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This paper documents the provenance and palaeoclimatic conditions of the Cretaceous Lumshiwal Formation near the western margin of the Indian plate in the Surghar Range, northwest Pakistan. The combined techniques of petrography, X-ray diffraction (XRD), and bulk rock geochemistry were utilized to reveal the source and mineral-geochemical composition and sandstone type. The petrographic analysis of the sandstones confirms the dominance of quartz, feldspar, and rock fragments, with subordinate occurrences of muscovite, magnetite, and hematite. Heavy minerals consist of tourmaline, titanite (sphene), rutile, cassiterite, monazite, and zircon. The cementing material includes ferruginous clays, jarosite, glauconite, calcite, minor dolomite, gypsum, and silica. The modal composition plot of the sandstones falls into arkose to sub-arkose, with a few lithic arkose varieties. Lithic fragments mainly include granite, with a minor occurrence of granitic gneiss, chert, phyllite, and quartz mica schist. The discriminatory provenance diagram of the sandstones suggests a transitional continental provenance. The bulk rock geochemistry of the sandstones reveals the presence of SiO2, TiO2, Al2O3, Fe2O3, FeO, MnO, CaO, MgO, Na2O, K2O and P2O5. The petrographical mineral findings were corroborated with XRD, SEM, and bulk rock geochemistry analysis (major element concentrations and their ratios), which collectively all confirm a felsic igneous source. The tectonic discrimination diagram (SiO2-log K2O/Na2O wt %) implies a dominant influx of sediment sourced from the passive continental margin of the uplifted Gondwana Indian plate. Terrigenous sediments found in the Lumshiwal Formation are interpreted to have been derived from granites and granitic gneisses of the Indian Shield. The palaeo-weathering index, including the chemical index of alteration (CIA) and the chemical index of weathering (CIW) of the Lumshiwal Formation confirms a low to moderately weathered source area. The climate discrimination plot (SiO2 versus Al2O3 + Na2O + K2O) shows that humid to semi-humid climatic conditions during the deposition of the Lumshiwal Formation.

Since the inaugural issue of Current Oncology was published 30 years ago, we have witnessed significant advancements in cancer research and care [...]

Abstract Micronutrient application has a crucial role in mitigating salinity stress in crop plants. This study was carried out to investigate the effect of zinc (Zn) and boron (B) as foliar applications on fenugreek growth and physiology under salt stress (0 and 120 mM). After 35 days of salt treatments, three levels of zinc (0, 50, and 100 ppm) and two levels of boron (0 and 2 ppm) were applied as a foliar application. Salinity significantly reduced root length (72.7%) and shoot length (33.9%), plant height (36%), leaf area (37%), root fresh weight (48%) and shoot fresh weight (75%), root dry weight (80%) and shoot dry weight (67%), photosynthetic pigments (78%), number of branches (50%), and seeds per pod (56%). Fenugreek’s growth and physiology were improved by foliar spray of zinc and boron, which increased the length of the shoot (6%) and root length (2%), fresh root weight (18%), and dry root weight (8%), and chlorophyll a (1%), chlorophyll b (25%), total soluble protein content (3%), shoot calcium (9%) and potassium (5%) contents by significantly decreasing sodium ion (11%) content. Moreover, 100 ppm of Zn and 2 ppm of B enhanced the growth and physiology of fenugreek by reducing the effect of salt stress. Overall, boron and zinc foliar spray is suggested for improvement in fenugreek growth under salinity stress.

Abstract The orthogonal frequency division multiplexing (OFDM) system applies coherent demodulation to achieve high spectral efficiency at a bandwidth cost by the pilot tones. Considering the statistical property of the down-link channels to the users, it can be found that there is an opportunity to reduce the pilot number in the conventional designs while maintaining the same signal demodulation performances. The design philosophy involves utilizing the difference of the channel coherent bandwidths (CCBs) by allocating data to appropriate positions upon the fact that different CCBs can tolerate different minimized pilot spacing. The proposed design allows each user’s equipment’s data not to exceed its CCB with the sparser pilots. The theoretical analysis is carried out based on the concept of channel frequency response using linear interpolation with channel estimation employing the least squares (LS) method. The gain of the proposed method is demonstrated in terms of the ergodic capacities and confirmed by the simulations.

Abstract The investigation focused on creating and studying a new 2D–2D S-scheme CdS/g-C3N4 heterojunction photocatalyst. Various techniques examined its structure, composition, and optical properties. This included XRD, XPS, EDS, SEM, TEM, HRTEM, DRS, and PL. The heterojunction showed a reduced charge recombination rate and more excellent stability, helping to lessen photocorrosion. This was due to photogenerated holes moving more quickly out of the CdS valence band. The interface between g-C3N4 and CdS favored a synergistic charge transfer. A suitable flat band potential measurement supported enhanced reactive oxygen species (ROS) generation in degrading 4-nitrophenol and 2-nitrophenol. This resulted in remarkable degradation efficiency of up to 99% and mineralization of up to 79%. The findings highlighted the practical design of the new 2D–2D S-scheme CdS/g-C3N4 heterojunction photocatalyst and its potential application in various energy and environmental settings, such as pollutant removal, hydrogen production, and CO2 conversion.

Introduction A patient safety culture primarily refers to the values, beliefs, attitudes, and behaviors within a healthcare setup in a community that assists in prioritizing patient safety and encouraging the reporting of errors and near-misses in that facility. There is a direct impact of patient safety culture on how well patient safety and quality improvement programs work. The aim of this cross-sectional descriptive study was to investigate the practices to improve patient safety culture and adverse event reporting practices among healthcare professionals in a tertiary care hospital located in Mirpur Azad Jammu and Kashmir. Methods In the non-probability convenience sampling of this cross-sectional study, Divisional Headquarters Teaching Hospital in Mirpur, Azad Kashmir used the Agency for Healthcare Research and Quality Surveys on Patient Safety Culture Hospital Survey to collect data about the perceptions of healthcare professionals regarding patient safety culture within their hospital to assess the trends of patient safety culture by obtaining longitudinal data. A pre-validated questionnaire that has undergone a rigorous trial of testing to maximize the reliability and accuracy of the outcomes was distributed among clinical staff (healthcare professionals who interact with patients on a daily basis, such as nurses, doctors, pharmacists, and laboratory technicians) and administrative staff (medical superintendent, deputy medical superintendent, assistant medical superintendent, heads of departments). Results A total of 312 questionnaires were returned (response rate, 76%). The study found that the dimension “supervisor/manager expectation and action promoting safety” had the highest positive response rate (65.16%), and “nonpunitive response” had the lowest (27.4%). Higher scores in “nonpunitive response to error” were associated with lower rates of medication errors, pressure ulcers, and surgical site infections, and higher scores in “frequency of event reporting” were associated with lower rates of medication errors, pressure ulcers, falls, hospital-acquired infections, and urinary tract infections. Conclusion We suggest that in order for hospital staff to continue providing excellent, clinically safe treatment, a well-structured hospital culture promoting patient safety is necessary. Moreover, further study is needed to determine strategies to improve patient safety expertise and awareness, and lower the frequency of adverse occurrences.

Ibrahim Abdel Aziz Ibrahim,1 Abdullah R Alzahrani,1 Ibrahim M Alanazi,1 Naiyer Shahzad,1 Imran Shahid,1 Alaa Hisham Falemban,1 Mohd Fahami Nur Azlina,2 Palanisamy Arulselvan3 1Department of Pharmacology and Toxicology, Faculty of Medicine, Umm Al-Qura university, Makkah, Saudi Arabia; 2Department of Pharmacology, Faculty of Medicine, Universiti Kebangsaan, Bangi, Selangor, Malaysia; 3Department of Chemistry, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, 602 105, IndiaCorrespondence: Ibrahim Abdel Aziz Ibrahim, Associated professor in Pharmacology, Department of Pharmacology and Toxicology, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia, Email iamustafa@uqu.edu.saBackground: Liver cancer is the sixth most prevalent form of cancer and the second major cause of cancer-associated mortalities worldwide. Cancer nanotechnology has the ability to fundamentally alter cancer treatment, diagnosis, and detection.Objective: In this study, we explained the development of graphene oxide/polyethylene glycol/folic acid/brucine nanocomposites (GO/PEG/Bru-FA NCs) and evaluated their antimicrobial and anticancer effect on the liver cancer HepG2 cells.Methodology: The GO/PEG/Bru-FA NCs were prepared using the co-precipitation technique and characterized using various techniques. The cytotoxicity of the GO/PEG/Bru-FA NCs was tested against both liver cancer HepG2 and non-malignant Vero cells using an MTT assay. The antimicrobial activity of the GO/PEG/Bru-FA NCs was tested against several pathogens using the well diffusion technique. The effects of GO/PEG/Bru-FA NCs on endogenous ROS accumulation, apoptosis, and MMP levels were examined using corresponding fluorescent staining assays, respectively. The apoptotic protein expressions, such as Bax, Bcl-2, and caspases, were studied using the corresponding kits.Results: The findings of various characterization assays revealed the development of GO/PEG/Bru-FA NCs with face-centered spherical morphology and an agglomerated appearance with an average size of 197.40 nm. The GO/PEG/Bru-FA NCs treatment remarkably inhibited the growth of the tested pathogens. The findings of the MTT assay evidenced that the GO/PEG/Bru-FA NCs effectively reduced the HepG2 cell growth while not showing toxicity to the Vero cells. The findings of the fluorescent assay proved that the GO/PEG/Bru-FA NCs increased ROS generation, reduced MMP levels, and promoted apoptosis in the HepG2 cells. The levels of Bax, caspase-9, and − 3 were increased, and Bcl-2 was reduced in the GO/PEG/Bru-FA NCs-treated HepG2 cells.Conclusion: The results of this work demonstrate that GO/PEG/Bru-FA NCs suppress viability and induce apoptosis in HepG2 cells, indicating their potential as an anticancer candidate. Keywords: graphene oxide, brucine, nanomedicine, apoptosis, caspases, photoluminescence

Despite many revolutionary asset pricing models developed over the past decades, traditional finance does not explain investor behavior very well. The purpose of this study is to examine the influence of behavioral biases on the investment decisions of investors of Pakistan Stock Exchange. In addition, the moderating influence of investment experience investigated in this study. The findings were reported using a sample of 230 individual investors, who make their own investments, typically through a mutual fund, bank, or internet broker. They make investments to achieve their unique investment objectives, such as saving for retirement, a child’s education, or increasing their overall wealth. The influence of behavioral biases on investment decisions was calculated using regression analysis. Regression results show that beta and t-values are significant and have a significant impact on investment decisions. Regression findings show that Confirmation Bias, Gamblers Fallacy Bias, Negativity Bias, Bandwagon Effect Bias, Loss Aversion Bias, and Overconfidence Bias all have a substantial impact on Investment Decisions. Status quo prejudice and endowment bias have a favorable but minor influence on Investment Decisions. Investment Experience is regarded as an essential component that contributes to successful decision making under risk and uncertainty, however the results of this study show that moderating variables have a minor influence. According to the findings, the moderating variable had no effect on the connection between behavioral biases and investment decisions. And the reason for this is that behavioral biases persist regardless of investing experience.

Although research on microplastics (MPs) interactions with other soil pollutants is increasingly becoming available, most studies do not consider risks to soil fertility or plant growth. This review aims: 1) to summarize the results of current studies on interactions between MPs, heavy metals, and organic pollutants; and 2) subsequently evaluate risks to the soil-plant nexus. Available-literature shows that polypropylene, polyethylene and polylactic acid increase cadmium (Cd) bioavailability and subsequently reduce root growth. Such effects are not evident in sandy or clay soils due to the formation of CdCO3 and iron-oxide by altered bacterial communities that stabilize Cd contamination. Chronic instead of short-term exposure to polystyrene in copper (Cu) - polluted soils decreases crop yield. With coexistence of MPs and lead (Pb) in soil, the uptake of Pb in crops increases, causing altered malondialdehyde content and superoxide dismutase and guaiacol peroxidase activities. Moreover, co-toxicity of polystyrene or polytetrafluoroethylene with arsenic (As) decreases root biomass, photosynthesis rate and the chlorophyll-a content. In alkaline soil, polyvinyl-chloride could decrease the bioavailability of MeHg due to changes in the abundance of Proteobacteria, and Firmicutes. We also found strong interactions between MPs and organic pollutants. Polystyrene decreases negative impacts of sulfamethazine on bacterial diversity, and structure in soil. Polyethylene, polyvinyl-chloride and polystyrene have a strong adsorption capacity for 17β-estradiol. This implies that 17β-estradiol toxicity can be reduced by these MPs. At low concentrations, polyethylene, polypropylene, and polystyrene have low affinity to diazepam. In conclusion, serious ecological risks are associated with MPs and other pollutants' interactions to soil-plant system.

Task scheduling is a crucial aspect in cloud computing paradigm as tasks with different processing capacities arise from heterogeneous resources and scheduling these different tasks to precise virtual machines (VM) in cloud datacenters is a crucial job for the Cloud Service Provider (CSP) as it is an NP-hard problem. Ineffective scheduling of tasks in cloud paradigm leads to increase in execution time in turn it effects quality of service which is a huge challenge in cloud paradigm. Scheduling tasks to VMs in datacenters which are located at far distances also plays a major part in quality of service of CSP especially in the case of time sensitive tasks. Many of the earlier authors proposed task offloading mechanisms using metaheuristic techniques and machine learning approaches but still there is a chance to improve offloading process by prioritizing tasks based on their runtime capacities, sizes onto fog nodes and schedule them onto precise VMs while addressing important concerns in cloud/fog computing paradigm such as makespan, consumption of energy. In this research, we proposed a task offloading mechanism (PTOMCFIA3C) which considers priorities of tasks and based on the priorities induced into the scheduling offloading mechanism will decide whether to offload tasks to fog/cloud nodes. PTOMCFIA3C modeled using improved Asynchronous Advantage Actor Critic algorithm a deep reinforcement learning technique which is improved by inducing RCNN which can accelerate its learning capability and optimizes scheduling process by offloading tasks to Fog/cloud nodes. Extensive simulations are conducted using simpy by taking the real-time computing worklogs. Simulations conducted in two phases. In the first phase, parameters evaluated with fixed nodes while in second phase parameters evaluated with variable nodes. Proposed PTOMCFIA3C evaluated over existing baseline approaches RATS-HM, MOABCQ, FOG-AMOSM and our proposed task offloading mechanism PTOMCFIA3C generated optimized schedules while improving makespan by 30.2%, consumption of energy by 31.25% significantly over compared approaches.

In the field of real-time systems, component-based models for mixed criticality systems (MCS) on multicore platform are gaining significant attention of the researchers from the recent past. The concept of mixed-criticality is firstly applied on single core systems but after gaining attention, it evolved to multicore platform. Initially, the tasks of MCS executes in low mode however if a task having high criticality does not its scheduling requirements complete its low mode, then the system switch to high mode. In high mode, only high critical tasks executes its high mode scheduling requirements, while all low tasks are discarded from further execution. In the realm of MCS, the component-based model have high importance and has been used in large and complex real-time systems. In component-based model, the component of a system is splatted into multiple components. In this study, a new component-based scheduling approach is proposed for multicore MCS. In the multicore mixed-criticality compositional framework, special emphasis is given on deriving the supply bound function, which represents available core supply, and the demand bound function, which indicates the requirements of resources for scheduling. A mixed-criticality multicore compositional framework (MMCF) is introduced for component-based MCS having tightened deadline. For the proposed strategy, a schedulability analysis is conducted, ensuring that the system can be scheduled only if the supply bound function is equal to or less than the demand bound function. On a 4-core platform, the scheduleability for the three scheduling strategies i.e. MMCF, MC-Partition, and Global are shown at different utilization levels of resource. 100% schedulability is shown by the proposed MMCF, MC-Partition, and global techniques for target utilizations of 0.7, 0.5, and 0.4, respectively. For a given utilization U=0.6, the proposed MMCF approach shows 100 % schedulability, while the MC-partition and global approaches show 90% and 20% schedulabilities respectively. It shows that the proposed MMCF approach schedules 10% and 80% more task sets than the MC-partition and global approaches respectively. The experimental results reveal that the proposed MMCF approach offers better schedulability than the other two approaches.

In this study, a flexible and fully printable four element patch antenna array bent on a cylindrical-shaped surface for main beam steering is proposed. The individual phases of the microstrip patches in the array are varied by using novel planar phase shifters constituted of microstrip composite right/left-handed (CRLH) lines ingrained with microparticles. The reinforcement of newly introduced microparticles in the CRLH transmission line structure makes it a variable phase shifter, which is fabricated on a flexible substrate in completely planar and conformal configurations. The magnetic particles-based CRLH-TL phase shifter (MP-CRLH) consists of multiple cavities in the substrate filled with micron-sized silver-coated ferrite particles. These particles in the cavity are magnetically aligned by a small magnet placed beneath the cavity. A particular phase shift can be achieved by activating particles in multiple cavities in the MP-CLRH phase shifter. The optimum locations of the cavities partially filled with magnetic particles for a particular phase shift have been explored after extensive 3D simulations in the frequency domain using CST Studio Suite. The main advantage of using the MP-CRLH phase shifter is that it can be conformed to non-planar surfaces, such as cylindrical-shaped in this scenario. As compared to conventional IC/discrete components-based phase shifters, the working of the novel MP-CRLH phase shifter is independent of external SMT and lumped circuit components for its biasing. The four unit-cell cascaded MP-CRLH phase shifter is then integrated with a four-element patch array on a cylindrical-shaped surface with radii of 30 cm and 10 cm. The main pattern of the array has been successfully scanned at 0°, −15°, and +30° using the integrated MP-CRLH phase shifter at.2.45 GHz. The full-wave simulation results are validated with the measured radiation patterns in a fully anechoic chamber facility. The conformal phased array integrated with MP-CRLH phase shifters on a flexible substrate can be used for applications like on-body communication systems, wearable devices, flexible electronics, and aircraft communication systems.

This paper features an effective and novel method to characterize dielectric materials using the reflected group delay of an over-coupled partially-filled coaxial resonator. The method employs a simple and cost-effective approach by utilizing one-port measurements from a Vector Network Analyzer to determine the dielectric constant and loss tangent of a dielectric material filled inside a coaxial resonator. Simulations using Advanced Design System by PathWave and ANSYS Electronics Desktop are done to test the mathematical model under different scenarios. Based on the results obtained from simulations, the proposed method can determine the dielectric constant and loss tangent with an error of less than 2% and 30% respectively, when operated to have a coupling coefficient in the range of 2.5 to 10. A region of confidence is derived by taking into account the effect of systematic errors to determine the maximum expected error in the calculated values of dielectric constant and loss tangent. The region of confidence describes that the resonator when filled to more than 50% of its capacity to have coupling coefficient greater than 2.5 can determine the dielectric constant and loss tangent of a material with an error of less than 5% and 80% respectively. However, by limiting the value of coupling coefficient the error in the loss tangent can be restricted under 30%. A coaxial resonator is designed and fabricated, and Teflon is tested in order to validate the proposed method. A hollow Teflon rod is tested twice for different values of coupling capacitance to confirm the repeatability of the method. Soil samples with different moisture contents are tested to confirm the capacity of the method in testing different types of materials. Each of the soil sample is tested three times and standard deviation in the calculated values of the dielectric properties re-confirmed the repeatability of the method. The proposed method is considered appropriate to test the dielectric properties of solid materials which can be machined to fit inside the hollow cavity of the resonator and other materials like soils, fine particle solids, grains and liquids.

Due to the properties of high thrust density, power density, and efficiency, Linear Hybrid Excited Flux Switching Machines (LHFSM) are centric for many researchers, especially in rail transit applications. A segmented mover dual stator LHFSM (SMDS-LHFSM) with concentrated armature and field windings is proposed in this paper. The concentrated windings help in reducing the overall copper losses. Flux gaps are added between the mover segments, making the machine a fault-tolerant. A small quantity of permanent magnets is used to reduce the overall cost of the machine. The suitable coil combination and open-circuit flux linkage are derived by magnetic circuit modeling and validated by FEM to reduce time and drive storage. Genetic algorithm (GA) is used to globally optimize the varying parameters of the proposed design and improve the initial performance. After GA, the thrust and thrust ripples are improved by 35.92% and 13.16%, respectively. A mathematical model is developed to verify the fault-tolerant capability and thrust of the proposed machine. Key electromagnetic performance parameters are investigated in depth. The proposed design has 17.6% higher thrust than the conventional machine. Finally, a prototype is fabricated to validate the theoretical analysis. The proposed SMDS-LHFSM has achieved fault-tolerant capability by achieving the mutual inductance value close to zero, flux regulation of 61.16% that is important in variable speed applications and a good temperature distribution after putting it for 2 hours under test.

Artificial intelligence (AI) systems have emerged as a powerful tool for management decision-making. However, there is a lack of a comprehensive framework that aims to bridge the gap between AI systems and human decision-makers. The proposed approach provides a novel framework, stepping towards the enhancement of AI-human communication with real-time feedback, iterative refinements, and user-centric interface designs. The framework is based on design principles of modularity, scalability, user-centricity, and adaptability, which were conceptualized to make it robust, flexible, and highly effective. Finally, it is expected that the indicated case studies and application scenarios will show the applicability and effectiveness of the framework in different contexts of industry, and therefore, provide concrete examples of how the benefits of the framework might be in practice. Simulation results show that the proposed mechanism has been significantly adopted, demonstrating a 10-20% increase in efficiency, user satisfaction, and feedback responsiveness compared to the existing mechanisms such as EHIDM and HCADMR. These results underscore the potential of the proposed framework to significantly enhance interaction dynamics between AI systems and human users.

Task Scheduling is a crucial challenge in cloud computing as diversified tasks come rapidly onto cloud console dynamically from heterogeneous resources which consists of different task lengths, processing capacities. Generating schedules for these type of tasks is a challenge for Cloud Service Provider(CSP). Therefore, to generate task schedules in cloud paradigm effectively by considering type of task arising to cloud console and match it with respective Virtual Machine (VM), a task scheduler is formulated by using Deep Deterministic Policy Gradient (DDPG) algorithm which is used as methodology to design scheduler. This scheduler works in three stages. In the initial stage, tasks are classified based on length and processing capacity to identify them whether they are High Performance Computing (HPC) tasks or High Throughput Computing (HTC) tasks. After classification, in the second stage, resources are to be tracked which matches the corresponding nature of tasks. Finally, in the third stage, according to the VM priorities calculated based on electricity unit cost and tasks are mapped according to the priorities to the corresponding VMs. Simulations are conducted using Cloudsim with fabricated workload distributions and realtime worklogs. Finally, our proposed Hybrid workload Deep Deterministic Policy Gradient Task scheduler(HDDPGTS) evaluated over DQN, A2C algorithms. From results, it proved that our proposed HDDPGTS significantly improved makespan, Energy consumption, scheduling overhead, scalability over baseline approaches.

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in recent years, with a wide range of applications in areas such as surveying, delivery, and security. UAV technology plays an important role in human life. Integrating Artificial Intelligence (AI) techniques into UAVs can significantly enhance their capabilities and performance. After the integration of AI in UAVs, their efficiency can be improved. It can automatically detect any object and highlight those objects with detailed information using AI. In most of the security surveillance places, UAV technology is beneficial. In this paper, we comprehensively reviewed the most widely used UAV communication protocols, including Wi-Fi, Zigbee, and Long-Range Wi-Fi (LoRaWAN). The review further explores valuable insights into the strengths and weaknesses of these protocols and how cognitive abilities such as perceptions and decision-making can be incorporated into UAV systems for autonomy. This paper provides a comprehensive overview of the state-of-the-art UAV object detection in remote sensing environments, as well as its types and use cases in different applications. It highlights the potential applications of these techniques in various domains, such as wildlife monitoring, search and rescue operations, and surveillance. The challenges and limitations of these methods and open research issues are given for future research.

This study presents the dynamic analysis for the fractional order economic and financial supply chain dynamical system using the supervised neural network performances aided with scale conjugate gradient. The investigations based on the fractional derivatives have been implemented to achieve the realistic and accurate performances of the fractional order economic and financial supply chain dynamical system. The mathematical form of the fractional order economic and financial supply chain dynamical system is categorized into three dynamics: rate of interest, investment cost, and price index. Three different variations based on the fractional order form of the economic and financial supply chain dynamical system have been numerically presented using the supervised neural network performances based on the scale conjugate gradient scheme. The selection of the data for solving the economic and financial supply chain dynamical system is taken as 80% for training, and 12% for testing, and 8% for endorsement. The accuracy of the proposed stochastic scheme is presented using the obtained and referenced Adam results. Rationality, capability and perfection are performed through the supervised neural network with scale conjugate gradient scheme performances-based together with the performances of correlation/regression, mean square error, state transition measures, and error histograms. Finally, a comparison of numerical results is examined and observed that the range of absolute error is between $10^{-05}$ to $10^{-07}$ which indicates that the proposed stochastic computing model can effectively analyze the economic and financial supply chain dynamical system.

Locating veins accurately is a common challenge in clinical settings, particularly with patients where veins are difficult to discern. The development of effective vein visualization technology is crucial for improving patient outcomes and minimizing procedural complications. This study aimed to design, develop, and evaluate a low-cost, high-resolution portable vein finder device. It was developed using affordable components and integrated imaging technology. The device captured images of veins in challenging patient scenarios. Four conventional image enhancement techniques were initially applied to these images. Subsequently, a novel image enhancement technique is proposed. The performance of each technique was qualitatively accessed using visual representation and histogram distribution and quantitatively assessed using the peak signal-to-noise ratio and structural similarity index measure. The proposed technique significantly outperformed the conventional methods. This indicates a superior enhancement of vein visibility, confirming the effectiveness of the proposed method. Thus, the newly developed vein finder, enhanced with the proposed image enhancement technique demonstrates significant potential for clinical use. It offers a reliable solution for vein detection in patients, potentially improving treatment accuracy.

Abstract The coronavirus (COVID-19) pandemic has not only had a severe impact on global health but also poses a threat to the environment. This research aims to explore an innovative approach to address the issue of increased waste generated by the pandemic. Specifically, the study investigates the utilization of discarded face masks in combination with recycled concrete aggregate (RCA) and Silica Fume (SFM) in civil construction projects. The disposable face masks were processed by removing the ear loops and nose strips, and then cutting them into small fibers measuring 20 mm in length, 5 mm in width, and 0.46 mm in thickness, resulting in an aspect ratio of 24. Various proportions of SFM and RCA were incorporated into the concrete mix, with a focus on evaluating the compressive strength, split tensile strength, and durability of the resulting material. The findings indicate that the addition of SFM led to improvements in both compressive and split tensile strength, while no significant impact on durability was observed.

Abstract Graphyne and Graphdiyne Nanoribbons reveal significant prospective with diverse applications. In electronics, they propose unique electronic properties for high-performance nanoscale devices, while in catalysis, their excellent surface area and reactivity sort them valuable catalyst supports for numerous chemical reactions, contributing to progresses in sustainable energy and environmental remediation. The topological indices (TIs) are numerical invariants that provide important information about the molecular topology of a given molecular graph. These indices are essential in QSAR/QSPR analysis and play a significant role in predicting various physico-chemical characteristics. In this article, we present a formula for computing reduced reverse (RR) degree-based topological indices for graphyne and graphdiyne nanoribbons, including the RR Zagreb indices, RR hyper-Zagreb indices, RR forgotten index, RR atom bond connectivity index, and RR Geometric-arithmetic index. We also execute a graph-theoretical analysis and comparison to demonstrate the critical significance and validate the acquired results. Our findings provide insights into the structural and chemical properties of these nanoribbons and contribute to the development of new materials for various applications.

Image dehazing is a crucial task in the early stages of real-time image processing pipelines used in applications such as surveillance and advance driver assistance systems. Dehazing algorithms mitigate the spatially selective degradation of image details caused by natural phenomenon such as fog and sandstorms. The problem is exacerbated in the presence of color cast which can affect the color-sensitive processing in the downstream tasks. In order to correct these two aberrations, we have proposed a light-weight algorithm which is not only quantitively more effective than the state-of-the-art works but also uses minimal computational resources. Specifically, it has been proposed to tackle both aforementioned problems by processing luminance and chrominance channels separately through a custom resource-efficient colorspace transform. Moreover, it has been proposed to employ online calculation of the relevant video stream statistics to estimate the degradation model over several temporally adjacent frames. This approach not only reduces the hardware resource utilization when implemented inside the video processing pipeline but also reduces the flicker effect observed when frames are processed individually. It has been demonstrated through quantitative analysis on standard datasets that the proposed approach either works at par or better than the reference works in terms of image quality metrics. Furthermore, the proposed framework has been developed as a real-time video processing system on an FPGA platform. The synthesis results of this implementation suggest that the proposed framework achieves this performance using minimal logic resources.

This paper presents a design optimization for grid-connected modular Active Front-End (AFE) rectifiers with an evaluation of efficiency, lifetime, cost, volume, and weight. This tool optimizes the rectifier switching frequency, component sizing and selection, and the number of parallel converters by designing and evaluating every possible configuration of the AFE rectifier system defined by the end user. The design of the LCL filter, magnetic design of inductors, selection of the inductor core and winding, and selection of SiC switches and MLC capacitors are discussed. Rapid Low-Fidelity (Lo-Fi) electro-thermal and lifetime models that are fast enough to be used in an optimization process have been developed. The rapid Lo-Fi models estimate component losses, temperatures, and lifetime for given load profiles and converter configurations. This Lo-Fi approach accelerates the processing time to generate the thermal data for the converter mission profile and allows us to skip rainflow-counting algorithm to assess the accumulated thermal damage to the SiC switches at the design and development stage. This in turn allows engineers to design converters with a longer predicted lifetime. Moreover, optimization of the grid-side three-phase LCL filter is performed considering efficiency, cost, and volume trade-off between the grid-side and converter-side inductors to achieve up to 50% decrease in losses, and 23% decrease in cost. Moreover, a 21-22% decrease in the system losses, 23-27% decrease in system cost, and tenfold improvement of the system lifetime can be achieved by optimizing the converter switching frequency and number of parallel modules. A 15 kW hardware prototype consisting of three 5 kW AFE rectifier modules is built and used to validate the efficiency from the fast Lo-Fi models. The validation of the detailed loss model and junction temperature swing is performed against a High-Fidelity (Hi-Fi) simulation in MATLAB Simulink environment.

Business has a massive effect on both people and society as a whole in today’s culture. Businesses make significant contributions to humanity’s general well-being and progress by promoting economic growth, supporting innovation, producing wealth, and addressing social challenges. However, choosing the right location for a business is a complex task, involving multiple criteria and qualitative and quantitative factors that heavily rely on expert judgement. The researchers propose a unique approach called the SVNZN multi-attributed decision-making method to aid decision-makers in this process. They introduce new operating laws for SVNZNs based on the sine trigonometric (ST) function, known for its periodicity and symmetry over the origin, making it favorable for decision-makers over multi-time phase parameters. Additionally, novel AOs such as SVNZN weighted averaging and geometric operators are defined to combine SVNZNs effectively. Based on these AOs, a decision-making technique for MADM problems is presented, and its applicability is demonstrated through a numerical example of selecting the best location for a business. To validate and enhance the understanding of these proposed techniques, the researchers conducted a comprehensive comparison analysis, including a sensitivity analysis, considering existing literature on MADM difficulties. Figure 1 provides a thorough graphical summary by visually representing all of the contributions and outcomes.

Reconfigurable intelligent surfaces (RIS) have been developed to exploit the stochastic characteristics of the propagation environment for next generation wireless systems. On the other hand, the integration of wireless physical network coding (PNC) and multiple antennas yields notable enhancements in system performance. This paper presents a multiuser system, employing RIS enabled PNC alongside multiple antennas to minimize both delay and error probability. Our aim is to establish reliable communication between the user pairs, which communicate through a base station (BS) via RIS. Therefore, the reflecting coefficients including both phases and amplitudes of the RIS are optimized by using the alternating direction method of multipliers (ADMM) algorithm for both single and multiple RIS cases. Extensive results are presented to compare the proposed algorithm with random phase shift, network coding (NC) and the search algorithm to illustrate its superiority.

Aim: The aim of this study was to study the treatment outcome and survival of squamous cell carcinoma anal canal using concurrent chemoradiotherapy in a tertiary care hospital. Materials and Methods: This was a retrospective study performed in our hospital, in which all necessary clinical and epidemiological details of the 38 cases diagnosed with squamous cell carcinoma anal canal from January 2015 to December 2021 were analyzed. Results: Most patients were male in gender, 22 (57.90%) and 16 (42.10%) were females, with a male: female ratio of (1.37:1). All patients received concurrent chemoradiation as the definitive treatment. The mean 5-year survivals were (48 months). Five years of overall survival was 68% and for clinical stage I, II, and III was 100%, 64%, and 37%, respectively. Conclusion: The standard of care for stage I to III anal squamous cell carcinoma is concurrent chemoradiotherapy with 5-fluorouracil and mitomycin-C with excellent response rates.

This article presents an extensive examination of antennas rooted in nature and biology, showcasing their remarkable performance across a wide spectrum of frequencies—from microwave to terahertz. The limitations of traditional antenna design have become increasingly evident in the face of burgeoning demands for novel communication technologies. Conventional analytical-equation-based approaches struggle to deliver the combined performance characteristics– encompassing bandwidth, gain, radiation pattern, and miniaturization– that emerging technologies necessitate. This has fueled an interest in bio-inspired antenna designs, a paradigm shift drawing inspiration from the ingenious structural solutions found in the living and non-living world, from plant leaves to bird feathers. These bio-inspired designs offer distinct advantages such as broader bandwidth and reduced sizes, making them highly appealing alternatives to the limitations of conventional antenna designs. This review explores a diverse range of bio-inspired designs. Among them are fractal geometries, inspired by self-repeating patterns in nature, which achieve optimal performance. Numerous designs in this category draw inspiration from nature, incorporating patterns observed in snowflakes, tree branches, clouds, and butterflies. Furthermore, nano-antennas have attracted significant attention for their vast potential applications in microwave and optical frequencies, playing a pivotal role in high-resolution spectroscopy, biomedical diagnosis and sensing, quantum photonics, and solar cell applications. By examining design methodologies and potential benefits, this article highlights the transformative potential of nature-inspired antennas. The compelling advantages of bio-inspired approaches necessitate a thorough exploration of their potential, paving the way for the development of next-generation communication systems with unprecedented capabilities.

The rise of smart cities, smart homes, and smart health powered by the Internet of Things (IoT) presents significant challenges in design, deployment, and security. The seamless data processing across a complex network of interconnected devices in unprotected conditions makes it vulnerable to potential breaches, underscoring the need for robust security at various levels of the network. Traditional security methods based on statistics often struggle to comprehend data patterns and provide the desired level of security. This work proposes a novel hybrid framework that combines Whale Optimization and Deep Learning with a trust-index to identify malicious nodes engaging in various attacks such as DoS, DDoS, Drop attack, and Tamper Attacks, thus enhancing IoT node security. The developed framework first calculates a trust-index score for IoT nodes based on drop attack, tamper attack, replay attack, and multiple-max attack. Subsequently, it utilizes the trust index score in the Optimized Neural Network model to effectively identify the malicious IoT node. The neural network optimization is achieved through a fitness function that determines optimal weights using the Whale Optimization Algorithm. The proposed framework has been tested across varying network sizes, comprising 100, 500, and 1000 nodes. The resulting outcomes were evaluated against benchmark security methods such as Logical regression, Random Forest, Support Vector Machine, Bayesian models, ANN, Elephant herding optimization, and Lion algorithm using metrics like specificity, sensitivity, accuracy, precision, False Positive Rate, False Negative Rate, False Discovery Rate, Error, F1 score, Matthews Correlation Coefficient, and Negative Predictive Value. The results reveal a notable enhancement in accuracy (26.63%, 13.04%, 17.78%, 30.52%, 22.45%, 4.26%, and 2.24%) for a 100-node network when compared to the benchmark security methods. Furthermore, the proposed framework consistently demonstrates strong performance even when applied to larger IoT networks with a higher node count.

Objective: To assess the nephroprotective potential of agmatine in a rat model of streptozotocin-induced diabetic nephropathy. Methods: A single dose of streptozotocin (40 mg/kg) coupled with a fructose diet induced diabetes in Wistar rats. Agmatine (40 and 80 mg/kg) was administered to rats for 12 weeks. The body weight and fasting blood glucose were measured weekly. Insulin level, urine output, total protein, albumin, blood urea nitrogen, creatinine, and cystatin-C were also determined at the end of the experiment. Furthermore, superoxide dismutase, glutathione, interleukin-1β, interleukin-6, and tumor necrosis factor-alpha were evaluated in kidney tissue. Histopathological study was also performed using hematoxylin and eosin staining. Results: Agmatine at both doses significantly increased final body weight, and lowered fasting blood glucose, urine output, insulin, total protein, albumin, blood urea nitrogen, creatinine, and cystatin-C levels compared with the diabetic group (P < 0.05). Inflammatory markers and antioxidant effect were significantly improved in agmatine-treated rats. Moreover, the histopathological changes in renal structure were ameliorated by agmatine treatment. Conclusions: Agmatine alleviates diabetic nephropathy by improving renal functions and reducing inflammation and oxidative stress. The molecular mechanisms of its nephroprotective actions need to be investigated in future study.

Abstract The study aims to investigate causal recipes to improve restaurant customers’ loyalty (LOY) during COVID-19 post-pandemic. The study utilizes Fuzzy-set Qualitative Comparative Analysis (fsQCA) within the framework of complexity theory to examine the intricate relationship between antecedent conditions, including Socially Responsible COVID-19 practices (CSR), Service Quality (SR), Customer Experience (EXP), Customer Satisfaction (SAT), Trust (TR), and Customer Commitment (COMM). The study used a quantitative survey approach, using a Likert scale to achieve the study aim. The survey has been strategically developed to gather intricate replies, taking influence from well-established scales within the area. The research purposefully recruited customers from the restaurant industry in Pakistan. A total of 450 full and valid replies were obtained via the use of Google Forms and paper questionnaires. The fsQCA approach is used to examine the data and ascertain configurations or combinations of antecedent situations that contribute to enhanced levels of loyalty. The results indicate that LOY is a multifaceted phenomenon in the post-pandemic phase of COVID-19 and is not only influenced by individual antecedent situations. This study identifies many routes that lead to increased levels of loyalty, highlighting the need to adopt a comprehensive and integrated strategy. The research emphasizes the diverse impacts of important factors, including CSR, SR, EXP, SAT, TR, and COMM, on loyalty. The novelty of this study is in its utilization of fsQCA and complexity theory to investigate LOY inside the restaurant sector among the distinctive circumstances of the post-pandemic period of COVID-19. This paper presents a critique of conventional symmetrical methods and proposes a comprehensive viewpoint on LOY, highlighting the need for sophisticated and integrated theoretical frameworks. Through the exploration of several routes to increased levels of LOY and the identification of the intricate influences of numerous preceding factors, this study enhances both theoretical and practical comprehension. The study places significant importance on an innovative research methodology and its potential impact on restaurant management, making it a noteworthy contribution to the current body of literature.

Abstract Background Multidrug-resistant bacterial strains cause several serious infections that can be fatal, such as Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumonia, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacteriaceae (often referred to as ESKAPE pathogens). Since ancient times, several indigenous medical systems in India have utilized diverse medicinal plants (approximately 80,000 species) as conventional treatments for a variety of illnesses. A member of the Fabaceae family, also referred to as “Himalayan indigo,” Indigofera heterantha Wall, is well known for its therapeutic properties. Methods The present study investigated the antibacterial, antifungal and antihelmintic properties of the roots, bark, leaves, and flowers of I. heterantha from the Kashmir Himalayas. The effectiveness of the extracts against bacteria, fungi, and earthworms. Three of the tested organisms for bacteria were ESKAPE pathogens, as they are responsible for creating fatal bacterial infections. The antifungal potency of I. heterantha aqueous and methanolic extracts was evaluated using the Agar Well Diffusion Assay. The antihelmintic activity was carried out on an adult Pheretima posthuma Indian earth worm, which shares physiological and anatomical similarities with human intestinal roundworm parasites. Results The methanolic extracts of root and bark have shown prominent activity against all bacterial strains, whereas aqueous extracts of flower, root, and leaves have shown promising activity against Staphylococcus aureus. The aqueous extract demonstrated good activity against S. cerevisiae at a concentration of 200 mg/ml with a zone of inhibition of 16 mm, while the methanolic extract displayed comparable activity against the fungal strains. The remaining two strains, P. crysogenum and A. fumigatus, were only moderately active in response to the extracts. All the extracts have shown anthelmintic activity except aqueous flower. Conclusion These results will pave the way for the bioassay-guided isolation of bioactive constituents that may act as hits for further development as potential antibacterial agents against drug-resistant microbial and helminthic infections.

Abstract This study investigates the no-wait flow shop scheduling problem and proposes a hybrid (HES-IG) algorithm that utilizes makespan as the objective function. To address the complexity of this NP-hard problem, the HES-IG algorithm combines evolution strategies (ES) and iterated greedy (IG) algorithm, as hybridizing algorithms helps different algorithms mitigate their weaknesses and leverage their respective strengths. The ES algorithm begins with a random initial solution and uses an insertion mutation to optimize the solution. Reproduction is carried out using (1 + 5)-ES, generating five offspring from one parent randomly. The selection process employs (µ + λ)-ES, allowing excellent parent solutions to survive multiple generations until a better offspring surpasses them. The IG algorithm’s straightforward search mechanism aids in further improving the solution and avoiding local minima. The destruction operator randomly removes d-jobs, which are then inserted one by one using a construction operator. The local search operator employs a single insertion approach, while the acceptance–rejection criteria are based on a constant temperature. Parameters of both ES and IG algorithms are calibrated using the Multifactor analysis of variance technique. The performance of the HES-IG algorithm is calibrated with other algorithms using the Wilcoxon signed test. The HES-IG algorithm is tested on 21 Nos. Reeves and 30 Nos. Taillard benchmark problems. The HES-IG algorithm has found 15 lower bound values for Reeves benchmark problems. Similarly, the HES-IG algorithm has found 30 lower bound values for the Taillard benchmark problems. Computational results indicate that the HES-IG algorithm outperforms other available techniques in the literature for all problem sizes.

Abstract Two new subclasses of the class of bi-Bazilevič functions, which are related to the Fibonacci-number series and the square-root functions, are introduced and studied in this article. Under a special choice of the parameter involved, these two classes of Bazilevič functions reduce to two new subclasses of star-like biunivalent functions related with the Fibonacci-number series and the square-root functions. Using the Faber polynomial expansion (FPE) technique, we find the general coefficient bounds for the functions belonging to each of these classes. We also find bounds for the initial coefficients for bi-Bazilevič functions and demonstrate how unexpectedly these initial coefficients behave in relation to the square-root functions and the Fibonacci-number series.

This study presents a novel, compact, diamond-shaped Multiple Input Multiple Output (MIMO) antenna system for 5G wireless communication. The proposed MIMO antenna system encompasses an array configuration, with each array unit comprising two antenna elements. The array elements are then orthogonally oriented making it a dual-port MIMO antenna system. The system is printed on a commercially available Rogers 5880, characterized by a permittivity ( $\varepsilon _{\mathrm {r}}$ ) of 2.2 and a loss tangent (tan( $\delta)$ ) of 0.0009. The overall dimensions of the MIMO antenna system are $26\times 16\times0.254$ mm3, operating in an mm-wave band with a frequency spectrum spanning from 26.2 to 34.2 GHz. The single antenna element exhibited a gain of 3.8 dBi and is then improved up to 7.6 dBi with two elements array arrangement. The system exhibited satisfactory MIMO characteristics with a maximum diversity gain of 10 dB, an envelope correlation coefficient of less than 0.0005, and an impressive 25 dB isolation between the antenna elements. Moreover, the proposed MIMO antenna array consistently maintained a radiation efficiency exceeding 90% across the entire desired frequency spectrum. Measurements were conducted with fabricated prototypes to validate the simulation results and were found in good agreement. The remarkable features of the proposed MIMO antenna system, such as compact size, wide bandwidth, good efficiency, and satisfactory gain values make it a promising candidate for 5G millimeter-wave wireless communication networks.

The human brain is an incredible and wonderful organ that governs all body actions. Due to its great importance, any defect in the shape of its regions should be reported quickly to reduce the death rate. The abnormal region segmentation helps to plan and monitor the treatment. The most critical procedure is isolating normal and abnormal tissues from each other. So far, remarkable imaging modalities are being used to diagnose abnormalities at their early stages, and magnetic resonance imaging (MRI) is renowned and noninvasive among those modalities. This paper investigates the current landscape of brain tumor segmentation (BTS) by exploring emerging deep learning (DL) methods for brain MRI analysis. The findings offer a comprehensive comparison of recent DL approaches, emphasizing their effectiveness in handling diverse tumor types while addressing limitations associated with data scarcity and robust validation. DL has shown a vital improvement for BTS, so our primary focus is to include significant DL robust models to analyze the brain MRI. However, DL outperforms traditional methods; still, there are several limitations, especially related to the diverse tumor types, lack of datasets, and weak validations. The future perspectives of DL-based BTS present significant potential for revolutionizing the diagnosis and treatment of brain tumors.

In contemporary business organizations, the pivotal role of automation in control processes is evident through Inventory Management Systems (IMS), which leverage advanced techniques and data analytics algorithms to optimize inventory levels, enhance accuracy, and minimize costs. However, existing security techniques for IMS, including access controls, firewalls, and audits, face challenges in effectively addressing the evolving threat landscape. These limitations, including struggles with dynamic user roles, susceptibility to data manipulation, and challenges in thwarting various cyber threats, necessitate innovative solutions for robust real-time management and security. Consequently, this work proposes a novel hybrid approach that integrates blockchain with RFID data, Generative Adversarial Networks, and Elliptic Curve Diffie-Hellman cryptographic techniques. In the developed hybrid approach, RFID readers are leveraged to collect inventory data, while the Generative Adversarial Network is specifically designed for processing the raw dataset, encompasses data filtering, normalization, and error correction tasks. The utilization of the Elliptic Curve Diffie-Hellman technique is integral for generating both private and shared keys, facilitating secure transmission between the IMS client and cloud-based servers. The blockchain module is engineered to enhance data security and protect shared secret keys, which is achieved through a two-layer mechanism involving encryption via the Advanced Encryption Standard algorithm and SHA-256 hashing function. Additionally, it incorporates the Artificial Algae Algorithm and an Elman Neural Network to ensure robust data access and integrity. To assess the effectiveness of the proposed hybrid approach, it is implemented on a publicly available dataset. The performance assessment involves a comparison with state-of-the-art security methods, considering key metrics such as encryption time, decryption time, key generation time, throughput, latency, and data confidentiality rate. Simulation results conclusively demonstrate that the proposed hybrid approach significantly reduces encryption time, decryption time, key generation time, and latency. Furthermore, it notably improves throughput and data confidentiality rates while aligning with stringent IMS security requirements.

Modern Electric Vehicles (EVs) require high power and high efficient powertrains to extend their power range. A key element of the electric powertrain is its drive with an electric motor controlled by a traction inverter. A cooling system dissipates heat generated due to the losses in this inverter and keeps its temperature within limits, i.e. below the operational maximum value. Indirect cooling systems are often the preferred solution due to their easy implementation and robust separation of the electric/electronic parts and the coolant circuit. Indirect cooling comes with additional surface interfaces, hence thermal barriers and increased thermal resistance for the losses’ heat flow path. One way to increase the system’s heat transfer coefficient is by implementing power electronics with dual-sided cooling (DSC) solutions and by enhancing surface structures for the cold plates. Manufacturing complex cold plate solutions with internal surface-enhancing structures by way of classical techniques (e.g. aluminum extrusion with CNC machining) can be difficult, costly, or even not possible. Sealed one-piece solutions are preferred, without the need to weld parts or to use screws, glue, gaskets, etc. 3D metal printing allows to manufacture of a one-unit compact, light, and reliable cold plate. This study shows the advantages and limitations of a 3D metal-printed inverter cold plate by presenting the microchannel design, numerical thermal simulations, and experimental results for the liquid cooled DSC SiC and Si inverters. This work explores the compatible use of 3D metal printing solutions, which will aid the development of modern high-power density EVs.

Hirsutism is characterized by the excessive growth of coarse hair in women, resembling the typical pattern seen in men. It affects between 5 to 10 % of females of reproductive age. Excessive hair growth often leads to significant mental and emotional anguish. Hirsutism is the result of an overabundance of androgens being secreted by the ovaries or adrenal glands. This article offers a comprehensive review of numerous causes that might provoke hirsutism which includes polycystic ovarian syndrome, Cushing syndrome, idiopathic hirsutism, insulin resistance, congenital adrenal hyperplasia, ovarian or adrenal tumors, menopause, and the use of certain drugs. Polycystic ovary syndrome and ovarian tumors are frequent underlying factors of hyperandrogenism, which subsequently results in the development of Hirsutism. Mechanical and cosmetic hair removal methods are viable options for managing hirsutism. This study examines the pharmacological therapies that have proven essential in enhancing the quality of life for patients. These treatments include oral contraceptives, antiandrogen therapy like spironolactone, cyproterone acetate, flutamide, and finasteride, specific insulin-lowering medicines, gonadotrophin-releasing hormone agonists, and topical treatment such eflornithine hydrochloride cream (13.9% w/w cream). This review also explored significance of nanotechnology-based methods like nanostructured lipid carriers, solid lipid nanoparticles, liposomes, cerosomes, and nanogel in enhancing the effectiveness of medications used to treat hirsutism. A comprehensive update has been made to the latest information on clinical studies and patents linked to the treatment of hirsutism.

In this research, we present two Numerical algorithms for studying the dynamics of spatially extended coupled nonlinear reaction-diffusion model. The difference in this work is to model a system of reacting and diffusing chemicals, and how to predict the dynamics of ecologically relevant behavior, including chaos. The goal can be achieved by applying a proposed staggered grid finite volume method for solving the Reaction model, rigorously validating the numerical method and grid generation techniques against established results. Additionally, we integrate an Alternating Direction Implicit formulation to compare the efficiency and accuracy of the model’s results. Dynamical system analysis, including linear stability analysis, is applied to comprehend the fundamental qualitative features of the inhibitor-activator system inherent in the coupled reaction-diffusion equations. Results, presented in tables and graphical representations, show that the schemes’ high accuracy at fourth-order precision in space and second-order in time, with conditional stability. Numerical results showed that the proposed finite volume approach was exceptionally proficient and accurate for tackling the two-dimensional nonlinear coupled reaction-diffusion model. Overall, the dialog highlights the significance of the proposed staggered grid finite approach and calculations for fathoming coupled reaction-diffusion equations are widely studied in biological and chemical systems of nonlinear differential equations.

IoT-based remote health monitoring is a promising technology to support patients who are unable to travel to medical facilities. Due to the sensitivity of health data, it is important to secure it against all possible threats. While a great deal of work has been done to secure IoT device-cloud communication and health records on the cloud, insider attacks remain a significant challenge. Malicious insiders may tamper, steal or change patients’ health data, which results in a loss of patient trust in these systems. Audit logs in the cloud, which may point to illegal data access, may also be erased or forged by malicious insiders as they tend to have technical knowledge and privileged access to the system. Thus, in this work, we propose a Cloud Access Security Broker (CASB) model that (a) logs every action performed on user data and (b) secures those logs by placing them in a private blockchain that is viewable by the data owners (i.e., patients). Patients can query the blockchain, track their data’s movement, and be alerted if their data has been accessed by an administrator or moved outside the cloud storage. In this work, we practically implement a web application that receives health data from patients, a CASB that securely stores the records in the cloud, and integrate a private blockchain that immediately logs all actions happening in the backend of the web application and CASB. We evaluate the system’s security and performance under varying numbers of patients and actions.

Task scheduling is a crucial challenge in cloud computing paradigm as variety of tasks with different runtime processing capacities generated from various heterogeneous devices are coming up to cloud application console which effects system performance in terms of makespan, resource utilization, resource cost. Therefore, traditional scheduling algorithms may not adapt to this paradigm efficiently. Many existing authors developed various task schedulers by using metaheuristic approaches to solve Task scheduling problem(TSP) to get near optimal solutions but still TSP is a highly dynamic challenging scenario as it is a NP hard problem. To tackle this challenge, this paper introduces a multi objective prioritized task scheduler using improved asynchronous advantage actor critic(a3c) algorithm which uses priorities of tasks based on length of tasks, runtime processing capacities and priorities of VMs based on electricity unit cost using multi cloud environment. Scheduling process carried out in two stages. In the first stage, all incoming tasks, VM priorities are calculated at the task manager level and in the second stage, Priorities are fed to (MOPTSA3C) scheduler to generate scheduling decisions to map tasks effectively onto VMs by considering priorities and schedule tasks based on cost, resource utilization, makespan in the available multi cloud environment. Extensive simulations are conducted on Cloudsim toolkit by giving input trace different fabricated data distributions and real time worklogs of HPC2N, NASA datasets to the scheduler. For evaluating the efficacy of proposed MOPTSA3C, it compared against existing techniques i.e. DQN, A2C, MOABCQ. From the results, it is evident that proposed MOPTSA3C outperforms existing algorithms for makespan, resource utilization, resource cost, reliability.

In the ever-changing world of decision-making, when game theory and reinforcement learning(RL) come together, they create a fascinating combination that shows a new way to solve complex problems in many fields. The combination of game theory and RL is a powerful convergence that opens up a hopeful new frontier for dealing with complex decision-making problems in many different fields. Research on the convergence of game theory and RL has shown to be beneficial, providing essential insights into challenging decision-making issues in various disciplines. This study investigates the recent developments of game theory and RL approaches through a systematic review and highlights the significance of game theory in boosting reinforcement algorithms and increasing the interaction of autonomous vehicles, safeguarding edge caching, and more. It offers a thorough account of the developments at the confluence of game theory and RL. The reviewed papers mainly focus on broad themes and address three important research questions: the impact of game theory on multi-agent reinforcement learning (MARL), the significant contributions of game theory to RL, and the significant impact areas. Following the methodology, search outcomes, and study areas is a discussion on game theory-related terminology, followed by study findings. The review’s conclusions offer ideas for further study and open research questions. The importance of game theory in advancing MARL, the potential of game theory in promoting RL strategies, and the opportunities for combining game theory and RL in cutting-edge fields like mobile edge caching and cyber-physical systems(CPS) are all emphasized in the conclusion. This review article advances our knowledge of the theoretical underpinnings and real-world applications of game theory and RL, laying the groundwork for future improvements in decision-making techniques and algorithms.

Maximum efficiency tracking (MET) with multiple concurrently-activated transmitters (TXs) has not yet been investigated thoroughly. This paper proposes a fast real-time estimation of coupling coefficients and load resistance for multiple concurrently-activated TXs and a moving RX. This is the first paper that demonstrates iteration-less, fast, communication-less MET with power regulation when the RX is moving across multiple activated TX array. The controller senses only the voltage magnitude of TX coil-capacitor resonator, and the input DC voltage and DC current of inverter. Once the parameters are estimated, the controller sets the converters to maximum efficiency point immediately. The benefits of proposed method are that it does not require a communication channel, extra circuit components for MET, time-consuming iterations and computations, and periodic interruption of power flow due to load disconnection or initialization, all of which were limitations in typical methods. Experimental results show that the proposed method provides a fast response (3.8ms) for an RX moving at 6.84 km/h across multiple TXs.

The 5-Methyluridine (m5U), predominantly present in RNA and especially enriched in transfer RNA (tRNA), significantly enhances translational accuracy and protein synthesis by ensuring precise genetic information decoding and optimal tRNA functionality within cellular mechanisms. The identification of m5U modification sites is crucial, as this modification has gained significant attention in diseases such as breast cancer, stress response, and viral infections, offering insights into its molecular mechanisms and regulatory functions in disease contexts. Nevertheless, due to the arduous nature, intricate procedures, reliance on sophisticated and expensive instrumentation, and the need for specialized expertise, conventional biochemical approaches for identifying m5U modification sites result in substantial resource expenditures and notable temporal investments. Consequently, the pressing need for a precise and efficient computational method highlights the urgency for alternative approaches in identifying m5U modification sites. In this study, we introduce a novel computational approach called “Deep-m5U,” which combines the strengths of Convolutional Neural Networks (CNNs) and tetranucleotide composition to accurately identify methyluridine modification sites and improve overall performance. The developed Deep-m5U method leverages CNNs to accurately detect protein-coding regions aand capture relevant motifs, while incorporating tetra-nucleotide composition to capture global compositional characteristics, resulting in a more robust model that significantly enhances performance. We evaluated the Deep-m5U model on two publicly available benchmark datasets: the full transcript and mature mRNA datasets. Our results showcase superior performance, achieving accuracies of 91.26% and 95.63% respectively, surpassing the current cutting-edge methods. Moreover, the open-source code for Deep-m5U is freely accessible at: https://github.com/waleed551/Deep-m5U.

In this paper, we introduce the concepts of Riemannian warped-twisted product submersions and examine their fundamental properties, including total geodesicity, total umbilicity and minimality. Additionally, we investigate the Ricci tensor of Riemannian warped-twisted product submersions, specifically about the horizontal and vertical distributions. Finally, we obtain Einstein condition for base manifold if the horizontal and vertical distributions of the ambient manifold is Einstein.

Abstract This research studies how recent Finnish smart mobility innovation projects have addressed sustainability in their objectives. A framework and a coding scheme were built upon scientific literature and was used for analysing documented project materials. A truth table was generated that quantifies how the different aspects of sustainability have been addressed in the projects’ objectives. The observed differences between the projects were analysed from two directions: in terms of funding sources (European Union vs. national) and project location (urban vs. rural areas). The results of qualitative comparative analysis showed that while sustainability was by and large present in the projects, some of the aspects of sustainability were more dominantly addressed than others. Also, there were differences in how sustainability was addressed between the projects in urban areas and the projects in rural areas.

This paper presents a compact penta-band Multiple Input Multiple Output (MIMO) Dielectric Resonator Antenna (DRA). The proposed MIMO DRA consists of two rectangular DRs incorporating complementary split ring resonators (CSRRs) and an inverted T-shaped slot. The MIMO DRA achieves low impedance matching through simple microstrip feedlines, which is further enhanced by integrating a microstrip line with the DRA itself. Remarkably, the introduction of CSRRs enables the design to resonate at five distinct frequency bands, namely 2.8-GHz, 4.4-GHz, 5.8-GHz, 6.4-GHz, and 6.9-GHz. The simulated properties of the MIMO DRA were validated through over-the-air measurements performed on a fabricated prototype. The proposed MIMO design demonstrates impedance bandwidths of 2.09 %, 2.43 %, 2.17 %, 2.55 %, and 4.91 % at the above respective resonance frequencies. The proposed design exhibits exceptional stability in radiation pattern, featuring a noteworthy peak gain of 6.08 dBi and an efficiency of 91.35 %. Notably, the incorporation of an inverted T-shaped slot effectively enhances isolation between the MIMO elements, achieving a maximum diversity gain of 10 dB and an envelope correlation coefficient of 0.005 over 20-dB isolation. A good agreement between the simulation and measured results is obtained, which underscores the suitability of the CSRR-based MIMO DRA for multiband wireless applications (blue tooth, radio astronomy, remote sensing, WIFI, satellite television and 6G), making it a very valuable contribution to the field.

Power Density and cost are the key design criteria for the modern power electronics unit in Electric Vehicles (EVs). EVs widely adopt inductive wireless charging technology, which contains the receiving coil and the AC-DC converter on the vehicle side. However, the vertical space on the chassis for charging electronics is very limited. Moreover, with a non-compact package comes a high damaging probability due to unpredictable road conditions, higher costs, and vehicle integration issues. This research presents the concept of distributed planar magnetics with the integrated rectifier hardware design for the vehicle side charging electronics by combining the concept of planar-distributed magnetics with the design space optimization research. A planar-distributed inductor module consisting of 16 EI cores with PCB-embedded windings was designed, modeled (using a novel high-fidelity model), and fabricated using the design space optimization technique, which resulted in a 66% increase in the power density. This research treated and solved the power density problem as an optimization problem considering every constraint involved and presented a thorough investigation from the design space utilization, power loss, loss and frequency relation along with the magnetostatic and electrostatic analysis perspective. It also provides a comparative study of various materials for the inductor fabrication keeping in view the automotive application environment. Moreover, a hybrid approach (metalcore and FR4) PCB was used instead of a regular FR4 PCB after an in-depth study and performance comparison.

The fifth generation of wireless communication is anticipated to provide improved quality of service and enhanced data rates to the end users. One such technology that stands out as a potential transmission scheme for 5G systems is Filter Bank Multicarrier using Offset Quadrature Amplitude Modulation (FBMC/OQAM) with an effective channel estimation technique for improved performance. However, due to the inherent imaginary interference, channel estimation methods relying on preamble structures in FBMC/OQAM systems exhibit sub-optimal performance, particularly within Multiple-Input Multiple-Output (MIMO) setups. For channel estimation schemes based on compressed sensing, the inherent sparsity of wireless channels can be exploited for accurate channel reconstruction and overall performance improvement.We propose a novel compressed sensing based algorithm namely, Adaptive Refined Random Orthogonal Matching Pursuit (ARROMP), for MIMO-FBMC system with Coordinated MultiPoint (CoMP) scheduling. This algorithm adaptively selects a support set by utilizing a double threshold for the minimization of mean squared error and for accurate channel reconstruction. The proposed algorithm's performance is compared with existing Orthogonal Matching Pursuit (OMP) schemes such as random OMP, refined random OMP, and least square-based estimation. The numerical simulations suggest that the proposed adaptive algorithm provides performance improvement in terms of reduced Mean Squared Error (MSE) of channel reconstruction and Bit Error Rate (BER). Moreover, the proposed ARROMP algorithm for MIMO-FBMC is rigorously tested with CoMP scheduling for a cellular network using frequency division duplex mode. The proposed system presents significant improvements in throughput and spectral efficiency for all types of cell users, including cell-edge users. The simulation results validate the improved performance of the proposed algorithm with CoMP scheduling over the existing single-cell system with no coordination.