Assignment 1: Computer Systems Building Blocks and

logical processes: Evolution, Components, and Features

BSc (Hons)/FdSc Computer Science

Computer Systems

Table of Contents

Introduction 2

Task 1: Building Blocks and Logical Processes 2

1. Processor (CPU) and Its Logical Processes 2
2. Memory (RAM) and Data Handling Processes 3
3. Registers and Instruction Execution 4
4. Cache Memory and Optimization of Data Flow 4

Task 2: Impact of Key Individuals and Their Contributions to Emerging Technologies 6

1. Foundational Contributions to Hardware Evolution 6
2. Virtualization and Cloud Computing 6
3. Internet of Things (IoT) and Edge Computing 7
4. Artificial Intelligence and Machine Learning 8
5. Quantum Computing and Future Technologies 8

Conclusion 9

References 9

Introduction 

Within the current computing landscape, hardware is crucial in supporting the various disciplines of computer science, software engineering, and information systems. However, hardware is often treated as a second-class entity by software designers and developers. Yet, the new technologies of virtualization, such as cloud computing, fog computing, and their resultant offspring-the Internet of Things (IoT)-have shown enough of the importance of appreciating the basic hardware foundations that support these systems. The discussion in this report is directed toward the core building blocks from which a computer is constructed, that is the processor, memory-primarily RAM-registers, and cache, and it analyzes their logical processes and evaluates their developmental paths in comparison with the key players behind the emergence of new technologies.

Task 1: Building Blocks and Logical Processes

1. Processor (CPU) and Its Logical Processes

The central processing unit (CPU), often referred to as the processor, is the principal computational device of a computer system. The software operates instruction and controls the flow of information to different subsystems. In the fetch stage, the processor pulls an instruction from memory; usually, the program counter shows the address it carries. The Control Unit in the decode stage interprets the instruction in binary form and converts it into a form that will make the computer execute a command. Lastly, in the execution phase, all the ALU or some other part executes the many operations that were intended. This cycle demonstrates how one cycle involves the completion of not only the binary signalling and instruction pipelines but also points to how modern processors move with such incredible speed owing to such organisation (Johnsen,  2024). 

The speed of the processor also depends on its design and the count of cores which means how capable it is to perform the operations. Current generation cores in most processors provide the facility for parallel execution of instructions due to the current multi-core designs. This capability is important for managing additional application sorts, for example, real-time data processing in the cloud computing system or AI operations. Another named feature is hyper-threading technology which allows us to solve in parallel more than one instruction for each core (He et al., 2024).  Also, reasoning or the analytical capability of a CPU is linked with frequency, called GHz, defining how many cycles the processor can perform per second. The higher rates of clock speed mean that the chip can process large amounts of data in a shorter time while at the same time, the system uses more power and produces more heat. Modern features such as pipelining and especially speculative execution are applied to internal structures to manage the execution of instructions. These techniques prevent the formation of bottlenecks hence guaranteeing a continued feed of instructions through the processor technology (Venkatesha and Parthasarathi, 2024).

2. Memory (RAM) and Data Handling Processes

RAM is the abbreviation for Random Access Memory and this one is the computer’s temporary storage that keeps data needed by the processor for an individual computation. Thus, its volatility guarantees high speed of read and write operations that are critical for an effective operation of the system. Signalling and addressing can be described as logical processes within the memory called RAM. In the case where a program needs information, the processor will prompt the address signal towards the RAM module. It tells the memory controller where the particular location is to be found and the memory controller gets this data and sends it back to the processor through the system bus. RAM’s design is oriented towards speed; DRAM and SRAM are enhanced for access times and present less latency. Data in RAM is also binary and each memory cell in the RAM is maintained at high or low voltage (Aswini, 2024). 

 The role of RAM is crucial keeping it in between the processor and storage systems because it holds data and instructions that are relevant to current activities. RAM is a temporary storage device, bringing data quickly to a computer’s processing chips, but it loses its contents when power is shut off, different from long-term storage devices. This speed is made possible by its nature which permits data to be retrieved on a random basis (random access) as opposed to a sequential one. The processes that take place in RAM are the addressing and signalling activities. Whenever the processor needs some data it sends the memory address of the required location using a memory controller. This controller also helps in decoding the request made to it and getting the data from the particular cell into the system bus and then forwarded to the CPU (Wilson, 2024).  

Therefore, different types of RAM have been invented because of technological enhancements namely DRAM and SRAM. The most widespread type is DRAMS working with capacitors and transistors and needs to be refreshed periodically to avoid losing data. SRAM on the other hand is much faster and much more reliable, but also a lot more costly and is used typically in CPU caches (Gajaria et al., 2024).  

3. Registers and Instruction Execution

Registers are small fast storage units that exist within the CPU. They temporarily store data and instructions in the meantime of processing which greatly shorten the time necessary to access the data than RAM. Control registers are part of the instruction cycle where operands and results can be stored during a computation process of hardware mathematics and logic data inputs and outputs. Registers in general; follow the processing of data using transfer and can use binary logic gates to perform complex operations internally at the lower level. Because they are close to the processor core, and response rates are fast, cache memory is essential for computations. Registers are important sub-complexes of the CPU that contain very high-speed storage spots of the data and control instructions used in processing. As for registers, it is said they are within the processor’s architecture and therefore, the access is immediate. Its primary purpose is to hold intermediates, addresses or instructions that are in between the CPU used as a part of the program. Registers run with the same clock as the CPU which makes them even greater than the RAM or cache (Li et al., 2024).  

A register may be of diverse forms and all the forms play various roles in the execution of instructions in the computer. For instance, the Accumulator Register, ALU holds the answers to an arithmetical and/or logic operation. The Instruction Register (IR) stores an instruction at a given time and the general purpose register holds operands or immediate data for arithmetic. Locally registers contain logic data transfer and logic data manipulation in addition to storage during the f–d–e cycle. Transmission gates in an actual CPU mean that registers can read, write and modify data at the rate determined by the control unit. Registers also work with other elements such as the cache or a storage area called RAM in the effective management of data (Lerner and Alonso, 2024).  

4. Cache Memory and Optimization of Data Flow

Cache memory is an abstract layer of high accessibility storage that is located between the processor and the RAM to help reduce the separation between the two. They hold often-needed data and instructions, decrease the time needed to access it and lessen the processor’s dependence on the much slower main memory. Cache levels are divided into structurally subordinated L1, L2, and even L3, which differentiate in terms of capacity and speed (Hässig,  2024). The workings at the managerial level of cache involve such concepts as prediction algorithms and data replacement. The processor instructs caches with temporal and spatial locality, to determine what information will be required in future and stores it. This optimization makes the system improve on the flow of data hence providing faster results. Cache memory is used to close the gap between processor and main memory (RAM), it is faster than the main memory. As time has moved on, this difference has continued to grow as processors have improved and continue to speed up the more the RAM access time is slower. Cache memory has an elegant solution for this issue; it helps place data and instructions that are most used closer to the CPU itself.  

Figure 1: Cache Memory

(Source: Putert, 2025)

Usually, the cache is divided into different levels, each with a different size and speed, The Level 1 (L1) cache resides on the processor core. While Level 2 (L2) cache is larger than Level 1 and has somewhat lower clock speed, Level 3 (L3) addresses separate processor cores and gives them joint access to shared information. All levels utilize predictive algorithms and replacement policies such as LRU (Least Recently Used) for dealing with the storage of data. Cache memory operation is based on the principles which are associated with temporal and spatial locality. Temporal locality helps to store the recent data in the cache because of regular usage of the same data. While the spatial locality loads nearby data blocks can be useful in optimizing sequential data processing. A cache hit occurs when the processor accesses data from the cache and this takes nearly negligible time. A cache miss occurs when a CPU issues permission for the data to be fetched from the RAM or other storages there will be a delay (Falahati et al., 2024).  

Task 2: Impact of Key Individuals and Their Contributions to Emerging Technologies

Progress in computing technologies and its related hinges on those influential personalities that have contributed to the development of both the hardware and software and their new adaptations. Some of these contributions include the provision of a hardware architecture which formed the basis for future modern computing systems: Cloud computing: Others have to do with the Internet of Things:

1. Foundational Contributions to Hardware Evolution 

Some essentials which contributed to the emergence of new, independent and rather refined platforms of hardware for computing were from various people, for example, John von Neumann. He defined a stored-program concept in the mid-twentieth century which pointed out an architecture of computing where both the instructions and data can be stored in memory. From this model, there was a possibility for sequential instruction execution which can be considered to be the basis of present-day processors, memory and registers (Venkatesha and Parthasarathi,  2024). Moore, founder of Intel, forecasted the continued increase of transistors in integrated circuits a trend that owes its name: to Moore’s Law. constitution this insight spurred the development of better processor architecture that in turn formulated improved and faster CPUs. Moore was influential not only in altering the path for processor architecture but as an indirect catalyst for RAM and cache memory as well, because transistor density increased thus allowing for next-generation system designs. 

2. Virtualization and Cloud Computing

The start of virtualization and cloud computing as the two most important discourses in the contemporary society of technology was made possible by personalities such as Diane Greene and Mendel Rosenblum of VMware. They developed software that made it possible to execute more than one operating system on a physical computer, more commonly known as virtualization today. Through the abstraction of hardware resources, virtualization achieved the optimization of hardware resources and the change of the data centre from a fixed environment to a dynamic and very cost-effective one. This innovation proved critical in support of cloud computing where distributed resources depend much on memory optimality and computing capabilities (Rajagopalan et al., 2024). In cloud computing, Werner Vogels has been one of the biggest stakes in developing AWS. Vogels was a pioneer behind massively scalable server architectures, which capitalize on improvements in processor speed as well as memory storage. Not only would this on-demand resource allocation and speedy data access ensure massive computational loads at the level of cloud platforms, but thus also hardware efficiency, which is very important for optimized use of caches and memory. For that reason, it has made the cloud service real, usable, and trustworthy around the world.

3. Internet of Things (IoT) and Edge Computing

Figure 2: IoT and edge computing

(Source: Onat, 2022)

The technology often referred to as the Internet of Things (IoT) developed into an innovative solution that was helped along by pioneers such as Kevin Ashton who came up with the term in 1999 (Alaba, 2024). What Ashton did was to propose a scenario in which devices can communicate and exchange data on their own through enhancements in microprocessors and memory to process and store heaps of real-time data. IoT devices require ultra-low power, high-performance processors and frugal memory organizations to accomplish part of the processing before sending the data to the cloud. In like manner, Intel visionary and Tilera Corporation founder Anant Agarwal drove the multi-core processors that remain critical for edge computing. Edge computing is all about making data processing near the place where it is generated and minimizing dependency on the main data centres. Some valuable changes to multi-core architectures were made by Agarwal which made parallel processing better so that the edge devices can process data and make decisions in real-time. This development best illustrates how special attention has been placed in processor and cache memory design to address the new emergence technologies. 

4. Artificial Intelligence and Machine Learning

AI and ML have benefited from the efforts of many hardware visionaries, including Lisa Su, CEO of Advanced Micro Devices Inc. Su has been instrumental in propelling the company that produces high-performance processors and graphics processing units (GPUs), all important in carrying out Artificial Intelligence tasks. GPUs are highly used to train complex artificial models because they provide parallel-processing memory access. The changes on the hardware part spearheaded by Su have made it possible to process big data in real-time, making AI popular and effective (Chen et al., 2024). Likewise, Andrew Ng, one of the pioneers in AI explicitly highlighted the role of hardware in scaling up machine learning. In deep learning frameworks, Ng uses high-memory capability and efficient cache systems to perform computations on, and to store, model parameters. He has made direct involvement in the areas of autonomous systems, natural language processing and computer vision by correlating the enhanced hardware with the research on artificial intelligence.

5. Quantum Computing and Future Technologies 

Quantum computing is the next big leap in the evolution of computations and leading personalities in this research field is John Preskill. Preskill’s research involves quantum processors in which algorithms require sets and not binary forms. While traditional processors perform calculations in a linear or a series manner quantum processors use superposition as well as entanglement to compute these at ultra-high speed. These processors need new configurations and architecture for memory systems to address quantum conditions, something different from classical computing. Others including Dario Gil of IBM have come up with quantum systems that can be interfaced with cloud frameworks. These developments showed how quantum technologies and quantum hardware co-integrate with scalable cloud structures. Memory and processor stakeholders remain essential in new Quantum systems as their advancement can significantly determine the scalability and functionality of Quantum computing (Proctor et al., 2025).

Conclusion

The hardware components and Logical processes’ relationship remains key to studying the advanced Computing systems. That is why by analysing the processor, memory, register, and cache components it is easy to identify how all the described components support modern technologies. The cases of Intel, GPU, foundational work from AMD, and diverse contributions toward emerging ideas from cloud to IoT specify the importance of a platform in the form of hardware. It can be said that, with motion continuing forward, an intertwining understanding of hardware and software will continue to be mandatory for driving future computing circumstances/ Developments will link past victory to the potential opportunities of tomorrow. 

References

Alaba, F.A., 2024. The Evolution of the IoT. In Internet of Things: A Case Study in Africa (pp. 1-18). Cham: Springer Nature Switzerland.

Aswini, V., 2024. Design of a CNTFET based Low Power Ternary Content Addressable Memory.

Chen, Y., Wu, C., Sui, R. and Zhang, J., 2024. Feasibility Study of Edge Computing Empowered by Artificial Intelligence—A Quantitative Analysis Based on Large Models. Big Data and Cognitive Computing, 8(8), p.94.

Falahati, H., Sadrosadati, M., Xu, Q., Gómez-Luna, J., Saber Latibari, B., Jeon, H., Hesaabi, S., Sarbazi-Azad, H., Mutlu, O., Annavaram, M. and Pedram, M., 2024. Cross-core Data Sharing for Energy-efficient GPUs. ACM Transactions on Architecture and Code Optimization, 21(3), pp.1-32.

Gajaria, D., Gomez, K.A. and Adegbija, T., 2024. STT-RAM-based Hierarchical In-Memory Computing. IEEE Transactions on Parallel and Distributed Systems.

Hässig, M., 2024. A fully-functional Cache Control Coprocessor for Enzian (Master’s thesis, ETH Zurich).

He, M., Liu, F. and Do, S.W.S., 2024, May. Heterogeneous Hyperthreading. In 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 68-78). IEEE.

Johnsen, M., 2024. Computer Engineering. Maria Johnsen.

Lerner, A. and Alonso, G., 2024, May. Data flow architectures for data processing on modern hardware. In 2024 IEEE 40th International Conference on Data Engineering (ICDE) (pp. 5511-5522). IEEE.

Li, M., Bi, Z., Wang, T., Wen, Y., Niu, Q., Liu, J., Peng, B., Zhang, S., Pan, X., Xu, J. and Wang, J., 2024. Deep learning and machine learning with gpgpu and cuda: Unlocking the power of parallel computing. arXiv preprint arXiv:2410.05686.

Onat (2022). [online] Ieee.org. Available at: https://innovationatwork.ieee.org/wp-content/uploads/2019/06/Real-Life-Use-Cases-for-Edge-Computing_1024X684.png. 

Proctor, T., Young, K., Baczewski, A.D. and Blume-Kohout, R., 2025. Benchmarking quantum computers. Nature Reviews Physics, pp.1-14.

Putert (2025). [online] Ecomputertips.com. Available at: https://r2.ecomputertips.com/imgs/glossary/cache-memory/cover.webp 

Rajagopalan, A., Swaminathan, D., Bajaj, M., Damaj, I., Rathore, R.S., Singh, A.R., Blazek, V. and Prokop, L., 2024. Empowering power distribution: Unleashing the synergy of IoT and cloud computing for sustainable and efficient energy systems. Results in Engineering, p.101949.

Venkatesha, S. and Parthasarathi, R., 2024. Survey on Redundancy Based-Fault tolerance methods for Processors and Hardware accelerators-Trends in Quantum Computing, Heterogeneous Systems and Reliability. ACM Computing Surveys.

Venkatesha, S. and Parthasarathi, R., 2024. Survey on Redundancy Based-Fault tolerance methods for Processors and Hardware accelerators-Trends in Quantum Computing, Heterogeneous Systems and Reliability. ACM Computing Surveys.

Wilson, K., 2024. Computer Jargon: The Illustrated Glossary of Basic Computer Terminology. Elluminet Press.

FdSc Computer Science 

BSc (Hons) Computer Science

Mathematics for Computer Science

Table of Contents

Task 1: Number Systems in Computer Science 2

Task 2: Arithmetic Operations on Computers 3

Task 3: Set Theory in Computer Science 4

Task 4: Probability and Its Applications in Computer Science 5

Task 5: Boolean Algebra and Real-World Binary Problems 7

References 9

Task 1: Number Systems in Computer Science

To define computer science, number systems must also be included since the main operation of computers depends on numerical data to perform computations, store data, and generally represent or manipulate other types of digital content. The most significant number systems used in computer science are: binary, octal, decimal, and hexadecimal. Each plays an important part in computer operations.

The binary number system (base-2) is the essence of computer science. This is because it comprises just 2 digits; those are 0 and 1. These 0s and 1s are known as binary digits or bits, and they represent the basic units of all the data present in computing, such as numbers, characters, images, and even audio. Binary is the language of computers because just like their wiring, a 0 state refers to ‘off’ and a 1 to ‘on.’ For instance, the binary number 1010 equals the decimal number 10 and is very good for low-level operations such as memory addressing and execution of instructions. 

The octal number system (base 8) uses digits from 0 to 7. It represents the simplification of binary forms by grouping their digits by three, hence making it easier to interpret and debug large binary sequences. For example, the binary 110011 can be interpreted simplistically into 63 in octal.

Not only humans but also decimal numbers use this system for daily living purposes, employing digits from 0 to 9. But it is tricked with converting into a binary numeral system for the computers to understand it, making it possible to work with human-readable formats and machine operations.

The hexadecimal number system (base-16) uses digits acquired by numbers from 0-9 and letters A, which represent values from 10-15. It is usually used in computer sciences for compact representations of binary data. For example, the binary 110110101011 can complete itself as DAB in hexadecimal form, which makes managing memory addresses, colour codes, and error messages easier. As such, number systems can be understood as a vital presence in computer science because they provide data representation, manipulation, and storage in the format of digital systems. A binary system lays the first foundation of computation, while octal and hexadecimal simplify operations of long strings of binary numbers (Strickland and Lewis,  2022). Decimal maintains an intuitive interaction between humans and machines. Thus, learning the way these number systems work is essential for tasks like encoding data, detecting errors, and algorithm development, making them integral features of computer science.

Task 2: Arithmetic Operations on Computers

Process Flow of Arithmetic Operations Inside Computer Arithmetic operations in a computer’s systems are based on local systems through which machines can add, subtract, multiply, and divide. The hardware parts such as adders, subtractors, multipliers, and dividers perform these operations at the hardware level.

To add in binary, the same process is followed as in decimal addition, with the following rules: 0 + 0 = 0; 1 + 0 = 1; and 1 + 1 = 10; here, the result will be 0 but a carry of 1 is generated. For instance, 110 added to 101 would result in 1011. With the help of logic gates like AND, OR and XOR, it is done in the arithmetic logic unit (ALU).

As for subtraction, it makes use of the two’s complement which would simplify binary subtraction by changing the subtraction into an addition. Taking the two’s complement of 101 and adding it to 110 would allow for the subtraction of 101 from 110. 

Thus, in binary, multiplication uses the shift and add method. In the multiplication of 10 (2 in decimal) by 11 (3 in decimal), shift and add partial results. This is very much done in modern processors using dedicated circuits for speed. When we repeatedly subtract or shift, we are performing division similar to that in long division in decimal. Remainders in the division also are treated in a similar way as done with decimal division. Arithmetic on computers is the most basic aspect of the execution of software instructions and exercise solutions to a problem. It is binary arithmetic that has formed the backbone of digital systems.

All these operations are done on a computer built to carry out instructions within a software program, run mathematical algorithms, or process raw data (Seetho et al., 2022). Arithmetic operations involve addition, subtraction, multiplication, and division, all done based on binary numbers. A computer relies on digital circuits such as adder, subtractor, multiplier, and divider circuits to execute all these operations efficiently at the hardware level.

A multiplication operation is usually achieved via the shift-and-add technique in computers. It can be shown that binary multiplication is a repeated process of bitwise shifts and addition. For example, to multiply 10 (2 in decimal) by 11 (3 in decimal), shift 10 to the left and add the shifted parts together. Modern processors employ optimized algorithms, such as Booth’s algorithm, to enhance performance. Like the long division method practised in decimal arithmetic, division in binary involves repeated subtraction or shifting bits.

Computers in turn do more than just these simple arithmetic operations; they perform such complex tasks as floating-point arithmetic to represent real numbers (Boldo et al., 2023). These involve very specific formats that confer values on fractional and serial portions during an operation standard, as in IEEE 754. Error detection and correction systems like the parity bits and checksums are fully dependent on binary arithmetic. Such mechanisms ensure that data are accurately kept while being transmitted and processed. Hence, there is great relevance of arithmetic operations in computers. Essentially, a computer’s arithmetic operations enable computation that ranges from simple calculations to the execution of complicated algorithms and tasks thereof. By binary arithmetic and digital circuits, the speed and accuracy of computation made available in computers are unmatched for applications today.

Task 3: Set Theory in Computer Science

The set theory provides a mathematical framework behind many concepts in computer science, for instance, data structures, databases, algorithms, or even artificial intelligence. A set is defined in set theory as a collection of unique elements, primarily used to model relationships and grouping concerning almost all computational problems.

In databases, set theories govern the operations of union, intersection, differences, etc., which are necessary for querying the data. For example, the union of two sets, say A and B, combines all the distinct elements in both sets. Similarly, the intersection determines the common elements that are very necessary to filter data in relational databases. In algorithms, certain set theory concepts are employed to optimize solutions. For instance, graph algorithms such as Dijkstra’s algorithm use sets for representing nodes along with edges that help find the shortest path in a particular network.

Set theory is equally important in artificial intelligence and machine learning and represents the features, clusters, or decision boundaries (Ezugwu et al., 2022). For instance, in the problems of classification, set intersections and complements can further define the rules for assigning points in a data set to specific classes. Indeed, in such a way almost all models organize and manipulate the data required from an important point of view; therefore, it will serve as an essential tool in computer science to solve problems quite easily across the different domains.

The application of set theory to database systems is indeed modelling and manipulating data. Union, intersection, and difference operations find their counterparts by the corresponding SQL operations of UNION, INTERSECT, and EXCEPT. For example, a union of two sets of student records from different departments provides a complete list of all students. Set theory encompasses the querying and processing of data in a more streamlined and effective manner. Search algorithms are designed based on set theory. For example, sets that represent the vertices and edges of a graph are employed in fast-pathfinding algorithms such as Dijkstra’s or A. In these algorithms, some critical operations on sets are done to find visited and unvisited nodes which optimize computation performance. A very strong use of set theory is formal verification that makes sure that the system will behave as it was expected to. As formal methods rely on mathematical models, sets are used to prove the correctness of an algorithm or program. Thus the errors occurring in critical systems such as the ones in aviation and healthcare are reduced. 

For instance, if set theory offers the capabilities for programming languages, then it would mean that sets, lists, and maps are data structures in programming languages. Python, for example, has a built-in set type, which supports mathematical operations on sets. These purposes are managing collections of data such as removing duplicate entries or making fast membership tests. Besides, set theory applies pretty much in artificial intelligence (AI) and machine learning. Sets serve as the building blocks of data representation points, features, or clusters that perform classification, clustering, or recommendation tasks (Sarker, 2021). One example is how intersection can be generalized as a joint belonging to sets, which describes the shared attributes between data points in making classification decisions in algorithms. 

Task 4: Probability and Its Applications in Computer Science 

Rules of Probability

Multiply the independent events so that you can calculate the possible outcome of two events occurring simultaneously. If events Even-d beckons, Even-d beckons:

 The Addition Rule is used to determine the probability of either of two mutually exclusive events occurring. For example, if the probability of event AAA is P(A)P(A)P(A) and the probability of event BBB is P(B)P(B)P(B), then P(A∪B)=P(A)+P(B)P(A \cup B) = P(A) + P(B)P(A∪B)=P(A)+P(B). This rule is particularly useful in decision-making algorithms where multiple outcomes are possible.

The Multiplication Rule applies to independent events and calculates the joint probability of two events happening simultaneously (Rumsey, 2024). If events AAA and BBB are independent, the probability of both occurring is P(A∩B)=P(A)⋅P(B)P(A \cap B) = P(A) \cdot P(B)P(A∩B)=P(A)⋅P(B). This concept is foundational in areas such as cryptography and machine learning.

An important rule in Bayesian inference that is responsible for many algorithms in artificial intelligence such as filters and recommendation systems.

Probability trees are yet another very important thing in visualizing and calculating probabilities of sequenced events. They, for example, can represent the probability of passing a test depending on how much someone prepared for it and how difficult the test was, assisting in modelling quite complex decision processes, such as in adaptive testing.

Applications in Computer Science

Probability, indeed, has numerous applications in computer science. One of the fundamental areas of probability applications is performance evaluation and predictive modelling. The usage of probability extends into the areas of machine learning and artificial intelligence. Such probabilistic models include but are not limited to, Bayesian networks, Hidden Markov Models, and Gaussian distributions, and they are quite relevant in classification, regression, or natural language processing tasks. For example, Naive Bayes classifiers are used to predict results based on the conditional probabilities involved in features, making probability theory fundamental for many applications in machine learning. Probability is one crucial variable for the performance analysis of computer systems. In queueing theory, one demonstrates the probabilistic model studying system performance, such as response times from servers network traffic, and so on. Thus, for example, the probability distribution of times of arrival for requests should help optimize cloud resource” allocation.

In cryptography, probability measures the strength of algorithms for encrypting data. The probability could also predetermine the value of brute-force attacks, thus ensuring designers that their systems are created much stronger. Randomized algorithms such as Monte Carlo algorithms and Las Vegas algorithms are facets of probability. These algorithms use random sampling to approximate solutions to problems such as numerical integration and optimization (Premkumar et al., 2021). For example, the Monte Carlo method uses random points within a square and compares them to randomly generated points within a circle to estimate the value of π. 

In conclusion, probability theory is all-important for computer science since uncertainty modelling, system performance evaluation, and intelligent algorithm development are all made possible.

Task 5: Boolean Algebra and Real-World Binary Problems

The Boolean algebra is a mathematical system which deals with binary-valued variables, and logical operations over these binary values. This makes it the most important in digital computing and electronics. The principles of this mathematics are used broadly in practical binary problems-they range from designing and optimizing digital circuits to algorithm construction and solving logical puzzles. One of the most common examples of the application of Boolean algebra to everyday problem-solving is found in traffic light control systems which form a very integral part of modern transportation networks.

Real-World Binary Problem: Traffic Light Control System

A traffic light control system acts like 1s and 0s because each of the light colours-red, yellow, and green- has a binary expression: ON (1) or OFF (0). It also requires the handling of logical states for lights on intersecting roads so that there will never be a conflict for safety and smoothness in traffic flow. The design logic of these lights is done using Boolean algebra. For example, the logic for one traffic light may be represented using Boolean equations:

• R=A∧¬BR = A \land \neg BR=A∧¬B: Red light is ON when the opposing green light is OFF.
• G=¬A∧BG = \neg A \land BG=¬A∧B: Green light is ON when the opposing red light is OFF.
  Here, AAA and BBB represent the binary states of other lights. Logic gates implement these equations in the control system hardware, ensuring smooth operation. 

Fault tolerance and efficiency are realized through Boolean simplifications such as using Karnaugh maps or through Quine-McCluskey methods. The techniques minimize the logic equations and hence number of gates in the circuit and improve performance. 

Boolean Algebra in Problem Solving

Boolean algebra finds application in computer algorithms that perform searches in and filters through databases. For example, searching for certain records in a database would require Boolean operators like AND, OR, and NOT to produce a refined outcome. A query such as “students who passed AND enrolled in physics OR math” is now filtered using Boolean operators that join as well as exclude conditions competitively. 

Broader Implications of Boolean Algebra

Not only traffic lights and databases, but also applications of Boolean algebra extend to creating error detection and correction algorithms, such as parity checks and cyclic redundancy checks, which verify the integrity of data in communication systems. Boolean algebra underpins the design of today’s computer processors, where millions of logic gates are used to perform calculations and execute instructions. It lays down the logical foundation for binary problem solvers embedded within digital systems. Boolean algebra is then combined with other areas of mathematics, such as calculus, to empower the same engineers and scientists to build efficient, reliable, and intelligent systems for solving real-world problems (Yazar, 2024). Boolean algebra is used as a primary math tool to solve binary problems, but calculus can also be applied in its cases to improve system operations. Such as in the traffic light system, calculus would model and optimize the light changes based on actual traffic flow data.  

References

Boldo, S., Jeannerod, C.P., Melquiond, G. and Muller, J.M., 2023. Floating-point arithmetic. Acta Numerica, 32, pp.203-290.

Bose, S.K., 2023. Application of Polynomials in Coding of Digital Data.

Champley, K.M., Willey, T.M., Kim, H., Bond, K., Glenn, S.M., Smith, J.A., Kallman, J.S., Brown, W.D., Seetho, I.M., Keene, L. and Azevedo, S.G., 2022. Livermore tomography tools: Accurate, fast, and flexible software for tomographic science. NDT & E International, 126, p.102595.

Ezugwu, A.E., Ikotun, A.M., Oyelade, O.O., Abualigah, L., Agushaka, J.O., Eke, C.I. and Akinyelu, A.A., 2022. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, p.104743.

Premkumar, M., Jangir, P., Kumar, B.S., Sowmya, R., Alhelou, H.H., Abualigah, L., Yildiz, A.R. and Mirjalili, S., 2021. A new arithmetic optimization algorithm for solving real-world multiobjective CEC-2021 constrained optimization problems: diversity analysis and validations. IEEE Access, 9, pp.84263-84295.

Rumsey, D.J., 2024. Probability for dummies. John Wiley & Sons.

Sarker, I.H., 2021. Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), p.160.

Strickland, L. and Lewis, H.R., 2022. Leibniz on binary: the invention of computer arithmetic. MIT Press.

Yazar, S., 2024. CAN SYMBOLIC COMPUTATION AND FORMALIST SYSTEMS ENHANCE MATH EDUCATION WITH ARTIFICIAL INTELLIGENCE?. Trakya Üniversitesi Sosyal Bilimler Dergisi, 26(2), pp.487-504.

BMA4000-20

The Business Environment

A2: Individual Reflective Performance Report

Individual Reflective Performance Report

Table of Contents

Reflection on the Fast Fashion Case Study 2

GIBB’s Reflection Model 2

Description 2

Feelings 2

Evaluation 3

Analysis 4

Conclusion 4

Action Plan 5

References 6

Appendix 7

1. SWOT Analysis 7
2. Competitors in the Fast Fashion Marketplace 7
3. Current Customer Demographics 8
4. Promotion Strategies 8
5. Advertising Platforms 8
6. Tackling Social and Environmental Issues 9
7. Impact of Being a Private Limited Company (Ltd.) 9

 Reflection on the Fast Fashion Case Study

GIBB’s Reflection Model

Description 

Intended to analyze the fast fashion case studies, considering both opportunities and challenges, of a private limited company under the fast-changing scenario. The company operated under similar business modes as used among others such as Zara and H & M. Like all fast fashion retailers, it designs, manufactures, and markets clothing all at a pace that makes them affordably trendy. But then, the case study talked of how enticing and demerit this method was. On one side, it satisfied a consumer need for variety at very low prices, but raised big fat social-ecological issues: In this case, overshooting thresholds in almost all parameters of overconsumption-from water to greenhouse gas emissions, low-paid and poorly protected labour in manufacturing plants.

Group discussions on the case study were collaborative and insightful along the lines of Tuckman and Jensen, following their group development. The forming stage was where the group worked around the case goals. It was storming when we entered moments of conflict on when to put priority on profitability and when to prioritize sustainability. Because of this, we got to norming by reconciling differences and marking our views along with ethical concerns and competitive advantage. In the end, we performed in producing effective strategies towards the company’s adoption against the challenges in the industry, such as the circular economy principles and sustainable supply chain practices.

Feelings 

I was both very excited and partly apprehensive while working on a fast fashion case study. The area is interesting with relevance and complexity; concerns were partly around the ethical dilemmas of the industry. It has led me to an internal debate on ethics and profitability with many questions regarding the practicality of encouraging a fast fashion enterprise to consider an ethics-based approach without compromising its market position. 

At the same time, I felt quite responsible for meaningful engagement with the case study. I came to understand that empathy for the organization on business goals and social and environmental consequences was critical to providing balanced and actionable recommendations. There was, however, a sense of group camaraderie as we fought through challenges and pushed each other into critical thinking. Sometimes, however, as a group, we would disagree or arrive at different alignments within ourselves. Therefore, I felt frustrated because, as a group, they sometimes turned our momentum upside down and made it more difficult than it was supposed to be. 

I want to provide meaningful solutions to the issues posed, especially those of environmental sustainability and worker welfare. However, I also doubt that I have enough knowledge of business strategies to give sound recommendations. Working in a group alleviated some of this fear due to the perspectives brought to discussions from diverse thoughts. Still, I sometimes got frustrated when there were disagreements, particularly during the storming stage, as it was tricky to align differing opinions on how to approach the case.

Evaluation

The experience of analyzing the fast fashion case study possessed a good and a bad side. It was good because the group discussion produced arguments that were transformed into generating ideas and solutions. For instance, we proposed that the company invest in water-saving technologies and explore partnerships with recycling initiatives like those of H&M and Levi’s. These ideas were reflective of a solid understanding of how businesses could address sustainability concerns without losing their competitive edge. Challenges were also present. One issue was that, from the start, alignment within the group was a challenge. Some members cared only about profit, while others highlighted sustainability, which made it very difficult to progress during the storming stage. Additionally, I found applying theoretical frameworks such as Mintzberg’s strategic configurations and Porter’s Five Forces somewhat challenging to the case. Although these frameworks seem to offer a comparative structure, generating actual strategies towards translating their insights married with practice proved harder than I had envisaged. The dynamics of the group were close to Tuckman and Jensen’s theory. For example, we needed to have established normative practices to have clear roles and responsibilities that would enhance communication and thus make tasks efficient during group work. The second phase revealed the gaps we’ve left in conflict management to indicate that facilitation skills would do well off the charts. I sometimes felt, during discussions in the group, that the problems were too huge for us to deal with. Balancing the demands of sustainability and profit is an overwhelming task, especially for a relatively new entrant in the business fighting for survival against long-standing giants such as Zara and H&M. I wondered whether having these recommendations would implement anything into practice. 

Analysis 

Studying the fast fashion case study has opened my lenses toward strategic business decisions. One such decision was the most effective in identifying the necessity of merging sustainability with the value proposition of the organization. This assertion was based on Porter’s Generic Strategies, which pointed out that differentiation could take place through ethical and sustainable practices by putting forward reforms, such as following a circular economy model and transparent supply chain practices, as far as risk mitigation would go.

Well, not all decisions were so lucky. For instance, our first reference point was profit, and on the side, we neglected a few points on stakeholder engagement in the initial phase of analysis. It became clear when looking at possible risks like bad publicity due to unethical practices. In addition, it has been so inconvenient for me to analyze the operations of production, like improving the manufacturing process that would minimize resource consumption. Mintzberg’s theories on organizational structures helped a bit, but our recommendations were not developed enough to be put into practical implementations. Thus, the case study highlighted the complexity of strategic decision-making in a fast-changing, ethical-challenging industry, like balancing different priorities of profitability, sustainability, and stakeholder expectations.

Conclusion 

After the experience, I realized that there were various areas where I could have improved my approach. Having devoted more time to understanding the fast fashion industry and the challenges therein would have allowed me to prepare nuanced and actionable recommendations. I should thus improve my skill in applying theoretical frameworks like Mintzberg’s and Porter’s to real-life cases. Though insightful framework grounds, I found it difficult to adapt their insights to the specific context of the case. Teamwork further identified other areas I could work on in collaboration and conflict resolution. That would call for stronger facilitation skills, which would help to articulate disagreements in a more effective way at the storming stage. The inclusion of tools like a structured brainstorming format or a decision matrix could also have cut down on the length of time the team spent making decisions. 

However, initially, I was very curious but worried about working on the fast fashion case study. I find the topic so close to a real everyday-life issue, and I was also completely on the verge of casting aspersions regarding the ethics of the industry. While I admired the innovation and efficiency driving the fast fashion business model, I found it disturbing to think about environmental degradation and labour exploitation that too commonly underlies such success. I think in the past, these feelings were the distinguishing features of the complexity of addressing ethical and strategic issues in fast fashion, as much as their troubles as their payoffs.

Action Plan

The following actionable measures may give me good performance boosts in case the same situation comes up in future:

Step 1: Understand the Industry Remotely

Commence with an extensive research study on the industry such as its market trends, sustainability challenges, and best practices of top companies to form a strong foundation for analysis.

Step 2: Theory Application Early

Apply theories such as Porter’s Five Forces and Mintzberg’s configurations to formulate the analysis and determine the main strategic priorities right from the beginning.

Step 3: Cultivate Effective Group Synergy

While in the forming phase, designate clear roles and responsibilities within the group. Employ such facilitation methods as rotation of leadership and structured debate to manage conflict in the storming phase.

Step 4: Recommend Practical Solutions

Focus on result-oriented idea generation that can solve both immediate and systemic problems. For example, propose concrete technologies or partnerships that complement the vision and capacities of the organization.

Step 5: Constant Reflection and Learning

At the end of a case, conduct a postmortem analysis to assess what worked and what did not. This reflection may then be used to modify skills and strategies for future real-world case studies.

I will be better placed to tackle the diverse problems in the fast fashion industry and contribute more effectively to group discussions and decision-making processes by implementing these measures. 

References

Arakawa, R., Maeda, K. and Yakura, H., 2024. ConverSearch: Supporting Experts in Human Behavior Analysis of Conversational Videos with a Multimodal Scene Search Tool. ACM Transactions on Interactive Intelligent Systems.

Bloomfield, J., 2024. ” But My Favorite Influencer Told Me to”: How and When to Assign Liability to Influencers When Their Followers Commit Torts. U. Ill. JL Tech. & Pol’y, p.371.

Chen, L., Haider, M.J. and He, J., 2024. Should “green information” be interactive? The influence of green information presentation on consumers’ green participation behavior for driving sustainable consumption of fashion brands. Journal of Cleaner Production, 470, p.143329.

Dholakia, N. and Ziliberberg, C., 2024. Change and Legitimation Narratives in the Intertwined Market Discourses of Sustainability and Neoliberalism. Critical Perspectives in the Study of Art, Fashion and Wine: Sustainability and Artification, Berlin: de Gruyter (forthcoming).

Ghosh, J. and Ghosh, R., 2024. Exploration of Fashion Industry Protection as Need of Hour on Intellectual Property. In Illustrating Digital Innovations Towards Intelligent Fashion: Leveraging Information System Engineering and Digital Twins for Efficient Design of Next-Generation Fashion (pp. 397-415). Cham: Springer Nature Switzerland.

Hadi, R., Melumad, S. and Park, E.S., 2024. The Metaverse: A new digital frontier for consumer behavior. Journal of Consumer Psychology, 34(1), pp.142-166.

Huihui, W., Alharthi, M., Ozturk, I., Sharif, A., Hanif, I. and Dong, X., 2024. A strategy for the promotion of renewable energy for cleaner production in G7 economies: By means of economic and institutional progress. Journal of Cleaner Production, 434, p.140323.

Kuruppu, S.C., Milne, M.J. and Tilt, C.A., 2024. Sustainability control systems in short-term operational and long-term strategic decision-making. Meditari Accountancy Research, 32(1), pp.234-265.

Pradana, A.A., 2024. Gap Analysis of Green Supply Chain Management Using Green Supply Chain Operation Reference Method in The Garment Industry (Doctoral dissertation, Universitas Islam Indonesia).

Su, Y., 2020. The Internationalization Strategies of Fast Fashion Clothing Retailer Brands: A Cases Study of ZARA, H&M, UNIQLO, and Gap. Wenzao Ursline University of Languages.

Appendix

1. SWOT Analysis

Table 1: SWOT analysis 

(Source: Self-created)

2. Competitors in the Fast Fashion Marketplace

To keep pace in the competition for fast fashion, the closest rival retailers in this industry include global giants like Zara, H&M, and Uniqlo. These companies completely dominate the industry on the grounds of their well-established supply chains, extensive product ranges, and wide market reach. In addition, regional fast fashion brands that can serve a particular demographic or market can even harass competition on the grounds of offering local product tastes (Dholakia and Ziliberberg, 2024). The competitive scenario thus calls for improved innovations and more refined strategies to differentiate the company.

3. Current Customer Demographics

The bulk of the company’s customers comprises young adults and millennials, aged about 18 to 35 years, who are interested in staying trendy while comparing all their purchasing decisions against their budgets. These customers are mainly housed in urban and suburban environments, where fast fashion retail outlets are readily available. The target population consists of people from either studying or working at entry-level jobs that allow them some disposable income, depending on their level in their careers. Many of these customers would show interest in activities such as socialization, travel, or some digitally-mediated activities like online shopping and content consumption using social media platforms (Hadi et al., 2024). The psychographic profile of the company’s customers points to their preference for self-expression through clothing.

4. Promotion Strategies  

The company is preparing to unveil an integrated marketing campaign that will shine a light on an essential and fashionable company offering. The very first step in this scheme would be to develop a loyalty program where customers would be encouraged to repeat their purchases through discounts and special offers. This is aimed at increasing customer retention and nurturing brand loyalty. Next, the company plans to collaborate with different popular social media influencers, who will come in to assist in creating content about their most recent collections. This influencer sampling would help tap a giant follower base ‘urging the ‘impressionable masses’ to consider buying the items in question (Bloomfield, 2024).

5. Advertising Platforms

It will be an ongoing collaboration that will be part of the overall marketing strategy of the company. It will be in addition to the seasonal sales and other promotional events that your brand uses to keep its consumers engaged year-round. The company will rely on affairs such as mounting a blended approach to online and offline media efforts to reach the target audiences. The use of online forums, for instance, Instagram and TikTok, would allow communication with the audience in the target group by demonstrating visual elements of the product such as outfit ideas and styling tips. These two channels offer the opportunity for participation and all allow targeted advertising by the company. The company would also take advantage of YouTube to upload behind-the-scene clips, tutorials, and testimonials relating to the audience to build emotional affinity with the audience (Arakawa et al., 2024). The sales channels would be integrated e-commerce and the website of the company, complemented by offline modes-the latter namely pop-up outlets and stores in events-paired with digital outreach to deploy a more intimate touch to interaction with customers. 

6. Tackling Social and Environmental Issues 

The establishment has established a position on the social and international environmental challenges. Accordingly, to address the social issues, the organization proposed to subject its supply chain to a policy of transparency that will assure fair wages with working conditions safe for all employees productive. The company will also engage partners of trust to undertake audits for compliance with ethical standards. In addition, the company is committed to the reduction of the carbon footprint of its products. Searching for and incorporating renewable materials in its collections, including findings from organic cotton and recycled fabrics, are some of the initiatives that this company has engaged in. The clothing recycling program, which greets customers with returns of old clothes for proper recycling or reuse, is also going to be launched (Pradana, 2024). In line with this development, growing public demand for sustainable and ethical practices in the industry is considered.

7. Impact of Being a Private Limited Company (Ltd.)

First, limited liability, meaning shareholders are protected from personal financial loss beyond their investments, is enjoyed by the private limited company. This means that the company operates without financial security, while harbored by attractiveness from investors who appreciate limited risk. Though being an Ltd. presents a company with the obligation of maintaining rigorous financial reporting and compliance standards by filing annual financial statements and, in some cases, adhering to rules of corporate governance. For this element, raising capital is said to be more difficult compared to a private limited company than that public traded firms as it doesn’t issue shares on the stock market but relies on internal funding or private investors. However, it allowed the company to keep the business under more control in its operating and decision-making processes to ensure the institution keeps functioning on track towards long-term goals (Kuruppu et al., 2024).

GBM3ARW Academic Research and Writing for Global Business Management

Executive Summary

AstraZeneca PLC is evaluated based on its alignment with two key United Nations Sustainable Development Goals: Goal 3 (Good Health and Well-being) and Goal 13 (Climate Action). This research considers the activities and strategies of the company as well as evidence to examine the effectiveness in meeting those global targets.  SDG 3 programs such as Healthy Heart Africa, and AZ&Me Prescription Savings Program have placed AstraZeneca on track concerning SDG 3. Healthy Heart Africa tackles non-communicable diseases in parts of underserved communities, while AZ&Me offers low-cost drugs. Because it is not affordable to many people and limited in scope around the world, its impact is not greater. On the other hand, SDG 13 can be measured by the strong progress in direct emissions reductions and conversion to renewable energy through AstraZeneca’s Ambition Zero Carbon initiative. The sustainability gaps in the supply chain remain. Ultimately, the report provides recommendations on increasing affordability, enhancing health programs, and improving supply chain sustainability. These will further consolidate AstraZeneca as a world leader in sustainable health care and climate action.

Table of Contents

Introduction 3

Method 3

Results 4

Discussion 5

Conclusion, Limitations, and Recommendations 6

References 7

Introduction

AstraZeneca PLC is a world-leading biopharmaceutical company focusing on drug discovery, development, and commercialization of prescription medicines primarily in oncology, cardiovascular, renal, and respiratory diseases. Innovation is a collective effort at AstraZeneca to meet some of the key unmet health needs in the world with balances between the challenges of the enterprise and the responsibilities of the enterprise towards wider society (Aoki, 2024).

The core objective of this report, which was privileged to research the implementation of some selected points in the endeavours of the United Nations Sustainable Development Goals by AstraZeneca, is to generate some insight on the sustainability practices of the company and into her action toward global health and environmental wellness. The report thus scrutinizes the accomplishments of AstraZeneca in two specific but very significant SDGs-Goal 3 (Good Health and Well-being) and Goal 13 (Climate Action). These goals are very significant in ensuring that access to healthcare can be sustainably adapted into practice within efforts against climate change. The assessment of whether or not AstraZeneca meets the SDG criteria will come from pieces of evidence and visuals to nullify ambiguity, and recommendations for improvement of that much are also to be given.

Method

This, according to the United Nations of 2023, is Sustainable Development Goal 3 concerning Good Health and Well-being, defining healthy lives and appropriate schedules for different ages of the people (Vyas-Doorgapersad, 2024). One of the major targets identified is access to essential medicines and healthcare services, for example, reducing mortality from non-communicable diseases. Such an agenda is similar to that of AstraZeneca in the Healthy Heart Africa program, which is meant to provide cardiovascular care to underserved areas in Africa. Still, there are many challenges in broadening access to lower-income markets due to prices and infrastructure problems.

Sustainable Development Goal 13 (Climate Action) urges all nations and people to undertake urgent efforts to halt climate change and its effects, such as cutting greenhouse gas emissions and improving resource efficiency. As part of its Ambition Zero Carbon program, AstraZeneca has committed to achieving net-zero greenhouse gas emissions in its whole value chain by 2045 (Willette, 2024). Much has been achieved, but a considerable amount of criticism has been levelled at it over its slower rate of implementation in the application of sustainable practices in the supply chain, especially in that of third-party providers. The evidence drawn for AstraZeneca’s compliance with these SDGs is from the sustainability reports, public domain information, and evaluations of the third parties. This report would evaluate the level of efforts to arrive at some of the strengths and weaknesses of AstraZeneca in addressing these vital global problems.

Results

The evaluation is somewhat mixed under evaluation about AstraZeneca’s alignment with SDG 3 and SDG 13. AstraZeneca has made very significant contributions to Goal 3 (Good Health and Well-being), for instance, through Healthy Heart Africa and the AZ&Me Prescription Savings Program, which have provided access to lifesaving medicines to underserved communities. On the other hand, affordability and accessibility gaps within lower-income settings indicate opportunities for further improvement. For Goal 13 (Climate Action), AstraZeneca is well committed, particularly evident through its Ambition Zero Carbon program, which facilitated the 59% reduction in Scope 1 and 2 emissions by 2023. Yet again, the participation of its global supply chain partners in the journey still prevails. The assessment of AstraZeneca’s performance regarding SDG 3 and SDG 13 shows areas of strengths and weaknesses. In SDG 3 (Good Health and Well-being), there are significant programs that were implemented by AstraZeneca such as Healthy Heart Africa where over 30 million individuals have been screened for hypertension, and thousands of healthcare workers have been trained in underprivileged regions (See et al., 2024). Additionally, programs like the AZ&Me Prescription Savings Program in the US provide free or very low-cost medicines to eligible patients. However, these efforts are scattered, with poor-program area outreach to low-income countries. Affordability issues also persist for patented medicines, leaving access limited for the poorest populations.

Table 1: UN SDG

(Source: Self-created)

Discussion

The findings show that AstraZeneca has good intentions toward both SDG 3 and SDG 13 because there are still some gaps that they need to fill. Regarding SDG 3, the company should be commended because it initiated Healthy Heart Africa, which specifically targeted NCD-related programming. It has led to successful results: over 30 million blood pressure screening tests have been conducted across Africa since 2014. However, the affordability of medicines is the biggest obstacle in income-restricted nations, as the cost of essential medicines may be prohibitive (Napier-Raman et al., 2024). The same would recommend more partnerships with governments and other NGOs to fill this and seek more extensive access to essential health care. On SDG 13, AstraZeneca has created an Ambition Zero Carbon program that talks about how proactive it is against climate change. Some successes earned through this initiative, such as getting to 100% renewable electricity in most of its main markets, demonstrate that commitment. However, using a third-party supplier proves challenging in achieving the same sustainable practices throughout the entire value chain. This gap can be closed through greater emphasis on supplier engagement and monitoring. Visuals such as a graph comparing AstraZeneca’s emissions reduction trajectory against its net-zero target for 2045 would better articulate the progress made. Furthermore, the geographical distribution of initiatives related to health would demonstrate the disproportionate impact on the world (Kuteesa et al., 2024).

The results indicate that AstraZeneca has made commendable strides in attuning itself to both SDG 3 and SDG 13. They still have some distances to cover. For example, on SDG 3, they might consider the company’s Healthy Heart Africa initiative on the NCD-related programming. It has been very successful: since 2014, there have been more than 30 million blood pressure screenings completed all over Africa. However, affordability of medicines is a major obstacle, particularly in lower-income markets, where the price of essential medicines may prohibit purchase. More partnerships with government and NGOs would go a long way in addressing these voids and expanding access to key health care (Dumalanède et al., 2024). On SDG 13, AstraZeneca’s Ambition Zero Carbon program demonstrates how proactive it intends to be against climate change. The program has showcased successes, such as transitioning to 100% renewable electricity in most of its key markets, which demonstrate this commitment. However, third-party supplier reliance makes it difficult to enforce sustainable practices across the entire value chain. There should be a greater emphasis on supplier engagement and monitoring to close this gap. Furthermore, visual information, such as a graph depicting AstraZeneca’s emissions reduction trajectory against its 2045 net-zero goal, would add clarity to this progress. Additionally, a geographical breakdown of its health initiatives would reveal disparities in the global impact (Prust et al., 2024).

Conclusion, Limitations, and Recommendations

Ultimately, AstraZeneca shows a great match with SDG 3 and SDG 13 by making impactful health programs and ambitious climate commitments. However, there are some limits regarding the affordability of medicines and the consistent sustainability of its supply chain.

This analysis relies solely on publicly available data, which may have neglected to include some of the most concerning internal problems or proprietary strategies. In addition, it focuses on only two SDGs, and therefore cannot be comprehensive about all that AstraZeneca does for sustainability (Zec, 2024).

For example, AstraZeneca could launch pricing initiatives in collaboration with governments and NGOs to bring medicines closer to vulnerable communities in countries where they operate (Sideri, 2024). And it needs to strengthen the supply chain management to ensure that third-party vendors adopt sustainable practices uniformly across their operations. These will improve the SDG performances of AstraZeneca and also put it in a better position as a leader in sustainable and equitable health care.

References

Aoki, T., 2024. How Pharmaceutical Companies Utilize Platform Strategy: A Study of the COVID-19 mRNA Vaccine Development (Doctoral dissertation, Massachusetts Institute of Technology).

Dumalanède, C., Ciambotti, G. and Lashitew, A.A., 2024. Addressing Health Care Inequality Through Social Franchising: The Role of Network Stewardship in Impact Intermediation. Business & Society, p.00076503241255479.

Kuteesa, K.N., Akpuokwe, C.U. and Udeh, C.A., 2024. Financing models for global health initiatives: lessons from maternal and gender equality programs. International Medical Science Research Journal, 4(4), pp.470-483.

Napier-Raman, S., Hossain, S.Z., Mpofu, E., Lee, M.J., Liamputtong, P. and Dune, T., 2024. Abortion Experiences and Perspectives Amongst Migrants and Refugees: A Systematic Review. International Journal of Environmental Research and Public Health, 21(3), p.312.

Prust, M.L., Forman, R. and Ovbiagele, B., 2024. Addressing disparities in the global epidemiology of stroke. Nature Reviews Neurology, 20(4), pp.207-221.

See, C., Hegde, K., Reid, L., Shi, R., Luthra, R., Dong, W., Lee, V. and Kang-Giaimo, A., 2024. Peer Reviewed: The Cost of Medications at a Student-Run Free Clinic in New Haven, Connecticut, 2021–2023. Preventing Chronic Disease, 21.

Sideri, K., 2024. mRNA vaccine politics: responsible governance coordination for vaccine innovation in times of urgency. Journal of Responsible Innovation, 11(1), p.2425121.

Vyas-Doorgapersad, S., 2024. Understanding employee wellness for improved performance and achieving sustainable development goal 3. International Journal of Religion, 5(11), pp.2889-2899.

Willette, D., 2024. Paths to Achieving Scope 1 Carbon Neutrality in Building Utilities (Doctoral dissertation, Massachusetts Institute of Technology).

Zec, D.N., 2024. Media framing of sustainability: The case of British pharmaceutical companies (2000–2020). Sustainable Development.

BSc (Hons)/FdSc Computer Science

Systems Analysis and Design

Table of Contents

Part A 2

Task 1: Assessing SDLC Models 2

Task 2: Information Gathering with Interviews and Surveys 3

Task 3: Detailed Design Documentation 4

Task 4: User Interface Design 5

Part B 5

Task 1: Factors Affecting the Success and Failure of the New System 5

Task 2: Evaluation of Design and Future Improvements 7

References 9

References 11

Part A

Task 1: Assessing SDLC Models

Two SDLC models will thus be assessed for their suitability concerning parameters such as the expected size of the project, the degree of technical complexity, and the ability to accommodate potential changes while building the driving school system effectively. The Waterfall Model consists of a strict linear spanning process in which one stage must be completed before a move takes place to the next stage; therefore, it is suitable for smaller projects, which have a clear requirement. Advantages include transparency in planning and easy manageability. The main disadvantage of the model is that for larger or more complex projects, the requirements might change once the development has started, and further activities in this mainline might lead to prohibitive costs. Other than that, the Agile Model provides interactivity that accounts for the changing requirements through ongoing collaboration with the different stakeholders. It must be remembered that this ease of access as regards flexibility and adaptability is good for developing projects where the said requirements are either prone to changes or are not fully known at the start (Franch et al., 2023). Quite on the other side, Agile requires active customer involvement during the entire development process to manage scope well and avoid issues such as scope creep. 

The Waterfall Model 

The Waterfall Model comprises the traditional processes of SDLC, with linear and sequential characters. The model is subdivided into phases such as requirements gathering, system design, implementation, testing, deployment, and maintenance. Each phase ends before the starting of another phase, with very little recourse to return to or modify earlier phases once completed. 

One of the biggest advantages of the Waterfall Model is clarity and simplicity (Thesing et al., 2021). Each phase has clearly defined goals and deliverables; this makes it simple to follow development and the project manager’s tasks to check if everything has been done. Generally, this structured approach works well for smaller projects or those that have clearly defined requirements and are not likely to change. For example, suppose IT Driving School defines all its features and functionalities, such as booking a lesson, student record management, and scheduling tests; then the Waterfall Model will help in assuring systematic compliance with these requirements.

Another area where the Waterfall Model excels is in its insistence on documentation. At every stage of development, highly detailed documentation is prepared that serves as a reference point for stakeholders and also developers. This is especially relevant for the driving school system since this helps the development team to keep a very clear record of requirements, design specifications, and test results. The documentation helps ensure continuity even when team changeovers happen during the project. On the other hand, the Waterfall Model is also associated with certain disadvantages. The most significant disadvantage is that it is inflexible (Gumiński et al., 2023). When a phase is completed, it is difficult and expensive to return and make changes.

The Agile Model

Agile is an iterative and flexible model for software development. The Waterfall Model is not linear and unlike this. Here, the product development process is through small iterations or sprints, where each iteration is expected to deliver a functional element of the overall system (Iderima, 2023). Thus, stakeholders engage throughout the development process and provide continuous insight for possible changes to be made as per the growth of the project. Flexibility is one strength of the Agile Model. Being workable to changing requirements makes it well-fitted for projects where the exact scope may not be very well understood from the beginning. The Pass IT Driving School system could also take the initial functionality development in modules, such as lesson booking and managing student records, to build on other features like automatic test scheduling or progress analytics continuously with users’ changing requirements.

The Agile Model puts a high premium on collaboration between developers and stakeholders. It therefore follows that regular meetings or stand-ups would keep everyone on the same page, and possible issues can be majorly resolved the moment they crop up. With this well-coordinated effort, a significant possibility arises that the resultant product may qualify to meet user needs and expectations (Lelo and Israel, 2024). For instance, about Pass IT Driving School, there would also be input from instructors, office staff, and even students, while the specific requirements would be ensured. One of the advantages of Agile is that early and continued testing is emphasized. This allows developers to pull together various testing efforts, which can help detect and fix problems at any time during software development. Through this, as most major problems are easily resolved, less time is wasted and fewer resources are consumed.

Task 2: Information Gathering with Interviews and Surveys

Interviews will also be conducted to map the stakeholder’s needs completely. The interviewees will include instructors, office staff, and students. The conduct of the interviews will revolve around finding out the pain points for each of them under the old system, finding out what would be expected of the new system to improve the old system, and finding out what would be in a new system for each stakeholder. For instance, the instructors could be expected to field questions about their current issues on lesson scheduling and tracking student progress while the office staff could talk freely about administration tasks and data management necessities. Questionnaires will also be constructed to ascertain and validate a larger audience with implementation issues concerning the new system. Surveys will focus on investigating ways the system can improve efficiency, the user experience, and overall effectiveness in managing driving lessons and student records (Nousopoulou et al., 2022).

Surveys extend interviews instead of using opinions from a single audience. A good survey collects opinions on possible features, user interface desires, and improvements to the system. For example, students might be asked about their preferred methods for booking a lesson or receiving progress updates. This work would inform how to create a user-centered booking process. Surveys provide the additional benefit of finding ongoing problems or cross-verifying what has been stated in interviews (Schippers, 2024). This twofold strategy collects qualitative and quantitative data, thereby anticipating a problem that needs addressing, as well as a problem that has to be resolved.

Task 3: Detailed Design Documentation

Full-fledge details of design documentation for the Driving School System will comprise complete data flows at all standard levels such as context/0-level, level 1, and level 2 to publicly convey the flow of information in the system. These key processes include booking a lesson, student records, testing times, and assigning an instructor. In addition to the data flow diagrams, flow charts will be used to depict and detail each of the processes, including the decision points, inputs, and outputs for system operations and functionality (Seifermann et al., 2022). 

Detailed design documentation Pass IT driving school system targets the clear and structured representation of data flow and other processes of the system. Data flow diagrams describe how information moves through the system stepwise. The context-level diagram will show that the system functions as a process with some external entities comprised of students, instructors, and the school office. The process will subsequently be broken down in the Level 1 DFD into sub-processes such as booking lessons, managing payments, and scheduling tests. The Level 2 DFD will break this down further for detailed DFD representation of such sub-processes as input fields for the student’s booking request or the workflow for updating lesson progress (AHISHAKIYE, 2024). In addition to the DFDs, flowcharts for each of these will also be created to show the sequence of actions in such significant operations as booking a lesson or processing a payment. All these will contain decision points, inputs, and outputs for identifying logical workflows.

Task 4: User Interface Design

Classy modern designs are meant the user interface of driving school systems for the data entry forms. These will have structured formats for specified fields starting from students’ details, lesson schedules, instructor allocations, and even payment records. The methods of data entry shall keep in mind easy to use as well as highly accurate measures necessary for efficient capture and management of data. Hardware and software requirements will also be provided for use in the day-to-day operations of the system while considering scaling up and integrating with the existing IT infrastructure (Khan et al., 2023). This design approach will help to improve usability while making operations more efficient for staff and instructors at Pass IT Driving School. 

The UI design that will be involved in the development of the Pass IT Driving School system will always be a design that focuses on simplicity and usability. Each screen layout will have a clear layout and easily understandable navigation so that users can use their time accessing and managing information more easily (Alarcón et al., 2022). As an illustration, the data entry fields on the student registration form will include the following fields: name, contact detail, lesson type, and preferred instructor. Possible methods of data entry may include drop-down menus, radio buttons, and calendar pickers, and will help simplify the data input such that not much chance for errors exist. It will also show the instructor and staff dashboard features that show the schedules, upcoming lessons, and their payment statuses at a glance. The design will work well if accessed from any mobile device enabling users to easily access the system using smartphones or tablets.

Part B

Task 1: Factors Affecting the Success and Failure of the New System

Timeliness and Budget

Most importantly, completing the project on time and to budget affects a project adversely if there are delays. Operationally, a school can be highly disrupted by these delays, leading to huge stakeholder frustration, and therefore the project generally costs more as a result. Strong project management ensures that anything gets accomplished on time as agreed to. Once again, appropriate measures are put in place for budget constraints. Effective allocation of resources and prioritization of significant features make it possible to avoid overspending (Ahsun and Elly,   2024).

Organizational Policies

One more critical factor is compliance with the accepted policies and procedures of the driving school. The system must meet the current workflows related to booking lessons, progress tracking for students, and administration of tests. Where a policy conflicts with these processes, there would be a tendency for staff resistance to the system because it makes it difficult for them to use it.

Staff Skills and Training

The staff who would use the system should have sound skills and technical capabilities because the fate of the system would lie on them. In the case of being unused to using the system or lacking requisite technical skills, their actions can lead to errors as well as inefficiencies and thus to reluctance in adopting the system. Elaborate training programs have to be in place and available for employees to become familiarized with the system and to promote confidence in its use (Ugbebor et al., 2024).

Change Control

Implementing a new system will call for the appropriate change management strategies. Resistance to change is a historical fact especially when the affected stakeholders receive little manual processes. To avoid such, participation in field development processes should be extended to staff and instructors so that they can own the system and hence reduce resistance. Periodically communicate to them the benefits and improvement of the work processes through the system to promote acceptance of the use of the new system.

User Involvement and Feedback

The user involvement in developing the system is going to determine the success of the system. Interventions such as teacher feedback, student feedback, and office staff feedback during development would help build a system that meets an appropriate measure of needs for all its users. The absence of user involvement in the design of any new product can send the product in dire straits failing to develop a tool addressing the true source of pain and hence garnering dissatisfaction in use. The Pass IT Driving School will be positively or negatively affected depending on the completion schedule, budget, organization policies, and personnel skills (Nousopoulou et al., 2022). In summary, because new systems depend on effective project management, the appropriate alignment of organizational policies, capacity building in training, proactive management of change, and continued involvement of users, Pass IT Driving School will be in the best position to achieve smooth implementation and desired results from the system at that time.

Task 2: Evaluation of Design and Future Improvements

The system is very much aligned with the client requirements collated in the information phase for Pass IT Driving School. Features such as lesson booking, progress tracking, and instructor scheduling were had to address the operational issues of the school, whereas these are made with user-friendly interfaces for easy navigation by the user category, whether instructor, office staff, or student. Online and database searching options and reporting menus are also valuable for making data easier to retrieve and manage in operations.

Scalability is considered one of the major strengths of the system. Future iterations can include additional services such as the automated processing of payments and real-time notifications for lesson updates (Johnson et al., 2024). Another improvement area that can be targeted is developing a mobile app to accompany the web system, thus allowing easy access to the system via smartphones. Regular updates and maintenance will ensure that the system runs correctly according to the evolving user needs. Feedback mechanisms such as surveys or suggestion forms can also be put in place to capture change requirements. This is how the system will evolve with the working of the driving school to maintain a better future in success and satisfaction.

The computerized systems’ design for Pass IT Driving School aligns with the major requirements identified from the preliminary gathering of information of the user for the system (Rao, 2024). The functionalities of the computerized system include the following: booking, progress tracking, instructor scheduling, and report generation, among other school operation requirements. Interfaces ensuring user-friendliness and easy layouts ensure the systems, as it is for students, instructors, and office staff identities, without extensive learning curves and errors. Quick access to information will further enhance the overall management and decision-making processes with the reporting menus and searching options.

The system is scalable and adaptable in design. Some things can subsequently be added to the system to enhance it, like automatic payment processing for lessons, real-time notifications on bookings for lessons, and integrations with third-party tools like calendars or communications applications. Armed with these features, the system’s functionality and workability for its end-users can only get better. Future improvement requires the development of mobile compatibility (Kashyap, 2024). A mobile app would enable the instructor, students, and staff to access the system anytime to arrange lessons and update progress and schedule for easy access.  

References

AHISHAKIYE, M., 2024. ONLINE EQUIVALENCE APPLICATION MANAGEMENT SYSYTEM Case study: ONECS (Chad) (Doctoral dissertation, ULK).

Ahsun, A. and Elly, B., 2024. Optimizing Resource Allocation for Enhanced Project Efficiency.

Alarcón, J., Balcázar, I., Collazos, C.A., Luna, H. and Moreira, F., 2022. User interface design patterns for infotainment systems based on driver distraction: A Colombian case study. Sustainability, 14(13), p.8186.

Franch, X., Palomares, C., Quer, C., Chatzipetrou, P. and Gorschek, T., 2023. The state-of-practice in requirements specification: an extended interview study at 12 companies. Requirements Engineering, 28(3), pp.377-409.

Gumiński, A., Dohn, K. and Oloyede, E., 2023. Advantages and disadvantages of traditional and agile methods in software development projects–case study. Zeszyty Naukowe. Organizacja i Zarządzanie/Politechnika Śląska, (188 Nowoczesność przemysłu i usług= Modernity of industry and services), pp.191-206.

Iderima, E.C., 2023. The Impact Of Software Engineering On The Systematic Design And Development Of Instructional Systems.

Johnson, E., Seyi-Lande, O.B., Adeleke, G.S., Amajuoyi, C.P. and Simpson, B.D., 2024. Developing scalable data solutions for small and medium enterprises: Challenges and best practices. International Journal of Management & Entrepreneurship Research, 6(6), pp.1910-1935.

Kashyap, A.N., 2024. Development of Firmware for Control and Feedback System in an Upper-Body Exoskeleton (Doctoral dissertation, CALIFORNIA STATE UNIVERSITY, NORTHRIDGE).

Khan, K.A., Quamar, M.M., Al-Qahtani, F.H., Asif, M., Alqahtani, M. and Khalid, M., 2023. Smart grid infrastructure and renewable energy deployment: A conceptual review of Saudi Arabia. Energy Strategy Reviews, 50, p.101247.

Lelo, J.M. and Israel, B., 2024. Supply Chain Innovative Practices and Customer Satisfaction: Insights from Manufacturing SMEs. Management Dynamics in the Knowledge Economy, 12(1), pp.54-69.

Nousopoulou, E., Kamariotou, M. and Kitsios, F., 2022. Digital transformation strategy in post-COVID era: Innovation performance determinants and digital capabilities in driving schools. Information, 13(7), p.323.

Nousopoulou, E., Kamariotou, M. and Kitsios, F., 2022. Digital transformation strategy in post-COVID era: Innovation performance determinants and digital capabilities in driving schools. Information, 13(7), p.323.

Rao, W., 2024. Design and implementation of college students’ physical education teaching information management system by data mining technology. Heliyon, 10(16).

Schippers, D., 2024. Understanding the Value and Adoption of the International Patient Summary: Exploring Clinical Value, Facilitators, and Barriers (Master’s thesis, University of Twente).

Seifermann, S., Heinrich, R., Werle, D. and Reussner, R., 2022. Detecting violations of access control and information flow policies in data flow diagrams. Journal of Systems and Software, 184, p.111138.

Thesing, T., Feldmann, C. and Burchardt, M., 2021. Agile versus waterfall project management: decision model for selecting the appropriate approach to a project. Procedia Computer Science, 181, pp.746-756.

Ugbebor, F., Aina, O., Abass, M. and Kushanu, D., 2024. EMPLOYEE CYBERSECURITY AWARENESS TRAINING PROGRAMS CUSTOMIZED FOR SME CONTEXTS TO REDUCE HUMAN-ERROR RELATED SECURITY INCIDENTS. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), pp.382-409.