Search Results (2,853)

The mechanical and structural design of railway vehicles is heavily influenced by their lifetime. Because fatigue is a significant factor that impacts the longevity of railway components, it is imperative that the fatigue resistance properties of crucial components, like leaf springs, be thoroughly investigated. This research investigates the fatigue resistance of 51CrV4 steel under bending and axial tension, considering different stress ratios across low-cycle fatigue (LCF), high-cycle fatigue (HCF), and very-high-cycle fatigue (VHCF) regimes, using experimental data collected from this work and prior research. Data included fractographic analyses aiming to help in understanding some of failures for different loads. The presence of geometric discontinuities, such as notches, amplifies stress concentrations, requiring a probabilistic approach to fatigue assessment. To address notch effects, the theory of critical distances (TCD) was employed to evaluate fatigue strength. TCD model was integrated in fatigue statistical models, such as the Walker model (WSN) and the Castillo–Fernández-Cantelli model adapted for mean stress effects (ACFC). Extending the application of the TCD theory, this research provides an improved probabilistic fatigue model that integrates notch sensitivity, mean stress effects, and fatigue regimes, contributing to more reliable design approaches of railway leaf springs or other components produced in 51CrV4 steel. Full article

Wireless sensor networks (WSNs) are fundamental to applications in the Internet of Things, smart cities, and environmental monitoring, where coverage optimization is critical for maximizing monitoring efficacy under constrained resources. Conventional approaches often suffer from low global coverage efficiency, high computational overhead, and a tendency to converge to local optima. To address these challenges, this study proposes the fusion multi-strategy gray wolf optimizer (FMGWO), an advanced variant of the Gray Wolf Optimizer (GWO). FMGWO integrates various strategies: electrostatic field initialization for uniform population distribution, dynamic parameter adjustment with nonlinear convergence and differential evolution scaling, an elder council mechanism to preserve historical elite solutions, alpha wolf tenure inspection and rotation to maintain population vitality, and a hybrid mutation strategy combining differential evolution and Cauchy perturbations to enhance diversity and global search capability. Ablation studies validate the efficacy of each strategy, while simulation experiments demonstrate FMGWO’s superior performance in WSN coverage optimization. Compared to established algorithms such as PSO, GWO, CSA, DE, GA, FA, OGWO, DGWO1, and DGWO2, FMGWO achieves higher coverage rates with fewer nodes—up to 98.63% with 30 nodes—alongside improved convergence speed and stability. These results underscore FMGWO’s potential as an effective solution for efficient WSN deployment, offering significant implications for resource-constrained optimization in IoT and edge computing systems. Full article

This paper presents the research outcomes of the Golden Seal project, which addresses the omnipresent issue of plastic pollution in coastal areas while enhancing their touristic value through the deployment of Internet of Things (IoT) technologies integrated into a gamified recycling framework. The developed system employs an IoT-enabled Wireless Sensor Network (WSN) to systematically collect, transmit, and analyze environmental data. A centralized, cloud-based platform supports real-time monitoring and data integration from Unmanned Aerial and Surface Vehicles (UAV and USV) equipped with sensors and high-resolution cameras. The system also introduces the Beach Cleanliness Index (BCI), a composite indicator that integrates quantitative environmental metrics with user-generated feedback to assess coastal cleanliness in real time. A key innovation of the project’s architecture is the incorporation of a Serious Game (SG), designed to foster public awareness and encourage active participation by local communities and municipal authorities in sustainable waste management practices. Pilot implementations were conducted at selected sites characterized by high tourism activity and accessibility. The results demonstrated the system’s effectiveness in detecting and classifying plastic waste in both coastal and terrestrial settings, while also validating the potential of the Golden Seal initiative to promote sustainable tourism and support marine ecosystem protection. Full article

The transition to a sustainable chemical industry necessitates efficient valorization of biomass, with polyols serving as versatile, renewable feedstocks. This comprehensive review, focusing on advancements within the last five years, critically analyzes the selective hydrogenolysis of key biomass-derived polyols—including glycerol, erythritol, xylitol, and sorbitol—into valuable diols. Emphasis is placed on the intricate catalytic strategies developed to control C–O bond cleavage, preventing undesired C–C scission and cyclization. The review highlights the design of bifunctional catalysts, often integrating noble metals (e.g., Pt, Ru, Ir) with oxophilic promoters (e.g., Re, W, Sn) on tailored supports (e.g., TiO₂, Nb₂O₅, N-doped carbon), which have led to significant improvements in selectivity towards specific diols such as 1,2-propanediol (1,2-PD), 1,3-propanediol (1,3-PD), and ethylene glycol (EG). While substantial progress in mechanistic understanding and catalyst performance has been achieved, challenges persist regarding catalyst stability under harsh hydrothermal conditions, the economic viability of noble metal systems, and the processing of complex polyol mixtures from lignocellulosic hydrolysates. Future directions for this field underscore the imperative for more robust, cost-effective catalysts, advanced computational tools, and intensified process designs to facilitate industrial-scale production of bio-based diols. Full article

This article presents a study of the energy efficiency of a wireless sensor network for measuring the main physicochemical parameters of soil. The main physicochemical parameters of soil are measured—acidity and electrical conductivity. The study on the transmission of measured data on the main soil parameters is conducted through simulation, with program modules developed in the MATLAB environment. Four main protocols for data routing are studied—the LEACH (Low-Energy Adaptive Clustering Hierarchy), EAMMH (Energy-Aware Multi-Hop Multi-Path Hierarchical), SEP (Stable Election Protocol for clustered heterogeneous WSN), and TEEN (Threshold-sensitive Energy Efficient Network). The results of the main energy indicators are obtained and a comparative analysis of the two protocols is carried out. The results obtained show that the SEP and TEEN routing protocols have better performance and efficiency with respect to inactive nodes in the network compared to the other two protocols. The EAMMH and LEACH routing protocols are the best in terms of the energy consumption by sensors in the network. Full article

The Dulong Sn-polymetallic deposit in Yunnan Province of southwestern China serves as a unique case study for unraveling the evolution of skarn systems and tin mineralization. Four distinct garnet types (Grt I to Grt IV) were classified based on petrographic observations. Compositional analysis reveals a progression from Grt I to Grt III, marked by increasing andradite components, and elevated tin concentrations, peaking at 5039 ppm. These trends suggest crystallization from Sn-enriched magmatic-hydrothermal fluids. In contrast, Grt IV garnet exhibits dominant almandine components and minimal tin content (<2 ppm). Its association with surrounding rocks (schist) further implies its metamorphic origin, distinct from the magmatic origin of the other garnet types. Combined with previously published sulfur and lead isotopic data, as well as trace element compositions of garnet, our study suggests that Laojunshan granites supply substantial ore-forming elements such as S, Pb, W, Sn, In, and Ga. In contrast, elements such as Sc, Y, and Ge are inferred to be predominantly derived from, or buffered by, the surrounding rocks. The geochemical evolution of the garnets highlights the critical role of redox fluctuations and fluid chemistry in controlling tin mineralization. Under neutral-pH fluid conditions, early-stage garnets incorporated significant tin. As the oxygen fugacity of the ore-forming fluid declined, cassiterite precipitation was triggered, leading to tin mineralization. This study reveals the interplay between fluid redox dynamics, garnet compositional changes, and mineral paragenesis in skarn-type tin deposits. Full article

Uncooled microbolometers play a pivotal role in infrared detection owing to their compactness, low power consumption, and cost-effectiveness. This review comprehensively summarizes recent progress in thermistor materials and focal plane arrays (FPAs), highlighting improvements in sensitivity and integration. Vanadium oxide (VO_x) remains predominant, with Al-doped films via atomic layer deposition (ALD) achieving a temperature coefficient of resistance (TCR) of −4.2%/K and significant 1/f noise reduction when combined with single-walled carbon nanotubes (SWCNTs). Silicon-based materials, such as phosphorus-doped hydrogenated amorphous silicon (α-Si:H), exhibit a TCR exceeding −5%/K, while titanium oxide (TiO_x) attains TCR values up to −7.2%/K through ALD and annealing. Emerging materials including GeSn alloys and semiconducting SWCNT networks show promise, with SWCNTs achieving a TCR of −6.5%/K and noise equivalent power (NEP) as low as 1.2 mW/√Hz. Advances in FPA technology feature pixel pitches reduced to 6 μm enabled by vertical nanotube thermal isolation, alongside the 3D heterogeneous integration of single-crystalline Si-based materials with readout circuits, yielding improved fill factors and responsivity. State-of-the-art VO_x-based FPAs demonstrate noise equivalent temperature differences (NETD) below 30 mK and specific detectivity (D*) near 2 × 10¹⁰ cm⋅Hz ^1/2/W. Future advancements will leverage materials-driven innovation (e.g., GeSn/SWCNT composites) and process optimization (e.g., plasma-enhanced ALD) to enable ultra-high-resolution imaging in both civil and military applications. This review underscores the central role of material innovation and system optimization in propelling microbolometer technology toward ultra-high resolution, high sensitivity, high reliability, and broad applicability. Full article

Wireless Sensor Network (WSN) technology has constrained computing resources that require efficient and low-cost cryptographic hardware to provide security services, particularly when dealing with large modular polynomial multiplication in cryptography. In this paper, a cost-efficient reconfigurable Karatsuba modular polynomial multiplier is proposed for general modulus polynomials. The modulus polynomial can be changed easily depending on the application. The proposed modular polynomial multiplier is synthesized and simulated by the AMD Vivado Design Tool. The design’s performance on ADP (Area Delay Product) has been improved compared to previous designs. It can be applied in ECC encryption to speed up the security services in WSN. Full article

Industrial Wireless Sensor Networks (IWSNs) play a critical role in Industry 4.0 environments, enabling real-time monitoring and control of industrial processes. However, existing lightweight authentication protocols for IWSNs remain vulnerable to sophisticated security attacks because of inadequate initial authentication phases. This study presents a security analysis of Gope et al.’s PUF-based authentication protocol for IWSNs and identifies critical vulnerabilities that enable man-in-the-middle (MITM) and denial-of-service (DoS) attacks. We demonstrate that Gope et al.’s protocol is susceptible to MITM attacks during both authentication and Secure Periodical Data Collection (SPDC), allowing adversaries to derive session keys and compromise communication confidentiality. Our analysis reveals that the sensor registration phase of the protocol lacks proper authentication mechanisms, enabling attackers to perform unauthorized PUF queries and subsequently mount successful attacks. To address these vulnerabilities, we propose an enhanced authentication scheme that introduces a sensor-initiated registration process. In our improved protocol, sensor nodes generate and control PUF challenges rather than passively responding to gateway requests. This modification prevents unauthorized PUF queries while preserving the lightweight characteristics essential for resource-constrained IWSN deployments. Security analysis demonstrates that our enhanced scheme effectively mitigates the identified MITM and DoS attacks without introducing significant computational or communication overhead. The proposed modifications maintain compatibility with the existing IWSN infrastructure while strengthening the overall security posture. Comparative analysis shows that our solution addresses the security weaknesses of the original protocol while preserving its practical advantages for industrial use. The enhanced protocol provides a practical and secure solution for real-time data access in IWSNs, making it suitable for deployment in mission-critical industrial environments where both security and efficiency are paramount. Full article

Optical wireless sensor networks (OWSNs) offer promising capabilities for high-speed, energy-efficient communication, particularly in mission-critical environments such as industrial automation, healthcare monitoring, and smart buildings. However, dynamic spectrum management and fault tolerance remain key challenges in ensuring reliable and timely data transmission. This paper proposes an adaptive spectrum management framework (ASMF) that addresses these challenges through a mathematically grounded and implementation-driven approach. The ASMF formulates the spectrum allocation problem as a constrained Markov decision process and leverages a dual-layer optimization strategy combining Lyapunov drift-plus-penalty for queue stability with deep reinforcement learning for adaptive long-term decision making. Additionally, ASMF integrates a hybrid fault-tolerant mechanism using LSTM-based link failure prediction and lightweight recovery logic, achieving up to 83% prediction accuracy. Experimental evaluations using real-world datasets from industrial, healthcare, and smart infrastructure scenarios demonstrate that ASMF reduces critical traffic latency by 37%, improves reliability by 42% under fault conditions, and enhances energy efficiency by 22.6% compared with state-of-the-art methods. The system also maintains a 99.94% packet delivery ratio for critical traffic and achieves 69.7% faster recovery after link failures. These results confirm the effectiveness of ASMF as a robust and scalable solution for adaptive spectrum management in dynamic, fault-prone OWSN environments. Full article

Wireless Sensor Networks (WSNs) are very important for monitoring complex 3D environments like forests, where energy efficiency and reliable communication are critical. This paper presents EEL-GA, an Energy Efficient LEACH-based clustering protocol optimized using a Genetic Algorithm. Cluster head (CH) selection is guided by a dual-metric fitness function combining residual energy and intra-cluster distance. EEL-GA is evaluated against EEL variants using Particle Swarm Optimization (PSO), Differential Evolution (DE), and the Artificial Bee Colony (ABC) across key performance metrics, including energy efficiency, packet delivery, and cluster lifetime. Simulations using real environmental data confirm EEL-GA’s superiority in sustaining energy, minimizing delay, and improving network stability, making it ideal for smart forestry and mission-critical WSN deployments. The model also incorporates environmental dynamics, such as temperature and humidity, enhancing its robustness in real-world applications. These findings support EEL-GA as a scalable, adaptive solution for future energy-aware 3D WSN frameworks. Full article

The Bi-Sn, Bi-Sn-Ag, and Bi-Sn-Sb solder alloy systems represent lead-free, environmentally friendly alternatives for reliable electronic assembly. These alloys comply with increasingly strict environmental and health regulations, while offering low melting points suitable for soldering temperature-sensitive components. Microstructural analysis revealed distinct phase segregation in all alloys, with Sb promoting coarse Sn₂Sb₃ intermetallic compounds and Ag inducing fine needle-like Ag₃Sn precipitates. Eutectic refinement and compositional contrast were confirmed by SEM-BSE and EDS mapping. Vickers microhardness measurements revealed increased hardness in Sb- and Ag-modified Bi–Sn alloys, with Ag₃Sn dispersion yielding the highest strengthening effect, indicating enhanced mechanical potential. This study also reports the thermal and electrical conductivities of Bi60Sn40, Bi60Sn35Ag5, and Bi60Sn35Sb5 alloys over the 25–140 °C range. Bi60Sn40 showed an increase in thermal conductivity across the full temperature range from 16.93 to 26.93 W/m·K, while Bi60Sn35Ag5 reached 18.28 W/m·K at 25 °C, and Bi60Sn35Sb5 exhibited 13.90 W/m·K. These findings underline the critical influence of alloying elements on microstructure, phase stability, and thermophysical behavior, supporting their application in low-temperature soldering technologies. Full article

Wireless sensor networks (WSNs) have gained significant attention across various industries and scientific fields. Localization, a crucial aspect of WSNs, involves accurately determining node positions to track events and execute actions. Despite the development of numerous localization algorithms, real-world environments pose challenges such as anisotropy, noise, and faults. To improve accuracy amidst these complexities, researchers are increasingly adopting advanced methodologies, including soft computing, software-defined networking, maximum likelihood estimation, and optimization techniques. Our comprehensive review from 2020 to 2024 reveals that approximately 29% of localization solutions employ optimization techniques, 48% of which utilize nature-inspired swarm-based algorithms. These algorithms have proven effective for node localization in a variety of applications, including smart cities, seismic exploration, oil and gas reservoir monitoring, assisted living environments, forest monitoring, and battlefield surveillance. This underscores the importance of swarm intelligence algorithms in sensor node localization, prompting a detailed investigation in our study. Additionally, we provide a comparative analysis to elucidate the applicability of these algorithms to various localization challenges. This examination not only helps researchers understand current localization issues within WSNs but also paves the way for enhanced localization precision in the future. Full article

The Internet of Things (IoT) has become an integral part of today’s smart and digitally connected world. IoT devices and technologies now connect almost every aspect of daily life, generating, storing, and analysing vast amounts of data. One important use of IoT is in utility management, where essential services such as water are supplied through IoT-enabled infrastructure to ensure fair, efficient, and sustainable delivery. The large volumes of data produced by water distribution networks must be safeguarded against manipulation, theft, and other malicious activities. Incidents such as the Queensland user data breach (2020–21), the Oldsmar water treatment plant attack (2021), and the Texas water system overflow (2024) show that attacks on water treatment plants, distribution networks, and supply infrastructure are common in Australia and worldwide, often due to inadequate security measures and limited technical resources. Lightweight cryptographic algorithms are particularly valuable in this context, as they are well-suited for resource-constrained hardware commonly used in IoT systems. This study focuses on the in-house developed ChaosFortress lightweight cryptographic algorithm, comparing its performance with other widely used lightweight cryptographic algorithms. The evaluation and comparative testing used an Arduino and a LoRa-based transmitter/receiver pair, along with the NIST Statistical Test Suite (STS). These tests assessed the performance of ChaosFortress against popular lightweight cryptographic algorithms, including ACORN, Ascon, ChaChaPoly, Speck, tinyAES, and tinyECC. ChaosFortress was equal in performance to the other algorithms in overall memory management but outperformed five of the six in execution speed. ChaosFortress achieved the quickest transmission time and topped the NIST STS results, highlighting its strong suitability for IoT applications. Full article

Addressing global energy and environmental issues calls for the development of effective photocatalysts capable of enabling solar-driven water splitting, a key route toward sustainable hydrogen generation. In this work, we conducted a detailed density functional theory (DFT) study on three bilayer van der Waals (vdW) heterojunctions, Ga₂SSe/GaP, Ga₂SSe/PtSSe, and Ga₂SSe/SnSSe, each explored in four distinct stacking configurations, with Ga₂SSe serving as the base monolayer. We assessed their structural stability, electronic properties, and optical responses to determine their suitability for photocatalytic water splitting. The analysis showed that Ga₂SSe/GaP and Ga₂SSe/SnSSe exhibit type-II band alignment, while Ga₂SSe/PtSSe displays a type-I alignment. Electrostatic potential profiles and Bader charge calculations identified SeGa₂S/SSnSe and SeGa₂S/SeSnS as direct Z-scheme systems, offering efficient charge carrier separation and robust redox potential. For effective water splitting, the band edges must straddle the water redox potentials. Our results indicate that configurations A and B in Ga₂SSe/GaP, along with C and D in Ga₂SSe/SnSSe, fulfill this requirement. These four configurations also exhibit strong absorption in both the visible and ultraviolet spectral ranges. Notably, configurations C and D of Ga₂SSe/SnSSe achieve high solar-to-hydrogen (STH) efficiencies, reaching 38.44% and 21.75%, respectively. Overall, our findings suggest that these direct Z-scheme heterostructures are promising candidates for water splitting photocatalysis. Full article