Search Results (677)

Near-infrared spectroscopy (NIRS) was applied to measure the moisture content (MC) of newly fallen deciduous leaves from four broadleaf tree species, namely Zelkova serrata (Keyaki), Quercus serrata (Konara), Quercus acutissima (Kunugi), and Fagus crenata (Buna), collected in 2016 and 2017. Leaves were sampled immediately after abscission, an ecologically important condition that has received direct measurement, providing initial MC data for litter decomposition research. Two multivariate calibration approaches were compared, partial least squares regression (PLSR) and random forest regression (RFR), each applied with and without spectral preprocessing (moving average, multiplicative scatter correction, second-order Norris Gap derivative, and mean centring), and both evaluated by leave-one-out cross-validation (LOOCV) for direct comparability. PLSR with preprocessing achieved R²cv = 0.75–0.86 and an RPD (the ratio of performance to deviation) = 2.01–2.70 across species and years; RFR without preprocessing yielded R²cv = 0.47–0.75 (RPD = 1.39–2.03), while RFR with preprocessing improved markedly to R²cv = 0.78–0.88 (RPD = 2.13–2.94). Wavelength importance, assessed by variable importance in projection (VIP) for PLSR under both preprocessing conditions, showed that preprocessed models concentrated importance sharply in the water combination band near 1900 nm, whereas non-preprocessed models distributed importance more broadly but still situated their single strongest wavelength within this same band in six of seven datasets. RFR feature importance shifted from the water absorption region (without preprocessing) toward the cellulose/lignin band near 2100–2300 nm (with preprocessing); a model-independent correlation analysis indicated that this shift corresponds, in most datasets, to a genuine change in the underlying MC–spectrum relationship rather than solely reflecting how RFR handles collinear wavelengths. Independent, bidirectional year-to-year prediction (2016-calibrated models tested on 2017 data, and vice versa) showed that the high within-year accuracy summarised above did not reliably transfer across years—most severely for Z. serrata, for which cross-year prediction failed in both directions—indicating that annual recalibration is advisable for operational use of this method. Full article

Industry 4.0 depends on the ability to connect heterogeneous systems, yet students and practitioners rarely have a low-risk environment in which to practice this kind of systems integration. This article presents a virtual-prototyping architecture developed and tested, linking a discrete-event simulation tool with external machine learning models through industrial communication protocols. The resulting artifacts and method are a contribution to systems engineering education, practice, and development. A two-phase empirical virtual-prototyping approach was used. First, Open Platform Communications Unified Architecture and Message Queuing Telemetry Transport were prototyped and compared as communication layers between Siemens Tecnomatix Plant Simulation and Python-based machine learning clients. Second, for this project, the more suitable protocol was applied to three increasingly complex use cases, addressing automated guided vehicle capacity, conveyor speed control, and process bottleneck identification. The use cases were assessed against the Technology Readiness Level scale. Open Platform Communications Unified Architecture provided reliable, real-time, bidirectional data exchange, while Message Queuing Telemetry Transport proved less stable for this application. The three use cases each demonstrated feasible simulation-machine learning integration, and the overall prototype reached Technology Readiness Level 4. Beyond its contribution to I4.0 practice, the staged research design, the use of Technology Readiness Levels as a maturity and reflection instrument, and the low-cost, risk-free nature of virtual prototyping constitute a transferable pedagogical pattern for systems engineering curricula, capstone projects, and competency-based training. Full article

The gut–skin axis is a bidirectional communication network linking the gastrointestinal tract, skin, microbiota, and immune system. As major barrier organs, the gut and skin harbor complex microbial communities that contribute to tissue homeostasis, immune regulation, and protection from environmental insults. Increasing evidence indicates that dysbiosis of the gut and skin microbiota is associated with inflammatory diseases through interconnected microbial, metabolic, and immune pathways. Clinical observations further reveal strong associations between gastrointestinal disorders, including inflammatory bowel disease (IBD) and celiac disease, and cutaneous manifestations. Atopic dermatitis (AD) is characterized by epithelial barrier dysfunction, immune dysregulation, microbial imbalance, and environmental influences. Patients with AD frequently exhibit reduced gut microbial diversity, depletion of beneficial short-chain fatty acid (SCFA)-producing bacteria, and enrichment of potentially pathogenic microorganisms. Cutaneous dysbiosis, particularly expansion of Staphylococcus aureus, can further impair the skin barrier and sustain inflammation. Microbial metabolites, including SCFAs, tryptophan-derived aryl hydrocarbon receptor ligands, and bile acid metabolites, may mediate gut–skin communication by regulating epithelial integrity, immune tolerance, and inflammatory signaling. IBD is also increasingly recognized as a systemic disorder involving alterations in skin microbiota and cutaneous immunity. Intestinal inflammation may disrupt immune tolerance to skin commensals and promote cutaneous inflammation through cytokine signaling and immune-cell trafficking, while emerging evidence suggests reciprocal skin-to-gut effects. Epidemiologic, genetic, and clinical studies indicate an association between AD and IBD, potentially reflecting shared genetic susceptibility, barrier dysfunction, dysbiosis, and immune pathways. This review summarizes current evidence linking the gut–skin axis to AD and IBD. Full article

Large-scale photovoltaic (PV) power plants are expanding rapidly across arid desert regions, driving land-cover transformations and associated changes in surface properties. Most existing remote sensing studies focus primarily on mapping PV distribution, while quantitative assessments of PV-induced land-cover transitions and associated surface property changes remain limited. This study develops an integrated assessment framework that combines time-series Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) prior parameters. Three semantic segmentation models are compared for PV extraction, with independent generalization validation conducted over desert areas in Xinjiang. Constrained by coarse-resolution BRDF products, 10 m broadband white-sky albedo (WSA) is retrieved. The results show that SegFormer outperforms the other two models for PV identification. From 2021 to 2025, the PV-covered area of the Talatan region expanded from 160.35 km² to 303.26 km². For the newly converted PV area, surface albedo decreased by 0.0391 and 0.0310 during 2021–2023 and 2023–2025, respectively, corresponding to local albedo-driven shortwave energy changes of 27.12 W·m⁻² and 23.55 W·m⁻². Consistent variation patterns are observed in the Xinjiang validation site. This study offers an integrated framework for characterizing PV expansion-induced land-cover changes and associated surface property variations, while providing an observation-based assessment of local shortwave energy variations related to surface albedo changes in arid regions. Full article

Accurate punctuation and capitalization are essential for the readability, interpretability, and structural coherence of machine-generated text. Their absence is particularly problematic in automatic speech recognition outputs and other forms of unstructured text, where missing punctuation and incorrect capitalization reduce both human readability and the effectiveness of downstream natural language processing tasks. This study proposes a hybrid XLM-RoBERTa–BiLSTM model for joint punctuation restoration and text capitalization on English-language data. The proposed architecture combines contextual representations produced by the multilingual pre-trained XLM-RoBERTa encoder with the sequential modeling capabilities of a bidirectional long short-term memory layer, followed by token-level classification in a unified label space. The model was trained and evaluated on a dataset derived from the IWSLT 2012 TED Talks corpus. Experimental evaluation on a held-out random test subset demonstrates strong performance. Excluding the dominant no-punctuation class, the model achieves an accuracy of 0.929, precision of 0.892, recall of 0.914, and an F1-score of 0.903. Including the dominant no-punctuation class increases these values to 0.961, 0.927, 0.919, and 0.923, respectively, reflecting the pronounced class imbalance in the dataset. Class-wise analysis shows high effectiveness for frequent punctuation classes and reliable capitalization prediction, whereas rare punctuation–capitalization categories remain more challenging because of their limited representation in the training and test subsets. Overall, the proposed hybrid XLM-RoBERTa–BiLSTM architecture achieves strong performance in joint punctuation restoration and text capitalization and represents an effective approach to improving the readability and structural quality of automatic speech recognition transcripts and other machine-generated text. Full article

Geothermal energy is a critical low-carbon resource in the global carbon neutrality transition, but expanding exploitation has raised growing concerns over geothermal fluid quality degradation and fluoride-related public health risks in the tectonically active coastal region of Guangdong, South China. However, the hydrochemical mechanisms governing fluoride enrichment remain poorly constrained, and conventional qualitative analytical approaches cannot quantitatively disentangle the superimposed effects of multiple subsurface geochemical processes. Based on 20 geothermal groundwater samples, this study integrates hydrochemical characterization, stable hydrogen and oxygen isotope tracing, and positive matrix factorization (PMF) modeling to quantitatively identify dominant hydrochemical processes and decipher the genetic mechanism of fluoride enrichment. The results demonstrate that the geothermal groundwaters belong to Cl–Na hydrochemical facies, with temperatures ranging from 60 °C to 96 °C and total dissolved solids (TDSs) varying from 560 mg/L to 9862 mg/L. Water–rock interaction dominates hydrochemical evolution: congruent dissolution of halite and other evaporite minerals serves as the primary source of bulk salinity, while bidirectional cation exchange on clay mineral surfaces substantially modifies the ionic assemblage. Stable isotope compositions (δD: −47.2‰ to −39.0‰; δ¹⁸O: −7.3‰ to −5.2‰) confirm a dominant meteoric recharge origin, with notable positive ¹⁸O shifts in multiple samples reflecting prolonged deep water–rock interaction with silicate host rocks. Recharge elevations are estimated at 239~682 m, delineating the northwestern medium–low mountain zone as the primary recharge area. Fluoride concentrations (2~13 mg/L) universally exceed the drinking water standard, and their enrichment is governed by a coupled geochemical feedback mechanism: hydrolytic weathering of fluor-bearing silicates releases structural fluoride, while widespread calcite precipitation scavenges aqueous Ca²⁺, weakens the common-ion effect, and promotes fluorite dissolution. The PMF model quantitatively resolves three geochemically meaningful controlling factors with clear process implications. These findings advance the mechanistic understanding of fluoride geochemistry in coastal granitic geothermal systems within the western Pacific tectonic belt, and provide a robust scientific basis for sustainable geothermal resource development and public health risk management. Full article

Trimethylamine N-oxide (TMAO), a gut microbiota-derived metabolite of dietary choline and L-carnitine, has emerged as a leading molecular mediator of the gut–heart–kidney axis in heart failure (HF). This state-of-the-art narrative review synthesizes evidence from 14 observational studies (13 prospective cohorts and one cross-sectional analysis), encompassing a heterogeneous range of HF settings, including established chronic HF, acute decompensated HF, incident HF in community cohorts, and subclinical myocardial injury. Across these populations, elevated circulating TMAO has been associated with adverse outcomes, including mortality, rehospitalization, and major adverse cardiovascular events, although the strength and consistency of associations vary by HF phenotype, renal function status, and population ancestry. The prognostic independence of TMAO is attenuated after adjusting for renal function in several cohorts, reflecting both obligatory renal clearance and a potentially bidirectional relationship with kidney injury. TMAO was not reduced during neurohormonal GDMT uptitration in the BIOSTAT-CHF study, suggesting that gut microbiota dysbiosis may represent a pathophysiological axis not reached by current HF treatment. The field has evolved through three thematic stages: TMAO as a single prognostic biomarker, TMAO within cardiorenal pathophysiology, and multimetabolite and multipathway risk profiling. Beyond the TMAO pathway, emerging evidence for phenylacetylglutamine (PAGln), short-chain fatty acids (SCFAs), and protein-bound uremic toxins as parallel gut-derived cardiovascular mediators supports a multidimensional metabolite profiling approach. Several cohorts suggest that selected multimetabolite panels may provide additional prognostic information beyond TMAO alone, although their composition, calibration, and external validity remain uncertain. Population-specific variation in TMAO levels and prognostic thresholds complicates universal clinical application. Without human interventional data demonstrating that lowering TMAO improves cardiovascular outcomes, TMAO remains a prognostic risk marker rather than a validated clinical target. Clinical translation requires resolving the renal confounding problem, validating multimetabolite panels across diverse populations, and conducting intervention trials targeting the gut–heart–kidney axis. Full article

Background: The gut microbiota–brain axis is a highly integrated bidirectional communication network operating through neural, neuroendocrine, immune, and metabolic pathways that maintain central nervous system homeostasis and has emerged as a promising complementary therapeutic target for neurological and neuropsychiatric disorders. Objective: The objective of this study is to systematically evaluate the effects of prebiotics, probiotics, and psychobiotics on gut–brain communication and neurological and behavioral outcomes, including neuroinflammatory markers, HPA-axis parameters, and microbial metabolites. Methods: A systematic review was conducted in accordance with the PRISMA 2020 guidelines. Literature searches of PubMed, Scopus, and Google Scholar from 2020 up to March 2026 identified 81 eligible studies (48 preclinical studies, 27 randomized controlled trials, and 6 quasi-experimental clinical studies) from 7358 records. Primary outcomes were systematically categorized into four domains: (1) cognitive performance and social/adaptive behavior; (2) neuroinflammatory markers (TNF-α, IL-6, IL-1β) and barrier integrity; (3) HPA-axis parameters (cortisol/corticosterone); and (4) neuroactive microbial metabolites (short-chain fatty acids and tryptophan derivatives). Methodological quality and risk of bias were assessed independently by two reviewers using the SYRCLE tool for preclinical studies and the Joanna Briggs Institute (JBI) tools for randomized and quasi-experimental clinical trials, with summary visualizations generated using the robvis web application (version 0.3.0). Results: Neurodegenerative diseases (n = 31; 38.3%) and mood disorders (n = 29; 35.8%) were the most frequently investigated conditions. Probiotics predominated (n = 61), followed by prebiotics (n = 12) and synbiotics (n = 8). Descriptively, 92% of included studies reported improvements in at least one evaluated outcome. However, this unweighted observation reflects effect direction rather than clinical magnitude, encompassing primary and secondary endpoints across highly heterogeneous sample sizes, study designs, and risk-of-bias profiles. Conclusions: Microbiota-targeted interventions show promise as complementary strategies for neurological disorders; however, substantial methodological heterogeneity, unstandardized dosing, and the absence of quantitative meta-analysis preclude definitive clinical recommendations. Successful translation will require harmonized protocols, strain-specific functional characterization, and precision microbiota-based trials. Full article

Winter wheat is a cornerstone of China’s grain production, contributing substantially to national food security and overall cereal output. This study modeled the nonlinear associations between multitemporal remote sensing variables and winter wheat yield. To produce high-spatiotemporal-resolution inputs, we used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to integrate Sentinel-2 normalized difference vegetation index (NDVI) data with MODIS NDVI data, generating NDVI composites at 8-day intervals with a 10-m spatial resolution. The NDVI, actual evapotranspiration (ET), land surface temperature (LST), precipitation (PRE), and soil moisture (SM) were selected as predictors for yield estimation because they are closely associated with winter wheat growth and yield formation during primary growth stages. By integrating the local temporal feature-learning capacity of a one-dimensional convolutional neural network (1-D CNN) with the strength of a bidirectional long short-term memory (BiLSTM) model in capturing temporal dependencies within time series, a BiLSTM–CNN model was constructed for wheat yield estimation and prediction. The BiLSTM–CNN model showed higher estimation accuracy than individual BiLSTM and 1-D CNN models, with an R² of 0.69 and root mean square error (RMSE) of 478.68 kg/hm². The use of all the parameters produced the best estimation performance among all the parameter combinations. Approximately two months before harvest, the model still provided satisfactory yield prediction accuracy. This study provides an important theoretical basis for high-accuracy regional winter wheat yield estimation and pre-harvest forecasting. Full article

End-of-Line (EoL) quality inspection of electric motors requires reliable detection of manufacturing faults before products leave the production line. However, conventional supervised deep learning approaches depend on a sufficient number of labeled instances of faulty motors, which are scarce in high-quality manufacturing environments. This limits their applicability to newly introduced products and previously unobserved fault types. This paper presents an unsupervised deep learning framework for anomaly detection in motor acoustic and vibration signals, designed for EoL quality inspection in manufacturing. The proposed method leverages Mel-frequency spectrograms (MFSs) as input features and employs a hybrid neural network combining a convolutional neural network (CNN) and bidirectional gated recurrent units (BiGRUs), effectively capturing both local spectral patterns and temporal dependencies. The method is evaluated on real industrial production data from over 2400 motors, of which approximately 4.3% were faulty, reflecting the highly imbalanced nature of high-quality manufacturing. The model was trained exclusively on healthy motor data and therefore does not require labeled faulty samples during training. Experimental results demonstrate strong discrimination between healthy and faulty motors, indicating that the proposed approach is suitable for automated EoL quality inspection in manufacturing environments where labeled faulty data are scarce. Full article

Multi-angle measurements provide essential data on the anisotropic reflectance properties of vegetation, enabling more robust retrievals of leaf area index (LAI), clumping index (CI), and canopy gap fraction compared to conventional single-view remote sensing. While Unmanned Aerial Vehicles (UAVs) offer unprecedented centimeter-level spatial resolution and flexible deployment, their application exposes a fundamental scale mismatch between ultra-high-resolution imagery and traditional bidirectional reflectance distribution function (BRDF) models. This review explicitly identifies that conventional 1D radiative transfer models (RTMs), which rely on the assumption of a statistically homogeneous canopy, suffer from severe scale-dependent biases, such as systematically underestimating hotspot reflectance (e.g., observed biases of 25% to 40% in 5-cm resolution UAV studies over specific vegetation canopies), and structural-optical confounding when directly applied to UAV data. At centimeter scales, macroscopic structural heterogeneity disrupts this homogeneity, necessitating the use of 3D RTMs that can explicitly simulate geometric occlusion and complex multiple scattering processes in highly heterogeneous environments. To bridge these theoretical and operational gaps, this review uniquely synthesizes UAV-specific multi-angle methodologies, systematically correlating canopy architectural types with optimal sensor configurations, flight strategies, and BRDF modeling frameworks. By evaluating recent advancements in multimodal data fusion, physics-informed machine learning, and physiological parameter retrieval, this review provides a comprehensive roadmap for decoupling structural and biochemical traits, highlighting how multi-angle directional signatures can substantially elevate classification accuracy, with specific experiments on spectrally similar crops and mixed tree species demonstrating improvements from roughly 40% to over 89%. Ultimately, it establishes practical, decision-oriented guidelines for overcoming transient illumination and co-registration errors, advancing high-fidelity quantitative monitoring and stress detection in complex forest ecosystems. Full article

Digital transformation has come to the fore as a pivotal force behind corporate green innovation in the digital economy. Although previous research has documented that digital transformation can promote green innovation, the underlying organizational mechanisms remain theoretically fragmented. Moreover, the external conditions that shape the effectiveness of this relationship are not yet fully understood. To address these gaps, we develop an integrated analytical framework anchored in Organizational Information Processing Theory (OIPT) and the Resource-Based View (RBV). This framework explains how digital transformation promotes green innovation through two complementary organizational mechanisms—resource investment and technology application—and incorporates the moderating role of digital infrastructure. Using panel data on Chinese A-share-listed firms covering 2015–2024, this study examines the proposed relationships through a Bidirectional Fixed Effects Model, mediation analysis, and moderation analysis. The findings demonstrate a significant positive association between digital transformation and green innovation. This effect is partially mediated by increased R&D(Resource and Development) investment and deeper digital organizational embedding, which constitute complementary pathways reflecting resource investment and enhanced resource utilization, respectively. Additionally, digital infrastructure positively moderates this relationship by providing a more supportive external digital environment. This study enriches the existing literature by synthesizing previously dispersed perspectives on mechanisms into an OIPT-RBV framework, offering a more comprehensive account of how and under what circumstances digital transformation facilitates green innovation. Our findings also carry practical implications for managers seeking to strengthen innovation capabilities and for policymakers aiming to advance digital infrastructure and firms’ sustainable development. Full article

Log parsing converts raw system logs into structured templates and is a key preprocessing step for Artificial Intelligence for IT Operations (AIOps). Existing parsers face a practical trade-off: rule-based methods offer high throughput but limited adaptability across heterogeneous log sources, whereas Large Language Model (LLM)-based parsers achieve broader semantic coverage at the cost of inference latency, privacy exposure, and cloud dependency. This paper presents FlanBC, a log parsing framework that formulates template extraction as a BIO (Beginning, Inside, Outside) sequence-labeling task and integrates a Flan-T5 semantic encoder, Bidirectional Long Short-Term Memory (BiLSTM) layers for local sequential modeling, and a Conditional Random Field (CRF) decoder for structured label prediction. Log-specific preprocessing and a subword-to-token alignment mechanism adapt the general-purpose encoder to semi-structured log data. A layer-freezing strategy reduces the number of parameters updated during training. The framework supports local inference without external API dependency. Experiments on three benchmark datasets from LogHub (HDFS, BGL, OpenStack) under a supervised random-split setup evaluate parsing accuracy, training efficiency, statistical stability across random seeds, and component contributions. FlanBC achieves a Group Accuracy of 99.32% on HDFS and 98.47% on BGL, with an inference throughput of 700+ logs/s on a consumer-grade GPU. On OpenStack, performance is lower (GA = 92.54%), reflecting the challenge that diverse natural-language-like logs pose for compact encoder-based models. Under a stricter template-disjoint split that prevents template overlap between training and test sets, FlanBC achieves an average Group Accuracy of 91.14%, indicating that the model generalizes to unseen templates beyond in-distribution recognition. Ablation results indicate that the semantic encoder, BiLSTM module, and CRF decoder each contribute to prediction accuracy. These findings suggest that domain-adapted semantic encoders combined with structured decoding offer a practical accuracy–efficiency balance for log parsing in settings where local, cloud-free inference is preferred. Full article

Deterministic channel models are essential for high-frequency communication system design in complex urban environments, where multiple propagation mechanisms including reflection, diffraction, and vegetation scattering coexist. This paper proposes a hybrid forward–backward ray tube tracing (HFB-RTT-3D) approach that extends the established ray tracing fusion with multiple diffuse scattering (RT-MDS) framework from natural terrain to urban scenarios by introducing pyramid-shaped diffraction and vegetation scattering ray tubes. A vegetation scattering model based on a leaf-level bidirectional scattering distribution function (BSDF) is established, enabling computationally feasible representation of vegetation effects in deterministic channel prediction. The framework thereby covers buildings, vegetation, and terrain within a unified ray tube data structure. Simulation analyses quantify vegetation modulation of multipath structure and received power across frequencies from 5 to 15 GHz and different canopy sizes. Measurement validation in a campus tree-lined avenue scenario at 2.3 to 5.9 GHz demonstrates that incorporating vegetation scattering reduces the root mean square error (RMSE) of the prediction to 6.11 to 6.30 dB, an improvement of 0.57 to 1.67 dB over the case without vegetation, confirming the effectiveness of the proposed method. Full article

Distributed generation (DG) is reshaping distribution networks through bidirectional power flows, operational variability, and dependence on coordinated regulation, pricing, and control. This paper examines how regulatory architecture, grid-code requirements, tariff design, and Distributed Energy Resource Management Systems (DERMS) influence DG integration, emphasizing Honduras. A structured mixed-source review is applied, combining Scopus-based bibliometric analysis of 2438 records (2000–2026) with targeted synthesis of technical, regulatory, tariff-related, and institutional sources. The bibliometric results show sustained growth and a thematic shift from conventional voltage-control studies toward active distribution networks, DER coordination, storage, demand response, tariff reform, and digital energy management. The analytical synthesis shows that effective DG integration requires more than interconnection compliance: it depends on grid-support functions, cost-reflective and equitable tariffs, and operational tools capable of managing voltage deviations, reverse power flow, congestion, protection coordination, and limited visibility. DERMS is an enabling layer for voltage control, active and reactive power management, congestion mitigation, adaptive protection, and predictive operation. For Honduras, current regulatory progress should be complemented by phased modernization focused on observability, smart metering, data infrastructure, local flexibility, and progressive DERMS deployment. The study provides an integrated framework for aligning regulatory, economic, and operational dimensions of DG integration in emerging distribution systems. Full article