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In the field of vehicular communication, the Internet of Vehicles (IoV) serves as a new era that guarantees increased connectivity, efficiency, and safety. The modern area and new technology have their challenges and constraints, though. This paper thoroughly examines these constraints significantly; we show how blockchain technology is being used to overcome them. This paper primarily explores the complexities of Blockchain-enabled Internet of Vehicles (BIoV) architectures, the applications they serve, and the robust security features they provide through a systematic literature review (SLR). In addition, we look at the several ways that blockchain and IoV might be integrated and investigate the subtle factors that should be considered when choosing consensus algorithms to maximize performance on different blockchains. This paper also addresses the methods and tools used to identify and avoid fraudulent activities in BIoV networks at a maximum level of security. It also reveals the wide range of BIoV applications and analyzes the different security levels they provide. In closing, we give an idea of the possibilities that will continue to develop the blockchain and IoV environment, reducing the roadblocks and advancing this combination toward a more secure, effective, and connected future for vehicle communication systems.

LiNi0.8Co0.1Mn0.1O2 (NCM) layered oxide is contemplated as an auspicious cathode candidate for commercialized lithium-ion batteries. Regardless, the successful commercial utilization of these materials is impeded by technical issues like structural degradation and poor cyclability. Elemental doping is among the most viable strategies for enhancing electrochemical performance. Herein, the preparation of surface tellurium-doped NCM is done by utilizing the methodology solid-state route at high temperatures. Surface doping of the Te ions leads to structural stability owing to the inactivation of oxygen at the surface via the binding of slabs of transition metal-oxygen. Remarkably, 1 wt% of Te doping in NCM exhibits enhanced electrochemical characteristics with an excellent discharge capacity, i.e., 225.8 mAh/g (0.1C), improved rate-capability of 156 mAh/g (5C) with 82.2% retention in capacity (0.5C) over 100 cycles within 2.7–4.3V as compared to all other prepared electrodes. Hence, the optimal doping of Te is favorable for enhancing capacity, cyclability along with rate capability of NCM.

In this study, copper (Cu)- and manganese (Mn)-based layered double hydroxide (LDH) nanosheets were produced by modest and low-cost hydrothermal technique to display an improved photocatalytic performance toward the degradation of aqueous methylene blue (MB). The morphological and structural properties of the as-prepared photocatalysts were characterized through various techniques comprising XRD, FT-IR, SEM, EDS, and their MB degradation activity was evaluated under visible light irradiation. SEM results explore that the synthesized LDH materials have a sheet-like morphology and are stacked layer by layer. Various analysis parameters, such as the effect of the contact time, concentration and pH of MB solutions were performed to optimize the performance of fabricated LDH materials. The results revealed that the as-synthesized CuAl-LDH and MnAl-LDH exhibited a 74.95 and 70.93% removal of MB under solar light within 180 min. Moreover, synthesized photocatalysts showed an excellent performance of up to four regeneration cycles. We believe that this study provides novel mechanistic insights into the design and preparation of highly competent photocatalysts using low-cost materials, with applications in environmental remediation.

The primary objective of the study was to determine the mean drain output after systemic tranexamic acid injection in patients undergoing ventral wall hernia repair. This was a descriptive cross-sectional study conducted in the Department of General Surgery, Hamdard University Hospital Karachi, Pakistan for a period of six months from Oct 2020 to April 2021. The patients undergoing ventral hernia repair were included. Injection tranexamic acid 1gm intravenous twice a day for 1st post-operative day and then capsule Tranexamic acid 500mg per oral three times a day for two consecutive days was given and drain output was measured for 3 days. A total of 63 patients with ventral hernia were included, mean age of patients was 41.476 (±SD= 8.693). There were 41 male patients (65.1%) and 22(34.9%) females. The mean drain output on first day, second and third day was 115.047+14.678 ml/day, 91.919+9.947 ml/day, 66.914+7.070 ml/day and 273.254+29.165 ml/day respectively. The results showed overall reduction in postoperative drain output after oral and intravenous tranexamic acid for 72 hours. However further large scale randomized controlled trials are recommended for confirmation of these findings.

Helicobacter pylori (H. pylori) is mainly considered for causing chronic gastritis, which can lead to several secondary complications like peptic ulcer and pre-malignant lesions for example atrophic gastritis, intestinal dysplasia and metaplasia, with the etiological factor of developing gastric cancer. Recent research demonstrates that H.pylori colonizes the stomach mucosa of more than fifty populations around the globe. This research focuses on unveiling hub genes, and diagnostic and drug targets against said organism by utilizing various types of networking biology and biophysical approaches. In data retrieval, the GSE19826 dataset was obtained from the gene expression omnibus database and microarray data set from array express. Geo2r analysis predicted a total number of 7 DEGs and 10 hub genes, next functional protein association network analysis (STRING) unveiled that among 10 Hub genes only 3 genes were found more interactive with other genes and involved in pathogenesis, The shortlisted three genes were further analyzed for survival analysis using Gene Expression Profiling Interactive Analysis (GEPIA) and predicted the survival rate of targeted genes. Moreover, functional enchainment analysis was done using the ToppFun server, the server predicted that COL11A1 and COL10A1 were more involved in the pathogenesis of the H. pylori infection. Furthermore, the COL10A1 gene was subjected to protein structure prediction. In molecular docking analysis, the asinex antibacterial library was screened for potential inhibitors, and one compound was predicted as a strong inhibitor with the best binding at −10.23 kcal/mol. The docking results were further validated through molecular dynamic simulation analysis and the MD simulation analysis evaluated the dynamic movement of the docked complex in various nanoseconds, the MD simulation results predicted that the docked complexes are stable throughout the simulation and can be used as a potential inhibitor against the said pathogen, however experimental study is required to further validate the predicted results and design drug against targeted pathogen.

Introduction: LSG is the gold standard sole bariatric procedure for morbid obesity. Small (2cm) and large (6cm) distance of resection margin from pylorus are both being used for neogastric sleeve formation however what is optimal distance remains controversial. Aims & Objectives: To assess excess weight loss results and complication rate with 6cm and 2cm distance of staple line from pylorus during a prolonged surveillance period. Place and Duration of Study: This retrospective cohort study was conducted at Fatima Memorial Hospital from Dec 2013 to Dec 2022 spanning 9 years. Material & Methods: Medical records of 50 morbidly obese patients aged 41- 50 yrs were segregated into two groups of 25 each based on the LSG procedure conducted. Group 1 had LSG with 6cm resection margin from pylorus while group 2 had a staple line distance of 2cm from pylorus for neogastric sleeve formation. The patients were subsequently followed up for excess weight loss (EXL) and BMI at 6 months, 1 year, 2 years, 3 years & 4-year interval. Data was entered and analyzed using SPSS version 21.P value ? 0.05 was considered significant. Results: EWL in Group 1 & 2 was 39.2± 6.77kg & 41.9± 4.35kg at 6 months,73.64± 6.49kg & 71.06± 7.77kg at 1 year, 65.52± 5.53kg & 63.92± 7.07kg at 2 years,58.16± 4.97kg & 57.28± 5.75kg at 3 years and 55.48± 3.39kg & 55.36± 3.72kg for 4 years were, not significant statistically. BMI in group 1 & 2 were 39.47± 5.11 & 35.88± 4.40 at 6 months, 29.22± 2.68 & 29.12± 2.00 at 1 year, 25.44± 1.96 & 24.73± 2.86 at 2 years, 22.84± 1.79 & 22.40±2.20 at 3 years and 21.66± 1.32, 21.67± 1.62 at 4 years were found to be statistically insignificant. Conclusion: With regards to extra weight loss, distance of resection margin from pylorus of 2cm or 6cm have identical effects but the latter has lowest complication incidence after 4 yrs of surveillance. Hence, a distance of 6cm is favoured to ensure safety of LSG for morbidly obese patients. And the findings of this study will help bariatric surgeons to improve technique of LSG in a safe and successful way for effective weight loss in morbidly obese patients.

Data offloading is considered as a potential candidate for alleviating congestion on wireless networks and for improving user experience. However, due to the stochastic nature of the wireless networks, it is important to take optimal actions under different conditions such that the user experience is enhanced and congestion on heavy-loaded radio access technologies (RATs) is reduced by offloading data through lower loaded RATs. Since artificial intelligence (AI)-based techniques can learn optimal actions and adapt to different conditions, in this work, we develop an AI-enabled Q-agent for making data offloading decisions in a multi-RAT wireless network. We employ a model-free Q-learning algorithm for training of the Q-agent. We use stochastic geometry as a tool for estimating the average data rate offered by the network in a given region by considering the effect of interference. We use the Markov process for modeling users’ mobility, that is, estimating the probability that a user is currently located in a region given its previous location. The user equipment (UE) plays the role of a Q-agent responsible for taking sequence of actions such that the long-term discounted cost for using network service is minimized. Q-agent performance has been evaluated and compared with the existing data offloading policies. The results suggest that the existing policies offer the best performance under specific situations. However, the Q-agent has learned to take near-optimal actions under different conditions. Thus, the Q-agent offers performance which is close to the best under different conditions.

Abstract This research study aims to examine the impact of sustainability on firm performance and analyze how vertical integration moderates the connection between performance and sustainability in the oil and gas sector. We analyzed a sample dataset of oil and gas companies from the top ten oil-producing countries spanned over ten years (2011–2020). The pool-fixed regression technique confirms that sustainability and its three components, i.e., social, environmental, and governance, are negatively related to performance. However, vertical integration moderated the connection between sustainability and performance in the case of the oil and gas sector. We have identified firm size, age, and return share price positively related to firm performance in the oil and gas industry. At the same time, the debt ratio negatively impacts the firm’s performance. The findings are significant for the management of oil and gas firms and the policymakers and regulatory authorities of oil-producing and exporting countries.

Abstract Solid‐state lithium batteries (SSLBs) have received considerable attention due to their advantages in thermal stability, energy density, and safety. Solid electrolyte (SE) is a key component in developing high‐performance SSLBs. An in‐depth understanding of the intrinsic bulk and interfacial properties is imperative to achieve SEs with competitive performance. This review first introduces the traditional electrochemical approaches to evaluating the fundamental parameters of SEs, including the ionic and electronic conductivities, activation barrier, electrochemical stability, and diffusion coefficient. After that, the characterization techniques to evaluate the structural and chemical stability of SEs are reviewed. Further, emerging interdisciplinary visualization techniques for SEs and interfaces are highlighted, including synchrotron X‐ray tomography, ultrasonic scanning imaging, time‐of‐flight secondary‐ion mass spectrometry, and three‐dimensional stress mapping, which improve the understanding of electrochemical performance and failure mechanisms. In addition, the application of machine learning to accelerate the screening and development of novel SEs is introduced. This review article aims to provide an overview of advanced characterization from a broad physical chemistry view, inspiring innovative and interdisciplinary studies in solid‐state batteries.

Abstract Internet of Things (IoT) vision has astoundingly transcended environmental sensing with integrated computing systems and smart devices, providing seamless connectivity among humans, machines, and their environment to cooperate for convenience and economical benefits. Apart from all the tremendous benefits of IoT, this paradigm still suffers from challenges of security and privacy vulnerabilities and demands a secure system for effective utilization of services in real-world IoT scenarios relying on which the IoT consumers expect secure and trustworthy communications. Trust Management (TM), which is a crucial aspect of security, plays a vital role in ensuring the exchange of information in a secure manner and maintaining the reliability of a system by measuring the degree of trust on IoT devices, reducing the uncertainties and risks involved in the systems. Thus, in recent years, Blockchain technology has been utilized for developing security innovations in TM field for different classes of IoT applications. It can provide tamper-proof data by enabling more reliable trust information and integrity verification, ultimately enhancing its availability and privacy when storing and sharing information. This paper provides a comprehensive survey that aims at analyzing and assessing Blockchain-based decentralized trust management systems (BCDTMS) for IoT. The contributions of this study are threefold; first, we provide the comprehensive and comparative analysis of state-of-the-art BCDTMS devised for different IoT classes such as Internet of Medical of Things (IoMT), Internet of Vehicles (IoV), Industrial IoT (IIoT), and Social IoT (SIoT). To make it an extensive study, we perform a detailed assessment of the existing BCDTMS in the literature in the aspects of Blockchain and TM. Second, we present requirements for developing Blockchain-based TM systems for IoT, and third we have highlighted the challenges in the context of using Blockchain for TM in various IoT applications.

To understand the variables affecting Pakistan's cotton output, our research paper study focuses on applying the PowerBI tool to analyze data related to the cotton crop. We gathered data from two fields in Rahimyar Khan and Shah Alam Shah, Matiari, Sindh, to analyze the soil moisture content, availability of fertilizer, and environmental factors to improve agricultural practices and increase crop yields. The dataset contains data from monitoring dates and factors like temperature, humidity, soil moisture content, and signal intensity. We forecast cotton crop output, improve planting schedules, and foresee possible issues like bug outbreaks using predictive analytics. The study offers practical suggestions for decision-making procedures about fertilizer application schedules, irrigation schedules, and the sustainability of cotton crops. Limitations include data quality and scalability challenges, and future research will focus on improving agricultural techniques for better cotton growing.

This study provides an understanding of tax salience through the review of the literature. The tax salience refers to the prominence and visibility of the taxes and their influence over the taxpayer’s behavior. This study reviews the background literature on tax salience, how the tax policy is influenced by it, and the reaction of consumer behavior towards tax salience. The study uses the review of literature as a research methodology. The study uses the review strategy of narrative review by synthesizing, organizing, and assembling the earlier literature related to tax salience. The google scholar database was used to retrieve relevant studies for the narrative review. The time period of the key studies is mainly from 1987 to 2023. The paper concludes that tax salience despite being a relatively young field of economics has a pivotal role in influencing tax policies and consumer behavior. This study provides guidelines for future researchers with respect to how the consumer reacts to salience of taxation. For the policy framework it can be mandated that the policy makers in order to optimize the tax systems should not overlook the concept and area of tax salience. Since the tax salience has a significant role in influencing and shaping the consumer behavior.

Software-defined networking (SDN) plays a crucial role in the enterprise and wide-area networking. The increasing demand for strict service-level agreement applications on the Internet requires networks to be scalable and resilient in the face of link and switch failure. However, there is a lack of systematic reviews on SDN data plane failure recovery techniques. This review article assesses SDN current state-of-the-art link and switches failure recovery solutions. We cover the root causes of failures in the traditional core network and their detection and classify the current failure recovery techniques for SDN into two categories: traditional and artificial intelligence (AI) approaches. AI-based techniques enable efficient failure recovery and enhance the quality of service. We also consider performance measure metrics to evaluate and determine the limitations of existing solutions. This study reviews 188 papers from 2010 to 2021, selecting 70 articles that are highly relevant to our work. All articles are written in English. Our research aims to collect a large amount of evidence that will assist the industry and academic researchers in networking to address current research gaps in failure recovery solutions for the SDN data plane.

Sign language recognition (SLR) enables the deaf and speech-impaired community to integrate and communicate effectively with the rest of society. Word level or isolated SLR is a fundamental yet complex task with the main objective of using models to correctly recognize signed words. Sign language consists of very fast and complex hand, body, face movements, and mouthing cues that make the task very challenging. Several input modalities; RGB, optical Flow, RGB-D, and pose/skeleton have been proposed for SLR. However, the complexity of these modalities and the state-of-the-art (SOTA) methodologies tend to be exceedingly sophisticated and over-parametrized. In this paper, our focus is to use the hands and body poses as an input modality. One major problem in pose-based SLR is extracting the most valuable and distinctive features for all skeleton joints. In this regard, we propose an accurate, efficient, and lightweight pose-based pipeline leveraging a graph convolution network (GCN) along with residual connections and a bottleneck structure. The proposed architecture not only facilitates efficient learning during model training providing significantly improved accuracy scores but also alleviates computational complexity. With the proposed architecture in place, we are able to achieve improved accuracies on three different subsets of the WLASL dataset and the LSA-64 dataset. Our proposed model outperforms previous SOTA pose-based methods by providing a relative improvement of 8.91%, 27.62%, and 26.97% for WLASL-100, WLASL-300, and WLASL-1000 subsets. Moreover, our proposed model also outperforms previous SOTA appearance-based methods by providing a relative improvement of 2.65% and 5.15% for WLASL-300 and WLASL-1000 subsets. For the LSA-64 dataset, our model is able to achieve 100% test recognition accuracy. We are able to achieve this improved performance with far less computational cost as compared to existing appearance-based methods.

Introduction: Globally, acute generalized peritonitis ranks among the top surgical emergencies. Different studies have been conducted to show the amplitude of peritonitis worldwide eliciting a huge impact on overall patient morbidity and mortality. Largely peritonitis is caused by a gastrointestinal perforation or anastomotic leak. In peritonitis, anaerobes & gram-negative organisms are mostly responsible for sepsis and morbidity due to the overactive inflammatory cascade by endotoxins which is amenable to timely intervention. Aims & Objectives: The study's aim was to evaluate whether using normal saline or metronidazole solution during intraoperative peritoneal lavage (IOPL), results in a lower rate of postoperative wound infection. Place and Duration of Study: This study was undertaken at the South Surgical Ward, Mayo Hospital Lahore for 6 months from February 2nd, 2021, to August 1st, 2021. Material & Methods: Consecutive sampling strategy followed by a randomized controlled trial were used to induct and provide intervention to 90 patients aged 15-65 years with peritonitis caused by hollow viscus perforation. The patients were subdivided into 2 groups A& B(n=45 each).Two liters of normal saline were used for peritoneal lavage in group A, while two liters of normal saline were combined with 200 mL of metronidazole solution and administered to group B. intraoperatively.Baseline physiological parameters such as age, sex, BMI , intra operative surgical parameters as duration of operation and post-operative course were recorded till discharge. On 10th POD, patients returned to OPD for further monitoring. An infection was diagnosed if the patient had post-operative symptoms such as a high temperature, increased TLC, wound discharge, redness, or pain. Data was entered and analyzed using SPSS version 23. Results: A majority (54.44%) of the patients were young adults. Mean age of 37.33 ± 10.53 years of patients in the metronidazole group was comparable to mean age 40.04 ± 11.96 years in the saline group, difference was not significant (p=0.067). Male/female ratio in Metronidazole and Saline groups were 17/25 and 10/18, respectively. Patients who received intraperitoneal lavage with normal saline were more likely to develop wound infections (17/45) (37.78%), while only 3/45) (6.67% of those who received metronidazole solution did so (p 0.0001). Conclusion: Based on the results of this experiment, using metronidazole solution for intraoperative peritoneal lavage instead of normal saline reduces the occurrence of postoperative wound infection.

Niosomes are multilamellar vesicles that efficiently deliver active substance into skin systemic circulation or skin layers. They are used in topical drug delivery system to enhance the skin permeation of active substance. So, the prime objective of this study was to develop a niosomal gel of fusidic acid to increase its skin permeation. Different formulations of niosomes of fusidic acid were designed by varying the cholesterol to surfactant ratio. Formulations containing fusidic acid, cholesterol, dihexadecyl pyridinium chloride, Span 60, or Tween 60 were prepared by thin film hydration method in rotary evaporator. The thin film formed in rotary flask was hydrated by phosphate buffer saline of pH 7.2. The niosomes formed were characterized through entrapment efficiency, size, polydispersity index (PDI), and zeta potential. The S3 formulation containing span 60 showed the highest entrapment efficiency (EE) of niosomes, so it was incorporated into Carbopol gel. Determination of pH, spreadability, rheological, and ex vivo permeation studies was conducted of niosomal gel. The results of ex vivo permeation studies showed high permeation of fusidic acid when gel was applied to an albino rat skin. According to the results and previous studies of niosomes, it can be concluded that niosomes enhanced the permeation of fusidic acid through the skin.

Chitosan-based nano-sized particles increase the penetration of the drug through the narrow junction into the bloodstream and target the specific site. The objective of this study was to prepare chitosan nanoparticles to entrap a hydrophobic drug (tamoxifen citrate), followed by the alginate coating of the developed nanoparticles to decrease their degradation in the acidic pH. Drug-loaded chitosan nanoparticles were prepared by the ionic gelation method. Alginate coating was done by dissolving sodium alginate to buffer solution and drug-loaded chitosan nanoparticles drop-wise under mild agitation. The size of alginate coated chitosan nanoparticles, zeta potential, surface morphology, in-vitro drug release, and entrapment efficiency was measured. The optimised formulation of both uncoated (SH3) and coated (SH7) formulation showed the particle size, PDI, and zeta potential with values 221 & 338 nm, 0.161 & 0.230 and 36.5 & −20.7 mV, respectively. The resulted nanoparticle surface was non-porous. The percentage yield of the optimised formulation SH3 was 28% and SH6 was 33%. The entrapment efficiency of the optimised formulation SH3 (uncoated formulation) and SH6 (coated formulation) is 69.5 and 58.51%, respectively. Chitosan nanoparticles were successfully prepared to entrap tamoxifen citrate. The coating of chitosan nanoparticles decreased their degradation in the acidic pH.

Software metrics plays a very vital role in life cycle of software development. Rapid software development techniques and tools have made it very complex to fully control the quality of a software. Software metrics are required to make sure that the quality of software is fully under control. Many software metrics have already been developed and applied to control the quality of software products. Software metrics is the measurement of quality in which performance is measured against quality standards to check whether they are according to the expectations. Quality metrics are also used to determine customer requirements into acceptable performance measures. This paper discusses the concepts of software quality, quality factor model, mapping according to McCall Quality Model & the quality metrics. The act of applying software quality measurements to functional components and to keep up with factors is a mind-boggling task. Effective software quality affirmation is exceptionally reliant upon quality methods. Future examination is needed to expand out and work on the approach to widen measurements that have been accepted on one venture, utilizing our rules, legitimate proportions of value on future software project. This paper also dives deep into the impacts of the various software metrics over different quality factors and explains the relationship between them.

The forthcoming era of the automotive industry, known as Automotive-Industry 5.0, will leverage the latest advancements in 6G communications technology to enable reliable, computationally advanced, and energy-efficient exchange of data between diverse onboard sensors, drones and other vehicles. We propose a non-orthogonal multiple access (NOMA) multi-drone communications network in order to address the requirements of enormous connections, various quality of services (QoS), ultra-reliability, and low latency in upcoming sixth-generation (6G) drone communications. Through the use of a power optimization framework, one of our goals is to evaluate the energy efficiency of the system. In particular, we define a non-convex power optimization problem while considering the possibility of imperfect successive interference cancellation (SIC) detection. Therefore, the goal is to reduce the total energy consumption of NOMA drone communications while guaranteeing the lowest possible rate for wireless devices. We use a novel method based on iterative sequential quadratic programming (SQP) to get the best possible solution to the non-convex optimization problem so that we may move on to the next step and solve it. The standard OMA framework, the Karush–Kuhn–Tucker (KKT)-based NOMA framework, and the average power NOMA framework are compared with the newly proposed optimization framework. The results of the Monte Carlo simulation demonstrate the accuracy of our derivations. The results that have been presented also demonstrate that the optimization framework that has been proposed is superior to previous benchmark frameworks in terms of system-achievable energy efficiency.

The purpose of this research is to investigate the impact of moisture content, closing speed, and pressurizing speed of hot press on the density, uniformity of density distribution, and properties of Medium Density Fiberboard (MDF) products. Moisture content affects the plasticity, heat conductivity, and hydrolysis reaction of the fiber and appropriate moisture content ensures the quality of the board. Excessive moisture content can lead to defects such as pollution and bubbles on the surface of the board. Closing time and pressurizing speed affect the nature, quality, and section structure of the MDF. The section density of the board varies with the closing time, and the product properties, especially the strength differs largely. The pressurizing speed influences the density distribution and board properties. Fast pressurizing speed results in high Modulus of Rupture (MoR) and low Internal Bonding (IB), while slow pressurizing speed leads to low MoR and high IB. Pre-plasticizing layer can improve the quality of MDF by increasing the density and hardness of the board surface. The findings provide guidance for optimizing the MDF production process and improving the quality of MDF products. Applying a fast pressurizing speed during the manufacturing process of MDF has resulted in a 15.7% increase in the MoR, which is a measure of the material’s ability to withstand stress before breaking as compared to using a slow pressurizing speed. Additionally, using a fast pressurizing speed has led to a reduction of 17.4% in the IB, which is a measure of the ability of the board to resist internal separation or delamination. Therefore, a fast pressurizing speed is more effective in improving the mechanical properties of MDF.

Purpose: The aim of this research was to prepare adhesive nanoparticles for the topical application of Minoxidil (MXD). Methods: Thiolated β-CDs were prepared via conjugation of cysteamine with oxidized CDs. MXD was encapsulated within thiolated and unmodified β-CDs. Ionic gelation method was used to prepare nanoparticles (Thio-NP and blank NP) of CDs with chitosan. Nanoparticles were analyzed for size and zetapotential. Inclusion complexes were characterized via FTIR. Drug dissolution studies were carried out. An in vitro adhesion study over human hair was performed. An in vivo skin irritation study was performed. Ex vivo drug uptake was evaluated by using a Franz diffusion cell. Results: Thiolated β-CDs presented 1804.68 ± 25 μmol/g thiol groups and 902.34 ± 25 μmol/g disulfide bonds. Nanoparticles displayed particle sizes within a range of 231 ± 07 nm to 354 ± 13 nm. The zeta potential was in the range of −8.1 ± 02 mV, +16.0 ± 05 mV. FTIR analyses confirmed no interaction between the excipients and drug. Delayed drug release was observed from Thio-NP. Thio-NP retained over hair surfaces for a significantly longer time. Similarly, drug retention was significantly improved. Thio-NP displayed no irritation over rabbit skin. Conclusion: Owing to the above results, nanoparticles developed with MXD-loaded thiolated β-CDs might be a potential drug delivery system for topical scalp diseases.

Controlling an uncertain mechatronic system is challenging and crucial for its automation. In this regard, several control-strategies are developed to handle such systems. However, these control-strategies are complex to design, and require in-depth knowledge of the system and its dynamics. In this study, we are testing the performance of a rather simple control-strategy (Adaptive Neuro- Fuzzy Inference System) using an uncertain Ball and Beam System. The custom- designed apparatus utilizes image processing technique to acquire the position of the ball on the beam. Then, desired position is achieved by controlling the beam angle using Adaptive Neuro-Fuzzy and PID control. We are showing that adaptive neuro-fuzzy control can effectively handle the system uncertainties, which traditional controllers (i.e., PID) cannot handle.

Micro Metal-Insulator-Metal (M-I-M) capacitor structures are well-known passive components that have been broadly used in integrated circuits, Radio Frequency (RF) decoupling, Micro Electro Mechanical Systems (MEMS) sensors, and health monitoring systems. Thanks to its small dimensions, it can be easily integrated into microelectronics. With the acceleration of the scalling dawn of integrated circuits and systems, the size of the capacitor and other components must be reduced. It has become challenging to fabricate a micro M-I-M capacitor with low leakage current and high-capacity density since the leakage current and depletion effect are reported as the main factors of the gradual loss of electrical energy in the micro-M-I-M capacitor. Thus, minimizing the leakage current and the depletion effect became a new research trend. This paper presents a penta-layer high-K dielectric between the electrodes to reduce the leakage current in the micro-M-I-M capacitor. For this purpose, various dielectric materials were investigated. It was found that niobium pentaoxide (Nb2O5) and hafnium dioxide (HfO2) as penta-layer dielectric materials provide the lowest leakage current between the two electrodes. The recorded values of leakage current density are reduced to a mere 0.95 µAmps/mm2 from several µAmps/mm2 at the operating voltage of 1 V. The reported micro-M-I-M capacitor has potential application as an energy storage device.

For next-generation all-solid-state metal batteries, the computation can lead to the discovery of new solid electrolytes with increased ionic conductivity and excellent safety. Based on computational predictions, a new proposed solid electrolyte with a flat energy landscape and fast ion migration is synthesized using traditional synthesis methods. Despite the promise of the predicted solid electrolyte candidates, conventional synthetic methods are frequently hampered by extensive optimization procedures and overpriced raw materials. It is impossible to rationally develop novel superionic conductors without a comprehensive understanding of ion migration mechanisms. In this review, we cover ion migration mechanisms and all emerging computational approaches that can be applied to explore ion conduction in inorganic materials. The general illustrations of sulfide and oxide electrolyte structures as well as their fundamental features, including ion migration paths, dimensionalities, defects, and ion occupancies, are systematically discussed. The major challenges to designing the solid electrolyte and their solving strategies are highlighted, such as lattice softness, polarizability, and structural disorder. In addition to an overview of recent findings, we propose a computational and experimental approach for designing high-performance solid electrolytes. This review article will contribute to a practical understanding of ion conduction, designing, rapid optimization, and screening of advanced solid electrolytes in order to eliminate liquid electrolytes.

Machine learning and deep learning algorithms are widely used in computer science domains. These algorithms are mostly used for classification and regression problems in almost every field of life. Semantic segmentation is an instantly growing research topic in the last few decades that refers to the association of each pixel in the image to the class it belongs. This paper illustrates the systematic survey of advanced research in the field of semantic segmentation till date. This study provides the brief knowledge about the latest proposed methods in the domain of semantic segmentation. The proposed study comprehends the concepts, techniques, tool, and results of different research frameworks proposed in the context of semantic segmentation. This study discusses the latest research papers in which machine learning and deep learning techniques are exploited for semantic segmentation and published between 2016 and 2021. The systematic literature review collected from seven different article libraries including ACM digital Library, Google Scholar, IEEE Xplore, Science Direct, Google Books, Refseek and Worldwide Science. For assuring the quality of the paper those papers are selected which have several citations on standardized platforms. Most of the studies used COCO, PASCAL, Cityscapes and CamVid dataset for training and validation of the machine learning and deep learning models. The results of the selected research articles are collected in the form of accuracy, mIoU value, F1 score, precision, and recall. In this study, we also conclude that most of the semantic segmentation studies use ResNet as the backbone of the architecture and none of the researchers used ensemble learning methods for semantic segmentation that is the loophole of the selected studies.

Globalization breeds increasing competition. In today’s dynamic climate, lean thinking has been found a promising business continuous improvement strategy for improving quality while reducing product cost and delivery time. However, its implementation has dynamic nature of challenges that varies from industry to industry and country to country, necessitating a specific framework by taking all stakeholders onboard. This study aims to propose a lean implementation framework to reduce defects and waste to improve the performance of the metal casting industry. The structure of the framework has been divided into three phases namely the lean conception phase, lean implementation phase, and lean sustainability phase. The proposed framework integrates the six sigma DMAIC methodology with lean tools and techniques to reduce defects and achieve performance improvement. A solid cast software has been used as a computer-assisted casting simulation tool to perform the analysis of defects within the casting. Further, the proposed framework is demonstrated and validated by employing a real-time case study that was manufactured using the sand casting process. The obtained results show remarkable improvements in poured metal weight (33.3%), mold weight including gating system (40%), casting yield (24.56%, rejection rate (90%), and financial saving (24.63%). As a result of analysis of percentage improvements data, the proposed framework can provide the practitioners with a standard roadmap and motivate the casting industries to implement lean for performance improvement through organizational change. Through the effective application of the lean implementation framework, quality enhancement has been demonstrated.

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be very helpful for the prediction of some phenomena, such as chances of getting a heart attack and possibility of a tumor, from the collected and analysed healthcare data. To mitigate these issues, in this paper, a new AI-enabled lightweight, secure communication scheme for an IoMT environment has been designed and named as ASCP-IoMT, in short. The security analysis of ASCP-IoMT is performed in different ways, such as an informal way and a formal way (through the random oracle model). ASCP-IoMT performs better than other similar schemes and provides superior security with extra functionality features as compared those for the existing state of art solutions. A practical implementation of ASCP-IoMT is also performed in order to measure its impact on various network performance parameters. The end to end delay values of ASCP-IoMT are 0.01587, 0.07440 and 0.17097 seconds and the throughput values of ASCP-IoMT are 5.05, 10.88 and 16.41 bits per second (bps) under the different considered cases, respectively. For AI-based Big data analytics phase, the values of computation time (seconds) for decision tree, support vector machine (SVM), and logistic regression are measured as 0.19, 0.23, and 0.27, respectively. Moreover, the different values of accuracy for decision tree, SVM and logistic regression are 84.24%, 87.57%, and 85.20%, respectively. From these values, it is clear that decision tree method requires less time than the other considered techniques, whereas accuracy is high in case of SVM.

The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).

Background COVID-19 was detected in China in December 2019. The rapid dissemination and novelty of the disease resulted in an epidemic. This study aimed to identify biochemical parameters at admission that can be used to categorize severity and outcome of COVID −19 infection. Materials and Methods This cross-sectional study was conducted at Allied Hospitals of RMU from April 2020 to July 2020. It included 128 randomly selected confirmed COVID-19 patients. At admission, biochemical profile (total bilirubin, alanine aminotransferases {ALT}, aspartate aminotransferases {AST}, urea, creatinine, uric acid, sodium, potassium, and chloride were correlated with severity and outcome of COVID-19 by employing t-tests and ANOVA where required. Cut-off values to predict disease severity and outcome were calculated using ROC curve. Results The study comprised 46.1% non-severe, 29.7% severe, and 24.2% critical COVID-19 patients. 84.4% patients improved and 15.6% expired. Urea was increased in critical disease patients (p 42.7 mg (sensitivity of 64.7%, specificity of 87.5%), AST >43.5 IU/L (64% sensitivity, 60% specificity), and sodium

Abstract Background The significance of investigation for diagnosing and managing thyroid dysfunction in pregnant females has been extensively documented in the medical literature. This study aimed to determine trimester-specific reference ranges for thyroid-stimulating hormones (TSH), free T3 (FT3), and free T4 (FT4) in apparently healthy pregnant women attending tertiary care hospitals in Lahore. Methods This cross-sectional study was conducted at two tertiary care Hospitals in Lahore, Pakistan. In this multi-centric study, 500 pregnant females were initially enrolled from September 2019 to December 2019 who fulfilled the inclusion criteria. For measurement of serum FT3, FT4, thyroid stimulating hormone (TSH), anti-thyroid peroxidase (anti-TPO), and thyroglobulin antibodies, 5 ml of the blood sample was drawn, under aseptic conditions, from each subject using Maglumi 800 chemiluminescence immunoassay (CLIA) system. Results Out of 500 subjects, 23 subjects with positive anti-TPO, 19 subjects with anti-TG antibodies, and 12 subjects due to less volume of serum yielded from whole blood (serum less than 3 ml) were excluded from the analysis. Ten samples were hemolyzed and not included in the analysis. A total of 436 samples were examined for analysis. Of the remaining 436 subjects, 133 (30.5%) were from 1st trimester, 153 (35.1%) from 2nd trimester, and 150 (34.4%) from 3rd trimester. As the data were non-normal, the 2.5th, 50th, and 97.5th percentiles were calculated to express each group's results. Trimester specific range of TSH 0.168-4.294, 0.258-4.584 and 0.341-4.625 mIU/mL, FT31.857-4.408, 1.958-4.621 and 2.025-4.821 pmol/L and FT4 8.815-18.006, 8.306-17.341 and 7.402-17.292 pmol/L. Conclusion In this study, we established a trimester-specific reference range for our local population's thyroid function test. The results of this study have complemented the results of previous studies.

In the last decades, embroidery getting much attention in industries and academics. Although the technique is very old, it is still very much adopted. The current era of machine learning and smart devices makes this method even more attractive, easy to adopt and high accuracy. The proposed research work aims to highlight the implementation of new smart devices in this field, and the work is based on microcontrollers. Adopting the proposed work will be very easy and feasible to transform from the old sewing machine to a new smart method. Using smart devices, it is very easy and precise to get the embroidery fulfilling the user's demands. It gives simple raster images, which are used to convert into digital images to portray any new product. The current work is a novel idea of generating simple images rather than complicated ones, which are difficult to draw. In our method, we introduce the digitization process to input any image file used for vectorizing raster images, where vector images can easily convert into a DST embroidery file, a commonly used embroidery format originally from Tajima. It also has a Gcode file. The machine runs on a Gcode file which can be easily generated and runs on a product.

Background: The prime objective of CPR is to maintain enough circulation to preserve existence till specialized treatment is provided. As mentioned, early CPR is an essential aspect in the chain of survival in Emergency Cardiovascular Care (ECC). Objective: To compare and study the attitude and awareness level related to Cardiopulmonary Resuscitation (CPR) among physical therapists in Karachi. Methods: The total sample was 126 physical therapists in this study. This was an observational study based on a cross-sectional design conducted for the assessment of CPR attitude and awareness of physical therapists through simple convenient sampling technique during the period of December 2020 to June 2021 from various public and private sector hospitals and rehabilitation centers of Karachi. Data collection was done through a questionnaire consisting 31 questions categorized into three sections including demographics, CPR attitudes and CPR awareness. Results: Results revealed more female physical therapists (76%). Most of them (70%) were aged less than 35 years and had less than 15 years of working experience. Most of them were engaged in musculoskeletal practice (40.5%). Almost 31% participants had CPR certifications. Less than half of the physical therapists (38.9%) reported ≤ 25% success rate of CPR. Majority of the participants stated that their ability to perform CPR was satisfactory (44.4%). Conclusion: The overall awareness of physical therapists regarding CPR was evident, but the attitude regarding CPR need improvement. It was also evident from the results that mandatory professional training programs should be conducted. Keywords: Attitude, Awareness, Cardiac Arrest, Cardiopulmonary Resuscitation (CPR), Physical Therapists

Edge computing is an extension of cloud computing where physical servers are deployed closer to the users in order to reduce latency. Edge data centers face the challenge of serving a continuously increasing number of applications with a reduced capacity compared to traditional data center. This paper introduces ImpalaE, an agent based on Deep Reinforcement Learning that aims at optimizing the resource usage in edge data centers. First, it proposes modeling the problem as a Markov Decision Process, with two optimization objectives: reducing the number of physical servers used and maximize number of applications placed in the data center. Second, it introduces an agent based on Proximal Policy Optimization, for finding the optimal consolidation policy, and an asynchronous architecture with multiple workers-shared learner that enables for faster convergence, even with reduced amount of data. We show the potential in a simulated edge data center scenario with different VM sizes based on Microsoft Azure real traces, considering CPU, memory, disk and network requirements. Experiments show that ImpalaE effectively increases the number of VMs that can be placed per episode and that it quickly converges to an optimal policy.

Accurate large-scale biomass prediction is crucial for assessing forest carbon storage and dynamics. It can also inform sustainable forest management practices and climate change mitigation efforts. However, stand-level biomass models are still scarce worldwide. Our study aims to introduce the generalized additive model (GAM) as a convenient and efficient approach for forest biomass estimation. Data from 311 sample plots of three conifer species in northeastern China were used to evaluate the performance of the GAM model and compare it with traditional nonlinear seemingly unrelated regression (NSUR) models in predicting stand biomass, including total, aboveground, and component biomass. The results indicated that the goodness of fit of GAM was better than that of NSUR in two model systems. In the majority of cases, the scatter plots and prediction performance revealed that the stand total and component biomass models utilizing GAM outperformed those based on NSUR. Disregarding heteroscedasticity and requiring fewer statistical assumptions provide additional support for the replacement of NSUR-based models with GAM-based models. This study implies that the GAM approach has greater potential for developing a system of stand biomass models.

Hypotrichosis is an uncommon type of alopecia (hair loss) characterized by coarse scalp hair caused by the reduced or fully terminated activity of the Lipase-H (LIPH) enzyme. LIPH gene mutations contribute to the development of irregular or non-functional proteins. Because several cellular processes, including cell maturation and proliferation, are inhibited when this enzyme is inactive, the hair follicles become structurally unreliable, undeveloped, and immature. This results in brittle hair, as well as altered hair shaft development and structure. Because of these nsSNPs, the protein’s structure and/or function may be altered. Given the difficulty in discovering functional SNPs in genes associated with disease, it is possible to assess potential functional SNPs before conducting broader population investigations. As a result, in our in silico analysis, we separated potentially hazardous nsSNPs of the LIPH gene from benign representatives using a variety of sequencing and architecture-based bioinformatics approaches. Using seven prediction algorithms, 9 out of a total of 215 nsSNPs were shown to be the most likely to cause harm. In order to distinguish between potentially harmful and benign nsSNPs of the LIPH gene, in our in silico investigation, we employed a range of sequence- and architecture-based bioinformatics techniques. Three nsSNPs (W108R, C246S, and H248N) were chosen as potentially harmful. The present findings will likely be helpful in future large population-based studies, as well as in drug discovery, particularly in the creation of personalized medicine, since this study provides an initial thorough investigation of the functional nsSNPs of LIPH.

With the rapid growth in wireless communication and IoT technologies, Radio Frequency Identification (RFID) is applied to the Internet of Vehicles (IoV) to ensure the security of private data and the accuracy of identification and tracking. However, in traffic congestion scenarios, frequent mutual authentication increases the overall computing and communication overhead of the network. For this reason, in this work, we propose a lightweight RFID security fast authentication protocol for traffic congestion scenarios, designing an ownership transfer protocol to transfer access rights to vehicle tags in non-congestion scenarios. The edge server is used for authentication, and the elliptic curve cryptography (ECC) algorithm and the hash function are combined to ensure the security of vehicles’ private data. The Scyther tool is used for the formal analysis of the proposed scheme, and this analysis shows that the proposed scheme can resist typical attacks in mobile communication of the IoV. Experimental results show that, compared to other RFID authentication protocols, the calculation and communication overheads of the tags proposed in this work are reduced by 66.35% in congested scenarios and 66.67% in non-congested scenarios, while the lowest are reduced by 32.71% and 50%, respectively. The results of this study demonstrate a significant reduction in the computational and communication overhead of tags while ensuring security.

Robust hybrid g-C3N4/ZnO-W/Cox heterojunction composites were synthesized using graphitic carbon nitride (g-C3N4) and ZnO-W nanoparticles (NPs) and different concentrations of Co dopant. The hybrid heterojunction composites were prepared by simple and low-cost coprecipitation methods. The fabricated catalyst was explored and investigated using various characterization techniques such as FTIR, XRD, FESEM and EDX. The surface morphology of the as-prepared hybrid nanocomposites with particle sizes in the range of 15–16 nm was validated by SEM analysis. The elemental composition of the synthesized composites was confirmed by EDS analysis. Photocatalysis using a photon as the sole energy source is considered a challenging approach for organic transformations under ambient conditions. The photocatalytic activity of the heterojunctions was tested by photodegrading methylene blue (MB) dye in the presence of sunlight. The reduced band gap of the heterojunction composite of 3.22–2.28 eV revealed that the incorporation of metal ions played an imperative role in modulating the light absorption range for photocatalytic applications. The as-synthesized g-C3N4/ZnO-W/Co0.010 composite suppressed the charge recombination ability during the photocatalytic degradation of methylene blue (MB) dye. The ternary heterojunction C3N4/ZnO-W/Co0.010 composite showed an impressive photocatalytic performance with 90% degradation of MB under visible light within 90 min of irradiation, compared to the outcomes achieved with the other compositions. Lastly, the synthesized composites showed good recyclability and mechanical stability over five cycles, confirming them as promising photocatalyst options in the future.

Reduction in friction ensures fuel economy, control on emissions and durability of components in internal combustion engines. A modern gasoline internal combustion engine was instrumented to determine the friction values at the cam–roller interface considering the effects of surface treatment and engine operating state. A series of tests under different operating speeds and lubricant inlet temperatures were undertaken using both an original surface roller and a Wonder Process Craft (WPC) surface-treated engine roller. The results clearly revealed a substantial reduction in friction magnitude for the WPC surface-treated engine roller in comparison to the original roller while operating under similar conditions, indicating their strong potential for employment in engines. An increase in friction with the rise in temperature was also observed for both types of rollers, whereas increased lubricant entraining velocity due to higher operating speed had the opposite impact. A considerable reduction in frictional drive torque ranging from 8% to 28% was observed by employing the WPC-treated roller in comparison to original/untreated roller at various operating conditions, which signifies the strong potential for employment of WPC surface treatment in the roller/follower valve train engines.

CR enabled radio environment provides the seamless operating framework to cope with the requirements of next generation wireless systems like higher data rates, massive machine time communication and interference free coexistence scenarios of wireless technologies. Traffic characterization studies assist to optimize coexistence framework by providing necessary information about the usage patterns of wireless services in observed radio bands. Prior knowledge about the observed sparse wireless local area network (WLAN) data traffic is a key for opportunistic radio resource allocation and utilization in the given coexistence scenarios of WLAN either with cellular systems (including 5G-NR/4G Long Term Evolution (LTE)) or with low power wireless systems (including Bluetooth and ZigBee). Parametric methodology is adapted to model the sparse WLAN data traffic observed in 2.4 GHz Industrial, Scientific and Medical (ISM) band as spectral and temporal activity. The multivariate Gaussian mixture model (MGMM) is proposed in both scenarios where either dependency is considered between the observed WLAN data traffic or assuming that the observed traffic is independent and identically distributed (i.i.d). It is to be validated that in order to have an efficient characterization of sparse WLAN data traffic, the dependency (correlation) between neighbored frequency subbands must be considered. By considering the dependency either between neighbored frequency subbands or neighbored time domain signals actually help to characterize the sparse WLAN data traffic in more realistic way which could not be possible by assuming them i.i.d. Such statistics to characterize sparse data traffic really help as user activity for efficient allocation and utilization of radio resources in CR enabled coexistence scenarios of wireless technologies where WLAN acitivity is considered nominal either as secondary user or classify it grey spectrum as primary user.

Palmprint has attracted increasing attention due to its several advantages in the biometrics field. Deep learning has achieved remarkable performance in the computer vision area, so a large number of deep-learning-based methods have been proposed by the research community for palmprint recognition. The outputs of a deep hashing network (DHN) can be represented as a binary bit string, so DHN can reduce the storage and accelerate the matching/retrieval speed. In this paper, DHN is employed to extract the binary template for palmprint and palmvein verification. Spatial transformer network is used to overcome the rotation and dislocation. Palmprint and palmvein can be acquired from visible-light spectrums, including red (R), green (G), blue (B), and near infrared (NIR) spectrum, respectively. Since the features in different spectrums are different, their complementary advantages can be exploited to the full by fusion. Image-level fusion and score-level fusion are developed for palmprint-palmvein fusion recognition. The experiments demonstrate that score-level fusion can improve the accuracy efficiently.

Reinforcement learning (RL) is an unsupervised learning technique used in many real-time applications. The essence of RL is a decision-making problem. In RL, the agent constantly interacts with the environment and selects the next action according to previous feedback in terms of reward. In this paper, RL trains Software-Defined Wireless Sensor Networks (SDWSNs) controller to optimize the routing paths. We combine RL and SDN, where RL is applied to the SDN controller to generate the routing tables. We also propose four different reward functions for optimization of the network performance. RL-based SDWSN improves network performance by 23% to 30% in terms of lifetime compared with RL-based routing techniques. RL-based SDWSN performs well because it can intelligently learn the routing path at the controller. In addition, it has a faster network convergence rate than RL-based WSN.

Blockchain is a revolutionary technology that is making a great impact on modern society due to its transparency, decentralization, and security properties. Blockchain gained considerable attention due to its very first application of Cryptocurrencies e.g., Bitcoin. In the near future, Blockchain technology is determined to transform the way we live, interact, and perform businesses. Recently, academics, industrialists, and researchers are aggressively investigating different aspects of Blockchain as an emerging technology. Unlike other Blockchain surveys focusing on either its applications, challenges, characteristics, or security, we present a comprehensive survey of Blockchain technology’s evolution, architecture, development frameworks, and security issues. We also present a comparative analysis of frameworks, classification of consensus algorithms, and analysis of security risks & cryptographic primitives that have been used in the Blockchain so far. Finally, this paper elaborates on key future directions, novel use cases and open research challenges, which could be explored by researchers to make further advances in this field.

Weed management is becoming increasingly important for sustainable crop production. Weeds cause an average yield loss of 11.5% billion in Pakistan, which is more than PKR 65 billion per year. A real-time laser weeding robot can increase the crop’s yield by efficiently removing weeds. Therefore, it helps decrease the environmental risks associated with traditional weed management approaches. However, to work efficiently and accurately, the weeding robot must have a robust weed detection mechanism to avoid physical damage to the targeted crops. This work focuses on developing a lightweight weed detection mechanism to assist laser weeding robots. The weed images were collected from six different agriculture farms in Pakistan. The dataset consisted of 9000 images of three crops: okra, bitter gourd, sponge gourd, and four weed species (horseweed, herb paris, grasses, and small weeds). We chose a single-shot object detection model, YOLO5. The selected model achieved a mAP of 0.88@IOU 0.5, indicating that the model predicted a large number of true positive (TP) with much less prediction of false positive (FP) and false negative (FN). While SSD-ResNet50 achieved a mAP of 0.53@IOU 0.5, the model predicted fewer TP with significant outcomes as FP or FN. The superior performance of the YOLOv5 model made it suitable for detecting and classifying weeds and crops within fields. Furthermore, the model was ported to an Nvidia Xavier AGX standalone device to make it a high-performance and low-power computation detection system. The model achieved an FPS rate of 27. Therefore, it is highly compatible with the laser weeding robot, which takes approximately 22.04 h at a velocity of 0.25 feet per second to remove weeds from a one-acre plot.

With the rapid development of information and communication technologies, artificial intelligence and IoTs, more and more advanced technologies, such as machine learning, reinforcement learning, neural networks and fuzzy systems, have been introduced into industrial practices. The application of advanced technologies has greatly promoted the process of industrial revolution. However, there is big gap between controlled simulation and real evolving environment, which results in the unsatisfactory performance of the typical algorithms in practical environments. For example, in Underwater IoTs, a dynamic and uncertain marine environment can cause equipment damage, resulting in huge financial losses. Therefore, improving the robustness and adaptability of algorithms and systems, and proposing new solutions in practical applications to meet the requirements of self-developing, self-organizing, and evolving systems is essential to promote intelligent industrial applications.

Weed management has become a highly labor-intensive activity, which is the reason for decreased yields and high costs. Moreover, the lack of skilled labor and weed-resistant herbicides severely impact the agriculture sector and food production, hence increasing the need for automation in agriculture. The use of agricultural robots will help in the assurance of higher yields and proactive control of the crops. This study proposes a laser-based weeding vehicle with a unique mechanical body that is adjustable relative to the field structure, called the Robot Operating System (ROS) based robust control system, and is customizable, cost-effective and easily replicable. Hence, an autonomous-mobile-agricultural robot with a 20 watt laser has been developed for the precise removal of weed plants. The assembled robot’s testing was conducted in the agro living lab. The field trials have demonstrated that the robot takes approximately 23.7 h at the linear velocity of 0.07 m/s for the weeding of one acre plot. It includes 5 s of laser to kill one weed plant. Comparatively, the primitive weeding technique is highly labor intensive and takes several days to complete an acre plot area. The data presented herein reflects that implementing this technology could become an excellent approach to removing unwanted plants from agricultural fields. This solution is relatively cost-efficient and provides an alternative to expensive human labor initiatives to deal with the increased labor wages.

The ongoing antibiotic-resistance crisis is becoming a global problem affecting public health. Urgent efforts are required to design novel therapeutics against pathogenic bacterial species. Brucella melitensis is an etiological agent of brucellosis, which mostly affects sheep and goats but several cases have also been reported in cattle, water buffalo, yaks and dogs. Infected animals also represent the major source of infection for humans. Development of safer and effective vaccines for brucellosis remains a priority to support disease control and eradication in animals and to prevent infection to humans. In this research study, we designed an in-silico multi-epitopes vaccine for B. melitensis using computational approaches. The pathogen core proteome was screened for good vaccine candidates using subtractive proteomics, reverse vaccinology and immunoinformatic tools. In total, 10 proteins: catalase; siderophore ABC transporter substrate-binding protein; pyridoxamine 5′-phosphate oxidase; superoxide dismutase; peptidylprolyl isomerase; superoxide dismutase family protein; septation protein A; hypothetical protein; binding-protein-dependent transport systems inner membrane component; and 4-hydroxy-2-oxoheptanedioate aldolase were selected for epitopes prediction. To induce cellular and antibody base immune responses, the vaccine must comprise both B and T-cells epitopes. The epitopes were next screened for antigenicity, allergic nature and water solubility and the probable antigenic, non-allergic, water-soluble and non-toxic nine epitopes were shortlisted for multi-epitopes vaccine construction. The designed vaccine construct comprises 274 amino acid long sequences having a molecular weight of 28.14 kDa and instability index of 27.62. The vaccine construct was further assessed for binding efficacy with immune cell receptors. Docking results revealed that the designed vaccine had good binding potency with selected immune cell receptors. Furthermore, vaccine-MHC-I, vaccine-MHC-II and vaccine-TLR-4 complexes were opted based on a least-binding energy score of −5.48 kcal/mol, 0.64 kcal/mol and −2.69 kcal/mol. Those selected were then energy refined and subjected to simulation studies to understand dynamic movements of the docked complexes. The docking results were further validated through MMPBSA and MMGBSA analyses. The MMPBSA calculated −235.18 kcal/mol, −206.79 kcal/mol, and −215.73 kcal/mol net binding free energy, while MMGBSA estimated −259.48 kcal/mol, −206.79 kcal/mol and −215.73 kcal/mol for TLR-4, MHC-I and MHC-II complexes, respectively. These findings were validated by water-swap and entropy calculations. Overall, the designed vaccine construct can evoke proper immune responses and the construct could be helpful for experimental researchers in formulation of a protective vaccine against the targeted pathogen for both animal and human use.

The hydrogen evolution reaction (HER) is a developing and promising technology to deliver clean energy using renewable sources. Presently, electrocatalytic water (H2O) splitting is one of the low-cost, affordable, and reliable industrial-scale effective hydrogen (H2) production methods. Nevertheless, the most active platinum (Pt) metal-based catalysts for the HER are subject to high cost and substandard stability. Therefore, a highly efficient, low-cost, and stable HER electrocatalyst is urgently desired to substitute Pt-based catalysts. Due to their low cost, outstanding stability, low overpotential, strong electronic interactions, excellent conductivity, more active sites, and abundance, transition metal tellurides (TMTs) and transition metal phosphides (TMPs) have emerged as promising electrocatalysts. This brief review focuses on the progress made over the past decade in the use of TMTs and TMPs for efficient green hydrogen production. Combining experimental and theoretical results, a detailed summary of their development is described. This review article aspires to provide the state-of-the-art guidelines and strategies for the design and development of new highly performing electrocatalysts for the upcoming energy conversion and storage electrochemical technologies.

In the field of computational biology, electronic modeling of bio-cellular processes is in vogue for about a couple of decades. Fast, efficient and scalable electronic mimetics of recurrently found bio-chemical reactions are expected to provide better electronic circuit simulators that can also be used as bio-sensors or implantable biodevices at cellular levels. This paper presents some possible electronic circuit equivalents to model dynamics of one such bio-chemical reaction commonly involved in many bio-cellular processes, specifically pathways in living cells, known as the Hill process. The distinguishing feature of this process is cooperativity which has been modeled in silicon substrate using a pair of transistors, one transistor driving current in the other the same way ligand binding to one receptor site controls the binding affinity of the other receptor sites. Two possible circuits have been proposed and compared to electronically model cooperativity of a Hill reaction. The main idea is to exploit the natural analogies found between structures and processes of a bio-cell and electronic transistor mechanics, to efficiently model fundamental bio-chemical reactions found recurring in bio-processes. These circuits can then be combined and rearranged quickly to form larger, more complex bio-networks, thus mitigating the intricacies involved in modeling of such systems.