We show how the Full Bayesian Significance Test (FBST) can be used as a model selection criterion. The FBST was presented by Pereira and Stern as a coherent Bayesian significance test. Key Words: Bayesian test; Evidence; Global optimization; Information; Model selection; Numerical integration; Posterior density; Precise hypothesis; Regularization. AMS: 62A15; 62F15; 62H15.

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)

A Bayesian measure of evidence for precise hypotheses is presented. The intention is to give a Bayesian alternative to significance tests or, equivalently, to p-values. In fact, a set is defined in the parameter space and the posterior probability, its credibility, is evaluated. This set is the “Highest Posterior Density Region” that is “tangent” to the set that defines the null hypothesis. Our measure of evidence is the complement of the credibility of the “tangent” region.

A new Evidence Test is applied to the problem of testing whether two Poisson random variables are dependent. The dependence structure is that of Holgate’s bivariate distribution. These bivariate distribution depends on three parameters, 0 < theta_1, theta_2 < infty, and 0 < theta_3 < min(theta_1, theta_2). The Evidence Test was originally developed as a Bayesian test, but in the present paper it is compared to the best known test of the hypothesis of independence in a frequentist framework. It is (...) shown that the Evidence Test is considerably more powerful when the correlation is not too close to zero, even for small samples. (shrink)

In this survey, a recent computational methodology paying a special attention to the separation of mathematical objects from numeral systems involved in their representation is described. It has been introduced with the intention to allow one to work with infinities and infinitesimals numerically in a unique computational framework in all the situations requiring these notions. The methodology does not contradict Cantor’s and non-standard analysis views and is based on the Euclid’s Common Notion no. 5 “The whole is greater than the (...) part” applied to all quantities (finite, infinite, and infinitesimal) and to all sets and processes (finite and infinite). The methodology uses a computational device called the Infinity Computer (patented in USA and EU) working numerically (recall that traditional theories work with infinities and infinitesimals only symbolically) with infinite and infinitesimal numbers that can be written in a positional numeral system with an infinite radix. It is argued that numeral systems involved in computations limit our capabilities to compute and lead to ambiguities in theoretical assertions, as well. The introduced methodology gives the possibility to use the same numeral system for measuring infinite sets, working with divergent series, probability, fractals, optimization problems, numerical differentiation, ODEs, etc. (recall that traditionally different numerals lemniscate; Aleph zero, etc. are used in different situations related to infinity). Numerous numerical examples and theoretical illustrations are given. The accuracy of the achieved results is continuously compared with those obtained by traditional tools used to work with infinities and infinitesimals. In particular, it is shown that the new approach allows one to observe mathematical objects involved in the Hypotheses of Continuum and the Riemann zeta function with a higher accuracy than it is done by traditional tools. It is stressed that the hardness of both problems is not related to their nature but is a consequence of the weakness of traditional numeral systems used to study them. It is shown that the introduced methodology and numeral system change our perception of the mathematical objects studied in the two problems. (shrink)

Abstract: The aims of this study are to introduce acceleration methods that called relative and algorithmic acceleration methods, which we generally call Al-Tememe's acceleration methods of the second kind discovered by (Ali Hassan Mohammed). It is very useful to improve the numerical results of continuous integrands in which the main error is of the 4th order, and related to accuracy, the number of used partial intervals and how fast to get results especially to accelerate the results got by Simpson's (...) method. Also, it is possible to utilize it in improving the results of differential equations numerically of the main error of the forth order. (shrink)

rd species identification plays a vital role in biodiversity conservation and ecological studies, offering insights into habitat health and species distribution. Traditional methods for identifying bird species are time-intensive and prone to human error, necessitating automated solutions. This project, Bird Species Identification Using Deep Learning, proposes an advanced system leveraging the power of deep learning to accurately identify bird species from images. The system utilizes a convolutional neural network (CNN), renowned for its proficiency in image classification tasks. A dataset comprising (...) diverse bird species images is preprocessed and augmented to enhance model robustness and generalization. (shrink)

A computational methodology called Grossone Infinity Computing introduced with the intention to allow one to work with infinities and infinitesimals numerically has been applied recently to a number of problems in numerical mathematics (optimization, numerical differentiation, numerical algorithms for solving ODEs, etc.). The possibility to use a specially developed computational device called the Infinity Computer (patented in USA and EU) for working with infinite and infinitesimal numbers numerically gives an additional advantage to this approach in comparison (...) with traditional methodologies studying infinities and infinitesimals only symbolically. The grossone methodology uses the Euclid’s Common Notion no. 5 ‘The whole is greater than the part’ and applies it to finite, infinite, and infinitesimal quantities and to finite and infinite sets and processes. It does not contradict Cantor’s and non-standard analysis views on infinity and can be considered as an applied development of their ideas. In this paper we consider infinite series and a particular attention is dedicated to divergent series with alternate signs. The Riemann series theorem states that conditionally convergent series can be rearranged in such a way that they either diverge or converge to an arbitrary real number. It is shown here that Riemann’s result is a consequence of the fact that symbol ∞ used traditionally does not allow us to express quantitatively the number of addends in the series, in other words, it just shows that the number of summands is infinite and does not allows us to count them. The usage of the grossone methodology allows us to see that (as it happens in the case where the number of addends is finite) rearrangements do not change the result for any sum with a fixed infinite number of summands. There are considered some traditional summation techniques such as Ramanujan summation producing results where to divergent series containing infinitely many positive integers negative results are assigned. It is shown that the careful counting of the number of addends in infinite series allows us to avoid this kind of results if grossone-based numerals are used. (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)

Abstract: The aims of this study are to introduce acceleration methods that are called triangular acceleration methods, which come within the series of several acceleration methods that generally known as Al-Tememe's acceleration methods of the second kind which are discovered by (Ali Hassan Mohammed). These methods are useful in improving the results of determining numerical integrals of continuous integrands where the main error is of the forth order with respect to accuracy, partial intervals and the fasting of calculating the (...) results specifically to accelerate results come out by Simpson's method. Also, it is possible to make use of it to improve the results of solving differential equations numerically of the main error of the forth order. (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)

In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated by (...) modern applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. (shrink)

Integrating machine learning (ML) into network management and orchestration has revolutionized edgebased networking paradigms, where real-time decision-making is critical. Traditional network management approaches often struggle with edge environments' dynamic and resource-constrained nature. By leveraging ML algorithms, networks at the edge can achieve enhanced efficiency, automation, and adaptability in areas such as traffic prediction, resource allocation, and anomaly detection (Wang et al., 2021). Supervised and unsupervised learning techniques facilitate proactive network optimization, reducing latency and improving quality of service (QoS) (Li (...) & Zhang, 2020). Furthermore, reinforcement learning (RL) models enable autonomous network orchestration, allowing edge devices to adapt intelligently to fluctuating workloads and environmental conditions (Patel et al., 2019). However, challenges such as data privacy, computational overhead, and model interpretability remain key concerns in deploying ML-driven network orchestration at the edge (Chen et al., 2022). This paper explores state-of-the-art advancements, key challenges, and future research directions in ML-based edge network management, highlighting its potential to drive the next generation of intelligent, self-optimizing networks. (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)

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 Genetic Ranking Optimization Algorithm (GROA) is used to rank phishing content based on multiple features by optimizing the ranking system through iterative selection and weighting. Dynamic weighting further enhances the process by adjusting the weights of features based on their importance in real-time. This hybrid approach enables the model to learn from the data, improving classification over time. The classification system was evaluated using benchmark phishing datasets, and the results demonstrated a significant improvement in detection accuracy and reduced (...) false positives. (shrink)

In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated by (...) modern applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. (shrink)

: In today's data-driven world, the efficient management of network resources is crucial for optimizing performance in data centers and large-scale networks. Load balancing is a critical process in ensuring the equitable distribution of data across multiple paths, thereby enhancing network throughput and minimizing latency. This paper presents a comprehensive approach to load balancing using advanced optimization techniques integrated with multipath routing protocols. The primary focus is on dynamically allocating network resources to manage the massive volume of data generated (...) by modern applications. By leveraging algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method efficiently distributes data across multiple paths, ensuring balanced network utilization. The combination of these algorithms with multipath routing significantly reduces congestion and improves overall network performance. Simulations conducted on various network scenarios demonstrate the effectiveness of this approach, showcasing improvements in data throughput, reduced packet loss, and enhanced quality of service (QoS). (shrink)

Today, the space industry is undergoing a period of significant technological advancement. Continuous progress in additive manufacturing technologies and the adoption of modern materials for 3D printing are driving this transformation. This trend has intensified competition among various space companies—both state-owned and private—each striving to introduce innovative and unique solutions. FlightControl Propulsion, a private space company in Ukraine, is one such example. This study focuses on the design of the power frame for a low-thrust liquid rocket engine. Power frames in (...) rocket engines must withstand significant mechanical loads. The application of topology optimization and high-strength steels enables the development of compact and efficient power frames that are substantially lighter than traditional designs. Additive manufacturing not only significantly reduces the production time of various parts and assemblies but also offers nearly unlimited possibilities for creating complex geometric shapes. What was previously challenging or even impossible to manufacture using conventional methods has now become feasible. This work examines a component fabricated using Selective Laser Melting (SLM) technology. A structured algorithm for topology optimization is described, along with the fundamental principle of the SIMP (Solid Isotropic Material with Penalization) topology optimization method. Numerical modeling of the stress-strain state for the initial design variant is performed using the finite element method (FEM) in a computer-aided engineering (CAE) environment. A computational finite element mesh is generated, and the safety margins of the design are evaluated. Following topology optimization, the final design variant is determined, and numerical modeling of the stress-strain state is repeated for the optimized design. The final design is then prepared for manufacturing and subsequent experimental testing. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized (...) through techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

Abstract: The main aim of this work is to introduce acceleration methods called a hyperbolic acceleration methods which are of series of numerated methods. In general, these methods named as AL-Tememe’s acceleration methods of first kind discovered by (Ali Hassan Mohammed). These are very beneficial to acceleration the numerical results for definite integrations with continuous integrands which are of 2nd order main error, with respect to the accuracy and the number of the used subintervals and the fasting obtaining results. (...) Especially, for acceleration the results which are obviously obtained by trapezoidal and midpoint methods. Moreover, these methods could be enhancing the results of the ordinary differential equations numerically which are of 2nd order main error. (shrink)

Abstract: The main aim of this work is to introduce acceleration methods called a Trigonometric acceleration methods which are of series of numerated methods. In general, these methods named as AL-Tememe’s acceleration methods of first kind to his discoverer ''Ali Hassan Mohammed''. These are very beneficial to acceleration the numerical results for definite integrations with continuous integrands which are of 2nd order main error, with respect to the accuracy and the number of the used subintervals and the fasting obtaining (...) results. Especially, for acceleration the results which are obviously obtained by trapezoidal and midpoint methods. Moreover, these methods could be enhancing the results of the ordinary differential equations numerically which are of 2nd order main error. (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)

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)

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)

We present a novel approach to defining the mathematical constant π through the infinite den- sification of a sweeping net, which approximates a circle as the net becomes infinitely dense. By developing and enhancing notation related to sweeping nets and saddle maps, we establish a rigor- ous framework for expressing π in terms of the densification process using reverse integration. This method, inspired by the concept that numbers ”come from infinity,” leverages a reverse integral approach to model the transition (...) from infinite densification to the finite circle. Our work not only offers a new perspective on the geometric interpretation of π but also provides insights into reverse integration techniques and their applications in mathematical analysis. (shrink)

The Integrated Information Theory (IIT) formulated for the first time in 2004 by the neuroscientist Giulio Tononi, is a theoretical framework aiming to scientifically explain phenomenal consciousness. The IIT is presented in the first part of this work. Broadly speaking, integrated information is an abstract quantitative measure of the causal power a system has on itself. The main claim of IIT is the identity between informational structures and experience. The nature of this identity will be the subject of the second (...) part. One can interpret the IIT as a fundamental law of nature connecting the physical domain to the mental domain. The philosophical implications of such a claim are numerous and they are subject to criticism. This will be the main concern of the third and last part. (shrink)

There exist many applications where it is necessary to approximate numerically derivatives of a function which is given by a computer procedure. In particular, all the fields of optimization have a special interest in such a kind of information. In this paper, a new way to do this is presented for a new kind of a computer - the Infinity Computer - able to work numerically with finite, infinite, and infinitesimal number. It is proved that the Infinity Computer is (...) able to calculate values of derivatives of a higher order for a wide class of functions represented by computer procedures. It is shown that the ability to compute derivatives of arbitrary order automatically and accurate to working precision is an intrinsic property of the Infinity Computer related to its way of functioning. Numerical examples illustrating the new concepts and numerical tools are given. (shrink)

This study conducts a detailed friction-based performance analysis of stabilizer bar bush seals through numerical simulations. Utilizing state-of-the-art Finite Element Analysis (FEA) tools, the research aims to evaluate how varying levels of friction coefficients impact the mechanical integrity and functionality of bush seals within automotive stabilizer bars. By systematically altering the friction coefficients from 0.1 to 0.5, the investigation assesses the resulting changes in reaction forces and stress distribution across the seal components. The primary focus is on understanding how (...) different friction settings influence the operational performance of the seals, particularly in terms of wear patterns, durability, and the ability to maintain effective load transmission between the stabilizer bar and its adjoining components. Key performance metrics such as seal deformation, stress resistance, and longevity under cyclic loading conditions are analyzed to provide insights into the optimal frictional properties required for maximizing seal efficiency. The outcomes of this simulation study are expected to offer valuable guidelines for the design and material selection of bush seals, aiming to enhance the overall stability and noise, vibration, and harshness (NVH) characteristics of automotive suspension systems. Recommendations for future research will include exploring the impact of material heterogeneity and geometric modifications on the frictional behavior of stabilizer bar bush seals. This work sets a foundation for advancing the design standards of automotive bush seals by integrating friction considerations into the simulation protocols for component testing. (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)

Traditional encryption methods provide a layer of security, but they often lack the robustness needed to address emerging threats. This paper introduces an optimized framework for secure cloud storage that integrates data encryption, decryption, and dispersion using cutting-edge optimization techniques. The proposed model enhances data security by first encrypting the data, then dispersing it across multiple cloud servers, ensuring that no single server holds the complete dataset. Decryption occurs only when the dispersed data fragments are reassembled, which adds an (...) additional layer of security. We also explore various optimization algorithms to improve the efficiency of encryption and dispersion processes, thereby reducing computational overhead while maintaining high security. The implementation of this framework is evaluated on multiple cloud platforms, demonstrating its effectiveness in safeguarding data with minimal performance impact. (shrink)

In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm (...) class='Hi'>optimization, and simulated annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. (shrink)

Strategies for tackling educational inequality take many forms, though perhaps the argument most often invoked is school integration. Yet whatever the promise of integration may be, its realization continues to be hobbled by numerous difficulties. In this paper we examine what many of these difficulties are. Yet in contrast to how many empirical researchers frame these issues, we argue that while educational success in majority-minority schools will depend on a variety of material and non-material resources, the presence of (...) these resources does not require school integration; indeed sometimes the most crucial resources are easier to foster in its absence. To that end, we briefly canvass the evidence from the United States on high performing majority-minority schools serving poor and minority students. Yet because these debates are so contentious in the American context, we pivot away from the U.S. to consider a different country, the Netherlands. We invite the reader to consider an analogous case where racial injustice and educational inequality are just as serious, yet where differences in the state school system might prove instructive concerning how some majority-minority schools choose to respond to existing segregation, but more importantly how educational success can occur in the absence of integration. (shrink)

As the prevalence of deepfake videos continues to escalate, there is an urgent need for robust and efficient detection methods to mitigate the potential consequences of misinformation and manipulation. This abstract explores the application of Long Short-Term Memory (LSTM) networks in the realm of deepfake video detection. LSTM, a type of recurrent neural network (RNN), has proven to be adept at capturing temporal dependencies in sequential data, making it a promising candidate for analysing the dynamic nature of videos. The research (...) delves into the intricacies of utilizing LSTM architectures for the detection of deepfake videos, emphasizing the significance of understanding temporal patterns inherent in manipulated content. The proposed methodology involves preprocessing of video data, including the creation of high-quality training datasets and the application of data augmentation techniques to enhance model generalization. The training process and optimization strategies specific to LSTM networks are explored to achieve optimal performance in deepfake detection. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the model’s effectiveness in distinguishing between genuine and manipulated content. The abstract also addresses challenges and limitations inherent in deepfake detection, including mitigating false positives and negatives, and discusses potential avenues for future research to enhance the robustness of LSTM-based detection systems. The findings of this research have implications for real-world applications, particularly in the context of social media platforms and video hosting services, where the integration of LSTM-based deepfake detection can contribute to a safer and more secure online environment. k. (shrink)

Zero provides a challenge for philosophers of mathematics with realist inclinations. On the one hand it is a bona fide cardinal number, yet on the other it is linked to ideas of nothingness and non-being. This paper provides an analysis of the epistemology and metaphysics of zero. We develop several constraints and then argue that a satisfactory account of zero can be obtained by integrating an account of numbers as properties of collections, work on the philosophy of absences, and recent (...) work in numerical cognition and ontogenetic studies. (shrink)

Integration sustainability outcomes give attention to construction ecology in the design review of urban environments to comply with Earth’s System that is composed of integral parts of the (i.e., physical, chemical and biological components). Naturally, exchange patterns of industrial ecology have consistent and periodic cycles to preserve energy flows and materials in Earth’s System. When engineering topology is affecting internal and external processes in system networks, it postulated the valence of the first-level spatial outcome (i.e., project compatibility success). These (...) instrumentalities are dependent on relating the second-level outcome (i.e., participant security satisfaction). The construction ecology-based topology (i.e., as feedback energy system) flows from biotic and abiotic resources in the entire Earth’s ecosystems. These spatial outcomes are providing an innovation, as entails a wide range of interactions to state, regulate and feedback “topology” to flow as “interdisciplinary equilibrium” of ecosystems. The interrelation dynamics of ecosystems are performing a process in a certain location within an appropriate time for characterizing their unique structure in “equilibrium patterns”, such as biosphere and collecting a composite structure of many distributed feedback flows. These interdisciplinary systems regulate their dynamics within complex structures. These dynamic mechanisms of the ecosystem regulate physical and chemical properties to enable a gradual and prolonged incremental pattern to develop a stable structure. The engineering topology of construction ecology for integration sustainability outcomes offers an interesting tool for ecologists and engineers in the simulation paradigm as an initial form of development structure within compatible computer software. This approach argues from ecology, resource savings, static load design, financial other pragmatic reasons, while an artistic/architectural perspective, these are not decisive. The paper described an attempt to unify analytic and analogical spatial modeling in developing urban environments as a relational setting, using optimization software and applied as an example of integrated industrial ecology where the construction process is based on a topology optimization approach. (shrink)

Artificial Intelligence (AI) is a technology that makes machines smart and capable of doing things that usually require human intelligence. It is a rapidly evolving field with ongoing research and development to advance its capabilities and address its limitations. AI has permeated various aspects of our daily lives, and its applications can be found in numerous products and services. The integration of AI continues to expand across multiple sectors, providing convenience, personalization, and efficiency in our daily lives. While Punjabi (...) culture is rich and diverse, characterized by several distinctive traits that define its identity, AI has started to make its presence felt in Punjabi culture through various avenues. Though AI is gradually enriching Punjabi culture, it is still in its early stages, and there is room for further exploration and integration. However, it is essential to ensure that AI's influence aligns with the values and aspirations of Punjabi culture while preserving its authenticity and uniqueness. (shrink)

AI and the Universal Law of Economic Balance: A Homeostatic Model for Sustainable Prosperity -/- Introduction -/- Modern economies are primarily driven by the profit motive, which, while encouraging innovation and efficiency, often leads to wage stagnation, wealth inequality, and resource exploitation. The imbalance between corporate profits, wages, purchasing power, and market demand has resulted in recurring economic crises, social unrest, and environmental degradation. -/- To resolve these systemic issues, economic policies must align with the universal law of balance in (...) nature, ensuring that financial systems function as self-regulating ecosystems rather than unstable, profit-driven markets. Achieving this balance requires both gradual reform and fundamental restructuring, first at the national level and then integrated into a global economic framework. -/- The key to Implementing and maintaining this balance lies in artificial intelligence (AI), which can analyze economic conditions in real time and adjust wages, prices, taxation, and resource distribution to maintain stability. However, to prevent AI from becoming a tool of economic control for powerful entities, it must be open-source, decentralized, and governed by ethical principles. -/- This essay explores how AI-driven economic balance can replace market-driven instability with a self-regulating, sustainable model that ensures fair wages, stable prices, and equitable wealth distribution. -/- The Problem: Profit Motive and Economic Imbalance -/- The traditional economic system relies on profit margins to sustain businesses. However, when companies focus on maximizing profits, several imbalances emerge: -/- 1. Lower Wages Reduce Purchasing Power -/- When businesses lower costs to remain competitive, wages stagnate. -/- Lower wages mean consumers cannot afford the products they produce. -/- 2. Weaker Demand Forces Businesses to Cut Costs Further -/- If people cannot buy goods, businesses cut jobs and wages further, deepening the problem. -/- Economic growth becomes dependent on debt, leading to financial instability. -/- 3. Wealth Concentration Creates Social and Political Instability -/- Extreme profit accumulation among a small elite leads to economic inequality. -/- Governments either increase taxes (which businesses resist) or cut social services (which leads to public unrest). -/- This cycle of imbalance proves that unregulated profit-seeking is unsustainable. Instead, an economy must be structured to self-regulate, ensuring a balance between wages, prices, profits, and demand. -/- A Homeostatic Economic Model: Balancing Wages, Prices, and Profits -/- A homeostatic economic model ensures that all components of the economy—wages, prices, profit margins, taxation, and demand—adjust dynamically to maintain stability. The following reforms would create this balance: -/- 1. Wage-Price Equilibrium -/- Wages must be indexed to productivity and inflation, ensuring purchasing power remains stable. -/- Profit-sharing incentives should be standardized, allowing workers to benefit from company success. -/- 2. Profit Regulation Without Killing Innovation -/- Essential industries (e.g., healthcare, food, energy) should have profit caps to prevent price exploitation. -/- Businesses that reinvest in worker wages, technology, or sustainability should receive tax breaks. -/- 3. Market Demand and Supply Stability -/- Governments and businesses must collaborate to prevent artificial scarcity and price manipulation. -/- Tax policies should support lower-income groups, ensuring their purchasing power remains intact. -/- 4. Dynamic Feedback Mechanism for Economic Stability -/- Economic decisions must be based on real-time data, avoiding speculation-based policies. -/- Policies should be adjustable based on changes in market conditions, preventing booms and busts. -/- These policies require both national reforms and global cooperation, ensuring that no single country or corporation can disrupt the balance for personal gain. -/- AI as the Key to Homeostatic Economic Balance -/- Maintaining economic balance in real time is too complex for human policymakers alone. This is where artificial intelligence (AI) can play a critical role in: -/- 1. Real-Time Wage and Price Adjustments -/- AI can analyze inflation, productivity, and cost of living to dynamically adjust wages and prices. -/- 2. Profit Optimization Without Exploitation -/- AI can track corporate profits and suggest profit-reinvestment policies that benefit workers and society. -/- 3. Smart Taxation and Wealth Redistribution -/- AI-driven progressive taxation models ensure fair wealth distribution while preventing economic stagnation. -/- 4. Market Demand and Supply Optimization -/- AI can predict consumer demand shifts and help businesses adjust production to prevent shortages or oversupply. -/- 5. Global Trade and Resource Management -/- AI can analyze trade patterns to ensure fair pricing and sustainable resource allocation worldwide. -/- 6. Public Awareness and Education -/- AI can identify economic misconceptions and suggest educational initiatives to promote financial literacy. -/- For AI to function effectively in this role, it must operate under ethical and decentralized governance to serve humanity rather than corporate or political interests. -/- Ensuring AI Remains Open-Source and Decentralized -/- To prevent AI from being misused as a tool of economic control, its development and governance must follow these principles: -/- 1. Open-Source AI for Economic Balance -/- AI models should be transparent and publicly auditable to prevent hidden biases. -/- Developers from multiple disciplines should contribute to ensure fairness and accuracy. -/- 2. Decentralized Governance Model -/- AI should be managed through global economic councils, not a single government or corporation. -/- Blockchain technology can ensure transparency in AI-driven economic decisions. -/- 3. Ethical Safeguards Against Manipulation -/- AI decisions should prioritize sustainability and human well-being over profit. -/- Economic policies should be auditable in real-time by independent researchers and civil organizations. -/- Failsafe mechanisms must prevent AI from implementing harmful policies due to data misinterpretation. -/- By ensuring AI-driven economic balance is open-source, decentralized, and ethically governed, it can create a self-correcting system that prevents economic crises, stabilizes wealth distribution, and ensures long-term prosperity. -/- Implementation Strategy: Gradual Reform Leading to Systemic Change -/- Since economic transition cannot happen overnight, a phased approach is necessary: -/- Phase 1: National-Level Reforms -/- 1. Implement wage-price indexing and profit-sharing policies. -/- 2. Reduce corporate taxes for businesses that reinvest in workers and sustainability. -/- 3. Introduce regulated price stabilization for essential goods. -/- Phase 2: Global Economic Integration -/- 1. Establish international economic councils to guide balance-driven policies. -/- 2. Reform global trade agreements to prioritize fair wages and sustainability. -/- 3. Create a universal economic sustainability index to track progress. -/- Phase 3: AI-Governed Dynamic Economic System -/- 1. Develop real-time AI monitoring for wage-price adjustments. -/- 2. Implement AI-driven smart taxation to prevent wealth inequality. -/- 3. Ensure AI operates under decentralized, transparent governance. -/- Conclusion: The Future of an AI-Driven Balanced Economy -/- By integrating AI with economic policy, we can create a self-regulating economic system that follows the universal law of balance, preventing both extreme poverty and extreme wealth accumulation. This homeostatic economic model ensures that profits, wages, prices, and demand remain in equilibrium, fostering sustainable prosperity for all. -/- . (shrink)

The dissemination of information in times of crisis or emergency is distinctive since the affected individuals may take, process, and act on information differently. As the Centers for Disease Control and Prevention noted “the right message at the right time from the right person can save lives.” This study elaborates on the principles of crisis and emergency risk communication (CERC) in the realistic narrative, and notes that a successful CERC should be prompt, accurate, veracious, empathetic, respectful, and promote meaningful action. (...) Since pandemics are not only biomedical but also social, political, economic, and cultural incidents, critical communication in the time of a pandemic is of crucial significance. Hence, the Coronavirus (COVID-19) pandemic is assessed as a recent and important case study. This pandemic had four chief challenges; including that since the disease is caused by the novel SARS-CoV-2 virus the assumptions about the disease have been revised numerously and rapidly. In addition, the cumulative scale and scope of COVID-19 which is in excess of 627 million confirmed cases, and approximately 6.58 million deaths worldwide remained a strain. Another concern was the overwhelming uncertainty and evolving trajectory of the pandemic about how and when it ends or enters the endemic stage of the outbreak. Finally, the overabundance of amplification and dissemination of both accurate and inaccurate information led to an infodemic. Long before the COVID-19 pandemic, many individuals used the internet as an important resource for seeking health-related information. However, the infodemic and the other aforementioned challenges made distinguishing up-to-date accurate information a more arduous task, and CERC and pandemic-related communication are even more crucial. Hence, a multi-dimensional set of skills is required to overcome these challenges whether amidst or after the pandemic. Therefore, a multi-stakeholder approach towards an overarching and comprehensive metaliteracy that “provides an integrated and inclusive framework to cultivate critical thinking and proficiency in comprehending data, information, visuals and the media” is essential. This study recommends and elaborates on how to incorporate metaliteracy into crisis and risk communication at governmental, organizational, and individual levels. (shrink)

The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. By leveraging (...) advanced optimization techniques such as Grid Search and Particle Swarm Optimization (PSO), the proposed SVM-based IDS achieves superior classification results. (shrink)

Multisensory processing encompasses all of the various ways in which the presence of information in one sensory modality can adaptively influence the processing of information in a different modality. In Part I of this survey article, I begin by presenting a cartography of some of the more extensively investigated forms of multisensory processing, with a special focus on two distinct types of multisensory integration. I briefly discuss the conditions under which these different forms of multisensory processing occur as well (...) as their important perceptual consequences and interrelations. In Part II, I then turn to examining of some of the different possible ways in which the structure of conscious perceptual experience might also be characterized as multisensory. In addition, I discuss the significance of research on multisensory processing and multisensory consciousness for philosophical attempts to individuate the senses. (shrink)

The increasing computational demands of artificial intelligence (AI) workloads have significantly escalated energy consumption in data centers. AI-driven applications, including deep learning, natural language processing, and autonomous systems, require substantial computing power, primarily provided by Graphics Processing Units. These GPUs, while enhancing computational efficiency, contribute to significant power consumption and heat generation, necessitating advanced cooling strategies. This study provides a quantitative assessment of AI-specific hardware power usage, focusing on the NVIDIA H100 GPU. The analysis compares AI data center energy consumption (...) to the average US household power usage, demonstrating that a single AI rack consumes approximately 39 times the energy of a typical household. Additionally, a scalability analysis estimates that approximately 87 new hyper-scale data centers consume the electricity as much as consumed by New York City. This emphasizes that with rapid growth of AI Data Centers, the large-scale deployment could lead to an unprecedented rise in global energy demand. Furthermore, the study evaluates the impact of heat dissipation on cooling requirements, highlighting the need for energy- efficient cooling solutions, including liquid and immersion cooling techniques. Future research directions include energy- efficient AI models, renewable energy integration, sustainable AI accelerator designs, and intelligent workload optimization to mitigate the environmental impact of large-scale AI adoption. This research provides critical insights for designing more sustainable AI-driven data centers while maintaining high-performance computing efficiency. (shrink)

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and confidentiality (...) of the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. Furthermore, the implementation of an optimized keyword indexing mechanism facilitates fast and accurate search operations, making the protocol well-suited for real-time IoT applications. Extensive experiments were conducted using various IoT datasets to evaluate the performance of the proposed protocol in terms of encryption speed, search efficiency, and overall security. The results demonstrate that the optimized encryption protocol significantly outperforms existing methods, offering a scalable solution that meets the stringent requirements of IoT systems. This research contributes to the field by providing a practical and secure encryption solution that addresses the unique challenges of IoT environments, paving the way for more secure and efficient IoT networks. (shrink)

The relevance of the problem under study is stipulated by the need to stabilize the market for tourist services in existing restrictions caused by the SARS-CoV-2 pandemic. The purpose of the research: The purpose of the article is to develop integrated measures for the formation of transport and logistics clusters to increase the synergetic effect of the proposed activities aimed at raising the mobility level of potential consumers of tourist services. Methods of the research: The main research methods include predicting (...) and simulating; they make it possible to consider the problem of forming the package of tourist and transport services as a process of targeted monitoring and optimization of tourist routs. Results of the research: The article presents the concept of creation of the network of transport and tourist clusters in Poland and its functioning; large rail carriers are the core of the clusters. Practical significance: The proposed models for the formation of a package of tourist services are aimed at increasing the synergetic effect for all cluster participants, as well as at the development of tourist activities during the pandemic. (shrink)

Numerous problems arising in engineering applications can have several objectives to be satisfied. An important class of problems of this kind is lexicographic multi-objective problems where the first objective is incomparably more important than the second one which, in its turn, is incomparably more important than the third one, etc. In this paper, Lexicographic Multi-Objective Linear Programming (LMOLP) problems are considered. To tackle them, traditional approaches either require solution of a series of linear programming problems or apply a scalarization of (...) weighted multiple objectives into a single-objective function. The latter approach requires finding a set of weights that guarantees the equivalence of the original problem and the single-objective one and the search of correct weights can be very time consuming. In this work a new approach for solving LMOLP problems using a recently introduced computational methodology allowing one to work numerically with infinities and infinitesimals is proposed. It is shown that a smart application of infinitesimal weights allows one to construct a single-objective problem avoiding the necessity to determine finite weights. The equivalence between the original multi-objective problem and the new single-objective one is proved. A simplex-based algorithm working with finite and infinitesimal numbers is proposed, implemented, and discussed. Results of some numerical experiments are provided. (shrink)

Numerous research groups are now utilizing Basic Formal Ontology as an upper-level framework to assist in the organization and integration of biomedical information. This paper provides elucidation of the three existing BFO subcategories of realizable entity, namely function, role, and disposition. It proposes one further sub-category of tendency, and considers the merits of recognizing two sub-categories of function for domain ontologies, namely, artifactual and biological function. The motivation is to help advance the coherent ontological treatment of functions, roles, and (...) dispositions, to help provide the potential for more detailed classification, and to shed light on BFO’s general make-up and use. (shrink)

Efficient rake scheduling and demand forecasting in railway operations are essential to address the complexities of passenger demand, minimize delays, and enhance utilization. This project uses advanced machine learning methods, specifically LSTM (Long Short-Term Memory) networks and GBM (Gradient Boosting Machine), to predict demand and optimize rake scheduling dynamically. Integrating a user-friendly web interface allows realtime data monitoring, enabling railway operators to make informed decisions. By leveraging real-time data sources, including rake movement, schedules, weather, and traffic conditions, this project aims (...) to improve operational efficiency and responsiveness. Keywords. Railway Scheduling, Rake Optimization, Demand Forecasting, LSTM, GBM, Machine Learning, RealTime Data. (shrink)

This paper provides a comprehensive workflow for organizations considering a move from legacy database systems to Snowflake, demonstrating how a structured migration process can result in enhanced data accessibility, operational flexibility, and long-term scalability. Future directions for research are also discussed, including integration with advanced analytics and machine learning applications, which could further augment Snowflake’s utility in big data ecosystems.

The present paper aims at contributing to the ethical debate on the impacts of artificial intelligence (AI) systems on human autonomy. More specifically, it intends to offer a clearer understanding of the design challenges to the effort of aligning driving automation technologies to this ethical value. After introducing the discussion on the ambiguous impacts that AI systems exert on human autonomy, the analysis zooms in on how the problem has been discussed in the literature on connected and automated vehicles (CAVs). (...) On this basis, it is claimed that the issue has been mainly tackled on a fairly general level, and mostly with reference to the controversial issue of crash-optimization algorithms, so that only limited design insights have been drawn. However, integrating ethical analysis and design practices is critical to pursue the implementation of such an important ethical value into CAV technologies. To this aim, it is argued, a more applied approach targeted at examining the impacts on human autonomy of current CAV functions should also be explored. As an example of the intricacy of this task, the case of automated route planning is discussed in some detail. (shrink)

Modern society faces major environmental problems, but there are many difficulties in studying the nature–human relationship from an integral psychosocial perspective. We propose the ecomind sponge conceptual framework, based on the mindsponge theory of information processing. We present a systematic method to examine the nature–human relationship with conceptual frameworks of system boundaries, selective exchange, and adaptive optimization. The theoretical mechanisms were constructed based on principles and new evidence in natural sciences. The core mechanism of ecomindsponge is the subjective sphere (...) of influence, which is the limited mental representation of information received from and processed based on the objective sphere of influence–actual interactions in reality. The subjective sphere is the sum of two sub-spheres: influencing (proactive) and being influenced (reactive). Maladaptation in thinking and behavior of the mind as an information collection-cum-processor results from the deviation of the subjective sphere from reality, which includes two main types: “stupidity” and “delusion”. Using Bayesian Mindsponge Framework (BMF) analytics on a dataset of 535 urban residents, we provide consistent statistical evidence on the proposed properties of subjective spheres. The dynamic framework of ecomindsponge can be used flexibly and practically for environmental research as well as other psychosocial fields. (shrink)