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In this paper, we analyze a hybrid Heterogeneous Cellular Network (HCNet) framework by deploying millimeter Wave (mmWave) small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data rate. We consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations (SBSs) are deployed in the areas with high User Equipment (UE) density. Such user centric deployment of mmWave SBSs inevitably incurs correlation between UE and SBSs. For a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave communication. By using tools from stochastic geometry, we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power association. For UE clustering we considered Thomas cluster process and derive expressions for the association probability, coverage probability, area spectral efficiency, and energy efficiency. We also provide Monte Carlo simulation results to validate the accuracy of the derived expressions. Furthermore, we analyze the impact of mmWave operating frequency, antenna gain, small cell biasing, and BSs density to get useful engineering insights into the performance of hybrid mmWave HCNets. Our results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

To drive safely, one must be attentive, coordinated, have good judgment, and be able to respond quickly to changing conditions. In certain countries, improving safety may depend largely on reducing the number of impaired drivers on the road. Therefore, solutions are required to reduce the risk that is posed on the road by drivers who have been consuming alcohol while driving. Previous research has proposed the use of sensors for detecting driver impairment caused by alcohol intoxication. However, relying on a gas sensor alone may not be appropriate for detection. To reduce drunk driving, this study proposes an Internet of Things (IoT)-based tool that measures heart rate and analyzes the breath of a driver for traces of alcohol. The tool represents a vehicle that is made up of a DC motor. In the circumstance that the tool detects a higher than resting heart rate in the driver as well as an amount of alcohol in the driver’s breath sample, the tool will immediately power down the DC motor and send an SMS to the registered emergency contact with the driver’s precise position using the GPS module. The initial prototype demonstrates the tool as a potential aftermarket accessory for vehicles. The implication of this paper is that the designed tool might be of practical use to researchers in their attempts to determine and obtain information on alcohol intoxication. Doi: 10.28991/HIJ-2024-05-02-012 Full Text: PDF

Background: Measles has been a significant public health concern in Pakistan, especially in the Khyber Pakhtunkhwa (KPK) province, where sporadic and silent epidemics continue to challenge existing control measures. This study aimed to estimate the prevalence and investigate the molecular epidemiology of the measles virus (MeV) in KPK and explore the vaccination status among the suspected individuals. Methods: A cross-sectional study was conducted between February and October 2021. A total of 336 suspected measles cases from the study population were analyzed for IgM antibodies using Enzyme-Linked Immunosorbent Assay (ELISA). Throat swabs were randomly collected from a subset of positive cases for molecular analysis. Phylogenetic analysis of MeV isolates was performed using the neighbor-joining method. The vaccination status of individuals was also recorded. Results: Among the suspected participants, 61.0% (205/336) were ELISA positive for IgM antibodies, with a higher prevalence in males (64.17%) compared to females (57.04%). The majority of cases (36.0%) were observed in infants and toddlers, consistent with previous reports. The majority of IgM-positive cases (71.7%) had not received any dose of measles vaccine, highlighting gaps in vaccine coverage and the need for improved immunization programs. Genetic analysis revealed that all MeV isolates belonged to the B3 genotype, with minor genetic variations from previously reported variants in the region. Conclusion: This study provides valuable insights into the genetic epidemiology of the MeV in KPK, Pakistan. The high incidence of measles infection among unvaccinated individuals highlights the urgency of raising awareness about vaccine importance and strengthening routine immunization programs.

This study examines a single-fed, self-isolated, dual-band, 8-element multiple-input multiple-output (MIMO) antenna system for fifth-generation (5G) and beyond-5G (B5G) handsets. The proposed antenna is designed by an innovative composition of two F-shaped slots backed by a slotted ground plane, enabling a dual-band operation in the sub-6 GHz (4.7 to 4.9 GHz, n79) and millimeter wave (mmWave) (28 to 29 GHz, n257) frequency bands. High isolation of >14.4 dB and >25 dB and a high diversity gain of >9.9 dB were achieved at the respective frequency bands without using additional decoupling techniques, exhibiting an efficiency of approximately 90% with high gains of 4.9 and 7.2 dBi at the n79 and n257 bands, respectively. The antenna demonstrates excellent MIMO performance regarding very low envelope correlation coefficient ( $\lt -7.3$ dB and −7 dB). In addition to the safety evaluations, the performance is verified in a realistic mobile device to ensure practical feasibility. The evaluations showed promising results, demonstrating that the proposed design is a suitable, compact, and highly effective MIMO solution for 5G and B5G handsets.

The rapid advancements in wireless technologies have led to numerous research studies that provide evidence of the successful utilization of wireless signals, particularly, WiFi signals for human activity sensing in the indoor environment. As a promising technology, WiFi-based human sensing can be utilized for a variety of applications such as smart healthcare, smart homes, security, industry, office indoor environments etc., due to the availability of rich infrastructure. Furthermore, compared to other radio frequency (RF) based systems such as radio detection and ranging (RADAR) and radio frequency identification (RFID), WiFi is non-invasive, has low-cost, and provides ubiquitous coverage in the indoor setup. However, due to the limited accuracy and high complexity of the model-based approaches for human sensing, the systems empowered by the deep learning (DL) techniques have achieved remarkable performance gains and showed more robustness in dealing with complicated human sensing tasks. The article explores the physical layer parameters used in WiFi sensing such as received signal strength indicator (RSSI) and channel state information (CSI), the estimated parameters such as angle-of-arrival (AoA) and Doppler shift (DS) along with frequency modulated continuous wave (FMCW) RADAR technology. Moreover, the preliminary signal processing stages that are applied to the received WiFi signals in the DL assisted systems are discussed. This article provides a comprehensive literature survey on the recent advances in DL-empowered WiFi sensing focusing on human activity recognition and movement tracking followed by fall detection, single task-multi task classification, crowd monitoring and sensing, indoor localization, gaits recognition, and pose estimation. Furthermore, the paper highlight the challenges in the existing literature and discusses the possible future research directions in WiFi-based human sensing assisted by DL techniques.

Abstract Hepatitis E is a global health concern. Hepatitis E virus (HEV) infection is endemic in Pakistan. HEV has four genotypes: HEV-1 through HEV-4. The genotypes HEV-1 and HEV-2 are associated with infection in humans, especially in countries with poor sanitation. The genotypes HEV-3 and HEV-4 are zoonotic and human infection takes place by consuming undercooked meat or being in contact with animals. The present study was designed to ascertain the presence of HEV in the Southern Punjab region of Pakistan. First, blood samples (n = 50) were collected from patients suspected of infection with the hepatitis E virus from the Multan District. The serum was separated and the samples were initially screened using an HEV IgM-ELISA. Second, the ELISA-positive samples were subjected to PCR and were genetically characterized. For PCR, the RNA extraction and complementary DNA synthesis were done using commercial kits. The HEV ORF2 (Open Reading Frame-2, capsid protein) was amplified using nested PCR targeting a 348 bp segment. The PCR amplicons were sequenced and an evolutionary tree was constructed using MEGA X software. A protein model was built employing the SWISS Model after protein translation using ExPASy online tool. The positivity rate of anti-HEV antibodies in serum samples was found as 56% (28/50). All Pakistani HEV showed homology with genotype 1 and shared common evolutionary origin and ancestry with HEV isolates of genotype 1 of London (MH504163), France (MN401238), and Japan (LC314158). Sequence analysis of motif regions assessment and protein structure revealed that the sequences had a similarity with the reference sequence. These data suggest that genotype 1 of HEV is circulating in Pakistan. This finding could be used for the diagnosis and control of HEV in the specific geographic region focusing on its prevalent genotype.

The joint optimization of spectral efficiency (SE) and energy efficiency (EE) through power allocation (PA) techniques is a critical requirement for emerging fifth-generation and beyond networks. The trade-off between SE and EE becomes challenging in the massive multiple-input-multiple-output (MIMO) equipped base stations (BSs) in multi-cell cellular networks. Various algorithmic approaches including genetic algorithms and convex optimization have been considered to optimize the trade-offs between SE and EE in cellular networks. However, these methods suffer from high computational costs. A promising deep reinforcement learning technique is capable of addressing the computational challenges of single-objective optimization problems in wireless networks. Furthermore, multi-objective reinforcement learning has been employed for multi-objective optimization problems and can be utilized to jointly enhance the SE and EE in cellular networks. In this paper, we propose a downlink (DL) transmit PA method based on a multi-objective asynchronous advantage single actor-multiple critics (MO-A3Cs) architecture. The proposed architecture aims to optimize SE and EE trade-offs in massive MIMO-assisted multi-cell networks. Furthermore, we also propose a Bayesian rule-based preference weight updating mechanism, multi-objective advantage function, and balanced-reward aggregation method to effectively train and avoid biased objective reward during the training process of the proposed model. Extensive simulations depict that the proposed model is better capable of dealing with the joint optimization of SE and EE in dynamic changing scenarios. Compared to the existing benchmarks such as Pareto front approximation-based multi-objective, reinforcement learning-based single objective, and iterative methods, the proposed approach provides a better SE-EE trade-off by achieving a higher EE in multi-cell massive MIMO networks.

Objective This study investigated the impact of wearable technologies, particularly advanced biomechanical analytics and machine learning, on sports performance monitoring and intervention strategies within the realm of physiotherapy. The primary aims were to evaluate key performance metrics, individual athlete variations and the efficacy of machine learning-driven adaptive interventions. Methods The research employed an observational cross-sectional design, focusing on the collection and analysis of real-world biomechanical data from athletes engaged in sports physiotherapy. A representative sample of athletes from Bahawalpur participated, utilizing Dring Stadium as the primary data collection venue. Wearable devices, including inertial sensors (MPU6050, MPU9250), electromyography (EMG) sensors (MyoWare Muscle Sensor), pressure sensors (FlexiForce sensor) and haptic feedback sensors, were strategically chosen for their ability to capture diverse biomechanical parameters. Results Key performance metrics, such as heart rate (mean: 76.5 bpm, SD: 3.2, min: 72, max: 80), joint angles (mean: 112.3 degrees, SD: 6.8, min: 105, max: 120), muscle activation (mean: 43.2%, SD: 4.5, min: 38, max: 48) and stress and strain features (mean: [112.3 ], SD: [6.5 ]), were analyzed and presented in summary tables. Individual athlete analyses highlighted variations in performance metrics, emphasizing the need for personalized monitoring and intervention strategies. The impact of wearable technologies on athletic performance was quantified through a comparison of metrics recorded with and without sensors. Results consistently demonstrated improvements in monitored parameters, affirming the significance of wearable technologies. Conclusions The study suggests that wearable technologies, when combined with advanced biomechanical analytics and machine learning, can enhance athletic performance in sports physiotherapy. Real-time monitoring allows for precise intervention adjustments, demonstrating the potential of machine learning-driven adaptive interventions.

In this study, we investigated how electroosmsis, viscous dissipation, and slippage affect the peristaltic flow of complex cilia-generated flow of hybrid nanofluid in a ciliated tube. The complex cilia-generated flow is characterized by a complex sinusoidal wave that transmits along the wall of the tube with a uniform speed having distinct amplitudes. The main objective of this work is to examine how this complex cilia on the wall drives the hybrid nanofluid flow under electroosmosis. The Helmholtz–Smoluchowski equation is used to model the electroosmosis, and the Poisson equation is solved analytically using the Debye-Huckel approximation. The lubrication approach is utilized to simplify the governing equations. The governing non-dimensional equations for hybrid nanofluid are solved exactly using the DSolve command of Mathematica. The results are presented graphically to accomplish the theoretical results from the complex-cilia wave evolution from the necessary flow parameters in an asymmetric tube. The study shows that increasing the electroosmotic parameter leads to an increase in the velocity profile at the boundary and a decrease in the middle of the tube. Conversely, the Helmholtz–Smoluchowski velocity parameter has the opposite effect. The Brinkman parameter increases the temperature of the hybrid nanofluid. The study also presents a graphical analysis of pressure rise and pressure gradient for various values of the parameters. The pressure rise against volumetric flow is rate and pressure gradient increases by increasing the electroosmotic parameter and Helmholtz-Smoluchowski parameter, while it decreases for increasing the velocity slip parameter. The streamlines are drawn to study the trapping phenomena of blood in the hybrid nanofluid flow in an asymmetric tube by the formation of bolus and the division of streamlines. It is observed that the size and number of bolus close to the artery walls increases for electroosmotic parameter while reverse behavior is observed for Helmholtz-Smoluchowski parameter. The tabular presentation of the heat transfer coefficient at the wall is presented. The current results can be used in bio mathematical models to control fluid flow through micro-channels and to investigate ways to cure artery blockages and cancer tumors in biomedicine. The study can also be used to design and optimize microfluidic devices for drug delivery, lab-on-a-chip devices, and other biomedical applications.

Abstract Corals are sessile invertebrates living underwater in colorful structures known as reefs. Unfortunately, coral’s temperature sensitivity is causing color bleaching, which hosts organisms that are crucial and consequently affect marine pharmacognosy. To address this problem, many researchers are developing cures and treatment procedures to restore bleached corals. However, before the cure, the researchers need to precisely localize the bleached corals in the Great Barrier Reef. The researchers have developed various visual classification frameworks to localize bleached corals. However, the performance of those techniques degrades with variations in illumination, orientation, scale, and view angle. In this paper, we develop highly noise-robust and invariant robust localization using bag-of-hybrid visual features (RL-BoHVF) for bleached corals by employing the AlexNet DNN and ColorTexture handcrafted by raw features. It is observed that the overall dimension is reduced by using the bag-of-feature method while achieving a classification accuracy of 96.20% on the balanced dataset collected from the Great Barrier Reef of Australia. Furthermore, the localization performance of the proposed model was evaluated on 342 images, which include both train and test segments. The model achieved superior performance compared to other standalone and hybrid DNN and handcrafted models reported in the literature.

Abstract Background High-Affinity Potassium ions represent one of the most important and large group of potassium transporters. Although HAK genes have been studied in a variety of plant species, yet, remain unexplored in common bean. Results In the current study, 20 HAK genes were identified in common bean genome. Super-family “K_trans” domain was found in all PvHAK genes. Signals for localization of PvHAK proteins were detected in cell membrane. Fifty three HAKs genes, across diverse plant species, were divided into 5 groups based on sequential homology. Twelve pairs of orthologs genes were found in various plant species. PvHAKs genes were distributed unequally on 7 chromosomes with maximum number (7) mapped on chromosome 2 while only 1 PvHAK found on each chromosome 1, 4, and 6. Tandem gene duplication was witnessed in 2 paralog pairs while 1 pair exhibited segmental gene duplication. Five groups were made in PvHAK gene family based on Phylogeny. Maximum PvHAKs (10) were detected in Group-V while group-II composed of only 1 PvHAK gene. Variation was witnessed in number and size of motifs, and structure of PvHAKs associated with different groups. Light and hormone responsive elements contributed 57 and 24% share, respectively, to cis regulatory elements. qRT-PCR based results revealed significant increase in expression of all 4 PvHAK genes under low-potassium stress. Conclusion The current study provides valuable information for further functional characterization and uncovering the molecular mechanism associated with Potassium transportation in plants.

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Neurodegeneration of the substantia nigra (SN) and diminished release of dopamine are prominent causes of this progressive disease. The current study aims to evaluate the protective potential of ethanolic extract of Mentha piperita (EthMP) against rotenone-mediated PD features, dopaminergic neuronal degeneration, oxidative stress and neuronal survival in a mouse model. Swiss albino male mice were assigned to five groups: control (2.5% DMSO vehicle), PD (rotenone 2.5 mg/kg), EthMP and rotenone (200mg/kg and 2.5mg/kg, respectively), EthMP (200 mg/kg), and Sinemet, reference treatment containing levodopa and carbidopa (20 mg/kg and rotenone 2.5mg/kg). Behavioral tests for motor functional deficit analysis were performed. Anti-oxidant capacity was estimated using standard antioxidant markers. Histopathology of the mid-brain for neurodegeneration estimation was performed. HPLC based dopamine level analysis and modulation of gene expression using quantitative real-time polymerase chain reaction was performed for the selected genes. EthMP administration significantly prevented the rotenone-mediated motor dysfunctions compared to PD group as assessed through open field, beam walk, pole climb down, stepping, tail suspension, and stride length tests. EthMP administration modulated the lipid peroxidation (LPO), reduced glutathione (GSH), and superoxide dismutase (SOD) levels, as well as glutathione-s-transferase (GST) and catalase (CAT) activities in mouse brain. EthMP extract prevented neurodegeneration in the SN of mice and partially maintained dopamine levels. The expression of genes related to dopamine, anti-oxidant potential and synapses were modulated in M. piperita (MP) extract treated mice brains. Current data suggest therapeutic capacities of MP extract and neuroprotective capacities, possibly through antioxidant capacities. Therefore, it may have potential clinical applications for PD management.

Metal-oxide-based gas sensors are extensively utilized across various domains due to their cost-effectiveness, facile fabrication, and compatibility with microelectronic technologies. The copper (Cu)-based multifunctional polymer-enhanced sensor (CuMPES) represents a notably tailored design for non-invasive environmental monitoring, particularly for detecting diverse gases with a low concentration. In this investigation, the Cu-CuO/PEDOT nanocomposite was synthesized via a straightforward chemical oxidation and vapor-phase polymerization. Comprehensive characterizations employing X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), X-ray diffraction (XRD), and micro Raman elucidated the composition, morphology, and crystal structure of this nanocomposite. Gas-sensing assessments of this CuMPES based on Cu-CuO/PEDOT revealed that the response current of the microneedle-type CuMPES surpassed that of the pure Cu microsensor by nearly threefold. The electrical conductivity and surface reactivity are enhanced by poly (3,4-ethylenedioxythiophene) (PEDOT) polymerized on the CuO-coated surface, resulting in an enhanced sensor performance with an ultra-fast response/recovery of 0.3/0.5 s.

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials. In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at https://github.com/Arif-PhyChem/datasets_and_databases_4_MLPs . This resource also has the overview in a machine-readable database format with the Jupyter notebook example for analysis. Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.

Flow and heat transfer dynamics are considered quite complex phenomenon in different enclosures. Heat transport mechanism and its improvement due to the presence of nanoscale particles is vital topic of research. Owing to the widespread importance of free convection through fluids with nanoparticle in various configuration and constraint, we have addressed in this article the natural convection nanofluid flow in a hexagon shaped enclosure with heated cylinder. Simulation has been performed in the influence of porous medium and inclined magnetic field. The physical model is translated in terms of differential systems. For solutions purpose, the finite element method is utilized. The quantities of interest include the fluid flow and heat transfer aspects. The obtained results against flow controlling parameters are offered by means of contour plots and line graphs. It is noticed that the at lower Darcy number the heat transfer rates has diminishing behavior because of the higher porosity of medium. At higher Hartman number the velocity profile tends to decrease due to resistive nature of Lorentz forces. At maximum radius of heat source, the Nusselt number and velocity profile has higher values.

Simulations of energy transfer in light-harvesting complexes are computationally very demanding. Here the authors apply an artificial intelligence quantum dissipative algorithm to study the excited state energy transfer dynamics in a light-harvesting complex.

Because of its exceptional qualities, aluminium composites are frequently employed in aircraft applications. Aluminium alumina composite was manufactured in-situ using the AA-6061 T6 matrix alloy and 3 %age, 6 %age (Nichrome) powder. Optical metallography, Image Analysis, and micro hardness tests were used to assess the in-situ composites. The specimens then liquid preheated at 550 °C for 3 hours and then aged at 180 °C for 7 hours to improve the durability in mechanical characteristics. In comparison to the as cast samples, the heat-treated samples showed significantly higher mechanical properties. AA-6061 T6 matrix composite reinforced with in-situ alumina particle with TiC was also studied for grain refining. The Al-4.5 % Ti-1.5 % C master alloy has been shown to be an effective grain refiner.

Cloud computing is an on-demand availability of computing resources. The current cloud computing environment uses different algorithms for its security; RC4 is one of them. However, due to the serial implementation of RC4 over cloud computing, the process of encryption and data transmission is badly affected by the factors of poor latency. This research is proposed to cover this factor by implementing a parallel RC4 algorithm in a cloud computing environment. The parallel RC4 model has been developed in web-based architecture relevant to the cloud computing environment. The method processed data simultaneously in four parallel pipelines encrypted through the RC4 algorithm. The proposed method used RC4 algorithms’ parallel implementation to encrypt and decrypt the data. The proposed method increases the efficiency of data encryption and decryption and transmission over a cloud environment using four pipelines at once. These four pipelines receive data and encrypt them using a key and then merge those four streams into a single cipher. These four pipelines enhance the speed of communication over cloud and solve the latency issue majorly. Data sending from node devices to cloud servers is divided into four equal streams and then encrypted using RC4 distinctly but parallel. Then after the roaming, the data are again first combined and then decrypted using the RC4 algorithm. The sample data sets are evaluated on the criteria of the starting time and ending time of the encryption and decryption of data. Results show that the proposed method is able to enhance the speed of the whole process up to average 3.7% due to the implementation of parallel technique and had reduced the time notably. The future work on this research involves enhancement of security process of RC4 algorithm since it can be breached easily.

The present study based on petrophysical computations assess the conventional fuel resources of the Yageliemu Formation. The Yageliemu Formation was evaluated using log data from four wells (YK19, YK20, YK21, and YK32) and observed over the entire reservoir interval. Yageliemu Formation originates from the Lower Cretaceous reservoir, which is located in the Yakela gas condensate field, Kuqa Depression of Tarim Basin China. The studied gas bearing zones are primarily composed of sandstone, with small amounts of shale and clay contents. The petrophysical properties of the gas-bearing zones were carefully examined, which are rated to be better quality sand layers with average effective porosities varying from 7 to 10%, permeability varying from 3 to 8.6 mD, average water saturation values ranging from 43 to 59%, and the average range of gas saturation is 40–57%. The Yageliemu Formation cutoff outcomes for shale volume (Vsh), effective porosity, water saturation, and permeability were characterized at 40% and 5%, 65%, and 0.1 mD, respectively. Thomas Stieber model was used to successfully identify shale structural behavior in reservoirs and it revealed that the dominant shale is mainly laminated with a minor volume of structural and dispersive shale. Lithofacies and axial variations of reservoir properties are assessed by constructing self-organizing maps, isoparametric maps of the petrophysical properties, and litho-saturation cross-plots, respectively. The reservoir characteristics of the Yageliemu Formation demonstrate that the prospective region for hydrocarbons accumulation trends is in the southwestern and northwestern parts of the research area. The study techniques can be used in the Tarim Basin and similar basins around the world to estimate the petrophysical properties of oil and gas wells and comprehend reservoir prospects.

Abstract Background Prevalence of brucellosis and MLVA genotyping in animals and humans, isolated from different regions of Pakistan was performed. Animals having history of brucellosis from the field and local farms of Bannu, Mardan, Peshawar, Swat, Lahore and Islamabad were selected for blood collection. Humans that work with them were also selected for sampling in this study. Total of 600 samples were taken from cattle and humans and subjected to Rose Bengal plate Test (RBPT) for the initial screening of positive samples. Designed primers of B.abortus for cattle and B.melitensis for humans were utilised to perform PCR. Culturing and isolation was carried to further to perform MLVA genotyping assay through the selection of two panels of primer markers. Results RBPT showed more number of cases of brucellosis in animals and humans compared to the PCR findings. Genotyping findings based upon MLVA‐15 set of markers demonstrated that the isolated strains of B.abortus fall in the same clade with strain A1, P8 and A2 from Pakistan and also similar with BCCN#02‐45 strain from India. On the other hand, B.melitensis isolated from different districts of Pakistan shared the same clade with BwIM‐AFG 63, BwIM‐AFG 44 strains from Afghanistan and BwIM IRN 37 strain from Iran. Selected VNTR alleles were sequenced for calibration purposes. Conclusion It is concluded that Brucella is prevalent in animals and humans in studied districts of Pakistan. Moreover, A1, P8, BwIM‐AFG 63, BwIM‐AFG 44 and A2 were found the common genotypes in Pakistan.

Abstract In this paper, we discuss the Anthroponotic Cutaneous Leishmania transmission. In addition, we develop a mathematical model for the Anthroponotic Cutaneous Leishmania transmission and consider its qualitative behavior. We derive the threshold number R 0 $R_{0}$ of the model using the next generation method. In the disease-free case, we carry out the local and global stability under the condition R 0 < 1 $R_{0} 1 $R_{0}>1$ . We also establish the global stability at the endemic equilibrium point with the help of a geometrical approach, which is a generalization of Lyapunov theory, by using a second additive compound matrix. Finally, we take into account the sensitivity analysis of the threshold number with other parameters. We also discuss several graphs of important parameters.

This study aims to demonstrate the impact of economic growth and energy consumption on environmental degradation in China, the top country that produced the highest carbon dioxide (CO2) emissions, by considering that environmental degradation is one of the extreme challenges that the world and China have been facing. Parallel to this aim, this study uses dynamic ARDL (DYNARDL) simulations to investigate the long-run and short-run cointegration amongst the selected parameters from 1979 to 2019. The results of the long-run and short-run simulations illustrate that 1) economic growth increases environmental degradation; 2) growth in energy consumption worsens the environmental degradation situation; 3) urbanization improves the environmental situation in the long run, whereas growth in urban population increases CO2 emissions in the short-run. The research argues that improved energy production and management should be included in economic policy planning and the government should invest more in renewable energy to prevent environmental degradation.

The deployment of low power small base stations (SBSs) in high density user areas enhances the performance of heterogeneous cellular networks (HCNets). Such user-centric SBS deployment (UCSD) is more appropriate as compared with the conventional uniformly deployed user equipment (UE) based HCNets, because it captures the correlation between UEs and SBSs. In the literature, the downlink (DL) performance of UCSD based HCNet (UC-HCNet) has been analyzed under maximum received signal power (MRSP) association strategy. In this paper, we develop and analyze the uplink (UL) performance of UC-HCNets with decoupled UL-DL association (DUDA) using stochastic geometery. We evaluate the total UL interference considering two special cases of Poisson cluster process (PCP), i.e., Matern cluster process (MCP) and Thomas cluster process (TCP), in UC-HCNet. The proposed DUDA scheme enhances the system performance gain by assuming dual connectivity of UE and BSs in the UL and DL directions. Furthermore, fractional power control (FPC) policy is considered to limit the UL power consumption. The system performance is evaluated in terms of outage probability and rate coverage. The numerical results are validated through simulations. Numerical results show that the performance improvement of DUDA in sparse scenario is higher as compared with dense scenario. Furthermore, it is investigated that the performance gain of DUDA is higher for larger UE variance and cluster radius in case of MCP and TCP, respectively.

The investigation of nanofluid’s cross flow, which is caused by a nonlinear stretching sheet within the boundary layer, is presented. The proper mathematical detail is provided for three distinct cross flow instances with the streamwise flow. A uniform transverse stream located far above the stretched plate, in one instance, creates the cross flow. Two further situations deal with cross flows caused by surface transverse shearing motions. Weidman’s work was used to find a similarity solution by making the necessary changes. It has been found that two parameters, namely nanoparticle volume frictions ϕ and a nonlinear stretching parameter β, have a significant impact on the flow of fluids in cross flow scenarios. Graphical representations of transverse and streamwise shear stresses and velocity profiles are provided. From this study, we found that nanoparticle volume fraction ϕ reduces the momentum boundary layer in both streamwise and cross flow scenarios while increasing the temperature of the fluid and, hence, increasing thermal boundary layer thickness. The same is observed for the nonlinear stretching parameter β.

Climate change is a reality recognized globally. Although global efforts are accelerating, there are fears in the underdeveloped world regarding the erosion of their sovereignty through climate change action and response mechanisms. Remedial actions taken at various levels are not a compensating reflection of this reality. There is a need to establish a well-thought-out mechanism and support fast-track climate change action and responses. This study, therefore, highlights the impact of climate change action on state sovereignty through in-depth analysis by interviewing climate experts and officials. It reckons that the issue revolves around interference in internal policies through the prism of climate change action incorporating world organisations. It concludes that developing states may have fears regarding the overreach of developed states in their remedial actions, as seen in the Global South and Global North divide. Bibliography Entry Shafi, Khalid Mahmood, Arif Ullah Khan, and Rafaqat Islam. 2021. "Climate Change Action and State Sovereignty." Margalla Papers 25 (2): 98-108.

The present investigation imparts the mixed convection non-linear Falkner-Skan flow and heat flux analysis of ZnO-SAE50 nanolubricant over a stationary/moving Riga-plate in presence of thermal radiation. Additionally, velocity and thermal slip boundary conditions are considered. In the current study, a micro-convection model (Patel model) is considered. The present model has great influence in significant enrichment of heat transport capability of the nanoliquids. The similar transformed nonlinear coupled governing expressions are examined numerically by the Runge-Kutta-Fehlberg integration scheme. The important outcome of the present study is that the augmented pressure gradient impedes the motion of nano-lubricant while reverse trend is attained due to the amplification of modified Hartmann number and velocity slip at the boundary. Further, the growth of thermal slip peters out the heat transfer rate from the Riga plate.

Herein, we have presented a unique strategy for aqueous synthesis of semiconductor CuInS2 QDs which showed multiple fluorescence. These synthesized CuInS2 QDs were prepared in aqueous media with biocompatible glutathione (GSH), as capping ligand and stabilizer, while the fluorescence and crystallinity were controlled by varying the reaction time. The investigation of various other experimental parameters including precursor’s concentration, effect of pH, and stability of QDs has also been carried out. These QDs were characterized by XRD, TEM, and FT-IR. Meanwhile, optical properties of as synthesized QDs were also investigated by fluorescence spectroscopy. Furthermore, the synthesized QDs were also less cytotoxic and maintained remarkable cell viability up to 100 μg/mL. When bioconjugated with Arginyl-glycyl-aspartic acid (RGD) moiety, the obtained CuInS2-RGD QDs have shown higher biocompatibility and good bioimaging performance. Keywords: Aqueous synthesis, CuInS2, Quantum dots, Fluorescence, Bioimaging

In modern data centres of cloud computing contains virtualization system. In order to improve network stability, energy efficiency, and makespan proper virtualization need. The virtual machine is one of the examples of virtualizations. Cloud computing data centres consist of millions of virtual machine to manage load balancing. In this study check the different number of virtual machine role in data centres, for that purpose, we established a network with the help of cloudsim and compare different data centres at each zones taking a different number of the virtual machine with different paramater and network banwith.After the simulation the result shows that increasning in the number of VM can affect the netwok accuracy in term of energy ,processing time ,coast and network stabality .

Abstract Background Cytochrome P450 (CYP2B6) is an important enzyme that metabolizes about 3.0% of therapeutic drugs. Drugs metabolized mainly by CYP2B6 include artemisinin, bupropion, cyclophosphamide, efavirenz, ketamine, and methadone. The genetic polymorphisms in the CYP2B6 gene have earlier been studied in many populations, but the data are lacking for the Pakistani population. This research study aimed to determine the frequencies of the three of the most important variant alleles and genotypes of the CYP2B6 gene in the Pakistani population. Methods Blood was withdrawn from healthy volunteers after taking informed consent. DNA was extracted using commercial kits, and allelic and genotype frequencies were determined after PCR amplification followed by restriction fragment length polymorphism (RFLP) and gel electrophoresis. Results Our results show a minor allele frequency of 33.8% for CYP2B6*6, 25.8% for CYP2B6*4, 6.5% for CYP2B6*3, whereas wild‐type genotype frequency was 48.57% for CYP2B6*6, 59.79% for CYP2B6*4, and 90.20% for CYP2B6*3. A significant interethnic variation was also observed. Conclusions Our results suggest that the frequency of poor metabolizers of CYP2B6, especially *6 variant, is significant enough in the Pakistani population to be given an important consideration when drugs metabolized by this enzyme are prescribed.

The behaviors of quantum Fisher information (QFI) and quantum signal to noise ratio (QSNR) of a qutrit interacting with random telegraphic noise (RTN) are studied. Analytical relations for both quantities are obtained for the ground and first excited states of qutrit as initial states and numerical procedure is followed for the superposition of these states. Every choice of the initial state leads to considerably larger values of QFI. The ground initial state can be favorable for precise estimation of the smaller values of unknown parameter of RTN for all choices of the time of interaction. Depending on the time of interaction, the excited initial state of qutrit could do better for both smaller and larger values in the slow regime of RTN. Different choices of the initial superposition state could serve better for precise estimation purposes. Our results suggest that a qutrit may perform better than a qubit as a probe for precise estimation of unknown parameters.

Stroke is the leading cause of morbidity and mortality worldwide. About 87% of stroke cases are ischemic, which disrupt the physiological activity of the brain, thus leading to a series of complex pathophysiological events. Despite decades of research on neuroprotectants to probe for suitable therapies against ischemic stroke, no successful results have been obtained, and new alternative approaches are urgently required in order to combat this pathological torment. To address these problems, drug repositioning/reprofiling is explored extensively. Drug repurposing aims to identify new uses for already established drugs, and this makes it an attractive commercial strategy. Nuclear factor-kappa beta (NF-κB) is reported to be involved in many physiological and pathological conditions, such as neurodegeneration, neuroinflammation, and ischemia/reperfusion (I/R) injury. In this study, we examined the neuroprotective effects of atorvastatin, cephalexin, and mycophenolate against the NF-κB in ischemic stroke, as compared to the standard NF-κB inhibitor caeffic acid phenethyl ester (CAPE). An in-silico docking analysis was performed and their potential neuroprotective activities in the in vivo transient middle cerebral artery occlusion (t-MCAO) rat model was examined. The percent (%) infarct area and 28-point composite neuro score were examined, and an immunohistochemical analysis (IHC) and enzyme-linked immunosorbent assay (ELISA) were further performed to validate the neuroprotective role of these compounds in stroke as well as their potential as antioxidants. Our results demonstrated that these novels NF-κB inhibitors could attenuate ischemic stroke-induced neuronal toxicity by targeting NF-κB, a potential therapeutic approach in ischemic stroke.

The initial criteria for evaluating a researcher's output is the number of papers published. Furthermore, for the measurement of author's research quality, the number of citations is significant. Typically, citations are directly linked with the visibility of a research paper. Many researches had shown that the visibility of a research paper can be improved further by using the search engine optimization techniques. In addition, some research already proved that the visibility of an article could improve the citation results. In this article, we analysed the impact of search engine optimization techniques that can improve the visibility of a research paper. Furthermore, this paper also proposing some strategies that can help and making the research publication visible to a large number of users.

Today, heart diseases have become one of the leading causes of deaths in nationwide. The best prevention for this disease is to have an early system that can predict the early symptoms which can save more life. Recently research in data mining had gained a lot of attention and had been used in different kind of applications including in medical. The use of data mining techniques can help researchers in predicting the probability of getting heart diseases among susceptible patients. Among prior studies, several researchers articulated their efforts for finding a best possible technique for heart disease prediction model. This study aims to draw a comparison among different algorithms used to predict heart diseases. The results of this paper will helps towards developing an understanding of the recent methodologies used for heart disease prediction models. This paper presents analysis results of significant data mining techniques that can be used in developing highly accurate and efficient prediction model which will help doctors in reducing the number of deaths cause by heart disease.

The increasing of energy cost and also environmental concern on green computing gaining more and more attention. Power and energy are a primary concern in the design and implementing green computing. Green is of the main step to make the computing world friendly with the environment. In this paper, an analysis on the comparison of green computer with other computing in E-learning environment had been done. The results show that green computing is friendly and less energy consuming. Therefore, this paper provide some suggestions in overcoming one of main challenging problems in environment problems which need to convert normally computing into green computing. In this paper also, we try to find out some specific area which consumes energy as compared to green computing in E –learning centre in Malaysia. The simulation results show that more than 30% of energy reduction by using green computing.

This article characterizes the influence of magnetic dipole in a non-Newtonian ferrofluid. The flow is caused by an incompressible stretchable cylinder. The effects of homogeneous and heterogeneous reactions are taken into account. Heat flux is evaluated by the Fourier’s law of heat conduction. Characteristics of pertinent parameters on magneto-thermomechanical coupling and chemical reactions are explored numerically. It is depicted that the magneto-thermomechanical interaction slows down the motion of fluid particles, thereby increases skin friction and decreasing rate of heat transfer at the surface of a cylinder. Comparison with available results for some cases is found good agreements. Keywords: Ferrofluid, Homogeneous and heterogeneous reactions, Magnetic dipole, Stretching cylinder

Ischemic stroke is characterized by permanent or transient obstruction of blood flow, which initiates a cascading pathological process, starting from acute ATP loss and ionic imbalance to subsequent membrane depolarization, glutamate excitotoxicity, and calcium overload. These initial events are followed by neuroinflammation and oxidative stress, eventually causing neuronal neurosis and apoptosis. Complicated interplays exist between these steps happening across various stages, which not only represent the complicated nature of ischemic pathology but also warrant a detailed delineation of the underlying molecular mechanisms to develop better therapeutic options. In the present study, we examined the neuroprotective effects of polydatin against ischemic brain injury using a rat model of permanent middle cerebral artery occlusion (MCAO). Our results demonstrated that polydatin treatment reduced the infarction volume and mitigated the neurobehavioral deficits, sequentially rescued neuronal apoptosis. Ischemic stroke induced an elevation of neuroinflammation and reactive oxygen species, which could be attenuated by polydatin via the reduced activation of p38 mitogen-activated protein kinase and c-Jun N-terminal kinase. In addition, polydatin upregulated the endogenous antioxidant nuclear factor erythroid 2-related factor 2, heme oxygenase-1, the thioredoxin pathway, and eventually reversed ischemic-stroke-induced elevation of ROS and inflammation in ischemic cortical tissue. The diverse and broad actions of polydatin suggested that it could be a multiple targeting neuroprotective agent in ameliorating the detrimental effects of MCAO, such as neuroinflammation, oxidative stress, and neuronal apoptosis. As repetitive clinical trials of neuroprotectants targeting a single step of stroke pathological process have failed previously, our results suggested that a neuroprotective strategy of acting at different stages may be more advantageous to intervene in the vicious cycles in MCAO.

The future forecasting ability of machine learning (ML) makes ML a promising tool for predicting long-time quantum dissipative dynamics of open systems. In this article, we employ nonparametric ML algorithm (kernel ridge regression as a representative of the kernel methods) to study the quantum dissipative dynamics of the widely-used spin-boson (SB) model. Our ML model takes short-time dynamics as an input and is used for fast propagation of the long-time dynamics, greatly reducing the computational effort in comparison with the traditional approaches. Presented results show that the ML model performs well in both symmetric and asymmetric SB models. Our approach is not limited to SB model and can be extended to complex systems.

ABSTRACT: This study aimed to investigate factors influencing the adoption of improved cultivars (ICs) in peach production in Khyber Pakhtunkhwa province of Pakistan. A total of 270 respondents were randomly selected from the three different cultivated areas of Khyber Pakhtunkhwa, namely, Peshawar, Nowshera and Swat. Binary choice model was used in this study to categorise the ICs of peach farmers into adoption and non-adoption. The study identifies that socio-economic, institutional farm resources, and climatic factors are influencing the adoption of ICs of peach production. Results of the estimated model reveal that farmer’s age, education, household size, membership, cell phone, farm size, extension services and the role of the non-government organization have a positive effect on adoption of ICs. In addition, farmer’s experience, off-farm income, livestock and machinery ownership, credit access and inputs prices have a positive and significant impact on ICs adoption. Moreover, results of the logit model demonstrate that climatic related factors have a highly significant and positive impact on the adoption of ICs. These results suggested that institutional services should be strengthened to provide managerial and technical skills on ICs technology adoption and on time provision of financial services to enhance the productivity of peach farmers.

ABSTRACT: This paper analyses the long-run relationship between agricultural gross domestic product (GDP) and fruits production of Pakistan’s economy over a period of 1961-2015 by employing Johansen and autoregressive distributed lag (ARDL) modern econometric technique. Three fruits were selected namely mango, apple and peach in this study. Augmented Dickey-Fuller and Phillips-Perron tests were used to check the data stationarity and conclude that the series are integrated of order one. The Johansen approach was applied to check the joint evolution of all the variables for co-integration. The Johansen test suggests that a long-run co-integration exists between agricultural GDP and fruits production. Results of the ARDL model (Bound test) detect the existence of long-run relationship between agricultural GDP and fruits production. The coefficient of the short-run form of ARDL model reveals that all the fruits have a positive impact on agricultural GDP. Moreover, the coefficients of the long-run form of ARDL model have apositive and significant influence on agricultural GDP of Pakistan. These results suggest that a 1% increase in mango, apple and peach production will increase agricultural GDP by 0.06%, 0.03% and 0.03% respectively. Finally, forecast error variance decomposition and impulse response function results depict that mango, apple and peach production significantly contributes to agricultural GDP in the case of Pakistan.

In the present work we consider a numerical solution for laminar, incompressible, and steady oblique stagnation point flow of Cu − water nanofluid over a stretching/shrinking sheet with mass suction S . We make use of the Cattaneo−Christov heat flux model to develop the equation of energy and investigate the qualities of surface heat transfer. The governing flow and energy equations are modified into the ordinary differential equations by similarity method for reasonable change. The subsequent ordinary differential equations are illuminated numerically through the function bvp4c in MATLAB. The impact of different flow parameters for example thermal relaxation parameter, suction parameter, stretching/shrinking parameter, free stream parameter, and nanoparticles volume fraction on the skin friction coefficient, local Nusselt number, and streamlines are contemplated and exposed through graphs. It turns out that the lower branch solution for the skin friction coefficient becomes singular in shrinking area, although the upper branch solution is smooth in both stretching and shrinking domain. For oblique stagnation-point flow the streamlines pattern are not symmetric, and reversed phenomenon are detected close to the shrinking surface. Also, we observed that the free stream parameter changes the direction of the oncoming flow and controls the obliqueness of the flow. The existing work mostly includes heat and mass transfer as a mechanism for improving the heat transfer rate, which is the main objective of the authors.