Search Results (21,991)

This study presents a comparative assessment of the Johnson–Cook (J-C) and Ductile Damage (DD) material models, evaluating their capability to replicate the tensile behavior and fracture development in ductile metals. Numerical models of AL6063-T4 aluminium and A36 steel dog-bone specimens with two different thicknesses were developed in ABAQUS to assess force–displacement response, stress–strain characteristics, and crack evolution under quasi-static loading. Results showed that specimen thickness directly doubled load capacity, while both models captured the overall elastic and plastic behavior of the materials. A key finding is that the DD model provided yield stresses closely matching the reference material values, whereas the J-C model exhibited higher apparent yields due to its intrinsic strain-rate sensitivity. Differences in damage behavior were also pronounced: the DD model better reproduced the gradual, inclined fracture path in aluminium, while the J-C model more accurately captured the strong necking-localization response characteristic of steel. Comparisons with experimentally tested specimens further supported these fracture tendencies. By analysing both materials under identical conditions, this work highlights the relative strengths and limitations of the two fracture formulations. The originality of the study lies in its systematic comparison across materials and thicknesses, providing clear guidance for selecting appropriate constitutive models in structural and computational mechanics research. Full article

Signal intersections are key nodes in urban road traffic networks, and real-time queue length information serves as a core performance indicator for formulating effective signal management schemes in modern adaptive traffic signal control systems, thereby enhancing traffic efficiency. In this study, a roadside Light Detection and Ranging (LiDAR) sensor is employed to acquire 3D point cloud data of vehicles in the road space, which acts as an important method for queue length detection. However, during queue-length detection, vehicles in different lanes are prone to occlusion because of the straight-line propagation of laser beams. This paper proposes a queue-length detection method based on variations in vehicle point cloud features to address the occlusion of queue-end vehicles during detection. This method first preprocesses LiDAR point cloud data (including region-of-interest extraction, ground-point filtering, point cloud clustering, object association, and lane recognition) to detect real-time queue lengths across multiple lanes. Subsequently, the occlusion problem is categorized into complete occulusion and partial occlusion, and corresponding processing is performed to correct the detection results. The performance of the proposed queue length detection method was validated through experiments that collected real-world data from three urban road intersections in Suzhou. The results indicate that this method’s average accuracy can reach 99.3%. Furthermore, the effectiveness of the proposed occlusion handling method has been validated through experiments. Full article

Generative artificial intelligence (GenAI) is increasingly transforming creative industries through its ability to generate high-quality content, raising critical questions about authorship, ownership, and the future of creative labor. This paper addresses these challenges by conducting a systematic bibliometric review of 119 peer-reviewed articles on GenAI in the creative sectors, published between 2023 and 2025. The study applies PRISMA 2020 guidelines and keyword co-occurrence analysis using VOSviewer to identify thematic clusters and map research trends. The central research question is how the academic literature conceptualizes the role and impact of GenAI within creative industries and how this has evolved over time. Findings reveal nine major thematic areas, ranging from technical implementations to ethical, economic, and institutional perspectives. The analysis shows that recent research emphasizes not only the technological capacities of GenAI, but also its implications for value creation, creative agency, and industry structures. The main contribution of the paper lies in offering a structured overview of current research trajectories, clarifying conceptual ambiguities, and highlighting understudied areas—particularly regarding the intersection of GenAI, platform economies, and labor dynamics. The review also identifies a methodological gap in comparative empirical studies and proposes directions for future research. By mapping the evolving discourse on GenAI in creative industries, this study contributes to both scholarly understanding and policy development. It provides a foundation for interdisciplinary inquiry and a forward-looking agenda for critically assessing GenAI’s role in reshaping creative work. Full article

To meet the challenge of microbial contamination of food, smart packaging materials with active controlled-release functions have become a research hotspot. In this study, a humidity-responsive antimicrobial composite film was constructed by introducing cinnamaldehyde@β-cyclodextrin inclusion complexes (CIN@β-CD ICs) into a konjac glucomannan/polyvinyl alcohol/lithium chloride (KGM/PVA/LiCl) matrix. Characterization results showed that the CIN@β-CD ICs formed a dense structure through hydrogen bonding, which enhanced the thermal stability, mechanical strength (tensile strength: 20.83 MPa) and surface hydrophilicity (water contact angle < 60°) of the film. The film acted as a humidity-triggered release system for CIN, enabling controlled antimicrobial delivery: at high humidity (98% RH), the film rapidly swelled and accelerated the release of CIN, with a cumulative release rate of 87.29% over 7 days, whereas the release slowed significantly at low humidity (43% RH). The antimicrobial activity of the released CIN was strongly influenced by ambient humidity, with the effect enhanced under high humidity conditions. It is noteworthy that the film containing 0.2% ICs exhibited the optimal antimicrobial performance among the formulations studied. This study elucidates a mechanism for humidity-triggered release through multicomponent synergism, which provides a feasible strategy for the design of environmentally friendly, smart packaging materials with high antimicrobial activity. Full article

Non-point source pollution poses a severe threat to the water quality of the Li River. This study conducted field monitoring of pollution loads from different land-use types on Maozhou Island in the Li River during the 2023 rainy season. Runoff water quality from vegetable plots, orchards, and bamboo forests consistently exceeded standards, with vegetable plots being the primary source of pollution. Their total phosphorus (TP) concentration exceeded standards by nearly 25 times, contributing the highest annual load. The transport of pollutants (TP, total nitrogen(TN), chemical oxygen demand(COD_Cr)) was closely correlated with suspended solids (SS), with the finest particles (<5 μm) identified as the primary carrier exhibiting the strongest pollutant enrichment capacity (e.g., in vegetable fields, the correlation coefficient r between < 5 μm particles and TP was >0.85, p < 0.01). Rainfall patterns significantly influenced pollutant concentrations; TN and TP levels increased with preceding dry days, while phosphorus output from vegetable plots decreased with rising average rainfall temperature. Compared to bamboo forests, vegetable plots and orchards exhibited lower soil adsorption capacity. This study recommends a connectivity-based strategy prioritizing the interception of heavily enriched fine particulate matter (<5 μm) through runoff control and enhanced wetland retention functions. These findings underscore the importance of controlling fine particulate matter for reducing non-point source pollution and maintaining ecological health in the Lijiang River basin. Full article

Lithium-ion batteries are widely used in portable devices and electronic vehicles (EVs) due to their excellent performance. Because of their internal chemistry, these batteries have non-linear characteristics, their parameters being dependent on temperature and varying over time due to aging. Since electric vehicles are marketed in different regions of the globe with different climates, this has led to increased attention to the problem of the reduced performance of EVs in colder environments. The purpose of this research is to study the effects of preconditioning on Li-ion cells and determine the need for preconditioning in EVs that operate under low-temperature conditions. Additionally, based on the results, alternative coping strategies are also suggested which can be used instead of preconditioning when this is not a viable option. Given this, the 18650 Li-ion cells studied were divided into two categories, cells to be charged/discharged permanently at low temperatures and cells that were to be exposed to the same low temperatures but then preconditioned to ambient temperature before the charge/discharge cycle for a total of 100 performed cycles. It was observed that low temperatures have a direct negative impact on the usable capacity of the cells, accounting for a drop of 8% of the initial value. These effects can be completely negated by preconditioning the cells prior to charging/discharging. After that, the effects of medium-term storage on the capacity of the batteries were investigated to study the possible recovery in the capacity of the cells. Finally, the need for preconditioning the cells is analyzed and alternative methods to mitigate the issues are suggested for equipment where preconditioning is not possible. Full article

This study investigates the geological, mineralogical, and geochemical features of the Alday ore occurrence (Central Kalba, East Kazakhstan) and aims to identify indicators of rare-metal mineralization, with lithium considered to be one of its principal components. In this study, the structural–stratigraphic position of the occurrence is refined; three series of albite–spodumene pegmatites are identified; the compositions of the ore-bearing schists and the granitoids of the Kunush and Kalba complexes are compared; and the role of metasomatic alteration in the concentration of Li, Ta, Nb, Be, and Sn is established. The plagiogranites and dikes of the Kunush complex are characterized by Li anomalies (up to 306 g/t), Ta (up to 64 g/t), and a fractionated REE spectrum (La/Yb up to 108). In addition, the following predictive criteria are formulated: the presence of tectonically disrupted dikes in the Kunush complex with Na₂O/K₂O > 4, the presence of albite and muscovite alteration zones, and the presence of ladder-type spodumene-bearing pegmatites controlled by northwest-trending faults. The ⁴⁰Ar/³⁹Ar muscovite age of the Alday pegmatites (~292 Ma) aligns with the age range of the Kalba granite complex. Based on the main principles of rare-metal pegmatite generation, it is determined that the Tochka pegmatites were formed during the fluid–magmatic fractionation of magma in large granitic reservoirs of the Kalba complex. The Karagoin–Saryozek zone—located between several large granite massifs of the Kalba complex, where host rocks function as a roof—may be promising for investigating rare-metal pegmatite mineralization. Full article

As a fundamental mathematical model for complex systems, complex networks describe interactions among social, infrastructural, and biological systems. However, the complete connection structure is often unobservable, making topology reconstruction from limited data—such as time series of unit states—a crucial challenge. To address network reconstruction under sparse local observations, this paper proposes a novel framework that integrates epidemic dynamics with Dempster–Shafer (DS) evidence theory. The core of our method lies in a two-level belief fusion process: (1) Intra-node fusion, which aggregates multiple independent SIR simulation results from a single seed node to generate robust local evidence represented as Basic Probability Assignments (BPAs), effectively quantifying uncertainty; (2) Inter-node fusion, which orthogonally combines BPAs from multiple seed nodes using DS theory to synthesize a globally consistent network topology. This dual-fusion design enables the framework to handle uncertainty and conflict inherent in sparse, stochastic observations. Extensive experiments demonstrate the effectiveness and robustness of the proposed approach. It achieves stable and high reconstruction accuracy on both a synthetic 16-node benchmark network and the real-world Zachary’s Karate Club network. Furthermore, the method scales successfully to four large-scale real-world networks, attaining an average accuracy of 0.85, thereby confirming its practical applicability across networks of different scales and densities. Full article

As a key component of sustainable economic development, digital transformation has become a fundamental driver for developing and upgrading the modern economic system. While existing research has identified resources and dynamic capabilities as foundational elements, a critical yet underexplored factor lies in the cognitive foundations that enable firms to strategically direct and leverage these assets. Based on 19,062 observation samples of more than 3000 listed companies in Shanghai and Shenzhen stock markets from 2010 to 2023, this paper constructs a theoretical framework of entrepreneurship, organizational attention and digital transformation from the Attention-Based View, and examines a moderated mediation model of the relationship between entrepreneurship and digital transformation. The results show that entrepreneurship significantly promotes digital transformation; organizational attention to “cooperation orientation” and “future orientation” plays a mediating role in it; and the regional innovation atmosphere positively strengthens the “cooperation orientation” path, facilitating the diffusion of innovative knowledge and technologies within the region. Meanwhile, online media reports negatively regulate the “future orientation” path, reflecting that short-term public pressure may weaken enterprises’ attention to long-term sustainable technology investment. In addition, different dimensions of entrepreneurship have varied effects on digital transformation. Heterogeneity analysis revealed significant variations across ownership type, scale, region, industry competition intensity, and technological intensity. This study expands the theoretical mechanism of entrepreneurship and digital transformation from the perspective of attention allocation, and provides theoretical and empirical foundation for fostering a strategic cognitive orientation and advancing digital transformation. Full article

Owing to the low cost, small size, and convenience for installation, 2D LiDAR has been widely used in mobile robots for simultaneous positioning and mapping (SLAM). However, traditional 2D LiDAR SLAM methods have low robustness and accuracy in LiDAR-degenerated environments. To improve the robustness of the SLAM method in such environments, an innovative SLAM method is developed, which mainly includes two parts, i.e., the front-end positioning and the back-end optimization. Specifically, in the front-end part, the AKF (adaptive Kalman filter) method is applied to estimate the pose of the mobile robot, zero bias of acceleration and gyroscope, lever arm length, and the mounting angle. The adaptive factor of the AKF can dynamically adjust the variance of the process and measurement noises based on the residual. In the back-end part, a particle filter (PF) is employed to optimize the pose estimation and build the map, where the pose domain constraint from the output of the front-end is introduced in the PF to avoid mismatch and enhance positioning accuracy. To verify the performance of the method, a series of experiments is carried out in four typical environments. The experimental results show that the positioning precision has been improved by about 61.3–97.9%, 35.7–99.0%, and 43.8–93.0% compared to the Karto SLAM, Hector SLAM, and Cartographer, respectively. Full article

Background: Whole-slide imaging and algorithmic advances have moved computational pathology from research to routine consideration. Despite notable successes, real-world deployment remains limited by generalization, validation gaps, and human-factor risks, which can be amplified in resource-constrained settings. Content/Scope: This narrative review and implementation perspective summarizes clinically proximate AI capabilities in cancer pathology, including lesion detection, metastasis triage, mitosis counting, immunomarker quantification, and prediction of selected molecular alterations from routine histology. We also summarize recurring failure modes, dataset leakage, stain/batch/site shifts, misleading explanation overlays, calibration errors, and automation bias, and distinguish applications supported by external retrospective validation, prospective reader-assistance or real-world studies, and regulatory-cleared use. We translate these evidence patterns into a practical checklist covering dataset design, external and temporal validation, robustness testing, calibration and uncertainty handling, explainability sanity checks, and workflow-safety design. Equity Focus: We propose a stepwise adoption pathway for low- and middle-income countries: prioritize narrow, high-impact use cases; match compute and storage requirements to local infrastructure; standardize pre-analytics; pool validation cohorts; and embed quality management, privacy protections, and audit trails. Conclusions: AI can already serve as a reliable second reader for selected tasks, reducing variance and freeing expert time. Safe, equitable deployment requires disciplined validation, calibrated uncertainty, and guardrails against human-factor failure. With pragmatic scoping and shared infrastructure, pathology programs can realize benefits while preserving trust and accountability. Full article

The mandate of Heaven (tianming 天命) is one of the fundamental beliefs of ancient China. Its origin can be traced back to at least the Shang and Zhou dynasties. During the era of the Hundred Schools of Thought, it was developed into a philosophical concept by Confucianism. Nevertheless, its religiosity was still inherited and developed by Confucianism, particularly the form of Confucianism that served as the state religion since the Han Dynasty. Hence, these two distinct yet intertwined Confucian perspectives on tianming coexist harmoniously. This symbiotic relationship serves a dual purpose: it nurtures the humanistic spirit and belief among the intellectual elite while simultaneously offering a universal religious belief accessible to the common people. The underlying essence that enables Confucianism to accommodate these two disparate spiritual temperaments lies in its core tenets of unremitting self-improvement and profound humanistic concern. The Confucian concepts of tianming and related religious ideas inherently encapsulate a humanistic spirit that resonates with the ethos of modern society. Full article

Transformers have recently been applied to change detection (CD) of multitemporal remote sensing images because of their ability to model global information. However, the rigid patch partitioning in vanilla self-attention destroys spatial structures and consistency in observed scenes, leading to limited CD performance. In this paper, we propose a novel dual-scale pixel aggregation transformer (DSPA-Former) to mitigate this issue. The core of DSPA-Former lies in a dynamic superpixel tokenization strategy and bidirectional dual-scale interaction within the learned feature space, which preserves semantic integrity while capturing long-range dependencies. Specifically, we design a hierarchical decoder that integrates multiscale features through specialized mechanisms for pixel superpixel dialogue, guided feature enhancement, and adaptive multiscale fusion. By modeling the homogeneous properties of spatial information via superpixel segmentation, DSPA-Former effectively maintains structural consistency and sharpens change boundaries. Comprehensive experiments on the LEVIR-CD, WHU-CD, and CLCD datasets demonstrate that DSPA-Former achieves superior performance compared to state-of-the-art methods, particularly in preserving the structural integrity of complex change regions. Full article

We report on the study of the immobilization process of non-radioactive rhenium (Re), a chemical analogue of technetium-99 (⁹⁹Tc), in compounds based on magnesium potassium phosphate (MKP), as well as the possibility of enhancing their properties with iron-bearing additives/fillers. Powdered Re₂O₇ was used as the initial Re-containing source. Because of the solubility and high leachability of Tc (VII), which is also volatile at high temperatures, its immobilization for long-term storage and disposal poses a serious challenge to researchers. Taking this into account, low-temperature stabilization technology based on MKP, a cementitious material, is currently considered promising. We prepared experimental specimens based on Re-incorporated MKP matrices and analyzed their microstructure in detail using analytical methods of X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Considering that iron-bearing substances can reduce Tc (VII) to the lower-valence form Tc (IV), which is more stable, attention was also paid to evaluate the effect of fillers (Fe₂O₃, Fe₃O₄, Fe, FeS and blast furnace slag (BFS)) on strength, oxidation state, and water resistance (expressed as leaching cumulative concentration). The addition of fillers ensures the formation of denser compounds based on MKP after 28 days of curing under ambient conditions and increases their mechanical strength. The oxidation state of Re and the reduction from Re (VII) to Re (IV) was estimated using X-ray-absorption near-edge structure (XANES) analysis. Considering the Re leaching concentrations from tests using the ANS-16.1 standard in water, enhanced leachability indices (LI) for Re from MKP matrices were determined with the addition of iron-bearing fillers. Overall, the average LI values were greater than the minimum limit, indicating their acceptance for disposal recommended by the U.S. Nuclear Regulatory Commission. Full article

Accurate and robust positioning is a critical enabler for Unmanned Aerial Vehicle (UAV) applications, ranging from mapping and inspection to emerging Urban Air Mobility (UAM). While Global Navigation Satellite Systems (GNSS) remain the backbone of absolute positioning, their performance is severely constrained by UAV platform characteristics and complex low-altitude environments. This paper presents a system-level review of GNSS-based UAV positioning. Instead of treating GNSS in isolation, we first link mission requirements and platform constraints, such as aggressive dynamics and Size, Weight, and Power (SWaP) limitations, to specific positioning challenges. We then critically evaluate the spectrum of GNSS techniques, from standalone and Satellite-Based Augmentation System (SBAS) modes to high-precision carrier-phase methods including Real-Time Kinematic (RTK), Post-Processed Kinematic (PPK), Precise Point Positioning (PPP), and PPP-RTK. Furthermore, we discuss multi-sensor fusion with inertial, visual, and Light Detection and Ranging (LiDAR) sensors to mitigate vulnerabilities in urban canyons and GNSS-denied conditions. Finally, we outline key challenges and future directions, highlighting integrity-aware architectures, Artificial Intelligence (AI)-enhanced signal processing, and multi-layer Positioning, Navigation, and Timing (PNT) concepts. The review provides a structured framework and system-level insights to guide resilient navigation for UAV operations in low-altitude airspace. Full article