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The rapid development of next-generation wireless networks has intensified the need for robust security measures, particularly in environments susceptible to eavesdropping. Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have emerged as a transformative technology, offering full-space coverage by manipulating electromagnetic wave propagation. However, the inherent flexibility of STAR-RIS introduces new vulnerabilities, making secure communication a significant challenge. To overcome these challenges, we propose a deep reinforcement learning (DRL) based secure and efficient beamforming optimization strategy, leveraging the deep deterministic policy gradient (DDPG) algorithm. By framing the problem as a Markov decision process (MDP), our approach enables the DDPG algorithm to learn optimal strategies for beamforming and transmission and reflection coefficients (TARCs) configurations. This method is specifically designed to optimize phase-shift coefficients within the STAR-RIS environment, effectively managing the coupled phase shifts and complex interactions that are critical for enhancing physical layer security (PLS). Through extensive simulations, we demonstrate that our DRL-based strategy not only outperforms traditional optimization techniques but also achieves real-time adaptive optimization, significantly improving both confidentiality and network efficiency. This research addresses key gaps in secure wireless communication and sets a new standard for future advancements in intelligent, adaptable network technologies.

Itaconic acid is an industrially crucial organic acid due to its broad range of applications. The main hurdle in itaconic acid production is the high cost of the substrate, i.e., pure glucose, required for the fermentation process. Pakistan annually produces about 1.7 to 1.8 million metric tonnes of mango fruit. Keeping this in view, the potential of a sugar-rich fruit by-product, i.e., mango peels, was analyzed to be used as a substrate for the biosynthesis of itaconic acid using Aspergillus niger by submerged fermentation. Different physicochemical parameters (incubation period, temperature, agitation rate, inoculum size, and pH) were optimized using the central composite design (CCD) design of response surface methodology (RSM). The maximum production of itaconic acid, i.e., 4.6 g/L, was analyzed using 10% mango peels w/v (water hydrolysate), 3 mL inoculum volume after 5 days of fermentation period at pH 3, and a temperature of 32 °C when the media was kept at a 200-rpm agitation speed. The itaconic acid extraction from mango peels was done using the solvent extraction method using n-butanol. The identification and quantification of itaconic acid produced in the study were done using the Fourier Transform Infrared Spectroscopy (FTIR) spectrum and the High-Performance Liquid Chromatography (HPLC) method. According to HPLC analysis, 98.74% purity of itaconic acid was obtained in the research. Hence, it is concluded from the results that sugar-rich mango peels can act as a promising substrate for the biosynthesis of itaconic acid. Further conditions can be optimized at the bioreactor level to meet industrial requirements.

Background: High-velocity trauma causes proximal tibial fractures, which have extensive articular involvement, numerous displaced condylar fracture lines, and soft tissue injuries. Complications are possible with the standard treatment, which is open reduction and internal fixation by a dual midline incision and dual plating. Nevertheless, a single midline incision is sufficient for the procedure. That's why we decidedto conduct this intervention. Objective: To evaluate the effectiveness and outcomes of proximal tibial fracture fixation using a double plating technique through a single midline incision. Study type,settings& duration:This quasi-experimental study was conducted at Department of Orthopaedic Surgery, PAEC Hospital,Islamabad, from March 2020 to March 2023. Methodology:Patients, aged 16 or above were enrolled who presented with proximal tibial fracture and underwent dual plating by giving single midline incision. Patients were followed-up and outcomes including infection, union, needed bone grafting, additional split skin graft and flap surgery and others were noted. SPSS version 25 was used to analyse the data. Results:Total 100 patients were enrolled in the study with the mean age of 44.71±17.88 years. There were 58 (58.0%) male patients and 42 (42.0%) female patients. The mean duration of surgery was 74.11±8.91 minutes. Out of 100 patients, 57 (57.0%) patients had excellent functional outcome, 34 (34.0%) patients had good fictional outcome, while 7 (7.0%) patients had fair functional outcome and 2 (2.0%) had poor functional outcome. Out of 100 patients, union achieved in 81 (81.0%) cases. Infection occurred in 2 (2.0%) cases, Knee stiffness in 12 (12.0%) cases, body graft was applied in 1 (1.0%) cases and skin graft and flap surgery was done in 1 (1.0%) cases and deep venous thrombosis in 2 (2.0%) cases. Conclusion:Dual plating for proximal tibial fracture via single midline is an appropriate method and is more than 90% successful in achieving good to excellent results and can replace other techniques which need more incision and increases the risk of infection.

The utilization of solar radiation by converting them into thermal energy is discussed in this paper. Nanoparticles improve the ability of heat transfer therefore, it is beneficial in the use of solar thermal systems and energy storage devices. The novel mixture of nanoparticles Graphene and Polythiophene in base fluid, which has high thermodynamic properties for the improvement of thermal effect with electromagnetic effect by using Maxwell fluid model is discussed. Polyvinyl alcohol water is taken as base fluid flowing through a moveable flat plat. The governing partial differential equations are transformed into ordinary differential equations. The semi-analytical technique, homotopy analysis method is used to obtain the solution of the ordinary differential equations. The velocity is enhanced with magnetic and electric field strength. The increase of the Prandtl number, Eckert number and chemical reaction parameter, exceeds the thermal effect which produces more entropy generation and heat enhancement. The results show that the hybrid nanofluid with this Novel mixture is highly thermodynamic with higher entropy and rapid thermal augmentation which can be used in energy production and energy storage devices. A novel intelligent numerical computing technique multi-layer perceptron with feed-forward back-propagation, an artificial neural networking method with the Levenberg-Marquard algorithm is used in this model. The data is gathered for the neural networking method training, validation, and testing. The efficiency of the model is obtained and mean square error is obtained by artificial neural networking.

In the present study, a double-hybrid nanofluid's Darcy-Forchheimer flow behavior over a Riga plate with the presence of radiation and heat source impacts is examined. Two distinct nanoparticles as Silicon Carbide (SIC), and Graphite (C) with a base fluid of Ethylene glycol (C2H6O2) are taken into consideration. The model of Cattaneo-Christov heat flux is discussed in the energy and mass equation. The symmetry variables are utilized to remodel the boundary conditions and governing flow models into non-linear ordinary differential equations. The redesigned ordinary differential equations are numerically computed using bvp4c in MATLAB. Graphical representations are used to study how distinct parameters create variations in the concentration, temperature, and velocity profile. The present study shows that the velocity profile decreases with an upsurge in the Hartmann number. The current analysis determined that the fluid velocity declines and the Forchhiemer number (Fr) increases. The fluid concentration increases when temperature temperature-dependent coefficient (B) rises. The significance of emerging parameters on skin friction, Nusselt number, and Sherwood number are computed in tabular shape. Artificial neural networking (ANN) is applied to optimize physical quantities of data, by training, validation, and testing, to check the accuracy of the data.

In recent years, deep learning (DL) has become indispensable in medical image segmentation (MIS), proven by numerous studies showcasing its effectiveness. This paper presents two significant original contributions and conducts a comprehensive thematic evaluation of DL approaches in MIS. Diverging from traditional surveys that categorize DL literature into multiple groups and analyze individual works within each category, we adopt a multi-level classification approach. This method organizes existing literature from a broad overview to finer details. Furthermore, to address the inherent challenges of this field, we introduce an innovative method called L-UNet. The L-UNet method strategically utilizes filters with reduced parameters to construct a U-shaped convolutional neural network. This not only demonstrates impressive performance but also alleviates complexities associated with architectural parameters. Extensive evaluation across multiple datasets, including lung image segmentation, PH2 skin cancer segmentation, liver image segmentation, Chest X-ray segmentation, and COVID-19 chest X-ray segmentation, highlights the exceptional performance of L-UNet. Notably, experimental results showcase competitive accuracies, achieving figures such as 99.15% for lung image segmentation, 99.53% for liver image segmentation, 95.45% for PH2 skin cancer segmentation, and 98.99% for Chest X-ray segmentation. Moreover, for COVID-19 chest X-ray segmentation, a commendable accuracy of 93.45% is observed. The simplification of L-UNet renders it a highly practical choice for deployment in resource-limited environments or real-world scenarios necessitating faster performance and enhanced efficiency.

The escalating demands of advanced automotive technologies exert considerable pressure on modern vehicles’ computational capabilities. This scenario underscores the critical need for vehicular edge computing (VEC) networks, which leverage 5G/6G communications to facilitate computational offloading. However, providing seamless access to these services while simultaneously adhering to stringent latency and security requirements presents a formidable challenge. The advent of reconfigurable intelligent surfaces (RIS) heralds a new era of possibilities, which includes enhancing connectivity, boosting data transmission rates, consequently reducing delay, and improving the physical layer security of communication channels. This paper delves into the utilization of zero-energy RIS (ze-RIS) in the context of vehicular computation offloading. Our primary goal is to ensure secure access while optimizing operational efficiency in compliance with various task-related and environmental requirements. The ze-RIS-assisted secure task efficient offloading (DRSTO) scheme is a novel deep reinforcement learning (DRL) framework that cleverly switches communication connections to optimize task offloading efficiency and security thereby resolving this issue. At its core, our assessment strategy revolves around the DRSTO model’s secrecy and efficiency factor which serve as both a performance measure and a reward function. Time efficiency and rate of confidentiality are used to evaluate this aspect which provides a thorough evaluation of the scheme’s success. Extensive testing and comparison have shown that the DRSTO scheme’s efficiency factor can be significantly increased, from 6.05 to 18.10. In addition, the rate of job success has increased dramatically, from 2.12% to 4.63%. When compared to other models that were evaluated, the DRSTO scheme consistently better on several metrics including reward, time frames per step (TFPS) ratio and DRL characteristics.

The deployment of 5G has exposed capacity constraints in realizing the key vision of the Internet of Everything (IoE). Therefore, the researchers are exploring potentials of Digital Twin Network (DTN) in wireless networks. DTN is a novel technology to create virtual replicas of physical environment for testing, optimizing, and managing wireless networks. The integration of Artificial Intelligence (AI) and DTN appears to be a promising approach to address communication systems by providing an efficient environment for testing and improving AI models before deployment in real networks for effective network management, optimal resource allocation, and precise behavior prediction. Therefore, AI-enabled DTN in 6G represents a compelling avenue to address multifaceted challenges faced by wireless networks. In this comprehensive work, we offer a holistic survey that delves into the state-of-the-art approaches for AI-enabled DTNs in 6G. Firstly, we discuss the evolution of wireless networks and concept of AI-enabled DTN in 6G. Secondly, we discuss the role of AI-enabled DTN in 6G and driving advancements in fundamental components of 6G including resource allocation, caching, data offloading, and data security. Thirdly, we conduct a detailed discussion on key enabling technologies for realizing the capabilities of AI-enabled DTN in 6G. Fourthly, several applications of AI-enabled DTN in 6G are discussed for the practical relevance and significance in various industries such as smart cities, healthcare, and transportation etc. Finally, we provide lessons learned and highlight existing challenges and research directions to embark on further research efforts in the realm of AI-enabled DTN in 6G.

Automated Machine Learning (AutoML) enhances productivity and efficiency by automating the entire process of machine learning model development, from data preprocessing to model deployment. These tools are accessible to users with varying levels of expertise and enable efficient, scalable, and accurate classification across different applications. This paper evaluates two popular AutoML tools, the Tree-Based Pipeline Optimization Tool (TPOT) version 0.10.2 and Konstanz Information Miner (KNIME) version 5.2.5, comparing their performance in a classification task. Specifically, this work analyzes autism spectrum disorder (ASD) detection in toddlers as a use case. The dataset for ASD detection was collected from various rehabilitation centers in Pakistan. TPOT and KNIME were applied to the ASD dataset, with TPOT achieving an accuracy of 85.23% and KNIME achieving 83.89%. Evaluation metrics such as precision, recall, and F1-score validated the reliability of the models. After selecting the best models with optimal accuracy, the most important features for ASD detection were identified using these AutoML tools. The tools optimized the feature selection process and significantly reduced diagnosis time. This study demonstrates the potential of AutoML tools and feature selection techniques to improve early ASD detection and outcomes for affected children and their families.

The surge of data traffic in wireless networks necessitates the provision of high-quality data services to meet users’ satisfaction levels. However, the limited spectral resources of the current network infrastructures and inherent challenges of achieving reliable line-of-sight (LoS) probability for ground users (GUs) in urban environments often lead to disruption to communication services delivery. This paper aims to address the challenges of frequent handover (HO) failures and disrupted communication services for mobile GUs by deploying an unmanned aerial vehicle as a flying base station (UAV-BS) in heterogeneous networks (HetNets). A channel model is investigated that considers both LoS and non-line-of-sight (NLoS) paths in three-dimensional (3D) air-to-ground (A2G) links using a detailed mathematical model with urban infrastructure parameters like building density and heights. In addition, a reinforcement learning (RL) algorithm is presented in this work to optimize UAV trajectories in response to the dynamic mobility of GUs for enhancing LoS connections. The proposed algorithm dynamically adjusts the UAV positions and enhances transmission channels by identifying both LoS and NLoS paths. Simulation results demonstrate that the proposed algorithm outperforms existing benchmarks through learning-based adaptive control of UAVs’ mobility, ensuring ubiquitous network connectivity for GUs and reducing HO failures in HetNets.

Aim: This study aimed to compare the tight rope technique and clavicular hook plate for the treatment of acute acromioclavicular joint dislocations. Study type, settings & duration: This randomized control trial was conducted at PAEC General Hospital, Islamabad from January 2019 to December 2021. Methodology: A total of 50 patients were selected. Patients have been divided into two groups Group A patients underwent the Tight-rope technique and Group B patients underwent the clavicular hook technique. Patients presenting with acute acromioclavicular joint dislocations of Rockwood type III and V, both males and females were included aged 18 to 50. Patients presenting with type I and type II injuries, AC joint arthritis, and fractures of the distal clavicle were excluded. Both techniques were compared in terms of VAS score and CMS score postoperatively. Results: The mean postoperative VAS pain score in group A was 1.76±0.77 and 2.32±1.03 in group B. The mean CMS score in group A was 88.92±2.3 and 84.6±3.4 in group B. The outcomes in Group A showed significant improvement as compared to group B. Conclusion: The Tight-rope technique showed significantly better outcomes as compared to the clavicular hook plate technique.

Abstract Herein, we synthesized Cr3+/Ln3+ (Er3+, Tm3+)‐codoped rare earth‐based Cs2NaScCl6 double perovskite, and the near‐infrared emission of Ln3+ can be excited by visible light through the energy transfer (ET) from Cr3+ to Ln3+. Moreover, there are two independent emission bands, which stems from 4T2 → 4A2 transition of Cr3+ (970 nm) and f‐f transition of Ln3+ (1542 nm for Er3+ and 1220 nm for Tm3+), respectively. Particularly, both compounds have ultra‐high photoluminescence quantum yield (PLQY) of 60% for 10%Cr3+/6%Er3+‐codoped Cs2NaScCl6 (Er3+ emission: ∼26%) and 68% for 10%Cr3+/4.5%Tm3+‐codoped Cs2NaScCl6 (Tm3+ emission: ∼56%), which can be attributed to the ultra‐high ET efficiency from Cr3+ to Ln3+ and the similar ionic activity of Sc3+ and Ln3+ allowing more dopants enter the host lattice. Considering the excellent stability of the samples, we demonstrated Cr3+/Tm3+‐codoped Cs2NaScCl6 in the applications of near‐infrared imaging and night vision. Finally, we reported 10%Cr3+/4.5%Tm3+/9%Er3+‐tridoped Cs2NaScCl6 and further applied it for optical thermometry.

Concrete is weak in tension, causing brittle failure without warning. Fiber is one of the simplest techniques to increase tensile strain. Several kinds of fibers (synthetic) are available such as steel fiber, glass fiber, and carbon fiber. However, these fibers are expensive and cannot be easily accessible. Researchers use agricultural fiber in concrete instead of synthetic fibers to offset this deficiency. Although, several studies have shown that agricultural fiber may be utilized to increase concrete tensile strength. However, a details review is required which combines all relevant information and the reader can evaluate the benefits of agricultural fiber. Therefore, this review focus on a comprehensive and up-to-date overview of the impact of agricultural fiber on concrete slump flow, mechanical quality, and durability. Furthermore, scanning electronic microscopy, enhancement methods, and agricultural fiber-reinforced concrete (AFRC) applications are also reviewed. Five different types of agricultural fiber including coconut, jute, banana, rice straw, and hemp fibers were selected. According to the findings, agricultural fiber increased concrete’s mechanical and durability qualities while comparably decreasing the slump. The optimum dose is essential as the higher dose adversely affects mechanical performance. The typical optimum amount varies from 1% to 2% by weight/volume of the binder. Among various types of agricultural fiber, coconut fiber is super performance. Less research is carried out on hemp, straw ash, and banana fibers than on coconut and jute fibers.

Retired electric vehicle (EV) batteries are reused in second-life energy storage applications. However, the overall performance of repurposed energy storage systems (ESSs) is limited by the variability in the individual batteries used. Therefore, battery grading is required for the optimal performance of ESSs. Electrochemical impedance spectroscopy (EIS)-based evaluation of battery aging is a promising way to grade lithium-ion batteries. However, it is not practical to measure the impedance of mass-retired batteries due to their high complexity and slowness. In this paper, a broadband multi-sine binary signal (MSBS) perturbation integrated with a multichannel EIS system is presented to measure the impedance spectra for the high-speed aging evaluation of lithium-ion batteries or modules. The measurement speed is multiple times higher than that of the conventional EIS. The broadband MSBS is validated with a reference sinusoidal sweep perturbation, and the corresponding root-mean-square error (RMSE) analysis is performed. Moreover, the accuracy of the presented multichannel EIS system is validated by impedance spectra measurements of Samsung INR18650-29E batteries and compared with those measured using a commercial EIS instrument. A chi-squared error under 0.641% is obtained for all channels. The non-linearity of batteries has a significant impact on the quality of impedance spectra. Therefore, Kronig–Kramer (KK) transform validation is also performed.

Rapid urban growth necessitates focused attention regarding its policy and governance to ensure affordable housing, transparent and efficient real-world systems, reduce social inequalities, and promote sustainable development. This study delves into the semantics and ontology for developing a Land Administration Domain Model (LADM) profile in the context of Pakistan’s Land Administration Systems (LASs), which currently face issues due to manual record-keeping, lack of transparency, frauds, and disintegration. Establishing a baseline through Record of Rights (RoR) and Property Information Report (PIR), alongside surveying and mapping procedures defined by laws and rules, forms the foundation for LADM profile development. This study explores the transition from manual LAS to 2D/3D representation, using LADM as a conceptual guideline. The LADM profile’s three key packages—PK_Party, PK_Administrative, and PK_SpatialUnit—a sub-package, and external classes are examined, with proposals for digitalisation and modernisation. Additionally, the study includes expert consultation, and highlights the significant support that the LADM implementation offers to achieve Sustainable Development Goals (SDGs) in Pakistan. In conclusion, the study underscores the need for a comprehensive and inclusive approach to address organisational overlaps and ambiguities within LAS, positioning PK LADM as a transformative force for sustainable urban LAS in Pakistan, aligning with broader SDGs. Recommendations include exploring realistic land valuation, integrated ownership and location verification systems, addressing historical survey data challenges, and promoting wider stakeholder adoption for sustainable 2D/3D urban LAS using LADM and its edition II as a way forward towards the creation of a smart city and digital twin.

Wireless Body Area Networks (WBANs) healthcare application is a group of heterogeneous-natured Bio-Medical Sensor Nodes (BMSNs) used to sense and monitor a patient’s vital signs, in the long run, under natural physiological conditions, without affecting the regular activities. Heterogeneous-natured BMSNs demand prioritized channel access during contention access phases to transmit data or allocate time slots in contention-free phases. Thus, contention-based prioritization of heterogeneous traffic is required in WBANs. Therefore, many researchers have considered and presented various traffic-priority Medium Access Control (MAC) protocols. This paper provides a thematic review of contention-based traffic priority MAC protocols in WBANs. Slotted Carrier Sense Multiple Access / Collision Avoidance (CSMA/CA) scheme in beacon-enabled modes of IEEE 802.15.4 and IEEE 802.15.6 MACs is also analyzed regarding contention-based traffic prioritization. In addition, a critical and comparative analysis of existing contention-based traffic priority MAC protocols is obtained. A hierarchy of contention-based traffic priority MAC protocols is proposed. This review on contention-based traffic priority MAC protocols in WBANs extends the body of knowledge. This paper, therefore, serves as the first and enhances the concept of contention-based traffic prioritization at the MAC layer in WBANs. We have confidence that this paper will stimulate a better way of solving the contention-based traffic prioritization problem.

Article highlights Compressive, tensile, and flexural capacity waste rubber tires-based concrete were discussed. The durability properties of waste rubber tires-based concrete were discussed. The suitability of waste rubber tires-based concrete for roadside barriers were discussed.

Data caching is a promising technique to alleviate the data traffic burden from the backhaul and minimize data access delay. However, the cache capacity constraint poses a significant challenge to obtaining content through the cache resource that degrades the caching performance. In this paper, we propose a novel two-tier caching mechanism for data caching on mobile user equipment (UE) and the small base station (SBS) level in ultra-dense 6G heterogeneous networks for reducing data access failure via cache resources. The two-tier caching enables users to retrieve their desired content from cache resources through device-to-device (D2D) communications from neighboring users or the serving SBS. The cache-enabled UE exploits millimeter-wave (mmWave)-based D2D communications, utilizing line-of-sight (LoS) links for high-speed data transmission to content-demanding mobile UE within a limited connection time. In the event of D2D communication failures, a dual-mode hybrid system, combining mmWave and microwave μWave technologies, is utilized to ensure effective data transmission between the SBS and UE to fulfill users’ data demands. In the proposed framework. the data transmission speed is optimized through mmWave signals in line-of-sight (LoS) conditions. In non-LoS scenarios, the system switches to μWave mode for obstacle-penetrating signal transmission. Subsequently, we propose a reinforcement learning (RL) approach to optimize cache decisions through the approximation of the Q action-value function. The proposed technique undergoes iterative learning, adapting to dynamic network conditions to enhance the content placement policy and minimize delay. Extensive simulations demonstrate the efficiency of our proposed approach in significantly reducing network delay compared with benchmark schemes.

Data preprocessing is crucial in the Machine Learning pipeline because the models’ learning ability directly affects the quality of data and the underlying information acquired from this stage. Nevertheless, surprisingly, there are many alternatives for each transformation task, which makes an inexperienced user overwhelmed. A simple Python-based Auto-preprocessing architecture for Automated Machine Learning is developed to offer automated, interactive, and data-driven support to help the users perform data preprocessing tasks efficiently. The suggested method provides valuable insights into a dataset and can handle standard data preprocessing tasks adeptly. Initially, it detects the data problem and presents it to the end-user using compelling visualizations. Then, it recommends the most effective data cleaning and preparation method to the user after evaluating the state-of-the-art candidate techniques. For evaluation, the proposed architecture is employed on ten different and diverse datasets for automatic data preprocessing before passing it to an ML algorithm. The results are then compared with the results generated by the same ML algorithm but implemented on manually preprocessed data. The results have shown that not only did this approach make the whole process uncomplicated and facile, but it was also able to improve the performance of the model significantly.

The urban land administration system (LAS) of any country serves as a key pillar for good governance, resource planning, service delivery, infrastructure development, and revenue collection. To reform their LASs, countries need a thorough understanding of their existing context and global relevance. The goal of this paper is to examine the status and challenges of urban LASs in Pakistan using the United Nations Framework for Effective Land Administration (FELA). The exploratory case study method used in the paper employs a mixed approach, which includes FELA-based questionnaire surveys, group discussions, and desk reviews. A total of 525 urban LAS stakeholders, including owner-buyers, real estate agents, bankers, lawyers, and LAS organizations, participated in the activity. The results show that more than half of the stakeholders are not satisfied with existing urban LASs, their governance and accountability, laws, and policies. Corruption is prevalent mostly in government organizations. Fraud and joint ownership are the most common sources of dispute, with 67 percent of the respondents stating that the cases take more than two years to resolve in court. The financial aspect of urban LASs is suffering due to property undervaluation and low revenue collection. Manual data and record keeping in LASs further complicate the system, with 87 percent of all respondents interested in innovating the urban LAS using modern technologies. Furthermore, 92 percent of all respondents expressed the need to standardize the existing LASs. There is a lack of capacity and skills, and 89 percent of organizations’ respondents believe that human resources skilled in Geographical Information Systems (GIS) and Remote Sensing (RS) can improve the efficiency of urban LASs. There is a lack of partnership among LAS organizations and a gap in the accessibility of LAS-related quality information. The country’s vision of building smart cities can be realized through LAS standardization and 3D and GIS innovation.

Objective: To evaluate the severity of depression and anxiety among patients of burns and correlate depression and anxiety with areas of body involved in burns. Study Design: A cross-sectional study. Place and Duration of Study: The departments of Psychiatry and department of Plastic Surgery, Combined Military Hospital Multan, from Oct 2019 to Mar 2020. Methodology: Through consecutive sampling, 56 patients of burns reporting to burns unit were assessed for anxiety and/ or depression based on the diagnostic criteria of International Classification of Diseases version 10. Symptom severity was assessed using Beck Depressive Inventory for depression and Beck Anxiety Inventory for anxiety. Descriptive statistics like mean with standard deviation was calculated for age. Frequency along with percentages was calculated for sociodemographic variables, Anxiety and depression. Results: Depression was present among 30 (53%) of participants out of which 16 (53.5%) had mild, 11 (37.9%) had moderate while only 3 (12.5%) had severe depression. Anxiety was seen among 50 (89%) of participants out of which sixteen (32.1%) had mild, 26 (51.7%) had moderate while 8 (16%) had severe anxiety. Significant correlation existed among the major area involved in burn with both anxiety and depression (p

This research aimed to determine the dynamic endogeneity nexus among energy consumption (EC), financial development (FD), foreign direct investment (FDI), globalization (GI), and urbanization (URBAN). The study used 64 countries’ annual panel data on “the Belt and Road Initiative (BRI)” from 2009 to 2019. Moreover, it employed a two-step system GMM, robust and results, that indicates financial development and urbanization are positively correlated with energy consumption, suggesting that these two factors raise the energy demand. Contrastingly, globalization negatively impacts energy demand, implying that global connectivity is essential for BRI countries. Foreign direct investment (FDI) has a positive but insignificant connection with energy consumption. Additionally, the Granger causality test was employed to explore the causal association among the variables, and outcomes reveal a bidirectional causal connection between FD and energy consumption. The study also suggests sustainable energy policy implications, which will be helpful to policymakers and governments for ensuring a balanced, sustainable growth.JEL Code: P48; P25; Q4; F6; G00; E2

Objectives: To determine the frequency and severity of psychotic symptoms in users of methamphetamine. Study Design: Cross sectional analytic study. Place and Duration of Study: Combined Military Hospital, Quetta, from Aug 2018 to Jan 2019. Methodology: Sixteen to sixty years old patients already using methamphetamine for at least 1 month were included in this study. Purposive non-probability sampling technique was used. A total of 134 patients of methamphetamine use were studied after consent, using Brief Psychiatric Rating Scale (BPRS). Data was entered in a specially designed semi structured proforma. The data was analysed by using One Way Anova Calculator. Results: Psychotic symptoms were found in 42 (31.3%) enrolled patients in our study. Out of 134 patients 63 (47%) were >25 years and 71 (53%) were < 25 years of age. Conclusion: The results of our study showed that a significant proportion of methamphetamine users had psychotic symptoms. A strategy should be made to promote awareness in population about harmful consequences of methamphetamine use, timely identification of psychosis in its users and their rehabilitation.

Deep learning is one of the most unexpected machine learning techniques which is being used in many applications like image classification, image analysis, clinical archives and object recognition. With an extensive utilization of digital images as information in the hospitals, the archives of medical images are growing exponentially. Digital images play a vigorous role in predicting the patient disease intensity and there are vast applications of medical images in diagnosis and investigation. Due to recent developments in imaging technology, classifying medical images in an automatic way is an open research problem for researchers of computer vision. For classifying the medical images according to their relevant classes a most suitable classifier is most important. Image classification is beneficial to predict the appropriate class or category of unknown images. The less discriminating ability and domain-specific categorization are the main drawbacks of low-level features. A semantic gap that exists between features of low-level as machine understanding and features of human understanding as high-level perception. In this research, a novel image representation method is proposed where the algorithm is trained for classifying medical images by deep learning technique. A pre-trained deep convolution neural network method with the fine-tuned approach is applied to the last three layers of deep neural network. The results of the experiment exhibit that our method is best suited to classify various medical images for various body organs. In this manner, data can sum up to other medical classification applications which supports radiologist's efforts for improving diagnosis.

Human-robot interaction is inevitable due to the increase of the autonomous intelligent machines in the human vicinity. The autonomous machines should synchronize with the interacting human. The key factor for synchronized Human-Robot Interaction (HRI) is a human intention. The interacting machine must have the clue about the intention of the interacting human for a useful interaction. In this review paper, recently proposed intention-based approaches for human-robot interaction are discussed. The approaches are categorized concerning different aspects, e.g., application area, specialized and generalized estimation techniques, etc. The review categorized the recently proposed approaches into five categories. The categorization is mainly based on the application areas of the intention recognition approaches. The type of approaches includes general and application-specific approaches. The application areas include synchronized physical human-assistance, human-synchronized vehicles, etc. The vehicles are synchronized with the driver and the pedestrian. The study highlighted the currently active as well as the dormant areas of the intention-based human-robot interaction and indicated new directions.

White blood cells (WBCs) protect human body against different types of infections including fungal, parasitic, viral, and bacterial. The detection of abnormal regions in WBCs is a difficult task. Therefore a method is proposed for the localization of WBCs based on YOLOv2-Nucleus-Cytoplasm, which contains darkNet-19 as a basenetwork of the YOLOv2 model. In this model features are extracted from LeakyReLU-18 of darkNet-19 and supplied as an input to the YOLOv2 model. The YOLOv2-Nucleus-Cytoplasm model localizes and classifies the WBCs with maximum score labels. It also localize the WBCs into the blast and non-blast cells. After localization, the bag-of-features are extracted and optimized by using particle swarm optimization(PSO). The improved feature vector is fed to classifiers i.e., optimized naïve Bayes (O-NB) & optimized discriminant analysis (O-DA) for WBCs classification. The experiments are performed on LISC, ALL-IDB1, and ALL-IDB2 datasets.

Skin cancer is developed due to abnormal cell growth. These cells are grown rapidly and destroy the normal skin cells. However, it's curable at an initial stage to reduce the patient's mortality rate. In this article, the method is proposed for localization, segmentation and classification of the skin lesion at an early stage. The proposed method contains three phases. In phase I, different types of the skin lesion are localized using tinyYOLOv2 model in which open neural network (ONNX) and squeeze Net model are used as a backbone. The features are extracted from depthconcat7 layer of squeeze Net and passed as an input to the tinyYOLOv2. The propose model accurately localize the affected part of the skin. In Phase II, 13-layer 3D-semantic segmentation model (01 input, 04 convolutional, 03 batch-normalization, 03 ReLU, softmax and pixel classification) is used for segmentation. In the proposed segmentation model, pixel classification layer is used for computing the overlap region between the segmented and ground truth images. Later in Phase III, extract deep features using ResNet-18 model and optimized features are selected using ant colony optimization (ACO) method. The optimized features vector is passed to the classifiers such as optimized (O)-SVM and O-NB. The proposed method is evaluated on the top MICCAI ISIC challenging 2017, 2018 and 2019 datasets. The proposed method accurately localized, segmented and classified the skin lesion at an early stage.

A minimal entropy martingale measure problem is studied to investigate risk-neutral densities and interest rate modelling. Hunt & Devolder focused on the method of Shannon minimal entropy martingale measure to select the best measure among all the equivalent martingale measures and, proposed a generalization of the Ho & Lee model in the semi-Markov regime-switching framework [1]. We formulate and solve the optimization problem of Hunt & Devolder for deriving risk-neutral densities using a new non-extensive entropy measure [2]. We use the Lambert function and a new type of approach to obtain results without depending on stochastic calculus techniques.

Diabetes is a chronic disease, if not treated in time may lead to many complications including diabetic foot ulcers (DFU). DFU is a dangerous disease, it needs regular treatment otherwise it may lead towards foot amputation. The DFU is classified into two categories such as infection (bacteria) and the ischaemia (inadequate supply of the blood). The DFU detection at an initial phase is a tough procedure. Therefore in this research work 16 layers convolutional neural network (CNN) for example 01 input, 03 convolutional, 03 batch-normalization, 01 average pooling, 01 skips convolutional, 03 ReLU, 01 add (element-wise addition of two inputs), fully connected, softmax and classification output layers for classification and YOLOv2-DFU for localization of infection/ischaemia models are proposed. In the classification phase, deep features are extracted and supplied to the number of classifiers such as KNN, DT, Ensemble, softmax, and NB to analyze the classification results for the selection of best classifiers. After the experimentation, we observed that DT and softmax achieved consistent results for the detection of ischaemia/infection in all performance metrics such as sensitivity, specificity, and accuracy as compared with other classifiers. In addition, after the classification, the Gradient-weighted class activation mapping (Grad-Cam) model is used to visualize the high-level features of the infected region for better understanding. The classified images are passed to the YOLOv2-DFU network for infected region localization. The Shuffle network is utilized as a mainstay of the YOLOv2 model in which bottleneck extracted features through ReLU node-199 layer and passed to the YOLOv2 model. The proposed method is validated on the newly developed DFU-Part (B) dataset and the results are compared with the latest published work using the same dataset.

Lithium-ion batteries are gaining more attention due to the rapid growth of electrical vehicles (EVs). Additionally, the industry is putting a lot of effort into reusing EV batteries in energy storage systems (ESS). The optimal performance of the repurposed battery system is highly dependent on the individual batteries used in it. These batteries need to be similar in terms of battery capacity, state of health (SOH), and remaining useful life (RUL). Therefore, battery grading techniques are expected to play a vital role in this newly emerging industry. There are various methods suggested to evaluate the aging of a battery in terms of capacity, SOH, and RUL. The use of ohmic resistance is one approach, as it varies with the aging of the battery. In order to measure the ohmic resistance, electrochemical impedance spectroscopy (EIS) is used, followed by the curve fitting procedures. In this research a novel method is suggested to measure the ohmic resistance without performing the broadband conventional EIS test and the curve fitting. Since the battery is perturbed for a specified frequency band (1 kHz to 100 Hz) using the linearly distributed phased multi-sine signal, only 1 sec perturbation is required, and the ohmic resistance can be directly calculated by using two impedance values. Thus, the measurement speed is several times faster than that of the conventional EIS methods. Hence, it is a suitable and convenient technique for the mass testing of the batteries. The accuracy and validity of the proposed technique are verified by testing three types of batteries. The percentage difference in the measured ohmic resistance value between the conventional and the proposed technique is less than 0.15% for all the batteries tested.

The aim of this paper consists in developing an entropy-based approach to risk assessment for actuarial models involving truncated and censored random variables by using the Tsallis entropy measure. The effect of some partial insurance models, such as inflation, truncation and censoring from above and truncation and censoring from below upon the entropy of losses is investigated in this framework. Analytic expressions for the per-payment and per-loss entropies are obtained, and the relationship between these entropies are studied. The Tsallis entropy of losses of the right-truncated loss random variable corresponding to the per-loss risk model with a deductible d and a policy limit u is computed for the exponential, Weibull, χ2 or Gamma distribution. In this context, the properties of the resulting entropies, such as the residual loss entropy and the past loss entropy, are studied as a result of using a deductible and a policy limit, respectively. Relationships between these entropy measures are derived, and the combined effect of a deductible and a policy limit is also analyzed. By investigating residual and past entropies for survival models, the entropies of losses corresponding to the proportional hazard and proportional reversed hazard models are derived. The Tsallis entropy approach for actuarial models involving truncated and censored random variables is new and more realistic, since it allows a greater degree of flexibility and improves the modeling accuracy.

This paper aims to empirically examine long memory and bi-directional information flow between estimated volatilities of highly volatile time series datasets of five cryptocurrencies. We propose the employment of Garman and Klass (GK), Parkinson’s, Rogers and Satchell (RS), and Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC) volatility estimators to estimate cryptocurrencies’ volatilities. The study applies methods such as mutual information, transfer entropy (TE), effective transfer entropy (ETE), and Rényi transfer entropy (RTE) to quantify the information flow between estimated volatilities. Additionally, Hurst exponent computations examine the existence of long memory in log returns and OHLC volatilities based on simple R/S, corrected R/S, empirical, corrected empirical, and theoretical methods. Our results confirm the long-run dependence and non-linear behavior of all cryptocurrency’s log returns and volatilities. In our analysis, TE and ETE estimates are statistically significant for all OHLC estimates. We report the highest information flow from BTC to LTC volatility (RS). Similarly, BNB and XRP share the most prominent information flow between volatilities estimated by GK, Parkinson’s, and GK-YZ. The study presents the practicable addition of OHLC volatility estimators for quantifying the information flow and provides an additional choice to compare with other volatility estimators, such as stochastic volatility models.

BACKGROUND & OBJECTIVE: Benign prostatic hyperplasia (BPH) is a prevalent cause of voiding problems in older males and is most commonly associated with acute urological complications, most important of which is acute urinary retention (AUR). α1- antagonists are the treatment of choice for the management of patients with BPH. Our objective was to evaluate the efficacy of tamsulosin for the treatment of patients with AUR due to BPH in terms of trial without catheter (TWOC). METHODOLOGY: This randomized control trial was conducted in a Urology Section, Department of Surgery, SIUT Karachi, from 21-12-2016 to 20-06-2017. Our study included 136 patients who presented AUR due to BPH as per selection criteria. Patients were randomized into two groups. Group A patients received tamsulosin (0.4 mg), and group B patients received placebo drug immediately after insertion of Foley catheter. The success of TWOC was evaluated after 3 days of medical treatment. Statistical analysis was carried out using SPSS v20.0. RESULTS: The mean age of the study patients was 59.45 + 8.05 years. The mean duration of BPH disease was 15.39 + 6.84 months. There were 33 (48.5%) patients in the tamsulosin group in which TWOC was successful and only 22 (32.4%) patients in placebo group in which TWOC was successful (p-value 0.04). There was no effect of age and duration of BPH disease on the outcome of this study. CONCLUSION: Tamsulosin is effective for the treatment of patients with AUR due to BPH.

Corona virus infection (Covid-19) originated from Chinese city of Wuhan, Hubei in late December 2019 . While the disease is thought to be highly contagious, WHO declared it as public health emergency on 30th January 2020. Pakistan reported its first corona virus infection on 26th February 2020. Initially infection was limited to those travelling from endemic areas but on 13th March 2020, Pakistan reported its first corona virus infection from local transmission. With limited testing ability and lack of uniform national corona virus response strategy, Pakistan reported total of 38,799 confirmed corona virus cases by mid May 2020. Confirmed cases reached 905,852 on 24th May 2021. Lack of definitive guidelines for health care workers (HCWs) and poor existing hospital strategies in response to COVID-19 pandemic increased the agitation and stress level among HCWs. More than half of the doctors working in our orthopedic department suffered from COVID-19. In this COVID-19 pandemic, orthopedic community should continuously evolve taking into consideration resource optimization, social distancing, innovation and best practices to minimize COVID-19 infection among health care workers who are working day and night to combat COVID- 19 in already overburdened health care system of developing country. Adopting these recommendations will not only give a uniform policy for health care workers (HCWs) of orthopedic department working in resource limited government hospitals, where thousands of patients present daily, but also limit the COVID-19 infection among health care workers (HCWs). We hope to get the best possible outcome out of current circumstances where developing nation like ours cannot afford the luxury of PPE for every health care worker and PCR COVID-19 testing for every patient.

Objective: To determine the frequency of depression in post stroke patients visiting a tertiary care hospital. Study Design: Cross-sectional analytic study. Place and Duration of Study: The study was carried at Armed Forces Institute of Mental Health Rawalpindi over 6 months, from Nov 2016 to Apr 2017. Material and Methods: The present study involved 130 post-stroke patients of either gender aged between 30-70 years who presented at the outpatient department of Armed Forces Institute of Mental Health Rawalpindi with stroke within past 2 years. A structured questionnaire was used to collect demographic data. Depression was assessed using ICD-10 diagnostic criteria. Attributing factors like patient’s age, gender, duration of stroke, educational, employment, socioeconomic and marital status and presence or absence of siblings, obesity and diabetes were noted. A signed written consent was taken from all patients. Results: The mean age of the patients was 57.1 ± 10.9 years. There were 75 (57.7%) male and 55 (42.3%) female patients with a male to female ratio of 1.4:1. Depression was diagnosed in 51 (39.2%) post-stroke patients. There was no statistically significant difference in the frequency of depression across various subgroups based on patient’s age, gender, duration of stroke, educational, employment, socioeconomic and marital status and presence or absence of siblings, obesity and diabetes. Conclusion: Depression was observed in a substantial proportion of post-stroke patients presenting at a tertiary care hospital which warrants routine screening for timely identification and management of depression in future practice to improve the quality of life and outcome of such patients.

Medical images are an important source of diagnosis. The brain of human analysis is now an advanced field of research for computer scientists and biomedical physicians. Services provided by the healthcare units usually vary, the quality of treatment provided in the urban and rural generally not same. Unavailability of medical equipment and services can have serious consequences in patient disease diagnosis and treatment. In this context, we developed. MRI (Magnetic Resonance Imaging) based CAD (Computer Aided Diagnosis) system which takes MRI as input and detects abnormal tissues (Tumors). MRI is the safe and well reputed imaging methodology for prediction of tumors. MRI modality assists the medical team in diagnosis and proper treatment plan (Medication/Surgery) of different types of abnormalities in the soft tissues of the human body. This paper proposes a framework for brain cancer detection and classification. The tumor is segmented using a semi-automatic segmentation algorithm in which the threshold values selection for head and cancer regions are premeditated automatically. Segmented tumors are further sectioned into malignant and benign using SVM (Support Vector Machine) classifier. Detailed experimental work indicates that our proposed CAD system achieves higher accuracy for the analysis of brain MRI analysis.

Today's power systems are subject to the high penetration of dynamic load. Volatility and intermittency of the dynamic load demand need to be compensated through optimization and scheduling without compromising user comfort. This paper proposes a multi-agent-based multi-layered hierarchical control system for residential load management under real-time pricing environment. The major objectives are to reduce peak load demand, electricity cost, and user discomfort. In doing so, different types of agents, i.e., price agent pa, sensor agent sa, decision agent da, load agent la, and action agent aa, are developed to control residential loads, such θas normal load (nl) and heavy load (hl). To handle price uncertainty, dynamically, optimal stopping rule (OSR) theory has been used. Two variants of OSR are proposed: 1) priority inversion logic-based OSR to subsidize the responsive consumers and 2) maximum energy consumption limit Q-based OSR-Q to maximize the profit of energy retailers. Finally, the proposed mechanism is validated on a set of loads to show the applicability and proficiency under a dynamic environment.

The purpose of the current study is to investigate the non-Newtonian unsteady Williamson fluid on a stretching/shrinking surface along with thermophoresis and Brownian effects. Basically, the model consists of a time-dependent magnetic field. The fluid is considered to be electrically conducting due to the effect of the external magnetic field. The values of magnetic Reynolds number are so small that the induced magnetic field is assumed to be negligible. In the concentration equation, the effects of Brownian motion and thermophoresis are discussed. Employing the similarity transformations, the governing nonlinear Partial Differential Equations (PDEs) are converted into the Ordinary Differential Equations (ODEs). The resulting ODEs are solved with the combined effects of the Successive Over Relaxation (SOR) method and Finite Difference Method (FDM). The impact of all the including parameters such as suction parameter, injection parameter, stretching/shrinking parameter, the ratio of viscosity, local Weissenberg number, unsteadiness parameter, Eckert number, Prandtl number, Lewis number, Nusselt number, Brownian motion parameter, shear stress, heat transfer rate, and mass transfer rate are analyzed using graphs and tables. Results show that the values of fluid velocity are better for S=8, −S=0, λ=0.3, β*=0.9, Wi=0.3, and Aa=2.0. It is also depicted from the results that the values of boundary layer thickness are better for S=0, −S=−8, λ=0.3, β*=0.1, Wi=1.5, and Aa=0.25. From the above numeric results, it is concluded that the fluid velocity is reduced and the thermal boundary layer thickness is enhanced by the enhancement of the stretching parameter.

Keyword extraction is essential in determining influenced keywords from huge documents as the research repositories are becoming massive in volume day by day. The research community is drowning in data and starving for information. The keywords are the words that describe the theme of the whole document in a precise way by consisting of just a few words. Furthermore, many state-of-the-art approaches are available for keyword extraction from a huge collection of documents and are classified into three types, the statistical approaches, machine learning, and graph-based methods. The machine learning approaches require a large training dataset that needs to be developed manually by domain experts, which sometimes is difficult to produce while determining influenced keywords. However, this research focused on enhancing state-of-the-art graph-based methods to extract keywords when the training dataset is unavailable. This research first converted the handcrafted dataset, collected from impact factor journals into n-grams combinations, ranging from unigram to pentagram and also enhanced traditional graph-based approaches. The experiment was conducted on a handcrafted dataset, and all methods were applied on it. Domain experts performed the user study to evaluate the results. The results were observed from every method and were evaluated with the user study using precision, recall and f-measure as evaluation matrices. The results showed that the proposed method (FNG-IE) performed well and scored near the machine learning approaches score.

Real-world optimization problems are getting more and more complex due to the involvement of inter dependencies. These complex problems need more advanced optimizing techniques. The Traveling Thief Problem (TTP) is an optimization problem that combines two well-known NP-Hard problems including the 0/1 knapsack problem and traveling salesman problem. TTP contains a person known as a thief who plans a tour to collect multiple items to fill his knapsack to gain maximum profit while incurring minimum cost in a standard time interval of 600 s. This paper proposed an efficient technique to solve the TTP problem by rearranging the steps of the knapsack. Initially, the picking strategy starts randomly and then a traversal plan is generated through the Lin-Kernighan heuristic. This traversal is then improved by eliminating the insignificant cities which contribute towards profit adversely by applying the modified simulated annealing technique. The proposed technique on different instances shows promising results as compared to other state-of-the-art algorithms. This technique has outperformed on a small and medium-size instance and competitive results have been obtained in the context of relatively larger instances.

Cereals are the major contributors to global food supply, accounting for more than half of the total human calorie requirements. Sustainable availability of quality cereal grains is an important step to address the high-priority issue of food security. High concentrations of heavy metals specifically lead (Pb) in the soil negatively affect biochemical and physiological processes regulating grain quality in cereals. The dietary intake of Pb more than desirable quantity via food chain is a major concern for humans, as it can predispose individuals to chronic health issues. In plant systems, high Pb concentrations can disrupt several key metabolic processes such as electron transport chain, cellular organelles integrity, membrane stability index, PSII connectivity, mineral metabolism, oxygen-evolving complex, and enzymatic activity. Plant growth-promoting rhizobacteria (PGPR) has been recommended as an inexpensive strategy for remediating Pb-contaminated soils. A diverse group of Ascomycetes fungi, i.e., dark septate endophytes is successfully used for this purpose. A symbiotic relationship between endophytes and host cereal induces Pb tolerance by immobilizing Pb ions. Molecular and cellular modifications in plants under Pb-stressed environments are explained by transcription factor families such as bZIP, ERF, and GARP as a regulator. The role of metal tolerance protein (MTP), natural resistance-associated macrophage protein (NRAMP), and heavy metal ATPase in decreasing Pb toxicity is well known. In the present review, we provided the contemporary synthesis of existing data regarding the effects of Pb toxicity on morpho-physiological and biochemical responses of major cereal crops. We also highlighted the mechanism/s of Pb uptake and translocation in plants, critically discussed the possible management strategies and way forward to overcome the menace of Pb toxicity in cereals.

The need for smart e-learning environments is resulting in new challenges for researchers and practitioners to develop intelligent systems that can be used to automate the Higher Education (HE) activities in an intelligent way. Some common examples of such activities are “analyzing, finding, and ranking the right resource to teach a course,” “analyzing and finding the people with common research interests to start joint research projects,” and “using data analytics and machine reasoning techniques for conducting the exams with different levels of complexities.” Ontological reasoning and smart data analytics can play an important role in analyzing and automating these HE activities and processes. In this paper, we present a framework named as Higher Education Activities and Processes Automation Framework (HEAPAF). The HEAPAF framework can be used to identify, extract, process, and produce the semantically enriched data in machine understandable format from different educational resources. We also present the Higher Education Ontology (HEO) that we designed and developed to accommodate the HE data and then to perform analysis and reasoning on it. As a proof of concept, we present a case study on the topic, “analyzing, finding, and ranking the right resources to teach a course,” which can dramatically improve the learning patterns of students in the growing smart educational environment. Finally, we provide the evaluation of our framework as evidence of its competency and consistency in improving academic analytics for educational activities and processes by using machine reasoning.

Data caching has emerged as a promising technique to handle growing data traffic and backhaul congestion of wireless networks. However, there is a concern regarding how and where to place contents to optimize data access by the users. Data caching can be exploited close to users by deploying cache entities at Small Base Stations (SBSs). In this approach, SBSs cache contents through the core network during off-peak traffic hours. Then, SBSs provide cached contents to content-demanding users during peak traffic hours with low latency. In this paper, we exploit the potential of data caching at the SBS level to minimize data access delay. We propose an intelligence-based data caching mechanism inspired by an artificial intelligence approach known as Reinforcement Learning (RL). Our proposed RL-based data caching mechanism is adaptive to dynamic learning and tracks network states to capture users’ diverse and varying data demands. Our proposed approach optimizes data caching at the SBS level by observing users’ data demands and locations to efficiently utilize the limited cache resources of SBS. Extensive simulations are performed to evaluate the performance of proposed caching mechanism based on various factors such as caching capacity, data library size, etc. The obtained results demonstrate that our proposed caching mechanism achieves 4% performance gain in terms of delay vs. contents, 3.5% performance gain in terms of delay vs. users, 2.6% performance gain in terms of delay vs. cache capacity, 18% performance gain in terms of percentage traffic offloading vs. popularity skewness (γ), and 6% performance gain in terms of backhaul saving vs. cache capacity.

Opportunistic network is emerging as a research domain nowadays with the introduction of Internet of things phenomena. In recent years, storage level congestion issue due to handheld devices is considered as a key challenge to be handled in the opportunistic networks. The prime objective of conducting this research is to develop artificial intelligence rule-based fire engine model to be tested using artificial intelligence latest classification algorithms further implemented using ONE simulator tool over MaxProp protocol. The achieved results show 98% accuracy in terms of classification using k-fold validation technique over six algorithms. The achieved results have been compared with MaxProp protocol over evaluation parameters such as delivery ratio, throughput, routing load, and overhead; whereas delivery ratio increase about 20% for node level and 5% for buffer level and throughput tends to increase 500 and 150 kbps for network and buffer levels, respectively.

Internet of things grew swiftly and many services, software, sensors-embedded electronic devices and related protocols were developed and still in progress with full swing. Internet of things enabling physically existing things to see, hear, think and perform a notable task by allowing them to talk to each other and share useful information while making decision and caring-on/out their important tasks. Internet of things is greatly promoted by wireless sensor network as it becomes a perpetual layer for it. Wireless sensor network works as a base-stone for most of the Internet of things applications. There are severe general and specific threats and technical challenges to Internet of things–based sensor networks which must overcome to ensure adaptation and diffusion of it. Most of the limitations of wireless sensor networks are due to its resource constraint objects nature. The specified open research challenges in Internet of things–based sensor network are power consumption, network lifespan, network throughput, routing and network security. To overcome aforementioned problems, this work aimed to prolong network lifetime, improve throughput, decrease packet latency/packet loss and further improvise in encountering malicious nodes. To further tune the network lifetime in terms of energy, wireless harvesting energy is suggested in proposed three-layer cluster-based wireless sensor network routing protocol. The proposed mechanism is a three-tier clustering technique with implanted security mechanism to encounter malicious activities of sensor nodes and to slant them into blacklist. It is a centred-based clustering protocol, where selection of cluster head and grid head is carried out by sink node based on the value of its cost function. Moreover, hardware-based link quality estimators are used to check link effectiveness and to further improve routing efficiency. At the end, excessive experiments have been carried out to check efficacy of the proposed protocol. It outperforms most of its counterpart protocols such as fuzzy logic–based unequal clustering and ant colony optimization–based routing hybrid, Artificial Bee Colony-SD, enhanced three-layer hybrid clustering mechanism and energy aware multi-hop routing in terms of network lifetime, network throughput, average energy consumption and packet latency.

Objective: To compare mean change in the Mayo Elbow Performance Score in olecranon fracture after treatment with tension band wiring and locking compression plate. Study Design: Randomized controlled trial. Place and Duration of Study: Department of Orthopaedics, Benazir Bhutto Hospital Rawalpindi, from Aug 2015 to Aug 2016. Material and Methods: Sixty patients were included in the study. Patients were divided into two groups, A and B. For group A, Locking compression plate while in group B tension band wiring was used. Follow-up was carried out at the end of 1st week and then at the end of 6th week post-operatively. Functional outcome was assessed by mean change in Mayo Elbow Performance Score. Results: Mean age in the study was 43.62 ± 12.187 years. There were 32 (53%) male patients and 28 (47%) female patients, while 34 (57%) patients had Mayo IIB fractures and 26 (43%) had mayo IIA fractures. Twenty five patients (42%) belonged to ASA class II while 35 (58%) belonged to ASA class I. Twenty Six patients (43%) presented within 24 hours of the injury while 34 (57%) presented within 48 hours. Mean change in the Mayo Elbow Performance Score in TBW group was 8.77 ± 3.54 versus 10.63 ± 3.479 for the LCP group (p=0.044). Conclusion: Locking Compression plate is superior to Tension Band Wiring in patients with olecranon fracture in terms of Mayo Elbow Performance Score.

Objective: To compare the corneal endothelial cell density in patients of Pseudoexfoliation syndrome (PXS) with normal healthy controls. Study Design: Cross sectional comparative. Place and Duration of Study: This study was carried out at Armed Forces Institute of Ophthalmology, Rawalpindi from Mar 2016 to Jan 2017. Patients and Methods:Patients of PXS and healthy subjects of both genders between ages of 40 and 50 years, with best corrected visual acuity of 6/6 on snellen visual acuity chart were included in the study. All the participants including controls and PXS patients were subjected to ophthalmic clinical examination including corrected distance visual acuity, slit lamp examination by the single trainee researcher to exclude bias. Participants in both groups were evaluated for corneal endothelial cell density (ECD) by using non-contact, TOPCON SP 3000P Specular microscope. Sampling bias was eliminated by analyzing 100 cells in the center of each specular microscope photograph and mean of three readings was analyzed. Results: Both the groups were similar with respect to gender and age. In our study sample there were 29 (58%) males in PXS group and 24 (48%) males in control group with no statistical significant (p-value > 0.05) difference between gender distributions of both groups. The mean age of the PXS group was 46.34 ± 2.932 years, and in control group was 45.54 ± 2.887 years (p-value >0.05). The mean value of ECD was found to be 2021.548 ± 528.142 cells/mm2 in PXS patients and the mean value of ECD in healthy subjects was noted to be 2772.692 ± 383.395 cells/mm2, showing significantly lower ECD in PXS cases. Similarly, significantly higher number of cases 22 (44%) had CED

Background: To determine the functional outcome of retrograde femoral nailing in terms of Tegner Lysholm score for distal femoral farctures. Methods: This prospective cross-sectional study was done at the Department of Orthopaedics, Benazir Bhutto Hospital, Rawalpindi from 20th January 2018 to 19th January 2019. The study comprised of 35 patients who presented with simple, extra-articular distal femur fracture. All patients were treated with retrograde femoral nailing using anterior (Para patellar) approach. Functional outcome was assessed at 6-months using Tegner Lysholm score. Data was analyzed using SPSS version 22. Results: Out of the 35 patients, majority were females (60%). Mean age of patients was 51.4±11.4 years. Most common age group in males was 41-50 years while in females it was 61 years and above. Average BMI of patients was 22.7±2.8 kg/m2. At 6-months post-operatively, Tegner Lysholm score was between 86-100, 71-85 and 56-70 in 65.7%, 25.7% and 8.6% cases, respectively. Tegner Lysholm score between genders was insignificant (p=0.4). Conclusion: Retrograde femoral nailing has excellent functional outcome in patients with extra-articular distal femoral fractures.

Persistent hepatitis B virus (HBV) infection relies on the establishment and maintenance of covalently closed circular (ccc) DNA, a 3.2 kb episome that serves as a viral transcription template, in the nucleus of an infected hepatocyte. Although evidence suggests that cccDNA is the repair product of nucleocapsid associated relaxed circular (rc) DNA, the cellular DNA polymerases involving in repairing the discontinuity in both strands of rcDNA as well as the underlying mechanism remain to be fully understood. Taking a chemical genetics approach, we found that DNA polymerase alpha (Pol α) is essential for cccDNA intracellular amplification, a genome recycling pathway that maintains a stable cccDNA pool in infected hepatocytes. Specifically, inhibition of Pol α by small molecule inhibitors aphidicolin or CD437 as well as silencing of Pol α expression by siRNA led to suppression of cccDNA amplification in human hepatoma cells. CRISPR-Cas9 knock-in of a CD437-resistant mutation into Pol α genes completely abolished the effect of CD437 on cccDNA formation, indicating that CD437 directly targets Pol α to disrupt cccDNA biosynthesis. Mechanistically, Pol α is recruited to HBV rcDNA and required for the generation of minus strand covalently closed circular rcDNA, suggesting that Pol α is involved in the repair of the minus strand DNA nick in cccDNA synthesis. Our study thus reveals that the distinct host DNA polymerases are hijacked by HBV to support the biosynthesis of cccDNA from intracellular amplification pathway compared to that from de novo viral infection, which requires Pol κ and Pol λ.