Search Results (9,581)

A monolithic digital Silicon Photomultiplier (SiPM) featuring 1024 microcells with a 30-micrometer pitch and a 50% fill factor has been designed in a 110-nanometer CMOS image sensor technology. The device under consideration integrates both SPAD sensors and front-end electronics in the same substrate. It can count up to 1024 photons in less than 22 ns, while assigning timestamps to the first and last detected photons with a time resolution of less than 100 ps. A parallel counter structure combined with a fast adder tree provides photon counting in digital form with low latency, whereas a carefully balanced fast NAND tree ensures a fixed-pattern time uncertainty not exceeding 26 ps. The architecture incorporates in-pixel memory for individual cell disabling and configurable thresholding on the timing signal for noise mitigation. In order to optimize the fill factor, a part of the electronics is placed outside the array, while the most sensitive elements of the timing and counting circuits are laid out close to the sensor, in the SPAD array. A serial readout is employed to provide a single output connection per SiPM, thereby simplifying system integration. Full article

In situ tunnel expansion provides a cost-effective and environmentally sustainable alternative to new tunnel construction. However, staged widening disturbs the lining–rock system, triggering complex, non-monotonic stability responses. This study integrates physical model tests and FLAC3D simulations to investigate the mechanical evolution of a limestone tunnel widened by the Center Diaphragm (CD) method. Seven cross-sections (S1–S7) were fabricated and tested under uniaxial compression with digital image correlation. Results show that the peak load decreases from 385.73 kN in the lined baseline (S1) to 184.14 kN at the first unilateral cut (S3), a 49% reduction, but recovers to 262.28 kN at the left-half closure (S4) before dropping to 128.16 kN at the upper-right excavation (S5). The final relined stage (S7) regains 200.69 kN, a 40% improvement over the unlined enlarged state (S6). Numerical analyses confirm this non-monotonic trajectory in terms of the peak plastic-zone fraction. It reaches at 86.32% in S3, decreases to 74.03% in S4, and rises to 76.43% in S5. The fractions further reach 88.51% in S6 and 87.70% in S7, reflecting the enlarged span and redistributed yielding. Targeted bolting at weak stages S3 and S5 reduced plastic-zone fraction by 14.73 and 4.75 percentage points, and reduced crown settlement by 68% and 41%, respectively. These findings challenge the conventional monotonic degradation assumption, identify S3 and S5 as critical weak links, and validate selective reinforcement for enhancing stability during tunnel expansion. Full article

The utilisation of composite materials has the potential to play a vital role in the development of lightweight structures for future generations of aircraft, with the objective to reduce emissions. Ultrasonic welding is a process that has been proven to exhibit advantageous qualities, including the capacity to achieve welds with a comparatively short process time. Furthermore, its capacity to function as both a static and a continuous process makes it a viable candidate for facilitating the realisation of this objective. The present study investigates the potential of a novel explicit modelling approach for the static ultrasonic welding process to more accurately represent the welding process by incorporating a more precise representation of the hammering effect. The hammering effect describes the partial loss of contact between the sonotrode and the upper adherend. The model’s validation was achieved through a multifaceted approach that incorporates high-speed camera recording, encompassing digital image correlation, laser displacement sensor measurements, and static ultrasonic welding experiments. These experiments encompassed varying welding times, followed by fracture surface analysis. The findings showed that an explicit time-domain model can effectively represent the static welding process of unidirectional materials utilising a film energy director. The experimental validation demonstrated a high degree of correlation between the thermal behaviour of the welding interface and the simulation results. The study demonstrated that the neutral position of the sonotrode exhibited an increase during the initial phase of the welding process due to dynamic stresses. This phenomenon enables reduced constraint movement of the adherends and the energy director, which results in the disconnection of the sonotrode from both the upper adherend and the energy director, as well as the adherends and the anvil. The higher neutral position of the sonotrode was then implemented in an explicit simulation of the static ultrasonic welding process. Full article

The uniaxial tensile test is a common and fundamental test in materials science and engineering, in which a specimen is subjected to controlled tension until failure. From this, the stress–strain curve and many property parameters of the material can be calculated, such as tensile strength, ultimate strength, maximum elongation, Young’s modulus, Poisson’s ratio, and yield strength. As fibrous materials, such as paper and paperboard, become more popular, accurately measuring their mechanical properties becomes essential for developing and applying these materials, especially in packaging. However, since they are anisotropic and inherently inhomogeneous due to the arrangement of the fibers, accurately determining their mechanical properties is not straightforward. This study investigated how several key factors influence the results of tensile tests on fiber-based materials: sample size and deformation measurement techniques using three fiber materials. This study also compared three different strain recording methods: digital image correlation (DIC), video extensometer, and conventional extensometer (Traverse). The DIC technique emphasized the effect of the inherent inhomogeneity of the paperboard on the overall mechanical properties obtained from tensile tests. The results indicated that sample size has a negligible effect on the stress–strain curve, and any apparent influence likely stems from slip at the grips during tensile testing. However, sample size does affect paperboard fracture to some extent. The study also provided recommendations for optimal specimen geometry and deformation recording methods to improve the accuracy and repeatability of tensile testing of fiber-based materials. Full article

Present-day artificial intelligence systems (AI), virtual assistants, and devices connected to the Internet of Things (IoT) are playing an increasingly important role in decision-making processes in the everyday lives of individuals and daily operations of organizations. In this respect, the users’ trust is a key factor determining their acceptance and effective use. In contemporary digital ecosystems, this trust increasingly becomes a component of sustainable digital marketing, in which transparent data practices and responsible communication shape long-term consumer–technology relationships. This paper analyzes the halo effect as a psychological mechanism affecting the perception of competences, reliability, and ethics in the case of technologies based on AI. Based on the literature on behavioral economics, it was shown how positive associations with the interface, brand, or previous experience of the user may lead to excessive trust in technology. Such mechanisms also play a significant role in shaping sustainable consumption patterns, as users—guided by cognitive shortcuts—can adopt technologies in ways that either strengthen or weaken responsible digital behaviors. Moreover, the potential risks associated with this phenomenon were also indicated. The aim of this paper was to present how the utilization of the halo effect influences the generation of trust in smart systems and the formulation of implication for management practices and technology design. These implications are increasingly important in the context of sustainable digital marketing policy, where organizations must align persuasive communication with ethical standards and with rising expectations regarding sustainable digital transformation. Relationships between variables were analyzed using structural equation modeling (SEM), making it possible to verify complex dependencies between the perceived image of technology, the halo effect, and the users’ trust. This study tested three core hypotheses regarding the halo effect’s role, the foundational importance of security, and the mediating function of trust in technology adoption. The results of these analyses indicate that the halo effect significantly affects the level of trust in each of the investigated areas, with the strongest effect observed in the case of virtual assistants, where perception of the human-like characteristics of the interface considerably strengthened trust in the competences and reliability of the system. This finding has particular relevance for AI-driven personalization mechanisms, which increasingly guide consumer decision-making and shape their long-term behavioral patterns in online environments, with direct implications for sustainable consumption. This paper provides contribution to innovation management and technical marketing, stressing the importance of cognitive and emotional factors in the acceptance of new technologies. At the same time, it highlights the theoretical need to integrate responsible AI design with sustainable digital marketing strategies The findings suggest that ensuring trust, once established, has the potential to support not only technological innovation but broader societal goals related to responsible consumption, environmental stewardship, and long-term digital well-being aligned with sustainable development principles. However, this study stops short of empirically measuring sustainable consumption behaviors, offering instead a conceptual link that requires further empirical validation. Full article

This study employed Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth data meteorological data, Digital Elevation Model (DEM), Normalized Difference Vegetation Index (NDVI), and ground monitoring data for particulate matter (PM2.5) to construct a model for estimating the PM2.5 concentration in Yangquan City, Shanxi Province, from 2015 to 2024. The spatial and temporal changes in the PM2.5 concentration were analyzed. The results revealed the following: (1) The random forest model was more accurate than the multiple linear regression model. The spring model R² increased by 38.7%, and the Root Mean Square Error (RMSE) decreased by 92.6%. The summer model R² increased by 65.1%, and the RMSE decreased by 92.5%. The autumn model R² increased by 2.7%, and the RMSE decreased by 83.4%. The winter model R² increased by 25.4%, and the RMSE decreased by 95.5%. (2) The PM2.5 concentration in Yangquan City showed an upward trend from 2015 to 2017, and then a downward trend from 2018 to 2024, with an average decrease of 18.3 μg/m³. The highest concentration of PM2.5 was 55–85 μg/m³ in winter, and the lowest concentration of PM2.5 was 25–40 μg/m³ in summer. In terms of spatial distribution, the PM2.5 concentration in Yangquan City exhibits a pattern of being lower in the northwest and higher in the southeast. The high values are primarily concentrated in the central urban areas and major industrial zones in the southeast. Full article

The synergy between additively manufactured (AM) polymers and functional PVD coatings is crucial for advanced applications, yet the reliability of these hybrid systems is dictated by the residual stresses induced during deposition. This work presents the first in-depth, nanoscale profiling of residual stresses in Ti6Al4V and SS316 coatings on 3D-printed Acrylonitrile Styrene Acrylate (ASA) and Silicon (Si) substrates. A cutting-edge methodology combining Focused Ion Beam (FIB) milling with Digital Image Correlation (DIC), rigorously interpreted through the non-integral eigenstrain theory, is employed. Our findings reveal a consistent pattern of compressive stresses near the coating surface but expose a significant tensile stress peak at the coating-substrate interface, a feature not observed on reference silicon substrates. High-resolution electron microscopy and elemental analysis suggest that this stress concentration is associated with the presence of a thin, brittle oxide interlayer formed on the substrate surface. Furthermore, this study quantifies the dominant effect of the low-stiffness polymer substrate, which leads to a strain relief magnitude an order of magnitude higher than in rigid substrates. This work provides critical quantitative data on the failure-driving mechanisms in these emerging material systems and establishes a robust, optimized metrological protocol for their characterization. Full article

The integration of outdoor camera images with three-dimensional (3D) geographic information on the observed scene is of interest for many video acquisition applications. To solve this data fusion problem, camera images have to be matched with the 3D geometry provided by a geographic information system (GIS). Considering a camera with a known geographical position, this paper proposes the use of a dense local azimuth–elevation map (LAEM) derived from a gridded digital elevation model (DEM) to represent the data and thus facilitate the matching of GIS and image data. To each regularly sampled azimuth and elevation angle pair, this map assigns the geographic point derived from the DEM viewed in this direction. The problem of computing the LAEM from the DEM is closely related to that of surface rendering, for which solutions exist in computer graphics. However, rendering software cannot be used directly in this case, since their view directions are constrained by the pinhole camera model and the apparent colour, rather than the position of the viewed point, is assigned to the viewing direction. Therefore, this paper also proposes a specific algorithm for the computation of the LAEM from the DEM. A MATLAB^® implementation of the algorithm is also provided, which is tailored to process the DEM dataset swissALTI3D from the Swiss Federal Office of Topography swisstopo. Full article

Differentiating AI-generated, real, or imitated artworks is becoming a tedious and computationally challenging problem in digital art analysis. AI-generated art has become nearly indistinguishable from human-made works, posing a significant threat to copyrighted content. This content is appearing on online platforms, at exhibitions, and in commercial galleries, thereby escalating the risk of copyright infringement. This sudden increase in generative images raises concerns like authenticity, intellectual property, and the preservation of cultural heritage. Without an automated, comprehensible system to determine whether an artwork has been AI-generated, authentic (real), or imitated, artists are prone to the reduction of their unique works. Institutions also struggle to curate and safeguard authentic pieces. As the variety of generative models continues to grow, it becomes a cultural necessity to build a robust, efficient, and transparent framework for determining whether a piece of art or an artist is involved in potential copyright infringement. To address these challenges, we introduce ArtUnmasked, a practical and interpretable framework capable of (i) efficiently distinguishing AI-generated artworks from real ones using a lightweight Spectral Artifact Identification (SPAI), (ii) a TagMatch-based artist filtering module for stylistic attribution, and (iii) a DINOv3–CLIP similarity module with patch-level correspondence that leverages the one-shot generalization ability of modern vision transformers to determine whether an artwork is authentic or imitated. We also created a custom dataset of ∼24K imitated artworks to complement our evaluation and support future research. The complete implementation is available in our GitHub repository. Full article

This experimental study investigates the influence of selected bed joint reinforcement systems on the evolution of damage and crack development in masonry elements subjected to axial compression. Autoclaved aerated concrete masonry samples reinforced with steel truss reinforcement, unidirectional carbon fibre mesh and steel cords embedded in a fibreglass matrix were tested and compared to an unreinforced reference specimen. Full-field deformation and strain localisation were monitored using digital image correlation (DIC). The results indicate that bed joint reinforcement does not lead to a measurable increase in compressive load-bearing capacity, as differences in ultimate load remain within experimental uncertainty. However, clear differences in the evolution and spatial distribution of damage were observed. Steel truss reinforcement promoted strain redistribution and delayed localisation of tensile strains, while the remaining reinforcement systems exhibited only limited influence on crack morphology. The findings confirm that bed joint reinforcement in compressed masonry should be classified as a nonstructural solution and demonstrate the diagnostic value of full-field deformation monitoring for assessing damage evolution and crack control in masonry structures. Full article

The formability of AA7021-T4 sheets under changing strain paths was investigated via a novel crystal plasticity model and associated experimentation. The motivation was to advance simulation tools for process design of limited-ductility 7xxx alloys, with important applications in the automotive industry. Pre-strains were applied in biaxial and plane-strain tension using Marciniak tooling, followed by uniaxial tensile testing to failure. Strain measurements were obtained by digital image correlation, while dislocation structures were characterized using high-resolution EBSD. A strain-gradient elasto-plastic self-consistent (SG-EPSC) model incorporating dislocation density-based hardening and backstress from geometrically necessary dislocations (GNDs) was employed to predict the stress–strain response and dislocation evolution. Results showed that pre-strains normalized by forming limit diagram (FLD) criteria produced comparable residual uniaxial tensile ductility, regardless of whether biaxial or plane-strain tension was applied, despite differences in absolute pre-strain levels. Both experiments and simulations revealed that GND density correlated with remaining ductility better than simple strain magnitude values. These findings indicate that AA7021-T4 retains greater formability under multiaxial strain path changes than expected from FLD-based considerations. The combined experimental–modeling approach demonstrates the value of incorporating microstructure-based variables, such as GNDs, into forming assessments of high-strength aluminum alloys, with implications for their potential use in automotive lightweighting development. Full article

Background/Objectives: The multifaceted concept of body image (BI) refers to an individual’s attitudes and impressions of their body. Negative BI is associated with a number of harmful health consequences, including obesity, eating disorders, and symptoms of sadness. The contemporary digital era, marked by the dominance of platforms, has brought about a considerable transformation in the landscape of BI issues. This study’s goal is to compile and assess the connections between social media (SM) use, body weight, and BI in adult populations. Methods: This is a narrative review that comprehensively searches across multiple academic databases, such as PubMed, Medline, Scopus, Web of Science, and Google Scholar. Studies that used SM (online blogs, microblogs, content communities, or social networking sites) for engagement (e.g., sharing, commenting, liking) or image-related activities (e.g., viewing, posting, or engaging with images) with healthy adults (aged 18–70 years) of any body mass index (BMI kg/m²) met the inclusion criteria. Included were observational and experimental studies that examined habitual SM use. Only peer-reviewed works published in English between 2015 and 2025 met the search criteria. Results: The currently available findings suggest that obese people are more dissatisfied with their bodies than people of normal weight, and obese women are more dissatisfied with their bodies than their peers of normal weight. Furthermore, experimental studies have demonstrated that immediate BI is adversely affected by acute exposure to idealized social media photographs. Conclusions: Policies should support specialized training that emphasizes a holistic approach to health and puts functionality and health above attractiveness. This training is crucial for dispelling weight-related stigmas and enabling healthcare providers to offer compassionate treatment that supports mental and physical health. Future research must concentrate on internalization and social pressure or reinforcement because these subjects have not gotten as much emphasis in prior studies. Such mechanism research could help better contextualize the role of recently introduced SM items. Full article

Background and Objectives: Male infertility has emerged as a growing global health concern, contributing to 20–30% of all infertility cases. It is a multifactorial condition, arising from genetic, endocrine, structural, environmental and lifestyle factors. This narrative review synthesizes current evidence on epidemiology, diagnostic advances and therapeutic strategies while highlighting emerging trends and research priorities. Materials and Methods: This review adheres to SANRA guidelines. Literature was sourced from PubMed, Saudi Digital Library, Google Scholar, and PsycINFO using MeSH terms including “Male Infertility,” “Diagnosis,” “Treatment,” and “Epidemiology.” Results: Diagnostic evaluation of male infertility includes clinical assessment, advanced semen analysis, imaging techniques, hormonal assays and molecular testing. Despite significant advances in the evaluation of male infertility, idiopathic causes (30–40%) remain challenging. Management strategies include lifestyle modifications, medical therapies including hormones and drugs, surgical interventions, and assisted reproductive technologies (ARTs). However, outcomes remain suboptimal in idiopathic and severe cases, particularly regarding sperm DNA fragmentation and environmental exposures. Conclusions: Substantial knowledge gaps exist in male infertility, particularly in idiopathic cases, molecular mechanisms of environmental pollutants, and long-term ART offspring outcomes. Future research priorities include: (1) molecular and epigenetic biomarkers for improved diagnosis and prognosis; (2) environmental exposure assessment and mitigation strategies; (3) metabolomics-guided personalized therapies; (4) regenerative medicine approaches including spermatogonial stem cell therapy; and (5) multidisciplinary integrative care models. Addressing these gaps through coordinated research and clinical innovation is essential for improving male reproductive health globally. Full article

Neuromorphic computing seeks to mimic structure and function of biological neural systems to enable energy-efficient, adaptive information processing. A critical component of this paradigm is neural encoding—the translation of analog or digital input data into spike-based representations suitable for spiking neural networks (SNNs). This paper provides a comprehensive overview of major neural encoding schemes used in neuromorphic systems, including rate and temporal encoding, as well as latency, interspike interval, phase, and multiplexed encoding. The purpose of this paper is to explore the use of encoding techniques for deep learning applications. We discussed the underlying principles of spike encoding approaches, their biological inspiration, computational efficiency, power consumption, integrated circuit design and implementation, and suitability for various neuromorphic applications. We also presented our research on a hardware-and-software co-design platform for different encoding schemes and demonstrated their performance. By comparing their strengths, limitations, and implementation challenges, we aim to provide insights that will guide the development of more efficient and application-specific neuromorphic systems. We also performed an encoder performance analysis via Python 3.12 simulations to compare classification accuracies across these spike encoders on three popular image and video datasets. The performance of neural encoders working with both deep neural networks (DNNs) and SNNs is analyzed. Our performance data is largely consistent with the benchmark data on image classification from other papers, while limited performance data on the University of Central Florida’s 101 (UCF-101) video dataset were found in comparable studies on spike encoders. Based on our encoder performance data, the Interspike Interval (ISI) encoder performs well across all three datasets, preserving continuous, detailed spike timing and richer temporal information for standard classification tasks. Further, for image classification, multiplexing encoders outperform other spike encoders as they simplify timing patterns by enforcing phase locking and improve stability and robustness to noise. Within the SNN testbenches, the ISI-Phase encoder achieved the highest accuracy on the Modified National Institute of Standards and Technology (MNIST) dataset, surpassing the Time-To-First Spike (TTFS) encoder by 1.9%. On the Canadian Institute For Advanced Research (CIFAR-10) dataset, the ISI encoder achieved the highest accuracy. This ISI encoder had 22.7% higher accuracy than the TTFS encoder on the CIFAR-10 dataset. The ISI encoder performed best on the UCF-101 dataset, achieving 12.7% better performance than the TTFS encoder. Full article

Precision Agriculture (PA) is increasingly applied to nutraceutical cropping systems, where agronomic productivity must be integrated with the stabilization of phytochemical quality and environmental sustainability. This structured narrative review synthesizes scientific evidence (primarily 2010–2025) on the use of Unmanned Aerial Vehicle (UAV)-based multispectral and thermal sensing, LiDAR-derived canopy characterization, Internet of Things (IoT) monitoring, and artificial intelligence (AI)-driven analytics in medicinal, aromatic, and functional crops. The literature indicates that PA enhances high-resolution monitoring of crop–environment interactions, supporting site-specific irrigation, nutrient management, and stress detection. Under validated conditions, these interventions are associated with improved yield stability, resource-use efficiency, and modulation of secondary metabolite accumulation. However, reported outcomes vary substantially across species, agroecological contexts, and experimental scales, and most studies remain plot-scale or pilot-scale, limiting large-scale generalization. Moringa oleifera Lam. is examined as a model species for Mediterranean and semi-arid systems. Evidence suggests that integrated spectral, structural, and environmental monitoring can support optimized irrigation scheduling, canopy uniformity, and phytochemical consistency. Nonetheless, genotype-specific calibration, multi-season validation, standardized metabolomic benchmarking, and cross-regional transferability remain significant research gaps. Overall, PA represents a scientifically promising but still maturing framework for nutraceutical agriculture. Future progress will require rigorous multi-site validation, improved model robustness, standardized sustainability metrics, and comprehensive economic assessments to ensure scalability and long-term impact. Full article