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.
AI-Powered Cyber Threat Intelligence Platform
Cybersecurity threats are becoming more complex and challenging to identify using conventional security measures. While traditional signature-based systems work well against known assaults, they frequently miss new threats like ransomware, zero-day exploits, and advanced persistent threats. This study suggests an AI-Powered Cyber Threat Intelligence (CTI) Platform that combines behavioral analysis and machine learning methods for proactive threat identification and categorization in order to address these issues. The platform uses a Long Short-Term Memory (LSTM) model to identify anomalies in sequential log data and a Random Forest Classifier to classify network traffic. Data is gathered from publicly accessible cybersecurity sources, system logs, and network traffic databases. Data collection, preprocessing, model building, threat detection, and dashboard visualization layers make up the suggested design. A classification accuracy of roughly 97.8% is shown by experimental evaluation using the CICIDS2017 dataset, with precision and recall levels surpassing 96%. The findings show that the suggested platform reduces the need for human monitoring while efficiently identifying cyber threats. For academic institutions, researchers, and small businesses looking to improve their cybersecurity capabilities, the platform provides a scalable and affordable alternative.
Copyright (c) 2026 Ms. Ambreen, Umair Adeel, Muhammad Usama Khan, Tayyaba Bibi, Nakhshab M. javed (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Identification and Documentation of Medicinal Flora of Pabbi Hills Adjacent to River Jhelum, Punjab, Pakistan
Plants have played a crucial role in human life by providing for a variety of needs. In addition to providing humans with food, fuel, shelter, and fodder, also be used to make herbal remedies for a variety of illnesses. Objective: The aim of this study was to document the medicinal uses of plants in Pabbi Hills Punjab Province, Pakistan. Ethnobotanists investigate the interactions between humans and plants. Methodology: For the first time in the region, the ethnobotanical results include quantitative data on the variety of therapeutic plants. Semi-structured interviews, in-person interviews, open-ended questionnaires, and firsthand observations were used to gather the data. Quantitative indicators of information consent factor (ICF), fidelity level (FL), usage value (UV), frequency citation (FC), and relative frequency citation (RFC) were used to examine the results. Result: There have been reports of 60 plant species from 37 families being used medicinally. With seven species, the Fabaceae family was shown to be the most prevalent in the region. The most commonly utilized portions were found to be the whole plant and its leaves (31%). The most popular form of preparation was decoction (20.9% with 31 species). The categories of vomiting, flatulence, and mouth freshing had the highest ICF value (1). Twelve plant species, including Mentha longifolia (L.) L., Trachyspermum ammi (L.) Sprague, Foeniculum vulgare Mill, Azadirachta indica A. Juss., Acacia nilotica Willd., Punica granatum L., Euclyptus globulens L., Cannabis sativa L., Ficus benghalensis L., Brassica campestris L., Ficus religiosa L., and Bryophyllum pinnatum (Lam.) Oken, were found to have 100% fidelity level. Mentha longifolia was reported to have the greatest usage value (1.09), while its RFC value (0.94) was the highest. Menthalongifolia (L.) L. Trachyspermum ammi (L.) Sprague was determined to have the highest utilization report. Conclusion: There are many therapeutic plants in the region, and people still frequently use these plants in their daily life. However, the younger generation is gradually losing their traditional understanding of these plants. The foundation for the preservation of the native flora and its usage as food and medicine is provided by this study. Additionally, it offers a range of socioeconomic aspects related to the average person.
Copyright (c) 2026 Sobia Yousaf, Asma Ibrahim, Farooq Ahmad, Sehrish Imran, Zainab Batool, Hafiz Muhammad Usman, Tayyaba Munawar (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Magnesium Fertilization Improves Grain Yield, Net Photosynthetic Rate, and Thousand Grain Weight of Spring Hybrid Maize (Zea mays L.) under Alkaline-Calcareous Soils of Southern Punjab
Maize (Zea mays L.) productivity in southern Punjab is increasingly limited by latent secondary-nutrient deficiencies on the region's alkaline-calcareous soils, where free CaCO₃, low organic matter, and Ca²⁺-dominated cation exchange sites jointly constrain the plant availability of magnesium. Despite magnesium's central role as the chlorophyll metal, an essential Rubisco cofactor, and the principal cation for sucrose phloem loading, calibrated soil-applied Mg dose–response data for spring-planted hybrid maize on Pakistani alkaline soils are limited. A two-location field trial was therefore conducted during the Rabi 2024–25 season at Hasilpur and Chishtian (District Bahawalnagar) to evaluate the response of spring-planted DK6317 hybrid maize to five graded levels of magnesium sulphate heptahydrate (MgSO₄·7H₂O), at 0, 20, 40, 60, and 80 kg MgSO₄·7H₂O ha⁻¹ and applied as a broadcast basal alongside a uniform NPK (225–150–125 kg ha⁻¹) plus Zn–B supply. The experiment was laid out as a randomized complete block design with three replications at each site and analyzed as a two-factor combined ANOVA. Treatment effects were highly significant (p < 0.01) for grain yield, net photosynthetic rate (Pn), and thousand-grain weight (TGW). Pooled mean grain yield rose progressively from 7,624.1 kg ha⁻¹ at the control to 7,964.8 kg ha⁻¹ at the highest Mg rate (+4.5%), while Pn increased significantly from 24.67 to 29.90 mmol CO₂ m⁻² s⁻¹ (+21.2%) and TGW increased from 281.1 to 328.1 g (+16.7%) over the same Mg gradient. The disproportionately large gain in TGW, set alongside the parallel rise in Pn, established a coherent mechanistic chain in which magnesium supply enhanced photosynthetic capacity through the late grain-filling window and translated into greater photo-assimilate partitioning to the developing kernel. All three response curves were still rising at the highest tested rate. On this evidence, the inclusion of 80 kg MgSO₄·7H₂O ha⁻¹ as a basal soil broadcast input is recommended as a regional Mg fertilization practice for spring hybrid maize across the canal-irrigated districts of southern Punjab.
Copyright (c) 2026 Muhammad Javid Qamar, Muhammad Rashid Farooq, Sehrish Jameel, Muhammad Bilal, Abdul Ghaffar Khan, Muhammad Khalid, Abdul Sattar, Muhammad Asif, Hafiz Muhammad Rafiq, Saeed-ur-Rehman, Ali Rizwan, Alamgir Alvi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Multi-Location Evaluation of Copper Fertilization on Cane Yield of Sugarcane (Saccharum officinarum L.) under Alkaline-Calcareous Soils of Southern Punjab, Pakistan
Sugarcane (Saccharum officinarum L.) productivity in southern Punjab is constrained not only by macronutrient imbalance but increasingly by latent micronutrient deficiencies on the region's alkaline-calcareous soils, where free CaCO₃ and chronically low organic matter immobilize plant-available copper. A multi-location field trial was therefore conducted during the 2023–24 crop season at three contrasting sites of the Bahawalpur–Bahawalnagar tract ; Haroonabad-1, Haroonabad-2, and Bahawalpur; to evaluate the response of spring-planted CPF-246 cane to graded levels of broadcast copper sulphate pentahydrate (CuSO₄·5H₂O). Five treatments comprising 0, 6, 8, 10, and 12 kg CuSO₄·5H₂O ha⁻¹, applied over a uniform NPK basal of 250–175–125 kg ha⁻¹, were arranged in a two-factor randomized complete block design with three replications at each site. Soil analysis confirmed alkaline pH (8.22–8.40), low organic matter (0.23–0.86%), and contrasting EC (1.31–2.9 dS m⁻¹) and macronutrient status across the three sites. Combined analysis of variance revealed highly significant effects of treatment (F = 14.34, p < 0.01) and location (F = 46.36, p < 0.05) on cane yield, while their interaction was marginally non-significant (F = 2.29, p = 0.0825). Cu fertilization progressively improved cane yield, with pooled mean yields rising from 104,993.8 kg ha⁻¹ in the unfertilized control to 112,843.2 kg ha⁻¹ at the highest tested dose. The greatest cane yield response over the NPK-only control was recorded at 12 kg CuSO₄·5H₂O ha⁻¹ (+7,849.4 kg ha⁻¹; +7.5%), which was therefore identified as the recommended Cu rate for spring-planted sugarcane on the alkaline-calcareous soils of southern Punjab. The inclusion of 12 kg CuSO₄·5H₂O ha⁻¹ as a basal soil-broadcast input, alongside the standard NPK schedule, is recommended to capture the latent Cu limitation on cane productivity in this agro-ecological zone.
Muhammad Javid Qamar (Corresponding Author)
Copyright (c) 2026 Muhammad Javid Qamar (Corresponding Author), Muhammad Rashid Farooq, Sehrish Jameel, Muhammad Bilal, Aftab Ahmad Sheikh, Beenish Butt, Muhammad Asif, Tahir Akbar, Ali Rizwan, Samina Hamid, Muhammad Imran, Muhammad Asadullah (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Sustainable Zinc Biofortification of Mung Bean Using Cellulose-Based Slow-Release Fertilizer Hydrogel under Normal and Drought Stress Conditions
Zinc (Zn) deficiency in edible legumes is a persistent nutritional problem that is exacerbated in systems with low micronutrient availability and recurrent drought, where conventional fertilizers often show poor synchronization between nutrient release and crop demand. This study aimed to evaluate cellulose-based slow-release hydrogel fertilizer formulations to improve Zn biofortification of mung bean (Vigna radiata L.) under normal irrigation (100% field capacity) and drought stress (40% field capacity), and to quantify treatment and irrigation effects on Zn accumulation and Zn use-efficiency indices. A controlled-environment pot experiment was conducted using a factorial arrangement of seven nutrient treatments (control, SRHG5, SRHG10, SRFHG5, SRFHG10, NPK-Fe, Zn, and Zn, Fe) across two irrigation regimes with three replicates (42 units). Cellulose-derived hydrogels were synthesized via cross-linking, loaded with Zn from ZnSO₄·7H₂O (5.6% elemental Zn) at an equivalent rate of 25 lb/A (~28 kg/ha; 0.08 g ZnSO₄ per pot), characterized for swelling and nutrient-release behavior, and applied near the root zone. At physiological maturity, seed and stover Zn concentrations were determined after acid digestion using AAS/ICP-OES, and data were analyzed by two-way ANOVA. Seed Zn ranged from 29.40-46.57 mg/kg, with SRFHG10 achieving the highest values under both 100% (46.57 mg/kg) and 40% irrigation (38.77 mg/kg); only SRFHG10 met the ≥40 mg/kg biofortification target under optimal irrigation, and no treatment reached the target under drought. Stover Zn was higher (25.80-59.40 mg/kg), again maximized by SRFHG10 (59.40 and 52.00 mg/kg under 100% and 40% irrigation). Nutrient treatment significantly affected Zn in seeds (F=9.32, p<0.001) and stover (F=756.26, p<0.001); irrigation had a smaller effect on seeds (F=7.16, p=0.01) but a strong effect on stover (F=277.71, p<0.001), with a significant treatment×irrigation interaction for stover (p<0.001) but not seeds (p=0.57). SRFHG10 also showed the highest Zn use efficiency (38.68 and 37.04) and agronomic Zn use efficiency (11.00 and 10.82), whereas Zn, Fe produced negative agronomic efficiency, indicating poor agronomic response. Overall, cellulose-based slow-release fertilizer hydrogel, particularly SRFHG10, enhanced Zn accumulation and fertilizer efficiency and partially mitigated drought-associated declines, supporting hydrogel-mediated micronutrient delivery as a promising strategy for improving mung bean nutritional quality, although further optimization is needed for consistent target attainment under water limitation.
Copyright (c) 2026 Marya Siraj Nabi, Afia Zia (Corresponding Author) (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
AI Capability and Performance: The Strategic Roles of Cyber Risk Management Firm and Knowledge Management Systems in Pakistan Halal Foods
The fast development of digital technology has changed the way organizations operate and increased the importance of artificial intelligence capabilities, cyber risk management and knowledge management systems, in enhancing business performance. The present study examined the impact of artificial intelligence capabilities on organizational performance, and the mediating role of cyber risk management and knowledge management systems in halal food manufacturing enterprises in Pakistan. The study is based on the Resource-Based View, the Theory of Dynamic Capabilities and the Knowledge-Based View. The study has adopted a quantitative research methodology with a cross-sectional survey design. Data was collected from 300 management workers working in halal food production companies in Pakistan using a standardised questionnaire. The collected data were analyzed using SPSS and SmartPLS. Results show that artificial intelligence capabilities have a significant positive effect on corporate performance, cyber risk management and knowledge management systems. In addition, the cyber risk management and knowledge management systems contribute greatly to the performance of the organization. The results also reveal that the proposed model possesses adequate explanatory power, predictive relevance, reliability and validity. The study contributes to the existing literature on digital transformation and organizational performance by providing real findings from the halal food manufacturing industry of Pakistan. The results suggest that organizations should spend money on AI technology, cybersecurity measures and knowledge management systems to enhance operational efficiency, organizational resilience and sustained competitive advantage.
Copyright (c) 2026 Ghulam Zara, Hassan Raza Ahsan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Explainable AI-Based Ransomware Early Detection Using File-System Behavioral Features and Random Forest
Ransomware is considered one of the major cybersecurity threats that has the capability to either encrypt or blocks access to organizational data. The Hacker demands compensation for recovery and identifications or prevent signature-based detection is useful, but it may fail against new variants that target by changing file names, using packed binaries, or exploit trusted system tools. The Research presented in this paper proposes to develop a method shortly referred as RF-RBD, an Artificial Intelligence-based ransomware detection model that make use of a Random Forest classifier. The model has the capability to identify ransomware like behavior by using filesystem features such as write rate, rename rate, and delete rate, entropy change, extension-change ratio, ransom-note count, shadow copy command count, and network-share access. In this study a synthetic defensive dataset is used for safe experimentation to avoid in system down time risk. The model is evaluated using parameters such as accuracy, precision, recall, F1 score, ROC-AUC, confusion matrix, and feature importance. The Experiments performed and the associated results show a strong performance on the simulated dataset, wherein a high recall is observed along with interpretable feature rankings. The paper also explores the limitations of the approach and associated ethical concerns, adversarial risks, and suggests future improvements for real system deployment.
Usman Imtiaz Member, IEEE (Corresponding Author)
Copyright (c) 2025 Dr. Ajab Khan, Fahad Amin, Member, IEEE, Usman Imtiaz Member, IEEE (Corresponding Author) (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Work-Life Balance, Employee Performance and Health in Pakistan’s Private Sector Industries: Evaluating the Effectiveness of HR Practices
This study investigates the critical interface between Strategic Human Resource Management (SHRM) practices, work-life balance (WLB), physiological and psychological employee health and overall job performance within Pakistan's rapidly expanding private sector. The sample comprises 350 professional employees spanning four major private industries: Information Technology (IT), Textiles, Telecommunications and Manufacturing across urban industrial hubs (Karachi, Lahore, Islamabad and Faisalabad). Utilizing a structural equation framework, a quantitative survey design was executed to evaluate how human resource interventions specifically flexible working arrangements, institutional leave benefits, organizational wellness initiatives and structural workload management protocols—influence internal work-life operational dynamics. Furthermore, the inquiry models the sequential bridge where enhanced work-life equilibrium yields improvements in employee holistic health, which systematically drives objective and subjective task performance. Empirical evaluations executed through standard correlation and multiple linear regression indicate that all four core HR dimensions exert a statistically significant, positive direct effect on WLB. Crucially, the data supports the assertion that WLB serves as an essential strategic operational bridge, substantially cascading into optimized physical and psychological employee health profiles and maximized task performance output. In contrast, systemic failures in mitigating occupational stressors and unmanaged operational targets correspond directly with pronounced clinical and sub-clinical exhaustion trends, psychological alienation and diminished aggregate corporate output. The paper concludes with actionable, localized HR policy structural re-engineering models tailored explicitly for the distinct socio-cultural and macroeconomic realities governing the contemporary Pakistani corporate ecosystem.
Copyright (c) 2026 Mazhar Ali Alyani, Dr. Sumaiya Qadir Buledi, Engr. Muhammad Bilal Ahmad, Ahar Ali Alyani, Sayyed Faheem Ali Shah, Kinza Bibi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
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