Search Results (34,000)

Maintaining a safe working environment for construction workers is critical to the improvement of urban areas. Several issues plague the present safety helmet detection technologies utilized on construction sites. Some of these issues include low accuracy, expensive deployment of edge devices, and complex backgrounds. To overcome these obstacles, this paper introduces a detection method that is both efficient and based on an improved version of YOLOv8n. Three components make up the superior algorithm: the C2f-SCConv architecture, the Partial Convolutional Detector (PCD), and Coordinate Attention (CA). Detection, redundancy reduction, and feature localization accuracy are all improved with coordinate attention. To further enhance feature quality, decrease computing cost, and make corrections more effective, a Partial Convolution detector is subsequently constructed. Feature refinement and feature representation are made more effective by using C2f-SCConv instead of the bottleneck C2f module. In comparison to its predecessor, the upgraded YOLOv8n is superior in every respect. It reduced model size by 2.21 MB, increased frame rate by 12.6 percent, decreased FLOPs by 49.9 percent, and had an average accuracy of 94.4 percent. This method is more efficient, quicker, and cheaper to set up on-site than conventional helmet-detection algorithms. Full article

Emotional Footprint Identification refers to the process of recognizing or understanding the emotional impact that a person, experience, or interaction leaves on others. Emotion Recognition plays an important role in human–computer interaction for identifying emotions such as fear, sadness, anger, happiness, and surprise on the human face during the conversation. However, accurate emotional footprint identification plays a crucial role due to the dynamic changes. Conventional deep learning techniques integrate advanced technologies for emotional footprint identification, but challenges in accurately detecting emotions in minimal time. To address these challenges, a novel Divergence Shepherd Feature Optimization-based Stochastic-Tuned Deep Multilayer Perceptron (DSFO-STDMP) is proposed. The proposed DSFO-STDMP model consists of three distinct processes namely data acquisition, feature selection or reduction, and classification. First, the data acquisition phase collects a number of conversation data samples from a dataset to train the model. These conversation samples are given to the Sokal–Sneath Divergence shuffling shepherd optimization to select more important features and remove the others. This optimization process accurately performs the feature reduction process to minimize the emotional footprint identification time. Once the features are selected, classification is carried out using the Rosenthal correlative stochastic-tuned deep multilayer perceptron classifier, which analyzes the correlation score between data samples. Based on this analysis, the system successfully classifies different emotions footprints during the conversations. In the fine-tuning phase, the stochastic gradient method is applied to adjust the weights between layers of deep learning architecture for minimizing errors and improving the model’s accuracy. Experimental evaluations are conducted using various performance metrics, including accuracy, precision, recall, F1 score, and emotional footprint identification time. The quantitative results reveal enhancement in the 95% accuracy, 93% precision, 97% recall and 97% F1 score. Additionally, the DSFO-STDMP minimized the in training time by 35% when compared to traditional techniques. Full article

Encapsulated formulations have emerged as a promising tool for increasing nutrient absorption in the food supplement and cosmetic industries. Although the theoretical amplification factors for improving the bioavailability of encapsulated formulations are very high for poorly soluble active compounds, it has long been known that encapsulation can also enhance the absorption of water-soluble ingredients. These findings have led to the development of new technologies for encapsulating nutrients for use in the food industry. However, accurate quantification of nutrients in encapsulated formulations in the food supplement industry remains a challenge. This study presents the development and validation of novel analytical procedures for determining artemisinin in various food supplement formulations. Three formulations were prepared using different emulsifying procedures for artemisinin encapsulation. High-performance liquid chromatography with UV/Vis detection (HPLC-UV/Vis) was used for analysis. Separation was performed using a Waters ACQUITY Premier BEH C18 column. Specialized sample preparation procedures were designed to efficiently disrupt encapsulation and extract artemisinin for precise quantification. Three different sample preparation procedures were required to accurately determine the artemisinin content in the tested formulations. All methods were validated. The precision, linearity expressed as R², LOD, and LOQ of the chromatographic method were 0.39%, 0.9995, 18 µg/mL, and 26 µg/mL, respectively. Recoveries of the sample preparation methods were above 94%. The developed procedures enable accurate determination of artemisinin in encapsulated formulations, ensuring product quality and safety. These findings suggest that, for quality control of encapsulated food products, specialized analytical procedures for individual formulations may need to be developed and validated. Full article

Building a resilient and efficient supply chain system is critical for sustaining firm operations in an increasingly uncertain global environment. This study examines whether the firm-level exposure to industry-wide robot penetration mitigates firm-level supply chain risks. By adopting Bartik’s instrumental variable approach to decompose industry-level robot data to the firm level (from the International Federation of Robotics, IFR), and using a novel text-mining-based supply chain risk index, constructed via a tailored “supply chain risk” dictionary, to quantify sentences containing both keywords from firms’ annual report MD&A sections, we apply a fixed effects model, and find that robot adoption significantly reduces supply chain risk by enhancing firms’ discourse power and improving supply chain coordination. The effect is more pronounced in firms with higher capital intensity, greater international exposure, stronger regulatory oversight, and better ESG (Environmental, Social, and Governance) performance. By integrating automation adoption with supply chain risk management, this study extends the literature on production economics and supply chain resilience. Our findings reveal that industrial robots, beyond enhancing productivity, function as a risk-mitigating technology that strengthens supply chain stability and operational continuity in volatile global production networks. Full article

Urban Green Spaces (UGSs) are essential for ecological sustainability and public health, offering benefits such as air pollution reduction, urban cooling, and recreational opportunities. However, existing evaluation tools remain inconsistent, often assessing isolated dimensions like accessibility or aesthetics without fully integrating health considerations. A systematic approach is needed to understand how these tools measure UGS quality and their relevance to health outcomes. This study employs a literature review (PRISMA framework) to analyze UGS evaluation tools with a focus on quality and health implications. A search in Scopus and Web of Science identified 14 relevant studies. Data extraction examined tool structure, assessed dimensions, data collection methods, geographic applications, and integration of health indicators. The review identified 13 distinct tools varying in complexity and methodology, from standardized checklists to GIS-based analyses. While key dimensions included accessibility, safety, aesthetics, and biodiversity, health-related factors were inconsistently integrated. Few tools explicitly assessed physical, mental, or social health outcomes. Technological innovations, such as Google Street View and AI-based analysis, emerged as enhancements for UGS evaluation. Despite methodological advances, gaps remain in linking UGS quality assessments to health outcomes. The lack of standardized health metrics limits applicability in urban planning. Future research should focus on interdisciplinary frameworks integrating environmental and health indicators to support the creation of sustainable and health-promoting UGS. Full article

The neural mechanisms of auditory and visual processing are not only a core research focus in cognitive neuroscience but also hold critical importance for the development of brain–computer interfaces, neurological disease diagnosis, and human–computer interaction technologies. However, EEG-based studies on classifying auditory and visual brain activities largely overlook the in-depth utilization of spatial distribution patterns and frequency-specific characteristics inherent in such activities. This paper proposes an analytical framework that constructs symmetrical spatio-temporal–frequency feature association vectors to represent brain activities by computing EEG microstates across multiple frequency bands and brain functional connectivity networks. Then we construct an Adaptive Tensor Fusion Network (ATFN) that leverages feature association vectors to recognize brain activities related to auditory, visual, and audiovisual processing. The ATFN includes a feature fusion and selection module based on differential feature enhancement, a feature encoding module enhanced with attention mechanisms, and a classifier based on a multilayer perceptron to achieve the efficient recognition of audiovisual brain activities. The feature association vectors are then processed by the Adaptive Tensor Fusion Network (ATFN) to efficiently recognize different types of audiovisual brain activities. The results show that the classification accuracy for auditory, visual, and audiovisual brain activity reaches 96.97% using the ATFN, demonstrating that the proposed symmetric spatio-temporal–frequency feature association vectors effectively characterize visual, auditory, and audiovisual brain activities. The symmetrical spatio-temporal–frequency feature association vectors establish a computable mapping that captures the intrinsic correlations among temporal, spatial, and frequency features, offering a more interpretable method to represent brain activities. The proposed ATFN provides an effective recognition framework for brain activity, with a potential application for brain–computer interfaces and neurological disease diagnosis. Full article

Frequent crude oil spills during offshore oil and gas production and transportation have inflicted irreversible detrimental effects on both human activities and marine ecosystems; with particular risks of secondary disasters such as combustion and explosions. To address these challenges; advanced oil sorption technologies have been developed to overcome the inherent limitations of conventional remediation methods. In this study, a flame-retardant protective coating was fabricated on melamine sponge (MS) through precipitation polymerization of octa-aminopropyl polyhedral oligomeric silsesquioxane (POSS) and hexachlorocyclotriphosphazene (HCCP), endowing the MS@PPOS-PDMS-Si composite with exceptional char-forming capability. Secondary functional layer: By coupling the complementary physicochemical properties of polydimethylsiloxane (PDMS) and SiO₂ nanofibers, we enabled them to function jointly, achieving superior performance in the material systems; this conferred enhanced hydrophobicity and structural stability to the MS matrix. Characterization results demonstrated a progressive reduction in peak heat release rate (PHRR) from 137.66 kW/m² to118.35 kW/m², 91.92 kW/m², and ultimately 46.23 kW/m², accompanied by a decrease in total smoke production (TSP) from 1.62 m² to 0.76 m², indicating significant smoke suppression. Furthermore, the water contact angle (WCA) exhibited substantial improvement from 0° (superhydrophilic) to 140.7° (highly hydrophobic). Cyclic sorption–desorption testing revealed maintained oil–water separation efficiency exceeding 95% after 10 operational cycles. These findings position the MS@PPOS-PDMS-Si composite as a promising candidate for emergency oil spill response and marine pollution remediation applications, demonstrating superior performance in fire safety, environmental durability, and operational reusability. Full article

This paper mainly studies tax inspection decision-making technology, aiming to improve the accuracy and robustness of target recognition, state estimation, and autonomous decision making in complex environments by constructing an application that integrates visual, radar, and inertial navigation information. Tax inspection is a universally complex phenomenon, but little is known about the use of innovative technology to arm tax auditors with tools in monitoring it. Thus, based on the legitimacy theory, there is an agreement between taxpayers and the tax authorities regarding adequate compliance with tax legislation. The use of systemic controls by tax authorities is essential to track stakeholders’ contracts and ensure the upholding of this mandate. The case study is exploratory, using participant observation, and interventionist approach to a tax auditing. The results indicated that partnership between experienced tax auditors and IT tax auditors offered several tangible benefits to the in-house development and monitoring of an innovative application. It also indicates that OCR supports a data lake for inspectors in which stored information is available on standby during inspection. Furthermore, auditors’ use of mobile applications programmed with intelligent perception and tracking resources instead of using searches on mainframes streamlined the inspection process. The integration of professional skepticism, empathy among users, and technological innovation created a surge in independence among tax auditors and ensured focus. This paper’s contribution lies in the discussion of the enhancement of tax inspection through target recognition, drawing on legitimacy theory to rethink the relationship between taxpayers and tax authorities regarding adequate compliance with tax legislation, and presenting an exploratory case study using a participant observation, interventionist approach focused on a tax auditor. The implications of this study for policy makers, auditors, and academics are only the peak of the iceberg, as innovation in public administration presupposes efficiency. As a suggestion for future dimensions of research, we recommend the infusion of AI into these tools for further efficacy and effectiveness to mitigate fraud in the undue appropriation of taxes and undue competition. Full article

The global deterioration of water quality due to climate change and industrialization has intensified the need for real-time monitoring systems. In South Korea’s automated water quality monitoring networks, measuring total organic carbon (TOC) and nitrate nitrogen (NO₃-N) as a proxy for total nitrogen (TN) is critical; however, conventional analytical instruments face limitations such as high costs, long analysis times, and the need for chemical reagents. In this study, we developed and evaluated a simultaneous TOC and NO₃-N measurement method using HASM-4000, a domestically developed optical sensor based on ultraviolet (UV) absorption spectroscopy. The sensor measures absorbance at 254 nm (TOC) and 220 nm (NO₃-N) based on the Beer–Lambert law, with signal processing techniques including optical power compensation (OPC) and Binning–Interpolation (BinInterp) applied to enhance measurement accuracy. Calibration using standard solutions demonstrated excellent linear correlations (R² > 0.99) between actual and estimated concentrations for both TOC and NO₃-N, with accuracy and reproducibility validated against standard methods under laboratory conditions. However, performance degradation was observed in turbid mixed samples due to the optical limitations of the 10 mm pathlength, suggesting the need for future improvements such as adopting a 5 mm pathlength and upgrading optical components. The HASM-4000 sensor enables real-time measurement without a reagent, and preliminary testing with river water samples demonstrates its potential to advance Korea’s water quality monitoring infrastructure by reducing dependence on foreign technologies and facilitating network expansion with cost-effective domestic solutions. Full article

Aquaculture is the fastest-growing food production sector globally. As its largest producer, China plays a pivotal role in ensuring aquatic food supply and supporting the blue economy. Despite its massive scale, a systematic understanding of the geographic distribution, structural composition, and drivers of China’s aquaculture value chain remains limited. We comprehensively characterized the sector’s composition, spatiotemporal evolution, and structural dynamics. We compiled and analyzed over 2.85 million enterprise registration records from the TianYanCha database, applying rigorous industry classification, spatial mapping, correlation analysis, and bottleneck assessment with natural and socioeconomic variables. Results show that policy reforms, notably the 2013 Company Law amendment and 2016 aquaculture certification measures, drove sharp increases in enterprise registrations, particularly in retail and farming. Enterprises are highly clustered in the Yangtze River Basin, Pearl River Delta, and southeastern coast, with inland expansion along major river systems. Strong interdependencies exist among sectors, while wholesale remains numerically scarce, forming a structural bottleneck. Standardization levels are low. Foreign investment, though under 5%, concentrated in processing and distribution, contributed to advanced technologies in the 1990s–2000s. These findings highlight rapid formalization, regional clustering, and structural imbalances, suggesting that enhancing formalization and addressing intermediary bottlenecks could improve sector resilience and efficiency. Full article

High-strength oil and gas pipeline girth welds exhibit significant material and geometric discontinuities with high susceptibility to defects, making them a critical weak link in oil and gas pipelines. Researching the fracture assessment technology pipeline’s girth welds is essential for enhancing the pipeline’s inherent safety and protection levels. Key issues and research progress related to fracture assessment technology are systematically addressed from the perspectives of pipeline fracture behavior and fracture assessment methods in this paper. The core focus of fracture behavior research is determining the crack driving force at the girth weld and the material’s fracture toughness. Fracture assessment methods include failure assessment diagrams and limited tensile strain capacity models. The development of single-parameter and multi-parameter fracture mechanics theories in establishing the relationship between in-plane and out-of-plane constraints and material fracture toughness is reviewed. Four commonly used methods for calculating crack driving forces in pipelines are presented. Moreover, the usage scenarios of various failure assessment diagrams in pipeline fracture assessment are analyzed. A comparison of the parameter ranges and applicability of commonly used international tensile strain capacity models is also provided. The paper highlights existing issues in current research on the fracture assessment of high-strength pipelines and outlines directions for further study. Lastly, this paper aims to provide theoretical and technical support for improving the inherent safety level of high-strength pipeline girth welds. Full article

The long-standing separation of agro-pastoral relations has adversely affected the agricultural economy and ecology, hindering sustainable agricultural development. The process of reconstructing agro-pastoral relations involves moving from separation to reintegration. To further verify the scientific validity of reconstructing agro-pastoral relations to improve economic and ecological benefits and enhance the capacity for sustainable agricultural development in the major corn-producing areas of Northeast China, this study used survey data from 521 sample farmers in Jilin Province, China, collected during the agricultural production cycle from 2020 to 2022. Using an endogenous switching regression (ESR) model and a counterfactual scenario, the integrated crop–livestock family farm (ICFF) model was shown to have a comparative advantage in improving economic and ecological benefits. The ICFF model can serve as a foundation for reconstructing agro-pastoral relations, thereby enhancing sustainable agricultural development capacity. Heterogeneity analysis indicates that larger-scale cultivated land, intensive cultivated land management, and higher education have a more significant impact on farmers’ choice of the ICFF model. To promote the restructuring of agro-pastoral relations through the ICFF model, farmers should be encouraged and supported to standardize the transfer of farmland, engage in livestock farming according to the principle of land-based livestock management, implement large-scale and intensive management, improve agricultural production technologies and improved varieties, strengthen publicity on the positive role of integrated crop-livestock management, and improve the financial support system. Full article

The emergence of electrically conductive polymeric materials has revolutionized the landscape of sustainable energy technologies, presenting unprecedented opportunities for advancing both photovoltaic conversion systems and electrochemical energy-storage platforms. These remarkable macromolecular materials exhibit distinctive characteristics including adjustable electronic band structures, exceptional mechanical adaptability, solution-phase processability, and cost-effective manufacturing potential. This extensive review provides an in-depth examination of the fundamental principles governing charge carrier mobility in conjugated polymer systems, explores diverse synthetic methodologies for tailoring molecular architectures, and analyzes their transformative applications across multiple energy technology domains. In photovoltaic technologies, electrically conductive polymers have driven major advancements in organic solar cells and photoelectrochemical systems, significantly improving energy conversion efficiency while reducing manufacturing costs. In electrochemical energy storage, their integration into supercapacitors and rechargeable lithium-based batteries has enhanced charge storage capability, accelerated charge–discharge processes, and extended operational lifespan compared with conventional electrode materials. This comprehensive analysis emphasizes emerging developments in hybrid composite architectures that combine conductive polymers with carbon-based nanomaterials, metal oxides, and other functional components to create next-generation flexible, lightweight, and wearable energy systems. By synthesizing fundamental materials chemistry with device engineering perspectives, this review illuminates the transformative potential of electrically conductive polymers in establishing sustainable, efficient, and resilient energy infrastructures for future technological landscapes. Full article

Background: Biopolymer-based active packaging has experienced significant growth in the food industry due to its capacity to enhance product stability and reduce reliance on synthetic preservatives. However, its application in cosmetics remains limited, despite increasing consumer demand for sustainable and preservative-free solutions. Objective: This review evaluates the feasibility of transferring biopolymer-based active packaging technologies from the food sector to cosmetic applications, identifying relevant materials, processing methods, and implementation challenges. Methodology: A bibliographic search was conducted across nine databases (2000–2025) using the keywords “active packaging,” “antioxidant,” “antimicrobial,” and “biopolymers.” Results: The most recurrent biopolymers identified were chitosan, fish gelatin, zein, and kafirin, all of which exhibit biodegradability, film-forming capacity, and compatibility with natural additives. Although their intrinsic antioxidant and antimicrobial properties are limited, these can be enhanced through the incorporation of bioactive compounds. Processing techniques such as casting, coating, dry forming, and electrospinning were found to be the most effective, enabling customized packaging designs. Key challenges include cost, sensory attributes, mechanical limitations, and regulatory compliance. Conclusion: Active packaging systems based on biopolymers—either alone or combined with natural bioactive ingredients—offer a viable innovation pathway for the cosmetics industry. These systems support clean-label claims and ecological positioning, representing a strategic opportunity to adapt validated technologies from the food sector to meet emerging cosmetic market demands. Full article

Recent advancements in food packaging have transitioned from passive containment toward innovative smart systems that integrate active and intelligent functionalities to improve product preservation, safety, and consumer interaction. This review examines the evolution of these technologies, focusing on biodegradable polymers and nanomaterial-enhanced substrates that combine environmental sustainability with superior barriers and antimicrobial performance. Developments in embedded sensing systems, including chemical, temperature, and humidity sensors, enable the continuous monitoring of food quality and environmental conditions, supporting extended shelf-life and early contamination detection. Intelligent packaging further incorporates indicators, sensors, and data carriers that enhance transparency and traceability across supply chains. These systems are often connected through blockchain and Internet of Things (IoT) platforms for real-time data analysis. The review also addresses consumer engagement via interactive labels and personalized nutritional feedback, along with the economic, behavioral, and regulatory aspects influencing large-scale adoption. Life cycle assessments are analyzed to evaluate trade-offs between enhanced functionality and environmental impact, emphasizing recyclability and end-of-life strategies within circular economy frameworks. Finally, the article discusses current technical challenges while highlighting emerging trends such as AI-driven predictive analytics and IoT-enabled connectivity as key enablers of sustainable, efficient, and safe food packaging systems. Full article