Engaging visual media shifts taste-related neural processing: An fMRI study on distracted eating.

BackgroundOccupational distress is currently an underappreciated epidemic in modern work environments, which erodes both productivity and well-being in the general population. Growing evidence suggests that chronic job-related stress disrupts sleep architecture, accelerates cardiometabolic and neurocognitive decline and perpetuates a cycle of diminished resilience.ObjectiveIn this commentary, the authors call for an integrated framework that places sleep as a core dimension of occupational health policy, rather than a mere individual lifestyle choice.MethodsAn extensive literature review was conducted to search for relevant articles using relevant keywords. Articles matching the subject and relevant to occupational distress and sleep crisis were extracted, studied in detail, and included in the commentary.ResultWe found existing gaps in the literature on occupational distress and sleep crisis, emphasizing the need for wider discussion and generous debate on this subject. Most organizations recommend individual-level stress and sleep management to address occupational distress and sleep crisis in workplaces rather than advocating for organizational reforms.ConclusionAs organizations increasingly acknowledge employee wellbeing as a strategic priority, there is an urgent need to reconceptualize recovery and sleep not as personal lifestyle choices but as outcomes shaped by organizational structures, cultures, and policies. By reframing recovery as a shared responsibility, this commentary contributes to ongoing debates about sustainable work, health-protective organizational design, and the ethical obligations of employers in safeguarding employee wellbeing and also seeks to propel both academic inquiry and organizational practice towards structural change rather than mere adaptation.

The increasing complexity of modern enterprise IT environments, encompassing both cloud-based and on-premise systems, has made vulnerability management a critical challenge. Traditional approaches are often manual, reactive, and fragmented, leading to delays in detection and remediation. This paper presents an AI-driven centralized vulnerability management system that automates vulnerability detection, intelligent risk prioritization, remediation orchestration, and continuous monitoring through a unified platform. The proposed system integrates distributed scan engines, workflow automation, and an AI-based analytical module to enhance decision-making. Experimental evaluation demonstrates improved prioritization accuracy, reduced manual effort, and faster remediation cycles, thereby strengthening overall cybersecurity resilience.

This paper explores the structural design and material application of seating systems in science museums, focusing on safety and ergonomic comfort. To address the requirements of modern exhibition environments, the study employs finite element analysis (FEA) and body pressure distribution experiments to evaluate the mechanical performance and user comfort of four distinct seat structures and materials: polyurethane, polyester fiber, polypropylene foam, and memory foam. The investigation analyzes their effects on stress distribution, strain, and pressure relief. Results indicate that while structural design is critical for ensuring uniform stress distribution, material properties primarily dictate comfort and pressure alleviation. Specifically, memory foam and polyurethane demonstrate the optimal balance between comfort and load-bearing capacity, whereas polypropylene presents risks of localized stress concentration. Consequently, the study proposes a "structural optimization coupled with material matching" strategy, utilizing rigid materials for load-bearing and flexible materials for comfort interface. These findings provide practical guidance for the engineering design and material selection of public seating in science museums.

This study extends the conventional skip-lot sampling (SkSP) framework by proposing a two-level skip-lot sampling plan (SkSP-2L) based on variables inspection using the process capability index Cpk. While the traditional SkSP-2L, rooted in attributes inspection, has demonstrated efficiency in reducing the average sample number (ASN), it becomes increasingly impractical in modern high-quality manufacturing environments where defect rates are extremely low. In such contexts, attributes-based methods often require prohibitively large sample sizes to ensure statistical validity. To overcome this limitation, the proposed variables-based SkSP-2L integrates the widely adopted Cpk index to facilitate more efficient and informative lot evaluation, aligned with contemporary process performance assessment practices. The study outlines the plan’s operating procedure, mathematical formulation, and sensitivity analysis. Comparative evaluations demonstrate the proposed method’s superiority over traditional SkSP-2 and single sampling plans in reducing ASN. A real-world case study further validates its practical applicability in precision-focused manufacturing settings with stringent quality requirements.

Genomic fingerprint of polyethylene-degrading bacteria.

Targeted biocontrol of Escherichia coli O157:H7 biofilms in food environments using the novel bacteriophage CAU_ECP01.

Aim. To consider theoretical substantiation of designing technological environment for environmental education for students, the strategic guidelines of which are the formation of environmental culture of students and their readiness for productive activity in conditions of a high degree of uncertainty and instability in the development of society. Methodology. The research is based on systemic, activity-based, environmental, and technological approaches. Theoretical methods used included analysis of philosophical, psychological, pedagogical, and methodological literature on environmental education and digitalization; modeling of the concept and structural-functional model of the technological environment; and generalization and systematization. Empirical methods included analysis of pedagogical experience and educational and methodological complexes. Results. The concept of the technological design for environmental education for schoolchildren has been developed and presented, revealing its essence as a global and local information and communication infrastructure designed to bridge the gap between awareness of environmental issues and the acquisition of survival technologies. Key characteristics of a technological environment for environmental education for schoolchildren are identified: openness, variability, functional diversity, criticality, high technology, and safety. A theoretical model is proposed that reveals the mechanism by which the modern technological environment transforms the fundamental components of the pedagogical system in environmental education, namely, goal setting, content selection, organization of the educational process, and the assessment system. Research implications. The scientific novelty lies in the development of a technological design for environmental education of schoolchildren. The theoretical significance lies in enriching the theory of environmental education through the conceptual justification of a technological environment as a pedagogical innovation integrating traditional and digital technologies. The practical value lies in the development of methodological principles and tools (including augmented reality teaching aids and immersive learning scenarios) for creating an effective technological environment in general education organizations and institutions of additional education. Conclusions. 1. The methodological foundations of a technological design for environmental education are defined, revealing its essence as an integrative pedagogical phenomenon arising at the intersection of traditional and innovative approaches. 2. A model of technological design is developed and theoretically substantiated, demonstrating its transformative influence on the target, content, process, and evaluative components of the environmental education system for schoolchildren. 3. The design principles of this environment are identified and characterized, and the practical aspects of its implementation are outlined. This, taken together, ensures the development of environmental knowledge, value orientations, critical thinking, and readiness for practical activities in schoolchildren in a rapidly changing world.

Fraud in public financial systems has become a critical challenge due to the rapid expansion of digital government services and the increasing volume of high-frequency financial transactions. Traditional fraud detection mechanisms, which rely on rules and batch processing, are no longer sufficient to address the complexity, speed, and evolving nature of modern fraudulent activities. This study proposes a big data analytics framework for real-time fraud detection in public financial systems by integrating streaming analytics, machine learning algorithms, and distributed computing architectures. The framework enables continuous monitoring of financial transactions, allowing for immediate detection of anomalies and fraudulent behavior with minimal latency. It incorporates supervised learning models for classification, unsupervised anomaly detection techniques for unknown fraud patterns, and hybrid ensemble approaches to improve detection robustness. Additionally, streaming data processing ensures scalability and real-time responsiveness in large-scale government financial infrastructures. The proposed model is expected to enhance detection accuracy, reduce false positives, and strengthen the overall security and transparency of public financial systems. The study contributes to advancing intelligent financial security systems and supports the development of adaptive, scalable, and real-time fraud prevention mechanisms for modern digital governance environments.

Keyloggers are among the most severe forms of malware, designed to silently record keystrokes in order to steal sensitive user information such as passwords, banking credentials, and personal data. With the increasing sophistication of modern malware, traditional signature-based antivirus solutions have become ineffective against stealthy and zero-day keylogger attacks. To address this growing security concern, this project presents an Advanced Keylogger Detection System based on behavioral analysis using Isolation Forest and Long Short-Term Memory (LSTM) models. The proposed system continuously monitors low-level system activities including keystroke dynamics, process execution behavior, system API calls, file access operations, and network communication patterns. Isolation Forest, an unsupervised anomaly detection algorithm, is utilized to identify deviations from normal system behavior and to detect unknown or previously unseen keylogger activities by isolating anomalous behavior patterns. To further strengthen detection accuracy, an LSTM-based deep learning model analyzes sequential and time-series behavioral data to identify persistent and stealthy keylogging activities that evolve over extended periods. By integrating anomaly detection with temporal behavior analysis, the system effectively distinguishes legitimate applications from malicious keylogging processes. Upon detection of suspicious activity, the system generates real-time alerts and initiates automated response mechanisms such as process termination and detailed activity logging for forensic analysis. Experimental evaluation demonstrates that the proposed system achieves improved detection accuracy with reduced false positives, providing a robust and proactive defence mechanism suitable for modern endpoint security environments.

Background: In modern office environments, maintaining adequate air quality is essential for cognitive performance and overall well-being. However, the physiological effects of ventilation (CO2 control) during short daytime breaks, particularly midday naps, remain insufficiently explored. This study aimed to investigate the impact of ventilation on autonomic nervous system (ANS) activity using heart rate variability (HRV) metrics. Methods: A crossover experiment was conducted with six office workers (mean age: 28 ± 2 years). Two conditions were compared: Condition A (with ventilation/CO2 control) and Condition B (without ventilation). The experimental protocol consisted of three phases: Phase 1 (desk work, 11:00–12:00), Phase 2 (nap, 12:00–13:00, including a 20–25 min nap), and Phase 3 (post-nap desk work, 13:00–14:00). HRV indices (SDNN, RMSSD, CVRR, LF, HF, and LF/HF) were calculated from 5-min segments within each phase. Results: A two-way mixed ANOVA revealed a significant main effect of ventilation on the LF/HF ratio during the post-nap phase (p = 0.0050, η2p = 0.9901), indicating improved autonomic stability upon awakening. Furthermore, a three-way mixed ANOVA (Sex × Order × Condition) showed that pNN50, an index of parasympathetic activity, exhibited significant interactions during the nap phase, including Condition × Sex (p = 0.0092) and the three-way interaction (p = 0.0333). Significant Order effects were also observed for heart rate (HR) across all phases (p < 0.05), suggesting habituation to the experimental environment. Conclusions: These findings indicate that ventilation is a critical environmental factor influencing physiological recovery during midday naps. Optimizing air quality may enhance autonomic regulation and improve the restorative effects of short daytime sleep in office settings. These findings should be interpreted as exploratory due to the small sample size (n = 6). While LF/HF was used as an index of autonomic balance, its physiological interpretation remains debated and should be considered with caution.

Abstract Multi-factor authentication (MFA) is widely used to strengthen account access, yet it does not by itself ensure rapid containment once authentication has succeeded. In modern authentication environments, especially enterprise and federated deployments, compromised access may persist across application sessions, identity provider sessions, access and refresh tokens, and remembered trust state. This fragmentation makes post-authentication containment a multi-layer problem rather than a single revocation event. This paper defines the post-authentication containment problem and presents a taxonomy of containment gaps and controls in MFA-protected systems. The taxonomy organizes incident classes and containment mechanisms across session, token, trust-state, and credential layers, and highlights where current controls remain partial, delayed, or dependent on product-specific integration. The paper further identifies user-driven deauthorization as an underdeveloped but potentially valuable containment capability. By clarifying this design space, the paper aims to inform future authentication architecture and support more systematic incident response. Keywords: Multi-factor authentication; post-authentication containment; session revocation; token revocation; federated identity; identity and access management; user-driven deauthorization; incident response

Abstract: The modern business environment is characterized by dynamic changes and digital transformation. Therefore, internal strategic communication plays a crucial role in the successful implementation of changes and transformations. With the increasing complexity of organizations and the growing diversity of employees, the traditional centralized approach with one type of message for all is no longer effective. The hypothesis is that target audience analysis within change management offers different approaches to communication that can transform organizations. The methodology includes several methods. The methods of analy¬sis and synthesis are used, which are relevant in view of the theoretical overview and presentation of a model that can be implemented in a modern business environ¬ment. The descriptive method is applied in deriving the stages for imple¬menting organizational change and achieving proper segmentation of inter¬nal audiences in a business environment. The aim is to explain how stake¬holder iden¬ti¬fication and audience segmentation can be used in John Kotter's organiza¬tional change model to influence employee engagement, reduce resistance to change, and improve effectiveness. The stages are described with a view to using this model and achieving practical results in business organizations. Attention is paid to business communication as an essential element in the successful imple¬men¬ta¬tion of employee management, motivation, and adaptation in a changing dynamic business environment. Keywords: internal strategic communications, organizational change manage¬ment, target audience analysis, stakeholder analysis, employee engagement.

ABSTRACT Cloud infrastructure governance increasingly depends on the ability to enforce measurable service-level objectives (SLOs)—the contractual commitments that underpin availability, performance, and operational resilience in modern enterprise environments. When SLOs are violated, organizations face compounding consequences: service disruptions, compliance exposure, audit findings, and degraded trust. Predictive autoscaling offers a proactive control mechanism, but its governance value is realized only when capacity decisions are grounded in calibrated, risk-aware uncertainty rather than deterministic point forecasts. This article introduces SLO-Aware Heteroscedastic LSTM (SLOAH-LSTM), a probabilistic workload forecasting model that embeds asymmetric risk weighting into its learning objective, directing forecast precision where it matters most for compliance. To support auditable and explainable capacity decisions, we evaluate three interpretable decision mappings (mean, quantile, and cost-aware Monte Carlo selection). A simulation framework replicating realistic cloud controller constraints demonstrates our approach reduces SLO violation rates from ≈6.5 percent to ≈3.0 percent with only a moderate increase in provisioned resources (avg pods ≈20 → 25) and a markedly improved combined cost index (≈55 vs. ≈85). These findings establish a practical, low-overhead framework for governing cloud resource allocation with quantifiable compliance guarantees, directly supporting IT audit objectives around availability assurance, risk-aware capacity planning, and the operational integrity of cloud-native enterprise infrastructure.

Environmental, Social, and Governance (ESG) has emerged as an important framework for evaluating corporate responsibility in modern business environments. This review examines the evolution, benefits, limitations, and ethical foundations of ESG while assessing its effectiveness as a tool for measuring responsible corporate conduct. The study adopts a narrative review approach, synthesizing existing literature on ESG, corporate governance, ethical theory, and sustainability frameworks. It traces the development of ESG from socially responsible investing to its integration into contemporary corporate strategies and its growing alignment with global sustainability agendas such as the United Nations Sustainable Development Goals (SDGs). The analysis highlights that ESG practices can enhance corporate governance, financial stability, risk management, and stakeholder trust while supporting long-term value creation and sustainable business strategies. However, several limitations remain, including inconsistencies in ESG rating methodologies, the risk of symbolic compliance and greenwashing, stakeholder skepticism, and structural constraints within shareholder-oriented economic systems. Ethical perspectives further reveal tensions between profit maximization, stakeholder interests, and broader institutional responsibilities in evaluating corporate conduct. The findings suggest that although ESG provides useful guidance for improving transparency and sustainability practices, it remains an incomplete framework for assessing corporate responsibility. Strengthening regulatory frameworks, improving measurement standardization, and integrating ethical reasoning into corporate governance will be necessary to enhance the credibility and effectiveness of ESG initiatives.

ABSTRACT PentestPro is an easy-to-use and powerful cybersecurity tool that automatically scans websites and networks to find vulnerabilities that hackers might exploit. It combines several trusted tools to perform thorough chec checks, uncovering weaknesses such as SQL injection, cross-site scripting, and API misconfigurations. The platform tests both public and private areas to reveal hidden risks and provides clear, actionable guidance on fixing them. With a user -friendly web interface and secure access controls , PentestPro is suitable for businesses of all sizes, helping them improve their security without needing expert knowledge. Designed for modern environments, PentestPro supports continuous security testing by integrating with development workflows, ensuring that security checks keep pace with software updates and evolving threats. Its modul scalable design allows it to fit into cloud deployments,offering detailed reports that help organizations stay compliant with regulations and manage risks effectively. Overall, PentestPro makes cybersecurity accessib accessibl , proactive, and efficient, empowering companies to protect their digital assets and maintain trust in an ever-changing threat landscape. Keywords: Pen Testing, Security, Network Security, Automation, Compliance

Effective and locally supported river restoration requires attention to the social dimensions of rivers. This paper examines local perceptions of hydropower and restoration in the river Rönne å, an early case under Sweden's National Plan for Modern Environmental Conditions for Hydropower. A mixed-methods approach, including a questionnaire and qualitative interviews, explores how inhabitants relate to their river and view the removal of three low-production dams, offering one of the most detailed assessments of stakeholder values in a Swedish hydropower river. The findings reveal important tensions between energy production and ecological restoration: While hydropower retains cultural legitimacy, support declines when ecological costs outweigh energy benefits. The river holds strong recreational, cultural, and symbolic meanings, embedding dam removals in broader sociocultural contexts rather than solely technical or ecological. These findings highlight the importance of participatory efforts and governance that align ecological goals with the diverse ways people relate to rivers.

Context-dependent effects of high-frequency pulsed electromagnetic fields on cytokine responses in human THP-1 monocytes in vitro.

Modern digital infrastructures including enterprise networks, cloud computing platforms, IoT ecosystems, and online service environments continuously generate vast volumes of real-time data such as network packets, authentication logs, and system performance metrics. This rapid data generation necessitates efficient, intelligent, and real-time security monitoring mechanisms. To address these challenges, this work proposes an automated and scalable AI-driven security framework for real-time anomaly detection and authentication threat analysis. Initially, machine learning models such as K-Nearest Neighbor (KNN) and Support Vector Classifier (SVC) are utilized to learn network behavior patterns and distinguish between normal and malicious activities. Although effective, these models exhibit limitations including high computational complexity, sensitivity to feature scaling, and inefficiency in handling large-scale or probabilistic data scenarios. To overcome these constraints, a Naive Bayes Classifier (NBC) is adopted as the primary probabilistic inference model within the framework. By leveraging Bayesian decision theory and modeling conditional feature dependencies, NBC enables efficient estimation of threat probabilities with reduced computational overhead and improved scalability for high-dimensional datasets. The proposed system incorporates data preprocessing, class balancing techniques, multi-model training, and deployment through a web-based Flask interface to facilitate real-time threat detection. Performance evaluation is conducted using standard metrics such as accuracy, precision, recall, and F1-score, demonstrating reliable anomaly detection and classification capabilities. The results validate that the proposed framework provides a robust, scalable, and efficient solution for enhancing real-time security intelligence in modern digital environments.

Artificial intelligence (AI)-driven graduate education and research in pharmacology and pharmaceutical sciences (AIPPS) aims to address the rapidly-growing role of AI and machine learning (ML) applications in biomedical sciences. This review provides perspectives on why and how the next-generation of biomedical scientists equip themselves with skills necessary to integrate AI and ML tools into their current fields of study, particularly pharmacology and pharmaceutical sciences. The AI-enabled approaches discussed in this article highlight opportunities for improving competitiveness in an evolving scientific landscape, that includes academia, pharmaceutical and biotech industries and regulatory science. Furthermore, this review discusses how graduate education and research can be enhanced through training in AI-driven disease prediction, molecular target identification drug design and discovery, drug repurposing and pharmacometric modelling. The knowledge outlined here may help graduate students and early career researchers navigate the challenges associated with applying AI-based methodologies in fundamental research, product and process development, service delivery, and regulatory policy and ethics. Overall, the insights provided in the review aim to support the development of skilled forward-thinking biomedical and pharmaceutical scientists capable of leveraging AI technologies in modern research environments.