Search Results (1,160)

This study presents a cost-effective and accurate method for assessing nitrogen concentration in vermicompost fertilizer using a low-cost TCS3200 color sensor and a K-Nearest Neighbors (KNN) machine learning model. The objective was to evaluate the performance of four different color spaces—RGB, Lab, LCh, and CMYK—identify the most effective feature representation for a multi-class classification task based on accuracy and theoretical robustness to ambient light variations. A total of 2400 data points were collected from a standard chemical test kit and processed. A rigorous 60-fold cross-validation approach was used to determine the optimal model hyperparameters and to ensure the robustness of the findings. The results demonstrate that the model trained on the LCh color space achieved the highest classification accuracy of 0.9708 with an optimal K-value of 6, significantly outperforming Lab (0.9688), RGB (0.9625), and CMYK (0.9583). A detailed analysis of the confusion matrix revealed that the model successfully classified the ‘High’ and ‘Medium’ nitrogen levels with near-perfect accuracy, while minor misclassifications occurred between the ‘Low’ and ‘Trace’ categories (5 Low ⟶ Trace, 6 Trace ⟶ Low). The proposed system offers a practical, robust, and accessible tool for precision agriculture, enabling farmers to make informed decisions regarding fertilization, and directly supporting sustainable agriculture and responsible resource management. The findings indicate that the LCh color space is highly effective for this application, providing a viable solution for the rapid and reliable assessment of vermicompost quality. Most importantly, this inexpensive, on-site system removes the need for costly, time-consuming laboratory analyses, giving farmers and compost users the instantaneous, accurate nitrogen data they need to maximize crop yield, optimize nutrient application, and significantly reduce input costs from overfertilization. Full article

Caregivers of a child with a serious medical condition are often confronted with difficult and stressful medical information. While they commonly seek out health-related information to better care for their child and help with their decision-making, sometimes caregivers engage in healthcare information avoidance. Healthcare information avoidance is the decision to prevent or delay the acquisition of available, but potentially unwanted, health-related information. We begin by defining the construct of healthcare information avoidance and exploring key theoretical frameworks that illuminate its underlying mechanisms including emotion regulation theory, attentional and cognitive models, approach-avoidance coping strategies, and dispositional theories. A lack of validated measures to assess caregiver healthcare information avoidance was noted as contributing to the dearth of empirical work in this area. Common areas of caregiver healthcare information avoidance were identified at various points throughout the pediatric palliative care illness trajectory. The review concludes with directions for future research and practical recommendations for clinical care, highlighting the importance of identifying the occurrence and reasons for caregiver information avoidance as well as optimizing approaches to information provision. Full article

Autonomous driving relies on multimodal sensors to acquire environmental information for supporting decision making and control. While significant progress has been made in 3D object detection regarding point cloud processing and multi-sensor fusion, existing methods still suffer from shortcomings—such as sparse point clouds of foreground targets, fusion instability caused by fluctuating sensor data quality, and inadequate modeling of cross-frame temporal consistency in video streams—which severely restrict the practical performance of perception systems. To address these issues, this paper proposes a multimodal video stream 3D object detection framework based on reliability evaluation. Specifically, it dynamically perceives the reliability of each modal feature by evaluating the Region of Interest (RoI) features of cameras and LiDARs, and adaptively adjusts their contribution ratios in the fusion process accordingly. Additionally, a target-level semantic soft matching graph is constructed within the RoI region. Combined with spatial self-attention and temporal cross-attention mechanisms, the spatio-temporal correlations between consecutive frames are fully explored to achieve feature completion and enhancement. Verification on the nuScenes dataset shows that the proposed algorithm achieves an optimal performance of 67.3% and 70.6% in terms of the two core metrics, mAP and NDS, respectively—outperforming existing mainstream 3D object detection algorithms. Ablation experiments confirm that each module plays a crucial role in improving overall performance, and the algorithm exhibits better robustness and generalization in dynamically complex scenarios. Full article

Rainfall-driven non-point source (NPS) pollution has become a critical issue for water environment management in urban watershed systems. However, single-model use is limited to fully represent the intricate processes of rainfall-correlated NPS pollution generation and dispersion for effective decision-making. This study develops a novel cross-scale, multi-factor coupled model framework to characterize hydrologic and NPS pollution responses to different rainfall events in Shenzhen, China, a representative worldwide metropolis facing challenges from rapid urbanization. The calibrated and validated coupled model achieved remarkable agreements with observed hydrologic (Nash–Sutcliffe efficiency, NSE > 0.81) and water quality (NSE > 0.85) data in different rainfall events and demonstrated high-resolution dynamic changes in flow and pollutant transfer within the studied watershed. In an individual rainfall event, heterogeneous spatial distributions of discharge and pollutant loads were found, highly correlated with land use types. The temporal change pattern and risk of flooding and NPS pollution differed significantly with rainfall intensity, and the increase in the pollutants (mean 322% and 596%, respectively) was much larger than the discharge (207% and 302%, respectively) under intense rainfall conditions. Based on these findings, a decision-support framework was established, featuring land-use-driven spatial prioritization of industrial hotspots, rainfall-intensity-stratified management protocols with event-triggered operational rules, and integrated source-pathway-receiving end intervention strategies. The validated model framework provides quantitative guidance for optimizing infrastructure design parameters, establishing performance-based regulatory standards, and enabling real-time operational decision-making in urban watershed management. Full article

Against the backdrop of the dual carbon goals, the construction industry—as the primary source of carbon emissions accounting for 50.9%—is increasingly relying on green technological innovation to drive its sustainable development transformation. However, construction enterprises currently face three core challenges: the significant incremental costs associated with adopting green technologies, insufficient green credit supply from financial institutions, especially banks, and inadequate policy coordination among government departments. Furthermore, misaligned interests among multiple stakeholders exacerbate the implementation challenges of green technological innovation, hindering the industry′s low-carbon transition. Therefore, systematically exploring the interaction patterns and functional mechanisms among construction enterprises, government agencies, and banks in green technology innovation decision-making is crucial. This study will provide theoretical and empirical support for the green transformation of the construction industry within the dual-carbon framework. This study establishes a tripartite game model involving construction companies, governments, and banks, centered around the decision-making phase of green technology innovation. By integrating evolutionary game theory with system dynamics (SD) approaches, it uncovers the evolutionary trajectories and underlying mechanisms of strategies adopted by each stakeholder. Research indicates that construction companies, governments, and banks ultimately maintain equilibrium at the (1,1,1) point. The study underscores the pivotal role of government guidance during the decision-making stage, highlighting that sustained implementation of proactive policies can foster positive interactions and a balance between construction companies’ pursuit of green technology innovation and banks’ provision of green credit. It can shorten the time required for enterprises and banks to evolve their strategies. Suppressing the probability of innovation failure moderates both parties′ strategies, and adjusting parameters such as green credit interest rates and government subsidies can optimize choices. This research not only enhances the theoretical understanding of green technology innovation in the construction sector but also offers practical insights for promoting industry-wide green innovation, improving the quality of green buildings, and regulating market order. Full article

Reservoir operation serves as a critical non-structural measure for real-time flood management, aimed to minimize downstream flood damage while ensuring dam safety. This study develops and evaluates a Gated Recurrent Unit (GRU)-based reservoir operation model with data integration (DI) to enhance flood management capabilities. Optimal reservoir outflows are first determined for historical flood events using the Interior Point Optimizer (IPOPT), a deterministic optimization model designed to minimize peak outflows. The optimized hydrographs are compared with observed outflows to assess the benefits of improved operational strategies. GRU models are then trained and validated using inflow hydrographs and resulting optimal reservoir storage and release data. Various input configurations are tested, incorporating DI of lagged observations and forecasted values to evaluate their influence on model accuracy. The study also examines multiple hyperparameter settings to identify the optimal configuration. The methodology is applied to the Namgang Dam in South Korea, simulating hourly operations during flood events. Results indicate that historical reservoir inflow and storage are the most influential inputs, while adding precipitation (historical or forecasted) and/or forecasted inflows does not improve model performance. The GRU model with DI successfully replicates optimized reservoir operations, demonstrating its reliability and efficiency in flood management. This framework supports timely and informed decision-making and offers a promising approach for enhancing flood risk mitigation through improved reservoir operations. Full article

The importance of Last-Mile Delivery (LMD) in the current economy cannot be overstated, as it is the final and most crucial step in the supply chain between retailers and consumers. In major cities, absent intervention, urban LMD emissions are projected to rise by >30% by 2030 as e-commerce grows (top-100-city “do-nothing” baseline). Sustainable, innovative ground-based solutions for LMD, such as Electric Vehicles, autonomous delivery robots, parcel lockers, pick-up points, crowdsourcing, and freight-on-transit, can revolutionize urban logistics by reducing congestion and pollution while improving efficiency. However, developing these solutions presents challenges in Operations Research (OR), including problem modeling, optimization, and computations. This systematic review aims to provide an OR-centric synthesis of sustainable, ground-based LMD by (i) classifying these innovative solutions across problem types and methods, (ii) linking technique classes to sustainability goals (cost, emissions/energy, service, resilience, and equity), and (iii) identifying research gaps and promising hybrid designs. We support this synthesis by systematically screening 283 records (2010–2025) and analyzing 265 eligible studies. After the gap analysis, the researchers and practitioners are recommended to explore new combinations of innovative solutions for ground-based LMD. While they offer benefits, their complexity requires advanced solution algorithms and decision-making frameworks. Full article

Compared with other fossil energy sources, natural gas is characterized by compressibility, low energy density, high storage costs, and imbalanced usage. Natural gas pipeline supply systems possess unique attributes such as closed transportation and a highly integrated upstream, midstream, and downstream structure. Moreover, pipelines are almost the only economical means of onshore natural gas transportation. Given that the upstream of the pipeline features multi-entity and multi-channel supply including natural gas, coal-to-gas, and LNG vaporized gas, while the downstream presents a competitive landscape with multi-market and multi-user segments (e.g., urban residents, factories, power plants, and vehicles), there is an urgent social demand for non-discriminatory and fair opening of natural gas pipeline network infrastructure to third-party entities. However, after the fair opening of natural gas pipeline networks, the original “point-to-point” transaction model will be replaced by market-driven behaviors, making the verification and allocation of gas transmission capacity a key operational issue. Currently, neither pipeline operators nor government regulatory authorities have issued corresponding rules, regulations, or evaluation plans. To address this, this paper proposes a multi-dimensional quantitative evaluation model based on the Analytic Hierarchy Process (AHP), integrating both commercial and technical indicators. The model comprehensively considers six indicators: pipeline transportation fees, pipeline gas line pack, maximum gas storage capacity, pipeline pressure drop, energy consumption, and user satisfaction and constructs a quantitative evaluation system. Through the consistency check of the judgment matrix (CR = 0.06213 < 0.1), the weights of the respective indicators are determined as follows: 0.2584, 0.2054, 0.1419, 0.1166, 0.1419, and 0.1357. The specific score of each indicator is determined based on the deviation between each evaluation indicator and the theoretical optimal value under different gas volume allocation schemes. Combined with the weight proportion, the total score of each gas volume allocation scheme is finally calculated, thereby obtaining the recommended gas volume allocation scheme. The evaluation model was applied to a practical pipeline project. The evaluation results show that the AHP-based evaluation model can effectively quantify the advantages and disadvantages of different gas volume allocation schemes. Notably, the gas volume allocation scheme under normal operating conditions is not the optimal one; instead, it ranks last according to the scores, with a score 0.7 points lower than that of the optimal scheme. In addition, to facilitate rapid decision-making for gas volume allocation schemes, this paper designs a program using HTML and develops a gas volume allocation evaluation program with JavaScript based on the established model. This self-developed program has the function of automatically generating scheme scores once the proposed gas volume allocation for each station is input, providing a decision support tool for pipeline operators, shippers, and regulatory authorities. The evaluation model provides a theoretical and methodological basis for the dynamic optimization of natural gas pipeline gas volume allocation schemes under the fair opening model. It is expected to, on the one hand, provide a reference for transactions between pipeline network companies and shippers, and on the other hand, offer insights for regulatory authorities to further formulate detailed and fair gas transmission capacity transaction methods. Full article

Monitoring plant water status is vital for optimizing irrigation in precision agriculture. This study explores the use of two simple, affordable, and non-invasive sensor systems, electrical impedance spectroscopy (EIS) and infrared (IR) spectroscopy, to assess plant water status directly from leaf tissues. This approach is well-suited for the realization of large networks of distributed sensors wirelessly connected to a central hub. An outdoor experiment was conducted over two phases of 20 day-experiment involving six Hydrangea macrophylla plants subjected to two irrigation treatments: a control group (well-irrigated) and a test group (poorly irrigated) designed to induce water stress. The standard relative water content (RWC) method validated the treatment effects on the plants, and both EIS and IR sensors effectively distinguished between the two groups. Impedance-derived parameters, particularly the normalized intracellular resistance (R0) and the cell membrane capacitance (C0), exhibited statistically significant differences between the treatments. In addition, the IR measurements showed moderate correlations with RWC, with determination coefficients of R² = 0.56 and R² = 0.51 for first and second phases of the experiment, respectively. Despite some limitations concerning the electrode–leaf conformity and external sunlight interference, the results point to the advantages of these methods for real-time plant monitoring and decision-making in smart irrigation systems. Full article

Spread Through Air Spaces (STAS) is an emerging pattern of tumor invasion in lung cancer, first recognized by the World Health Organization in 2015. This narrative review examines STAS from a multidisciplinary perspective, integrating pathologic, radiologic, oncologic, and surgical points of view, together with molecular biology to assess its clinical significance, diagnostic challenges, and therapeutic implications. Pathologically, STAS is characterized by tumor cells floating beyond the main tumor, contributing to recurrence and poor prognosis. Radiologic advancements suggest potential imaging markers for STAS, such as spiculation, the absence of an air bronchogram, solid tumor components, as well as high fluorodeoxyglucose uptake, though definitive preoperative identification remains challenging. Oncologic studies link STAS to aggressive tumor behavior and lympho-vascular invasion, suggesting a role for adjuvant chemotherapy even in the earliest stages of disease; furthermore, specific molecular alterations have been discovered, including EGFR wild-type status and ALK/ROS1 rearrangements together with high Ki-67 expression, tumor necrosis, and alterations in cell adhesion proteins like E-cadherin. Surgical aspects highlight the increased risk of recurrence following limited resection, raising concerns about optimal surgical strategies. The debate over STAS as a true invasion mechanism versus an artifact from surgical handling underscores the need for standardized pathological evaluation. This review aims to refine STAS detection, integrate it into multidisciplinary treatment decision-making, and assess its potential as a staging criterion in lung cancer management. Full article

Predicting the quality of freshwater, both surface and groundwater, is essential for the sustainable management of water resources. This study collected 1822 articles from the Scopus database (2000–2024) and filtered them using Topic Modeling to create the study corpus. The B-SLR analysis identified exponential growth in scientific publications since 2020, indicating that this field has reached a stage of maturity. The results showed that the predominant techniques for predicting water quality, both for surface and groundwater, fall into three main categories: (i) ensemble models, with Bagging and Boosting representing 43.07% and 25.91%, respectively, particularly random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGB), along with their optimized variants; (ii) deep neural networks such as long short-term memory (LSTM) and convolutional neural network (CNN), which excel at modeling complex temporal dynamics; and (iii) traditional algorithms like artificial neural network (ANN), support vector machines (SVMs), and decision tree (DT), which remain widely used. Current trends point towards the use of hybrid and explainable architectures, with increased application of interpretability techniques. Emerging approaches such as Generative Adversarial Network (GAN) and Group Method of Data Handling (GMDH) for data-scarce contexts, Transfer Learning for knowledge reuse, and Transformer architectures that outperform LSTM in time series prediction tasks were also identified. Furthermore, the most studied water bodies (e.g., rivers, aquifers) and the most commonly used water quality indicators (e.g., WQI, EWQI, dissolved oxygen, nitrates) were identified. The B-SLR and Topic Modeling methodology provided a more robust, reproducible, and comprehensive overview of AI/ML/DL models for freshwater quality prediction, facilitating the identification of thematic patterns and research opportunities. Full article

To address the issue that prototypical networks in existing few-shot text classification methods suffer from performance limitations due to prototype shift and metric constraints, this paper proposes a meta-learning-based few-shot text classification method: Threshold Dynamic Multi-Source Decisive Prototypical Network (TDMP-Net) to solve these problems. This method designs two core components: the threshold dynamic data augmentation module and the multi-source information Decider. Specifically, the threshold dynamic data augmentation module achieves the optimization of the prototype estimation process by leveraging the multi-source information of query set samples, which thereby alleviates the prototype shift problem; meanwhile, the multi-source information Decider performs classification by relying on the multi-source information of the query set, thus alleviating the metric constraint problem. The effectiveness of the proposed method is verified on four benchmark datasets: under the five-way one-shot and five-way five-shot settings, TDMP-Net achieves average accuracies of 78.3% and 86.5%, respectively, which are an average improvement of 3.3 percentage points compared with current state-of-the-art methods. Experimental results show that this TDMP-Net can effectively alleviate the prototype shift problem and metric constraint problems, and has stronger generalization ability. Full article

Ecosystem services provide the scientific foundation and optimization objectives for constructing ecological security patterns, and their spatial characteristics directly affect planning decisions such as ecological source identification and corridor layout. However, current methods for constructing ecological security patterns rely excessively on static spatial optimization of landscape structure and ecological processes, while overlooking the dynamic variations in ecosystem service values under climate change. Taking Yunnan Province as a case study, this paper calculates ecosystem service values, analyzes their spatiotemporal variations, and based on ecosystem service value hotspots, applies the MSPA model and circuit theory to identify ecological sources, corridors, pinch points, barrier areas, and improvement areas. On this basis, we construct and optimize the ecological security pattern of Yunnan Province and propose ecological protection strategies. The results show that: (1) From 2000 to 2030, ecosystem service values in Yunnan exhibit significant spatiotemporal heterogeneity. From 2000 to 2020, they first declined and then increased, with aquatic ecosystems contributing the most. Under future climate scenarios, ecosystem service values continue to increase, with the greatest growth under the SSP2-4.5 scenario. The spatial pattern is characterized by higher values in the central region and lower values in the eastern and western areas. (2) In 2020, 56 ecological sources were identified; under the SSP1-1.9 scenario, 61 were identified, while 57 were identified under both SSP2-4.5 and SSP5-8.5 scenarios. These sources are mainly distributed in northwestern Yunnan and the Nujiang and Lancang River basins, presenting a “more in the west, fewer in the east” pattern. (3) In 2020, 132 ecological corridors and 74 pinch points were identified. By 2030, under SSP1-1.9, there are 149 corridors and 84 pinch points; under SSP2-4.5, 135 corridors and 55 pinch points; and under SSP5-8.5, 134 corridors and 60 pinch points. (4) By integrating results across multiple scenarios, an ecological security pattern characterized as “three screens, two zones, six corridors, and multiple points” is constructed. Based on regional ecological background characteristics, differentiated strategies for ecological security protection of territorial space are proposed. This study provides a scientific reference for the synergistic optimization of ecosystem services and ecological security patterns under climate change. Full article

The concept of synergistic water resources, water environment, water ecology remediation, and restoration (3WRR) is essential for addressing the interlinked challenges of water scarcity, pollution, and ecological degradation. An intelligent platform of remediation and restoration project optimization was developed, integrating multi-source data fusion, a coupled air–land–water model, and dynamic decision optimization to support 3WRR in river basins. Applied to the Jinshan Lake Basin (JLB) in China’s Greater Bay Area, the platform assessed 894 scenarios encompassing diverse remediation and restoration plans, including point/non-point source reduction, sediment dredging, recycled water reuse, ecological water replenishment, and sluice gate control, accounting for inter-annual meteorological variability. The results reveal that source control alone (95% reduction in point and non-point loads) leads to limited improvement, achieving less than 2% compliance with Class IV water quality standards in tributaries. Integrated engineering–ecological interventions, combining sediment dredging with high-flow replenishment from the Xizhijiang River (26.1 m³/s), increases compliance days of Class IV water quality standards by 10–51 days. Concerning the lake plans, including sluice regulation and large-volume water exchange, the lake area met the Class IV standard for COD, NH₃-N, and TP by over 90%. The platform’s multi-objective optimization framework highlights that coordinated, multi-scale interventions substantially outperform isolated strategies in both effectiveness and sustainability. These findings provide a replicable and data-driven paradigm for 3WRR implementation in complex river–lake systems. The platform’s application and promotion in other watersheds worldwide will serve to enable the low-cost and high-efficiency management of watershed water environments. Full article

Reverse logistics (RL) plays a crucial role in promoting circularity and sustainability in supply chains, particularly in the face of increasing waste generation and growing environmental demands. In recent years, machine learning (ML) has emerged as a strategic tool to enhance processes, decision-making, and outcomes in RL. This article presents a systematic review of ML applications in reverse logistics, highlighting trends, challenges, and research opportunities. The analysis covers 52 articles retrieved from the Scopus and Web of Science databases, following the PRISMA protocol. The results show that the most frequently employed techniques are supervised models, followed by unsupervised methods and, to a lesser extent, reinforcement learning. The main ML applications in RL focus on return and waste generation forecasting, process optimization, classification, pricing, reliability assessments, and consumer behavior analysis. The studies examined predominantly use traditional evaluation metrics, such as MAPE and F1-score, while few consider multidimensional indicators encompassing long-term social or environmental impacts. Key challenges identified include data scarcity and quality, inherent uncertainties in reverse supply chains, and the high computational cost of models. This article also points to research gaps concerning metadata standardization, the absence of public benchmarks, model explainability, and the integration of ML with simulations and digital twins, indicating pathways toward more robust, transparent, and sustainable RL. Full article