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There are several materials and settings that determine the strength of a 3D printer. Energy, matter, and thermodynamics are essential considerations in a 3D Printer. 3D printing most occurs with a shell and closed-cell infill. This saves material and printing time while maximizing length. The top and bottom surfaces experience the most force when bent. The strength of the beam can optimize this. (Physics of 3D Printing, 2021) Energy is also vital for printing. It provides mechanical energy to power the electronic parts of the printer. It also allows the user to know the status of the printer.   MATTER The matter of a 3D printer explains the components of the printer. This includes its extruders. They are crucial components in 3D printers. The extruder is the tool that holds the filament of the printer. It controls the amount of energy fed into the filament's hot end. The hot ends are attached to the extruder. They are the primary location tasked with the melting process. (Physics of 3D Printing, 2021) The extruders contain a stepper motor that allows the filament to be fed through. They also form a gearing and hobbed shaft that hold the filament in place. The fan regulates the temperature in the filament. Some 3D printers come with dual extruders. The dual extruders support holds the designs of the printer. It also can print with multiple materials within a single object. 3D printers have two intruders, direct extruders and Bowden. Direct extruders have their motors driving the filament. (Physics of 3D Printing, 2021) They also have hot ends directly attached to the extruder body. Bowden contains a separation tube between the extruder. It includes the motor and the other components attached to the printer chassis. Print Bed: It is the part that the printed objects rest on during printing. (Physics of 3D Printing, 2021) It always moves down to allow for the next layering step. It should provide the sufficient adhesion to the molten material to ensure that objects adhere to the bed. It has aluminium and glass materials that offer a smooth surface for the things to rest on. The user can apply the gluing agent to improve cohesion. Hot Ends: it is where the filament is melted before being extruded through a nozzle. There is also Enclosure that provides safety and better temperature management for better printing results. It also ensures that the internal ambient temperature of the printer is stable, thus reducing printing issues such as warping and cracking. The filament is the coil of composite or thermoplastic. It is fed through the printer and then into the extruders, which are melted and then extruded. It is cost-effective and thus preferred by many organizations. (Physics of 3D Printing, 2021) Layer height is the thickness of the printed layers of the objects. A smaller layer yields better printing quality objects because of better interlayer cohesion. However, it requires more printing time. The Fibers are thin wires that connect different electrical components of the printer. Their strength depends on the bond materials they are made of—for example, carbon fiber and fiberglass. ENERGY Energy in a 3D printer is required to power the machinery components in the printer. It mainly consists of both mechanical and electrical energy. 3D printing uses different ways to produce and store energy. The energy produced is transformed from one form to another. It includes electricity generation technologies such as wind turbines and solar panels. There is also conversion hardware, such as batteries and generators. (Physics of 3D Printing, 2021) Scaled models and prototypes can also improve and transform products into their complete final form. The energy consumption in a 3D printer depends on the type of printer and the general status of the printer. There are 3D printers that consume more power while others consume relatively less. Printers with low energy efficiency consume more power and thus electricity costs. The filament also determines the amount of energy to be consumed. Each material of the filament melts at different temperatures. For example, printing with PLA reduces energy consumption compared to printing with ABS. (Physics of 3D Printing, 2021) 3D printers have a power supply that converts high voltage Alternating Current to low voltage Direct Current. Their power supply has a maximum rating that cannot succeed. The amount of energy consumed by a 3D printer depends on the type and status of the filament. For example, a BCN3D Epsilon W50 printer has a maximum power consumption of 840W per hour. When printing with both extruders, the filament must be melted to a higher temperature to consume as much energy as possible. When printing with only one extruder, the filament should be melted at a lower temperature in order to consume less power. This is recommendable as it is cost-effective. The printing speed also determines the energy consumption rates. (Physics of 3D Printing, 2021) Printing at higher speeds significantly reduces overall energy consumption. This is because the print heads need more energy to go faster. There is also direct energy deposition that only focuses on thermal energy such as electric arc, electron beam, or laser to fuse the wire or powder feedstock as it is deposited. The energy consumption in a printer can be reduced or minimized. The user should print in supplication or mirror mode. This means that the printing should be with the two extruders simultaneously. It reduces energy consumption by almost half. Printing with the recommended settings and configurations also minimizes energy consumption. This is because these configurations have been tested in a laboratory and proved perfect and efficient. (Physics of 3D Printing, 2021) Using modern printing technology also reduces energy consumption compared to traditional methods. This is highly recommended. 3D printers operate at a complete power supply. They do not store energy to use when the power supply is off. THERMODYNAMICS OF 3D PRINTERS The thermodynamics in 3D printers relates to heat, energy, and temperature. It deals with the transfer of power in one form to another and from one place to another. Thermodynamic concepts in 3D printers are applied in different parts of the printer. The hot end and the filament apply heat concepts and thus thermodynamics. There are also heated print beds that convert heat to thermal energy for transmission. (Dynamics, Thermodynamics and Mechanics in 3D printing, 2020) The heated print beds minimize the chances of object warping. This is because they provide the necessary heat to the print bed's first layer, ensuring faster cooling of random pockets. The printer also contains aluminum beds that offer uniform heat distribution. It can convert heat to thermodynamic energy for equal distribution. The filament uses heat to melt it in the hot end. They contain a thermal barrier tube with a heat break that cuts off the circuit when it exceeds the desired temperature. It also has a heatsink that absorbs all the heat emitted from the printer's filaments and other heating materials. The thermal sink also provides twofold properties. (Dynamics, Thermodynamics and Mechanics in 3D printing, 2020) The thermal barrier is connected to the heat block from the bottom section where the filament is melted. Thermodynamic temperatures are controlled and lowered by the heat break to prevent overheating of the filament. This process is called heat creep. There is also a sealed-off printing environment in the Enclosure. The sealed-off printing environment ensures better printing results and proper utilization of resources. High temperature in the Enclosure causes overheating and cause fumes on the printing materials. (Dynamics, Thermodynamics and Mechanics in 3D printing, 2020) As a result, the printer needs glass filters to reduce the dangerous particles due to fumes. The Enclosure also ensures that the internal temperature of the printer is stable, thus reducing warping and cracking. Thermodynamics concepts are also applied in the filament. The filament contains a coil of thermoplastic with various diameters. The filament is fed through the printer and intruders, where it is melted and extruded. The filament also has other materials, such as wood, to control temperatures. Warping causes shrinkage of the 3D printed object. It is caused by non-uniform cooling, where the printing layers have different cooling times than the heated parts. It can be prevented by ensuring that the 3D printer has a heated bed with a metal plate. This ensures uniform temperature distribution throughout the material. (Dynamics, Thermodynamics and Mechanics in 3D printing, 2020) Ambient temperature fluctuations also cause cracking of the printer objects. It can be controlled by using an enclosure that allows changes in the ambient temperature. References Dynamics, Thermodynamics, and Mechanics in 3D printing. (n.d.). Frontiers. Retrieved September 17, 2022, from https://www.frontiersin.org/research-topics/31193/dynamics-thermodynamics-and-mechanics-in-3d-printing Physics of 3D Printing. (2021, April 8). Markforged. Retrieved September 16, 2022, from https://markforged.com/resources/learn/design-for-additive-manufacturing-plastics-composites/understanding-3d-printing-strength/physics-of-3d-printing

Abstract There is a dire need for creating complex solutions to the problem of cybercrime. Developers have effortlessly placed themselves on the fore front of for creating solutions backed with programming to help the monitor large businesses networks. This paper is a documentation of the use of python in cyber security. The many solutions that can be used in python have left developers spoilt for choice. There are various programming languages within python that can be used for digital forensics some of which can be created to be used in LANS. Through the use of open-source software, the program is built to run on hardware of lower cost. The program is also meant to run on demand and scan across networks Introduction Cyber experts have experienced the growth of the popularity for python since it was first introduced. Python has become popular and as such making it a programming language which is quite high-level. Python has gained all the popularity owing to its effectiveness and much more number of libraries. The adherence to readability code and simple syntax has also made python to be used globally by users from all walks of life. Python is so effective such that whatever command it receives, it does the task swiftly and with precision. A user may use python to send TCP- packets to machinery, create intrusion prevention measures, and perform an analysis on malware without any assistance from the third party tools. Any cyber expert would be keen and excited to learn more about python and the use of programming skills. Python in essence offers the ability to identify any possible challenges and fix them in time. The use of python in cyber security is one of the many ways that digital programing has come to gain traction. Many people have come to embrace the role that programming languages have over cyber security. It experts are expected to write scripts and applications. The python language beats all the rest and no other language can match with it in terms of cyber security solutions. Python has earned the admiration of many because it is easy to use and quite flexible and the development needs are quite little compared to other programming languages. This task evaluates the use of python in cyber security and digital forensics. Background information Cyber security experts require programming skills to effectively expedite cyber security. The knowledge on programming is essential for analyzing any likelihood of vulnerability. Malicious software can be identified through programing languages along other risks and as such security analysts should be well versed with that kind of knowledge. An ideal cyber security professional can have knowledge on administration of systems, virtualization software, operating systems and several others. This research is crucial in an attempt to identify how python is used to fight cybercrime as well as the merits it tags along. The research paper contains a brief history on the subject, literature review of the previous research done on the use of python in cyber security, a discussion on the findings and the conclusion. Literature review Technology tools and threats are increasingly evolving and if the experts in the cyber security crime sector do not go with the pace then there are chances that they will be deemed ineffective and lacking relevance. There is chance that if experts do not embrace the ever growing technology then with time they will be unable to provide the increasing demand of the organization defense. In a scenario where one would be using an operating system with new features creating interesting artifacts for forensics but sadly they don’t halve the tools for accessing it. Guido Van Rossum between the years 1985 and 1990 invented the python. The design for python has a focus on the ability of allowing programmers to code and write instructions with fewer codes. Python compared to java has a lower case lower than the likes of Java. The automatic memory management system that gives it the permission to support various programing efforts in python is very unique. There are many possible options which can be used for cyber security but nonetheless python is the most preferred. There are many factors that leave programing being the best for the cyber security task which include easy to learn, wide usage , easy to debug , and is object oriented. Python provides a language library that enables hacker’s testers experts with all the features in a spectrum to create novel programs that are strong and powerful as well as fit for the task. The modules that pythons come with supports web activities like building clients, XML and parsing HTTP. A rapid methodology of application development can be used by web developers needing open source frameworks. Third party tools comes with some advantages such as extra features like optimized handling of calculations making python a more reliable language for programming and application of data. Cyber experts benefit from the use of python and having deep knowledge about the language can be very instrumental. Coding programs is done with ease by the web experts as the language provides with a rapid and compatible platform for the same. Serious security professions need to be up-to speed with the use of python and the kind of combination used to make the very basis of this python. With python, one can be either completely lacking in the knowledge about python or very experienced in the field. Whether or not one has knowledge about python, they can easily make programs in the language that make their work is made easier. There is a dire need for creating complex solutions to the problem of cybercrime. The python language beats all the rest and no other language can match with it in terms of cyber security solutions Developers have effortlessly placed themselves on the fore front of for creating solutions backed with programming to help the monitor large businesses networks. Many people have come to embrace the role that programming languages have over cyber security. It experts are expected to write scripts and applications. Python has earned the admiration of many because it is easy to use and quite flexible and the development needs are quite little compared to other programming languages. The use of python in cyber security is one of the many ways that digital programing has come to gain traction. Organizations in the future need to embrace the use of python in their networks to ensure protection against cybercrime. There is a need for all experts to emphasize on the use of python for cyber security. The programming language has been for so long and its effectiveness has seen python become popular in cybercrime. Experts have experienced the growth of the tool that works without the need for any external interference. Discussion The versatile python language of programming has very many uses and one of them is in cyber security. Python programming language has been used widely on cyber security, digital forensics and penetration testing. There are various aspects and dimension within which python operates such as port scanning, socket programming and geo-location. Domains like mobile app development, cloud computing, web development big data analytics, and network forensics largely depend on the powerful programming language python. For two decades now, users have relied on python and it has been very effective. Python programming also supports other various programming aspects such as functional, object, procedural and imperative oriented. There are various high performance uses of python in computing applications. Companies like Google, IBM, Microsoft, Red hat and amazon among many others rely on python programming for most of their computing solutions. Python delivers as an open source on implementations and other interfaces for dynamic languages and programs. It is this high reliability of python that has made it such a huge and effective tool for cyber security. There extensive use of python for cyber security and digital forensics is nothing to be ignored. The base programming in python, basic activities can be performed without intervention from other tools. Simulation of attacks, wireless scanning of networks, access to mail servers, port scanning, website cloning and creating systems for detection and prevention of intrusion are just but the few tasks that python can successfully expedite. Other implementations that relate to security applications and digital fingerprints can also be carried out by use of python. Network port scanning Implementation of network port scanning is done using nmap tool alongside python socket programming. The implementation of network port scanning can be done without any interference from a third party. Python incorporates just the use of a few instructions lines to expedite and implement network related forensics. Although there are many other available solutions to solve network related and digital forensics, python is the most effective and fastest. Geo-location extraction Python and Google APIS enable real-time locations for any IP address that can be extracted using the pygeoip module. Python in collaboration with Google APIS offers IP addresses that have been scanned thoroughly and with global location and visibility. Real time social media extraction. Python scripts enable the extraction of data in real-time from social media. The many modules in python can help interface connections with twitter, whatsaap, LinkedIn and Facebook. The python programming language is preferred by many users and experts for the following reasons: Easy to learn- python compared to any other programing language is quite easy to learn about it. The programming language is user friendly and works on minimal or no codes. Code readability for python is improves fat. Wide usage The work and roles of python are so diverse and it can be used for very many activities. Python programming language has essentially has a very extensive use and is not limited to little software. Python can also be used to enquire and support web products like Reddit, quora and YouTube. Object oriented language The knowledge about python is very beneficial to users who might be well informed of the programing language. Individuals can easily have more understanding about object –oriented languages for instance java. The only thing required for the user to learn and understand is to learn the syntax for the new language. Open source language Python is availed to developers at zero or minimal for these reasons, many consumers are very excited about this task making python the most preferred language was developers and company. Conclusion The extensive library with rapid application packages and rapid development in python makes it the better alternative for cyber security. Python also benefits from the following few features, modular design with clean syntax code. Better response time, structure, data security which absolutely the best idea. Digital forensic applications and cyber networks have the ability to activate python as one of the best cyber security solution. The programing C language (c++) is the best programming in python. Experts in the field of cyber security, forensic analysts, penetration testers and incident responders have all experienced one common aspect over the years in the industry; change. There have been numerous changes in the field such as a change in technologies tools and threats. The theme of change is completely constant in this field and the networks have been experiencing revolutionary change. Python has essentially provided a long lasting solution to the experts who no longer have to stress about how to use it. Python programing language is completely user friendly. The penetration testers and pother experts are able to keep up with the pace of technology change in the field. Experts are enabling through python, to write tools that are custom and automate activities for easy management and response to unique threats. References Abomhara, M. (2015). Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks. Journal of Cyber Security and Mobility, 4(1), 65-88. Architects, C. (2017). Python, Hacking & Advanced Hacking: 3 BOOKS IN 1 THE BLUEPRINT Everything You Need To Know For Python Programming and Hacking!(Volume 5). Architects, C. (2018). PYTHON & HACKING BUNDLE: 3 BOOKS IN 1 THE BLUEPRINT Everything You Need To Know For Python Programming and Hacking!. Barbu, I. D., Pascariu, C., Bacivarov, I. C., Axinte, S. D., & Firoiu, M. (2017, June). Intruder Monitoring system for local networks using Python. In 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1-4). IEEE. Benson, V., McAlaney, J., & Frumkin, L. A. (2019). Emerging Threats for the Human Element and Countermeasures in Current Cyber Security Landscape. In Cyber Law, Privacy, and Security: Concepts, Methodologies, Tools, and Applications (pp. 1264-1269). IGI Global. Cui, H., & Li, F. (2018, September). ANDES: A Python-Based Cyber-Physical Power System Simulation Tool. In 2018 North American Power Symposium (NAPS) (pp. 1-6). IEEE. Cohen, A., Stern, A., & Fatakhov, G. (2019). U.S. Patent No. 10,262,133. Washington, DC: U.S. Patent and Trademark Office. Gupta, B., Agrawal, D. P., & Yamaguchi, S. (Eds.). (2016). Handbook of research on modern Cryptographic solutions for computer and cyber security. IGI global. Miao, Q., Li, H., & He, Z. (2018, July). Campus Network Control Management System Based on Python. In International Conference on Applications and Techniques in Cyber Security 6and Intelligence (pp. 856-865). Springer, Cham. Naik, S., & Maral, V. (2017, May). Cyber security—IoT. In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 764-767). IEEE. Pandey, R. K., & Misra, M. (2016, December). Cyber security threats—Smart grid infrastructure. In 2016 National Power Systems Conference (NPSC) (pp. 1-6). IEEE. Raj, P. S., Silambarasan, G., & Scholar, M. P. (2017). Role of data mining in cyber security. International Journal of Engineering Science, 13932. Raval, R., Maskus, A., Saltmiras, B., Dunn, M., Hawrylak, P. J., & Hale, J. (2018, April). Competitive Learning Environment for Cyber-Physical System Security Experimentation. In 2018 1st International Conference on Data Intelligence and Security (ICDIS) (pp. 211-218). IEEE. Thakur, K., Qiu, M., Gai, K., & Ali, M. L. (2015, November). An investigation on cyber security threats and security models. In 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing (pp. 307-311). IEEE. Tesfahun, A., & Bhaskari, D. L. (2016). A SCADA testbed for investigating cyber security vulnerabilities in critical infrastructures. Automatic Control and Computer Sciences, 50(1), 54- 62. Tweneboah-Koduah, S., Skouby, K. E., & Tadayoni, R. (2017). Cyber security threats to IoT applications and service domains. Wireless Personal Communications, 95(1), 169-185. [16]. Vaughn Jr, R. B., & Morris, T. (2016, April). Addressing critical industrial control system cyber security concerns via high fidelity simulation. In Proceedings of the 11th Annual Cyber and Information Security Research Conference (p. 12). ACM.

Machine learning is a technique of data analyses used by computer scientists and programming experts to automate analytical model building. It is a special area in artificial intelligence based on the idea that computers can identify data patterns, learn from them, and make appropriate decisions with minimal human assistance (Holzinger, 2016). This paper describes and evaluates machine learning processes and techniques used in modern computer systems and automated machines. Labeled vs. unlabeled data sets Labeled data comes with a label, while the counterpart does not. Supervised learning uses labeled data because it contains meaningful tags used modeling. On the other hand, the unlabeled dataset bears natural and human-created artifacts that can be used by unsupervised learning. Supervised Machine Learning Supervised learning is common in various applications in modern-day computing, including text processing, image recognition, recommendation systems, and several others. This kind of machine learning is characterized by using labeled datasets to train the algorithms and subsequently class data or make an accurate prediction of the outcomes. The input data is fed into the model, which then adjusts the data weights using a reinforced learning process to ascertain that the model is appropriately fitted. A vast majority of organizations use supervised machine learning to address real-world problems at a scale. For instance, the ability of an email application to classify a message as spam and put it in a different folder aside from the inbox is one capability of supervised machine learning (Holzinger, 2016). Generally, this learning language can be used in business organizations to eliminate the manual classification of work and predicting the future using the labeled data. Human expertise and intervention are needed to evade potentially overfitting data models when formatting the machine learning algorithms. Supervised machine learning uses neural network tools that train the model by mimicking the interconnectivity of the human through layers of nodes. Every node has inputs, weights, thresholds, and output. According to Sidey-Gibbons et al. (2019), supervised machine learning uses a training set to teach models that eventually yield desired system functionality. The training dataset comes with inputs and correct outputs that the model has to learn for a specified period. The language is also endowed with algorithms that measure its accuracy using the loss function, which adjusts until errors are minimized to zero levels. Supervised learning can either be classification or regression, depending on the nature of data mining. Classification uses an algorithm to assign test data into specific categories (TensorFlow, 2019). This process identifies entities in a dataset and makes conclusions on how the model can label or define those entities. Some of the common classifications include decision trees, random forest, and support vector machines. On the other hand, regression defines the relationship between dependent and independent variables. The technique to estimate projections such as the sale revenues and commissions for the business. Popular regression models include polynomial regression, linear and logistical regressions. Supervised machine learning used the Scitik-learn tool, whose development uses the Python programming language (TensorFlow, 2019). The tool is very useful in data mining and analysis. It also provides models and algorithms for classification and regression, which are the categories of supervised learning. The tool is easy to understand and learn, and most of its parameters are flexible to change for any algorithm while calling objects. The rationale for Selecting an Analytic Tool The choice of an analytic tool for machine learning depends on several factors that serve as the rationale for the ultimate decisions over-analytical tools. One of them is the business objectives in relation to the cost of acquiring the tool. User interface and visualization are often another crucial consideration. Scikit-learn as one of these tools is often preferred because of the ease of use and higher user interface. It also has advanced analytics that allows it to recognize data patterns and predict future trends and outcomes. It is also flexible in that it allows standalone solutions and the integration of other technological capabilities. Machine Learning Process The machine process is all about using tags and training the machine to learn those tags. For instance, in training image recognition, the expert would need to tag photos of natural features such as lakes, rivers, forests, and mountains with appropriate names. This exercise is data labeling. When the user is working with machine learning text analysis, he/she would feed the text analysis model with text training data and then tag it, depending on the nature of the analysis being done (TensorFlow, 2019). For sentimental analysis, customer feedback, for instance, would be fed into the model and then train the model by tagging each comment as neutral, positive, or negative. Generally, the machine learning process involves three steps. The first step involves feeding the machine learning training input. In this case, it could be customer reviews and feedback from customer service data and social media. The second step is tagging the training data with the desired output. For the case of the business-customer relationship, the sentimental analysis model would be told whether the customer review is positive, negative, or neutral. The model then transforms the training data into text vectors representing data features (Holzinger, (2016). The third step involves testing the modes by feeding it testing data. Accordingly, algorithms are trained to associate feature vectors with tags using the manually tagged samples and make predictions when handling and processing unseen data. Uses of Machine Learning in Healthcare Machine learning has several applications in healthcare and has been useful in meeting the growing needs of medical demands and improving operations at lower costs. For instance, at the bedside, machine learning innovation can assist healthcare practitioners in detecting and treating diseases more efficiently and with more precision and personalize care (Dai et al., 2015). Generally, this innovation has revealed how technology can yield holistic care strategies to improve the quality of care and subsequent patient outcomes. One of the complex machine learning that mimics the functioning of the human brain is currently being used in radiology and medical imaging. Deep learning uses neural networks to detect, recognize and analyze cancerous lesions from images (Dai et al., 2015). Machine learning in health informatics is also streamlining record-keeping through electronic health records. The use of artificial intelligence in EHR improves patient care, lowers healthcare and administrative costs, and optimizes healthcare operations. Disease identification and diagnosis and medical imaging diagnosis are other areas of machine learning applications in healthcare practice. The machine learning algorithms can detect patterns associated with health conditions and diseases using information from thousands of healthcare records and existing patient data (Sidey-Gibbons et al., 2019). Conclusion Machine learning can either be supervised or unsupervised, and in most industry applications, supervised learning is preferred because it uses labeled data to make ideal predicting future events and outcomes. Additionally, supervised learning can be classification or regression whose difference is the nature of the output. Machine learning has been applied in many areas to improve the efficiency and speed of operations while lower errors and costs. In healthcare, machine learning has been used in medical and imaging diagnoses, treatment interventions, and healthcare records, and data management. References Dai, W., Brisimi, T. S., Adams, W. G., Mela, T., Saligrama, V., & Paschalidis, I. C. (2015). Prediction of hospitalization due to heart diseases by supervised learning methods. International journal of medical informatics, 84(3), 189-197. Holzinger. (2016). Holzinger Group Welcome to Students. Youtube.com. Retrieved 5 May 2021, from https://www.youtube.com/watch?v=lc2hvuh0FwQ&feature=youtu.be. Sidey-Gibbons, J. A., & Sidey-Gibbons, C. J. (2019). Machine learning in medicine: a practical introduction. BMC medical research methodology, 19(1), 1-18. TensorFlow. (2019). Machine Learning Zero to Hero. Youtube.com. Retrieved 5 May 2021, from https://www.youtube.com/watch?v=VwVg9jCtqaU.

Thermodynamic processes that occur spontaneously are all irreversible; that is, they proceed naturally in one direction but never reverse. A rolling wheel across a rough road converts mechanical energy into heat due to friction. The former is irreversible, just as it is impossible that a wheel at rest would spontaneously start moving and getting colder as it moves instead. In this paper, the second law will be introduced by considering several thermodynamic devices: (1) heat engines, which are partly successful in converting heat into mechanical work, and (2) refrigerators and heat pumps, which are partly successful in transferring heat from cooler to hotter regions. Heat Engines The essence of our technological society is the ability to utilize energy resources other than muscle power. These energy resources come in many forms (e.g. solar, geothermal, wind, and hydroelectric). But even though we have a number of them available in the environment, most of the energy used for machinery comes from burning fossil fuels. This process yields heat, which then can be directly used for heating buildings in frigid climate, for cooking and pasteurization, and chemical processing. But to operate motors and machines, we need to transform heat into mechanical energy. Any device that converts heat partly into mechanical energy or work is called a heat engine. They absorb heat from a source at a relatively high temperature, i.e. a hot reservoir (like combustion of fuel), perform mechanical work, and discard some heat at a lower temperature (Young & Freedman, 2019). In correspondence to the first law of thermodynamics, the initial and final internal energies of this system are equal when carried through a cyclic process, as in Fig. 1 Schematic energy-fiow diagram for a heat engine Thus, we can say that net heat flowing into the engine in a cyclic process is equal to the net work done by the engine (Brown et al., 2017). We can illustrate how energy is transformed in a heat engine using the energy-flow diagram (Fig. 1). The engine itself is represented by the circle. The amount of heat QH supplied to the engine by the hot reservoir is directly proportional to the width of the incoming “pipeline” at the top of the diagram. The width of the outgoing pipeline at the bottom is proportional to the magnitude |QC| of the heat discarded in the exhaust. The branch arrow to the right represents the portion of the heat supplied that the engine converts to mechanical work, W. When an engine repeats the same cycle over and over, QH and QC represent the quantities of heat absorbed and rejected by the engine during one cycle; QH is positive, and QC is negative. The net heat Q absorbed per cycle is =       +     =||−|| The useful output of the engine is the net work W done by the working substance. From the first law, =                 =             +           =||−|| Ideally, we would like to convert all the heat QH into work; in that case we would haven QH = W and QC = 0. Experience shows that this is impossible; there is always some heat wasted, and QC is never zero. We define the thermal efficiency of an engine, denoted by e as the quotient The thermal efficiency e represents the fraction of QH that is converted to work. To put it another way, e is what you get divided by what you pay for. This is always less than unity, an all-too-familiar experience! In terms of the flow diagram of Fig. 1, the most efficient engine is one for which the branch pipeline representing the work output is as wide as possible and the exhaust pipeline representing the heat thrown away is as narrow as possible.   When we substitute the two expressions for W given by Eq. 1.2 into Eq. 1.3, we get the following equivalent expressions for e: Fig. 2.1 Schematic energy-flow diagram for a refrigerator Refrigerator and Heat Pump We can understand the mechanism of a refrigerator as opposed to a heat engine. As explained in the first part, a heat engine takes heat from a hot reservoir and gives it off to a colder place. A refrigerator operates in reverse, i.e. it takes heat from a cold place (inside of the refrigerator) and gives off that heat into a warmer place, often the surrounding air in the room where the refrigerator is located. In addition, while a heat engine has a net output of mechanical work, the refrigerator requires a net input of mechanical work (Poredoš, 2021). Fig 2.1 shows an energy-flow diagram for a refrigerator. From the first law of thermodynamics for a cyclic process, +−=0 −=− or because both QH and W are negative, ||=                   +|               | It only shows that the heat |QH| given off from the working substance and given to the hot reservoir is always greater than the heat QC taken from the cold reservoir. From an economic point of view, the most efficient refrigeration cycle is one that takes off the greatest amount of heat |QC| from inside the refrigerator for the least use of mechanical work, |W|. The relevant ratio is |QC|/|W|, called the coefficient of performance, K, which implies that the larger this ratio is, the better the refrigerator. A variation on this is the heat pump, which functions like a refrigerator, but turned inside out. A heat pump is used to heat buildings by cooling the air outside. The evaporator coil is placed outside, as it takes heat from cold air, while the condenser coils are inside, which gives off heat to the warmer air. In this design, the heat |QH| taken inside a building can be considerably greater than the work |W| needed to get it there. Conclusion In the bottom line, it is impossible to create a heat engine that completely converts heat to work, i.e. 100% thermal efficiency. It only corresponds to the second law of thermodynamics which states that it is impossible for any system to undergo a process in which it absorbs heat from a reservoir at a single temperature and converts the heat completely into mechanical work, with the system ending in the same state in which it began. Heat flows spontaneously from hotter to colder objects, never the reverse. A refrigerator does take heat from a colder to a hotter object, but its operation requires an input of mechanical energy or work. We can deduce that it is impossible for any process to have as its sole result the transfer of heat from a cooler to a hotter object.   References Brown, T. L., LeMay, Jr., H. E., Bursten, B. E., Murphy, C. J., Woodward, P. M. (2017, January 1). Chemistry: The Central Science (14th ed.). Pearson. Ozerov, R. P., & Vorobyev, A. A. (2007). 3 - Molecular Physics. Physics for Chemists. (), 169–250. https://doi.org/10.1016/B978-044452830-8/50005-2 Poredoš, A. (2021, April 25). Thermodynamics of Heat Pump and Refrigeration Cycles. Entropy, 23(5), 524. https://doi.org/10.3390/e23050524 Young, H. D., & Freedman, R. A. (2019). University Physics with Modern Physics (15th ed.). Pearson.

Artificial intelligence (AI), a fast-developing area, has recently attracted considerable attention. Programmers use several programming languages, which give the essential frameworks and tools to complete AI tasks. As an alternative to more antiquated languages like FORTRAN, BASIC, or COBOL, I will explore two programming languages frequently used in AI in this response and emphasize their applicability. Programming languages used to accomplish AI One of the most popular programming languages for usage in AI applications is Python. It provides several AI-specific libraries and frameworks, including TensorFlow and PyTorch. Python is popular in AI development due to its simplicity of use and the availability of libraries that make difficult AI tasks (Gunning & Aha,2019). Python's clear syntax and extensive ecosystem of AI-focused libraries make it ideal for quick experimentation and prototyping, which speeds up the development process. R is yet another popular programming language in AI. R excels at statistical analysis and data visualization, whereas Python shines in general-purpose programming and machine learning (Alvarez-Dionisi et al.,2019). One of R's key strengths is its extensive collection of statistical libraries, including caret and dplyr, which make complex data processing and modeling possible. The authors emphasized R's strengths for statistical modeling and exploratory data analysis, allowing researchers to conclude large datasets quickly. Comparison with the older languages Python and R offer several benefits in AI compared to more antiquated languages like FORTRAN, BASIC, or COBOL. First, developers can use pre-existing tools and algorithms by utilizing the large libraries and frameworks both Python and R provide that are designed expressly for AI applications (Ribeiro et al.,2021). It drastically reduces the time and effort needed to create and execute AI solutions. Additionally, developers may readily obtain resources, documentation, and support from other professionals because of Python and R's larger and more active communities. Conclusion In conclusion, Python and R have become widely used programming languages in artificial intelligence because of their community support, libraries, and ease of use. These languages give programmers the frameworks and tools to implement AI algorithms effectively. The readability, community support, and accessibility of Python and R are significantly better than those of more antiquated languages like FORTRAN, BASIC, or COBOL. They are the preferred languages for researchers and professionals due to their adaptability to AI tasks.   References Alvarez-Dionisi, L. E., Mittra, M., & Balza, R. (2019). Teaching artificial intelligence and robotics to undergraduate systems engineering students. International Journal of Modern Education and Computer Science, 11(7), 54-63. http://www.mecs-press.net/ijmecs/ijmecs-v11-n7/IJMECS-V11-N7-6.pdf Gunning, D., & Aha, D. (2019). DARPA’s explainable artificial intelligence (XAI) program. AI magazine, 40(2), 44-58. http://ojs.aaai.org/index.php/aimagazine/article/view/2850 Ribeiro, J., Lima, R., Eckhardt, T., & Paiva, S. (2021). Robotic process automation and artificial intelligence in industry 4.0–a literature review. Procedia Computer Science, 181, 51-58. https://www.sciencedirect.com/science/article/pii/S1877050921001393