Breakdown Of System Research Articles

Machine Learning Techniques for Robust Security in Wireless Sensor Networks

Abstract: The security of wireless sensor networks (WSNs) is critical for ensuring the reliability and accuracy of real-time monitoring systems. A key vulnerability in WSNs is the risk of system breakdown or erroneous decision-making, which can lead to adverse consequences. Given the sensitivity of data handled by these networks, robust protection against various attacks and intrusions is imperative. However, existing security algorithms often fall short when applied to large-scale WSNs due to challenges such as high energy consumption, limited throughput, and excessive computational overhead. To address these challenges, this research introduces an intelligent middleware layer designed to enhance security in WSNs. The proposed solution utilizes Generative Adversarial Networks (GANs), an unsupervised machine learning technique, featuring generator (G) and discriminator (D) networks working adversarially to enhance WSN security. This intelligent middleware acts as a protective layer between WSNs and end-users, addressing vulnerabilities and mitigating threats. The approach ensures robust security while maintaining energy efficiency and scalability for real-world applications. By integrating advanced machine learning techniques, this research establishes a foundation for creating resilient and trustworthy wireless sensor networks capable of handling modern challenges effectively.

Construction of a risk assessment and prediction model for athlete doping use based on bioinformatics

A suitable approach to identifying doping behavior among athletes is to use advanced techniques. Bioinformatics can analyze large biological databases. It has potential approaches for mapping out decision models. Doping substances can severely distort an athlete’s biomechanical performance. For example, stimulants may enhance short-term power output but disrupt the natural rhythm and coordination of muscle contractions, leading to imbalanced forces and increased risk of musculoskeletal injuries. This abnormal biomechanical loading can affect joint stability and movement efficiency. n training, doping gives a false impression of enhanced capacity. Athletes might overtrain, ignoring proper recovery periods. Their bodies, under the influence of doping, can’t follow the normal adaptive process of training, leading to a breakdown in the physiological systems. Recovery is also hampered. Doping can disrupt the body’s hormonal and metabolic balance, slowing down tissue repair and regeneration. Genetic predispositions, which might make an athlete more receptive to doping’s effects, along with lower recovery rates and high competitive stress levels, are identified as key doping risk factors. Bioinformatics collects multi-source data like genomic profiles, hormone levels, and metabolic markers. Advanced tools analyze these to expose patterns and correlations related to doping risks. Machine learning trains a prediction model using historical doping data and biological signatures. Validated via simulations and real-world tests, it predicts doping risks. Sports authorities can use the resulting risk matrix to detect potential dopers early, promoting clean sports.

Remembering Anne Frank: Marjorie Agosín’s Multidirectional Feminist Postmemory

Abstract Chilean Jewish author, professor, and human rights activist, Marjorie Agosín ties her own fate to that of Anne Frank through a multi-genre literary alignment spanning twenty years. The article traces the mechanisms by which Agosín engages the social, political, and cultural preconditions for catastrophe, whereby systemic violations against humanity are effected through a precursing systemic breakdown of meaning and human connection. Engaging contemporary scholarship on trauma memory-with trauma defined as the psychic and physical effects of catastrophe-this article approaches the intersection of his­torical fact and transnational memory in Agosín’s work, understood here as “feminist mul­tidirectional postmemory.”

Challenges in Wastewater Treatment Plant

Effective wastewater operation and maintenance are critical for ensuring sustainable water management and protecting public health and the environment. However, these systems face numerous challenges, including equipment durability issues, frequent breakdowns in treatment systems, regulatory compliance pressures, and the impacts of climate change. Financial constraints, such as limited budgets and the high costs of advanced technologies, further exacerbate these issues, while human resource challenges, such as skill gaps and inadequate training, hinder operational efficiency. This research paper explores these challenges in detail, drawing on real-world case studies from leading wastewater treatment companies and municipalities worldwide. Innovative solutions, including the adoption of artificial intelligence (AI), the Internet of Things (IoT), and decentralized wastewater treatment systems, are analyzed as potential pathways to address these hurdles. The findings underscore the need for comprehensive strategies that integrate technology, skilled workforce development, and adaptive policy frameworks to improve wastewater management. This paper also highlights the disparities between developed and developing nations in addressing these challenges and provides actionable insights for future improvements.

Cross-Sections for Projectile Ionization, Electron Capture, and System Breakdown of C5+ and Li2+ Ions with Atomic Hydrogen

For many disciplines of science, all conceivable collisional cross-sections and reactions must be precisely known. Although recent decades have seen a trial of large-scale research to obtain such data, many essential atomic and molecular cross-section data are still missing, and the reliability of the existing cross-sections has to be validated. In this paper, we present projectile ionization, electron capture, and system breakdown cross-sections in carbon (C5+) ions and lithium (Li2+) ion collisions with atomic hydrogen based on the Monte Carlo models of classical and quasi-classical trajectories. According to our expectation, the QCTMC results show higher results in comparison to standard CTMC data, emphasizing the role of the Heisenberg correction constraint, especially in the low-energy regime. On the other hand, at high energy, the Heisenberg correction term has less influence as the projectile momentum increases. We present the total cross-sections of projectile ionization, electron capture, and system breakdown in C5+ ions and Li2+ ion collisions with atomic hydrogen in the impact energy range from 10 keV to 160 keV, which is of interest in astrophysical plasmas, atmospheric sciences, plasma laboratories, and fusion research.

Gaussian Derivative Change-point Detection for early warnings of industrial system failures

An early warning of future system failure is essential for conducting predictive maintenance and enhancing system availability. This paper introduces a three-step framework for assessing system health to predict imminent system breakdowns. First, the Gaussian Derivative Change-Point Detection (GDCPD) algorithm is proposed for detecting changes in the high-dimensional feature space. GDCPD conducts a multivariate Change-Point Detection (CPD) by implementing Gaussian derivative processes for identifying change locations on critical system features, as these changes eventually will lead to system failure. To assess the significance of these changes, Weighted Mahalanobis Distance (WMD) is applied in both offline and online analyses. In the offline setting, WMD helps establish a threshold that determines significant system variations, while in the online setting, it facilitates real-time monitoring, issuing alarms for potential future system breakdowns. Utilizing the insights gained from the GDCPD and monitoring scheme, Long Short-Term Memory (LSTM) network is then employed to estimate the Remaining Useful Life (RUL) of the system. The experimental study of a real-world system demonstrates the effectiveness of the proposed methodology in accurately forecasting system failures well before they occur. By integrating CPD with real-time monitoring and RUL prediction, this methodology significantly advances system health monitoring and early warning capabilities.

Excess Mortality Estimation

Estimating the mortality associated with a specific mortality crisis event (for example, a pandemic, natural disaster, or conflict) is clearly an important public health undertaking. In many situations, deaths may be directly or indirectly attributable to the mortality crisis event, and both contributions may be of interest. The totality of the mortality impact on the population (direct and indirect deaths) includes the knock-on effects of the event, such as a breakdown of the health care system, or increased mortality due to shortages of resources. Unfortunately, estimating the deaths directly attributable to the event is frequently problematic. Hence, the excess mortality, defined as the difference between the observed mortality and that which would have occurred in the absence of the crisis event, is an estimation target. If the region of interest contains a functioning vital registration system, so that the mortality is fully observed and reliable, then the only modeling required is to produce the expected deaths counts, but this is a nontrivial exercise. In low- and middle-income countries it is common for there to be incomplete (or nonexistent) mortality data, and one must then use additional data and/or modeling, including predicting mortality using auxiliary variables. We describe and review each of these aspects, give examples of excess mortality studies, and provide a case study on excess mortality across states of the United States during the COVID-19 pandemic.

The mass person and their speech roles of a simpleton, a buffoon, a mentor in the Russian prose of the 1920s

The scientific review deals with the understanding of the image of the mass person in the literature of the 1920s. The focus of our attention includes both the actual literary texts of the writers of this decade, as well as fundamentally important studies devoted to this problem. Historical-typological and comparative methods are chosen as leading research technologies. They allow us to study the subjective organization of prose works and create a scientific classification of the most representative phenomena in this field. Modern achievements in narrative theory are taken into account. It is widely known that in the revolutionary breakdown of the entire symbolic system of being the ratio of everydayness and non-everydayness has changed. The role of the public dimension increased, human life went out onto the street, the square, and there was a tilt towards the collectivist "we". An ordinary person found their voice and inevitably became, to some extent, a historical "figure". For writers, this person was of interest as a subject of consciousness and a subject of speech. The range of speech roles of the mass person is quite large. There is a simpleton, and a buffoon, and a joker, and a performer, and a cheat, and a singer, and a limited person, and a fool, and a teaching mentor, and a narrow-minded judge. A mass person represents himself/herself in texts in different ways: 1) in the individual lines assigned to him/her by the author, 2) in detailed monologues, and, finally, 3) in prose. In the latter case comically-colored and holistic forms of fantasy-narrative became extremely active: there was a demand for a narrator figure capable of replacing the traditional figure of an "omniscient" narrator close to the author. This narrator completely subordinates the narrative, and the phraseological point of view of the character who initiated the storytelling event begins to dominate the text. The scientific review deals with the works of M.M. Zoshchenko, P.S. Romanov, A.N. Tolstoy, V.Ya. Shishkov, M.Ya. Kozyrev, Y.L. Slezkin. At the same time, attention is paid to such opposite speech reflections of the mass person as a harmless buffoon who turns every thing and every being around themselves into a theater of life, and a severely instructive mentor, distinguished by speech aggression. All these examples show that the traditional "little person" (familiar from previous literature and becoming a "mass person" in new historical conditions and gaining the status of the initiator of storytelling) does not at the same time acquire proper personal development and reveals his or her stereotypicality and shallowness.

Electrical faults classification in permanent magnet synchronous motor using ResNet neural network

AbstractThe predictive maintenance of permeant magnet synchronous motor is highly required as this kind of motor has been commonly employed in electric vehicles, industrial systems, and other applications owing to its high power density output, as well as the regenerative operation characteristics during braking and deceleration driving conditions. One of the most important causes of PMSM failure is the stator short and drive switches failure. These problems have attracted more attention in the field of deep learning for fault detection purposes in the early stages, to avoid any system breakdown, and to decrease the risk and price of maintenance. In this paper, we investigate the possibility of detecting the electrical faults in PMSM by generating our data which includes current signals that have been analyzed and preprocessed by applying Continuous Wavelet Transform (CWT) to select the reliable features this conversion will be used to train ResNet 50. The evaluation metrics have shown that ResNet 50 achieves an accuracy of 100% for the classification of faults.

Fading ergodicity

Eigenstate thermalization hypothesis (ETH) represents a breakthrough in many-body physics since it allows us to link thermalization of physical observables with the applicability of random matrix theory (RMT). Recent years were also extremely fruitful in exploring possible counterexamples to thermalization, ranging, among others, from integrability, single-particle chaos, many-body localization, many-body scars, to Hilbert-space fragmentation. In all these cases the conventional ETH is violated. However, it remains elusive how the conventional ETH breaks down when one approaches the boundaries of ergodicity, and whether the range of validity of the conventional ETH coincides with the validity of RMT-like spectral statistics. Here we bridge this gap and we introduce a scenario of the ETH breakdown in many-body quantum systems, dubbed fading ergodicity regime, which establishes a link between the conventional ETH and nonergodic behavior. We conjecture this scenario to be relevant for the description of finite many-body systems at the boundaries of ergodicity, and we provide numerical and analytical arguments for its validity in the quantum sun model of ergodicity-breaking phase transition. For the latter, we provide evidence that the breakdown of the conventional ETH is not associated with the breakdown of the RMT-like spectral statistics. Published by the American Physical Society 2024

Spanish (Neo-)Party Press: The Case of the Digital Native Newspaper La Última Hora and Podemos

ABSTRACT This paper argues that the traditional party press model is currently making a comeback, due to social polarisation, digital technologies and the online-only press, whose partisan and ideological nature provides a favourable context for media outlets acting at the service of a specific political party. In Spain, the consolidation of the online-only press has coincided with the breakdown of the two-party system, leading to the appearance of La Última Hora Noticias (LUH), a digital native newspaper that defines itself as independent, but whose discourse is identifiable with the position of political party Podemos, in what can be regarded as a sort of “neo-party press”. This paper aims to shed light on this trend by analysing LUH in terms of the characteristics of the traditional party press, the topics that it covers, the frequency with which it mentions Podemos and its leaders and how it represents them, and the different parties that it defends or criticises. The results reveal that LUH uses party-press techniques, clearly identifies with Podemos’ political interests, and resorts to formulas that are closer to propaganda than to political journalism.

Current State of Scientific Knowledge on Curcumin Encapsulation and Applications.

The yellow pigment curcumin has long been used in traditional medicine for its anti-inflammatory, antibacterial and antioxidant activities. Over the past half-century, scien-tific investigations have shown that curcumin is endowed with additional health benefits be-cause it can modify key molecular targets associated with a number of pathologies, such as diabetes, cancer, and arthritis, in addition to cardiovascular, multiple sclerosis, Alzheimer's, and Crohn's diseases. However, this molecule has several disadvantages, such as low bioavail-ability and solubility, severe oxidative destruction, light sensitivity, fast systemic clearance and breakdown at alkaline pH levels. To address these drawbacks, several methods of micro-encapsulation employing a variety of shell materials have been investigated. These techniques contributed toward the increase of curcumin's solubility and stability against heat, light, oxy-gen, and an alkaline pH. The various shell materials and methods used to microencapsulate this chemical are the main topics of this review. The use of microencapsulated curcumin in food, medicine, and cosmetics is also discussed in more detail. Recent relevant research from the last few years has been given in this area, along with future difficulties.

Immune-Molecular Link between Thyroid and Skin Autoimmune Diseases: A Narrative Review.

Autoimmune skin disorders, including Psoriasis, Lichen Planus, Vitiligo, Atopic Dermatitis, and Alopecia Areata, arise from a combination of genetic predisposition, external factors, and immunological dysfunction. It is well-documented that there is a strong correlation between autoimmune thyroid diseases and a range of dermatological disorders, especially urticaria. This review investigates possible links between autoimmune thyroiditis and a broader spectrum of autoimmune skin conditions, analyzing shared genetic markers, immunological mechanisms, and clinical correlations. Common pathogenic mechanisms include disrupted immune tolerance and oxidative stress, leading to chronic inflammation. Genetic factors, such as IL-23 receptor gene variants, increase the risk for Psoriasis, Alopecia Areata, and Hashimoto's thyroiditis. Additionally, CTLA-4 mutations enhance susceptibility to autoimmune thyroid and skin disorders. Shared genetic susceptibility was also reported in Lichen Planus and Vitilgo, even if different genetic loci might be involved. The breakdown of the immune system can determine a pro-inflammatory state, facilitating the development of autoimmunity and auto-antibody cross-reactions. The presence of similar antigens in skin cells and thyrocytes might explain why both tissues are affected. The significant overlap between these conditions emphasizes the necessity for a comprehensive diagnosis workup and treatment. Future research should focus on clarifying specific immunological pathways and identifying novel biomarkers.

INCREASING THE STABILITY OF ORGANISATIONAL AND TECHNICAL SYSTEMS THROUGH THE USE OF MACHINE LEARNING

The stability of technical and organisational-technical systems is an integral factor in the functioning of a wide range of complex technical and organisational-technical systems, such as energy systems, industrial facilities, transport systems, and others. While it depends on various factors, there are currently no means to support decision-making for automated dispatch control systems users in case of failures. The article analyses the effects of failures and breakdowns in complex technical and organisational-technical systems. It provides examples of major technogenic disasters. The study examines the human factor influence on the emergence of technogenic disasters. Currently, automated dispatch control systems do not have any means to facilitate decision-making by providing the dispatcher with a clear plan of action to troubleshoot and eliminate a possible disaster. The authors propose a concept of support means for decision-making in response to crises using automated dispatch systems for managing complex organisational-technical systems based on machine learning and artificial intelligence. The means is based on machine learning methods and has a two-component structure: an artificial neural network that defines the type of failure and a programme component that selects the algorithm. The neural network is implemented in the Python programming language, and the user interface is in the C# language. The neural network fulfils the classification task defining the failure based on an array of logical variables that demonstrate the change of state of system elements. The programme component uses the database created with SQL language to define the algorithm of actions for the dispatcher. The authors describe the dataset for the model in case of its use in energy systems. There is also a description of the model components. The model’s distinguishing features are adaptivity due to machine learning methods and the possibility of further model training to increase the classification accuracy based on its use cases. Keywords: machine learning, automated dispatch control systems, decision-making support, system stability.

The recovery strategy of interdependent networks under targeted attacks

To effectively mitigate failures in interdependent systems during targeted-attack scenarios, a common approach is to pre-store repair resources. The question arises: what constitutes an appropriate quantity of these pre-stored repair resources? The paper introduces a novel recovery strategy aimed at providing guidance for this issue. Current recovery strategies frequently emphasize the dynamic interplay between cascading failures and recovery processes, indicating that interventions during the recovery phase are permissible. In this context, the recovery strategy focus on recovering a predetermined number of failed nodes that are adjacent to the largest connected component of each individual network, along with their dependent nodes, at each recovery stage. Simulation results demonstrate that this strategy significantly enhances the capacity to prevent system breakdowns for interdependent networks subjected to targeted attacks. Therefore, by determining the necessary recovery steps to prevent system failures and the appropriate repair resources required for each step, this novel strategy can serve as a valuable reference for the pre-storage of repair resources. Significantly, the strategy can be effectively applied to interdependent networks associated with critical infrastructure, such as power grids and communication networks.

Psychosocial Causes of Crime; Offender as Victim. Post-Soviet Georgian Perspective

This study explores the psychosocial causes of crime, particularly focusing on the unique socio-cultural context of post-Soviet Georgia. It examines how offenders can be both perpetrators and victims due to tough environments. It shows how socio-economic issues and the breakdown of traditional support systems influence criminal behavior. This study explores the experiences and motivations behind criminal behavior among older individuals in post-Soviet Georgia. It aims to understand how the collapse of socio-economic structures and the influence of criminal subcultures contributed to the rise in crime during this period. Using mixed approache, the study combines a thorough literature review with in-depth interviews of 12 men aged 35 to 50, who were convicted of violent crimes in Georgia during and shortly after the collapse of the Soviet Union. The findings suggest that economic hardship, social stigmatization, and the appeal of criminal subcultures were major drivers of criminal behavior. Many participants pointed to tough economic conditions, societal pressures, and a feeling of learned helplessness as key factors behind their actions. The study also highlights the long-lasting effects of early stigmatization and the socio-economic challenges faced during the post-Soviet era. The study concludes that to effectively address criminal behavior, it’s essential to create comprehensive rehabilitation and reintegration programs. These programs should address both psychological and biological factors, provide economic support, build social networks, and offer trauma-informed care. There’s a clear need for targeted interventions that not only prevent crime but also help former offenders reintegrate into society.

A Critical Analysis of Challenges and Prospects of the National Health Insurance Scheme in Zambia-A Case Study of Lusaka District

Zambia’s National Health Insurance (NHI) was established by the enactment of the NHI Act No. 2 of 2018 and supported by statutory instrument No. 63 of 2019 which led to the establishment of the National Health Insurance Scheme (NHIS) under the management of the National Health Insurance Management Authority (NHIMA). The goal of the NHIS was to guarantee the provision of universal health insurance coverage for all residents, regardless of their socioeconomic status. As one of the prospective financing solutions for protecting populations from high health care service prices, NHIS is receiving increased attention from the international community. This study set out to investigate the difficulties and opportunities that the plan faces from the standpoint of the service providers and subscribers in Lusaka, Zambia. The study used the descriptive research design since it seeks to analyze the prospects and challenges of the national health insurance scheme in Lusaka, Zambia. The study came to a conclusion that in Lusaka, Zambia, consumers who wish to utilize the NHIS plan face challenges related to health literacy and awareness, fraud, and corruption, as well as, subpar service quality. The survey also discovered that consumers in Lusaka have difficulties with enrolment and registration while attempting to use the NHIS program due to intermittent availability and constant breakdown of the NHIMA system. The study further came to conclude that some of the prospects for the NHIS plan in Lusaka is public knowledge and involvement, enhancing monitoring and evaluation of the private and public health facilities accredited with NHIMA and improving the internet connectivity of the NHIMA system to enhance service delivery.

Predictive Modeling of Conveyor Belt Deterioration in Coal Mines Using AI Techniques

Conveyor belts are vital for material transportation in coal mines due to their cost-effectiveness and versatility. These belts endure significant wear from harsh operating conditions, risking substantial financial losses if they fail. This study develops five artificial neural network (ANN) models to predict conveyor belt damage using 11 parameters from the Belchatow brown coal mine in Poland. The models target five outputs: number of repairs and cable cuts, cumulative number of repairs and cable cuts, and their ages. Various optimizers (Adam, Nadam, RMSprop, Adamax, and stochastic gradient descent or SGD) and activation functions (ReLU, Swish, sigmoid, tanh, Leaky ReLU, and softmax) were tested to find the optimal configurations. The predictive performance was evaluated using three error indicators against actual mine data. Superior models can forecast belt behavior under specific conditions, aiding proactive maintenance. The study also advocates for the Diagbelt+ system over human inspections for failure detection. This modeling approach enhances proactive maintenance, preventing total system breakdowns due to belt wear.

Do Algorithms Dream of Electric Requirements? Leveraging AI‐Based Approaches for Automated Allocation and Classification of Requirements in Railway Engineering

AbstractIn recent years, Artificial Intelligence has experienced an extraordinary growth. Systems Engineering is a discipline where the implementation of AI can be challenging, but that could immensely benefit from its capabilities. This paper presents one of the many implementations that AI can have within the Systems Engineering field. AI has been leveraged to create an algorithm that allows for the automatic identification and classification of requirements within a specific engineering sector: large railway projects. While text classification algorithms are well established, the key to a successful implementation of a requirements classification algorithm lays on the effective structurization of the data, as well as the high quality of the training datasets. This paper describes how an AI‐based requirements classification algorithm has been planned and trained to effectively classify requirements in future documents based on systems and subsystems from a System Breakdown Structure (SBS), as well as to predict the adequate method of verification for both the Design and Testing and Commissioning stages of a railway project.Finally, the paper showcases how the use of this AI‐based requirements classifier does not only lower the probability of human error, but also reduces ∼75% human workload per project. Additionally, overall ∼30% cost savings to organizations are expected in a 10‐year period in the task of classifying requirements with respect to manual classification performed by subject matter experts.

Optimal DGs coordination strategy for managing unbalanced and islanded distribution networks

Abstract Distributed generators (DGs) have the potential to act as alternative power sources for balancing and restoring service in distribution system breakdowns. Nevertheless, DGs variety of characteristics and uncertain behavior pose difficulties in optimizing their operation while restoring the system. In this paper, a hierarchical structure is introduced to manage the unbalanced and islanded distribution networks with the optimal coordination strategy of DGs. The proposed approach encompasses multiple control tiers that manage DGs coordination process into three specific levels, each accountable for distinct functions involving voltage and frequency regulation, power distribution within individual islands, and overseeing power exchange between these units for minimizing imbalance and maximizing the load restoration. This research delivers a comprehensive solution for optimally coordinating DGs in unbalanced and isolated networks during restoration, thus elevating system reliability and resilience. The proposed model for distributed control restoration is validated using a modified IEEE 123-bus test setup.