Search Results (56,446)

Deep neural networks (DNNs) have achieved superior performance in diagnosing process faults, but they lack robustness when encountering novel fault types absent from training sets. Such unknown faults commonly appear in industrial settings, and conventional DNNs often misclassify them as one of the known fault types. To address this limitation, we formulate the concept of open-set fault diagnosis (OSFD), which seeks to distinguish unknown faults from known ones while correctly classifying the known faults. The primary challenge in OSFD lies in minimizing both the empirical classification risk associated with known faults and the open space risk without access to training data for unknown faults. In order to mitigate these risks, we introduce a novel approach called hypersphere-guided reciprocal point learning (SRPL). Specifically, SRPL preserves a DNN for feature extraction while constraining features to lie on a unit hypersphere. To reduce empirical classification risk, it applies an angular-margin penalty that explicitly increases intra-class compactness and inter-class separation for known faults on the hypersphere, thereby improving discriminability among known faults. Additionally, SRPL introduces reciprocal points on the hypersphere, with each point acting as a classifier by occupying the extra-class region associated with a particular known fault. The interactions among multiple reciprocal points, together with the deliberate synthesis of unknown fault features on the hypersphere, serve to lower open-space risk: the reciprocal-point interactions provide an indirect estimate of unknowns, and the synthesized unknowns provide a direct estimate, both of which enhance distinguishability between known and unknown faults. Extensive experimental results on the Tennessee Eastman process confirm the superiority of the proposed method compared to state-of-the-art OSR algorithms, e.g., an 82.32% AUROC score and a 71.50% OSFDR score. Full article

As a zero-carbon fuel, ammonia holds significant potential for achieving the “dual carbon” strategic goals. However, its extremely low laminar burning velocity (LBV) limits its direct application in combustion systems. This work systematically reviews the research progress on the LBV of ammonia flames, focusing on three key aspects: measurement methods, effects of combustion conditions, and reaction kinetic models. In terms of measurement methods, the principles, applicability, and limitations of the spherical outwardly propagating flame method, Bunsen-burner method, counter-flow flame method, and heat flux method are discussed in detail. It is pointed out that the heat flux method and counter-flow flame method are more suitable for the accurate measurement of ammonia flame LBV due to their low stretch rate and high stability. Regarding the effects of combustion conditions, the LBV characteristics of pure ammonia flames under ambient temperature and pressure are summarized. The influence patterns of three factors on LBV are analyzed systematically: blending high-reactivity fuels (e.g., hydrogen and methane), oxygen-enriched conditions, and variations in temperature and pressure. This analysis reveals effective approaches to improve ammonia combustion performance. Furthermore, the promoting effect of high-reactivity fuel blending on liquid ammonia combustion was also summarized. For reaction kinetic models, various chemical reaction mechanisms applicable to pure ammonia and ammonia-blended fuels (ammonia/hydrogen, ammonia/methane, etc.) are sorted out. The performance and discrepancies of each model in predicting LBV are evaluated. It is noted that current models still have significant uncertainties under specific conditions, such as high pressure and moderate blending ratios. This review aims to provide theoretical references and data support for the fundamental research and engineering application of ammonia combustion, promoting the development and application of ammonia as a clean fuel. Full article

Stock trend prediction is a complex and crucial task due to the dynamic and nonlinear nature of stock price movements. Traditional models struggle to capture the non-stationary and volatile characteristics of financial time series. To address this challenge, we propose the Hybrid Relational Transformer (HRformer), which specifically decomposes time series into multiple components, enabling more accurate modeling of both short-term and long-term dependencies in stock data. The HRformer mainly comprises three key modules: the Multi-Component Decomposition Layer, the Component-wise Temporal Encoder (CTE), and the Inter-Stock Correlation Attention (ISCA). Our approach first employs the Multi-Component Decomposition Layer to decompose the stock sequence into trend, cyclic, and volatility components, each of which is independently modeled by the CTE to capture distinct temporal dynamics. These component representations are then adaptively integrated through the Adaptive Multi-Component Integration (AMCI) mechanism, which dynamically fuses their information. The fused output is subsequently refined by the ISCA module to incorporate inter-stock correlations, leading to more accurate and robust predictions. Extensive experiments on the NASDAQ100 and CSI300 datasets demonstrate that HRformer consistently outperforms state-of-the-art methods, e.g., achieving about 0.83% higher Accuracy and 1.78% higher F1-score than TDformer on NASDAQ100, with Sharpe Ratios of 1.5354 on NASDAQ100 and 0.5398 on CSI300, especially in volatile market conditions. Backtesting results validate its practical utility in real-world trading scenarios, showing its potential to enhance investment decisions and portfolio performance. Full article

This study investigated the effects of transcranial direct current stimulation (tDCS) over the dorsolateral prefrontal cortex (dlPFC) at three intensities (sham, 1 mA, 2 mA) on postural control, isokinetic strength, and cognitive performance in women with fibromyalgia (FM) and healthy controls (HCs). Using a double-blind, sham-controlled, crossover design, 26 participants (13 FM, 13 HC) completed sessions in randomized order, performing tasks under single- and dual-task conditions. Cognitive accuracy improved in both groups following 1 mA and 2 mA stimulation, particularly during single-task scenarios in static balance tasks. Notably, 2 mA tDCS reduced dual-task cost (DTC) in cognitive performance for the FM group, indicating decreased cognitive–motor interference. However, postural and strength outcomes showed no consistent intensity-dependent changes, with only selected nonlinear centers of pressure metrics (e.g., Lyapunov exponent, DFA) indicating possible modulation in FM. Isokinetic strength measures remained largely unaffected by tDCS across all intensities. Overall, the findings suggest that dlPFC-tDCS may selectively enhance cognitive function and reduce cognitive–motor interference in FM, especially under low-demand or higher-intensity stimulation conditions, while offering limited benefits for physical strength and balance. Full article

Over 292 million tons of municipal solid waste (MSW) are generated annually in the United States, with more than half disposed of in landfills. Municipal solid waste landfills (MSWLFs) are stationary sources of air pollution and potential health risks for nearby communities. The Agency for Toxic Substances and Disease Registry (ATSDR) has completed over 300 public health assessments (PHAs) and related investigations at MSWLFs and open dumps since the 1980s. This paper reviews the ATSDR’s evaluations of air pathway concerns at 125 MSWLF sites assessed between 1988 and early 2025, with many being evaluated during the 1990s. Most sites were located in the Midwest and Northeast, and only 25% remained active. The ATSDR found no air-related public health hazard at 86% of sites. At sites where hazards were identified, common issues included elevated outdoor or indoor toxicants (e.g., hydrogen sulfide, benzene, trichloroethylene, and mercury) and unsafe methane accumulations. Contributing factors included older site designs, inadequate gas-collection, subsurface fires, and distance from nearby residences. Corrective actions effectively reduced exposures at the affected sites. Results suggest that well-located and maintained landfills minimize public health hazards, while aging or poorly managed sites pose risks. Continued monitoring and research are warranted as waste management shifts toward reducing, reusing, recycling, composting, and energy-recovery technologies to improve efficiency, advance technologies, and address systemic public health challenges. Full article

One of the best-known flavonoid chrysin was coupled at position 7 with several trisubstituted phosphine derivatives with a flexible spacer, and their in vitro anticancer activities were investigated on 60 human tumor cell lines (NCI60) and on several gynecological cancer cells. The trisubstituted phosphines contained different substituents on the aromatic ring(s), e.g., methyl and methoxy groups or fluoro atoms. The phosphorus atom was substituted not only with aromatic rings but with cyclohexyl substituents. The ionic phosphonium building block is important because it allows the therapeutic agents to transfer across the cell membrane. Therefore, the pharmacophores linked to it can exert their effects in the mitochondria. Instead of the ionic phosphonium element, a neutral moiety, namely the triphenylmethyl group, was also added to the side chain, being sterically similar but without a charge and phosphorus atom. Most of the hybrids exhibited low micromolar growth inhibition (GI₅₀) values against the majority of the tested cell lines. Notably, conjugate 3f stood out, demonstrating nanomolar antitumor activity against the K-562 leukemia cell line (GI₅₀ = 34 nM). One selected compound (3i) with promising cancer selectivity elicited cell cycle disturbances and inhibited the migration of breast cancer. The tumor-selectivity of 3a and 3f was assessed based on their effects on non-tumor Chinese hamster ovary (CHO) cells using the CellTiter-Glo Luminescent Cell Viability Assay. Given their estimated half-maximal inhibitory concentration (IC₅₀) values on non-tumor CHO cells (2.65 µM and 1.15 µM, respectively), these conjugates demonstrate promising selectivity toward several cancer cell lines. The excellent results obtained may serve as good starting points for further optimization and the design of even more effective flavonoid- and/or phosphonium-based drugs. Full article

Multi-drug resistance in Acinetobacter baumannii poses a significant human health threat. For multidrug-resistant pathogens, ‘last line of defense’ antibiotics like the polymyxins are implemented. Concerningly, polymyxin-resistance is evidenced in Acinetobacter baumannii and is mediated by the PmrAB two-component system. The response regulator PmrA upregulates pmrC, leading to lipooligosaccharide modifications that reduce polymyxin binding. Sequencing of A. baumannii resistant isolates has identified point mutations in the receiver domain of PmrA that correlate with increased resistance. To investigate functional impacts of these mutations, we characterized five PmrA mutations (D10N, M12I, I13M, G54E, and S119T) by assessing changes in PmrA DNA-binding affinity, dimerization, phosphorylation, and structure. Our findings suggest that these mutations impact the ability of PmrA to receive the activating phosphoryl group from the sensor kinase PmrB. The slow phosphoryl uptake is likely due to (1) disruption of the PmrB-PmrA interaction by interfering with the recognition site on PmrA, or (2) perturbation of PmrA’s active site via steric hindrance or displacement of residues and ions necessary for coordination within the aspartic acid pocket. Slowed phosphorylation of a response regulator can lead to enhanced gene transcription through several mechanisms. These insights advance our understanding of PmrA-mediated resistance in A. baumannii. Full article

Federated representation learning (FRL) is a promising technique for learning shared data representations that capture general features across decentralized clients without sharing raw data. However, there is a risk of sensitive information leakage from learned representations. The conventional differential privacy (DP) mechanism protects the privacy of the whole data by randomizing (adding noise or random response) at the cost of deteriorating learning performance. Inspired by the fact that some data information may be public or non-private and only sensitive information (e.g., race) should be protected, we investigate the information-theoretic protection on specific sensitive information for FRL. To characterize the trade-off between utility and sensitive information leakage, we adopt mutual information-based metrics to measure utility and sensitive information leakage, and propose a method that maximizes the utility performance, while restricting sensitive information leakage less than any positive value $ϵ$ via the local DP mechanism. Simulation demonstrates that our scheme can achieve the best utility–leakage trade-off among baseline schemes, and more importantly can adjust the trade-off between leakage and utility by controlling the noise level in local DP. Full article

Pyrenophora tritici-repentis (Ptr), the causal agent of tan spot, is a necrotrophic fungus that represents a significant threat to wheat production worldwide. The development of resistant cultivars is limited by an incomplete understanding of wheat defence responses against Ptr. Here, weighted gene co-expression network analysis (WGCNA) was applied to RNA-seq data from resistant (Robigus) and susceptible (Hereward) wheat lines before and after Ptr infection to identify coordinated host responses. Eight co-expression modules were identified, three of which were linked to either resistance, susceptibility, or Ptr infection. The resistance-associated module was enriched with chloroplast ribosomal machinery genes (e.g., 50S ribosome-binding GTPase, L28, L6), and transcriptional regulators. This suggested that maintaining chloroplast function, coupled with large-scale transcriptional reprogramming, was important for resistance. The susceptibility-associated module indicated the high expression of post-transcriptional modifiers, including SGS3, RBX1, and SENPs. The Ptr-responsive module showed common responses in both genotypes and included several defence-related genes (nucleotide-binding domain leucine-rich repeat R-genes [NLRs], chitinases, beta-1,3-glucanases) and metabolic pathways, such as phenylpropanoid biosynthesis and nitrogen metabolism (phenylpropanoid ammonia lyase [PAL], cytochrome P450s, glutamine synthase, and ammonium transporters). These results define distinct and shared molecular networks that are linked to resistance and susceptibility, providing valuable candidate genes for functional validation that could ultimately be exploited to enhance wheat resilience against necrotrophic fungal pathogens. Full article

Background: Cesarean section (CS) frequently results in abdominal scarring, affecting recovery, aesthetics, and quality of life. Platelet-rich plasma (PRP), an autologous concentrate rich in growth factors, may enhance wound healing. This pilot trial assessed the effect of intraoperative PRP on CS scar outcomes. Methods: In this single-center, single-blind randomized controlled trial (February 2023–December 2024), 100 women undergoing elective CS were randomized to PRP treatment (n = 50) or standard care (n = 50). PRP, prepared from 20 mL autologous blood, was infiltrated into uterine incision margins and subcutaneously before skin closure. Scar healing was evaluated at day 7 and day 40 postpartum using the Patient and Observer Scar Assessment Scale (POSAS; physician and patient), Vancouver Scar Scale, Manchester Scar Scale, REEDA (Redness, Edema, Ecchymosis, Discharge, Approximation) Scale, Visual Analog Scale (VAS), and Numeric Rating Scale (NRS). Mann–Whitney U tests and Cohen’s d effect sizes were calculated. Results: Follow-up was complete for all participants. On day 7, PRP-treated patients had lower mean scores across most scales (e.g., Vancouver: 1.74 ± 1.58 vs. 2.54 ± 2.30; p = 0.063). At day 40, improvements persisted, with POSAS Patient scores significantly lower in the PRP group (7.24 ± 1.81 vs. 8.00 ± 2.06; p = 0.029). Effect sizes were small-to-moderate (<0.5), suggesting underpowering. No adverse events occurred. Conclusions: PRP administration during CS showed favorable trends toward improved scar quality and reduced patient-reported discomfort, with statistical significance for POSAS Patient scores at 40 days. Larger, multicenter trials with extended follow-up are needed to confirm these findings. Full article

Snow construction plays a crucial role in military operations in cold regions, providing tactical fortifications, thermal insulation, and emergency infrastructure in environments where conventional building materials are scarce or require extensive infrastructure for support. Current snow construction methods, including manual piling and compaction, are labor-intensive and inconsistent, limiting their use in large-scale or time-sensitive operations. This study explores the feasibility of adapting a compressed earth block (CEB) machine to produce compressed snow blocks (CSBs) as modular, uniform building units for cold-region applications. Using an AECT Impact 2001A hydraulic press, naturally occurring snow was processed with a snowblower and compacted at maximum operating pressure (i.e., 20,684 kPa) to evaluate block formation, dimensional consistency, and density. The machine successfully produced relatively consistent CSBs, but the initial 3–4 blocks following block height adjustment were generally unsuccessful (e.g., incorrect block height or collapsed/broke) while the machine reached its steady state cyclic condition. These blocks were discarded and excluded from the dataset. The successful CSBs had mean block heights of 7.76 ± 0.56 cm and densities comparable to ice (i.e., 0.83 g/cm³). Variations in block height and mass may be attributed to manual snow loading and minor material impurities. While the dataset is limited, the results warrant further investigation into this technology, particularly regarding CSB strength (i.e., hardness and compressive strength) and performance under variable snow and environmental conditions. Mechanized snow compaction using existing CEB technology is technically feasible and capable of producing uniform, structurally stable CSBs but requires further investigation and modifications to reach its full potential. With design improvements such as automated snow feeding, cold-resistant components, and system winterization, this approach could enable scalable CSB production for rapid, on-site construction of snow-based structures in Arctic environments, supporting the military and civilian needs. Full article

Silicon carbide (SiC) power converters are attracting increasing interest due to their significant advantages in terms of efficiency, switching speed, and greater temperature tolerance compared to traditional silicon-based converters. Tools to improve the design process, such as those to predict the switching behavior of silicon carbide-based power converters, can be of great help, e.g., in studying critical electrical/thermal stress in power devices. This work aims to present an effective multi-objective optimization method to identify the main parasitic parameters of a SiC half-bridge power converter related to the board layout and device packaging. This goal was achieved by minimizing the errors between the system responses carried out by the simulated power converter and the measurements collected from a limited number of experimental tests. The feasibility and effectiveness of the method are verified by tests performed on a 1200 V, 75 A, SiC half-bridge converter. Although this methodology has been validated for a specific converter topology, it can be extended to model more complex power converter structures. Full article

Circulating tumor DNA (ctDNA) profiling offers non-invasive insights for personalized prostate cancer management. This systematic review provides the first comprehensive appraisal of ctDNA assay methods, genomic targets, and their clinical correlations and proposes practical recommendations to guide future standardization and validation. We searched PubMed, ScienceDirect, Scopus, and the Cochrane Library starting December 2024 following PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. From 229 records, 44 studies (10,631 patients) met the inclusion criteria. Plasma ctDNA analyzed by NGS predominantly profiled TP53 (72.7%), AR (70.4%), BRCA1/2 (61.3%), ATM (50%), RB1 (47.7%), and PTEN (41%). ctDNA positivity and specific key alterations correlated with poorer overall and progression-free survival. BRCA1/2-mutant patients benefited from Olaparib plus Abiraterone, while persistent alterations predicted early progression. Beyond synthesizing existing evidence, we identify key gaps, such as inconsistent reporting of variant allele fractions, limited diversity in study populations, and underexplored rare alterations. We recommend unified reporting standards (e.g., variant allele frequency thresholds and panel composition) and prioritized prospective trials to validate high-impact targets. These steps will accelerate the integration of ctDNA into routine precision oncology practice worldwide. Full article

Genealogy is a powerful analytical framework for understanding the cultural evolution of tribes, communities, and societies. This article demonstrates that the recurrent reliance on genealogical structures is a common feature of human societies, serving as a fundamental mechanism for cultural evolution through time, space, and culture. Based on comparative analysis of indigenous tribal societies (e.g., Aboriginal Australian kinship, Polynesian chiefly genealogies), agrarian civilizations (e.g., European feudal lineages, Chinese patriliny), and modern nation-states (e.g., nationalist mythmaking, DNA-based ancestry movements), this study reveals consistent patterns in genealogical functions. Drawing on an interdisciplinary perspective from anthropology, sociology, history, and evolutionary biology, it is argued that genealogical systems are not passive records of descent but dynamic forces of cultural continuity and adaptation. The evidence shows that, despite vast sociocultural differences, genealogy widely operates as a dual-purpose instrument. It preserves cultural memory and legitimizes political authority while simultaneously facilitating social adaptation and innovation in response to new challenges. The paper also critiques contemporary trends like commercial genetic genealogy, highlighting its potential for reconnecting diasporic communities alongside its risks of biological essentialism. Ultimately, the work establishes that the persistent and patterned reliance on genealogy from oral traditions to genetic data offers a critical lens for understanding the deep structures of cultural continuity and transformation in human societies. It further underscores the importance of genealogy in cultural evolution, historical persistence, societal transformation, and the construction of belonging in an increasingly globalized world. Full article

Soil moisture plays a key role in the critical zone of the Earth and has extensive value in the understanding of hydrological, agricultural, and environmental processes (among others). Long-term (in situ) monitoring of soil moisture measurements is generally not practical; however, short-term measurements are often found. Limited soil moisture measurements can be employed to develop a numerical model for long-term and accurate soil moisture estimations. A key input variable to the model is precipitation, which is also not easily accessible, particularly at a finer spatial resolution; hence, publicly available remote sensing data can be used as an alternative. This study, therefore, aims to develop a numerical model HYDRUS-1D to estimate soil moisture in the data-scarce state of the Northern Territory, Australia, with a land cover of shrubland and a Tropical-Savannah type climate. The HDYRUS-1D is based on the numerical solution of Richards’ equation of variably saturated flow that relies on information about the soil water retention characteristics. This study utilized the van Genuchten model parameters, which were optimized (against measured soil moisture) through parameter optimization with initial estimates obtained from the HYDRUS catalogue. Initial estimates from different sources can differ for the same soil texture (e.g., loamy sand) and can induce uncertainties in the calibrated model. Therefore, a comprehensive uncertainty analysis was conducted to address potential uncertainties in the calibration process. The HYDRUS-1D was calibrated for a period between March 2012 and February 2013 and was independently validated against three different periods between March 2013 and October 2016. Root Mean Square Error (RMSE), Pearson’s correlation coefficient (R), and Mean Absolute Error (MAE) were used to assess the efficiency of the model in simulating the measured soil moisture. The model exhibited good performance in replicating measured soil moisture during calibration (RMSE = 0.00 m³/m³, MAE = 0.005 m³/m³, and R = 0.70), during validation period 1 (RMSE = 0.035 m³/m³ and MAE = 0.023 m³/m³, and R = 0.72), validation period 2 (RMSE = 0.054 m³/m³ and MAE = 0.039 m³/m³, and R = 0.51), and validation period 3 (RMSE = 0.046 m³/m³ and MAE = 0.032 m³/m³, and R = 0.61), respectively. Remotely sensed precipitation data were used from the CHRS-PERSIANN, CHRS-CCS, and CHRS-PDIR-Now to assess their capabilities in estimating soil moisture. Efficiency evaluation metrics and visual assessment revealed that these products underestimated the soil moisture. The CHRS-CCS outperformed other products in terms of overall efficiency (average RMSE of 0.040 m³/m³, average MAE of 0.023 m³/m³, and an average R of 0.68, respectively). An integrated approach based on numerical modelling and remote sensing employed in this study can help understand the long-term dynamics of soil moisture and soil water balance in the Northern Territory, Australia. Full article