Experimental Findings Research Articles

Yield Performance Study of Bina Developed Sesame Varieties under Agro-Ecological Conditions of Mymensingh District in Bangladesh

An investigation was done during the Kharif-I (Summer) season 2022 at Sadar Upazilla of Mymensingh district in Bangladesh to explore the yield and factors affecting the yield of four sesame varieties developed by the Bangladesh Institute of Nuclear Agriculture (BINA). Three replications of a Randomized Complete Block Design (RCBD) were used, where Binatil-1, Binatil-2, Binatil-3, and Binatil-4 were the varieties. The experiment's findings showed that Binatil-1 contained the tallest plant (107.00 cm), whereas Binatil-3 contained the shortest plant (79.55 cm). It was noted that Binatil-2 had the greatest number of plant-1 branches (4.95), whereas Binatil-1 had the least amount (2.14). Additionally, Binatil-3 displayed the highest number of plant-1 pods (95.65), whereas Binatil-1 provided the lowest amount (51.27). Binatil-1 had the largest pod (3.56 cm), whereas Binatil-4 had the smallest (2.57 cm). It was noted that Binatil-1 displayed the greatest count of seeds in pod-1 (78.15), while Binatil-4 displayed the least amount (61.12). The highest 1000 seed weight was obtained from Binatil-2 (3.15 g), whereas the lowest 1000 seed weight was recorded in Binatil-1 (2.95 g). The established genotype Binatil-2 had the maximum seed yield (1.42 t ha-1), whereas Binatil-1 had the least (1.27 t ha-1). Among the cultivars, Binatil-3 matured in the fewest days (87), whereas Binatil-2 required the most days (95). At Binatil-2, significant yield performance was found from the Kharif-I (summer) season experiment. A simple correlation was done where branches per plant per thousand seed weight had a significant positive correlation and pod breadth had a negative impact on seed yield for all genotypes. This experiment will be helpful for oil crop breeders and the farming community to choose promising sesame genotypes with high yield potential and future breeding stock. Jagannath University Journal of Life and Earth Sciences, 9(2): 125–132, 2023 (December)

Exploring Bimanual Haptic Feedback for Spatial Search in Virtual Reality.

Spatial search tasks are common and crucial in many Virtual Reality (VR) applications. Traditional methods to enhance the performance of spatial search often employ sensory cues such as visual, auditory, or haptic feedback. However, the design and use of bimanual haptic feedback with two VR controllers for spatial search in VR remains largely unexplored. In this work, we explored bimanual haptic feedback with various combinations of haptic properties, where four types of bimanual haptic feedback were designed, for spatial search tasks in VR. Two experiments were designed to evaluate the effectiveness of bimanual haptic feedback on spatial direction guidance and search in VR. The results from the first experiment reveal that our proposed bimanual haptic schemes significantly enhanced the recognition of spatial directions in terms of accuracy and speed compared to spatial audio feedback. The second experiment's findings suggest that the performance of bimanual haptic feedback was comparable to or even better than the visual arrow, especially in reducing the angle of head movement and enhancing searching targets behind the participants, which was supported by subjective feedback as well. Based on these findings, we have derived a set of design recommendations for spatial search using bimanual haptic feedback in VR.

Adaptive switching and routing protocol design and optimization in internet of things based on probabilistic models

Through smart cities, Intelligent Transportation Systems (ITS), the agricultural sector, and wearable devices, the Internet of Things (IoT) has revolutionized several human interests. Through the development of new cluster tasks, the Decision-Making System (DMS) of Cluster Heads (CHs), and improving the accuracy of traffic prediction and reliability of transportation, the present study intends to improve the energy depletion of IoT devices. The paper explores the subject of data flow optimization using Fuzzy Assisted Cuckoo Search Optimization (FACSO), traffic flow using Gaussian Process Regression (GPR), and CH prediction using the Stochastic Optimization Algorithm (SOA). Optimizing network lifetime while minimizing Energy Consumption (EC) is feasible through the practical application of the SOA, GPR, and FACSO models. Increasing End-to-End Delay (EED), Network Throughput (NT), and energy efficiency can be rendered feasible through a real-time DMS regarding routing employing a novel approach referred to as FACSO. This approach has enhanced the efficacy and reliability of Wireless Sensor Networks (WSN). With up to 500 nodes and an EC of 0.3451 J, the experiment's findings demonstrate that a proposed SOA-FACSO model achieves superior EED.

A new track finding algorithm based on a multi-dimensional extension of the Hough Transform

We introduce a new pattern recognition algorithm for track finding in High Energy Physics Experiments based on an extension of the Hough Transform to multiple dimensions. A remarkable property of this algorithm is that the execution time is simply proportional to the total number of the hits to be processed, making it particularly attractive for high occupancy situations. The algorithm needs to be trained using a sufficiently large set of simulated tracks. The same track finding algorithm can be used for very different detector geometries and only the set of simulated tracks used for training needs to be changed. The particular structure of the algorithm also lends itself naturally to parallel hardware implementations which, combined with its intrinsic flexibility, should provide a most powerful tool for triggering at future colliders.

Mechanism of lightning damage to glass fiber composite wind turbine blades

Wind turbine blades (WTBs) are susceptible to lightning damage, resulting in significant costs for repair and replacement, which poses a considerable economic burden on wind farms. Therefore, this study investigates the mechanism of lightning damage of glass fiber composite (GFC)-WTBs to reduce the risk of such damage. The damage of GFC-WTBs caused by lightning strikes was analyzed using a numerical simulation method. The lightning pilot was simulated using high-voltage rod electrodes, and subsequently, the electric field strength and damage area on the blade surface under different conditions were measured. Additionally, a simulation model based on finite element analysis was developed to further predict and validate the experimental findings. The results reveal that the maximum electric field intensity was observed in the blade tip. Notably, the electric field intensity was found to be over 798,000 V/m when the rod electrode was positioned at a 0-degree angle with the blade tip. Further investigation revealed a non-linear and positive correlation between the damage area of GFC and lightning current amplitude, charge, and specific energy. In conclusion, the study provides a comprehensive examination of the relationship between lightning current and the damage to GFC-WTBs, as well as elucidates the mechanism of lightning damage to GFC-WTBs.

The effects of identity salience on product judgment in a domain of trade-offs

Purpose This paper aims to investigate a condition under which identity salience effects are weakened. By examining how identity salience influences individuals’ product judgment in a domain of trade-offs, the current research demonstrates that the utilitarian value of a product is an important determinant of the effectiveness of identity salience on product judgment. Design/methodology/approach This research consists of two experiments. In Experiment 1, the authors examined whether identity salience effects were mitigated when the level of the perceived utilitarian value of an identity-incongruent product was greater than that of an identity-congruent product. In Experiment 2, the authors examined the effectiveness of internal attribution as a moderator that strengthens identity salience effects when the perceived utilitarian value of an identity-incongruent (vs. identity-congruent) product is higher. Findings In Experiment 1, the authors show that when the utilitarian value of a product with an attribute congruent (vs. incongruent) with one’s salient identity is lower, individuals do not show a greater preference for the identity-congruent (vs. identity-incongruent) product, mitigating the identity salience effects. Experiment 2 demonstrates that when individuals with a salient identity attribute a decision outcome to the self, they display a greater preference for the identity-congruent product even when its utilitarian value is lower compared to that of the identity-incongruent product. Research limitations/implications The research contributes to previous research examining conditions under which identity salience effects are weakened [e.g. social influence by others (Bolton and Reed, 2004); self-affirmation (Cohen et al., 2007)] by exploring the role of the utilitarian value of a product, which has not been examined yet in prior research. Also, by doing so, the current research adds to the literature on identity salience in a domain of trade-offs (Benjamin et al., 2010; Shaddy et al., 2020, 2021). Finally, this research reveals that when a decision outcome is attributed to the self, identity salience effects become greater. By finding a novel determinant of identity salience effects (i.e. internal attribution), the present research contributes to the literature that has examined factors that amplify identity salience effects [e.g. cultural relevance (Chattaraman et al., 2009); social distinctiveness (Forehand et al., 2002); different types of groups (White and Dahl, 2007)]. Practical implications The findings provide managerial insights on identity-based marketing by showing a condition under which identity-based marketing does not work [i.e. when the utilitarian value of an identity-congruent (vs. identity-incongruent) product is lower] and how to enhance the effectiveness of identity-based marketing by using internal attribution. Originality/value By exploring the role of utilitarian value, not yet examined in prior research, the present research adds to the knowledge of the conditions under which identity salience effects are weakened. Furthermore, by finding a novel determinant of identity salience effects (i.e. internal attribution), the research contributes to the literature on factors that amplify identity salience effects.

Overview of physics results from the ADITYA-U tokamak and future experiments

Abstract The ADITYA Upgrade (ADITYA-U), a medium-sized (R_0=75 cm,a=25 cm) conventional tokamak facility in India, has been consistently producing experiments findings by using circular and shaped-plasmas. Recognizing the plasma parameters aligning closely with the design parameters of circular limited plasmas, ADITYA-U shifted its focus toward exploring the operational regime for experimentation on saw-tooth and MHD phenomena. Moreover, ADITYA-U has made consistent advancements toward conducting preliminary plasma shaping experiments through the activation of top and bottom divertor coils utilizing hydrogen as well as deuterium fuels. Confinement is improved by a factor of ~ 1.5 in D_2 plasmas when compared to H_2 plasmas of ADITYA-U. Further, ADITYA-U operations emphasize preventing disruptions and runaway electrons (REs) to ensure safe operations for future fusion devices. Significant suppression of REs has been achieved in ADITYA-U with the application of pulsed localized vertical magnetic field (LVF) perturbation, thereby establishing the technique's independence from the tokamak device. The successful RE mitigation requires a critical threshold of LVF pulse magnitude, which is approximately 1% of the toroidal magnetic field, and a minimum duration of ~ 5 ms. Apart from this, several novel findings have been achieved in the ADITYA-U experiments, including the modification of sawtooth duration through gas-puff, the emergence of MHD-induced GAM-like oscillations, the propagation of fast heat pulses induced by MHD activity, the control of RE dynamics through Gas-puffs, the propagation of pinch-driven cold-pulses, the transport and core accumulations of argon impurities, the mass dependency of plasma toroidal rotation and the detection of “RICE” scaling, as well as the characterization of edge plasma using wall conditioning methods, such as glow discharge cleaning using a combination of Ar-H2 mixture, localized wall cleaning by Electron Cyclotron (EC) resonant plasma, and the development of machine learning-based disruption predictions, will be discussed in this paper.

IoT-Cloud-based Smart Healthcare Monitoring System based on Seahorse Optimization-driven Mutated CNN

Healthcare is undergoing a transformation due to predictive analytics, which offers clinical decision support in real-time, facilitating effective, tailored therapy. By lowering unfavorable events and raising the standard of care, this approach supports patients at every step of diagnosis, prognosis and therapy. Predictive algorithms driven by artificial intelligence (AI), machine learning (ML) and Internet-of-Things (IoT) handle enormous amounts of data, with cardiovascular illnesses as the primary cause of death worldwide. IoT devices make sophisticated deep learning (DL) analytics and remote patient monitoring possible. This paper presents a novel 5-stage disease prediction model as an IoT-cloud-based smart healthcare management framework, constructed using a seahorse optimization-driven mutated convolutional neural network (SHO-MuCNN). In stage-1, data is gathered from a variety of sources through data collection. The stage-2 of data pre-processing involves the use of standardization to get the data acceptable for additional processing. Using a customized principal component analysis (CPCA), the set of characteristics is determined in stage-3. Next, the stage-4 diseases are predicted by the suggested SHO-MuCNN methodology. According to the prediction results, physicians are educating patients in stage-5 on exactly what measures can be taken to reduce the risk. The smart healthcare monitoring measures for accuracy (98.56%), precision (96.69%), F1-score (97.86%), Recall (94.59%)and AUC (95.02), were employed in the models' performance. In addition, this study utilizes the SPSS and t-test for statistical analysis. The experiment's findings indicate that the SHO-MuCNN strategy outperformed the remaining techniques in an intelligent healthcare monitoring system.

Sol-Gel technique, characterization and photocatalytic degradation activity of Manganese doped ZnO nanoparticles

Photocatalysis using semiconductor metal oxide stands out as a highly effective and efficient method for eliminating organic pollutants from wastewater. This study aims to assess the photocatalytic capabilities of Mn doped ZnO nanocomposites in degrading methylene blue (MB) dye under ultra-violet light exposure. This study details the synthesis of ZnO photocatalysts through a straightforward one-step sol-gel method, incorporating varying levels of Mn-doping (0%, 2% & 4%). The structural and optical attributes were examined using techniques such as powder X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS) and UV-Vis diffuse reflectance spectra. X-ray diffraction analyses verified the presence of a hexagonal wurtzite crystal structure in all synthesized samples, exhibiting a high degree of crystallinity. In addition, an investigation into the impact of Mn impurities on the photocatalytic performance of ZnO catalysts was conducted in the context of methylene blue (MB) degradation. The experimental findings revealed that the Mn doped ZnO nanoparticles produced exhibited significantly superior photocatalytic performance compared to pure ZnO when used in breaking down methylene blue under UV-light exposure. This study proposes that these Mn doped ZnO could serve as a highly effective photocatalyst for treating water contaminated with certain chemically persistent synthetic organic dyes. The improved photocatalytic capabilities of ZnO nanostructures doped with Mn were ascribed to the synergistic impact of increased surface area in ZnO nanosphere and enhanced efficiency in charge separation resulting from optimized Mn doping. A potential explanation for the heightened photocatalytic performance of Mn-doped ZnO nanostructures is proposed tentatively.

Guava fruit disease identification based on improved convolutional neural network

Guava fruit cultivation is crucial for Asian economic development, with Indonesia producing 449,970 metric tons between 2022 and 2023. However, technology-based approaches can detect disease symptoms, enhancing production and mitigating economic losses by enhancing quality. In this paper, we introduce an accurate guava fruit disease detection (GFDI) system. It contains the generation of appropriate diseased images and the development of a novel improved convolutional neural network (improved-CNN) that is built depending on the principles of AlexNet. Also, several preprocessing techniques have been used, including data augmentation, contrast enhancement, image resizing, and dataset splitting. The proposed improved-CNN model is trained to identify three common guava fruit diseases using a dataset of 612 images. The experimental findings indicate that the proposed improved-CNN model achieve accuracy 98% for trains and 93% for tests using 0.001 learning rate, the model parameters are decreased by 50,106,831 compared with traditional AlexNet model. The findings of the investigation indicate that the deep learning model improves the accuracy and convergence rate for guava fruit disease prevention.

RDGT-GAN: Robust Distribution Generalization of Transformers for COVID-19 Fake News Detection

Social media platforms have become a vital source of information during the outbreak of the pandemic (COVID-19). The phenomena of fake information or news spread through social media have become increasingly prevalent and a powerful tool for information proliferation. Detecting fake news is crucial for the betterment of society. Existing fake news detection models focus on increasing the performance which leads to overfitting and lag generalizability. Hence, these models require training for various datasets of the same domain with significant variations in the distribution. In our work, we have addressed this overfitting issue by designing a robust distribution generalization of transformers-based generative adversarial network (RDGT-GAN) architecture, which can generalize the model for COVID-19 fake news datasets with different distributions without retraining. Based on our experimental findings, it is evident that the proposed model outperforms the current state-of-the-art (SOTA) models in terms of performance.

How Reproducible Are Manual Therapy Interventions in Trials for Low Back Pain? A Scoping Review.

OBJECTIVES: To assess the reproducibility of manual therapy interventions used in clinical trials for low back pain (LBP), and summarize knowledge gaps in assessing the reproducibility of manual therapy interventions for LBP. DESIGN: Scoping review. LITERATURE SEARCH: PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Central Register of Controlled Trials (CENTRAL), and Embase were searched for trials from inception through April 2023. STUDY SELECTION CRITERIA: Randomized controlled trials were included if they described the use of manual therapy to treat LBP in adults 18 to 65 years old and were accessible in English. DATA SYNTHESIS: The Consensus on Exercise Reporting Template (CERT) checklist, used for exercise reporting, was previously modified for manual therapy reporting. This 11-item modified CERT was used to extract details of manual therapy reporting in the included trials. Frequency counts were calculated to identify items most and least commonly reported. RESULTS: Of 128 trials, none reported all 11 items of the modified CERT. The most commonly reported items were the description of how the application of manual therapy was decided (n = 113, 88.3%) and a description of adjunct interventions provided (n = 82, 64.1%). The least reported items were the description of an associated home program (n = 27, 21.1%) and a detailed description of the application of manual therapy (n = 22, 17.2%). CONCLUSION: Reporting of manual therapy interventions in trials investigating LBP was poor overall, limiting the reproducibility of these treatments. Using a checklist designed explicitly for manual therapy intervention reporting may improve reproducibility of these interventions and help align clinical outcomes with experimental findings. J Orthop Sports Phys Ther 2024;54(4):1-10. Epub 29 January 2024. doi:10.2519/jospt.2024.12201.

Thiazole Derivatives as RORγt Inhibitors: Synthesis, Biological Evaluation, and Docking Analysis

Background: Cancer is a serious concern in developing countries. Due to various worldwide impacts, it has surpassed coronary disorders becoming the leading cause of mortality. In cancer progression, the involvement of RORs (retinoic acid receptor-related orphan receptors) has gained attention in the search for versatile agents. Thiazole has the potential to be used as a scaffold in the development of anticancer drugs. Objective: This work’s major goal was to design and synthesize promising anticancer candidates. Methods: Docking investigations concerning the RORγt receptor were utilized in the design of the compounds. A series of novel thiazole derivatives was synthesized employing Hantsch synthesis. Melting points, TLC development, and spectral analyses were considered for the synthesized compounds' characterization. Corresponding spectra were analyzed and determined to be consistent with the assigned structure. On 60 human cancer cell lines, selected compounds were investigated in vitro by NCI (National Cancer Institute), USA, for anticancer action. Results: Varying ratios of anticancer potential were presented by the compounds in in vitro studies. Compound 3c was determined to be the most active compound in the series, with a -15.03 mean growth percent. In silico study revealed that the synthesized compounds could have anticancer possibilities. In the targeted pdb id (6q7a) binding site, compound 3c demonstrated excellent docking scores in molecular docking analyses, supporting its ability to be utilized as a lead compound in rational drug design. Conclusion: Compounds with satisfactory docking scores within the binding pocket of chosen pdb id (6q7a) validated their suitability for usage as lead compounds in rational drug design. Outcomes of in silico investigations were in agreement with the findings of the cytotoxicity experiments. Leukemia, renal, breast, and lung cancer cell lines were most sensitive to the substance-treated tumor cells. Electronwithdrawing groups were essential for antiproliferative action, according to the findings.

Efficient Motif Discovery in Protein Sequences Using a Branch and Bound Algorithm.

Identifying motifs within sets of protein sequences constitutes a pivotal challenge in proteomics, imparting insights into protein evolution, function prediction, and structural attributes. Motifs hold the potential to unveil crucial protein aspects like transcription factor binding sites and protein-protein interaction regions. However, prevailing techniques for identifying motif sequences in extensive protein collections often entail significant time investments. Furthermore, ensuring the accuracy of obtained results remains a persistent motif discovery challenge. This paper introduces an innovative approach-a branch and bound algorithm-for exact motif identification across diverse lengths. This algorithm exhibits superior performance in terms of reduced runtime and enhanced result accuracy, as compared to existing methods. To achieve this objective, the study constructs a comprehensive tree structure encompassing potential motif evolution pathways. Subsequently, the tree is pruned based on motif length and targeted similarity thresholds. The proposed algorithm efficiently identifies all potential motif subsequences, characterized by maximal similarity, within expansive protein sequence datasets. Experimental findings affirm the algorithm's efficacy, highlighting its superior performance in terms of runtime, motif count, and accuracy, in comparison to prevalent practical techniques.

Advancing spacecraft rendezvous and docking through safety reinforcement learning and ubiquitous learning principles

As spacecraft rendezvous and docking missions become increasingly complex, the need for advanced solutions has surged. In recent years, the application of reinforcement learning techniques to tackle spacecraft rendezvous guidance challenges has emerged as a prominent international trend. Vital to ensuring the secure rendezvous and docking of spacecraft is the task of obstacle avoidance. However, traditional reinforcement learning algorithms lack the ability to enforce safety constraints within the exploration space, which presents a formidable obstacle in the design of spacecraft rendezvous guidance strategies. In response to this challenge, a spacecraft rendezvous guidance methodology founded on safety reinforcement learning is proposed. Firstly, a Markov model is crafted for autonomous spacecraft rendezvous in scenarios involving collision avoidance. A reward system, contingent on obstacle warnings and collision avoidance constraints, is introduced to establish a safety reinforcement learning framework for devising spacecraft rendezvous guidance strategies. Secondly, within the framework of safety reinforcement learning, two deep reinforcement learning (DRL) algorithms, Proximal Policy Optimisation (PPO) and Deep Deterministic Policy Gradient (DDPG), are leveraged to generate these guidance strategies. Experimental findings validate the effectiveness of this approach in successfully executing obstacle avoidance and achieving rendezvous with remarkable precision. Furthermore, through an analysis of the performance and generalization capabilities of these two algorithms, the efficacy of the proposed methodology is further underscored. This fusion of advanced space guidance technology with the principles of Ubiquitous Learning marks a significant step forward in the quest for safer and more efficient spacecraft rendezvous and docking operations.

Design Aspects of Lower Limb Exoskeleton

Aim: This research work aimed at the design, simulation, and validation of a lower limb exoskeleton for rehabilitation. The device can provide regressive gait training for patients suffering from lower limb mobility disorders. Background: People suffering from mobility disorders, such as spinal cord injuries, and other related diseases are in high proportion. Exoskeletons play a vital role in enhancing the lifestyle of people with disorders. Devices that provide locomotion assistance and help in reducing the burden of therapists through effective and repetitive gait training are in high demand. Exoskeletons have further extended to the fields of the military to enhance the performance of physically abled persons. Prototype development of lower limb exoskeletons is too expensive and many of them are patented. The requirement for this system to perform human trials is subjective to several medical and ethical norms. Thus, there exists a need to evaluate and validate the exoskeleton designs. Methods: In this work, the design has been made inclusive of different body shapes and sizes. The device has been modeled in SOLIDWORKS and its structural integrity has been analyzed using the ANSYS software. Later, the model has been subjected to environmental assessment and then motion analysis using the ADAMS software. Results: The structural integrity analysis has revealed the design to be adequate to carry the applied load as the stresses induced were less than the yield strength of the material. The sustainability analysis showed that LLE made of aluminium alloy had less impact on the environment relative to the other two materials. Conclusion: The kinematic simulation revealed that the angular amplitudes, the reaction force of the right hip and knee joint, and the contact force between the shoe and the ground of the exoskeleton agreed well with the experimental findings of the literature.

Atomistic description of conductive bridge formation in two-dimensional material based memristor

In-memory computing technology built on 2D material-based nonvolatile resistive switches (aka memristors) has made great progress in recent years. It has however been debated whether such remarkable resistive switching is an inherent property of the 2D materials or if the metal electrode plays any role? Can the metal atoms penetrate through the crystalline 2D materials to form conductive filaments as observed in amorphous oxide-based memristors? To find answers, here we investigate MoS2 and h-BN-based devices with electrochemically passive and active (metal) electrodes using reactive molecular dynamics with a charge equilibration approach. We find that the SET and RESET processes in active electrode-based multilayer devices involve the formation and disruption of metal filaments linking the two electrodes exclusively through the grain boundaries, the configuration of which affects the volatility of the resistive switching. Whereas the switching mechanisms in passive electrode-based devices require the formation of interlayer B-N bonds and popping of the S atom to the Mo plane at the point defects. We also show that metal atom adsorption at the point defects causes resistive switching in monolayer MoS2. Our atomic-level understanding provides explanations to the apparently contradictory experimental findings and enables defect-engineering guidelines in 2D materials for such disruptive technology.

SIoV Mobility Management using SDVN-enabled Traffic Light Cooperative Framework

Social Internet of Vehicles (SIoV) is an emerging connected vehicular networking framework among specialized social vehicles to share and disseminate important information like traffic updates, weather conditions, parking slots, etc. This study aims to form an SIoV network among emergency vehicles for their frequent communication to improve the throughput, average waiting time, queue length, and speed during vehicular movement while crossing the intersection in the city. To address this, we propose a novel smart traffic light controller-assisted software-defined vehicular networking-enabled SIoV framework for emergency vehicles. Emergency vehicles form an SIoV network by utilizing Software-Defined Vehicular Networking (SDVN) architecture in Vehicle to Vehicle and Vehicle to Infrastructure communication. The SDVN module is used to offer two essential services: 1) SIoV-based road-lane prioritization, and 2) congestion prevention signal generation for the smart traffic light controller. An SDVN-MP algorithm is proposed to generate an effective traffic light control signal with an SDVN controller feedback signal. Furthermore, to improve the SIoV movement in the city, two levels of prioritization: 1) SIoV, and 2) the road lane with SIoV, are done. The first level of prioritization is to assign higher weightage to the social vehicular entities, and the second level is to prioritize the respective road lane based on SIoV quantity. The proposed framework is validated through a realistic simulation study on the Indian city OpenStreetMap utilizing the Simulation of Urban MObility simulator. The experimental findings demonstrate that the SDVN-MP model enhances (state-of-the-art) comparative performance by 22.5% to 55.2%, 1.2% to 82.7%, 1.6% to 38.4%, and 1.8% to 12.4% for average waiting time, average speed, average queue length, and average throughput metrics, respectively.

Effects of textile type on the flexural properties of prefabricated hollow-core slabs strengthened with PVA fibers-improved TRM

In this study, the utilization of textile-reinforced mortar (TRM) in the strengthening and rehabilitation field is investigated. To regulate crack width, polyvinyl alcohol (PVA) fibers were incorporated into the TRM matrix. Nine prefabricated hollow-core slabs strengthened with TRM, along with one reference slab, were subjected to static loading using a 4-point bending apparatus. The experimental variables consisted of the type of textile (glass, basalt, and carbon) and the number of textile layers (one, two, and three layers). The experimental findings demonstrated that the inclusion of PVA fibers improved the TRM by effectively controlling crack width and increasing energy absorption capacity. Among the textile types, carbon TRM-strengthened slabs exhibited the highest flexural capacity. This capacity increased by 74%, 119%, and 132% for slabs strengthened with one, two, and three layers of textile, respectively. Glass TRM-strengthened slabs demonstrated superior deformability, with an increase rate ranging from 202% to 331%. Finally, to predict the flexural capacity and deflection of the tested slabs, a theoretical analysis was conducted and validated by comparing the theoretical predictions to the actual test results for accuracy and reliability.

Construction of programmed time-released multifunctional hydrogel with antibacterial and anti-inflammatory properties for impaired wound healing

The successful reprogramming of impaired wound healing presents ongoing challenges due to the impaired tissue microenvironment caused by severe bacterial infection, excessive oxidative stress, as well as the inappropriate dosage timing during different stages of the healing process. Herein, a dual-layer hydrogel with sodium alginate (SA)-loaded zinc oxide (ZnO) nanoparticles and poly(N-isopropylacrylamide) (PNIPAM)-loaded Cu5.4O ultrasmall nanozymes (named programmed time-released multifunctional hydrogel, PTMH) was designed to dynamically regulate the wound inflammatory microenvironment based on different phases of wound repairing. PTMH combated bacteria at the early phase of infection by generating reactive oxygen species through ZnO under visible-light irradiation with gradual degradation of the lower layer. Subsequently, when the upper layer was in direct contact with the wound tissue, Cu5.4O ultrasmall nanozymes were released to scavenge excessive reactive oxygen species. This neutralized a range of inflammatory factors and facilitated the transition from the inflammatory phase to the proliferative phase. Furthermore, the utilization of Cu5.4O ultrasmall nanozymes enhanced angiogenesis, thereby facilitating the delivery of oxygen and nutrients to the impaired tissue. Our experimental findings indicate that PTMHs promote the healing process of diabetic wounds with bacterial infection in mice, exhibiting notable antibacterial and anti-inflammatory properties over a specific period of time.