SASTRA University

Although 1-alkanols are widely used as anesthetics and membrane-active agents, the molecular basis of their chain-length-dependent cutoff behavior remains unclear. Here, we perform extensive atomistic molecular dynamics simulations to investigate the partitioning of 1-alkanols with varying chain lengths in a raft-like lipid bilayer composed of dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylcholine (DOPC), and cholesterol (Chol), which exhibits coexistence of liquid-ordered (lo) and liquid-disordered (ld) domains. We observe pronounced lateral heterogeneity in alkanol distribution, membrane thickness, number density, and lateral pressure profiles across coexisting phases. A distinct cutoff chain length, ncutoff = 12, is identified: alkanols with n < ncutoff preferentially partition into DOPC-rich ld domains, whereas alkanols with n ≥ ncutoff preferentially localize within DPPC- and cholesterol-rich lo domains. Our results indicate a reduction in the magnitude of the lateral pressure profile and the associated elastic moments upon incorporation of 1-alkanols relative to the alkanol-free membrane, within statistical uncertainty. The results provide a detailed molecular characterization of how alkanol chain length modulates the membrane structure and mechanical response in laterally heterogeneous lipid membranes.

We investigate the collective dynamics of the two-dimensional swarmalator model under external forcing and phase lag. Although forcing and phase lag effects have been studied individually in swarmalator models, we extend the investigation by examining their competing interactions, offering a more realistic representation of the swarmalator dynamics. We analyze how phase lag affects both spatial and phase dynamics of the system, resulting in translational motion of the swarmalator collectives. In addition to the static chimera and static asynchronous states, the swarmalator collectives exhibit three distinct translational states: active translational state, coherent translational state, and active translational chimera. Our results show that swarmalator collectives under external forcing drift away from the static external stimulus to a larger distance under the attractive coupling than the repulsive coupling. The critical forcing strength (Fc) required to suppress translational motion increases with phase lag, highlighting its role in sustaining translational motion. We numerically characterize the different dynamical collective states in the phase diagram by employing appropriate order parameters and evaluating the Euclidean distance. We expect this work to advance our understanding of systems with non-reciprocal interactions under external forcing, which is commonly found in sociology, ecology, and robotics.

Computational blind challenges offer critical, unbiased opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the past decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral Platform Discovery Consortium's pan-coronavirus antiviral discovery program, in partnership with Polaris and the OpenADMET project. This collaborative initiative invited global participants from both academia and industry to develop and apply computational methods to predict the biochemical potency and crystallographic ligand poses of small molecules against key coronavirus targets, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (Mpro), as well as multiple ADMET assay end points, using previously undisclosed comprehensive experimental drug discovery data sets as benchmarks. By evaluating submissions across multiple tasks and compounds, we established performance leaderboards and conducted meta-analyses to assess methodological strengths, common pitfalls, and areas for improvement. This analysis provides a foundation for best practices in real-world machine learning evaluation, grounded in community-driven benchmarking. We also highlight how next-generation platforms, such as Polaris, enable rigorous challenge design, embedded evaluation frameworks, and broad community engagement. This paper reports the collective findings of the challenge, offering a high-level overview of the data, evaluation infrastructure, and top-performing strategies. We further provide context and support for the accompanying papers authored by the challenge participants in this special issue, which explore individual approaches in greater depth. Together, these contributions aim to advance reproducible, trustworthy, and high-impact computational methods in drug discovery, and to explore best practices and pitfalls in future blind challenge design and execution, including planned initiatives for the OpenADMET project.

Detection of defects in diverse domains requires a specialized object detection system capable of identifying various types of flaws of different sizes and shapes. This research addresses detection challenges across six critical domains: saline bottle level monitoring, screw defect detection, magnetic tile inspection, road crack analysis, fabric flaw identification, and potato leaf disease recognition. These applications exhibit unique visual characteristics, including variable defect morphologies, subtle texture variations, and domain-specific features, which conventional detection models often fail to adequately address. Standard Faster R-CNN implementations with ResNet-50 and VGG-16 backbones offer general feature extraction but lack domain-specific optimization for these specialized applications. We propose LightDefectNet-18, a custom CNN backbone for Faster R-CNN featuring dual residual blocks with skip connections, strategic kernel sizing, and progressive channel expansion, integrated with a Feature Pyramid Network (FPN) architecture. The FPN component creates a multi-scale feature hierarchy through top-down pathways and lateral connections, effectively detecting defects across scales. The architecture incorporates batch normalization layers, calibrated dropout, and proper weight initialization to enhance feature preservation and gradient flow. When integrated with Faster R-CNN, we implement refined anchor configurations optimized for multi-scale defect detection across our target applications, with tailored anchor sizes and aspect ratios for each pyramid level. The detection pipeline employs an adaptive optimization strategy with learning rate scheduling and early stopping mechanisms. The quantitative evaluation demonstrates superior detection performance across all target applications compared to standard backbones, with significant improvements in Average Precision using a relaxed IoU threshold specifically calibrated for industrial defect detection scenarios. The model's FPN-enhanced architecture effectively addresses the challenges of capturing fine-grained visual features essential for distinguishing subtle anomalies at multiple scales in specialized materials while maintaining computational efficiency suitable for deployment in real-world industrial and agricultural monitoring systems, even with limited training data.

With rising pollution, depleting fossil fuels, and the urgent need to combat climate change, switching to electric vehicles is no longer a choice but a necessity. Conducting constructive research is crucial for selecting the right electric vehicle to ensure a sustainable and energy-secure future. An effective decision-making process requires a method that evaluates multiple criteria to ensure the best choices. To support this goal, this study develops a decision model using the spherical linear Diophantine fuzzy (SLDF) method. SLDF sets enhance traditional fuzzy sets by incorporating three control parameters, which better capture human judgment and provide deciders with the flexibility to handle complex decision-making scenarios. Additionally, distance-similarity metrics serve as key information tools for ranking alternatives based on their closeness. So, this study presents a new distance-based similarity metric for spherical linear Diophantine fuzzy sets (SLDFSs) and thoroughly examining its attributes. To evaluate the effectiveness of the proposed metric, we perform a comparative analysis with existing methods in the literature. Furthermore, for the selection process, we adapt the Vlse Kriterijuska Optimizacija I Komoromisno Resenje (VIKOR) method to the SLDF framework, leading to the innovative SLDF-VIKOR approach. Additionally, we apply the proposed similarity metric to clustering analysis, demonstrating its practical value in Multi-Criteria Group Decision Making (MCGDM). To double-check the veracity of SLDF-clustering methodology, we conduct sensitivity analysis in three special cases.