Phishing attacks remain one of the most prevalent cybersecurity threats, affecting individuals and organizations globally. The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns (...) to enhance the detection system's decision-making process. (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 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)

The rapid evolution of phishing techniques necessitates more sophisticated detection and classification methods. In this paper, we propose a novel approach to phishing content classification using a Genetic Ranking Optimization Algorithm (GROA), combined with dynamic weighting, to improve the accuracy and ranking of phishing versus legitimate content. Our method leverages features such as URL structure, email content analysis, and user behavior patterns to enhance the detection system's decision-making process. 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. (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)

: 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)

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)

Wireless Sensor Networks (WSNs) have become increasingly prevalent in various applications, ranging from environmental monitoring to smart cities. However, the limited energy resources of sensor nodes pose significant challenges in maintaining network longevity and data transmission efficiency. Duty-cycled WSNs, where sensor nodes alternate between active and sleep states to conserve energy, offer a solution to these challenges but introduce new complexities in data transmission. This paper presents an optimized approach to aggregated packet transmission in duty-cycled WSNs, utilizing advanced optimization (...) techniques to enhance energy efficiency, reduce latency, and improve network throughput. By aggregating data packets from multiple nodes before transmission, the proposed method minimizes the number of transmissions, thereby conserving energy. Optimization algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to determine the optimal aggregation and transmission schedules, taking into account factors such as network topology, node energy levels, and data urgency. T. (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)

The classification system was evaluated using benchmark phishing datasets, and the results demonstrated a significant improvement in detection accuracy and reduced false positives. The proposed model outperformed traditional machine learning algorithms, showing promise for real-world deployment in phishing detection systems. We conclude with suggestions for future improvements, such as incorporating more behavioral data and deploying the system in realtime monitoring applications.

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)

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)

The research methodology involves data preprocessing, feature engineering, model training, and performance evaluation. We employ optimization methods such as Genetic Algorithms and Grid Search to fine-tune model parameters, ensuring robust and generalizable models. The dataset used includes patient medical records, with features like age, blood pressure, cholesterol levels, and lifestyle habits serving as inputs for the ML models. Evaluation metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC), assess the model's predictive power.

During the last two decades the role of quantitative genetics in evolutionary theory has expanded considerably. Quantitative genetic-based models addressing long term phenotypic evolution, evolution in multiple environments (phenotypic plasticity) and evolution of ontogenies (developmental trajectories) have been proposed. Yet, the mathematical foundations of quantitative genetics were laid with a very different set of problems in mind (mostly the prediction of short term responses to artificial selection), and at a time in which any details of the genetic machinery (...) were virtually unknown. In this paper we discuss what a model is in population biology, and what kind of model we need in order to address the complexities of phenotypic evolution. We review the assumptions of quantitative genetics and its most recent accomplishments, together with the limitations that such assumptions impose on the modelling of some aspects of phenotypic evolution. We also discuss three alternative appr oaches to the theoretical description of evolutionary trajectories (nonlinear dynamics, complexity theory and optimization theory), and their respective advantages and limitations. We conclude by calling for a new theoretical synthesis, including quantitative genetics and not necessarily limited to the other approaches here discussed. (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)

Optimization algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to determine the optimal aggregation and transmission schedules, taking into account factors such as network topology, node energy levels, and data urgency. The proposed approach is validated through extensive simulations, demonstrating significant improvements in energy consumption, packet delivery ratio, and overall network performance. The results suggest that the optimized aggregated packet transmission method can effectively extend the lifespan of duty-cycled WSNs while ensuring reliable (...) data communication. Future work will explore the integration of machine learning techniques for adaptive scheduling and the application of the proposed method to heterogeneous WSNs. (shrink)

: Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as (...) Genetic Algorithms (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify (...) cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. (shrink)

Mit den Fortschritten in Generativer Künstlicher Intelligenz, Large Language Models, Brain-Computer Interfaces und genetischen Eingriffen gewinnt auch der Transhumanismus an Relevanz. Der Transhumanismus ist ein beliebtes Thema der Medien und wird in Tages- und Wochenzeitungen sowie im Fernsehen gerne aufgegriffen. Außerdem gibt es inzwischen viele Filme, die den Transhumanismus thematisieren, z. B. die Dokumentation „Endlich unendlich“ (2021, Regie: Stephan Bergmann). Der Transhumanismus wurde in Österreich auch Gegenstand einer Verschwörungserzählung, über die der Bayrische Rundfunk aufgeklärt hat. Auf den Wahlplakaten der „Partei (...) für Gesundheitsforschung“ ist der Transhumanist Aubrey de Grey abgebildet, der ein in Cambridge lebender Brite ist, aber auf Plakaten in Deutschland dargestellt wird. Diese und weitere Entwicklungen machen es notwendig, sich mit dem Transhumanismus aus wissenschaftlicher Perspektive auseinanderzusetzen. Was ist der Transhumanismus? Alle sprechen von Transhumanismus, aber was der Transhumanismus letztlich ist, bleibt häufig unklar. Der Transhumanismus (TH) ist eine philosophisch-technologische Bewegung des 20. und 21. Jahrhunderts, die es sich zum Ziel setzt, mittels neuer Technologien den Menschen grundlegend zu transformieren. Wichtig ist dabei, dass sich der TH nicht mit den Optimierungsbestrebungen in der Gesellschaft oder technologischem Fortschritt gleichsetzen lässt. Vielmehr verstehe ich unter TH die Gruppe, die sich um die Organisation Humanity+ in Kalifornien versammelt und die Personen, die sich explizit alsVertreter*innen des TH verstehen. Der TH hat eine ganz eigene Agenda, eigene Institutionen, Organisationen und Publikationsorgane.4 Zu den Themen und Visionen gehören die Lebensverlängerung von mehreren Hundert Jahren bis zur Unsterblichkeit, Kryonik (das Einfrieren des Körpers, der wieder aufgetaut werden soll, wenn nach transhumanistischer Vorstellung Unsterblichkeit ermöglicht werden kann), umfassendes Enhancement und die Verschmelzung von Körper und Technik sowie Beseitigung allen Leids, aller Krankheiten und Aufhebung des Alterns. Der TH strebt danach, mittels Technik neue Sinnesfähigkeiten zu erlangen und neue (virtuelle) Erfahrungswelten zu erschließen. Außerdem wird von vielen Transhumanist*innen auch die hochspekulative Vision des Mind Uploading vertreten, die streng genommen dem Posthumanismus zugeordnet werden muss Gemäß dieser Vision soll durch das maschinelle Lesen des Gehirns der ganze Mensch auf ein externes Medium hochgeladen werden können. Die Vision des Mind Uploadings ist aus naturwissenschaftlicher Perspektive hochumstritten, ist aber ein sehr beliebtes Motiv, das auch außerhalb des TH Wirkung zeigt und in viele Science-Fiction- Filme Eingang gefunden hat (z.B. die Amazon Serie „Upload“, 2020, Regie: Greg Daniels). Das Bestreben des TH, den Menschen grundlegend zu transformieren, wirft zunächst die Frage auf, wie der TH den Menschen überhaupt versteht. Dieser Frage nach dem Menschenverständnis des TH habe ich mich in meinem Buch „Über-Menschen. Philosophische Auseinandersetzung mit der Anthropologie des Transhumanismus“ gewidmet. Philosophische Anthropologie bezeichnet die wissenschaftlichen, philosophischen Reflexionen auf den Menschen. Welches Menschenverständnis vertritt der TH und lässt sich unsere Anthropologie auf dieser Grundlage weiterentwickeln? Das Buch gliedert sich in drei Teile: Teil 1 führt zuerst in die Bewegung des TH ein, worauf Teil II aufbaut, der sich mit dem Menschenverständnis des TH beschäftigt. Teil III betrachtet schließlich heutige Optimierungsbestrebungen in der Gesellschaft sowie neue Ansätze für die Technikanthropologie (z. B. Kritischer Posthumanismus und Neuer Materialismus). Die nachfolgenden Kapitel dieses Aufsatzes sind ein Ausschnitt aus Teil II, der Untersuchung des transhumanistischen Menschenverständnisses. Dieser Aufsatz widmet sich dem „genetisch codierten Menschen“ im TH, also der transhumanistischen Sicht auf den Menschen als jemanden, die*der vollständig von ihren*seinen Genen bestimmt ist. Der genetische Diskurs ist nur einer von fünf im TH dominanten Diskursen, die das transhumanistische Menschenverständnis ausmachen. Zu den anderen Diskursen bzw. transhumanistischen Bestimmungen des Menschen gehören: der Diskurs um die „Natur des Menschen“, der Maschinendiskurs – die Bestimmung des Menschen als Maschine –, der neurowissenschaftliche Diskurs – die Gleichsetzung des Menschen mit dem Gehirn bzw. Neuronalem und zuletzt die metaphysischen Verhältnisbestimmungen von Geist und Körper (PUZIO, Über-Menschen, Kap. 4). Diese transhumanistischen Bestimmungen des Menschen werden am Ende ausgewertet. Ohne die Ergebnisse dieses Kapitels im Detail vorwegnehmen zu wollen, kann für die Gesamtuntersuchung des TH gesagt werden, dass das transhumanistische Menschenverständnis sich als hochproblematisch erweist. Die transhumanistischen Argumentationsstrukturen sind nicht tragfähig und der TH behauptet sich auf die Naturwissenschaften zu berufen, widerspricht allerdings den aktuellen Ergebnissen der Naturwissenschaften. Dies wird in den nachfolgenden Kapiteln gut deutlich werden. Der TH hat ein reduktionistisches Menschenverständnis und reduziert den Menschen beispielsweise auf „Information“. Viele Ausführungen sind diskriminierend z. B. gegenüber Kranken, Alternden und Menschen mit Behinderungen und letztendlich wird sogar jeder Mensch, auch der gesunde junge Mensch, gegenüber der Maschine abgewertet, die aus transhumanistischer Sicht den Menschen weit übertrifft. Im weiteren Verlauf der Arbeit werden sogar „ideologische Elemente“ im TH identifiziert. Daher wird in der Arbeit abgelehnt, den TH als Grundlage für die Weiterentwicklung der Anthropologie zu verwenden und stattdessen werden im letzten Teil der Arbeit alternative Ansätze für die Technikanthropologie präsentiert. Wie bereits erwähnt, richtet sich die Kritik am TH jedoch nicht gegen die heutige Optimierung in der Gesellschaft, die ebenfalls im letzten Teil der Arbeit diskutiert wird. Optimierungsbestrebungen hat es schon immer gegeben und sie werden nun durch neue Technologien weiter verändert, sie sind aber nicht mit der Bewegung des TH gleichzusetzen. Das Kapitel ist eingebettet zwischen dem Maschinendiskurs (Mensch als Maschine) mit seinen Ausführungen zu Information und Kybernetik und dem neurowissenschaftlichen Diskurs („Der Mensch ist ihr*sein Gehirn“). Es wird im Folgenden der genetische Diskurs im TH untersucht, also wie der TH den Menschen vollständig auf Gene zurückführt, und dies schließlich kritisch geprüft. Sind „gute Gene“ alles? Kann der Mensch vollständig über ihre*seine Gene bestimmt werden? Und was hat es mit der trügerischen „genetischen Information“ auf sich? Zunächst wird der genetische Diskurs im TH dargestellt, also die genetischen Ausführungen des TH präsentiert (Kap. 2), danach wird der Blick auf den Gebrauch der genetischen Metaphern im TH gerichtet (Kap. 3) und zuletzt werden die genetischen Vorstellungen des TH kritisiert (Kap. 4). Der Aufsatz schließt in Kap. 5mit einer (vorläufigen) Zusammenführung der Ergebnisse und einem Ausblick. (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. The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify (...) cyberbullying behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)

In this paper, we explore the properties and optimization techniques related to polyhedral cones and energy numbers with a focus on the cone of positive semidefinite matrices and efficient computation strategies for kernels. In Part (a), we examine the polyhedral nature of the cone of positive semidefinite matrices, , establishing that it does not form a polyhedral cone for due to its infinite dimensional characteristics. In Part (b), we present an algorithm for efficiently computing the kernel function on-the-fly, leveraging (...) a polyhedral description of the convex hull generated by the feature mappings and . By restructuring the problem and using gradient-based optimization techniques, our approach minimizes memory usage and computational overhead, thus enabling scalable computation. Through examples and visualizations, we demonstrate the practical applications and efficiency of the proposed algorithm in optimizing these kernel computations. (shrink)

Air pollution is a significant environmental concern that affects human health, ecosystems, and climate change. Effective monitoring and management of outdoor air quality are crucial for mitigating its adverse effects. This paper presents an advanced approach to outdoor pollution measurement utilizing Internet of Things (IoT) technology, combined with optimization techniques to enhance system efficiency and data accuracy. The proposed framework integrates a network of IoT sensors that continuously monitor various air pollutants, such as particulate matter (PM), carbon monoxide (CO), (...) sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃), across different geographic locations. The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. (shrink)

The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. The system's performance is evaluated through simulations and real-world experiments, demonstrating its capability to deliver reliable and timely pollution (...) data. Future work will explore the integration of machine learning techniques for predictive analytics and the expansion of the sensor network for broader coverage. (shrink)

Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as (...) class='Hi'>Genetic Algorithms (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. Data from multiple sources, including camera feeds and wearable sensors, are used to train and validate the models, ensuring a comprehensive understanding of drowsiness indicators. (shrink)

The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. The system's performance is evaluated through simulations and real-world experiments, demonstrating its capability to deliver reliable and timely pollution (...) data. Future work will explore the integration of machine learning techniques for predictive analytics and the expansion of the sensor network for broader coverage. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as (...) accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. (shrink)

Data preparation, feature engineering, model training, and performance evaluation are all part of the study methodology. To ensure reliable and broadly applicable models, we utilize optimization techniques like Grid Search and Genetic Algorithms to precisely adjust model parameters. Features including age, blood pressure, cholesterol levels, and lifestyle choices are employed as inputs for the machine learning models in the dataset, which consists of patient medical information. The predictive capacity of the model is evaluated using evaluation measures, such as (...) accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC-ROC). Our findings show that improved machine learning models perform better than conventional methods, offering trustworthy forecasts that can help medical practitioners with early diagnosis and individualized treatment planning. In order to achieve even higher predicted accuracy, the study's conclusion discusses the significance of its findings for clinical practice as well as future improvements that might be made, like adding wearable device data in real-time or investigating deep learning techniques. (shrink)

Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive dimensional data. Many service providers envisage anomaly detection criteria to guarantee availability to avoid breakdowns and complexities caused due to large-scale operations. The streaming log data generated is associated with multi-dimensional complexity and thus poses a considerable challenge to detect the anomalies or unusual occurrences in the data. In this research, a hybrid model is proposed that is motivated by deep (...) belief criteria and meta-heuristics. Using Search-and-Rescue—BrainStorm Optimization (SAR-BSO), a hybrid feature selection (FS) and deep belief network classifier is used to localize and detect anomalies for streaming data logs. The significant contribution of the research lies in FS, which is carried out using SAR-BSO which increases the detection power of the model as it selects the most significant variables by minimizing redundant features. The evaluation of accuracy is efficiently improved when compared with the predictable methods, such as Extract Local Outlier Factor (ELOF), Track-plus, Hybrid Distributed Batch Stream (HDBS), IForestASD, DBN, BSO-based Feature Selection with DBN, Genetic Algorithm-Deep Belief Network (GA-DBN), Mutual Information-Deep Belief Network (MI-DBN), information entropy-Deep Belief Network(I + DBN), Flat Field-Deep Belief Network (FF + DBN), African Vulture Optimization Algorithm-Deep Belief Network(AVOA + DBN), Gorilla Troop Optimizer-Deep Belief Network(GTO-DBN), and SARO-based Feature Selection with DBN. Further, the accurate detection of the anomalies in the data stream is established by the Deep Belief Neural Network (DBN) classifier. The model’s efficacy is determined using Apache, Hadoop, HDFS, Spark, and Linux datasets and evaluated against existing similar models. The model efficiency is provided using multiple evaluation metrics and is found effective. From the experimentation, the accuracy of the proposed model is found to be 93.3, 95.4, 93.6, 94.2, and 93.5% respectively for the dataset such as Apache, Hadoop, HDFS, spark, and Linux. This enhancement in accuracy is due to the selection of optimal features by the proposed SAR-BSO algorithm. (shrink)

Darwin’s claim that Natural Selection, through optimization of fitness, explains complex biological design has not yet been properly formalized. Alan Grafen’s Formal Darwinism Project aims at providing such a formalization and at demonstrating that fitness maximization is coherent with results from Population Genetics, usually interpreted as denying it. We suggest that Grafen’s proposal suffers from some limitations linked to its concept of design as optimized fitness. In order to overcome these limitations, we propose a classification of evolutionary facts based (...) on a bi-dimensional complexity space, which adds robustness to fitness as measure of the quality of a design. In this space, each point represents an organismic architecture, and movement between two points would be an evolutionary fact. Natural Selection explains movement along the fitness axis, while non-selective mechanisms explain movement on the robustness axis. Moreover, we propose a thermodynamic metaphor to draw parallelisms between notions of fitness and entropy, robustness and energy, and movement in this space and reversible and irreversible cycles. We argue that this metaphor illustrates different evolutionary processes usually overlooked by proposals of formalization of Darwinian theory, such Grafen’s Formal Darwinism Project. (shrink)

Driver drowsiness is a significant factor contributing to road accidents, resulting in severe injuries and fatalities. This study presents an optimized approach for detecting driver drowsiness using machine learning techniques. The proposed system utilizes real-time data to analyze driver behavior and physiological signals to identify signs of fatigue. Various machine learning algorithms, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forest, are explored for their efficacy in detecting drowsiness. The system incorporates an optimization technique—such as (...) class='Hi'>Genetic Algorithms (GA) or Particle Swarm Optimization (PSO)—to enhance the accuracy and response time of the detection process. The integration of optimization methods ensures that the model adapts to various driving conditions and individual differences, providing a more reliable and robust detection mechanism. (shrink)

Synthetic biology is a field of research that concentrates on the design, construction, and modification of new biomolecular parts and metabolic pathways using engineering techniques and computational models. By employing knowledge of operational pathways from engineering and mathematics such as circuits, oscillators, and digital logic gates, it uses these to understand, model, rewire, and reprogram biological networks and modules. Standard biological parts with known functions are catalogued in a number of registries (e.g. Massachusetts Institute of Technology Registry of Standard Biological (...) Parts). Biological parts can then be selected from the catalogue and assembled in a variety of combinations to construct a system or pathway in a microbe. Through the innovative re-engineering of biological circuits and the optimization of certain metabolic pathways, biological modules can be designed to reprogram organisms to produce products or behaviors. Synthetic biology is what is known as a “platform technology”. That is, it generates highly transferrable theoretical models, engineering principles, and know-how that can be applied to create potential products in a wide variety of industries. Proponents suggest that applications of synthetic biology may be able to provide scientific and engineered solutions to a multitude of worldwide problems from health to energy. Synthetic biology research has already been successful in constructing microbial products which promise to offer cheaper pharmaceuticals such as the antimalarial synthetic drug artemisinin, engineered microbes capable of cleaning up oil spills, and the engineering of biosensors that can detect the presence of high concentrations of arsenic in drinking water. One of the potential benefits of synthetic biology research is in its application to biofuel production. It is this application which is the focus of this entry. The term “biofuel” has referred generally to all liquid fuels that are sourced from plant or plant byproducts and are used for energy necessary for transportation vehicles (Thompson 2012). Biofuels that are produced using synthetic biological techniques re-engineer microbes into biofuel factories are a subset of these. (shrink)

The overall goal of the approach developed in this paper is to estimate the likelihood of a given kinetic kill scenario between hostile spacebased adversaries using the mathematical framework of Complex Conceptual Spaces Single Observation. Conceptual spaces are a cognitive model that provide a method for systematically and automatically mimicking human decision making. For accurate decisions to be made, the fusion of both hard and soft data into a single decision framework is required. This presents several challenges to this data (...) fusion framework. The first is the challenge involved in handling multiple complex terminologies, which is addressed by drawing on a set of Space Domain Ontologies. Another challenge is the complex combinatorics involved when considering all possible feature combinations. This can be mitigated by using integer linear programming optimization that is outlined by the Complex Conceptual Spaces Single Observation mathematical model framework. A third challenge is the complicated physics that is involved in a spacecraft collision that must be addressed to obtain a better understanding of threat assessment. Overcoming these various challenges allows for a quantitative ranking for the potential of a kinetic kill collision across multiple spacecraft pairs. In addition to overcoming these challenges this paper will break down threat assessment into four domains and identify a ranking of threat both for each individual domain and for the four domains combined. Simulation results are shown to verify the developed concepts. (shrink)

In the case of introduction of visa-free regime between Ukraine and the European Union (EU) there will arise a problem of matching throughput capacity of the Ukrainian checkpoints to the quantity of people who would want to cross the border. The absence of need for obtaining a visa does not eliminate compulsory customs and border control, but it potentially increases the number of people who intend to cross the border. A growing number of border crossings in case of their mismatch (...) with the criteria for checkpoints are a potential non-tariff restricting barrier in international trade. A threat to the image of the country from the occurrence of such a tool of non-tariff regulation indicates relevance of present study. -/- The aim of the work was to study the dynamics of actual and potentially possible border crossings and to define directions for the optimization of procedures of such a crossing to eliminate negative impact of this non-tariff instrument of regulating international trade on the overall image of the country. -/- We analyzed the peculiarities of crossing the border between Ukraine and the EU through automobile crossing points based on quantitative data regarding crossing the Ukrainian–Polish border. The goals and structure of movement across the border by frequency are clarified. We defined criteria for increase in the discrepancy between actual and nominal throughput capacities of the Ukrainian checkpoints. -/- The scientific novelty of present research is establishing the nature of quantitative effect from restricting the crossing of border on the image of the country with the help of world rankings. -/- A practical significance of the study is to identify ways to improve a procedure for crossing the border: 1) by combining Polish and Ukrainian infrastructure of control at checkpoints; 2) by creating separate lined for crossing the border by persons without transportation vehicles; 3) by simplifying a procedure of control at the border both by checking only by border staff and by increasing the value of goods that a citizen can bring into country; 4) by financing technical equipment for crossing points and their infrastructure by introducing a targeted general tax for persons who cross the border; 5) by compiling open and regular statistics of border crossing. -/- A promising direction for further research is the economic substantiation of appropriateness of the proposed ways to improve the throughput capacities of checkpoints. (shrink)

...If, in some places, backward science determines the remaining period by the lack of optimism only by the number 123456789, then our progressive science expands it to 987654321, which is eight times more advanced than theirs. However, due to the inherent caution of scientists, both sides do not specify the measuring unit of reference — year, day, hour or minute are meant. Leonid Leonov. Collected Op. in ten volumes. Volume ten. M.: IHL, 1984, p.583. -/- The modern men being as (...) a rule is proceeding in three relatively autonomous, but immanently coexisting, and each penetrating dialectically through other, and each contesting even cruelly with other, and each more or less dominating on other, differently in various societies, worlds: the first one of the barbarity, the second one the culture, the third one the civilization... Barbarity is the mode of coexistence of autonomous human societies based on the unlimited will to act so as subject wants to do without any limitations of his activities... Culture is the mode of coexistence of autonomous human societies based on the most ancient testaments, customs, traditions etc of the highestly sacrified appearence... Civilisation is the mode of coexistence of autonomous human societies based on the reasoned or legitimated by social contracts, by profits, by expediency and so more constitutions, institutions etc... -/- Andrei Melnikov-Pechersky in his epic "In the Woods" says that there was a movement among the Old Believers in Russia, or a current that proclaimed Kostroma the new and, it seems, the only true Jerusalem, in an apocalyptic sense, of course, which in its spiritual scale was probably not inferior to the Bogomils, shakers, Mormons, or else and the whole Reformation in potential. In Western Europe, every more or less convex nuance of the history of a state or region (even if it is a small San Marino) is overgrown with almost a dozen institutions, not always state ones, by the way, in which almost every sneeze and step of very ambivalent historical subjects in a broad sense is studied. Especially in the young, but very pretentious America of the United States, which embodies all world justice. In our country, for example, even many leading humanitarian specialists do not realize the spiritual value of the Ipatievsky MONASTERY. The Russian writer Boris Shiryaev, who died quite a long time ago and in a very far abroad, devoted a special part of his documentary and artistic epic of wanderings "The Inextinguishable Lampada" to an entertaining and instructive, tragic fate of a kind of village monarchy in some Villages of the Kostroma province (I could not identify the area, and, frankly, I did not really try) in the early years The Soviet government. At the same time, all Russians learn about the French emperor Napoleon almost with their mother's milk how many percent of cyanide potassium was found in his hair, they reflect on Lermontov's "Airship" (probably, just not to "memorize" the imposed "Borodino" by heart). The events, of course, are incomparable, but the fact is significant. The outstanding national artist (if not to say a thinker of the Chesnyakov level) Grigory Pavlovich Kusochkin was glorified wherever, in whatever catalogues he was not included. Paris, London, New York, St. Petersburg, Moscow... To look at some smear of luminaries like Picasso or Modigliani, international tourist routes are organized in the restaurant toilet using the finest marketing technologies, and we did not even know Grigory Pavlovich by sight until, thank God, a series of TV shows on all Russian channels was dedicated to him in one gulp. Why is this happening? Is it a consequence of poverty or the result of the conscious policy of the ruling elite of the country and the world? The regions of the Kostroma Region are Austria, Switzerland, Bavaria, Armenia, Moldova, and Georgia... Georgia! I served in the army there. In this small but very great country, judging even by the appearance of its President Shevardnadze, although it is smaller than the Kostroma region, there are so many different, but Georgian-speaking peoples! Kakhetians, Meskhetians, Imereti, Iveretians, Mingrelians, Khevsurs... Adjarians (mostly Georgian-speaking Muslims) even have their own autonomy. And some peoples are not Georgian—speaking at all — South Ossetians, Abkhazians, Kurds, Armenians, Greeks, even Assyrians. In the Kostroma Region, and in any Russian "province" — not much less: there are plenty of the same Georgians or Armenians everywhere, quite moderate, loyal, and so on. Having, as a rule, their own officially registered associations within the framework of cultural autonomy. Well done! And Israel! He is the only one with all his "illegal" (and I am for the fact that every nation knows better where its ancestral lands are) territories half the size of the Kostroma region. The total number of Jews worldwide, according to the "Soviet Encyclopedic Dictionary" in 1987, is about 14 million. Meanwhile, the fate of Israel is being preoccupied (it is very instructive how he achieved this!) all the united nations of the world. In the Kostroma Region and its diaspora, it seems to everyone, and especially to its semi-migrant, Varangian leadership. I don't care if they build a public atomic bomb here in the form of a nuclear power plant or not. Because they do not know the value of this land, the greatness of its spirit. Kostroma is a holy land. I will immediately make a reservation that by Kostroma I mean not only the modern region, but the entire area that has ever historically been associated with Kostroma. The USSR is also a part of the great historical Russia. In this sense, even the entire era of the Russian Romanov Empire is the Kostroma region. Or: many toponymic objects of our native land are called mysteriously beautiful: Sharya, Shunga, Shekshema, Sholezhka, - we need to find out what kind of peoples — and where are they now? — we left such euphonious archetypal combinations, because these are our Kostromsky peoples, these are our Kostroma people. Or, say, the classic of American and world sociology Pitirim Alexandrovich Sorokin writes in his autobiographical scientific novel "The Long Way" that he studied at the Khrenovskaya (what a strong word!) teachers' seminary of Kostroma province, but in an administrative row for propaganda activities, gendarmes were taken by sledge to the prison of the city of Kineshma - is this not a fact for Kostroma, and primarily for him, local history, although Kineshma has long been attributed to the Ivanovo region, and Pitirim Alexandrovich is buried on the other side of the planet?! I have not the slightest doubt that Kostroma provided spiritual food, gave birth to and nurtured a huge intellectual diaspora with extremely valuable material on a national, global scale. And this is both in direct and indirect order, in the form of a chain reaction. Apparently, it is easy for an intelligent person to grow up in Kostroma, but it is impossible to live, and somewhere on the contrary. Therefore, it is not surprising that the geneticist academician D. K. Belyaev, who was born in the village of Protasov near Nerekhta, very close to Kostroma, glorified Moscow and Siberia with his discoveries and practical results, which immeasurably enriched the Soviet state treasury with foreign exchange income from the sale of the most valuable fur grown according to the biotechnology developed by him in a much shorter time than it usually turns out in nature. And in Kostroma, most likely, few people know who he is and what he is famous for. I will focus in more detail on one of the aspects of his great life. This happened back in the so-called pre-Perestroika times. In a series of his speeches, Dmitry Konstantinovich, based on the results of his and colleagues' research, emphasized, in his opinion, the determining role of the biological factor in shaping the content of human essence, of course, only in a philosophical, conceptual aspect. For some reason, this was opposed by a geneticist, as they say, persecuted at one time by the "senile" in selectionism (including human nature) by the Michurino-Lysenkoists, academician N. P. Dubinin, which personally remains incomprehensible to me until now. With a grateful curtsey to Academician Dubinin and his point of view, the famous French humanist, member of the Politburo of the French Communist Party Lucien Sav appeared on the pages of the Communist magazine. The material was clearly titled: "Problems are not only scientific, but also political." He very indecently attributed the views of academician Belyaev to "a reactionary ideology that tries to put genetics at its service, and more broadly, biology, in order to present all kinds of social inequalities of all kinds as inherent in nature itself"(1). I would like to see any ideology that does not try to "put this or that at its service," much less something worthwhile... A little later, the editorial of the Communist hit the "biologizers", and soon Dmitry Konstantinovich was gone. In short, it is only on the example of our fellow countrymen that you can write interesting, world-class essays on science and its history, but instead our scientists should fit their global potencies into a page and a half (good yet. if not entirely at their own expense), and then for at least three years to guess whether they will be published or not. Now it no longer surprises me how at one time, when, after reading the listener card I filled out, the sweetest girl from the IPKPON office at USU congratulated me for something. It turned out that the dean of the Faculty of Philosophy of USU, doctor, professor, Honored Scientist, and now academician Konstantin Niko-laevich Lyubutin is a countryman, Kostromsky, or rather Manturovsky by origin, as I write below in the notes — we are Kostromendors. To him, my fellow citizen in the most subtle micropatriotic dimensions, I owe a lot in my professional and social development, even in survival. Because that's how it often turns out: you can be born here, sometimes you can live here... It's very hard. Maybe the Kostroma origin of an outstanding Russian historian of philosophy, organizer of science and education, educator and friend of a galaxy of people devoted to the Motherland and the cause is not a great milestone in the history of the region, but name me at least a dozen Russian cities on the scale of Kostroma, from where such intellectual ones would come out into the world and light luminaries like ours. No matter who you take from the world significance of the pillars of national (and world, and foreign, - even the great Rabbi of Lubavitcher spent three years in exile in a Campfire, — I learned from some foreign voices) history, literature, culture in general, technology, science and so on - you will always find someone, which is very directly related to Kostroma. Pushkin, Lermontov, Gorky. Alexander Nikolaevich Ostrovsky, Pisarev, Uspensky, Katenin, Florensky, Savrasov, Kustodiev, Dyagilev, Mikhalkov, Alexander Alexandrovich Zinoviev; writers, artists, entrepreneurs, revolutionaries, inventors, politicians... In some world-famous for its classic Russian provincialism, Kologriv has been storing, figuratively speaking, unique sketches, if not of Leo-Nardo da Vinci himself, then certainly of his school, almost since the seventeenth century. And not only that. Nevertheless, the well-known Chukhloma to the Fatherland, in addition to thinkers of the rank of Rozanov and Zinoviev (where should domestic philosophers mature and not be born, if not in Chukhloma, Manturov or Khrenknowswhere?), gave an actor of amazing, national, originality — Mikhail Pugovkin, who can not be confused with anyone, a role that no one will repeat. (shrink)

The importance, and urgency, of a Roboethics lay in the lesson of our recent history. Two of the front rank fields of science and technology, Nuclear Physics and Genetic Engineering, have already been forced to face the ethical consequences of their research’s applications under the pressure of dramatic and troubling events. In many countries, public opinion, shocked by some of these effects, urged to either halt the whole applications, or to seriously control them. Robotics is rapidly becoming one of (...) the leading field of science and technology, so that we can forecast that in the XXI century humanity will coexist with the first alien intelligence we have ever come in contact with - robots. It will be an event rich in ethical, social and economic problems. Public opinion is already asking questions such as: “Could a robot do "good" and "evil”? “Could robots be dangerous for humankind?”. (shrink)

Given the recent explosion of interest in applications of evolutionary biology to understanding human psychology, we think it timely to assure better understanding of modern evolutionary theory among the psychologists who might be using it. We find it necessary to do so because of the very reducd version of evolutionary theorizing that has been incorporated into much of evolutionary psychology so far. Our aim here is to clarify why the use of a reduced version of evolutionary genetics will lead to (...) faulty science and to indicate where other resources of evolutionary biology can be found that might elevate the standard of the evolutionary component of evolutionary psychology. (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)

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)

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)

Ranking theory is one of the salient formal representations of doxastic states. It differs from others in being able to represent belief in a proposition (= taking it to be true), to also represent degrees of belief (i.e. beliefs as more or less firm), and thus to generally account for the dynamics of these beliefs. It does so on the basis of fundamental and compelling rationality postulates and is hence one way of explicating the rational structure of doxastic states. (...) Thereby it provides foundations for accounts of defeasible or nonmonotonic reasoning. It has widespread applications in philosophy, it proves to be most useful in Artificial Intelligence, and it has started to find applications as a model of reasoning in psychology. (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.

Ranking theory is a formal epistemology that has been developed in over 600 pages in Spohn's recent book The Laws of Belief, which aims to provide a normative account of the dynamics of beliefs that presents an alternative to current probabilistic approaches. It has long been received in the AI community, but it has not yet found application in experimental psychology. The purpose of this paper is to derive clear, quantitative predictions by exploiting a parallel between ranking theory (...) and a statistical model called logistic regression. This approach is illustrated by the development of a model for the conditional inference task using Spohn's ranking theoretic approach to conditionals. (shrink)

An organizing theme of the dissertation is the issue of how to make philosophical theories useful for scientific purposes. An argument for the contention is presented that it doesn’t suffice merely to theoretically motivate one’s theories, and make them compatible with existing data, but that philosophers having this aim should ideally contribute to identifying unique and hard to vary predictions of their theories. This methodological recommendation is applied to the ranking-theoretic approach to conditionals, which emphasizes the epistemic relevance and (...) the expression of reason relations as part of the semantics of the natural language conditional. As a first step, this approach is theoretically motivated in a comparative discussion of other alternatives in psychology of reasoning, like the suppositional theory of conditionals, and novel approaches to the problems of compositionality and accounting for the objective purport of indicative conditionals are presented. In a second step, a formal model is formulated, which allows us to derive quantitative predictions from the ranking-theoretic approach, and it is investigated which novel avenues of empirical research that this model opens up for. Finally, a treatment is given of the problem of logical omniscience as it concerns the issue of whether ranking theory (and other similar approaches) makes too idealized assumptions about rationality to allow for interesting applications in psychology of reasoning. Building on the work of Robert Brandom, a novel solution to this problem is presented, which both opens up for new perspectives in psychology of reasoning and appears to be capable of satisfying a range of constraints on bridge principles between logic and norms of reasoning, which would otherwise stand in a tension. (shrink)

Lara Buchak argues for a version of rank-weighted utilitarianism that assigns greater weight to the interests of the worse off. She argues that our distributive principles should be derived from the preferences of rational individuals behind a veil of ignorance, who ought to be risk averse. I argue that Buchak’s appeal to the veil of ignorance leads to a particular way of extending rank-weighted utilitarianism to the evaluation of uncertain prospects. This method recommends choices that violate the unanimous preferences of (...) rational individuals and choices that guarantee worse distributions. These results, I suggest, undermine Buchak’s argument for rank-weighted utilitarianism. (shrink)

In traditional consequentialism the good is position-neutral. A single evaluative ranking of states of affairs is correct for everyone, everywhere regardless of their positions. Recently, position-relative forms of consequentialism have been developed. These allow for the correct rankings of states to depend on connections that hold between the state being evaluated and the position of the evaluator. For example, perhaps being an agent who acts in a certain state requires me to rank that state differently from someone else who (...) lacks this connection. In this chapter several different kinds of position-relative rankings related to agents, times, physical locations, and possible worlds are explored. Arguments for and against adopting a position-relative axiology are examined, and it is suggested that position-relative consequentialism is a promising moral theory that has been underestimated. (shrink)

Genetically engineered (GE) insects, such as the GE OX513A Aedes aegypti mosquitoes, have been designed to suppress their wild-type populations so as to reduce the transmission of vector-borne diseases in humans. Apart from the ecological and epidemiological uncertainties associated with this approach, such biotechnological approaches may be used by individual governments or the global community of nations to avoid addressing the underlying structural, systemic causes of those infections... We discuss here key ethical questions raised by the use of GE mosquitoes, (...) with the aim of fostering discussion between the public, researchers, policy makers, healthcare organizations, and regulatory agencies at the local, national, and international levels. We affect that goal by outlining a procedural approach to decision-making about the use of the “biotechnology” that goes beyond “community engagement.” The protocol we advocate for entails informed deliberations and decision-making at the community level. It is designed to ensure that the voices of the marginalized and vulnerable groups that would be disproportionately affected by the decision are heard during the community-wide discussions. Moreover, we make the case that the values embedded in the risk assessment should be identified so that the community can make an informed decision about the use of GE insects. In addition, we advocate for the involvement of a variety of actors whose responsibility would be to ensure that the community has the opportunity to make an informed decision based on deliberations about the use of the “biotechnology.”. (shrink)

The cumulative impact of enhancement technologies may alter the human species in the very long-term future. In this article, I will start showing how radical genetic enhancements may accelerate the conversion into a novel species. I will also clarify the concepts of ‘biological species’, ‘transhuman’ and ‘posthuman’. Then, I will summarize some ethical arguments for creating a transhuman or posthuman species with a substantially higher level of well-being than the human one. In particular, I will present what I shall (...) call the Principle of the Best Interests of Posthumanity, which states that the enhancement of the human and transhuman species must be directed towards the creation of a posthuman existence that is substantially more valuable than its predecessors. I suggest that human extinction may be considered, within that principle, as one of the best interests of posthumanity. Finally, I will develop three objections that make that principle unattractive and that show that pursuing a full-blown programme of posthuman evolution is ethically flawed. (shrink)

In this paper I argue that the extent to which a human trait is genetically caused can causally depend upon whether the trait is categorized within human genetics as genetically caused. This makes the kindgenetically caused traitan interactive kind. I demonstrate that this thesis is both conceptually coherent and empirically plausible. I outline the core rationale of this thesis and demonstrate its conceptual coherence by drawing upon Waters’ (2007) analysis of genetic causation. I add empirical plausibility to the thesis (...) by describing a hypothetical but empirically plausible mechanism by which the fact that obesity is categorized as genetically caused within human genetics increases the extent to which obesity is in fact genetically caused. (shrink)

We introduce a ranking of multidimensional alternatives, including uncertain prospects as a particular case, when these objects can be given a matrix form. This ranking is separable in terms of rows and columns, and continuous and monotonic in the basic quantities. Owing to the theory of additive separability developed here, we derive very precise numerical representations over a large class of domains (i.e., typically notof the Cartesian product form). We apply these representationsto (1)streams of commodity baskets through time, (...) (2)uncertain social prospects, (3)uncertain individual prospects. Concerning(1), we propose a finite horizon variant of Koopmans’s (1960) axiomatization of infinite discounted utility sums. The main results concern(2). We push the classic comparison between the exanteand expostsocial welfare criteria one step further by avoiding any expected utility assumptions, and as a consequence obtain what appears to be the strongest existing form of Harsanyi’s (1955) Aggregation Theorem. Concerning(3), we derive a subjective probability for Anscombe and Aumann’s (1963) finite case by merely assuming that there are two epistemically independent sources of uncertainty. (shrink)