The behavior of macroeconomic indicators (reflecting the development of both the national economy as a whole and that of individual economic sectors) testifies to the low current efficiency of the functioning of economic entities in the context of economic activities. This in turn necessitates scientific substantiation of developing and introducing theoretical and methodological recommendations to ensure innovative development of the country. One of the most effective ways to practically implement this task is to substantiate methods for quantitative assessment of potential (...) in the context of economic activities. The practical value of applying these methods is that the results of the integrated assessment of potential can be used as a criterion for the priority development of certain economic sectors or economic activities. (shrink)

Causal understanding is a defining characteristic of human cognition. Like many animals, human children learn to control their bodily movements and act effectively in the environment. Like a smaller subset of animals, children intervene: they learn to change the environment in targeted ways. Unlike other animals, children grow into adults with the causal reasoning skills to develop abstract theories, invent sophisticated technologies and imagine alternate pasts, distant futures and fictional worlds. In this Review, we explore the development of human-unique causal (...) learning and reasoning from evolutionary and ontogenetic perspectives. We frame our discussion using an ‘interventionist’ approach. First, we situate causal understanding in relation to cognitive abilities shared with non-human animals. We argue that human causal understanding is distinguished by its depersonalized (objective) and decontextualized (general) representations. Using this framework, we next review empirical findings on early human causal learning and reasoning and consider the naturalistic contexts that support its development. Then we explore connections to related abilities. We conclude with suggestions for ongoing collaboration between developmental, cross-cultural, computational, neural and evolutionary approaches to causal understanding. (shrink)

The mind is not structured anatomically, as emphasized by modern pathology. Instead, it is expanded as a whole in a subtle form behind the physical body. In the Indian system of medical philosophy, the mind is considered as the astral nerves made third body, which identified as the ‘Manomaya-sharira’ (subconscious mind). The mind is composed of millions of astralnadis, through which Pranic-energies circulate freely into the astral anatomy of mind. Seven-chakras are found parallel to the spine, serving as the major (...) seven innovation centres in the body. These Chakras facilitate the connection of the mind with the seven dimensions of the inner world. From these dimensions the Pranic-energies including Sattva, Raja, Tama are exported to the mind and its Nadi-tantras, influencing both the outer and inner subconscious minds. Subsequently, these elements, viz., Sattva, Raja and Tama are thrown from the mind to the outer world through the seven sense organs, manifesting as either constructive or destructive activities. Thus the mind exists due to ‘Prana’, (energy of Omkara) which increases through deeper meditation, sound sleep, and breathing exercises (O2). The positional behaviour of the mind shifts according to the physical and mental temperament, namely Triguna and Tridosha, or Panchavikara. The negative energy (Tama-guna) of the fourth dimension enters the astral Nadis-system of the mind via the interspace of the solar plexus, which crushes the mind’s structures, and consequently, all the Chakras are corrupted and the Nadis are blocked, so it generates a break in the circulation of ‘Prana’ into the Nadis. As a result, many socio-economic challenges and psychosomatic disorders occur. (shrink)

There is no doubt that custard apple diseases are among the important reasons that destroy the Custard Apple plant and its agricultural crops. This leads to obvious damage to these plants and they become inedible. Discovering these diseases is a good step to provide the appropriate and correct treatment. Determining the treatment with high accuracy depends on the method used to correctly diagnose the disease, expert systems can greatly help in avoiding damage to these plants. The expert system correctly diagnoses (...) Custard Apple disease to make it easier for farmers to find the right treatment based on the appropriate diagnosis. Objectives: A specialized syllable language system was established for the diagnosis of Custard Apple plant disease. (shrink)

Using cutting-edge technologies like artificial intelligence (AI), machine learning and Bluetooth connectivity, this paper introduces a state-of-the-art assistive device made especially for blind and visually impaired people. The primary objective of the device is to enhance user mobility, safety and independence in navigating public spaces. Key features include Al-powered real-time navigation, camera-based text recognition and audio feedback, as well as emergency SOS functionality to address critical situations. The Al system processes environmental data through integrated sensors and cameras, providing auditory feedback (...) and voice-assisted navigation to guide users effectively. Bluetooth connectivity enables seamless integration with smartphones and other compatible devices, enhancing accessibility in smart environments. The device’s capacity to instantaneously recognize and vocalize text from signs, labels and papers promises to empower users in daily activities. Security and privacy are prioritized through encrypted data transmission and customized settings. The benefits of this method are compared to those of current technologies based on attributes like ease of use, real-time responsiveness, and user independence. The latest developments in wearable assistive technology are highlighted in a thorough literature review, with an emphasis on user-centered design and useful AI applications for people with visual impairments. The findings suggest that the proposed device could significantly improve the quality of life for blind and visually impaired individuals by offering an intuitive and reliable tool for independent navigation and information access. (shrink)

An intelligent virtual interview system has been developed to automate and optimize remote hiring processes. Leveraging React.js, TypeScript, and Material-UI for the frontend, the platform integrates generative AI for dynamic question generation, NLP and speech recognition for candidate answer evaluation, and face detection for behavioural monitoring. It supports bulk resume upload and screening through PDF parsing, enabling efficient shortlisting based on job-specific requirements. The multi-step interview process includes real-time webcam interaction, automated scoring, and seamless communication of results via emails. Designed (...) for scalability and accuracy, the system reduces manual effort, minimizes bias, and enhances the overall experience for both recruiters and candidates. (shrink)

During the microscopic anatomical study of a fresh water siluridfish, Wallago attu,a new trematode species Opisthorchis varunai was recorded from the gallbladder. The fish was collected from the Varuna River in Varanasi. The newly reported species has been described in detail, discussed and compared with other species of the genus Opisthorchis. It has elongated spinose body, bluntly pointed at anterior and posterior extremities, entire ovary, ventral sucker pre-equatorial, unlobed testes and presence of pre-pharynx. It has been distinguished by other species (...) through the absence of a crown of spines around the excretory pore. (shrink)

Background: A bean is the seed of one of several genera of the flowering plant family Fabaceae, which are used as vegetables for human or animal food. They can be cooked in many different ways, including boiling, frying, and baking, and are used in many traditional dishes throughout the world. Beans are one of the longest-cultivated plants. Broad beans, also called fava beans, in their wild state the size of a small fingernail, were gathered in Afghanistan and the Himalayan foothills. (...) In a form improved from naturally occurring types, Beans were an important source of protein throughout old and new world history, and still are today. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper, the design of the proposed Expert System was produced to help farmers and those interested in agriculture in diagnosing many of the Bean diseases such as Fusarium wilt, Charcoal rot or ashy stem blight, Bacterial leaf spot and blight, Mung bean yellow mosaic virus, Cercospora leaf spot. The proposed expert system presents an overview of Bean diseases are given, the cause of diseases outlined and the treatment of disease whenever possible is given out. CLIPS Expert System language was used for designing and implementing the proposed expert system. Results: The proposed Bean diseases diagnosis expert system was evaluated by Agricultural experts and some friends interested in agriculture and they were satisfied with its performance. Conclusions: The proposed expert system is very useful for Farmers and those interested in agriculture. (shrink)

Background: Persimmon is a grassy, perennial plant, belonging to the oral platoon, square-shaped leg, bifurcated, erect, and ranging in height from (10 - 201 cm). Home to Europe and Asia. The Persimmon plant has many benefits, the most important of which are pain relief, treatment of gallbladder disorders, the expulsion of gases, anti-inflammatory, and relaxing nerves. While the Persimmon plant is the ideal option for the start of gardens, it is prone to some common diseases that affect the plant's growth. (...) Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper, the design of the proposed Expert System was produced to help Farmers and those interested in agriculture in diagnosing many of the Persimmon diseases such as Persimmon rust, Verticillium wilt, Anthracnose, Powdery mildew, Black Stem Rot, Stem and stolon canker, Septoria leaf spot. The proposed expert system presents an overview of Persimmon diseases are given, the cause of diseases outlined and the treatment of disease whenever possible is given out. CLIPS Expert System language was used for designing and implementing the proposed expert system. Results: The proposed Persimmon diseases diagnosis expert system was evaluated by Agricultural Students at AL Azhar University and some friends interested in agriculture and they were satisfied with its performance. Conclusions: The proposed expert system is very useful for Farmers and those interested in agriculture. (shrink)

Cross-species comparisons are benefited by compatible datasets; conclusions related to phylogenetic comparisons, questions on convergent and divergent evolution, or homologs versus analogs can only be made when the behaviors being measured are comparable. A direct comparison of the social function of physical contact across two disparate taxa is possible only if data collection and analyses methodologies are analogous. We identify and discuss the parameters, assumptions and measurement schemes applicable to multiple taxa and species that facilitate cross-species comparisons. To illustrate our (...) proposed guidelines for evaluating the role played by tactile contact in social behavior across disparate taxa, this paper presents data on mother-offspring relationships in the two species studied by the authors: chimpanzees (Pan troglodytes schweinfurthii) and dolphins (bottlenose and spotted, Tursiops truncatus and Stenella frontalis, respectively). Cross-species comparative studies allow for a more comprehensive assessment of the similarities and differences with respect to how animals traverse the relationships that form their social groups and societies. (shrink)

This introduction to the Journal of Business Research special issue on anti-consumption briefly defines and highlights the importance of anticonsumption research, provides an overview of the latest studies in the area, and suggests an agenda for future research on anti-consumption.

This chapter identifies and explores a series of challenges raised by the clinical concept of delusion for theories which conceive autonomy as an agency rather than a status concept. The first challenge is to address the autonomy-impairing nature of delusions consistently with their role as grounds for full legal and ethical excuse, on the one hand, and psychopathological significance as key symptoms of psychoses, on the other. The second challenge is to take into account the full logical range of delusions, (...) which may take the form of true or false factual beliefs, positive or negative evaluations, as well as the paradoxical delusion of mental illness. The third and final challenge is to make room for non-pathological or, autonomy-preserving delusions and to offer a credible way of distinguishing between these and pathological or, autonomy-impairing delusions. By setting out these challenges, we are able to, firstly, distinguish between two separate conceptions of objectivity that may be at work in existing accounts of delusions and, secondly, to put a spotlight on an elusive yet inescapable notion of agential success that underlies our thinking about autonomy as well as mental disorder. (shrink)

With the exponential growth of online social networks, now boasting over 4 billion active users, maintaining data integrity and ensuring user security has become increasingly challenging. Social media platforms face numerous security threats, including the creation of fake profiles by malicious users seeking to steal sensitive information, spread misinformation, or engage in fraudulent activities. Due to the anonymity afforded by these platforms, detecting such deceptive accounts manually is both difficult and inefficient. This project focuses on the detection of fake profiles (...) using machine learning and deep learning techniques, trained on platform-specific datasets to improve accuracy. A web-based application has been developed featuring four core modules, each dedicated to a major social media platform experiencing rapid user growth. Each module integrates a trained model to assess and classify user profiles as real or fake. Currently, the system has been implemented for Instagram, with plans to expand to other platforms. This approach aims to provide a scalable and intelligent solution to enhance user trust and safety across social media environments. (shrink)

New trends in the economic systems management in the context of modern global challenges: collective monograph / scientific edited by M. Bezpartochnyi, in 2 Vol. // VUZF University of Finance, Business and Entrepreneurship. – Sofia: VUZF Publishing House “St. Grigorii Bogoslov”, 2020. – Vol. 1. – 309 p.

It is important to provide security and efficient data exchange in cloud infrastructure and achieve traceability and anonymity of data. mean For high levels of safety and performance in one Anonymously, this article addresses the topic It allows data to be exchanged and stored between members of the same group in the cloud. Proposed arrangement creates unique and traceable group data sharing policies using group signatures and special agreements Strategies to accomplish these goals. this Facilitates anonymous communication between systems Public (...) clouds have many users and. Real people following up when needed. Also, the system implements the main agreement programs to make it easier for team members to. Obtain a shared session key for secure data exchange and storage facilities. Basic generation processes a Symmetric Balanced Incomplete Block Theory (SBIBD), significantly reducing the workload of team members a shared session key must be introduced. In cloud computing contexts, the suggested system guarantees efficiency and security for group data sharing, as shown by theoretical analysis and experimental validation. (shrink)

This edited volume illustrates the central importance of diversity of human values throughout healthcare. The readings are organised around the main stages of the clinical encounter from the patient's perspective. This introductory chapter opens up crucial issues of methodology and of practical application in this highly innovative approach to the role of ethics in healthcare.

Many of our most important goals require months or even years of effort to achieve, and some never get achieved at all. As social psychologists have lately emphasized, success in pursuing such goals requires the capacity for perseverance, or "grit." Philosophers have had little to say about grit, however, insofar as it differs from more familiar notions of willpower or continence. This leaves us ill-equipped to assess the social and moral implications of promoting grit. We propose that grit has an (...) important epistemic component, in that failures of perseverance are often caused by a significant loss of confidence that one will succeed if one continues to try. Correspondingly, successful exercises of grit often involve a kind of epistemic resilience in the face of failure, injury, rejection, and other setbacks that constitute genuine evidence that success is not forthcoming. Given this, we discuss whether and to what extent displays of grit can be epistemically as well as practically rational. We conclude that they can be (although many are not), and that the rationality of grit will depend partly on features of the context the agent normally finds herself in. In particular, grit-friendly norms of deliberation might be irrational to use in contexts of severe material scarcity or oppression. (shrink)

This document is based on integrating healthcare system and create a self-sustaining ecosystem that can help healthcare providers and hospitals to provide adequate as well as accurate treatment. This is now the age of smart computer. Machines have started to impersonate as human, with the advent of artificial intelligence, machine learning, and deep learning. Chatbot is classified as conversational software agents enabled by natural language processing, and is an excellent example of such system. A Chatbot is a program which allows (...) the user to start a conversation with the machine. These Chatbots can be used in health sector. Health Chatbots are capable of running one-on-one patient communications and of reviewing specific patient queries. The integration of Artificial Intelligence (AI) in healthcare is revolutionizing patient interaction through intelligent virtual health assistants. This project explores the development of an AI-powered virtual health assistant that leverages machine learning to enhance patient engagement, provide personalized health recommendations, and assist in preliminary diagnostics. The system utilizes Natural Language Processing (NLP) for understanding patient queries and predictive analytics for offering real-time responses based on medical data. By incorporating machine learning algorithms, the assistant continuously learns from user interactions to improve accuracy and efficiency. This approach aims to reduce the workload on healthcare professionals, improve accessibility to medical information, and enhance patient experience. The study evaluates the impact of AI-driven virtual health assistance on patient satisfaction, response accuracy, and healthcare delivery efficiency. The proposed solution demonstrates the potential of AI in creating a more accessible and intelligent healthcare ecosystem. (shrink)

This volume of articles, literature and case studies illustrates the central importance of human values throughout healthcare. The readings are structured around the main stages of the clinical encounter from the patient's perspective.

The growing use of smart technology in personal grooming has resulted in novel uses such as smart mirrors to analyze the skin health and offer skincare advice. Here is a machine learning-based smart mirror framework that inspects facial skin conditions and gives individualized skincare advice. The framework employs computer vision and deep learning methods to identify and evaluate typical skin issues like acne, wrinkles, pigmentation, dryness, oiliness, and dark circles. By taking facial images with high resolution, the smart mirror derives (...) dermatological attributes and identifies skin conditions based on a CNN-driven model. The analysis is then tuned using image processing techniques and predictive analytics powered by artificial intelligence, providing high accuracy in the assessment of skin. Depending on the identified condition of the skin, the system recommends individualized skincare regimens involving appropriate products, lifestyle changes, and home remedies. The suggestions are based on a knowledge base learned on dermatological databases, skin care literature, and user-dependent considerations like age, skin type, climatic conditions, and past skin care. The intelligent mirror includes IoT sensors that sense environmental parameters like humidity, temperature, and UV index that impact skin well-being. Users interact with the intelligent mirror through voice recognition, touchpad controls, or gesture sensors, so it can be a seamless and convenient user interface. Progress tracking can be provided by the system too, where users get to track changes in the skin over a period of time and accordingly adjust their regimens. Besides, it ties in with cloud platforms and mobile apps, allowing consumers to access their skincare information online and receive professional advice. (shrink)

Suppose some person 'A' sets out to accomplish a difficult, long-term goal such as writing a passable Ph.D. thesis. What should you believe about whether A will succeed? The default answer is that you should believe whatever the total accessible evidence concerning A's abilities, circumstances, capacity for self-discipline, and so forth supports. But could it be that what you should believe depends in part on the relationship you have with A? We argue that it does, in the case where A (...) is yourself. The capacity for "grit" involves a kind of epistemic resilience in the face of evidence suggesting that one might fail, and this makes it rational to respond to the relevant evidence differently when you are the agent in question. We then explore whether similar arguments extend to the case of "believing in" our significant others -- our friends, lovers, family members, colleagues, patients, and students. (shrink)

The rise of fake news on digital platforms has become a major societal issue, shaping public opinion and eroding trust in legitimate news sources. As misinformation spreads rapidly, it becomes increasingly difficult to discern fact from fiction, leading to harmful consequences in various domains such as politics, public health, and social relationships. To combat this, there is a growing need for advanced systems that can accurately detect and classify fake news. This project aims to develop a robust machine learning system, (...) leveraging Natural Language Processing (NLP) techniques and machine learning algorithms, to identify deceptive information in textual data with high precision. By focusing on automation, this system promises to scale effectively and provide a timely solution to the growing problem of fake news. The proposed solution incorporates a pre-processing phase that includes Part-of-Speech (POS) tagging, a technique used to analyze the grammatical structure of the text. POS tagging helps in understanding the syntactic roles of words, which can significantly enhance the ability of the model to detect subtle linguistic patterns often associated with misinformation. The system will align these POS-tagged texts with their original content, ensuring that the underlying meaning is preserved while enabling further analysis. This pre-processing is a crucial step before integrating advanced models, such as General Pre-trained Transformers (GPT), which are known for their strong language modeling capabilities. The combination of these techniques will ensure the model achieves a high level of accuracy in detecting fake news. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. Additionally, the (...) paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

The Automated Data Analytics System leverages Generative AI, specifically OpenAI’s GPT-4, to empower non-technical users to interact with enterprise data through natural language queries. Seamlessly integrated with a Snowflake data warehouse and built on a Streamlit-based user interface, the tool automatically translates user input into SQL queries, executes them, and delivers structured insights along with dynamic visualizations. This eliminates the need for manual SQL writing and custom development, significantly lowering the technical barriers traditionally associated with data analysis. The intuitive interface (...) enables business analysts and decision-makers to independently explore data, identify trends, and generate real-time reports without relying on developers or data teams. By automating the entire analytics workflow from natural language query generation to result visualization the platform simplifies data exploration, enhances user autonomy, and fosters a data-driven culture across the organization. As a result, businesses can make faster, smarter decisions and extract actionable insights from raw data with minimal effort and no coding required. (shrink)

Abstract: The rapid advancement of artificial intelligence (AI) has catalyzed significant developments in autonomous systems, which are increasingly shaping diverse sectors including transportation, robotics, and industrial automation. This paper explores the evolution of AI technologies that underpin these autonomous systems, focusing on their capabilities, applications, and the challenges they present. Key areas of discussion include the technological innovations driving autonomy, such as machine learning algorithms and sensor integration, and the practical implementations observed in autonomous vehicles, drones, and robotic systems. Additionally, (...) the paper addresses critical challenges including safety, ethical concerns, and regulatory issues that influence the deployment and acceptance of autonomous technologies. By examining current trends and future prospects, this research aims to provide a comprehensive overview of how AI is transforming the landscape of autonomous systems and to identify key areas for future research and development. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...) it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying (...) PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%. (shrink)

The AI-driven Virtual Health Consultant is a Python-based GUI healthcare application using Tkinter that integrates AI, machine learning, and voice interaction to deliver a secure, user-friendly experience. It features a voiceenabled chatbot powered by SpeechRecognition and pyttsx3 for symptom input and health inquiries, enhancing accessibility for all users. The system predicts diseases using Random Forest, Decision Tree, and Naive Bayes algorithms [5] based on a trained medical dataset. With cryptography for data security, it supports general users, doctors, and administrators. The (...) modular, scalable design ensures future upgrades, making it a reliable and intelligent tool for early diagnosis and improved healthcare access. (shrink)

The authors propose a new IoT-enabled bio-impedance device using the ESP32 microcontroller to facilitate real-time health monitoring. Bioimpedance analysis is a non-invasive method to assess various physiological parameters, including body composition, hydration status, and tissue health. By integrating bio-impedance analysis with IoT capabilities, the proposed device enables continuous data acquisition and remote monitoring, thereby enhancing personalized healthcare management. The ESP32's low-power consumption and built-in Wi-Fi and Bluetooth functionalities make it an ideal platform for this application. The device transmits bioimpedance measurements (...) to a cloud-based platform, where data is stored and analyzed, providing healthcare professionals and users with accessible, real-time health information. The proposed device demonstrates accurate impedance measurements and reliable data transmission, highlighting the potential of IoT-based bioimpedance devices in advancing remote health monitoring solutions. (shrink)

Felix Klein and Abraham Fraenkel each formulated a criterion for a theory of infinitesimals to be successful, in terms of the feasibility of implementation of the Mean Value Theorem. We explore the evolution of the idea over the past century, and the role of Abraham Robinson's framework therein.

The study aimed to identify the effect of applying detection and prevention tools for career fraud in combating and preventing fraud and reducing its risks through an applied study on Palestinian Ministry of Health in Gaza Strip, Palestine. To achieve the objectives of the study, the researchers used the questionnaire as a main tool to collect data, and the descriptive and analytical approach to conducting the study. The study population consisted of (501) supervisory employees working at MOH in Gaza Strip, (...) Palestine. The researchers used the stratified random sample method and the multiple regression method to measure the effect. The study concluded a set of results, the most important of which is a positive evaluation among respondents about the levels of application of tools to detect and prevent job fraud within MOH, and the presence of a high interest in tools to prevent job fraud during the implementation of its various work. The study recommended the necessity for the Palestinian National Authority to develop and approve laws regulating health sector, so that those laws guarantee the prevention of conflicts of interest, especially for the category of doctors, by preventing job duplication of doctors working in the government sector. Also study recommended the need for MOH to adopt principles of health sector governance and begin immediately to implement them. This process to ensure transparency, disclosure and accountability in the business framework in a manner that realizes the effective protection of stakeholders and does not compromise or discriminate in the application of these principles, which ensures that all forms of job fraud are organized in an organized manner and within fixed legal frameworks. (shrink)

Much problem solving and learning research in math and science has focused on formal representations. Recently researchers have documented the use of unschooled strategies for solving daily problems -- informal strategies which can be as effective, and sometimes as sophisticated, as school-taught formalisms. Our research focuses on how formal and informal strategies interact in the process of doing and learning mathematics. We found that combining informal and formal strategies is more effective than single strategies. We provide a theoretical account of (...) this multiple strategy effect and have begun to formulate this theory in an ACT-R computer model. We show why students may reach common impasses in the use of written algebra, and how subsequent or concurrent use of informal strategies leads to better problem-solving performance. Formal strategies facilitate computation because of their abstract and syntactic nature; however, abstraction can lead to nonsensical interpretations and conceptual errors. Reapplying the formal strategy will not repair such errors; switching to an informal one may. We explain the multiple strategy effect as a complementary relationship between the computational efficiency of formal strategies and the sense-making function of informal strategies. (shrink)

Comparing the degree of openness of the economy of Poland and the Netherlands, we can say the following. The Netherlands is more dependent on foreign trade than Poland. The Netherlands export quota reaches almost 50%, unlike 41,37% in Poland in 2016. However, Poland has become more import-dependent. Poland, in contrast to the Netherlands, is continuing to increase the indicators of "economic globalization". To date, the Netherlands has been pursuing more moderate foreign trade policy and trying to protect itself from external (...) risks by focusing on an internal market with high purchasing power. On the contrary, Polish politics tries to focus on the new markets for its products in order to increase the prosperity and well-being of its citizens. Poland as a country and an economic entity has made a qualitative leap in its development. The economy of this country has become more open and integrated into the world economy. (shrink)

The utilization of machine learning optimization methods is of utmost importance in the identification of liver tumors, attracting considerable interest in this domain. After obtaining a liver tissue sample, magnetic resonance imaging (MRI), computed tomography (CT), and ultrasonography (US) are used as imaging techniques to separate the tumor and liver. Nevertheless, the utilization of shades of gray and forms is insufficient for achieving accurate segmentation in computed abdominal CT images, mostly because of the presence of overlapping intensities and the unpredictable (...) placements and shapes of soft tissues. The results demonstrate that our proposed technique outperforms previous state-of-the-art models in terms of overall accuracy in tumor identification. The 2D Convolutional Neural Network (CNN) model had a remarkable training accuracy of 96.47%, while the auto-encoder network closely followed with an accuracy of 95.63%. In addition, the 2D CNN network exhibited an impressive average recall rate of 95%, beating the auto-encoder network's rate of 94%. The ROC curve regions for both networks exhibited remarkable performance, with values ranging from 0.99 to 1. Out of the many machine learning approaches used, the ResUNet had the least accurate results, while the K-Nearest Neighbors (KNN) achieved the greatest accuracy rate of 86%. Conversely, the MLP demonstrated a paltry accuracy rate of about 28%. The statistical tests conducted in this study indicated a significant difference (p-value < 0.05) between the suggested approach and many existing machine learning algorithms. (shrink)

In order to solve the increasing amount of Object detection is a core part in computer vision, with applications spreading throughout the process of constructing a smart city, city managers always invest much energy and capital in cleaning street trash because of the random emergence of street trash, As deep network solutions become increasingly deep and convoluted, they tend to be constrained by the quantity of training data. Keeping this in view, to drive developments in analysing and interpreting images, Open (...) CV or Google AI has made the Open Images dataset public. Open Images is an extension of the tradition initiated by PASCAL VOC, Image Net and COCO, but now in an unprecedented scale. In this project we to execute the Hence, visual street cleanliness evaluation is especially significant. Yet, current assessment methods have some evident shortcomings, i.e., collecting street garbage data is not automated and street cleanliness data is not real-time best performing algorithm for automatically detecting objects. Lastly, the results are integrated into the street cleanliness calculation model to finally visualize street cleanliness levels, which brings convenience to city managers in organizing clean-up staff effectively. (shrink)

This project focuses on improving vehicle damage assessment efficiency and user experience through the development of an AI-powered web application. The system is designed to streamline workflows for users by automating tasks such as damage detection, severity classification, and repair cost estimation. It leverages Convolutional Neural Networks (CNN) and Detectron2 to accurately identify and segment damaged vehicle parts, while providing users with detailed results, including estimated repair costs derived from a predefined dataset. The application further enhances efficiency by eliminating manual (...) inspections, reducing processing time, and improving accuracy. Built with Flask for backend development, it ensures seamless data handling and scalability. Ultimately, the integration of advanced AI and data analytics improves decision-making and operational outcomes, offering a reliable solution for insurance automation, automobile services, and fleet management. (shrink)

RESUMO O presente estudo trata-se de uma revisão de literatura que buscou reunir informações sobre o efeito da utilização da vagem de faveira sobre a qualidade da carne e características de carcaça de pequenos ruminantes. A faveira é uma leguminosa presente nos estados do Piauí e Maranhão. Suas vagens apresentam elevados teores nutricionais, com aproximadamente 11,18% de proteína bruta e 10,79% de compostos fenólicos, incluindo taninos. Embora estejam disponíveis apenas nos meses de setembro e outubro, podem ser secas e utilizadas (...) durante a estação seca, representando uma alternativa alimentar interessante para os animais. Os taninos presentes na composição da Parkia platycephala, quando inseridos na alimentação de cordeiros podem modular a biohidrogenação e, consequentemente, melhorar o perfil de ácidos graxos (AG) da carne de cordeiros e minimizando a oxidação lipídica, ajudando a preservar o valor nutricional e as características sensoriais do produto cárneo. A utilização da vagem de faveira na alimentação de pequenos ruminantes, pode ser utilizada em até 100%, sem interferir nas características de carcaça e na qualidade da carne. Sendo considerado um alimento alternativo com potencial nutritivo e viável para produtores. Palavras-chave: proteína animal; valor nutricional; vagem de faveira. (shrink)

Cyberbullying, a contemporary menace within the realm of social media, has emerged alongside the surge in social media usage, leading to the exploitation of freedom of expression. Statistics indicate that a substantial 36.5% of individuals believe they have experienced cyberbullying at some point in their lives. This figure has more than doubled since 2007, and there has been a noticeable increase from 2018 to 2023, indicating an unfavorable trend. Although existing solutions in the market aim to mitigate this issue, they (...) often come with usage limitations or inefficient algorithms. (shrink)

An advanced audio generation application built using Streamlit and Meta’s Audiocraft framework. It integrates two state-of-the-art models, MusicGen and AudioGen, allowing users to generate both musical compositions and ambient soundscapes from text prompts. Users can refine the generated audio by adjusting parameters such as Top-k sampling and Temperature to control creativity and coherence. Additionally, the app supports optional features like reverb effects and stereo output conversion, enhancing the overall sound quality. The intuitive interface permits combining general music prompts with optional (...) instrumental details, enabling dynamic and personalized audio production. This flexible system caters to a wide range of creative needs, from orchestral-inspired anthems to immersive environmental sounds, and demonstrates the potential of generative AI to democratize audio creation for both enthusiasts and professionals. (shrink)

The time window between the disclosure of a new cyber vulnerability and its use by cybercriminals has been getting smaller and smaller over time. Recent episodes, such as the Log4j vulnerability, exemplify this well. Within hours after the exploit being released, attackers started scanning the internet looking for vulnerable hosts to deploy threats like crypto currency miners and ransom ware on vulnerable systems. Thus, it becomes imperative for the cybersecurity defense strategy to detect threats and their capabilities as early as (...) possible to maximize the success of prevention actions. Although crucial, discovering new threats is a challenging activity for security analysts due to the immense volume of data and information sources to be analyzed for signs that a threat is emerging. In this sense, we present a framework for automatic identification and profiling of emerging threats using Twitter messages as a source of events and MITRE ATT&CK as a source of knowledge for threat characterization. The framework comprises three main parts: identification of cyber threats and their names; profiling the identified threat in terms of its intentions or goals by employing two machine learning layers to filter and classify tweets; and alarm generation based on the threat’s risk . The main contribution of our work is the approach to characterize or profile the identified threats in terms of their intentions or goals, providing additional context on the threat and avenues for mitigation. In our experiments, the profiling stage reached an F1 score of 77% in correctly profiling discovered threats. (shrink)

The total early-stage entrepreneurial activity (TEA) in South Africa is said to be extremely low compared to those of other sub-Saharan countries. This is despite the concerted efforts of the government to establish, develop and nurture entrepreneurship at all levels, especially among the youths. This calls for concern given the current state of the economy and the challenges faced by South Africa’s future generation. This paper is anchored on two theoretical frameworks to substantiate our argument for the inclusion of entrepreneurship (...) education in the curricula of non-business programmes at universities of technology. The theoretical frameworks are the contingency organizational theory and the magnet versus radiant model. The study adopted an exploratory cross-sectional research design which allowed us to collect data from a cross-section of a population: the universities of technology in South Africa. The findings suggest that only fifteen (out of the 46) of the programmes showed visible evidence of entrepreneurship/business studies in their content. Such finding implies that there is a need for entrepreneurship to be integrated into the curricula of all non-business departments if not for the sake of its perceived employment generation attributes, but for its other attributes such as innovation, and more importantly employability. (shrink)

Abstract: Background: Vitamin D insufficiency/deficiency prevalent in all age groups across the world is common in obesity and may play an important role in the risk factors of metabolic syndrome (MS). Objectives: This cross-sectional study is to evaluate the relationship between levels of adiponectin and circulating 25(OH)D, and its effect on metabolic biomarker among overweight/obese female students. Methods: Three hundred female students; with mean age 20.9 ± 3.2 years were attending the Aljouf University, Sakaka, Saudi Arabia. They were randomly selected (...) from university during the studying year 2015 – 2016. Anthropometric and biochemical indices were determined. Results: The study showed 19% of the female’s student were either overweight or obese (15% and 4%, respectively). The frequency of MS diagnosis among the students was 17%, with 13% and 4% had either three or four risk factors, respectively. Overweight/obese subjects had significantly worse anthropometric and biochemical characteristics, including waist/hip ratio, blood pressure (BP), fasting blood glucose (BG), insulin, insulin resistance (HOMA-IR), triglyceride levels (TG), low-density lipoprotein cholesterol levels (LDL-C), leptin, adiponectin, leptin/adiponectin ratio and high-density lipoprotein cholesterol levels (HDL-C) compared to normal weight. Of the subjects diagnosed with MS, 59% had mild and 8.6% had severe 25(OH)D deficiency. There was negative association between 25(OH)D and both FBG and HOMA-IR among young women obese/overweight. Conclusion: In our study, it suggested that low level of adiponectin was strongly correlated with low 25(OH)D levels. Also, the prevalence of MS tends to increase with high occurrence rate of low circulating 25(OH)D levels that is, known cause poor glycemic control and prediction of cardiovascular outcomes. (shrink)

Plant diseases pose a severe threat to global agriculture, causing up to 40% annual crop losses due to delayed and inaccurate diagnostics. The AI Plant Doctor: Leaf Disease Scanner is an innovative Android-based solution that transforms plant health management using Artificial Intelligence. By integrating Convolutional Neural Networks (CNNs) for precise disease classification, TensorFlow Lite for efficient on-device processing, and OpenCV for robust image preprocessing, the system delivers real-time diagnostics with 92% accuracy. Hosted on a user-friendly Streamlit interface, it offers offline (...) functionality, cloud-based scalability, and seamless access to curated treatment resources via Google API. This platform empowers farmers to detect diseases swiftly and adopt sustainable practices, significantly enhancing crop yields and food security. (shrink)

The early diagnosis of brain stroke is crucial to reducing damage and increasing the probability of survival of the patient. Historically, stroke diagnosis has been made by getting the images processed through the interpretation of medical imaging. This method is expensive, complicated, and prone to error. The advancements in machine learning that we have seen today, particularly in real-time image processing, offer a potential solution towards making stroke detection faster and more efficient. The objective of this project, therefore, is to (...) create a real-time brain stroke detection system with Federated Learning in combination with YOLOv8 thus, transforming the private collaborative learning model with the state-of-the-art techniques for object detection. The Federated Learning concept allows a number of health institutions to perform collaborative training on a stroke detection model with their local data, keeping the sensitive patient information to themselves and making it privacy-compliant. The leading real-time object detection algorithm, YOLOv8, is altered in a way to allow the accurate identification of features of brain strokes in medical imagery such as CT scans and MRIs. The infrastructure developed will cover a great storm with rapid and reliable detection of brain-stroked patients without exposing their data. Stroke detection through these means should not only be faster and more accurate but also would help enhance data privacy and create collaborations among medical institutions towards better outcomes and timely interventions to patients. (shrink)

In December 2013, the Nonhuman Rights Project (NhRP) filed a petition for a common law writ of habeas corpus in the New York State Supreme Court on behalf of Tommy, a chimpanzee living alone in a cage in a shed in rural New York (Barlow, 2017). Under animal welfare laws, Tommy’s owners, the Laverys, were doing nothing illegal by keeping him in those conditions. Nonetheless, the NhRP argued that given the cognitive, social, and emotional capacities of chimpanzees, Tommy’s confinement constituted (...) a profound wrong that demanded remedy by the courts. Soon thereafter, the NhRP filed habeas corpus petitions on behalf of Kiko, another chimpanzee housed alone in Niagara Falls, and Hercules and Leo, two chimpanzees held in research facilities at Stony Brook University. Thus began the legal struggle to move these chimpanzees from captivity to a sanctuary, an effort that has led the NhRP to argue in multiple courts before multiple judges. The central point of contention has been whether Tommy, Kiko, Hercules, and Leo have legal rights. To date, no judge has been willing to issue a writ of habeas corpus on their behalf. Such a ruling would mean that these chimpanzees have rights that confinement might violate. Instead, the judges have argued that chimpanzees cannot be bearers of legal rights because they are not, and cannot be persons. In this book we argue that chimpanzees are persons because they are autonomous. (shrink)

Cho and Martinez-Martin provide a wide-ranging analysis of what they label “digital simulacra”—which are in essence data-driven AI-based simulation models such as digital twins or models used for i...

A portion of the dietary nutrients is excreted due to the presence of antinutritional factors in some ingredients. This study aimed to determine the total and ileal digestibility coefficients of crude protein, as well as the passage rate of diets with different chemical compositions, supplemented or not with an enzymatic complex, in broiler chickens. No effect of the enzymatic complex was observed on the crude protein digestibility coefficient (P>0.05). However, the ileal digestibility coefficients of crude protein showed significant differences (P<0.05) (...) within each group (with or without enzyme supplementation). The control treatment with the enzymatic complex presented a lower ileal digestibility coefficient of crude protein (P<0.05) compared to the control treatment without supplementation. The passage rate did not differ between treatments (P>0.05). It was concluded that the inclusion of the enzymatic complex, composed of calcium carbonate, Yucca extract, protease, phytase, and cellulase, in vegetable-based diets for broiler chickens did not affect digestibility coefficients or passage rate. Keywords: poultry farming, enzyme additives, nutrition, crude protein. (shrink)

Shortages of new therapeutics to treat coronavirus disease (COVID-19) have forced clinicians, public health officials, and health systems to grapple with difficult questions about how to fairly allocate potentially life-saving treatments when there are not enough for all patients in need (1). Shortages have occurred with remdesivir, tocilizumab, monoclonal antibodies, and the oral antiviral Paxlovid (2) -/- Ensuring equitable allocation is especially important in light of the disproportionate burden experienced during the COVID-19 pandemic by disadvantaged groups, including Black, Hispanic/Latino and (...) Indigenous communities, individuals with certain disabilities, and low-income persons. However, many health systems have resorted to first-come, first-served approaches to allocation, which tend to disadvantage individuals with barriers in access to care (3). There is mounting evidence of racial, ethnic, and socioeconomic disparities in access to medications for COVID-19 (4, 5). -/- One potential method to promote equitable allocation is to use a weighted lottery, which is an allocation strategy that gives all eligible patients a chance to receive the scarce treatment while also allowing the assignment of higher or lower chances according to other ethical considerations (6). We sought to assess the feasibility of implementing a weighted lottery to allocate scarce COVID-19 medications in a large U.S. health system and to determine whether the weighted lottery promotes equitable allocation. (shrink)

Throughout the biological and biomedical sciences there is a growing need for, prescriptive ‘minimum information’ (MI) checklists specifying the key information to include when reporting experimental results are beginning to find favor with experimentalists, analysts, publishers and funders alike. Such checklists aim to ensure that methods, data, analyses and results are described to a level sufficient to support the unambiguous interpretation, sophisticated search, reanalysis and experimental corroboration and reuse of data sets, facilitating the extraction of maximum value from data sets (...) them. However, such ‘minimum information’ MI checklists are usually developed independently by groups working within representatives of particular biologically- or technologically-delineated domains. Consequently, an overview of the full range of checklists can be difficult to establish without intensive searching, and even tracking thetheir individual evolution of single checklists may be a non-trivial exercise. Checklists are also inevitably partially redundant when measured one against another, and where they overlap is far from straightforward. Furthermore, conflicts in scope and arbitrary decisions on wording and sub-structuring make integration difficult. This presents inhibit their use in combination. Overall, these issues present significant difficulties for the users of checklists, especially those in areas such as systems biology, who routinely combine information from multiple biological domains and technology platforms. To address all of the above, we present MIBBI (Minimum Information for Biological and Biomedical Investigations); a web-based communal resource for such checklists, designed to act as a ‘one-stop shop’ for those exploring the range of extant checklist projects, and to foster collaborative, integrative development and ultimately promote gradual integration of checklists. (shrink)