Search Results (4,601)

Distributed denial of service (DDoS) attacks pose a serious risk to the operational stability of a network for companies, often leading to service disruptions and financial damage and a loss of trust and credibility. The increasing sophistication and scale of these threats highlight the pressing need for advanced mitigation strategies. Despite the numerous existing studies on DDoS detection, many rely on large, redundant feature sets and lack validation for real-time applicability, leading to high computational complexity and limited generalization across diverse network conditions. This study addresses this gap by proposing a feature-optimized and computationally efficient ML framework for DDoS detection and mitigation using benchmark dataset. The proposed approach serves as a foundational step toward developing a low complexity model suitable for future real-time and hardware-based implementation. The dataset was systematically preprocessed to identify critical parameters, such as packet length Min, Total Backward Packets, Avg Fwd Segment Size, and others. Several ML algorithms, involving Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Cat-Boost, are applied to develop models for detecting and mitigating abnormal network traffic. The developed ML model demonstrates high performance, achieving 99.78% accuracy with Decision Tree and 99.85% with Random Forest, representing improvements of 1.53% and 0.74% compared to previous work, respectively. In addition, the Decision Tree algorithm achieved 99.85% accuracy for mitigation. with an inference time as low as 0.004 s, proving its suitability for identifying DDoS attacks in real time. Overall, this research presents an effective approach for DDoS detection, emphasizing the integration of ML models into existing security systems to enhance real-time threat mitigation. Full article

Digital governance requires transparent, auditable, and secure mechanisms for document circulation across public institutions. Existing workflow management and e-government systems, especially in the legislative field, often lack end-to-end auditability, leaving gaps in accountability and verification. This article introduces a blockchain-based workflow audit application designed to ensure integrity, traceability, and transparency of document exchanges and transitions in central and local administrations. This article presents a solution that oversees the auditing and monitoring of document circulation between different public institutions or within a single institution. The system is based on blockchain technology that stores data and preserves history, making every action traceable and auditable. The process of document creation involves the encryption, timestamping and addition of the document to the blockchain, the access to which is restricted only to authorised stakeholders. The system aims to enhance transparency and accuracy in the presentation of legislative process documents for public consultation. The preliminary prototype was subjected to a validation process by an end-user from the parliamentary legislative authority in Romania. Full article

The increasing global demand for electricity has heightened the need for stable and reliable power distribution systems. Disruptions in power distribution can cause substantial economic losses and societal impact, underscoring the importance of accurate, timely, and scalable monitoring. The integration of Internet of Things (IoT) technologies into smart grids offers promising capabilities for real-time data collection and intelligent control. However, the application of IoT has created new challenges such as high communication overhead and insufficient user privacy protection due to the continuous exchange of sensitive data. In this paper, we propose a method for power distribution analytics in smart grids based on IoT called PSDA. PSDA collects real-time power usage data from IoT sensor nodes distributed across different grid regions. The collected data is spatially organized using Hilbert curves to preserve locality and enable efficient encoding for subsequent processing. Meanwhile, we adopt a dual-server architecture and distributed point functions (DPF) to ensure efficient data transmission and privacy protection for power usage data. Experimental results indicate that the proposed approach is capable of accurately analyzing power distribution, thereby facilitating prompt responses within smart grid management systems. Compared with traditional methods, our scheme offers significant advantages in privacy protection and real-time processing, providing an innovative IoT-integrated solution for the secure and efficient operation of smart grids. Full article

The deployment of deep learning models in Internet of Things (IoT) systems is increasingly threatened by adversarial attacks. To address the challenge of effectively detecting adversarial examples generated by Generative Adversarial Networks (AdvGANs), this paper proposes an unsupervised detection method that integrates spatial statistical features and multidimensional distribution characteristics. First, a collection of adversarial examples under four different attack intensities was constructed on the CIFAR-10 dataset. Then, based on the VGG16 and ResNet50 classification models, a dual-module collaborative architecture was designed: Module A extracted spatial statistics from convolutional layers and constructed category prototypes to calculate similarity, while Module B extracted multidimensional statistical features and characterized distribution anomalies using the Mahalanobis distance. Experimental results showed that the proposed method achieved a maximum AUROC of 0.9937 for detecting AdvGAN attacks on ResNet50 and 0.9753 on VGG16. Furthermore, it achieved AUROC scores exceeding 0.95 against traditional attacks such as FGSM and PGD, demonstrating its cross-attack generalization capability. Cross-dataset evaluation on Fashion-MNIST confirms its robust generalization across data domains. This study presents an effective solution for unsupervised adversarial example detection, without requiring adversarial samples for training, making it suitable for a wide range of attack scenarios. These findings highlight the potential of the proposed method for enhancing the robustness of IoT systems in security-critical applications. Full article

The application of Artificial Intelligence (AI) in river basin pollution control shows great potential to improve governance efficiency through real-time monitoring, pollution prediction, and intelligent decision-making. However, its rapid development also brings regulatory challenges, including data privacy, algorithmic bias, responsibility definition, and cross-regional coordination. Based on the SETO loop framework (Scoping, Existing Regulation Assessment, Tool Selection, and Organizational Design), this paper systematically analyzes the regulatory needs and pathways for AI in watershed water pollution control through typical case studies from countries such as China and the United States. The study first defines the regulatory scope, focusing on protecting the ecological environment, public health, and data security. It then assesses the shortcomings of existing environmental regulations in governing AI, such as their inability to adapt to dynamic pollution sources. Subsequently, it explores suitable regulatory tools, including information disclosure requirements, algorithmic transparency standards, and hybrid regulatory models. Finally, it proposes a multi-tiered organizational scheme that integrates international norms, national legislation, and local practices to achieve flexible and effective regulation. This study demonstrates that the SETO loop provides a viable framework for balancing technological innovation with risk prevention and control. It offers a scientific basis for policymakers and calls for establishing a dynamic, layered regulatory system to address the complex challenges of AI in environmental governance. Full article

Micro and nano-plastics pose a significant threat to the global environment, affecting agricultural systems, food security, and human health. Some studies indicate that microplastics can induce physiological damage in plants, including oxidative stress, reduced germination, stunted biomass growth, and impaired photosynthesis. The extent of the damage varies depending on the type of microplastics, their size, and concentration. Moreover, micro- and nano-plastics can disturb the delicate balance of the soil microbiome. Microbial communities play a significant role in the health and functioning of ecosystems by facilitating nutrient turnover, breaking down organic matter, preserving soil integrity, and controlling diseases caused by soil-dwelling pathogens. This review highlights the role of omics technologies in elucidating the molecular mechanisms underlying plant responses to micro- and nanoplastics. The findings can enhance our comprehension of how micro- and nanoplastics affect agricultural systems when they contaminate soil. Full article

Renewable natural gas is an innovative alternative fuel source that has the potential to integrate seamlessly into the current energy and fuel sector. In addition, growing concerns related to energy security and environmental impact are incentivizing the development of RNG technologies. In conjunction with this document, current technologies related to biogas conditioning and biogas upgrading were covered in a separate analysis deemed Part I. With the current technologies, however, issues such as compositional quality, combustion efficiency, and high operational costs still need to be addressed before RNG can reach its true capability in use. Recent innovations have focused on optimizing techniques and introducing new methods to maximize methane yield and purity while minimizing costs and energy consumption. This document, Part II, provides an overview of emerging technologies related to further biogas upgrading, such as cryogenics, methane enrichment, and hybrid treatments, aimed at increasing cleaned biogas purity. Processes in development are also discussed, including industrial lung, supersonic separation, chemical hydrogenation, hydrate formation, and various biological treatments. The benefits of these advancements are increased purity for the ability to pipeline renewable natural gas in existing infrastructure, help industries reach sustainability goals, and contribute to a more resilient energy system. Together, Parts I and II offer a comprehensive understanding of both current and future technological developments. Full article

The increasing complexity of mechanical manufacturing demands intelligent, integrated solutions to maintain high levels of precision, efficiency, and traceability. While ERP systems provide centralized management for core business functions, they often fall short in addressing operational-level workflows on the shopfloor. This paper presents the development and implementation of GIP (Gestão Integrada de Produção—Integrated Production Management), a custom software solution designed to bridge this gap for a small-to-medium enterprise (SME) specializing in precision mechanical components. GIP automates manual tasks such as technical drawing validation, file management, and part tracking, significantly reducing approval times and human error while enhancing traceability through unique DataMatrix part marking and centralized data logging. Developed with a modular, user-centered design using C# and SQL Server, the system integrates seamlessly with existing ERP infrastructure, following Industry 4.0 principles. Its deployment resulted in quantifiable improvements in productivity, data security, interdepartmental communication, and project delivery times. The success of GIP underscores the benefits of complementing ERP platforms with task-specific tools tailored to real user workflows. This approach aligns with smart manufacturing trends such as digital threads and digital twins, laying the groundwork for future enhancements in predictive maintenance and real-time analytics. GIP demonstrates how agile, scalable digital tools can drive competitiveness in modern industrial environments. Full article

Background: In the evolving landscape of pathology, Laboratory Information Systems (LISs) have become essential tools for ensuring traceability, efficiency, and data security in diagnostic workflows. Methods: This study presents a comprehensive comparative analysis of three major LIS platforms used in Italian pathology laboratories in 2025: Armonia (Dedalus), Pathox Web (Tesi Group), and WinSAP 3.0 (Engineering). Each system is evaluated across key parameters, including sample traceability, integration with hospital systems, digital reporting, user interface, and compliance with regulatory standards such as GDPR and ISO 15189. Results: Armonia stands out for its advanced integration capabilities, scalability, and support for digital pathology, making it ideal for large institutions. Pathox Web offers a balanced solution with strong usability and web-based accessibility, suitable for medium-sized laboratories. WinSAP 3.0, while more limited in modern features, remains a stable and cost-effective option for many facilities. This study emphasizes the strategic importance of selecting an LIS aligned with institutional needs, highlighting its role in enhancing diagnostic quality, operational safety, and future integration with artificial intelligence and automation. Conclusions: The findings support informed decision-making in LIS adoption, critically contributing to the management of scientific and economic data of pathology services in Italy. Full article

This paper presents a systematic methodology for identifying and integrating safety and security requirements in autonomous driving systems, demonstrated through the case of an autonomous intersection. The study focuses on modeling the intelligent intersection using the MBSE Grid Framework, the SysML modeling language, and the Cameo Systems Modeler tool. Two specific use cases are modeled to illustrate the system’s functionality. A multidisciplinary approach is developed to incorporate safety and security requirements into the system model, combining theoretical foundations with practical implementation techniques. The methodology includes both a generalizable framework and domain-specific strategies tailored to autonomous driving. The proposed approach is applied and critically evaluated using the intelligent intersection as a case study. By extending SysML to systematically address safety and security concerns, the work contributes to the development of safer and more efficient autonomous transportation systems. The results provide a foundation for future research and practical applications in the field of intelligent mobility and cyber–physical systems. Full article

The dynamic confrontation between medical image-encryption technology and cryptanalysis enhances the security of sensitive healthcare information. Recently, Lai et al. proposed a color medical image-encryption scheme (LG-IES) based on a 2D Logistic-Gaussian hyperchaotic map (Applied Mathematics and Computation, 2023). This paper identifies that the LG-IES suffers from vulnerabilities stemming from the existence of equivalent keys and the linear solvability of the diffusion equation, enabling successful attacks through crafted chosen-plaintext attacks and known-plaintext attacks. For an $M\times N$ image, a system of linear equations with rank r can be constructed, resulting in a reduction of the key space from $2^{32\times M\times N}$ to $2^{32\times (M\times N-r)}$. To address these security flaws, the improved ILG-IES integrates the SHA-3 Edge-Pixel Filling Algorithm (SHA-3-EPFA), which includes plaintext-related SHA-3 hashing for parameter generation, a chaos-driven 3 × 3 × 3 Unit Rubik’s Cube rotation to achieve cross-channel fusion, and edge-pixel filling rules for diffusion encryption. ILG-IES outperforms LG-IES in attack resistance (resists CPA/KPA/differential attacks) while maintaining comparable security indicators (e.g., NPCR 99.6%, UACI 33.5%) to reference schemes. In future work, SHA-3-EPFA can be embedded as an independent module into most permutation-diffusion-based image-encryption systems, offering new perspectives for securing sensitive color images. Full article

The coordinated scheduling of diesel generators, photovoltaic (PV) systems, and energy storage systems (ESS) is essential for improving the reliability and resilience of islanded microgrids in remote and mission-critical applications. This review systematically analyzes diesel–PV–ESSs from an “energy symbiosis” perspective, emphasizing the complementary roles of diesel power security, PV’s clean generation, and ESS’s spatiotemporal energy-shifting capability. A technology–time–performance framework is developed by screening advances over the past decade, revealing that coordinated operation can reduce the Levelized Cost of Energy (LCOE) by 12–18%, maintain voltage deviations within 5% under 30% PV fluctuations, and achieve nonlinear resilience gains. For example, when ESS compensates 120% of diesel start-up delay, the maximum disturbance tolerance time increases by 40%. To quantitatively assess symbiosis–resilience coupling, a dual-indicator framework is proposed, integrating the dynamic coordination degree (ζ ≥ 0.7) and the energy complementarity index (ECI > 0.75), supported by ten representative global cases (2010–2024). Advanced methods such as hybrid inertia emulation (200 ms response) and adaptive weight scheduling enhance the minimum time to sustain (MTTS) by over 30% and improve fault recovery rates to 94%. Key gaps are identified in dynamic weight allocation and topology-specific resilience design. To address them, this review introduces a “symbiosis–resilience threshold” co-design paradigm and derives a ζ–resilience coupling equation to guide optimal capacity ratios. Engineering validation confirms a 30% reduction in development cycles and an 8–12% decrease in lifecycle costs. Overall, this review bridges theoretical methodology and engineering practice, providing a roadmap for advancing high-renewable-penetration islanded microgrids. Full article

This research explores how coalitions of leaders who score high in the Dark Triad traits—narcissism, Machiavellianism, and psychopathy—rebuild moral architectures in organizations to consolidate power, suppress dissent, and secure their rule. Contrary to work that has focused predominantly on individual toxic leaders, this research examines the collective processes that emerge when multiple high-DT-scoring leaders coalesce and unify their moral leadership front. Adopting a qualitative, article-based document analysis methodology, this study synthesizes and critiques evidence from 55 peer-reviewed articles published between 2015 and 2025. Thematic analysis identified three fundamental dynamics through which Dark Triad leaders collectively exercise dominance. The first, the Ethics Cartel, involves the construction of a shared moral façade that legitimates power and shields wrongdoing. The second, Mutual Cover, outlines forms of mutual protection in which leaders shield one another from accountability and scrutiny. The third, Cultural Capture, outlines processes through which organizational culture is increasingly reconfigured such that “ethics” are structured to favor leadership over employees or wider stakeholders. This study illustrates how these coalitions cross over into individual transgressions, creating systemic risk that warps the fabric of organizational culture. Employees are confronted with a work culture that positions ethics as a means of developing survival adaptive mechanisms, such as silence, withdrawal, or compliance. These processes not only harm psychological safety and break trust but also disable accountability mechanisms established to maintain integrity. This study contributes to the study of leadership and organizational ethics by framing ethics not as merely an individual moral stance but as a collective instrument of power. It calls for more attention to the risks that follow collaboration among toxic leaders and for governance arrangements that address the organizational and systemic consequences of these unions. By situating these findings within the broader debate on power, people, and performance, this paper aligns with the focus of the Special Issue “Power, People, and Performance: Rethinking Organizational Leadership and Management” by showing how collective Dark Triad leadership distorts organizational performance outcomes while reshaping power relations in ways that undermine people’s trust and well-being. These insights extend Alowais & Suliman’s findings, highlighting the systemic feedback loops sustaining ethical distortion. Full article

The sustainable development of agrifood systems is a pressing global challenge, highlighting the need for frameworks that guide responsible investment and community engagement. The Principles for Responsible Investment in Agriculture and Food Systems (CSA-IRA), approved by the Food Security Council in 2014, provide such a framework. Recognizing this opportunity, the FAO selected the Gesplan Research Group of the Polytechnic University of Madrid in 2016 to promote these principles in Latin America, the Caribbean, and Spain, leveraging the expertise of PhD graduates in Projects and Planning for Sustainable Rural Development. The main objective of this research was to explore how teaching, research, and civil society engagement can be integrated to operationalize CSA-IRA principles and foster sustainable development. To achieve this, the study applied the “Working with People” model across multiple countries and contexts, using university–business collaborations to implement practical, socially responsible initiatives. Over nine years, the approach generated a network of 46 universities and 52 agrifood companies across 12 countries, demonstrating effective multi-stakeholder collaboration. The accumulated experience led to the proposal of the Metauniversity—a “university of universities”—as an innovative instrument to scale knowledge transfer, research, and community engagement. These findings highlight that structured, collaborative networks can translate CSA-IRA principles into tangible actions, offering a replicable model for sustainable agrifood development globally Full article

This study aims to provide an efficient framework for the coordinated integration of AC and DC chargers, intermittent solar Photovoltaic (PV) Distributed Generation (DG) units, and a Distribution Static Compensator (DSTATCOM) across residential, commercial, and industrial zones of a Radial Distribution Network (RDN) considering the benefits of various stakeholders: Electric Vehicle (EV) charging station owners, EV owners, and distribution network operators. The model uses a multi-zone planning method and healthy-bus strategy to allocate Electric Vehicle Charging Stations (EVCSs), Photovoltaic Distributed Generation (PVDG) units, and DSTATCOMs. The proposed framework optimally determines the numbers of EVCSs, PVDG units, and DSTATCOMs using Harris Hawk Optimization, considering the maximization of techno-economic benefits while satisfying all the security constraints. Further, to showcase the benefits from the perspective of EV owners, an EV waiting-time evaluation is performed. The simulation results show that integrating EVCSs (with both AC and DC chargers) with solar PVDG units and DSTATCOMs in the existing RDN improves the voltage profile, reduces power losses, and enhances cost-effectiveness compared to the system with only EVCSs. Furthermore, the zonal division ensures that charging infrastructure is distributed across the network increasing accessibility to the EV users. It is also observed that combining AC and DC chargers across the network provides overall benefits in terms of voltage profile, line loss, and waiting time as compared to a system with only AC or DC chargers. The proposed framework improves EV owners’ access and reduces waiting time, while supporting distribution network operators through enhanced grid stability and efficient integration of EV loads, PV generation, and DSTATCOM. Full article