Search

The aim of this paper is to introduce a generalized $(3+1)$-Kadomtsev-Petviashvili equation which is used to describe waves in a ferromagnetic medium. The equation's bilinear form is created and the new homoclinic test approach based on the Hirota bilinear form is used to find numerous novel precise solutions. These accurate solutions, which are depicted in the contour, two-dimensional and three-dimensional graphs, show the evolution of periodic characteristics. The modulation instability is used to investigate the stability of the obtained solutions. Additionally, the development of the fusion soliton is examined, as well as the fusion phenomenon in the traveling wave solution is described in the physical discussion. For this evolution equation, the study indicates new mechanical structures and various characteristics. The derived results back up the model that was proposed. These discoveries open up a new avenue for us to investigate the concept further.

Abstract Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (deoxyribonucleic acid microarray] facilitates in computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This novel algorithm establishes a balance between the exploitation and exploration capabilities of the improved inertia weight adaptive particle swarm optimization. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) algorithm is employed for solution explorations. Each particle in the SIW-APSO increases its position and velocity iteratively through an evolutionary process. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been employed to identify several genes in the cancer dataset. The classification algorithm contains ELM, K-centroid nearest neighbor, and support vector machine to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.

Objective: The objective is to evaluate dental auxiliaries’ knowledge, attitudes, and practices for the prevention of noma disease. Materials and Methods: This was a cross-sectional study conducted over 6 months. After obtaining ethical clearance from the Sokoto and Zamfara State Ministries of Health, all consenting dental assistant staff who met the selection criteria were administered a validated questionnaire containing 17 questions assessing the respondents’ knowledge, attitudes, and practices. They were recruited using a self-administered questionnaire. The overall score was classified as “low” if the average score was 50% or less (50% or less), satisfactory (51%–69%), or good (70% or more). Data were analyzed using IBM SPSS version 20 software. Results: A total of 149 respondents returned the questionnaire, of which 80 (53.7%) were men and 69 (46.3%) were women. The mean ± standard deviation age of respondents was 27.37 ± 7.00 years, with a range of 20–58 years. The age of respondents was divided into four groups, and the majority of 112 respondents (75.2%) were in their 30s. The majority of respondents, 141 (94.6%), know about the prevention of noma disease. The total respondent score was 1827 points (72.1%), which was evaluated as “good.” Conclusion: Knowledge, attitudes, and practices of dental auxiliaries in the field of noma prevention are good. Dental auxiliaries, if supervised and appropriately trained, can play a role in noma prevention by actively participating in and disseminating information on specific preventive measures such as scaling and polishing.

Abstract The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

Modern agricultural machinery such as harvesting robotic arms are necessary to optimize the semi-automated agricultural processes to meet the ever-growing food needs of the population of the world. Production of low cost and highly efficient robotic arms are needed to meet the needs of the small-scale growers of underdeveloped countries. In this regard, mechanical design, sensor placement, and object identification are essential to optimize the performance of agricultural robots. This research is conducted to design a fully automated robotic arm for fruit picking in hydroponics system and research farm using machine vision to help the smallholders. For this purpose, a robotic arm with 1.3 m in height is designed using aluminum (18-gauge) and mild steel (MS-16) as fabrication material for the mechanical structure. The prototype design is tested for stress estimation at various payloads using SolidWorks simulations. The proposed robotic arm is based on a four degrees of freedom (4-DoF) design with the gripper having an opening range of 5.5 to 12 cm depending upon the task to handle various objects. The robotic arm is installed on an expandable vertical mobile platform that enables an expanded operational workspace with lower energy consumption. The total weight of the robotic arm is 60 kgs housing a payload capacity ranging between 8 and 10 kgs in accordance with industry-standard load-bearing specifications. The inverse kinematic algorithm using DH-table is computed with an accuracy of up to 95 % and target height of gripper is cross checked by mounting the ultrasonic sensor at the base of robotic arm. The YOLOv8 algorithm is used for object detection using the depth sense camera having angle range of 0.3 m to 3 m with the precision of up to 96 %. The time needed for recognition of fruits and pitching was about 15 s, with a success rate of up to 90 %. This research produces a low-cost and efficient solution for stallholders by developing a fully automated robotic arm for fruit picking. It optimizes agricultural productivity through improved mechanical design, sensor accuracy, and object detection which reduces labor costs by increasing efficiency of semi-automated farming systems. Further research is required to optimize the overall weight of the system and automation of the movement of robotic arm system between the consecutive rows to build a fully autonomous fruit picking operation in hydroponics.

Abstract During the fourth energy revolution, artificial intelligence implementation is necessary in all fields of technology to meet the increasing energy demands and address the diminishing fossil fuel reserves, necessitating the shift towards smart grids. The authors focus on predicting parameters accurately to minimise loss and improve power generation capacity in smart grids, given that accurate parameter prediction is essential for traditional power grid stations converting to smart grids. The authors employ an artificial intelligence‐based machine learning model, namely the long short‐term memory, to predict parameters of a solar power plant. After analysing the results obtained from the long short‐term memory model in graphical visualisation, the model is further improved using two different techniques namely, a convolutional neural network‐long short‐term memory and the authors proposed an autoencoder long short‐term memory. Comparing the results of these models, the study finds that autoencoder long short‐term memory outperforms the convolutional neural network‐long short‐term memory as well as simple long short‐term memory. Thus, the use of artificial intelligence in this study substantially enhances the precision of parameter prediction by augmenting the performance of rudimentary machine learning models, thereby facilitating the attainment of a resilient and resourceful power system that overcomes power losses and ameliorates production capacity in the context of Smart Grids.

Canine pathologies refer to the various diseases, disorders, and abnormalities that can affect a dog’s different body systems and cause mortality. The primary aim of this study was to investigate the common pathologies that are associated with the mortality of dogs in Ilorin, Kwara State. A retrospective review of post-mortem records from 7 years was undertaken at a necropsy unit of the Department of Veterinary Pathology, University of Ilorin. Data collected included age, sex, breed, disease pathogens, and year. A total of 150 dog carcasses were presented for necropsy during the period between January 2016 and December 2022. In this study, the cases used were confirmed based on PCR results in some cases, bacterial culture and isolation, parasite identification, gross lesions, and histopathological findings in other cases. Descriptive statistics were employed to evaluate the effect of disease pathogens on age, sex, and breed distribution patterns associated with the mortality of dogs. The prevalence of viral infection among other diseases was statistically significant (34.00%; OR = 0.53; P

The prevalence of inorganic pollutants in the environment, including heavy metals (HMs), necessitates a sustainable and cost-effective solution to mitigate their impacts on the environment and living organisms. The present research aimed to assess the phytoextraction capability of spinach (Spinach oleracea L.), under the combined effects of ascorbic acid (AA) and microwave (MW) irradiation amendments, cultivated using surgical processing wastewater. In a preliminary study, spinach seeds were exposed to MW radiations at 2.45 GHz for different durations (15, 30, 45, 60, and 90 seconds). Maximum germination was observed after the 30 seconds of radiation exposure. Healthy spinach seeds treated with MW radiations for 30 s were cultivated in the sand for two weeks, after which juvenile plants were transferred to a hydroponic system. Surgical industry wastewater in different concentrations (25 %, 50 %, 75 %, 100 %) and AA (10 mM) were provided to both MW-treated and untreated plants. The results revealed that MW-treatment significantly enhanced the plant growth, biomass, antioxidant enzyme activities and photosynthetic pigments, while untreated plants exhibited increased reactive oxygen species (ROS) and electrolyte leakage (EL) compared with their controls. The addition of AA to both MW-treated and untreated plants improved their antioxidative defense capacity under HMs-induced stress. MW-treated spinach plants, under AA application, demonstrated relatively higher concentrations and accumulation of HMs including lead (Pb), cadmium (Cd) and nickel (Ni). Specifically, MW-treated plants with AA amendment showed a significant increase in Pb concentration by 188 % in leaves, Cd by 98 %, and Ni by 102 % in roots. Additionally, the accumulation of Ni increased by 174 % in leaves, Cd by 168 % in roots, and Pb by 185 % in the stem of spinach plant tissues compared to MW-untreated plants. These findings suggested that combining AA with MW irradiation of seeds could be a beneficial strategy for increasing the phytoextraction of HMs from wastewater and improving overall plant health undergoing HMs stress.

Amorphous solid dispersion (ASD) has emerged to be an outstanding strategy among multiple options available for improving solubility and consequently biological activity. Interestingly several binary SD systems continue to exhibit insufficient solubility over time. Therefore, the goal of current research was to design ternary amorphous solid dispersions (ASDs) of hydrophobic model drug curcumin (CUR) to enhance the solubility and dissolution rate in turn, presenting enhanced anti-bacterial, antioxidant and anti-inflammatory activity. For this purpose several ternary solid dispersions (TSDs) consisting of Soluplus®, Syloid® XDP 3150, Syloid® 244 and Poloxamer® 188 in combination with HPMC E5 (binary carrier) were prepared using solvent evaporation method. Both solubility and dissolution testing of prepared solid dispersion were performed to determine the increase in solubility and dissolution. Solid state investigation was carried out utilizing infrared spectroscopy, also known as Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM),Differential scanning calorimetry (DSC) and X-ray diffraction (XRD).Optimized formulations were also tested for their biological effectiveness including anti-bacterial, anti-oxidant and anti-inflammatory activity. Amid all Ternary formulations F3 entailing 20 % soluplus® remarkably improved the solubility (186 μg/ml ± 3.95) and consequently dissolution (91 % ± 3.89 %) of curcumin by 3100 and 9 fold respectively. These finding were also supported by FTIR, SEM, XRD and DSC. In-vitro antibacterial investigation of F3 also demonstrated significant improvement in antibacterial activity against both gram positive (Staphylococcus aureus, Bacillus cereus) and gram negative (Pseudomonas aeruginosa, Escherichia coli) bacteria. Among all the tested strains Staphylococcus aureus was found to be most susceptible with a zone of inhibition of 24 mm ± 2.87. Antioxidant activity of F3 was also notably enhanced (93 % ± 5.30) in contrast to CUR (69 % ± 4.79). In vitro anti-inflammatory assessment also exhibited that F3 markedly protected BSA (bovine serum albumin) from denaturation with percent BSA inhibition of 80 % ± 3.16 in comparison to CUR (49 % ± 2.91). Hence, F3 could be an effective solid dispersion system for the delivery of model hydrophobic drug curcumin.

Stuttering is a speech disorder characterised by the repetition, prolongation, or blocking of sounds, syllables, or words, which can cause significant social and emotional difficulties for those who experience it. To help find and diagnose stuttering early, it is important for clinicians and researchers to accurately separate stuttering from normal speech. This helps them understand the disorder, find possible causes, and come up with effective interventions and treatments. The UCLASS dataset was used in this study, and 40 MFCC features were taken out to see how well different machine learning classifiers could indicate the difference between normal and stuttering speech. The major problem is the UCLASS imbalanced dataset. The authors address it with the synthetic minority oversampling technique. After machine learning experimentation’s, we propose a novel hybrid model that performs better than individual machine learning. In hybrid, the lightweight SutterNet model takes the best features from the data and then make prediction. The results indicate that the evaluated classifiers showed varying levels of performance. Overall, the results suggest that hybrid classifiers have the potential to accurately classify normal and stuttering speech, which could have important implications for the early identification and diagnosis of stuttering, as well as the development of assistive technologies and effective interventions and treatments.

Fusarium species are commonly found in soil, water, plants, and animals. A variety of secondary metabolites with multiple biological activities have been recently isolated from Fusarium species, making Fusarium fungi a treasure trove of bioactive compounds. This mini-review comprehensively highlights the newly isolated secondary metabolites produced by Fusarium species and their various biological activities reported from 2019 to October 2024. About 276 novel metabolites were revealed from at least 21 Fusarium species in this period. The main metabolites were nitrogen-containing compounds, polyketides, terpenoids, steroids, and phenolics. The Fusarium species mostly belonged to plant endophytic, plant pathogenic, soil-derived, and marine-derived fungi. The metabolites mainly displayed antibacterial, antifungal, phytotoxic, antimalarial, anti-inflammatory, and cytotoxic activities, suggesting their medicinal and agricultural applications. This mini-review aims to increase the diversity of Fusarium metabolites and their biological activities in order to accelerate their development and applications.

Due to the quick development of insecticide resistance, it is crucial to optimize management programs by understanding the sublethal effects of effective insecticides like chlorantraniliprole on Aedes aegypti L. populations. Using age-stage and two-sex life tables, we investigated the sublethal impacts of chlorantraniliprole on Ae. aegypti. Larval duration in the progeny of exposed parents was reduced by 0.33–0.42 days, whereas, the longevity of male and female adults was decreased by 1.43–3.05 days. Similarly, the egg-laying capacity of F1 and F2 progeny of the exposed parents was significantly reduced from 27.3% to 41.2%. The mean generation time (T) increased up to 11.8% in exposed populations, and the net reproduction rate (Ro) decreased by 51.50–55.60%. After 24 h of chlorantraniliprole treatment, there was a significant increase in cytochrome P450 activity. Contrarily, the activity of glutathione S-transferase (GST) initially declined but started increasing after 48 h of treatment. This research highlights the importance of chlorantraniliprole in mosquito management, as well as the importance of considering sublethal effects when developing strategies to handle them. Having a thorough understanding of the harmful effects of insecticides on mosquito populations can greatly enhance the effectiveness of insecticide-based interventions, while also minimizing the risk of pest resurgence.

Background One of the major reasons for unsuccessful treatment outcomes among patients with drug-resistant tuberculosis (DR-TB) is the high rate of loss to follow-up (LTFU). However, in Pakistan, no qualitative study has been conducted to explore the perceptions of LTFU patients with regard to DR-TB treatment, the problems they face and the reasons for LTFU in detail.Methods This was a qualitative study that involved semistructured, indepth, face-to-face interviews of 39 LTFU patients with DR-TB. All interviews were carried out in Pakistan’s national language ‘Urdu’ using an interview guide in two phases: the first phase was from December 2020 to February 2021 among patients with extensively drug-resistant tuberculosis and the second phase from July 2021 to September 2021 among patients with multidrug-resistant tuberculosis.Results The inductive thematic analysis of audio-recorded interviews generated the following four key themes, which were the major reasons reported by the participants of the current study to have led to LTFU: (1) patient-related factors, such as lack of awareness about the total duration of DR-TB treatment, fatigue from previous multiple failed episodes, lack of belief in treatment efficacy and perception of DR-TB as a non-curable disease; (2) medication-related factors, such as use of injectables, high pill burden, longer duration and adverse events; (3) socioeconomic factors, such as gender discrimination, poor socioeconomic conditions, non-supportive family members, social isolation and unemployment; and (4) service provider-related factors, such as distant treatment centres, non-availability of a qualified person, lack of adequate counselling and poor attitude of healthcare professionals.Conclusion In the current study, patients’ perceptions about DR-TB treatment, socioeconomic condition, medication and service provider-related factors emerged as barriers to the successful completion of DR-TB treatment. Increasing patients’ awareness about the duration of DR-TB treatment, interacting sessions with successfully treated patients, availability of rapid drug susceptibility testing facilities at treatment centres, decentralising treatment and using the recently recommended all-oral regimen may further decrease the rate of LTFU.

To address the rising energy demands in industrial and public sectors, integrating zero‐carbon emission energy sources into the power grid is crucial. Smart grids, equipped with advanced sensing, computing, and communication technologies, offer an efficient way to incorporate renewable energy resources and manage power systems effectively. However, improving solar energy efficiency, which currently contributes around 3.6% to global electricity, is a challenge in smart grid infrastructures. This research tackles this issue by deploying machine learning models, specifically recurrent neural network (RNN), long short‐term memory (LSTM), and gate recurrent unit (GRU), to predict measurements that could enhance solar power generation in smart grids. The objective is to boost both performance and accuracy of solar power generation in the smart grid. The study conducts experimental analyses and performance evaluations of these models in smart grid environments, considering factors like power output, irradiance, and performance ratio. The results, presented through graphical visualizations, show notable improvements, particularly with the LSTM model, which achieves a 97% accuracy, outperforming the RNN and GRU models. This outcome highlights the LSTM model's effectiveness in accurately predicting measurements, thereby advancing solar power generation efficiency in the smart grid framework.

Achieving sustainable green building design is essential to reducing our environmental impact and enhancing energy efficiency. Traditional methods often depend heavily on expert knowledge and subjective decisions, posing significant challenges. This research addresses these issues by introducing an innovative framework that integrates building information modeling (BIM), explainable artificial intelligence (AI), and multi-objective optimization. The framework includes three main components: data generation through DesignBuilder simulation, a BO-LGBM (Bayesian optimization–LightGBM) predictive model with LIME (Local Interpretable Model-agnostic Explanations) for energy prediction and interpretation, and the multi-objective optimization technique AGE-MOEA to address uncertainties. A case study demonstrates the framework’s effectiveness, with the BO-LGBM model achieving high prediction accuracy (R-squared > 93.4%, MAPE < 2.13%) and LIME identifying significant HVAC system features. The AGE-MOEA optimization resulted in a 13.43% improvement in energy consumption, CO2 emissions, and thermal comfort, with an additional 4.0% optimization gain when incorporating uncertainties. This study enhances the transparency of machine learning predictions and efficiently identifies optimal passive and active design solutions, contributing significantly to sustainable construction practices. Future research should focus on validating its real-world applicability, assessing its generalizability across various building types, and integrating generative design capabilities for automated optimization.

OBJECTIVE: To note the functional outcome of recurrent anterior shoulder dislocation with Bankart repair (BR) treated with arthroscopic stabilisation with suture anchors. METHODOLOGY: This prospective cohort study was conducted at the Department of Orthopedic Surgery, Lahore General Hospital, Lahore, from January 2021 to June 2022. Patients undergoing arthroscopic BR with anterior instability of the glenohumeral joint with at least >6 episodes of dislocation/subluxation in the last six months were analysed. Functional outcomes were assessed at one-month, three-month and six month intervals post-surgery using "Modified University of California Los Angeles (UCLA)" and "Simple Shoulder Test (SST)". RESULTS: Of 25 patients, 24 (96.0%) were male. The mean age was 27.20±4.0 years. The mean duration of symptoms was 9.1±6.2 months. In 16 (64.0%) patients, 2-suture anchors were utilised, while 3-suture anchors were used in the remaining 9 (36.0%) patients. Pre-surgery, the mean UCLA score was 16.4±5.6 and improved to 1-month, 3-month and 6-month as 16.6±5.1, 22.8±6.2 and 28.4±6.6 respectively (p

Foods are substances that when consumed provide the body with nutrients for growth and development. Foods for consumption should be within the acceptable limit of microbial load, exceeding it may lead to food-borne intoxication and illness. This study aimed to determine the microbial quality of commonly sold foods in some restaurants within Katsina metropolis. Physiochemical properties of the food namely; pH, temperature, colour, and taste were determined using a pH meter, thermometer, and organoleptic approach respectively. Conventional methods of identification were used to identify the isolates and a modified method by Kirby Bauer was employed to determine antimicrobial assays of the identified isolates. The results obtained showed that the physiochemical parameters of pH and temperature range between 5.50 to 7.40, and 30.0 oC to 32.0 oC respectively with a normal taste and colour. Total bacterial (1.04 x107 CFU/g) and fungal (4.8 x106 CFU/g) counts of the samples fall within the unsatisfactory limits set by the International Commission on Microbiological Specifications for Food (ICMSF). A total of nine (9) bacteria and five (5) fungi were identified from fifteen (15) samples examined. E. coli (26.31%) was most prevalent followed by Enterobacter aerogens and Staphylococcus epidermis (15.78%), Bacillus subtilis, and Salmonella spp (10.52%) while Proteus mirabilis, Micrococcus luteus, Pseudomonas aeroginosa, and Clostridium spp. have the least occurrence (5.26%). Aspergillus flavus (45.45%) was most prevalent among the fungal isolates followed by Mucor (27.27%) and Botrytis, Alternaria, and Fusarium chlamydosporum (9.09%) have the least occurrence. The antimicrobial assay shows that Gentamycin, levofloxacin, Erythromycin, and Ciprofloxacin were effective against Micrococcus luteus, Staphylococcus spp., Clostridium spp., and Bacillus subtilis. Norfloxacin and Amoxicillin were resisted by Micrococcus luteus and Staphylococcus spp., Ketoconazole and Fluconazole inhibited Mucor and Aspergillus flavus but were resisted by Alternaria, Botrytis, and Fusarium chlamydosporum. It was concluded that foods sold in some restaurants within the Katsina metropolis fall within the unsatisfactory limits of ICMSF standards, thus, improved strict hygiene compliance is recommended.

Targeted analysis of contaminants in water are often focused on a narrow range of chemicals, falling short of the true pollution status of water bodies. Non targeted screening presents several advantages in identifying less prioritized pollutants, entirely unknown compounds and transformation products. In this study we employed a non-targeted screening workflow established on a high-resolution quadrupole time-of-flight mass spectrometer coupled to an ultrahigh performance liquid chromatograph (UHPLC-QTOF-MS) to identify known and unknown pollutants in South African waste and surface waters. Level two confidence identification of 315 compounds was achieved based on mass accuracy, isotope patterns and MS/MS spectra match. Pharmaceuticals, drugs, and metabolites made up 40% of the detected compounds, biological compounds and industrial chemicals along with their metabolites constituted 24 and 18% respectively, while personal care products, pesticides and food additives made up approximately 5, 4 and 4% respectively. Several antiretroviral drugs were confirmed with level one confidence using isotope labelled standards. A wide range of “new” pharmaceuticals, pesticides and metabolites were documented in South African waters for the first time. Seventeen (17) pharmaceuticals were reported for the first time in South African waters, of which four are reported for the first time ever in surface water.The result of this exploratory study highlights the presence of several contaminants of public health concern that have hitherto received little to no attention in previous wastewater-based epidemiological studies. We provide a detailed list of priority contaminants for future studies on targeted-analysis.

Introduction Mechanical thrombectomy (MT) has demonstrated a significant improvement in functional outcomes for patients with anterior circulation strokes and large‐vessel occlusion (LVO). However, despite successful recanalization, certain patients still experience large hemispheric ischemia (LHI), necessitating decompressive hemicraniectomy (DHC). This study aims to investigate the characteristics and predictive factors associated with LHI requiring DHC in patients with successfully recanalized anterior circulation LVO, shedding light on potential factors influencing patient outcomes in this context. Methods We retrospectively analyzed 117 patients with LHI admitted to OLSU from November 2015 to March 2023. These individuals were then matched into two groups: a study group who had undergone decompressive hemicraniectomy (38) and a control group of individuals who had not (79). Additionally, within the DHC recipients, these individuals were further subgrouped into those who had undergone MT. Information was collected on demographic variables, medical history and clinical parameters. Functional outcomes were assessed via binarization of the following variables: the modified Rankin scale score on discharge (0‐4: good outcome vs. 5‐6: poor outcome). Results Among the 38 patients with LHI who underwent DHC (mean age 52.47 + 12.27 years, 63.1% males), 16 (42.1%) patients received intravenous tPA administration, and 13 (34%) underwent mechanical thrombectomy (MT) with successful reperfusion (TICI ≥ 2b) achieved in 8 (61.5%) of these patients. The assessment of the modified Rankin Scale (mRS) at discharge (0‐4: good outcome, 5‐6: poor outcome) revealed that both intravenous tPA (p‐value ‐ 0.206) and MT (p‐value 0.50) were not significantly associated with a good clinical outcome at discharge. Conclusion In the overall analysis of all individuals with complete outcome data, none of the variables (DHC receipt, MT receipt, tPA receipt) were significantly associated with mRS at discharge. The study did not find significant associations between the examined variables and the outcome (mRS on discharge) in patients with LHI who underwent DHC, regardless of whether they underwent reperfusion therapy or not. Advancements in acute ischemic stroke therapies such as thrombolytics and thrombectomy may reduce the need for DHC.

Introduction: Breast-conserving surgery (BCS) has been historically linked with a high rate of re-excision. To address this issue, the Society of Surgical Oncology (SSO) and the American Society for Radiation Oncology (ASTRO) developed consensus guidelines in 2014 to standardise practices and improve clinical outcomes for BCS patients. In our tertiary cancer care hospital, we assessed the impact of these guidelines on the re-excision rate following BCS. Materials and Methods: We conducted a retrospective study on breast cancer patients who underwent BCS at the Shaukat Khanum Memorial Cancer Hospital and Research Centre in Lahore, Pakistan. The study compared the re-excision rate before the implementation of the SSO-ASTRO consensus guidelines (November 2015 to July 2017) and after the implementation (January 2018 to August 2019). Margins were considered positive if "ink on tumour" was present and negative if "no ink on tumour" was present. Fisher's exact test or chi-square test was used to compare the re-excision rates between the pre- and post-guideline periods. Results: A total of 919 patients were identified, with 533 from the pre-guideline period and 386 from the post-guideline period. Of the 919 patients, 31 with ductal carcinoma in situ (DCIS) were excluded from the re-excision analysis because the guidelines were not implemented on the DCIS. Furthermore, the overall rate of re-excision in our data was 4.3%. The re-excision rate decreased from 71.1% to 28.9% (P ≤0.05) following the adoption of the guidelines. We observed a statistically significant decrease in the re-excision rate after implementing the SSO-ASTRO guidelines. Conclusion: Implementation of the SSO-ASTRO margin guidelines led to a notable decrease in the overall re-excision rate in our data set. These findings suggest that continued adherence to the guidelines may lead to a further reduction in the re-excision rate in the future.

Islamic banks cannot launch any operations and offerings unless a Sharī’ah approval is issued from their respective Sharī’ah boards. Hence, it becomes of utmost important and useful to find out the factors that affect the credibility of Sharī’ah approvals. To achieve this objective, first, six factors, namely, the methodology, format, product manual (istiftā), the role of Islamic banks as petitioners (mustaftī), the role of the Sharī’ah board, and the role of Sharī’ah governance framework, have been extracted from the relevant literature. Next, regression analysis is carried out based on the responses of 1000 Sharī’ah scholar respondents collected through a well-structured questionnaire to confirm the role of the identified factors in establishing the credibility of Islamic banking Sharī’ah approvals. The results indicate that all of these factors are the core factors affecting the credibility of Islamic banks' Sharī’ah approvals. The study recommends that the Islamic banking division of the State Bank of Pakistan should develop proper guidelines in this regard.

Power quality disturbances (PQDs) occur as the use of non-linear load and renewable-based micro-grids increases. This paper presents a new algorithm that consists of the discrete orthogonal S-transform (DOST) in the feature extraction stage, compressive sensing (CS) in the feature reduction stage, and a deep stacking network (DSN) for the automatic classification of single and multiple PQDs. It compresses the extracted feature matrix (orthogonal S-matrix coefficients) to minimize the computational process and provide more diversified features. Firstly, PQDs data is generated from a modified IEEE 13 bus system with wind grid integration, both synthetically and in real time. Moreover, compressive measurements of 24 types of multiple PQDs events and nine types of single PQDs events of synthetic and real data, and 12 type of three-phase single and multiple PQDs from the modified IEEE wind grid integration are fed to a proposed DSN classifier for PQD recognition. The DOST-based CS feature extraction technique achieves good robustness and time-frequency localization while retaining useful information. The DSN classifier method utilizes a Batch-mode gradient as a fine-tune, which has less noise gradient and improved efficiency of PQD classification. A noise level of 20 dB to 50 dB is considered. Other models, such as k-Nearest Neighbor (KNN), Multiclass Support Vector Machine (MSVM), and ensemble learner methods, are also developed to compare the efficiency. The high classification results demonstrate that the DOST-CS feature extraction and the DSN classifier have high precision in identifying multiple power quality events, even in noisy conditions.

The main objective of the paper is to theoretically as well as empirically analyze the Sharī’ah compliance of running Musharkah (RM) being practiced by Islamic banks. The study employs both theoretical and empirical descriptive analytical approaches. The empirical part of the study uses primary data of 1000 Shariah scholar respondents. The data was collected through questionnaire throughout the four provinces and capital city of Pakistan. The study employs univariate and bivariate analysis. The results reveals that an RM is quite close to running finance. An RM is causing the concentration of wealth in the hands of corporate class who invest less of their capital but get more of profit. It is found that an RM violates obvious principles of Islamic jurisprudence. In RM, fixation of profit at two levels as being practiced is nothing but stratagem to legalize the illegal interest based running finance.

The current study aims to explore the Sharī‘ah Governance (SG) and the management of Sharī‘ah Non-Compliance Risk (SNCR) in Islamic Banks (IBs) of Pakistan through the lens of Maqasid Sharī‘ah. For this purpose, the content analysis method was employed on the Sharī‘ah Governance Framework (SGF) issued by the State Bank of Pakistan (SBP), by concerning the related literature. Content analysis revealed that SGF explicitly defined the roles and responsibilities of various organs of SG of IBs towards sharī‘ah compliance. Moreover, the study also revealed that IBs were exposed to SNCR whenever they failed to comply with Sharī‘ah and consequently, SNCR lead to such a situation that threatened the stability and viability of IBs. The findings of this paper suggested that the viability and stability of IBs, as one of Maqāsid Khassah of the Islamic financial system, is being fully realized through different SG mechanisms provided in SGF, which strengthened the overall sharī‘ah compliance environment of IBs in Pakistan. It was also confirmed that different Maqasid Sharī‘ah , such as transparency, protection and growth of wealth, equity and justice in return distribution, and two dimensions of the well-being of society from the perspective of Maqasid Sharī‘ah , namely, safeguarding religiosity and safeguarding wealthwere being realized through SG mechanisms of IBs in Pakistan.

الملخص: أهداف البحث: يعد خلل الشم والذوق من الأعراض السريرية المبكرة الهامة لمرض كوفيد-19. تهدف الدراسة إلى حساب الانتشار التجميعي لهذه الأعراض؛ ثم مناقشة الآثار المحتملة على الممارسة السريرية مثل استخدامها كأدوات فحص ومؤشرات تشخيص محتملة. طرق البحث: باستخدام مجموعة من الكلمات المفتاحية ومصطلحات الميدلاين المعيارية، بحثنا عن الدراسات بالملاحظة في قواعد البيانات الخمس التالية: ''ميدلاين'' و ''سكوبس'' و''مكتبة كوكرين'' و ''شبكة العلوم'' و '' قوقل العلمي''. قام اثنان من الباحثين بفحص واختيار الدراسات، كما تم تقييم احتمالات التحيز في الدراسات المضمنة باستخدام مقياس نيوكاسل-أوتاوا. تم اتباع إرشادات ''بريزما'' لفحص المقالات النهائية وتحديدها وفقا لمعايير التضمين. كانت النتيجة المبلغ عنها للتحليل التجميعي هي معدل انتشار ضعف الشم والذوق المحسوب باستخدام نموذج التأثير العشوائي. النتائج: تم تضمين 17 دراسة مع حجم عينة إجمالي 4149 في هذا التحليل التلوي. من بين هؤلاء، أبلغ 2106 و 2676 مريضا عن درجة من الخلل الوظيفي الشمي و/أو الذوقي مع كوفيد-19؛ على التوالي. كانت النتائج المبلغ عنها من حيث الانتشار التجميعي، مع ضعف الذوق 57.33٪ والخلل الشمي 59.69٪، والذي يعتبر انتشارا كبيرا. الاستنتاجات: هناك معدل مرتفع لحدوث ضعف في حاسة الشم والذوق في مرض كوفيد-19. نظرًا لعدم وجود اختبار موضوعي للكشف عن ضعف الشم والذوق في معظم الدراسات، فمن المرجح أن يكون معدل الانتشار المرتفع الذي اكتشفناه أقل من الانتشار الحقيقي. هذا يعني أنه يجب على الأطباء استخدامها كمؤشرات مبكرة موثوقة لمرض كوفيد-19 وتوظيفها قبل فحص المرضى باختبارات باهظة الثمن. Abstract: Objective: Olfactory and gustatory dysfunction (OGD) are important early clinical symptoms of COVID-19. We aim to calculate the pooled prevalence of these symptoms and discuss the likely implications on clinical practice such as their use as screening tools and potential prognosis indicators. Methods: Using a combination of keywords and medical subject headings, we searched for observational studies in the following five databases: Medline/PubMed, Scopus, Cochrane Library, Web of Science, and Google Scholar. Two authors independently screened and selected the final articles according to the inclusion criteria. Two investigators independently assessed the risk of bias in individual studies using the Newcastle–Ottawa Scale. Heterogeneity and publication bias were also assessed. The reported outcome of the pooled analysis was the prevalence of OGD calculated using a random-effect model. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed to report results. Results: Seventeen studies with a total sample size of 4149 were included in this meta-analysis. Out of these, 2106 and 2676 patients reported some degree of olfactory and/or gustatory dysfunction with COVID-19, respectively. The reported outcomes were in terms of pooled prevalence, with gustatory dysfunction being 57.33% and olfactory dysfunction being 59.69%, a significantly high occurrence. Conclusion: There is a high occurrence of smell and taste impairment in COVID-19. Given the lack of objective testing for detecting OGD in most studies, the high prevalence found is likely to be an underestimation of the true prevalence. This implies that physicians must use them as reliable early indicators of COVID-19 and employ them before using expensive tests.

The increasing share of converter-based renewable energy sources in the power system has forced the system operators to demand voltage support from the converters in case of faults. In the case of symmetric faults, all the phases have equal voltage support, but in the case of asymmetric faults, selective voltage support is required. The grid codes define the voltage support required in the case of symmetric/asymmetric faults, which is the reactive current injection in the respective sequence proportional to its voltage dip, but studies confirm that it does not result in a minimum unbalance factor in the case of asymmetric faults. The unbalance factor is an indication of the level of imbalance voltage among the phases. Moreover, it also results in fluctuated active power injection in the case of asymmetric faults, which causes dc link voltage fluctuations, and the power reversal may also occur due to such fluctuations, which leads to higher protection costs for the dc link. In order to (1) enhance the uniformity of voltage among different phases in the case of asymmetric faults and (2) minimize the real power fluctuations in such conditions, a novel control scheme is presented in this paper. It optimally distributes the negative sequence current phasor into its active and reactive components to achieve the minimum voltage unbalance factor. It also confirms the minimum real power fluctuations by adjusting the positive and negative sequence current phasors. The proposed scheme also ensures the current limit of the converter. The proposed scheme is developed in Matlab/Simulink and tested under different faulty conditions. The results confirm the better performance of the proposed scheme against the grid code recommendation under different faulty conditions.

Abstract Background There was a lack of information about prognostic accuracy of time to sputum culture conversion (SCC) in forecasting cure among extensively drug-resistant tuberculosis (XDR-TB) patients. Therefore, this study evaluated the prognostic accuracy of SCC at various time points in forecasting cure among XDR-TB patients. Methods This retrospective observational study included 355 eligible pulmonary XDR-TB patients treated at 27 centers in Pakistan between 01-05-2010 and 30-06-2017. The baseline and follow-up information of patients from treatment initiation until the end of treatment were retrieved from electronic nominal recording and reporting system. Time to SCC was analyzed by Kaplan–Meier method, and differences between groups were compared through log-rank test. Predictors of time to SCC and cure were respectively evaluated by multivariate Cox proportional hazards and binary logistic regression analyses. A p-value 40 years (hazards ratio [HR] = 0.632, p-value = 0.004), baseline sputum grading of scanty, + 1 (HR = 0.511, p-value = 0.002), + 2, + 3 (HR = 0.523, p-value = 0.001) and use of high dose isoniazid (HR = 0.463, p-value = 0.004) were significantly associated with early SCC. Only SCC at 6 month of treatment had statistically significant association with cure (odds ratio = 15.603, p-value

Abstract Worldwide, breast cancer has an eminent morbidity and mortality rate, as it is a neoplastic disease among females. The query of the prospective danger of bovine leukemia virus (BLV) to humans is an old but exceedingly topical focus of scientific debate. The objective of the current study was to determine the possible relationship between BLV and breast cancer. A total of 2710 formalin-fixed paraffin-embedded (FFPE) breast cancer samples were selected regardless of the age, ethnicity, or municipality origin of the subjects. The presence of BLV in human breast cancer was determined through nested PCR by amplifying tax and gag genes followed by partial sequencing. Homology was confirmed by using the online BLAST Tool. BLV genes were found to be positive in 26.8% (728/2710) of the samples from breast cancer patients and 10% (10/80) of the samples without cancer (negative control). The results indicated a correlation between the presence of the BLV gene and breast cancer (odds ratio = 0.3889; confidence interval = 1,18; p = 0.0029). The current findings suggest a possible link between BLV and human breast carcinoma. Therefore, screening cattle herds and milk products is suggested to reduce the viral transmission risk to humans.

Milk is seen as a chief source of protein and other biologically available nutrients for human beings. Pakistan, the fourth largest milk-producing country, is badly affected by the contagious transboundary apthoviral disease of ungulate animals; the foot and mouth disease (FMD) virus. FMD is endemic in Pakistan and has caused significant economic loss to the dairy industry in the form of a profound decrease in milk production and increased morbidity and deaths of dairy animals. Inclusively, the case fatality ratio of FMD was 15.11%. Of the seven FMDV serotypes, (O, A, C, Asia 1, SAT 1, SAT 2, and SAT 3), three serotypes (O, A, and Asia-1) are endemic in Pakistan. Rapid and highly sensitive diagnostic tools are required for efficient control of this disease. Presently, FMD in the laboratory is diagnosed via ELISA and molecular approaches, i.e., RT-PCR. Serotype-specific RT-PCR analysis not only confirms ELISA serotyping results but can also be used for the screening of ELISA negative samples. Genotypically, FMDV serotype O has a topotype (Middle East–South Asia (ME–SA) and lineage PanAsia-2) that is reported frequently from different areas of Pakistan. Confirmed cases of serotype A and Asia-1 are also reported. The information gathered can be used for understanding the molecular epidemiology of FMD in Pakistan. Further studies on the molecular dynamics of FMD could be useful for ensuring the timely diagnosis of this deadly pathogen, which would ultimately be beneficial for the mass vaccination programs of FMD in Pakistan.

The focus of this paper was on Amazon product reviews. The goal of this is to study is two (NLP) for evaluating Amazon product review sentiment analysis. Customers can learn about a product's quality by reading reviews. Several product review characteristics, such as quality, time of evaluation, material in terms of product lifespan and excellent client feedback from the past, will have an impact on product rankings. Manual interventions are required to analyse these reviews, which are not only time consuming but also prone to errors. As a result, automatic models and procedures are required to effectively manage product reviews. (NLP) is the most practical method for training a neural network in this era of artificial intelligence. First, the Naive Bayes classifier was used to analyse the sentiment of consumer in this study. The (SVM) has categorized user sentiments into binary categories. The goal of the approach is to forecast some of the most important characteristics of an amazon-based product reviews, and then analyse Customer attitudes about these aspects. The suggested model is validated using a large-scale real-world dataset gathered specifically for this purpose. The dataset is made up of thousands of manually annotated product reviews gathered from amazon. After passing the input via the network model, (TF) and (IDF) pre-processing methods were used to evaluate the feature. The outcomes precision, recall and F1 score are very promising

Abstract Background There was a complete lack of information about the treatment outcomes of rifampicin/multidrug resistant (RR/MDR) childhood TB patients (age ≤ 14 years) from Pakistan, an MDR-TB 5th high burden country. Therefore, this study evaluated the socio-demographic characteristics, drug resistance pattern, treatment outcomes and factors associated with unsuccessful outcomes among childhood RR/MDR-TB patients in Pakistan. Methods This was a multicentre retrospective record review of all microbiologically confirmed childhood RR/MDR-TB patients (age ≤ 14 years) enrolled for treatment at seven units of programmatic management of drug-resistant TB (PMDT) in Pakistan. The baseline and follow-up information of enrolled participants from treatment initiation until the end of treatment were retrieved from electronic nominal recording and reporting system. World Health Organization (WHO) defined criterion was used for deciding treatment outcomes. The outcomes of “cured” and “treatment completed” were collectively grouped as successful, whereas “death”, “treatment failure” and “lost to follow-up” were grouped together as unsuccessful outcomes. Multivariable binary logistic regression analysis was used to find factors associated with unsuccessful outcomes. A p-value

Introduction: The corona virus pandemic has brought with it psychological problems like anxiety, stress, and depression. Research studies are urgently required to generate data that would help formulate strategies to manage the ensuing psychiatric and psychological maladies at societal level. Aims & Objectives: The aim of our study is to better comprehend the initial psychological response of society to COVID-19 outbreak though a survey. Place and duration of study: This cross-sectional study was conducted in King Edward Medical University, Lahore from April 2020 to October 2020. Material & Methods: An online questionnaire comprising of 20 items including the Zung self-rating Anxiety Scale (SAS) was disseminated to three fifty (350) participants. Two hundred and seventy-one (n=271) responses were received. Collected data was entered and analyzed using the SPSS software version 20. Categorical variables were presented as frequencies and percentages. Relationships between categorical variables were tested by Chi square test. Regression analysis was conducted to investigate the effect of various variables on total stress score. A p-value of

In this study, terrace soil investigation was carried out in project E-35 (phase-I) China-Pak Economic Corridor, Lesser Himalayas, North Pakistan. The methodology in current research is based on tests that include sieve analysis, plastic index, proctor, California Bearing Ratio, Los Angeles, sand equivalent and specific gravity. The results of these tests for different layers were compared with AASHTO and NHA specifications. The results show that the embankment, subgrade and subbase layers were composed of silt, sand and gravel, respectively while the aggregate base coarse was composed of sand, aggregate and less amount of fine clay material. The sieve analysis test shows that soil and aggregate base coarse has less clay with high silt, sandy material and index plastic to low plastic, which is appropriate for the construction. The California Bearing Ratio shows that the soil and aggregate base coarse have high load-bearing capacity. The Los Angeles abrasion reveal that the sub base and aggregate base coarse are resistive. The sand equivalent shows that aggregate base coarse has high sand material. The specific gravity illustrates that aggregate base coarse material is denser. The current study shows that terrace soil is suitable for the construction of the road in project E-35 (phase-I) China-Pak Economic Corridor.

Abstract Background and Aim The coronavirus‐19 is an ongoing global pandemic resulting in millions of deaths worldwide. For a patient population at higher risk of infection, telemedicine is a promising means of providing safe and alternative care routes while minimizing their risk of exposure. This study gives insight into patients' experiences and satisfaction with telemedicine during this pandemic. Methods We conducted a cross‐sectional study on 297 patients (RR: 85%) at the National Center for Cancer Care and Research (NCCCR), Qatar. Data was collected through electronic medical records of the eligibe patient population, and phone calls were made whereby the physician read a standard introductory script followed by a survey questionnaire. We focused on patients' experience with telemedicine services amid the pandemic. This was done using a six‐point Likert scoring system of seven questions that were scaled from 1 to 6. Results More than 80% of patients somewhat to strongly agreed that telemedicine met their healthcare needs, improved their confidence in their healthcare system, and were generally satisfied with the quality of care provided. Nearly all patients (90%) understood their physicians' recommendations over the phone. In addition, more than half of the patients (89%) felt they could freely communicate their concerns. Patients also showed an inclination towards face‐to‐face consultations at 68%; however, 90% were willing to participate in future teleconsultations. Conclusion Our study indicates an overall positive experience among patients towards the use of telemedicine. Telemedicine is a safe, futuristic approach toward patient care management and, thus, provides healthcare professionals a platform to implement further patient and physician education. Even though our data also showed that patients liked in‐person visits to some degree, this needs to be looked into more in future studies.

BACKGROUND & OBJECTIVE: Piperacillin is an antibiotic that belongs to the ureidopenicillin family. Piperacillin-tazobactam provides cover against gram-positive, gram-negative bacteria and some bacteria of anaerobic nature. It is an extended-spectrum antibiotic and is used along with tazobactam which is an inhibitor of beta-lactamase. The main objective of our research was to identify the hypersensitivity reactions of piperacillin-tazobactam in pharmaceuticals factory and office workers. METHODOLOGY: A Cross-sectional study was conducted at Stallion Pharmaceuticals (Pvt) Ltd located at Sundar Industrial Estate Lahore, Pakistan, from March to April 2018, after approval from the institutional research committee. All the workers, including factory workers and office workers at Stallion Pharmaceuticals (Pvt) Ltd were included in this study. Data was collected from each respondent by immediate-type skin testing. Data analysis was done by SPSS version 25, the demographic data was evaluated by descriptive statistics, Fisher Exact test was used for comparison and p≤0.05 was taken as significant. RESULTS: The results of the study presented that hypersensitivity reaction was present in (7.5%) of males and (3.8%) of females. It was also observed that (8.8%) of factory workers had hypersensitivity reactions when they are gone through skin tests, and only (2.5%) of office workers were skin test positive. From the above results (11.2%) of all the respondents developed hypersensitivity reactions and (88.8%) showed no reaction when they were gone through skin tests, which contain 0.1 ml of piperacillin and tazobactam. Fisher exact test was applied, and results showed no significant difference between hypersensitivity reactions among factory and office workers as the p-value was 01. CONCLUSION: Hypersensitivity reaction to piperacillin-tazobactam was minimal in the factory & office workers, and this was not significant.

Teratomas are a common form of germ cell tumor. Teratomas are commonly found in the gonadal organs, such as the ovaries and testes. Treatment of choice for ovarian teratomas is complete surgical excision, which exhibits a good prognosis in benign teratomas; however, chemotherapy treatment is needed for malignant components. Neurological paraneoplastic presentation of gynecological tumors is rare; however, ovarian tumors account for 10% of this presentation. In literature, paraneoplastic limbic encephalitis, anti-N-methyl-D-aspartate receptor encephalitis, and paraneoplastic cerebellar degeneration have been reported in ovarian teratomas and tumors; however, myasthenia gravis has been reported only twice. In both of those cases, manifestation of myasthenia gravis was preceding the diagnosis of ovarian cancer. We describe the first case of a 21-year-old female who presented with new-onset myasthenia gravis after finishing chemotherapy for ovarian teratoma. Another unusual aspect of our case is the rare co-occurrence of gliomatosis peritonei with mature teratoma.

Wireless capsule endoscopy (WCE) is an efficient tool to investigate gastrointestinal tract disorders and perform painless imaging of the intestine. Despite that, several concerns make its wide applicability and adaptation challenging like efficacy, tolerance, safety, and performance. Besides, automatic analysis of the WCE provided dataset is of great importance for detecting abnormalities. Imaging of the patient's digestive tract through WCE produces a large dataset that requires a substantial amount of time and a special skill set from a medical practitioner for analysis. Several computer-aided and vision-based solutions have been proposed to resolve these issues, yet, they do not provide the desired level of accuracy and further improvements are still needed. The current study aims to devise a system that can perform the task of automatic analysis of WCE images to identify abnormalities and assist practitioners for robust diagnosis. This study adopts a deep neural network approach and proposes a model name BIR (bleedy image recognizer) that combines the MobileNet with a custom-built convolutional neural network (CNN) model to classify WCE bleedy images. BIR uses the MobileNet model for initial-level computation for its lower computation power requirement and subsequently the output is fed to the CNN for further processing. A dataset of 1650 WCE images is used to train and test the model using the measures of accuracy, precision, recall, F1 score, and Cohen's kappa to evaluate the performance of the BIR. Results indicate the promising outcomes with achieved accuracy, precision, recall, F1 score, and Cohen's kappa of 0.993, 1.000, 0.994, 0.997, and 0.995 respectively. The accuracy of the BIR model is 0.978 with the Google collected WCE image dataset which is better than the state-of-art approaches.

Abstract: Sustainable development means that human societies survive and meet their needs without compromising the ability of future generations to meet their needs. It is as if existing resources are to be preserved for future generations and the resources that we are running out of are to be reduced or used in such a way that they remain usable for a long time. In modern times, due to population expansion and overuse of natural resources, resources are dwindling day by day and the world's population is not benefiting from these resources. Therefore the concept of sustainable development has become very popular globally and the United Nations Educational, Scientific and Cultural Organization (UNESCO) also introduced the Sustainable Development Goals (SDGs 2030) to ensure the balanced and sustainable economic and social development of human society. We find in the Qur'an and Sunnah the basic concept of the proposed measures for sustainable development to protect and augment the resources of the state. Many verses in the Holy Qur'an draw attention to the best management of resources. While some verses are clearly related to natural collective resources and directly or indirectly call for proper arrangement of natural collective resources. This article will explain the concept of safe use of natural resources in the light of the Qur'an, Sunnah, and Sīrah of the Prophet (S.A.W). Keywords: Safe Use of Natural Resources, Sustainable Development, Sustainable Development and Islamic point of view, Sustainable Development in light of Sirah, Use of Natural Resources in light of Sirah

Today the big challenge for NEWS organization to well organize the news and well categorize the news in automatically no need the data entry people to enter and select the category and then based on the category and its sub-category they will be manually selected and enter the details and then after this the analysis will later on used for different aspects. The news is almost every second used in different sources of media in soft and hard. We use the both sources of the Pakistan News in dual languages English and Urdu both and process them and prepare them for machine learning and based on the Machine learning trained data we build a very effective and efficient model that can predict the title category of the news and category of description of the news. We use different machine learning algorithms and different features extraction finally we build the model using the machine learning algorithm with 89% accuracy with logistic regression.

YouTube ranking are the most impressive topic for researchers and the researcher try to get the qualitative content of the YouTube video without watching the video if the user is new its most important for us to enable the user to find the best video as per quality of the content for this purpose we proposed as model which gives us the classification of the comments with its class and also predict the how much the fake comments are written in the comments section to help the video content to ranked on the YouTube. This qualitative work helps the research after the preprocessing the comments and filter the fake comments and then find the qualitative comments from the video feedback section. Our proposed model help the user to classify his comments about the skin care videos the skin care means skin care and treatment and if there is some fake comments and fake ideas are putted in the video or comments this will classify and help the user about the video with pure qualitative analysis of the video with comments and this helps us to truly rank the video and the user will get benefits truly. With the help of Machine learning algorithms and feature engineering we meet the target and after this we received the 0.94 F1 score.

Today YouTube is the first one application when the people need to learn something this is due to the robust service of the YouTube. Every second 500Hours video uploaded on the YouTube. On the one side this huge data is so big edge for YouTube but on the other side this is the big challenge for YouTube to provide efficient and effective search results for each user. People when search on the YouTube they are facing the difficulty to find the best video matched with their required content the only way to find the quality of the video is user feedback in term of comments but we also faced some problem too in comments most important are these two we focus 1.Huge comments 2.Multi-language comments for this problem our proposed method help the user to processes the all comments into the single English language and find the sentiment of the each video category and one the basis of the polarity score we find the best video tutorial and also compare the polarity results with and without our proposed method the results shows the method is effective and efficient.

The brain tumor is widely seen as cancer which is considered as the second leading cause of human bereavement. Abnormal growth of cells can be found inside or in the boundary of the brain. This kind of abnormality can affect the functionality of the brain or can harm the natural behavior of a person. In general, the brain tumor has two types: one is Benign and another is a Malignant tumor. The Benign tumor cannot spread out suddenly and cannot detriment the other parts of the brain, but the Malignant brain tumor is one type of cancer tumor that can uninterrupted to the patient’s death and it will be prolonged with the worst condition and also affect the neighboring healthy brain tissues. The difficulty is that the tumor cell is not be identified at its initial stage and when they identified it’s difficult to recover the patient in this means patients meet death. This study is contributing to the field of image processing so that the tumor can be identified earlier and with the help of early treatment lives can be saved as the burden on society will be reduced especially in the poor countries. The main technique for tumor identification is MRI imaging. There are many other techniques used for this purpose like CT, MRI, and X-Rays scientists are now working on the new techniques every day a lot of new ideas are developed and implemented. Still, now MRI imaging is a more reliable technique for tumor identification. But there is a need to improve accuracy on which better results are dependent. There is a lot of burden on the doctors to identify and separate the tumor from the images so there is a need to develop an automatic system that will reduce this burden. There is a bundle of algorithms that have been developed to sort this problem. But still now due to some problems or limitations of algorithms it is still unsolved. In this paper, we have proposed an automatic model with a complete framework of tumor identification, segmentation, and classification is proposed with 94% accuracy results achieved by Support Vector Machine (VSM). We used 200 MRI images for this experiment.

With the global spread of COVID-19, the governments advised the public for adopting safety precautions to limit its spread. The virus spreads from people, contaminated places, and nozzle droplets that necessitate strict precautionary measures. Consequently, different safety precautions have been implemented to fight COVID-19 such as wearing a facemask, restriction of social gatherings, keeping 6 feet distance, etc. Despite the warnings, highlighted need for such measures, and the increasing severity of the pandemic situation, the expected number of people adopting these precautions is low. This study aims at assessing and understanding the public perception of COVID-19 safety precautions, especially the use of facemask. A unified framework of sentiment lexicon with the proposed ensemble EB-DT is devised to analyze sentiments regarding safety precautions. Extensive experiments are performed with a large dataset collected from Twitter. In addition, the factors leading to a negative perception of safety precautions are analyzed by performing topic analysis using the Latent Dirichlet allocation algorithm. The experimental results reveal that 12% of the tweets correspond to negative sentiments towards facemask precaution mainly by its discomfort. Analysis of change in peoples' sentiment over time indicates a gradual increase in the positive sentiments regarding COVID-19 restrictions.

Soil moisture sensors and hydraulic modeling play a vital role in managing surface irrigation systems. Crop water productivity can be improved by managing the inflow cut-off time and optimizing the other field scale measurements. As such, hydraulic modelling and field experiments were carried out at the University of Agriculture Faisalabad-Pakistan. The soil moisture sensor (SEN-13322) and the WinSRFR model were used for this purpose. In total, nineteen treatments including eighteen simulated treatments and one conventional treatment were designed at two levels of discharge (Q1:0.0025 and Q2:0.0035 m3s−1), at three sensor positions (S1:55%, S2:65%, and S3:75%) across the field length, as well as with three different border widths (B1:6.4m, B2:8.5m, and B3:10.7m) after successful sensor and model calibration during the two growing seasons of 2016–2017 and 2017–2018. The results revealed a significant difference between the means and the treatment T10 i.e., Q2S1B1 that were found to be highly efficient and uniform.

With the increasing penetration of converter-based power sources into the power system, the performance of the converter has become a key factor for enhancing grid reliability, especially during asymmetrical faults. To meet the low voltage ride-through requirements, the converter should feed the reactive power to the grid for voltage support while ensuring the maximum current limitation for the converter’s safety. For such injections, the grid codes are defined. This paper presents a novel and simplified reference current generation scheme to fulfill the requirement of recent grid codes, ensure the current limit of the converter and confirm better utilization of the converter’s current capacity during asymmetrical faults. Moreover, it also discusses the new sequence extraction scheme based on the delay sample method in the stationary reference frame and the control modifications for the negative sequence current injection. The proposed scheme was tested for different priority injection schemes. Its performance was also compared with other control schemes. Detailed simulation studies, in MATLAB/Simulink, were presented to confirm the performance of the proposed scheme under different faulty conditions. The results confirmed the supremacy of the proposed scheme over the available schemes for better utilization of the converter’s current capacity during asymmetrical faults. It also ensured the peak current limitation of the converter while fulfilling the recent grid code requirements. Moreover, the results showed that the new scheme has 10% more current capacity compared to the other schemes due to better incorporation of the angle between the positive and negative phase sequences of the voltage.

Published in August 2021

Khyber Pakhtunkhwa launched the Billion Tree Afforestation Project (BTAP) in 2014. Pakistan also initiated a “10 billion trees in five years” project in 2018. The soil and water assessment tool (SWAT) model was used to forecast the impacts of LULC changes on water yield under three scenarios: before planting, after 1 billion trees planted, and after 10 billion trees planted. Model calibration and validation were undertaken at the Bisham Qila gauging station from 1984 to 2000 and 2001 to 2010. The Tarbela reservoir’s mean annual runoff declined from 53.70 mm to 45.40 mm after 1 billion trees planted, while under the third scenario it approximated 35.05 mm.

Irrigated agriculture is highly dependent on groundwater resources in Pakistan. Due to the fiber and food requirements, the reliance on groundwater has been increasing during the past two decades. This research work was conducted at the canal command area of the Dera Ghazi Khan (D.G Khan) canal for years 2017–2019. There were two products of the satellite i.e., AQUA (EOS PM) and TERRA (EOS AM), which were used in this research for determining groundwater demand in the area. For Rabi seasons 2017–2018 and 2018–2019, cropping areas of the major crops i.e., wheat and sugarcane, were 98,712.5 and 131,856.2 ha, and 100,568.7 and 132,743.7 ha, respectively. For the Kharif seasons of 2018 and 2019, cropping areas of major crops i.e., rice, cotton, and sugarcane, were 82,093.7, 74,150, and 98,712.5 ha, and 75,687.5, 79,275, and 132,743.7 ha, respectively.