Search Results (2,412)

Blockchain systems have been widely adopted in today’s society, with consensus algorithms serving as their core component to ensure all participants in the network agree on a specific data state. Existing consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and the Practical Byzantine Fault-Tolerant Algorithm (PBFT) exhibit certain limitations in terms of scalability, security, and efficiency. To address these limitations, this paper proposes a novel Network-based Reputation Consensus (NRC) algorithm. The main research contributions of this work include the following: (1) An intelligent grouping mechanism that dynamically groups nodes based on network awareness, forming consensus groups with low internal latency and high bandwidth utilization, significantly reducing intra-group communication overhead. (2) A dynamic reputation system incorporating a “diminishing returns” reward function and a “multiplicative penalty” mechanism, effectively incentivizing honest node participation while preventing power monopoly. (3) A two-phase model of “intra-group BFT consensus + global communication committee ordering” that decomposes complex global consensus into parallel intra-group processing and coordination among a small set of elite nodes, thereby drastically improving efficiency. (4) Comprehensive simulations comparing the NRC algorithm with mainstream consensus algorithms, demonstrating its superior performance in communication overhead, throughput, latency, and tolerance to malicious nodes, thereby laying the foundation for large-scale applications. Full article

Face recognition systems are widely used for biometric authentication but face two major problems. First, processing high-resolution images and large databases requires extensive computational time. Second, emerging quantum computers threaten to break the encryption methods that protect stored facial templates. Quantum computers will soon be able to decrypt current security systems, putting biometric data at permanent risk since facial features cannot be changed like passwords. This paper presents a solution that uses quantum computing to speed up face recognition while adding quantum-resistant security. It applies quantum principal component analysis (QPCA) and the SWAP test to reduce the computational complexity and implement lattice-based cryptography, which quantum computers cannot break. Experimental evaluation demonstrates a significant overall speedup with improved accuracy. The proposed framework achieves a significant improvement in performance, provides 125-bit security against quantum attacks and compresses the data storage requirements significantly. These results demonstrate that quantum-enhanced face recognition can solve both the speed and security challenges facing current biometric systems, making it practical for real-world deployment as quantum technology advances. Full article

Higher education institutions (HEIs) are increasingly becoming vulnerable to cyberattacks as they adopt digital technologies to support their administrative, research and academic activities. These institutions, which typically operate in open and decentralized environments, face serious challenges as a result of the growing complexity of cyberattacks such as phishing, ransomware and data breaches. This systematic review synthesizes existing literature on cybersecurity in HEIs, identifying key challenges, emerging solutions and current trends. The review analyses the adoption of advanced technologies such as zero trust architectures (ZTAs), artificial intelligence (AI)-driven security and cloud-based systems. Furthermore, it investigates the underlying causes of cybersecurity vulnerabilities, including fragmented security procedures, lack of proper awareness about cybersecurity among users and associated technology gaps. The review also examines how governance frameworks, institutional policies and the incorporation of state-of-the-art security technologies can significantly mitigate these threats. Findings reveal that considerable progress has been made by some institutions in implementing security measures. However, comprehensive cybersecurity plans that integrate technological solutions with a robust institutional culture of cybersecurity awareness are still critically needed. The review concludes by highlighting the need for HEIs to collaborate and foster institution-wide partnership to strengthen cybersecurity measures. Finally, an in-depth study into the strategies and best practices for handling emerging cyberthreats in the HEIs is recommended. Full article

This paper presents fine-grained Field Programmable Gate Arrays (FPGA) architectures for the Advanced Encryption Standard (AES) MixColumns and InvMixColumns transformations, targeting improved performance and resource utilization. The proposed method reformulates these operations as boolean functions directly mapped onto FPGA Lookup-Table (LuT) primitives, replacing conventional xor-based arithmetic with memory-level computation. A custom MATLAB-R2019a-based pre-synthesis optimization algorithm performs algebraic simplification and shared subexpression extraction at the polynomial level of Galois Field $GF\left(2^8\right)$, reducing redundant logic memory. This architecture, LuT-level optimization minimizes the delay of the complex InvMixColumns stage and narrows the delay gap between encryption (1.305 ns) and decryption (1.854 ns), resulting in a more balanced and power-efficient AES pipeline. Hardware implementation on a Xilinx Virtex-5 FPGA confirms the efficiency of the design, demonstrating competitive performance compared to state-of-the-art FPGA realizations. Its fast performance and minimal hardware requirements make it well suited for real-time secure communication systems and embedded platforms with limited resources that need reliable bidirectional data processing. Full article

Precision agriculture increasingly recognizes the transformative potential of unmanned aerial systems (UASs) for crop monitoring and field assessment, yet research consistently highlights significant usability barriers as the main constraints to widespread adoption. Complex mission planning processes, including detailed flight plan creation and way point management, pose substantial technical challenges that mainly affect non-expert operators. Farmers and their teams generally prefer user-friendly, straightforward tools, as evidenced by the rapid adoption of GPS guidance systems, which underscores the need for simpler mission planning in UAS operations. To enhance accessibility and safety in UAS control, especially for non-expert operators in agriculture and related fields, we propose a Secure UAS Control Framework (SAUCF): a comprehensive system for natural-language-driven UAS mission management with integrated dual-factor biometric authentication. The framework converts spoken user instructions into executable flight plans by leveraging a language-model-powered mission planner that interprets transcribed voice commands and generates context-aware operational directives, including takeoff, location monitoring, return-to-home, and landing operations. Mission orchestration is performed through a large language model (LLM) agent, coupled with a human-in-the-loop supervision mechanism that enables operators to review, adjust, or confirm mission plans before deployment. Additionally, SAUCF offers a manual override feature, allowing users to assume direct control or interrupt missions at any stage, ensuring safety and adaptability in dynamic environments. Proof-of-concept demonstrations on a UAS plat-form with on-board computing validated reliable speech-to-text transcription, biometric verification via voice matching and face authentication, and effective Sim2Real transfer of natural-language-driven mission plans from simulation environments to physical UAS operations. Initial evaluations showed that SAUCF reduced mission planning time, minimized command errors, and simplified complex multi-objective workflows compared to traditional waypoint-based tools, though comprehensive field validation remains necessary to confirm these preliminary findings. The integration of natural-language-based interaction, real-time identity verification, human-in-the-loop LLM orchestration, and manual override capabilities allows SAUCF to significantly lower the technical barrier to UAS operation while ensuring mission security, operational reliability, and operator agency in real-world conditions. These findings lay the groundwork for systematic field trials and suggest that prioritizing ease of operation in mission planning can drive broader deployment of UAS technologies. Full article

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions. Full article

Geometric inaccuracies in the design of X-ray baggage scanners can lead to significant image artifacts, such as banding and discontinuities, which compromise security screening effectiveness. Although comprehensive commercial solutions are available, constructing a custom X-ray scanner requires the precise alignment of detector arrays. This is a complex and time-consuming process when performed manually. The core of the proposed method is a computational model that calculates the optimal position and orientation for each detector card based on user-defined scanner dimensions and hardware parameters. To validate the geometry created with this method, its performance was compared against flat and arc-shaped geometries. The results demonstrate that the proposed method successfully generates geometries that produce continuous and artifact-free images. The study concludes that the developed software tool provides a robust and practical solution, significantly simplifying the complex task of scanner construction and accelerating the development of reliable, custom X-ray inspection systems. Full article

Authentication mechanisms attract considerable research interest due to the protective role they offer, and when they fail, the system becomes vulnerable and immediately exposed to attacks. Blockchain technology was recently incorporated to enhance authentication mechanisms through its inherited specifications that cover higher security requirements. This article proposes a dynamic multi-factor authentication (MFA) mechanism based on blockchain technology. The approach combines a honeytoken authentication method implemented with smart contracts and deploys the dynamic change of honeytokens for enhanced security. Two additional random numbers are inserted into the honeytoken within the smart contract for protection from potential attackers, forming a triad of values. The produced set is then imported into a dynamic hash algorithm that changes daily, introducing an additional layer of complexity and unpredictability. The honeytokens are securely transferred to the user through a dedicated and safe communication channel, ensuring the integrity and confidentiality of this critical authentication factor. Extensive evaluation and threat analysis of the proposed blockchain-based MFA dynamic mechanism (BMFA) demonstrate that it meets high-security standards and possesses essential properties that give prospects for future use in many domains. Full article

Anaerobic fungi (phylum Neocallimastigomycota) play a crucial role in degrading forages and fibrous foods in the gastrointestinal tract of mammalian herbivores, particularly ruminants. Currently, they are classified into twenty-two genera; however, recent research suggests the occurrence of several novel taxa that require further characterization. Anaerobic rumen fungi play a pivotal role in lignocellulose degradation due to their unique enzymatic capabilities. This review explores the enzymatic systems of rumen anaerobic fungi, highlighting their ability to produce a diverse array of carbohydrate-active enzymes (CAZymes), such as cellulases, hemicellulases, and pectinases. These enzymes facilitate the breakdown of complex plant polymers, making anaerobic fungi essential contributors to fiber degradation in the rumen ecosystem and valuable resources for biotechnological applications. This review summarizes the structural and functional diversity of fungal CAZymes, and the mechanical disruption of plant cell walls by fungal rhizoidal networks is discussed, showcasing the ability of fungi to enhance substrate accessibility and facilitate microbial colonization. Recent studies using genomic, transcriptomic, and biochemical approaches have uncovered several novel CAZymes in anaerobic fungi, including multifunctional xylanases, β-glucosidases, and esterases. These findings highlight the continued expansion of fungal enzyme repertoires and their potential for biotechnology and feed applications. Continued research in this field will enhance our understanding of microbial ecology and enzyme function, paving the way for applications that address global challenges in energy, food security, and environmental sustainability. Full article

The monitoring and security of large-scale terminal infrastructures represent a critical application domain for industrial cyber-physical systems. However, real-time 3D visualization in such environments faces significant challenges from dense crowds, specular reflections, and complex architectural layouts. This paper presents a cyber-physical system for terminal infrastructure monitoring, underpinned by a novel, depth-free camera registration framework. At its core, the system establishes explicit geometric mappings across four coordinate systems (world, 3D model, camera, image), leveraging known installation parameters to eliminate dependency on depth sensors. Dynamic inconsistencies are resolved through a multi-stage layout refinement process, enabling robust operation under terminal-specific challenges. The framework maintains real-time performance at over 25 FPS when processing 16 concurrent video streams on commercial hardware. Extensive evaluations demonstrate a 44.9% reduction in registration error compared to state-of-the-art methods, validating the system’s practicality for enhancing situational awareness and security in large-scale, dynamic terminals. Full article

Digital Twins are becoming central enablers of Europe’s digital and green transitions, yet their data-intensive and autonomous nature exposes them to one of the most complex regulatory environments in the world. This article presents a comprehensive scoping review of how six principal European digital laws—the General Data Protection Regulation, Data Governance Act, Data Act, Artificial Intelligence Act, NIS2 Directive, and Cyber Resilience Act—jointly govern the design, deployment, and operation of Digital Twin systems. Building on the PRISMA-ScR methodology, the study constructs a Unified Digital Twin Compliance Framework (UDTCF) that consolidates overlapping obligations across data governance, privacy, cybersecurity, transparency, interoperability, and ethical responsibility. The framework is operationalised through a Digital Twin Compliance Evaluation Matrix (DTCEM) that enables qualitative assessment of compliance maturity in research and innovation projects. Applying these tools to representative European cases in Smart Cities, Industrial Manufacturing, Transportation, and Energy Systems reveals strong convergence in data governance, security, and interoperability, but also persistent gaps in the transparency, explainability, and accountability of AI-driven components. The findings demonstrate that European digital legislation forms a coherent yet fragmented ecosystem that increasingly requires integration through compliance-by-design methodologies. The article concludes that Digital Twins can act not only as regulated technologies but also as compliance infrastructures themselves, embedding legal, ethical, and technical safeguards that reinforce Europe’s vision for trustworthy, resilient, and human-centric digital transformation. Full article

In today’s digital era, real-time applications rely heavily on cloud environments for computation, storage, and data retrieval. Data owners outsource sensitive information to cloud storage servers managed by service providers such as Google and Amazon, who are responsible for ensuring data confidentiality. Traditional cryptographic algorithms, though widely adopted, face challenges related to key management and computational complexity when implemented in the cloud. To overcome these limitations, this research proposes a Secure DNA Cryptosystem (SDNA) based on DNA molecular structures and biological processes. The proposed system generates encoding/decoding tables and encryption/decryption algorithms, using dynamically generated key files to secure communication between data owners and users in the cloud. The DNA-based cryptographic approach enhances data confidentiality, ensures faster computation, and increases resistance to cryptanalysis through dynamic key operations. The experimental results demonstrate the efficiency of the proposed system. For a character count of 16,384, the encryption and decryption times are 852 ms and 822 ms, respectively. Similarly, for a word count of 16,384, the encryption and decryption times are significantly reduced to 75 ms and 62 ms, respectively. These results highlight the superior computational performance and adaptability of the SDNA compared to conventional cryptographic schemes. Overall, performance and security analysis confirm that the proposed SDNA is computationally secure, faster, and flexible for implementation in cloud environments, offering a promising solution for real-time secure data storage and retrieval. Full article

This study examines the role of public procurement of artificial intelligence (AI) as a catalyst for transformative change in State functions. Building on the concept of transformative law, it argues that law should not merely regulate technological innovation but actively guide and shape it in accordance with democratic values and the rule of law. Within this framework, public procurement emerges as a strategic instrument for (re)structuring the very configuration of public governance and institutions. This analysis highlights key legal issues surrounding the procurement of AI, starting with the premise of its dual function: on the one hand, as a tool for optimising acquisition procedures and, on the other, as the object of acquisition itself. Among the most pressing issues analysed are the definitions of algorithmic legality and accountability, the asymmetry of expertise between public authorities and private suppliers, and the regulatory complexity that characterises the field, especially in light of the recently adopted EU AI Act. Finally, this study conceptualises the public procurement of AI as a form of legal infrastructure, capable of securing systemic and enduring transformations for the State and its institutions. Full article

Using lithium batteries to supply electric machinery and/or equipment in underground mines requires an adequate level of security. This is particularly important in coal mines, especially under the threat of methane explosions and/or fire hazards. Lithium battery cells with a BMS should be effectively isolated from the impact of the surrounding mine environment. This can be achieved by storing all battery systems in a certified explosion-proof enclosure (Ex) in accordance with the relevant regulations and standards. Preliminary tests conducted by the authors indicated that use of lithium cells without a BMS in mines is risky and, in practice, unacceptable. BMSs with passive cell balancing are most commonly employed. They allow for the equalization of cell voltages primarily during the charging process. However, the lowest-capacity cell still determines the overall lifetime of a battery. Furthermore, the use of active balancing systems (BMSs) is rare in practice due to their greater complexity and price. Nevertheless, they can significantly extend battery life through the much more efficient redistribution of energy among the cells, including during the discharge process. This article presents the operation of a modified (hybrid) BMS architecture, combining both passive and active balancing methods when employed for the selected suspended mine vehicle. It enables more safe and more effective charging process, as well as discharging process, which results in the longer time of operation of lithium battery packs, for one charge. This system is intended for use in mining machinery and equipment, as well as in selected energy storage systems powered by lithium-based battery modules. Full article

Nature reserves serve as core spatial units for maintaining regional ecological security and biodiversity. Owing to their high ecosystem integrity, extensive vegetation cover, and low levels of disturbance, they play a crucial role in sustaining ecological processes and ensuring functional stability. Taking the Giant Panda National Park (GPNP), which spans the provinces of Gansu, Sichuan, and Shaanxi in China, as the study region, the vegetation net primary productivity (NPP) during 2001–2023 was simulated using the Carnegie–Ames–Stanford Approach (CASA) model. Spatial and temporal variations in NPP were examined using Moran’s I, Getis-Ord Gi* hotspot analysis, Theil–Sen trend estimation, and the Mann–Kendall test. In addition, the Optimal Parameters-based Geographical Detector (OPGD) model was applied to quantitatively assess the relative contributions of natural and anthropogenic factors to NPP dynamics. The results demonstrated that: (1) The mean annual NPP within the GPNP reached 646.90 gC·m⁻²·yr⁻¹, exhibiting a fluctuating yet generally upward trajectory, with an average growth rate of approximately 0.65 gC·m⁻²·yr⁻¹, reflecting the positive ecological outcomes of national park establishment and ecological restoration projects. (2) NPP exhibits significant spatial heterogeneity, with higher NPP values in the northern, while the central and western regions and some high-altitude areas remain at relatively low levels. Across the four major subregions of the GPNP, the Qinling has the highest mean annual NPP at 758.89 gC·m⁻²·yr⁻¹, whereas the Qionglai–Daxiaoxiangling subregion shows the lowest value at 616.27 gC·m⁻²·yr⁻¹. (3) Optimal NPP occurred under favorable temperature and precipitation conditions combined with relatively high solar radiation. Low elevations, gentle slopes, south facing aspects, and leached soils facilitated productivity accumulation, whereas areas with high elevation and steep slopes exhibited markedly lower productivity. Moderate human disturbance contributed to sustaining and enhancing NPP. (4) Factor detection results indicated that elevation, mean annual temperature, and land use were the dominant drivers of spatial heterogeneity when considering all natural and anthropogenic variables. Their interactions further enhanced explanatory power, particularly the interaction between elevation and climatic factors. Overall, these findings reveal the complex spatiotemporal characteristics and multi-factorial controls of vegetation productivity in the GPNP and provide scientific guidance for strengthening habitat conservation, improving ecological restoration planning, and supporting adaptive vegetation management within the national park systems. Full article