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Abstract Autonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient transportation. The existing works provide achievable results but lack effective solutions, as accumulation on roads can obscure lane markings and traffic signs, making it difficult for the self-driving car to navigate safely. Heavy rain, snow, fog, or dust storms can severely limit the car’s sensors’ ability to detect obstacles, pedestrians, and other vehicles, which pose potential safety risks. So, we have presented a multi-sensor fusion and segmentation for multi-object tracking using DQN in self-driving cars. Our proposed scheme incorporates the handling of pipelines for camera and LiDAR data and the development of an autonomous solution for object detection by handling sensor images. An Improved Adaptive Extended Kalman Filter (IAEKF) was used for noise reduction. The Contrast enhancement was done using a Normalised Gamma Transformation based CLAHE (NGT-CLAHE), and the adaptive thresholding was implemented using an Improved Adaptive Weighted Mean Filter (IAWMF) which was used for preprocessing. The multi-segmentation based on orientation employs various segmentation techniques and degrees. The dense net-based multi-image fusion gives more efficiency and a high memory in terms of fast processing time. The Energy Valley Optimizer (EVO) approach is used to select grid map-based paths and lanes. This strategy solves complicated tasks in a simple manner, which leads to ease of flexibility, resilience, and scalability. In addition, the YOLO V7 model is used for detection and categorization. The proposed work is evaluated using metrics such as velocity, accuracy rate, success rate, success ratio, mean squared error, loss rate, and accumulated reward.

Abstract The field of photovoltaics relies heavily on compound semiconductors, particularly those based on selenium. However, challenges in maintaining stoichiometry and the scarcity of elements like Indium and Gallium hinder their widespread adoption. Tin monoselenide (SnSe) emerges as a promising alternative to silicon, thanks to its favorable properties, including a high absorption coefficient, suitable band gap, and excellent chemical stability. Additionally, SnSe exhibits superior thermal stability and can maintain efficiency under a wider range of operating conditions. Its unique crystal structure allows for enhanced charge carrier mobility, facilitating better performance in thin-film applications. These attributes collectively position SnSe as a highly competitive material in the pursuit of efficient and reliable solar energy solutions. Despite its potential, there is a lack of research on low-efficiency solar cells based on SnSe, highlighting the need for further exploration. Our study advocates for a theoretical investigation into the factors affecting SnSe-based solar cell efficiency, focusing on loss mechanisms like bulk and interface recombination. We propose an analytical model that examines various recombination mechanisms and their interactions to enhance efficiency. Experimental results validate our model, revealing the pivotal role of recombination mechanisms at both bulk and interface levels. Besides, the suggested model is used as a fitness function for the Multi Objective Particle Swarm Optimization (MOPSO) technique to identify the optimal combination of design parameters that produced the highest efficiency. The optimized design with In2O3 and MgZnO as Electron transport layer (ETL) materials surpasses current efficiency limitations and explore efficient Cd-free electron transport layer with suitable band alignment. Overall, our strategy by combining analytical model with optimization technique provides insights and solution for enhancing SnSe-based solar cell efficiency, to exceed 13%.

Abstract Wireless sensor networks' most prominent concern is energy optimization. It faces significant problems like high energy consumption, data loss, delay, and low network lifetime. To improve, it uses clustering. However, during clustering, coverage holes are most likely to appear near the network's edge, within the cluster, and between clusters. As a result, there are more energy holes and dead nodes; therefore, the goal of this work is to maximize node network lifetime and minimize energy consumption during data transmission in the wireless sensor network (WSN). The proposed work includes three entities: sensor nodes, an edge-assisted unmanned aerial vehicle (UAV), and a base station. It uses an edge-assisted unmanned aerial vehicle to provide additional resources to the UAV, which helps reduce energy consumption during data transmission. This research proposes using communication to enhance the speed and bandwidth of data transmission and reduce transmission latency. This work attempts to improve performance by increasing throughput.

Abstract The Schockley–Quisser (SQ) limit of 28.64% is distant from the Sb2S3 solar cells’ record power conversion efficiency (PCE), which is 8.00%. Such poor efficiency is mostly owing to substantial interface-induced recombination losses caused by defects at the interfaces and misaligned energy levels. The endeavor of this study is to investigate an efficient Sb2S3 solar cell structure via accurate analytical modeling. The proposed model considers different recombination mechanisms such as non-radiative recombination, Sb2S3/CdS interface recombination, Auger, SRH, tunneling-enhanced recombination, and their combined impact on solar cell performance. This model is verified against experimental work (Glass/ITO/CdS/Sb2S3/Au) where a good coincidence is achieved. Several parameters effects such as thickness, doping, electronic affinity, and bandgap are scrutinized. The effect of both bulk traps located in CdS and Sb2S3 on the electrical outputs of the solar cell is analyzed thoroughly. Besides, a deep insight into the effect of interfacial traps on solar cell figures of merits is gained through shedding light into their relation with carriers’ minority lifetime, diffusion length, and surface recombination velocity. Our research findings illuminate that the primary contributors to Sb2S3 degradation are interfacial traps and series resistance. Furthermore, achieving optimal band alignment by fine-tuning the electron affinity of CdS to create a Spike-like conformation is crucial for enhancing the immunity of the device versus the interfacial traps. In our study, the optimized solar cell configuration (Glass/ITO/CdS/Sb2S3/Au) demonstrates remarkable performance, including a high short-circuit current (J SC ) of 47.9 mA/cm2, an open-circuit voltage (V OC ) of 1.16 V, a fill factor (FF) of 54%, and a notable improvement in conversion efficiency by approximately 30% compared to conventional solar cells. Beyond its superior performance, the optimized Sb2S3 solar cell also exhibits enhanced reliability in mitigating interfacial traps at the CdS/Sb2S3 junction. This improved reliability can be attributed to our precise control of band alignment and the fine-tuning of influencing parameters.

The advancement of autonomous vehicle technology relies heavily on sophisticated machine-learning models that facilitate real-time object detection and classification. To address this requirement, we propose VINO_EffiFedAV, a novel federated learning framework specifically designed for autonomous vehicles. By leveraging NVIDIA T4 GPUs and integrating camera sensor data, this framework develops and trains customized YOLOv8 models directly within each vehicle, ensuring localized processing and enhanced data privacy and security. YOLOv8′s superior real-time performance, lightweight and scalable architecture, and improved accuracy in detecting small objects make it an ideal choice. Our approach effectively manages Non-Independent and Identically Distributed (Non-IID) data from diverse environmental conditions through an efficient selective client update mechanism integrated with the Federated Averaging (FedAvg) algorithm. This strategy strategically filters and aggregates client contributions, reducing the impact of outlier data and maintaining a robust global model. The system's versatility and effectiveness are validated across various datasets, including KITTI for IID conditions and Nuimages and Cityscapes for Non-IID scenarios, enhancing the model's ability to generalize across different driving environments. Notably, our framework reduces average communication cost by up to 50 % and 64 % and computational complexity by 72 % and 84 % for IID and Non-IID scenarios. Moreover, consistent latency performance ensures reliable real-time object detection, achieving impressive mean average precision scores of 84.3 % and 61.6 %. These results underscore the potential of VINO_EffiFedAV to enhance object detection systems in autonomous vehicles, contributing to safer and more efficient navigation.

The need for sustainable fuel sources and efficient waste management has led researchers to explore innovative methods for converting waste into fuel. One promising avenue is the utilization of 100 % tyre oil (TO), which could offer a profitable and environmentally friendly solution for disposing of waste tyres. With rising fossil fuel costs, environmental concerns, and the challenges of waste tyre landfilling, there is increased interest in waste tyre pyrolysis oils (WTPO) as an alternative energy source. This study examines the hydro processed WTPO (HWTPO) was analysed using a metallic catalyst, specifically Co/SBA-15. This method involves hydrolysing WTPO with the metallic catalyst, assessing the physical-chemical properties, formulation efficiency compared to petroleum products, and diesel engine performance like the impact on fuel consumption, combustion, and emissions. The synthetic HWTPO's chemical and physical properties were found to be comparable to European diesel specifications (European standard 590). Under hydroprocessing conditions (80 bar and 375 °C), the Co/SBA-15 catalyst produced isoalkanes, n-alkanes, and aromatics in quantities nearly equivalent to 100 % diesel. HWTPO demonstrated the potential to maximize greenhouse gas emissions reduction and enhance the performance of diesel-powered engines. The favourable properties of HWTPO suggest that waste tyre pyrolysis oil could be a viable transportation fuel.

Abstract In the backdrop of today's environmental priorities, where toxicity and stability hinder lead‐based perovskite solar cell (PSC) progress, the emergence of lead‐free alternatives like Cs2AgBiBr6 perovskites has gained significance. This study revolves around the comprehensive evaluation of Cs2AgBiBr6 as a potential photovoltaic (PV) material, using density functional theory (DFT) calculations with CASTEP. Revealing a vital bandgap of 1.654 eV and emphasizing the contributions of Ag‐4d and Br‐4p orbitals, this analysis also underscores Ag atoms' dominance in charge distribution. Optically, Cs2AgBiBr6 exhibits UV absorption peaks around 15 eV, intensifying with photon energy up to 3.75 eV, hinting at its promise for solar applications. Guided by DFT, forty configurations involving various electron transport layers (ETLs) and hole transport layers (HTLs) are explored. Among these, CNTS emerges as the prime HTL due to ideal absorber alignment. The spotlight architecture, FTO/AZnO/Cs2AgBiBr6/CNTS/Au, boasts exceptional efficiency (23.5%), Voc (1.38 V), Jsc (21.38 mA cm−2), and FF (79.9%). In contrast, FTO/CdZnS/Cs2AgBiBr6/CNTS/Au achieves a slightly lower 23.15% efficiency. Real‐world intricacies are probed, encompassing resistances, temperature, current–voltage (J–V) traits, and quantum efficiency (QE), enhancing practical relevance. These findings are thoughtfully contextualized within prior literature, showcasing the study's contributions to non‐toxic, inorganic perovskite solar technology. This work aspires to positively steer sustainable PV advancement.

The current research involved the synthesis of ZnO nanoparticles from the leaf and fruit extracts of Azaratchida indica and Phyllanthus acidus. ZnO nanoparticles exhibit diversified applications in medicine, agriculture and food industry. The synthesized nanoparticles were characterized using techniques such as Powder X-ray Diffraction (PXRD) and High-Resolution Transmission Electron Microscopy (HRTEM) studies which showed the shape and size of nanoparticles to be spherical with 16 mm and 20 mm. Invitro biological effects of ZnO NPs were studied including antidiabetic, antiinflammatory, anticancer, antibacterial, antifungal and DNA protection nicking assay. The synthesized ZnO NPs demonstrated cytotoxic effects on the cell lines at higher concentration. However, there was a significant increase in the inhibition of alpha amylase and alpha- glucosidase activity. Similar results were obtained in the study of antiinflammatory activity of ZnO NPs. The nanoparticles of ZnO showed prominent antibacterial and antifungal activity against E. coli, S aureus, K pneumoniae, S.typhi, Candida albicans and Aspergillus species respectively. DNA nicking protection assay studies have also shown pronounced anticancer effects. The Fourier-transform infrared (FTIR) spectrum indicates the presence of Zn-O stretching vibrations, confirming the formation of zinc oxide bonds. These findings confirms that ZnO nanoparticles from these extracts have properties that can be used safely and efficiently.

Polyvinyl alcohol (PVA) Sodium alginate (NaAlg) polymer composites were synthesized by solution casting technique and doping with ammonium nitrate (NH4NO3). The functionalization structure of the produced electrolytes was investigated using XRD and AC impedance spectroscopy. XRD confirms amorphous nature. The ionic conductivity of the electrolytes is evaluated using ac impedance measurements at temperatures ranging from 303 to 353 K. PVA: NaAlg: NH4NO3 (60:40:15 wt%) blend polymer electrolyte was observed to have the highest conductivity of 1.05 × 10−6 S cm−1. The Arrhenius plot appears to be obeyed by the temperature dependent ionic conductivity and is of non-debye type. Activation energy for best conducting electrolyte was 0.2364 eV. The dielectric relaxation of the polymer electrolyte has been studied, and the results are described. Relaxation time estimated from loss tangent analysis also affirms that value is low for highest conducting sample. Results of CV and LSV studies gave a plausible approach for electrochemical application.

Seagrass is a natural, renewable, and much unexplored marine resource, which are capable, and reliable sources in the field of pharmaceuticals and drug discovery. The seagrass, Syringodium isoetifolium is our target plant for the study, which was collected from the Ramanathapuram district, Tamil Nadu. The current study is focused to evaluate the cytotoxic, cell cycle arrest, and apoptotic induction activities of the hydroalcoholic extract of S. isoetifolium against the human breast cancer (MCF-7) cell line. The cytotoxic evaluation revealed that the extract inhibited MCF-7 proliferation with IC50 value of 230.32 μg/ml. Evaluation of MCF-7 cell cycles demonstrated that the extract arrested the cell cycle in the S phase and continued to the G2/M phase at half of the IC50 value. The extract induced apoptotic of MCF-7 cells about 30.61% which was nearly the same with aripiprazole as a positive control (52.35%). Nootkatone significantly binds to the target proteins – HSP 90 and HER2 kinase with the least binding energy was predicted as the most active anticancer compounds by a molecular docking study. In light of the findings, it can be said that the hydroalcoholic extract of S. isoetifolium has the potential to be a therapeutic agent for breast malignancies by acting as an anticancer component. It can be used as an anticancer agent on its own and/or as a scaffold for analog synthesis to develop novel anticancer agents with improved therapeutic efficacy.

Ferrite nanoparticles are an emerging material for industrial and biomedical applications. Herein, a simple non-aqueous sol–gel method is used to synthesize CuxZn(1-x)Fe2O4 (x = 0.0, 0.25, 0.5, and 0.75) nanoparticles. X-ray diffraction (XRD), scanning electron microscopy (SEM), UV–Vis spectroscopy, and a vibrating-sample magnetometer (VSM) were utilized to investigate the structural formation and magnetic merits of the prepared ferrite nanoparticles. The Rietveld refinement of the X-ray diffraction pattern confirmed the formation of single-phase cubic structures with Fd3¯m space groups for all samples. The increase in cu2+ concentration in zinc ferrite nanoparticles decreases the lattice parameters from 8.4418 to 8.4368. The energy gap of cu2+-doped zinc ferrite increases from 1.89 to 2.04 eV with a decrease in particle size. MH loop revealed an increase in Ms and Mr Parameters as Hc reduces with an increase in Cu2+ concentration in the zinc ferrite matrix. It was discovered that an increase in Cu2+ content improved the antibacterial activities of Cu2+-doped zinc ferrite against all bacterial species.

A potential photocatalytic activity of MoS2-modified g-C3N4 composite for methylene blue (MB) dye degradation is processed under sunlight radiation. The photocatalyst materials (g-C3N4, MoS2, and g-C3N4/MoS2) were prepared by low-temperature hydrothermal method and the synthesized samples were further characterized through the X-ray diffraction (XRD), UV–vis diffuse reflectance spectrometer (DRS), scanning electron microscope (SEM), energy dispersive X-ray spectroscopy (EDS) and Photoluminescence (PL) spectroscopy techniques. Initially, the formation of the crystalline structure of (g-C3N4, MoS2, and g-C3N4/MoS2) samples was identified through the XRD technique. Further, the morphology of the MoS2-modified g-C3N4 when observed through SEM was found to be a regular structure with minute pores and voids when compared to the other two samples resulting in better light absorption capacity. The elemental composition of synthesized composite is analyzed by XPS spectra. The energy band gap investigated through the DRS spectra was 2.9 eV, 1.8 eV and 1.82 eV for the g-C3N4, MoS2, and g-C3N4/MoS2 samples respectively. Further BET, TOC and scavenger radical analysis was conducted on the tested sample to analyze the degradation process. Thus, the composite photocatalyst material of MoS2-modified g-C3N4 has an enhanced recombination electron-hole pair rate. Finally, the photocatalytic feature of the composite (g-C3N4/MoS2) tested on MB dye solution under sunlight irradiation for 2 hr was found to be successful.

Abstract Background: Escherichia coli O157:H7 infection causes hemorrhagic colitis and is diagnosed based on symptoms such as cramps, stomach pain, and watery diarrhea. Shiga-like toxins (Verotoxin) produced by Escherichia coli O157:H7 damages endothelial cells of both kidney and brain, causing renal dysfunction and neurological problems. Methods: The present study focuses on identifying the prevalence of Verotoxin-producing Escherichia coli O157:H7among diarrheal inpatients at Erode Government Hospital, India, and its antibiogram. Further, the Verotoxins were characterized by using SDS-PAGE analysis. A total of 123 diarrheal stool samples were collected, and 37 of them (30.08% of the total samples) were found to have the presence of E. coli. The organisms were identified based on their colony morphology on various media, cell morphology, and biochemical tests. The Shiga-like toxin production was identified by non-fermentation of sorbitol on SMAC agar plates. Confirmation of Shiga-like toxin was performed using agglutination assay. Results: In total,12 isolates showed agglutination and these isolates were confirmed to be E. coli O157:H7. The molecular weight of the Verotoxin was found to be between 20 and 29 kD. The antibiogram profile of the four isolated strains against 10 standard antibiotics was determined. Conclusion: The results of this study show the occurrence of drug resistance on hemorrhagic colitis causing E. coli O157:H7.

To make tungsten trioxide nanostructures, hydrothermal methods were used. A temperature of 200 °C was used to prepare the samples. 48-hour reactions were used to examine how reaction time affected the characteristics of tungsten trioxide nanostructures. For one hour, the samples were calcined at 500 °C. After using ammonium metavanadate to dope vanadium, the same method for making WO3 was used. Many different characterization techniques have been performed on the calcined and prepared samples. X-ray diffraction (XRD) and scanning electron microscopy (SEM) and Transmission electron microscopy (TEM) were used to examine the samples' structural and morphological characteristics (SEM). EDAX has verified that vanadium has been doped. Fourier transformed infrared spectroscopy was used to do the vibrational analysis (FTIR).

The Orf virus (ORFV) is an epitheliotropic virus causing a highly contagious skin disease mainly in sheep and goats. Several diagnostics including molecular tools like Loop mediated isothermal amplification (LAMP) assay are available to detect ORFV in affected species. However, the carry‑over contamination associated with LAMP as open tube format prevents the assay applicability as point‑of‑care test in field diagnostic settings. In this study, the B2L gene based LAMP assay was optimized in a closed tube format using hydroxynaphthol blue (HNB) and calcein as pre‑addition dyes and it has shown a clear positive and negative signal at 60 °C using 4 and 5 mM concentrations of MgSO4 for these dyes, respectively. This optimized assay that could reveal the result within one hour is highly specific and sensitive with a limit of detection of 12.5 femtogram of viral genomic DNA or ~ 85 virus genome equivalent. This improved method can also prevent the cross‑contamination of LAMP reactions in the laboratory without compromising diagnostic sensitivity and specificity when compared to open tube system. This closed tube LAMP method has potential to act as a simple visual detection assay for the rapid and specific diagnosis of ORFV in sheep and goats.