Search Results (56,778)

The growing demand for environmentally responsible lubricants motivates the use of bio-based base stocks and benign solid additives. This study assesses the tribological performance of two Amazonian vegetable oils, Carapa guianensis (andiroba) and Copaifera spp. (copaiba resin) and a paraffinic mineral oil (PNL30) formulated with different zinc oxide (ZnO) particles, namely nanocrystals and microcrystals, at 0.01, 0.05, and 0.10 wt.%. Reciprocating sliding tests, coupled with 3D profilometry, viscosity, and sedimentation analyses, were used to link dispersion stability with friction and wear responses. A preliminary stability screening constrained the practical loading window to ≤0.10 wt.% for reproducible suspensions. Performance depended on the interplay between particle type and base-oil chemistry. Andiroba exhibited the most pronounced gains, with ZnO microcrystals near 0.05 wt.% delivering the best friction outcomes and the largest wear reductions (up to ~35%). In copaiba resin oil, nanocrystals produced small, sometimes non-significant improvements, whereas microcrystals tended to worsen wear consistent with abrasive third-body effects in a less polar matrix. In PNL30, the overall benefits were modest: nanocrystal additions generally increased wear, whereas microcrystals particularly at the highest loading 0.10 wt.% achieved a 36.4% reduction in SWR, representing a measurable and statistically significant improvement in wear resistance. These results highlight that eco-efficient lubricant design should co-optimize particle characteristics and dosage with base-oil polarity and film-forming tendencies, prioritizing dispersion stability alongside tribological targets. Full article

Cooperative searches by unmanned aerial vehicles (UAVs) have wide applications in urban environments. However, the dense obstacles and limited communication networks in urban settings often lead to repeated searches and inefficient information sharing among UAVs. To address these challenges, this article proposes a novel cooperative strategy named the Asynchronous Collaborative Hybrid Architecture (ACHA), which is tailored for urban flight. Specifically, a digital pheromone mechanism is devised to create artificial potential field to guide UAVs’ search efficiently within local communication constraints. Moreover, UAVs switch between the dual decision mode, namely Chain-Following Mode (CFM) and Tree Expansion Mode (TEM) based on the urban environmental topology. When UAVs arrive at a bifurcation node, the TEM is activated, asynchronously triggering the Collaborative-aware Pruning Search Tree (CPT) algorithm to generate subsequent paths, after which they switch back to CFM. Theoretically, it is demonstrated that the collaborative-aware pruning scheme can avoid the “cooperative benefit trap”, where there is a significant divergence between the actual and predicted cooperative benefits. The simulation results confirm that the proposed method outperforms existing approaches in terms of cooperative search accuracy, collision risk and convergence speed in complex urban search scenarios. Full article

Cyberbullying behaviors manifest uniquely in different regions, shaped strongly by local slang, dialectal expressions, and cultural context. Code-mixed Chinese–English colloquial language (Cantonese) is commonly used in Hong Kong, Macau, and parts of southern China. Code-mixing is the use of multiple languages concurrently, and Cantonese text includes distinct phonetic, lexical, and syntactic features that are not exhibited in datasets developed for either Chinese or English applications. In this study, a privacy-aware code-mixed cyberbullying dataset (PCCD), containing 14,115 annotated tweets organized into 1668 sessions, was developed. Personally identifiable information and well-known identifiers, such as the names of famous celebrities, politicians, and organizations, were replaced with randomly generated dummy names. The anonymized data empirically demonstrated improved performance in terms of precision, recall, and F₁ score, indicating a greater generalization ability when handling unseen participants. To the best of our knowledge, the PCCD is the first code-mixed Chinese–English dataset that includes abuser and victim identity annotation. Our dataset facilitates the development of robust cyberbullying detection tools that researchers and developers can use to accurately measure aggressiveness, attack frequency, and abuser–victim power imbalance in a dialogue session. Full article

As a next-generation serial communication protocol employed in automotive electronics and industrial control domains, Controller Area Network with Flexible Data-Rate (CAN-FD) enhances communication efficiency via the introduction of a dual-rate transmission mechanism, yet it still inherits the security vulnerabilities of traditional CAN networks. To enhance the security of node identity authentication in CAN-FD networks—a critical prerequisite for secure communication—we present an electronic control unit (ECU) authentication scheme that utilizes voltage hardware fingerprints (VHFs) as the core identity credential. Specifically, a single frame of data is utilized to integrate the control field’s voltage characteristics and data field’s edges, forming stable and distinguishable hardware fingerprints. We also analyze the VHF offset characteristics under typical spoofing attacks and wire-tapping attacks, and then propose a lightweight vehicle intrusion detection system (VIDS) scheme to identify attack scenarios and locate the compromised ECU in CAN-FD networks. Lastly, we conducted research on and discussed other VHF-influencing factors and put forward detailed specific solutions. Attack tests are conducted under four representative scenarios, namely substitution attack, masquerade attack, injection attack, and wire-tapping attack. The findings reveal that our scheme can not only accurately distinguish between various CAN-FD nodes but also identify specific attack types in real time. In detail, a single-frame node recognition rate exceeding 99% is achieved in approximately 2 ms, and in experiments covering multiple attack scenarios on this six-node prototype system, 100% recognition accuracy for attack types is realized in approximately 500 ms. Full article

The integration of renewable energy sources (RESs) into modern power systems has introduced significant challenges in maintaining system stability and reliability. Among these challenges, load frequency control (LFC) has become a vital area of research. The variable nature of RESs, such as wind and solar, along with their intermittent availability, necessitates advanced management systems for effective frequency regulation. LFC plays a crucial role in ensuring the stability and performance of electrical power systems by managing frequency through the balance of supply and demand, accounting for variations in load, generation, and other disturbances within the system. In traditional power systems, LFC is achieved through a combination of primary, secondary, and tertiary control mechanisms. However, the advent of smart grids has considerably complicated and enhanced the potential for LFC. In these smart grids, which leverage digital communication, sensors, and automation technologies, LFC becomes more intricate and adaptable. These systems not only utilize traditional centralized control but also incorporate RESs, decentralized resources, energy storage solutions, and real-time data to improve frequency management. This research methodically evaluates current LFC techniques using a hierarchical control and technology-focused framework, classifying approaches as conventional, intelligent, and hybrid control schemes within centralized and decentralized system architectures. An evaluative analysis reveals that while intelligent and hybrid control strategies markedly enhance dynamic frequency response and robustness with substantial renewable energy source (RES) integration, persistent challenges remain regarding controller coordination, scalability, computational requirements, and real-time execution. The analysis highlights adaptive hybrid intelligent control schemes, namely those that combine data-driven learning with physical system models, as the most promising avenue for future research, particularly in low-inertia and highly dispersed smart grid scenarios. Full article

The continuous increase in power density of electronic devices imposes stringent requirements on the design of lightweight, high-efficiency heat sinks. To overcome the limitations of conventional single-gradient or monomaterial heat sinks—namely, their suboptimal heat-transfer efficiency and poor structural adaptability—this study proposes a dual-gradient, triply periodic minimal surface (TPMS)-based multimaterial heat sink architecture fabricated from CuCrZr and AlSi₇Mg. Thermal performance was quantified experimentally using infrared thermography, while the underlying flow-field mechanisms were investigated numerically via computational fluid dynamics (CFD) simulations employing the standard k–ε turbulence model. With the TPMS material volume ratio fixed at 3:3 (CuCrZr:AlSi₇Mg), the Z-axis gradient configuration P-Z4-5 delivered the best overall thermal performance, achieving a heat-transfer coefficient (HTC) of 1557.63 W·m⁻²·K⁻¹ and a thermal resistance as low as 1.83 K·W⁻¹ at an inlet velocity of 5 m·s⁻¹. In contrast, the Y-axis gradient configuration P-Y3-6 yielded the most uniform temperature distribution, exhibiting a maximum surface temperature difference of only 21.5 °C under the same inlet condition. Velocity and turbulence distribution analyses reveal that the dual-gradient design enhances both the narrow-tube effect and flow-induced disturbances; furthermore, increasing the inlet velocity from 5 m·s⁻¹ to 21.65 m·s⁻¹ significantly intensifies vorticity-driven fluid mixing. Among all configurations evaluated, P-Z4-5 exhibited the highest j/f factor (i.e., the ratio of Colburn j-factor to Fanning friction factor), followed by P-Z3.5-5.5 and P-Z3-6. These findings establish a promising new pathway for the development of high-performance, lightweight heat sinks tailored for next-generation high-power electronics. Full article

Multi-label data usually carries a complex structural class imbalance, which significantly affects the overall predictive performance of multi-label learning models. Although many studies have investigated this problem, most existing methods rely on resampling, static cost weighting, or ensemble learning. Few studies simultaneously consider cost information and neighborhood size within the local statistical model of ML-kNN. To address this issue, this paper proposes a cost-sensitive adaptive k-nearest neighbors algorithm, named CS-MLAkNN, for imbalanced multi-label learning. The algorithm implements a dual cost-sensitive strategy at both the feature and label levels within the ML-kNN framework. Specifically, feature-level cost sensitivity is achieved through distance weighting during the training phase. In the prediction phase, label distribution information is incorporated into the posterior probability calculation to achieve label-level cost sensitivity. Moreover, the optimal number of neighbors (k) is determined adaptively through cross-validation. CS-MLAkNN maintains the simplicity and interpretability of the original ML-kNN, and meanwhile it explicitly introduces cost sensitivity and adaptiveness into three key steps: distance metric, posterior decision, and neighbor determination. Experimental results on 14 benchmark datasets demonstrate that the proposed method achieves optimal or near-optimal performance across various evaluation metrics. It also shows significant advantages over other state-of-the-art imbalanced multi-label learning algorithms. Full article

Human civilization embodies a rich cultural heritage shaped over long historical periods by numerous ethnic groups, each employing distinctive motifs and patterns in religious spaces, architecture, clothing, utensils, and other artifacts. Such motifs commonly originate from elementary geometric primitives that are organized through symmetric or asymmetric compositions to convey symbolic and esthetic meaning. This study focuses on Mongolian patterns derived from the nomadic heritage of Mongolia and still prevalent in contemporary design. These patterns draw inspiration from nature, geometry, animals, plants, and symbolic forms. This article proposes a mathematical modeling-driven digitization framework for the systematic analysis and digitization of Mongolian patterns, with the objective of generating accurate digital representations in the form of computer-aided design (CAD) models. A concise review of related work is first presented, followed by a structured digitization framework and a taxonomy of representative Mongolian motifs. A case study demonstrates that, when combined through distance-preserving and shape-preserving geometric operations such as translation, rotation, and reflection, four fundamental geometric entities, namely the circle, circular arc, spiral, and astroid, are sufficient to retain the intrinsic symmetry and compositional coherence of complex patterns observed in selected artifacts. Furthermore, the proposed analytical modeling approach enables the generation of vector-based line drawings that support precise CAD model construction. Accordingly, this study establishes a computational design workflow that integrates cultural heritage patterns into CAD-based modeling environments, thereby supporting digital preservation and fabrication with high geometric fidelity. Full article

Wheelchair users face environmental barriers that limit their mobility and social participation. Although existing navigation tools support urban mobility, they often lack clear orientation and confirmation cues, and information on accessible and safe routes to meet wheelchair users’ needs. This study aims to identify the most adapted route instructions for wheelchair users, examine characteristics’ (sociodemographic information and profiles) impact on their instructions’ choices, and evaluate instruction’s delivery modalities. An online questionnaire collected participants’ characteristics and agreement with the proposed route instruction formulations (different combinations of information like turn-by-turn instructions, landmarks, and accessibility information) regarding clarity, sufficiency, adaptability, and safety criteria. Formulations were evaluated across 14 navigation situations involving accessibility and safety challenges. Participants also rated communication modalities. 32 wheelchair-users (19 males, 13 females; mean age = 45.8 years; mean wheelchair experience = 23.5 years) participated. Data analysis reveals the importance of enriched turn-by-turn instructions, including non-turning actions, alerts, landmarks, and/or street names for participants. Alert-based formulations were favored in most situations, like uneven sidewalks, slopes and intersections. More enriched instructions were significantly acceptable among women and participants with greater wheelchair experience. Multimodal delivery, particularly visual and audio information, was also preferred. These findings help develop adaptive navigation tools, improving wheelchair users’ safe, confident mobility, autonomy, and social participation. Full article

This study investigates the extent of corporate risk disclosure (CRD) in Jordanian non-financial organizations while also looking at the impact of four unique board of directors’ features—namely, board size, the regularity of board meetings, CEO duality, and board experience—on the degree of risk disclosure. The study also examines the risk management committee’s (RMC) moderating function in improving the correlation between the traits of the board of directors and risk disclosure, a subject that has not been covered well in the Jordanian setting. The study analyzed 900 annual reports from non-financial companies listed on the Amman Stock Exchange (ASE) between 2014 and 2023. To evaluate risk disclosure, the reports were subjected to content analysis using counts of risk-related phrases. The hypotheses were tested using a random effects regression model. The number of risk disclosure statements varies from 2 to 10 per business, with an average of 24. Although CEO duality has a detrimental impact on risk disclosure levels, the findings demonstrate that industry sector and board competence have a positive effect. The leverage, the sort of audit company, the size of the firm, the frequency of board meetings, or the size of the board have no discernible effect. In particular, having an RMC significantly enhances the positive effects of board features on risk reporting. This study provides the first empirical data in Jordan on the impact of the RMC on the relationship between the traits of the board of directors and corporate risk disclosure in the non-financial industry. As a result, it fills a major gap in the literature on risk disclosure and corporate governance. Furthermore, it is the first study to use a modern and thorough measure for evaluating risk disclosure while also taking into account data from both before and after the changes to the Jordanian Corporate Governance Code. The study’s findings are made more relevant, rigorous, and contextual by this two-way contribution. Full article

As Guangdong is a pivotal province in China’s national forest city initiative, examining the spatiotemporal evolution and key drivers of eco-environmental quality (EEQ) in Guangdong is essential for advancing regional sustainable development. To address the complexity of EEQ assessments in areas that are characterized by dense hydrological networks, extensive vegetation cover, and rapid urban expansion, the Google Earth Engine platform was utilized in this study, and remote sensing indices with heightened sensitivity to vegetation and moisture dynamics—namely, the kernel normalized difference vegetation index and the kernel normalized difference moisture index—were introduced to develop an improved water benefit-based ecological index (ImWBEI). Through an integrated analytical framework incorporating Theil–Sen trend analysis, Mann–Kendall significance testing, Hurst exponent analysis, an optimal parameter-based geographical detector, and a coupled coordination degree model, this research systematically evaluated the spatiotemporal patterns, future trends, driving mechanisms, and coordination with urbanization of the EEQ in Guangdong from 2000 to 2021. The results demonstrated that the ImWBEI enhanced the detailed characterization of complex underlying surfaces, such as urban built-up areas and land–water transition zones. Throughout the study period, the EEQ in Guangdong displayed a stable spatial distribution characterized by higher values in the north and lower values in the south. Concurrently, the EEQ significantly improved at a rate of 0.0092 per year. Hurst index analysis indicated that this trajectory would likely persist, with the future trend dominated by a pattern of weak persistent improvement. The comprehensive urbanization index was identified as the most critical factor influencing the spatial differentiation of the EEQ in Guangdong. Although notable north–south disparities were observed in the coordination between the EEQ and comprehensive urbanization, the provincial-level coupled coordination consistently improved. Consequently, this work yielded actionable insights and a replicable framework for ecological monitoring and coordinated development in similar water–forest integrated urban regions. It was particularly relevant for informing ecological restoration prioritization and development restriction decisions in critical land–water transition zones—areas where the ImWBEI demonstrated enhanced sensitivity. Full article

Porous tubular structures are of significant interest in engineering due to their exceptional potential for lightweight design, energy absorption, and multifunctional integration. Inspired by the unique net architecture of natural luffa sponges, this study introduces a novel design approach for such structure, namely bio-inspired Voronoi Tube (BVT). This design employs Voronoi tessellation patterns, parametrically controlled through the spatial distribution of seed points and integrates iterative optimization algorithms, to achieve precise coordinated regulation over the randomness and continuity of the resulting spatial network, closely mimicking the biological paradigm. Then, specimens are fabricated via additive manufacturing and then quasi-statically compressed axially, followed by systematic mechanical testing of the base material. The experimental results are analyzed to reveal the BVT structure’s mechanical responses and simultaneously validate finite-element simulation model. Subsequently, a systematic numerical analysis is performed to further understand the deformation mechanisms of the BVT structure and the influence of key geometric parameters. The results indicate that the iteratively optimized BVT structure successfully replicates the characteristic energy absorption behavior of the natural luffa sponge, confirming the effectiveness of the bio-inspired design. A rise in diameter from 0.6 mm to 1.0 mm results in a 78.32% increase in the specific energy absorption (SEA). Under identical mass conditions, tailored adjustments to the geometry can enhance the SEA by up to 34.57%. Full article

This work presents a systematic design flow for the fundamental building blocks (namely, the low-noise amplifier and the down-conversion mixer) of an analog receiver for 5G backhauling systems implemented in SiGe BiCMOS technology. The proposed methodology enables the sizing and optimization of receiver blocks up to post-layout simulations, starting from the specified performance requirements. It accounts for both the parasitic effects of active devices and the distributed effects of interconnects. The design flow was applied using STMicroelectronics BiCMOS55X technology to develop low-noise amplifiers and D-band to E-band downconverters capable of covering the 130–150 GHz and 150–165 GHz sub-bands. Preliminary measurement results obtained from both the standalone LNA blocks and the complete receivers are presented and discussed. Full article

In the intelligent operation and maintenance of industrial equipment, labeling failure data remains a challenging task due to its high cost and low efficiency. Although incorporating a large amount of unlabeled data alongside limited labeled samples can partially alleviate this “labeling bottleneck,” the performance and robustness of models still heavily depend on the scale and quality of annotated data, which often leads to generalization issues in real industrial scenarios. To address these challenges, this paper proposes an unsupervised fault diagnosis method based on an efficient domain adaptation model named E-DANNMK. This approach reduces reliance on manually labeled fault data, thereby mitigating annotation-related issues such as high cost and potential bias. The E-DANNMK model integrates residual networks, an efficient channel attention mechanism, and domain adversarial neural networks to improve both feature discriminability and cross-domain adaptability. To validate its effectiveness, experiments were conducted on two major bearing fault datasets. The results demonstrate that the proposed E-DANNMK model achieves an average diagnostic accuracy of 94.21%, outperforming mainstream domain adaptation methods—including CDAN, CORAL, DANN, CNN-Transformer, DMT and DANN-MK—by a margin ranging from 3.12% to 7.15%. Full article

Previously, research has primarily focused on how the environment affects fruit quality, but there is a lack of studies investigating the impact of different growth stages on fruit quality. In this study, a total of 192 differentially abundant metabolites and 5546 differentially expressed genes were categorized into eight modules exhibiting distinct trends, along with an additional module that remained unchanged throughout all growth stages. These modules elucidate the primary metabolic alterations and transcriptional regulatory networks underlying quality variations in tomato fruits at the mature stage across different growth stages (Spike1–Spike3). Furthermore, an investigation was conducted on the module that remained constant throughout the growth stages. It was observed that the soluble sugar content remained stable across the different growth stages, whereas the levels of total phenols and flavonoids exhibited significant variation. Additionally, the principal metabolites influencing tomato flavor—namely aspartic acid, glutamic acid, glucose, fructose, citric acid, α-linolenic acid, and linoleic acid—did not demonstrate significant changes in content. The findings of this study provide novel insights into the formation of tomato quality and establish a theoretical foundation for the cultivation of long-season tomatoes with stable fruit quality. Full article