A dual-functional needle-based VOC sensing platform for rapid vegetable phenotypic classification.

Volatile organic compounds (VOCs) are common constituents of fruits, vegetables, and crops, and are closely associated with their quality attributes, such as firmness, sugar level, ripeness, translucency, and pungency levels. While VOCs are vital for assessing vegetable quality and phenotypic classification, traditional detection methods, such as Gas Chromatography-Mass Spectrometry (GC-MS) and Proton Transfer Reaction Mass Spectrometry (PTR-MS) are limited by expensive equipment, complex sample preparation, and slow turnaround time. Additionally, the transient nature of VOCs complicates their detection using these methods.

Defect functionalized morphological, structural, and optical properties of MnS incorporated MoS heterostructure: Experimental and theoretical insight.

The low-cost hydrothermal method has been successfully applied to synthesize MnS-incorporated MoS nanoflowers (MoS/MnS). The FE-SEM, TEM, XRD, Raman, UV-VIS, and density functional theory (DFT) were used to investigate the surface morphology, structural property, optical property, and simulated optical and dielectric properties. FE-SEM and TEM images reveal the 3D flower-like structure of MoS and the flower-like structure of the nanocomposite.

An alignment-free method for phylogeny estimation using maximum likelihood.

Background: While alignment has traditionally been the primary approach for establishing homology prior to phylogenetic inference, alignment-free methods offer a simplified alternative, particularly beneficial when handling genome-wide data involving long sequences and complex events such as rearrangements. Moreover, alignment-free methods become crucial for data types like genome skims, where assembly is impractical. However, despite these benefits, alignment-free techniques have not gained widespread acceptance since they lack the accuracy of alignment-based techniques, primarily due to their reliance on simplified models of pairwise distance calculation.

CFD based neural network model framework for mixed convection in a lid-driven cavity with conductive cylinder.

The current study investigates the application of an Artificial Neural Network (ANN) model to analyze and predict mixed convection heat transfer within a 2-dimensional square cavity with a conductive cylinder at the centre. The top lid of the cavity is maintained at a constant cold temperature and slides with a constant linear velocity, while the bottom wall is heated to maintain a constant temperature. The governing equations are discretized using Galerkin Weighted Residual Method and numerically solved using Gauss Quadrature procedure.

Impact of fixed phosphorus position on activity of triazole bisphosphonates as geranylgeranyl diphosphate synthase inhibitors.

Geranylgeranyl diphosphate synthase (GGDPS) produces the 20-carbon isoprenoid species used in protein geranylgeranylation reactions. Inhibition of GGDPS has emerged as a novel means of disrupting the activity of geranylgeranylated proteins in cancers such as myeloma and osteosarcoma. We have focused on developing a series of isoprenoid triazole bisphosphonate-based GGDPS inhibitors, demonstrating a complex structure-activity relationship (SAR), not only at the enzymatic level, but also at the cellular and whole organism levels.

Thermally Stable Ruthenium Contact for Robust -Type Tellurium Transistors.

Tellurium (Te) is attractive for -channel transistors due to its high hole mobility. Despite having a low thermal budget suitable for back-end-of-line (BEOL) monolithic integration, the practical realization of Te transistors is hindered by its thermal stability. In this work, we investigate thermal stability for Te thin films grown via scalable thermal evaporation.

Facile Synthesis of Bioactive Silver Nanocomposite Hydrogels with Electro-Conductive and Wound-Healing Properties.

Bioactive metal and metal oxide-based nanocomposite hydrogels exhibit significant antibacterial properties by interacting with microbial DNA and preventing bacterial replication. They offer potential applications as coating materials for human or animal skin injuries to prevent microbial growth and promote healing. In this study, silver nanoparticles (AgNPs) were synthesized using a chemical reduction method and incorporated into a polymer network to fabricate silver nanocomposite hydrogels (AgNCHGs) through a simple free radical polymerization method.

Identifying built environment factors influencing driver yielding behavior at unsignalized intersections: A naturalistic open-source dataset collected in Minnesota.

Introduction: Many factors influence the yielding result of driver-pedestrian interactions, including traffic, vehicle, roadway, pedestrian attributes, and more. While researchers have examined the individual influence of these factors on interaction outcomes, there is a noticeable absence of comprehensive, naturalistic studies in current literature, particularly those investigating the impact of the built environment on driver-yielding behavior.

Method: To address this gap, our study introduces an extensive open-source dataset, compiled from video data at 18 unsignalized intersections across Minnesota.

Integrating design and system approaches for analyzing road traffic collisions in low-income settings.

Road crashes have become a critical issue in low- and middle-income countries (LMICs), where complex social and systemic factors exacerbate road safety challenges. This study combines Systems-Theoretic Accident Model and Processes-Systems Theoretic Process Analysis (STAMP-STPA) and Design with Intent (DwI), aiming to address both systemic and design-based factors, providing a comprehensive framework for enhancing road safety interventions in LMICs. A case study of a head-on collision in Bangladesh is analyzed using STAMP-STPA and DwI independently where the former identifies control and feedback failures, and the latter generates creative design solutions for road safety improvements.

Chaotic behavior, sensitivity analysis and Jacobian elliptic function solution of M-fractional paraxial wave with Kerr law nonlinearity.

This study investigates the paraxial approximation of the M-fractional paraxial wave equation with Kerr law nonlinearity. The paraxial wave equation is most important to describe the propagation of waves under the paraxial approximation. This approximation assumes that the wavefronts are nearly parallel to the axis of propagation, allowing for simplifications that make the equation particularly useful in studying beam-like structures such as laser beams and optical solitons.

Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans.

This study presents a semi-automated approach for assessing water quality in the Sundarbans, a critical and vulnerable ecosystem, using machine learning (ML) models integrated with field and remotely-sensed data. Key water quality parameters-Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH-were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. The predictive framework leverages Google Earth Engine (GEE) and AutoML, utilizing deep learning libraries to create dynamic, adaptive models that enhance prediction accuracy.

Biodegradable Chitosan-Based Stretchable Electronics with Recyclable Silver Nanowires.

The combination of biodegradability and biocompatibility makes chitosan a principal bioresourced material in biomedical engineering, wearable technology, and medical diagnostics, particularly for integration in human interfaces for soft electronic applications. However, this requires the introduction of soft electronic circuits with the capability of recycling the functional materials, while biodegrading the substrate. This paper presents the development and characterization of biodegradable soft circuits that are constructed using stretchable and flexible substrates from plasticized chitosan and conductive functional wiring from recyclable silver nanowires (AgNWs).

Influence of shape memory alloy in seismic response of coupled shear wall of concrete structures.

Super-elastic Shape Memory Alloys (SMAs) can undergo reversible phase transitions, experience considerable inelastic deformation, and restore their original shape after heating or stress reduction. These features of SMAs make them suitable candidates as the reinforcement of concrete shear walls particularly in seismic events. A ten-story reinforced concrete building with a SMA reinforced coupled shear wall in a high seismic zone of Bangladesh is investigated using to better understand the dynamic response of SMAs in the walls.

Developing decision making framework on built-up site suitability assessment for urban regeneration in the industrial cities of Eastern India.

Unprecedented urban growth in developing countries impacts the existing urban planning as well as prospective urban regeneration. Therefore, evaluating the prospective suitable sites for built-up area development is important to make sustainable urban planning through the urban regeneration process in the industrial-based urban area Asansol Municipal Corporation (AMC). Hence, we analyzed the area-specific built-up suitability using machine learning soft-computing techniques: Artificial Neural Network, Random Forest, and Support Vector Machine.

Mechanistic insights into the enhanced photocatalytic efficiency of MoS-tuned DyFeO heterojunction nanocomposites for pollutant degradation.

In this study, DyFeO-MoS heterojunction nanocomposites were synthesized by integrating porous DyFeO nanoparticles (an n-type semiconductor) with MoS nanosheets (a p-type semiconductor). The resulting p-n heterojunction substantially improved the photocatalytic efficiency for degrading methylene blue (MB) and levofloxacin (LFX). This design introduces a built-in electric field at the interface, promoting efficient charge separation and suppressing electron-hole recombination, thereby significantly enhancing photocatalytic performance under solar irradiation compared to DyFeO alone.

Multidrug-resistant ESBL in urban surface waters and public health implications: A Case Study from Goranchatbari, Dhaka.

Surface water pollution from rapid urbanization, industrialization, inadequate sanitation, and excessive agrochemical use is a global crisis. In developing countries, water quality is one of the most urgent environmental issues. The presence of pathogenic extended spectrum β-lactamase (ESBL) producing in surface water poses a critical public health concern by increasing the risk of waterborne diseases and spreading multidrug-resistant (MDR) infections.

Explainable AI-driven scalogram analysis and optimized transfer learning for sleep apnea detection with single-lead electrocardiograms.

Sleep apnea, a fatal sleep disorder causing repetitive respiratory cessation, requires immediate intervention due to neuropsychological issues. However, existing approaches such as polysomnography, considered the most reliable and accurate test to detect sleep apnea, frequently require multichannel ECG recordings and advanced feature extraction algorithms, significantly restricting their wider application. Deep learning has recently emerged as a viable method for detecting sleep apnea.

Data analysis for performance index of plate heat exchanger filled by ionanofluid-oil: parallel versus counterflow.

Plate heat exchangers ensure their continued relevance and adaptability in meeting modern energy, environmental, and industrial demands. Innovations in this area significantly improve energy efficiency, reduce environmental impact, and enhance industrial processes. However, the ionanofluid significantly enhanced the thermal conductivity and heat transfer coefficient compared to conventional fluids.

Machine learning for predicting strength properties of waste iron slag concrete.

This study investigates the utilization of waste iron slag (WIS) as a sustainable alternative in concrete production to reduce environmental impact and preserve natural resources. The experimental investigation of WIS-incorporated concrete focused on compressive and tensile strength with machine learning (ML) models for prediction. Among the tested ML algorithms, Decision Tree (DT) and XGBoost showed the highest accuracy (R = 0.

Insights into the electronic structure, optical properties, and photocatalytic potential of GdCoCrO perovskite: a comprehensive theoretical and experimental investigation.

In this study, we present a comprehensive theoretical and experimental investigation into the electronic structure, optical properties, and photocatalytic potential of GdCoCrO (GCCO) double perovskite. Using first-principles calculations with the generalized-gradient-approximation plus Hubbard (GGA + ) method, we explored the effects of Coulomb interactions on the electronic properties. Our calculations revealed that GCCO exhibits a half-metallic nature, displaying metallic behavior for up-spin and semiconducting behavior for down-spin states.

Quantification of external disturbance forces in sliding microwire.

Due to microscale size and infinitesimal stiffness, the undesirable surface and external forces influence the mechanical behaviors of microstructures. It hinders MEMS functions, degrades reliability, and acts as a disturbance. Since MEMS functions based on microstructure mechanical behaviors, therefore, their quantification in microstructures is vital.

ONUBAD: A comprehensive dataset for automated conversion of Bangla regional dialects into standard Bengali dialect.

Despite significant research on the Bangla language in Natural Language Processing (NLP), there remains a notable resource deficit for its diverse regional dialects, such as those spoken in Chittagong, Sylhet, and Barisal. These dialects, often considered unintelligible to speakers of Standard Bengali, pose challenges due to their unique grammatical structures and phonetic variations. Some linguists categorize them as distinct languages.

Analyzing disease control through testing game approach embedded with treatment and vaccination strategies.

This research introduces an expanded SEIR (Susceptible-Exposed-Infected-Recovered) model that incorporates the components of testing, treatment, and vaccination. The study utilizes an evolutionary game theory (EGT) framework to investigate the impact of human behavior on the acceptance and implementation of these interventions. The choice to undergo testing and vaccination is considered a strategic decision influenced by perceived risks and benefits.

Wintertime black carbon assessment in dhaka, Bangladesh: Integrated health risk analysis.

This study investigated the ramifications of black carbon (BC) emissions on human health during the winter season of December 2019 to February 2020 in Dhaka, Bangladesh. BC, arising from incomplete combustion of fossil and biofuels, underwent meticulous measurement of densities, concentrations, and emissions at two pivotal sites. Employing low-volume air samplers with Quartz filters and subsequent analysis with an Aethalometer (Soot scanner, OT21, USA), the study unveiled monthly average BC densities of 1.