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Functional magnetic resonance imaging has revealed correlated activities in brain regions even in the absence of a task. Initial studies assumed this resting-state functional connectivity (FC) to be stationary in nature, but recent studies have modeled these activities as a dynamic network. Dynamic spatiotemporal models better model the brain activities, but are computationally more involved. A comparison of static and dynamic FCs was made to quantitatively study their efficacies in identifying intrinsic individual connectivity patterns using data from the Human Connectome Project. Results show that the intrinsic individual brain connectivity pattern can be used as a ‘fingerprint’ to distinguish among and identify subjects and is more accurately captured with partial correlation and assuming static FC. It was also seen that the intrinsic individual brain connectivity patterns were invariant over a few months. Additionally, biological sex identification was successfully performed using the intrinsic individual connectivity patterns, and group averages of male and female FC matrices. Edge consistency, edge variability and differential power measures were used to identify the major resting-state networks involved in identifying subjects and their sex.

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were measured using an EEG. The CSNN’s performance was compared to a standard CNN, EEGNet and three graph neural networks via 10-fold cross-validation. The CSNN outperformed all the other neural networks, and had a predictive accuracy of 99.06% with a true positive rate of 98.50%, a true negative rate of 99.20% and an F1-score of 0.98. Performance of the CSNN was comparable to the CNN in an ablation study using a subset of EEG channels that localized SCPs. Classification performance of the CSNN degraded only slightly when the floating-point EEG data were converted into spike trains via delta modulation to mimic synaptic connections.

The present work explores the synergistic impact of mullite-based thermal barrier coatings (TBCs) and varying compression ratios (CRs) on the performance, combustion, and emission behavior of a single-cylinder, four-stroke variable compression ratio (VCR) diesel engine operated with Scum Oil Methyl Ester (SOME) diesel fuel blends. Mullite ceramic (3Al 2 O 3 ·2SiO 2 ) was applied via plasma spraying onto engine components including the piston crown, cylinder head, and intake/exhaust valves to minimize thermal losses. Experimental tests were carried out at CRs of 16.0, 17.5, and 19.0 using conventional diesel and blends of SOME (B20, B40, B60, B80, and B100 samples) in both coated and uncoated engine setups. The coated engine exhibited its best performance at CR 19, recording a BTE improvement of 7.51% for diesel and 5.75% for B20, alongside BSFC reductions of 14.28% and 9.09%, respectively, compared to the uncoated configuration. Additional enhancements were observed in terms of peak in-cylinder pressure, heat release rate, combustion efficiency, and reduced ignition delay and combustion duration. Emission measurements showed notable decreases in CO, HC, and smoke11.41%, 10.56%, and 9.43% for B20, and 6.16%, 7.10%, and 8.84% for diesel while only slight increases in NO x and CO 2 emissions were recorded. These findings highlight the effectiveness of combining biodiesel and thermal barrier coatings in achieving improved efficiency and lower emissions, with the B20 blend at CR 19 showing the most favorable results for sustainable diesel engine operation.

This study seeks to optimize and evaluate the wear behavior of glass fiber–reinforced epoxy (G-E) nanocomposites incorporating organomodified montmorillonite (oMMT) nanoclay through a rigorous statistical modeling framework. Nanocomposites were prepared with different grades of oMMT (NC-I, NC-II, NC-III, NC-IV) and compared with neat G-E + NC-00 and unmodified NC-V composites. Structural characterization through XRD confirmed enhanced interlayer spacing (up to 5.04 nm for E + NC-III), indicating successful intercalation/exfoliation, while FTIR spectra verified the incorporation of epoxy chains within organoclay galleries. Dry-sliding wear behaviour was evaluated on a pin-on-disc tribometer as per ASTM G99-17 standards, with applied load (8.18–41.82 N), sliding velocity (0.25–2.44 m/s), and sliding distance (318–2177 m) as process variables. Among all tested systems, the G-E + NC-III nanocomposite consistently exhibited the lowest specific wear rate (9.54–40.29 × 10 − 6 mm 3 /Nm), outperforming the neat matrix (10.11–43.33 × 10 − 6 mm 3 /Nm) across all loading and velocity conditions. Response Surface Methodology (RSM) and Central Composite Design (CCD) were employed to reduce experimental runs and evaluate parameter interactions. ANOVA revealed that applied load was the dominant factor influencing wear, contributing 68.73% in G-E + NC-00 and 69.03% in G-E + NC-III composites, followed by sliding velocity (≈ 7%) and quadratic effects of AL 2 and SV 2 (≈ 12%). Regression models developed for both systems demonstrated excellent correlation with experimental data (R 2 = 96.4% for G-E + NC-00 and 95.65% for G-E + NC-III), with prediction errors ranging between 4.1% and 11.9%. Overall, the incorporation of oMMT nanoclay, particularly NC-III, significantly enhanced interfacial adhesion, promoted uniform dispersion, and reduced wear loss under varying tribological conditions. The statistical analysis confirms Response Surface Methodology (RSM) as a robust and effective approach for predicting wear performance and identifying optimal processing parameters. The optimized formulation (G-E + NC-III) exhibits strong potential for use in high-wear engineering domains such as automotive body panels, aerospace components, and structural assemblies, where the combination of lightweight construction and enhanced durability is critical.

Oppositional defiant disorder and conduct disorder, collectively referred to as disruptive behavior disorders (DBDs), are prevalent psychiatric disorders in children. Early diagnosis of DBDs is crucial because they can increase the risks of other mental health and substance use disorders without appropriate psychosocial interventions and treatment. However, diagnosing DBDs is challenging as they are often comorbid with other disorders, such as attention-deficit/hyperactivity disorder, anxiety, and depression. In this study, a multimodal ensemble three-dimensional convolutional neural network (3D CNN) deep learning model was used to classify children with DBDs and typically developing children. The study participants included 419 females and 681 males, aged 108–131 months who were enrolled in the Adolescent Brain Cognitive Development Study. Children were grouped based on the presence of DBDs ( n = 550) and typically developing ( n = 550); assessments were based on the scores from the Child Behavior Checklist and on the Schedule for Affective Disorders and Schizophrenia for School-age Children-Present and Lifetime version for DSM-5. The diffusion, structural, and resting-state functional magnetic resonance imaging (rs-fMRI) data were used as input data to the 3D CNN. The model achieved 72% accuracy in classifying children with DBDs with 70% sensitivity, 72% specificity, and an F1-score of 70. In addition, the discriminative power of the classifier was investigated by identifying the cortical and subcortical regions primarily involved in the prediction of DBDs using a gradient-weighted class activation mapping method. The classification results were compared with those obtained using the three neuroimaging modalities individually, and a connectome-based graph CNN and a multi-scale recurrent neural network using only the rs-fMRI data.

In this work, a DC microgrid consists of a solar photovoltaic, wind power system and fuel cells as sources interlinked with the utility grid. The appropriate sizing and positioning of electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) are concurrently determined to curtail the negative impact of their placement on the distribution network’s operational parameters. The charging station location problem is presented in a multi-objective context comprising voltage stability, reliability, the power loss (VRP) index and cost as objective functions. RES and EVCS location and capacity are chosen as the objective variables. The objective functions are tested on modified IEEE 33 and 123-bus radial distribution systems. The minimum value of cost obtained is USD 2.0250 × 106 for the proposed case. The minimum value of the VRP index is obtained by innovative scheme 6, i.e., 9.6985 and 17.34 on 33-bus and 123-bus test systems, respectively. The EVCSs on medium- and large-scale networks are optimally placed at bus numbers 2, 19, 20; 16, 43, and 107. There is a substantial rise in the voltage profile and a decline in the VRP index with RESs’ optimal placement at bus numbers 2, 18, 30; 60, 72, and 102. The location and size of an EVCS and RESs are optimized by the modified teaching-learning-based optimization (TLBO) technique, and the results show the effectiveness of RESs in reducing the VRP index using the proposed algorithm.

Black pepper ( L.), renowned as the \"King of Spices,\" holds significant economic and medicinal value but is highly susceptible to drought stress, which impacts its growth and productivity. Several studies have reported the impact of drought stress on morphological, physiological and biochemical characteristics, while the molecular mechanism underlying drought tolerance remains largely unexplored. This study focusses on the molecular basis of drought tolerance in black pepper through identification of differentially expressed genes (DEGs) by comparative transcriptome analysis involving drought-tolerant Accession (No. 4226) under control and water deficit conditions, and validation of these DEGs by co-expression analysis involving drought-tolerant (IISR Thevam and Acc. No. 4226) and drought-susceptible (Panniyur-1) genotypes under water-deficit conditions. Reference based assembly of RNAseq data and differential gene expression analysis revealed 2,780 DEGs such as , , , associated with photosynthetic carbon assimilation, stress-induced regulation of protein synthesis and phosphate homeostasis under nutrient and drought stress, respectively. Functional annotation highlighted enriched biological processes such as metabolic reprogramming and secondary metabolite biosynthesis, while pathway analyses emphasized the role of starch and sucrose metabolism and RNA processing pathways in drought adaptation. Validation of key DEGs such as catalase, defensin, , , , and through RT-qPCR confirmed the transcriptome data and the higher expression in drought tolerant accessions, indicated their involvement in imparting tolerance to drought. The findings also provided valuable insights regarding correlation of molecular and physiological mechanisms underlying drought tolerance in black pepper thereby laying the groundwork for developing high-yielding, drought-tolerant black pepper cultivars.

Recent advancement in controllability of digital hydraulic is similar to the performance of a proportional/servo hydraulic system, and several studies show that digital hydraulic will be an alternative for proportional/servo hydraulic. In this paper, tip point tracking of a single-link arm is taken as the subject of the study. Here, the single-link arm is controlled by a digital hydraulic system, which is established with parallel-connected on/off valves. In order to attain stepwise flow control, the pulse code modulation technique is used. By referring to the previous work, the control signal for the trajectory tracking is calculated by taking account of cylinder chamber pressure and velocity. But in this study, the required volume flow rate for trajectory tracing is taken into account for generating control signal. This approach improves the performance of the digital hydraulic system at lower velocity tracking and also reduces the computational complexity. The analysis is conducted with the proposed algorithm for 4-bit and 5-bit digital flow control units and tip point response of single link is presented. The results show that the 5-bit system has significantly better performance than the 4-bit system. In addition, the analysis is conducted with different stroke lengths such as 200, 100 and 50 mm for studying the behaviour of the system at lower velocity tracking. Better controllability is achieved at lower velocity tracking, and the results obtained with the proposed algorithm have nearly 2% tracking error.

Untreated and treated jute fiber and nano-clay in various ratios were used to fabricate jute/nano-clay/epoxy hybrid composites through compression molding method. The dynamic mechanical and free vibration behaviours were evaluated by varying the concentration of NaOH (2.5%, 5% and 7.5%) and wt.% of nano-clay (1, 3, 5 and 7 wt.%). Experimental outcomes disclosed that the storage and loss modulus, damping factor and natural frequency are influenced by concentration of NaOH solution and nano-clay content. A positive shift (towards higher temperature) in glass transition temperature and enhanced natural frequency of the composites after NaOH treatment and nano-clay addition confirmed that superior interfacial bonding exists between the jute fibers and epoxy matrix. Finally, the composites incorporated with 5% treated fiber and 5 wt.% of nano-clay is suggested for low strength structural applications in construction and automobile industries.

Drought or water deficit stress is one of the main environmental stresses affecting plants, resulting in reduced productivity and crop loss. Black pepper, a major spice cultivated across the globe, is drought sensitive and water stress often results in plant death. The present study compared the difference in physiological parameters: relative water content (RWC) and cell membrane leakage, and also analyzed the differential expression of 11 drought responsive genes in drought tolerant and drought sensitive black pepper genotypes. Tolerant black pepper genotype exhibited significantly higher RWC and lower cell membrane leakage 10 days after stress induction than the sensitive genotype. The relative expressions of the 11 selected drought responsive genes were normalized against ubiquitin and RNA-binding protein which was identified as the most stable reference genes in black pepper under the present experimental condition using the RefFinder software. Dehydrin showed the highest transcript accumulation in both the black pepper genotypes under drought stress condition and the relative expression of the gene was higher in the tolerant genotype compared to the susceptible. Similar pattern of higher relative expression was also observed in the stress responsive gene, osmotin. The membrane protein aquaporin and the transcription factor bZIP were relatively down-regulated in the tolerant genotype. The differential expression of these important drought responsive genes in tolerant genotype of black pepper indicates its further usefulness in developing varieties with improved water stress tolerance.