Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirusbased pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the (...) virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. (shrink)

The jurisprudence under international human rights treaties has had a considerable impact across countries. Known for addressing complex agendas, the work of expert bodies under the treaties has been credited and relied upon for filling the gaps in the realization of several objectives, including the peace and security agenda. -/- In 1982, the Human Rights Committee (ICCPR), in a General Comment observed that “states have the supreme duty to prevent wars, acts of genocide and other acts of mass violence ... (...) Every effort … to avert the danger of war, especially thermonuclear war, and to strengthen international peace and security would constitute the most important condition and guarantee for the safeguarding of the right to life.” Over the years, all treaty bodies have contributed in this direction, endorsing peace and security so as “to protect people against direct and structural violence … as systemic problems and not merely as isolated incidents …”. A closer look at the jurisprudence on peace and security, emanating from treaty monitoring mechanisms including state periodic reports, interpretive statements, the individual communications procedure, and others, reveals its distinctive nature. (shrink)

This chapter presents the perceptual reality monitoring theory of consciousness (PRM). PRM is a higher-order theory of consciousness. It holds that consciousness involves monitoring the reliability of one’s own sensory signals. I explain how a perceptual reality monitoring mechanism computes the higher order representations that are crucial for consciousness. While PRM accounts for the difference between conscious and unconscious states, it does not explain, on its own, why experiences feel the way they do—the phenomenal character of experience. (...) PRM is compatible with multiple theories of phenomenal character. Here, I show how to combine it with ‘Mental Quality Space’ theory. I finish by presenting empirical evidence for PRM, as well as some of its predictions. (shrink)

Over the last few years, personal health monitoring wearable devices have emerged as innovative applications of Artificial Intelligence (AI) in the healthcare industry as they help in real time analysis and prediction of health standardized check-ups and health management. To navigate through the current trends, new technologies and developments, the prospects are as follows: The article also gives a logical look at the state of the art of such devices, enumerating the advantages and drawbacks, as well as outlining the (...) main ethical issues. In terms of method, it also discusses different AI approaches, data acquisition procedures, and those integrating systems used in the context of health monitoring. The finding and implication section discusses the findings of the current trend and the possibility of improvement in application and user acceptance. The last section provides a prognosis on what the area of personal health monitoring might look like in the future, as well as the position of AI in the optimisation of patient experience. (shrink)

This article discloses topical issues of the need for constant monitoring of changes in the business activity in enterprise structures. The main purpose of the study is to monitor the business activity of industrial enterprise structures of Ukraine to predict their development under martial law. A critical analysis of the content of scientific publications to solve the problem of improving the management of business activity of business structures revealed the lack of attention of scientists to the problems under study. (...) The relevance of its solution is due to the scale of the impact of military conflicts on the business activity in enterprise structures, which requires its monitoring to further establish the state and ensure compliance of the business with the norms that are considered necessary to maintain a certain level of stability of development or to survive, to determine areas where the business can improve its activities and become more profitable. The methodological basis is the system of methods used to obtain the final results of the study: theoretical generalization – to deepen the essence of the concept of «monitoring business activity»; analysis, synthesis and logical generalization – to obtain conclusions during monitoring of business confidence and business climate in industry; prognostic – to predict the development of industrial business structures of Ukraine under martial law. The object of research in the work is the business activity of business structures. The article presents the results of monitoring business confidence and business climate in the industry of Ukraine under martial law. Analysis, synthesis and logical generalization of monitoring results became the basis for further forecasting the development of business structures. The results of the study can be useful for business structures that seek to maintain a certain level of stability of development and identify areas of improvement. (shrink)

Type 1 diabetes (T1D) is a chronic illness that requires intensive lifelong management of blood glucose concentrations by means of external insulin administration. There have been substantial developments in the ways of measuring glucose levels, which is crucial to T1D self-management. Recently, continuous glucose monitoring (CGM) has allowed people with T1D to keep track of their blood glucose levels in near real-time. These devices have alarms that warn users about potentially dangerous blood glucose trends, which can often be shared (...) with ther people. CGM is consistently associated with improved glycemic control and reduced hypoglycemia and is currently recommended by doctors. However, due to the costs of CGM, only those who qualify for hospital provision or those who can personally afford it are able to use it, which excludes many people. In this paper, I argue that unequal access to CGM results in: (1) unjust health inequalities, (2) relational injustice, (3) injustice with regard to agency and autonomy, and (4) epistemic injustice. These considerations provide prima facie moral reasons why all people with T1D should have access to CGM technology. I discuss the specific case of CGM policy in the Netherlands, which currently only provides coverage for a small group of people with T1D, and argue that, especially with additional considerations of cost-effectiveness, the Dutch government ought to include CGM in basic health care insurance for all people with T1D. (shrink)

The increasing demand for reliable power sources has made diesel generators essential in various industries. However, traditional monitoring methods often rely on manual inspections, resulting in inefficiencies, unplanned downtimes, and higher operational costs. This project proposes a digital monitoring system for diesel generators that leverages advanced sensor technologies and real-time data analytics to overcome these challenges. The system will utilize Internet of Things (IoT) devices to collect key operational parameters, such as fuel levels, temperature, vibration, and performance metrics, (...) enabling comprehensive monitoring of generator health. By integrating machine learning algorithms, the system will analyze historical and real-time data to predict potential failures and optimize maintenance schedules, shifting from reactive to proactive management. A user-friendly interface will allow operators to visualize generator performance and receive instant alerts for detected anomalies, enhancing decision- making processes, reducing downtime, and lowering maintenance costs—ultimately increasing the reliability and efficiency of power supply. Additionally, the system facilitates remote monitoring, providing continuous oversight regardless of location. This project not only aims to enhance the operational efficiency of diesel generators but also lays the groundwork for smarter energy management solutions in the future. The successful implementation of this digital monitoring system could serve as a model for similar applications across various sectors, contributing to sustainability and operational excellence in energy management. Through this innovative approach, we aim to ensure diesel generators operate at optimal performance levels while minimizing environmental impact. Keywords. Diesel Generators, Digital Monitoring, IoT Devices, Real-time Data Analytics, Machine Learning. (shrink)

In today’s fast-paced supply chain environment, efficient inventory management is essential for small and medium-sized warehouses striving to reduce manual errors, avoid stockouts, and improve operational productivity. This paper presents the design and implementation of a smart warehouse system that leverages Internet of Things (IoT) technologies for real-time inventory monitoring and automated order fulfilment. The proposed system integrates RFID readers, barcode scanners, and various IoT sensors—including weight, temperature, and motion detectors—with a cloud-based inventory management platform. These components work together (...) to continuously monitor stock levels, detect item movement, and maintain environmental conditions for sensitive goods. When stock falls below a predefined threshold, the system automatically triggers restocking requests and can even notify or place orders with suppliers. A predictive analytics engine powered by machine learning analyses historical data to optimize reorder points, minimize waste, and forecast demand. The implementation demonstrates how IoTbased automation can enhance transparency, reduce costs, and increase scalability for small warehouse operations. Experimental results confirm improved efficiency, higher picking accuracy, and reduced operational downtime. This research offers a cost-effective and scalable solution tailored to meet the growing demands of inventory control in the digital era. (shrink)

his project focuses on developing a community-based water monitoring and conservation tool. The system allows users to track daily water consumption, receive real-time feedback on usage, and view eco-friendly tips. It integrates data visualization and eco-points for promoting conservation behavior. Built using node js, JavaScript, and Chart.js, this platform encourages users to reduce water consumption and share their achievements. The goal is to foster community engagement and sustainable water usage practices, addressing the global concern of water scarcity.

Air pollution is a critical challenge for urban areas, contributing to public health crises and environmental degradation. Traditional air quality monitoring systems often lack the granularity and adaptability needed to address dynamic pollution sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance air quality management in smart cities by enabling real-time monitoring, pollution source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, satellite imagery, and traffic systems, cities can reduce (...) pollution levels, improve public health outcomes, and promote sustainable urban environments. Experimental results demonstrate significant improvements in pollution detection accuracy, mitigation efficiency, and public awareness, offering a sustainable blueprint for urban air quality management. (shrink)

Noise pollution is a growing concern in urban areas, contributing to public health issues such as stress, sleep disturbances, and hearing loss. Traditional noise monitoring systems often lack the granularity and adaptability needed to address dynamic noise sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance noise pollution management in smart cities by enabling real-time monitoring, source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, traffic systems, and urban infrastructure, (...) cities can reduce noise levels, improve public health outcomes, and enhance the quality of life for residents. Experimental results demonstrate significant improvements in noise detection accuracy, mitigation efficiency, and public awareness, offering a sustainable blueprint for urban noise pollution management. (shrink)

The rapid urbanization and growing population have led to increased waste generation, necessitating efficient and intelligent waste management systems. This project presents a Smart Garbage Monitoring and Clearance Alert System utilizing the Internet of Things (IoT) to address this challenge. The system employs a load sensor to measure the weight of waste in the dustbin, and an ultrasonic sensor to monitor the garbage level. When the bin is nearly full, a servo motor is triggered to close the lid, preventing (...) overflow and environmental hazards. The ESP32 microcontroller serves as the central unit, managing sensor data and system operations. Real-time garbage status is communicated wirelessly to municipal authorities through IoT, enabling timely waste clearance and improving sanitation services. This solution ensures a cleaner environment by optimizing garbage collection routes and reducing manual inspection efforts. College campuses have waste management concerns due to insufficient collection and overflowing bins, which cause environmental and logistical issues. An IoTenabled smart trash system improves collection, lowers costs, and increases sustainability by assuring timely garbage disposal and reducing buildup, resulting in a cleaner and more efficient campus environment. (shrink)

Traffic congestion in urban areas is a growing challenge, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal systems operate on fixed timing schedules, failing to adapt to real-time traffic conditions. This research proposes an AI-based smart traffic management system that dynamically adjusts traffic signal timings using CCTV cameras and deep learning models. The system continuously monitors stopped vehicles at intersections using YOLO-based object detection and assigns adaptive green light durations based on vehicle density. The proposed (...) solution is tested using a custom-trained YOLO model, YOLOv8, and YOLOv5, comparing performance metrics such as mAP, precision, and recall. Experimental results demonstrate that the proposed model achieves improved traffic flow efficiency by reducing waiting times and prioritizing lanes with higher congestion. The system is scalable for smart cities and can integrate IoT-based sensors and real-time traffic analytics for further optimization. (shrink)

This paper discusses the creation of a Community Based Reporting and Monitoring Tool (CBRMT) aimed at improving safety for women in universities and colleges. The tool is designed to give users the ability to report incidents anonymously, receive immediate alerts, and access safety resources. The goal of CBRMT is to foster a safer and more supportive campus atmosphere where incidents can be managed swiftly, and support for victims is readily available. Through these initiatives, the tool aspires to encourage a (...) safer, more responsible, and inclusive culture across campuses globally. (shrink)

Software-Defined Networking (SDN) is transforming network management by offering centralized control and programmability. As SDN environments grow more complex, effective monitoring and analytics become crucial to ensure the performance, reliability, and security of networks. This paper explores the tools and techniques used for monitoring and analyzing SDN networks, emphasizing the importance of real-time data, traffic analysis, and anomaly detection. The paper reviews a variety of existing tools and techniques used to monitor SDN networks, such as OpenFlow monitoring, (...) SNMP (Simple Network Management Protocol) based tools, and machine learning approaches for predictive analytics. Challenges such as network visibility, scalability, and the integration of SDN monitoring with traditional network management systems are discussed. The paper also presents case studies of SDN monitoring implementations and offers recommendations for organizations seeking to optimize their SDN monitoring practices. (shrink)

As organizations increasingly adopt cloud-native architectures, Kubernetes has emerged as the leading container orchestration platform. However, monitoring such dynamic and distributed environments remains a significant challenge. -/- Traditional observability tools often fall short in detecting real- time anomalies and understanding contextual issues across -/- multiple clusters. This study introduces an enhanced Kubernetes monitoring system based on Distributed Event Processing (DEP). The proposed system integrates telemetry collection, event stream processing, and complex event correlation to offer intelligent and scalable (...) class='Hi'>monitoring capabilities. It enables proactive identification of issues such as pod failures, network delays, and performance bottlenecks by analyzing real-time event data from Kubernetes clusters. The architecture supports multi-cluster observability using distributed tools like Apache Kafka, Flink, Prometheus, and Fluentd. Empirical results demonstrate improved detection speed, reduced false positives, and faster recovery time, making this approach highly effective for modern DevOps workflows. (shrink)

Air pollutants pose a threat to biodiversity throughout the world. This study was conducted to evaluate atmospheric element accumulation in Isparta city, including Gölcük Nature Park, located in the Western Mediterranean Region of Turkey. It is aimed to determine the air quality and potential pollutant sources in the region through lichen biomonitoring. Specimens of the epiphytic foliose lichen Physcia aipolia (Erh. ex Humb.) Fürnr. were sampled from 14 sites in the study area and analyzed by ICP-MS with reference material in (...) order to detect 11 trace elements; Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn. The spatial distribution of these airborne elements in the study area was mapped. The results showed that the elemental sequence in terms of average concentrations measured was as follows: Ni >Cr >V >Fe >As >Al >Zn >Cu> >Cd >Mn >Pb. The results of cluster analyses of the elemental data indicated that the 3 major contaminants in the urban area were Cr, Ni, and V, probably due to the extensive use of coal in heating facilities and gasoline in vehicles. P. aipolia specimens accumulated maximal concentrations of As, Cr, Fe, Ni, and V in the northern and the southeastern localities surrounding the city, while Cd, Pb were most abundant in the eastern edge of Gölcük Nature Park. The pollution in Isparta city with heavy metal is worse than expected, mostly due to the extensive use of coal as fossil fuel. In addition, it seems to be associated with the city's topographic and climatic characteristics, e.g. inversion effects. (shrink)

The target of Second- Language Acquisition (SLA), emerged in the second half of the 20th century, was to be helpful in foreign- language education/ teaching. It denotes mostly the study of individuals (or sometimes groups) who are learning a language consequent to learning their first language when they are young children. At the same time, it signifies the process of learning a second language. The added language is named a second language, but it might indeed be the third, fourth or (...) more which is going to be acquired. The range of SLA comprises informal Second- Language Learning occurring in natural milieus, formal second- language learning occurring in classroom or the one that contains a combination of them both, that is, settings and conditions. The three main aspects for the study of SLA process are the linguistic, psychological and social aspects. The Monitor Theory/ Model postulated by Krashen in the 1970s is a psychological approach in nature. With its five hypotheses (The Acquisition– Learning Hypothesis, The Monitor Hypothesis, The Natural Order Hypothesis, The Input Hypothesis, and The Affective Filter Hypothesis), it tries to find answers to the problems of SLA, such as what does a second- language learner come to know, how the acquisition process takes place, and why some learners are more successful than others? The Monitor Theory (MT) received extensively many criticisms after its appearance and was rejected. Its teaching implications were also at the centre of criticisms. What place does MT occupy in SLA today? This study aims to try to find an answer. The other questions are: How important is the MT for SLA? What kind of criticisms are expressed against it? How fair is the criticism by McLaughlin (1978, 1987)? The working hypotheses of the present work are: The hypotheses developed by Krashen are not/ will not be rejected. Because science is still lying in the so- called agony phase, and cannot find any answers to all questions in psychology (e.g. how exactly is the processing of language; in particular and of mind in general). Moreover, the problems related to memory etc., the thoughts emanated from the MT can probably not be refuted. They have evolved so far and will be evolved further, perhaps with small differences. This research is completely based on the literature written since the time the theory was developed. In other words, it was carried out using a descriptive method without using a special data collection tool. The sources written on the subject were reviewed and an answer to the research questions was tried to be found. Even though the theory is expressed with different names and different meanings today, it has survived all the criticisms made, and it has been concluded that it still occupies an important place in the discipline of second- language acquisition (SLA) and foreign- language teaching. Again, the inquiries carried out since the 1970s delineate that the implications in favour of language education are not very different from those stated by Krashen (1982), which were the products of his opinions in that period. There are still basic consequences grounded on MT for language teaching today. (shrink)

This paper addresses the critical role of automated data quality monitoring in Machine Learning Operations (MLOps) pipelines. As organizations increasingly rely on machine learning models for decision-making, ensuring the quality and reliability of input data becomes paramount. The paper explores various types of data quality issues, including missing values, outliers, data drift, and integrity violations, and their potential impact on model performance. It then examines automated detection methods, such as statistical analysis, machine learning-based anomaly detection, rule-based systems, and data (...) profiling. The integration of data quality monitoring into different stages of the MLOps pipeline is discussed, emphasizing continuous monitoring at data ingestion, pre-training validation, post- deployment drift detection, and feedback loops for model retraining. The paper also addresses key challenges in implementing automated data quality monitoring, including balancing precision and recall in anomaly detection, handling high-dimensional and unstructured data, managing false positives and alert fatigue, and adapting to evolving data distributions. By providing a comprehensive framework for automating data quality monitoring in MLOps pipelines, this paper aims to equip practitioners with the knowledge and strategies necessary to enhance the reliability and performance of machine learning systems in production environments. (shrink)

Decision-making by a Data and Safety Monitoring Board (DSMB) regarding clinical trial conduct and termination is intricate and largely limited by cases and rules. Decision-making by legal jury is also intricate and largely constrained by cases and rules. In this paper, I argue by analogy that legal decision-making, which strives for a balance between competing demands of conservatism and innovation, supplies a good basis to the logic behind DSMB decision-making. Using the doctrine of precedents in legal reasoning as my (...) central analog will lead us to a decision-theoretic analogy for much more systematic documentation of decisions in clinical trials. My conclusion is twofold: every DSMB decision should articulate a clear general principle (a ratio decidendi) that gives reason for the decision; and all such decisions should be made public. I use Bartha’s (2010) theory of analogical arguments to both frame and assess my argument by analogy. (shrink)

Smart Traffic Analysis, Monitoring, and Density Estimation System Using YOLOv8 and DeepSORT, aims to provide an efficient and real-time solution for vehicle detection, tracking, and speed estimation in traffic surveillance. The system leverages YOLOv8 (You Only Look Once version 8) for high-precision object detection and integrates DeepSORT (Simple Online and Realtime Tracker with a Deep Association Metric) for multi-object tracking, ensuring accurate identification and movement tracking of vehicles across video frames. The YOLOv8 model is trained on large-scale datasets for (...) object detection and efficiently identifies vehicles in real-time. The detected bounding boxes, confidence scores, and class labels are then passed to the DeepSORT tracking algorithm, which assigns a unique ID to each detected object and maintains its trajectory across frames. DeepSORT employs an advanced Kalman filter for motion prediction and a deep appearance descriptor to ensure robust re-identification even in complex traffic conditions. Additionally, the system integrates speed estimation by tracking the movement of each vehicle across frames, calculating the pixel displacement over time, and converting it into an approximate speed (km/h). This enables real-time monitoring of traffic flow and assists in identifying potential rule violations, such as over speeding. The system processes video streams efficiently using OpenCV, NumPy, and PyTorch, making it suitable for both offline video analysis and live surveillance feeds. The output video includes annotated bounding boxes, unique tracking IDs, and estimated vehicle speeds. This solution is highly scalable and can be deployed for traffic density estimation, congestion monitoring, and automated traffic law enforcement. By combining deep learning-based detection with robust tracking and speed estimation, this project offers a faultless, real-time, and automated approach to traffic analysis, helping urban planners and law enforcement agencies manage road safety effectively. (shrink)

The attendance is taken in every organization. Traditional approach for attendance is, professor calls student name & record attendance. For each lecture this is wastage of time. To avoid these losses, we are about to use automatic process which is based on image processing. In this project approach, we are using face detection & face recognition system. The first phase is pre-processing where the face detection is processed through the step image processing. It includes the face detection and face recognition (...) process. Second phase is feature extraction. Step by step execution of these techniques (Image Processing) helps to achieve the final output. The working of this project is to detect and recognize the face and mark the attendance for the corresponding face in the database. Input of this project is face detection and recognition and output is to mark the attendance. Our project is being presented as a solution for the Automatic Attendance Marking System. It is designed to be reliable and low power. The Automatic face detection and recognition proposed to attendance marking in database acts as the solution for the automatic attendance marking system.. (shrink)

Wildlife conservation and habitat monitoring are essential for maintaining biodiversity and ecological balance. Traditional conservation methods face challenges such as limited manpower, inaccessible terrain, and the need for continuous surveillance. Artificial Intelligence (AI) has emerged as a transformative tool, enabling more efficient, scalable, and real-time approaches to wildlife protection. This paper explores the use of AI technologies such as computer vision, machine learning, and bioacoustic analysis in monitoring animal populations, detecting poaching activity, and assessing habitat health. A structured (...) framework is proposed for integrating AI into conservation practices, highlighting benefits and future potential. (shrink)

Vibration, especially basic vibration, may cause loose contacts, open circuits, or other contact problems, which account for a large proportion of system failure causes. Due to the complexity of the chassis structure used in the biomedical motors, the vibration analysis of a single component cannot solve the vibration problems encountered in the work of the system. Therefore, it is necessary to use system simulation and experiments to monitor and enhance the health of the structure. Finite element method is used to (...) carry out the dynamic analysis from the component to the system, and calculate the natural frequency, modal shape and harmonic response. Then, the effectiveness and accuracy of the analysis results are verified by experiments through simulation and experiments. The results show that the measures taken have effectively reduced the vibration of the chassis equipment and improved its safety for better health safety of patients. There is just a 2% variation between the natural frequencies acquired from the experiment and the simulation computation. In terms of natural frequency, the first 6-order natural frequencies all have errors of less than 10%, and the natural frequency error is comparable to that of fireproof board. (shrink)

Do self‐monitoring accounts, a dominant account of the positive symptoms of schizophrenia, explain auditory verbal hallucination? In this essay, I argue that the account fails to answer crucial questions any explanation of auditory verbal hallucination must address. Where the account provides a plausible answer, I make the case for an alternative explanation: auditory verbal hallucination is not the result of a failed control mechanism, namely failed self‐monitoring, but, rather, of the persistent automaticity of auditory experience of a voice. (...) My argument emphasizes the importance of careful examination of phenomenology as providing substantive constraints on causal models of the positive symptoms in schizophrenia. (shrink)

The Internet of Things (IoT) has revolutionized healthcare by establishing realtime and remote monitoring systems, thereby significantly enhancing patient care and operational efficiency. Traditional monitoring systems, however, focus on basic parameters such as heart rate and body temperature, which can only provide minimal functionality. This work proposes an advanced IoT-enabled solution that makes use of sensors to monitor a variety of health parameters, such as glucose and lactate levels. The system gives real time alerts to ensure timely interventions (...) in critical areas, such as intensive care unit, ICU. The aim should be to optimize patient treatment while using IoT technology such that it does not burden much the healthcare staff but remote monitoring is effective. Of especial importance, this idea can be used in healthcare in rural setups and other settings where pandemics and flu season have rendered traditional monitor setups impractical. The proposed system bridges the gap between patients and providers by providing a scalable and efficient healthcare solution. It ensures better outcomes and streamlined care delivery. Not only does it enhance critical care response times, but it also reduces the need for constant on-site personnel, providing a significant improvement in accessibility, efficiency, and responsiveness in healthcare. Keywords: IoT, Remote Monitoring, Healthcare, Real- Time Alerts, Critical Care, Patient Monitoring Systems, Sensor Technology, Pandemic Response. (shrink)

This dissertation is about the relationship between Epistemology and other domains. In it I want to show that in an important sense, Epistemology is an autonomous domain. That is, epistemic vocabulary (e.g., “knowledge”, “justification”, “rationality”, “epistemic reason”, etc.) cannot be analyzed without remainder into non-epistemic vocabulary. Epistemic phenomena must be explained in terms of epistemic reasons and the form of assessment proprietary to them. Although epistemic vocabulary cannot be reduced to the vocabulary of other domains, Epistemology is nonetheless connected with (...) other domains in an important way. In this dissertation I focus on the connection between Epistemology and the Philosophy of Mind. I distinguish different forms cognitive self-monitoring can take and corresponding forms of cognitive autonomy. The kind of self-monitoring that receives the most attention involves simulating interpersonal argumentation. I argue that the capacity to self-monitor in this way grounds familiar epistemic standards (e.g., knowledge and justification) and explains how reasons get their grip on us. On the picture that emerges, Epistemology and Philosophy of Mind need to be done in tandem. Epistemology gives us the normative framework that is modeled by the self-monitoring system. The self-monitoring system, conversely, explains the kind of activity we engage in that makes that normative framework applicable to us. Epistemic reasons themselves are just episodes of this kind of activity. In Chapter 1 I argue that epistemic luck cannot be analyzed in non-epistemic terms. Here I generalize Jennifer Lackey’s criticism of Duncan Pritchard’s safety-based account of epistemic luck to all reductive theories. I argue that they will either be extensionally inadequate, or they will illicitly presuppose the vocabulary they are trying to reduce. The upshot is that epistemic luck can only be understood in terms of the epistemic assessment of reasons. By extension, we can’t give a reductive analysis of knowledge. This is not to say we cannot analyze knowledge, just that we can’t analyze away the epistemic vocabulary without remainder. In chapter 2 I explain the relation between epistemic luck, epistemic justification, and knowledge. I argue that previous work in the defeasible reasoning tradition, despite being on the right track, fell short of realizing its full potential. This is because the account of how these three things relate given by its proponents was insufficiently unified, which encouraged many epistemologists to abandon the defeasible reasoning tradition entirely. I argue that a unified and principled account of their relation is possible, and it allows for an elegant analysis of knowledge. In slogan form: good reasons are apparent to the knowing subject. In chapter 3 I extend the defeasible reasoning tradition to explain how knowledge is gradable. I show that this enables us to bring the debates about fake barns and knowledge from falsehoods to ecumenical resolutions, while also shedding light on a new kind of case. Furthermore, I explain how background assumptions are themselves defeasible and some of the epistemic significance of this fact. In chapter 4 I make the connection between Epistemology and Philosophy of Mind. I argue that the self-monitoring system of cognitively mature humans uses a model of the normative framework described in the previous chapters to regulate its own cognition. Here I build on that claim to develop an account of the role played by knowledge and justification in doxastic social coordination. I build on the claim that background assumptions are themselves defeasible to develop an account of the foundations of knowledge according to which they are social but not brutely sociological. In chapter 5 I discuss the relation between the epistemic states of cognitively mature humans, children, and non-human animals. I do this in a way meant to integrate an insight from virtue epistemology into the reasons-first picture developed here: positive epistemic standing is a state attributable to the subject. In chapter 6 I discuss the metaphysics of epistemic reasons. I claim that they are episodes of the activity of reasoning. In the preceding chapters, we get a sense of what that activity involves. Here I argue that the materials developed there put us in a position to understand what epistemic reasons are. This account incorporates the insights of rival positions while sidestepping familiar objections to them. The resulting picture is in line with the non-reductive approach to the epistemic developed so far. In fact, it goes further than other “reasons-first” approaches to Epistemology by claiming that reasons are not only basic within Epistemology, but they are also basic full-stop. One key consideration for my argument here is that reasons are, by their very nature, competitors. Although this point is generally acknowledged in the literature on reasons, its consequences for the metaphysics of reasons hasn’t been. Participants in the debate about the metaphysics of reasons typically try to identify reasons with something (e.g., facts or mental states) that are either not competitors, or only competitors insofar as they constitute, or bear on the epistemic assessment of, episodes of reasoning. (shrink)

Highway construction projects are often complicated and need careful management. This proposes the development of real time monitoring dashboard specially designed for highway road construction projects. The project is mainly useful for transportation planners, civil engineers, project managers, surveyors, constructors, government authorities, safety inspectors. Traditional monitoring methods often involve manual inspections and periodic reporting, which can lead to delays and inefficiencies. Existing approaches generally rely on static data collection and do not provide real-time insights into construction progress, resource (...) allocation. The proposed solution integrates advanced technologies such as data analytics to provide an interactive platform. This dashboard will offer real-time visualization of construction progress, resource usage, expenses, facilitating immediate decision-making and management. (shrink)

This project focuses on developing a community-based reporting and monitoring tool to improve women’s safety in colleges and universities. The tool allows users to anonymously report unsafe incidents, harassment, or suspicious activities, with features for immediate alerting to campus authorities and support groups. It integrates GPS tracking for emergencies and data visualization to monitor safety trends. Built using Java, XML, and Android Studio, the platform fosters collaboration between students, faculty, and administrators, encouraging proactive safety measures. By enabling discreet reporting (...) and real-time monitoring, the tool aims to create a safer campus environment for women. (shrink)

Many philosophers and psychologists have sought to explain experiences of auditory verbal hallucinations and “inserted thoughts” in schizophrenia in terms of a failure on the part of patients to appropriately monitor their own inner speech. These self-monitoring accounts have recently been challenged by some who argue that AVHs are better explained in terms of the spontaneous activation of auditory-verbal representations. This paper defends two kinds of self-monitoring approach against the spontaneous activation account. The defense requires first making some (...) important clarifications concerning what is at issue in the dispute between the two forms of theory. A popular but problematic self-monitoring theory is then contrasted with two more plausible conceptions of what the relevant self-monitoring deficits involve. The first appeals to deficits in the neural mechanisms that normally filter or attenuate sensory signals that are the result of one’s own actions. The second, less familiar, form of self-monitoring approach draws an important analogy between Wernicke’s aphasia and AVHs in schizophrenia. This style of self-monitoring theory pursues possible connections among AVHs, inserted thoughts, and the disorganized speech characteristic formal thought disorder. (shrink)

Environmental pollution poses a serious threat to public health, biodiversity, and climate stability. Traditional monitoring methods are often reactive, limited in scale, and incapable of providing real-time responses. With the integration of Artificial Intelligence (AI), particularly in machine learning, computer vision, and edge computing, it is now possible to develop tools for continuous, real-time monitoring of various pollutants. This paper presents the design and development of AI-driven tools to monitor air, water, and soil pollution in real-time. These tools (...) enhance data accuracy, facilitate predictive analysis, and enable rapid response to hazardous changes in the environment. (shrink)

Personal Health Monitoring (PHM) uses electronic devices which monitor and record health-related data outside a hospital, usually within the home. This paper examines the ethical issues raised by PHM. Eight themes describing the ethical implications of PHM are identified through a review of 68 academic articles concerning PHM. The identified themes include privacy, autonomy, obtrusiveness and visibility, stigma and identity, medicalisation, social isolation, delivery of care, and safety and technological need. The issues around each of these are discussed. The (...) system / lifeworld perspective of Habermas is applied to develop an understanding of the role of PHMs as mediators of communication between the institutional and the domestic environment. Furthermore, links are established between the ethical issues to demonstrate that the ethics of PHM involves a complex network of ethical interactions. The paper extends the discussion of the critical effect PHMs have on the patient’s identity and concludes that a holistic understanding of the ethical issues surrounding PHMs will help both researchers and practitioners in developing effective PHM implementations. (shrink)

The continuous growth of healthcare data has made it essential to develop efficient systems that not only alert healthcare providers but also visualize patient data in a comprehensible way. This study introduces a Health Alert System integrated with Report Visualization powered by Data Analytics to improve patient monitoring and alerting mechanisms. By leveraging real-time data from wearable sensors and hospital records, the system generates health alerts based on deviations from normal parameters. 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)

One method for preventing the spread of the coronavirus and other contagious diseases is through social distancing. Therefore, creating a tool to measure and quickly discover the precise distance is necessary. In order to prevent physical contact between individuals, this study aimed to detects individuals’ physical distance, through an inaugurated battery-powered device that monitors physical distance through wireless technology. Specifically, in public or crowded areas, to lessen the spread of the virus. This study focuses on detecting people’s physical distance in (...) the region of interest utilizing an Ultrasonic and VL53L0X sensors and determining the significant difference between the two sensors in monitoring physical distance. This study employs an applied experimental research design. The efficiency of both sensors in measuring physical distance was evaluated through analysis. The Ultrasonic and VL53L0X sensors underwent 15 tests. Furthermore, the researchers effectuated a t-test to determine the significant difference between the two sensors. Data gathered revealed that the sensors’ combined mean in terms of measuring physical distance indicates that the ultrasonic sensor performed better than VL53L0X, measuring 134.7 cm, notwithstanding 99.8 cm for the VL53L0X sensor. Complementary to this, the results of the t-test show that the VL53L0X sensor and Ultrasonic sensor have a significant difference in terms of distance precision. (shrink)

. The Road Safety Violation Reporting System is an innovative approach to improve road safety by enabling community members to report traffic rule violations. India faces over 150,000 traffic fatalities each year due to non-compliance with laws like wearing seat belts, helmets, and avoiding mobile phone use while driving. Our platform allows citizens to report violations using photographic evidence, which includes location and timestamp data. The system automates fine generation while maintaining the anonymity of the reporters, encouraging public participation in (...) traffic law enforcement. This project leverages community engagement and advanced technology to significantly reduce traffic violations and enhance road safety. (shrink)

With the rising global prevalence of cardiovascular diseases and the growing need for accessible healthcare solutions, there is a critical demand for systems that can provide both continuous monitoring and early diagnosis. This project presents a Smart Health Monitoring System with Real-Time Vital Tracking and Predictive Diagnostics, a comprehensive and intelligent health management platform that combines Internet of Things (IoT) technology with machine learning- based predictive analytics. The system integrates a heartbeat sensor and DS18B20 temperature sensor connected to (...) an ESP32 microcontroller, which continuously captures vital physiological parameters. This data is transmitted wirelessly to a Flask-powered web server, where it is logged, visualized in real time through an interactive dashboard, and stored for further analysis. Users can monitor trends over time, download data in CSV format, and reset logs as needed. (shrink)

The increasing complexity of regulatory requirements in the banking sector has created a critical need for real-time, automated compliance solutions. Traditional compliance mechanisms, which rely on periodic audits and manual rule enforcement, are proving inadequate in detecting violations promptly or adapting to dynamic regulations. This paper proposes an AI-driven framework for real-time compliance monitoring that leverages machine learning, natural language processing, and anomaly detection techniques to ensure continuous supervision of banking operations. The system is designed to ingest and process (...) live transactional data, analyze regulatory texts using NLP, detect suspicious behavior patterns, and adapt to evolving compliance rules through continuous learning. Experimental evaluations demonstrate the effectiveness of the framework in identifying policy violations with high accuracy and low latency, while case studies highlight its practical relevance across multiple banking scenarios. The proposed solution significantly enhances operational transparency, auditability, and regulatory responsiveness, ultimately reducing risks of non-compliance in financial institutions. (shrink)

An invited commentary on Ara Norenzayan's Big Gods: How Religion Transformed Cooperation and Conflict, focusing on whether early China constitutes an exception to his general theory.

Water is a fundamental resource for life, yet maintaining its quality remains a significant global challenge due to pollution from industrial, agricultural, and domestic sources. Traditional water quality monitoring methods are time-consuming and limited in scope. Machine Learning (ML) offers advanced capabilities for analyzing complex datasets, predicting pollution levels, and optimizing water resource management in real-time. This paper explores the integration of ML in water quality monitoring and management, reviewing current applications, methodologies, and future research directions. We present (...) a framework for real-time monitoring using ML models to enhance decision-making and resource planning. (shrink)

Open-content communities that focus on co-creation without requirements for entry have to face the issue of institutional trust in contributors. This research investigates the various ways in which these communities manage this issue. It is shown that communities of open-source software—continue to—rely mainly on hierarchy (reserving write-access for higher echelons), which substitutes (the need for) trust. Encyclopedic communities, though, largely avoid this solution. In the particular case of Wikipedia, which is confronted with persistent vandalism, another arrangement has been pioneered instead. (...) Trust (i.e. full write-access) is ‘backgrounded’ by means of a permanent mobilization of Wikipedians to monitor incoming edits. Computational approaches have been developed for the purpose, yielding both sophisticated monitoring tools that are used by human patrollers, and bots that operate autonomously. Measures of reputation are also under investigation within Wikipedia; their incorporation in monitoring efforts, as an indicator of the trustworthiness of editors, is envisaged. These collective monitoring efforts are interpreted as focusing on avoiding possible damage being inflicted on Wikipedian spaces, thereby being allowed to keep the discretionary powers of editing intact for all users. Further, the essential differences between backgrounding and substituting trust are elaborated. Finally it is argued that the Wikipedian monitoring of new edits, especially by its heavy reliance on computational tools, raises a number of moral questions that need to be answered urgently. (shrink)

As organizations increasingly depend on data-driven decision-making, the complexity of Business Intelligence (BI) systems and their data pipelines has grown exponentially. This complexity introduces significant challenges in maintaining data quality, ensuring traceability, and guaranteeing system reliability. Unmanaged data dependencies in upstream BI components can lead to data inconsistencies, system failures, and compromised analytics. This paper presents a comprehensive framework for monitoring and managing data dependencies in upstream BI systems, with a primary focus on the Dependency Discovery Engine utilizing Static (...) Code Analysis. The framework provides a systematic approach to identifying, tracking, and managing data dependencies across the entire BI ecosystem using advanced static code analysis techniques. Detailed methodologies, algorithms, and implementation considerations for static code analysis are discussed. This framework offers a robust solution for organizations seeking to enhance data quality, system reliability, and operational efficiency in their BI systems. Future research will expand on dynamic runtime analysis and machine learning- based methods to further enhance the framework. (shrink)

The rapid growth of data generated from applications, servers, and network devices has emphasized the need for efficient real-time monitoring solutions. The ELK Stack—comprising Elasticsearch, Logstash, and Kibana—provides a robust framework for ingesting, storing, visualizing, and analyzing log and event data in real time. This paper presents an end-to-end real-time monitoring architecture built with the ELK Stack. It compares traditional log analysis systems with the ELK-based solution, outlines the deployment methodology, and proposes an enhanced monitoring system optimized (...) for high-volume environments. (shrink)

The continuous growth of healthcare data has made it essential to develop efficient systems that not only alert healthcare providers but also visualize patient data in a comprehensible way. This study introduces a Health Alert System integrated with Report Visualization powered by Data Analytics to improve patient monitoring and alerting mechanisms. By leveraging real-time data from wearable sensors and hospital records, the system generates health alerts based on deviations from normal parameters. 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)

The project centers on the development and deployment of live digital twins for power plants, designed to seamlessly integrate with existing monitoring and database systems. These digital twins act as dynamic, real-time virtual replicas of physical power plants, capturing the intricate details of plant operations, infrastructure, and environmental interactions. The primary objective is to leverage this technology to enhance the construction, operation, and maintenance phases of power plant management. By continuously receiving real-time data from various operational sensors, equipment, and (...) external monitoring sources, the digital twin models can simulate real-world conditions, providing a holistic view of plant performance. This includes critical output parameters such as energy generation, spill rates, and head loss, particularly in the context of hydroelectric plants. The digital twins serve as a powerful tool for optimizing energy production, identifying potential issues before they escalate, and facilitating proactive maintenance strategies, thereby minimizing downtime and maximizing efficiency. Furthermore, these models allow for a more informed and data-driven approach to plant management, ensuring that every aspect of plant performance, from structural integrity to environmental impact, is closely monitored and analyzed. The integration of digital twin technology into operational workflows enables plant operators and engineers to make smarter, more efficient decisions based on real-time, actionable insights. This project not only aims to improve the day-to-day functionality of power plants but also sets the stage for future advancements in power plant technology, contributing to the broader goals of sustainability, energy efficiency, and the long-term viability of energy infrastructure. By embracing this innovative approach, the project seeks to usher in a new era of smarter, more sustainable power plant management. (shrink)

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

The current increase of air pollution rates and its consequences of global warming, health problems and threats to human lives necessitate the continuous search for more efficient and cost effective gas monitoring devices. Devices that is easily available and implementable worldwide. In developing countries the cost and the availability of the equipment is one of the obstacles that contribute to not having an efficient monitoring system. Hence, modifying cheap and easily available devices to work as pollution monitoring (...) system will be an asset in this direction. In this work I am presenting the design of a computer based pollution detection and measuring system using cheap CO (Carbon Monoxide) alarms used in homes. The outcome is a cost-effective computer based CO monitoring system that is easily available and could be easily modified to work as a network based CO nose. A C# based program is also designed to show and log the sensor readings in an easy to use and read Window. (shrink)

Vibration, especially basic vibration, may cause loose contacts, open circuits, or other contact problems, which account for a large proportion of system failure causes. Due to the complexity of the chassis structure used in the biomedical motors, the vibration analysis of a single component cannot solve the vibration problems encountered in the work of the system. Therefore, it is necessary to use system simulation and experiments to monitor and enhance the health of the structure. Finite element method is used to (...) carry out the dynamic analysis from the component to the system, and calculate the natural frequency, modal shape and harmonic response. Then, the effectiveness and accuracy of the analysis results are verified by experiments through simulation and experiments. The results show that the measures taken have effectively reduced the vibration of the chassis equipment and improved its safety for better health safety of patients. There is just a 2% variation between the natural frequencies acquired from the experiment and the simulation computation. In terms of natural frequency, the first 6-order natural frequencies all have errors of less than 10%, and the natural frequency error is comparable to that of fireproof board. (shrink)

This study analyzed the monitoring and evaluation of HIV/AIDS prevention programs in Southern Senatorial District of Cross River State, Nigeria. The study considered different levels of care/support and tested for locational variations in the monitoring/evaluation of HIV/AIDs prevention programs. A descriptive survey research design was utilized. This study covered 596 public health employees (doctors, nurses, pharmacists, and laboratory employees) in the study area. A sample of 239 respondents was chosen using the proportional stratified random sampling procedure. Data was (...) collected using a questionnaire constructed by the researchers and validated by specialists. Data gathered were analyzed using descriptive and inferential statistics. Results indicated that HIV/AIDs programs have been successfully monitored/evaluated to a high extent. High rates of success in the monitoring/evaluation of HIV/AIDs prevention programs are attributable to high rates of care/support provided to people living with HIV/AIDs. There was a significant variation in the monitoring/evaluation of HIV/AIDs prevention programs based on the location of health facilities, with higher rates recorded for urban areas. Based on the findings, it was concluded that the monitoring/evaluation of HIV/AIDs prevention programs in the Southern Senatorial District of Cross River State had recorded a significant level of success. The study recommended, among others, that there should be even distribution of medical facilities, resources and personnel to both urban and rural areas to promote equity and access to materials needed to contain or mitigate the spread of the pandemic across all locations. (shrink)

Closed-Circuit Television (CCTV) systems are essential in modern security setups because they provide continuous surveillance, acting as both a deterrent and a critical tool for monitoring and evidence collection. Unlike human guards who can be limited by fatigue and blind spots, CCTV cameras offer consistent, 24/7 coverage of key areas. They fill gaps in the current security system by enabling real-time monitoring and recording incidents for later review, ensuring that potential security breaches are detected and addressed more effectively. (...) This enhances overall security effectiveness and reduces the reliance on human intervention. The integration of Artificial Intelligence and Machine Learning (AIML) techniques with existing CCTV networks presents a promising approach to address critical challenges in urban environments. This project examines how AIML can be leveraged for crowd management, crime prevention, and work monitoring using CCTV infrastructure. For crowd management, AIML enables automated crowd counting and density estimation, facilitating efficient allocation of resources during events and emergencies. In crime prevention, AIML algorithms analyze video feeds in real-time to detect suspicious activities and identify anomalies, aiding law enforcement in proactive interventions. Additionally, AIML enhances workplace monitoring by tracking productivity metrics, ensuring compliance with safety protocols, and optimizing operational workflows. Through these applications, AIML empowers cities and organizations to improve public safety, enhance operational efficiency, and make data-driven decisions based on insights derived from CCTV footage. The integration of AIML with existing CCTV networks represents a transformative advancement in urban surveillance and management practices, offering scalable solutions for diverse urban challenges. (shrink)

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.