Synthesis and characterization of spin coated ZnTe thin films for improving the efficiency of ZnTe/ZnS solar cell using SCAPS-1D.

Cadmium telluride (CdTe) absorber layer in solar cells (SCs) is environmentally dangerous for the toxic behavior of cadmium (Cd). Alternatively, zinc telluride (ZnTe) is deliberated as a promising PV material for its adoptable absorption coefficient, better conversion efficiency and low production cost of materials requirements. The main objective of this study is to synthesis and characterization analysis of ZnTe thin films to enhance the performance of ZnS/ZnTe solar cell.

Ultra-sensitive immunosensing of snake venom by functionalized Sm-Co doped antimony-tungstate.

Snake venom has long-term physiological effects on survivor's life. An electrochemical immunosensor based on samarium-cobalt-doped antimony tungstate (SbWO@Sm-Co) is developed via a solvothermal method to detect snake venom antigens (SVA). The fabricated nanospheres are functionalized with carboxyl groups to enhance the linkage of the 3-mercaptopropionic acid linker (3-MPA).

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.

Integrating generative adversarial networks with IoT for adaptive AI-powered personalized elderly care in smart homes.

The need for effective and personalized in-home solutions will continue to rise with the world population of elderly individuals expected to surpass 1.6 billion by the year 2050. The study presents a system that merges Generative Adversarial Network (GAN) with IoT-enabled adaptive artificial intelligence (AI) framework for transforming personalized elderly care within the smart home environment.

The compressive strength development and pH of cement mortars incorporating high volume supplementary cementitious materials under accelerated curing.

The durability and service life of concrete structures is primarily dependent on the performance of cement-based material (CBM). This performance of CBMs is linked to their pH directly. Literature suggests that when supplementary cementitious materials (SCMs) are used, the pH of CBMs decreases as a result Ca(OH)₂ consumption during the Pozzolanic reaction (PR).

A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities.

The traditional consensus algorithm based on the Internet of Vehicles (IoV) system has the disadvantages of high latency, low reliability, and weak fault tolerance, and it cannot make real-time adjustments according to the actual environment, making the system vulnerable to malicious control, inefficiency, and poor environmental adaptability. To solve this problem, we propose a gradually accelerating environment adaptive consensus algorithm, AE-PBFT, that can be applied to IoV. It includes a trust management model that achieves gradual acceleration by recording the historical continuous behavior of nodes, thereby improving the efficiency of screening nodes with different intentions, accelerating the consensus process, and reducing latency.

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.

Peptide classification landscape: An in-depth systematic literature review on peptide types, databases, datasets, predictors architectures and performance.

Peptides are gaining significant attention in diverse fields such as the pharmaceutical market has seen a steady rise in peptide-based therapeutics over the past six decades. Peptides have been utilized in the development of distinct applications including inhibitors of SARS-COV-2 and treatments for conditions like cancer and diabetes. Distinct types of peptides possess unique characteristics, and development of peptide-specific applications require the discrimination of one peptide type from others.

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.

Strigolactone and karrikin receptors regulate phytohormone biosynthetic and catabolic processes.

Karrikin plays a more critical role in affecting the homeostasis of ABA and cytokinins, while strigolactones play a more critical role in influencing the homeostasis of jasmonic acid and gibberellins. Strigolactones (SLs) and karrikins (KARs) regulate plant growth and development through their crosstalk, and through the crosstalk between them and other phytohormones, such as abscisic acid (ABA) and auxin. However, how SL and KAR signaling pathways influence the levels of other phytohormones is still unknown.

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.

Newly predicted halide perovskites MgAB (A = N, Bi; B = F, Br, I) for next-generation photovoltaic applications: a first-principles study.

The research examines the exceptional physical characteristics of MgAB (A = N, Bi; B = F, Br, I) perovskite compounds through density functional theory to assess their feasibility for photovoltaic applications. Mechanical characterization further supports their stability where out of all the compounds, MgBiI demonstrates high ductility, while MgNF and MgBiBr possess a brittle nature. The calculated elastic constants and anisotropy factors also substantiate their mechanical stability, while there is an observed declining trend in Debye temperature with increase in atomic number.

FaultSeg: A Dataset for Train Wheel Defect Detection.

Wheels are a critical component of railway infrastructure and work as the load carrier of the train. However, defective wheels pose a serious risk to safety that can gravely jeopardize people's safety. There is a significant risk of injury or death from defective wheels, endangering the lives of individuals.

A personalized reinforcement learning recommendation algorithm using bi-clustering techniques.

Recommender systems have become a core component of various online platforms, helping users get relevant information from the abundant digital data. Traditional RSs often generate static recommendations, which may not adapt well to changing user preferences. To address this problem, we propose a novel reinforcement learning (RL) recommendation algorithm that can give personalized recommendations by adapting to changing user preferences.

Evaluating environmental flows for the targeted fish habitats in selected rivers.

This study aims to assess environmental flow (EF) to ensure fish habitats while serving as a drinking water source, determined as the Mean Annual Flow (MAF). The research estimates the EF requirements for fish populations and drinking water supply in the selected rivers, i.e.

Numerical and machine learning based evaluation of ethylene glycol based hybrid nano-structured (TiO-SWCNTs) fluid flow.

A better mechanical resistance and improved thermal conductivity, as compared to mono nano-liquids, can be attained by the ethylene glycol-based hybrid nanofluids. These fluids have substantial uses in several engineering systems. The main focus, in the recent work, is to assess the dynamics of the ethylene glycol-based hybrid nano-structured fluid via the computational fluid dynamics (CFD) and machine learning (ML) approaches.

Correlation awareness evolutionary sparse hybrid spectral band selection algorithm to detect aflatoxin B1 contaminated almonds using hyperspectral images.

Aflatoxin B1 is a harmful metabolite that frequently contaminates almonds, other nuts, and grains. Prolonged consumption of foods contaminated with aflatoxin B1 can lead to severe health issues. Hyperspectral imaging enables rapid, non-destructive detection of aflatoxin B1, but its high dimensionality complicates data analysis and increases complexity of classification models.

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.

Development of a sustainable portable Archimedes screw turbine for hydropower generation.

Portable hydropower turbines are turbines with a scale below 5 kW and which can be carried from one place to another easily by hand due to their light weight. This study was carried out to evaluate the potential of Archimedes Screw Turbine (AST) as an improved portable hydro-power turbine (PHPT) to address shortcomings in available portable turbines. The design of Archimedes screw hydro-power turbine is mainly concerned with screw geometry, which is determined by a variety of internal and external characteristics, including its length, external and internal diameter, Pitch of blades, and Number of the blades, which were 80 cm, 18 cm, 9.

Expression profile and characterization of respiratory burst oxidase homolog genes in rice under MeJA, SA and Xoo treatments.

Respiratory burst oxidase homologs (Rboh) genes is essential for synthesizing reactive oxygen species, which play a crucial role in environmental stress response. The Rboh gene family has been studied in model plants such as Arabidopsis. Nevertheless, Rboh remained largely unexplored in Rice (Oryza sativa L.

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.

Revealing large-scale surface subsidence in Jincheng City's mining clusters using MT-InSAR and VMD-SSA-LSTM time series prediction model.

Jincheng City's mining areas have long been plagued by surface subsidence, posing significant threats to local residents' safety and impacting the region's economic and social stability. Understanding and effectively monitoring the driving factors and mechanisms of surface subsidence are crucial for devising scientific prevention measures and promoting the sustainable development of mining areas. This article aims to comprehensively reveal the large-scale surface subsidence phenomenon in Jincheng City's mining clusters by utilizing advanced remote sensing technology and machine learning models, identifying its main driving forces, and predicting future subsidence trends to provide scientific evidence for geological disaster prevention in mining areas.