Search Results (19,005)

Arsenic contamination in groundwater is a severe and widespread environmental and public health challenge. Recent years have witnessed rapid advances in catalytic remediation technologies, particularly those integrating advanced oxidation processes (AOPs), bifunctional materials, and field-scale applications. This comprehensive review synthesizes recent developments, emphasizing the synergy between catalytic oxidation and adsorption, the design of innovative and recyclable materials, and the practical translation of laboratory findings to real-world remediation scenarios. Key breakthroughs include dual-function catalysts for combined contaminant removal, scalable systems compatible with renewable energy, and hybrid strategies integrating conventional and catalytic routes. Case studies from arsenic hotspots worldwide demonstrate not only technological feasibility but also highlight knowledge gaps and sustainability challenges. By evaluating catalytic mechanisms, operational performance, and environmental impact, this review identifies promising directions for the next generation of arsenic remediation and offers a critical roadmap to guide future research and practice. Full article

Flexible acceleration sensors demonstrate revolutionary potential in healthcare, structural vibration monitoring, and consumer electronics owing to their unique conformal adhesion capability and mechanical adaptability. However, current academic research presents two distinct paradigms for realizing flexibility: one is the hybridly flexible sensor, which incorporates traditional micro-electro-mechanical System (MEMS) acceleration sensor chips with flexible packaging/substrates; the other is the intrinsically flexible sensor, whose sensing unit and substrate are entirely composed of flexible materials enabled by microstructural design. This review first analyzes the fundamental differences and design challenges between these two flexible architectures. It then systematically elucidates five core sensing mechanisms—capacitive, piezoresistive, triboelectric, piezoelectric, and electromagnetic—comparing their working principles, material systems, structural designs, and performance metrics. Among these, piezoelectric and triboelectric types exhibit distinctive advantages in self-powering capability, whereas resistive and capacitive approaches offer greater ease of integration. Furthermore, the applications of intrinsically flexible acceleration sensors in structural health monitoring, wearable devices, automotive safety, and other fields are discussed, with particular emphasis on their unique strengths in real-time vibration monitoring. Finally, the review summarizes existing challenges, such as the trade-off between sensitivity and flexibility, and provides theoretical insights to guide future innovations in intrinsically flexible acceleration sensor technology. Full article

The use of colored LED lights is a tool for controlling the development of lymphoid organs and the immune system in general. This study aims to analyze the effects of using simple and combined colored LED lights throughout a 6 week period (1–42 days of age). In this study, 336 one-day-old chicks were used, separated randomly into four groups with different sex and lighting systems, with each group being divided into four separate replicates (4 × 21 birds). The chicks in the WL-Male and WL-Female were exposed to white LED light (WL, 400–760 nm) for 6 weeks, while the chicks in the G-GxB-BL-Male and G-GxB-BL-Female were exposed to a combination of monochromatic lights as follows: green (560 nm) from 1 to 14 days of age, green and blue (480–560 nm) for 15–28 days of age, and blue lights (480 nm) for 29–42 days of age. The use of a mixture of green and blue LED lights (G-GxB-BL) resulted in a significant decrease in the average daily feed intake and feed conversion ratio compared to white light, without causing changes in the body weight of the chicks, average daily gain, mortality rate, and coefficient of variability. G-GxB-BL lights also improved the morphological development of the bursa of Fabricius (BF) compared to white light by significantly increasing the organ index and the lymphoid follicle area. At the same time, G-GxB-BL light compared to white light improved B lymphocytes proliferation in the BF by significantly increasing the lymphocyte density in lymphoid follicles, as well as the number of PCNA-positive cells. This light treatment had these results due to the activation of melatonin receptors, which led to a significant increase in Mel1a-positive cells and a significant decrease in the number of RORα-positive cells. These results demonstrate that G-GxB-BL lights improved the growth performance and immune response in the BF of broiler chickens. Full article

This study investigates how environmental consciousness motivations—grounded in Corporate Social Responsibility (CSR) theories (instrumental, political, integrative, and ethical)—influence environmental management performance (MP) and indirectly affect operational performance (OP). Specifically, the research examines these motivations under the intensifying pressure of the Carbon Border Adjustment Mechanism (CBAM) within manufacturing firms in Türkiye. From a cost–benefit perspective, the study addresses whether dominant instrumental (cost-oriented) consciousness acts as a barrier to innovation-led gains predicted by the Porter Hypothesis. Analyzing data from 400 managers using the PLS-SEM method, findings reveal that while ethical and political consciousness positively enhance MP and OP, instrumental consciousness—driven by short-term cost-compliance pressures—exerts a significant negative impact. Furthermore, the statistical insignificance of integrative consciousness highlights a strategic integration gap for manufacturing enterprises in Türkiye. These results demonstrate that perceiving environmental regulations merely as a “cost burden” creates a structural barrier that breaks the strategic productivity cycle. The study concludes that to achieve a positive multiplier effect on competitiveness, firms must transition from instrumental compliance to integrated strategic commitment, guiding managers to distinguish between short-term instrumental efforts and long-term strategic commitments. Full article

In low-latency edge-intelligence scenarios such as autonomous driving and industrial edge analytics, the processing of large-scale sensor data imposes extremely stringent requirements on communication latency. However, the high overhead of the traditional TCP protocol makes it difficult to satisfy such demands, while the semantic gap between the high-performance RoCE protocol and the standard Socket API prevents existing applications from directly exploiting its advantages. To address this problem, this paper proposes TransBridge, a lightweight user-space communication middleware that transparently bridges TCP and RoCE. Its design is realized through three key innovations: a transparent user-space compatibility architecture that enables unmodified Socket-based applications to benefit from RoCE performance; a microsecond-level low-latency transmission engine that bypasses kernel and protocol stack overhead; and a lightweight lock-free resource management mechanism based on a decentralized peer-to-peer architecture and deferred buffer updates. Experiments on a real RoCE network show that TransBridge significantly outperforms mainstream schemes: it achieves an average round-trip latency of 5.926 $\mathsf{\mu }$s for 16 B messages and a throughput of 20.254 Gbps for 16 KB messages; in the Fast DDS application-level evaluation, it achieves a throughput of 188 Mbps and an average round-trip latency of about 150 $\mathsf{\mu }$s. The results indicate that TransBridge can provide transparent and effective RoCE acceleration for existing Socket-based applications in resource-constrained edge environments. Full article

In recent years, cross-border e-commerce and digital trade activities in transition economy countries and regions have continued to grow. Based on resource orchestration theory and empowerment theory, this paper examines the influence mechanism of digital leadership on international entrepreneurial performance and investigates the moderating effect of platform support. Analyzing survey data from 227 Chinese cross-border e-commerce enterprises using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the study finds that: (1) Digital leadership positively influences the international entrepreneurial performance of cross-border e-commerce enterprises through the mediating roles of brand management capability and product innovation capability; (2) Platform support plays a positive moderating role in the relationship between brand management capability and international entrepreneurial performance in cross-border e-commerce; (3) Platform support moderates the mediating effect of brand management capability in the relationship between digital leadership and international entrepreneurial performance of cross-border e-commerce enterprises; (4) Based on fsQCA analysis, two antecedent configurations for achieving high international entrepreneurial performance in cross-border e-commerce are identified. These findings hold significant theoretical implications for research on cross-border digital platforms and international new ventures, while also providing robust empirical support for enterprises seeking to achieve international entrepreneurial success through the implementation of digital strategies. Full article

This paper presents a comprehensive experimental assessment of electromagnetic field (EMF) exposure dynamics during the transition from IEEE 802.11ax (Wi-Fi 6) to IEEE 802.11be (Wi-Fi 7). Using a human-centric experimental setup, we evaluate the impact of Wi-Fi 7’s core innovations—4096-QAM modulation, 320 MHz bandwidth, and Multi-Link Operation—under iPerf3-controlled high-traffic conditions. A key contribution of this study is the analysis of multi-client influence, comparing EMF emission profiles when one versus two devices are active. Our results reveal a significant paradigm shift: while Wi-Fi 7 generates higher near-field peaks (up to 955.92 mV/m in MLO mode at 20 cm) to sustain high-order modulation, it exhibits an aggressive spatial decay, with E-field intensity collapsing by up to 76.6% at one meter. We demonstrate that the transition from a single-client to a dual-client configuration significantly alters the stochastic nature of the field, increasing the probability of transient high-power events, as characterized by our Complementary Cumulative Distribution Function (CCDF) framework. The findings confirm that Wi-Fi 7’s performance gains are decoupled from long-range exposure; the high-intensity field remains strictly localized, providing a natural safety buffer. This study provides new experimental vista into how next-generation WLAN systems trade near-field strength for far-field safety, maintaining compliance with international limits while supporting multi-device gigabit connectivity. Full article

Electroencephalogram (EEG) signals serve as the core information carrier for brain–computer interfaces (BCIs); however, their highly non-stationary nature, extremely low signal-to-noise ratio, and significant inter-individual variability pose considerable challenges for signal decoding. Existing deep learning methods struggle to strike a balance between multi-scale, fine-grained feature extraction and efficient long-range temporal modeling. To overcome this limitation, this study proposes a novel deep cascaded architecture, MSResBiMamba, which deeply integrates multi-scale spatiotemporal feature learning with cutting-edge long-sequence modeling techniques. The model first utilizes an enhanced multi-scale spatiotemporal convolutional network (MS-CNN) combined with a SE-channel attention mechanism to adaptively extract local multi-band features and dynamically suppress redundant artefacts. Subsequently, it innovatively introduces an enhanced bidirectional Mamba (Bi-Mamba) module to efficiently capture non-causal long-range temporal dependencies with linear computational complexity, whilst cascading multi-head self-attention mechanisms to establish global higher-order feature interactions. Extensive experiments on the BCI Competition IV-2a dataset demonstrate that MSResBiMamba achieves outstanding classification performance in multi-class motor imagery tasks, significantly outperforming traditional methods and existing state-of-the-art neural networks. Ablation studies and t-SNE visualisations further confirm the model’s robustness in feature decoupling and cross-subject applications, providing a high-precision, high-efficiency decoding solution for BCI systems. Full article

Full fine-tuning of large-scale vision-language models for video action recognition incurs prohibitive computational cost and often degrades pre-trained spatial representations. To address this, we propose VETA-CLIP, a Video Efficient Temporal Adaptation framework that enhances temporal modeling while preserving cross-modal alignment. By incorporating lightweight adapters into a frozen backbone, VETA-CLIP introduces only 3.55M trainable parameters (a 98% reduction compared to full fine-tuning). Our approach features two key innovations: (1) an Efficient Spatio-Temporal Attention (ESTA) mechanism with a parameter-free boundary replication temporal shift (BRTS) module, which explicitly decouples spatial and temporal attention heads to capture inter-frame dynamics while minimizing disruption to the pre-trained spatial representations; and (2) a novel Variation Loss that maximizes both local inter-frame differences and global temporal variance, encouraging the model to focus on action-related changes rather than static backgrounds. Extensive experiments on HMDB-51, UCF-101, and Something-Something v2 demonstrate that VETA-CLIP achieves competitive performance across zero-shot, base-to-novel, and few-shot protocols, while and remains competitive on the Kinetics-400 dataset. Notably, our eight-frame variant requires only 4.7 GB of peak GPU memory and 2.47 ms of inference per video, demonstrating exceptional computational efficiency alongside consistent accuracy gains. Full article

In recent years, Visual Education has emerged as an innovative and interdisciplinary teaching approach aimed at promoting meaningful learning through the conscious use of visual tools and languages. This educational paradigm helps to facilitate the understanding of complex concepts, translating them into clear and intuitive visual representations, while enhancing memorisation skills, critical information processing and the practical application of acquired knowledge. This systematic review, conducted according to the PRISMA (2020) protocol, analyses the most recent empirical evidence on the effectiveness of Visual Education in educational contexts. The main objective is to assess how the intentional use of visual tools—images, concept maps, educational videos, interactive digital materials, and virtual manipulatives—contributes to enhancing learning processes and developing transversal skills. Through a comparative analysis of fourteen international contributions published between 2020 and 2025, selected from the Scopus, Web of Science and EBSCO databases, the research highlights how Visual Education significantly influences the improvement of academic performance, motivation and cognitive and emotional engagement of students. The results also confirm the inclusive function of visual teaching, which can encourage participation, self-esteem and cooperation even in individuals with special educational needs. The discussion emphasises the need for the systematic integration of Visual Education into school curricula as a strategy to enhance soft skills and promote more equitable, effective learning geared towards the integral development of the individual. Full article

Background/Objectives: Bud sports (somatic mutations) offer a quick way to develop new bougainvillea varieties by altering specific traits while keeping the desirable genetic background of the original cultivar. However, we still lack a comprehensive understanding of their genomic architecture and the molecular mechanisms behind their formation. This study aimed to characterize the population genomic characteristics of bud sports derived from the commercial variety Bougainvillea × buttiana ‘Miss Manila’. Methods: We employed genotyping by sequencing (GBS) on 39 accessions, including 27 bud sports and 12 conventional varieties. Population genomic analyses, such as principal component analysis (PCA), phylogenetic reconstruction, ADMIXTURE, and diversity statistics (π, He, Tajima’s D), were performed on 64,810 high-quality SNPs. Genome-wide scans for differentiation (FST) and selective sweeps (XP-CLR) were also conducted. Results: Bud sports showed significantly lower genetic diversity (π and He) than conventional varieties, which matches their clonal origin. PCA, phylogenetic, and ADMIXTURE analyses (optimal K = 4) revealed clear genetic differentiation and distinct population structures between the two groups. The bud sport population possessed fewer private alleles and a less negative Tajima’s D value. Genomic scans identified regions under selection in bud sports, with functional annotation pointed to genes involved in ubiquitin-mediated proteolysis and RNA transport. Notably, Bou_119143 (UDP-rhamnose rhamnosyltransferase 1) showed a high mutation frequency specifically in bud sports. Conclusions: We provide the first population-genomic evidence that bud sports of ‘Miss Manila’ are genetically distinct clonal lineages, shaped by somatic mutation and selection. These findings support bud sports as efficient sources for germplasm innovation. The identified genomic regions and candidate genes lay a foundation for future marker-assisted selection and molecular breeding in bougainvillea. Full article

In the context of a rapidly evolving data-driven economy and increasingly stringent carbon reduction policies, the impact of supply chain digitalization (SCD) on corporate carbon emission intensity (CEI) has become an important research topic. Using panel data on Chinese A-share listed firms from the Shanghai and Shenzhen stock exchanges over the period 2013–2023, this study employs Python-based text analysis of corporate annual reports to explore the effect of SCD on corporate CEI. The results show that SCD significantly reduces corporate CEI. Mechanism analysis further indicates that this effect operates through three channels: alleviating financing constraints, promoting green innovation, and reducing supply chain disruption risk. Heterogeneity analysis reveals that the mitigating effect of SCD on corporate CEI is more pronounced among non-state-owned firms, large-scale firms, firms in non-high-tech industries, firms in highly environmentally sensitive industries, and firms located in regions with more developed digital infrastructure. Further analysis shows that SCD contributes to improvements in both firms’ sustainability and financial performance. Overall, this study provides important policy implications for both governments and firms. Full article

Global freshwater resources face severe water quality degradation, with chlorophyll-a (Chl-a) concentration serving as a critical eutrophication indicator. While deep learning methods enable accurate Chl-a retrieval from remote sensing reflectance (Rrs) spectra, the scarcity of paired Rrs-Chl-a samples limits model generalization and causes overfitting, particularly in optically complex inland waters. To address this data bottleneck, we propose a physics-constrained latent diffusion model for synthesizing high-fidelity paired Rrs-Chl-a data to augment limited training sets for deep learning-based water quality retrieval. Our framework integrates three key innovations: (1) a lightweight variational autoencoder achieving 8.6:1 latent space compression, reducing computational overhead while preserving spectral features; (2) band-selective attention mechanisms targeting chlorophyll-sensitive wavelengths (440, 550, 680, and 700–750 nm) based on bio-optical principles; and (3) physics-guided conditional encoding that captures concentration-dependent spectral responses across oligotrophic to eutrophic regimes. Evaluated on the GLORIA dataset, our model demonstrates superior performance in spectral similarity (0.535), sample diversity (0.072), and distribution matching (Fréchet distance 0.0008) compared to conventional generative models. When applied to data augmentation, synthetic spectra improved downstream Chl-a retrieval from R²= 0.75 to 0.91, reducing RMSE by 39%. This physics-informed generative approach addresses data scarcity in aquatic remote sensing research, supporting global needs for enhanced understanding of inland and coastal water quality dynamics in data-limited regions. Full article

This paper presents a reproducible system innovation for processing Digital Imaging and Communications in Medicine (DICOM) mammography images based on pixel-monotonic dynamic range management and engineering-verifiable intensity transformations. Standard DICOM conversion schemes to 8-bit representation often result in irreversible luminance-range compression, locality-dependent contrast distortions, and reduced robustness of deep learning models. The proposed framework preserves the physical consistency of the Modality LUT and photometric polarity, performs breast-aware robust Winsor normalization, and applies strictly monotonic global tone mapping while preserving the 16-bit depth of the training data. System validation was performed using architecture-independent metrics. Compared to standard processing, the median value of normalized mutual information increased from 0.878 to 0.892, the effective number of bits increased from 7.88 to 10.11 (+2.25), the representation entropy increased by 1.42 bits, and the clipping rate was reduced to almost zero. Experiments with the Faster R-CNN detector showed stable or improved calcification localization at Intersection over Union (IoU) ≥ 0.5 under controlled augmentation conditions. The results confirm that pixel-monotonic dynamic range control provides a reproducible, engineering-verifiable basis for AI-based mammography analysis within the evaluated dataset and experimental setting. Full article

The Bureau of Fisheries and Aquatic Resources Tilapia Industry Roadmap (2022–2025) emphasizes the need for technological innovation in Philippine aquaculture. We developed an automated IoT-based monitoring and control system for Oreochromis niloticus using fuzzy logic for the dynamic regulation of temperature, dissolved oxygen, pH, ammonia, total dissolved solids, and turbidity. The system integrates sensors and a web-based interface for real-time data access and management of aeration, filtration, and temperature. Experimental results show the improved stability of water quality, reduced fish mortality, and enhanced growth performance compared with conventional setups. The system demonstrates a practical and sustainable approach to intensifying tilapia aquaculture through smart automation. Full article