Search Results (4,352)

With the escalating global cyber threats, Distributed Denial of Service (DDoS) attacks have become one of the most disruptive and prevalent network attacks. Traditional DDoS detection systems face significant challenges due to the unpredictable nature, diverse protocols, and coupled behavioral patterns of attack traffic. To address this issue, this paper proposes a novel approach for DDoS attack detection by leveraging the Transformer architecture to model both temporal dependencies and behavioral patterns, significantly improving detection accuracy. We utilize the global attention mechanism of the Transformer to effectively capture long-range temporal correlations in network traffic, and the model’s ability to process multiple traffic features simultaneously enables it to identify nonlinear interactions. By reconstructing the CIC-DDoS2019 dataset, we strengthen the representation of attack behaviors, enabling the model to capture dynamic attack patterns and subtle traffic anomalies. This approach represents a key contribution by applying Transformer-based self-attention mechanisms to accurately model DDoS attack traffic, particularly in handling complex and dynamic attack patterns. Experimental results demonstrate that the proposed method achieves 99.9% accuracy, with 100% precision, recall, and F1 score, showcasing its potential for high-precision, low-false-alarm automated DDoS attack detection. This study provides a new solution for real-time DDoS detection and holds significant practical implications for cybersecurity systems. Full article

Electrical energy supply services are characterised by unpredictable risks that affect both distribution network operators (DSOs) and electricity consumers. This paper presents an innovative cooperative game theory (GT) framework to enhance electric energy supply reliability and demand-side flexibility by aligning the interest of DSOs and consumers. The research investigates the performance of the proposed GT model under different distribution network (DN) topologies and fault intensities, explicitly considering outage durations and restoration times. A cooperation mechanism based on penalty compensation is introduced to simulate realistic interactions between DSOs and consumers. Simulation results confirm that adaptive cooperation under this framework yields significant reliability improvements of up to 70% in some DN configurations. The GT-based approach supports informed investment decisions, improved stakeholder satisfaction, and reduced risk of service disruptions. Findings suggest that integrated GT planning mechanisms can lead to more resilient and consumer-centred electricity distribution systems. Full article

Robotic systems play an increasingly significant role in both education and industry; however, access to physical robots remains a challenge due to high costs and operational risks. This work presents a training platform based on Digital Twins, aimed at active learning in the control of robotic manipulators, with a focus on the UFACTORY 850 arm. The proposed approach integrates mathematical modeling, interactive simulation, and experimental validation, enabling the implementation and testing of control strategies in three virtual scenarios that replicate real-world conditions: a laboratory, a service environment, and an industrial production line. The system relies on kinematic and dynamic models of the manipulator, using maneuverability velocities as input signals, and employs ROS as middleware to link the Unity 2022.2.14 graphics engine with the control algorithms developed in MATLAB R2022a. Experimental results demonstrate the accuracy of the implemented models and the effectiveness of the control algorithms, validating the usefulness of Digital Twins as a pedagogical tool to support safe, accessible, and innovative learning in robotic engineering. Full article

Shaft deflection degrades roller alignment and intensifies stress concentration/edge effects at roller-ends and raceway edges, ultimately compromising service performance of tapered roller bearings (TRBs). Therefore, a dynamic model was developed for a TRB subjected to a deflected shaft in which Johnson’s load–deformation relationship was applied to reflect non-uniform cross-sectional structures of the tapered rollers and raceways, viscous damping was integrated into the roller/cage interaction, and friction actions at the raceways and flange areas were treated separately. Then, moment load and angular misalignment of the tapered roller were analyzed under various shaft deflection and operating conditions. Results indicate that tilt angle remains orders of magnitude smaller than skew angle. Shaft deflection amplifies both skew and tilt, and the influence level is proportional to the bearing size. Centrifugal effect primarily affects skew motion, whereas gyroscopic effect mainly influences tilt motion. Axial forces exert greater influence on roller skew than tilt. The flange typically constrains roller skew, whereas both raceways may induce bidirectional tilt/skew motion. Full article

Riparian zones, located at the interface between terrestrial and aquatic systems, are among the most dynamic and ecologically valuable landscapes. These transitional areas play a pivotal role in maintaining environmental health by supporting biodiversity, regulating hydrological processes, filtering pollutants, and stabilizing streambanks. At the core of these functions lie the unique characteristics of riparian soils, which result from complex interactions between water dynamics, sedimentation, vegetation, and microbial activity. This paper provides a comprehensive overview of the origin, structure, and functioning of riparian soils, with particular attention being paid to their physical, chemical, and biological properties and how these properties are shaped by periodic flooding and vegetation patterns. Special emphasis is placed on Mediterranean riparian environments, where marked seasonality, alternating wet–dry cycles, and increasing climate variability enhance both the importance and fragility of riparian systems. A bibliographic study, covering 25 years (2000–2025), was carried out through Scopus and Web of Science. The results highlight that riparian areas are key for carbon sequestration, nutrient retention, and ecosystem connectivity in water-limited regions, yet they are increasingly threatened by land use change, water abstraction, pollution, and biological invasions. Climate change exacerbates these pressures, altering hydrological regimes and reducing soil resilience. Conservation requires integrated strategies that maintain hydrological connectivity, promote native vegetation, and limit anthropogenic impacts. Preserving riparian soils is therefore fundamental to sustain ecosystem services, improve water quality, and enhance landscape resilience in vulnerable Mediterranean contexts. Full article

Vibration loads during deep drilling are one of the main causes of reduced service life of drilling tools and emergency failure of downhole motors. This work investigates the adaptive operation of an original elastic element based on an open cylindrical shell used as part of a drilling shock absorber. The vibration protection device contains an adjustable radial clearance between the load-bearing shell and the rigid housing, which provides the effect of structural nonlinearity. This allows effective combination of two operating modes of the drilling shock absorber: normal mode, when the clearance does not close and the elastic element operates with increased compliance; and emergency mode, when the clearance closes and gradual load redistribution and increase in device stiffness occur. A nonconservative problem concerning the contact interaction of an elastic filler with a coaxially installed shaft and an open shell is formulated, and as the load increases, contact between the shell and the housing, installed with a radial clearance, is taken into account. Numerical finite element modeling is performed considering dry friction in contact pairs. The distributions of radial displacements, contact stresses, and equivalent stresses are examined, and deformation diagrams are presented for two loading modes. The influence of different cycle asymmetry coefficients on the formation of hysteresis loops and energy dissipation is analyzed. It is shown that with increasing load, clearance closure begins from local sectors and gradually covers almost the entire outer surface of the shell. This results in deconcentration of contact pressure between the shell and housing and reduction of peak concentrations of equivalent stresses in the open shell. The results confirm the effectiveness of the adaptive approach to designing shell shock absorbers capable of reliably withstanding emergency overloads, which is important for deep drilling where the exact range of external impacts is difficult to predict. Full article

Existing building management systems face critical limitations in real-time data integration, primarily relying on static models that lack dynamic updates from IoT sensors. To address this gap, this study proposes a novel system integrating MQTT over WebSocket with Three.js visualization, enabling real-time sensor-data binding to Building Information Models (BIM). The architecture leverages MQTT’s lightweight publish-subscribe protocol for efficient communication and employs a TCP-based retransmission mechanism to ensure 99.5% data reliability in unstable networks. A dynamic topic-matching algorithm is introduced to automate sensor-BIM associations, reducing manual configuration time by 60%. The system’s frontend, powered by Three.js, achieves browser-based 3D visualization with sub-second updates (280–550 ms latency), while the backend utilizes SpringBoot for scalable service orchestration. Experimental evaluations across diverse environments—including high-rise offices, industrial plants, and residential complexes—demonstrate the system’s robustness: Real-time monitoring: Fire alarms triggered within 2.1 s (22% faster than legacy systems). Network resilience: 98.2% availability under 30% packet loss. User efficiency: 4.6/5 satisfaction score from facility managers. This work advances intelligent building management by bridging IoT data with interactive 3D models, offering a scalable solution for emergency response, energy optimization, and predictive maintenance in smart cities. Full article

Background: Clinical pharmacy services (CPSs) play a key role in ensuring medication safety, optimizing pharmacotherapy, and improving patient outcomes. While their benefits are well-documented globally, their specific impact within the Saudi healthcare system remains underexplored. Objective: This study aimed to evaluate the impact of pharmacist-led interventions in a tertiary medical center in Saudi Arabia. Methods: A retrospective chart review was conducted at a 1200-bed academic hospital in western Saudi Arabia. Pharmacist interventions documented between 1 January 2023 and 31 December 2023 were analyzed. Interventions were categorized into 13 types, including dosage errors, unavailable medications, and drug–drug interactions. Descriptive statistics were used to summarize the data. Results: A total of 38,143 pharmacist interventions were recorded. Dosage errors accounted for 77.2% (n = 29,584) of interventions, followed by issues with medication availability (6.57%, n = 2519) and incorrect medication orders (4.59%, n = 1761). The most frequently implicated medications were acetylsalicylic acid, enoxaparin, and paracetamol, collectively representing 43.55% of interventions. The highest intervention rates were in the Emergency Department (25.3%, n = 11,050), Oncology Clinics (9.81%, n = 4285), and Male Medical Units (9.43%, n = 4119). Conclusions: Clinical pharmacists play a significant role in reducing medication errors and improving patient safety across various specialties. Their targeted interventions optimize therapeutic outcomes, highlighting the need for integrating advanced tools and expanding CPSs to meet evolving healthcare demands in Saudi Arabia. Full article

Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber–physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bilevel formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed graph using nodal pressure evolution and edge-based Weymouth-type relations, including control-aware equipment such as valves and compressors. An extended Kalman filter estimates the full network state from partial SCADA telemetry. The controller computes pressure-safe control inputs via MPC under actuator constraints and forecasted demands. Adversarial manipulation is formalized as a bilevel optimization problem where an attacker perturbs sensor data to degrade throughput while remaining undetected by bad-data detectors. This attack–control interaction is solved via Karush–Kuhn–Tucker (KKT) reformulation, which results in a tractable mixed-integer quadratic program. Test gas pipeline case studies demonstrate the covert reduction in service delivery under attack. Results show that undetectable attacks can cause sustained throughput loss with minimal instantaneous deviation. This reveals the need for integrated detection and control strategies in cyber–physical infrastructure. Full article

Wheat (Triticum aestivum L.) is a vital food crop for majority of the world’s population. However, its yield potential is significantly threatened by insect pests, which adversely affect production, quality, and overall food security. The diverse array of insect pests throughout wheat’s growth stages necessitates a comprehensive understanding of their interactions with wheat cultivation. This review critically assesses the diversity, biology, ecology, and management strategies of major insect pests in North India, including aphids, termites, pink stem borer, gram pod borer, armyworm, and brown wheat mite. These pests infest wheat at various growth stages, posing significant challenges to sustainable production. Moreover, existing pest control strategies are challenged by evolving agronomic practices in the region and climate change globally. As agricultural systems worldwide aim for sustainability and resilience in the face of climate change, this review advocates for the adoption of an integrated pest management (IPM) approach combining innovative and traditional pest control strategies to enhance ecosystem services and fortify the resilience of agricultural systems. By interlinking these pivotal elements, this review presents a valuable perspective on the important pests affecting wheat and the currently used IPM practices, emphasizing the need for adaptive management in the context of evolving climate challenges. Full article

In higher education, institutional quality is traditionally assessed through metrics such as academic programs, research output, educational resources, and community services. However, it is important that their activities align with student expectations, particularly in relation to interactive learning environments, learning management system interaction, curricular and co-curricular activities, accessibility, support services and other learning resources that ensure academic success and, jointly, career readiness. The growing popularity of student engagement metrics as one of the key measures to evaluate institutional efficacy is now a feature across higher education. By monitoring student engagement, institutions assess the impact of existing resources and make necessary improvements or interventions to ensure student success. This study presents a comprehensive analysis of student feedback from the StudentSurvey.ie dataset (2016–2022), which consists of approximately 275,000 student responses, focusing on student self-perception of engagement in the learning process. By using classical topic modelling techniques such as Latent Dirichlet Allocation (LDA) and Bi-term Topic Modelling (BTM), along with the advanced transformer-based BERTopic model, we identify key themes in student responses that can impact institutional strength performance metrics. BTM proved more effective than LDA for short text analysis, whereas BERTopic offered greater semantic coherence and uncovered hidden themes using deep learning embeddings. Moreover, a custom Named Entity Recognition (NER) model successfully extracted entities such as university personnel, digital tools, and educational resources, with improved performance as the training data size increased. To enable students to offer actionable feedback, suggesting areas of improvement, an n-gram and bigram network analysis was used to focus on common modifiers such as “more” and “better” and trends across student groups. This study introduces a fully automated, scalable pipeline that integrates topic modelling, NER, and n-gram analysis to interpret student feedback, offering reportable insights and supporting structured enhancements to the student learning experience. Full article

This study investigates the effectiveness of virtual simulations in developing intercultural competence (IC) among pre-service teachers in Israel. In the context of cultural diversity and historical conflict, 283 Arab and Jewish students participated in a course utilizing either a 3D virtual world (VW) or video conferencing via Zoom. The mixed-methods research combined quantitative surveys with qualitative interviews. Quantitative results showed a modest but significant increase in intercultural competence for both groups, with no significant difference between VW and Zoom platforms. Qualitative findings revealed that while VW participants faced more technical challenges, they also reported more opportunities for empathy development and cross-cultural understanding. Both groups valued the exposure to different cultures and expressed a desire for face-to-face encounters. This study highlights the potential of virtual simulations in fostering IC, emphasizing the importance of collaborative problem-solving and intercultural interaction, regardless of the specific digital platform used. These findings contribute to the growing body of research on technology-mediated intercultural education and offer insights for designing effective virtual learning environments in multicultural contexts. Full article

Developing secure and well-structured web applications using Spring Boot presents a significant challenge, as it requires developers to manage multiple layers, employ framework-specific annotations, and ensure authentication, authorization, and compliance with architectural standards. These complexities often lead to errors among students and novice developers. Although current low-code platforms reduce coding effort, they frequently compromise clarity, modularity, and maintainability. This paper introduces XBoot, a lightweight framework that utilizes a straightforward XML-based domain-specific language (DSL) to automatically generate modular and secure Spring Boot applications. By providing concise specifications for entities, services, routes, and user roles, XBoot generates database entities, service classes, controllers, user interface templates, and integrated security rules. Validation rules are directly enforced from the DSL, and built-in Swagger documentation facilitates interactive API testing. The evaluation was conducted in two phases. Initially, XBoot was validated by generating applications for student–course and flight-booking domains, where less than 50 lines of DSL resulted in 950–1350 lines of Java and HTML code, complete with security and documentation. Subsequently, 10 undergraduate students utilized XBoot in practice. All participants successfully generated and deployed applications within 2–20 min (average ≈ 7), compared to 45–120 min for manual implementation. On a 5-point Likert scale, students rated the reinforcement of layered architecture at an average of 4.0. These findings suggest that XBoot effectively eliminates common structural and security errors, reduces boilerplate complexity through concise DSL specifications, and maintains modularity and transparency-limitations often observed in traditional coding and other low-code platforms. Full article

Manuscript 50 (46) from the library of the monastery of Putna contains a text entitled “Discourse on the appropriate manner of standing in the church.” The first part explains the Eucharistic liturgy, from the vesting of the priest to the moment before the epiclesis. The service is dramatized as an interaction between the priest and an angel of God, who later enters a battle with a demon that distracts the congregation. The second part of the text consists of the vision of the monk who lost his faith in the Eucharist. At the prayers of the community, he receives a revelation of the reality of the liturgical mystery, in which he is shown a child slaughtered on the altar table. The visionary text in the first section is part of a tradition attested in the Slavonic environment of the Balkans, which later became popular in the Russian world. These Slavonic versions are based on a similar visionary text attested in Greek manuscripts, but the similarities are only partial. The present study places the text from the Putna manuscript in relation to the iconography of the liturgical space and highlights the relevance of this type of literature for understanding the local monastic culture. Full article

In recent years, the convenience and potential for information extraction offered by Remote Sensing Image–Text Retrieval (RSITR) have made it a significant focus of research in remote sensing (RS) knowledge services. Current mainstream methods for RSITR generally align fused image features at multiple scales with textual features, primarily focusing on the local information of RS images while neglecting potential semantic information. This results in insufficient alignment in the cross-modal semantic space. To overcome this limitation, we propose a Multi-Scale Semantic-Aware Remote Sensing Image–Text Retrieval method (MSSA). This method introduces Progressive Spatial Channel Joint Attention (PSCJA), which enhances the expressive capability of multi-scale image features through Window-Region-Global Progressive Attention (WRGPA) and Segmented Channel Attention (SCA). Additionally, the Image-Guided Text Attention (IGTA) mechanism dynamically adjust textual attention weights based on visual context. Furthermore, the Cross-Modal Semantic Extraction Module (CMSE) incorporated learnable semantic tokens at each scale, enabling attention interaction between multi-scale features of different modalities and the capturing of hierarchical semantic associations. This multi-scale semantic-guided retrieval method ensures cross-modal semantic consistency, significantly improving the accuracy of cross-modal retrieval in RS. MSSA demonstrates superior retrieval accuracy in experiments across three baseline datasets, achieving a new state-of-the-art performance. Full article