Search Results (1,940)

This study contributes to scientific theory by analyzing the models proposed within the framework of circular agriculture to determine how the three dimensions of sustainability—environmental, economic, and social—are integrated into their implementation. A systematic review was conducted on articles published between 2016 and 2025, indexed in the Scopus and Web of Science databases, as well as the relevant grey literature. The methodology employed an extensive content analysis designed to minimize bias, applying filters related to specific knowledge areas to delimitate the search scope and enhance the precision of the research. The findings reveal that the research on circular agriculture models is predominantly grounded in the principles of the circular economy and its associated indicators. Moreover, these models tend to focus on environmental metrics, often neglecting a comprehensive exploration of the social and economic dimensions of sustainable development. It can be concluded that a significant gap persists in the literature regarding the circularity of agriculture and its socio-economic impacts and the role of regulatory frameworks, aspects that future research must address in order to achieve sustainability in circular agriculture. Full article

“The Internet of Things” is a relatively new idea that refers to objects that can connect to the Internet and exchange data. The Internet of Things (IoT) enables novel interactions between objects and people by interconnecting billions of devices. While there are many IoT-related products, challenges pertaining to their effective implementation, particularly the lack of knowledge and confidence about security, must be addressed. To provide IoT-based enterprises with a platform for efficiency and sustainability, this study aims to identify the critical elements that influence the growth of a successful company integrated with an IoT system. This study proposes a decision support tool that evaluates the influential features of IoT using the Pythagorean Fuzzy and Interval Grey approaches within the Analytical Hierarchy Process (AHP). This study demonstrates that security, value, and connectivity are more critical than telepresence and intelligence indicators. When both strategies are used, market demand and information privacy become significant indicators. Applying the Pythagorean Fuzzy approach enables the identification of sensor networks, authorization, market demand, and data management in terms of importance. The application of the Interval Grey approach underscores the importance of data management, particularly in sensor networks. The indicators that were finally ranked are compared to obtain a good coefficient of agreement. These findings offer practical insights for promoting sustainability in enterprise operations by optimizing IoT infrastructure and decision-making processes. Full article

Accurate finite element (FE) models are essential for the safety assessment of civil engineering structures. However, obtaining reliable model parameters for existing bridges remains challenging due to the inability to conduct static load tests without disrupting traffic flow. To address this, this study proposes an FE model updating framework that integrates the response surface method and the nutcracker optimization algorithm (NOA). This framework is characterized by the incorporation of ambient vibration data into parameter optimization, thereby enhancing model accuracy. The stochastic subspace identification method is first adopted to extract the bridge’s natural frequencies from vibration data. The response surface method is then employed to construct a response surface function that approximates the FE model. The NOA is subsequently applied to iteratively optimize this response surface function, ensuring rapid convergence and the precise adjustment of the FE model parameter. To validate the effectiveness of the proposed framework, a continuous beam–arch composite bridge with a span of 204.783 m was selected as a case study. The results indicate that the proposed method reduced the average frequency error from 5.58% to 2.75% by updating the model parameters. While the whale optimization algorithm required 21 iterations and the grey wolf optimizer needed 41 iterations to converge near the minimum, the NOA achieved this in merely 13 iterations, demonstrating the NOA’s superior convergence speed. Furthermore, the NOA significantly outperformed both the whale optimization algorithm and the grey wolf optimizer in reducing the error of the first transverse vibration frequency. Full article

Additive manufacturing (AM) techniques, especially fused deposition modeling (FDM), offer significant advantages in terms of cost, material efficiency, and design flexibility. In this study, the mechanical performance of biodegradable PLA/PHA composite samples produced via FDM was optimized by evaluating the influence of key printing parameters—layer height, printing orientation, and printing speed—on both the tensile and compressive strength. A full factorial design (3 × 3 × 3) was employed, and all of the samples were triplicated to ensure the consistency of the results. Grey relational analysis (GRA) was used as a multi-objective optimization method to determine the optimal parameter combinations. An analysis of variance (ANOVA) was also conducted to assess the statistical significance of each parameter. The ANOVA results revealed that printing orientation is the most significant parameter for both tensile and compression strength. The optimal parameter combination for maximizing mechanical properties was a layer height of 0.1 mm, an X printing orientation, and a printing speed of 50 mm/s. This study demonstrates the effectiveness of GRA in optimizing the mechanical properties of biodegradable composites and provides practical guidelines to produce environmentally sustainable polymer parts. Full article

Coupled grey and green infrastructure (CGGI) offers a promising pathway toward sustainable stormwater management in historic urban environments. This study compares CGGI and conventional grey infrastructure (GREI)-only strategies across four degrees of layout centralization (0%, 33.3%, 66.7%, and 100%) in the Quanzhou West Street Historic Reserve, China. Using a multi-objective optimization framework integrating SWMM simulations, life-cycle cost (LCC) modeling, and resilience metrics, we found that the decentralized CGGI layouts reduced the total LCC by up to 29.6% and required 60.7% less green infrastructure (GI) area than centralized schemes. Under nine extreme rainfall scenarios, the GREI-only systems showed slightly higher technical resilience (Tech-R: max 99.6%) than CGGI (Tech-R: max 99.1%). However, the CGGI systems outperformed GREI in operational resilience (Oper-R), reducing overflow volume by up to 22.6% under 50% network failure. These findings demonstrate that decentralized CGGI provides a more resilient and cost-effective drainage solution, well-suited for heritage districts with spatial and cultural constraints. Full article

In the present work, the optimization of ceramic-based composite WC(Co,Ni) welds by laser cladding was carried out using response surface methodology based on finite element analysis. The heat distribution and temperature field of laser-melted WC(Co,Ni) ceramic coatings were simulated using ANSYS software, which allowed the computation of the distribution of residual stresses. The results show that the isotherms in the simulation of the temperature field are elliptical in shape, and that the isotherms in front of the moving heat source are dense with a larger temperature gradient, while the isotherms behind the heat source are sparse with a smaller temperature gradient. In addition, the observed microstructural evolution shows that the melting zone domains of WC(Co,Ni) are mainly composed of unmelted carbides. These carbides are dendritic, rod-like, leaf-like, or net-like, and are agglomerated into smaller groups. The W content of these unmelted carbides exceeds 80%, while the C content is around 1.5–3.0%. The grey areas are composed of WC, Co and Ni compounds. Based on the regression model, a quadratic model was successfully constructed. A three-dimensional profile model of the residual stress behaviour was further explored. The estimated values of the RSM-based FEA model for residual stress are very similar to the actual results, which shows that the model is effective in reducing residual stress by laser cladding. Full article

The piston-type expander (PTE), as the primary output component, significantly influences the performance of an energy storage system. This paper proposes a non-cam variable valve actuation system for the PTE, supported by a mathematical model. An enhanced S-curve trajectory planning method is used to design the valve lift curve. The study investigates the effects of various valve lift design parameters on output power and efficiency at different rotational speeds, employing orthogonal design and SPSS Statistics 27 (Statistical Product and Service Solutions) simulations. A grey comprehensive evaluation method is used to identify optimal valve lift parameters for each speed. The results show that valve lift parameters influence PTE performance to varying degrees, with intake duration having the greatest effect, followed by maximum valve lift, while intake end time has the least impact. The non-cam PTE outperforms the cam-based PTE. At 800 rpm, the optimal design yields 7.12 kW and 53.5% efficiency; at 900 rpm, 8.17 kW and 50.6%; at 1000 rpm, 9.2 kW and 46.8%; and at 1100 rpm, 12.09 kW and 41.2%. At these speeds, output power increases by 18.37%, 11.42%, 11.62%, and 9.82%, while energy efficiency improves by 15.01%, 15.05%, 14.24%, and 13.86%, respectively. Full article

With climate change, the spatial and temporal patterns of precipitation are altered to a certain degree, which potentially affects the grey water footprint (GWF) of total nitrogen (TN) in agriculture, thereby threatening water security in the Yangtze River Basin (YRB), the largest river in China. The current study constructs an assessment framework for climate change impacts on the GWF of agricultural TN by coupling Shared Socioeconomic Pathways (SSPs) with the InVEST model. The framework consists of four components: (i) data collection and processing, (ii) simulating the two critical indicators (L_TN and W) in the GWF model based on the InVEST model, (iii) calculating the GWF and GWF index (GI) of TN, and (iv) calculating climate change impact index on GWF of agricultural TN (CI) under two SSPs. It is applied to the YRB, and the results show the following: (i) GWFs are 959.7 and 961.4 billion m³ under the SSP1-2.6 and SSP5-8.5 climate scenarios in 2030, respectively, which are both lower than that in 2020 (1067.1 billion m³). (ii) The GI values for TN in 2030 under SSP1-2.6 and SSP5-8.5 remain at “High” grade, with the values of 0.95 and 1.03, respectively. Regionally, the water pollution level of Taihu Lake is the highest, while that of Wujiang River is the lowest. (iii) The CI values of the YRB in 2030 under SSP1-2.6 and SSP5-8.5 scenarios are 0.507 and 0.527, respectively. And the CI values of the five regions in the YRB are greater than 0, indicating that the negative effects of climate change on GWFs increase. (iv) Compared with 2020, L_TN and W in YRB in 2030 under the two SSPs decrease, while the GI of TN in YRB rises from SSP1-2.6 to SSP5-8.5. The assessment framework can provide strategic recommendations for sustainable water resource management in the YRB and other regions globally under climate change. Full article

Accurate oil debris analysis and wear monitoring of a gearbox are essential to ensure its stable and reliable operation. Element types of wear debris and their changes in the lubrication oil of the gearbox can be monitored by spectral analysis. However, it is still difficult to identify wear parts of the gearbox due to the complex composition of elements of wear debris. An improved multi-variable grey prediction model by incorporating a multi-objective genetic algorithm (MOGA-GM(1, N)) is proposed to evaluate weight coefficients of element concentrations of wear debris in the lubrication oil of the gearbox. Moreover, a wear growth rate of each element in the lubrication oil is proposed as an index for oil debris analysis to analyze the multi-variable correlation between the common element of iron (Fe) and other related elements of wear parts of the gearbox. Oil debris analysis of the gearbox is conducted on optimal weight coefficients of related elements to the common element Fe using the MOGA-GM(1, N) model. Wear experiment results verify feasibility of the proposed oil debris analysis method. Full article

In the aluminum electrolysis production process, the traditional cell control method based on cell voltage and series current can no longer meet the goals of energy conservation, consumption reduction, and digital-intelligent transformation. Therefore, a new digital cell control technology that is centrally dependent on various process parameters has become an urgent demand in the aluminum electrolysis industry. Among them, the real-time online measurement of alumina concentration is one of the key data points for implementing such technology. However, due to the harsh production environment and limitations of current sensor technologies, hardware-based detection of alumina concentration is difficult to achieve. To address this issue, this study proposes a soft-sensing model for alumina concentration based on a long short-term memory (LSTM) neural network optimized by a weighted average algorithm (WAA). The proposed method outperforms BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-LSTM-Attention, and CNN-BiLSTM-Attention models in terms of predictive accuracy. In comparison to LSTM models optimized using the Grey Wolf Optimizer (GWO), Harris Hawks Optimization (HHO), Optuna, Tornado Optimization Algorithm (TOC), and Whale Migration Algorithm (WMA), the WAA-enhanced LSTM model consistently achieves significantly better performance. This superiority is evidenced by lower MAE and RMSE values, along with higher R² and accuracy scores. The WAA-LSTM model remains stable throughout the training process and achieves the lowest final loss, further confirming the accuracy and superiority of the proposed approach. Full article

The interconnections among water, energy, and food (WEF) systems are growing increasingly complex, making it essential to understand their evolutionary mechanisms and coordination barriers to enhance regional resilience and sustainability. In this study, we investigated the WEF system in Northeast China by constructing a comprehensive indicator system encompassing resource endowment and utilization efficiency. The coupling coordination degree (CCD) of the WEF system was quantitatively assessed from 2001 to 2022. An obstacle degree model was employed to identify key constraints, while grey relational analysis was used to evaluate the driving influence of individual indicators. Furthermore, a co-evolution model based on logistic growth and competition–cooperation dynamics was developed to simulate system interactions. The results reveal the following: (1) the regional WEF-CCD increased from 0.627 in 2001 to 0.769 in 2022, reaching the intermediate coordination level, with the CCDs of the food, water, and energy subsystems rising from 0.39 to 0.62, 0.38 to 0.60, and 0.40 to 0.55, respectively, highlighting that the food subsystem had the most stable and significant improvement; (2) Jilin Province attained the highest WEF-CCD, 0.850, in 2022, while that for Heilongjiang remained the lowest, at 0.715, indicating substantial interprovincial disparities; (3) key indicators, such as food self-sufficiency rate, electricity generation, and ecological water use, functioned as both core constraints and major drivers of system performance; (4) co-evolution modeling revealed that the food subsystem exhibited the fastest growth, followed by water and energy (${\alpha }_3$ >${ \alpha }_1$ > ${ \alpha }_2$ > 0), with mutual promotion between water and energy subsystems and inhibitory effects from the food subsystem, ultimately converging toward a stable equilibrium state; and (5) interprovincial co-evolution modeling indicated that Jilin leads in WEF system development, followed by Liaoning and Heilongjiang, with predominantly cooperative interactions among provinces driving convergence toward a stable and coordinated equilibrium despite structural asymmetries. This study proposes a transferable, multi-method analytical framework for evaluating WEF coordination, offering practical insights into bottlenecks, key drivers, and co-evolutionary dynamics for sustainable resource governance. Full article

Breast cancer (BRCA) remains a significant cause of mortality among women, particularly in developing and underdeveloped regions, where early detection is crucial for effective treatment. This research introduces an innovative hybrid model that combines Improved Grey Wolf Optimizer (IGWO) with the Seagull Optimization Algorithm (SOA), forming the IGWO–SOA technique to enhance BRCA detection accuracy. The hybrid model draws inspiration from the adaptive and strategic behaviors of seagulls, especially their ability to dynamically change attack angles in order to effectively tackle complex global optimization challenges. A deep neural network (DNN) is fine-tuned using this hybrid optimization method to address the challenges of hyperparameter selection and overfitting, which are common in DL approaches for BRCA classification. The proposed IGWO–SOA model demonstrates optimal performance in identifying key attributes that contribute to accurate cancer detection using the CBIS-DDSM dataset. Its effectiveness is validated using performance metrics such as loss, F1-score, precision, accuracy, and recall. Notably, the model achieved an impressive accuracy of 99.4%, outperforming existing methods in the domain. By optimizing both the learning parameters and model structure, this research establishes an advanced deep learning framework built upon the IGWO–SOA approach, presenting a robust and reliable method for early BRCA detection with significant potential to improve diagnostic precision. Full article

Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of the grey level co-occurrence matrix (GLCM) and topographic–hydrologic features on automatic gully extraction and guide future practices in adjacent regions. To accomplish this, GaoFen-2 (GF-2) satellite imagery and high-resolution digital elevation model (DEM) data were first collected. The GLCM and topographic–hydrologic features were generated, and then, a gully label dataset was built via visual interpretation. Second, the study area was divided into training, testing, and validation areas, and four practices using different feature combinations were conducted. The DeepLabV3+ and ResNet50 architectures were applied to train five models in each practice. Thirdly, the trainset gully intersection over union (IOU), test set gully IOU, receiver operating characteristic curve (ROC), area under the curve (AUC), user’s accuracy, producer’s accuracy, Kappa coefficient, and gully IOU in the validation area were used to assess the performance of the models in each practice. The results show that the validated gully IOU was 0.4299 (±0.0082) when only the red (R), green (G), blue (B), and near-infrared (NIR) bands were applied, and solely combining the topographic–hydrologic features with the RGB and NIR bands significantly improved the performance of the models, which boosted the validated gully IOU to 0.4796 (±0.0146). Nevertheless, solely combining GLCM features with RGB and NIR bands decreased the accuracy, which resulted in the lowest validated gully IOU of 0.3755 (±0.0229). Finally, by employing the full set of RGB and NIR bands, the GLCM and topographic–hydrologic features obtained a validated gully IOU of 0.4762 (±0.0163) and tended to show an equivalent improvement with the combination of topographic–hydrologic features and RGB and NIR bands. A preliminary explanation is that the GLCM captures the local textures of gullies and their backgrounds, and thus introduces ambiguity and noise into the convolutional neural network (CNN). Therefore, the GLCM tends to provide no benefit to automatic gully extraction with CNN-type algorithms, while topographic–hydrologic features, which are also original drivers of gullies, help determine the possible presence of water-origin gullies when optical bands fail to tell the difference between a gully and its confusing background. Full article

Earthquakes are sudden and highly destructive events that severely disrupt infrastructure and logistics systems, making accurate and timely emergency material demand forecasting a critical challenge in disaster response. However, the scarcity of reliable data during the early stages of an earthquake limits the effectiveness of traditional forecasting methods. To address this issue, this study proposes a hybrid forecasting framework that integrates the Grey Model (GM(1,1)) with Bayesian Dynamic Linear Models (BDLMs), aiming to improve both the accuracy and adaptability of demand predictions. The approach operates in two phases: first, GM(1,1) generates preliminary forecasts using limited initial observations; second, BDLMs dynamically update these forecasts in real time as new data become available. The model is validated through a case study of the 2010 M7.1 Yushu earthquake in Qinghai Province, China. The results indicate that the hybrid method produces reliable forecasts even at the earliest stages of the disaster, with increasing accuracy as more observational data are incorporated. Our case study demonstrates that the integrated GM(1,1)-BDLM framework substantially reduces prediction errors compared to standalone GM(1,1). Using the first five days’ data to forecast fatalities and emergency material demand for days 6–10, the hybrid model achieves a 4.01% error rate—a 19.62 percentage point improvement over GM(1,1)’s 23.63% error rate. This adaptive forecasting mechanism offers robust support for evidence-based decision-making in emergency material allocation, enhancing the efficiency and responsiveness of post-disaster relief operations. Full article

Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance. Full article