Search Results (37,422)

Against the background of China’s ecological civilization construction and sustainable development strategies, how remote sensing courses adapt to the demands of the artificial intelligence era has become an urgent issue for undergraduate education in relevant disciplines at universities. This study proposed a trinity teaching reform path of “deep learning and remote sensing, and ecological sustainability”, aiming to cultivate interdisciplinary talents with capabilities in intelligent interpretation and practical application. The study established a three-stage curriculum objective system, integrating knowledge, ability, and literacy, designed a five-dimensional linkage teaching method combining case-driven teaching, modular training, and blended learning, and conducted teaching practices using mainstream deep learning frameworks and cloud platforms. Through hierarchical teaching practice cases and multi-dimensional evaluation data, it was shown that the reform effectively enhanced the experiment group students’ abilities in deep learning applications, complex remote sensing data processing, and ecological problem-solving. The achievement values for all five evaluation indicators exceeded 80%, with the highest improvement reaching 28% compared to the control group. The results indicate that this teaching reform not only enhances learning outcomes but also provides a valuable framework and practical pathway for remote sensing education empowered by artificial intelligence and the cultivation of professional talent in future sustainable development fields. Full article

Background/Objectives: Benign prostatic hyperplasia (BPH) is a prevalent urological disorder in aging men, characterized by the enlargement of prostate epithelial and stromal cells, which leads to lower urinary tract symptoms. Paeonol, a bioactive compound derived from Moutan Cortex (Paeonia suffruticosa), exhibits multiple pharmacological properties; however, its therapeutic potential in BPH remains unclear. This study aimed to elucidate the mechanisms of paeonol in BPH treatment using network pharmacology and in vivo experiments. Methods: Network pharmacology and molecular docking were conducted to identify potential targets of paeonol against BPH. For the in vivo study, testosterone-induced BPH rat models were employed, and efficacy was evaluated through prostate weight assessment, histological examination, and the quantitative real-time polymerase chain reaction (qRT-PCR) analysis of prostate tissues. Results: In silico analysis revealed key signaling pathways involved in apoptosis, proliferation, phosphatidylinositol 3-kinase (PI3K)–protein kinase B (Akt), and inflammation. Paeonol administration significantly reduced prostate weight, volume, and histological hyperplasia in BPH rats. qRT-PCR analysis demonstrated that paeonol may suppress dihydrotestosterone production by inhibiting 5α-reductase 2 (5AR2) and the androgen receptor (AR), while also downregulating local growth factors, alpha serine/threonine-protein kinase (Akt1), nuclear factor-κB (NF-κB), and glutathione reductase (GR) expression. Conclusions: These findings provide novel insights into the multitargeted therapeutic potential of paeonol in BPH by inhibiting 5AR and AR and suppressing proliferation via NF-κB and Akt pathway modulation. Full article

Salt stress severely constrains agricultural productivity in arid and semi-arid regions. This study evaluated exogenous proline as an osmoprotector in Physalis ixocarpa Brot. (Mexican husk tomato) under salinity. Germination screening identified 75 mM NaCl as a threshold stress level, reducing germination by 38.9% while maintaining seedling viability. Proline pretreatment (30-min imbibition) at 8 mM restored germination to 78% and fresh weight to control levels under salt stress. In vitro experiments revealed that 8 mM proline enhanced chlorophyll content above salt-stressed controls while reducing root length from 9.72 to 5.08 cm, indicating resource reallocation toward photosynthetic protection. Infrared spectroscopy showed characteristic polysaccharide shifts and bands potentially associated with proline incorporation. Gas chromatography–mass spectrometry metabolomics of stem–leaf extracts revealed salt-induced synthesis of nitrogenous osmolytes (such as long-chain amines) and carbohydrate reorganization from α-D-glucopyranoside to β-D-riboside. Proline treatment restored the original carbohydrate profile while generating pyrrolidine derivatives (2.83%), evidence of active proline metabolism. Phenolic antioxidants (e.g., catechol) present in controls were absent under both salt stress and proline treatment, suggesting that proline’s protective mechanism may operate through metabolic regulation of osmolyte pathways and membrane stabilization rather than inducing phenolic antioxidant synthesis. These findings demonstrate proline’s multifaceted protective mechanisms and support its potential application for enhancing salt tolerance in this crop. Full article

This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem. Full article

Grapefruit (Citrus × paradisi Macfad.) is susceptible to Huanglongbing (HLB) disease, which prominently affects tree health and leads to a substantial loss of productivity. HLB-affected trees exhibit a nutritional imbalance expressed in either deficiencies or toxicities of the essential minerals required for plant growth, as well as changes in the production of plant metabolites. Hence, understanding foliar nutritional and metabolite fluctuations as HLB-elicited symptoms progress can assist growers in improving tree health management strategies. This study evaluated changes in foliar nutrient and phloem sap amino acid concentrations of HLB-affected grapefruit trees showing a mixed canopy of HLB-induced blotchy mottle and asymptomatic mature leaves. The trees used in our experiment were fruit-bearing seven-year-old grapefruit trees (cv ‘Rio Red’ on sour orange rootstock) grown in South Texas. Two types of foliage from HLB-affected trees were studied, (a) HLB-symptomatic and confirmed Candidatus Liberibacter asiaticus (CLas)-positive (IS) and (b) CLas-negative and HLB-asymptomatic (IA) mature leaves, which were compared to asymptomatic and CLas-free mature foliage from healthy trees (HY) in terms of their leaf nutrient and phloem sap amino acid contents. Hierarchical clustering based on leaf nutrient contents showed that 70% of IA samples clustered with HY samples, thus indicating that the levels of some nutrients were statistically similar in these two types of samples. The concentrations of the macronutrients N, Ca, Mg, and S and the micronutrients Mn and B were significantly reduced in HLB-symptomatic (IS) leaves, as compared to their IA and HY counterparts, which did not show statistically significant differences. Conversely, leaf Na concentration was approximately two-fold higher in leaves from HLB-affected trees (IA and IS) independent of symptom expression as compared to leaves from healthy trees. Significantly higher concentrations of glutamine and the S-containing amino acids taurine and cystathionine were observed in the IS leaves relative to the phloem sap of IA leaves from HLB-affected trees. In contrast, the phloem sap of IA (14%) and IS (41%) leaves from HLB-affected trees exhibited lower levels of γ-amino butyric acid (GABA) as compared to HY leaves. The results of this study highlight the changes in leaf nutrient and phloem sap amino acid profiles following CLas infection and HLB symptom development in grapefruit, and we discuss these results considering the strategies that growers can implement to correct the nutritional deficiencies and/or toxicities induced by this disease. Full article

Aerobic soils serve as significant sinks for atmospheric methane, with their effectiveness influenced by the diversity and activity of soil methanotrophs. Land-use changes, particularly the conversion of natural ecosystems to agriculture, can substantially alter these microbial communities. A promising strategy to restore methane oxidation capacity is the introduction of active, ambient methane-oxidizing bacteria. The stable methane-oxidizing microbial consortium T1, dominated by Methylocystis (74%), was isolated from the soil of the unique Chernevaya Taiga forest ecosystem. The effects of inoculating this consortium were evaluated in a four week laboratory incubation experiment, using microcosms of soddy-podzolic agro-soil. Methane oxidation potential was assessed to measure methanotroph activity; methanotrophs were quantified using qPCR targeting pmoA genes; and the diversity of soil microbial communities was examined through 16S rRNA gene profiling. Inoculated soils exhibited significantly higher methane oxidation potentials compared to non-inoculated soils. Furthermore, pmoA gene copy numbers in the inoculated soils were significantly elevated (10⁶ copies pmoA g⁻¹), indicating stable persisted methanotrophic populations throughout the incubation period. These findings suggest that enriched methanotrophic consortium inoculation into agro-soils may be a promising strategy for restoring methane-oxidizing activity. Full article

Salt stress is a major constraint on cowpea cultivation in semi-arid regions, primarily due to excess salts in irrigation water and soils. We aimed to investigate the effects of foliar magnesium (Mg) application on the production, phenology, and seed vigor of the cowpea landraces “Pingo de Ouro” and “Costela de Vaca” under salt stress conditions. Two experiments were conducted. The first was carried out in a greenhouse using a randomized block design with five replicates, in a 2 × 3 × 4 factorial scheme: two cowpea landraces (“Pingo de Ouro” and “Costela de Vaca”), three irrigation water salinity levels (0.54, 3.50, and 5.00 dS m⁻¹), and four foliar doses of a product (0.0, 1.0, 2.0 and 3.0 mL L⁻¹) containing 8% magnesium. Morphological traits and seed production were evaluated. The second experiment was conducted in a laboratory using a completely randomized design, also in a 2 × 3 × 4 factorial, with four replicates of 25 seeds each. In the first experiment, the 1 mL L⁻¹ dose provided the best results for pod length in the variety “Pingo de Ouro” under an electrical conductivity salinity of 5.00 dS m⁻¹. In the variety “Costela de Vaca”, this same dose increased the number of seeds per pod and the 100-seed weight under the same salinity level. In the second experiment, seedlings of “Pingo de Ouro” grown from seeds produced by plants treated with 2 and 3 mL L⁻¹ doses showed greater shoot length, root length, stem diameter, and shoot fresh mass, particularly under 0.54 dS m⁻¹ salinity. Therefore, “Pingo de Ouro” exhibited superior seedling growth at doses of 2 and 3 mL L⁻¹, particularly under conditions of low salinity. These findings support the use of foliar magnesium fertilization as an effective agronomic strategy to enhance seed production and quality in cowpea landraces under salt stress conditions. Full article

A CO₂-responsive TMPDA–SDS–SiO₂ gel system was developed and evaluated through formulation optimization, structural characterization, rheological testing, and core flooding experiments. The optimal formulation was identified as 7.39 wt% SDS, 1.69 wt% TMPDA, and 0.1 wt% SiO₂, achieving post-CO₂ viscosities above 10³–10⁴ mPa·s. Spectroscopic and microscopic analyses confirmed that CO₂ protonates TMPDA amine groups to form carbamate/bicarbonate species, which drive the micellar transformation into a wormlike network, thereby enhancing gelation and viscosity. Rheological tests showed severe shear-thinning behavior, excellent shear recovery, and reversible viscosity changes under alternating CO₂/N₂ injection. The gel demonstrated rapid responsiveness, reaching stable viscosities within 8 min, and maintained good performance after 60 days of thermal aging at 90 °C and in high-salinity brines. Plugging tests in sand-packed tubes revealed that a permeability reduction of 98.9% could be achieved at 0.15 PV injection. In heterogeneous parallel core flooding experiments, the gel preferentially reduced high-permeability channel conductivity, improved sweep efficiency in low-permeability zones, and increased incremental oil recovery by 14.28–34.38% depending on the permeability contrast. These findings indicate that the CO₂-responsive TMPDA–SDS–SiO₂ gel system offers promising potential as a novel smart blocking gel system for improving the effectiveness of CO₂ flooding in heterogeneous reservoirs. Full article

To address the shortcomings of the RIME algorithm’s weak global exploration ability, insufficient information exchange among populations, and limited population diversity, this work proposes a distributed data-guided RIME algorithm called DRIME. First, this paper proposes a data-distribution-driven guided learning strategy that enhances information exchange among populations and dynamically guides populations to exploit or explore. Then, a soft-rime search phase based on weighted averaging is proposed, which balances the development and exploration of RIME by alternating with the original strategy. Finally, a candidate pool is utilized to replace the optimal reference point of the hard-rime puncture mechanism to enrich the diversity of the population and reduce the risk of falling into local optima. To evaluate the performance of the DRIME algorithm, parameter sensitivity analysis, policy effectiveness analysis, and two comparative analyses are performed on the CEC-2017 test set and the CEC-2022 test set. The parameter sensitivity analysis identifies the optimal parameter settings for the DRIME algorithm. The strategy effectiveness analysis confirms the effectiveness of the improved strategies. In comparison with ACGRIME, TERIME, IRIME, DNMRIME, GLSRIME, and HERIME on the CEC-2017 test set, the DRIME algorithm achieves Friedman rankings of 1.517, 1.069, 1.138, and 1.069 in different dimensions. In comparison with EOSMA, GLS-MPA, ISGTOA, EMTLBO, LSHADE-SPACMA, and APSM-jSO on the CEC-2022 test set, the DRIME algorithm achieves Friedman rankings of 2.167 and 1.917 in 10 and 30 dimensions, respectively. In addition, the DRIME algorithm achieved an average ranking of 1.23 in engineering constraint optimization problems, far surpassing other comparison algorithms. In conclusion, the numerical optimization experiments successfully illustrate that the DRIME algorithm has excellent search capability and can provide satisfactory solutions to a wide range of optimization problems. Full article

Clustering techniques significantly enhance recommender systems by improving predictive accuracy and interpretability, particularly in sparse, high-dimensional datasets. This research presents a comprehensive comparative analysis of traditional clustering methods such as K-means and Fuzzy C-Means (FCM) against advanced probabilistic clustering methodologies based on Non-negative Matrix Factorization (NMF), focusing specifically on Bayesian NMF. Experiments conducted using the widely recognized MovieLens 1M dataset reveal Bayesian NMF’s superior performance in terms of predictive accuracy, intra-cluster cohesion, and interpretability compared to classical methods. The study systematically evaluates the influence of key parameters such as overlap ($\alpha$) and evidence threshold ($\beta$) in Bayesian NMF, demonstrating that careful parameter tuning substantially improves recommendation quality. The results highlight the inherent trade-off between cluster cohesion and predictive accuracy, emphasizing the flexibility and robustness of probabilistic approaches in accurately modeling user preferences and behaviors. The paper concludes by proposing future directions, including the exploration of hybrid clustering methods, dynamic adaptation to evolving user preferences, and integration of contextual information, thereby fostering continued advances in personalized-recommendation research. Full article

This research seeks to reduce wind-blown sand hazards in saline deserts by introducing hyperbranched PVAc copolymer emulsion as a novel ecological sand-fixing material. The study began with the preparation of the emulsion, then evaluated its fundamental properties and the salt tolerance of latex films through FTIR, SEM, and mechanical strength assessments. The sand-fixing properties (compressive strength, anti-water erosion, anti-wind erosion, thermal aging, freeze–thaw stability, and water retention) were evaluated. In addition, their effects on increasing both the growth of microbes and plants in salty deserts have been evaluated by field experiments to understand their ecological effects. The experimental results showed that the hyperbranched PVAc copolymer emulsion has excellent salt resistance and can be used as an ecological sand-fixing material in salty deserts. The research findings demonstrate that the hyperbranched PVAc copolymer emulsion exhibits superior salt tolerance, rendering it an effective ecological sand-fixing material for saline deserts. Notable attributes encompass its capacity to significantly mitigate NaCl-induced aggregate damage to sand-fixing materials, thereby enhancing sand fixation performance; its robust thermal aging resistance, freeze–thaw stability, and salt tolerance, which enable it to withstand environmental temperature variations; and experimental assessments of sand-based plant and microbial growth confirming favorable ecological impacts. This study presents novel methodologies for designing ecological sand-fixing materials in saline deserts to combat desertification. Full article

Micro-expressions, characterized by brief and subtle facial muscle movements, are essential for conveying nuanced emotions in digital humans, yet existing rendering techniques often produce rigid or emotionally monotonous animations due to the inadequate modeling of temporal dynamics and action unit interdependencies. This paper proposes a graph-driven framework for micro-expression rendering that generates emotionally diverse and lifelike expressions. We employ a 3D-ResNet-18 backbone network to perform joint spatio-temporal feature extraction from facial video sequences, enhancing sensitivity to transient motion cues. Action units (AUs) are modeled as nodes in a symmetric graph, with edge weights derived from empirical co-occurrence probabilities and processed via a graph convolutional network to capture structural dependencies and symmetric interactions. This symmetry is justified by the inherent bilateral nature of human facial anatomy, where AU relationships are based on co-occurrence and facial anatomy analysis (as per the FACS), which are typically undirected and symmetric. Human faces are symmetric, and such relationships align with the design of classic spectral GCNs for undirected graphs, assuming that adjacency matrices are symmetric to model non-directional co-occurrences effectively. Predicted AU activations and timestamps are interpolated into continuous motion curves using B-spline functions and mapped to skeletal controls within a real-time animation pipeline (Unreal Engine). Experiments on the CASME II dataset demonstrate superior performance, achieving an F1-score of 77.93% and an accuracy of 84.80% (k-fold cross-validation, k = 5), outperforming baselines in temporal segmentation. Subjective evaluations confirm that the rendered digital human exhibits improvements in perceptual clarity, naturalness, and realism. This approach bridges micro-expression recognition and high-fidelity facial animation, enabling more expressive virtual interactions through curve extraction from AU values and timestamps. Full article

Efficient multimodal transportation from a passenger’s doorstep to the airport is critical for ensuring timely arrivals, reducing travel uncertainty, and optimizing overall travel experience. However, coordinating different modes of transport—such as walking, public transit, ride-hailing, and private vehicles—poses significant challenges due to varying schedules, traffic conditions, and transfer times. Traditional route planning methods often fail to account for real-time disruptions, leading to delays and inefficiencies. As air travel demand grows, optimizing these multimodal routes becomes increasingly important to minimize delays, improve passenger convenience, and enhance transport system resilience. To address this challenge, we propose an optimization framework combining Mixed-Integer Linear Programming (MILP) and Dynamic Programming (DP) to generate optimal travel routes from a passenger’s location to the airport gate. MILP is used to model and optimize multimodal trip decisions, considering time windows, cost constraints, and transfer dependencies. Meanwhile, DP allows for adaptive, real-time adjustments based on changing conditions such as traffic congestion, transit delays, and service availability. By integrating these two techniques, our approach ensures a robust, efficient, and scalable solution for multimodal transport routing, ultimately enhancing reliability and reducing travel time variability. The results demonstrate that the MILP solver converges within 20 iterations, reducing the objective value from 15.2 to 7.1 units with an optimality gap of 8.5%; the DP-based adaptation maintains feasibility under a 2 min disruption; and the multimodal analysis yields a total travel time of 9.0 min with a fare of 3.0 units, where the bus segment accounts for 6.5 min and 2.2 units of the total. In the multimodal transport evaluation, DP adaptation reduced cumulative delays by more than half after disruptions, while route selection demonstrated balanced trade-offs between cost and time across walking, bus, and train segments. Full article

This document proposes a new evaluation model to be applied to a training course on health and safety at work based on virtual reality. The model refers to three macro-levels (design, delivery, and evaluation), which extend throughout the training life cycle. At macro level 1, design, the quality of the model intended for the virtual reality experience is evaluated, as well as its adaptation to the work environment and its compliance with applicable voluntary and mandatory standards; in macro level 2, delivery, the performance of the model, the individual reactions of users with headsets, their performance and psycho-physical state, the time, and the score achieved are evaluated; in macro level 3, evaluation, the long-term effects of subjective training and the social and economic impact that virtual reality training has had on the organisation are evaluated. The study investigates assessment models for virtual-reality-based occupational health and safety courses and identifies a model outlining general criteria that can be adapted to several types of courses and different work sectors. By examining the typical stages of the training life cycle and drawing on training evaluation models such as Kirkpatrick or Molenda and Information and Communication Technology metrics, the study identifies the key elements for assessing the effectiveness of virtual reality training in occupational health and safety. Full article

In the context of industrial applications, ensuring medium access control is a fundamental challenge. Industrial IoT devices are resource-constrained and must guarantee reliable communication while reducing energy consumption. The IEEE 802.15.4e standard proposed time-slotted channel hopping (TSCH) to meet the requirements of the industrial Internet of Things. TSCH relies on time synchronization and channel hopping to improve performance and reduce energy consumption. Despite these characteristics, configuring an efficient schedule under varying traffic conditions and interference scenarios remains a challenging problem. The exploitation of reinforcement learning (RL) techniques offers a promising approach to address this challenge. AI enables TSCH to dynamically adapt its scheduling based on real-time network conditions, making decisions that optimize key performance criteria such as energy efficiency, reliability, and latency. By learning from the environment, reinforcement learning can reconfigure schedules to mitigate interference scenarios and meet traffic demands. In this work, we compare various reinforcement learning (RL) algorithms in the context of the TSCH environment. In particular, we evaluate the deep Q-network (DQN), double deep Q-network (DDQN), and prioritized DQN (PER-DQN). We focus on the convergence speed of these algorithms and their capacity to adapt the schedule. Our results show that the PER-DQN algorithm improves the packet delivery ratio and achieves faster convergence compared to DQN and DDQN, demonstrating its effectiveness for dynamic TSCH scheduling in Industrial IoT environments. These quantifiable improvements highlight the potential of prioritized experience replay to enhance reliability and efficiency under varying network conditions. Full article