Search Results (78)

Illegal mining operations induce cascading ecosystem degradation by causing extensive ground subsidence, necessitating accurate underground goaf localization for effectively induced-hazard mitigation. The conventional locating method applied the synthetic aperture radar interferometry (InSAR) technique to obtain ground deformation to estimate underground goaf parameters, and the locating accuracy was crucially contingent upon the appropriateness of nonlinear deformation function models selection and the precision of geological parameters acquisition. However, conventional model-driven underground goaf locating frameworks often fail to sufficiently integrate prior geological information during the model selection process, potentially leading to increased positioning errors. In order to enhance the operational efficiency and locating accuracy of underground goaf, deformation model selection must be aligned with site-specific geological conditions under varying cases of prior information. To address these challenges, this study categorizes prior geological information into three different hierarchical levels (detailed, moderate, and limited) to systematically investigate the correlations between model selection and prior information. Subsequently, field validation was carried out by applying two different non-linear deformation function models, Probability Integral Model (PIM) and Okada Dislocation Model (ODM), with three different prior geological information conditions. The quantitative performance results indicate that, (1) under a detailed prior information condition, PIM achieves enhanced dimensional parameter estimation accuracy with 6.9% reduction in maximum relative error; (2) in a moderate prior information condition, both models demonstrate comparable estimation performance; and (3) for a limited prior information condition, ODM exhibits superior parameter estimation capability showing 3.4% decrease in maximum relative error. Furthermore, this investigation discusses the influence of deformation spatial resolution, the impacts of azimuth determination methodologies, and performance comparisons between non-hybrid and hybrid optimization algorithms. This study demonstrates that aligning the selection of deformation models with different types of prior geological information significantly improves the accuracy of underground goaf detection. The findings offer practical guidelines for selecting optimal models based on varying information scenarios, thereby enhancing the reliability of disaster evaluation and mitigation strategies related to illegal mining. Full article

Land consolidation (LC) is a sustainability-oriented policy tool designed to address land fragmentation, inefficient spatial organization, and ecological degradation in rural areas. This research proposes a Production–Living–Ecological (PLE) spatial utilization efficiency evaluation system, based on an integrated methodological framework combining Principal Component Analysis (PCA), Entropy Weight Method (EWM), Attribute-Weighting Method (AWM), Linear Weighted Sum Method (LWSM), Threshold-Verification Coefficient Method (TVCM), Jenks Natural Breaks (JNB) classification, and the Obstacle Degree Model (ODM). The framework is applied to Qian County, located in the Guanzhong Plain in Shaanxi Province. The results reveal three key findings: (1) PLE efficiency exhibits significant spatial heterogeneity. Production efficiency shows a spatial pattern characterized by high values in the central region that gradually decrease toward the surrounding areas. In contrast, the living efficiency demonstrates higher values in the eastern and western regions, while remaining relatively low in the central area. Moreover, ecological efficiency shows a marked advantage in the northern region, indicating a distinct south–north gradient. (2) Integrated efficiency consolidation potential zones present distinct spatial distributions. Preliminary consolidation zones are primarily located in the western region; priority zones are concentrated in the south; and intensive consolidation zones are clustered in the central and southeastern areas, with sporadic distributions in the west and north. (3) Five primary obstacle factors hinder land use efficiency: intensive utilization of production land (PC1), agricultural land reutilization intensity (PC2), livability of living spaces (PC4), ecological space security (PC7), and ecological space fragmentation (PC8). These findings provide theoretical insights and practical guidance for formulating tar-gated LC strategies, optimizing rural spatial structures, and advancing sustainable development in similar regions. Full article

Photovoltaic (PV) energy generation has seen rapid growth in recent years due to its sustainability and environmental benefits. However, accurately identifying PV panel parameters is crucial for enhancing system performance, especially under varying environmental conditions. This study presents an enhanced approach for estimating PV panel parameters using a Modified Quasi-Opposition-Based Killer Whale Optimization (MQOB-KWO) technique. The research aims to improve parameter extraction accuracy by optimizing the one-diode model (ODM), a widely used representation of PV cells, using a modified metaheuristic optimization technique. The proposed algorithm leverages a Quasi-Opposition-Based Learning (QOBL) mechanism to enhance search efficiency and convergence speed. The methodology involves implementing the MQOB-KWO in MATLAB R2021a and evaluating its effectiveness through experimental I-V data from two unlike photovoltaic panels. The findings are contrasted to established optimization techniques from the literature, such as the original Killer Whale Optimization (KWO), Improved Opposition-Based Particle Swarm Optimization (IOB-PSO), Improved Cuckoo Search Algorithm (ImCSA), and Chaotic Improved Artificial Bee Colony (CIABC). The findings demonstrate that the proposed MQOB-KWO achieves superior accuracy with the lowest Root Mean Square Error (RMSE) compared to other methods, and the lowest error rates (Root Mean Square Error—RMSE, and Integral Absolute Error—IAE) compared to the original KWO, resulting in a better value of the coefficient of determination (${\mathrm{R}}^2$), hence effectively capturing PV module characteristics. Additionally, the algorithm shows fast convergence, making it suitable for real-time PV system modeling. The study confirms that the proposed optimization technique is a reliable and efficient tool for improving PV parameter estimation, contributing to better system efficiency and operational performance. Full article

Ensilage is an efficient storage method for preserving maize stalks for use as biogas feedstocks. However, maize stalk silages are susceptible to secondary fermentation, which degrades feedstock quality. This study explored the effects of ensilage and secondary fermentation on methane production from maize stalk and microbial community dynamics in anaerobic digestion (AD). Both ensilage and secondary fermentation decreased the specific methane yield (SMY) of maize stalks. Ensilage inhibited the acidogenesis process in AD. Secondary fermentation reduced bacterial richness and hydrolytic activity, and thus decreased the SMY of silage. After 6 months of ensilage, 97.06% organic dry matter (ODM) and 94.28% methane yield were preserved. SF greatly reduced the storage efficiency by causing 34.11% ODM loss and 52.60% methane yield loss in 40 days. Losses in ODM or methane yield during air exposure followed the Zwietering-modified Gompertz model. Metagenomic analysis showed a shift from Ruminoccoccaceae and Lachnospiraceae to Rikenellaceae in AD of maize stalk silage following secondary fermentation. Carnobacteriaceae, Moraxellaceae, Lachnospiraceae, Porphyromonadaceae, and Corynebacteriaceae were positively correlated with the content of water-soluble carbohydrates, whereas Anaerolineaceae and Ruminococcaceae were positively correlated with total organic acid content in stalks. Full article

The biology literature addresses two puzzles: (i) the increase in specific metabolic rate of organs (SOrMR, W/kg of organ) with a decrease in body mass (M_B) of biological species (BS), and (ii) how the organs recognize they are in a smaller or larger body and adjust metabolic rates of the body (${\dot{q}}_B$) accordingly. These puzzles were answered in the author’s earlier work by linking the field of oxygen-deficient combustion (ODC) of fuel particle clouds (FC) in engineering to the field of oxygen-deficient metabolism (ODM) of cell clouds (CC) in biology. The current work extends the ODM hypothesis to predict the whole-body metabolic rates of 114 BS and demonstrates Kleiber’s power law {${\dot{q}}_B = a {M_B}^b$}. The methodology is based on the postulate of Lindstedt and Schaeffer that “150 ton blue whale. and the 2 g Etruscan shrew.. share the same.. biochemical pathways” and involve the following steps: (i) extension of the effectiveness factor relation, expressed in terms of the dimensionless group number G (=Thiele Modulus²), from engineering to the organs of BS, (ii) modification of G as G_OD for the biology literature as a measure of oxygen deficiency (OD), (iii) collection of data on organ and body masses of 116 species and prediction of SOrMR_k of organ k of 114 BS (from 0.0076 kg Shrew to 6650 kg elephant) using only the SOrMR_k and organ masses of two reference species (Shrew, 0.0076 kg: RS-1; Rat Wistar, 0.390 kg: RS-2), (iv) estimation of ${\dot{q}}_B$ for 114 species versus M_B and demonstration of Kleiber’s law with a = 2.962, b = 0.747, and (v) extension of ODM to predict the allometric law for maximal metabolic rate (under exercise, {${\dot{q}}_{B,MMR} = a_{MMR} {M_B}^{b_{MMR}}$}) and validate the approach for MMR by comparing b_MMR with the literature data. A method of detecting hypoxic condition of an organ as a precursor to cancer is suggested for use by medical personnel Full article

Developing new-quality productivity in water conservancy (NQPWC) is vital for advancing economic and social development, with a focus on sustainability. An evaluation of NQPWC and the identification of key barriers can help define the challenges and guide the development of targeted solutions. This study established an evaluation indicators system for NQPWC through four dimensions (3H1G): High-technology, High-efficiency, High-quality, and Green. Utilizing a multi-attribute decision approach based on the Real-Code Accelerated Genetic Algorithm Projection Pursuit model (RAGA-PP model), an evaluation of NQPWC at the provincial level in China from 2011 to 2022 was conducted. The results revealed a curvilinear upward trend in NQPWC in most regions, with southeastern coastal provinces (cities) outperforming those in the northwest. Further, the major obstacles affecting NQPWC’s development were identified through an Obstacle Degree Model (ODM), with High-technology being the most significant dimension, followed by High-quality, Green, and High-efficiency. Full article

Biogas production from lignocellulosic biomass, such as wheat and rapeseed straw, is an essential strategy for sustainable energy generation. However, the efficiency of anaerobic digestion depends on the physical characteristics of the substrate, particularly the particle size, which influences microbial accessibility and biogas yield. This study aims to optimize straw particle size for enhanced methane production by evaluating different fractionation levels. The straw was processed using a hammer mill and separated into three size fractions (2.4 mm, 1 mm) alongside non-separated and finely ground (2 mm) samples. The chemical composition was analyzed using X-ray fluorescence (XRF), and key parameters such as pH, dry matter (DM), and organic dry matter (ODM) were assessed. The results indicated that rapeseed straw had lower pH (6.05) and DM than wheat straw (7.01). Biogas yield analysis demonstrated that methane production varied with particle size. For rapeseed straw, non-separated samples achieved the highest methane yield (132.87 m³ Mg⁻¹), whereas for wheat straw, methane yield decreased with increased fragmentation, with the highest yield observed for non-separated material (206.65 m³ Mg⁻¹). The carbon-to-nitrogen (C/N) ratio was highest in rapeseed straw (153.82), potentially limiting microbial activity, while finer fractions had more balanced ratios. These findings highlight the importance of mechanical pretreatment in optimizing biogas production and provide insights into improving the efficiency of straw-based anaerobic digestion systems. Full article

Three-dimensional (3D) object detection is crucial for autonomous driving, yet current PointPillar feature-based methods face challenges like under-segmentation, overlapping, and false detection, particularly in occluded scenarios. This paper presents a novel dual-stage improved PointPillar feature-based 3D object detection method (S2*-ODM) specifically designed to address these issues. The first innovation is the introduction of a dual-stage pillar feature encoding (S2-PFE) module, which effectively integrates both inter-pillar and intra-pillar relational features. This enhancement significantly improves the recognition of local structures and global distributions, enabling better differentiation of objects in occluded or overlapping environments. As a result, it reduces problems such as under-segmentation and false positives. The second key improvement is the incorporation of an attention mechanism within the backbone network, which refines feature extraction by emphasizing critical features in pseudo-images and suppressing irrelevant ones. This mechanism strengthens the network’s ability to focus on essential object details. Experimental results on the KITTI dataset show that the proposed method outperforms the baseline, achieving notable improvements in detection accuracy, with average precision for 3D detection of cars, pedestrians, and cyclists increasing by 1.04%, 2.17%, and 3.72%, respectively. These innovations make S2*-ODM a significant advancement in enhancing the accuracy and reliability of 3D object detection for autonomous driving. Full article

The tetrahydroisoquinoline skeleton is a pharmacologically significant core structure containing chiral centers, making enantiomeric separation crucial due to the potentially distinct biological effects of each enantiomer. In this study, laudanosine (N-methyl-tetrahydropapaverine) and its three derivatives (6′-bromo-laudanosine, norlaudanosine, and N-propyl-norlaudanosine) were synthesized and used as model compounds to investigate chiral recognition mechanisms. Screening over twenty cyclodextrins (CyDs) as chiral selectors in capillary electrophoresis (CE), we found anionic CyDs to be the most effective, with sulfated-γ-CyD (S-γ-CyD) achieving a maximum R_s of 10.5 for laudanosine. Notably, octakis-(6-deoxy-6-(2-carboxyethyl)-thio)-γ-CyD (sugammadex, SGX), heptakis-(2,3-O-diacetyl-6-O-sulfo)-β-CD (HDAS), heptakis-(2,3-O-dimethyl-6-O-sulfo)-β-CD (HDMS), and octakis-(2,3-O-dimethyl-6-O-sulfo)-γ-CD (ODMS) provided excellent enantioseparation for all four analytes. Following HPLC screening on CyD-based and polysaccharide-based chiral stationary phases, semi-preparative HPLC methods using amylose and cellulose-based columns were optimized to isolate enantiomers. The purity of the isolated enantiomers was evaluated by HPLC, and their configurations were confirmed via circular dichroism spectroscopy. The isolated enantiomers allowed us to explore enantiomer migration order reversals in CE and enantiomer elution order reversal in HPLC. Further ¹H and 2D ROESY NMR experiments provided atomic-level insights into enantioselective complex formation, confirming enantiomer differentiation by SGX and elucidating the inclusion complex structure, where the ring C immersion into the CyD cavity is prevalent. Full article

To reduce the experimental costs associated with tool condition monitoring (TCM) under new cutting conditions, a finite element modeling (FEM)-assisted deep subdomain adaptive network (DSAN) approach is proposed. Initially, an FEM technique is employed to construct a cutting tool model for the new cutting condition (target domain), and the similarity between simulated and experimental data is assessed to obtain valid simulated samples for the target domain. Subsequently, the time–frequency Markov representation method is utilized to extract imaging features from the simulated samples, which serve as input features for the monitoring model. Then, a DSAN model is established to facilitate the transfer from simulation to reality, with the source domain comprising a simulated sample set under new cutting conditions that includes various types of tool conditions obtained through FEM, and the target domain containing only a limited number of normal tool condition samples under new cutting conditions. The application analysis has demonstrated the effectiveness of the proposed method, achieving a classification accuracy of 99%. The proposed approach can significantly reduce experimental costs and obtain high-precision diagnostics of tool conditions with a small sample size. Full article

The addition of dexamethasone in membranes for guided bone regeneration is promising due to its dual effect: (1) anti-inflammatory action and (2) induction of osteogenesis in host stem cells. Electrospun fiber coating with dexamethasone using the layer-by-layer (LBL) technique offers an interesting alternative for the gradual release of the drug, aiming for enhanced osteodifferentiation activity. This study aimed to develop synthetic poly-L-lactide (PLLA) membranes with dexamethasone incorporated into the fibers or coated on their surface, and to evaluate the drug release rate, as well as the material’s ability to promote proliferation, osteoconduction, and osteodifferentiation of human periodontal ligament stem cells (hPDLSCs). PLLA membranes were produced by electrospinning. Dexamethasone was incorporated using three techniques: (A) electrospinning of a co-solution of PLLA with 2.5 w/w% dexamethasone; (B) deposition of four layers on the PLLA membrane using alternating solutions of chitosan and heparin/dexamethasone; (C) deposition of 10 layers on the PLLA membrane using the same solutions. hPDLSC proliferation was measured via CCK-8 at 1, 7, 14, and 21 days. Cellular differentiation was assessed by alkaline phosphatase activity (7 days) and alizarin red staining (21 days) in clonogenic and osteogenic media (ODM). Data were analyzed using one or two-way ANOVA and Tukey test. Electrospun membranes with dexamethasone and those with 4 layers showed immediate drug release within 24 h, whereas 10 layers exhibited gradual release over 14 days. Cumulative drug release was higher for electrospun membranes at 1 and 7 days, similar to 10 layers at 14 and 21 days. The 4 LBL membrane promoted lower hPDLSC proliferation compared to the 10 LBL and electrospun membranes at 21 days but showed increased extracellular matrix mineralization in osteogenic media. No significant differences in alkaline phosphatase expression were observed between materials. Therefore, the addition of dexamethasone in 10 layers, combined with heparin, enables gradual drug release. However, lower drug release in the first 24 h by four LBL membranes improved the material’s osteogenesis properties. None of the materials improved the osteodifferentiation in the clonogenic medium. Full article

Climate change is a major global issue because transportation is a major source of pollutants and greenhouse gases that affect human health and air quality. However, to effectively prioritize and fund mitigating actions, decision-makers lack scientific rigor and diagnoses with sufficient spatial resolution. Based on the Origin-Destination Matrix (ODM), this study suggests a methodology to measure and identify mobility emissions (CO₂, Nox, PM) at the neighborhood level with high spatial resolution. Testing of the methodology was performed in Valencia, Spain. Even though many studies calculate carbon footprint, few make use of precise geographic information and openly accessible data, and they frequently concentrate on entire cities rather than smaller areas. To determine all potential routes for each Origin-Destination (OD) trip, the process uses geostatistics to estimate daily trip activity data (kilometers traveled). The COPERT calculator methodology from the European Union is used to analyze these routes to calculate the total emissions and the distance traveled per neighborhood. Based on road infrastructure, the methodology determines which neighborhoods receive emissions and creates measures of equitable environmental responsibility. It also identifies short trips that might be replaced by cycling or walking, as well as possible improvements to public transportation. Full article

In the future, the cultivation of maize will become more and more prominent. As the world’s demand for food and animal feeding increases, remote sensing technologies (RS technologies), especially unmanned aerial vehicles (UAVs), are developing more and more, and the usability of the cameras (Multispectral-MS) installed on them is increasing, especially for plant disease detection and severity observations. In the present research, two different maize hybrids, P9025 and sweet corn Dessert R78 (CS hybrid), were employed. Four different treatments were performed with three different doses (low, medium, and high dosage) of infection with corn smut fungus (Ustilago maydis [DC] Corda). The fields were monitored two times after the inoculation—20 DAI (days after inoculation) and 27 DAI. The orthomosaics were created in WebODM 2.5.2 software and the study included five vegetation indices (NDVI [Normalized Difference Vegetation Index], GNDVI [Green Normalized Difference Vegetation Index], NDRE [Normalized Difference Red Edge], LCI [Leaf Chlorophyll Index] and ENDVI [Enhanced Normalized Difference Vegetation Index]) with further analysis in QGIS. The gathered data were analyzed using R-based Jamovi 2.6.13 software with different statistical methods. In the case of the sweet maize hybrid, we obtained promising results, as follows: the NDVI values of CS 0 were significantly higher than the high-dosed infection CS 10.000 with a mean difference of 0.05422 *** and a p value of 4.43 × 10⁻⁵ value, suggesting differences in all of the levels of infection. Furthermore, we investigated the correlations of the vegetation indices (VI) for the Dessert R78, where NDVI and GNDVI showed high correlations. NDVI had a strong correlation with GNDVI (r = 0.83), a medium correlation with LCI (r = 0.56) and a weak correlation with NDRE (r = 0.419). There was also a strong correlation between LCI and GNDVI, with r = 0.836. NDRE and GNDVI indices had the correlation coefficients with a CCoeff. of r = 0.716. For hybrid separation analyses, useful results were obtained for NDVI and ENDVI as well. Full article

The vibration pretreatment–microwave curing process can achieve high-quality molding under low-pressure conditions and is widely used in the curing of resin-based composites. This study investigated the effects of the vibration pretreatment process parameters on the void content and the fiber weight fraction of T700/TRE231; specifically, their influence on the interlaminar shear strength and impact strength of the composite. Initially, an orthogonal experimental design was employed with interlaminar shear strength as the optimization target, where vibration acceleration was determined as the primary factor and dwell time as the secondary factor. Concurrently, thermogravimetric analysis (TGA) was performed based on process parameters that corresponded to the extremum of interlaminar shear strength, revealing a 2.17% difference in fiber weight fraction among specimens with varying parameters, indicating a minimal effect of fiber weight fraction on mechanical properties. Optical digital microscope (ODM) analysis identified interlaminar large-size voids in specimens treated with vibration energy of 5 g and 15 g, while specimens subjected to a vibration energy of 10 g exhibited numerous small-sized voids within layers, suggesting that vibration acceleration influences void escape pathways. Finally, impact testing revealed the effect of the vibration pretreatment process parameters on the impact strength, implying a positive correlation between interlaminar shear strength and impact strength. Full article

Illegal coal mining is prevalent worldwide, leading to extensive ground subsidence and land collapse. It is crucial to define the location and spatial dimensions of these areas for the efficient prevention of the induced hazards. Conventional methods for goaf locating using the InSAR technique are mostly based on the probability integral model (PIM). However, The PIM requires detailed mining information to preset model parameters and does not account for the layered structure of the coal overburden, making it challenging to detect underground goaves in cases of illegal mining. In response, a novel method based on the InSAR technique and the Stratified Optimal Okada Dislocation Model, named S-ODM, is proposed for locating goaves with basic geological information. Firstly, the S-ODM employs a numerical model to establish a nonlinear function between the goaf parameters and InSAR-derived ground deformation. Then, in order to mitigate the influence of nearby mining activities, the goaf azimuth angle is estimated using the textures and trends of the InSAR-derived deformation time series. Finally, the goaf’s dimensions and location are estimated by the genetic algorithm–particle swarm optimization (GA-PSO). The effectiveness of the proposed method is validated using both simulation and real data, demonstrating average relative errors of 6.29% and 7.37%, respectively. Compared with the PIM and ODM, the proposed S-ODM shows improvements of 19.48% and 52.46% in geometric parameters. Additionally, the errors introduced by GA-PSO and the influence of ground deformation monitoring errors are discussed in this study. Full article