Search Results (3,607)

Food packaging constitutes a pivotal enabler within the contemporary food industry, requiring continuous innovation to address evolving challenges. Traditional packaging systems typically provide passive protection, which is inadequate for addressing dynamic microbial shifts and spoilage-induced microenvironmental instabilities. In contrast, corrective responsive food packaging (CRFP) takes a distinct approach through the integration of sensing capabilities and targeted active intervention. Upon detection of specific stimuli, CRFP systems precisely deliver bioactive agents to mitigate food deterioration. This review systematically summarizes recent advances in CRFP technology, offering a comprehensive overview of its core response mechanisms, functional materials, advanced carrier systems, and future research priorities. Special emphasis is given to (i) stimuli-responsive systems, including single-stimulus (pH, enzyme, humidity, temperature, and light) and multi-stimulus-responsive systems, detailing their triggering mechanisms and practical applications; and (ii) functional materials and carriers, exploring their synergistic effects for optimized bioactive release. This review aims to provide a structured framework for the design and implementation of CRFP, facilitating its translation from laboratory to industrial practice and contributing to the development of sustainable and efficient food preservation strategies. Full article

Novel highly sensitive two-dimensional gas-sensing materials for detecting hazardous gases are crucial for human health, climate protection, and industrial development. In this study, density functional theory (DFT) was employed to investigate the adsorption and sensing properties of four representative hazardous gas molecules (NO, NO₂, F₂, and Cl₂) on pristine and vacancy-defective (S vacancy and Te vacancy) Janus MoSTe monolayer. The introduction of a vacancy into the MoSTe monolayer significantly reduces the adsorption distances and enhances the adsorption energies and charge transfers. Notably, an S vacancy induces a transition in the adsorption behaviors of NO, NO₂, and Cl₂ on MoSTe from physisorption to chemisorption, and a Te vacancy leads to strong physisorption of NO and NO₂ on the MoSTe monolayer. Electronic structure analysis further reveals that gas molecule adsorption can modulate band gaps. Adsorption of F₂ and Cl₂ on the Te surface of pristine MoSTe converts the indirect bandgap into a direct bandgap. However, the calculation results for O₂ adsorption indicate that the S and Te vacancies in Janus MoSTe may be readily occupied by O₂, suggesting that it is not a good sensing material under atmospheric conditions. This study provides valuable theoretical insights and guidance for future experiments on vacancy-defective Janus MoSTe monolayer. Full article

Per- and polyfluoroalkyl substances (PFAS) have been extensively used for many years in the manufacturing of industrial and commercial goods. Their toxicity and their extensive use, stability, durability, persistence, and bioaccumulation are responsible for the contamination of water, soil, air, and food, causing significant harm to human health and the environment. The objective of this chapter is to evaluate the ability of advanced (bio)sensing strategies for the sensitive, accurate, rapid, simple, and low-cost detection of PFAS in drinking water and the environment. We address advanced bio(sensing) strategies by emphasizing the electrochemical (bio)sensing strategies and the optical bio(sensing) strategies. The principle of each method, the mechanisms involved in the detection, the linear range, the limit of detection, and the applicability are underlined. Finally, this review outlines the major challenges and outlook to move advanced (bio)sensing strategies from the laboratory stage to practical applications in the environment, food, and health. Full article

Natural disasters such as floods and earthquakes cause severe physical, social, and economic losses, highlighting the critical need for timely and reliable early warning systems. Conventional water level and structural health monitoring technologies are often costly, limiting deployment to high-priority infrastructure only. This paper presents the development and validation of two low-cost Internet of Things (IoT) systems for multi-hazard disaster monitoring and early warning, explicitly supporting UN Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities) by enabling equitable monitoring of rural or minor bridges. The proposed system achieves a significant cost reduction (approximately $300 compared to conventional systems typically exceeding $5000), highlighting its potential for scalable and sustainable deployment. The first system integrates a Raspberry Pi, Pi Camera, Lidar Lite V3, and ADXL355 accelerometer to simultaneously capture floodwater images, measure water levels, and record bridge vibrations, with distance measurements recorded at user-defined intervals and vibration data sampled up to 100 Hz. Laboratory repeatability and uncertainty analyses of the Lidar Lite V3 indicate a root mean square error of ~2.4 cm over a 0–25 cm range, demonstrating stable performance for flood monitoring and sufficient accuracy for early warning applications using low-cost sensing systems. The ADXL355 accelerometer is validated through harmonic excitation tests (0.1–2 Hz) and real earthquake recordings, confirming its suitability for low-frequency structural response monitoring. The second system combines a Raspberry Pi, an HX711 amplifier, and a CDP25 displacement transducer to measure bridge-bearing displacements up to 25 cm, with data acquisition at sampling rates of up to 80 Hz, with laboratory tests demonstrating consistent and repeatable measurements during both loading and unloading cycles. The IoT framework is resilient, incorporating solar power and local data storage to ensure operation during power or network outages. Unlike prior studies focusing on individual sensors, this work delivers a fully integrated multi-sensor platform with formalized early warning logic based on predefined thresholds. The results demonstrate the feasibility of scalable, real-time, low-cost monitoring for disaster risk reduction and infrastructure resilience, providing a sustainable solution for community-scale early warning applications. Full article

Digital Twins (DTs) have emerged as a key technology for sensor-driven cyber–physical systems, enabling such features as real-time monitoring, predictive maintenance, and operational optimization. Despite rapid progress, existing research in the area remains fragmented, mostly addressing only singular aspects, such as data acquisition, modeling, or control, lacking a unified lifecycle-oriented methodology capable of integrating heterogeneous sensor infrastructures, hybrid analytical models, and continuous feedback mechanisms. This paper presents a comprehensive state-of-the-art review of Digital Twin technologies, focusing on sensor-centric architectures, data integration strategies, and hybrid modeling approaches. Based on the identified limitations, a novel Federated Digital Twin Lifecycle Model (F-DTLM) is proposed as a unifying framework for industrial applications. The model structures the DT lifecycle into four iterative phases—Definition and Scoping; Sensor Data and Infrastructure Federation; Hybrid Modeling and State Synchronization; and Operational Optimization and Closed-Loop Control, supported by cross-cutting layers addressing interoperability and governance. The integration of federated sensing infrastructures with hybrid physics-informed and data-driven models enables scalable synchronization between physical and digital systems. A comparative analysis and an illustrative predictive maintenance scenario illustrate the potential applicability of the proposed approach. Full article

Long short-term memory (LSTM) networks demonstrate superior time-series feature extraction capabilities and have exhibited significant advantages in the soft sensing of key indicators in complex industrial processes. However, conventional LSTM networks rely solely on the output information from forward propagation through network units, neglecting the residual information between the LSTM cell outputs and the key indicators. Moreover, unidirectional LSTM networks fail to fully exploit the inherent bidirectional temporal dependencies in industrial data. These issues lead to excessive redundancy in the features learned by the network and suboptimal prediction efficiency. This paper proposes a novel dual residual-enhanced deep bidirectional LSTM (DResBiLSTM) framework that integrates bidirectional temporal modeling and dual residual learning for the soft sensing of key variables in complex industrial processes. Firstly, residual information derived from the discrepancy between previous network outputs and key indicators is introduced into the input of the traditional LSTM cell, thereby constructing a residual bidirectional LSTM (ResBiLSTM) network. Secondly, a deep neural architecture is established using residual structures to incorporate input variable residuals, enabling effective soft sensing of key industrial indicators. This framework simultaneously extracts and utilizes latent features characterized by nonlinearity and dynamics from both process and quality variables, significantly enhancing prediction performance. Finally, through both numerical simulations and experimental validations employing real-world operational data from the LaCe/PrNd solvent extraction process, the proposed method demonstrates superior predictive accuracy and better practical effectiveness compared to existing soft sensing approaches. Full article

The construction of green mines is a core strategy for promoting ecological civilization in China’s mining sector, yet its long-term ecological effects require quantitative assessment. Using a cement-grade limestone mine operated by Linyi Zhonglian Cement Co., Ltd. in Shandong Province as an illustrative case, we employed Landsat 8 OLI/TIRS imagery acquired in 2015, 2020, and 2025 to develop a five-indicator framework for assessing ecological environment quality. The selected indicators comprised greenness (NDVI), wetness, dryness (NDBSI), land surface temperature (LST), and dust concentration (MECDI). These five indicators were subsequently integrated via principal component analysis to generate the Mine Ecological Quality Index (Mine-EQI). Using this index, we applied the Theil–Sen median slope estimator alongside zonal statistics to examine ecological change trajectories across the full study area and three functional zones—the industrial square, haul roads, and active mining area—over the 2015–2025 period. The ecological outcomes attributable to the green mine policy were then quantified. The results show that (1) the mean Mine-EQI of the study area decreased from 0.3713 in 2015 to 0.3460 in 2025, exhibiting a slight overall decline. However, the rate of decline decreased from −6.1% during 2015–2020 to −0.7% during 2020–2025, yielding a Temporal Change Intensity index (TCI) of +88.5%, indicating that the ecological degradation trend has been effectively curbed. (2) Significant spatial heterogeneity was observed. The industrial square showed substantial improvement (Theil–Sen slope = +0.0726), while the haul roads (slope = −0.0705) and mining area (slope = −0.0408) continued to exhibit degradation trends. The improved areas (9.7% of the study area) were spatially coincident with green mine engineering projects. (3) The dust indicator (MECDI) decreased by 24.7% during 2020–2025, and the vegetation index (NDVI) increased by 19.5% over the decade, representing the dominant contributors to ecological improvement. This study reveals that China’s green mine policy has yielded remarkable ecological improvements in relatively stable functional zones such as industrial squares. In contrast, ecological restoration within persistently disturbed areas, including haul roads and mining pits, demands long-term sustained investment and governance. By integrating remote sensing techniques with policy analysis, this research establishes a replicable framework for evaluating progress toward sustainable mining practices. The findings directly support the monitoring of SDG 12 (Responsible Consumption and Production) and SDG 15 (Life on Land), providing a quantitative pathway to balance mineral resource extraction with ecological protection—a core sustainability challenge for resource-dependent regions. Full article

Quantifying the detection efficiency of buoy-based sonar and optimizing deployment strategies in complex marine environments remain significant challenges. This study proposes a transceiver depth optimization method based on the Bellhop ray model to enhance underwater remote sensing data quality. For the first time, we validated the applicability of acoustic reciprocity in deep-sea environments exceeding 5000 m, characterized by non-uniform sound speed profiles, horizontal inhomogeneity, and steep seamount terrain, with a maximum relative error of <1.2%. This extends the applicable boundaries of the acoustic reciprocity theorem from idealized simple waveguides to complex, realistic deep-sea environments. Building on this validation, we developed a novel, equivalent, superposition modeling framework for bidirectional transmission loss (TL), which converts the computationally intractable TL from target to receiver into the calculable TL from receiver to target, thus significantly reducing computational complexity. Systematic simulations uncovered a depth-layered dependency mechanism: shallow sources (23.14~69.42 m) and deep sources (≥347.10 m) show robustness to large depth differences exceeding 500 m, whereas mid-layer sources (161.98~231.40 m) exhibit a distinct critical threshold effect. Static simulations identify a performance degradation cliff with an onset at an approximate depth difference of 185 m, leading to a 50% reduction in detection range and fragmented near-field detection coverage. To accommodate environmental temporal variability (e.g., internal waves), a conservative safety margin was incorporated, establishing a robust engineering threshold of 150 m. Accordingly, we define 160~350 m as the optimal detection depth window and propose a layered deployment protocol that fills a critical industry gap in quantitative deployment design for deep-sea acoustic detection. Specifically, transceiver depth differences should be strictly constrained to <150 m for mid-layer operations, while more-flexible depth configurations are permissible for shallow and deep sources. These findings furnish quantitative engineering criteria for the design of reliable underwater remote sensing networks, while balancing long-range detection stability and near-field coverage integrity. Full article

Ascosphaera apis, an obligate lethal fungal pathogen that infects bee larvae, and causes chalkbrood disease, poses a significant threat to the global beekeeping industry. Long non-coding RNAs (lncRNAs) are employed by pathogens to enhance infectivity and evade host immunity. Here, lncRNAs in A. apis spores (AaCK group) and the guts of 4-, 5-, and 6-day-old Apis cerana cerana worker larvae inoculated with A. apis spores (AaT1, AaT2, and AaT3 groups) were identified, characterized, and validated. Additionally, the expression pattern of fungal lncRNAs during infection was analyzed, followed by an investigation of the regulatory manners and roles of differentially expressed lncRNAs (DElncRNAs). A total of 1379 lncRNAs were identified in AaCK, AaT1, AaT2, and AaT3 groups using bioinformatics, involving various types such as sense lncRNAs, antisense lncRNAs, bidirectional lncRNAs, intergenic lncRNAs, and intronic lncRNAs. Additionally, 4, 9, and 75 up-regulated lncRNAs as well as 2, 1, and 15 down-regulated ones were identified in the 4-, 5-, and 6-day-old larval guts following A. apis inoculation. Fifteen DElncRNAs as potential antisense lncRNAs may interact with 15 sense-strand mRNAs in the AaCK vs. AaT3 comparison group. Cis-acting analysis identified 10, 16, and 136 upstream and downstream genes of DElncRNAs in the aforementioned comparison groups, involving a series of GO terms and KEGG pathways like metabolic process and biosynthesis of secondary metabolites. Following the trans-acting investigation, 752, 821, and 1327 co-transcribed genes with DElncRNAs were discovered, spanning an array of functional terms and pathways such as biological processes and glycerophospholipid metabolism. Analysis of a competing endogenous RNA (ceRNA) network indicated that 1 and 5 DElncRNAs in the AaCK vs. AaT1 and AaCK vs. AaT3 comparison groups potentially targeted 1 and 2 miRNAs, further targeting 208 and 286 mRNAs, respectively. Further analysis identified one ceRNA axis relevant to the MAPK signaling pathway and several ceRNA networks associated with the biosynthesis of secondary metabolites. Finally, RT-qPCR results confirmed that the expression trends of six randomly selected DElncRNAs were consistent with those in the transcriptome data. These findings not only offer a foundation for elucidating the mechanisms underlying DElncRNA-mediated A. apis infection but also enrich our understanding of honeybee host–fungal pathogen interactions. Full article

Industry 4.0 (I4.0) is transforming industrial systems through interconnected, data-driven technologies, raising questions about how these developments affect Occupational Health and Safety (OHS). This study investigates research trends, thematic structures, and knowledge gaps at the intersection of I4.0 and OHS using a multilevel bibliometric framework applied to Scopus records published from 2011 to 2025. The analysis moves from a broad overview of the I4.0 landscape to more focused examinations of specific I4.0–OHS publications, prevention-oriented studies, and emerging-risk research. The results show that OHS has limited visibility in the general I4.0 literature and is more prominent mainly in targeted subsets, where digital sensing, artificial intelligence, machine learning, and immersive technologies drive prevention-focused research. Conversely, emerging risks such as cognitive load, psychosocial stressors, and human–autonomy interaction appear in smaller, more dispersed clusters. Overall, the findings suggest that the relationship between I4.0 and OHS is unevenly developed, with established prevention mechanisms and early-stage conceptualization of new risks. Strengthening this field will require integrating human factors with digital indicators, better characterizing emerging risks, and ensuring that digital transformation supports SDG 8 by fostering safe and healthy working environments. Full article

Mechanical engineering systems must sense, inspect, and navigate constrained environments and operate adaptively under uncertainty—requirements that map structurally onto capabilities achieved by biological systems through distributed sensing, morphology-driven locomotion, multimodal perception, and decentralised control. Biomimetics can therefore be interpreted not merely as a source of design inspiration but also as a functional engineering framework relevant to industrial monitoring, inspection, maintenance, and autonomous operation. This study presents a PRISMA 2020-guided systematic mapping review of the biomimetics literature explicitly relevant to mechanical-engineering functions over the decade 2016–2026. A Scopus corpus of 11,114 records was screened through a two-stage abstract-level process. After deduplication and broad relevance filtering, a stricter eligibility audit retained 505 studies assignable to five predefined functional clusters: robotics and access (235 records; 46.5%), mechanical surfaces and tribology (141; 27.9%), sensing and monitoring (106; 21.0%), vision and inspection (14; 2.8%), and control and computation (9; 1.8%). Publication output accelerated markedly after 2022, with 2025 yielding the highest annual count. The principal gap identified is not a shortage of biomimetic concepts, but their limited consolidation into deployable industrial inspection and maintenance architectures. A translational taxonomy connecting biological principles, engineering abstractions, enabling technologies, and mechanical use cases is proposed as an interpretive structuring tool for future research prioritisation and technology-readiness discussion. Full article

Existing research on the landscape patterns of urban parks and green spaces demonstrates a disproportionate focus across tiers within China’s urban hierarchy. Numerous studies have concentrated on economically developed first-tier cities, such as Beijing, Shanghai, and Guangzhou. In contrast, medium-to-large non-first-tier cities, especially provincial capitals and emerging cities within the first- and second tiers, have been relatively understudied, although they have received increasing attention in recent years. This bias extends regionally, with studies predominantly examining cities in the more developed central and eastern regions, while less-developed areas and lower-tier cities receive significantly less attention. This study tracks changes in park quantity, spatial concentration, patch structure and driver associations at three planning-related time points. Shenyang provides a distinct cold-region and old industrial city case, shaped by long winters, industrial renewal and outward urban growth. Furthermore, to inform park and green-space planning in Northeast China’s cold-climate cities, exemplified here by Shenyang, a major metropolis with a monsoon-influenced humid continental climate (Köppen Dwa), long cold winters, and relatively short warm summers, we document a shift in park distribution from the urban core to peripheral areas. Based on park vector layers reconstructed from planning documents, remote sensing interpretation and field verification, this study combined spatial analysis, landscape metric calculation and driver-association modeling. ArcGIS Pro was used to identify changes in distribution centers, directional extension and local clustering; FRAGSTATS 4.2 was used to calculate park landscape metrics; and SIMCA-P 14.1 was used to examine the statistical associations between selected landscape indicators and potential driving variables. The results show that the number and total area of parks in central Shenyang increased substantially from 2000 to 2024. Spatially, park distribution became less concentrated in the traditional inner city, while new clusters gradually appeared in peripheral districts and newly developed urban areas. The old urban core remained important, but its dominance weakened as park provision expanded outward. The landscape metric results further indicate that park expansion was accompanied by more irregular patch forms, stronger fragmentation and declining structural continuity. The driver association analysis suggests that climate conditions, population change, industrial restructuring, real estate investment, road construction and urban greening policies were related to different aspects of park landscape change. These associations should be interpreted as statistical relationships rather than direct causal effects. Overall, this study clarifies the spatial restructuring of park green spaces in a cold-region old industrial city and provides planning evidence for improving park connectivity, coordinating green space expansion with urban construction and supporting sustainable park system development in Northeast China. Full article

Intelligent detection in modern manufacturing, healthcare, process industries, and structural monitoring is fundamentally enabled by heterogeneous sensor systems. Rather than being viewed as a purely image-centered recognition task, intelligent detection is more appropriately formulated as a sensor-driven state inference problem in which sensing physics, signal quality, temporal synchronization, modality availability, and deployment conditions jointly determine what can be reliably detected, localized, interpreted, and acted upon. Against this background, this review provides a structured synthesis of the field through three coupled dimensions, namely methods, systems, and governance, and organizes the literature around four recurring engineering components: signal unification, representation unification, alignment mechanisms, and robustness mechanisms. Using a structured review protocol with explicit source selection, screening, and study coding, the paper traces the methodological evolution from traditional feature-engineering and model-based pipelines to deep learning for visual, temporal, multimodal, generative, and mechanism-constrained sensing, and further to foundation-model-based and multimodal sensor intelligence. Cross-domain evidence is synthesized from industrial defect detection, fault diagnosis, remaining useful life prediction, non-destructive testing, structural health monitoring, medical lesion analysis, and process monitoring. The review argues that recent progress has substantially strengthened learned representations, multimodal interaction, and semantic extensibility, but has not removed persistent constraints arising from domain shift, missing modalities, calibration instability, privacy-preserving collaboration, and edge-side resource limits. Accordingly, the central challenge is no longer how to optimize isolated detection models, but how to build sensor-enabled intelligent systems that remain physically grounded, trustworthy, transferable, and maintainable under real operational conditions. On this basis, the paper concludes by identifying future directions in mechanism-aware modeling, trustworthy evaluation, missing-modality-robust multimodal systems, privacy-preserving cross-site collaboration, and edge-native lifecycle-aware deployment. Full article

Continuous monitoring of impurity content and breakage rate in combine harvester grain flow remains challenging because representative samples are difficult to acquire online, and the visual targets are small, dense, and imbalanced. In this study, a prototype monitoring system integrating sample collection, controlled conveying, image acquisition, and embedded processing was developed for online grain-quality sensing during harvesting. To satisfy the requirement for sidewall sampling from the vertical grain conveying auger, centrifugal sampling and screw conveying were used to extract and transport grain-flow samples, and a stable imaging environment was established using an industrial camera and dedicated illumination. Pixel-area-to-mass mapping models were established for broken grains and impurity targets, with coefficients of determination higher than 0.93. In addition, a lightweight improved YOLOv8-Seg model was developed to recognize and segment broken grains and impurity targets under dense small-target conditions. Bench-scale validation showed that the relative error of impurity content ranged from 1.02% to 13.04%, with an average of 6.09%, while the absolute error of breakage rate ranged from 0.01 to 0.02 percentage points. These results demonstrate the feasibility of the proposed method for online estimation of impurity content and breakage rate under bench-scale conditions and provide a basis for future machine integration and field validation. Full article

Coal-fired power units remain integral to electricity supply in many regions while facing increasingly stringent environmental expectations. Bridging reliable generation with sustainability requires more than end-of-pipe controls; it demands continuous intelligence embedded in plant operations. This study introduces an industry-oriented monitoring framework that transforms historical operational records into actionable foresight, enabling on-the-fly orchestration of combustion conditions to anticipate sulfur oxide (SOx) concentrations. Leveraging 919 empirical data points collected in 2019 from Unit 8 of the Taichung Thermal Power Plant, the framework integrates robust data governance, targeted feature curation, and a neural network-based analytics core. Eight process variables—sulfur content, coal feed rate, fixed carbon, grinding rate, calorific value, excess air, air flow, and boiler efficiency—emerge as the most influential drivers through systematic selection and feature importance attribution. The resulting forecasting module exhibits near-perfect alignment with observed emissions (R² = 0.99), enabling near-real-time guidance for setpoint adjustments and facilitating compliance strategies under varying load and fuel-quality conditions. Beyond accuracy, the system is architected for scalability and portability, aligning with Industry 4.0 paradigms by coupling continuous sensing, data-driven decision support, and stakeholder transparency. By reframing emission oversight as a proactive, intelligent service rather than a static reporting function, the proposed approach advances operational resilience, regulatory compliance, and community trust, with direct implications for resource efficiency and circular economy initiatives across heavy industry. The framework reduces potential SOx emissions and improves energy utilization efficiency under varying operational conditions. This approach contributes to environmental sustainability by enabling proactive emission reduction and cleaner production practices. It supports regulatory compliance and aligns with global sustainability goals, including SDG 7 and SDG 13. Full article