Search Results (606)

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

Background: Small Island Developing States (SIDS) face high rates of non-communicable diseases (NCDs), and a key structural driver includes SIDS’ heavy reliance on imported food. Yet, our knowledge about food sources in SIDS is limited. Methods: We systematically searched 14 peer-reviewed databases and 17 grey literature repositories, identifying 56 articles and 96 documents concerning food sources in SIDS. Our study aimed to map these sources while considering broader societal, cultural, and environmental aspects. Results: We found high heterogeneity of food sources beyond store-bought foods, highlighting the complexity of food landscapes in this context. To explore these food sources and their interconnections, we developed a classification including Aid, Buy, Grow, Share, State and Wild food sources, and offered contextually-sensitive insights into their variety (types), extent (relevance), nature (characteristics) and changes over time. We developed an interactive open-access evidence map that outlined the identified interconnections between food sources following our proposed classification. There are numerous interrelations between food sources, showing that pathways from food sourcing to consumption can be unexpected and complex. Conclusions: In 2014, SIDS governments collectively committed to ending malnutrition by 2030. A deeper understanding of food sourcing is essential to achieve this goal. Full article

Traditional cementitious coating materials struggle to meet the performance criteria for protective coatings in complex environments. This study developed a polymer-modified cement-based coating material with polymer, silica fume (SF), and quartz sand (QS) as the principal admixtures. It also investigated the influence of material composition on the coating’s mechanical properties, durability, interfacial bond characteristics with concrete, and the durability enhancement of coated concrete. The results demonstrated that compared with ordinary cementitious coating material (OCCM), the interfacial bonding performance between 3% Styrene Butadiene Rubber Powder (SBR) coating material and concrete was improved by 42%; the frost resistance and sulfate erosion resistance of concrete protected by 6% polyurethane (PU) coating material were improved by 31.5% and 69.6%. The inclusion of polymers reduces the mechanical properties. The re-addition of silica fume can lower the porosity while increasing durability and strength. The coating material, mixed with 12% SF and 6% PU, exhibits mechanical properties not lower than those of OCCM. Meanwhile, the interfacial bonding performance and durability of the coated concrete have been improved by 45% and 48%, respectively. The grey relational analysis indicated that the coating material with the best comprehensive performance is the one mixed with 12% SF + 6% PU, and the grey correlation degree is 0.84. Full article

The realization of high-precision target positioning requires the systematic suppression of nonlinear perturbations in the UAV optoelectronic system and the optimization of the cumulative deviation of coordinate transformations through error transfer modeling. This study proposes an error allocation method based on the improved raccoon optimization algorithm (KYCOA) to resolve the problem of degradation of positioning accuracy due to multi-source error coupling during UAV target positioning. Firstly, a multi-coordinate system transformation model is established to analyze the nonlinear transfer characteristics of the error, and the Taylor expansion is used to linearize the error transfer process and derive the synthetic error model under the geocentric coordinate system. Secondly, the KYCOA is proposed to optimize the error allocation by combining the good point set initialization strategy to enhance the population diversity, and the golden sine algorithm to improve the position updating mechanism in response to the defect of the traditional optimization algorithm, which easily falls into the local optimum. Simulation experiments show that the positioning error distance of the KYCOA is reduced by 66.75%, 41.89%, and 62.06% when compared with that of the original Coati Optimization Algorithm (COA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA), respectively. In the real flight test, the target point localization error of the KYCOA is reduced by more than 40% on average when compared with that of other algorithms, which verifies the effectiveness of the proposed method in improving the target localization accuracy and robustness of UAVs. Full article

Rapid urbanization and climate extremes expose cities to multi-dimensional risks, necessitating the coordinated development of new urbanization and urban resilience for achieving urban sustainability. While existing studies focus on core economic zones like the Yangtze River Delta, secondary economic cooperation regions remain understudied. This study examined the Huaihai Economic Zone (HEZ)—a quadri-provincial border area—by constructing the evaluation systems for new urbanization and urban resilience. The development indices of the two systems were measured using the entropy weight-CRITIC method. The spatiotemporal evolution characteristics of their coupling coordination degree (CCD) were analyzed through a CCD model, while key driving factors influencing the CCD were investigated using a grey relational analysis model. The results indicated that both the new urbanization construction and urban resilience development indices in the HEZ exhibited a steady upward trend during the study period, with the urban resilience development index surpassing the new urbanization construction index. The new urbanization index increased from 0.3026 (2013) to 0.4702 (2023), and the urban resilience index increased from 0.3520 (2013) to 0.6366 (2023). The CCD between new urbanization and urban resilience reached 0.7368 by 2023, with 80% of cities in the HEZ achieving good coordination types. The variation of the CCD among cities was minimal, revealing a spatially clustered coordinated development pattern. In terms of driving factors, economic development level, public service capacity, and municipal resilience level were identified as core drivers for enhancing coupling coordination. Infrastructure construction, digital capabilities, and spatial intensification served as important supports, while ecological governance capacity remained a weakness. This study establishes a transferable framework for the coordinated development of secondary economic cooperation region, though future research should integrate diverse data sources and expand indicator coverage for higher precision. Moreover, the use of linear models to analyze the key driving factors of the CCD has limitations. The incorporation of non-linear techniques can better elucidate the complex interactions among factors. Full article

This study presents a comprehensive multi-objective optimization framework for optimal placement and sizing of distributed generation (DG) units in electric bus (E-bus) transit systems integrated with a high-power flash charging infrastructure. An enhanced Multi-Objective Grey Wolf Optimizer (MOGWO), utilizing Euclidean distance-based Pareto ranking, is developed to minimize power loss, voltage deviation, and voltage violations. The framework incorporates realistic E-bus operation characteristics, including a 31-stop, 62 km route, 600 kW pantograph flash chargers, and dynamic load profiles over a 90 min simulation period. Statistical evaluation on IEEE 33-bus and 69-bus distribution networks demonstrates that MOGWO consistently outperforms MOPSO and NSGA-II across all DG deployment scenarios. In the three-DG configuration, MOGWO achieved minimum power losses of 0.0279 MW and 0.0179 MW, and voltage deviations of 0.1313 and 0.1362 in the 33-bus and 69-bus systems, respectively, while eliminating voltage violations. The proposed method also demonstrated superior solution quality with low variance and faster convergence, requiring under 7 h of computation on average. A five-method compromise solution strategy, including TOPSIS and Lp-metric, enabled transparent and robust decision-making. The findings confirm the proposed framework’s effectiveness and scalability for enhancing distribution system performance under the demands of electric transit electrification and smart grid integration. Full article

Accurately predicting recycling prices at battery recycling sites helps reduce transportation and dismantling costs, ensures economies of scale in the recycling, and supports the sustainable development of the new energy vehicle industry. However, this prediction typically relies on easily accessible surface data, such as battery characteristics and market prices. These data have complex correlations with recycling price, general price prediction methods have low prediction accuracy. To this end, an improved prediction method is proposed to enhance the accuracy of predicting recycling prices through surface data. Firstly, factors influencing recycling prices are selected based on self-factor and market fluctuations, a bidirectional denoising autoencoder and support vector regression model (BDAE-SVR) is established. BDAE is used to adjust the weights of influencing factors to remove noise, extract features related to recycling price. The extracted features are introduced into the SVR model to establish a correspondence between the features and recycling price. Secondly, to have better applications for different batteries, the Grey Wolf algorithm (GWO) is used to adjust the SVR parameters to improve the generalization ability of the prediction model. Finally, taking retired power batteries as an example, the effectiveness of the method is verified. Compared with methods such as random forest (RF), the RMSE predicted by BDAE is decreased from 1.058 to 0.371, indicating better prediction accuracy. Full article

Green technology innovation (GTI) is pivotal for driving energy transition and low-carbon development in manufacturing. This study evaluates the spatiotemporal efficiency and predicts trends of GTI in China’s Yangtze River Economic Belt (YREB, 2010–2022) using a combined “input-desirable output-undesirable output” framework. Combining the SBM and super-efficiency SBM models, we evaluate regional GTI efficiency (2010–2022) and reveal its spatiotemporal patterns. An improved GM(1,N|λ,γ) model with a new information adjustment parameter (λ) and nonlinear parameter (γ) is applied for prediction. Key findings include: (1) The GTI efficiency remains generally low during the study period (provincial average: 0.7049–1.4526), showing an “east-high, west-low” spatial heterogeneity. Temporally, provincial efficiency peaked in 2016, with intensified fluctuations around 2020 due to policy iterations and external shocks. (2) Regional efficiency displays a stepwise decline pattern from downstream to middle-upstream areas. Middle-upstream regions face efficiency constraints from insufficient inputs and undesirable output redundancy, yet exhibit significant optimization potential. (3) Parameter analysis highlights that downstream provinces (γ ≈ 1) exhibit mature green adoption, while mid-upstream regions (e.g., Hubei) face severe technological lock-in and reliance on traditional energy. Additionally, middle and downstream provinces (e.g., Sichuan, Anhui) with low λ values show rapid policy responsiveness, but face efficiency volatility from frequent shifts. (4) The improved GM(1,N|λ,γ) model shows markedly enhanced prediction accuracy compared to traditional grey models, effectively addressing the “poor-information, grey-characteristic” data trend extraction challenges in GTI research. Based on these findings, targeted policy recommendations are proposed to advance GTI development. Full article

Understanding how urbanisation and habitat fragmentation influence wildlife is critical for biodiversity conservation. Fragmentation and population isolation can reduce genetic diversity, yet few studies have assessed these genetic impacts in urbanised environments. Eastern grey kangaroos (Macropus giganteus), widespread across eastern Australia, often inhabit landscapes shaped by urbanisation. Using single nucleotide polymorphism (SNP) data from scat and tissue samples, we compared genetic characteristics of kangaroo populations in urban and non-urban areas across three regions. We assessed the influence of habitat isolation on genetic diversity and relatedness at 18 study sites. Overall, urban populations did not show significantly lower genetic diversity than those in less developed areas (p > 0.05; Urban mean H_O = 0.196, Non-urban mean H_O = 0.188). However, populations fully isolated by roads, buildings, and fences exhibited reduced genetic diversity and increased inbreeding. Additionally, significant genetic differences were observed among regions. These findings suggest that while urbanisation alone may not always reduce genetic diversity, complete physical isolation poses greater risks to population genetic health. This study highlights how urban landscape features can shape the genetics of large terrestrial mammals and underscores the need for spatially informed urban planning and management strategies that maintain or restore habitat connectivity. Full article

Accurate prediction of ship fuel consumption is essential for improving energy efficiency, optimizing mission planning, and ensuring operational integrity at sea. However, during complex tasks such as high-speed maneuvers, fuel consumption exhibits complex dynamics characterized by the coexistence of baseline drift and transient peaks that conventional models often fail to capture accurately, particularly the abrupt peaks. In this study, a hybrid prediction model, DGM-SVR, is presented, combining a rolling dynamic grey model (DGM (1,1)) with support vector regression (SVR). The DGM (1,1) adapts to the dynamic fuel consumption baseline and trends via a rolling window mechanism, while the SVR learns and predicts the residual sequence generated by the DGM, specifically addressing the high-amplitude fuel spikes triggered by maneuvers. Validated on a simulated dataset reflecting typical fuel spike characteristics during high-speed maneuvers, the DGM-SVR model demonstrated superior overall prediction accuracy (MAPE and RMSE) compared to standalone DGM (1,1), moving average (MA), and SVR models. Notably, DGM-SVR reduced the test set’s MAPE and RMSE by approximately 21% and 34%, respectively, relative to the next-best DGM model, and significantly improved the predictive accuracy, magnitude, and responsiveness in predicting fuel consumption spikes. The findings indicate that the DGM-SVR hybrid strategy effectively fuses DGM’s trend-fitting strength with SVR’s proficiency in capturing spikes from the residual sequence, offering a more reliable and precise method for dynamic ship fuel consumption forecasting, with considerable potential for ship energy efficiency management and intelligent operational support. This study lays a foundation for future validation on real-world operational data. Full article

Grey models have attracted considerable attention as a time series forecasting tool in recent years. Nevertheless, the linear characteristics of the differential equations on which traditional grey models rely frequently result in inadequate predictive accuracy and applicability when addressing intricate nonlinear systems. This study introduces a conformable fractional order unbiased kernel-regularized nonhomogeneous grey model (CFUKRNGM) based on statistical learning theory to address these limitations. The proposed model initially uses a conformable fractional-order accumulation operator to derive distribution information from historical data. A novel regularization problem is then formulated, thereby eliminating the bias term from the kernel-regularized nonhomogeneous grey model (KRNGM). The parameter estimation of the CFUKRNGM model requires solving a linear equation with a lower order than the KRNGM model, and is automatically calibrated through the Bayesian optimization algorithm. Experimental results show that the CFUKRNGM model achieves superior prediction accuracy and greater generalization performance compared to both the KRNGM and traditional grey models. Full article

Cyclic voltammetry (CV) is an accessible, readily available, non-expensive technique that can be used to search for the interaction of compounds with DNA and detect the strongest DNA-binding from a set of compounds, therefore allowing for the optimization of the number of cytotoxicity assays. Focusing on this electrochemical approach, the study of twenty-seven camphorimine silver complexes of six different families was performed aiming at detecting interactions with calf thymus DNA (CT-DNA). All of the complexes display at least two cathodic waves attributed respectively to the Ag(I)→Ag(0) (higher potential) and ligand based (lower potential) reductions. In the presence of CT-DNA, a negative shift in the potential of the Ag(I)→Ag(0) reduction was observed in all cases. Additional changes in the potential of the waves, attributed to the ligand-based reduction, were also observed. The formation of a light grey product adherent to the Pt electrode in the case of {Ag(OH)} and {Ag₂(µ-O)} complexes further corroborates the interaction of the complexes with CT-DNA detected by CV. The morphologic analysis of the light grey material was made by scanning electronic microscopy (SEM). The magnitude of the shift in the potential of the Ag(I)→Ag(0) reduction in the presence of CT-DNA differs among the families of the complexes. The complexes based on {Ag(NO₃)} exhibit higher potential shifts than those based on {Ag(OH)}, while the characteristics of the ligand (^AL-Y, ^BL-Y, ^CL-Z) and the imine substituents (Y,Z) fine-tune the potential shifts. The energy values calculated by docking corroborate the tendency in the magnitude of the interaction between the complexes and CT-DNA established by the reaction coefficient ratios (Q_[Ag-DNA]/Q_[Ag]). The molecular docking study extended the information regarding the type of interaction beyond the usual intercalation, groove binding, or electrostatic modes that are typically reported, allowing a finer understanding of the non-covalent interactions involved. The rationalization of the CV and cytotoxicity data for the Ag(I) camphorimine complexes support a direct relationship between the shifts in the potential and the cytotoxic activities of the complexes, aiding the decision on whether the cytotoxicity of a complex from a family is worthy of evaluation. Full article

Accurate prediction of natural gas production is of great significance for optimizing development strategies, simplifying production management, and promoting decision-making. This paper utilizes partial differentiation to effectively capture the spatiotemporal characteristics of natural gas data and the advantages of grey prediction models. By introducing the fractional damping accumulation operator, a new fractional order partial grey prediction model is established. The new model utilizes partial capture of details and features in the data, improves model accuracy through fractional order accumulation, and extends the metadata of the classic grey prediction model from time series to matrix series, effectively compensating for the phenomenon of inaccurate results caused by data fluctuations in the model. Meanwhile, the principle of data accumulation is effectively expressed in matrix form, and the least squares method is used to estimate the parameters of the model. The time response equation of the model is obtained through multiplication transformation, and the modelling steps are elaborated in detail. Finally, the new model is applied to the prediction of natural gas production in Qinghai Province, China, selecting energy production related to natural gas production, including raw coal production, oil production, and electricity generation, as relevant variables. To verify the effectiveness of the new model, we started by selecting the number of relevant variables, divided them into three categories for analysis based on the number of relevant variables, and compared them with five other grey prediction models. The results showed that in the seven simulation experiments of the three types of experiments, the average relative error of the new model was less than 2%, indicating that the new model has strong stability. When selecting the other three types of energy production as related variables, the best effect was achieved with an average relative error of 0.3821%, and the natural gas production for the next nine months was successfully predicted. Full article

Background: Olfactory dysfunction (OD)—including anosmia and hyposmia—is a common and often persistent outcome of viral infections. This systematic review consolidates findings from structural and functional MRI studies to explore how COVID-19 SARS-CoV-2-induced smell loss alters the brain. Considerable heterogeneity was observed across studies, influenced by differences in methodology, population characteristics, imaging timelines, and OD classification. Methods: Following PRISMA guidelines, we conducted a systematic search of PubMed/MEDLINE, Scopus, and Web of Science to identify MRI-based studies examining COVID-19’s SARS-CoV-2 OD. Twenty-four studies were included and categorized based on imaging focus: (1) olfactory bulb (OB), (2) olfactory sulcus (OS), (3) grey and white matter changes, (4) task-based brain activation, and (5) resting-state functional connectivity. Demographic and imaging data were extracted and analyzed accordingly. Results: Structural imaging revealed consistent reductions in olfactory bulb volume (OBV) and olfactory sulcus depth (OSD), especially among individuals with OD persisting beyond three months, suggestive of inflammation and neurodegeneration in olfactory-associated regions like the orbitofrontal cortex and thalamus. Functional MRI studies showed increased connectivity in early-stage OD within regions such as the piriform and orbitofrontal cortices, possibly reflecting compensatory activity. In contrast, prolonged OD was associated with reduced activation and diminished connectivity, indicating a decline in olfactory processing capacity. Disruptions in the default mode network (DMN) and limbic areas further point to secondary cognitive and emotional effects. Diffusion tensor imaging (DTI) findings—such as decreased fractional anisotropy (FA) and increased mean diffusivity (MD)—highlight white matter microstructural compromise in individuals with long-term OD. Conclusions: COVID-19’s SARS-CoV-2 olfactory dysfunction is associated with a range of cerebral alterations that evolve with the duration and severity of smell loss. Persistent dysfunction correlates with greater neural damage, underscoring the need for longitudinal neuroimaging studies to better understand recovery dynamics and guide therapeutic strategies. Full article

Laser weld quality remains a critical priority across nearly all industries. However, identifying optimal laser parameter sets continues to be highly challenging, often relying on costly, time-consuming trial-and-error experiments. This difficulty is largely attributed to the severe fluctuations and instabilities inherent in laser welding, particularly keyhole instabilities. This study examines the impact of laser power modulation parameters, which, when properly applied, have been found effective in controlling and minimizing process instabilities. The investigated parameters include different pulse shapes (sinusoidal and cosinusoidal) and their associated characteristics, namely frequency (100–800 Hz) and amplitude (1000–4000 W). The impact of these modulation parameters on keyhole mode laser spot welding performance in aluminum is investigated. Using a Taguchi experimental design, a series of tests were developed, focusing on eight key welding responses, including keyhole dimensions, mean temperature, and the variability of instability-inducing forces and related factors affecting process stability. Grey relational analysis (GRA) combined with analysis of variance (ANOVA) is applied to identify the optimal combinations of laser parameters. The results indicate that low amplitude (1000 W), low to intermediate frequencies (100–400 Hz), and cosinusoidal waveforms significantly enhance weld quality by improving process stability and balancing penetration depth. Among the factors, amplitude has the greatest impact, accounting for over 50% of the performance variation, followed by frequency and pulse shape. The findings provide clear guidance for optimizing laser welding parameters to achieve stable, high-quality aluminum welds. Full article