Global Research Journal of Natural Science and Technology
The Global Research Journal of Natural Science and Technology (GRJNST) (ISSN: 2790-7643 Print | 2790-7651 Online) is a HEC-Pakistan recognized Y Category, multidisciplinary journal dedicated to advancing knowledge across the natural sciences, engineering, technology, health sciences, and agricultural disciplines. GRJNST provides a prestigious platform for the dissemination of original, high-quality research, reviews, and analytical essays that promote scientific rigor, innovation, and interdisciplinary dialogue. Founded to foster global research excellence, GRJNST operates as a double-blind peer-reviewed, open access journal, committed to maintaining the highest standards of research integrity, ethical publishing, and transparency. The journal rigorously evaluates submissions through a network of over 300 certified editorial board members, professional reviewers, and expert collaborators, ensuring that published work reflects originality, methodological soundness, and relevance to the field. To date, GRJNST has received over 1,200 research submissions, publishing approximately 220 top-quality articles, demonstrating its selectivity, quality control, and dedication to scholarly excellence. The journal prioritizes contributions that advance understanding, offer innovative solutions, and bridge disciplinary boundaries, aligning with contemporary challenges and research frontiers. GRJNST operates under a fully open access policy, allowing unrestricted access to all content in PDF format, thereby maximizing visibility, accessibility, and citation impact. Each article is assigned a Digital Object Identifier (DOI), ensuring persistent identification, interoperability with international databases, and integration into global research ecosystems. The journal adheres to rigorous ethical standards, including plagiarism detection, conflict of interest disclosure, and compliance with research data policies. The journal’s scope spans, but is not limited to, Physics, Chemistry, Biology, Zoology, Botany, Geology, Mathematics, Biotechnology, Food Science, Agricultural Sciences, Sustainable Energy, Soil and Water Sciences, Environmental and Earth Sciences, Computer Science, Mechanical, Civil, and Electrical Engineering, Information Technology, Health Sciences, Medicine, Public Health, Veterinary Science, and Health Policy. GRJNST emphasizes international collaboration, supported by its network of expert reviewers and editorial advisors, which includes leading researchers and academics from top universities and institutions worldwide. This network ensures an impartial, rigorous, and timely peer-review process, fostering credibility and academic recognition. Through its comprehensive publishing model, GRJNST contributes to the global knowledge ecosystem, promoting research dissemination, innovation, and evidence-based solutions. By combining high-quality scholarly content, open access, and robust indexing in national and international databases, the journal has steadily emerged as a trusted, impactful, and influential publication platform for researchers, practitioners, and policy-makers alike. In essence, GRJNST embodies the principles of academic excellence, interdisciplinary collaboration, and global knowledge exchange, serving as a cornerstone for advancing science, technology, and applied research worldwide.
Influence of Different Nitrogen and Phosphorus Fertilizer Rates on Growth and Yield Components of Sesame (Sesamum indicum L.)
A field study was conducted to evaluate the effect of different nitrogen and phosphorus (NP) fertilizer rates on the growth and yield performance of sesame (Sesamum indicum L.). The experiment was laid out in a Randomized Complete Block Design (RCBD) with six treatments including a control (0, 30, 40, 50, 60, and 70 kg NP ha⁻¹). The results showed a significant and consistent increase in all growth and yield parameters with increasing NP levels. Plant height (104.35–181.94 cm), stem diameter (4.89–10.97 cm), capsules per plant (70.42–181.08), and seeds per capsule (19.12–44.63) increased progressively with higher fertilizer application. Similarly, seed weight per plant (21.46–51.58 g), seed index (2.69–4.26 g), and biological yield (3124.8–5225.4 kg ha⁻¹) also showed marked improvement across treatments. The maximum values for all studied traits were recorded at 70 kg NP ha⁻¹, while the minimum values were observed in the control treatment. Statistical analysis indicated highly significant differences among treatments (P ≤ 0.05) for all parameters. The study concludes that balanced application of nitrogen and phosphorus at higher levels (especially 70 kg NP ha⁻¹) significantly enhances sesame growth and yield performance under field conditions.
Copyright (c) 2026 Asif Ali Kaleri (Corresponding Author), Bina Khanzada, Tanveer Hussain Nohri, Sheharyar Ali, Mian Noor Hussain Asghar Ali, Ain-ul-Abad Syed, Manzoor Ali Magsi, Nawab Ali Buledi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Digital Pure Tone Audiometer: A Smart and Self-Administered Hearing Test System
Hearing loss affects over 430 million people globally, with the World Health Organization projecting this figure to rise to 700 million by 2050. Despite its prevalence, access to affordable and accurate diagnostic tools remains limited, particularly in low-resource settings. Traditional audiometry systems rely on complex hardware and proprietary software, limiting their scalability and adaptability. This device reimagines such systems by utilizing open-source technologies to create a user-friendly device. The core problem lies in bridging the gap between affordability and diagnostic accuracy while ensuring compliance with international audiometric standards. The solution integrates a Raspberry Pi with headphones and a touchscreen interface, capable of performing pure-tone audiometry across frequencies (125 Hz–8 kHz) at intensities up to 120 decibel Hearing Level. Key features include automated threshold detection, real-time audiogram visualization, and dual operational modes (manual/auto) to accommodate diverse clinical workflows. The auto mode uses adaptive algorithms to reduce testing time, while the manual mode allows clinicians fine control for unusual cases. The procedures encompassed iterative prototyping, software development for tone generation and response logging, and rigorous clinical validation. Calibration was performed using a reference sound level meter, while usability metrics (e.g., touch responsiveness, test duration) were quantified through timed trials. Results demonstrated a mean threshold deviation of ±4 decibel Hearing Level compared to the commercial device. Testing involved 49 participants, including clinicians and patients, to evaluate accuracy, usability, and efficiency. This device underscores the viability of open-source, low-cost solutions in bridging healthcare disparities, offering a scalable model for hearing loss diagnosis.
Saeed Ahmed Maitlo (Corresponding Author)
Copyright (c) 2026 Pareesa Shoro, Aliza, Hamna Khan Alias Palwasha, Saeed Ahmed Maitlo, Sarmad Shams (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Remote Sensing–Based Assessment of Forest Carbon Sequestration and Land Use Change Dynamics under Climate Variability
Forest ecosystems are among the most significant terrestrial carbon reservoirs and play a crucial role in regulating the global carbon cycle through carbon sequestration. However, rapid land use and land cover change (LULC), combined with increasing climate variability, has significantly altered the stability and efficiency of these ecosystems. This review paper examines the application of remote sensing technologies in assessing forest carbon sequestration and monitoring land use dynamics under changing climatic conditions. The study highlights how urbanization, agricultural expansion, deforestation, and wildfire activities contribute to carbon emissions and ecosystem degradation across tropical, coastal, semi-arid, and urban forest landscapes. Special emphasis is placed on the integration of optical, LiDAR, and Synthetic Aperture Radar (SAR) systems for estimating above-ground biomass (AGB), soil organic carbon (SOC), and vegetation structural characteristics. The review further explores the role of vegetation indices, multi-sensor fusion, and machine learning algorithms such as Random Forest (RF), Support Vector Machines (SVM), XGBoost, and Convolutional Neural Networks (CNNs) in improving biomass estimation accuracy and carbon accounting. Additionally, the paper discusses the influence of climatic variables, including temperature rise, altered precipitation regimes, droughts, and wildfires, on forest carbon sequestration potential and ecosystem resilience. Emerging technologies such as ESA Biomass, NISAR missions, and Digital Twin Earth systems are also evaluated for their transformative potential in global forest monitoring and carbon management. The findings suggest that integrating advanced remote sensing with artificial intelligence and climate modeling provides an effective framework for sustainable forest management, climate mitigation strategies, and accurate carbon monitoring at regional and global scales.
Muhammad Hassan Ali (Corresponding Author)
Copyright (c) 2026 Muhammad Hassan Ali (Corresponding Author), Fateh Ali Chohan, Asad ullah, Kalsoom, Muhammad Ismail Chohan, Walliullah Lagahri, Muhammad Afzal (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Rewiring Carbon Strategy: How High-Voltage Systems Shape Corporate Emissions and Sustainability Outcomes
This study investigated the influence of high-voltage systems on corporate carbon emissions and sustainability outcomes within energy-intensive industries. The research examined how advanced electrical infrastructure contributed to energy efficiency, environmental governance, and organizational sustainability performance. A quantitative research design was employed using survey data collected from 320 professionals working in manufacturing, energy, logistics, engineering, and technology sectors. Data analysis was conducted through descriptive statistics, correlation analysis, and multiple regression analysis using SPSS. The findings revealed strong positive relationships between high-voltage system efficiency, energy optimization, carbon reduction, and sustainability performance. Correlation analysis indicated a significant relationship between high-voltage systems and energy efficiency improvement (r = .783, p < .01), while sustainability performance showed a strong association with ESG governance (r = .734, p < .01). Regression analysis demonstrated that high-voltage system efficiency significantly influenced corporate sustainability performance (β = .412, p = .000). The model summary showed an R² value of .659, indicating that 65.9% of the variation in sustainability performance was explained by the independent variables. The findings suggested that organizations utilizing modern transmission systems achieved lower operational emissions, improved energy reliability, and stronger environmental governance practices. The study concluded that high-voltage infrastructure modernization supported industrial decarbonization, sustainable energy management, and long-term corporate sustainability objectives. The research provided practical implications for policymakers, industrial managers, and sustainability professionals seeking effective strategies for environmental transformation and climate-oriented industrial development.
Copyright (c) 2026 Ali Raza Chachar, Muhammad Asif Ramzan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Management of Ti-6Al-4V and SS316L Stress Shielding and Stability of Femoral Fracture Fixation Bone Plates by Finite Element Analysis in COMSOL Metaphysics
The human femur is known as the longest bone in the human body. For compression, it is known as the most robust bone. The femur bears the majority of body weight and facilitates several essential functions, including ambulation and leaping from a height. Bone fractures typically result from an excessive stress that exceeds the maximum strain that a human bone can withstand. Due to the loss of the bone's capacity for self-healing, a bone fracture with a fragment larger than 5 mm needs extra support, such as a bone implant, for recovery. The repair process is accelerated with a bone implant that holds the parts in place, permits realignment, and limits excessive movement. The commercially available bone plates comprised of biocompatible metallic materials or metal alloys are used in the current fracture fixation technique. To aid in the healing process, these bone plates are often attached to one side of the fracture. Prosthetic bone bioimplants have been designed and developed using conventional metallic biomaterials like titanium alloy (Ti-6Al-4V) and stainless steel (SS 316L). Two typical internal fixation materials are relatively evaluated with Finite Element Method (FEM) in COMSOL Multiphysics and they comprise Titanium alloy (Ti-6Al-4V) and Stainless Steel (SS316L) in realistic conditions of loads in the fractures of the diaphyseal segment of the femur. The fixation of the fracture shall be stable and the issue of stress shielding is the issue due to the use of the relatively inelastic material that is SS316L (Young) which are the ones that bear the majority of the load and causes bone resorption A complex 3D model of the femur-plate-screw system with transverse fracture of 1 mm has been modeled with an intention of equaling the material with the same physiological biomechanical boundary loads. These results showed that theTi-6Al-4V plate system was in a superior state concerning the mechanical performance of an overall displacement of 3.887599mm, compared to the huge displacement of SS316L (25.1252). It proves that Ti-6Al-4V is better and provides greater structural stability and thus is even more suitable material which can be used as load-bearing orthopedic implants, where structural stability is of significant determinant of clinical success.
Copyright (c) 2026 Kajal Dev, Hajira Fatima, Kashaf Khanzada, Sehreen Moorat, Natasha Mukhtiar (Corresponding Author) (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Design and Implementation of an IoT-based Air Quality Monitoring System with Mobile App Interface
The climate change and variations in environmental conditions such as temperature, humidity and air quality have increased considerably in recent years. With the advancement of smart technologies and the growing need for real-time, data-driven decision-making, there is a strong demand for automated environmental monitoring systems. Continuous monitoring and representation of environmental data are crucial for safety of humans and environment. This need is addressed by the proposed system which is designed and implemented as an Internet of Things (IoT) based air quality and smoke detection system with mobile application interface. An ESP32 microcontroller is used to monitor the environmental conditions continuously with the help of DHT11 temperature and humidity sensor, MQ-2 gas sensor and MQ-135 gas sensor. The chosen components are affordable and small, allowing for reliable deployment. Processed data is shown locally on an OLED screen for on-site monitoring and at the same time uploaded to the cloud platform ThingSpeak for storage and remote access. A Flask based web application receives the data and uses the threshold-based classification to show safe and hazardous environmental conditions in an interactive dashboard. Moreover, RESTful API integration can help to communicate with a mobile application, which means that the remote monitoring can be done in real-time from anywhere. System evaluation shows that manual supervision needs are reduced and real-time visualization of environmental conditions is effective. The proposed system is overall low cost, scalable and efficient for smart environmental monitoring applications.
Copyright (c) 2025 Muhammad Sobait, Rauf Ahmad, Hammad Shahab (Corresponding Author) (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
CRISPR-Cas–Driven Molecular Genetics and Immunology: Transforming Precision Therapeutics in the Post-Genomic Era
Precision genome editing holds transformative potential for immunotherapy, yet translating CRISPR-Cas interventions into clinically robust therapeutics remains constrained by variable functional outcomes and limited predictive biomarkers. Here, we engineered primary human T cells using high-fidelity Cas9 and adenine base editor ribonucleoproteins targeting [target locus], integrating multi-omic profiling (scRNA-seq, CITE-seq, ATAC-seq), unbiased off-target screening, and functional validation in humanized in vivo models. Machine learning harmonized multi-modal data to predict editing efficacy and immune persistence. On-target editing achieved 64–78% efficiency with negligible off-target activity and preserved genomic stability. Edited cells underwent coordinated transcriptomic and epigenetic reprogramming toward a stem-cell memory phenotype, demonstrating enhanced TCR signaling, cytotoxicity, and sustained proliferation. In humanized mice, adoptive transfer conferred durable tumor control (median survival: 48 vs. 29 days; p < 0.001) without cytokine storm or off-target toxicity. Chromatin accessibility at the target locus and early phospho-ZAP70 dynamics accurately predicted in vivo persistence (AUC = 0.96). This study establishes a causally validated, translation-ready CRISPR-Cas platform that bridges post-genomic discovery with precision immunotherapy,
Copyright (c) 2026 Nadia Bibi, Muhammad Saad Abbasi, Maryam Farooq, Mahtab Ali shah, Noor-Ul-Huda, Muhammad Aarab, Muqaddas Fida (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Safety, Quality, and Sustainability: The Environmental Benefits of HSEQ in Oil Fields
The oil and gas industry provides a large part of the current energy needs; however, it produces a wide variety of severe environmental problems such as air pollution, water pollution, contamination of soil, and alteration of ecosystems. The development of Health, Safety, Environment, and Quality (HSEQ) management systems has become a key way to help reduce the adverse effects caused by the oil and gas sector and create a more sustainable future for the oil industry. This research will focus on how HSEQ management systems can help achieve the goal of sustainability in relation to the oil and gas sector, specifically in the case of Pakistan. Through a qualitative research approach using thematic analysis as the primary analysis method, the current state of the literature regarding HSEQ management systems will be explored through the synthesis of theoretical perspectives, case study data, and literature-based evidence. Using these data sets, it will evaluate how HSEQ management systems are able to provide a contribution to the reduction of the environmental impact of the oil industry while still meeting efficiency goals for the oil industry. This research indicates that there are four main categories in which HSEQ supports environmentally sustainable development: (1) Protection from risk & Control of hazards; (2) Compliance with environmental laws & Regulations; (3) Waste and Pollution; (4) Resource and Energy efficiency. The four categories are reviewed in the framework of an operational idea for an oilfield where most of these processes have demonstrated limited positive results due to lack of proactive measures such as frequent assessments of environmental impacts, implementation of ISO 14001 systems and protocols for spill containment and the reduction of flaring. The theoretical framework includes the Triple Bottom Line (TBL), Precautionary Principle, and Corporate Social Responsibility (CSR), so HSEQ is both an operational necessity and a company’s ethical obligation. The research indicates that environmentally responsible operations in Oil Field production is not only an aspect of legal compliance but also a strategic investment for operational resilience, corporate reputation, and trust with communities. The Oil Field case study demonstrates that well-integrated HSEQ systems can result in significant positive environmental impacts, including reduced GHG emissions, decreased freshwater use, and improved biodiversity protection around extraction activities. But the research also identifies gaps, such as a lack of availability to the public for performance related to the Environment, inconsistent enforcement of HSEQ standards within subcontracted operations, and not enough training of workers concerning sustainable practices. Based on these findings, the study makes recommendations to strengthen internal audit functions, to expand on the integration of renewable energy into oil field operations, to expand community engagement efforts, and to encourage the open disclosure of environmental metrics to stakeholders. The research places HSEQ in the broader context of Corporate Responsibility and Environmental Stewardship, thus highlighting that a fundamental reform in all aspects of oil field operations is needed, not just compliance, but also to take the lead in implementing Environmental Sustainability in oil field operations.
Copyright (c) 2026 Waqar Afzal, Muhammad Bilal Qamar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Efficient Explicit Improved Scheme for Numerical Solution of Cauchy Problems
This study work is focus on to design explicit splendid converging method to estimate numerical result of initial-value-problems (IVP’s) for differential equation in nature ordinary. The stability criteria of the improved scheme are investigated, and corresponding stability region is depicted. Error analysis carried out also affirms accuracy having third order. Error (LTE) is derived by utilizing Taylor’s series expansion to skip higher order term after matching corresponding coefficients. Adding a partial derivative inside function evaluation raises convergence and reduces both errors (maximum and last). Based on results for improved scheme to illustrate the accuracy and efficiency. The comparisons of the improved and existing schemes having same order of local accuracy are discussed. Looking at numerical results, the improved scheme gives the better result in the comparison of existing few same order method. Stability is proved to examine behavior of method and its region is visualized. Consistency is investigated that is showing how error shrink to zero. the numerical result is validated and illustrated graphically via utilizing MATLAB 2023a software.
Muhammad Daud Kandhro (Corresponding Author)
Copyright (c) 2026 Muhammad Daud Kandhro (Corresponding Author), Khushbu Rajput , Zohaib Ali (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Phytoremediation of Heavy Metals Using Native Plant Species
Heavy metal contamination has emerged as a critical environmental issue due to rapid industrialization, mining activities, and poor waste management practices. These contaminants persist in soil and water systems, posing long-term ecological and human health risks. In developing regions such as Pakistan, the problem is further aggravated by weak environmental governance and limited access to advanced remediation technologies. This issue requires urgent attention because heavy metals accumulate in crops, enter the food chain, and cause chronic health disorders. Sustainable and cost-effective remediation strategies are therefore essential for environmental protection and public health. This study investigates the potential of selected native plant species for phytoremediation of heavy metals in contaminated soils. The research focuses on three native species: Typha domingensis (cattail), Phragmites australis (common reed), and Prosopis juliflora (mesquite), which are widely distributed in Sindh and other parts of Pakistan. A mixed-method approach was adopted, integrating field sampling, laboratory analysis, and comparative evaluation using secondary data. Soil and plant samples were collected from industrial and mining-affected sites, and heavy metal concentrations were measured using standard analytical techniques. Key performance indicators such as bioaccumulation factor (BAF) and translocation factor (TF) were calculated to assess phytoremediation efficiency. The results show that Typha domingensis exhibited high accumulation of cadmium (Cd) and lead (Pb) with BAF values ranging from 1.6 to 2.3. Phragmites australis demonstrated strong translocation ability for chromium (Cr), with TF values reaching up to 0.72. Prosopis juliflora showed significant tolerance and accumulation of multiple metals, particularly in root tissues, indicating its suitability for phytostabilization. Overall, native species improved remediation efficiency by approximately 45–65% compared to non-native plants reported in previous studies. The study concludes that native plant species provide an effective, low-cost, and environmentally sustainable solution for heavy metal remediation. Their integration into environmental management strategies can significantly enhance ecological restoration efforts in Pakistan.
Copyright (c) 2026 Usra Saqib, Anbji, Imran Ali, Bushra fazal , Poorab Almas (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Vertical Farming and Soilless Cultivation Technologies for Urban Food Security
Vertical farming and soilless cultivation technologies represent a paradigm shift in urban agriculture, addressing the challenges of feeding a projected 9.7 billion global population by 2050 amid arable land loss (1.8–2.4% annually), freshwater scarcity (70% agricultural use), and climate volatility. This review synthesizes advancements in hydroponics, aeroponics, and aquaponics decoupling production from soil via nutrient film technique (NFT), deep water culture (DWC), and vertical stacking enabling year-round yields up to 390 times higher than traditional methods per unit area, with 70–95% water savings and reduced pesticide needs. Key innovations include LED-optimized spectral lighting (e.g., red:blue ratios for photosynthesis), AI-driven climate control (sensors for pH, EC, CO₂), and integrated pest management (IPM) with beneficial microbes. Economic analyses indicate viability in urban settings (ROIs of 3–5 years for leafy greens), while environmental benefits encompass lower carbon footprints (via localized production) and enhanced food security in megacities. Challenges like high energy demands (addressed by renewables) and initial costs are mitigated through modular designs and subsidies. The integration of these systems promises resilient, localized food chains, aligning with SDGs for zero hunger and sustainable cities.
Gul Muhammad shah (Corresponding author)
Copyright (c) 2026 Gul Muhammad shah (Corresponding author), Murtaza Ali , Iqra Sultan Rajput, Mir Baqar Raza Talpur (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Integrating Artificial Intelligence, Remote Sensing, and GIS for Sustainable Agro-Forestry Management and Land Resource Optimization
The increasing pressure on land resources due to climate change, population growth, and unsustainable land-use practices necessitates advanced approaches for sustainable agro-forestry management. This study proposes an integrated framework that combines Artificial Intelligence (AI), Remote Sensing (RS), and Geographic Information Systems (GIS) to support land suitability analysis and resource optimization. Multi-source geospatial data, including satellite imagery, environmental variables, and topographic information, are processed through a structured pipeline involving pre-processing, feature extraction, AI-based modelling, and GIS-driven spatial analysis. Machine learning and deep learning models are employed to perform land use classification and predict agro-forestry suitability, while multi-criteria decision analysis (MCDA) is used to integrate environmental factors and generate suitability maps. The results indicate that a significant portion of the study area falls within highly and moderately suitable categories, demonstrating strong potential for agro-forestry development. Model evaluation using 5-fold cross-validation shows stable performance across all models, with deep learning approaches providing slightly higher accuracy, while traditional models remain computationally efficient. The integration of NDVI-based environmental analysis with spatial modelling further validates the reliability of the framework. The resulting decision support outputs enable the identification of priority zones and optimized land-use strategies. Overall, the proposed framework offers a scalable and data-driven solution for sustainable land resource management and supports informed decision-making for agro-forestry planning.
Copyright (c) 2026 Muhammad Kashif Majeed (Corresponding Author), Kashif Akbar, Muhammad Hassan Ali, Muhammad Essa Siddique, Murtaza Ali , Gul Muhammad Shah (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Impacts of Climate Change on Cotton (Gossypium hirsutum L.): Growth, Yield and Resilience Strategies- A Global and Pakistan Perspective
Cotton (Gossypium hirsutum L.) is an important fiber crop which plays a vital role in industries, economics and rural livelihoods, globally and especially in Pakistan. But current climate change is increasingly becoming a threat to cotton productivity due to increased temperatures, variability in rainfall, droughts, flooding, salinity and increased infestation of pest and diseases. Heat and drought stress affects the rate of photosynthesis, maturation of the plant, boll retention and fiber development with significant yield and quality losses. Physiological instability and oxidative damage are exacerbated by combined stresses. Climate variability also increases the occurrence of key pests and diseases such as pink bollworm (Pectinophora gossypiella) and Cotton Leaf Curl Virus (CLCuV) with devastating consequences to crop yields, particularly in South Asia. New opportunities are provided by recent progress in biotechnology such as CRISPR-Cas genome editing, genomic selection, genome-wide association studies (GWAS) and multi-omics approaches to develop cotton cultivars that are more climate-resilient, have enhanced stress resistance and have more stable fiber properties. Besides that, climate-smart agriculture (CSA) techniques like deficit irrigation, conservation agriculture, integrated pest management, agroforestry and digital agriculture are gaining increasing significance as adaptation strategies. This review provides a summary of the physiological, agronomic, molecular, ecological and socio-economic climate change effects on cotton at the global and Pakistan perspectives and integrated approaches for sustainable and climate resilient cotton production. This work was supported by the Government of Pakistan, Ministry of Science and Technology, under the program Climate Resilient Agriculture with a seed grant from the ASL. This work was funded by Government of Pakistan, Ministry of Science and Technology under the program Climate Resilient Agriculture, with a seed grant from the ASL.
Copyright (c) 2026 Rashida Babi, Habib Ullah (Corresponding Author), Ali Nawaz , Haji Manzoor Ahmad , Khatir Ali , Abdulrehman Niazi , Sana Ullah (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Artificial Intelligence for Smart Infrastructure System: Integrating Civil and Electrical Engineering
The rapid convergence of Artificial Intelligence (AI) with civil and electrical engineering is transforming traditional infrastructure into intelligent, adaptive, and sustainable smart infrastructure systems. This study presents a comprehensive review of AI-driven integration frameworks that bridge the physical domain of civil engineering with the energy and communication networks of electrical engineering. It highlights the role of machine learning, deep learning, and reinforcement learning techniques in enabling predictive modeling, real-time monitoring, and autonomous decision-making across infrastructure lifecycles. Key applications discussed include structural health monitoring using data-driven models, computer vision-based damage detection, and time-series forecasting for infrastructure performance. The paper further explores the critical role of smart grids in modern energy systems, emphasizing AI-based load forecasting, fault detection, and self-healing capabilities. Emerging paradigms such as Vehicle-to-Grid (V2G) systems and microgrid integration are examined as essential components of future urban resilience. Digital Twin technology is identified as a cornerstone of smart infrastructure, enabling real-time synchronization between physical assets and virtual models for predictive maintenance, lifecycle optimization, and risk-informed design. Additionally, the integration of smart materials, including self-healing concrete, shape memory alloys, and piezoelectric systems, introduces a new layer of material intelligence that enhances infrastructure durability and efficiency. Despite significant advancements, challenges such as data interoperability, cybersecurity, energy demands of AI systems, and regulatory constraints remain critical barriers. The study underscores the importance of interdisciplinary collaboration, standardization frameworks, and sustainable design strategies to fully realize the potential of AI-enabled infrastructure. Ultimately, the integration of AI with civil and electrical engineering offers a transformative pathway toward resilient, efficient, and future-ready built environments.
Copyright (c) 2026 Mohib ur Rahman (Corresponding Author), Wasiq Attique, Ayesha Samreen, Maaz Bin Ubaid, Ahmad Saleem, Zohaib Akhtar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Smart Transportation Systems: Enhancing Traffic Flow and Reducing Urban Congestion through Intelligent Solutions
Smart Transportation Systems emerged as an effective solution for improving traffic flow efficiency and reducing urban congestion through intelligent and data-driven technologies. This study examined the impact of real-time traffic monitoring, intelligent traffic signal control, artificial intelligence-based predictive analytics, and IoT-enabled data integration on urban mobility outcomes. A quantitative research design was applied, and data was collected from a sample of 300 respondents, including transportation professionals, urban planners, and daily commuters. Descriptive statistical analysis was used to evaluate the effectiveness of Smart Transportation System components. The results indicated that real-time traffic monitoring recorded the highest mean value (M = 4.12), followed by traffic flow efficiency (M = 4.10), intelligent signal control (M = 4.05), and urban congestion reduction (M = 4.03). AI-based predictive analytics (M = 4.01) and IoT-enabled data integration (M = 3.98) also demonstrated strong positive contributions to traffic optimization. The findings showed that Smart Transportation Systems significantly improved travel time, reduced intersection delays, enhanced vehicle movement, and minimized congestion during peak hours. The study concluded that intelligent transportation technologies played a crucial role in enhancing urban mobility and supporting sustainable transportation development. The results provided valuable insights for policymakers and urban planners to design efficient, technology-driven traffic management systems for modern cities.
Copyright (c) 2026 Muhammad Bilal Israr, Zafreen Elahi, Anwaar Hazoor Ansari, Ahtsham Mustafa Awan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Mutational Analysis of TRAPPC9 IN Non-syndromic Intellectual Disability in Selected Patients
Intellectual incapacity (identity) is a neurodevelopmental circumstance characterized through good sized obstacles in cognitive functioning and adaptive behavior, with onset before the age of 18. It is usually diagnosed in early formative years due to developmental delays in motor, cognitive, and speech abilities, and is generally described by way of an IQ under 70. Identity is classed into syndromic and non-syndromic kinds and further divided into mild, mild, extreme, and profound categories based totally on severity. This takes a look at centered on non-syndromic identification patients, regarding medical evaluation and genetic evaluation. Blood samples have been accrued, and DNA became analyzed the usage of PCR with TRAPPC9 gene primers, followed by gel electrophoresis and sequencing. No mutation became detected in exon 14 of the TRAPPC9 gene, suggesting the want for broader genetic investigations to higher understand and help reduce the prevalence of intellectual incapacity.
Copyright (c) 2026 Mubara Mubashar, Farva Razzaq Maham, Saman Mumtaz, Farooq Ahmad, Fizza Hassan, Laraib Saleem, Hafiza Komal Naeem (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Advancing Nitrogen Use Efficiency in Modern Agriculture through the Synergistic Integration of Biotechnology, Microbial-Based Approaches, and Innovative Nutrient Delivery Systems for Sustainable Food Production
Global agricultural systems remain critically dependent on synthetic nitrogen fertilisers, yet systemic inefficiencies persist: less than half of applied nitrogen is assimilated by crops, with the remainder driving aquatic eutrophication, nitrous oxide emissions, and economic vulnerability. This study evaluates a tripartite integration framework, combining genome-edited crop lines, targeted rhizosphere microbial consortia, and precision nano-enabled nutrient delivery systems, to elevate nitrogen use efficiency (NUE) whilst mitigating environmental leakage. Deployed across a multi-site factorial design under reduced-input regimes, the integrated approach increased agronomic efficiency by 23% and reduced cumulative N₂O emissions by 41% relative to conventional broadcasting. Rhizosphere metagenomics revealed significant enrichment of functional nitrogen-cycling taxa, confirming enhanced biological nitrogen acquisition and tighter plant–microbe–soil coupling. These findings demonstrate that decoupling yield trajectories from fertiliser dependency requires coherent, systems-level integration rather than isolated technological interventions. By aligning genetic optimisation, microbial ecology, and precision delivery, contemporary agriculture can transition towards a regenerative nutrient paradigm that reconciles productivity imperatives with planetary boundaries.
Copyright (c) 2026 Nadia Jabeen, Gohar Ayoub Shah, Sayyad Waqas Umar, Ali Abdal, Muhammad Zeeshan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Evaluation of Genetic Parameters and Combining Ability for Drought Tolerance in Cotton (Gossypium hirsutum)
Global climate change especially water shortage and heat waves have drastically reduced cotton production. It is necessary to develop climate resistant coupled with high yielder cotton varieties suitable for various environments. For this purpose, ten cotton verities with different morphological, yield parameters, and drought tolerance/susceptibility were selected and hybridized using line × tester mating design. To study the inheritance pattern, twenty-five combinations along with their parents were grown in the field following augmented design with two different treatments i-e normal and water-deficit. Under stress environment, the GCA variance (σ2 gca) was higher than the SCA variance (σ2 sca) for CLCuD, number of bolls, boll weight, seed index, fiber length, and micronaiere value that shows additive type of gene action of investigated traits. Line L5 (VH-327), tester T5 (FH-Lalazar) and crosse 18 (L4 x T3) and cross 21 (L5 x T1) performed best for yield and yield contributing traits under water deficit condition environment. Cross 18 (L4 x T3), 21 (L5 x T1) and 22 (L5 x T2) had the higher SCA for the bolls number and cotton yield (15-20% higher from parents) both under normal and water deficit condition. Both under normal and water deficit condition, the best-performing genotypes and combinations (15 to 20% increase in seed cotton yield) may be used in the cotton breeding program to enahance the seed cotton yield and drought tolerance.
Copyright (c) 2026 Muhammad Ihsan Ullah, Saeed Ahmed, Shoaib Liaqat(Corresponding Author), Zaib-un-Nisa, Wajad Nazeer(Corresponding Author), Amna Bibi, Sadia Hakeem, Hammad Hussnain, Javed Iqbal, Nadia Hussain Ahmad, Sadia Kanwal, Muhammad Luqman (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Chemotypic Variation in Antioxidant Activity and Phytochemical Profiles of Terminalia arjuna (Roxb. ex DC.)
Terminalia arjuna (Roxb. ex DC.) Wight & Arn. is a medicinal tree widely used in South Asian traditional medicine for cardioprotection, primarily attributed to its antioxidant phytochemicals. However, the phytochemical profile of T. arjuna growing in Sindh, Pakistan where arid climate and saline soils may induce unique chemotypic variation remains uncharacterized. This study compared the phytochemical composition and in vitro antioxidant capacity of aqueous and methanolic extracts from leaves, stem bark and seeds of T. arjuna collected from Sindh, Pakistan. Plant material was extracted with distilled water and 80% methanol. Qualitative phytochemical screening (flavonoids, alkaloids) and quantitative analyses (total phenolic content; total antioxidant capacity via phosphomolybdenum assay; proteins, carbohydrates, reducing sugars) were performed (n=3 biological replicates). Statistical analysis used t-tests, two-way ANOVA and Pearson correlation (α=0.05). Flavonoids and alkaloids were present in all extracts. The aqueous leaf extract showed significantly higher TPC (49.8 ± 3.5 mg GAE/g) and TAC (38.4 ± 3.1 mg AAE/g) than its methanolic counterpart (p<0.001 and p<0.01, respectively), with a strong positive correlation (r=0.94, p<0.001). The methanolic seed extract contained the highest protein concentration (16.8 ± 1.5 mg/g). Significant variation was observed across plant parts and solvents (two-way ANOVA: plant part F(2,12)=45.32, p<0.001; solvent F(1,12)=38.76, p<0.001; interaction F(2,12)=12.45, p<0.001). This study establishes the first phytochemical baseline for Sindh-grown T. arjuna, confirming chemotypic variation. The aqueous leaf extract is a potent, renewable, sustainable antioxidant source, offering an ecologically responsible alternative to destructive bark harvesting.
Copyright (c) 2024 Safia Karim, Ghulam Farooque, Tayyaba , Mehran Ali Chhalgri, Nadir Ali Rind, Sana khokhar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Physicochemical and Sensory Attributes of Wheat Bread Supplemented with Mashed Potato
Rising wheat prices and import dependence are increasing the need for affordable, locally available alternatives in bread making. Potato (Solanum tuberosum L.) is widely grown but underused in bakery products. While dried potato flour typically cannot exceed 20–30% substitution due to gluten weakening, fresh mashed potato may behave differently because of its pre-gelatinized starch and intact cellular structure. This study examined bread in which wheat flour was replaced with fresh mashed potato at 0%, 30% and 50% levels. Standard baking procedures were applied and physicochemical properties (moisture, ash, protein, fat, fiber), pH and energy content were analyzed using AOAC methods. Consumer sensory evaluation was conducted with 30 participants using a 9-point hedonic scale. Statistical analysis included one-way ANOVA, LSD post-hoc testing, effect size estimation and confidence intervals. Results showed that increasing mashed potato significantly increased moisture, ash and crude fiber, while protein content decreased as expected. A marked increase in measured fat was observed, but this is most likely an analytical artifact caused by matrix effects during extraction rather than a true compositional change. The 50% substitution level produced the highest sensory acceptance, particularly for texture, flavor and overall liking. Overall, fresh mashed potato enabled up to 50% wheat replacement with improved hydration, mineral and fiber characteristics and acceptable sensory quality. However, absence of loaf volume, dough rheology and staling measurements limits full assessment of bread structure and functionality. Further studies should include these parameters and refine lipid analysis methods for starch-rich food systems
Copyright (c) 2024 Muhammad Irfan, Aasia Akbar Panwar, Ghulam Farooque, Fawad Ahmed (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Solvent‑Dependent Phytochemical Profiling and Total Reducing Capacity of Pongamia pinnata (L.) Pierre
Oxidative stress is implicated in numerous chronic diseases, driving the search for natural reducing agents from medicinal plants. Pongamia pinnata (L.) Pierre is a traditional medicinal tree; however, comparative data on the influence of solvent polarity and plant tissue on its phytochemical composition and reducing capacity remain limited. This study compared the phytochemical profiles and total reducing capacity of leaf, stem and seed extracts obtained using methanol (organic) and water (aqueous), with the aim of identifying the optimal plant part–solvent combination. Shade-dried plant materials were macerated in methanol or water (1:10 w/v, 48 h). Qualitative assays (Wagner’s test for alkaloids; lead acetate test for flavonoid-reactive compounds) and quantitative analyses, including total protein (Lowry), soluble carbohydrates (phenol–sulfuric acid), reducing sugars (DNS), total phenolic content (Folin–Ciocalteu) and total reducing capacity (phosphomolybdenum), were performed. All measurements were conducted in triplicate (n = 3). Data were analyzed using one-way ANOVA followed by Tukey’s HSD test (p < 0.05). Alkaloids and flavonoid-reactive compounds were detected in all extracts. The aqueous seed extract exhibited the highest mean total phenolic content (2.193 mg GAE/mL) and reducing capacity (1.337 mg TE/mL). However, standard deviations exceeding the corresponding means indicate substantial variability and limited statistical robustness. Both assays are non-specific and reflect total reducing capacity rather than direct radical scavenging activity. These findings provide preliminary evidence that aqueous seed extracts are enriched in polar reducing compounds, supporting traditional usage. Further validation using radical-specific assays (e.g., DPPH, ABTS), increased replication (n ≥ 6) and bioactivity-guided fractionation is required before potential nutraceutical application.
Copyright (c) 2024 Tayyaba , Ghulam Farooque, Mehran Ali Chhalgri, Safia Karim, Nadir Ali Rind, Sana khokhar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Simultaneous Shoot Proliferation, In Vitro Flowering and Rooting in Rose (Rosa hybrida L.)
Conventional micropropagation of rose relies on separate phases for shoot multiplication, rooting and acclimatization, which increases production time, cost and operational complexity. In vitro flowering is rarely incorporated into propagation systems despite its value for breeding and physiological studies. This study aimed to establish a simplified integrated protocol enabling simultaneous shoot multiplication and in vitro flowering, followed by efficient rooting in Rosa hybrida. Nodal explants were cultured on Murashige and Skoog (MS) medium supplemented with 6-benzylaminopurine (BAP; 0–4.0 mg L⁻¹) and indole-3-acetic acid (IAA; 0–3.0 mg L⁻¹) under a completely randomized design (n = 15 per treatment). For rooting, regenerated shoots were transferred to full- or half-strength MS medium containing indole-3-butyric acid (IBA; 1.5 mg L⁻¹), with or without gibberellic acid (GA₃; 0.5 mg L⁻¹). Data were analyzed using ANOVA followed by LSD tests (p ≤ 0.05). The combination of MS + 3.0 mg L⁻¹ BAP + 3.0 mg L⁻¹ IAA produced the best morphogenic response, with early shoot initiation (18.22 days), highest shoot proliferation (3.60 shoots/explant) and maximum in vitro flowering (2.48 flowers/culture). Rooting was most efficient on full-strength MS medium supplemented with IBA, yielding 93.3% rooting with 4.67 roots per shoot and 2.60 cm mean root length. GA₃ application significantly reduced root elongation. The proposed system integrates shoot regeneration, flowering and rooting into a streamlined process, reducing culture handling and production time. This protocol may support accelerated rose breeding and in vitro floral research following further validation across cultivars.
Copyright (c) 2024 Mehran Ali Chhalgri, Tayyaba Karim, Ghulam Farooque, Safia Karim, Nadir Ali Rind (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
A Theoretical Investigation of Local Truncation Error and Convergence of an Accelerated Third-Order Runge–Kutta Method for Non-Autonomous Ordinary Differential Equations
In this article, we theoretically examined well-organized third order numerical technique for IVP of ODE’s including partial derivative which has enhanced its competency regarding truncation error. Accelerated numerical method for local truncation error and convergence is theoretically investigated to assess how accurate and reliable proposed method is. Expansion of Taylor’s series is done that provides right pattern to expand and evaluate function evaluation. With the help of Taylor’s series, Local truncation error is an explicitly derived to clarify the order of accuracy. Linear standard test is discussed for calculating stability. Stability is investigated for knowing behavior of method. Stability region is drawn by MATLAB2023a software. Stability region is visualized to check numerical method possess a bounded solution when applied frequently and repeatedly. Consistency is proved which tells us error goes to zero as much as we decrease step size which guarantees the desired result. Convergence criteria theoretically discussed here by proving consistency and stability. Theoretically findings showing that the numerical method is stable, attains high accuracy and gives reliable performance. Therefore, method is efficient and applicable for extensive class of IVP arising in the area of ODE’s.
Muhammad Daud Kandhro(Corresponding Author)
Copyright (c) 2026 Muhammad Daud Kandhro(Corresponding Author), Naveed Ahmed Tunio, Zohaib Ali Qureshi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Knowledge Graphs for Software Engineering: A Systematic Review of Applications, Techniques, and Emerging LLM Integration
Knowledge graphs (KGs) have proven to be a powerful knowledge representation and reasoning paradigm for automating software engineering (SE) tasks such as bug localization, API recommendation, and vulnerability assessment. Although the topic is gaining traction, an overview of the use of KGs in SE is lacking. In this paper, we conduct a systematic review of 60 papers (2016-2025) on the application of KGs in SE. We pose four research questions, examining application areas, KG construction and reasoning techniques, data and tools, and future trends and challenges. We find that KGs are primarily used for code understanding and bug detection (40%), API development (25%) and security/vulnerability analysis (20%). Construction techniques encompass ontology design, information extraction, graph embeddings and neural KG completion. Although numerous papers are able to demonstrate high performance in controlled experiments (e.g., >80% precision), issues around scalability, updateability and deployment remain. Recent studies focus on the use of KGs in conjunction with large language models for vulnerability and code analysis. We introduce a classification of applications, review current techniques, and discuss possible directions for future work, including the need for benchmarks, ethical use cases and better integration with LLMs.
Copyright (c) 2026 Sajid Ahmed, Abdullah Soomro, Kishor Kumar, Ubaidullah alias Kashif, Muhammad Raees, Wajahat Akbar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Numerical Investigation of MHD Boundary Layer Flow of Non-Newtonian Fluids over Different Geometries with Heat and Mass Transfer
This research study is an extensive numerical study of steady two-dimensional magnetohydrodynamic (MHD) boundary layer flows of non-Newtonian fluids over different shapes considering simultaneous heat and mass transfer. In numerous real-world problems, fluids behave in a non-Newtonian way and the presence of a magnetic field further complicates momentum and energy transport processes. The conservation equations of mass, momentum, thermal energy and concentrations of the species are reduced into a system of coupled, nonlinear ordinary differential equations by applying similarity transformations. These are then numerically solved by employing the shooting and fourth order Runge-Kutta method. An extensive parametric study is carried out to investigate the effect of magnetic parameter, Prandtl number, Schmidt number, and non-Newtonian parameters on the velocity, temperature, and concentration profiles. It is found that a magnetic field remarkably slows down the fluid flow owing to the electromagnetic force and that the temperature is increased due to the increased energy dissipation. Additionally, the surface geometry is demonstrated to be crucial in determining the boundary layer characteristics. The results offer insights for industries like polymer processing, cooling systems, and chemical transport processes.
Copyright (c) 2026 Mohammad Osama Zaheer (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Microbiota-Induced Activation of the Integrated Stress Response in Sulcular and Junctional Keratinocytes Amplifies Immunopathogenic Mechanisms in Periodontal Disease
Periodontitis is driven by dysbiotic subgingival microbiota that trigger maladaptive host inflammation, yet the epithelial mechanisms converting microbial challenge into tissue destruction remain poorly defined. Here, we demonstrate that dysbiotic pathobionts chronically activate the integrated stress response (ISR) in human sulcular and junctional keratinocytes, transforming a cytoprotective pathway into a central amplifier of immunopathology. Multi-omics profiling of clinical biopsies and primary cultures revealed compartment-specific activation of PERK, GCN2, PKR, and HRI kinases, converging on eIF2α phosphorylation and sustained ATF4/CHOP signaling. This maladaptive ISR state directly amplifies NF-κB–driven pro-inflammatory cytokine production, primes NLRP3 inflammasome activation, and downregulates tight junction proteins, precipitating epithelial barrier failure and connective tissue invasion. Pharmacological inhibition (ISRIB, GSK2606414) or genetic ablation (EIF2AK3, ATF4) of ISR signaling restored barrier integrity, attenuated inflammatory mediator release, and significantly reduced alveolar bone loss in murine periodontitis models without perturbing microbial ecology. Spatial transcriptomics identified a disease-enriched ATF4⁺/CHOP⁺ keratinocyte subpopulation that colocalizes with immune infiltrates and epithelial erosion. Collectively, these findings position epithelial ISR activation as a critical mechanistic bridge between microbial dysbiosis and periodontal tissue destruction, highlighting host-directed ISR modulation as a promising adjunctive therapeutic strategy for managing chronic periodontitis.
Copyright (c) 2026 Jaweria Khan, Ayesha Rehman , Mamoona Fakhar (Corresponding Author), Rida Fayyaz, Muqaddas Fida , Nimra Ramzan , Ayesha Jamshaid, Kalsoom Fatima (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
AuthStateBench: A Standards-Aligned Benchmark for Stateful Authorization and Authentication Workflows
Authentication and authorization weaknesses in modern web applications rarely arise as isolated request-level defects. They often depend on role changes, session lifecycle conditions, object ownership boundaries, API authorization rules, and business workflow ordering. Existing vulnerability benchmarks and scanner evaluations remain valuable, but they often represent weaknesses as code-level or input-output defects and therefore underrepresent semantic failures such as IDOR/BOLA, function-level authorization bypass, stale-session reuse, tenant-boundary violation, privilege transition errors, and workflow bypass. This article introduces AuthStateBench, a standards-aligned benchmark design for modeling stateful authorization and authentication workflow vulnerabilities in web applications and APIs. The study uses a structured literature-based and standards-mapping methodology that draws on access-control testing research, stateful web testing, web logic flaw analysis, scanner-evaluation studies, vulnerability benchmark literature, AI-assisted vulnerability-analysis work, and major security guidance including OWASP Top 10, OWASP API Security Top 10, OWASP ASVS, OWASP WSTG, NIST SSDF, MITRE CWE, CISA Secure by Design, OAuth 2.0 security guidance, OpenID Connect, and software-assurance benchmark resources. AuthStateBench contributes a four-dimensional state model built around role state, session state, object-ownership state, and workflow state; a scenario taxonomy; a benchmark scenario template; standards-mapping logic; formal scenario and coverage equations; and comparison criteria for manual, scanner-assisted, AI-assisted, and standards-based assessment. The article does not claim empirical detection accuracy, tool execution, live-system testing, or dataset results. Instead, it provides a reproducible design artifact and validation roadmap for future controlled implementation and comparative evaluation.
Muhammad Shahzad Khadim(Corresponding Author)
Copyright (c) 2026 Muhammad Shahzad Khadim(Corresponding Author), Syed Mufassir Shah, Zubair Khan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Characterization of the humic acid isolated from Thar coal mines, Sindh, Pakistan
Pakistan is rich with 185 billion tons of coal reserves in all provinces but as fuel coal’s rank is poor because excessive ash, evaporative contents, sulfur and wetness, these coals fall in the category of lignite to sub-bituminous rank but lignite make the largest share of coal which is rich with significant amount of humic acid that upgrade the agricultural topsoil for increasing Pakistan’s crop production. The objective of this study was to investigate the larger extraction and characterization of humic acid from low rank coal of Thar (Sindh Province). Respective coal samples were initially oxidized with 2% HNO3 and then treated with 1.0, 2.5, 3, and 3.5% NaOH solutions for the extraction of humic acid. The 3.5% concentration of NaOH was found to be the optimum for the extraction of humic acid. The extracted humic acid was investigated for its quantitative assessment through UV-Vis spectrophotometer procedure. The humic acid determined by UV-Vis spectrophotometry was 32.23%. FTIR analyses of extracted humic acid samples have shown the presence of carboxylic, phenols, alcoholic, amines and ethers functional groups. Zeta potential analysis of humic acid was -20.3 mv, negative value indicating the larger number of negative ions than positive. In XRD analysis of humic acid samples the major peak was obtained at 44.727170. The results of the study could be useful for selection of suitable coals for humic acid extraction.
Copyright (c) 2025 Saima Malik (Corresponding Author), Tajnees Pirzada, Sana Malik (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
The Hybrid BERT-LSTM Model for the classification Sindhi Text in NLP
The traditional ML models lack to capture the relationship having deep semantic nature, while deep learning model alone cannot work better with temporal and contextual embeddings. In this context the need of efficient Hybrid approach BERT-LSTM for the improvement of the text classification. This study proposes the Hybrid approach BERT-LSTM on the sindhi text data. The text data is collected in sindhi language from hugging face. The dataset contains the labeled samples of the sindhi language text having their predefined classes. Total 150 sentences are used for the sindhi text classification. The model performed robust performance results by the all-evaluation matrices, which achieved macro-average of 0.88, 0.88 accuracy and 0.86 precision and recall 0.85. the significant use of the macro-average because it confirms the consistent model predictive ability across the sentimental data textual classes. The BERT embeddings provide sustainable granularity in sindhi text syntax might provide the miss classification with is shown in sense of minimal dispersion off-diagonal cells. This study provides the critical gap in sentimental analysis for the sindhi text data by providing the hybrid approach BERT-LSTM model architecture. The multilingual BERT is provided to add for feature extraction and for the modeling capability for the sequential capability the Bidirectional BERT is used. The semantic nuance and the low of sindhi text structural behavior is effectively captured by the Hybrid approach.
Copyright (c) 2026 Nimra Memon , Shabana, Waqas Ahmed Memon, Shahzad Ayaz, Shahzad Ayaz, Duaa Noor (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Genetic Diversity Analysis of Indigenous Flora Using Molecular Markers
The adaptability, survival and sustainable use of indigenous plants depend upon its genetic richness. This research focused on the diverse need for the assessment of genetic diversity of local flora of Sindh through the aid of molecular technique. The genetic structure and conservation of various indigenous plant species from different ecological zones were studied. This study explores genetic diversity patterns inferred from molecular marker techniques to illuminate population differentiation and ecological adaptation. It also draws attention to the need for incorporating novel genetic tools for biodiversity assessment in less developed areas. The objective of this study was to evaluate and compare the genetic diversity of indigenous plant populations from various ecological zones of Sindh using RAPD and SSR markers. The samples of leaves were taken from different ecozones coastal, desert and irrigated zone of Sindh. Genomic DNA was extracted using the CTAB-modiﬁed method and PCR ampliﬁcation was carried out using RAPD and SSR primers. Agarose gel electrophoresis was performed on amplified products and binary-coded genetic data were scored for statistical analysis with the use of POPGENE, NTSYS, and PAST software. High polymorphism (78.17% RAPD; 81.25% SSR) indicated large genetic diversity among populations of the two species, as shown from the results. The highest genetic diversity was noted in desert regions such as Tharparkar, and comparatively lower was observed in irrigated regions. Cluster and PCoA analyses further indicated that populations clustered according to ecological affinities. The authors conclude that the genetic diversity within species of the native flora of Sindh is considerable but not evenly distributed. It suggests the prioritization of genetically rich populations for in-situ conservation and advises embedding molecular data on genetic diversity into national biodiversity management programs for effective conservation planning.
Copyright (c) 2026 Sohail Raja jatoi, Sobia Sattar , Kosar parveen, Imran Ali Bhurt, Sadia Talpur (Author) https://creativecommons.org/licenses/by-nc-nd/4.0