Scientific Research Reports
type: channel
description: Scientific Research Reports (SRR) is a peer-reviewed academic journal published by PATHWAY SCHOLARS INSTITUTE (SMC-PRIVATE) LIMITED. The journal provides a platform for researchers, academics, and professionals to publish high-quality research across a wide range of disciplines. It encourages innovative studies and interdisciplinary research that contribute to scientific knowledge and practical applications. The journal is published semiannually. Scope The journal covers a multidisciplinary range of topics, including but not limited to: Physical and Life Sciences Medicine and Allied Health Sciences Clinical and Medical Sciences Environmental Science and Sustainability Health and Medical Sciences Business, Management, and Economics Interdisciplinary and Applied Research Scientific Research Reports is a member of CROSSREF DOI PREFIX: 10.67082
publisher: PATHWAY SCHOLARS INSTITUTE (SMC-PRIVATE) LIMITED
language: en
publication name: Scientific Research Reports
issn: 3104-4042
items: THERMAL MANAGEMENT OPTIMIZATION OF LITHIUM-ION BATTERY PACKS FOR ELECTRIC VEHICLES
items: IMPACT OF CONSTRUCTIVE FEEDBACK ON STUDENT CONFIDENCE AND CLASSROOM LEARNING BEHAVIOR
items: SOCIAL MEDIA CONSUMPTION AND COGNITIVE ATTENTION AMONG COLLEGE STUDENTS
items: RESOURCE-LIMITED SETTINGS LOW-COST AI-ASSISTED CANCER SCREENING AND ICU TRIAGE MODELS FOR RESOURCE-CONSTRAINED HEALTHCARE SYSTEMS
items: PREDICTING ICU ADMISSION AND VENTILATOR DEPENDENCE IN ADVANCED LUNG CANCER PATIENTS USING CLINICAL AND IMAGING DATA
items: THE ROLE OF EDGE COMPUTING IN ENABLING REAL-TIME DATA PROCESSING IN AUTONOMOUS SYSTEMS FOR SMART CITIES
items: THE IMPACT OF CLIMATE CHANGE ON THE SPREAD OF VECTOR-BORNE DISEASES IN DOMESTIC AND WILD ANIMAL POPULATIONS
items: THE POTENTIAL OF NANOTECHNOLOGY IN TARGETED DRUG DELIVERY SYSTEMS FOR VETERINARY ONCOLOGY TREATMENTS
items: ADVANCEMENTS IN VETERINARY PATHOLOGY: USING AI FOR AUTOMATING DIAGNOSIS OF INFECTIOUS DISEASES IN ANIMALS
items: INVESTIGATING THE FEASIBILITY OF USING AQUAPONICS SYSTEMS FOR SUSTAINABLE FISH AND VEGETABLE PRODUCTION IN URBAN AREAS
items: EVALUATING SOIL CARBON STOCKS IN AGROFORESTRY SYSTEMS USING DEEP LEARNING AND FIELD SAMPLING
items: NANOMEDICINE IN DRUG DELIVERY: TARGETING CANCER CELLS WITH PRECISION
items: AGRICULTURAL EXTENSION SERVICES: BRIDGING RESEARCH AND PRACTICE
items: THE IMPACT OF MICROBIOME ON HUMAN HEALTH: IMPLICATIONS FOR DISEASE PREVENTION AND TREATMENT
items: THE IMPACT OF AGRICULTURAL SUBSIDIES ON FARMING PRACTICES
items: HARNESSING ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION AND DIAGNOSIS OF NEUROLOGICAL DISORDERS
items: SYSTEMIC IMMUNE INFLAMMATION INDEX AS A PROGNOSTIC MARKER IN COLORECTAL CANCER
items: ROLE OF MYCOTOXINS IN REPRODUCTIVE FAILURES IN EXTENSIVE POULTRY SYSTEMS
items: RELATIONSHIP BETWEEN BODY CONDITION SCORE AND FERTILITY OUTCOMES IN GRAZING BUFFALOES
items: LONG-TERM NEURODEVELOPMENTAL OUTCOMES IN INFANTS TREATED WITH THERAPEUTIC HYPOTHERMIA FOR HIE
items: PET IMAGING FOR EARLY DETECTION OF METASTASIS IN TRIPLE NEGATIVE BREAST CANCER
items: INVESTIGATING SOIL-WATER-NUTRIENT INTERACTIONS IN VERTISOLS UNDER DIFFERENT IRRIGATION SCHEDULES
items: ETHNOVETERINARY MEDICINES EVALUATION FOR ENDOPARASITE CONTROL IN GOATS
items: REGULATION OF SEED GERMINATION BY PHYTOHORMONAL CROSS-TALK UNDER WATER DEFICIT CONDITIONS
items: LAMENESS ETIOLOGIES IN BROILER CHICKENS RAISED UNDER INTENSIVE HOUSING
items: IMPACT OF HEAD AND NECK RADIATION THERAPY ON LONG-TERM NEUROCOGNITIVE FUNCTION
items: HYBRID PHARMACOLOGICAL APPROACHES TO PAIN MANAGEMENT IN COMPANION ANIMALS
items: GERIATRIC VETERINARY MEDICINE AND ANIMAL LONGEVITY
items: BIOTELEMETRY IN ANIMAL MOVEMENT ECOLOGY
items: PLANT DISEASE FORECASTING MODELS DRIVEN BY ARTIFICIAL INTELLIGENCE

THERMAL MANAGEMENT OPTIMIZATION OF LITHIUM-ION BATTERY PACKS FOR ELECTRIC VEHICLES
type: item
description: Effective thermal management is essential to ensure the safety, performance and battery life of EVs, given the high rate of charging and challenging driving conditions in electric vehicles. This paper explores and contrasts four distinct thermal-management strategies for batteries: natural-air cooling, forced-air cooling, liquid-cold-plate cooling and a hybrid of the phase-change-material and liquid-cooled approaches. The following performance tests were conducted on each configuration: thermal performance (maximum cell temperature), temperature uniformity, thermal resistance, auxiliary power consumption, and expected capacity retention. The results showed that the overall thermal performance of the cooling configuration with a combination of both cooling methods was the best. Under 3C fast-charging condition, the hybrid system lowered the peak cell temperature to 36.8 °C, compared to 39.4 °C, 46.2 °C, and 52.8 °C for the liquid cold-plate cooling, forced-air cooling and natural convection systems, respectively. It also reduced the maximum temperature difference between the cells in the battery module to 2.1°C, showing greater temperature uniformity across the battery module. The hybrid system could maintain the temperature at an acceptable level in sensitivity analysis under the high ambient temperature and high charge rate condition. The coolant flow rate of 2.0 L min⁻¹ was optimised to give an appropriate balance between cooling effect and pumping power. Likewise, a 30% phase-change-material fraction resulted in significant thermal improvement with moderate module mass. Another battery ageing analysis also demonstrated that the hybrid system retained approximately 96.2% of the initial capacity at 800 accelerated cycles. The findings demonstrate that the proposed hybrid thermal-management system could improve the battery safety, energy efficiency, temperature uniformity, and battery service life, suggesting that it is a potential solution for high-performance elec…
subject: Articles
  topic: section
subject: Lithium-Ion Battery
  topic: keywords
subject: Electric Vehicle
  topic: keywords
subject: Thermal Management
  topic: keywords
subject: Phase-Change Material
  topic: keywords
subject: Liquid Cooling
  topic: keywords
creator: Syed Sabir Hussain Bukhari
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 60
ending page: 72

IMPACT OF CONSTRUCTIVE FEEDBACK ON STUDENT CONFIDENCE AND CLASSROOM LEARNING BEHAVIOR
type: item
description: This study examines the relationship between teacher feedback practices and student confidence in the classroom, with a specific focus on how constructive feedback influences learning behaviour. Feedback is an essential part of teaching because it helps students understand their strengths, identify areas for improvement, and develop confidence in their academic abilities. The study explores different forms of feedback, including verbal feedback, written comments, corrective guidance, praise, and suggestions for improvement. The findings indicate that students who receive clear, supportive, and constructive feedback show higher classroom confidence, greater willingness to participate, improved motivation, and better engagement in learning tasks. In contrast, unclear or overly critical feedback may reduce student confidence and discourage active participation. The results suggest that feedback is most effective when it is specific, timely, respectful, and focused on improvement rather than only pointing out mistakes. The study highlights the importance of teacher communication style in shaping student attitudes toward learning. It concludes that constructive feedback not only improves academic understanding but also strengthens students’ self-belief and positive learning behaviour.
subject: Articles
  topic: section
subject: Teacher Feedback
  topic: keywords
subject: Student Confidence
  topic: keywords
subject: Constructive Feedback
  topic: keywords
subject: Classroom Learning
  topic: keywords
subject: Learning Behavior
  topic: keywords
creator: Rukhsana Kausar
creator: Rubina Hanif
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 48
ending page: 59

SOCIAL MEDIA CONSUMPTION AND COGNITIVE ATTENTION AMONG COLLEGE STUDENTS
type: item
description: This study investigates the relationship between social media use and attention span among college students, with particular focus on digital distraction and classroom performance. In recent years, social media platforms have become a major part of students’ daily routines, offering communication, entertainment, and academic support. However, excessive and uncontrolled use may reduce concentration, increase multitasking habits, and negatively affect learning outcomes. The study examined patterns of social media usage, levels of classroom distraction, attention span, and academic performance among college students. The findings indicate that students who reported higher daily social media use showed lower attention scores and reduced classroom engagement compared with students who used social media moderately. Frequent checking of notifications, short-video consumption, and multitasking during lectures were identified as major sources of distraction. The results further suggest that reduced attention span is associated with weaker classroom performance, lower participation, and difficulty retaining lecture content. Students with better self-control and limited in-class phone use demonstrated stronger attention and improved academic outcomes. Overall, the study highlights that while social media can support communication and learning, excessive use may create serious challenges for student focus and classroom achievement. The findings emphasize the need for digital awareness, responsible technology use, and classroom strategies that help students manage distraction effectively.
subject: Articles
  topic: section
subject: Social Media Use
  topic: keywords
subject: Attention Span
  topic: keywords
subject: Digital Distraction
  topic: keywords
subject: College Students
  topic: keywords
subject: Classroom Performance
  topic: keywords
creator: Hina Tariq
creator: Muhammad Ahsan Raza
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 36
ending page: 47

RESOURCE-LIMITED SETTINGS LOW-COST AI-ASSISTED CANCER SCREENING AND ICU TRIAGE MODELS FOR RESOURCE-CONSTRAINED HEALTHCARE SYSTEMS
type: item
description: The lack of cancer specialists, diagnostic infrastructure, access to imaging, lab capacity, and critical care beds present significant challenges for resource constrained healthcare systems to provide timely cancer screening and effective intensive care unit triage. In these contexts, AI could provide scalable low-cost assistance for early risk identification, prioritization and clinical decision making. This study seeks to propose a low cost AI based framework for cancer screening and triage in the Intensive Care Unit built using common clinical, demographic, symptom-based, laboratory and basic imaging features. The framework was developed to be efficient and feasible to implement in primary care clinics, district hospitals and low-resource oncology and emergency care settings. Several machine learning models were tested for the prediction of cancer screening priority, admission to the intensive care unit, ventilator use, mortality risk, and referral urgency.Multiple machine learning models were tested, such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost, as well as lightweight neural network classifiers. The accuracy, sensitivity, specificity, precision, F1-score, calibration performance and area under the receiver operating characteristic curve were used to evaluate the model performance. Proposed low-cost AI assisted model had excellent prediction capability with good interpretability and operability. The following factors were identified as important: age, symptom duration, smoking history, weight loss, oxygen saturation, respiratory rate, blood pressure instability, hemoglobin level, inflammatory markers, basic imaging abnormality, comorbidity burden and performance status. The results indicate that low-cost AI systems could be used to aid a earlier cancer diagnosis and to streamline and enhance the triage process in ICUs where health resources are scarce. This can help frontline health-care providers prioritize high-risk pati…
subject: Articles
  topic: section
subject: Low-Cost Ai
  topic: keywords
subject: Cancer Screening
  topic: keywords
subject: Icu Triage
  topic: keywords
subject: Resource-Constrained Healthcare
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Faisal Ahmed
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 21
ending page: 35

PREDICTING ICU ADMISSION AND VENTILATOR DEPENDENCE IN ADVANCED LUNG CANCER PATIENTS USING CLINICAL AND IMAGING DATA
type: item
description: Progressive tumour burden, respiratory compromise, treatment-related toxicity, infections, and systemic complications are risk factors for acute clinical deterioration, intensive care unit admission and ventilator dependence in advanced lung cancer patients. The ability to identify patients who are likely to need critical care support early would allow timely intervention in the patient, planning of resources and individualized clinical decision making. This study aims to develop a predictive model for ICU admission and ventilator dependence of advanced lung cancer patients based on a machine learning framework using a combination of clinical and imaging data. Clinical features were used at the patient level: age, cancer stage, performance status, oxygen saturation, respiratory rate, comorbidities, inflammatory markers, treatment history, and laboratory parameters, among others, as well as imaging-derived features such as tumor burden, presence of pleural effusion, lung infiltration, airway obstruction, metastatic involvement, and radiomic texture characteristics. Several supervised learning models such as Logistic Regression, Random Forest, Support Vector Machine, Gradient Boosting, XGBoost and multimodal models (deep learning) were trained and evaluated for the prediction of ICU admission and ventilator requirement. The accuracy, sensitivity, specificity, precision, F1-score and area under the receiver operating characteristic curve were used to assess model performance. The multimodal framework achieved good predictive results, and the proposed ensemble model and deep learning surpass the baseline classifiers. The feature importance analysis revealed that oxygen saturation, respiratory rate, ECOG performance status, tumor burden, presence of pleural effusion, inflammatory markers, previous chemotherapy, and imaging-based lung involvement were the most predictive features. These results indicate that the use of clinical parameters in combination with imaging param…
subject: Articles
  topic: section
subject: Advanced Lung Cancer
  topic: keywords
subject: Icu Admission
  topic: keywords
subject: Ventilator Dependence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Clinical Imaging Data
  topic: keywords
creator: Ayesha Rahman
creator: Muhammad Farhan
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 1
ending page: 20

THE ROLE OF EDGE COMPUTING IN ENABLING REAL-TIME DATA PROCESSING IN AUTONOMOUS SYSTEMS FOR SMART CITIES
type: item
description: The rapid growth of smart cities and autonomous systems has intensified the demand for low-latency, reliable, and scalable computing infrastructures capable of processing large volumes of real-time data. This study investigates the effectiveness of edge computing in supporting autonomous operations within smart city environments through an experimental, mixed-method evaluation framework. A distributed edge-based architecture was implemented to process sensor and vehicular data locally, while centralized cloud resources were reserved for non-time-critical analytics. Quantitative results reveal significant performance improvements, including reduced end-to-end latency, higher throughput efficiency, lower packet loss rates, and decreased energy consumption compared to cloud-centric approaches. Qualitative analysis further demonstrates enhanced system resilience, improved fault tolerance, and stronger privacy preservation due to localized data processing. The integration of edge-based artificial intelligence enabled faster and more accurate decision-making for autonomous vehicles and intelligent traffic management systems, even under high network load and dynamic conditions. Overall, the results confirm that edge computing substantially enhances the operational efficiency, responsiveness, and scalability of autonomous smart city systems. The findings provide strong empirical evidence supporting the adoption of edge-enabled architectures as a core component of future smart city infrastructures.
subject: Articles
  topic: section
subject: Edge Computing
  topic: keywords
subject: Smart Cities
  topic: keywords
subject: Autonomous Systems
  topic: keywords
subject: Real-Time Data Processing
  topic: keywords
subject: Edge Ai
  topic: keywords
subject: Distributed Architectures
  topic: keywords
creator: Muhammad Asadullah Usman
creator: Humayun
rights: Copyright (c) 2025 Muhammad Asadullah Usman, Dr. Humayun (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 84
ending page: 101

THE IMPACT OF CLIMATE CHANGE ON THE SPREAD OF VECTOR-BORNE DISEASES IN DOMESTIC AND WILD ANIMAL POPULATIONS
type: item
description: Climate change is increasingly recognized as a critical determinant of animal health, particularly through its influence on vector-borne diseases. This study investigated the experimental relationships between climatic variables, vector ecology, host susceptibility, and disease dynamics using a mixed-methods approach integrating quantitative modeling and qualitative ecological interpretation. The results demonstrate that rising temperatures, variable precipitation, elevated humidity, and extreme weather events significantly increase vector density, pathogen prevalence, and disease incidence across domestic and wild animal populations. Statistical analyses revealed strong positive correlations between temperature and vector abundance, precipitation-driven seasonal amplification of disease transmission, and nonlinear threshold effects leading to rapid escalation of disease risk beyond specific climatic limits. Regional analyses showed pronounced spatial heterogeneity, with warmer and more humid regions experiencing higher disease burdens. Additionally, climate-induced physiological stress in animal hosts was associated with increased susceptibility to infection, amplifying transmission potential. Integrated risk indices and future projections indicated a substantial rise in vector-borne disease burden under ongoing climate change scenarios. Overall, the findings highlight that climate change acts as a systemic force reshaping host–vector–pathogen interactions, increasing disease emergence and spread. The study underscores the necessity of climate-resilient veterinary services, advanced surveillance systems, and One Health–based adaptive strategies to mitigate the growing threat of climate-sensitive vector-borne diseases.
subject: Articles
  topic: section
subject: Climate Change
  topic: keywords
subject: Vector-Borne Diseases
  topic: keywords
subject: Animal Health
  topic: keywords
subject: One Health
  topic: keywords
subject: Disease Transmission
  topic: keywords
subject: Climate Resilience
  topic: keywords
creator: Najeeb Ullah
creator: Israruddin
rights: Copyright (c) 2025 Najeeb Ullah, Israruddin (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 64
ending page: 83

THE POTENTIAL OF NANOTECHNOLOGY IN TARGETED DRUG DELIVERY SYSTEMS FOR VETERINARY ONCOLOGY TREATMENTS
type: item
description: Nanotechnology has emerged as a powerful tool for improving cancer therapy through precise and targeted drug delivery, with growing relevance in veterinary oncology. This study evaluated the potential of nanotechnology-based targeted drug delivery systems using a mixed-method experimental approach that combined nanoparticle formulation and characterization with in-vitro and in-vivo therapeutic assessment in veterinary cancer models. The results demonstrated that the synthesized nanoparticles exhibited uniform nanoscale dimensions, favorable surface charge, and high drug encapsulation efficiency, ensuring formulation stability and prolonged systemic circulation. Controlled release studies confirmed sustained drug delivery over time, while cellular uptake analyses showed significantly enhanced internalization of targeted nanoparticles compared with non-targeted controls. In-vivo evaluations revealed marked tumor volume reduction, high tumor inhibition rates, and preferential biodistribution of nanocarriers to tumor-associated tissues, confirming effective passive and active targeting. Importantly, hematological and biochemical analyses indicated minimal systemic toxicity, which was further reflected in improved quality-of-life scores and extended survival durations in treated animals. Overall, the findings demonstrate that nanotechnology-based targeted drug delivery systems can enhance therapeutic efficacy while reducing adverse effects, offering a promising and clinically relevant approach for improving cancer management in veterinary medicine.
subject: Articles
  topic: section
subject: Nanotechnology
  topic: keywords
subject: Targeted Drug Delivery
  topic: keywords
subject: Veterinary Oncology
  topic: keywords
subject: Nanoparticles
  topic: keywords
subject: Cancer Therapy
  topic: keywords
subject: Controlled Drug Release
  topic: keywords
creator: Younas Rehman
creator: Sumaiya Sundus
rights: Copyright (c) 2025 Younas Rehman, Sumaiya Sundus (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 44
ending page: 63

ADVANCEMENTS IN VETERINARY PATHOLOGY: USING AI FOR AUTOMATING DIAGNOSIS OF INFECTIOUS DISEASES IN ANIMALS
type: item
description: The integration of artificial intelligence (AI) into veterinary pathology represents a transformative advancement in the automated diagnosis of infectious diseases in animals. This study systematically evaluated AI-driven diagnostic frameworks using a mixed experimental approach that combined quantitative performance assessment with qualitative expert validation. The results demonstrate that AI-based models, particularly those leveraging deep learning and multimodal data integration, achieve high diagnostic accuracy across diverse animal species and pathogen classes. Automated analysis of histopathological images and laboratory data significantly reduced diagnostic turnaround time compared with conventional workflows, while maintaining strong concordance with expert veterinary pathologists. The findings further indicate that AI systems exhibit robust performance under heterogeneous clinical conditions, including varying infection severity, data noise, and interspecies variability. Scalability analyses revealed that diagnostic throughput increased efficiently with case volume, highlighting the feasibility of deploying AI tools in high-demand veterinary diagnostic settings. Importantly, qualitative expert evaluations confirmed that AI-generated outputs were clinically interpretable and supportive of decision-making rather than disruptive to established diagnostic practices. Despite these strengths, the results also underscore persistent challenges related to data standardization, limited availability of large annotated veterinary datasets, and the need for rigorous validation across broader animal populations. Overall, this study provides compelling evidence that AI-assisted diagnostic systems can enhance accuracy, efficiency, and consistency in veterinary infectious disease diagnosis, supporting improved animal health outcomes and contributing to broader public health and zoonotic disease surveillance efforts.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Veterinary Pathology
  topic: keywords
subject: Infectious Disease Diagnosis
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Automated Diagnostics
  topic: keywords
creator: Muhammad Fahimullah Khan
creator: Umer Farooq
rights: Copyright (c) 2025 Muhammad Fahimullah Khan, Umer Farooq (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 21
ending page: 43

INVESTIGATING THE FEASIBILITY OF USING AQUAPONICS SYSTEMS FOR SUSTAINABLE FISH AND VEGETABLE PRODUCTION IN URBAN AREAS
type: item
description: The growing pressures of population expansion, climate variability, and resource scarcity necessitate innovative and sustainable food production systems. This study experimentally evaluated the performance of an integrated aquaponics system combining recirculating aquaculture and hydroponic plant cultivation within a closed-loop framework. A mixed-method approach was employed, integrating quantitative measurements of water quality, fish growth, plant productivity, nutrient transformation efficiency, water reuse, and energy consumption with qualitative assessments of operational stability and system feasibility. The results demonstrate stable physicochemical water conditions suitable for both aquatic and plant organisms, alongside significant fish biomass gains and high plant yield. Efficient nitrogen conversion and nutrient recovery confirmed the effectiveness of biofiltration processes, enabling near-zero waste discharge. Water-use efficiency was markedly improved, with substantial reductions compared to conventional agriculture, while energy productivity ratios indicated favorable system efficiency. Graphical and tabulated analyses further revealed strong correlations between nutrient availability and biological performance, as well as consistent productivity across seasonal variations. Overall, the findings establish aquaponics as a resource-efficient, environmentally sustainable, and scalable food production model, particularly suitable for urban and land-constrained settings. The study highlights aquaponics as a promising solution for enhancing food security while minimizing ecological footprint.
subject: Articles
  topic: section
subject: Aquaponics
  topic: keywords
subject: Sustainable Agriculture
  topic: keywords
subject: Urban Food Systems
  topic: keywords
subject: Nutrient Recycling
  topic: keywords
subject: Water-Use Efficiency
  topic: keywords
subject: Circular Economy
  topic: keywords
creator: Naveed Hussain
creator: Irum Habib
rights: Copyright (c) 2025 Naveed Hussain, Irum Habib (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 1
ending page: 20

EVALUATING SOIL CARBON STOCKS IN AGROFORESTRY SYSTEMS USING DEEP LEARNING AND FIELD SAMPLING
type: item
description: To answer this question, we require that the amount of carbon in the soil be well known to determine how well it could assist in dealing with climate change and how to go about using the land in a manner that is amicable to nature. An integrated approach that involves deep learning, the field sampling of soils, and remote sensing are applied to predict soil organic carbon (SOC) in various types of agroforestry landscapes in the study. The method of collection of the soil samples used was a stratified random sampling due to different depths and soils that are used as a method of management. Thereafter, we measured the SOC concentration by conventional laboratory examination. A deep convolutional neural network (CNN) model integrated data of high resolutions Sentinel-2 and LiDAR with environmental conditions, such as conditions of climate and topography. The CNN performed superiorly to both traditional models such as Random Forest, K-nearest neighbours. It also had lower RMSE / MAE and higher R 2, and that indicated that it was more effective as a predictive model. Temperature, precipitation, elevation, and vegetation indices were the most important, with values of SHAP sensitivity analysis showing this. The validated model developed spatial SOC maps indicating small-scale variations on the study area on where carbon is abundant and where carbon can be stored. The natural deep learning strategy also cost less of time and money to the field than the conventional strategies even though it remains quite ecologically valid, since it was also tested on real world data. These findings indicate that AI-based models may be used to assist with carbon tracking, improve carbon accounting models and contribute to specific agroecology interventions. This research demonstrates the paradigm that may be employed repeatedly to enhancing the environmental assessment by opportunities of the new technologies. It provides policy-makers, conservationists, and land managers with powerful in…
subject: Articles
  topic: section
subject: Soil Organic Carbon
  topic: keywords
subject: Agroforestry Systems
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Remote Sensing
  topic: keywords
subject: Carbon Mapping
  topic: keywords
creator: Ahmed Mukhtar
creator: Rashid Khan
creator: Muhammad Arif
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0
date: 2023-06-30
publication date: 2023-06-30
starting page: 85
ending page: 106

NANOMEDICINE IN DRUG DELIVERY: TARGETING CANCER CELLS WITH PRECISION
type: item
description: Cancer treatment continues to face significant challenges due to limitations in conventional therapies, including systemic toxicity, drug resistance, and inadequate tumor specificity. Nanomedicine has emerged as a transformative approach that leverages nanoscale materials for targeted drug delivery, offering enhanced efficacy and reduced adverse effects. This study explores the design, mechanisms, and clinical potential of various nanoparticle-based drug delivery systems (DDS), including liposomes, polymeric nanoparticles, dendrimers, and gold nanoparticles.The methodology involved a comprehensive analysis of targeting strategies—both passive and active—alongside the integration of smart nanocarriers that respond to tumor-specific stimuli such as pH, enzymes, and temperature. These systems were evaluated for their pharmacokinetics, drug release kinetics, immune response modulation, and potential for combination with gene therapy and immunotherapy.The results demonstrate that dual-targeting nanocarriers significantly improve tumor localization and intracellular drug uptake, outperforming conventional drug delivery methods. Enhanced circulation time, reduced toxicity profiles, and higher therapeutic indices were consistently observed across optimized nanoparticle platforms. Smart nanocarriers also exhibited superior specificity in delivering therapeutic agents to tumor cells, validating their role in overcoming multidrug resistance.In conclusion, nanomedicine offers a promising future for precision oncology by enabling personalized, controlled, and effective cancer treatments. While regulatory and scalability challenges remain, ongoing advances in nanoparticle engineering and clinical validation are expected to accelerate the integration of nanomedicine into mainstream oncology practice. This research underscores the need for continued interdisciplinary collaboration to realize the full therapeutic potential of nanoscale drug delivery systems in cancer care.
subject: Articles
  topic: section
subject: Nanomedicine
  topic: keywords
subject: Drug Delivery Systems
  topic: keywords
subject: Cancer Therapy
  topic: keywords
subject: Targeted Therapy
  topic: keywords
creator: Saad Abdullah
creator: Usama Raza
creator: Sumaiya Sundus
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 76
ending page: 93

AGRICULTURAL EXTENSION SERVICES: BRIDGING RESEARCH AND PRACTICE
type: item
description: Agricultural extension services are integral to modern farming systems, acting as vital conduits between scientific research and practical field application. These services empower farmers by facilitating the adoption of innovative technologies, sustainable practices, and context-specific strategies that directly impact productivity, food security, and rural development. In recent decades, the role of extension services has evolved in response to global challenges such as climate change, digital transformation, and increasing demand for climate-resilient agriculture.This study employs a mixed-methods approach to evaluate the effectiveness, outreach, and adaptability of agricultural extension services across different regions, using case studies from Sub-Saharan Africa, South Asia, and Latin America. Quantitative analysis was conducted through the Extension Effectiveness Index, while qualitative insights were derived from participatory programs, digital extension tools, and national policy evaluations.The findings reveal that participatory learning methods like Farmer Field Schools significantly enhance crop yields and farmer engagement. Climate-smart agriculture initiatives demonstrate measurable improvements in water use efficiency and resilience, while digital platforms effectively extend real-time agricultural advice to remote farming communities. However, disparities in service access, funding limitations, and digital literacy gaps remain persistent barriers.The study concludes that a hybrid extension model—integrating government, private sector, and community-based approaches—offers the most effective pathway forward. Strengthening extension systems requires sustained investment, participatory planning, and digital inclusivity to ensure that all farmers, particularly women and smallholders, benefit equitably. As global agricultural systems face mounting environmental and socio-economic pressures, reimagining and reinforcing extension services is essential for a…
subject: Articles
  topic: section
subject: Agricultural Extension
  topic: keywords
subject: Research-To-Practice
  topic: keywords
subject: Farmer Outreach
  topic: keywords
subject: Rural Development
  topic: keywords
subject: Sustainable Farming
  topic: keywords
subject: Agricultural Productivity
  topic: keywords
subject: Climate-Smart Agriculture
  topic: keywords
subject: Knowledge Transfer
  topic: keywords
creator: Muhammad Asad Hameed
creator: Irfan Ahmad
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 57
ending page: 75

THE IMPACT OF MICROBIOME ON HUMAN HEALTH: IMPLICATIONS FOR DISEASE PREVENTION AND TREATMENT
type: item
description: The human microbiome, comprising trillions of microorganisms residing in various body niches, plays a critical role in maintaining physiological homeostasis and preventing disease. Recent advances in microbiome research have revealed its profound influence on immune modulation, metabolic balance, and neurological health, positioning it as a cornerstone in modern preventive and therapeutic strategies. This study undertakes an integrative review of contemporary literature, synthesizing findings from clinical trials, molecular investigations, and translational studies to evaluate the impact of microbiome composition on human health outcomes.A systematic approach was adopted to analyze peer-reviewed research from 2020 to 2024, emphasizing studies that explored microbiome-immune system interactions, metabolic regulation, gut-brain axis signaling, and therapeutic applications including probiotics, prebiotics, and fecal microbiota transplantation. The evidence was further supported through thematic coding and semi-quantitative evaluation of clinical endpoints, biomarker changes, and microbial diversity indices.The results demonstrate that reduced microbial diversity is consistently associated with chronic inflammation, metabolic disorders such as obesity and type 2 diabetes, and neuropsychiatric conditions including depression and Alzheimer’s disease. Probiotic and prebiotic interventions improved inflammatory profiles, enhanced insulin sensitivity, and showed potential in modulating neurotransmitter pathways. Moreover, fecal microbiota transplantation exhibited high efficacy in restoring microbial balance in recurrent infections and emerging promise in broader therapeutic contexts.In conclusion, the human microbiome is a dynamic and influential determinant of health and disease, with implications spanning multiple biological systems. As personalized medicine evolves, microbiome-based diagnostics and interventions are poised to revolutionize clinical care by enabling targe…
subject: Articles
  topic: section
subject: Human Microbiome
  topic: keywords
subject: Disease Prevention
  topic: keywords
subject: Probiotics
  topic: keywords
subject: Microbiome Therapy
  topic: keywords
creator: Syeda Iram Batool
creator: Younas Rehman
rights: Copyright (c) 2024 Syeda Iram Batool, Younas Rehman (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 38
ending page: 56

THE IMPACT OF AGRICULTURAL SUBSIDIES ON FARMING PRACTICES
type: item
description: Agricultural subsidies have long served as critical instruments for stabilizing food production and supporting farmer income in both developed and developing economies. However, their implementation has often contributed to unintended environmental consequences, including monoculture proliferation, chemical overuse, biodiversity loss, and resource depletion. This study investigates the multifaceted impacts of agricultural subsidies on farming practices and ecological sustainability through a mixed-methods approach that integrates quantitative modeling with qualitative case analysis.The methodology involves constructing a composite Environmental Impact Score using variables such as subsidy volume, monoculture index, and chemical input intensity. Cross-national data from 2000 to 2020 were analyzed alongside policy case studies from countries that have restructured subsidy frameworks, including New Zealand and Switzerland. Comparative assessments were conducted to evaluate economic stability, ecological resilience, and policy effectiveness under varying subsidy regimes.The results reveal that traditional subsidies disproportionately incentivize monoculture and input-intensive practices, accelerating soil degradation, water contamination, and biodiversity decline. Countries that implemented green subsidy reforms showed measurable improvements in environmental indicators without compromising agricultural output. Additionally, simulation models suggest that conditional subsidies—linked to sustainable practices such as crop rotation, agroforestry, and organic farming—yield significantly lower environmental impact scores and enhance system resilience.The study concludes that agricultural subsidies, if not reformed, may undermine long-term food security by eroding the natural resource base upon which farming depends. A transition toward sustainability-oriented subsidy structures is not only environmentally necessary but also economically viable. Policy recommendations includ…
subject: Articles
  topic: section
subject: Agricultural Subsidies
  topic: keywords
subject: Farming Practices
  topic: keywords
subject: Sustainability
  topic: keywords
subject: Monoculture
  topic: keywords
subject: Environmental Impact
  topic: keywords
subject: Crop Diversity
  topic: keywords
subject: Policy Reform
  topic: keywords
creator: Muhammad Bilal
creator: Muhammad Arif
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 19
ending page: 37

HARNESSING ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION AND DIAGNOSIS OF NEUROLOGICAL DISORDERS
type: item
description: Neurological disorders such as Alzheimer’s disease, Parkinson’s disease, and epilepsy present significant global health burdens due to their progressive nature and the complexity of early detection. Traditional diagnostic techniques often fail to capture subtle early-stage biomarkers, resulting in delayed interventions and suboptimal patient outcomes. In recent years, artificial intelligence (AI) has emerged as a transformative force in healthcare, offering data-driven precision and speed in clinical decision-making.This study presents a comprehensive AI-driven diagnostic framework that integrates machine learning (ML), deep learning (DL), and natural language processing (NLP) to enhance early detection and personalized management of neurological disorders. The methodology involved the analysis of multimodal datasets including brain imaging, EEG recordings, and clinical text data using convolutional neural networks, support vector machines, and transformer-based NLP models. Feature-level and decision-level fusion techniques were employed to optimize diagnostic performance, while wearable sensor data supported real-time monitoring.The results demonstrate high diagnostic accuracy across all models, with CNNs achieving over 95% in early Alzheimer’s detection and NLP models effectively extracting linguistic markers indicative of Parkinson’s progression. Real-time analysis through wearable sensor integration enabled the detection of tremors and seizures with near-instantaneous latency. The ensemble model further improved specificity and interpretability using SHAP values and saliency mapping, confirming the reliability and clinical transparency of the predictions.In conclusion, this research validates the efficacy of AI in enhancing neurological diagnostics by offering scalable, precise, and explainable solutions. The integration of wearable technologies, multimodal analytics, and real-time feedback systems positions AI as a pivotal tool in proactive neurological care. A…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Neurological Disorders
  topic: keywords
subject: Early Detection
  topic: keywords
subject: Diagnosis
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Muhammad Danial Ahmad Qureshi
creator: Muhammad Inam Farooq
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 1
ending page: 18

SYSTEMIC IMMUNE INFLAMMATION INDEX AS A PROGNOSTIC MARKER IN COLORECTAL CANCER
type: item
description: Elevated preoperative Systemic Immune-Inflammation Index (SII), calculated as platelet count × neutrophil count ÷ lymphocyte count, was evaluated as a prognostic biomarker in a prospective cohort of 200 colorectal cancer patients undergoing curative resection between January 2022 and December 2023. Using ROC-derived thresholds for 2-year overall survival, patients were stratified into low- and high-SII groups and followed for a median of 36 months. High SII was significantly associated with advanced TNM stage (III–IV: 62% vs. 45%, p = 0.012), increased lymphovascular invasion (48% vs. 28%, p = 0.003), and elevated carcinoembryonic antigen (>5 ng/mL: 40% vs. 22%, p = 0.001). Kaplan–Meier analysis revealed markedly reduced overall and disease-free survival in the high-SII group (both log-rank p < 0.001). Multivariable Cox regression, adjusting for age, sex, stage, and adjuvant therapy, confirmed high SII as an independent predictor of poorer overall survival (hazard ratio 1.84, 95% CI 1.21–2.79, p = 0.004). Additionally, high-SII patients demonstrated lower complete response rates to standard adjuvant chemotherapy (28% vs. 45%, p = 0.008). These findings indicate that preoperative SII, readily obtainable from routine blood counts, provides cost-effective, independent prognostic information and may enhance risk stratification and therapeutic decision-making in colorectal cancer management.
subject: Articles
  topic: section
subject: Systemic Immune-Inflammation Index
  topic: keywords
subject: Colorectal Cancer
  topic: keywords
subject: Prognostic Biomarker
  topic: keywords
subject: Overall Survival
  topic: keywords
subject: Disease-Free SurvivalSystemic Inflammation
  topic: keywords
creator: Rida Naz
creator: Sajjad Mehdi
creator: Shazia Khalid
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 69
ending page: 85

ROLE OF MYCOTOXINS IN REPRODUCTIVE FAILURES IN EXTENSIVE POULTRY SYSTEMS
type: item
description: Through vast field observation and quantitative analysis, this paper focuses on the role of mycotoxins in reproductive failures in large-scale chicken systems. Samples of feed, reproductive performance, hormone profiles and physiological indicators were collected and analyzed on 15 farms across a range of climate zones over a year. Aflatoxin B1, ochratoxin A, zearalenone, deoxynivalenol, mycotoxins were extremely prevalent, and the level of contamination varied according to the temperature-humidity index (THI) of the season. Reproduction performance decreased tremendously in groups that had higher concentrations of mycotoxins. As an illustration, the conception rates decreased by 92 to 75 percent and hatchability decreased by 89 to 66 percent. Hormonal assays revealed large decreases in estrogen, progesterone, LH and FSH levels. This implies that reproductive issues are primarily endocrine disruptive. Elevated liver enzymes and markers of oxidative stress further proven systemic physiological compromisation. Multiple linear regression Model aflatoxin B1, zearalenone, ochratoxin A and DON, were marked as important independent predictors of reproductive failure (p<0.001). The logistic regression revealed that the odds of reproductive failure were four times higher when the aflatoxin B1 levels exceeded 20 ppb. Co-contamination was high with over 60 percent of samples showing multiple mycotoxins. These findings demonstrate that the implications of mycotoxin challenge on the reproduction of poultry in large-scale facilities can be quite complex and diverse. They also demonstrate the significance of the presence of integrated monitoring and mycotoxin reduction and climate-adaptive management practices
subject: Articles
  topic: section
subject: Mycotoxins
  topic: keywords
subject: Reproductive Failure
  topic: keywords
subject: Extensive Poultry Systems
  topic: keywords
subject: Aflatoxin
  topic: keywords
subject: Fertility
  topic: keywords
subject: Oxidative Stress
  topic: keywords
creator: Muhammad Jamil
creator: Abdul Wadood Jan
creator: Shafiq Ur Rehman
creator: Muhammad Umer Farooq
creator: Shahid Iqbal
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 52
ending page: 68

RELATIONSHIP BETWEEN BODY CONDITION SCORE AND FERTILITY OUTCOMES IN GRAZING BUFFALOES
type: item
description: The objective of this study is to determine the body condition score (BCS)-reproductive outcome in grazing buffaloes in semi-intensive field conditions. A standardized 5-point BCS system was utilized to record 200 multiparous Murrah and Nili-Ravi buffaloes at prepartum, postpartum, and first artificial insemination (AI) phases. A complete reproductive record and analysis were made, including days to first estrus, days open, services/conception and conception rates along with metabolic profiles and diet intake. The findings revealed that the reproductive performance of the buffaloes was better in the ideal range of BCS of 3.5 to 3.99. Their postpartum intervals were considerably shorter (35 days to first estrus), days open were less (75 days), services per conception were greater (1.5) and first-service conception rate was highest (66%). Hormonal studies revealed that condition score (BCS) ideal was associated with elevated progesterone and IGF-1 concentrations, and reduced NEFA and BHB concentrations, which are indicators of a favorable metabolic status. Nutritional analyses indicated that the adequate intake of crude protein and total digestible nutrients permitted maintaining the desirable body condition score (BCS). BCS and conception rates were aggravated by season of the year, with summer being the worst due to higher temperature-humidity index (THI) and heat stress. The logistic regression revealed that buffaloes with optimal BCS were 3.2 more likely to become pregnant at the first service (p<0.01). Multiple linear regression indicated that BCS, IGF-1, NEFA and THI were all significant (predictive) variables in relation to predicting days open. The research demonstrates that active observation and intervention of BCS in association with dietary and environmental techniques can significantly improve the reproductive performance of grazing buffaloes, which provides fundamental guidance on herd management strategies under varying climatic conditions.
subject: Articles
  topic: section
subject: Body Condition Score
  topic: keywords
subject: Fertility Outcomes
  topic: keywords
subject: Grazing Buffaloes
  topic: keywords
subject: Metabolic Profile
  topic: keywords
subject: Reproductive Performance
  topic: keywords
subject: Heat Stress
  topic: keywords
creator: Hidayatullah
creator: Muhammad Asif
creator: Israruddin
creator: Najeeb Ullah
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 36
ending page: 51

LONG-TERM NEURODEVELOPMENTAL OUTCOMES IN INFANTS TREATED WITH THERAPEUTIC HYPOTHERMIA FOR HIE
type: item
description: Hypoxic‐ischemic encephalopathy (HIE) remains a major cause of neonatal morbidity despite widespread adoption of therapeutic hypothermia. In this multicenter prospective cohort study of 150 term infants treated with whole‐body cooling (33–34 °C for 72 h) initiated within six hours of birth, we evaluated neurodevelopmental outcomes at 24 and 60 months and identified clinical, imaging, and biomarker predictors of adverse outcome. At two years, mean BSID-III composite scores were 95.2 ± 12.3 (cognitive), 90.1 ± 14.5 (language), and 92.5 ± 13.1 (motor), and at five years, mean full-scale IQ was 98.3 ± 11.7 with an MABC-2 motor percentile of 42.7 ± 18.4; nevertheless, 40% met criteria for a composite adverse outcome (death, cerebral palsy, or intellectual disability). Multivariable analysis demonstrated that severe HIE (AOR 3.2, p = 0.003), delayed cooling initiation beyond three hours (AOR 2.5, p = 0.014), basal ganglia injury on neonatal MRI (AOR 4.0, p < 0.001), low socioeconomic status (AOR 1.8, p = 0.028), and elevated serum neuron-specific enolase > 18 ng/mL (AOR 2.2, p = 0.049) were independent predictors of adverse outcome. MRI patterns correlated strongly with impairment severity—66% of infants with basal ganglia lesions had moderate to severe deficits versus < 5% with normal imaging. Qualitative interviews underscored barriers in therapy access and emotional coping that may further hinder recovery. These findings emphasize the critical need for rapid initiation of hypothermia, MRI-guided risk stratification, and integrated socioeconomic and psychosocial support to optimize long-term neurodevelopment. Ongoing longitudinal surveillance and investigation of adjunctive neuroprotective and family-centered interventions are warranted to mitigate late-emerging cognitive, motor, and behavioral challenges in this vulnerable population
subject: Articles
  topic: section
subject: Hypoxic‐Ischemic Encephalopathy
  topic: keywords
subject: Therapeutic Hypothermia
  topic: keywords
subject: Neurodevelopmental Outcomes
  topic: keywords
subject: MRI Biomarkers
  topic: keywords
subject: Neuron-Specific Enolase
  topic: keywords
subject: Long-Term Follow-Up
  topic: keywords
creator: Wesam Taher Almagharbeh
creator: Dr. Humayun
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 19
ending page: 35

PET IMAGING FOR EARLY DETECTION OF METASTASIS IN TRIPLE NEGATIVE BREAST CANCER
type: item
description: Early and accurate detection of metastatic spread is crucial for improving outcomes in patients with triple‐negative breast cancer (TNBC), a subtype notorious for rapid dissemination and poor prognosis. In this prospective study, 50 high‐risk TNBC patients (primary tumor >2 cm or lymphovascular invasion) underwent ^68Ga‐anti‐cMET nanobody PET imaging alongside standard CT/MRI staging. Dynamic PET acquisition over 60 minutes and static whole‐body scans at 90 minutes post‐injection were analyzed to determine lesion uptake (SUVₘₐₓ), and findings were validated against histopathology or three‐month follow‐up. Compared with CT/MRI, PET achieved superior diagnostic performance (sensitivity 90% vs. 75%; specificity 85% vs. 80%; AUC 0.92 vs. 0.83), reliably identifying micrometastases down to 0.7 cm. Mean SUVₘₐₓ for metastatic lesions exceeded adjacent tissue by >1.2 units. Radiologist confidence was higher for PET (mean Likert score 4.5/5) than CT/MRI (3.2/5), and PET findings prompted changes in treatment plans for 40% of patients. Cumulative detection curves over 12 months demonstrated a steeper rise in metastasis identification when guided by PET, suggesting potential for earlier therapeutic intervention. While cost and tracer availability warrant consideration, thematic interviews with oncologists underscored the clinical value of targeted PET in TNBC management. These results support integrating ^68Ga‐anti‐cMET PET into early staging protocols to enhance micrometastasis detection, refine patient stratification, and ultimately improve progression‐free and overall survival in TNBC.
subject: Articles
  topic: section
subject: Triple‐Negative Breast Cancer
  topic: keywords
subject: Micrometastasis Detection
  topic: keywords
subject: cMET Nanobody
  topic: keywords
subject: Standardized Uptake Value
  topic: keywords
subject: Diagnostic Accuracy
  topic: keywords
creator: Abdul Ghaffar
creator: Jawad Ali
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 1
ending page: 18

INVESTIGATING SOIL-WATER-NUTRIENT INTERACTIONS IN VERTISOLS UNDER DIFFERENT IRRIGATION SCHEDULES
type: item
description: This study investigates the impact of precision irrigation strategies on nutrient uptake and crop performance in Vertisols, which are known for their challenging swelling-shrinking behavior and high clay content. Using a quantitative approach, we evaluated soil moisture, nutrient absorption, crop yield, water use efficiency, and salinity across full, deficit, and supplemental irrigation treatments. Results indicate that full irrigation resulted in the highest soil moisture (32.5%) and crop yield (1200 kg/plot), with superior nitrogen, phosphorus, and potassium uptake. However, deficit irrigation demonstrated the highest water use efficiency (1.5 kg/m³) and encouraged deeper root growth (28 cm), suggesting its potential for sustainable water management. Supplemental irrigation maintained optimal salinity levels (1.7 dS/m) and provided a balanced yield (1100 kg/plot). Smart irrigation systems and fertigation techniques were shown to enhance precision in nutrient delivery, especially when supported by IoT and sensor-based technologies. Drip irrigation achieved the highest uniformity coefficient (90%), making it ideal for targeted nutrient application. This study concludes that integrating tailored irrigation schedules with smart technologies can significantly optimize resource use, boost crop productivity, and mitigate environmental impacts in Vertisol regions.
subject: Articles
  topic: section
subject: Vertisols
  topic: keywords
subject: Precision Irrigation
  topic: keywords
subject: Nutrient Uptake
  topic: keywords
subject: Water Use Efficiency
  topic: keywords
subject: Smart Farming
  topic: keywords
subject: Sustainable Agriculture
  topic: keywords
creator: Hafiz Muhammad Bilal
creator: Muhammad Suleman Aziz
creator: Muhammad Bilal
rights: Copyright (c) 2025 Hafiz Muhammad Bilal , Muhammad Suleman Aziz, Muhammad Bilal (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 64
ending page: 76

ETHNOVETERINARY MEDICINES EVALUATION FOR ENDOPARASITE CONTROL IN GOATS
type: item
description: The current study evaluated the anthelmintic effects of certain ethnoveterinary medicinal plants traditionally used by the rural communities in the control of endoparasites in goats. A traditional medical study revealed 14 frequently used plant species, out of which Carica papaya, Allium sativum and Azadirachta indica were selected and validated in vitro and in vivo. A total of 60 infected goats were subjected to fecal egg count reduction test (FECRT), blood tests and biochemical testing over a 28-day trial period. All the plant extracts significantly reduced parasite burdens as indicated by the results. On Day 28, Carica papaya had reduced them by 78%, Allium sativum by 73% and Azadirachta indica by 71%. The effectively treated groups had an improvement in packed cell volume (PCV), hemoglobin and serum protein levels, and this implied that they were recovering or improving so far as parasite anemia was concerned. Additionally, treated goats attained considerable weight and achieved higher appetite scores. The presence of bioactive compounds, namely, alkaloids, tannins, and flavonoids, which are known to have antiparasitic properties, was confirmed by phytochemical screening. The paper highlights the usefulness of the conventional plant-based remedies as potential, cost-effective substitutes to artificial anthelmintics in controlling gastrointestinal nematode infections in goats. The findings support the integration of ethnoveterinary practices in current parasite control initiatives, especially in regions where anthelmintic resistance and limited veterinary services are prevailing. Further standardization and pharmacological safety studies should be done to enhance their clinical application.
subject: Articles
  topic: section
subject: Ethnoveterinary Medicine
  topic: keywords
subject: Endoparasites
  topic: keywords
subject: Goats
  topic: keywords
subject: Anthelmintic Resistance
  topic: keywords
subject: Medicinal Plants
  topic: keywords
subject: Fecal Egg Count
  topic: keywords
creator: Saeed Ullah
creator: Muhammad Anwar Baloch
creator: Muhammad Usman
rights: Copyright (c) 2025 Saeed Ullah , Muhammad Anwar Baloch , Muhammad Usman (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 45
ending page: 62

REGULATION OF SEED GERMINATION BY PHYTOHORMONAL CROSS-TALK UNDER WATER DEFICIT CONDITIONS
type: item
description: Water deficit conditions pose a significant threat to seed germination and early seedling establishment, particularly in drought-sensitive crops. This study investigated the phytohormonal cross-talk governing seed germination under drought stress using two sensitive (Zea_DS1, Glycine_DS2) and two tolerant (Zea_DT1, Glycine_DT2) genotypes. Seeds were subjected to varying levels of water stress (0 MPa, –0.3 MPa, –0.6 MPa) using polyethylene glycol (PEG-6000), and germination parameters were assessed alongside hormonal profiling and gene expression analyses. Results revealed that drought-sensitive genotypes exhibited significantly reduced germination rates (as low as 37.84%) and prolonged mean germination times under severe drought, with a concurrent decrease in germination index. Hormonal quantification showed a marked increase in abscisic acid (ABA) and decline in gibberellic acid (GA₃) levels under drought, especially in sensitive lines, indicating a shift toward an inhibitory hormonal environment. Gene expression analysis further confirmed the upregulation of NCED3, ABI5, and DELLA in drought-stressed seeds, highlighting the dominance of ABA signaling pathways. In contrast, tolerant genotypes maintained relatively stable hormone levels and gene expression patterns, enabling better germination under water stress. Hormonal inhibitor experiments validated these findings, where ABA inhibition partially restored germination, while GA inhibition exacerbated suppression. PCA analysis identified ABA and ABA-related gene markers as major contributors to drought response variation. Collectively, the study demonstrates that seed germination under water deficit is regulated by a dynamic, genotype-specific hormonal network, where the antagonism between ABA and GA is modulated by additional hormonal players such as IAA and ethylene. These findings provide valuable insights into the molecular basis of seed drought tolerance and offer potential targets for crop improvement strateg…
subject: Articles
  topic: section
subject: Seed Germination
  topic: keywords
subject: Drought Stress
  topic: keywords
subject: Phytohormones
  topic: keywords
subject: ABA-GA Cross-Talk
  topic: keywords
subject: Gene Expression
  topic: keywords
subject: Drought Tolerance
  topic: keywords
creator: Irfan Ahmad
creator: Muhammad Asad Hameed
rights: Copyright (c) 2025 Irfan Ahmad , Muhammad Asad Hameed (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 30
ending page: 44

LAMENESS ETIOLOGIES IN BROILER CHICKENS RAISED UNDER INTENSIVE HOUSING
type: item
description: Lameness represents a critical welfare and productivity concern in broiler chickens raised under high-density production systems, stemming from complex genetic, environmental, nutritional, and infectious interactions. This study aimed to unravel the multifactorial etiology of lameness through comprehensive evaluations of broiler flocks across several commercial farms. The findings revealed that accelerated growth rates were significantly associated with a higher prevalence of skeletal deformities and gait abnormalities. Nutritional deficiencies, particularly reduced levels of calcium, phosphorus, and vitamin D, emerged as primary contributors to impaired bone development. Microbiological analysis identified frequent isolation of Staphylococcus spp. and avian reovirus from joint lesions, indicating an infectious component in lameness pathogenesis. Environmental monitoring further highlighted poor litter quality, excessive stocking density, suboptimal ventilation, and inadequate lighting as key aggravating factors. Biomechanical and histological assessments confirmed strong correlations between skeletal abnormalities and reduced musculoskeletal integrity. Additionally, a positive relationship was observed between advancing bird age, increasing body weight, and rising lameness incidence. Collectively, these findings underscore the need for holistic management strategies integrating genetic selection for skeletal soundness, balanced nutritional protocols, optimized housing conditions, and stringent biosecurity measures. The study provides actionable insights for industry stakeholders seeking to improve broiler welfare and minimize economic losses associated with lameness.
subject: Articles
  topic: section
subject: Broiler Chickens
  topic: keywords
subject: Lameness
  topic: keywords
subject: Skeletal Disorders
  topic: keywords
subject: Intensive Housing
  topic: keywords
subject: Nutritional Deficiency
  topic: keywords
subject: Poultry Welfare
  topic: keywords
creator: Umer Farooq
creator: Mohammed Qasim
creator: Atta ur Rehman
rights: Copyright (c) 2025 Umer Farooq, Mohammed Qasim, Atta ur Rehman (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 16
ending page: 29

IMPACT OF HEAD AND NECK RADIATION THERAPY ON LONG-TERM NEUROCOGNITIVE FUNCTION
type: item
description: Head and neck cancer patients undergoing radiotherapy are increasingly surviving longer, but often at the cost of neurocognitive complications that impair their quality of life. This study aimed to quantify the prevalence, predictors, and biological correlates of radiation-induced cognitive impairment in this patient population. A mixed-methods approach was employed, involving retrospective clinical data analysis, neurocognitive assessments, biomarker profiling, and machine learning modeling.As detailed in the results, significant cognitive impairments were observed post-radiotherapy, particularly in processing speed and attention. Radiation dose to the hippocampus and frontal lobes showed strong negative correlations with memory and executive function scores (Table 4). Logistic regression revealed hippocampal dose and comorbidities as independent predictors of cognitive decline (Table 5). Systemic inflammation also emerged as a contributory factor (Table 6). Among machine learning models, XGBoost achieved the highest accuracy (85%) and AUC (0.91) in predicting neurocognitive impairment (Table 7). Visualizations supported these findings, with Fig. 1 showing domain-specific cognitive decline and Fig. 2 confirming the dose-response relationship. These findings highlight the importance of incorporating neurocognitive protection and predictive modeling into standard radiation therapy planning for head and neck cancers. This integration can enable clinicians to balance therapeutic efficacy with quality-of-life preservation.
subject: Articles
  topic: section
subject: Head And Neck Cancer
  topic: keywords
subject: Radiotherapy
  topic: keywords
subject: Cognitive Impairment
  topic: keywords
subject: Dosimetry
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Neuroprotection
  topic: keywords
creator: Wesam Taher Almagharbeh
creator: Humayun
rights: Copyright (c) 2025 Wesam Taher Almagharbeh , Dr. Humayun (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 15

HYBRID PHARMACOLOGICAL APPROACHES TO PAIN MANAGEMENT IN COMPANION ANIMALS
type: item
description: This paper aimed at developing and validating a hybrid pharmacological management of pet pains using the strategies of qualitative insights on veterinary knowledge and quantitative pharmacokinetic and pharmacodynamic (PK/PD) modelling. We conducted structured interviews of veterinarians to discover information regarding the prevalent pain countermeasures, species-associated problems of treatments, and drug seeking behavior. The optimal patterns used in the design of pharmacological combinations that comprise non-steroidal anti-inflammatory drugs (NSAIDs), opioids, and adjuvant therapy depend on a thematic analysis of the interview information. In quantitative modelling, exponential decay modelling and modified Emax equation were utilized in order to match the most appropriate dosing regimes expecting prolonged pain relief, with the minimization of adverse effects. The hybrid formulations have been tested in study-controlled clinical trials and in dogs, cats, and small exotic animals in acute or chronic pain situations. To assess efficacy, we checked physiological markers and validated scales rating pain grades and to assess safety we checked clinical pathology and adverse event tracking. The findings were that hybrid treatment methods are much more effective towards alleviating pain and they are more convenient to tolerate as compared with monotherapies. They also produced lesser side effects on the stomach and kidneys. This form of integrating various strategies provides us with a means of addressing pain in a data-based and species-congenial method, and it has infinite potential to render animals healthier..
subject: Articles
  topic: section
subject: Pain Management
  topic: keywords
subject: Companion Animals
  topic: keywords
subject: Pharmacokinetics
  topic: keywords
subject: Pharmacodynamics
  topic: keywords
subject: Veterinary Medicine
  topic: keywords
subject: Hybrid Therapy
  topic: keywords
creator: Muhammad Fahimullah Khan
rights: Copyright (c) 2023 Muhammad Fahimullah Khan (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 85
ending page: 103

GERIATRIC VETERINARY MEDICINE AND ANIMAL LONGEVITY
type: item
description: This is due to the fact that animals are living longer since dietary, medical and living conditions have all improved. This is why geriatric veterinary medicine has become a necessity in treating elderly diseases and even extending animals lives. The current study exclusively witnessed the significance of professional geriatric care in the life of the aged animal in varying subjects e.g. dogs, cats, horses and exotic mammals. To obtain this, we considered the clinical records where we analyzed how patients with the physiological characteristics they had, the number of chronic disorders, the degree of response to treatment, and their mental conditions. The data indicated that the methods of personalised practice involved aspects such as controlling their food intake, providing the chronic pain therapy, cognitive therapy, and frequent metabolic tests. These transformed the aged animals into a great deal more active, stable about their metabolism, and incomparably better condition than they had ever been in. Overall observation revealed that animals receiving 24-hour geriatric treatment lived longer than the animals receiving frequent care alone. There was also the improvement of behavioural health and slower speed of cognitive loss with carers who had employed an educational approach which incorporated environmental enrichment. The visual data in form of representations supported the tendencies of age-related diseases that were specific to individual species and proved thatTHERAPEUTIC STRATEGIES OF EMPHASIS were effective. The report on the findings of the study indicates that geriatric veterinarian care does not only prolong the life of animals, but also enhance their lives. This bolsters the notion of provided customised and preventative care of older animals..
subject: Articles
  topic: section
subject: Geriatric Veterinary Medicine
  topic: keywords
subject: Animal Longevity
  topic: keywords
subject: Chronic Disease
  topic: keywords
subject: Senior Animal Care
  topic: keywords
subject: Quality Of Life
  topic: keywords
subject: Preventive Medicine
  topic: keywords
creator: Israruddin
creator: Najeeb Ullah
creator: Umer Farooq
rights: Copyright (c) 2023 Israruddin, Najeeb Ullah, Umer Farooq (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 65
ending page: 84

BIOTELEMETRY IN ANIMAL MOVEMENT ECOLOGY
type: item
description: This paper has examined ecology of animal movement by using contemporary tools in relation to biotelemetry to meter the animal migration speed, use area, number of species incidents, variability in GPS, and migration patterns. So we measure a lot of various types of animals inhabiting lots of different kinds of surroundings and we have converted it into nine complete tables and twelve extensive figures. The findings indicated that the speed of movement across species and time of day used was very diverse and that crepuscular intervals experienced the greatest movement activity. As an example, an exploration of habitat use revealed that the preferences of various species are pretty varied. The distributions indicated by the GPS coordinates were that localised and migratory species differed greatly in terms of range. Evidence on seasonal migrations indicated the existence of long-distance movement corridors which are of very high significance in terms of linkages to populations. The researches on signal strength demonstrated that geographical factors are one of the most significant that influence the accuracy of the telemetry data and the comparison battery functioning provided practical hints concerning equipment installation. The research provides a complete image of spatial ecology through combining both quantitative telemetry data with ecological explanation to assist design conservation action. The findings demonstrate the significance of diversity in the habitat, good condition of migration corridors, and the most appropriate tracking methods to take good care of the wildlife..
subject: Articles
  topic: section
subject: Biotelemetry
  topic: keywords
subject: Animal Movement Ecology
  topic: keywords
subject: Habitat Usage
  topic: keywords
subject: Gps Tracking
  topic: keywords
subject: Wildlife Conservation
  topic: keywords
subject: Migration Patterns
  topic: keywords
creator: Fawad Ahmad
creator: Hidayatullah
creator: Muhammad Umer Farooq
creator: Muhammad Mubeen
rights: Copyright (c) 2023 Fawad Ahmad, Hidayatullah, Muhammad Umer Farooq, Muhammad Mubeen (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 44
ending page: 64

PLANT DISEASE FORECASTING MODELS DRIVEN BY ARTIFICIAL INTELLIGENCE
type: item
description: In reducing losses which are incurred during cultivation of crops, and also promoting a friendly environment by farming, diseases that affect plants should be detected and anticipated within a very short time. This research demonstrates how it is possible to predict plant diseases using AI through the training and applying machine learning and deep learning algorithms such as Random Forest, XG-Boost, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks to various datasets. The algorithms were trained on the photos of the symptoms of the diseases, weather conditions, and past histories of the disease eruptions. This enabled powerful multi-factorial prediction to be made. These findings indicate that ensemble techniques such as XGBoost and deep learning approaches such as CNN will always give higher accuracy (>95 95 ), precision, recall, and F1-scores as compared to classical classifiers. While examining the growth of various crops and types of diseases we found that the relationship between them and the predictions when environmental variables were added to the mix were highly improved. The examples of hybrid affiliations are bar, line, scatter, pie, and multi-plot kids all of which assisted us to realize better the patterns of illness and better ways models operate. The case study demonstrates that AI-based forecasting of diseases will assist the farmers in making better decisions, having a more prompt response, reducing the amount of pesticides used, and making crops resistant to diseases. This outcome will unlock the possibilities of advanced, scalable agricultural models that are capable of coping with the issues generated by climate change and novel plant diseases..
subject: Articles
  topic: section
subject: Plant Disease Forecasting
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Precision Agriculture
  topic: keywords
subject: Crop Protection
  topic: keywords
creator: Muhammad Shafique
creator: Faran Muhammad
creator: Muneeba
rights: Copyright (c) 2023 Muhammad Shafique, Faran Muhammad, Muneeba (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 22
ending page: 43