In the privacy preserving data mining, the utility mining casts a very vital part. The objective of the suggested technique is performed by concealing the high sensitive item sets with the help of the hiding maximum utility item first (HMUIF) algorithm, which effectively evaluates the sensitive item sets by effectively exploiting the user defined utility threshold value. It successfully attempts to estimate the sensitive item sets by utilising optimal threshold value, by means of the grey wolf optimisation (GWO) algorithm. The (...) optimised threshold value is then checked for its performance analysis by employing several constraints like the HF, MC and DIS. The novel technique is performed and the optimal threshold resultant item sets are assessed and contrasted with those of diverse optimisation approaches. The novel HMUIF considerably cuts down the calculation complication, thereby paving the way for the enhancement in hiding performance of the item sets. (shrink)

Plastic waste is a significant environmental problem for almost all countries; therefore, protecting the environment from the problem is crucial. The most sensible solution to these problems is substituting the natural aggregates with substantial plastic waste in various building materials. This study aimed to optimise the mixed design ratio of cement brick containing plastic waste as aggregate replacement. Plastic cement brick mixtures were prepared by the incorporation of four different types of plastic waste such as polyethylene terephthalate (PET), high-density polyethylene, (...) low-density polyethylene and polypropylene into cement bricks with different cement contents (150, 300 and 450 g) and plastic replacement percentages (0, 3 and 6%). Compressive strength and water absorption of the plastic cement bricks were analysed using a statistical model through the response surface methodology. It revealed the optimum cement brick mixed design is C3-1% PET with the compressive strength of 27.50 MPa and water absorption of 1.16%. The optimised plastic cement brick also satisfied the general ASTM C62-17 requirements for building bricks despite the higher porosity observed by the scanning electron microscopy. The results from Fourier transform infrared spectroscopy analysis also showed that the addition of the plastic waste into cement brick was unlikely to modify the chemical compound within the cement brick mixtures. Thus, the proposed mathematical model can predict the required hardened properties of plastic cement bricks and could lead to greater utilisation of plastic waste in building materials. (shrink)

In times of rapid technological progress, transhumanism, which strives for radical technological transformations of the human being, spreads its ideas with great publicity and media impact. Although these ideas are directed towards the future, they influence how we understand humans, bodies, and technology today. T his article exam­ ines the anthropology of transhumanism and investigates the extent to which it offers approaches for the contemporary anthropology of body optimisation. T he article comes to the conclusion that the understanding of the (...) human being in transhumanism is prob­ lematic in many respects and therefore not suitable for the further development of a contemporary philosophical anthropology. Nevertheless, corrective perspectives for an anthropology of contemporary body optimisation can be derived from these problems. (shrink)

The IPAP Schizophrenia Algorithm was originally designed in the form of a flow chart to help physicians optimise the treatment of schizophrenic patients. We examined the current version from the perspective of recent work on terminologies and ontologies thereby drawing on the resources of Basic Formal Ontology, and this with the objective to make the algorithm appropriate for Semantic Web applications. We found that Basic Formal Ontology is a rich enough theory to represent all the entities involved and that applying (...) the theory to the IPAP schizophrenia algorithm results in a representation that can be used by software agents to perform monitoring and control in a referent tracking environment. (shrink)

Within educational science and communication science, the concepts of scientific literacy and effectiveness of science communication have been intensely debated in relation to the free types of education, but the research did not focus on the specificity of their target (the general public) in relation to the specificity of their object (science). In general, research maintained an exclusively externalist view for these concepts and associated them with the complexity and diversity of teaching science and less with the epistemic dimension of (...) science communication as a transfer of understandable knowledge in particular conditions. This prospected research advances an internalist view on these concepts, aiming to answer the question of whether science communication (with a conceptual meaning that embeds understanding, message, and goal) to the general public is possible and in what form, within an epistemological theoretical framework. (shrink)

The relations between rationality and optimization have been widely discussed in the wake of Herbert Simon's work, with the common conclusion that the rationality concept does not imply the optimization principle. The paper is partly concerned with adding evidence for this view, but its main, more challenging objective is to question the converse implication from optimization to rationality, which is accepted even by bounded rationality theorists. We discuss three topics in succession: (1) rationally defensible cyclical choices, (2) (...) the revealed preference theory of optimization, and (3) the infinite regress of optimization. We conclude that (1) and (2) provide evidence only for the weak thesis that rationality does not imply optimization. But (3) is seen to deliver a significant argument for the strong thesis that optimization does not imply rationality. (shrink)

This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.

As in today's fast-changing technological context, organizations more and more need cost-effectiveness to maintain competitiveness and enhance organizational performance. Leading drivers of these trends are automation, cloud technology, data-based decision-making, and agile. Automation improves performance through minimizing redundancy and human mistake, especially supply chain, financial, and customer care processes. Predictive analysis also helps maintain cost savings with forecasting demand and optimization of assets. Cloud computing offers scalable and elastic infrastructure solutions, reducing the costs of traditional IT. Companies such as (...) Netflix and Amazon Web Services illustrate how cloud platforms facilitate dynamic resource allocation and cost-efficient service delivery. Datadriven decision-making enables businesses to gain a deeper understanding of consumer behaviour and market trends. Businesses such as Tesla and Google use big data analytics to optimize supply chains and minimize operational waste, improving responsiveness and profitability. Remote and blended work arrangements, enabled by tools like Zoom and Microsoft Teams, minimize overhead for office space and transportation, with additional saving opportunities. Lean and agile systems, embraced by companies like Toyota and Tesla, enhance flexibility and minimize waste through production and project management. Cybersecurity is an ongoing issue as digital integration continues to grow. Investments made by companies such as IBM and Microsoft in sophisticated security systems reduce financial and reputational risks. Although these measures have definite advantages, difficulties like large initial investment, resistance to change, and data security threats remain. Phased implementation facilitates smooth transitions. Finally, organizations adopting smart technologies and innovation stand a better chance of realizing sustainable cost reductions and long-term profitability. (shrink)

The Genetic Algorithm (GA) and Simulated Annealing (SA), two techniques for global optimization, were applied to a reduced (simplified) form of the phase problem (RPP) in computational crystallography. Results were compared with those of "enhanced pair flipping" (EPF), a more elaborate problem-specific algorithm incorporating local and global searches. Not surprisingly, EPF did better than the GA or SA approaches, but the existence of GA and SA techniques more advanced than those used in this study suggest that these techniques still (...) hold promise for phase problem applications. The RPP is, furthermore, an excellent test problem for such global optimization methods. (shrink)

Cognitive Optimization in the Age of AI: Enhancing Human Potential -/- Introduction -/- Cognitive optimization is the process of enhancing mental functions such as memory, learning, decision-making, and problem-solving to achieve peak intellectual performance. It is a multidisciplinary approach that integrates neuroscience, psychology, nutrition, lifestyle adjustments, and, increasingly, artificial intelligence (AI). In an era where information is abundant and rapid decision-making is crucial, optimizing cognitive abilities is more Important than ever. -/- AI-driven technologies, video games, mobile apps, and (...) digital platforms like YouTube are playing a transformative role in enhancing human cognition. YouTube, in particular, has become a powerful tool for self-directed learning, offering access to educational content, expert insights, and cognitive training exercises. -/- This essay explores cognitive optimization, key strategies involved, and how AI, video games, mobile apps, and platforms like YouTube are revolutionizing human intelligence. -/- The Science Behind Cognitive Optimization -/- Cognitive optimization is rooted in neuroplasticity—the brain’s ability to rewire itself through experience and learning. Several key factors contribute to cognitive enhancement: -/- 1. Neuroplasticity and Learning – Continuous learning strengthens neural connections, fostering intelligence and adaptability. Activities such as reading, solving complex problems, and learning new skills stimulate brain function. -/- 2. Nutrition and Brain Health – Diet plays a vital role in cognitive performance. Nutrients such as omega-3 fatty acids, antioxidants, and essential vitamins support brain function and protect against cognitive decline. -/- 3. Physical Exercise – Regular exercise increases blood flow to the brain, promotes neurogenesis (the growth of new neurons), and enhances memory and problem-solving skills. -/- 4. Sleep and Recovery – Sleep is essential for memory consolidation, emotional regulation, and mental clarity. Chronic sleep deprivation impairs cognitive function and decision-making. -/- 5. Mindfulness and Stress Management – Practices such as meditation, deep breathing, and yoga improve focus, reduce anxiety, and enhance mental resilience. -/- 6. Social Interaction and Collaboration – Engaging in meaningful discussions and debates strengthens cognitive agility and enhances problem-solving skills. -/- Beyond these traditional methods, digital tools such as AI-driven learning platforms, video games, mobile applications, and YouTube have expanded the possibilities for cognitive enhancement. -/- AI and Cognitive Optimization: A New Frontier -/- AI technology is revolutionizing cognitive optimization by providing personalized insights, accelerating learning, and even augmenting human intelligence. Below are several ways AI is transforming cognitive enhancement: -/- 1. AI-Powered Personalized Learning -/- AI-driven educational platforms such as adaptive learning systems customize lessons based on an individual’s learning style and pace. These systems use machine learning to analyze cognitive strengths and weaknesses, adjusting content dynamically to optimize learning efficiency. Platforms like Coursera, Duolingo, and Khan Academy utilize AI algorithms to provide personalized recommendations, ensuring users focus on areas that need improvement. -/- 2. Brain-Computer Interfaces (BCIs) -/- Brain-Computer Interfaces (BCIs) are AI-powered systems that enable direct communication between the human brain and computers. Companies like Neuralink and OpenBCI are developing BCIs that could enhance memory, restore lost cognitive functions, and even allow direct mental interaction with AI systems. This technology has the potential to revolutionize learning and decision-making by integrating human cognition with artificial intelligence. -/- 3. AI-Powered Cognitive Assistants -/- AI-driven virtual assistants such as ChatGPT, Google Assistant, and IBM Watson enhance cognition by providing instant access to information, summarizing complex topics, and even suggesting optimal decision-making strategies. These tools help individuals process vast amounts of information efficiently, reducing cognitive overload and enhancing productivity. -/- 4. AI in Mental Health and Stress Management -/- Mental health is a critical component of cognitive optimization. AI-driven mental health apps like Woebot and Wysa use machine learning to provide real-time emotional support, track mood patterns, and offer cognitive behavioral therapy (CBT) techniques. By reducing stress and anxiety, these AI-powered tools help maintain optimal cognitive function. -/- The Role of YouTube in Cognitive Optimization -/- YouTube has emerged as one of the most powerful platforms for cognitive enhancement. With millions of educational videos covering diverse subjects, YouTube serves as a free and accessible learning tool that fosters continuous cognitive growth. -/- 1. Learning New Skills and Expanding Knowledge -/- YouTube provides access to high-quality educational content in virtually every field. Channels like Veritasium, TED-Ed, and CrashCourse offer lessons on science, history, philosophy, and more. Learning new concepts regularly strengthens cognitive flexibility and enhances problem-solving skills. -/- 2. Enhancing Memory and Comprehension -/- Visual and auditory learning through YouTube helps reinforce memory retention. Watching documentaries, explainer videos, and step-by-step tutorials aids in deepening understanding and reinforcing neural pathways associated with knowledge acquisition. -/- 3. Improving Focus and Critical Thinking -/- Channels that discuss philosophy, logic, and critical thinking—such as Academy of Ideas or PBS Idea Channel—train the brain to analyze complex arguments, recognize biases, and develop sharper reasoning skills. Engaging in thought-provoking discussions through video content strengthens cognitive agility. -/- 4. AI-Generated Content for Personalized Learning -/- AI-powered recommendation algorithms help users discover personalized content based on their interests and learning habits. AI-curated video playlists allow individuals to engage in structured learning journeys, optimizing cognitive development in areas of personal interest. -/- 5. Interactive Learning and Problem-Solving -/- YouTube’s interactive features, such as quizzes, live Q&A sessions, and discussion threads, allow users to actively engage with educational content. This social interaction enhances cognitive engagement and retention. -/- By leveraging YouTube strategically, individuals can create personalized learning experiences that optimize cognitive function in a self-directed manner. -/- Video Games and Cognitive Optimization -/- Video games are increasingly recognized as tools for cognitive enhancement, providing interactive and engaging ways to improve various mental skills. -/- 1. Action Video Games for Reflexes and Decision-Making -/- Fast-paced action games like Call of Duty, Fortnite, and Counter-Strike train players to react quickly, improve hand-eye coordination, and develop strategic thinking under pressure. Studies suggest that such games enhance cognitive flexibility and decision-making speed. -/- 2. Puzzle and Strategy Games for Problem-Solving -/- Games like Portal, The Witness, and Tetris challenge players with complex puzzles that require logical thinking, spatial reasoning, and pattern recognition. -/- 3. Open-World and Role-Playing Games (RPGs) for Memory and Creativity -/- Games like The Legend of Zelda, Elden Ring, and The Witcher 3 encourage exploration, problem-solving, and strategic planning. -/- 4. Multiplayer and Cooperative Games for Social Cognition -/- Team-based games like League of Legends, Dota 2, and Among Us enhance communication skills, teamwork, and emotional intelligence. -/- The Future of Cognitive Optimization -/- The future of cognitive optimization lies in the seamless integration of AI, video games, mobile applications, and digital platforms like YouTube. Emerging technologies such as neuroprosthetics, AI-enhanced memory implants, and real-time brain augmentation could redefine human potential. -/- Furthermore, as AGI (Artificial General Intelligence) develops, AI could become an even more powerful tool in cognitive enhancement, potentially working alongside humans as cognitive partners. This could align with initiatives such as Angelito Malicse’s universal formula for decision-making, where AI-driven cognitive optimization ensures that human choices align with the natural law of balance. -/- Conclusion -/- Cognitive optimization is essential for maximizing human potential, and AI, video games, YouTube, and cognitive training apps are playing an increasingly significant role in this process. From personalized learning to interactive gaming and mobile applications, digital tools are transforming the way we enhance cognition. -/- By leveraging these tools responsibly and integrating them with fundamental cognitive enhancement strategies, humanity can unlock new levels of intelligence, creativity, and problem-solving ability—ensuring that the future of cognition is both powerful and sustainable. -/- . (shrink)

The theme of this issue of the St. Gallen Business Review is "Harmony". For this reason, we would like to discuss whether two aspects of our life- world are in harmony, namely economic optimization and morality. What is the relation between them? According to a widely shared view, which is one aspect of the doctrine of "mainstream economics", the functioning of an economic system does not require moral behaviour on the part of the individual economic agent. In what follows, (...) we will try to convince you that this is false. If all economic agents – managers, employees, bankers, consumers and so on – were purely self-interested, as mainstream economics assumes, our economic practice would fail. There are various reasons for this. Here we will focus on one of them, namely the pivotal role of interpersonal communication. (shrink)

In this study, the multipurpose objective of development in the Anambra River basin was examined. The study's goals are to determine the net benefits of the various objectives under each purpose currently carried out by the Anambra River Basin, to identify the best way to achieve the goals, and to show, through logical and mathematical reasoning, how money could be allocated to the various goals of a dam project in Anambra State effectively. Economic Efficiency (EE), Regional Economic Redistribution (RER), Social (...) Well-Being (SW), Youth Empowerment (YE), and Environmental Quality Improvement are the objectives taken into account in the study (EQI). The optimization problem in the multipurpose-objective development of the Anambra State river basin was solved utilizing the multi-start optimization method. The study showed that the basin would achieve enormous financial benefits if it used its N15.0 billion strategic development budget over four years for various purposes as illustrated. The results showed that the following allocation of the five purposes should be optimized simultaneously for an N15.0 billion four-year strategic development fund: EE should receive N0.762 billion, RER should receive N0.594 billion, SW should receive N0.649 billion, YE should receive N0.683 billion, and environmental quality improvement should receive N4.768 billion. It was found that Environmental Quality Improvement was allocated the higher amount while Regional Economic Redistribution was allocated the least amount for the development. The study's findings recommended that the management of the basin stop the practice of allocating funds to various uses at their discretion, which has led to significant oversight and the improper distribution of scarce resources. As a result, every fund distribution must be justified using logic and mathematics. To increase the accuracy of the data, the Anambra State River Basin Development Authorities must intensify record-keeping activities. (shrink)

Emergency response times play a critical role in saving lives, especially in urban settings where traffic congestion and unpredictable events can delay ambulance arrivals. This paper explores a novel framework for smart route optimization for emergency vehicles, leveraging artificial intelligence (AI), Internet of Things (IoT) technologies, and dynamic traffic analytics. We propose a real-time adaptive routing system that integrates machine learning (ML) for predictive modeling and IoT-enabled communication with traffic infrastructure. The system is evaluated using simulated urban environments, achieving (...) a 35% reduction in response times compared to traditional methods. This work lays the foundation for future advancements in intelligent emergency systems and their integration into smart cities. (shrink)

The generative AI system is being adopted across the several fields to provide novel solutions for text generation, image synthesis, and decision-making. But when they are used in multi-agent and multi-cloud systems, they are expensive in terms of computation and finance. Regarding the aforementioned factors, this paper aims to examine methods of reducing such costs while achieving system efficiency. Such measures as dynamic workload distribution, resource scaling, as well as cost-conscious model selection is described. Through the examples of case studies (...) and simulations, we show that incorporating these strategies can drastically decrease expenses and ensure immediate and accurate scalability across clouds of different ecosystems. (shrink)

Background/Objective: The primary intension is to provide utility trade off and good privacy for intermediate datasets in cloud. Methods: An efficient conditional entropy and database difference ratio is employed for the process. Utility is taken care with the employment of conditional entropy with the help of Swarm Optimization (PSO). Privacy handled by database difference ratio. Findings: Conditional entropy is found out between the first column and the original database and this is taken as the fitness function in Particle Swarm (...) Optimization (PSO). Database difference is taken between the original database and convoluted database in the consequent module to yield right selection data and tuple information from the intermediate datasets in cloud. Applications/Improvements: Improved methods are required to provide privacy and utility for the right selection of datasets. This approach has better results by having higher entropy values and lower dataset difference ratio. (shrink)

Emergency response times play a critical role in saving lives, especially in urban settings where traffic congestion and unpredictable events can delay ambulance arrivals. This paper explores a novel framework for smart route optimization for emergency vehicles, leveraging artificial intelligence (AI), Internet of Things (IoT) technologies, and dynamic traffic analytics. We propose a real-time adaptive routing system that integrates machine learning (ML) for predictive modeling and IoT-enabled communication with traffic infrastructure. The system is evaluated using simulated urban environments, achieving (...) a 35% reduction in response times compared to traditional methods. This work lays the foundation for future advancements in intelligent emergency systems and their integration into smart cities. (shrink)

This paper will be concerned with hard choices—that is, choice situations where an agent cannot make a rationally justified choice. Specifically, this paper asks: if an agent cannot optimize in a given situation, are they facing a hard choice? A pair of claims are defended in light of this question. First, situations where an agent cannot optimize because of incompleteness of the binary preference or value relation constitute a hard choice. Second, situations where agents cannot optimize because the binary preference (...) or value relation violates acyclicity do not constitute a hard choice. (shrink)

Automation testing has become an integral part of modern software development, significantly improving efficiency, reducing human error, and enhancing testing accuracy. Over the last seven years, significant advancements have been made in automation testing optimization, focusing on enhancing the effectiveness of testing procedures and optimizing resource allocation. This paper provides a comprehensive review of the recent techniques and trends in automation testing optimization. The review highlights the evolution of automation testing strategies, investigates novel optimization methods, identifies datasets (...) commonly used in research, and discusses the emerging trends that are shaping the future of automation testing. A comparative analysis of various optimization models and their performance is also presented, leading to the identification of the most effective approaches in current research. The paper concludes with insights into the future of automation testing optimization, exploring areas of potential improvement and innovation. (shrink)

The article examines the process of formation inventory of the enterprise and determines the optimal volume of commodity resources for sale. A generalization of author’s approaches to the formation and evaluation of inventories of the enterprise is carried out. The marketing-logistic approach was applied for the purpose of distribution groups of commodity resources due to the risk of non-fulfillment the order for the supply of goods of the enterprise. In order to ensure an effective process of commodity provision of the (...) enterprises, the costs associated with the formation of inventories are determined. The formalized scheme of the formation commodity provision and the process of optimization inventory of the enterprise is offered. The analysis the structure of the company’s inventory is carried out, the volume of goods turnover is defined, the stocks are grouped for various clustering characteristics. To conduct the study, statistical information was used on commodity resources of the enterprises, statistical methods (grouping, structure analysis, estimation of dynamic series), tools for assessing the efficiency of inventory use, HML-FMR clustering were used. The necessity of using XYZ and ABC analysis is indicated in order to obtain more reliable results and forecast values of the product support of the enterprise. Economic-mathematical modeling is applied and graphically shown the difference in the formation of commodity resources by various features of HML-FMR clustering. The calculations allow the enterprise to determine the optimal amount of commodity resources in accordance with the needs of consumers and their solvent demand, to plan financial resources for the formation of inventories, to develop assortment policy in accordance with demand for products and their implementation. The results of calculations the volume of merchandising of the enterprise, taking into account the HML-FMR clustering affect the formation of final financial performance of the enterprise — income and profits. (shrink)

Application of the reliability of repairable systems on solving problems from constructing production systems takes an important place in the process of finding the optimal solution among the suggested system choices. The basic hypothesis when using the reliability of the repairable systems is that every machine is representing a component, a fact that is debatable when talking about technical sciences. However, considering the second assumption of the stationary process, the function of the availability is introduced. It represents the measure between (...) the reliability and the benefit of maintaining the components and the system as a whole one. An additional and the last reason for analyzing constructing machine as a whole one has an economic reason. From the aspect of the total cost and the capacity of the system, the function of the availability is catching more and more impact in the process of determinating the total cost in bill of quantities as the most important part of tender documents. Until former works, techno-economic optimisation usually ran over when choosing the system of the construction machines. In the work, there are shown the methodologies for gathering and analyzing the indicators of availabilities per unit of a company's machine park. The time of demission and reparation have been investigated among 34 components in the period of 6 years. Based on the obtained result for different conditions of machine exploitation and facilities, it is suggested the methodology of the future behaviour of the corresponding within the model optimization availability of constructing production system with the aim of eliminating extra criterion. The suggested model takes in consideration the total cost of the components when they are functional, and when they are not. This kind of approach enables the one who take choices decision of the height of profits, in the lenght of work in function of the projection of cash flow and managing with real indicators of functioning the system at the project level. Given the fact that the maintanance policy of machines is based on availability, and not on reliability of components, with the aim of maximizing the availability of different budget scenarios, a logical approach to the selection of the objective function in the terms of management of construction production systems is to minimize costs for a given availability of system constructing machinery is based on the use of historical data base with the values of the operational availability of components, conditions in which they function, in the process of forecasting the project availability for projects that follow. Ranking variant solutions is the result of the adopted function of the aim as a consequence due to enlargement methods of availability for serial parallel systems with the associated restrictions. For optimal system availability is taken a value that belongs to minimal total cost. However, it is predicted that after the end of the financial year, the real and projected indicators of system functions get compared to each other. Informational support to the proposed model is informational company system, which was built in the PHP programming language on the Linux software platform, and with data based in My SQL. The suggested model is tested on the system for the production and embedding bitumen bound materials on the specific project. Obtained results confirm the assumptions used in work. (shrink)

This qualitative case study investigates the environmental implications of tourist arrivals in Matabungkay Beach, Lian, and their impact on the destination image. Through in-depth interviews with seven key stakeholders, including local residents, business owners, and environmental activists, the study explores perceptions, concerns, and potential solutions regarding the intersection of tourism and environmental sustainability. Findings reveal a complex relationship between tourism and environmental degradation, with participants expressing concerns about poor waste management, impacts on destination image, and health concerns. These challenges not (...) only threaten the natural beauty and biodiversity of the area but also tarnish the destination's image, affecting its appeal to tourists and potential investors. Despite these challenges, participants highlight existing initiatives and potential solutions, including community-based conservation efforts, sustainable tourism practices, and enhanced regulations. Collaboration among stakeholders is emphasized as crucial for achieving a balance between tourism development and environmental preservation. The study underscores the need for comprehensive strategies includes formulated an ordinance for comprehensive waste management and pollution control ordinance to optimize the destination image of Matabungkay Beach while addressing its environmental challenges. By integrating sustainable practices and fostering community engagement, the destination can enhance its appeal while safeguarding its natural resources for future generations. (shrink)

t Intentional sampling methods are non-randomized procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. In this paper we extend previous works related to intentional sampling, and address the problem of sequential allocation for clinical trials with few patients. Roughly speaking, patients are enrolled sequentially, according to the order in which they start the treatment at the clinic or hospital. The allocation problem consists in assigning each new patient to one, and (...) only one, of the alternative treatment arms. The main requisite is that the profiles in the alternative arms remain similar with respect to some relevant patients’ attributes (age, gender, disease, symptom severity and others). We perform numerical experiments based on a real case study and discuss how to conveniently set up perturbation parameters, in order to yield a suitable balance between optimality – the similarity among the relative frequencies of patients in the several categories for both arms, and decoupling – the absence of a tendency to allocate each pair of patients consistently to the same arm. (shrink)

Nature’s Way of Optimization and the Law of Balance -/- Nature is the ultimate example of efficiency, balance, and sustainability. Everything in the natural world—from how animals survive to how ecosystems function to how the human body works—is designed to maximize effectiveness while minimizing waste. If we observe how nature operates, we can learn valuable lessons about how to make decisions, govern societies, and live our daily lives. -/- This understanding aligns with the universal law of balance in nature, (...) which shows that everything must function in harmony to prevent failure or destruction. Throughout history, civilizations and economies that followed these natural principles thrived, while those that ignored them collapsed. -/- Survival and the Law of Cause and Effect -/- In nature, only the strongest and most adaptable species survive. This process, called natural selection, ensures that living things improve over time by keeping the traits that help them survive and eliminating the ones that don’t. This idea is similar to the law of cause and effect (karma)—if something is defective, it won’t function properly. -/- For example, consider the cheetah and the gazelle. Over generations, cheetahs have evolved to run faster to catch their prey, while gazelles have evolved to be more agile to escape. This balance keeps both species in check. If cheetahs became too fast and killed all gazelles, they would starve. If gazelles became too fast, cheetahs would die out, disrupting the ecosystem. -/- A similar pattern exists In human societies. Singapore is an example of a country that followed the law of balance in its development. It had limited resources but invested heavily in education, technology, and sustainable urban planning. Today, it is one of the most successful economies in the world, proving that proper planning and balance lead to success. -/- In contrast, Venezuela ignored economic balance. Despite having the world’s largest oil reserves, poor policies, corruption, and over-reliance on oil caused economic collapse. This is similar to an animal species that overuses its resources and faces extinction. -/- The Power of Feedback: How Nature Corrects Itself -/- Nature has built-in feedback systems to keep everything in balance. For example, if too many deer exist in a forest, they eat too many plants, reducing food availability. As a result, some deer starve, and their population naturally decreases, restoring balance. -/- A similar feedback system applies to human society. If governments make poor policies, people protest or suffer economic consequences, forcing leaders to change their approach. A clear example is South Korea’s response to the COVI”-19 pandemic. Unlike some countries that ignored early warnings, South Korea quickly implemented mass testing, strict quarantine measures, and public health campaigns. As a result, it controlled the virus better than many other nations, proving that listening to feedback leads to better outcomes. -/- On the other hand, the Chernobyl nuclear disaster (1986) happened partly because Soviet leaders ignored early warnings from engineers, leading to one of the worst nuclear accidents in history. Just like ignoring warning signs in nature leads to extinction, ignoring feedback in society leads to disaster. -/- Energy Efficiency: Why Wasting Resources Leads to Collapse -/- Nature never wastes anything. Plants absorb sunlight efficiently through photosynthesis, animals store energy for survival, and ecosystems recycle nutrients to sustain life. -/- One of the best examples of energy efficiency is the penguin. In the freezing Antarctic, emperor penguins huddle together to conserve heat, taking turns standing on the outside to prevent exhaustion. This natural cooperation ensures survival. -/- Humans, however, often fail to follow this principle. Look at Dubai, a city built in a desert. While it has grown into a major global hub, it also uses massive amounts of water and electricity, relying on desalination plants that consume enormous energy. Without sustainable solutions, this could become a long-term problem, just like a species that consumes resources faster than they can be replenished. -/- A positive example Is Denmark, which has invested heavily in wind energy. Today, nearly 50% of its electricity comes from wind power, making it one of the most energy-efficient nations in the world. -/- Self-Organization: Free Will Exists Within Natural Laws -/- Many people think free will means we can do anything we want, but nature shows that even freedom has limits. Birds can fly, but they must follow air currents. Fish can swim, but they must adapt to ocean conditions. Every action is guided by natural laws that ensure survival and efficiency. -/- A perfect example Is Japan’s railway system. Despite being one of the busiest in the world, it runs with near-perfect punctuality because every part of the system—from maintenance workers to train operators—functions with self-discipline and coordination. This proves that even large-scale human systems can work efficiently when they follow organized, natural principles. -/- However, when humans ignore balance, problems arise. Deforestation in Indonesia has destroyed rainforests at an alarming rate. Because of excessive logging and palm oil plantations, natural ecosystems are collapsing, leading to floods, wildfires, and loss of biodiversity. This is similar to how removing a key species in an ecosystem can lead to its collapse. -/- Resilience and Education: How Societies Can Heal -/- Nature is resilient—it recovers after disasters. Forests regrow after wildfires, coral reefs recover from bleaching, and animals adapt to changing climates. But this recovery only happens because nature learns and adjusts. -/- One of the best human examples of resilience is Germany after World War II. After its destruction, Germany rebuilt itself through education, innovation, and sustainable industry. Today, it is one of the world’s leading economies, proving that even nations can recover if they focus on knowledge, sustainability, and balance. -/- However, societies that fail to learn from their mistakes collapse. Easter Island, once a thriving civilization, was destroyed because its people cut down all their trees, leaving them without resources. Their lack of foresight and adaptation led to their downfall—just like an animal species that fails to adapt to environmental changes. -/- Nature’s Recycling: Why Good Leadership Must Be Sustainable -/- Nature wastes nothing—dead plants and animals decompose into nutrients that feed new life. The carbon and nitrogen cycles keep ecosystems functioning. Just as nature ensures that everything is reused efficiently, leadership and governance should be sustainable. -/- An excellent example of a balanced government is Bhutan. Instead of measuring success by GDP alone, it follows Gross National Happiness (GNH), which considers economic growth, environmental conservation, and cultural values. This balanced approach has made Bhutan one of the happiest and most sustainable countries in the world. -/- On the other hand, countries that ignore sustainability face collapse. Syria’s civil war was worsened by climate change—years of drought caused food shortages, leading to conflict. Ignoring nature’s balance led to political and humanitarian disaster. -/- Conclusion: The Path to a Balanced and Optimized Society -/- Nature teaches us that survival and success depend on balance, efficiency, and adaptation. Societies that respect these principles thrive, while those that ignore them suffer. -/- The universal law of balance in nature is not just a theory—it is a reality that governs everything from biology to economics to human behavior. If we integrate these lessons into education, governance, and leadership, we can create a world that is sustainable, just, and optimized for future generations. -/- If we fail to follow these natural laws, the consequences will be suffering, collapse, and chaos. But if we align with them, we can build a society that thrives in harmony with nature, ensuring peace, prosperity, and sustainability for all. -/- . (shrink)

Optimization algorithms play a critical role in the training of deep learning models, as they influence the convergence rate, accuracy, and stability of learning processes. Among the most popular optimization algorithms are Stochastic Gradient Descent (SGD) and its adaptive counterparts, such as Adam. While SGD has been widely used for years, Adam has gained significant popularity due to its adaptive learning rate and the ability to handle sparse gradients. However, the effectiveness of these algorithms varies depending on the (...) problem domain, the dataset, and the architecture of the neural network. This paper conducts a comparative study of popular optimization algorithms used in deep learning, focusing primarily on SGD, Adam, and other emerging optimization techniques. We investigate the characteristics, advantages, and disadvantages of these algorithms, with a particular focus on their convergence rates, robustness, and computational efficiency. The study also considers modern variants, such as RMSprop, Adagrad, and L-BFGS, which aim to improve upon the basic optimization techniques by addressing issues like vanishing gradients, overfitting, and slow convergence. Through a series of experiments using standard benchmark datasets, we analyze the performance of these optimization algorithms on different deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The results are analyzed to highlight the conditions under which each algorithm excels and provide practical recommendations for selecting the optimal optimizer based on specific problem requirements. The findings of this study offer valuable insights for deep learning practitioners, providing a detailed comparison of the strengths and weaknesses of popular optimization algorithms, and guide future research on enhancing optimization techniques for deep learning models. (shrink)

Distributed Constraint Optimization Problems (DCOPs) provide a framework for solving multi-agent coordination tasks efficiently. However, their black-box nature often limits transparency and trust in decision-making processes. This paper explores methods to enhance interpretability in DCOPs, leveraging explainable AI (XAI) techniques. We introduce a novel approach incorporating heuristic explanations, constraint visualization, and modelagnostic methods to provide insights into DCOP solutions. Experimental results demonstrate that our method improves human understanding and debugging of DCOP solutions while maintaining solution quality.

Intentional sampling methods are non-probabilistic procedures that select a group of individuals for a sample with the purpose of meeting specific prescribed criteria. Intentional sampling methods are intended for exploratory research or pilot studies where tight budget constraints preclude the use of traditional randomized representative sampling. The possibility of subsequently generalize statistically from such deterministic samples to the general population has been the issue of long standing arguments and debates. Nevertheless, the intentional sampling techniques developed in this paper explore pragmatic (...) strategies for overcoming some of the real or perceived shortcomings and limitations of intentional sampling in practical applications. (shrink)

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data (...) set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware specimens have been collected from the Mailing database for testing purposes. The experimental findings show that the suggested technique for measuring cyber security hazards in IoT has superior classification results to existing methods. Hence to detect the attack prevention in IoT process Whale with Grey wolf optimization (WGWO) and deep convolution network is used. (shrink)

This study focuses on developing a predictive model for classifying various crops based on key environmental factors such as soil composition and weather conditions. By integrating critical soil parameters, including Nitrogen, Phosphorus, Potassium, and pH, with weather variables like Temperature, Humidity, and Rainfall, the model aims to predict the most suitable crops for specific regions. The approach utilizes a Random Forest Classifier, enhanced through a Genetic Algorithm (GA) for optimizing hyperparameters, thereby improving the model's performance and adaptability to diverse agricultural (...) conditions. The hybrid model is designed to handle complex datasets and offer accurate classifications for multiple crops, including rice, maize, and jute. To ensure transparency and interpretability, the model incorporates Explainable AI (XAI) techniques, such as LIME and SHAP, which provide insights into the decision-making process. This allows users, such as farmers and policymakers, to understand the key factors influencing crop classification. By combining machine learning and optimization techniques, the system offers an effective tool for crop prediction, helping to reduce risks, increase agricultural productivity, and support sustainable farming practices. Ultimately, the model aims to enhance decision-making, mitigate crop failures, and contribute to food security and economic stability in agriculture-dependent regions. (shrink)

Urbanization poses significant challenges to healthcare systems, including overcrowded hospitals, inequitable access to care, and rising costs. Artificial Intelligence (AI) and the Internet of Things (IoT) offer transformative solutions for optimizing healthcare delivery in smart cities. This paper explores how AI-driven predictive analytics, combined with IoT-enabled wearable devices and telemedicine platforms, can enhance patient outcomes, streamline resource allocation, and reduce urban health disparities. By analyzing real-time health data and predicting disease outbreaks, this study demonstrates the potential of AI to revolutionize (...) urban healthcare systems. Experimental results highlight improvements in diagnostic accuracy, emergency response times, and operational efficiency, paving the way for equitable and sustainable healthcare in smart cities. (shrink)

As global manufacturing industries evolve, the demand for smarter, more efficient, and predictive production systems is intensifying. The integration of analyzer instrumentation with Artificial Intelligence (AI) in manufacturing environments presents a transformative opportunity to optimize processes, enhance product quality, and reduce operational costs. This research explores the convergence of analyzer technologies and AI-driven automation to create predictive manufacturing ecosystems. Through the use of smart sensors, real-time data analytics, and machine learning algorithms, modern manufacturing setups are becoming increasingly self-aware, adaptive, and (...) proactive. This paper reviews current advancements in analyzer instrumentation—such as gas chromatographs, spectrometers, and thermal analyzers—coupled with AI applications like predictive maintenance, quality forecasting, and anomaly detection. It also investigates key case studies from industries such as pharmaceuticals, automotive, and petrochemicals, highlighting how real-time monitoring and AI-based decision-making are reshaping production efficiency. The study utilizes a qualitative methodology, including a literature review and selected case analysis, to assess the benefits and challenges of these technologies. Findings reveal that predictive process optimization leads to significant improvements in throughput, energy consumption, and defect reduction. However, successful integration relies heavily on data quality, sensor accuracy, and workforce adaptability. The paper concludes by proposing a roadmap for industries aiming to transition toward nextgen manufacturing through AI-integrated analyzer instrumentation, emphasizing strategic planning, cross-disciplinary collaboration, and continuous learning. (shrink)

Clark acknowledges but resists the indirect mind–world relation inherent in prediction error minimization (PEM). But directness should also be resisted. This creates a puzzle, which calls for reconceptualization of the relation. We suggest that a causal conception captures both aspects. With this conception, aspects of situated cognition, social interaction and culture can be understood as emerging through precision optimization.

This capstone is centered around the development of an efficient electronic property inventory system tailored for school assets, driven by the overarching objective of resource optimization to ensure equitable access to vital materials for all learners. The methodology follows the “ISSO” framework (Ignite, Strategize, Systematize, Operationalize), complemented by a Logical Framework. The project employs a homegrown digitalized system constructed through a waterfall model approach, which undergoes alpha and beta testing. The study’s analysis utilizes a t-Test to evaluate its impact. (...) The outcomes reveal that real-time property monitoring significantly enhances custodianship, while digitalization yields improvements in reliability, efficiency, and usability, effectively overhauling the conventional inventory process. The electronic system fosters stakeholder engagement, supporting communication, budget allocation, and audits. Comparative testing demonstrates clear advantages over traditional manual methods. In conclusion, this study underscores the merits of a refined electronic property inventory system. Notably, the research suggests a need for careful consideration of process complexities in outsourcing decisions and showcases the swift, precise, and transparent accounting provided by e-AIMSS deployments, thereby nurturing transparency and public confidence. This innovative approach holds promise for broader application within the realm of e-government processes. (shrink)

Data mining technology for healthcare purposes transforms basic medical information into useful insights which helps doctors predict diseases along with diagnosing patients while tailoring personalized treatment plans. A successful healthcare analytics process requires a complete data pipeline which includes data preprocessing followed by extraction then selection after optimization until classification. Data preprocessing implements the Z-Score Method together with Isolation Forest along with Interquartile Range (IQR) Method to detect outliers which would potentially harm model performance. The Principal Component Analysis (PCA) (...) serves as an extraction method to reduce dimensions alongside log transformation which addresses data skewness to establish data stability. Wrapper methods Recursive Feature Elimination (RFE) together with Genetic Algorithms build predictive accuracy by continuously refining the selected feature subsets. Model performance enhancement relies on the Adam optimizer together with other optimization methods that aid in achieving high accuracy during medical outcome predictions. Adam Optimization optimization achieved a maximum training accuracy of 1.0 while validation accuracy reached 0.9 as the optimal value. Improvements in loss functionality decreased it from 45 to almost zero levels proving successful learning takes place. Analysis showed that the model demonstrated a positive trend in both validation accuracy levels despite periodic accuracy difficulties because the pipeline functioned well for predicting CKD conditions. Future work will direct its attention towards adjusting model performance by fine-tuning along with regularization techniques and additional data acquisition for enhancing generalization capabilities. (shrink)

Data mining technology for healthcare purposes transforms basic medical information into useful insights which helps doctors predict diseases along with diagnosing patients while tailoring personalized treatment plans. A successful healthcare analytics process requires a complete data pipeline which includes data preprocessing followed by extraction then selection after optimization until classification. Data preprocessing implements the Z-Score Method together with Isolation Forest along with Interquartile Range (IQR) Method to detect outliers which would potentially harm model performance. The Principal Component Analysis (PCA) (...) serves as an extraction method to reduce dimensions alongside log transformation which addresses data skewness to establish data stability. Wrapper methods Recursive Feature Elimination (RFE) together with Genetic Algorithms build predictive accuracy by continuously refining the selected feature subsets. Model performance enhancement relies on the Adam optimizer together with other optimization methods that aid in achieving high accuracy during medical outcome predictions. Adam Optimization optimization achieved a maximum training accuracy of 1.0 while validation accuracy reached 0.9 as the optimal value. Improvements in loss functionality decreased it from 45 to almost zero levels proving successful learning takes place. Analysis showed that the model demonstrated a positive trend in both validation accuracy levels despite periodic accuracy difficulties because the pipeline functioned well for predicting CKD conditions. Future work will direct its attention towards adjusting model performance by fine-tuning along with regularization techniques and additional data acquisition for enhancing generalization capabilities. (shrink)

The integration of performance optimization techniques in cloud security is increasingly becoming essential as businesses leverage cloud environments to store, manage, and process data. Cloud computing offers a multitude of advantages such as scalability, flexibility, and cost efficiency; however, it also exposes organizations to various security threats. This paper explores the latest trends in cloud security and investigates how performance optimization techniques can enhance security while ensuring minimal impact on system performance. By reviewing current research and emerging technologies, (...) we propose a framework for combining security and performance optimization strategies, emphasizing machine learning, cryptography, and automated threat detection systems. We also examine the role of virtualization and containerization in cloud security. The findings indicate that with the right balance, performance and security can coexist without compromising the quality of service. (shrink)

A comment on Paul Schoemaker's target article in Behavioral and Brain Sciences, 14 (1991), p. 205-215, "The Quest for Optimality: A Positive Heuristic of Science?" (https://doi.org/10.1017/S0140525X00066140). This comment argues that the optimizing model of decision leads to an infinite regress, once internal costs of decision (i.e., information and computation costs) are duly taken into account.

Cloud computing is the provision of IT resources (IaaS) on-demand using a pay as you go model over the internet. It is a broad and deep platform that helps customers builds sophisticated, scalable applications. To get the full benefits, research on a wide range of topics is needed. While resource over provisioning can cost users more than necessary, resource under provisioning hurts the application performance. The cost effectiveness of cloud computing highly depends on how well the customer can optimize the (...) cost of renting resources (VMs) from cloud providers. The issue of resource provisioning optimization from cloud-consumer potential is a complicated optimization issue, which includes much uncertainty parameters. There is a much research avenue available for solving this problem as it is in the real-world. Here, in this paper we provide details about various optimization techniques for resource provisioning. (shrink)

Hyperparameter optimization is a crucial aspect of deep learning, as the choice of hyperparameters significantly influences model performance. Finding the optimal set of hyperparameters can be a time-consuming and computationally expensive process. Traditional techniques, such as grid search and random search, often fail to efficiently explore the vast hyperparameter space, especially for deep learning models with numerous parameters. In this paper, we propose Bayesian Optimization (BO) as an effective approach for hyperparameter optimization in deep learning models. Bayesian (...) Optimization is a global optimization technique that is particularly suitable for optimizing complex, expensive to-evaluate functions. Unlike grid search or random search, BO builds a probabilistic model of the objective function and uses this model to make informed decisions about where to search next in the hyperparameter space. This approach reduces the number of evaluations required to find optimal or near-optimal hyperparameters, making it computationally efficient and well-suited for deep learning applications. The paper presents a detailed overview of Bayesian Optimization, its working principles, and how it can be applied to deep learning hyperparameter tuning. We explore the use of Gaussian Processes (GP) as surrogate models for BO and highlight the benefits of using acquisition functions to balance exploration and exploitation. Additionally, we compare BO with traditional methods, evaluating its performance in various deep learning tasks such as image classification, natural language processing, and time-series forecasting. Finally, we discuss the challenges and limitations of using Bayesian Optimization for hyperparameter tuning and offer insights into future directions for improving its efficiency and applicability in large-scale deep learning models. (shrink)

With the rapid growth of Internet of Things (IoT) devices and the increasing demand for real-time processing of large data volumes, traditional cloud-based systems struggle to meet latency and bandwidth requirements. Edge-Cloud convergence has emerged as a solution, combining the computational power of cloud data centers with the low-latency and high-throughput capabilities of edge devices. This paper explores the architecture, design principles, and best practices for building hybrid systems that integrate edge computing and cloud infrastructure. We investigate various methods to (...) optimize latency, improve real-time data processing capabilities, and ensure seamless communication across the hybrid environment. Additionally, the paper highlights challenges such as data consistency, scalability, security, and fault tolerance in Edge-Cloud systems. The performance of Edge-Cloud hybrid systems is analyzed through simulation, showcasing key advantages such as reduced latency, bandwidth optimization, and fault tolerance in real-time applications. The paper concludes by providing recommendations for future work and areas of research to advance the development of more robust and efficient Edge-Cloud systems. (shrink)

The rapid increase of the old age people imposes the reconsideration of the rehabilitation techniques and procedures and/or the development of the existing ones, at least from two points of view: the limitation use of the pharmaceutical drugs because of their secondary effects in the debilitated organisms and their avoidance; the high risk of the induced anxiety states, depression or other symptoms as a consequence of the main disease, i.e. the neuro-degenerative or mobility dysfunctions, limiting again the use of such (...) excessive drug medication. Moreover, Alzheimer’s or Parknson’s diseases are otherwise incurable, so the application of rehabilitating procedures like music-based therapy becomes preferential. The advances both in the identification of the specific auditory musical-sensitive circuits and of the non- musical neuro-connections but activated indirectly by music, otherwise inaccessible by other techniques, and in the understanding of consciousness, which can be described by seven cognitive centers, allows to improve and develop the music based therapy. A multitask procedure based on the activation of these cognition centers is presented, allowing to increase the personal psychical engagement of the patient in the recommended music based therapeutic program, addressed both to the rehabilitation of the main degenerative disease and to the secondary induced dysfunctions. This procedure combines the evidence based medicine assisted by laboratory expertise for the determination of a correct diagnostic, and narrative based medicine allowing to quantify the personal engaging qualities of the patient with respect to the proposed music based program, reducing in this way the gap between these two basic procedures and bringing in a first plane the patient care, not only the disease, with beneficial consequence for the therapy efficiency and for the patient-medical care relations. (shrink)

The predictive coding (PC) theory of attention identifies attention with the optimization of the precision weighting of prediction error. Here we provide some challenges for this identification. On the one hand, the precision weighting of prediction error is too broad a phenomenon to be identified with attention because such weighting plays a central role in multimodal integration. Cases of crossmodal illusions such as the rubber hand illusion and the McGurk effect involve the differential precision weighting of prediction error, yet (...) attention does not shift as one would predict. On the other hand, the precision weighting of prediction error is too narrow a phenomenon to be identified with attention, because it cannot accommodate the full range of attentional phenomena. We review criticisms that PC cannot account for volitional attention and affect-biased attention, and we propose that it may not be able to account for feature-based and intellectual attention. (shrink)

The purpose of this study is to develop a model that will optimize (minimize) the heat transfer coefficient of mild steel weldment using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The process input parameters are welding current, welding voltage, and gas flow rate, while the response variable is heat transfer coefficient. Tungsten inert gas (TIG) welding process was used to produce the welded joints. Optimizing this parameter is one sure way of producing a good weld with the desired (...) strength and quality. From the RSM analysis, the optimal solutions for the process input parameters are; welding current, 180.00Amps, welding voltage, 21.672Volts and gas flow rate, 15.504L/min. The optimal solution for the response variable is 238.819W/m0C. The Desirability of achieving the Optimum solution results is 83.62%. From the analysis of variance (ANOVA), it was observed that gas flow rate (GFR) has more significant effect on the response variable. The ANN analysis predicted an optimal solution for the response variable to be 256.663W/m2oC, with an overall strong correlation (R) between the input factors and the response variable to be 99.893%. Therefore, it is recommended that the models be used to navigate the design space. But, the optimal solutions of the artificial neural network (ANN) analysis (ANN) are better and more robust because of its higher Regression (R) value, and therefore recommended for application and use, and for systematic decision making. (shrink)

In today’s digital landscape, the exponential growth of unstructured and semi-structured data has led to increasing challenges in data management and retrieval. Metadata—descriptive data that provides context to content— plays a crucial role in mitigating this complexity. However, traditional metadata systems are often fragmented, manually curated, and inconsistent, leading to inefficiencies across data workflows. Artificial Intelligence (AI) introduces a transformative approach to metadata optimization by automating generation, improving accuracy, and enabling intelligent context understanding. This paper explores how AI technologies (...) such as machine learning (ML), natural language processing (NLP), and computer vision are reengineering metadata systems across industries. We present a review of current literature, a comparative table of traditional versus AI-powered methods, a research methodology for implementation, and a conceptual model of AI in the metadata lifecycle. The study demonstrates that AI not only enhances metadata quality and usability but also empowers organizations to derive deeper insights and ensure data governance at scale. (shrink)

The article discusses the implementation of AI within cartridge inventory systems to achieve improved inventory control with reduced operational expenses and superior efficiency results. Research shows that AI solutions succeed in practice because they eliminate stock shortages while managing excess inventory to achieve better supply chain operational results throughout real-world situations. The research approach examined existing AI usage in inventory management and documented success stories about HP and Canon when they applied AI technology to their cartridge supply operations. The research (...) confirms how AI technologies' predictive analytics and machine learning capabilities deliver better demand forecasting accuracy and improved resource allocation while reducing business costs. Implementing AI solutions provides business opportunities to industries dependent on cartridges since they can gain operational efficiency with enhanced inventory precision while cutting overhead expenses. This research paper ends with propositional suggestions about investigating how AI technology integrates with IoT systems for advanced real-time inventory monitoring. (shrink)

Purpose: Due to the increase in energy demand and the effects of global warming, energy-efficient buildings have gained significant importance in the modern construction industry. To create a suitable framework with the aim of reducing energy consumption in the building sector, the external walls of a residential building were considered with two criteria of global warming potential and energy consumption. -/- Design/methodology/approach: In the first stage, to achieve a nearly zero-energy building, energy analysis was performed for 37 different states of (...) thermal insulation. Then, the insulation materials’ life cycle assessment was performed. These results were used to find a set of optimal modes in the Pareto front by using non-dominated sorting genetic algorithm II multi-objective genetic algorithm. Thus, based on the data obtained from this method, it was possible to compare and choose different thermal insulation materials based on the distance from the Pareto front, reducing the environmental effects. -/- Findings: The results showed that replacing the windows was possible to save 3.24% in energy consumption. Also, selecting the proper insulation reduced energy consumption value by 63.13%. Finally, this building can save 69.31% of energy consumption compared to the base building by following the zero-energy building standard. As a result, the Pareto curve was introduced as a guide for the optimal design of the building’s wall insulation. -/- Originality/value: The proposed method provides designers with a framework for latent carbon analysis to access quickly and select optimal scenarios. It can also be used without restrictions for other decisions with different goals and criteria. (shrink)

Big data has increasingly been recognized as a transformative force in the sports industry, offering insights that were once impossible to gather through traditional methods. By leveraging vast amounts of real-time data from wearable devices, sensors, and video analysis, big data analytics can help athletes and teams make informed decisions to enhance performance, prevent injuries, and optimize training. This paper explores the role of big data in sports performance analytics, focusing on its application across various sports domains, including training (...) class='Hi'>optimization, injury prevention, and game strategy. The study highlights key data sources, methodologies, and real-world case studies where big data analytics has led to significant improvements in sports performance. The potential for big data to revolutionize the future of sports is vast, with its continued evolution poised to impact athlete training, team management, and overall game strategies. (shrink)

This paper will be concerned with hard choices—that is, choice situations where an agent cannot make a rationally justified choice. Specifically, this paper asks: if an agent cannot optimize in a given situation, are they facing a hard choice? A pair of claims are defended in light of this question. First, situations where an agent cannot optimize because of incompleteness of the binary preference or value relation constitute a hard choice. Second, situations where agents cannot optimize because the binary preference (...) or value relation violates acyclicity do not constitute a hard choice. (shrink)