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As wildfires become increasingly perilous amidst Pakistan’s expanding population and evolving environmental conditions, their global significance necessitates urgent attention and concerted efforts toward proactive measures and international cooperation. This research strives to comprehensively enhance wildfire prediction and management by implementing various measures to contribute to proactive mitigation in Pakistan. Additionally, the objective of this research was to acquire an extensive understanding of the factors that influence fire patterns in the country. For this purpose, we looked at the spatiotemporal patterns and causes of wildfires between 2000 and 2023 using descriptive analysis. The data analysis included a discussion on density-based clustering as well as the distribution of the data across four seasons over a period of six years. Factors that could indicate the probability of a fire occurrence such as weather conditions, terrain characteristics, and fuel availability encompass details about the soil, economy, and vegetation. We used a convolutional neural network (CNN) to extract features, and different machine learning (ML) techniques were implemented to obtain the best model for wildfire prediction. The majority of fires in the past six years have primarily occurred during the winter months in coastal locations. The occurrence of fires was accurately predicted by ML models such as random forest (RF), which outperformed competing models. Meanwhile, a CNN with 1D and 2D was used for more improvement in prediction by ML models. The accuracy increased from an 86.48 to 91.34 accuracy score by just using a CNN 1D. For more feature extraction, a CNN 2D was used on the same dataset, which led to state-of-the-art prediction results. A 96.91 accuracy score was achieved by further tuning the RF model on the total data. Data division by spatial and temporal changes was also used for the better prediction of fire, which can further be helpful for understanding the different prospects of wildfire. This research aims to advance wildfire prediction methodologies by leveraging ML techniques to explore the benefits and limitations of capturing complex patterns and relationships in large datasets. Policymakers, environmentalists, and scholars studying climate change can benefit greatly from the study’s analytical approach, which may assist Pakistan in better managing and reducing wildfires.

As the climate changes with the population expansion in Pakistan, wildfires are becoming more threatening. The goal of this study was to understand fire trends which might help to improve wildland management and reduction in wildfire risk in Pakistan. Using descriptive analyses, we investigated the spatiotemporal trends and causes of wildfire in the 2001–2020 period. Optimized machine learning (ML) models were incorporated using variables representing potential fire drivers, such as weather, topography, and fuel, which includes vegetation, soil, and socioeconomic data. The majority of fires occurred in the last 5 years, with winter being the most prevalent season in coastal regions. ML models such as RF outperformed others and correctly predicted fire occurrence (AUC values of 0.84–0.93). Elevation, population, specific humidity, vapor pressure, and NDVI were all key factors; however, their contributions varied depending on locational clusters and seasons. The percentage shares of climatic conditions, fuel, and topographical variables at the country level were 55.2%, 31.8%, and 12.8%, respectively. This study identified the probable driving factors of Pakistan wildfires, as well as the probability of fire occurrences across the country. The analytical approach, as well as the findings and conclusions reached, can be very useful to policymakers, environmentalists, and climate change researchers, among others, and may help Pakistan improve its wildfire management and mitigation.

Wildfires are predicted to occur more frequently and intensely as a result of global warming, posing a greater threat to human society, terrestrial ecosystems, and the atmosphere. Most existing methods for monitoring wildfire occurrences are based either on static topographical information or weather-based indices. This work explored the advantages of a new machine learning-based ‘soil properties’ attribute in monitoring wildfire occurrence in Pakistan. Specifically, we used satellite observations during 2001–2020 to investigate the correlation at different temporal and spatial scales between wildfire properties (fire count, FC) and soil properties and classes (SoilGrids1km) derived from combination with local covariates using machine learning. The correlations were compared to that obtained with the static topographic index elevation to determine whether soil properties, such as soil bulk density, taxonomy, and texture, provide new independent information about wildfires. Finally, soil properties and the topographical indices were combined to establish multivariate linear regression models to estimate FC. Results show that: (1) the temporal variations of FC are negatively correlated with soil properties using the monthly observations at 1° grid and regional scales; and overall opposite annual cycles and interannual variations between and soil properties are observed in Pakistan; (2) compared to the other static variables such as elevation, soil properties shows stronger correlation with the temperate wildfire count in Northern Pakistan but weaker correlation with the wildfire properties in Southern Pakistan; and it is found that combining both types of indices enhances the explained variance for fire attributes in the two regions; (3) In comparison to linear regression models based solely on elevation, multivariate linear regression models based on soil properties offer superior estimates of FC.

Human action recognition is still an uncertain computer vision problem, which could be solved by a robust action descriptor. As a solution, we proposed an action recognition descriptor using only the 3D skeleton joint’s points to perform this unsettle task. Joint’s point interrelationships and frame-frame interrelationships are presented, which is a solution backbone to achieve human action recognition. Here, many joints are related to each other, and frames depend on different frames while performing any action sequence. Joints point spatial information calculates using angle, joint’s sine relation, and distance features, whereas joints point temporal information estimates from frame-frame relations. Experiments are performed over four publicly available databases, i.e., MSR Daily Activity 3D Dataset, UTD Multimodal Human Action Dataset, KARD- Kinect Activity Recognition Dataset, and SBU Kinect Interaction Dataset, and proved that proposed descriptor outperforms as a comparison to state-of-the-art approaches on entire four datasets. Angle, Sine relation, and Distance features are extracted using interrelationships of joints and frames (ASD-R). It is all achieved due to accurately detecting spatial and temporal information of the Joint’s points. Moreover, the Support Vector Machine classifier supports the proposed descriptor to identify the right classification precisely.

The primary purpose of this study was to determine adherence and health-related quality of life (HRQoL) in PWE. Secondary aims were to assess association between adherence and HRQoL and determine predictors of HRQoL in PWE in Pakistan. A descriptive cross-sectional study was conducted among PWE receiving treatment from two tertiary care hospitals of Pakistan. The HRQoL and adherence were assessed with Urdu versions of Quality of Life in Epilepsy-31 (QOLIE-31), and Medication Adherence Rating Scale (MARS). Relationship between HRQoL and adherence was assessed by Pearson's product-moment correlation coefficient. Forced entry multiple linear models were used to determine relationship of independent variables with HRQoL. 219 PWE with a mean (±standard deviation) age, 34.18 (± 13.710) years, participated in this study. The overall weighted mean HRQoL score was (51.60 ± 17.10), and mean score for adherence was 6.17 (± 2.31). There was significant association between adherence and HRQoL in PWE (Pearson's correlation = 0.820-0.930; p ≤ .0001). Multiple linear regression found adherence (B = 16.8; p ≤ .0001), male gender (B = 10.0; p = .001), employment status (employed: B = 7.50; p = .030), level of education (Tertiary: B = 0.910; p = .010), duration of epilepsy (>10 years: B = -0.700; p ≤ .0001), and age (≥46 years: B = -0.680; p ≤ .0001), and ASM therapy (polypharmacy: B = 0.430; p = .010) as independent predictors of HRQoL in PWE from Pakistan. The findings suggest PWE from our center have suboptimal adherence which affects HRQoL. Independent factors such as male gender, employment status and duration of epilepsy are predictors of HRQoL.

Efficient task execution is critical to optimize the usage of computing resources in process scheduling. Various task scheduling algorithms ensure optimized and efficient use of computing resources. This article introduces an innovative dual-layer scheduling algorithm, Multi-Queue Adaptive Priority Scheduling (MQAPS), for task execution. MQAPS features a dual-layer hierarchy with a ready queue (RQ) and a secondary queue (SQ). New tasks enter the RQ, where they are prioritized, while the SQ contains tasks that have already used computing resources at least once, with priorities below a predefined threshold. The algorithm dynamically calculates the time slice based on process priorities to ensure efficient CPU utilization. In the RQ, the task’s priority level defines its prioritization, which ensures that important jobs are completed on time compared to other conventional methods where priority is fixed or no priority parameter is defined, resulting in starvation in low-priority jobs. The simulation results show that MQAPS better utilizes CPU resources and time than traditional round-robin (RR) and multi-level scheduling. The MQAPS showcases a promising scheduling technique ensuring a balanced framework for dynamic adjustment of time quantum and priority. The MQAPS algorithm demonstrated optimization, fairness, and efficiency in job scheduling.

Task scheduling algorithms are crucial for optimizing the utilization of computing resources. This work proposes a unique approach for improving task execution in real-time systems using an enhanced Round Robin scheduling algorithm variant incorporating dynamic time quantum and priority. The proposed algorithm adjusts the time slice allocated to each task based on execution time and priority, resulting in more efficient resource utilization. We also prioritize higher-priority tasks and execute them as soon as they arrive in the ready queue, ensuring the timely completion of critical tasks. We evaluate the performance of our algorithm using a set of real-world tasks and compare it with traditional Round Robin scheduling. The results show that our proposed approach significantly improves task execution time and resource utilization compared to conventional Round Robin scheduling. Our approach offers a promising solution for optimizing task execution in real-time systems. The combination of dynamic time quantum and priorities adds a unique element to the existing literature in this field.

Agriculture faces critical challenges such as timely disease detection, fragmented market access, and limited use of real‐time technology in the field. To address these issues, we developed AgriSage, an Android‐based intelligent mobile application that integrates artificial intelligence, weather forecasts, and governmental scheme updates to support farmers, sellers, customers, and policymakers. The application incorporates two optimized deep learning models designed for on‐device deployment. The first model, based on MobileNetV2, performs binary classification to detect the presence of plants in images. It achieved a precision, recall, and F1‐score of 1.00 for both classes, indicating perfect classification performance on the test set. On‐device inference testing of the converted TensorFlow Lite model resulted in an average prediction time of approximately 3736.44 ms per image when evaluated through the validation pipeline. Another deep learning model, that is, a convolutional neural network designed for disease classification, was trained on the PlantVillage dataset across 38 classes. It achieved a macro average F1‐score of 0.8207 and a weighted average F1‐score of 0.8703. The optimized TensorFlow Lite version demonstrated an average inference time of 35.6 ms per image, confirming its suitability for real‐time, on‐device deployment. AgriSage delivers a robust and scalable platform integrating AI‐powered crop monitoring and disease detection. It also provides real‐time agricultural support services, contributing to improved decision‐making and promoting sustainable farming practices. Android‐based application for empowering farmers with E‐commerce.

Background Tocilizumab (TCZ), an interleukin-6 (IL-6) receptor blocker, emerged as a treatment for cytokine release syndrome (CRS) in patients with severe COVID-19 pneumonia. The main objective of the study is to discuss the treatment response of TCZ in severe and critically ill patients with COVID-19 pneumonia. Patient demographics, laboratory parameters before and after TCZ therapy, and clinical outcomes in 20 patients in a single center were prospectively reviewed. Results Out of 120 patients, 96 (80%) were males and 24 (20%) were females. Only eight (10%) patients did not have any previously known comorbidity. There were 78 (65%) patients with severe disease, while 42 (35%) have critically severe disease. Of the 120 patients, only 36 required a second dose of TCZ in our study based on clinical background. Neutrophils and C-reactive protein (CRP) levels were observed to be raised in all patients, while lymphopenia was observed in 114/120, and D-dimer levels were elevated in 102 (85%) patients. After the second dose of tocilizumab, 102 (85%) patients reduced oxygen requirement within four days, and 14 patients were removed on the second dose of tocilizumab on clinical grounds. Of these 120 patients, in two weeks, 30 (25%) were discharged. Within three weeks, 60 of them were discharged, while 12 were discharged after three weeks, and 18 patients died in our study despite treatment. Conclusion TCZ appeared to be a good treatment option in patients with CRS and severe and critical pneumonia, and for patients with raised IL-6 levels despite single TCZ therapy, a repeat dose is recommended.

Abstract The glauconite deposits from Thub top near Ayubia, District Abbottabad (Pakistan) are characterized for their mineral contents, chemical composition, and beneficiation potential by using petrography, chemical analysis and high intensity wet magnetic separation. The petrographic analysis showed that the glauconitic sandstone is dominantly comprised of quartz, carbonate cements, and glauconite. Iron oxides has partially replaced the glauconite. The chemical analysis revealed a low K2O content of 3.23%, below the threshold for economic extraction and utilization in potassium fertilizer (K-fertilizer) production. In addition, the sample in its natural form had low P2O5 and iron oxide contents. The upgradation of glauconite showed an increased concentration of Fe2O3 and FeO (iron oxides) from 11.36% to 17.79% and 4.32% to 4.50% respectively. However, there was an insignificant increase in the concentrations of K2O, P2O5, and other oxides. Furthermore, the potential utilization of the beneficiated glauconite samples as K-fertilizer and water softener was evaluated. The results indicate the limited suitability of glauconite as a fertilizer because of its low potassium (K) contents. In its original form, the material displayed a negative water-softening property. Nevertheless, after regeneration through immersion in NaCl and NaOH solutions, a significant improvement in the water-softening capability of glauconite was observed and the treated glauconite reduced water hardness from 916 mg/L to 300 mg/L.