Search Results (7,187)

In reliability engineering research, obtaining accurate information about the life expectancy of products or materials is essential. However, collecting such data under normal operating conditions is often challenging, particularly for highly reliable items. This paper addresses the problem of statistical inference for lifetime data following the power Rayleigh distribution. To reduce experimental cost and time, a partially step-stress-accelerated life test is employed under a Type-I generalized hybrid censoring scheme (GHCS). Point estimators of the model parameters, as well as the acceleration factor, are derived using both maximum likelihood and Bayesian approaches. Furthermore, interval estimation is developed based on the asymptotic normality of maximum likelihood estimators, in addition to a bootstrap method and Markov-chain Monte Carlo techniques. A real-life dataset is analyzed to demonstrate the applicability of the proposed model. Finally, a Monte Carlo simulation study is conducted to evaluate and compare the performance of the suggested model and estimation procedures. Full article

Satellite-based land surface temperature (LST) products are frequently affected by cloud cover and atmospheric conditions, resulting in missing data that significantly limits the continuous monitoring of the thermal environment in complex terrains, such as the Tibetan Plateau. Existing spatiotemporal interpolation methods face clear accuracy limitations when addressing extensive data gaps, while physical models often struggle due to insufficient meteorological inputs in complex landscapes. Moreover, conventional data-driven approaches usually overlook local spatial variations, resulting in smoothed thermal patterns and systematic errors. To overcome these issues, we propose a Physically Constrained Spatial Residual Learning framework. In this framework, we use the Enhanced Annual Temperature Cycle (EATC) model to capture the temporal baseline of LST first. Then, we integrate multi-source auxiliary data into the Geographical-XGBoost (G-XGBoost) algorithm to model spatial nonlinear residuals. Using simulated cloud masks on the 2017 MODIS LST dataset from the Qinghai–Tibet Engineering Corridor, we show that the hybrid model outperforms both individual physical models and global machine learning models in accuracy and spatial detail recovery. Validation results yield an R² of 0.88, an RMSE of 1.92 K, and a mean bias of 0.07 K. Seasonal evaluations indicate best performance in winter (RMSE = 1.19 K) with robust performance in summer. Furthermore, the framework reduces boundary artifacts and accurately reproduces thermal spatial patterns in complex terrain through adaptive local bandwidth and weight adjustments. This approach provides a reliable method for high-precision LST reconstruction over heterogeneous alpine surfaces. Full article

Background: Inter-species hybridization has been extensively used in breeding cervids to improve productive traits by hybrid vigor, but the genomic processes behind the process are not well studied. Methods: In this study, we investigated restriction-site-associated DNA sequencing (RAD-seq) to conduct a whole-genome analysis of Hua-Ma hybrid individuals (a cross of sika deer and Tianshan red deer). Results: A total of 571,835 SNPs were obtained and 427,121 high-quality SNPs were obtained from five individuals upon strict filtering. An analysis of genetic diversity has shown that the observed heterozygosity (Ho = 0.2130) was lower than expected heterozygosity (He = 0.3560). Although these estimates provide preliminary insights into the genetic diversity of the sampled Hua-Ma hybrids, they should be interpreted cautiously because of the limited sample size. After the pruning of linkage disequilibrium, 189,975 independent SNPs underwent principal component analysis (PCA). The first two principal components (PC1 = 32.6%, PC2 = 26.2%) indicate relative genomic clustering rather than definitive ancestry assignments. These patterns reflect relative genomic clustering rather than definitive ancestry assignments. Conclusions: The high polymorphic loci were functionally annotated and revealed the existence of synonymous and nonsynonymous variants, including a conserved locus adjacent to the BEND5 gene that exhibited a nonsynonymous mutation generating a valine-to-isoleucine amino acid substitution which may have potential functional relevance, although no phenotypic effects were evaluated in this study. On the whole, these results suggest heterozygosity and mixed ancestry in Hua-Ma hybrids. This exploratory study provides an initial SNP resource that may support future genomic selection, parentage analysis, and breeding optimization efforts. Full article

Aluminum alloy hot rolling features diverse varieties, large variations in incoming strip thickness, and strong process nonlinearity. Traditional rolling force prediction models rely on simplified physical assumptions and poor adaptability, making it hard to satisfy high-precision production requirements. This paper presents a mechanism–data dual-driven PSO-BP neural network method for rolling force prediction which is applicable to the rolling temperature range of 320 °C to 520 °C. The SIMS mechanism model is employed as a physical constraint, and a hybrid PSO-GD algorithm optimizes the initial weights and thresholds of the BP network, avoiding the local optimum issue of conventional BP. The rolling mechanism model is embedded into the loss function to deeply integrate physical laws and data-driven learning. Validation using 508 sets of field data from 5083 aluminum alloy hot rolling shows that the model achieves a M_APE of 5.0794% and R² of 0.9254, significantly outperforming the traditional mechanism model (8.91%) and standard BP (8.77%). The proposed model preserves physical interpretability while utilizing data-driven adaptability, offering an effective approach for high-precision rolling force prediction and improving the dimensional accuracy of hot-rolled aluminum alloy sheets. Full article

This paper evaluates the potential for green hydrogen production in Tunisia using nearly 3 GW of renewable electricity distributed across four strategically selected sites: Haouaria, Zriba, Sbikha, and Feriana. These locations were chosen for their proximity to the Trans-Mediterranean (TransMed) natural gas pipeline linking Algeria to Italy, as well as their strong but underexploited solar and wind energy resources. Each site was optimized according to land availability and renewable energy potential: Haouaria is wind-dominant, Zriba employs a hybrid solar-wind configuration, Sbikha focuses on solar, and Feriana integrates both solar and wind over a large area. The analysis reveals a total green hydrogen production capacity supported by approximately 3.1 GW of installed renewable power, with a base-case LCOH ranging from $1.21 to $2.05 per kilogram. El Haouaria emerges as the most cost-effective site due to its highly favorable wind conditions, while the sensitivity analysis shows that LCOH can reach up to approximately $3.8 per kilogram under higher CAPEX assumptions. The findings underscore the viability of a multi-site development strategy and highlight northern Tunisia’s comparative advantage for low-cost green hydrogen production, thanks to its superior resource mix, existing infrastructure, and better water availability relative to Tunisia’s southern regions. Full article

Biobased wood adhesives are essential to reducing the dependence of wood-based panels on petrochemical and formaldehyde-emitting resins. This review critically synthesizes recent progress in lignin-, tannin-, starch-, furan/HMF-, organic acid-, and soy protein-based adhesive systems, with emphasis on chemical reactivity, curing mechanisms, water resistance, processability, and industrial relevance. The discussion distinguishes laboratory performance from industrial feasibility by considering specific press time, solids content, viscosity, raw material variability, emissions, cost, life-cycle performance, and compatibility with particleboard, medium-density fibreboard, plywood, and related engineered wood products. Lignin and tannins are highlighted as the most chemically compatible phenolic platforms, starch and soy systems as abundant but moisture-sensitive binders requiring targeted crosslinking, HMF and furan derivatives as promising aldehyde-type formaldehyde-free crosslinkers, and citric acid systems as attractive polyester-forming binders with pressing-temperature limitations. The review concludes that near-term adoption will most likely proceed through hybrid and partially biobased systems, whereas fully biobased adhesives require faster curing, standardized feedstocks, pilot-scale validation, and transparent techno-economic and life-cycle assessment. Full article

When a large language model (LLM) answers a question using retrieved documents, retrieval-augmented generation (RAG) is the standard approach. Retrieving more documents improves answer accuracy but increases cost and response time; retrieving fewer documents saves resources but may miss critical information. Most existing RAG systems sidestep this dilemma by applying the same retrieval setting to every query, regardless of how simple or complex the question is. This wastes budget allocation on easy questions and under-serves hard ones. This paper introduces Cost-Aware RAG (CA-RAG), a routing framework that solves this problem by treating each query individually. For every incoming question, CA-RAG selects the most suitable retrieval strategy from a fixed menu of four options, ranging from no retrieval to fetching the top $k=10$ most-relevant documents. The selection is driven by a scoring formula that balances expected answer quality against predicted cost and response time. The weights in this formula act as dials: adjusting them shifts the system toward speed, savings, or quality without any retraining. CA-RAG is built on Facebook AI Similarity Search (FAISS) for document retrieval, OpenAI gpt-4o-mini for generation, and text-embedding-3-small for dense retrieval embeddings. We evaluate CA-RAG on a benchmark of 28 queries. The router assigns different strategies to different queries, achieving 26% fewer billed tokens compared to always using heavy retrieval and 34% lower response time compared to always answering without retrieval, while maintaining answer-quality parity in both cases. Further analysis shows that most savings come from simpler queries, where heavy retrieval was unnecessary. All results are reproducible from logged comma-separated value (CSV) files. CA-RAG demonstrates that a small but well-designed set of retrieval strategies combined with lightweight per-query routing can meaningfully reduce the cost and latency of LLM deployments without compromising answer quality. Full article

3D Gaussian Splatting (3DGS) offers efficient explicit rendering, but large-scale remote-sensing use remains fragmented across UAV/aerial photogrammetry, satellite reconstruction, large-scene scaling, surface modeling, and geospatial evaluation. We present a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-informed scoping review based on 55 core studies identified through Web of Science, Scopus, IEEE Xplore, and supplementary searches completed on 3 June 2026. A faceted taxonomy organizes the literature by platform, sensor model, scalability strategy, and geometric supervision. The synthesis shows that partitioning, hierarchy, compression, and feed-forward inference improve scalability but do not guarantee metric geometry. Reliable deployment additionally requires sensor-consistent projection, geometric or georeferencing constraints, explicit supervision labels, and product-level evaluation. In control-point-free settings, internal consistency should be distinguished from independently validated accuracy. We therefore propose a platform-aware benchmark framework that jointly records visual fidelity, computational cost, metric geometry, product utility, failure behavior, and reproducibility metadata for UAV/aerial, satellite, and hybrid settings. Full article

The growing global population has increased energy and food demand, leading to a higher production of waste streams such as fly ash from the energy sector and wastewater from food and beverage industries. Without proper treatment, these wastes pose significant environmental concerns. One promising strategy is to repurpose industrial byproducts for wastewater treatment. Winery wastewater, for instance, contains acidic organic compounds and alcohol that are difficult to remove using conventional methods, while large amounts of fly ash remain underutilized. This study, therefore, examines a hybrid system that combines fly ash-assisted Fenton oxidation with membrane filtration for winery wastewater treatment. The process involved sequential Fenton pre-treatment followed by lab-scale nanofiltration using a 1 kg/mol ceramic membrane (13.1 cm²). A Design of Experiments approach was applied to evaluate system performance under varying H₂O₂ dosages (10–30 mL/L), fly ash loadings (1–3 g/L), and membrane fluxes (40–80 LMH). Filtration was performed through multiple constant-flux cycles, with energy requirements ranging from 400 to 800 kWh/m³ for the flux variations calculated from the lab-scale pump operating at a constant power supply. The hybrid method showed strong performance, achieving 70% TOC removal and 90% reduction of color and iron. However, considerable membrane fouling was observed, likely due to increased retention and deposition of organic matter, iron, and fly ash during filtration. Full article

Musculoskeletal (MSK) disorders—osteoporosis, osteoarthritis, rheumatoid arthritis, intervertebral disc degeneration, tendinopathy, and skeletal muscle injury—contribute the largest share of years lived with disability worldwide. Conventional therapy relies on systemic dosing or repeated intra-articular and peri-tissue injections, which suffer from off-target toxicity, poor lesional bioavailability, and low adherence. Polymer microneedles (MNs)—micron-scale projections of biodegradable, dissolving, hydrogel-forming, or composite polymers—have rapidly matured into a versatile platform for minimally invasive, spatially localized, and temporally programmable delivery of small molecules, biologics, nucleic acids, extracellular vesicles, and cells to MSK tissues. This review synthesizes 2018–2026 advances in polymer MN systems engineered specifically for MSK regeneration. We classify dominant polymer chemistries and MN architectures; map fit-for-purpose across bone, cartilage, joint, intervertebral disc, tendon, and skeletal muscle; and survey “smart” MN designs that exploit reactive oxygen species, pH, mechanical, triboelectric, optogenetic, and ultrasonic triggers. We close with a concise conclusion and forward perspective that identifies the key design levers—hybrid MN–scaffold combination products, stimuli-responsive platforms tuned to the MSK micro-environment, and cell- and EV-loaded formats—most likely to have clinical impact. Full article

The scheduling of crude oil operations in marine refineries is a complex combinatorial problem, exacerbated by stochastic disruptions like vessel delays and port congestion. Traditional deterministic and expected-value approaches fail to mitigate high-impact tail events, causing severe demurrage and production bottlenecks. To address this, we propose a novel CVaR-Driven Reactive GRASP Simheuristic. This framework hybridizes GRASP with Monte Carlo simulation, embedding Conditional Value-at-Risk (CVaR) into the adaptive memory to actively steer the search away from catastrophic logistical gridlocks. Overcoming standard “unlimited port capacity” assumptions, the model endogenously calculates demurrage dynamics and introduces an automated Failure Taxonomy for explainable insights. Evaluated on a 30-day industrial case study, representing a standard short-term operational scheduling horizon, under baseline conditions and severe dynamic disruptions (vessel delays, unit maintenance), the diagnostic reveals that over 80% of scheduling failures stem from endogenous port congestion rather than internal dead-ends. Furthermore, a comprehensive ablation study mathematically validates the superiority of the CVaR-driven memory over standard expected-cost optimization in preventing catastrophic tail-risk scenarios. Results demonstrate that this CVaR-driven approach effectively absorbs stochastic shocks, prevents stockouts, and minimizes worst-case costs, generating highly robust schedules in under three minutes. Ultimately, it provides a robust, risk-aware Decision Support System (DSS) for supply chain and operations managers. Full article

The decarbonization of maritime and inland waterway transport requires implementation pathways that go beyond fuel substitution and address energy, water, safety, infrastructure, and lifecycle constraints. This study proposes a sustainable technical pathway for hydrogen implementation in small-scale maritime and inland waterway vessels, using Colombia as a territorial case study. The methodology integrates technological surveillance, national energy-transition assessment, sectoral and territorial analysis, hydrogen pathway selection, water-resource management, safety and regulatory review, lifecycle criteria, and progressive validation under Technology Readiness Level principles. The results identify compressed gaseous hydrogen combined with Proton Exchange Membrane fuel cells and hybrid battery support as the most feasible short-term configuration for small vessels due to its modularity, operational flexibility, and compatibility with decentralized applications. The framework also shows that hydrogen production must be designed as a coupled water–energy–hydrogen system, prioritizing treated wastewater, rainwater, desalinated water, or other non-potable sources to avoid pressure on community and agricultural water demand. Laboratory and prototype validation demonstrated a progressive route from didactic hydrogen systems to small-vessel maquettes and scaled prototypes. The proposed pathway provides an implementation-oriented framework for safe, sustainable, and territorially adapted hydrogen deployment in small maritime systems. Full article

Ratoon rice cultivation is a significant practice for enhancing land productivity and food security. Ratooning ability is a key determinant of ratoon season crop (RC) yield and is influenced by genetic, agronomic, and hormonal factors. This study aimed to evaluate the effects of foliar-applied ratooning enhancers, formulated with plant hormones and botanical extracts, on the growth and regeneration of a japonica–indica hybrid rice cultivar, ‘Qingxiangyou 19 Xiang’. Treatments included gibberellin (GA), low, medium, and high concentrations of brassinosteroid (BR), each with or without jasmine extract (JE), alongside proline and zinc chloride (ZnCl₂) as supporting components. These solutions were applied twice at 5 and 15 days after flowering (DAF) of the main crop (MC). The results showed that GA treatment increased plant height and panicle length but reduced MC tiller number. BR treatments did not affect plant height but significantly increased the 1000-grain weight. Crucially, while BR alone had no significant effect on ratooning ability, the BR-JE combined application, particularly at medium (MBR-JE) and high (HBR-JE) concentrations, significantly increased ratoon tiller number and enhanced ratooning ability. However, the HBR-JE combination increased grain chalkiness. In conclusion, the foliar application of BR combined with JE during the flowering stage effectively promotes ratooning ability without compromising MC yield, offering a promising agronomic strategy for sustainable ratoon rice production. Full article

Weeds remain one of the primary constraints on crop productivity, making accurate detection and spatial localization essential for precision weeding systems. Over the past decades, weed detection has evolved from traditional feature-based image processing to deep learning-driven visual recognition, substantially improving detection accuracy under controlled and semi-controlled conditions. However, most existing approaches still follow a weed-centric paradigm in which models are trained to explicitly recognize diverse weed species or weed classes. Such strategies face persistent limitations caused by extreme weed morphological variability, crop-weed similarity, high annotation cost, and spatial-temporal heterogeneity across fields, seasons, and cropping systems. This review therefore reframes weed detection as a broader transition from feature-based vision and direct weed recognition toward crop-guided, context-aware, and decision-oriented intelligence. Specifically, we synthesize the literature from three perspectives: (i) methodological evolution, including handcrafted features, machine learning, deep learning, segmentation, and multimodal sensing; (ii) paradigm transformation, from weed-centric detection to crop-guided inference based on crop structure, crop rows, and non-crop vegetation; and (iii) deployment-oriented integration, including edge devices, latency-accuracy-energy trade-offs, and robotic actuation. We further summarize representative public datasets, method categories, crop-guided studies, and edge-platform reporting requirements. Finally, we outline a decision-aware hybrid framework in which crop-guided perception provides low-latency weed localization, while species-level recognition is conditionally activated when required by herbicide selection, resistance management, or high-risk weed control. This synthesis clarifies both the value and the limitations of crop-guided weed detection and outlines actionable directions for scalable, robust, and field-deployable intelligent weeding systems. Full article

Data has become a strategic production factor, but the institutional logic underlying data’s tradability, priceability, and governability remains insufficiently theorized. In response, this study develops a coevolutionary framework that connects conventional factor market theory with digital political economy, platform theory, and comparative institutional analysis. This study adopts a conceptual–analytical research design, integrating three research methods: theory synthesis, comparative institutional analysis, and policy-process interpretation. Through theoretical synthesis, institutional comparison, and policy-process interpretation, it analyzes the conditions under which data circulation becomes feasible, lawful, and economically sustainable. In addition, by combining transaction data, exchange listings, property rights registrations, network indicators, and regional policy variations, it formulates testable propositions and an empirical agenda. The study finds that data factor markets do not emerge automatically with digitalization; their formation requires three mutually reinforcing conditions: technologically reducing search, verification, privacy protection, and contract enforcement costs; institutionally realizing a modular definition of rights and establishing compliance boundaries; and market demand from firms, public agencies, and research organizations generating use-case-specific value. Meanwhile, this study revises the three-stage model of market evolution as a contingent and testable pathway—from administrative pilot allocation, through hybrid state–market professionalization, to ecosystem-based cross-domain circulation. It also clarifies a closed-loop dynamic mechanism consisting of external shocks, internal strategic feedback, and adaptive governance, which jointly shapes market boundaries, pricing rules, and competition patterns. Full article