Search Results (1,211)

The recent advancements in blockchain technology have also expanded its applications to smart agricultural fields, leading to increased research and studies in areas such as supply chain traceability systems and insurance systems. Policies and reward systems built on top of centralized systems face several problems and issues, including data integrity issues, modifications in data readings, third-party banking vulnerabilities, and central point failures. The current paper discusses how farming is becoming a leading cause of water and electricity wastage and introduces a novel idea called IncentiveChain. To keep a limit on the usage of resources in farming, we implemented an application for distributing cryptocurrency to the producers, as the farmers are responsible for the activities in farming fields. Launching incentive schemes can benefit farmers economically and attract more interest and attention. We provide a state-of-the-art architecture and design through distributed storage, which will include using edge points and various technologies affiliated with national agricultural departments and regional utility companies to make IncentiveChain practical. We successfully demonstrate the execution of the IncentiveChain application by transferring crypto-ether from utility company accounts to farmer accounts in a decentralized system application. With this system, the ether is distributed to the farmer more securely using the blockchain, which in turn removes third-party banking vulnerabilities and central, cloud, and blockchain constraints and adds data trust and authenticity. Full article

Landslide displacement prediction is vital for geohazard early warning and infrastructure safety. To address the challenges of modeling nonstationary, nonlinear, and multiscale behaviors inherent in GNSS time series, this study proposes a hybrid predicting framework that integrates Past Decomposable Mixing with an inverted Transformer architecture (PDM-iTransformer). The PDM module decomposes the original sequence into multi-resolution trend and seasonal components, using structured bottom-up and top-down mixing strategies to enhance feature representation. The iTransformer then models each variable’s time series independently, applying cross-variable self-attention to capture latent dependencies and using feed-forward networks to extract local dynamic features. This design enables simultaneous modeling of long-term trends and short-term fluctuations. Experimental results on GNSS monitoring data demonstrate that the proposed method significantly outperforms traditional models, with R² increased by 16.2–48.3% and RMSE and MAE reduced by up to 1.33 mm and 1.08 mm, respectively. These findings validate the framework’s effectiveness and robustness in predicting landslide displacement under complex terrain conditions. Full article

Learned systems in the domain of visual recognition and cognition impress in part because even though they are trained with datasets many orders of magnitude smaller than the full population of possible images, they exhibit sufficient generalization to be applicable to new and previously unseen data. Since training data sets typically represent such a small sampling of any domain, the possibility of bias in their composition is very real. But what are the limits of generalization given such bias, and up to what point might it be sufficient for a real problem task? There are many types of bias as will be seen, but we focus only on one, selection bias. In vision, image contents are dependent on the physics of vision and geometry of the imaging process and not only on scene contents. How do biases in these factors—that is, non-uniform sample collection across the spectrum of imaging possibilities—affect learning? We address this in two ways. The first is theoretical in the tradition of the Thought Experiment. The point is to use a simple theoretical tool to probe into the bias of data collection to highlight deficiencies that might then deserve extra attention either in data collection or system development. Those theoretical results are then used to motivate practical tests on a new dataset using several existing top classifiers. We report that, both theoretically and empirically, there are some selection biases rooted in the physics and imaging geometry of vision that challenge current methods of classification. Full article

The lossof ear tags in breeding pigs can lead to the loss or confusion of individual identity information. Timely and accurate detection, along with continuous tracking of breeding pigs that have lost their ear tags, is crucial for improving the precision of farm management. However, considering the real-time requirements for the detection of ear tag-lost breeding pigs, coupled with tracking challenges such as similar appearances, clustered occlusion, and rapid movements of breeding pigs, this paper proposed a dual-view synergistic method for detecting ear tag-lost breeding pigs and tracking individuals. First, a lightweight ear tag loss detector was developed by combining the Cascade-TagLossDetector with a channel pruning algorithm. Second, a synergistic architecture was designed that integrates a localized top-down view with a panoramic oblique view, where the detection results of ear tag-lost breeding pigs from the localized top-down view were mapped to the panoramic oblique view for precise localization. Finally, an enhanced tracker incorporating Motion Attention was proposed to continuously track the localized ear tag-lost breeding pigs. Experimental results indicated that, during the ear tag loss detection stage for breeding pigs, the pruned detector achieved a mean average precision of 94.03% for bounding box detection and 90.16% for instance segmentation, with a parameter count of 28.04 million and a detection speed of 37.71 fps. Compared to the unpruned model, the parameter count was reduced by 20.93 million, and the detection speed increased by 12.38 fps while maintaining detection accuracy. In the tracking stage, the success rate, normalized precision, and precision of the proposed tracker reached 86.91%, 92.68%, and 89.74%, respectively, representing improvements of 4.39, 3.22, and 4.77 percentage points, respectively, compared to the baseline model. These results validated the advantages of the proposed method in terms of detection timeliness, tracking continuity, and feasibility of deployment on edge devices, providing significant reference value for managing livestock identity in breeding farms. Full article

Green innovation (GI) is a key measure for enterprises to realize green transformation and sustainable development. Top management teams’ environmental attention (TMTEA) plays a critical role in shaping organizational strategic direction, value orientation, management mode, and behavioral patterns, serving as a micro-foundation for GI. Based on exploring the relationship between TMTEA and GI, this study adopts the ambidexterity theory to categorize dual green innovation (Dual_GI) into breakthrough green innovation (BGI) and progressive green innovation (PGI), and examines the impact of TMTEA on Dual_GI from the perspectives of external government environmental regulation (GER) and internal absorptive capacity (AC). Drawing on the attention-based view (ABV), this study uses data samples of Chinese A-share listed companies from 2010 to 2022 and establishes a fixed-effect model to empirically test this relationship. The results show the following: (1) TMTEA has a positive impact on corporate Dual_GI, and the promotion effect on PGI is more significant. (2) Both GER and AC can positively moderate the impact of TMTEA on Dual_GI, and both have a stronger moderating effect on TMTEA on PGI. (3) Further analysis shows that this driving effect is more obvious in state-owned enterprises, non-heavy polluting enterprises and enterprise maturity, and TMTEA can also drive Dual_GI to improve sustainable development performance. This study deepens the research scope and boundary conditions of TMT’s micro-psychological cognition and GI. It provides new insights for managers in emerging economies to rebalance their companies’ economic benefits and environmental transformation. Full article

In Nature-Based Solution (NBS) projects, stakeholder mapping is not merely a methodological step but a strategic process that enables meaningful engagement, co-creation, and the building of trust among diverse actors. This study describes the stakeholder mapping approach adopted in the NBSINFRA project, paying particular attention to methods designed to strengthen participation and co-creation. The process followed three inter-related steps: (1) stakeholder identification; (2) stakeholder analysis, filtering, and prioritization; and (3) stakeholder understanding. Drawing on a cross-methodological approach, including interviews, focus groups, direct observation, and on-site observations, the project engaged a wide spectrum of stakeholders, involving representatives of the local community. The findings point out that stakeholder mapping functioned as a catalyst for social engagement, co-design, informal collaborations, and the development of trustful and transparent relationships between team members and the community. The process made it possible to identify regional and national stakeholders, thereby opening avenues for international collaboration in later stages of the project. Finally, this study highlights persistent challenges that require attention, including information gaps, limited opportunities for participation due to time constraints, and the enduring prevalence of top-down decision-making. Full article

The surface quality of optical components plays a decisive role in advanced imaging, precision manufacturing, and high-power laser systems, where even defects can induce abnormal scattering and degrade system performance. Addressing the limitations of conventional single-view inspection methods, this study presents a panoramic multi-angle scattered light field acquisition approach integrated with deep learning-based recognition. A hemispherical synchronous imaging system is designed to capture complete scattered distributions from surface defects in a single exposure, ensuring both structural consistency and angular completeness of the measured data. To enhance the interpretation of complex scattering patterns, we develop a tailored lightweight network, SFD-YOLO, which incorporates the PSimam attention module for improved salient feature extraction and the Efficient_Mamba_CSP module for robust global semantic modeling. Using a simulated dataset of multi-width scratch defects, the proposed method achieves high classification accuracy with strong generalization and computational efficiency. Compared to the baseline YOLOv11-cls, SFD-YOLO improves Top-1 accuracy from 92.5% to 95.6%, while reducing the parameter count from 1.54 M to 1.25 M and maintaining low computational cost (Flops 4.0G). These results confirm that panoramic multi-angle scattered imaging, coupled with advanced neural architectures, provides a powerful and practical framework for optical surface defect detection, offering valuable prospects for high-precision quality evaluation and intelligent defect inversion in optical inspection. Full article

Deep learning-based water body extraction methods generally focus on maximizing accuracy while neglecting inference speed, which can make them challenging to apply in real-time applications. To address this problem, this paper proposes a lightweight u-shaped network (LU-Net), which improves inference speed while maintaining comparable accuracy. To reduce inference latency, a lightweight decoder block (LDB) is designed, which employs a depthwise separable convolution structure to accelerate the decoding process. To enhance accuracy, a lightweight convolutional block attention module (LCBAM) is designed, which effectively captures water-specific spectral and spatial characteristics through a dual-attention mechanism. To improve multi-scale water boundary extraction, a structurally re-parameterized multi-scale fusion prediction module (SRMFPM) is designed, which integrates multi-scale water boundary information through convolutions of different sizes. Comparative experiments are conducted on the GID and LoveDA datasets, with model performance assessed using the MIoU metric and inference latency. The results demonstrate that LU-Net achieves the lowest GPU latency of 3.1 MS and the second-lowest CPU latency of 36 MS in the experiments. On the GID, LU-Net achieves the MIoU of 91.36%, outperforming other tested methods. On the LoveDA datasets, LU-Net achieves the second-highest MIoU of 86.32% among the evaluated models, which is 0.08% lower than the top-performing CGNet. Considering both latency and MIoU, LU-Net demonstrates commendable efficiency on the GID and LoveDA datasets across all compared networks. Full article

Artificial Intelligence (AI) is reshaping higher education by enabling personalized learning (PL) and enhancing teaching and learning practices. To examine global research trends, pedagogical paradigms, equity and sustainability considerations, instructional strategies, learning outcomes, and interdisciplinary collaboration, this study systematically reviewed 29 articles indexed in the Social Sciences Citation Index (SSCI) Q1, representing the top 25% of cited articles, published between January 2020 and December 2024 in the Web of Science database. Results indicate that AI-PL research is concentrated in Asia, particularly China, and predominantly situated within education and computer science. Quantitative designs prevail, often complemented by qualitative insights, with supervised machine learning as the most common algorithm. While constructivist principles implicitly guide most studies, explicit theoretical grounding improves AI-pedagogy alignment and educational outcomes. AI demonstrates potential to enhance instructional approaches such as PBL, STEAM, gamification, and UDL, and to foster higher-order skills, yet uncritical use may undermine learner autonomy. Systematic attention to equity and SDG-related objectives remains limited. Emerging interdisciplinary collaborations show promise but are not yet fully institutionalized, constraining integrative system design. These findings underscore the need for stronger theoretical framing, alignment of AI with pedagogical and societal imperatives, and professional development to enhance educators’ AI literacy. Coordinated efforts among academia, industry, and policymakers are essential to develop scalable, context-sensitive AI solutions that advance inclusive, adaptive, and transformative higher education. Full article

Nanobiocomposites are a class of biomaterials that include at least one phase with constituents in the nanometer range. Nanobiocomposites, a new class of materials formed by combining natural and inorganic materials (metals, ceramics, polymers, and graphene) at the nanoscale dimension, are expected to revolutionize tissue engineering and bone implant applications because of their enhanced corrosion resistance, mechanical properties, biocompatibility, and antimicrobial activity. Titanium-based nanocomposites are gaining attention in biomedical applications due to their exceptional biocompatibility, corrosion resistance, and mechanical properties. These composites typically consist of a titanium or titanium alloy matrix that is embedded with nanoscale bioactive phases, such as hydroxyapatite, bioactive glass, polymers, or carbon-based nanomaterials. Common methods for synthesizing Ti-based nanobiocomposites and their parts, including bottom-up and top-down approaches, are presented and discussed. The synthesis conditions and appropriate functionalization influence the final properties of nanobiomaterials. By modifying the surface roughness at the nanoscale level, composite implants can be enhanced to improve tissue integration, leading to increased cell adhesion and protein adsorption. The objective of this review is to illustrate the most recent research on the synthesis and properties of Ti-based biocomposites and their scaffolds. Full article

Breast ultrasound imaging functions as a vital radiation-free detection tool for breast cancer, yet its low contrast, speckle noise, and interclass variability make automated interpretation difficult. In this paper, we introduce UltraScanNet as a specific deep learning backbone that addresses breast ultrasound classification needs. The proposed architecture combines a convolutional stem with learnable 2D positional embeddings, followed by a hybrid stage that unites MobileViT blocks with spatial gating and convolutional residuals and two progressively global stages that use a depth-aware composition of three components: (1) UltraScanUnit (a state-space module with selective scan gated convolutional residuals and low-rank projections), (2) ConvAttnMixers for spatial channel mixing, and (3) multi-head self-attention blocks for global reasoning. This research includes a detailed ablation study to evaluate the individual impact of each architectural component. The results demonstrate that UltraScanNet reaches 91.67% top-1 accuracy, a precision score of 0.9072, a recall score of 0.9174, and an F1-score of 0.9096 on the BUSI dataset, which make it a very competitive option among multiple state-of-the-art models, including ViT-Small (91.67%), MaxViT-Tiny (91.67%), MambaVision (91.02%), Swin-Tiny (90.38%), ConvNeXt-Tiny (89.74%), and ResNet-50 (85.90%). On top of this, the paper provides an extensive global and per-class analysis of the performance of these models, offering a comprehensive benchmark for future work. The code will be publicly available. Full article

Literature review plays a crucial role in research. This paper explores bibliometrics, which utilize statistical tools to evaluate the researcher’s scientific contributions. Its intent is to map frequently cited articles and authors, identify top sources, track publication years, explore keywords and their co-occurrences, and show article distribution by thematic area and country. Additionally, it provides a thematic map of relevance and progress, with special attention to interdisciplinary work. Finally, it also makes use of research findings in maintenance management decision-making, where the findings reveal that the literature provides valuable insights into the impact of the Analytic Hierarchy Process (AHP) method. Despite advancements in maintenance management, gaps persist in comprehensively addressing core themes, evolutionary trends, and future research directions. This research aims to bridge this gap by providing a detailed examination of the application of bibliometric analysis employing statistical tools to measure researchers’ scientific contributions, concerning the AHP method applications in maintenance management within the steel industry. The study confirmed that tools like VOSviewer and the Bibliometrix package in R can extract relevant information regarding bibliometric laws, helping us understand research patterns. These findings support strategic decision-making and the evaluation of scientific policies for researchers and institutions. Full article

Academic researchers in technical and policy fields tend to pay little attention to the metaphysical and ontological ‘priors’ that nevertheless structure and determine scientific strategies and results. Green political agendas rooted in ecological modernization (EM) are distinguished from antecedent visions predicated on biophysical limits. Net zero is shown to be rooted in a project of global EM. Ecomodernism is analyzed in relation to its principal actors, geopolitical context and underlying metaphysics and anthropology. It is driven by non-negotiable societal priorities (‘ends’), which themselves derive from a particular set of technical ‘means’. The top-down version of the Fourth Industrial Revolution (IR4.0) and new paradigm of global net zero constitute an integrated agenda of eco-modernism. Global net zero cannot hope to achieve its own metabolic goals in respect of either energy flows or the circular economy. A competing, bottom-up and distributed model of the IR4.0 could potentially achieve these targets without falling prey to the Jevons paradox. This potential turns on the greater capacity of low-overhead, prosumer models to nurture less materialist cultural priorities that are more communitarian and family-oriented. A smart energy system that emerges in the context of distributed, domestic and informal production is much more likely to mirror the complex, infinitely gradated and granular pattern of oscillating energy transfers that are characteristic of biological systems. From an ecological economic perspective, such a bottom-up approach to the IR4.0 is much more likely to see the orders of magnitude reduction in the unit energetic cost of social complexity envisaged, in principle, by net zero. Through this comprehensive review of the metaphysical and ontological priors of mainstream IR4.0, researchers in the linked fields of energy and circular economy are presented with a wider range of potential options less constrained by preconceived assumptions about the ‘ends’ of societal development and progress. Full article

Background: Healthcare workers face multiple, interlinked occupational health risks—burnout, post-COVID-19 sequelae (Long COVID), and extended medical leave. These outcomes often share predictors, contribute to each other, and, together, impact workforce capacity. Yet, existing tools typically address them in isolation. Objective: The objective of this study to develop and deploy an integrated, explainable artificial intelligence (AI) framework that predicts these three outcomes using the same structured occupational health dataset, enabling unified workforce risk monitoring. Methods: We analyzed data from 1244 Romanian healthcare professionals with 14 demographic, occupational, lifestyle, and comorbidity features. For each outcome, we trained a separate predictive model within a common framework: (1) a lightweight transformer neural network with hyperparameter optimization, (2) a transformer with multi-head attention, and (3) a stacked ensemble combining transformer, XGBoost, and logistic regression. The data were SMOTE-balanced and evaluated on held-out test sets using Accuracy, ROC-AUC, and F1-score, with 10,000-iteration bootstrap testing for statistical significance. Results: The stacked ensemble achieved the highest performance: ROC AUC = 0.70 (burnout), 0.93 (Long COVID), and 0.93 (extended leave). The F1 scores were >0.89 for Long COVID and extended leave, whereas the performance gains for burnout were comparatively modest, reflecting the multidimensional and heterogeneous nature of burnout as a binary construct. The gains over logistic regression were statistically significant (p < 0.0001 for Long COVID and extended leave; p = 0.0355 for burnout). The SHAP analysis identified overlapping top predictors—tenure, age, job role, cancer history, pulmonary disease, and obesity—supporting the value of a unified framework. Conclusions: We trained separate models for each occupational health risk but deployed them in a single, real-time web application. This integrated approach improves efficiency, enables multi-outcome workforce surveillance, and supports proactive interventions in healthcare settings. Full article

Zirconia is a high-strength ceramic and has increasing importance, particularly in the field of aesthetic dentistry for crown restorations. Multi-composition-layered-type (MCL) zirconia is attracting attention as a new material that integrates high light transmittance with mechanical strength. However, there are no reports on the deformation induced by sintering in MCL zirconia. Therefore, we aimed to investigate the sintering distortion of MCL zirconia. An experimental fixed dental prosthesis (FDP) was designed based on a 4-unit monolithic zirconia FDP. A MCL with no color gradation and an MCL with color gradation were selected. Particularly, three milling areas—the top end of the disk (area I) (n = 7), vertical center (area II) (n = 7), and bottom end of the disk (area III) (n = 7)—were investigated. Moreover, sintering distortions generated by experimental FDPs were measured. Sintering distortion was detected in all areas. The direction of distortion varied by area—positive in area I, negative in area II, and approximately zero in area III—with a significant difference between areas I and II (p = 0.001). The largest absolute distortion was observed in c-MCL-A (area I); the corresponding marginal gaps were ~89.4 μm (second molar) and ~56.9 μm (first premolar), both below the clinical threshold of 120 μm. Full article