Search Results (8,328)

Growing pressure on electricity grids and the increasing availability of smart meter data have intensified the need for accurate, interpretable, and scalable methods to analyze and forecast household electricity consumption. In this context, this study presents a general, data-agnostic methodology for predicting and classifying household energy consumption. The proposed workflow unifies data preparation, feature engineering, and machine learning techniques (including clustering, classification, regression, and time series forecasting) within a single interpretable pipeline that supports actionable insights. Rather than proposing new prediction algorithms, this work contributes a fully reproducible, end-to-end methodological pipeline that enables the controlled evaluation of the impact of contextual variables, customer segmentation, and cold-start conditions on household energy forecasting. A distinctive aspect of the pipeline is the explicit use of household- and dwelling-level contextual variables to derive customer typologies via clustering and to enrich forecasting models. The models are evaluated for predictive accuracy, reliability under varying conditions, and suitability for operational use. The results show that incorporating contextual variables and clustering significantly improves forecasting accuracy, particularly in cold-start scenarios where no historical consumption data are available. Although numerous public datasets of residential electricity consumption exist, they rarely provide, in an openly accessible form, both detailed load histories and rich contextual attributes, while many are subject to privacy or licensing restrictions. To ensure full reproducibility and to enable controlled experiments where contextual variables can be switched on and off, the experiments are conducted on a synthetically generated dataset that reproduces realistic behavior and seasonal usage patterns. However, the proposed methodology is independent of the specific data source and can be directly applied to any real or synthetic dataset with similar structure. The approach enables applications such as short- and long-term demand forecasting, estimation of household energy costs, and forecasting demand for new customers. These findings demonstrate that the proposed pipeline provides a transparent and effective framework for end-to-end analysis of household electricity consumption. Full article

A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and deployment challenges arising when applying existing AI techniques to safety-critical industrial environments with legacy PLC/SCADA infrastructure and real-time constraints. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions—purposively selected from 24 runs monitored during a six-month deployment—span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results in the evaluated CIP deployment show high time within specification in sanitizing stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency ${\Gamma }_s\ge 0.98$), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, though empirical validation in other process types remains future work. The contribution lies in demonstrating how to bridge the gap between AI theory and industrial practice through careful system architecture, data transformation pipelines, and integration patterns that enable reliable AI-enhanced decision support in production environments, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring. Full article

Targeting the thinly bedded and strongly heterogeneous tight sandstone gas reservoirs of the Shaximiao Formation in the Yingshan area of the Sichuan Basin, this study establishes an integrated workflow that combines high-fidelity 3D seismic processing with quantitative interpretation to address key challenges such as insufficient resolution of conventional seismic data under complex near-surface conditions and difficulty in depicting sand-body geometries. On the processing side, a 2D-3D integrated amplitude-preserving high-resolution strategy is applied. In contrast to conventional workflows that treat 2D and 3D datasets independently and often sacrifice true-amplitude characteristics during static correction and noise suppression, the proposed approach unifies first-break picking and static-correction parameters across 2D and 3D data while preserving relative amplitude fidelity. Techniques such as true-surface velocity modeling, coherent-noise suppression, and wavelet compression are introduced. As a result, the effective frequency bandwidth of the newly processed data is broadened by approximately 10–16 Hz relative to the legacy dataset, and the imaging of small faults and narrow river-channel boundaries is significantly enhanced. On the interpretation side, ten sublayers within the first member of the Shaximiao Formation are correlated with high precision, yielding the identification of 41 fourth-order local structural units and 122 stratigraphic traps. Through seismic forward modeling and attribute optimization, a set of sensitive attributes suitable for thin-sandstone detection is established. These attributes enable fine-scale characterization of sand-body distributions within the shallow-water delta system, where fluvial control is pronounced, leading to the identification of 364 multi-phase superimposed channels. Based on attribute fusion, rock-physics-constrained inversion, and integrated hydrocarbon-indicator analysis, 147 favorable “sweet spots” are predicted, and six well locations are proposed. The study builds a reservoir-forming model of “deep hydrocarbon generation–upward migration, fault-controlled charging, structural trapping, and microfacies-controlled enrichment,” achieving high-fidelity imaging and quantitative prediction of tight sandstone reservoirs in the Shaximiao Formation. The results provide robust technical support for favorable-zone evaluation and subsequent exploration deployment in the Yingshan area. Full article

Background/Objectives: Alzheimer’s disease (AD) exhibits marked genetic heterogeneity between early-onset (EOAD) and late-onset (LOAD) forms. EOAD is typically associated with highly penetrant variants, whereas LOAD follows a polygenic architecture dominated by non-coding variation. However, the tissue-specific regulatory consequences of these variants remain insufficiently characterized. This study aimed to compare the regulatory genomic architectures underlying EOAD and LOAD using a multi-tissue integrative approach. Methods: GWAS-associated variants for EOAD and LOAD were retrieved from the GWAS Catalog using a relaxed significance threshold (p < 1 × 10⁻⁵). Variants were functionally annotated and integrated with GTEx v8 eQTL data across 13 neurologically relevant tissues and peripheral blood. Regulatory effects were evaluated using eQTL slope estimates. Basal gene expression patterns were assessed using GTEx RNA-seq data, and protein–protein interaction and functional enrichment analyses were performed using the STRING database. Results: A total of 287 variants were analyzed (32 EOAD, 255 LOAD), with minimal overlap. EOAD exhibited a highly focal regulatory profile, identifying GSE1 as the sole eQTL-regulated gene, restricted to the dorsolateral prefrontal cortex (BA9). In contrast, LOAD displayed a broad multi-tissue regulatory architecture involving APH1B, APOE, CEP63, and HAVCR2, with heterogeneous tissue-specific effects. LOAD-regulated genes converged on pathways related to γ-secretase activity, amyloid precursor protein processing, and Notch signaling, whereas GSE1-associated interactions were enriched for chromatin organization and epigenetic repression. Conclusions: EOAD and LOAD exhibit distinct regulatory genomic architectures, with EOAD characterized by focal, region-specific regulation and LOAD by widespread, tissue-dependent effects, highlighting stage-specific molecular mechanisms contributing to AD heterogeneity. Full article

The northern segment of the Great Xing’an Range, northeastern China, hosts a previously unrecognized near-E–W-trending rhyolite belt in the Tulihe area. We conducted systematic geochronological and geochemical investigations to constrain its formation age, petrogenesis, and regional tectonic significance. Field investigation, petrographic observation, and zircon laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) U–Pb dating indicate that the rhyolite belt was formed during the Early Cretaceous, with emplacement ages directly determined from three samples ranging from 143.8 to 131.5 Ma. Geochemically, the rhyolites yielded high SiO₂ contents (74.44–75.88 wt.%), high total alkalis (K₂O + Na₂O = 8.50–8.99 wt.%), and low MgO contents (0.16–0.55 wt.%). They displayed strong enrichment in light rare earth elements and depletion in high field strength elements, weakly negative Eu anomalies, A/CNK ratios near unity, and relatively high Nb/Ta ratios. Trace element signatures and incompatible element abundances (Zr + Nb + Ce + Y = 193.2–338.3 × 10⁻⁶) are mostly consistent with highly fractionated I-type volcanic rocks, rather than S-type or M-type affinities. The geochemical data suggest that the rhyolites were mainly generated by partial melting of a medium- to high-K basaltic lower crust, with minor crustal assimilation and limited mantle input. Tectonically, Early Cretaceous magmatism in the northern Great Xing’an Range was governed by flat-slab subduction and subsequent rollback of the Paleo-Pacific (Izanagi) plate, while the local E–W-trending rhyolite belt was controlled by pre-existing faults, reflecting localized post-orogenic extension consistent with regional NE-trending volcanic belts. The northwest-to-southeast younging trend records asthenospheric upwelling and enhanced crust–mantle interaction induced by slab rollback. These results highlight the petrogenetic and tectonic evolution of medium- to high-K magmatism along the NE Asian continental margin and improve our understanding of Mesozoic volcanism in the Great Xing’an Range. Full article

Accurate patent valuation remains a persistent challenge in intellectual property management, particularly in the food industry, where technological homogeneity and rapid innovation cycles introduce substantial noise into observable performance indicators. Traditional valuation approaches, whether based on subjective expert judgment or citation-based metrics, often struggle to effectively reduce information uncertainty in this context. To address this limitation, this study proposes an objective, data-driven patent valuation framework grounded in information theory. We construct a multidimensional evaluation system comprising nine indicators across technological, legal, and economic dimensions and apply it to a large-scale dataset of 100,648 invention patents. To address the heavy-tailed nature of patent indicators without sacrificing the information contained in high-impact outliers, we introduce a square-root transformation strategy that stabilizes dispersion while preserving ordinal relationships. Indicator weights are determined objectively via Shannon entropy, capturing the relative scarcity and discriminatory information content of each signal, after which comprehensive value scores are derived using the TOPSIS method. Empirical results reveal that the entropy-based model assigns dominant weights to so-called “costly signals”, specifically PCT applications (29.53%) and patent transfers (24.36%). Statistical correlation analysis confirms that these selected indicators are significantly associated with patent value ($p<0.001$), while bootstrapping tests demonstrate the robustness of the resulting weight structure. The model’s validity is further evaluated using an external benchmark (“ground truth”) dataset comprising 55 patents recognized by the China Patent Award. The proposed framework demonstrates substantially stronger discriminatory capability than baseline methods, awarded patents achieve an average score 2.64 times higher than that of ordinary patents, and the enrichment factor for award-winning patents within the Top-100 ranking reaches 91.5. Additional robustness analyses, including benchmarking against the Weighted Sum Model (WSM), further confirm the methodological stability of the framework, with sensitivity analysis revealing an exceptional enrichment factor of 183.1 for the Top-50 patents. These findings confirm that the Entropy–TOPSIS framework functions as an effective information-filtering mechanism, amplifying high-value patent signals in noise-intensive environments. Consequently, the proposed model serves as a generalizable and theoretically grounded tool for objective patent valuation, with particular relevance to industries characterized by heavy-tailed data and high information uncertainty. Full article

Uncertain regression analysis is a powerful tool for analyzing and interpreting the complex relationships between explanatory and response variables under uncertain environments, and a crucial step in analyzing datasets containing complex uncertainties is statistical inference based on uncertain parameter estimation methods. However, the existing parameter estimation studies of uncertain regression models all fail to effectively avoid the negative impact of outliers on the estimation results. To solve the above problem and further enrich the parameter estimation research, this paper constructs a symmetric statistical invariant for the uncertain regression model based on observed data and uncertain disturbance terms. Based on this statistical invariant, the least absolute deviation criterion is applied to propose a least absolute deviation estimation for the uncertain regression model. Finally, two numerical examples are provided to illustrate the advantages of the proposed method compared to existing methods, and the comparative results show that in certain scenarios, the least absolute deviation estimation method exhibits superior performance compared to other existing methods in terms of mean squared error, mean absolute error, and mean absolute percentage error. Furthermore, as a byproduct of this paper, the proposed method is applied to sports statistics, and two empirical cases are also provided to demonstrate the effectiveness of this application. Full article

The southeastern Iberian Peninsula, particularly the Águilas Arc within the Neogene Volcanic Province (NVP), represents a promising geothermal domain with complex tectonics and geology. The Palomares Fault Zone (PFZ), a key shear structure initiated during the Late Miocene, acts as a conduit for fluid migration, promoting mineralization and potential anomalies of rare and critical metals through fluid–rock interaction. This study investigates such interactions in the southernmost Águilas Arc, focusing on the El Arteal fault segment within the eastern PFZ strand. Mineralogical, geochemical, and hydrogeological analyses were performed using XRD, SEM, and ICP-MS techniques. Results reveal six mineral assemblages (MA) within the fault segment where the fault gouge samples were characterized by cataclastic textures and the occurrence of authigenic minerals, including halite, kaolinite, illite, paragonite, goethite, hematite, gypsum, barite, celestine, and quartz. Geochemical data indicate enrichment signatures in large-ion lithophile elements (LILE) and minor chalcophile and light rare-earth elements (LREE). Two thermal hydrofacies with alkaline metals enrichment were identified in wells and mine shafts: (1) Na⁺SO₄²⁻ and (2) Na⁺Cl⁻, where the latter exhibits high Na⁺ and Cl⁻ concentrations toward deeper sectors. These findings suggest multiple stages of fluid–rock interaction controlled by temperature: an early phase dominated by epithermal mineralization, followed by late-stage circulation of hypersaline fluids. This evolution provides an abnormal geochemical signature that is unique in the Aguilas Arc Geothermal System. Full article

Neoadjuvant chemoimmunotherapy has emerged as a promising treatment strategy for head and neck squamous cell carcinoma (HNSCC). There is an urgent need to improve patient responses to this approach. In this study, we aim to elucidate the mechanisms underlying poor response to neoadjuvant chemoimmunotherapy and to identify strategies to enhance therapeutic efficacy in HNSCC. We identified a cancer stem-like cell (CSC) population enriched in patients with partial response (PR) to neoadjuvant chemoimmunotherapy, characterized by high CD80 expression. CD80 was likewise highly expressed in ALDH^highCD44⁺ and BMI1⁺ populations. Functionally, CD80 knockdown attenuated tumor-sphere-forming capacity and reduced the migration and invasion of tumor cells, whereas CD80 overexpression potentiated these pro-tumorigenic activities. Moreover, CD80 inhibition activated signaling pathways of Th1 immune responses and IL-2 production. CD80 blockade enhanced T cell cytotoxicity. In preclinical HNSCC models, inhibition of CD80 significantly decreased tumor burden, accumulated CD8⁺ T cells, and increased the production of cytotoxic effector molecules. Our data demonstrated that CD80 modulated tumor-cell stemness and malignant phenotype while restraining antitumor T cell immunity. Targeting CD80 augments antitumor immunity and provides a compelling strategy to enhance treatment responses to neoadjuvant chemoimmunotherapy in HNSCC. Full article

In recent years, the operational performance of agricultural enterprises has been influenced by both natural conditions and market environments, resulting in high uncertainty and volatility. When performance falls below expectations, agricultural enterprises consciously engage in strategic change and proactive risk-taking to alleviate performance pressures. Based on Firm Behavioral Theory, Performance Feedback Theory, and Prospect Theory, we examine how performance expectation gap affects risk-taking of agricultural enterprises by using panel data of Chinese A-share listed agricultural firms from 2007 to 2023. The results show that performance expectation gap has a positive effect on risk-taking, which means the greater the gap, the higher the level of risk-taking. And the better developed the institutional environment, the greater the tendency for risk-taking. Further analysis shows that performance expectation gap promotes risk-taking by driving strategic change within agricultural enterprises. This research enriches the study on the influencing factors of risk-taking in agricultural enterprises, offering decision-making insights for them to prudently assess and manage risks. Full article

Background: Sheep (Ovis aries) tail fat serves as a crucial energy reserve for adapting to harsh environments. However, excessive deposition can reduce farming efficiency and product quality. Elucidating the regulatory mechanisms of tail fat deposition is of great significance for genetic improvement in sheep. Methods: In this study, transcriptome sequencing was conducted on tail fat tissues from fat-tailed Kazakh sheep (KAZ), thin-tailed Suffolk sheep (SFK), and their F2 hybrid sheep (CSH) (3 individuals per group). Subsequently, qRT-PCR validation, Enrichr, and KEGG database analyses were performed to investigate the molecular pathways involved in tail fat deposition. Results: High-quality clean reads were obtained from sequencing, with a genome alignment rate ranging from 76.15% to 79.43% and good data reproducibility. Differential expression analysis revealed multiple differentially expressed genes (DEGs) between KAZ and CSH groups, KAZ and SFK groups, as well as SFK and CSH groups. Five core candidate genes (BDH1, EPHX1, BCAT2, FASN, ACACA) were identified, all enriched in the fatty acid synthesis pathway and highly expressed in fat-tailed sheep, which was confirmed by qRT-PCR. Additionally, 189 lncRNAs were identified to collectively regulate target genes (e.g., FABP family, AGPAT2), along with three common differentially expressed miRNAs (novel_120, novel_171, novel_440) targeting genes enriched in lipid transport and lipid droplet formation pathways. Conclusions: This study confirms that the lncRNA-mRNA-miRNA regulatory axis is a key pathway in tail fat formation, providing important theoretical support and molecular targets for genetic improvement of ovine tail fat deposition traits. Full article

Maximizing the agricultural output on inherently infertile land and minimizing the environmental cost remain central research imperatives. Albic black soil typifies such infertility. Conventional practice relies on fertilization and straw incorporation, but the albic layer’s impermeability funnels applied nutrients into adjacent aquatic systems. Therefore, this study developed deep placement fertilization by lodging fertilizer directly within the albic layer to block hydrologic loss. The feasibility of mechanization was first validated in pot experiments. Soybeans were allocated to six treatments simulating fertilizer placement at different soil depths: control (C), control and fertilizer (CF), surface soil mixing (SM), surface soil mixing and fertilizer (SMF), plow pan soil mixing (PM), and plow pan soil mixing and fertilizer (PMF). The treatments used 20 cm tillage, and the data were collected after 15, 25, and 35 days and at harvest. Integrative transcriptomic, proteomic, metabolomic, and soil microbiome profiling revealed that fertilizer positioned at 25 cm in the albic layer increased yield, restructured the rhizobiont community and promoted arbuscular mycorrhizal fungal colonization. Among the fertilizer treatments, CF had the best growth, and SMF was inhibited by a nutrient shortage. SMF and PMF lost water faster than CF. Abscisic acid (ABA) conveyed the subterranean fertilization signal to the leaf. The enrichment of Vicinamibacterales, Xanthobacteraceae, and Glomeromycota in soil lowered the ABA content in the roots, which upregulated thymidine kinase and peroxidase upon arrival in the leaf, increasing yield. These findings provide a transferable benchmark for any parent material exhibiting poor hydraulic conductivity. Full article

Although Micro, Small and Medium Enterprises (MSMEs) are the backbone of economic activity and inclusive growth in Indonesia, and recent data from Jambi Province reveal a disconnect between robust post-pandemic recovery and meaningful poverty reduction. While regional GDP climbed from 0.99% to 6% between 2020 and 2024, poverty declined only slightly, highlighting persistent inequality. This study addresses this gap by examining, for the first time in the context of Jambi Province, how e-commerce adoption mediates the link between Micro, Small and Medium Enterprises’ (MSMEs’) quality and the achievement of economic growth, innovation, and Sustainable Development Goals (SDGs) 1 and 9. Using Structural Equation Modeling–Partial Least Squares (SEM-PLS) on data from 250 Micro, Small and Medium Enterprises (MSMEs), the findings reveal that improvements in Micro, Small and Medium Enterprises’ (MSMEs’) quality alone do not drive growth or reduce poverty unless they are accompanied by the effective adoption of e-commerce. This integrated approach, combining Micro, Small and Medium Enterprises’ (MSMEs’) capacity, digital transformation and regional Sustainable Development Goal outcomes, offers new empirical evidence and practical recommendations for emerging economies. Despite a sectoral and regional focus, the framework and results are generalizable to similar contexts. Future research should expand into additional sectors and regions, and adopt longitudinal analysis to validate and enrich these findings. Full article

Colorectal cancer (CRC) is one of the leading causes of mortality worldwide, with rising cases in individuals under 50 years old, classified as early-onset CRC (EO-CRC). EO-CRC is characterized by having clinical features related to a worse prognosis and outcome. This underscores the critical need for early detection biomarkers. ncRNAs emerge as potential biomarkers for diagnosis, prognosis, and treatment response in other types of cancers. Sequencing data from the NCBI Bioproject PRJNA787417 were analyzed to identify differentially expressed miRNAs in early- and late-onset colorectal cancer (EO-CRC and LO-CRC). Differential expressions were assessed with a log fold change threshold of 1 and an adjusted p-value of 0.05. Predicted mRNA targets were identified via ENCORI and analyzed for pathway enrichment using the SHINYGO algorithm. RNA-seq analysis identified a 25-ncRNA EO-CRC signature, including hsa-miR-195 (downregulated) and hsa-miR-549a (upregulated), with enrichment analyses suggesting associations with MAPK, PI3K, VEGF, and KRAS pathways commonly linked to angiogenesis, migration, and invasion. This preliminary report highlights a 25-gene deregulated signature in EO-CRC, in which hsa-miR-195 and hsa-miR-549a emerge as biomarkers of clinical relevance, regulating key genes involved in angiogenesis, migration, and invasion. Their dysregulation could contribute to the aggressive clinical features and poor outcomes observed in EO-CRC. Full article

Background: Commercial feed formulations are increasingly being evaluated for their nutritional impacts on aquaculture species, yet the molecular consequences of commonly used commercial diets remain underexplored. Methods: This study investigated the effects of two commercial diets, diet A (higher land animal protein) and diet B (higher fish meal content), on the protein profile in the brain, liver, and intestine of barramundi (Lates calcarifer). A 12-week feeding trial was conducted with controlled water quality, and proteomic profiling was performed using data-independent acquisition. Results: Differential analysis revealed consistent changes between diets across all tissues, with a higher percentage of differentially abundant proteins observed in between-diet comparisons (12.99% in brain, 12.73% in liver, and 16.59% in intestine) than within-diet controls (<8%), confirming a measurable dietary effect size. In total, 3901 proteins in the brain, 3660 in the liver, and 5025 in the intestine were quantified. Functional enrichment highlighted upregulation of ferroptosis pathways, downregulation of apelin signaling in the brain, and increased digestive proteases in the liver. ICP-MS confirmed elevated iron concentrations in the brain, liver, and intestine of fish fed on diet B. Conclusions: These findings demonstrate that molecular pathways linked to iron metabolism, digestion, and growth regulation are very sensitive to dietary composition, highlighting how proteomics can help identify subtle impacts of compositional differences in aquaculture feeding. Although physiological parameters did not differ significantly, the proteomic alterations observed across tissues likely indicate organ-specific metabolic adaptations to the differing nutrient availability between diets. Full article