Global Research Journal of Natural Science and Technology
type: channel
description: 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, Bi…
publisher: Al-Qamar Islamic Research Institute
language: en
publication name: Global Research Journal of Natural Science and Technology
issn: 2790-7643
items: CRISPR-Cas–Driven Molecular Genetics and Immunology: Transforming Precision Therapeutics in the Post-Genomic Era
items: Safety, Quality, and Sustainability: The Environmental Benefits of HSEQ in Oil Fields
items: Efficient Explicit Improved Scheme for Numerical Solution of Cauchy Problems
items: Phytoremediation of Heavy Metals Using Native Plant Species
items: A Vertical Farming and Soilless Cultivation Technologies for Urban Food Security
items: Integrating Artificial Intelligence, Remote Sensing, and GIS for Sustainable Agro-Forestry Management and Land Resource Optimization
items: Impacts of Climate Change on Cotton (Gossypium hirsutum L.): Growth, Yield and Resilience Strategies- A Global and Pakistan Perspective
items: Artificial Intelligence for Smart Infrastructure System: Integrating Civil and Electrical Engineering
items: Smart Transportation Systems: Enhancing Traffic Flow and Reducing Urban Congestion through Intelligent Solutions
items: Mutational Analysis of TRAPPC9 IN Non-syndromic Intellectual Disability in Selected Patients
items: https://grjnst.com/index.php/view/article/view/353
items: Evaluation of Genetic Parameters and Combining Ability for Drought Tolerance in Cotton (Gossypium hirsutum)
items: Chemotypic Variation in Antioxidant Activity and Phytochemical Profiles of Terminalia arjuna (Roxb. ex DC.)
items: Physicochemical and Sensory Attributes of Wheat Bread Supplemented with Mashed Potato
items: Solvent‑Dependent Phytochemical Profiling and Total Reducing Capacity of Pongamia pinnata (L.) Pierre
items: Simultaneous Shoot Proliferation, In Vitro Flowering and Rooting in Rose (Rosa hybrida L.)
items: A Theoretical Investigation of Local Truncation Error and Convergence of an Accelerated Third-Order Runge–Kutta Method for Non-Autonomous Ordinary Differential Equations
items: Knowledge Graphs for Software Engineering: A Systematic Review of Applications, Techniques, and Emerging LLM Integration
items: Numerical Investigation of MHD Boundary Layer Flow of Non-Newtonian Fluids over Different Geometries with Heat and Mass Transfer
items: Microbiota-Induced Activation of the Integrated Stress Response in Sulcular and Junctional Keratinocytes Amplifies Immunopathogenic Mechanisms in Periodontal Disease
items: AuthStateBench: A Standards-Aligned Benchmark for Stateful Authorization and Authentication Workflows
items: Characterization of the humic acid isolated from Thar coal mines, Sindh, Pakistan
items: The Hybrid BERT-LSTM Model for the classification Sindhi Text in NLP
items: Genetic Diversity Analysis of Indigenous Flora Using Molecular Markers
items: Biochemical characterization and profiling of major seed storage proteins in selected medicinal plant found in Sindh Pakistan
items: Quality and Nutritional Evaluation of Oat Cupcakes Enriched with Carrot Pomace
items: Modern Approaches to Energy Physics: From Particle Interactions to Sustainable Technologies
items: Integrating Genomic Tools with Conservation Biology: A Framework for Managing Genetic Erosion in Endangered Mammals of Pakistan
items: Deep Learning for Intelligent Systems: Advancing Scalability, Explainability, and Real-World Applications
items: Comparative Analysis of Phytochemical Composition and Antioxidant Activity of Fresh vs. Processed Fruits

CRISPR-Cas–Driven Molecular Genetics and Immunology: Transforming Precision Therapeutics in the Post-Genomic Era
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description: 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,
subject: Articles
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creator: Nadia Bibi
creator: Muhammad Saad Abbasi
creator: Maryam Farooq
creator: Mahtab Ali shah
creator: Noor-Ul-Huda
creator: Muhammad Aarab
creator: Muqaddas Fida
rights: 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
date: 2026-04-20
publication date: 2026-04-20
doi: 10.53762/grjnst.04.03.12

Safety, Quality, and Sustainability: The Environmental Benefits of HSEQ in Oil Fields
type: item
description: 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 oblig…
subject: Articles
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creator: Waqar Afzal
creator: Muhammad Bilal Qamar
rights: Copyright (c) 2026 Waqar Afzal, Muhammad Bilal Qamar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-18
publication date: 2026-05-18
doi: 10.53762/grjnst.04.03.11

Efficient Explicit Improved Scheme for Numerical Solution of Cauchy Problems
type: item
description: 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.
subject: Articles
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creator: Muhammad Daud Kandhro (Corresponding Author)
creator: Khushbu Rajput
creator: Zohaib Ali
rights: Copyright (c) 2026 Muhammad Daud Kandhro (Corresponding Author), Khushbu Rajput , Zohaib Ali (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-18
publication date: 2026-05-18
doi: 10.53762/grjnst.04.03.10

Phytoremediation of Heavy Metals Using Native Plant Species
type: item
description: 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 …
subject: Articles
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creator: Usra Saqib
creator: Anbji
creator: Imran Ali
creator: Bushra fazal
creator: Poorab Almas
rights: Copyright (c) 2026 Usra Saqib, Anbji, Imran Ali, Bushra fazal , Poorab Almas (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-08
publication date: 2026-05-08
doi: 10.53762/grjnst.04.03.09

A Vertical Farming and Soilless Cultivation Technologies for Urban Food Security
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description: 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.
subject: Articles
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subject: Array
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creator: Gul Muhammad shah (Corresponding author)
creator: Murtaza Ali
creator: Iqra Sultan Rajput
creator: Mir Baqar Raza Talpur
rights: 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
date: 2026-05-18
publication date: 2026-05-18
doi: 10.53762/grjnst.04.03.08

Integrating Artificial Intelligence, Remote Sensing, and GIS for Sustainable Agro-Forestry Management and Land Resource Optimization
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description: 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.
subject: Articles
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creator: Muhammad Kashif Majeed
creator: Kashif Akbar
creator: Muhammad Hassan Ali
creator: Muhammad Essa Siddique
creator: Murtaza Ali
creator: Gul Muhammad Shah
creator: Dr. Qasim Mansoor Jalali
creator: Dr. Ajab Khan (Corresponding Author)
rights: 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
date: 2026-05-12
publication date: 2026-05-12
doi: 10.53762/grjnst.04.03.07

Impacts of Climate Change on Cotton (Gossypium hirsutum L.): Growth, Yield and Resilience Strategies- A Global and Pakistan Perspective
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description: 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 g…
subject: Articles
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subject: Array
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creator: Rashida Babi
creator: Habib Ullah (Corresponding Author)
creator: Ali Nawaz
creator: Haji Manzoor Ahmad
creator: Khatir Ali
creator: Abdulrehman Niazi
creator: Sana Ullah
rights: 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
date: 2026-05-11
publication date: 2026-05-11
doi: 10.53762/grjnst.04.03.06

Artificial Intelligence for Smart Infrastructure System: Integrating Civil and Electrical Engineering
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description: 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 inte…
subject: Articles
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subject: Array
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creator: Mohib ur Rahman (Corresponding Author)
creator: Wasiq Attique
creator: Ayesha Samreen
creator: Maaz Bin Ubaid
creator: Ahmad Saleem
creator: Zohaib Akhtar
rights: 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
date: 2026-05-10
publication date: 2026-05-10
doi: 10.53762/grjnst.04.03.05

Smart Transportation Systems: Enhancing Traffic Flow and Reducing Urban Congestion through Intelligent Solutions
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description: 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.
subject: Articles
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creator: Muhammad Bilal Israr
creator: Zafreen Elahi
creator: Anwaar Hazoor Ansari
rights: Copyright (c) 2026 Muhammad Bilal Israr, Zafreen Elahi, Anwaar Hazoor Ansari, Ahtsham Mustafa Awan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-09
publication date: 2026-05-09
doi: 10.53762/grjnst.04.03.03

Mutational Analysis of TRAPPC9 IN Non-syndromic Intellectual Disability in Selected Patients
type: item
description: 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.
subject: Articles
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creator: Mubara Mubashar
creator: Farva Razzaq Maham
creator: Saman Mumtaz
creator: Farooq Ahmad
creator: Fizza Hassan
creator: Laraib Saleem
creator: Hafiza Komal Naeem
rights: 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
date: 2026-05-08
publication date: 2026-05-08
doi: 10.53762/grjnst.04.03.04

https://grjnst.com/index.php/view/article/view/353
type: item
title: 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
description: 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.
subject: Articles
  topic: section
creator: Nadia Jabeen
creator: Gohar Ayoub Shah
creator: Sayyad Waqas Umar
creator: Ali Abdal
creator: Muhammad Zeeshan
rights: 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
date: 2026-05-07
publication date: 2026-05-07
doi: 10.53762/grjnst.04.03.02

Evaluation of Genetic Parameters and Combining Ability for Drought Tolerance in Cotton (Gossypium hirsutum)
type: item
description: 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.
subject: Articles
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creator: Muhammad Ihsan Ullah
creator: Saeed Ahmed
creator: Shoaib Liaqat(Corresponding Author)
creator: Zaib-un-Nisa
creator: Wajad Nazeer(Corresponding Author)
creator: Amna Bibi
creator: Sadia Hakeem
creator: Hammad Hussnain
creator: Javed Iqbal
creator: Nadia Hussain Ahmad
creator: Sadia Kanwal
creator: Muhammad Luqman
rights: 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
date: 2026-03-31
publication date: 2026-03-31
doi: 10.53762/grjnst.04.02.29

Chemotypic Variation in Antioxidant Activity and Phytochemical Profiles of Terminalia arjuna (Roxb. ex DC.)
type: item
description: 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.
subject: Articles
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creator: Safia Karim
creator: Ghulam Farooque
creator: Tayyaba
creator: Mehran Ali Chhalgri
creator: Nadir Ali Rind
creator: Sana khokhar
rights: 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
date: 2024-09-30
publication date: 2024-09-30
doi: 10.53762/grjnst.02.02.05

Physicochemical and Sensory Attributes of Wheat Bread Supplemented with Mashed Potato
type: item
description: 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
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Muhammad Irfan
creator: Aasia Akbar Panwar
creator: Ghulam Farooque
creator: Fawad Ahmed
rights: Copyright (c) 2024 Muhammad Irfan, Aasia Akbar Panwar, Ghulam Farooque, Fawad Ahmed (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2024-09-30
publication date: 2024-09-30
doi: 10.53762/grjnst.02.02.04

Solvent‑Dependent Phytochemical Profiling and Total Reducing Capacity of Pongamia pinnata (L.) Pierre
type: item
description: 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.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Tayyaba
creator: Ghulam Farooque
creator: Mehran Ali Chhalgri
creator: Safia Karim
creator: Nadir Ali Rind
creator: Sana khokhar
rights: 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
date: 2024-06-30
publication date: 2024-06-30
doi: 10.53762/grjnst.02.01.05

Simultaneous Shoot Proliferation, In Vitro Flowering and Rooting in Rose (Rosa hybrida L.)
type: item
description: 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.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Mehran Ali Chhalgri
creator: Tayyaba
creator: Ghulam Farooque
creator: Safia Karim
creator: Nadir Ali Rind
rights: 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
date: 2024-06-30
publication date: 2024-06-30
doi: 10.53762/grjnst.02.01.04

A Theoretical Investigation of Local Truncation Error and Convergence of an Accelerated Third-Order Runge–Kutta Method for Non-Autonomous Ordinary Differential Equations
type: item
description: 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.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Muhammad Daud Kandhro(Corresponding Author)
creator: Naveed Ahmed Tunio
creator: Zohaib Ali Qureshi
rights: Copyright (c) 2026 Muhammad Daud Kandhro(Corresponding Author), Naveed Ahmed Tunio, Zohaib Ali Qureshi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-30
publication date: 2026-04-30
doi: 10.53762/grjnst.04.02.30

Knowledge Graphs for Software Engineering: A Systematic Review of Applications, Techniques, and Emerging LLM Integration
type: item
description: 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.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Sajid Ahmed
creator: Abdullah Soomro(Corresponding Author)
creator: Kishor Kumar
creator: Ubaidullah alias Kashif
creator: Muhammad Raees
creator: Wajahat Akbar
rights: 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
date: 2026-01-31
publication date: 2026-01-31
doi: 10.53762/grjnst.04.01.31

Numerical Investigation of MHD Boundary Layer Flow of Non-Newtonian Fluids over Different Geometries with Heat and Mass Transfer
type: item
description: 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.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Mohammad Osama Zaheer
rights: Copyright (c) 2026 Mohammad Osama Zaheer (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-05-05
publication date: 2026-05-05
doi: 10.53762/grjnst.04.03.01

Microbiota-Induced Activation of the Integrated Stress Response in Sulcular and Junctional Keratinocytes Amplifies Immunopathogenic Mechanisms in Periodontal Disease
type: item
description: 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.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Jaweria Khan
creator: Ayesha Rehman
creator: Mamoona Fakhar (Corresponding Author)
creator: Rida Fayyaz
creator: Muqaddas Fida
creator: Nimra Ramzan
creator: Ayesha Jamshaid
creator: Kalsoom Fatima
rights: 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
date: 2026-04-30
publication date: 2026-04-30
doi: 10.53762/grjnst.04.02.28

AuthStateBench: A Standards-Aligned Benchmark for Stateful Authorization and Authentication Workflows
type: item
description: 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.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Muhammad Shahzad Khadim(Corresponding Author)
creator: Syed Mufassir Shah
creator: Zubair Khan
rights: Copyright (c) 2026 Muhammad Shahzad Khadim(Corresponding Author), Syed Mufassir Shah, Zubair Khan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-29
publication date: 2026-04-29
doi: 10.53762/grjnst.04.02.27

Characterization of the humic acid isolated from Thar coal mines, Sindh, Pakistan
type: item
description: 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.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Saima Malik (Corresponding Author)
creator: Tajnees Pirzada
creator: Sana Malik
rights: Copyright (c) 2025 Saima Malik (Corresponding Author), Tajnees Pirzada, Sana Malik (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-31
publication date: 2025-12-31
doi: 10.53762/grjnst.03.04.35

The Hybrid BERT-LSTM Model for the classification Sindhi Text in NLP
type: item
description: 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.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Nimra Memon
creator: Shabana
creator: Waqas Ahmed Memon
creator: Shahzad Ayaz
creator: Duaa Noor
rights: 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
date: 2026-04-27
publication date: 2026-04-27
doi: 10.53762/grjnst.04.02.24

Genetic Diversity Analysis of Indigenous Flora Using Molecular Markers
type: item
description: 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.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Sohail Raja jatoi
creator: Sobia Sattar
creator: Kosar parveen
creator: Imran Ali Bhurt
creator: Sadia Talpur
rights: 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
date: 2026-04-27
publication date: 2026-04-27
doi: 10.53762/grjnst.04.02.23

Biochemical characterization and profiling of major seed storage proteins in selected medicinal plant found in Sindh Pakistan
type: item
description: In this study investigated the composition and biochemical properties of seed storage proteins in important medicinal plants, including the seeds of Syzygium cumini L., Lawsonia inermis L., Calotrpis procera. R Bro, Ricinus communis L., Linum usitatissimum L., Brassica juncea L Czern., Withania coagulans Dun, and Cucumis melo var. agrestis Naudin. Protein extraction from seed flour was carried out using sequential steps of extraction: delipidation (removal of oil), water (albumin), 5.0 M NaCl (globulin), 70% ethanol (prolamin), and 150.2M sodium phosphate buffer, pH 8.0 (glutelin). Quantification was performed using the dye binding method of Bradford which showed substantial differences in their concentrations and total yields. Comparative analysis of seed storage proteins showed different dominance patterns among the plants studied. The highest levels of albumin were found in Brassica juncea L Czern. (50.40%), which suggests a strong water-soluble protein fraction, the highest globulin content was found in Ricinus communis (67.11%), suggesting a high salt-soluble protein fraction, the highest prolamin content was found in Withania coagulans (55.93%), suggesting a strong alcohol-soluble protein fraction, and the highest glutelin was found in Syzygium cumini (15.12%).
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: S.K. Khanzada
creator: AK Khanzada
creator: Beenish khanzada
creator: Shaheen Memon
creator: B. Sadiq
creator: Imran Ali Bhurt
rights: Copyright (c) 2026 S.K. Khanzada, AK Khanzada, Beenish khanzada , Shaheen Memon, B. Sadiq , Imran Ali Bhurt (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-27
publication date: 2026-04-27
doi: 10.53762/grjnst.04.02.22

Quality and Nutritional Evaluation of Oat Cupcakes Enriched with Carrot Pomace
type: item
description: Carrot pomace is the by-product of juice that is enrich in fiber. Carrot pomace contains fiber and provides nutritional benefits for humans. Oat (Avena sativa L.) is the member of member of the Poaceae family and the fourth most significant cereal crop in the world. The basic purpose of this research was to utilize the carrot pomace in oat cupcakes in order to enhance value chain of oat (neglected cereal) and carrot pomace (by product). In this research carrot pomace powder was prepared by using the hot air oven. After that proximate analysis (moisture, fat, fiber, ash and protein) of raw material was performed. Then the cupcakes were prepared by two different methods one by using 5%, 10% and 15% carrot pomace (CP) and secondly by using 5%, 10% and 15% of carrot pomace powder (CPP) with 45%, 40% and 30% oat flour. Density and pH of batter was observed in range of 1.03±0.05 to 89±0.04 and 6.23±0.29 to 7.02±0.24. Moisture, protein, fiber and ash were increased among treatments in both CP and CPP while on the basis of sensory analysis (overall acceptability) of prepared cupcakes was analyzed that T2 (10% CP) and T5 (10% CP was more accepted. It was concluded that cupcakes 10% CP and CPP can be utilized with oat flour for preparation of fiber enrich cupcakes.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Hira Jabeen
creator: Shabbir Ahmad
creator: Hafiza Mamoona Ahmad
creator: Sadaf Yaqoob
creator: Umair Raza
creator: Saba Kanwal
creator: Afshan Tehseen Asghar
rights: Copyright (c) 2026 Hira Jabeen, Shabbir Ahmad, Hafiza Mamoona Ahmad, Sadaf Yaqoob, Umair Raza, Saba Kanwal, Afshan Tehseen Asghar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-26
publication date: 2026-04-26
doi: 10.53762/grjnst.04.02.26

Modern Approaches to Energy Physics: From Particle Interactions to Sustainable Technologies
type: item
description: This study examined modern approaches in energy physics by linking particle interaction modeling with the development of sustainable energy technologies. The research aimed to evaluate how physics-based models influenced energy efficiency, renewable energy integration, and system optimization. A quantitative research design was employed using a sample size of 320 respondents, including physicists, engineers, and energy professionals. Data was analyzed through descriptive statistics, correlation analysis, regression modeling, and structural equation modeling. The results indicated that particle interaction modeling significantly improved energy efficiency (β = 0.47, p < 0.001), enhanced renewable energy integration (β = 0.42, p < 0.001), and supported system optimization (β = 0.39, p < 0.001). Correlation findings revealed moderate positive relationships among all variables, with values ranging from 0.47 to 0.55. The structural model demonstrated good fit indices (CFI = 0.95, RMSEA = 0.05, χ²/df = 2.10), confirming the validity of the proposed framework. These findings emphasized that integrating particle physics principles with modern energy systems improved performance, efficiency, and sustainability outcomes. The study concluded that physics-informed approaches provided a reliable pathway for enhancing renewable energy systems and addressing global energy challenges through innovative and data-driven solutions.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Ali Raza
creator: Murtaza Hussain Shar
creator: Gada Hussain Narejo
rights: Copyright (c) 2026 Ali Raza, Murtaza Hussain Shar, Gada Hussain Narejo (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-26
publication date: 2026-04-26
doi: 10.53762/grjnst.04.02.25

Integrating Genomic Tools with Conservation Biology: A Framework for Managing Genetic Erosion in Endangered Mammals of Pakistan
type: item
description: This study examined the integration of genomic tools with conservation biology to address genetic erosion in endangered mammals of Pakistan. The research focused on key species including the snow leopard, Indus river dolphin, and markhor, which faced increasing threats from habitat fragmentation, inbreeding, and environmental change. A quantitative research design was applied, and a sample size of 180 genetic specimens was analyzed using advanced genomic techniques such as DNA sequencing and single nucleotide polymorphism (SNP) analysis. The results indicated a moderate level of genetic diversity (mean = 3.21) alongside high inbreeding levels (mean = 4.05) and severe habitat fragmentation (mean = 4.18). Structural model findings showed that habitat fragmentation negatively affected genetic diversity (β = -0.42, p = 0.000), while inbreeding also exerted a significant negative effect (β = -0.39, p = 0.000). In contrast, genomic monitoring demonstrated a positive impact on genetic diversity (β = 0.36, p = 0.000), and conservation policy support also contributed positively (β = 0.31, p = 0.000). The study proposed an integrated framework combining genomic monitoring, habitat restoration, and policy interventions to enhance conservation effectiveness. The findings highlighted the importance of incorporating genomic data into conservation strategies to improve species resilience and reduce extinction risks. This research provided practical implications for policymakers, conservationists, and researchers aiming to preserve biodiversity and manage genetic erosion in endangered mammalian populations.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Quratulain shaikh
creator: Rehana Aslam
creator: Hira Mazhar Ujan
creator: Sadaf Shafique
rights: Copyright (c) 2026 Quratulain shaikh, Rehana Aslam, Hira Mazhar Ujan, Sadaf Shafique (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-24
publication date: 2026-04-24
doi: 10.53762/grjnst.04.02.21

Deep Learning for Intelligent Systems: Advancing Scalability, Explainability, and Real-World Applications
type: item
description: Deep learning emerged as a foundational technology in intelligent systems, enabling advanced data-driven decision-making across multiple domains. The study examined the role of deep learning in enhancing scalability, explainability, and real-world applicability of intelligent systems. A quantitative research design was adopted, and data were collected from a sample of 300 respondents including AI professionals, researchers, and IT experts. The results showed that real-world applicability (M = 4.02) and system efficiency (β = 0.31, p < 0.001) were the strongest predictors of intelligent system performance. Deep learning scalability (β = 0.28, p < 0.001), model interpretability (β = 0.25, p < 0.001), and explainable AI (β = 0.22, p < 0.001) also showed significant positive effects.Correlation analysis indicated strong relationships among all variables, particularly between real-world applicability and system performance (r = 0.75). The findings suggested that while deep learning significantly improved automation and predictive capabilities, challenges related to transparency and computational efficiency still persisted. The study concluded that integrating scalability with explainability was essential for developing trustworthy intelligent systems. It further recommended the adoption of lightweight architectures, hybrid AI models, and standardized explainability frameworks to enhance real-world deployment and ethical AI usage.
subject: Articles
  topic: section
subject: Array
  topic: keywords
creator: Shamikh Imran
creator: Rehan Ali Khan
creator: Dr Abdul Sattar
rights: Copyright (c) 2026 Rafique Haider, Rehan Ali Khan , Rehan Ali Khan , Dr Abdul Sattar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-23
publication date: 2026-04-23
doi: 10.53762/grjnst.04.02.20

Comparative Analysis of Phytochemical Composition and Antioxidant Activity of Fresh vs. Processed Fruits
type: item
description: Post harvest processing methods such as thermal treatment, freezing, drying, and industrial canning have a significant impact on the nutritional integrity of fruits. This paper is a systematic review of and comparison of the phytochemical profiles of ten economically and nutritionally important fruit species (both fresh and processed) in terms of total phenolic content (TPC), total flavonoid content (TFC), ascorbic acid, carotenoid levels and anthocyanin content and antioxidant activity. Based on the known in vitro assay protocols such as DPPH, FRAP, and ABTS assays, we compared the effects that different processing modalities have on the bioactive compounds content and functional antioxidant properties differently. Findings indicate that thermal treatment invariably led to a 18-42 percent reduction in TPC and ascorbic acid content by 30-65 percent, and freezing retained more than 85 percent of phytochemicals. Lycopene bioavailability in tomato was enhanced by up to 35% following heat treatment. The highest overall retention rates were with freeze-drying. The implications of these findings include considerable implications to dietary recommendations, food industry processing standards and within the nutritional policy of the population health.
subject: Articles
  topic: section
subject: Array
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creator: Aijaz Ahmed Bhutto
creator: Mohammad Younis Talpur
rights: Copyright (c) 2026 Aijaz Ahmed Bhutto , Dr. M.A Kazi Institute of Chemistry University of Sindh, Jamshoro 76080, Pakistan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-21
publication date: 2026-04-21
doi: 10.53762/grjnst.04.02.19