Search Results (1,324)

This study presents an integrated geochemical approach to distinguish between genetic and reservoir-related factors controlling oil compositional variability, evaluate reservoir compartmentalization, and reconstruct hydrocarbon migration pathways within the Nuraly field and the Akshabulak group of fields in the South Turgay Basin, Kazakhstan. The study aims to develop and validate an integrated approach combining biomarker analysis and oil fingerprinting to improve the reliability of oil genetic interpretation, assess reservoir fluid communication, and reconstruct secondary hydrocarbon migration pathways. This study analyzed 164 unique crude oil samples from the Akshabulak and Nuraly fields. Oil fingerprinting was performed on all 164 samples, including 128 samples from the Akshabulak group and 36 samples from the Nuraly field. A representative subset of 75 samples, comprising 39 Akshabulak oils and 36 Nuraly oils, was additionally analyzed for biomarkers. Oil fingerprinting was conducted using low thermal mass multidimensional gas chromatography (LTM-MD-GC), whereas biomarker analysis was performed using gas chromatography–mass spectrometry (GC–MS). Principal component analysis (PCA) and hierarchical cluster analysis were applied separately to the oil-fingerprinting and biomarker datasets. The resulting classifications were subsequently compared and integrated to distinguish source-related genetic variability from reservoir-related compositional effects, including hydrocarbon migration, oil mixing, and reservoir compartmentalization. The proposed approach is based on the complementary diagnostic capabilities of the applied geochemical methods. Biomarkers provide information on the origin of organic matter, depositional environment, and thermal maturity of the source rocks, whereas oil fingerprinting is sensitive to hydrocarbon migration processes and the degree of hydrodynamic connectivity between reservoirs. The results indicate that the investigated oils are predominantly derived from terrigenous organic matter of lacustrine origin. The Akshabulak group is characterized by genetic homogeneity of oils despite pronounced reservoir compartmentalization, whereas the Nuraly field contains at least two genetically distinct oil populations and hydrocarbon mixing zones. Regional hydrocarbon migration was reconstructed from southeast to northwest. Paleochannel sandstones were identified as high-permeability migration conduits, while tectonic faults and facies heterogeneity were recognized as the principal controls on reservoir hydrodynamic isolation. The results demonstrate that integrating biomarker analysis with oil fingerprinting provides an effective tool for distinguishing between genetic and reservoir-related controls on oil compositional variability, evaluating reservoir compartmentalization, and improving the reliability of geological and reservoir models in structurally complex petroleum systems. Full article

Triazole fungicides (TFs) are widely used in cereal production due to their potent fungicidal activity and broad-spectrum efficacy. Nonetheless, residues of TFs in food products may pose risks to food safety and human health. Therefore, the development of efficient and environmentally friendly sample preparation methods is paramount for the reliable determination of TFs. Herein, a novel green solvent-based dispersive liquid–liquid microextraction method coupled with high-performance liquid chromatography (DLLME-HPLC) was developed for the determination of TFs in cereal samples. The prepared magnetic deep eutectic solvents (MDESs), composed of nonanoic acid and ferric hydroxide, served as green, magnetically responsive extraction solvents, enabling rapid magnetic separation without centrifugation. Four bio-based solvents (BBSs) were investigated as green dispersive solvents to facilitate the dispersion of MDESs and replace conventional toxic organic dispersants, thereby further enhancing the environmental sustainability of the extraction procedure. Owing to the combined effects of hydrophobic interactions, hydrogen-bonding networks, and magnetic responsiveness, the proposed method achieved efficient extraction and rapid phase separation while minimizing solvent consumption and operational complexity. The greenness of the method was evaluated using multiple green analytical chemistry metrics, confirming its low environmental impact, reduced waste generation, and improved operational safety compared with conventional DLLME procedures. Under optimized conditions, the method was successfully applied to determine TFs in rice, wheat, corn, buckwheat, and oat samples, achieving recoveries ranging from 75.0% to 101.9% and relative standard deviations of 1.6–4.8%. The developed DLLME method provides a rapid, sensitive, and environmentally friendly strategy for cereal pesticide residue analysis and expands the application of MDESs and BBSs in green sample preparation. Full article

Background: Measurement reliability of the sagittal thoracic spine, e.g., thoracic kyphosis and balance, on radiographs has an unknown evidence base. This literature review aims to systematically identify and evaluate the reliability of biomechanical assessments of the sagittal thoracic spine on radiographs used in clinical practice. Methods: The study design was registered with PROSPERO (CRD42023431171). Chiropractic Biophysics Nonprofit, Inc (Eagle, ID, USA) funded this investigation. Inclusion criteria involved studies in English using human subjects, radiography, and reliability analysis of biomechanical analysis of the thoracic spine. Exclusion criteria involved animal and cadaveric studies, phantom mannequins, geometric studies, and non-radiographic studies. This review was conducted using the Peer Review of Electronic Search Strategies (PRESS) checklist to organize the search strategy. A combined approach using Medical Search Headings (MeSH) search terms and a systematic literature review (SLR) search strategy was used. This review followed the recommendations of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Databases searched included PubMed, CINAHL, Alt HealthWatch, Web of Science, and a grey literature search. Initial search results produced 990 results. A total of 658 records were screened by two independent reviews and 197 full text articles were assessed for eligibility. Results: Sixty-six studies were included in the final analysis and assessed for methodological quality and bias using the 11-item Quality Appraisal of Diagnostic Reliability (QAREL) tool. A total of 15 studies were of low methodological quality (high risk of bias), 28 were of moderate quality (moderate risk of bias), and 23 were of high quality (low risk of bias). Conclusions: This SLR found most articles investigating the reliability of biomechanical assessment of the sagittal thoracic spine on radiographs to be of moderate-to-high quality and show good-to-excellent reliability. Full article

Environmental DNA (eDNA) is a highly sensitive tool widely used in biodiversity studies and for detecting organisms of biosecurity or conservation importance. However, the persistence of eDNA, even after the removal of an organism, can complicate interpretation and lead to false indications of ongoing presence. In contrast, environmental RNA (eRNA) is short-lived and may offer improved spatiotemporal precision for detecting living organisms. In this study, we evaluated the viability of environmental messenger RNA (emRNA) for targeted detection of aquatic weeds, using Amazon frogbit (Hydrocharis laevigata) as a model species. A highly sensitive qPCR assay, targeting the chloroplast rpoB transcript, was used alongside positive and negative controls to ensure workflow reliability. Our results showed that emRNA was undetectable at low density (0.56 plants per litre) and was consistently detectable at very high abundance (over eight plants per litre), although copy numbers were very low. These findings suggest that the effective detection threshold for emRNA may exceed densities typically encountered during early invasion. Consequently, the qPCR-based emRNA approaches evaluated in this study showed limited applicability for biosecurity surveillance, where reliable detection at low abundance is essential. Future research should focus on improving detection sensitivity, including eRNA enrichment. Full article

High-dimensional engineering datasets often combine class imbalance, noisy structures, and limited ground truth, making unsupervised analysis difficult to evaluate reliably. This study quantifies how three properties—partition stability, minority class recovery, and topological interpretability—are traded off across clustering methods, using a capacity-matched 25-seed comparison on a TCGA-derived RNA expression dataset (10,095 samples, 19 cancer types, 13,634 genes). We compare K-means across cluster counts $k\in \{19,\dots ,400\}$, self-organizing maps (SOMs) across lattice sizes from 25 to 625 nodes, consensus K-means, a granularity-matched SOM-Super20 control, and four modern baselines (HDBSCAN, spectral clustering, Gaussian mixtures, and Leiden). At matched prototype budgets, K-means is both more reproducible and substantially better at recovering minority classes than SOMs: at 400 prototypes, K-means achieves pairwise NMI $0.819$ versus $0.621$ for the $20\times 20$ SOM and recovers the smallest cancers 6–$14\times$ more effectively (pancreas effective coverage $0.760$ vs. $0.054$).Crucially, the SOM does not close this gap even when given more prototypes ($0.07$ at 625 nodes), so, under matched capacity, minority recovery is better explained by representational capacity and centroid allocation freedom than by topology preservation. The recovery is not free: increasing k overfragments the partition and lowers the pairwise ARI stability ($0.643\to 0.419$ from $k=20$ to $k=400$), while the NMI remains robust ($\approx 0.82$). The hardest minority, pancreas, is recovered only by high-capacity K-means and by no other method evaluated, including SOMs at any size, consensus K-means, SOM-Super20, HDBSCAN, Gaussian mixtures, spectral clustering, and Leiden. The SOM’s distinct value is therefore not stability or recovery but the interpretable two-dimensional topological visualization that it uniquely provides, including a gradient-organized structure that is reproducible across seeds for kidney (weaker for uterus). No single method optimizes all three properties; the appropriate choice depends on whether a task prioritizes reproducibility, minority recovery, or visual interpretability. Because these conclusions follow from the shape of the data and the allocation behavior of the algorithms rather than from biological semantics, we expect them to transfer to high-dimensional imbalanced engineering data, such as those from fault clustering, condition monitoring, and anomaly detection. Full article

Background: Large language models (LLMs) show considerable promise for medical question answering and reasoning. Their use in medical microbiology, however, remains constrained by limited domain-specific knowledge and the risk of hallucinated outputs. Objective: To develop and evaluate Smart Medical Microbiology (SmartMM), a specialized LLM for accurate, reliable, and context-aware responses in medical microbiology. Methods: SmartMM integrates domain-adaptive continual pretraining, supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), knowledge distillation, and retrieval-augmented generation (RAG). We constructed a high-quality microbiology corpus from textbooks, clinical guidelines, the scientific literature, case reports, and other authoritative sources. Model performance was assessed using objective examinations, subjective generation tasks, expert review, and real-world user preference evaluation. Results: SmartMM achieved accuracies of 0.897 and 0.563 on true-or-false and fill-in-the-blank questions, respectively. In subjective generation tasks, it obtained the highest ROUGE-L score (0.265) and BERTScore F1 score (0.771) among all compared models. Expert assessment showed excellent inter-rater reliability, with all ICC(C,3) values exceeding 0.970. In a user evaluation involving 20 participants and 100 real-world questions, SmartMM received the largest number of first-place rankings (33), placing it among the top-performing systems overall. Conclusions: SmartMM showed strong domain adaptability in medical microbiology knowledge organization, semantic generation, and retrieval-augmented reasoning. These findings support its potential use in educational support, infectious disease knowledge assistance, and retrieval-enhanced medical question answering. Full article

Unmanned aerial vehicles (UAVs) used in low-altitude mobility and electric aviation are increasingly required to carry higher payloads, operate for longer durations and maintain reliable performance under constrained installation conditions. In compact power cabins, batteries, controllers, power distribution units and auxiliary actuators are densely arranged, making cabin thermal management a critical design issue. In this study, a full-scale conjugate flow and heat transfer model is developed for the power cabin of a UAV and validated against thermal management experiments. The validated model is then used to examine how a conventional rectangular ram-air inlet and a proposed stepped ram-air inlet affect air capture, internal flow organization and temperature distribution. The inlet area of the rectangular configuration is first varied to establish a baseline, after which the transition arc ratio, spacing ratio and area ratio of the stepped inlet are parametrically investigated. The results show that increasing the rectangular inlet area from 0.002 to 0.008 m² increases the total captured mass flow rate from 0.258 to 1.084 kg/s, whereas the cabin average temperature decreases by 0.34 °C. By contrast, the cabin maximum temperature decreases nonlinearly, with a 27.2% reduction when the area increases from 0.004 to 0.006 m². These results indicate that air capture and the cabin average temperature alone are insufficient to evaluate cooling effectiveness in a compact multi-source cabin. For the stepped inlet, the transition arc ratio controls the turning of the incoming flow, the spacing ratio governs shielding and backflow between adjacent inlet sections, and the area ratio redistributes the dominant inlet sections. The best-performing stepped-inlet configuration among the tested cases increases the captured mass flow rate by 32.8% compared with the rectangular baseline under the same opening constraint and improves the utilization of cooling air around high heat load components. This study demonstrates that ram-air inlet design for UAV power cabins should be treated as a coupled problem of the mass flow capture, internal flow path and component-level thermal response. Full article

Two-dimensional transition-metal carbides and nitrides (MXenes) are increasingly adopted in advanced electronic devices, where their metallic conductivity, optical tunability, and chemically addressable surfaces support next-generation multifunctional optoelectronics. Their practical performance, however, depends not only on their intrinsic properties but also on the heterogeneous interfaces where charges, photons, and ions interact. Unlike earlier reviews organized around synthesis routes or separate device categories, this review takes interfacial chemistry as a single organizing principle and follows it from surface terminations through to integrated systems. The structural and surface-chemical characteristics of MXenes are described first, showing how dynamic terminations and interfacial dipoles regulate work functions and energy-level alignment. We then discuss molecular functionalization, defect passivation, and heterojunction formation as strategies for reducing Schottky barriers and improving charge-transfer kinetics. Optoelectronic platforms built on these engineered interfaces, including high-efficiency photovoltaics, broadband photodetectors, and stretchable wearable systems, are subsequently detailed, together with emerging architectures that merge self-powered sensing with neuromorphic visual functions, a scope seldom treated alongside conventional devices in previous surveys. By connecting surface chemistry with device integration, this review outlines a materials-to-systems pathway toward more reliable and scalable MXene-based optoelectronic technologies. Full article

Indirect observation systems, such as hyperspectral remote sensing and passive geophysical measurements, retrieve useful information from redundant observations of the same scene. However, the resulting data are often contaminated by structured coherent artifacts caused by sensor nonuniformity, calibration residuals, or incomplete illumination. These artifacts are difficult to suppress because they are spatially organized components with directional continuity and non-negligible correlation. Their signal-like coherence allows them to mimic image textures or physical events, making conventional denoising methods prone to residual artifacts or signal leakage. To address this problem, we propose a consistency-guided collaborative filtering framework for suppressing structured coherent artifacts while preserving useful signals. The proposed framework extends paired-observation similarity analysis into a consistency-guided strategy for redundant observations. Paired observations of the same target are constructed to distinguish useful signals from physically inconsistent artifacts. This consistency contrast is incorporated into collaborative filtering to guide block matching and aggregation, while a coherent noise power spectral density model characterizes the directional and spatial correlation of the artifacts for targeted noise shrinkage. The proposed framework is evaluated primarily on hyperspectral remote-sensing images contaminated by simulated stripe artifacts, with additional validation on synthetic and field geophysical paired-observation data containing nonphysical coherent events. The results demonstrate that the proposed method can suppress structured coherent artifacts while preserving useful signals and maintaining high signal fidelity. This work provides a unified way to exploit observational redundancy for enhancing imaging reliability. Full article

Copper and its alloys are indispensable for electronics, communications, new energy systems, and aerospace engineering due to their exceptional electrical conductivity and mechanical properties. However, the thin cuprous oxide (Cu₂O) layer that naturally forms on copper and its alloys is prone to failure under elevated temperatures and high humidity, particularly in chloride-rich environments, leading to accelerated localized corrosion. While conventional chromate-based passivation has long been the industrial standard for preventing corrosion, its use has been increasingly restricted by global regulations (such as RoHS and REACH) due to its severe toxicity and health risks. To address the conflict between environmental compliance and protective performance, this review systematically evaluates recent advances in environmentally friendly, chromium-free anti-corrosion coatings in the present review. These alternative coatings are critically analyzed and categorized into four mechanistic groups: (i) inorganic conversion coatings (including molybdate, tungstate, rare earth, and phosphate systems); (ii) organic films formed via chemical or physical adsorption (such as organic inhibitors, thiol-based monolayers, and organosilane self-assembled films); (iii) conversion coatings engineered through covalent bonding, coordination chemistry, and microstructural tailoring; and (iv) multifunctional coatings that integrate self-healing capability with high electrical conductivity. Beyond providing a technical summary, this review explored how the swift progression of electronic information technology, new energy infrastructure, and robotics has imposed more exacting, multifunctional demands on copper components. This review provides a strategic roadmap for future research and prioritizes the creation of protection strategies that operate robustly in multi-physics coupling environments—integrating high conductivity, autonomous self-healing, and long-term chemical stability to ensure the reliability of next-generation infrastructure. Full article

Spike density is an important component of wheat spike architecture and is determined by the combined effects of spike-axis elongation and spikelet number. In this study, we characterized an ethyl methanesulfonate (EMS)-induced recessive dense-spike mutant, ds, which was obtained from EMS mutagenesis of the wheat cultivar YN21 in 2013 and displays a field-visible compact spike architecture associated with a shortened spike axis, reduced spike internode spacing, and increased spikelet number. Genetic analysis showed that all F₁ plants exhibited normal spikes and that segregation in the F₂ population fitted a 3:1 ratio, supporting control by a single recessive nuclear gene. Through genome-wide marker-based linkage screening, enlarged-population validation, and fine mapping, ds was delimited to an approximately 864.819 kb interval between ID2B2796 and ID2B7615 on chromosome 2B. This interval contains 11 high-confidence annotated genes, including F-box protein, actin, ubiquitin-conjugating enzyme, ribosomal protein L16, RecX, plant cysteine oxidase, PI4KIIγ, and two adjacent GA3OX-family-related genes. Integrated RNA-seq and RT-qPCR analyses showed that the two adjacent GA3OX-family-related genes were expressed in young spikes and exhibited reduced expression in ds-sib relative to WT-sib. These expression data support their retention as plausible, non-exclusive candidates but do not establish causality. Transcriptome analysis further revealed changes in hormone-related pathways, cell-wall organization, carbohydrate metabolism, and transcription-factor regulation. These results provide a reliable genetic basis for further molecular cloning of ds and suggest that altered hormone- and growth-related transcriptional responses may be associated with dense-spike formation in wheat. Full article

Soil organic carbon (SOC) is a key indicator of cropland quality, soil fertility, and the carbon sequestration potential of agroecosystems. Accurate characterization of its spatial distribution is essential for black soil conservation and regional soil carbon management. However, regional-scale SOC prediction is often constrained by limited field observations, which can reduce model generalizability and predictive reliability. In this study, we developed a limited-sample SOC prediction framework for the Liaohe Plain using 310 surface (0–20 cm) soil samples collected in 2025 and multi-source environmental covariates, including climate, vegetation, soil spectral, and terrain variables. The framework used the Tabular Prior-Data Fitted Network (TabPFN), whose performance was compared with that of Random Forest, Support Vector Machine, CatBoost, K-Nearest Neighbors, and XGBoost. Model performance was evaluated using 100 repetitions of random 80:20 holdout validation and repeated five-fold spatial cross-validation based on spatially constrained clustering, while sampling-induced relative uncertainty was quantified using 100 repeated random sampling and model-fitting runs. Under random holdout validation, TabPFN showed competitive predictive performance, with mean R² and RMSE values of 0.608 ± 0.038 and 4.026 ± 0.184 g kg⁻¹, respectively. Repeated spatial cross-validation yielded more conservative performance estimates, with mean R² and RMSE values of 0.535 ± 0.072 and 4.33 ± 0.31 g kg⁻¹, respectively, indicating that random splitting may overestimate model performance when sampling sites are spatially clustered. Spatial prediction showed that cropland SOC ranged from 4.63 to 27.04 g kg⁻¹, with generally lower values in the west and higher values in the northeast. Areas with high sampling-induced relative uncertainty were mainly concentrated in the northern, northeastern, and marginal regions. These findings provide a methodological basis for SOC mapping, supplementary sampling optimization, and regional soil carbon management under limited-sample conditions, although the temporal robustness of the results requires confirmation using independent data from additional years. Full article

Diets high in fat and refined carbohydrates reliably mimic key features of metabolic dysfunction-associated fatty liver disease (MAFLD), including hepatic lipid accumulation, oxidative stress, and ongoing liver injury. This study evaluated the long-term, low-dose administration of Callistemon citrinus ethanolic leaf extract and its primary bioactive compounds: d-limonene, ellagic acid, gallic acid, and p-coumaric acid to prevent lipotoxicity in rats fed a high-fat–sucrose diet (HFSD). Sixty male Wistar rats were randomly divided into 10 groups: normal control group, an untreated HFSD group, and eight HFSD groups treated for 23 weeks with metformin, C. citrinus extract, the individual bioactive compounds, or a mixture of these compounds. We assessed body and organ weights, liver macroscopic features, serum markers of liver injury, oxidative stress biomarkers, histopathological changes, and hepatic fatty acid profiles. HFSD feeding induced significant body weight gain, hepatomegaly, severe tissue damage, elevated serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), glucose and triglycerides, alongside heightened hepatic oxidative stress, and altered saturated fatty acid profiles. Conversely, treatment with metformin and the natural compounds attenuated HFSD-induced liver injury to varying degrees. Improvements in histopathological severity and serum liver enzymes correlated with decreased oxidative stress biomarkers. Notably, ellagic acid, gallic acid, d-limonene, and the bioactive mixture, significantly improved hepatic fatty acid profiles, whereas p-coumaric acid exhibited more modest effects. These results demonstrate that long-term administration of low-dose treatment can successfully improve all evaluated metabolic, biochemical, and histological parameter, suggesting that modulating oxidative stress and liver lipid composition is strongly linked to the mitigation of liver injury in this experimental MAFLD model. Full article

Carbon black (CB) nanoparticles (~20 nm) offer high specific surface area and conductivity for self-sensing cementitious composites, but strong interparticle van der Waals forces drive agglomeration in alkaline pore solutions, limiting sensing reliability. This study introduces a nanoscale interfacial engineering strategy in which CB nanoparticles are adsorbed onto polypropylene (PP) fiber surfaces as spatially organized conductive elements, with EDS evidence of enhanced hydrate coverage at the fiber–matrix interface. Three CB dosages (0.5%, 1.0%, and 1.5% by binder mass) with 0.5% PP fiber were investigated. Nanoparticle coating and interfacial micro-structure were characterized by SEM-EDS, while FTIR was used to verify that the fiber backbone remained chemically unmodified; piezoresistive response and durability were assessed via cyclic compression, DIC, and hygrothermal cycling. The 1.0% CB nanocomposite lies within the effective percolation window (~0.9–1.2%), showing high linearity, a stable gauge factor (~100), and distinct FCR acceleration for early-warning sensing. The 1.5% CB composite yields higher sensitivity but scattered responses due to nanoparticle clustering; 0.5% CB remains below the percolation threshold with a discontinuous network. After 60 hygrothermal cycles, the 1.0% nanocomposite retains >93% of its gauge factor with minimal resistance drift. The nano-engineered CB–PP fiber architecture offers a scalable route integrating crack bridging, percolation networking, and durable self-sensing in cementitious nanocomposites for structural health monitoring. Full article

High-mobility dual-functional radar–communication systems require a common waveform that combines delay–Doppler information organization, sensing resolution, and power-efficient transmission. We present a cyclically closed constant-envelope orthogonal time frequency space–continuous phase modulation–linear frequency modulation (OTFS–CPM–LFM) waveform and matched transceiver architecture. Hermitian delay–Doppler mapping and direct-current (DC) row nulling create a real, zero-sum drive with a reversible frame-level phase representation. The communication receiver combines a Tikhonov-regularized waveform inverse with reference-aided unwrapping and tail-biting phase regression, while the radar receiver reconstructs the data-dependent current-frame reference. Numerical results verify the structural waveform properties and characterize communication, radar, and computational tradeoffs. They also quantify degradation under controlled complex-gain channel-state-information mismatch and show that phase regression is less reliable at a low signal-to-noise ratio (SNR). The constant-envelope claim applies only to ideal discrete complex-baseband samples and does not include pulse shaping or radio-frequency hardware. The framework therefore provides a self-consistent waveform interface while exposing tradeoffs among payload, recovery reliability, sensing sidelobes, and implementation cost. Full article