Wigner's quantum-mechanical classification of particle-types in terms of irreducible representations of the Poincaré group has a classical analogue, which we extend in this paper. We study the compactness properties of the resulting phase spaces at fixed energy, and show that in order for a classical massless particle to be physically sensible, its phase space must feature a classical-particle counterpart of electromagnetic gauge invariance. By examining the connection between massless and massive particles in the massless limit, we also derive a classical-particle (...) version of the Higgs mechanism. (shrink)

The Disputationes Theologiae from 1655 of Bartolomeo Mastri (1602–1673) is structured after the model of the medieval commentaries on Peter Lombard’s Four Books of Sentences and hence has a large section in the first part on divine knowledge. Within this section, called Disputation on the Divine Intellect (Disputatio de Divino Intellectu), Mastri’s long and nuanced discussion of divine foreknowledge merits particular attention. In the time of Mastri, the theological issue of divine foreknowledge and its relation to human freedom had gained (...) particular prominence, with the Jesuits and the Thomists each opting for their particular doctrine on this subject as well as the related topics of grace and predestination, thereby competing to establish the definitive Roman Catholic reaction to the Protestant and Reformed views on these matters. Mastri, entering the debate at a rather late stage (almost half a century after Paul V’s famous attempt in 1607 to call off the controversy De Auxiliis by prohibiting any further polemics on the subject of grace), sets out to locate a clearly Scotist position in this rather peculiar historical landscape called Early Modern theology. This article discusses Mastri's contribution to the debate and highlights its coherence with other aspects of Mastri's doctrine of divine knowledge. (shrink)

Scientific fields frequently need to exchange data to advance their own inquiries. Data unification is the process of stabilizing these forms of interfield data exchange. I present an account of the epistemic structure of data unification, drawing on case studies from model-based cognitive neuroscience (MBCN). MBCN is distinctive because it shows that modeling practices play an essential role in mediating these data exchanges. Models often serve as interfield evidential integrators, and models built for this purpose have their own representational and (...) inferential functions. This form of data unification should be seen as autonomous from other forms, particularly explanatory unification. (shrink)

Consciousness is targeted by both philosophers and neuroscientists; but different methodological premises and even different conceptions about what conscious experience is and how the challenges and potential problems associated with consciousness research should be formulated underlie the different approaches. Namely, whereas empirical data and the constant refinement of experimental procedures to expand and modify this body of empirical data and resulting empirical theories are crucial to neuroscience, the significance of empirical knowledge to philosophy is less clear: Although empirical data certainly (...) can influence philosophical concepts, the latter are nonetheless prerequisites of empirical research itself and thus may themselves not be empirically testable. The present paper elaborates from a multidisciplinary, neuroscientist-philosopher’s perspective the relation of philosophical concepts and empirical research on consciousness, drawing on two exemplary controversies from the philosophy of mind – on the ontological status of experiential properties and on free will. Consequences from both the scientific and the philosophical standpoint are discussed. (shrink)

León and Zahavi (2023) have made a compelling case for the necessity of philosophy — and not only neuroscience — for investigating consciousness. In particular, they argue that any theory of consciousness cannot avoid philosophical enquiry and thus only can choose between good or bad philosophy. Also, the topics of self-consciousness and selfhood are highlighted as problems of consciousness sui generis next to the mind–body problem. I will try to elucidate a bit more the specific approaches to consciousness that philosophy (...) and neuroscience take and thus elaborate why the philosophy and the neuroscience of consciousness are complimentary rather than mutually exclusive. (shrink)

A style of argument that calls into question our freedom (in the sense that involves freedom to do otherwise) has been around for millennia; it can be traced back to Origen. The argument-form makes use of the crucial idea that the past is over-and-done-with and thus fixed; we cannot now do anything about the distant past (or, for that matter, the recent past)—it is now too late. Peter van Inwagen has presented this argument (what he calls the Consequence Argument) in (...) perhaps its clearest and most forceful way, but debate over the argument has arguably reached a stalemate. -/- Recently, however, Wes Holliday has attempted to break this seeming stalemate by presenting a new argument for the Principle of the Fixity of the Past. Holliday’s argument is subtle and ingenious, and worthy of serious consideration, especially given the promise it holds for genuinely advancing this old debate. In what follows, however, we argue that despite its considerable ingenuity, Holliday’s argument fails to convince, and the stalemate appears to remain. (shrink)

This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and (...) missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. (shrink)

Kin and kinship matter to us. We are social creatures and our kin or relatives are typically high on the list of those most important to us. Kin are those we care for and who care for us. Our family ties provide a sense of where and with whom we belong. Kin matters also impose boundaries on who we relate to and how, including in sexual and other intimate matters. The study of kinship has been a cornerstone of anthropology throughout (...) its history, but kin matters matter beyond the confines of any academic discipline. -/- Kin Matters: Relational Beings in the Fragile Sciences examines three related themes in the philosophy of anthropology concerning kin matters: the nature of relations, incest and its avoidance, and the study of kinship in cultural anthropology. It develops an integrative framework for thinking about kin matters recognizing that there should be much more fluidity between the cognitive, biological, and social sciences—the fragile sciences—than one typically finds both in those sciences and in philosophical reflection on them. Along the way, Kin Matters offers a novel account of relations, challenges culture-first explanations of incest avoidance, and advocates for a redirection in the study of kinship. (shrink)

Einstein's relativity emerged from his resolution of three key thought experiments. We show that Presentist Fragmentalism can systematically resolve eight fundamental paradoxes, including Einstein's classic train scenario and Schrodinger’s Cat.

Following the Russian invasion of Ukraine on 24 February 2022, German Chancellor Olaf Scholz announced a major reorientation of German security policy, committing the federal government to a significant increase in defence spending as well as arms deliveries to Ukraine. Scholz’s announcement of a Zeitenwende (sea change) has triggered a renewed debate about continuity and change in German security policy, with some observers claiming that the Zeitenwende marks the end of German ‘pacifism’, the country’s traditional culture of military reticence. In (...) this article, I challenge this framing of the issue. I argue that both talking about German military reticence in terms of ‘pacifism’ and conceptualizing it as a rather stable element of culture fail to grasp the messy empirical reality of post-Zeitenwende security policy and leads to false expectations regarding future foreign policy behaviour. I argue in favour of complementing this perspective with a more differentiated discursive approach. (shrink)

This project aims to control the speed and direction of a DC motor in four quadrants: clockwise, counterclockwise, forward braking, and reverse braking. An H-bridge motor drive IC is used to switch the motor’s polarity based on user inputs via four control switches. A slide switch enables alternative directional control. A 555 timer generates PWM pulses for speed regulation, while relays manage polarity changes and braking. In regenerative braking, the motor generates reverse Ftorque for instant stopping. The four-quadrant control is (...) achieved by varying the duty cycle of the PWM signal and switching polarity using the H-bridge IC. The paper provides an overview of DC motor operation, explaining key components and working principles. It discusses the importance of four-quadrant operation, including motoring (forward/reverse) and braking (regenerative/plugging). Various control strategies such as armature voltage control and field flux control are examined for their efficiency. Applications in electric vehicles, robotics, and industrial automation are explored, demonstrating the versatile use of DC motors in modern engineering. (shrink)

Breast cancer is one of the leading causes of death among women worldwide. Early detection plays a crucial role in improving survival rates, and machine learning (ML) provides powerful tools for identifying cancerous tumors in medical imaging and diagnostic data. This paper explores various machine learning techniques used for breast cancer detection, with a particular focus on the Wisconsin Breast Cancer Dataset (WBCD). We evaluate several classification models, including Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Random (...) Forest (RF), for their ability to accurately classify benign and malignant tumors. The goal of this paper is to demonstrate the use of supervised learning techniques to enhance early breast cancer detection. (shrink)

The model is trained on a large dataset containing images of various food items along with their nutritional information. After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their (...) daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. (shrink)

The increasing reliance on digital technologies within the power sector has introduced considerable cybersecurity risks, especially from malware and trojans. These threats can disrupt essential operations, manipulate grid functions, and compromise the integrity of energy systems, thereby endangering both economic stability and national security. This research aims to create a detection framework tailored to the specific challenges of the power sector. The proposed framework utilizes advanced methods such as behaviour based anomaly detection, machine learning algorithms, and both static and dynamic (...) analysis of software. By examining distinct patterns and signatures associated with malware and trojans targeting power sector software, this study seeks to enhance early detection capabilities and response strategies. Real-world case studies and simulations will be employed to evaluate the effectiveness of these detection techniques, highlighting the necessity of robust and adaptable security measures to protect critical energy infrastructure. (shrink)

As hybrid electric vehicles (HEVs) gain prominence in the automotive industry, managing the heat generated by their electric motors and batteries has become a critical challenge. The performance, safety, and longevity of HEV components, such as batteries and motors, are significantly affected by thermal conditions. This paper proposes a modular liquid cooling system designed to efficiently manage heat dissipation in hybrid electric vehicle motors and batteries. The system leverages advanced fluid dynamics and thermoelectric materials to achieve optimal thermal control while (...) maintaining energy efficiency. The modular design allows for flexibility, enabling the cooling system to be adapted for different configurations of electric motors and battery modules. This adaptability ensures that the cooling system can cater to various HEV architectures without extensive redesign. The liquid cooling system is composed of multiple channels that distribute coolant uniformly across the critical components, preventing hotspots and ensuring stable operating temperatures. The system is equipped with sensors and control units to monitor and regulate temperature levels in real time, enhancing system reliability. (shrink)

The global catastrophe known as COVID-19 has shattered the world’s socioeconomic structure. Effective and affordable diagnosis techniques are crucial for better COVID-19 therapy and the eradication of bogus cases. Due to the daily upsurge in cases, hospitals only have a small supply of COVID-19 test kits. The study describes a deep Convolutional Neural Network (CNN) design for categorizing chest x-ray images in the diagnosis of COVID-19. The lack of a substantial, high-quality chest x-ray picture collection made efficient and exact CNN (...) categorization problematic. The dataset has been pre-processed using an image enhancement strategy to provide an effective training dataset for the proposed CNN model to achieve performance. The proposed model achieves 99.73% of accuracy, 98.95% of specificity, 99.47% of precision, 99.62% of sensitivity, and 98.71% of F1 score. A comparative study between the proposed model and numerous CNN-based COVID-19 detection algorithms is carried out to demonstrate that it outperforms other models. When evaluated on a separate dataset, the suggested model excelled over all other models, generally and explicitly. (shrink)

This entry investigates the connections between neo-populism and neo-conspiracism in the USA. One central thread is the rhetoric of purity that fosters rigid dichotomies of thought about identities, contributing to both populism and conspiracism, eliciting a neologism: conspirapopulism.

A graph-theoretic account of logics is explored based on the general notion of m-graph (that is, a graph where each edge can have a finite sequence of nodes as source). Signatures, interpretation structures and deduction systems are seen as m-graphs. After defining a category freely generated by a m-graph, formulas and expressions in general can be seen as morphisms. Moreover, derivations involving rule instantiation are also morphisms. Soundness and completeness theorems are proved. As a consequence of the generality of the (...) approach our results apply to very different logics encompassing, among others, substructural logics as well as logics with nondeterministic semantics, and subsume all logics endowed with an algebraic semantics. (shrink)

This study aimed to assess the effectiveness of remedial instruction in improving the numeracy skills of Grade 7 students at Malbug National High School during the school year 2023-2024. Adopting a quasi-experimental research design, the research focused on Grade 7 students at Malbug National High School, Cawayan East District, Masbate Province Division, Philippines, identified as non-numerates, employing pre-tests and post-tests as essential research tools. The independent variable was the remedial instruction in numeracy, while the dependent variable was students' numeracy performance (...) measured through pre-tests and post-tests. Before the intervention, the pre-test numeracy performance exhibited a varied distribution of students across different score ranges. A majority fell into the "Needs Major Support" category, emphasizing the necessity for targeted interventions. The post-test results, however, revealed a remarkable improvement, with 87.88% of initially non-numerate students achieving scores in the "Transforming" range. A thorough statistical examination validated a notable disparity in the scores obtained before and after the instructional intervention, substantiating the favorable influence of remedial instruction. The calculated t-value of 19.594, exceeding the critical t-value, led to rejecting the null hypothesis. In conclusion, the study emphasizes the need for tailored interventions based on the initial deficiency in numeracy skills. The post-test results underscored the success of remedial instruction in fostering substantial growth. The study recommends sustaining the remedial program, implementing periodic assessments, providing teacher training, involving parents in students' development, allocating adequate resources, and further research in other schools to strengthen findings. (shrink)

The healthcare sector is undergoing a transformative shift towards smart healthcare, driven by advancements in technology, including Artificial Intelligence (AI) and Blockchain. As healthcare systems generate vast amounts of data from multiple sources, such as electronic health records (EHRs), medical imaging, wearable devices, and sensor-based monitoring, the challenge lies in securely integrating and analyzing this data for real-time, actionable insights. Blockchain technology, with its decentralized, immutable, and transparent framework, offers a robust solution for securing data integrity, privacy, and sharing across (...) healthcare networks. AI, on the other hand, enables advanced data analytics, providing healthcare professionals with predictive insights, early diagnosis, and personalized treatment plans. This paper explores the integration of Blockchain and AI for advanced data management in smart healthcare. We propose a system that utilizes Blockchain for secure and transparent data storage, ensuring that healthcare data from various sources is tamper-proof and can be shared across multiple stakeholders. AI algorithms, particularly machine learning and deep learning models, are applied to the integrated data to detect patterns, predict disease progression, and optimize treatment plans. By leveraging Blockchain, healthcare data can be securely shared across providers and patients, ensuring privacy and regulatory compliance, while AI technologies provide the intelligence required for enhancing clinical decision-making. This integrated system not only addresses the growing concerns surrounding data security and privacy but also promotes interoperability and collaboration within the healthcare ecosystem. The paper discusses the challenges, opportunities, and future directions of Blockchain and AI integration in smart healthcare systems, highlighting how this convergence can lead to more efficient, secure, and personalized healthcare services. (shrink)

The rapid advancements in healthcare technologies have resulted in an enormous increase in biomedical data, creating the need for innovative approaches to harness this information for early disease detection. Big Data Analytics (BDA) combined with Artificial Intelligence (AI) offers unprecedented opportunities to analyze complex biomedical signal patterns and predict the onset of diseases at an early stage. The application of AI techniques like machine learning and deep learning in conjunction with BDA allows for the detection of subtle patterns in large (...) datasets that might be missed by conventional diagnostic methods. This paper explores the integration of Big Data Analytics and AI for early disease detection through the analysis of biomedical signal patterns, such as electrocardiograms (ECG), electroencephalograms (EEG), and wearable sensor data. AI algorithms, trained on vast datasets, can recognize early warning signals of various diseases such as cardiovascular disorders, neurodegenerative diseases, and respiratory conditions. The ability to analyze these signals in real time enables early intervention, potentially improving patient outcomes and reducing healthcare costs. A comprehensive literature review highlights the use of BDA and AI in biomedical applications and the challenges associated with data volume, quality, and heterogeneity. We propose a system methodology that combines AI models with real-time Big Data Analytics to process biomedical signals efficiently and accurately. The proposed system leverages AI for feature extraction and classification while utilizing Big Data tools to handle the massive influx of data from biomedical devices and sensors. This approach not only enhances diagnostic precision but also paves the way for personalized healthcare solutions. (shrink)

In recent years, the combination of Artificial Intelligence (AI) and Blockchain technology has garnered significant attention, especially in the healthcare domain. With the increasing reliance on biomedical signal processing for disease diagnosis and treatment, ensuring the security, privacy, and integrity of data has become paramount. Biomedical signals, including electrocardiograms (ECG), electroencephalograms (EEG), and other physiological data, often contain sensitive information. AI models have shown great promise in processing and interpreting these signals, enabling accurate disease detection and personalized healthcare. However, the (...) potential for data tampering, unauthorized access, and privacy concerns pose significant challenges. Blockchain, with its decentralized, tamper-resistant, and secure framework, offers a solution to these challenges. By integrating Blockchain with AI for biomedical signal processing, data can be securely stored, verified, and accessed, ensuring that healthcare providers can rely on the integrity and accuracy of the signals used in AI-based diagnostics. This paper explores the role of Blockchain-enhanced AI solutions for secure biomedical signal processing and data integration, addressing both the technical and ethical challenges in this space. We propose a system that leverages Blockchain to manage the secure transfer and storage of biomedical signals while utilizing AI to analyze and interpret these signals in real time. Blockchain provides an immutable ledger for auditing and validating data provenance, ensuring that the data remains secure throughout the healthcare pipeline. The integration of these two technologies not only enhances the security of biomedical data but also enables more trustworthy AI-driven diagnostics and decision-making in healthcare. (shrink)

. In the evolving landscape of healthcare, the efficient processing of biomedical signals is critical for real-time diagnosis and personalized treatment. Conventional cloud-based AI systems for biomedical signal processing face challenges such as high latency, bandwidth consumption, and data privacy concerns. Edge computing, which brings data processing closer to the source, has emerged as a potential solution to these limitations. However, optimizing AI models for edge devices, which often have limited computational resources, remains a challenge. This paper proposes an innovative (...) approach to optimize AI models for biomedical signal processing by leveraging reinforcement learning (RL) techniques within edge computing environments. Reinforcement learning offers the potential to dynamically adapt model parameters based on real-time feedback from the edge devices, optimizing the trade-offs between model accuracy, resource utilization, and processing speed. The proposed system is designed to operate with high efficiency on edge devices, enabling faster signal processing while ensuring energy efficiency and maintaining diagnostic accuracy. A detailed review of existing literature highlights the benefits and challenges of edge computing in biomedical applications, the role of reinforcement learning in dynamic optimization, and the limitations of traditional AI models in constrained environments. The proposed system methodology integrates RL for model optimization, focusing on edge devices' unique constraints in terms of power, memory, and processing capacity. Simulation results demonstrate the improved efficiency and accuracy of biomedical signal processing with optimized AI models in edge computing setups, showing significant improvements in real-time diagnostic applications. (shrink)

This project, titled Bird Species Identification Using Deep Learning, aims to develop a robust system that can identify bird species from images with high precision. The core of this project involves training a CNN model on a diverse dataset of bird images. This dataset includes species from various geographical locations and environments, capturing a wide range of appearances, postures, and behaviors. By preprocessing and augmenting the dataset, the model is designed to handle challenges such as variations in lighting, background noise, (...) and partial occlusions. The implementation of this project has numerous practical applications. For instance, it can be integrated into mobile or web-based platforms to provide real-time bird identification tools for researchers, birdwatchers, and environmental agencies. It can also be utilized in wildlife monitoring programs to track species distribution and migration patterns. Furthermore, the system can support educational initiatives, allowing individuals to learn about bird species and their significance in the ecosystem. (shrink)

Underwater Wireless Sensor Networks (UWSNs) are pivotal for various applications, including oceanographic data collection, environmental monitoring, and naval operations. However, the harsh underwater environment poses challenges in designing efficient routing protocols, especially concerning energy consumption and data transmission reliability. This paper proposes an optimized depth-based routing protocol for energy-aware data transmission in UWSNs, focusing on minimizing energy usage while ensuring robust data delivery. The protocol dynamically adjusts transmission power based on node depth and residual energy, reducing communication overhead and prolonging (...) network lifetime. (shrink)

In what would become his last interview, Jacques Derrida raised the question about the legacy of his work. What would become of deconstruction after his death? -/- Derrida proposed two contradictory hypotheses: the legacy of deconstruction might be re-invested in a new philosophical undertaking that could produce new intellectual gains, or it might be deposited on a savings account – a dépôt légal – where it would attract only limited legal interest. Derrida suggested that at least part his legacy should (...) be re-invested in the development of a new European concept of international law. -/- This paper is an attempt to explore this hypothetical undertaking by translating the language of deconstruction into the language of international law. It turns out, in the course of this translation, that the relationship between international and domestic law is structured like the distinction between writing and speech – not as a conceptual distinction between equals, but as a conceptual hierarchy. While domestic law has developed organically, embodying the common sense (bon sens, gesunder Verstand) of the people, international law is an artefact, a creation of lawyers and diplomats; while domestic law is a full-fledged system, international law remains primitive – it has no basic norm, no centralized sanctioning system, no prohibition of self-help. It has no totem and taboo. -/- The narrative that supports this conceptual structure is a story of the modern, enlightened concept of civil law, the law of the contrat social. A deconstruction of this conceptual structure produces a new concept of international law that does not embrace a centralized system but challenge, uncertainty and a risk of failure – a law that has no totem and no taboo. (shrink)

An idea is to develop a software module for a leading Courier Service company, which handles over 20,00,000 parcels per day. However currently, all the parcel handling is managed through manual operations. The manual operation takes much of the time and the man-power. It obviously leads to the dissatisfaction of the customers, which automatically affects the company’s growth. So to automate the parcel management, in order to achieve these objectives, increase operational efficiencies, Increase customer experience, Reduce the operational cost The (...) proposed idea is to develop a software module, which satisfies the above complexities and to improve the growth of the courier companies. (shrink)

The term “international” refers to the movement of resources – goods, services, capital and labor – across international boundaries. Such movement may be voluntary, in which case it qualifies as a socioeconomic phenomenon, or involuntary. Involuntary movement of resources may be small-scale or (socioeconomically) microscopic, or it may be macroscopic – a large-scale transfer of resources over an international boundary. When resources are mobilized on a large scale and without the consent of the target State, the event amounts by definition (...) to an international conflict as it challenges the very ontological foundation of the classic State system – an international boundary. However, in the context of globalization, a shift has occurred from inter-State conflicts to a microscopic form of conflict – often referred to as international “terrorism.” Unlike a classic inter-State conflict, international “terrorism” is not a macroscopic (political) but a microscopic (socio-economic) phenomenon and, as such, a “traveling” conflict: it does not target international boundaries but socioeconomic differences – differences in language, race, ethnicity or religion. As such, international “terrorism” is not only one of the causes of deceleration of globalization, but also a symptom of its systemic failure. While globalization has involved liberalization of cross-border movement of goods, services and capital, it has not involved liberalization of cross-border movement of labor. The root cause of international “terrorism” lies in a systemic denial of socio-economic opportunity. (shrink)

The proposed system combines predictive analytics and historical data to flag potential emergencies before they occur. The visual analytics platform provides comprehensive reports to healthcare providers, enabling them to monitor trends, identify risk factors, and make informed decisions. This approach significantly enhances patient care by minimizing delays in response and improving overall health outcomes. The system's architecture, based on big data frameworks, supports scalable and efficient data processing. The study demonstrates how the integration of predictive models and data visualization tools (...) can revolutionize health alert systems, making them more responsive and adaptive to individual patient needs. Future enhancements will focus on incorporating machine learning models for more personalized predictions and extending the system's capabilities to remote patient care. (shrink)

This paper presents the design and development of an Android Grocery Management App aimed at simplifying the process of grocery shopping and inventory management for users. The app allows users to manage their grocery list, track inventory, set reminders for grocery purchases, and receive product suggestions. It also features integration with online stores for easy purchasing. The app is built with an emphasis on usability, providing an intuitive interface for seamless interaction.

The integration of robotics and stock management systems has revolutionized warehouse operations, offering greater efficiency, accuracy, and flexibility. This paper presents an optimal design for integrating robotic systems with stock management for pick-and-place operations in warehousing environments. The primary objective of this research is to enhance the precision and speed of stock handling while minimizing human intervention and error. Our design incorporates state-of-the-art sensors, real-time tracking systems, and autonomous robots programmed with advanced algorithms for object identification, gripping, and movement.

This study aims to determine the two emotion regulation strategies—cognitive reappraisal or expressive suppression—used by pre-service teachers enrolled in Philippine Normal University, and examine its relationship with their mental well-being and interpersonal relationships. This study utilized quantitative correlational research design by administering an online survey that contains three questionnaires: Emotion Regulation Questionnaire (ERQ) to determine what emotion regulation strategy is frequently used, Warwick-Edinburg Mental Well-Being Scale (WEMWBS) to determine the state of their well-being, and the Functional Idiographic Assessment Template-Questionnaire-Short Form (...) (FIAT-Q—SF) to determine their levels of interpersonal relationship and functioning problems. The results of this study shows that frequent use of expressive suppression leads to lower levels of mental well-being and higher levels of interpersonal relationships and functioning problems, while more frequent use of cognitive reappraisal results in higher levels of mental well-being, but shows no significance or causation to interpersonal relationship and functioning problems. It can be concluded that the majority of pre-service teachers use cognitive reappraisal as their emotion regulation strategy more frequently as it was found to be significantly associated with their mental well-being levels, but was insignificant in producing interpersonal relationship difficulties. Furthermore, expressive suppression displayed more respondents with severe levels of interpersonal relationship difficulties than those who more frequently use cognitive reappraisal. The results are further discussed in light of its implications to the pre-service teachers’ well-being and mental health. (shrink)

A precise formulation of the structure of modern evolutionary theory has proved elusive. In this paper, I introduce and develop a formal approach to the structure of population genetics, evolutionary theory's most developed sub-theory. Under the semantic approach, used as a framework in this paper, presenting a theory consists in presenting a related family of models. I offer general guidelines and examples for the classification of population genetics models; the defining features of the models are taken to be their state (...) spaces, parameters, and laws. The suggestions regarding the various aspects of the characterization of population genetics models provide an outline for further detailed research. (shrink)

Disintegration is the most important step for drug bioavailability because after, the disintegration process, the ingredients of solid dosage forms dissolve and become bioavailable. Generally, the tablets and capsules should be taken with a glass of water otherwise the manufacturer gives instructions to use the proper beverage. Several drugs are taken with different forms of beverages to ensure easy swallowing of the tablet, masking the bad taste of the drug and overcoming the drug aftertaste, these beverages can influence the disintegration (...) of the solid dosage form. The most common cold beverages consumed by the Libyan population are juice, milk, and Coca-Cola. These beverages are consumed without awareness of the possible effect of these beverages on drugs, especially over-the-counter drugs such as analgesic, antipyretic, and anti-inflammatory drugs that are taken without a prescription. This study aimed to evaluate the influence of three beverages, namely: orange juice, milk, and Coca-Cola on the disintegration time of four over-the-counter-drugs: Panadol, Congestal, Brufen, and Reality Extra using disintegration test of drugs in 0.1 N HCL and in hybrid medium prepared by combination of 150 ml 0.1 N HCL and 550 ml of beverage to simulate in vivo condition. The findings show that the orange juice significantly increases the disintegration time of all the investigated drugs. Coca-Cola significantly increases the disintegration time of Reality Extra, and slightly increases the disintegration time of Brufen, Congestal, and Panadol and the full-fat milk significantly increases the disintegration time of Reality Extra and Panadol while slightly increasing the disintegration time for Brufen and Congestal. It is concluded that commonly used beverages affect the disintegration time of the examined over-the-counter drugs. Patients should be advised to take these medications without beverages which may delay the disintegration and the onset of action of the drug. (shrink)

Understanding the policymaking process in an emerging economy in the global south, such as Bangladesh, holds significant importance. The country's remarkable socio-economic development, once the most impoverished in the region, has been facilitated by post-globalization economic transformation. While the literature on institutional change has predominantly focused on states in industrialist countries, this dissertation presents an innovative theoretical approach. It deeply explores primary case materials to illustrate how the state engages in policy evolution in a developing country's gradual shift from the (...) import substitution model towards globalization and deregulation. The central argument posited is that Bangladesh's globalization, particularly through its private sector-led garment exports, stands as a significant success story. Bangladesh is now the world's second-largest garment exporter after China. This achievement is attributed to the entrepreneurial development it fosters and its role in driving socio-economic change. Moreover, it has enabled the mobilization of resources, reducing aid dependence, enhancing climate resilience, improving public health and wellbeing, empowering women, and addressing critical needs in physical infrastructure and human development. The argument presented here diverges from the conventional explanation found in the literature, which tends to emphasize the role of external forces, particularly institutions like the World Bank, the International Monetary Fund, and donors, in triggering policy reforms in Bangladesh. These reforms, commonly linked with structural adjustment packages implemented during the balance of payments crisis, are frequently perceived as predominantly influenced by external pressures. Additionally, the influence of the business class or Bangladeshi non-governmental organizations (NGOs) is often asserted. This dissertation proposes an alternative explanation by suggesting that ideational viewpoints within the state, specifically the ideas espoused by top technocrats and political executives, played a significant role. It argues that these internal ideational factors were more influential than materialistic ones, highlighting the causal role of the policy process driven by endogenous imperatives. The introduction of the dissertation addresses the research problem and research puzzle while suggesting conjectures after problematizing existing arguments. The significant economic transformation observed in Bangladesh today is portrayed as the outcome of a gradual process spanning several decades. The transition towards promoting the private sector and embracing globalization commenced in 1991, following a series of incremental changes dating back to the 1970s and 1980s. During these periods, the institutional path progressed towards empowering the private sector and embracing globalization, laying the groundwork for a trajectory led by the private sector in the context of globalization. The theory chapter serves to establish the conceptual and theoretical foundation for analyzing both economic change and transformation by examining the evolutionary nature of the process. The study adopts an institutional change framework, drawing on the theory of gradual change and focusing on concepts such as layering and tipping points. Within this framework, causal variables such as powering and puzzling, social learning, and ideas are identified. The methodological approach is grounded in historical institutionalism, employing the process-tracing "within case" method. The selection of cases deliberately targets an important yet under-researched area—the policy process in Bangladesh concerning the success of garment-led industrialization. This dissertation fills a noticeable gap in the literature through rigorous analysis, enabling an in-depth exploration of the institutional dynamics spanning four governments from 1972 to 1991. The study relies on substantial primary materials, including interviews with key officials. Each empirical chapter not only presents data but also connects the findings to conceptual and theoretical conjectures, underscoring the significant contributions of this research. The key causal variables identified include the concepts of ‘puzzling’ and ‘social learning’ within the state capacity framework, along with a narrowly defined and developed concept of a "policy committee." The learning process entails technocrats engaging in what Hugh Heclo and Peter Hall term as "social learning," where they play a crucial role in addressing policy challenges, particularly during times of uncertainty and political turmoil, based on their learning from past experiences. Additionally, the concept of ‘powering’ is highlighted, emphasizing that while technocrats provide policy recommendations, it is ultimately the political executive who endorses policies. The interplay between ‘puzzling and powering’ and ‘social learning’ shapes the outcomes of the policy process. The first empirical chapter focusing on the Sheikh Mujibur Rahman Government (1972-1975) illustrates the emergence of policy learning during this period. Initially, this government pursued import substitution under strict government controls, with a predominant public sector. The historical context of exploitation under Pakistani rule and the centralization of industry by West Pakistan fostered socialism as an ideal for East Pakistan's economic emancipation. Additionally, close ties with India and the Soviet Union reinforced this socialist tendency. However, due to severe economic conditions resulting from stringent control on private investment and emphasis on large-scale public investment, policy puzzling and learning ensued. As a result, incremental changes were initiated, allowing limited private sector participation. The second empirical chapter explores the General Ziaur Rahman government's tenure (1975-1981) and how deregulation was consolidated within a layering step, paving the way for a shift towards globalization. General Zia and his technocrats, operating under authoritarian conditions, acknowledged the inefficacy of existing policies. Furthermore, General Zia's inclination towards the West fostered closer collaboration with donors. The Multifiber Agreement (MFA) presented an opportunity for garments, coinciding with the decline in jute exports. This government opened up industrial sectors for private participation, and banks provided funding across various sectors, with special emphasis on the burgeoning garment industry. The chapter also explores the origins of the garment industry during this period. Notably, Noorul Quader Khan, upon retiring as cabinet secretary, collaborated with a Korean multinational, Daewoo, with the support of General Zia, leading to the establishment of Desh Garments—a pioneering venture that catalyzed the export orientation of the sector. The third empirical chapter delves into the economic policies of the General Hussain Muhammad Ershad Government (1982-1990), highlighting its emphasis on greater private sector involvement. Under this administration, there was a political inclination towards globalization and deregulation, leading to the appointment of technocrats aligned with this vision. The leadership believed that promoting the private sector would be more effective in job creation. Despite facing significant political opposition, efforts towards disinvestment were made, eventually resulting in compromises like public-private partnerships. The decentralization of administration and restructuring of ministries aimed at promoting industrialization and economic globalization driven by the private sector at the regional level. Initiatives such as the Industrial Promotion and Development Company, with support from organizations like the International Finance Corporation, aimed to boost the private sector. Efforts towards globalization were synchronized with the establishment of an export processing zone in Chittagong, focusing on promoting private and foreign investment. Foreign investment promotion was encouraged through diplomatic efforts and legislative measures to safeguard investors. Currency devaluation was utilized to stimulate exports, along with some degree of import liberalization. The chapter convincingly argues that the groundwork for globalization, initiated under General Zia, came to fruition under General Ershad. The final empirical chapter contends that the system shifted towards globalization and deregulation in 1991. Although a severe balance of payments crisis occurred in 1989, it wasn't perceived as the sole cause of the industrial policy of 1991. The democratic uprising in 1990 led to the downfall of the military-backed government and the rise of a democratic government. The chapter argues that while economic and political changes may have been intertwined, the economic shift in 1991 was primarily driven by ideas within the state and driven by endogenous imperatives. It examines preceding periods to illustrate how the tipping mechanism might have operated, detailing the roles of top technocrats and political executives. The chapter provides insights into key policymakers and their ideas, as well as their interactions with industrialists, entrepreneurs, and representatives of multilateral organizations like the World Bank. Significant reform measures, including tariff reduction, the introduction of value-added tax, banking sector reform, and the adoption of floating exchange rates, are outcomes of the policy change. Additionally, sectors such as power, banks, and telecommunications, formerly monopolized by the public sector, are now deregulated. (shrink)

Optical Coherence Tomography (OCT) proves essential as an imaging modality for detecting early diseases especially by helping patients who age and face increased susceptibility to retinal and systemic conditions. The development of artificial intelligence technology now boosts OCT diagnostic features to identify conditions like diabetic retinopathy in addition to age-related macular degeneration and cardiovascular diseases at an early stage. This paper examines two main advancements in artificial intelligence for OCT imaging monitoring such as Google Health's Retinal Disease Predictor and AI (...) systems used to evaluate cardiovascular risks. This research develops HealthSight AI which combines deep learning algorithms with real-time predictive analytics to detect multiple diseases in healthcare. Medical studies demonstrate how AI-enhanced OCT technology can transform preventive healthcare delivery through its clinical implementations. The integration of AI in OCT imaging holds vast prospective advantages yet operational hurdles stem from ethical matters and system adherence needs together with healthcare structure implementation barriers. The findings emphasize the necessity to develop additional research together with collaboration so AI-powered OCT imaging can reach broad clinical implementation. (shrink)

Should a principle of charity be applied to the interpretation of the colour concepts exercised in visual experience? We think not. We shall argue, for one thing, that the grounds for applying a principle of charity are lacking in the case of colour concepts. More importantly, we shall argue that attempts at giving the experience of colour a charitable interpretation either fail to respect obvious features of that experience or fail to interpret it charitably, after all. Charity to visual experience (...) is therefore no motive for resisting the natural, Galilean response to a scientific understanding of light and vision. The best interpretation of colour experience ends up convicting it of widespread and systematic error. (shrink)

The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, ensuring comprehensive validation. Results indicate significant accuracy improvements over traditional machine learning approaches, demonstrating the potential of deep learning in species identification. This project holds promise for applications in wildlife monitoring, ecological research, and educational tools, promoting awareness and conservation efforts. Future work may include integrating the system into mobile applications or deploying it for real-time bird species identification in field conditions.

A. A. Long, one of the world's leading writers on ancient philosophy, presents eighteen essays on the philosophers and schools of the Hellenistic and Roman periods--Epicureans, Stoics, and Sceptics. The discussion ranges over four centuries of innovative and challenging thought in ethics and politics, psychology, epistemology, and cosmology.

Hans Frei, one of the most influential American theologians of the twentieth century, is generally considered a founder of postliberal theology. Frei never set forth his thinking systematically, and he has been criticized for being inconsistent, contradictory, and insufficiently rigorous. Jason Springs seeks here to offer a re-evaluation of Frei's work. Arguing that Hans Frei's theology cannot be understood without a meticulous consideration of the complex equilibrium of his theological and philosophical interests and influences, Springs vindicates Frei's christologically motivated engagement (...) with Ludwig Wittgenstein, Clifford Geertz, and Erich Auerbach, as well as his use of ordinary language philosophy and non-foundational philosophical insights, while illuminating his indebtedness to Karl Barth's theology. Moreover, by placing Frei's work in critical conversation with developments in pragmatist thought and cultural theory since his death, this re-reading aims to resolve many of the misunderstandings that vex his theological legacy. What emerges from Toward a Generous Orthodoxy is a sharpened account of the christologically anchored, interdisciplinary, and conversational character of Frei's theology, one he came to describe as a "generous orthodoxy"--modeling a way for academic theological voices to take seriously both their vocation to the Christian church and their roles as interlocutors in academic discourse. (shrink)

In the quantum-Bayesian approach to quantum foundations, a quantum state is viewed as an expression of an agent’s personalist Bayesian degrees of belief, or probabilities, concerning the results of measurements. These probabilities obey the usual probability rules as required by Dutch-book coherence, but quantum mechanics imposes additional constraints upon them. In this paper, we explore the question of deriving the structure of quantum-state space from a set of assumptions in the spirit of quantum Bayesianism. The starting point is the representation (...) of quantum states induced by a symmetric informationally complete measurement or SIC. In this representation, the Born rule takes the form of a particularly simple modification of the law of total probability. We show how to derive key features of quantum-state space from (i) the requirement that the Born rule arises as a simple modification of the law of total probability and (ii) a limited number of additional assumptions of a strong Bayesian flavor. (shrink)

Microlearning-based lessons are still new in the field of education because it primarily focuses on corporate training and adult learning. Hence, limited research was conducted in basic education even in higher education. Microlearning-based lessons are designed and delivered in the form of small, bite-sized information which allows students to achieve a certain learning objective. The main purpose of this research is to determine the effectiveness of Microlearning-based Lessons in Chemistry in the level of academic performance of 18 Grade 9 students (...) under the online learning modality. Moreover, the research instrument used was a researcher-made pretest-posttest based on the learning competencies of the Most Essential Learning Competency (MELC) consisting of 25 multiple choice items along the topics in Grade 9 Chemistry during the Second Quarter of the school year 2020-2021. Mean and paired t-tests were used as tools for analysis. Findings reveal that students’ exposure to Microlearning-based Lessons in Teaching Grade 9 Chemistry improved their level of academic performance in Chemistry. This implies that students perform better when they use microlearning-based lessons in learning Chemistry. Thus, it can be concluded that the level of performance in Chemistry of Grade 9 students greatly improved after exposure to microlearning-based lessons. Furthermore, this shows the effectiveness of the intervention material in improving their level of academic performance, thus the microlearning-based lessons be adopted in teaching Chemistry is recommended. (shrink)

In this paper I examine Grace A. de Laguna’s theory of universals in its historical context and in relation to contemporary debates in analytic metaphysics. I explain the central features of her theory, arguing that her theory should be classified as a form of immanent realism and as a powers ontology. I then show in what ways her theory affords a theory of modality in terms of potentialities and discuss some of its consequences along the way.

Abstract Most discussions of risk are developed in broadly consequentialist terms, focusing on the outcomes of risks as such. This paper will provide an alternative account of risk from a virtue ethical perspective, shifting the focus to the decision to take the risk. Making ethical decisions about risk is, we will argue, not fundamentally about the actual chain of events that the decision sets in process, but about the reasonableness of the decision to take the risk in the first place. (...) A virtue ethical account of risk is needed because the notion of the ‘reasonableness’ of the decision to take the risk is affected by the complexity of the moral status of particular instances of risk-taking and the risk-taker’s responsiveness to these contextual features. The very idea of ‘reasonable risk’ welcomes judgments about the nature of the risk itself, raises questions about complicity, culpability and responsibility, while at its heart, involves a judgement about the justification of risk which unavoidably focuses our attention on the character of the individuals involved in risk making decisions. Keywords: Risk; ethics; morality; responsibility; virtue; choice; reasons . (shrink)

Daniel Came's most recent edited collection features original essays from leading figures in the field. As most of its chapters are well-written and well-argued, it will interest Nietzsche scholars generally. It's difficult to narrow the volume's intended audience much further than this, however. The source of this difficulty is not merely titular, though one wonders what aspects of Nietzsche's philosophy could not plausibly be yoked under the dual headings of "morality" and "life affirmation." Rather, the difficulty stems from a shortcoming (...) of Nietzsche's. As Came puts the point in his introduction, "Nietzsche is greatly more forthcoming in his diagnosis of the life-denying nature of morality than he is about what should replace morality and in particular the type of life-affirming attitude which might then ensue" (7). The underdeveloped character of Nietzsche's positive pronouncements about life affirmation leaves many questions unanswered, the most basic of which might be, "What exactly is it to affirm life?" (7). This collection admirably attempts to tackle such questions head-on by foregrounding Nietzsche's "practical-existential concern with the value of existence" (4)—even if, in resisting the temptation to delve ever deeper into the complexities of Nietzsche's critical enterprise, this approach risks leaving some specialist concerns aside. In light of the inchoate nature of Nietzsche's remedy for life denial, a volume taking life affirmation as its starting point can be forgiven if its contributions are somewhat scattered. (shrink)

In this paper, I will reread the history of molecular genetics from a psychoanalytical angle, analysing it as a case history. Building on the developmental theories of Freud and his followers, I will distinguish four stages, namely: (1) oedipal childhood, notably the epoch of model building (1943–1953); (2) the latency period, with a focus on the development of basic skills (1953–1989); (3) adolescence, exemplified by the Human Genome Project, with its fierce conflicts, great expectations and grandiose claims (1989–2003) and (4) (...) adulthood (2003–present) during which revolutionary research areas such as molecular biology and genomics have achieved a certain level of normalcy—have evolved into a normal science. I will indicate how a psychoanalytical assessment conducted in this manner may help us to interpret and address some of the key normative issues that have been raised with regard to molecular genetics over the years, such as ‘relevance’, ‘responsible innovation’ and ‘promise management’. (shrink)

Text John Toland a jeho Křesťanství bez mystérií sestává ze dvou provázaných částí. První část představuje stručné biografické pojednání o Johnu Tolandovi (1670–1722), v němž se mimo jiné snažíme předložit obecný úvod do jeho myšlení. Druhá část pak nabízí první český překlad předmluvy k Tolandovu nejznámějšímu spisu Christianity not Mysterious (1696), který je považován za základní impulz deistické diskuze na Britských ostrovech.