Journal of Environmental Science and Engineering
Journal of Environmental Science and Engineering (JESE) 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 in environmental science and engineering. It promotes innovative studies and practical solutions addressing environmental challenges and sustainable engineering practices. The journal is published semiannually and welcomes contributions from scholars worldwide. Scope Environmental Engineering Water and Wastewater Treatment Air Pollution Control and Monitoring Solid Waste Management Environmental Sustainability and Green Technology Climate Change and Environmental Impact Renewable and Sustainable Energy Systems Environmental Modeling and Assessment Pollution Prevention and Control Technologies Ecological Engineering and Resource Management
PATHWAY SCHOLARS INSTITUTE (SMC-PRIVATE) LIMITED
SUSTAINABLE ENERGY SOLUTIONS FOR URBAN INFRASTRUCTURE: ASSESSING THE ROLE OF RENEWABLE TECHNOLOGIES IN REDUCING CARBON EMISSIONS
The issue of the carbon emissions as a result of the urban infrastructure has increased with the urbanization process and the rising usage of the energy, thus the need to utilize sustainable energy options cannot be overlooked. This paper used the mixed-methods experimental design and established the efficiency of renewable energy technology in reduction of carbon in urban setting. Whereas the qualitative works indicated the findings in the context of governance and policies, the comparison of scenarios through the prism of quantitative modeling was applied between the typical fossil-based system and different rates of renewable energy integration. The overall impact, as per the results, of the increased use of renewable technology is a drastic decrease in the total quantity of carbon intensity of the aggregate energy consumption and urban carbon emissions. The energy storage and renewable systems that were hybrid led to emission reductions that people could experience the most because of the improvement of matching the supply and the demand. Sensitivity analysis also showed that as renewable capacity is augmented by a small structure through the integration of energy saving measures the compounding benefits also go up in small steps. Qualitative research has established stakeholder involvement, institutional preparedness and positive policies as the most important agents in the attainment of effective implementation. On the whole, the article demonstrates that renewable energy technologies are needed in the urban infrastructure decarbonization and sustainable urbanization, and they can provide evidence-based information to support the process of climate mitigation and urban energy planning.
Sustainable energy, Urban infrastructure, Renewable energy technologies, Carbon emission reduction, Energy systems modeling, Climate mitigation
Copyright (c) 2025 Muhammad Danial Ahmad Qureshi, Abdul Jabbar (Author) https://creativecommons.org/licenses/by/4.0
THE INTEGRATION OF INTERNET OF THINGS (IOT) IN SMART CITIES: INNOVATIONS IN TRAFFIC MANAGEMENT AND PUBLIC SAFETY
The increased urbanization has exacerbated issues such as traffic congestion and safety of the population and therefore we must have innovative and technological solutions. This paper explores the integration of the Internet of Things (IoT) technology in smart cities, with a special focus on how it has enhanced the services of the development of traffic management systems and the security of citizens. The study utilizes the experimental mixed-methodology to evaluate the IoT-based traffic sensors, adaptive signal management systems, and real-time surveillance platforms in different urban environments. The quantitative findings indicate that the efficiency of the traffic flow has significantly increased after the implementation of the IoT. These are reduction in the congestion indices, reduced average travel time, and a noticeable reduction in traffic-related accidents. It also resulted in the improvement of public safety, including faster emergency response rates, improved situational awareness, and improved predicted future events based on data-driven analytics. The qualitative results demonstrate the positive stakeholder attitudes towards the system use and performance, and, at the same time, reveal the further problems related to the data management and privacy. The combined outcome is that the implementation of IoT within cities would make the process of traffic management and overall public safety much improved, which creates smarter, safer and more sustainable cities. The research contributes to the growing literature on smart city research with concrete facts and also provides politicians and city planners with practical information on the application of IoT-driven solutions in cities.
Internet of Things, Smart Cities, Traffic Management, Public Safety, Real-Time Analytics, Urban Sustainability
Copyright (c) 2025 Nida Hafeez, Muhammad Umair (Author) https://creativecommons.org/licenses/by/4.0
EXPLORING SMART GRID TECHNOLOGIES: ENHANCING ENERGY EFFICIENCY AND RELIABILITY IN URBAN POWER SYSTEMS
The traditional power grids are experiencing unprecedented pressure because of the accelerating urbanization and growing electricity demand prompting the need to transit towards smart and responsive energy systems. This paper analyzes the potential of the smart grid technology to enhance the operational reliability and resiliency of the power networks in urban areas and the energy efficiency of urban power systems. The performance improvements in most of the operational areas are measured through rigorous analytical approach taking into consideration the distributed energy resources, demand response mechanisms, enhanced metering infrastructure, and real time monitors. The results demonstrate that there have been significant reductions in transmission and distribution losses, improved load balancing, shorter recovery times and outage detection, and that the overall energy efficiency has been quantifiably increased. Although the integration of renewable energy sources increased the sustainability of the systems without compromising stability, the integration of smart grid-based demand-side management was beneficial in decreasing the peak load and optimizing the energy consumption pattern. Also, it is evident in the findings that predictive analytics and automated control schemes enhance grid reliability by avoiding the spread of faults and promoting proactive maintenance. The report further highlights the economic and environmental benefit of adopting smart grids, which include reduced cost, reduced carbon emissions, and improved service to the urban clients. In general, the results confirm that the smart grid technologies provide a sustainable and cost-effective path to sustainable energy management and electricity networks that are ready to withstand the future and therefore, can be seen as a groundbreaking approach to the modern city power systems.
Smart grid technologies, Energy efficiency, Urban power systems, Grid reliability, Demand response, Renewable energy integration
Copyright (c) 2025 Wajeeha Ahmed, Muzammil Ali (Author) https://creativecommons.org/licenses/by/4.0
THE ROLE OF 3D PRINTING IN REVOLUTIONIZING STRUCTURAL ENGINEERING: APPLICATIONS IN SUSTAINABLE BUILDING DESIGN AND CONSTRUCTIONS
The fast development of the 3D printing technology has presented new opportunities to the structural engineers to be creative, particularly in designing and constructing environmentally friendly buildings. This study focuses on the impact of 3D printing on the structural performance, material efficiency, the speed of construction, and environmental sustainability. The results show that 3D-printed structural elements should be able to achieve high dimensional accuracy and adequate load carrying capacity and reduce material utilization and construction waste significantly. Comparative analysis shows that in the case of 3D printing, the time required to construct and the amount of labor input reduces, and the design freedom is increased, a more complex and optimal structural form can be produced. The recycled and sustainable materials used in 3D printing further demonstrate how it can be used to reduce carbon emissions and promote such building practices that are environmentally friendly. The findings also indicate that process automation and consistency are improved by digital fabrication, which assists in quality control of building projects. Despite these advantages, the paper has identified issues with standardization of materials, durability of structures and regulatory approval. Overall, these findings indicate that 3D printing can transform structural engineering because it can create more efficient, sustainable, and innovative construction solutions. Nevertheless, they also emphasize the necessity of additional research in order to overcome the existing technical and institutional issues.
3D printing, structural engineering, sustainable construction, additive manufacturing, green building design, construction innovation
Copyright (c) 2025 Azmat Ullah, Syed Muhammad Shakir Bukhari (Author) https://creativecommons.org/licenses/by/4.0
EVALUATING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON PREDICTIVE MAINTENANCE IN INDUSTRIAL MANUFACTURING SYSTEMS
Artificial intelligence is now an indispensable part of modern industrial production and predictive maintenance is one of its uses. The study investigates the impact of AI-based predictive maintenance models on equipment performance, maintenance performance, and operation costs. The paper evaluates the accuracy of detection of problems, reduction of downtimes, maintenance savings, and system stability through the application of modern machine learning and deep learning methods to large volume of industrial sensor data. Its conclusion indicates that AI-based models are far more precise when forecasting when equipment will malfunction as compared to conventional maintenance approaches. The real life experience is that the maintenance planning has been significantly improved with reduced downtimes, improved utilization of resources and improved decision making capabilities. Besides, predictive maintenance with AI will assist equipment in achieving longer life and stabilize production. It is evident in the research that smart and data-driven maintenance plans enable taking action before things go wrong, reduce operational risks, and support long-lasting manufacturing operations. These data confirm the effectiveness of artificial intelligence as a key to the transformation of traditional methodologies of maintenance to intelligent, adaptive, and efficient solutions, which give considerable findings to the community of industry specialists and researchers.
Artificial Intelligence, Predictive Maintenance, Industrial Manufacturing, Machine Learning, Smart Maintenance Systems, Industry 4.0
Copyright (c) 2025 Moaz Israr, Muhammad Ammad (Author) https://creativecommons.org/licenses/by/4.0
IMPROVING THE STRUCTURAL INTEGRITY OF BRIDGES: TECHNIQUES FOR DETECTING AND MITIGATING CORROSION AND FATIGUE IN AGING INFRASTRUCTURE
Bridges have been the main avenue in developing the transportation and economic infrastructure in the cities, but now they are gradually succumbing to aging, corrosion, and fatigue. Degrading bridges actually pose significant safety threats but also great economic burdens. Recent research has drawn attention to advanced monitoring and maintenance techniques that can be introduced to elongate the service life of bridges. The current article reviews the state-of-the-art approaches for detecting and managing corrosion and fatigue in aging bridges, enriching them with insights gained from structural health monitoring (SHM) systems, vibration-based methods, and life-cycle optimization strategies. Structural Health Monitoring (SHM) has become the cornerstone of modern infrastructure management to provide continuous, real-time information about structural integrity. Vibration-based monitoring detection method detects changes in structural dynamics, which help to localize damage and predict the need for maintenance. SHM, combined with increasing development in sensor technologies, adopts a proactive approach to bridge management. Furthermore, lifecycle management approaches discussed during international conferences are also deliberating on predictive maintenance in risk analysis and integration of NDE methods. Amongst them, corrosion sensor, robotic inspections, and use of high-performance materials are important in preserving structural integrity. This research article tells the call that requires interdisciplinary approaches, combining engineering, data analytics, and maintenance planning. Improvement of safety and economy investments on public infrastructure may come by addressing corrosion and fatigue on aging infrastructure.
Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0
DESIGNING FAULT-TOLERANT CONTROL SYSTEMS FOR AUTONOMOUS DRONES: ENSURING SAFE OPERATION IN REAL-WORLD ENVIRONMENTS
The synergistic use of a number of modern UAV technologies is becoming essential in several spheres of life, including disaster relief, agricultural practices, surveillance, and logistics. The critical subsystems such as sensors, actuators, and controllers can suffer from various faults which threaten uninterrupted and safe operation of UAVs. Faults result in mission failure, safety threats, and operational inefficiencies. Therefore, development of FTCS became an important and active area of research. FTCS are aimed at the real-time detection, diagnosis, and mitigation of faults of UAVs, maintaining stability and reliability during adverse conditions. This paper provides an extensive discussion on recent advances in FTCS for UAVs; FDD in FTCS, reconfigurable control mechanisms, and fault compensation strategies based on machine learning. The review discusses data-driven methods such as RBFNNs and TDOA in the dynamic identification and fault accommodation respectively. Results of the hardware-in-the-loop simulation with respective insight into the replication of the real-world scenario through which FTCS validation is conducted before real life application are included in the paper. The transition is the fourth major change that FDD is undergoing; the first and second being the classic deterministic model-based techniques like Kalman filters and state observers, respectively; the third being AI techniques. The AI-based FDD will empower the responding UAV to sense subtle anomalies and more accurately predict failure, thus mitigating costly downtimes and enhancing operational resilience. Also importantly, noteworthy gains can be argued from the point of view of reconfigurable control with various dynamically adjusting actuator input compensation mechanisms, including extremum-seeking algorithms, to be able to stabilize the UAV under faulty conditions. The bedrock of the article is the reinforcement learning, deep learning, and hybrid AI techniques in FTCS. These help with improving UAV adaptability and fault resilience through continual learning of flight data and enhancement of the decision-making process. HILS has been increasingly advocated as a method to investigate FTCS performance in realistic situations, bridging yet another gap between the theory and practice.Integration of AI, machine learning, and reconfigurable control strategies have enhanced UAV fault tolerance greatly thus providing more robustness and reliability to autonomous flight operations. Future studies should refine the mechanisms for real-time adaptations, focusing more on computational efficiency and new AI-based strategies for enhancing FTCS capabilities.
ADVANCES IN BATTERY STORAGE TECHNOLOGIES FOR RENEWABLE ENERGY: IMPROVING EFFICIENCY AND REDUCING COST FOR LARGE-SCALE APPLICATIONS
To transition to renewable energy, advances in battery storage technologies must be made to their efficiency, reliability, and cost-effective performance in large-scale applications. This work reviews some of the recent advances important battery storage technologies for renewable energy sources integration within the existing grids. The analysis has focused on the enhancement of energy density, lifecycle performance, and capital and operational cost reductions. The electrochemical storage systems here referred to are lithium-ion and sodium-sulfur batteries, which are considered scalable, having high energy density and rapid response characteristics, making them suitable in operation for diverse energy needs. In addition, improvements in hybrid energy storage systems involving electrochemical and thermal storage have been shown to give promise regarding other efficiency and reliability gains. New technologies, including solid-state batteries and advanced flow batteries, show promise for eliminating existing barriers associated with energy storage, improving safety, increasing storage capability, and extending lifespan.Moreover, this research investigates the strategies of cost reduction essential for making the technology widely available: technological innovation, cum economies of scale, and recycling practices to limit environmental damage and depletion of resources. Great emphasis is put on..During integration with smart grid technologies and advanced energy management systems, it would further improve the performance efficiency and reliability of storage. Battery design optimization and manufacturing improvement process development and robust recycling framework formulation for a long-term cost reduction have further been emphasized.The findings further elucidate the importance of hybrid alternatives and novel technologies in some economically and sustainable realizations of large-scale systems for renewable energy storage. The research points to many important considerations for optimizing energy storage technologies for a sustainable energy future by policymakers, the industry, and researchers in dealing with energy security, grid stability, and environmental sustainability.
ADVANCING PRECISION SURGERY THROUGH THE DEVELOPMENT OF AUTONOMOUS ROBOTIC SYSTEMS: INNOVATIONS, CHALLENGES, AND FUTURE PERSPECTIVES
The advent of robotic systems in surgery has revolutionized the field, offering enhanced precision, minimal invasiveness, and faster recovery times for patients. This paper examines the evolution of robotic surgery, focusing on technological advancements, benefits, and challenges, alongside its clinical applications and future trends. Emerging technologies such as artificial intelligence, telesurgery, and multimodal imaging systems are redefining surgical possibilities, enabling procedures that were once deemed too complex. While the benefits of robotic surgery are undeniable, challenges related to training, ethical considerations, and system optimization remain. With continued innovation and integration, robotic surgery holds the promise of personalized, data-driven, and globally accessible healthcare.
ADVANCED WATER TREATMENT TECHNOLOGIES: LEVERAGING BIO-NANOTECHNOLOGY FOR EFFICIENT CONTAMINANT REMOVAL AND SUSTAINABLE RESOURCE MANAGEMENT
The essential challenge facing humanity during the twenty-first century centers on providing adequate amounts of economical and clean water for everyone. The situation grows worse because population has increased while water quality has declined alongside changing climate conditions. Nanotechnology acts as a fundamental technology enabler in integrated water operations by raising system effectiveness and permitting new water extraction methods from unconventional reserves. Nanotechnology enhances global drinking water supplies by developing innovative nanomaterials which treat surface water and groundwater and contaminated wastewater with dangerous inorganic and organic substances and harmful microorganisms and metal ions. Nanotechnology breakthroughs addressed crucial challenges within engineering physics and chemistry and other fields. The current wastewater treatment industry depends heavily on multiple products and processes derived from nanotechnology research including nano adsorbents, nano zero-valent iron, nano biocides, nanofiltration and magnetic nanoparticles and hybrid systems for wet air oxidation with nanoparticles. The review investigates recent breakthroughs in nanotechnology because it focuses on effective hazardous waste management through cost-efficient and energy-saving solutions. Different sectors including science research and healthcare along with food production have recognized both the rising value of nanotechnology and peculiar properties of nanobubbles.
URBAN GREEN INFRASTRUCTURE AND BIODIVERSITY: A META-ANALYSIS OF GLOBAL CITY TRENDS
Urban green infrastructure (UGI) is now a source of environmental protection against the adverse impacts of urbanization and promotion of biodiversity in rapidly urbanized cities. It is a meta-analysis of the findings of 80 peer-reviewed articles conducted to date at 25 sites in 31 cities across the world to understand the impact of various forms of green infrastructure, such as parks or green roofs and vegetated corridors, on urban biodiversity. The parameters we considered were city governance quality, climate zone, policy integration at city level along with important biodiversity indicators such as species richness, Shannon index and Simpson index. Basing on the results, UGI is found to be a substantial contributor to urban biodiversity with a pooled mean effect size (lnRR) of 0.42 (95% CI: 0.31-0.53), and therefore, it is found that it has moderate but consistent ecological advantages. The most species richness was found in temperate cities with sound urban government and integrative policy structures. Longitudinal study also depicted that gains in biodiversity increase with time particularly when researches take longer than 10 years. Meta-regression uncovered that the quality of governance (p < 0.01) and green space connectivity (p < 0.05) produced strong evidence concerning the outcome of biodiversity. Although such trends are positive, a large amount of variance existed between studies (I 2 = 57.3%). That happened largely due to differences in study design, climate and the scale of the green interventions. Moreover, though 70 percent of the research discussions discussed the framework of urban policies, a small percentage of 45 percent discussed being fair and inclusive of all, indicating a substantial variation in implementation. Funnel plots and the tests of Egger showed no publication bias at all hence giving the data further credibility. Overall, the research demonstrates that properly planned and location-specific UGI assists cities in being more resilient to environmental changes and assists in achieving the Sustainable Development Goals (SDGs) associated with life on land, climate goals, and cities resistant to change. In order to make the best of biodiversity, however, the governance inclusion, long term ecological monitoring, and socio-spatial justice will require to be at the top of the list in terms of green infrastructure arrangements in future cities.
INTEGRATED WASTE-TO-ENERGY SYSTEMS: ENVIRONMENTAL TRADE-OFFS AND CIRCULAR ECONOMY POTENTIAL
The waste-to-energy (WtE) processes, as an integrated waste management system, are emerging as an important component of sustainable waste management as not only do they eliminate the problem of landfills but also generate renewable energy. This paper explores in detail current WtE technologies assessing their effectiveness in terms of energy savings, emission reduction, material recovery and compatibility with the concepts of a circular economy. It was based on a mixed-methodology, and some experimental tests on 25 other types of garbage were made, along with assessment of possible environmental and economic impact. The findings demonstrated that the waste of the trash had varied a lot with the organic material representing between 30 and 65 per cent and plastics representing up to 25 per cent. This influenced the technological selection and the calorific value. The higher heating values were up to 28 MJ/kg and optimised systems delivered 6.0 kWh/kg of energy. Between 25 and 75 percent of conversion efficiency occurred. Incineration and gasification sucked more energy per mass compared to anaerobic digestion with increased emissions of CO 2 and PM emissions compared to AD. To assess the performance of circular economy, the indicators demonstrated that the top performing facilities recovered more than 90% of the material and a circularity of 0.89. This evidences the fact that the resource loop has great potential in being closed. Lifecycle greenhouse gas emissions were attributed largely to the conversion stage and this demonstrates the need to have increased control of emissions and the integration of green energy sources. Economical studies established that certain plants could gain as much as 25 percent of their investments, and more importantly, those that conducted combined heat and power (CHP) systems. Survey results on the perception of the people on matters revealed that the citizens were mildly familiar with the issue of policy integration but strongly favored it. This goes in line with the view that WtE is viable in the society. Overall, the findings demonstrate that when properly installed and managed, integrated WtE systems may become effective instruments of diversifying energy foundation, minimize emissions, and advancing the circular economy in general. The research indicates the ultimate value of utilizing data to optimize systems, develop mechanisms to reduce the emissions and engage the stakeholders in achieving the sustainable potential of the Waste-to-Energy technologies to the full.
THE ROLE OF BIOPRINTING IN PERSONALIZED MEDICINE: ENGINEERING 3D PRINTED DRUG DELIVERY SYSTEMS FOR CUSTOMIZED THERAPEUTICS
This study explores the potential of 3D printed drug delivery systems (DDS) in personalized medicine, focusing on the development and evaluation of customized therapeutics. The primary aim was to engineer bioprinted DDS capable of sustained drug release, improved bioactivity, and enhanced mechanical stability. The results demonstrated a controlled drug release over 72 hours, significantly improving therapeutic effectiveness compared to traditional drug delivery methods, which typically show rapid drug release followed by suboptimal effects. Bioactivity assays revealed a notable increase in cell viability, with the 3D printed DDS exhibiting a 95.6% cell viability rate, compared to 75.3% for traditional systems. Mechanical integrity testing confirmed that the 3D printed DDS maintained structural stability under compression, with a maximum stress at deformation of 3.5 MPa, outperforming traditional DDS. Tests on the synthesized drug delivery system indicated its high biocompatible nature by demonstrating low levels of toxicity in cytotoxicity measurements. The technical evaluations confirmed that 3D-printed DDS released medication at higher levels compared to standard versions and maintained excellent compatibility with biological networks alongside strong material strength thus establishing itself as a promising medicine customization platform. Studies indicate how 3D printed DDS system enables patient-customer tailored release methods resulting in enhanced drug control programs. This technology requires additional technical developments in suitable biomaterials along with improved clinical application scale to effectively use. The main research priority in future 3D bioprinting studies must include improvements to technology along with its clinical practicality.
DEVELOPMENT OF ENERGY-EFFICIENT POWER AMPLIFIERS FOR 5G COMMUNICATION NETWORKS USING GALLIUM NITRIDE (GAN) TECHNOLOGY
This study investigates the development of energy-efficient Gallium Nitride (GaN)-based power amplifiers for 5G communication networks, focusing on improving performance through advanced thermal management, linearity enhancement, and economic feasibility. GaN technology, known for its high power density, wide bandwidth, and thermal efficiency, has emerged as a key enabler for next-generation power amplifiers in 5G applications. The results demonstrate that incorporating advanced cooling solutions significantly improves the efficiency and output power of GaN amplifiers. Specifically, the active cooling configuration achieved the highest efficiency (85%) and output power (25W), outperforming other configurations, such as HEMT with heat sinks and microchannel cooling. The power gain performance was enhanced substantially through microchannel cooling because it produced 22 dB at 2 GHz although the HEMT without cooling reached only 18 dB. Among all testing conditions the active cooling technique achieved an EVM minimal value of 4% along with an ACPR maximum value of -48 dBc which established its superiority regarding signal quality. Numerous economic factors indicate active cooling systems provide the most practical mass-market solution due to their higher initial investment cost ($600) but profitable margin of $350. GaN power amplifiers for 5G uses require state-of-the-art thermal management methods because they enhance both performance and economic efficiency according to research. Our investigation demonstrates GaN technology provides solutions for power-efficient communication systems delivering high performance at sustainable prices as per 5G requirements.
DEVELOPMENT OF ADVANCED WEARABLE HEALTH DEVICES FOR REAL-TIME, CONTINUOUS MONITORING AND EARLY DETECTION OF CHRONIC DISEASES USING INTEGRATED BIOSENSORS AND MACHINE LEARNING
The increasing prevalence of chronic diseases has created a pressing need for innovative solutions to enable early detection and continuous monitoring. This study presents the development of an advanced wearable health device integrated with biosensors and machine learning algorithms for real-time monitoring and early detection of chronic diseases. The device was designed to measure critical biomarkers, including heart rate, blood glucose, oxygen saturation, and blood pressure, providing continuous, non-invasive monitoring. The device demonstrated high accuracy across these sensors, with the heart rate sensor achieving 98.2% accuracy and the blood glucose sensor reaching 95.1% accuracy.The simulation utilized machine learning models to forecast diseases where the random forest model achieved the highest performance rate of 93.1%. The system demonstrated strong connections to real medical diagnoses for cardiovascular problems and diabetes and respiratory problems during testing that reached a 91.5% accuracy level while verifying chronic illness indicators from clinical databases. Users from multiple age brackets show signs of adopting this gadget because it receives positive feedback about its easy operation and comfort features. Wearable sensors when combined with machine learning technology demonstrate their capability to generate individual disease information while offering early diagnosis which helps resolve important chronic disease treatment hurdles. Through continuous monitoring activities enabled by wearable technology human healthcare is revolutionizing because it enables enhanced predictive skills along with better patient outcomes. Future public health benefits from the device rely on enhancing sensor incorporation and machine learning algorithms while handling privacy issues and sensor integration.
DEVELOPMENT OF ULTRA-LOW POWER INTEGRATED CIRCUITS FOR REAL-TIME BIOMEDICAL SIGNAL PROCESSING IN IMPLANTABLE DEVICES
This study presents the development of ultra-low power integrated circuits for real-time biomedical signal processing in implantable devices, aiming to address the challenges of high power consumption and the need for continuous, autonomous operation in medical applications. The proposed solution integrates hybrid processing architectures, combining both digital and analog circuits, optimized for low power consumption, while maintaining high signal processing accuracy. Additionally, energy harvesting techniques, including piezoelectric and thermoelectric sources, are incorporated to ensure long-term, self-sufficient operation. Between 25% to 30% power consumption reductions result from this work while maintaining all signal quality measurements at current levels. Signal processing shows high accuracy levels because algorithms identify noise and extract features and run classifications with more than 97% precision. The hybrid processing system proves better at handling time requirements while using less power than classic methods. Long-term six-month testing demonstrates reliable signal performance with small power usage and a strong operational stability. The device presents proven security standards for implantation because of biocompatibility testing. Multiple research findings show sufficient potential for this system to function as an implantable biomedical device that consumes minimal power and requires minimal maintenance. The future research agenda aims to advance energy collection systems that will boost therapeutic system performance.
Copyright (c) 2025 Ume Habiba , Ahmed Ali (Author) https://creativecommons.org/licenses/by/4.0
IMPACT OF CLIMATE CHANGE ON INFRASTRUCTURE LONGEVITY: PREDICTIVE MODELING OF STRUCTURAL DEGRADATION IN COASTAL REGIONS
This study investigates the impact of climate change on the longevity of coastal infrastructure by evaluating the degradation rates of common construction materials under varying climate scenarios. Using predictive modeling and Monte Carlo simulations, we assess the accelerated degradation of materials such as concrete, steel, asphalt, and wood in response to environmental stressors like sea-level rise, temperature fluctuations, and increased storm frequency. Research indicates that infrastructure elements which face severe temperature conditions suffer such extensive deterioration that their lifespan shortens by more than 40%. In this process concrete and steel materials endure maximum degradation. High-risk coastal infrastructure repair costs will escalate because of storm surges and flooding so total expenditures will increase by 20–30 percent. Models indicate that coastal infrastructure suffers from two main degradation factors including storm frequency and rising sea-levels. The study provides critical details for urban designers along with government lawmakers who need information on resilient infrastructure design patterns. Future maintenance costs of infrastructures positioned in vulnerable coastal regions can be lowered through lifetime climate assessment since this method reduces exposure to future threats and maintenance expenses.
Copyright (c) 2025 Muhammad Ammad, Muhammad Umair, Muhammad Asad (Author) https://creativecommons.org/licenses/by/4.0
BLOCKCHAIN-BASED SECURE DATA SHARING AND PRIVACY PRESERVATION IN DECENTRALIZED EDGE COMPUTING NETWORKS
This study explores the integration of blockchain technology into decentralized edge computing networks to enhance secure data sharing and privacy preservation. With the proliferation of Internet of Things (IoT) devices and the increasing need for real-time data processing, edge computing presents a promising solution, though it faces significant challenges related to data security and privacy. The research proposes a blockchain-based framework that utilizes decentralized ledgers and cryptographic techniques to address these challenges. The study evaluates the performance of the system in terms of transaction throughput, latency, energy consumption, and scalability across various network sizes. The results show that blockchain significantly enhances data privacy, with cryptographic methods like public-key encryption and zero-knowledge proofs proving effective in maintaining confidentiality. However, scalability remains a concern, as transaction throughput decreases and latency increases with the number of nodes in the network. The study demonstrates that Proof of Stake (PoS) consensus protocols outperform Proof of Work (PoW) in terms of energy efficiency specifically designed for limited resource edge environments. Evaluation of privacy functions integrated into products demonstrated extensive protection which blocks unauthorized entry. The investigation examines various technical hurdles related to blockchain-edge computing integration that involve complex system architecture and insufficient available resources. The study develops understanding of blockchain technology and edge computing through analyzing its ability to secure private decentralized data sharing. The main research priority should be to enhance Blockchain protocol performance because this becomes the critical element for coping with scalability and speed problems.
Copyright (c) 2025 Moaz Israr, Fahad Ali (Author) https://creativecommons.org/licenses/by/4.0
DEVELOPMENT OF AUTONOMOUS ROBOTIC SYSTEMS FOR PRECISION MANUFACTURING USING AI-DRIVEN PREDICTIVE MAINTENANCE
This study investigates the application of AI-driven predictive maintenance systems for autonomous robotic systems in precision manufacturing. The objective was to develop a hybrid machine learning model combining recurrent neural networks (RNN), convolutional neural networks (CNN), and support vector machines (SVM) to predict robotic system failures and enhance operational efficiency. The results demonstrated that the hybrid model outperformed individual models, achieving the highest accuracy (92.7%), precision (93.2%), recall (94.1%), and F1 score (93.6%). These findings indicate that integrating multiple machine learning algorithms significantly improves the predictive capabilities of maintenance systems in autonomous robotic systems. The research demonstrated real-time data acquisition through IoT sensors plays an essential role by maintaining ongoing monitoring for scheduling maintenance in a timely manner. Compared to classic maintenance techniques the AI-based solution shortened machine availability loss and lower maintenance expenses without compromising system dependability. Research results confirm that hybrid predictive models produce superior operational outcomes as validated by previous studies done in predictive maintenance applications. This research joins other proof demonstrating the benefits of AI technology applications in industrial manufacturing operations which need smooth production continuity. Future investigations need to develop predictive maintenance systems for massive manufacturing operations and introduce emerging varieties of AI technology.
Copyright (c) 2025 Syed Muhammad Shakir Bukhari , Nida Hafeez (Author) https://creativecommons.org/licenses/by/4.0
DESIGN AND OPTIMIZATION OF QUANTUM DOT SOLAR CELLS FOR ENHANCED EFFICIENCY IN PHOTOVOLTAIC POWER SYSTEMS
This study investigates the design, optimization, and performance of quantum dot solar cells (QDSCs) with the goal of enhancing their efficiency for photovoltaic applications. Quantum dots (QDs) of varying compositions, including CdSe, PbS, CuInS2, and CdTe, were synthesized and incorporated into solar cell devices. Among these, CdSe-based QDSCs exhibited the highest power conversion efficiency (PCE) of 10.4%, outperforming the other materials. The study also explored the impact of fabrication methods, revealing that spin-coating provided superior results compared to drop-casting and inkjet printing, achieving the best device performance. Laboratory tests at room temperature revealed QDSC efficiency degradation amounted to 5.8% during six months of exposure to high temperature and continuous light. Additionally, humidity also caused degradation issues in these cells. Computer simulations show that enhancing materials would maximize light-to-energy conversion efficiency although experimental results have already been achieved. Research findings demonstrate quantum dot solar cells have potential to function as an effective silicon-based solar cell alternative yet additional research is needed for long-term performance improvement and stability enhancement and scale up capabilities. The investigation introduces significant novel insights into QDSC stability and performance elements which serve as a basis for future improvements seeking commercial deployment.
Copyright (c) 2025 Ghulam Mujtaba Noor , Olumhense Benedict Adoghe (Author) https://creativecommons.org/licenses/by/4.0
DESIGNING HYBRID ELECTRONIC-WIRELESS COMMUNICATION SYSTEMS FOR INTELLIGENT BUILDING AUTOMATION
The integration of hybrid electronic-wireless communication systems in intelligent building automation (IBA) holds significant potential for improving the performance, energy efficiency, and security of modern smart buildings. This study addresses the key challenges of interoperability, energy consumption, and security in the deployment of hybrid communication networks within IBA systems. A novel hybrid communication model combining both wired electronic and wireless technologies was designed, optimizing the strengths of each system while mitigating their limitations. Our results show that the hybrid model enhances the overall stability and efficiency of building automation systems by ensuring seamless data transmission between devices while minimizing network congestion.Systems using the hybrid approach reduce their energy consumption by 20% when installed in dense infrastructure areas compared to traditional wireless systems. The system utilized sophisticated machine learning methods to automatically adjust traffic flow while also predicting network congestion in order to prevent data bottlenecks which secured system dependability and responsiveness levels. Security performance of the hybrid system increased through advanced encryption and authentication methods which reduced the risk of communication network attacks by 35%. Next-generation smart buildings should embrace the proposed hybrid electronic-wireless communication system because it provides an overall robust effective and secure framework. The research in this paper provides essential information about hybrid communications for advanced building automation and contributes to existing scholarly knowledge on this topic.
Copyright (c) 2023 Muhammad Bilal, Muhammad Danial Ahmad Qureshi (Author) https://creativecommons.org/licenses/by/4.0
MICROPLASTICS IN TERRESTRIAL ECOSYSTEMS: SOIL HEALTH AND CROP UPTAKE ASSESSMENT
Microplastics are emerging as a typical pollutant in land-based ecosystems without much understanding of their impact on soil health and crop productivity. Through controlled greenhouse experiment, this paper examines the impact of microplastic pollution on soil physical and chemical properties, plant growth, and plastic uptake within farming regimes. We increased the amount of microplastics in the soil samples and observed changes in pH, bulk density, field capacity and organic matter. The findings demonstrated that availability of microplastics altered the Hydrological structure of the soil and the degree of its water holding capacity. With higher pollution rates, the bulk density reduced and the field capacity declined with a maximum of 15%. Plants cultivated on soils that contained a high level of microplastic were severely stressed as their height dropped by 20 per cent, their biomass decrease by 18 per cent, their chlorophyll and relative water content (RWC) decreased by a lot. Scientists also examined the content of microplastic in the tissues of plants. They established that roots contained the highest particles with an average of 15 particles per gramme. This demonstrates that the particles might end up in the entire plant via absorption and ultimately move to the food chain. The association between soil organic matter and plant biomass was high (r = 0.71) and similarly, there was high correlation between field capacity and chlorophyll content in the field (r = 0.66). It implies that the quality of the soil influences the extent to which crops can overcome that stress linked to microplastic. These links were also verified through regression and ANOVA, where all the plant characteristics under consideration were influenced by the treatment in a significant way (p < 0.05). The report further indicates that the composition of the soil microplastic contained mainly polyethylene and polypropylene and plants had the responsibility of absorbing the microplastic with a size of 100-300 um. These findings inform us of significant details concerning the impact of soil microplastic contamination on the activity of agroecosystems. They also illustrate how the necessity to address this issue on a timely basis (through the change of the rules, the use of a smaller number of plastics, and the restructuring of the farming process) is critical.
Copyright (c) 2023 Irfan Ahmad, Hafiz Muhammad Bilal (Author) https://creativecommons.org/licenses/by/4.0
AI-DRIVEN FAULT DIAGNOSIS IN AERONAUTICAL SYSTEMS USING CIRCUIT-LEVEL SENSORS FOR PREDICTIVE MAINTENANCE
This study investigates the application of AI-driven fault diagnosis systems using circuit-level sensors for predictive maintenance in aeronautical systems. The research demonstrates that AI-based systems outperform traditional fault detection methods in multiple areas, including fault detection accuracy, energy efficiency, thermal management, and cost-effectiveness. Traditional techniques achieved detection accuracy of 80% but the AI-based system performed better with 98%. Artificial intelligence technologies perform better than conventional methods and raise both reliability and efficiency together with reducing maintenance expenses by forty percent and downtime by fifty percent. The AI-based system demonstrated that it could improve energy efficiency by 10% while thermal management achieved a 15% enhancement which guaranteed better operational stability. Artificial intelligence optimization of predictive maintenance methods achieves better system diagnostics and reduces system failures and enhances aerospace system performance through these results. The research outcomes demonstrate that operation effectiveness together with substantial savings in long-term maintenance costs can be achieved with AI-driven servicing solutions. This research establishes artificial intelligence technology adoption in aircraft systems through its creation of an efficient and sustainable predictive maintenance system for aerospace components.
Copyright (c) 2023 Umair Saeed, Asim Raza (Author) https://creativecommons.org/licenses/by/4.0
LONG-TERM CHANGES IN TROPOSPHERIC OZONE CONCENTRATION: IMPLICATIONS FOR HUMAN HEALTH AND CROP YIELD
Ozone (Oplay-3) is a secondary emission that is formed via the reaction of light with nitrogen oxides in addition to volatile organic compounds. It has assumed colossal risk to both population and environmental health. In this study, authors examine the long-term trends (20082022) of tropospheric ozone levels and their impacts on human health (lungs) and the production of crops that grow in various regions of the planet. We have considered satellite measurements of OMI and TROPOMI, as well as ground-based monitoring data, to see how the level of ozone changed over the course of many years and geographical locations. The findings indicate that the urban-industrial regions in general (and in Asia and North America in particular) reported a consistently increased concentration of ozone in the atmosphere (with the mean or workable level being recorded at >75 ppb). Background accumulation on the other hand was gradual in the rural areas and this led to increase in ozone levels. The studies which applied population-weighted exposure measures and generalised linear model to health impact study indicated a significant association between high ozone concentrations on increased hospitalisation due to asthma, COPD and heart disease. Elderly people had the greatest relative risk (RR = 1.89), children, and adults in the second place. Meanwhile, crop yield estimations considering exposure indices of AOT40 revealed massive losses, particularly the crops that are prone to ozone like wheat and rice. Wheat in Asia alone declined up to 20 per cent in years where there was much ozone. The analysis conducted by the multivariate regressions and crop simulation such as WOFOST indicated that existing relationship between exposure to ozone and yield outcomes is direct and inverse.Some of the visiualisations that indicated good evidence of the two issues that ozone pollution was responsible of were heatmaps, line charts and infographics. In this paper the research shows that tropospheric ozone is not only a prominent air pollutant but also a long-term environmental plague and a health issue to certain individuals. We require combined mitigation strategies immediately to mitigate these dangers, which are all converging. Such actions are needed to venture into emission reduction measures, air monitoring routes, and the hardening of crops.
Copyright (c) 2023 Muhammad Umair, Rabia Nasir (Author) https://creativecommons.org/licenses/by/4.0
CARBON CAPTURE AND UTILIZATION (CCU): OPTIMIZATION OF CHEMICAL PROCESSES FOR CONVERTING CO2 INTO VALUE-ADDED PRODUCTS
Carbon Capture and Utilization (CCU) has emerged as a critical technology for mitigating climate change by converting CO₂ into valuable products. This study explores the optimization of chemical processes for CO₂ conversion, focusing on electrochemical and thermochemical methods using various catalysts. Cu/Ag hybrid catalysts were evaluated for their electrochemical CO₂ reduction performance, achieving a CO₂ conversion efficiency of 65% and a methane yield of 0.80 mol/mol CO₂, surpassing the performance of individual metal catalysts like copper (55%) and silver (40%). The study also investigates the thermochemical CO₂ reduction process, where the Ni-Ce composite catalyst demonstrated the highest CO yield (0.70 mol/mol CO₂) and hydrogen yield (0.30 mol/mol CO₂), outperforming other metal oxide catalysts such as ceria and iron oxide. The performance of Cu/Ag hybrids as catalysts was evaluated for durability since their operational capability endured across 15 sequential cycles. The combination of high revenue involvement through $1800/ton product together with $400/ton CO₂ profit demonstrates economic feasibility for large-scale CO₂ utilization according to financial research. The Ni-Ce composite delivers equal output quantities at a modest profit margin of $250 per ton CO₂ even though the selling price per ton product remains at $250. The research examines efficient CO₂ use technology design by demonstrating the requirement for balancing catalyst stability and economic affordability as well as performance levels. The study shows that hybrid catalyst systems may offer an effective solution for sustainable CO₂ conversion to strengthen circular carbon economies while developing real solutions against world climate issues.
Copyright (c) 2023 Kamran Iqbal, Rimsha Tariq (Author) https://creativecommons.org/licenses/by/4.0
ASSESSING THE ECOTOXICOLOGICAL RISKS OF EMERGING CONTAMINANTS IN AQUATIC ECOSYSTEMS: A MULTISCALE APPROACH TO ENVIRONMENTAL TOXICOLOGY
Emerging contaminants (ECs) have garnered increasing scientific and regulatory attention due to their widespread occurrence in aquatic ecosystems and their potential risks to environmental and public health. These unregulated compounds—such as pharmaceuticals, personal care products, endocrine disruptors, industrial chemicals, and microplastics—are often resistant to conventional wastewater treatment and have been detected in surface waters worldwide. Understanding their ecotoxicological impact is essential for safeguarding ecosystem integrity and human well-being.This study employed a multiscale methodological framework combining environmental sampling, molecular biomarker analysis, bioassays, and computational risk quantification to evaluate the impact of ECs in aquatic environments. Bioaccumulation, oxidative stress pathways, endocrine disruption, and biomagnification were examined in representative aquatic organisms, while cellular damage to enzymes, DNA, and membranes was assessed to understand mechanistic toxicity. A combination of analytical chemistry, ecological surveys, and risk modeling enabled the assessment of contaminant transfer through trophic levels and their long-term implications for ecosystem services.The results revealed a high frequency of bioaccumulative contaminants such as PFAS, heavy metals, and pharmaceuticals across sampled sites. Aquatic species demonstrated reproductive abnormalities, growth retardation, and disrupted population structures, while oxidative biomarkers confirmed chronic stress responses. Biomagnification trends were evident in predator species, and statistical models highlighted significant links between contaminant concentration and trophic level. Moreover, ecosystem-level effects included altered nutrient cycling, suppressed primary production, and biodiversity loss in impacted zones.This research underscores the urgent need for integrated monitoring systems and updated regulatory policies addressing emerging contaminants. The findings highlight the vulnerability of aquatic food webs to chemical stressors and support the development of predictive frameworks and mitigation strategies. Ultimately, the study advocates for a precautionary, science-informed approach to ensure the ecological sustainability and public health safety of global water systems.
Copyright (c) 2023 Maria Hanif (Author) https://creativecommons.org/licenses/by/4.0
INTEGRATED MONITORING APPROACHES FOR SOIL, WATER, AND AIR QUALITY: A FRAMEWORK FOR SUSTAINABLE ENVIRONMENTAL MANAGEMENT
Environmental degradation resulting from industrialization, urbanization, and unsustainable agricultural practices has intensified the need for systematic monitoring of soil, water, and air quality. These environmental components are interconnected and collectively influence ecosystem health, public well-being, and long-term sustainability. Traditional monitoring techniques often focus on isolated parameters, limiting the ability to detect and address cumulative environmental risks.This research proposes an integrated environmental monitoring framework that combines conventional assessment tools with advanced technologies such as remote sensing, sensor networks, and GIS-based spatial analysis. The methodology encompasses the evaluation of soil, water, and air quality using both field-collected and real-time data, subsequently synthesized into a unified Environmental Index (EI) model. The EI quantifies environmental health by weighting normalized scores of each component, providing an actionable metric for decision-makers.The results demonstrate significant variability in pollutant distribution across environmental domains, with soil showing elevated nutrient depletion, water bodies exhibiting high turbidity and nitrate levels, and air quality impacted by particulate matter and gaseous emissions. The hybrid data visualization and spatial mapping revealed critical pollution hotspots and emphasized the effectiveness of integrating cross-domain indicators for holistic environmental assessment.The study concludes that integrated monitoring offers a more precise and responsive mechanism for tracking environmental changes compared to traditional siloed approaches. It facilitates early detection of ecological threats, prioritizes resource allocation, and enhances evidence-based policy formulation. In resource-constrained settings, this framework also provides a scalable and cost-effective strategy for governments and institutions aiming to meet sustainable development goals. The integration of technology and interdisciplinary data within a unified platform paves the way for real-time environmental intelligence, ultimately contributing to healthier ecosystems and more resilient communities.
Copyright (c) 2023 Hina Saeed, Abdul Jabbar (Author) https://creativecommons.org/licenses/by/4.0
ASSESSING THE BIOLOGICAL CONSEQUENCES OF CLIMATE CHANGE: IMPACTS ON ECOSYSTEMS, SPECIES DISTRIBUTION, AND GENETIC DIVERSITY
The integration of Traditional Ecological Knowledge (TEK) with modern scientific conservation practices offers a transformative approach to biodiversity preservation, particularly in the context of ecological degradation and climate change. TEK, deeply rooted in the cultural traditions of Indigenous communities, provides valuable insights into sustainable ecosystem management across tropical and temperate biomes. This study employed a comparative methodology, analyzing biodiversity patterns, conservation outcomes, and community engagement metrics from both tropical and temperate regions. The research incorporated qualitative field observations and secondary data on species richness, soil health indices, ecological resilience, and the effectiveness of community-led conservation practices. Results indicate that TEK-informed conservation approaches significantly enhance biodiversity protection, ecosystem stability, and species adaptability across diverse ecological settings. The study found that TEK-based practices, such as agroforestry, controlled burning, and sacred groves, contribute not only to ecological balance but also foster community participation, policy inclusivity, and long-term sustainability. Metrics evaluating ecosystem services, resilience to environmental changes, and collaborative management frameworks further support the efficacy of TEK in achieving conservation goals. In conclusion, the integration of TEK into modern conservation strategies not only bridges cultural and scientific paradigms but also strengthens ecological outcomes and community ownership. This research underscores the need for inclusive, cross-cultural conservation policies that respect Indigenous knowledge systems, thereby offering a scalable and context-sensitive framework for global biodiversity preservation.
Copyright (c) 2023 Muhammad Asad, Muhammad Umair (Author) https://creativecommons.org/licenses/by/4.0
INTEGRATING TRADITIONAL ECOLOGICAL KNOWLEDGE AND MODERN CONSERVATION STRATEGIES FOR BIODIVERSITY PRESERVATION IN FRAGILE ECOSYSTEMS
Biodiversity loss in fragile ecosystems has become an escalating global concern, driven by anthropogenic pressures and climate change. In response, there is increasing recognition of the need to integrate Traditional Ecological Knowledge (TEK) with modern conservation strategies to develop holistic, sustainable solutions. TEK encompasses indigenous practices rooted in generations of interaction with nature, offering context-specific insights into species behavior, land management, and ecosystem resilience.This study employs ecological modeling, including species distribution models (SDMs), to simulate biodiversity outcomes under various conservation strategies. It incorporates qualitative analysis of policy frameworks and quantitative assessments of soil health, species richness, and community engagement across multiple global biomes. Advanced data sources such as environmental DNA (eDNA), remote sensing, and citizen science applications further enrich the methodology.The results reveal that ecosystems managed through TEK exhibit higher species richness, improved soil health indices, and stronger community involvement compared to technocratic models alone. Socioeconomic and ecological resilience is markedly enhanced when conservation efforts include participatory governance, cultural preservation practices, and co-management frameworks. Regions applying hybrid approaches show consistently superior conservation metrics.The study concludes that merging TEK with scientific frameworks not only strengthens conservation efficacy but also empowers indigenous communities and safeguards cultural heritage. This integrated approach offers a scalable and context-sensitive conservation model that aligns with global biodiversity goals and supports long-term ecosystem sustainability. Institutional recognition of TEK, equitable policy integration, and sustained funding are essential to fully realize the potential of this synergistic conservation paradigm.
Copyright (c) 2023 Mohamed Anouar Harrad (Author) https://creativecommons.org/licenses/by/4.0
SUSTAINABLE ENGINEERING PRACTICES: INNOVATIONS IN GREEN TECHNOLOGY FOR FUTURE INFRASTRUCTURE
Environmental issues have been aggravated by the increase in urbanisation and growth of infrastructure globally and therefore the current mode of engineer work needs to be transformed to a more sustainable one. The conventional construction techniques lead to excessive emission of carbon, waste human energy and consume resources indicating the significance of development of new environment-friendly constructional designs and techniques.The research examines the ways of applying green technology, e.g. solar and wind power plants, energy-saving materials and ways of saving water, to infrastructure construction plans. It is concerned about the application of such technologies in the developing world such as Pakistan. An organized case study, performance analysis, and comparison metrics were part of the means in which we saw the environmental and economic impacts of such sustainable methods.The findings indicate that the energy efficiency, cost savings as well as reduced carbon footprints ahave all been positively impacted in most of the case studies. Projects utilizing renewable energy experienced large reductions in the costs of the energy used. Much drinking water was also saved using rainwater harvesting systems. As well, green building materials enhanced thermal performance, i.e., reduced energy consumption and environmental pollution.These findings demonstrate that not only is sustainable engineering a possibility, technically speaking, but also economically wise and cost-saving in the long-term. Although issues exist, such as high start-up cost and opened regulation loopholes, the positive effects of such a switch on a large scale in the long-term perspective, (i.e. improved environmental resilience, less dependence on resources, and higher quality of urban life), are the factors which demonstrate that it is important to do it. According to the report, it is required to have in place a strong national framework alongside legislative support, stakeholder training, and engagement of the populace to ensure that sustainability becomes part of the future of infrastructure development.
Copyright (c) 2023 Muhammad Ammad, Uran Abazi (Author) https://creativecommons.org/licenses/by/4.0