Main Points: 

I.          Efficiency

a.     Ensures the functioning of critical healthcare systems without hindrance

                                          i.         The major advantage of using AI in healthcare operations (AIOps) is the significance of maintaining vital systems so that medical operations can continue functioning during any technical problem. 

                                        ii.         Response to the evidence: AI helps to ensure that no time is wasted as the healthcare setting is not affected in any way during technological issues, which consequently saves energy as well as ensure the patient’s wellbeing.

b.    Aids the doctors

                                          i.          

Main points:

--Stimulation of the immune system (composition of vaccines/ immune response)

--development of immunity (synthesis of antibodies/ memory cell activation)

-- safety and side effects (adverse reactions/ negligible side effects)

https://mylibrary.qu.edu.qa/search~S1?/YSpinal+muscular+atrophy+%28SMA%29&SORT=D/YSpinal+muscular+atrophy+%28SMA%29&SORT=D&SUBKEY=Spinal+muscular+atrophy+(SMA)/1%2C3%2C3%2CB/frameset&FF=YSpinal+muscular+atrophy+%28SMA%29&SORT=D&1%2C1%2C

Since the old times people have traded using things like pebbles or rocks, and as society developed throughout the years, multiple types of currencies have come out such as paper-based money, coins, and even digital currencies like bitcoins. Later credit cards were invented for its convenience and quicker access to cash, However, due to the recent impact of Covid-19 there’s been a change to cashless society. This means relying on cashless methods rather than using paper-based money or coins, it comes with its fair share of disadvantages for both individuals and financial banks where there is a rapid growth of criminal activities, risk of losing personal bank account data and loss of privacy.

Additionally, AI promotes productivity by encouraging automation of daily tasks of employees working in various companies. Working on several tasks simultaneously can be quite challenging for employees and might negatively affect company’s output production. Polak explained that AI makes the employees more efficient by taking responsibility of performing daily tasks which enhances labor skills and hence ensures that more work is done in less time (as cited in Shen and Zhang, 2024). In other words, when AI is used to complete a specific set of tasks, people who were previously responsible to perform them can develop prioritization skills by working on other important assignments. For instance, employees can work on a project which requires more attention and will bring more profit to the company without worrying about less important assignments, which will be managed by AI. Therefore, they will be able to complete more tasks within the same period, thus increasing company’s output and generating more sales.

            Despite this benefit, the major disadvantage of AI on our community is that it reduces student’s potential to be successful. AI hinders the development of fundamental skills needed by students to achieve high grades in school. Sánchez-Ruiz et. al. suggested that over-dependence on AI systems like ChatGPT can adversely affect critical thinking and problem-solving skills of students (as cited in Hasanein & Abu Elnasr, 2023). That is, students who use ChatGPT to solve their assignments are less likely to learn and are unable to think independently or solve problems on their own. Similarly, they hesitate to make key decisions that involve analyzing various aspects and measuring potential risks associated with the task. For this reason, they usually show poor academic performance and are unable to seek reputable jobs in future, thus hindering their way to success.

            Moreover, introduction of AI in educational institutes leads to lack of support given by the teachers to weak and newly enrolled students. Although AI can give rapid answers to the homework questions without the need of teacher’s assistance, they are still needed to provide guidance on certain aspects, where AI is of no use. Chiu et. al. explained that while exceptional students enjoy engaging with AI chatbots, newly enrolled students require emotional support of their teachers to get motivated. (as cited in Han & Lee, 2024). What the author means is that AI chatbots lack the ability to counsel newly enrolled students and provide them with emotional and moral support needed by them to get comfortable in new setting. This guidance can only be provided by teachers who can personally encourage these students to excel at college. However, the introduction of AI in schools leads to lack of this assistance as the teachers are getting replaced, which consequently leads to their poor academic performance and reduced ability to succeed.

            Lastly, the security and privacy issues associated with AI is its additional disadvantage on people. The use of AI has increased the number of cybercrimes around the world as significant rise in identity thefts incidents has been observed in last few years. Suwajanakorn et. al. argued that an important and well-known case of deepfake creation that has been reported is a one-minute video with over 4.8 million views since April 2018, featuring the former US President Barack Obama. (as cited in Sandotra & Arora, 2024). To put it more simply, primary threat and ethical concerns with implementing AI in various institutions is digital security, as hackers may attack networks and exploit data for unethical purposes. This includes impersonating other people without their knowledge and performing tasks which will cause harm to them. Hence, this can tarnish their reputation in a society and lead to unfavorable legal actions against the creator.

           Furthermore, by introducing AI to their settings, institutions can face financial issues owing to the increased need of hiring skilled staff.

The major difference between object-oriented and functional programming approaches is the principles and theory of each one. Functional programming, on one hand, primarily concentrates on the processes to do on data, rather than data itself. Based on a mathematical branch that is called Lambda Calculus, and using pure functions, one of its concepts, it does that. According to Jensen (2019) functions are used as the main building block of functional programming model. In other words, every piece of a code written using this style is a function, that is, the function has all the operations to do on data. Consequently, any functional program is totally constructed of functions, and all needed procedures are organized in these functions in a way that achieves this style’s goals. On the other hand, the concepts of class and object are the essential ones of object-oriented programming. In fact, object-oriented programming is an abstract simulation of the way life and nature are organized. A class is the blueprint or template for its objects, objects are instances of a class, and these are at the core of object-oriented programming (Sarcar, 2019). To put it simply, a class is a definition consists of some attributes and methods that describe a particular object. An object can or can be not a real-world entity. For instance, mammals are one of the many classes existing on earth, and humans are one example of this class. Similarly, data is the point of interest in an object-oriented program, and it is organized and classified into large groups that can be instantiated, and endless number of objects can be created of them.

Another difference between these two styles of programming is that each one provides unique benefits that cannot be provided by other programming paradigms. Although functional programming has various advantages, data caching and restoring is one of its most notable benefits. This great benefit of this style is a result of its structure of pure functions. Because the pure function is going to always return the same output for the given input, it is possible to cache the function’s outputs (Aravinth & Machiraju, 2018). In fact, whether a mathematical or a pure function, a function does some specific processes on inputs, and gives an output that will never change, which enables a program of saving the function’s outputs so it can be retrieved effortlessly any time without rerunning the function more than one time. Caching is not only beneficial for reducing the pressure on networks, but also enhances the user experience and shortens loading time. On the contrary, the object-oriented paradigm has its own benefits as well, and one of these benefits is inheritance. Inheritance, in simple words, is one of the four concepts that is produced by the idea of classes and objects, and it is the ability to have a subclass and a super-class in programs. The great thing about inheritance is that it provides a code reuse mechanism, and a code written in a super-class can be reused by its subclass (Sharan & Davis, 2022). That is, the subclass can have the definition of its super-class, as if it inherits it, its methods, and attributes, besides its own definition. In consequence of that, codes written in this style are shorter, and more readable.

The last difference between object-oriented and functional programming is how difficult each one is to learn and master. For functional programming paradigm, and even though it is a very practical and concise one, it is considered to be more challenging to grasp, and the reason behind that is the mathematical component of it. In fact, Lambda calculus, the base of the functional paradigm, is a formal system based on the definition of function and its applications (Machado, 2013). Thus, understanding what a function is and how it works is the basic step towards this approach, which means, as well, the need of some mathematical knowledge and experience. Because of that, this paradigm is more difficult to work and design with compared to other programming paradigms. In converse, the object-oriented style of programming represents an easy start to learning programming, specifically if compared to the functional paradigm. The special criterion about object-oriented programming that makes it close to most peoples’ understanding are the simple, yet complex concepts it has. It does not only help programmers relate their programs to daily life, but also makes programming a joyful job. Additionally, its neat and well-organized and arranged structure eases the process of maintaining any program written using it, which as a result reduces the confusion when dealing with it.

Additionally, facial recognition technology can offer customers a streamlined experience. Using facial recognition to make a purchase is seen to be much more convenient and efficient. Seng et. al.(2021), conducted a study and reported that instead of entering a credit or debit card, using facial recognition to make a purchase was seen to be more convenient in the eyes of the consumer. The speed of transactions made by facial recognition is faster than entering a physical card which is why many customers prefer it.

Additionally, gene editing improves our immune system in protecting us. Abdelnour et al. (2023) mentioned that CRISPR/Cas9 is a gene that was recognized as a bacterial defense mechanism that increases the immune response against viruses. By using gene editing, the body’s ability to prevent infections and fight off viruses can be increased. This technology can improve our immune system in detecting and destroying harmful viruses because, it alters specific genes like CRISPR/Cas9 to recognize viruses. It has the ability to fight diseases by changing the DNA of the organism that causes illness, thereby increase the disease prevention.

Another benefit of gene editing is boosting agriculture. It makes crops stronger against pests and diseases. By changing the genetic information, plants can grow faster and produce more food. This technology has also made fruits and vegetables last longer on the market rather than getting rotted in a short period of time. Permyakova et al. (2024) stated that advancements in genetic engineering techniques and the availability of plasmid libraries have enables researchers to accurately modify genes, creating new avenues for enhancing crops. This study shows how technology is helping scientists change genes in crops more precisely, leading to improved crop quality and productivity. By using these advanced tools and resources, researchers are revolutionizing how they enhance crops for the future. Creating genetically modified crops that are more resilient to pests, diseases, and environmental stressors increases food securing and improves agriculture.

Moreover, genome editing can be considered a important part of reducing food waste by preventing food from being wasted. Denver (2019) mentioned that gene editing tools present the potential for minimizing food waste by improving harvest efficiency and extending the shelf life of produce. By using gene editing tools, we can prevent food from spoiling quickly, ultimately reducing the amount of food that gets thrown away. This research shows that gene editing can help us produce and preserve food more effectively, contributing to minimizing food waste. It will decrease losses occurring before and after harvesting while transporting crops from the farm to storage and market.

However, Gene editing poses the potential risks if creating undetectable genetic modifications, leading to ethical dilemmas regarding inequality, transparency, and consent. Wadhwa (2017) asserted that advancements in gene editing are emphasizing an urgent necessity for ethical and legal standards to govern in vitro genetic manipulation, while also prompting worries about a future where wealthier individuals might use CRISPR to enhance their children. Secretly chsnjng genes can make people trust in science less, keep some people from getting the benefits, and take away others’ right to control their genes. To use gene editing fairly and responsibly, it’s important to be honest, think about ethics, and make sure everyone has a chance to benefit from it. Using genetic modification for purposes like making people stronger or smarter could create unfairness in society, because it costs a lot of money, not everyone will benefit equally. Thie could make existing social gaps even worse and keep them going.

 Moreover, obesity-related diabetes can lead to joint inflammation. The common condition of (IGT), in obesity can cause joint discomfort. This can damage the musculoskeletal system and limit mobility. Diabetes can also cause changes in nerves and small blood vessels, leading to abnormalities in the hands and other joints. According to Huang et al. (2024), studies have underscored an association between diabetes and Osteoarthritis (OA), with OA often linked to insulin resistance. This is further exemplified by a connection between two substances - leptin, a hormone that regulates body weight, and the antagonist of the interleukin-1 receptor, a protein that controls inflammation - and insulin resistance and (OA). The presence of certain substances in people with insulin resistance and OA indicates a connection between T2DM and OA, implying that diabetes-related factors also affect OA development.

The first and most important aspect of comparison between the two languages is readability and writing. Python stands out for its simplicity and readability, with a syntax designed to resemble natural language, as highlighted by Khoirom (2020). In contrast, Java's verbosity, acknowledged by Schildt (2018), requires more boilerplate code, making it less straightforward for developers, especially beginners or those accustomed to Python's concise syntax. For instance, while printing "Hello, World!" in Python requires just one line of code, Java necessitates defining a class and a main method, resulting in more cumbersome syntax. Overall, Python's simplicity and natural-language-like syntax contribute to its readability and ease of use, while Java's verbosity poses challenges for understanding and writing code, particularly for beginners or those familiar with Python's concise syntax.

The performance comparison between Python and Java is pivotal for developers' language choice. Java typically outperforms Python due to its static typing and bytecode compilation, as noted by Gosling (2014) Java's static typing enables early error detection and compiler optimization, enhancing efficiency. Conversely, Python's dynamic typing and interpretation process may lead to slower performance, noted by Lutz (2013). For instance, in complex algorithms, Java's optimization during compilation typically results in faster execution. While Python emphasizes readability and ease of use, Java's performance advantages suit speed-critical applications. Developers must weigh these factors when selecting the appropriate language, considering performance needs and maintainability.

            Compared to the previous comparisons, diversity and uses are no less important. Python's versatility is evident in its widespread adoption across domains such as rapid development, scripting, scientific computing, and AI applications (Downey, 2015). Python's concise syntax and rich libraries make it ideal for tasks like rapid prototyping, data analysis, and AI development, supported by renowned libraries like NumPy and TensorFlow. In contrast, Java's strength lies in enterprise-level applications, web development, and mobile app development (Schildt, 2018). Java's platform independence and comprehensive standard library enable building complex applications across diverse environments, illustrated by frameworks like Spring for web development and its support for Android app development. Understanding the diverse applications of Python and Java is crucial for selecting the most suitable language for specific project requirements.

            Compatibility and target platforms are crucial considerations when comparing Python and Java. Java's platform independence, facilitated by the Java Virtual Machine (JVM), ensures seamless execution across various operating systems and hardware architectures (Gosling, 2014). The JVM abstracts the underlying platform, allowing Java applications to run without recompilation. This compatibility is vital for enterprise and large-scale systems. Python also offers cross-platform support, but compatibility may vary due to library availability and versioning issues (Downey, 2015). Differences between Python versions can pose challenges. In essence, while Java ensures compatibility through the JVM, Python's cross-platform support may be subject to library dependencies and version variations, highlighting key considerations for developers.

Another aspect of comparison between Python and Java is their respective communities and libraries. Python is renowned for its vibrant community and extensive collection of libraries and frameworks, covering diverse application domains (Lutz, 2013). The Python community actively contributes to library development, fostering collaboration and innovation. From web frameworks like Django to data science tools like NumPy, Python offers a wide range of resources. Conversely, Java also has a sizable community, particularly in enterprise and Android development (Schildt, 2018). However, Java's community may lack the diversity and breadth of Python's. While Java boasts frameworks like Spring and Android SDK, Python's ecosystem often surpasses Java's in variety and specialization. Ultimately, Python's active community and rich library ecosystem contribute to its versatility and attractiveness among developers, highlighting the importance of community and library support in language selection.

The final comparison point is the learning curve and academic use of Python and Java. Python's popularity in academia stems from its simplicity and ease of learning (Lo, 2015). Its clear syntax and readability make it accessible to beginners, allowing them to focus on fundamental programming concepts without complexity. Python's versatility across different domains also makes it suitable for diverse educational needs. In contrast, Java may pose a steeper learning curve due to its verbosity and emphasis on object-oriented programming (Schildt, 2018). While Java offers insights into industry practices, its complexity may require more effort from students. Overall, Python's simplicity makes it ideal for academic use, fostering a supportive learning environment. Java, despite its challenges, provides valuable insights into object-oriented programming. Understanding these differences is crucial for educators when selecting languages for instructional purposes.

            In conclusion, the comparison between Python and Java highlights their distinct characteristics and applications. Python's simplicity and readability make it popular for beginners and professionals alike, while Java's verbosity offers robustness for complex enterprise projects. Python excels in rapid development and scientific computing, while Java dominates in web and mobile app development. Both languages have large communities and libraries, with Python's community being particularly vibrant. In academia, Python's ease of learning prevails, although Java remains prevalent. Ultimately, the choice between Python and Java depends on project requirements, performance needs, and developer familiarity, with each language offering unique strengths in software development.