Browse Articles

Dose and genotype dependent effects of foliar acetic acid on sweet corn under water deficit

Abstract Sweet corn ( Zea mays L. var. saccharata) faces significant challenges due to water deprivation caused by global water scarcity. This study investigates the potential of acetic acid to enhance sweet corn resilience by modulating morphophysiological and biochemical traits under water deficit conditions. Three genotypes were subjected to foliar application of acetic acid at altered concentrations under water deprivation. Water deprivation significantly decreased specific root and shoot length and root volume in all genotypes. However, acetic acid alleviated these adverse effects, particularly in Tyson, which exhibited a notable increase, indicating enhanced adaptability to stress. Specific leaf area increased after treatment with acetic acid; likewise, stomatal conductance showed a significant increase in Messenger and Tyson on application with acetic acid under water deprivation. Chlorophyll-a and chlorophyll-b exhibited genotype-specific and concentration-dependent responses to the treatments, with significant increases observed in Tyson with acetic acid application at 10 mM under water deprivation. Chlorophyll fluorescence parameters varied; while Messenger and GSS 8529 showed non-significant results, Tyson exhibited significant increases in actual photochemical efficiency, mainly with a low concentration of acetic acid under water deprivation. Acetic acid also reduced malondialdehyde levels, a marker of oxidative stress, across all genotypes under stress, and increased peroxidase activity in Tyson and GSS 8529, indicating enhanced antioxidant defences. These findings suggest that acetic acid application effectively mitigates the effects of water deprivation by enhancing photosynthetic efficiency, antioxidant defences, and growth parameters, showing substantial genotype differences. Further research is recommended to optimize acetic acid treatments, considering genotype-specific responses to maximize stress resilience and growth performance.

Seismic characterization of inland and coastal sabkhas using VP, VS, seismic anisotropy, and attenuation

Molecular Motif Learning as a pretraining objective for molecular property prediction

Surge arrester leakage current modeling based on pollution layer electrical conductivity estimation

Abstract The surface leakage current (LC) of metal oxide surge arresters (MOSA) is highly dependent on environmental conditions. This factor influences the modeling of MOSA leakage current as an alternative solution to laboratory tests. In this paper, a new approach to modeling the surface leakage current of MOSA based on the estimation of the electrical conductivity (EC) of the contaminated layer is presented, which provides the ability to model the surge arrester LC in different environmental conditions with high accuracy. The estimation of the EC of the polluted layer has been performed using artificial intelligence (AI) based on laboratory tests considering the effect of uniform and non-uniform pollution, humidity, pollution intensity and voltage on three types of 20 kV silicon rubber surge arresters. Mean Squared Error (MSE) and Coefficient of Determination were used for assessing the ability of AI based method in EC estimation. Finite element method (FEM)-based software has been used for surge arrester modeling. The use of the estimated electrical conductivity characteristic in the FEM model has made it possible to evaluate the effect of the internal and external currents of the MOSA on the total leakage current in different scenarios. The comparison of the results obtained from proposed model and laboratory test indicate the capability of the proposed method in estimating the EC, modeling the LC, and their generalization to cases for which laboratory test results are not available.

A creative approach for the protection coordination of microgrids considering parallel events via the pyramidal grouping technique

ER exit sites mediated by the COPII adaptor sec24D selectively recruit lipid raft-preferring proteins for rapid ER export

Abstract The determinants of sub-cellular trafficking for many membrane proteins are poorly understood. Lipid-driven membrane nanodomains known as lipid rafts have been widely implicated in post-Golgi traffic, but their involvement in protein sorting in the endoplasmic reticulum has not been widely considered. To assess the role of membrane domains in the early secretory pathway, we use the Retention Using Selective Hooks system to synchronize and quantitatively assess trafficking rates and destinations of model proteins with tunable raft affinities. We find that raft-preferring constructs exit the ER faster than raft-excluded and have distinct preferences for ER exit sites marked by specific isoforms of sec24 cargo adaptors. Namely, raft-excluded cargo localizes to sec24A-positive sites while raft-preferring cargo localizes to sec24D ERES, dependent on p24-family cargo adapters TMED2/10. Finally, sec24D, but not sec24A, ERES accumulate a fluorescent cholesterol analog. These observations suggest that association with raft-like domains affects protein export from the ER.

m6A regulators-based gene expression pattern is associated with immune microenvironment characteristics in hepatocellular carcinoma

Development and validation of machine learning models for assessing the risk of postoperative venous thromboembolism in cervical cancer patients

Low complexity hybrid algorithm for improving PAPR BER and PSD in OTFS under diverse channel conditions

A multidimensional, efficient, and secure data query based on privacy preservation in vehicular ad hoc networks

For vehicular ad hoc networks (VANET) to achieve intelligent transportation applications, efficient and secure data querying is essential. However, sophisticated multidimensional data processing, easy user privacy leaks, and low computational efficiency in resource-constrained contexts are some of the main issues that data querying in VANET environments encounters. To address these issues, this paper proposes an efficient fine-grained data query system (EFDA) based on lightweight masks that allows vehicle users to safely and in real-time query multidimensional traffic data. First, multifaceted data vectors are effectively integrated into a single cipher processing unit using a multidimensional CRT transformation method that counts the number of valid data. Paillier homomorphic encryption and the lightweight region feature masking technique are used to provide safe aggregation while preserving the privacy of the original data. Second, the ECDSA signature is used to ensure source dependability and data integrity. Lastly, to lower system risk and enhance data quality, an effective malicious node monitoring method based on dichotomous recursion and a reputation incentive mechanism based on user feedback is presented. According to security analysis, the EFDA scheme meets the threat model’s specified security requirements for data confidentiality, integrity, source reliability, and identity privacy. According to the performance simulation evaluation, the EFDA system lowers the computation overhead by 85.7% and 90.1% and the communication overhead by 69.1% and 39.2% when compared to the reference scheme. It achieves the balance between privacy protection and query efficiency and validates its viability and efficiency in the resource-constrained in-vehicle network environment.

Enhancing unsupervised bearing fault diagnosis through structured prediction in latent subspace

Abstract Fault diagnosis techniques are essential for preventing equipment failures, reducing maintenance costs, and enhancing operational efficiency by promptly identifying anomalies. The widespread deployment of industrial sensors has significantly increased the availability of machinery data, facilitating extensive research in data-driven fault diagnosis. However, real-world datasets frequently exhibit label scarcity and severe class imbalance, where fault instances are substantially fewer than normal samples. To address these challenges, this study proposes a robust unsupervised domain adaptation framework that synthesizes fault signals by interpolating real healthy samples with domain-specific knowledge. Although this synthetic augmentation effectively expands training data, the resulting distribution often deviates from actual fault scenarios, limiting model generalizability. To alleviate this domain discrepancy, our framework incorporates Conditional Domain-Adversarial Networks (CDAN) for domain-invariant feature extraction, complemented by structured pseudo-labeling to assign reliable predictions to unlabeled target samples. Subsequently, a Locality Preserving Projection (LPP) module constructs a shared latent space to achieve both domain alignment and enhanced class discrimination. Experimental evaluations conducted on a synthetic dataset derived from the CWRU bearing benchmark demonstrate that the proposed method achieves accuracies of 91.10% under imbalanced conditions and 84.65% in balanced scenarios, surpassing current state-of-the-art methods by 12.87% and 3.57%, respectively. Ablation studies further underscore the significant contribution of structured pseudo-labeling to the overall performance, confirming the proposed approach’s efficacy and robustness in real-world unsupervised industrial fault diagnosis tasks.

Time pressure alters takeoff but not landing biomechanics in single-leg countermovement jumps

This study examined how time pressure influences lower-limb biomechanics during single-leg maximal countermovement jumps (CMJs), with a focus on kinetic and kinematic responses during both jumping and landing phases. Participants performed single-leg CMJs under two conditions: self-paced (SP) and reaction-time (RT), the latter simulating time-constrained environments. Joint angles, ground reaction forces (vGRF), and joint moments were analyzed. Significant differences emerged between SP and RT tasks in jumping-phase kinetics and kinematics, with only kinematic differences present during landing. The RT condition led to reduced hip and knee flexion, increased peak vGRF, and shorter flight times, yet no improvement in jump height. This suggests inefficient energy transfer possibly due to reduced range of motion and increased muscle co-contraction or pretension strategies. Joint moment analysis revealed a shift from a hip-dominant strategy in SP to a knee-dominant strategy in RT. Landing in RT was characterized by reduced joint flexion and increased frontal plane loading, potentially elevating the risk of lower-limb injury. Time pressure modifies motor strategies in single-leg CMJs, promoting faster execution at the cost of performance efficiency. These findings underscore the importance of training for both explosive performance and neuromuscular control under time-constrained, sport-specific conditions.

Simulating nationwide coupled disease and fear spread in an agent-based model

Abstract Human cognitive responses, behavioral responses, and disease dynamics co-evolve over the course of any disease outbreak, and can result in complex feedbacks. We present a dynamic agent-based model that explicitly couples the spread of disease with the spread of fear surrounding the disease, implemented within the EpiCast simulation framework. EpiCast models transmission within a realistic synthetic population, capturing individual-level interactions. In our model, fear propagates through both in-person contact and broadcast media, prompting individuals to adopt protective behaviors that reduce disease spread. In order to better understand these coupled dynamics, we create and compare a range of compartmental models to ensure that introducing additional disease states does not prevent the emergence of multiple waves in these simpler models. Additionally, we compare a range of behavioral scenarios within EpiCast, varying the level and intensity of fear and behavior change. Our results show that the addition of asymptomatic, exposed, and pre-symptomatic disease states can impact both the rate at which an outbreak progresses and its overall trajectory in compartmental models. In EpiCast, the combination of non-local fear spread via broadcasters and strong behavioral responses by fearful individuals generally leads to multiple epidemic waves, an outcome that occurs only within a narrow parameter range when fear spreads purely through local contact. Accounting for the coupled spread of fear and disease is critical for understanding disease dynamics and designing timely, targeted responses to emerging infectious threats.

The effect of morpheme positional frequency on tibetan novel word acquisition: An eye-tracking study

Word segmentation is crucial for reading in unspaced languages like Tibetan, where readers rely on high-level cues like morpheme positional frequency (the statistical likelihood of a morpheme appearing at the beginning or end of a word). Using a novel word learning paradigm, this study investigated whether initial and final morpheme positional frequency facilitate word segmentation and lexical recognition in Tibetan. In two eye-tracking experiments, participants read sentences containing novel words manipulated for initial (Experiment 1) or final (Experiment 2) morpheme positional frequency across learning and testing phases. Results showed that while initial morpheme positional frequency influenced saccade target selection and the late stage of lexical recognition, final morpheme positional frequency primarily affected late stage of lexical recognition. Reading phase improved efficiency across all measures. The effects of morpheme positional frequency and the reading phase were independent at both early and late stages of lexical recognition. Findings provided evidence that morpheme positional frequency may serve as a functional word segmentation cue in the context of Tibetan novel word acquisition.

Hybrid experimental and machine learning approach for optimizing abrasive wear of microcrystalline cellulose modified hemp/bamboo fiber composites

Abstract In this work, hemp/bamboo hybrid fabric–epoxy composites reinforced with 0–9 wt% microcrystalline cellulose (µCC) is examined for their abrasive wear behavior. Compression molding was used to create composites with 0, 3, 6, and 9 wt% µCC. In accordance with ASTM G65 guidelines, wear tests were conducted under controlled dry sand abrasion. Using a Taguchi L 16 design, the effects of applied load (5–20 N), abrading distance (250–1000 m), and µCC content on wear loss were assessed. To predict abrasive wear and examine the role of µCC filler, several machine learning models were used, including Linear Regression, K-Nearest Neighbors, Artificial Neural Networks, Random Forest, Gradient Boosting, and eXtreme Gradient Boosting. By increasing the hardness and load-bearing capacity of the composite, µCC mechanistically increases wear resistance and lessens material removal during abrasion. According to ANOVA results, wear loss was most affected by abrading distance (44.08%), load (34.21%), and µCC content (18.01%). The Random Forest model had the lowest error (RMSE = 0.045) and the highest predictive accuracy (R 2  = 0.942). Abrading distance is the main factor influencing wear resistance, followed by load and µCC content, according to feature importance analysis. Accurately forecasting abrasive wear and creating high-performance, sustainable hybrid composites can be accomplished by combining machine learning and experimental data.

Combining transcriptomics with network pharmacology to explore the mechanism of Yiqi Huoxue decoction against liver fibrosis

Background Clinical practice commonly uses the Yi-qi Huo-xue formula (YQHX), a traditional Chinese herbal medicine comprising eight herbal components, to treat liver fibrosis resulting from various etiologies. Nevertheless, this formula’s specific active constituents and underlying mechanisms of action remain to be fully elucidated. Methods The drug components of YQHX and potential targets for liver fibrosis were identified via the screening of the various databases. Qualitative and quantitative identification of chemical components of drug-containing serum by Ultra Performance Liquid Chromatography (UPLC).Liver fibrosis was induced in mice through the intraperitoneal injection of carbon tetrachloride, followed by oral administration of YQHX. RNA-Seq quantified transcriptomic profiles in liver tissue.The degree of liver fibrosis was assessed via histopathology staining, the transcription and expression of relevant proteins were analyzed. Primary cells were isolated for in vitro experiments to validate the influence of YQHX on the associated signaling pathways. Results Network pharmacology identified IL-1β, IL-6, and TNF-α as potential targets for YQHX in treating liver fibrosis.The UPLC detected multiple potential active components. In vivo experiments showed that YQHX reduced serum AST and ALT levels in liver fibrosis-induced mice, decreased liverIL-1β, IL-6, and TNF-α levels, and improved liver fibrosis.The results of transcriptomics suggest that YQHX can reduce the expression of “collagen-activated signaling pathway,” “MyD88-dependent toll-like receptor signaling pathway,” “fibrinolysis” and “toll-like receptor 4 signaling pathway”. Furthermore, YQHX reduced the aggregation of M1 macrophages in the portal area and the deposition of α-SMA. Primary bone marrow-derived cells successfully transformed into M1 macrophages after induction, and YQHX reduced the levels of IL-1β, IL-6, and TNF-α in the supernatant of M1 macrophage culture and decreased the activation of primary hepatic stellate cells indirectly co-cultured with the supernatant. Interestingly, TLR4 agonists weakened this inhibitory effect. Both in vitro and in vivo experiments demonstrated that YQHX could inhibit the expression of the TLR4/TRAF6/MyD88 pathway in M1 macrophages. Conclusion We reveal here the molecular mechanism and signaling pathway of YQHX in treating liver fibrosis by utilizing network pharmacology in conjunction with in vivo and in vitro experiments. The findings offer insights that may advance the clinical application of YQHX.

Performance investigation of Xanthan gum polymer flooding for enhanced oil recovery using machine learning models

Who gets counted? Understanding low female death registration in India

Background Civil Registration and Vital Statistics (CRVS) systems are essential for governance, public health, and achieving SDGs however, gender gaps limit women’s access to rights and services, with under-registration of female vital events reinforcing their invisibility and distorting gender-responsive policies. Objectives This study examines the drivers of low female death registration across India’s States and Union Territories, focusing on the roles of age, gender and wealth, with an aim to inform policies to strengthen CRVS systems and reduce gender disparities in vital statistics. Methods The study utilizes data from NFHS-5 (2019–2021 for examining the factors associated with female death registration. Multivariable logistic regression models have been used to examine the impact of socio-economic and demographic factors on female death registration in India. Findings The results highlight a significant gender gap in death registration (73% male vs. 64% female). The gap is widest in states like Bihar and Uttar Pradesh, while states like Kerala and Goa report near universal registration for both sexes. Gender gaps in housing and land ownership align with gaps in death registration, suggesting a strong association between asset ownership and registration. The results highlight association between wealth and death registration, with rates rising across quintiles for both sexes; however males consistently have higher registration rates. Among the poorest, the gap is widest which narrows down in the richest group. A gender gap in death registration persists across all age groups in India, beginning early, widening during working ages, and continuing into old age; while registration rates improve with age and wealth, women especially among the poorest remain under-registered, particularly in early and later life stages. Conclusions Women in India encounter barriers to civil registration, and improving death registration demands systemic reforms, digital advancements, and community engagement Strengthening political commitment, collaboration, and public awareness will ensure inclusive, accurate records, enhancing CRVS for governance and policy.

Multiscale molecular dynamics investigation of high-efficiency hydrogen storage in titanium-doped carbon nanotubes

Exercise: The key to enhancing sleep quality and physical function in Parkinson’s disease? A systematic review and meta-analysis

Parkinson’s disease (PD) is a significant neurodegenerative disorder that affects millions of individuals worldwide and currently has limited effective treatment options. Exercise has been proposed as a non-pharmacological intervention to improve both motor and non-motor symptoms in PD. This study aims to systematically review and meta-analyze the impact of exercise interventions on sleep quality and physical functioning in PD patients. A comprehensive search of the literature up to December 15, 2023, identified randomized controlled trials that evaluated exercise interventions in PD patients. The primary outcomes were sleep quality, motor function, balance, gait performance, and quality of life. A total of 62 studies with 3,274 participants were included in the analysis. Exercise interventions led to significant improvements in sleep quality [SMD = −0.55, 95% CI (−0.91, −0.18), p  = 0.003], motor capability [SMD = −0.47, 95% CI (−0.66, −0.28), p  < 0.01], balance ability [SMD = 0.53, 95% CI (0.33, 0.74), p  < 0.0001], gait performance [TUGT: SMD = −0.44, 95% CI (−0.60, −0.29), p  = 0.0017; stride velocity: SMD = 0.38, 95% CI (0.15, 0.60), p  = 0.001; step length: SMD = 0.32, 95% CI (0.10, 0.54), p  = 0.004], and quality of life [SMD = −0.38, 95% CI (−0.73, −0.03), p  = 0.04] ( p   < 0.05). Exercise is an effective intervention for enhancing sleep quality and improving physical function in PD patients. These findings underscore the importance of incorporating exercise into the management strategies for PD.