Scientific Journal for Publishing in Health Research and Technology
The Scientific Journal for Publishing in Health Research and Technology (SJPHRT) (ISSN: 3105-949X) is an open-access, peer-reviewed academic journal published by the Academic University in Libya. Dedicated to advancing scientific knowledge, SJPHRT serves as a vital platform for researchers and scholars in the fields of health and technology sciences. The journal publishes four issues annually, providing timely access to cutting-edge research. Our Vision To be a leading scientific publication that promotes innovation and excellence in health and technology research in Libya and the broader region. Our Mission To publish high-quality, peer-reviewed research that contributes significantly to the body of knowledge in health and technology, while supporting the academic community through a transparent and efficient publication process. Our Objectives Disseminate knowledge: Publish original and impactful research. Encourage collaboration: Foster a global academic community. Ensure quality: Maintain rigorous peer-review standards.
Modern Olive Tree Irrigation and Fertilisation: Increasing Productivity and Sustainability
This study explores cutting-edge olive tree watering and fertilisation methods to improve olive production. The historical context highlights olive cultivation's importance and the need for change. The symbiotic relationship between irrigation and fertilisation is carefully examined in our research of traditional practices and their development. We concentrate on drip irrigation and sprinkler systems to study improved irrigation methods. Rainwater collection and smart irrigation technology alter resource management. Our study of fertilisation strategies highlights controlled-release fertilisers, organic practices, and precision technology, which all have a role in nutrient delivery and absorption efficiency. This study uses actual data and theoretical frameworks to show how harmonising irrigation and fertilisation might boost economic viability and environmental stewardship. As issues arise, we negotiate technological subtleties, economic concerns, and ecological sustainability to advance innovation, informed legislation, and sustainable olive agriculture.
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The impact of waste from the olive oil business on the biological characteristics of soil
This study explores the complex interactions between soil ecosystems and olive oil industry outputs, with an emphasis on waste from olive mills. Employing a multi-faceted approach encompassing field experiments and advanced laboratory analyses, our study discerns the nuanced impact of olive pomace and wastewater on the biological tapestry of soil. By meticulously scrutinizing alterations in microbial communities and soil nutrient dynamics, we aim to unravel the ecological ramifications of olive oil industry waste disposal. Our investigation not only catalogues the distinctive composition of these waste components but also elucidates their consequential effects on the microbial composition and nutrient richness of soil. Employing cutting-edge DNA sequencing techniques, we explore the subtleties of microbial diversity and abundance shifts, providing a comprehensive understanding of the intricate symbiosis within soil ecosystems. Furthermore, statistical analyses unravel correlations between waste constituents and soil biological properties, elucidating critical insights for sustainable agricultural practices. The results of this study not only broaden our understanding of the ecological consequences of waste from the olive oil business, but they also provide useful suggestions for reducing any possible negative impacts. This research highlights the need of sustainable waste disposal methods in maintaining the health of our agricultural landscapes and acts as a guide for knowledgeable soil management techniques.
Synthesis and Characterization of Functionalized Benzimidazole Derivatives: A Practical Experimental Study
A series of functionalized benzimidazole derivatives were successfully synthesized and characterized to enhance their biological activity, achieving yields ranging from 70% to 85% using standard condensation and functionalization processes. The structures of the compounds were verified through mass spectrometry (MS), infrared spectroscopy (IR), and nuclear magnetic resonance (NMR) spectroscopy, which provided comprehensive structural insights. Evaluation of antimicrobial activity against S. aureus and E. coli demonstrated that derivatives 1c and 1d exhibited notable inhibitory zones. Furthermore, cytotoxicity tests using HeLa and MCF-7 cell lines showed potent anticancer activity, with compounds 1c and 1d displaying lower IC50 values compared to those reported in earlier research. These results suggest that specific structural alterations can markedly increase the biological efficacy of benzimidazole derivatives, indicating their strong potential as antimicrobial and antitumor drugs. Future research should focus on investigating a larger spectrum of biological functions, employing new synthesis techniques, and carrying out comprehensive mechanistic and pharmacokinetic studies to fully realize the therapeutic promise of these molecules.
GC-MS Quantification of Bioactive Isothiocyanates in Sinapis alba Essential Oil and Validation of Rapid Bactericidal Kinetics Against Clinically Relevant Pathogens
White mustard (Sinapis alba L.), a member of the Brassicaceae family, is globally recognized for its culinary, agricultural, and medicinal applications. This study comprehensively investigates the chemical profile of S. alba essential oil (EO) using gas chromatography-mass spectrometry (GC-MS) and evaluates its antimicrobial and antioxidant properties. Hydro distilled EO yielded 0.8% (w/w), with GC-MS analysis identifying 32 volatile constituents, predominantly allyl isothiocyanate (72.4%), 4-pentenyl isothiocyanate (12.3%), and β-pinene (4.1%). Antimicrobial assays demonstrated potent activity against Staphylococcus aureus (MIC: 0.25 mg/mL) and Escherichia coli (MIC: 0.5 mg/mL), while antifungal effects against Candida albicans were moderate (MIC: 2 mg/mL). Antioxidant capacity, assessed via DPPH (IC₅₀: 45.2 µg/mL) and FRAP (0.8 mM Fe²⁺/g), indicated moderate free radical scavenging. These findings underscore the potential of S. alba EO as a natural antimicrobial agent and adjuvant in cancer therapy.
Edge Intelligence for Real-Time Image Recognition: A Lightweight Neural Scheduler Via Using Execution-Time Signatures on Heterogeneous Edge Devices
Mobile Edge Computing (MEC) has emerged as a pivotal paradigm for enabling low-latency, high-efficiency AI applications at the network edge. However, heterogeneous hardware constraints across edge devices pose significant challenges in dynamic task scheduling. This paper introduces a novel neural execution-time signature model that learns hardware- and load-aware patterns to predict optimal device assignment for image recognition tasks. Leveraging a rich dataset collected from diverse platforms, including MacBook Pro, Raspberry Pi, and virtual machines, this research trains lightweight Artificial Neural Networks (ANNs) and Multilayer Perceptron (MLPs) to classify execution efficiency with over 99% accuracy. This research model captures subtle temporal and computational load features, enabling a smart scheduler that minimizes latency while maximizing resource utilization. Experimental results demonstrate superior performance in both balanced and imbalanced label scenarios, highlighting the model’s robustness and scalability. This work bridges predictive analytics and edge orchestration, offering a practical blueprint for next-generation edge intelligence systems. This research introduces a novel, lightweight neural scheduler that uses execution-time signatures and engineered temporal and performance features to predict task efficiency with over 99% accuracy across diverse edge devices like Raspberry Pi and MacBook Pro. In addition, it enables real-time, hardware-agnostic scheduling without per-device calibration, offering a scalable, cloud-independent solution for next-generation edge intelligence systems.
The Relationship Between Vitamin D Deficiency and Bone Mineral Density Among Adults in Bani Walid, Libya
Background and Objectives: Despite abundant sunlight, Vitamin D deficiency has become a pressing global public health concern, a situation increasingly evident in Libya, particularly in Bani Walid. This paradox is driven by factors such as limited outdoor activity, clothing styles that restrict sun exposure, and diets low in vitamin D-rich foods. Low vitamin D levels directly impair calcium absorption and bone mineralization, leading to reduced bone mineral density (BMD) and a higher risk of osteoporosis and fractures. This study aimed to investigate the prevalence of vitamin D deficiency among adults in Bani Walid, Libya, and determine the relationship between vitamin D status and BMD. The research also sought to assess if gender differences play a role in this association. Methods and Results: A cross-sectional study was conducted on 200 adult participants in Bani Walid, Libya, between May and September 2025. Serum vitamin D levels and BMD were assessed using standardized tests and Dual-Energy X-ray Absorptiometry (DEXA). The results showed that 52.0% of participants were vitamin D deficient, with an additional 28.0% having insufficient levels. Vitamin D levels were significantly lower among females (67.0%) compared to males (25.0%). Low BMD values were observed in 70.0% of the study population. Crucially, a strong positive correlation was found between low vitamin D levels and reduced bone density (p = 0.0001). Conclusion and Recommendations: The findings confirm that vitamin D deficiency is widespread and strongly associated with reduced BMD in Bani Walid adults. Addressing this deficiency through preventive strategies- such as regular screening, dietary supplementation, and lifestyle modifications-is an urgent public health priority to protect bone health in the adult population of Bani Walid and similar regions in Libya
Study the effect of glass fibers and polypropylene fibers on the mechanical properties of concrete
The construction sector is experiencing rapid and increasing development every year. This rapid growth in concrete has led to the addition of chemicals that improve concrete performance and reduce the consumption of natural materials. Current development requires increasing the strength and durability of concrete to meet the requirements of modern construction. Natural and synthetic fibers are a good choice for improving concrete properties such as increasing compressive, flexural, and tensile strength. These fibers have been used since ancient times in different proportions and have proven effective in significantly enhancing compressive strength, flexural strength, tensile strength, and split tensile strength of cement. This study aims to determine the optimal percentage of fiber content as well as study the effect of high percentages of these fibers on concrete properties. Glass fibers (GF) and polypropylene fibers (PPF) were used in percentages: 0.5%, 1%, 1.5%, 2%, 2.5%, and 3% by weight of cement. Compressive, flexural, and tensile strengths of concrete were studied before and after fiber addition at a constant water-to-binder ratio of W/C = 0.5. Workability, compressive, flexural, and tensile strength were measured at 7 and 28 days. The results showed that workability decreased with increasing fiber content. Compressive strength was observed to decrease at all studied percentages, and the optimal fiber content for improving compressive strength was found at fiber contents less than 0.5% for both types of fibers. Compressive strength decreased with increasing fiber content, reaching its lowest values at 3% fiber content, which were 56.5% and 72% for glass fiber and polypropylene fibers, respectively. This decrease was accompanied by a decrease in split tensile strength. On the other hand, flexural strength increased for mixes containing glass fibers and polypropylene fibers with increasing fiber content. It reached its highest value at 1.5% fiber content with a 13.6% increase for glass fiber, and at 0.5% fiber content, with a 1.6% increase for polypropylene. Overall, glass fiber showed the best results for improving concrete properties.
Redefining Zero in Addition and Subtraction: From Neutral Element to Absence of Operation
Zero is one of the most fundamental concepts in mathematics, serving as a central pivot in arithmetic, algebra, and number systems. However, its traditional definition as a neutral element in addition and subtraction, an absorbing factor in multiplication, and a problematic case in division has produced philosophical and educational inconsistencies for centuries. This paper proposes a new perspective that redefines zero not as a neutral element but as the absence of an operation. According to this view, adding or subtracting zero does not constitute an actual arithmetic operation; rather, no operation takes place at all. This perspective eliminates philosophical ambiguity, simplifies mathematics education, and establishes a more logically consistent framework for arithmetic.
Analytical and Applied Study for Estimating the Heat Index in Gharyan City Using Artificial Intelligence Techniques
This research aims to study and analyze the Heat Index in Gharyan city, located in the Western Mountain region of Libya, used on climate data spanning a long period from 1995 to 2024. The index was calculated using the U.S. National Weather Service (NWS) equation for monthly temperature and humidity. The results showed that the summer months (June–September) are characterized a perceived temperature that exceeds the actual temperature, with the maximum difference reaching +0.8°C in August, highlighting the importance of this index in assessing thermal stress on the population. Five artificial intelligence models were developed and trained to predict the Heat Index: Linear Regression, Ridge Regression, Lasso Regression, Random Forest, and Neural Networks. The results demonstrated the superiority of the Lasso Regression model, achieving high prediction accuracy (R² = 0.9990, RMSE = 0.1554°C), making it an effective tool for rapid prediction. Additionally, the research provides practical recommendations to support urban planning, organize daily activities, and develop early warning systems for heat waves. It also lays the groundwork for roader future studies on thermal comfort in Libya amidst accelerating climate change.
Diagnosing Advanced Mathematics Learning Difficulties: An In-Depth Critical Analysis of Assessment Tools and Intervention Strategies (2000–2023)
This study aimed to provide a comprehensive critical analysis of diagnostic tools and developed intervention mechanisms for addressing Advanced Mathematics Learning Difficulties (AMLD), encompassing areas such as abstract algebra, calculus, and spatial geometry, within a timeframe spanning from 2000 to 2023. The study adopted a Systematic Critical Review methodology in accordance with the PRISMA protocol, analyzing 127 studies extracted from reputable academic databases. Pivotal results confirmed that diagnostic efficacy is contingent upon adopting a multidimensional integrative framework that includes cognitive, affective, and meta-cognitive aspects. Although cognitive assessment tools, such as the Algebra Thinking Test (ATT), demonstrated moderate efficacy (effect size d = 0.62), intervention strategies based on multiple representations and meta-cognitive training recorded the highest levels of effectiveness, with effect sizes reaching d = 0.79 and d = 0.75, respectively. The study also revealed three critical research challenges: firstly, a severe shortage of culturally calibrated assessment tools, with only 15% being valid for use in Arabic contexts; secondly, weak effective utilization of advanced technology in diagnosis (with only 12% employing artificial intelligence techniques); and thirdly, a systematic omission of diagnosing spatial geometry difficulties in approximately 73% of the published research.
Privacy and Security in Healthcare Framework: a Review
The healthcare framework is one of the components to secure patient information. The significance of the healthcare domains is actually focusing on privacy and security and accessibility of users. Cloud computing and the Electronic Health Records (EHRs) and the related reduction in cost cannot be disregarded as a component in the conveyance of medicinal services advantage. Be that as it may, decreased cost is one of the targets anticipated in the utilization of EHR, which is joined by potential dangers, either when clients utilize human services or circulation of the nation's part, to protection and safety. The structure for healthcare protection is a mix of established, statutory, and administrative law at the government and state levels to guarantee security and safety everywhere throughout the world. This study has an interest in observing the main advantages and showing the disadvantages of some healthcare frameworks as described by the authors. The result of this study, which is presented throughout tables of comparisons between healthcare frameworks, can be used as a guideline for other researchers and healthcare organizations.
Technical, Economical and Environmental Aspects of Hybrid Renewable Energy Systems
The world is witnessing a shift from the traditional fossil fuel-based generation system to a future with a larger share of clean renewable generation. Hybrid energy systems are connected with wind power, photovoltaic energy, fuel cells, biomass energy, geothermal energy, and tidal energy to generate electricity to meet energy demand at various scales and connect it to the main grid or microgrids, thereby reducing dependence on fossil fuels, which in turn reduces air emissions and mitigates environmental damage. The hybrid system is considered a better option for building modern electrical grids due to its economic, environmental, and social benefits. An overview of various distributed generation technologies has been presented. This research provides a comprehensive review of the energy, economic, environmental, and technical analysis of these sources, as well as the possibility of integrating different renewable energy sources to form a hybrid system.
Hybrid AI Models for Forecasting and Optimizing Solar Energy Generation Under Varying Weather Conditions
Accurate solar power forecasting is crucial for maximizing renewable energy integration and grid stability. This study reviews and proposes advanced hybrid AI models that combine convolutional neural networks (CNNs) with long short-term memory networks (LSTMs) to forecast photovoltaic (PV) generation under diverse weather conditions. We highlight how weather factors (irradiance, cloud cover, temperature, humidity) affect PV output and why traditional methods struggle to capture these nonlinear effects. Hybrid CNN-LSTM architectures extract spatial features (e.g. cloud patterns) and learn temporal dependencies in time-series data, yielding higher accuracy than standalone models. For example, Ladjal et al. (2025) report a CNN-LSTM model with R² ≈ 0.9993, far exceeding simpler ANN or SVR approaches. We describe the data preprocessing steps, model structures, and evaluation metrics (MSE, RMSE, MAE, MAPE) used in public solar datasets (e.g. NASA’s POWER data). Experiments on benchmark PV datasets demonstrate that the hybrid model consistently achieves lower errors (e.g. MAPE ≈1-7%) compared to feed-forward ANNs. Moreover, we discuss optimization applications: forecast-informed control (tilt adjustment, energy storage scheduling) can increase energy yields and reduce costs by anticipating weather variability.
Global Trends in Prescription Drug Utilization and Resistance Patterns: A 15-Year Comparative Study of Therapeutic Practices
Over the last 15 years, global antibiotic consumption has risen sharply, driving an increase in resistance across diverse pathogens. We analysed publicly available data from WHO GLASS and CDDEP’s ResistanceMap to quantify antibiotic use and resistance trends worldwide. Global use rose from ~9.8 defined daily doses (DDD) per 1,000 people per day in 2000 to 14.3 by 2018, with continued growth to 15.2 DDD/1,000/day by 2023. High-income countries (HICs) generally show lower per-capita use and resistance than low- and middle-income countries (LMICs). Figure data reveal geographic variation: e.g. Greece’s rate (45.9) far exceeds the Philippines (5.0). Resistance rates also vary: for instance, Enterococcus faecium shows 87% global resistance to broad-spectrum penicillins. LMICs now exhibit DRI (Drug Resistance Index) values as high as 80-90, reflecting heavy resistance burden. Veterinary antibiotic use remains intense, especially in Asia, but European livestock usage and sales have declined under stewardship policies. We compare national prescribing patterns and stewardship initiatives, highlighting the AWaRe classification targets and differences in clinical practice. Our findings underscore urgent need for stewardship: scaling up “Access” antibiotic use to ≥60% (WHO target), reducing unnecessary broad-spectrum use, and strengthening surveillance.
Assessment of Antibiotic Resistance Trends Across Regions Through a 10 Year Retrospective Data Analysis
Antibiotic resistance (AMR) poses a major global health threat. We examined trends in AMR over the past decade by region, emphasizing Africa and Libya. We combined data from global surveillance (e.g. WHO GLASS), interactive databases (OneHealth’s ResistanceMap), and published studies. A focal dataset from 14 African countries (2016-2019) comprised ~820,000 culture records, with 187,832 culture-positive isolates. Overall, resistance remained high with no significant decline over time. The highest resistance levels were found among third-generation cephalosporin-resistant Enterobacterales, and lowest among carbapenem-resistant bacteria. Resistance was higher in hospitalized patients and older males. In Libya, a literature review (43 studies, 18,160 isolates) found urinary tract infections (61%) were most common, with Pseudomonas and Staphylococcus aureus predominating. Notably, ~8.5% of S. aureus were MRSA, and Pseudomonas showed ≈10% resistance to piperacillin. Despite lower antibiotic consumption in Africa (per WHO GLASS), AMR burden is high. Global antibiotic use has risen sharply (∼46% since 2000), driven by Asia and LMICs. In livestock, antibiotic use is extreme: ~70% of all antibiotics go to animals (Fig.5), with Asia far exceeding Europe/Africa (Fig.6). Our study highlights persistent high AMR in Africa and Libya, linked to factors like overuse in both humans and animals. Regional differences and a lack of decline underscore the urgent need for stewardship.
Analyzing the Evolution and Impact of Drone Technology: Trends, Applications, and Regulatory Challenges
This paper examines the development of unmanned aerial vehicles (UAVs or “drones”) from their early military origins to modern civilian uses. We review key technological milestones in drone evolution, describe current market trends and usage growth, and survey major applications of drones across sectors such as agriculture, logistics, surveillance, environmental monitoring, and media production. The paper also analyzes economic and social impacts including job creation and cost benefits. A significant focus is on the regulatory landscape: we outline rules in leading jurisdictions (US, EU, India) and discuss challenges of privacy, safety, and standardization. Finally, we look ahead to future trajectories like autonomous swarms, beyond-visual-line-of-sight (BVLOS) operations, and urban air mobility (drone taxis). The analysis draws on recent market reports, academic studies, and industry sources to provide a comprehensive overview.
AI-Based Spatiotemporal Analysis of Solar and Wind Energy Potential Using Satellite and Ground Sensor Data
Renewable energy planning requires accurate estimates of solar and wind resources over space and time. This study develops a spatiotemporal framework that uses satellite and reanalysis data to assess solar irradiance and wind speed. We use NASA’s Power and PVOUT satellite data, ECMWF’s ERA5 reanalysis, and ground observations as inputs. These data are processed to capture both spatial variability (via convolutional neural networks) and temporal trends (via LSTM and graph neural networks) in renewable potential. Our approach is tested on a case region using Google Earth Engine and public datasets. Results show that combining spatial satellite information with temporal weather data improves prediction accuracy (e.g. solar PV output and wind power) over purely numerical models. For example, we observe that the windiest sites align with historical patterns, and solar irradiance maps match known sunny regions. The methodology also reveals solar-wind complementarity: in some areas wind peaks during seasons of weaker sun, helping balance supply. Key contributions of this study include integration of high-resolution satellite and ERA5 data, hybrid AI models capturing spatiotemporal dependencies, and case-study results demonstrating enhanced resource mapping for policy planning. The proposed framework can aid grid operators and planners in identifying high-potential solar and wind sites under varying conditions.