Search Results (5,844)

Numerical simulation has played a crucial role in modeling the behavior of natural gas hydrate (NGH). However, the existing numerical simulators worldwide have exhibited limitations in functionality, convergence, and computational efficiency. In this study, we present a novel numerical simulator, GPSFlow/Hydrate, for modeling the behavior of hydrate-bearing geologic systems and for addressing the limitations in the existing simulators. It is capable of simulating multiphase and multicomponent flow in hydrate-bearing subsurface reservoirs under ambient conditions. The simulator incorporates multiple mass components, various phases, as well as heat transfer, and sand is treated as an independent non-Newtonian flow and modeled as a Bingham fluid. The CH₄ or binary/ternary gas hydrate dissociation or formation, phase changes, and corresponding thermal effects are fully accounted for, as well as various hydrate formation and dissociation mechanisms, such as depressurization, thermal stimulation, and sand flow behavior. In terms of computation, the simulator utilizes a domain decomposition technology to achieve hybrid parallel computing through the use of distributed memory and shared memory. The verification of the GPSFlow/Hydrate simulator are evaluated through two 1D simulation cases, a sand flow simulation case, and five 3D gas production cases. A comparison of the 1D cases with various numerical simulators demonstrated the reliability of GPSFlow/Hydrate, while its application in modeling the sand flow further highlighted its capability to address the challenges of gas hydrate exploitation and its potential for broader practical use. Several successful 3D gas hydrate reservoir simulation cases, based on parameters from the Shenhu region of the South China Sea, revealed the correlation of initial hydrate saturation and reservoir condition with hydrate decomposition and gas production performance. Furthermore, multithread parallel computing achieved a 2–4-fold increase in efficiency over single-thread approaches, ensuring accurate solutions for complex physical processes and large-scale grids. Overall, the development of GPSFlow/Hydrate constitutes a significant scientific contribution to understanding gas hydrate formation and decomposition mechanisms, as well as to advancing multicomponent flow migration modeling and gas hydrate resource development. Full article

Global ambitions for net-zero emissions and resource circularity are propelling industry from linear “make-use-dispose”models toward closed-loop value creation. Remanufacturing, which aims to restore end-of-life products to a “like-new” condition, plays a central role in this transition. However, its stochastic inputs and complex, multi-stage processes pose significant challenges to traditional production planning methods. This study delivers an integrated overview of remanufacturing scheduling by combining a systematic bibliometric review of 190 publications (2005–2025) with a critical synthesis of modelling approaches and enabling technologies. The bibliometric results reveal five thematic clusters and a 14% annual growth rate, highlighting a shift from deterministic, shop-floor-focused models to uncertainty-aware, sustainability-oriented frameworks. The scheduling problems are formalised to capture features arising from variable core quality, multi-phase precedence, and carbon reduction goals, in both centralised and cloud-based systems. Advances in human–robot disassembly, vision-based inspection, hybrid repair, and digital testing demonstrate feedback-rich environments that increasingly integrate planning and execution. A comparative analysis shows that, while mixed-integer programming and metaheuristics perform well in small static settings, dynamic and large-scale contexts benefit from reinforcement learning and hybrid decomposition models. Finally, future directions for dynamic, collaborative, carbon-conscious, and digital-twin-driven scheduling are outlined and investigated. Full article

Hybrid plant and meat (HPM) products, in which a portion of meat is substituted with alternative plant protein-containing ingredients, offer a promising option for flexitarian consumers seeking to increase plant protein consumption while continuing to enjoy the sensory qualities of meat products. This study evaluated the effects of faba bean protein (FBP), pea protein (PP), and rice protein (RP) ingredients at a 12.5% meat protein substitution level, under varying pre-hydration conditions and, subsequently, on the technological properties of hybrid plant/beef patties (HPBP). Colour measurements indicated that plant protein ingredient addition to HPBP resulted in increased lightness (L*) and decreased redness (a*) values. HPBP showed reduced cooking loss compared to 100% beef patties, and cooking loss increased with higher pre-hydration levels of plant proteins. Faba bean hybrid patty (FBHP) exhibited lower texture scores, while the patty containing non-hydrated RP had the highest hardness values. The texture of patties with PP was comparable to the control, irrespective of the hydration status of the plant protein. Inclusion of plant proteins also reduced water mobility by restricting intracellular water. Overall, these findings provide valuable insights into the selection of suitable plant proteins and the requirement for optimal pre-hydration of plant proteins prior to incorporation into HPBP to ensure optimal technological properties. Full article

Methane concentration anomalies during coal mining operations are identified as important factors triggering major safety accidents. This study aimed to address the key issues of insufficient adaptability of existing detection methods in dynamic and complex underground environments and limited characterization capabilities for non-uniform sampling data. Specifically, an intelligent diagnostic model was proposed by integrating the improved Dung Beetle Optimization Algorithm (SGDBO) with Transformer-SVM. A dual-path feature fusion architecture was innovatively constructed. First, the original sequence length of samples was unified by interpolation algorithms to adapt to deep learning model inputs. Meanwhile, statistical features of samples (such as kurtosis and differential standard deviation) were extracted to deeply characterize local mutation characteristics. Then, the Transformer network was utilized to automatically capture the temporal dependencies of concentration time series. Additionally, the output features were concatenated with manual statistical features and input into the LSSVM classifier to form a complementary enhancement diagnostic mechanism. Sine chaotic mapping initialization and a golden sine search mechanism were integrated into DBO. Subsequently, the SGDBO algorithm was employed to optimize the hyperparameters of the Transformer-LSSVM hybrid model, breaking through the bottleneck of traditional parameter optimization falling into local optima. Experiments reveal that this model can significantly improve the classification accuracy and robustness of anomaly curve discrimination. Furthermore, core technical support can be provided to construct coal mine safety monitoring systems, demonstrating critical practical value for ensuring national energy security production. Full article

The global population surge and continuously rising energy demand have led to the rapid depletion of fossil fuel reserves. Over-exploitation of non-renewable fuels is responsible for the emission of greenhouse gases, air pollution, and global warming, which causes serious health issues and ecological imbalance. The present study focuses on the potential of algae-based biofuel as an alternative energy source for fossil fuels. Algal biofuels are more environmentally friendly and economically reasonable to produce on a pilot scale compared to lignocellulosic-derived biofuels. Algae can be cultivated in closed, open, and hybrid photobioreactors. Notably, high-rate raceway ponds with the ability to recycle nutrients can reduce freshwater consumption by 60% compared to closed systems. The algal strain along with various factors such as light, temperature, nutrients, carbon dioxide, and pH is responsible for the growth of biomass and biofuel production. Algal biomass conversion through hydrothermal liquefaction (HTL) can achieve higher energy return on investments (EROI) than conventional techniques, making it a promising Technology Readiness Level (TRL) 5–6 pathway toward circular biorefineries. Therefore, algal-based biofuel production offers numerous benefits in terms of socio-economic growth. This review highlights the basic cultivation, dewatering, and processing of algae to produce biofuels using various methods. A simplified multicriteria evaluation strategy was used to compare various catalytic processes based on multiple performance indicators. We also conferred various advantages of an integrated biorefinery system and current technological advancements for algal biofuel production. In addition to this, policies and market regulations are discussed briefly. At the end, critical challenges and future perspectives of algal biorefineries are reviewed. Algal biofuels are environmentally friendly as well as economically sustainable and usually offer more benefits compared to fossil fuels. Full article

Desertification poses critical threats to agricultural productivity and socio-economic stability, particularly in vulnerable regions like Thatta and Badin districts of Sindh, Pakistan. Traditional monitoring methods lack the accuracy and temporal resolution needed for effective early warning systems. This study presents a novel Spatio-Temporal Desertification Predictor (STDP) framework that integrates deep learning with next-generation satellite imaging for time-series desertification forecasting. The proposed encoder–decoder architecture combines Convolutional Neural Networks (CNNs) for spatial feature extraction from high-resolution satellite imagery with modified Long Short-Term Memory (LSTM) networks enhanced by multi-head attention to capture temporal dependencies. Environmental variables are fused through an adaptive data integration layer, and hyperparameter optimization is employed to enhance model performance for edge computing deployment. Experimental validation on a 15-year satellite dataset (2010–2024) demonstrates superior performance with MSE = 0.018, MAE = 0.079, and $R^2=0.94$, outperforming traditional CNN-only, LSTM-only, and hybrid baselines by 15–20% in prediction accuracy. The framework forecasts desertification trends through 2030, providing actionable signals for environmental management and policy-making. This work advances the integration of AI with satellite-based Earth observation, offering a scalable path for real-time environmental monitoring in IoT and edge computing infrastructures. Full article

The rapid growth of e-commerce has created substantial environmental impacts, driving the need for advanced optimization models to enhance supply chain sustainability. As consumer preferences shift toward environmental responsibility, organizations must adopt robust quantitative methods to reduce ecological footprints while ensuring operational efficiency. This study develops a novel hybrid multi-criteria decision-making (MCDM) model to evaluate and prioritize green suppliers under uncertainty, integrating the rough-Dombi best–worst method (BWM) and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed model addresses two key challenges: (1) inconsistency in expert judgments through rough set theory and Dombi aggregation operators and (2) ranking instability via an enhanced TOPSIS formulation that mitigates rank reversal. Mathematically, the rough-Dombi BWM leverages interval-valued rough numbers to model subjective expert preferences, while the Dombi operator ensures flexible and precise weight aggregation. The modified TOPSIS incorporates a dynamic distance metric to strengthen ranking robustness. A case study of five e-commerce suppliers validates the model’s effectiveness, with results identifying cost, green competitiveness, and external environmental management as the dominant evaluation dimensions. Key indicators—such as product price, pollution control, and green design—are rigorously prioritized using the proposed framework. Theoretical contributions include (1) a new rough-Dombi fusion for criteria weighting under uncertainty and (2) a stabilized TOPSIS variant with reduced sensitivity to data perturbations. Practically, the model provides e-commerce enterprises with a computationally efficient tool for sustainable supplier selection, enhancing resource allocation and green innovation. This study advances the intersection of uncertainty modeling, operational research, and sustainability analytics, offering scalable methodologies for mathematical decision-making in supply chain contexts. Full article

Cilantro (Coriandrum sativum L.) is a popular annual culinary herb grown for its leaves or seeds. With the increase in hydroponic herb production in controlled environments, a need exists for leaf tissue nutrient standards specific to this production system. The objective of this study was to develop comprehensive foliar mineral nutrient interpretation ranges for greenhouse-grown cilantro. Cilantro plants were grown in a hydroponic sand culture system to induce and document nutritional disorders. Plants were supplied with a modified Hoagland’s solution, which was adjusted to individually add or omit one nutrient per treatment while holding all others constant. Deficiency and toxicity symptoms were photographed, after which the plant tissue was collected to determine plant dry weight and critical tissue nutrient concentrations. Nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), boron (B), iron (Fe), and zinc (Zn) deficiencies, as well as B toxicity, were induced. Deficiencies of copper (Cu), manganese (Mn), and molybdenum (Mo) were not observed during the experiment. Additional foliar tissue analysis data (n = 463) were compiled to create nutrient interpretation ranges for 12 essential elements based on a hybrid meta-analysis Sufficiency Range Approach (SRA). This approach defines ranges for deficient, low, sufficient, high, and excessive values. For each element, the optimal distribution was selected according to the lowest Bayesian Information Criterion (BIC) value. A Normal distribution best represented K and S. A Gamma distribution best represented P, Ca, Mn, and Mo, whereas a Weibull distribution best represented N, Mg, B, Cu, Fe, and Zn. These interpretation ranges, along with descriptions of typical symptomology and critical tissue nutrient concentrations, provide useful tools for both diagnosing nutritional disorders and interpreting foliar nutrient analysis results of greenhouse-grown cilantro. Full article

Efficient production scheduling is a key challenge in industrial operations and continues to attract significant interest within the field of operations research. This paper investigates a range of methodological approaches designed to solve the permutation flow shop scheduling problem (PFSP) with sequence-dependent setup times (SDST). The main objective is to minimize the total weighted flow time (TWFT) while ensuring a no-wait production environment. The proposed solution strategy is based on using algorithms with a mixed integer linear programming (MILP) formulation, heuristics, and their combination. The heuristics utilized in this paper include an advanced greedy randomized adaptive search procedure (GRASP) based on a priority rule and Hybrid-GRASP-NEH (HGRASP), where Nawaz-Enscore-Ham (NEH) takes place to initiate solutions, based on iterative global and local search methods to refine exploration capabilities and improve solution quality. These approaches were validated using a comprehensive set of experiments across diverse instance sizes that proved the efficiency of HGRASP, with the results showing a high-performance level that closely matched that of the exact MILP approach. Statistical analysis via the Friedman test (χ² = 46.75, p = 7.04 × 10⁻¹¹) confirmed significant performance differences among MILP, GRASP, and HGRASP. While MILP guarantees theoretical optimality, its practical effectiveness was limited by imposed computational time constraints, and HGRASP consistently achieved near-optimal solutions with superior computational efficiency, as demonstrated across diverse instance sizes. Full article

UAV pollination holds significant promise for enhancing hybrid rice seed production, yet the mechanisms of pollen diffusion under UAV downwash and the lack of theoretical guidance for operational parameter optimization remain critical challenges. To address this, this study employed a coupled Computational Fluid Dynamics–Discrete Phase Model (CFD–DPM) numerical simulation to systematically investigate the interaction between the UAV-induced wind field and pollen particles. A validated CFD model was first developed to characterize the UAV wind-field distribution, demonstrating good agreement with field measurements. Building upon this, a coupled wind field–pollen CFD–DPM model was established, enabling a detailed visualization and analysis of airflow patterns and pollen transport dynamics under varying flight parameters (speed and height). Using the pollen disturbance area and effective settling range as key evaluation metrics, the optimal pollination parameters were identified as a flight speed of 3 m/s and a height of 4 m. Field validation trials confirmed that UAV-assisted pollination using these optimized parameters significantly increased the seed yield by 21.4% compared to traditional manual methods, aligning closely with simulation predictions. This study establishes a robust three-tier validation framework (“numerical simulation—wind-field verification—field validation”) that provides both theoretical insights and practical guidance for optimizing UAV pollination operations. The framework demonstrates strong generalizability for improving the efficiency and mechanization level of hybrid rice seed production. Full article

Timely, objective pain recognition in dairy cattle is essential for welfare assurance, productivity, and ethical husbandry yet remains elusive because evolutionary pressure renders bovine distress signals brief and inconspicuous. Without verbal self-reporting, cows suppress overt cues, so automated vision is indispensable for on-farm triage. Although earlier systems tracked whole-body posture or static grimace scales, frame-level detection of facial micro-expressions has not been explored fully in livestock. We translate micro-expression analytics from automotive driver monitoring to the barn, linking modern computer vision with veterinary ethology. Our two-stage pipeline first detects faces and 30 landmarks using a custom You Only Look Once (YOLO) version 8-Pose network, achieving a 96.9% mean average precision ($mAP$) at an Intersection over the Union (IoU) threshold of 0.50 for detection and 83.8% Object Keypoint Similarity (OKS) for keypoint placement. Cropped eye, ear, and muzzle patches are encoded using a pretrained MobileNetV2, generating 3840-dimensional descriptors that capture millisecond muscle twitches. Sequences of five consecutive frames are fed into a 128-unit Long Short-Term Memory (LSTM) classifier that outputs pain probabilities. On a held-out validation set of 1700 frames, the system records 99.65% accuracy and an F1-score of 0.997, with only three false positives and three false negatives. Tested on 14 unseen barn videos, it attains 64.3% clip-level accuracy (i.e., overall accuracy for the whole video clip) and 83% precision for the pain class, using a hybrid aggregation rule that combines a 30% mean probability threshold with micro-burst counting to temper false alarms. As an early exploration from our proof-of-concept study on a subset of our custom dairy farm datasets, these results show that micro-expression mining can deliver scalable, non-invasive pain surveillance across variations in illumination, camera angle, background, and individual morphology. Future work will explore attention-based temporal pooling, curriculum learning for variable window lengths, domain-adaptive fine-tuning, and multimodal fusion with accelerometry on the complete datasets to elevate the performance toward clinical deployment. Full article

This study evaluates the viability of a specific hybrid renewable energy system (HRES) installation designed for a remote community as a case study in Cuba. The system integrates solar, wind, and biomass resources to address localised challenges of energy insecurity and environmental degradation. Rather than offering a generalised evaluation of HRES technologies, this work focuses on the performance, impacts, and viability of this particular configuration within its unique geographical, social, and technical context. Using life cycle assessment (LCA) and input–output modelling, the research assesses environmental and socioeconomic impacts. The proposed HRES reduces greenhouse gas emissions by 60% (from 1.14 to 0.47 kg CO₂eq/kWh) and fossil energy consumption by 50% compared to diesel-based systems. Socioeconomic analysis reveals that the system generates 40.3 full-time equivalent (FTE) jobs, with significant employment opportunities in operation and maintenance. However, initial investments primarily benefit foreign suppliers due to Cuba’s reliance on imported components. The study highlights the potential for local economic gains through workforce training and domestic manufacturing of renewable energy technologies. These findings underscore the importance of integrating multiple renewable sources to enhance energy resilience and sustainability in Cuba. Policymakers should prioritise strategies to incentivise local production and capacity building to maximise long-term benefits. Future research should explore scalability across diverse regions and investigate policy frameworks to support widespread adoption of HRES. This study provides valuable insights for advancing sustainable energy solutions in Cuba and similar contexts globally. Full article

Lung cancer remains a foremost cause of cancer-related impermanence globally, demanding innovative and effective therapeutic strategies. Polymeric nanoparticles (NPs) have turned up as a promising transport system for drugs due to their biodegradability, biocompatibility, and capability to provide controlled and targeted release of therapeutic agents. This review offers a thorough examination of different polymeric NP platforms, such as chitosan, gelatin, alginate, poly (lactic acid), and polycaprolactone, highlighting their mechanisms, formulations, and applications in the treatment of lung cancer. These NPs facilitate the delivery of chemotherapeutic agents, gene therapies, and immune modulators, with enhanced bioavailability and reduced systemic toxicity. Additionally, advanced formulations such as ligand-conjugated, stimuli-responsive, and multifunctional NPs demonstrate improved tumor-specific accumulation and cellular uptake. The review also discusses quantum dots, magnetic and lipid-based NPs, and green-synthesized metallic polymeric hybrids, emphasizing their potential in theranostics and combination therapies. Preclinical studies show promising results, yet clinical translation faces challenges; for example, large-scale production, long-term toxicity, and regulatory hurdles. Overall, polymeric NPs represent a powerful platform for advancing personalized lung cancer therapy, with future prospects rooted in multifunctional, targeted, and patient-specific nanomedicine. Full article

Widespread Triassic granitic magmatism is archived in the East Kunlun Orogen Belt (EKOB) of Northern Qinghai–Tibet Plateau. Mafic magmatic enclaves (MMEs), commonly hosted in these plutons, are generally interpreted as products of magma mixing; however, the specific magmatic processes remain poorly understood. In this study, we present new data on the complex zoning patterns, in situ U–Pb ages, trace element compositions, and Nd isotopic characteristics of titanite grains from the MMEs and host granodiorite of Laningzaohuo Zhongyou pluton. Whole-rock geochemical data indicate that the pluton is composed of volcanic arc-related, calc-alkaline, metaluminous I-type granodiorite. Titanite in the MMEs and the granodiorite yield similar U–Pb ages of ~244 Ma but display distinct textural and compositional features. Titanite from the granodiorite is typically euhedral, characterized by magmatic core and mantle with deuteric rim, and exhibits sector and fir-tree zoning in the core. In contrast, titanite from the MMEs is generally anhedral, also showing magmatic core and mantle as well as deuteric rims, but exhibits oscillatory zoning and incomplete sector and fir-tree zoning in the core. Titanite cores in the MMEs have ε_Nd(t) ranging from −2.5 to −3.4, comparable to those of the coeval gabbro and MMEs elsewhere in the EKOB. These cores also show higher LREE/HREE ratios compared to titanite cores in the granodiorite, suggesting crystallization from mixed magmas with greater contributions from enriched lithospheric mantle sources. Titanite mantles in the MMEs yield ε_Nd(t) of −4.0 to −4.8, slightly lower than the cores in the MMEs but higher than those of titanite cores and mantles in the granodiorite (−4.6 to −5.5). The mantle can be interpreted as crystallized from mixed magmas with less mafic components. Titanite rims in the MMEs have ε_Nd(t) of −5.0 to −5.7, identical to those in the granodiorite, and have REE concentrations and Th/U and Nb/Ta ratios consistent with the titanite rims in the granodiorite, clearly indicative of crystallization from evolved, hydrated, granodioritic magmas. Plagioclase in the MMEs exhibits disequilibrium textures such as sieve texture and reverse zoning, with An_36–66, contrasting with the more uniform An contents (An_35–37) in the granodiorite. This suggests that plagioclase in the MMEs crystallized in an environment influenced by both mafic and felsic magmas. Amphibole thermobarometry indicates that amphibole in the MMEs crystallized at ~788 °C and ~295 MPa, slightly higher than the crystallization conditions in the granodiorite (~778 °C and ~259 MPa). We thus propose that the chemical and textural differences between titanite in the MMEs and granodiorite suggest that the MMEs formed within a mushy hybrid layer generated by injection of upwelling basaltic magma into a pre-existing granitic magma chamber. Titanite cores and mantles in the MMEs likely crystallized from variably mixed magmas. They subsequently underwent resorption and disequilibrium growth within the hybrid layer, and were eventually overgrown by rims formed from evolved interstitial granitic melts within the mushy enclaves. These findings demonstrate that the complex zoning and geochemical titanite in the MMEs provide valuable insights into magma mixing processes. Full article

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization. Full article