Search Results (18,580)

The strain on the global food sector to reconcile environmental sustainability with operational efficiency has been intensifying. In a growing economy, this study investigates the revolutionary potential of integrated digital ecosystems that include blockchain, big data analytics, and IoT-enabled real-time monitoring on the performance of Green Supply Chain Management (GSCM). The research, that relies on the Technology–Organization–Environment (TOE) framework, utilizes a rigorous mixed-methods approach which utilizes Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Structural Equation Modeling (SEM) on data from food-processing firms in Pakistan. Green innovation is an important moderating catalyst, and SEM results confirm that digital integration significantly enhances waste reduction and energy efficiency, explaining 62% of performance variance. A further configurational analysis indicates causal equifinality and reveals 3 distinct paths to superior sustainability, from “Innovation-Driven Institutionalization” to “Government-Supported Scaling.” It demonstrates that various combinations of external support and internal readiness may ultimately contribute to success. The findings are supported by post-implementation evaluations, which show a 29% decrease in energy consumption and a 55% reduction in cold-chain losses. These findings offer novel insights for practitioners and policymakers, validating that environmental stewardship and commercial profitability are mutually reinforcing objectives in the digital age. Full article

Remote sensing semantic segmentation is fundamental to applications such as land-cover mapping, urban analysis, and environmental monitoring. However, remote sensing scenes often exhibit pronounced scale variation, fragmented regions, dense small objects, and complex boundary transitions, making fine-grained prediction particularly challenging. Transformer-based architectures such as SegFormer have demonstrated a strong capability in modeling long-range context through hierarchical encoding, yet their lightweight decoders mainly rely on linear projection and feature fusion, providing limited capacity for local refinement after multi-scale aggregation. This limitation may reduce spatial precision in boundary-sensitive and small-object-rich regions. To address this issue, we propose the Post-fusion Enhanced Block (PFEB), a lightweight decoder-side refinement module inserted after multi-scale feature fusion and before pixel-wise classification. PFEB combines channel expansion, depthwise and pointwise convolutions, efficient channel attention (ECA), and residual learning to enhance local semantic refinement while largely preserving computational efficiency. Built upon SegFormer, the proposed method was evaluated on two widely used remote sensing benchmarks, i.e., LoveDA and ISPRS Vaihingen, under both Mix Transformer-B0 (MiT-B0) and Mix Transformer-B2 (MiT-B2) backbones. Experimental results show that PFEB consistently improves the SegFormer baseline across datasets and model scales. Under MiT-B2 backbone, our method achieves 53.82 ± 0.31 mean intersection over union (mIoU) on LoveDA and 74.84 ± 0.41 mIoU on ISPRS Vaihingen. Boundary- and size-aware evaluations further indicate that the gains are mainly reflected in improved semantic correctness near boundaries and in the recoverability of small objects. With only modest additional cost (approximately +0.53 M parameters and +8.7 G floating point operations (FLOPs)), PFEB provides a favorable accuracy–efficiency trade-off. These results suggest that PFEB is an effective and lightweight post-fusion refinement module for improving fine-grained remote sensing semantic segmentation. Full article

Existing Environmental, Social, and Governance (ESG) monitoring approaches rely primarily on static reports and dashboard-based interfaces, limiting real-time analysis and interactive exploration of sustainability data in complex built environments. In addition, current digital twin systems often lack integration with IoT-based sensing or depend on cloud-based rendering infrastructures, increasing deployment complexity and restricting accessibility. This study proposes a lightweight Web3D-based digital twin framework for real-time ESG monitoring in smart buildings. The system integrates an independently developed IoT sensor network with a browser-native 3D visualization platform, enabling real-time monitoring of ESG indicators—including electricity consumption—without requiring proprietary software or dedicated rendering hardware. ESG indicators are derived using a rule-based classification aligned with the WELL Building Standard v1. The framework was validated through a 12-month real-world deployment involving 60 IoT sensors. Results demonstrate stable performance, achieving 66 FPS rendering, 78 ms system latency, and 98% sensor data consistency based on cross-sensor agreement. The system also enabled timely detection of environmental anomalies, leading to measurable improvements in air quality and lighting conditions. Unlike prior digital twin systems, the proposed framework delivers a fully browser-native, lightweight architecture that integrates real-time IoT sensing, adaptive Web3D visualization, and structured ESG monitoring within a single deployable system. This approach provides a practical solution with potential for broader deployment in real-time sustainability monitoring for smart buildings. Full article

With the continuing intensification of modern agriculture, pesticides and antibiotics are extensively used to control pests and diseases, but their improper use and indirect inputs have resulted in widespread contamination of agricultural environments and food products. This review synthesizes how these contaminants enter agroecosystems, their occurrence across soils, waters and agricultural products, and the processes that redistribute residues across air–water–soil interfaces and into the soil–plant continuum. We summarize cross-media transport pathways (e.g., runoff/leaching, volatilization–deposition and irrigation-driven redistribution) and relate environmental exposure to plant uptake using a harmonized indicator set, including the bioconcentration factor (BCF), translocation factor (TF), octanol–water partition coefficient (log Kow) and soil organic carbon–water partition coefficient (Koc). We further discuss key determinants of crop accumulation, including compound-specific properties, soil characteristics and plant physiological traits, and highlight how these factors jointly shape residue profiles in edible tissues. Finally, we outline research priorities for source reduction, standardized multi-matrix surveillance, fate-to-uptake modeling, and microbiome-enabled remediation strategies to support pollution control, food safety and public health. Full article

This study presents a capacitive sensor with a zirconium oxide (ZrO₂) coating for real-time measurement of component concentration in liquid media. The ZrO₂ layer was formed on stainless steel electrodes by magnetron sputtering, and its structural, morphological, and chemical properties were characterized using SEM, EDS, FTIR, and XRD. It was found that increasing coating thickness results in more continuous and highly crystalline layers, while reducing the influence of the substrate on surface properties. The performance of the capacitive sensor was evaluated by analysing the dependence of capacitance on frequency and NaCl concentration. The results show that the thickness of the ZrO₂ layer has a significant influence on sensor sensitivity and measurement stability. A thinner layer (~2 µm) provides higher sensitivity but is more affected by parasitic effects, while thicker layers improve measurement stability at the expense of reduced sensitivity. An optimal trade-off between sensitivity and stability is achieved at a ZrO₂ layer thickness of approximately 4 µm, ensuring sufficient sensitivity and good measurement repeatability. The results indicate that ZrO₂-modified capacitive sensors are a promising technology for monitoring liquid quality, particularly in environmental protection and industrial process control. Full article

Robotics is increasingly positioned as an enabling technology for precision agriculture, where management actions must be spatially and temporally targeted under constraints on labour, input use, safety, and environmental impact. This review synthesises studies on agricultural field robotics and organises the literature along four complementary axes: task (monitoring, weeding, spraying, and harvesting), platform (UGV, UAV, gantry/fixed-structure, greenhouse robot, and hybrid systems), autonomy-stack module (perception, localisation, planning, control, actuation, safety, and human–robot interaction), and evaluation setting (lab, greenhouse, open-field single season, and open-field multi-season/multi-site). Across these dimensions, this review analyses how platform constraints shape sensing geometry, actuation capability, localisation reliability, energy/endurance, supervision burden, and safety requirements. It further examines enabling technologies that recur across tasks, including vision and multimodal perception under occlusion and illumination variability, localisation and mapping under weak or denied GNSS, uncertainty-aware planning in deformable and partially observed environments, and compliant end-effectors for contact-rich operations. Beyond cataloguing systems, this paper emphasises evaluation practice by synthesising core task-relevant metrics, comparing laboratory and field validation settings, and proposing a reporting checklist and benchmark ladder to improve reproducibility and cross-study comparability. This review identifies recurring bottlenecks in domain shift, long-term autonomy, calibration robustness, crop-safe actuation, and safety assurance near humans, and it concludes with a staged research roadmap linking near-term evaluation reform to longer-term credible multi-site autonomy. Overall, this paper provides a structured framework for interpreting agricultural robotic systems not only by application but also by deployment context, system maturity, and evaluation credibility. Full article

The rapid diffusion of industry 4.0 technologies has substantially transformed the maritime transportation sectors by enabling data-driven operations, enhanced connectivity, and more intelligent decision-making processes. Digital technologies such as the Internet of Things (IoT), simulation systems, and advanced data analytics are increasingly reshaping operational structures in maritime logistics, positioning technological transformation as a strategic priority for firms. However, the weighting and prioritization of components emerging with industry 4.0 technologies remain an underexplored area in the literature. The primary motivation of this study is to determine the weights of these industry 4.0 components using the Bayesian Best Worst Method (BWM) and to reveal their corresponding credal ranking levels. In this context, the present study aims to evaluate and prioritize the critical industry 4.0 components influencing technological transformation processes using the Bayesian BWM. Bayesian BWM is preferred over alternative Multi Criteria Decision Making (MCDM) approaches due to its ability to explicitly model uncertainty within a probabilistic framework, generate more consistent weighting results, and flexibly incorporate decision-makers’ judgments. The findings reveal that safety and security (0.2945) constitute the most influential main component, underscoring the necessity of robust digital infrastructures and reliable systems within highly digitalized operational environments. Among the sub-components, data privacy (0.1301) demonstrates the highest global weight, highlighting the growing importance of safeguarding sensitive information in data-intensive digital systems. The results further indicate that autonomous operation and coordination play significant roles in facilitating efficient digital operations, particularly through real-time equipment monitoring and IoT-based operational visibility. Moreover, sustainability (0.1968) emerges as the second most important component, suggesting that organizations increasingly assess technological investments not only in terms of operational efficiency but also with respect to long-term resilience. Within this dimension, continuous training (0.0614) is identified as the most influential component, indicating that the success of digital transformation depends not only on technological infrastructure but also on the development of human capabilities. With the increasing digitalization of the maritime industry, protection against cyber threats has become essential for ensuring operational continuity and safeguarding data integrity. In this regard, adopting proactive cybersecurity strategies and continuously monitoring and updating systems are of critical importance. In the digital transformation of maritime transportation, integrating sustainability considerations is essential to ensure long-term operational efficiency and environmental responsibility. These practical implications are particularly relevant for policymakers, port authorities, and shipping companies seeking to enhance both digital capabilities and sustainable performance. Full article

Environmental DNA (eDNA) offers a promising, non-invasive approach for monitoring infectious agents in aquaculture. While molecular techniques for detecting shrimp pathogens are well established in host tissues, there is a lack of standardized protocols for pathogen detection from environmental samples using conventional PCR. In this study, we developed and validated a universal conventional PCR protocol for monitoring DNA from major viral and bacterial shrimp pathogens within pond water and sediment samples. The method was applied to two commercial shrimp farms in Mexico, where eDNA was extracted from field-collected water and sediment. Using published primer sets, we successfully amplified DNA sequences corresponding to six key pathogens—Infectious hypodermal and hematopoietic necrosis virus (IHHNV), Baculovirus penaei (BP), Monodon baculovirus (MBV), Shrimp hemocyte iridescent virus (SHIV), Candidatus Hepatobacter penaei (NHP-B), and Acute hepatopancreatic necrosis disease (AHPND)-causing Vibrio spp.—in environmental samples. Sequencing of PCR amplicons confirmed 93–100% identity to previously reported pathogen strains, highlighting the method’s reliability. Pathogen detection rates varied by site, sample type, and date, with the percentage of positive samples ranging from 11.1% to 77.7%. Notably, this is the first report of SHIV DNA detection from environmental samples in the Americas, highlighting its value for pathogen surveillance even in the absence of documented outbreaks. This protocol offers a cost-effective and scalable tool for pathogen surveillance in shrimp aquaculture, enhancing early disease detection and contributing to improved biosecurity and risk assessment frameworks. Full article

The integration of digital tools and environmental design methods is widely recognised as essential for sustainable architectural practice. However, their influence on early design decisions and lifecycle continuity remains limited. This study introduces the concept of integration readiness and operationalises it through the Integrated Environmental Design Framework (ILPP+), which links environmental methods to project phases, decision leverage, and organisational conditions. An exploratory survey of 37 architectural design offices in the Lower Silesian region of Poland was conducted to examine how BIM, life cycle assessment (LCA), passive strategies, performance-based analysis, and monitoring practices are embedded in design workflows. The analysis combines descriptive statistics with exploratory correlation analysis to identify relationships between selected integration dimensions. The results indicate uneven patterns of integration. Passive strategies and simulations show moderate coupling (ρ = 0.60), while weaker relationships between simulations and structured decision processes (ρ = 0.40) suggest that analytical tools are not consistently used as decision-support mechanisms. Similarly, BIM shows only partial integration with LCA (ρ = 0.41) and post-occupancy evaluation (ρ = 0.46), indicating limited lifecycle continuity within the analysed sample. These findings suggest that environmental integration may be constrained not by the availability of tools but by their positioning within decision processes and across project phases. The study highlights the importance of aligning analytical methods with high-leverage design stages and strengthening feedback loops between design and operation. Full article

Carbon dots (CDs) have demonstrated promising application prospects in the field of corrosion protection due to their small size, excellent dispersibility, abundant and tunable surface functional groups, low cost, environmental friendliness, and unique fluorescence properties. However, existing reviews have predominantly focused on the synthesis and photoluminescence properties of CDs, lacking systematic integration and in-depth mechanistic analysis of their diverse applications in corrosion protection. This review systematically summarizes the recent research progress and underlying mechanisms of CDs in five key areas: corrosion inhibitors, anticorrosive coatings, photogenerated cathodic protection, chloride binding, and corrosion monitoring. As corrosion inhibitors, CDs form compact protective films on metal surfaces through synergistic physical and chemical adsorption. In anticorrosive coatings, CDs not only enhance the physical barrier effect but also impart intelligent functionalities such as self-healing and corrosion monitoring. In the field of photogenerated cathodic protection, CDs broaden the light absorption range of semiconductors and facilitate the separation of photogenerated carriers. As chloride binding promoters, CDs promote the formation of cement hydration products, thereby improving the durability of reinforced concrete structures. As sensing platforms, CDs enable early visual detection of corrosion through their specific fluorescence response to ions such as Fe³⁺. Despite significant progress, challenges remain in scalable preparation, practical application performance in complex environments, and multifunctional integration. This review systematically outlines the research advancements of CDs in corrosion protection, providing a practical reference for subsequent studies and engineering applications. Future research should focus on scalable synthesis, machine learning-assisted design, and the development of integrated multifunctional protection systems to promote the practical application of CDs in the field of corrosion protection. Full article

High-sensitivity rapid detection of ammonia (NH₃) in environmental monitoring, industrial safety, early diagnosis, and other fields is of great significance. Covalent organic frameworks (COFs) have shown great potential in the field of gas sensing due to their designable porous structure and active sites. However, the traditional solvothermal synthesis method of COFs has problems such as cumbersome steps, high energy consumption and serious environmental pollution. Therefore, it is of great significance to invent a new method for COF synthesis that is green and efficient and makes it easy to conduct flexible ammonia gas sensing. This study first reported a solvent-free synthesis of imine connection 1,3,5-Triformylbenzene (TFB) and p-Phenylenediamine (PDA)—a new strategy for COF. This method innovatively employs zinc trifluoromethyl sulfonate (Zn(OTf)₂) as a bifunctional catalyst. This catalyst not only efficiently catalyzes para-phenylenediamine, but its zinc ions also play a unique structural guiding role, guiding the reactants to be arranged in a directional manner, thereby constructing a highly ordered porous crystal structure. A series of characterizations confirmed that the obtained TFB-PDA-COF had good crystallinity and a high proportion of imine bonds (C=N). The powder material was coated onto a flexible polyimide (PI) substrate, successfully constructing a resistive ammonia gas sensor that operates at room temperature. The test results show that this sensor has a high response value, rapid response/recovery capability, and good selectivity for ammonia gas. More importantly, based on a flexible PI substrate, the device can maintain stable sensing performance even under repeated bending conditions, demonstrating its great potential in practical flexible electronic applications. This work not only provides a brand-new “zinc ion-guided” paradigm for the green and controllable synthesis of COF but also lays a material foundation for their application in the next-generation flexible sensing field. Full article

Inefficient urban traffic management remains a critical challenge, as conventional signal controllers—built on fixed timing plans—cannot cope with the dynamic nature of modern city traffic. This study addresses this limitation by developing a decentralized MARL-based framework capable of coordinating five interconnected intersections as a unified traffic cell. Central to the proposed solution is the Strategic Anti-Blocking Phase Adjustment (SAPA) module, which enables intersections to autonomously modify phase durations in response to real-time traffic conditions. The framework is designed to handle heterogeneous demand patterns, with particular emphasis on arterial corridors connecting urban centers to peripheral zones. Integration of a Visible Light Communication (VLC) network allows continuous monitoring of key variables, including vehicle kinematics and pedestrian activity, feeding the agents with rich environmental feedback. Experimental evaluation confirms the effectiveness of the approach: the SAPA-augmented DQN achieves roughly 33% shorter vehicle queues and a ~70% reduction in pedestrian waiting counts relative to a standard DQN baseline. Remarkably, these gains bring the value-based method to a performance level comparable to MAPPO, a considerably more complex multi-agent policy optimization algorithm, establishing SAPA as an efficient and scalable enhancement for intelligent urban traffic control. Full article

Glyphosate is the most widely used herbicide worldwide, but many current detection methods rely on lab-based chromatography, requiring costly equipment and expert users. Here, we describe a low-cost, field-deployable fluorescence biosensing platform for glyphosate detection in water and soil. An engineered variant of the Escherichia coli periplasmic binding protein PhnD was optimized through strategic fluorophore placement to produce a robust fluorescence signal increase upon glyphosate binding. The biosensor was integrated into a self-contained, 3D-printed device that functions as a miniature fluorometer, providing a simple yes-or-no output for non-expert users while retaining access to raw fluorescence data. The device exhibits nanomolar fluorescence sensitivity with results comparable to a benchtop fluorometer. Using this platform, glyphosate was reliably detected in buffered solutions, commercial herbicides, tap water, and soil extracts. To mitigate false positives arising from phosphate interference, we developed a dual-sensor strategy incorporating an independent phosphate biosensor and a second-generation device capable of multi-wavelength fluorescence detection. Together, these results demonstrate an affordable and versatile biosensing platform with strong potential for field-based environmental monitoring. Full article

Accurate classification of uranium slag origin and category is essential for nuclear environmental monitoring and safety. This study presents a hybrid framework combining laser-induced breakdown spectroscopy (LIBS), four preprocessing methods, and five machine learning algorithms for rapid uranium slag classification. A total of nine sample categories were collected from three mining areas, with categories defined by their U concentration levels within each origin. Standard normal variate (SNV), Savitzky–Golay smoothing (SG), and their combinations (SNV-SG, SG-SNV) were applied to evaluate preprocessing effects. To address ultra-high-dimensional spectral data (49,242 points per spectrum), principal component analysis (PCA) and random forest (RF) were employed for feature engineering, integrated with support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbors (KNN) classifiers. Hyperparameter optimization via five-fold cross-validation and Bayesian optimization enhanced accuracy and efficiency. RF-based hybrid models consistently outperformed PCA-based counterparts. Remarkably, the RF-LDA model with SNV-SG preprocessing achieved 100% classification accuracy across all test sets with a processing time of only 10.46 s, demonstrating exceptional discriminative power and computational efficiency. These findings establish that combining RF feature selection with advanced machine learning offers a robust solution for LIBS-based nuclear material classification, with significant implications for both nuclear safety and resource management. Full article

Accurate ecological prediction is critical for sustainable environmental management and carbon cycle assessment, yet model development is often constrained by limited datasets and inconsistent preprocessing practices. Reliable litterfall prediction plays a key role in understanding nutrient cycling and supporting sustainable forest ecosystem management. Although gradient boosting models have shown promising performance in ecological applications, structured evaluations integrating preprocessing strategies with synthetic data augmentation remain limited under data-scarce conditions. This study proposes the Hybrid Preprocessing and Augmented Boosting Framework (HPABF), which combines multi-stage preprocessing—including MICE imputation, log transformation, and feature engineering—with synthetic data augmentation to enhance predictive robustness. The framework was evaluated across eight machine learning models using a 968-sample forest ecological dataset. To mitigate data scarcity, 5000 synthetic samples were generated while preserving the statistical distribution and multivariate structure of the original data (91% fidelity). Fractal dimension analysis was further introduced as a geometric validation metric to assess prediction structure and stability beyond conventional performance measures. Within the HPABF, gradient boosting models achieved a 7% improvement over baseline performance (R² = 0.96, MAE = 0.06) under cross-validation strategies designed to reduce overfitting. Training with synthetic data further improved predictive accuracy (R² = 0.98), demonstrating the framework’s effectiveness for data-scarce ecological applications. By improving prediction reliability under limited data conditions, the proposed framework supports more accurate environmental monitoring, informed decision-making, and sustainable management of forest ecosystems. Full article