Volume 15, Issue 9, 09 2024 Edition - IJSER Journal Publication


Publication for Volume 15, Issue 9, 09 2024 Edition - IJSER Journal Publication

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Adoption of Digital Learning Technology: An Empirical Analysis of the Determinants in Telecom Sector []


Technology has advanced significantly from the analogue period to the digital era. Digital Learning Technology (DLT) is a learning paradigm based on the use of ubiquitous latest technologies, by using smart devices. It can be described as a learning environment that is assisted in daily life by wireless networks, mobile, and embedded computers. It aims to offer content and interaction to students wherever they are, at any time. The learning process has advanced thanks to the technology revolution, which has also fundamentally altered how knowledge is shared and learned. At present, there exist other frameworks too, but they are centered towards different paradigms, and point of view pertaining to DLT with its emphasis on Telecommunication Sector has not been taken into consideration. As, existing frameworks are centered towards different environments hence there exists a need to add dimensions of Empowered Learner, Digital Citizen, Knowledge Curator, Innovative Designer, Computational Thinker and Creator, Communicator & Global Collaborator. These have not been integrated together in existing available research. The study will ascertain level of knowledge of DLT and examine factors which affect the adoption rate, use, and role of DLT in telecoms setups. The results of this research will help create a framework that, if used in any academic or learning setting in a technology-based firm.

Benefits of Outsourcing Information and Communication Technology Infrastructure []


We have seen for the last few decades that Telecommunications Industry has shown a lot of progress relating to infrastructure development associated with access to telecommunications services, thus leading to reduction in customer services charges. Profit margins have seen reductions that lead operators to consider alternatives by adopting new models while keeping service quality unchanged. By deploying a network model, costs, risks, customer engagement profitability, new technological embracing and business costs can be substantially reduced. The research involves the analysis of the variables including, reduction in cost, performance of the organizations, performance of the employees, flexibility, risks of outsourcing and access to specialized skills & technologies. It was seen that improved organizational performance can be achieved by dividing the infrastructure to be outsourced into smaller units and then further outsourcing them to the competent vendors. Most employers felt that the outsourcing infrastructure has led to the increase in their performance and productivity.

Fabrication of Nanostructured Cadmium Selenide Thin Films and Study of its Electrical Properties []


Cadmium selenide (CdSe) thin films having different thicknesses were prepared by thermal vacuum evaporation method onto precleaned amorphous glass substrate. The surface morphological properties of as prepared CdSe films have been characterized by various techniques, such as, X-ray diffraction (XRD), EDAX, field emission scanning electron microscopy (FESEM). XRD studies identify that the as-deposited CdSe films are highly oriented to [100] direction and they belong to nanocrystalline hexagonal phase. The lattice parameters (a = 4.292 and c = 7.012) and crystallite size (D) were calculated and found to be 156 nm. FESEM investigation confirms that films were uniformly deposited over the surface and particles in irregular morphologies in the form of fibrous texture. The electrical properties of the films have been evaluated such as resistivity (5.115 x 10-3 ohm-cm), carrier concentration (4.79 x 1012 /cm3), mobility (2020.2 cm2/Volt-Sec), activation energy (0.295 – 0.305 eV). TEP measurement confirms the deposited films are of P type semiconducting in nature.

Revolutionizing Irrigation in Morocco: A Performance Analysis of Low-Pressure Drip Systems []


In the water-scarce MENA region, with a focal emphasis on Marrakech, Morocco, efficient water, and energy utilization in agriculture is paramount. This research aimed to evaluate and compare the performance of traditional drip emitters, commonly adopted by regional farmers, to that of emerging low-pressure emitters. Over a duration of 365 days, both systems underwent rigorous testing on their irrigation patterns, flow rates, and associated energy consumption. Findings from this study indicated a modest increase of 0.59% in water consumption with the low-pressure emitters. More notably, however, there was a substantial reduction in energy consumption of approximately 63.84%. When assessed on a specific energy basis, low-pressure systems demonstrated a remarkable efficiency, consuming just 6.04 W/m^3 compared to traditional emitters that consumed 17.14 W/m^3. This highlights a potential energy savings of 64.76% with the low-pressure system. Given the region's challenges with water and energy resources, this study provides compelling evidence in favor of adopting low-pressure emitters as a sustainable and efficient alternative in the MENA agricultural landscape.

Zero Trust Architecture: Enhancing Cybersecurity in Enterprise Networks []


Zero Trust Architecture (ZTA) offers a robust approach to enhancing cybersecurity in enterprise networks, replacing the traditional perimeter-based security models. This paper examines the application, challenges, and effectiveness of ZTA in contemporary corporate environments, with a focus on hybrid and cloud infrastructures. By emphasizing key principles such as least-privileged access, continuous authentication, and network segmentation, ZTA directly addresses the security risks that organizations face today. This research includes a review of relevant literature and an analysis of case studies to explore the difficulties companies encounter when adopting ZTA, including financial costs, integration complexities, and resistance to change. The study also identifies strategies that organizations have successfully employed to overcome these obstacles, leading to improved security and operational efficiency. The findings highlight ZTA’s ability to reduce security incidents through automation and enhanced monitoring. While there are technical challenges to implementing Zero Trust, the research concludes that the framework is essential for maintaining a strong security posture. Future areas for exploration include the role of technologies like artificial intelligence and machine learning in further improving ZTA.

Optimizing Energy Efficiency through Vertical Greenery Systems []


As modern buildings become more airtight to improve energy efficiency, indoor air quality can decline due to reduced ventilation and increased energy use. Plants offer a viable solution to this problem by enhancing indoor air quality through CO2 reduction, humidity control, and pollutant filtration, while also providing cooling and psychological benefits. The primary mechanisms involved are photosynthesis, which absorbs CO2 and releases oxygen, and transpiration/evapotranspiration, which cools indoor environments and improves air quality. Recent advancements in vertical greenery systems (VGS) have significantly enhanced indoor phytoremediation by optimizing space and plant biomass. VGS can reduce indoor temperatures by up to 6°C, decrease cooling energy consumption by up to 58.9%, and lower CO2 levels by up to 17%, offering considerable environmental and economic benefits. The effectiveness of VGS depends on factors such as plant species, light conditions, and CO2 levels, with optimal lighting and substrate moisture improving CO2 assimilation and cooling effects. However, challenges such as high initial costs, maintenance requirements, and climate-specific performance issues persist. This review examines the mechanisms by which plants regulate temperature, humidity, and CO2 levels, evaluates the effectiveness of VGS, and discusses factors influencing their performance. It also addresses the current limitations of VGS and provides recommendations for future improvements.

The Foldster Gen 1: Development of an Affordable Laundry Folding Robot for Domestic Use []


Laundry tasks, particularly folding, consume significant time in households. While advanced folding robots exist, their high cost limits widespread adoption. There is a need for affordable, efficient folding robots accessible to average consumers. This paper presents and evaluates a low-cost, high-speed folding robot for household use, addressing the gap between advanced prototypes and affordable solutions. The paper presents the design and construction of the Foldster Gen 1, a folding robot utilizing a simple three-flap mechanism controlled by an Arduino microcontroller and servo motors. Its performance was evaluated through speed tests and user comparisons with manual folding. The Foldster Gen 1 achieved a folding speed of 3.5 seconds per garment, representing an 80.5% time reduction compared to manual folding in user tests. The prototype's component cost was INR 1,279, significantly lower than existing commercial solutions. The Foldster Gen 1 demonstrates the feasibility of creating a low-cost, efficient folding robot for household use. While limitations in versatility exist, the system's performance suggests potential for widespread adoption of laundry automation technology. Keywords: Household robots, laundry automation, folding robot, affordable technology, domestic assistance

Condition Review on Photovoltaic Cell Cooling Techniques for Sub-Saharan African Region. []


Abstract Solar energy, a critical component of sustainable energy solutions, offers significant potential, especially in regions like Sub-Saharan Africa (SSA) that experience high solar irradiance. However, excessive heat and inefficient thermal management hinder the optimal performance of photovoltaic (PV) systems in these regions. High operating temperatures lead to thermal degradation, reducing both efficiency and lifespan of PV panels. This paper reviews the challenges of PV cooling in SSA, highlighting the urgent need for cost-effective cooling techniques to enhance efficiency and economic viability. It explores various methods to mitigate excessive heat and proposes a novel cooling technique tailored to the climatic conditions of SSA. The goal is to improve PV performance, extend panel lifespan, and support sustainable energy development in the region, addressing the pressing energy poverty that affects approximately 70% of SSA's population. This study underscores the importance of efficient PV systems in combating climate change and fostering economic growth in SSA.

Smart Technologies and GIS-Driven Approaches for Efficient Solid Waste Management: Addressing Toxicity, Segregation, and Sustainable Disposal Practices []


One of the globally pertinent issues is to manage the wastes generated in an organized manner. Mitigating solid waste generation is important alongside efficient management of the generated wastes is paramount. Thus, recycling has emerged as a viable alternative. Solid Waste Management comprises steps like collection, transportation, and disposal, and in an efficient waste management process, it is essential to monitor and design each process scientifically. Disposal in landfills is a general practice however, owing to inefficient segregation before disposal the wastes are highly toxic with the potential to affect the hygiene of the earth. It is observed that half of the wastes disposed of are toxic and this underpins the importance of segregation before disposing. Also, identification of appropriate sites for landfill development is important and it is preferred to build them in areas that are sparsely populated. The mismanagement of solid wastes is evident in developing countries and as a solution, various smart technologies have been developed that monitor the accumulation of wastes. The utilization of Geographical Information System (GIS) is fairly common in analyzing waste management as it brings integrates software-based technologies with data. Apart from monitoring the accumulation, several computational technologies assist in determining appropriate disposal sites or transportation routes, etc.

Federated Learning and GNNs for Explainable Network Intrusion Detection and Risk Prediction []


The thesis focuses on a novel Network Intrusion Detection System (NIDS) based on Federated Learning (FL) and Graph Neural Networks (GNNs) for some of the most critical challenges in cybersecurity. The traditional NIDS suffers from an inability to adapt and scale, which significantly impairs its performance in confronting the increasingly complex and sophisticated cyber threats. In contrast, this model overcomes such limitations by utilizing FL, which trains models across multiple data sources in a truly decentralized fashion without necessarily sharing data directly. This will significantly enhance data privacy and security against sensitive network environments. More importantly, we further leverage GNNs to analyze complex relational data embedded within network traffic. GNNs effectively map complex communications between the entities of the network, hence allowing the detection of sophisticated intrusion tactics that might, for example, leverage these relationships. In our case, this lets our system easily outperform traditional rule-based and simple machine learning-based NIDS, which cannot cope with the dynamic nature of modern network threats. We integrate XAI techniques into our system in order to show its decision-making process in a more transparent and trustworthy way. Explainable Artificial Intelligence (XAI) provides explainable interpretations from the model's decisions or predictions, which in turn enable more realistic validation to be performed by network administrators and security analysts. In real-world security applications, transparency is of primary importance since insight into the grounds for alerts is often required for proper and effective response. In addition, FL, GNNs, and XAI together allow one to advance the technical capability of NIDS to meet broader needs around scalability, privacy, and interpretability in cybersecurity tools. This system performs even better than some solutions in terms of detection rate and false positive rate, and it has much better privacy-preserving features that could stand tall in protecting modern digital infrastructures. The research reveals significant improvements on existing methods, hence showing great potential for wide applications in securing networks against various intrusion scenarios.

Status Review on Stability Analysis of Solar and Wind Electrical Energy Resource Based Microgrids integration into a Multi-Machine Power System. []


The world's interest in producing large amounts of electrical energy from renewable sources is mostly driven by global warming. One of the most promising renewable energy sources for producing large amounts of power is solar PV and wind energy, thanks to developments in converter technology. Wind and solar PV power could change the electrical grid in several ways and have an impact on its stability if the current commissioning rate persists. This paper provides a thorough analysis of the technological difficulties, including the stability concerns related to the integration of wind and solar PV on a large scale into the electricity system. The paper also analyzes solar PV for stability studies, HVDC, DFIG, FACTS devices, and generators' dynamic models. This report concludes by summarizing the research findings regarding the technical solutions to address the issues with power system stability.

EFFECT OF STIFFENER LOCATION ON THE FLEXURAL CAPACITY OF BUILT-UP HOT-ROLLED AND COLD-FORMED STEEL BEAMS []


The use of built-up steel beams, both hot-rolled and cold-formed, is prevalent in structural engineering applications where high flexural strength and material efficiency are critical. These beams are often subjected to high bending moments, making them susceptible to various forms of buckling, such as local buckling and lateral-torsional buckling (LTB). To mitigate these issues and enhance the flexural capacity of built-up beams, stiffeners are introduced. Stiffeners play an essential role in reinforcing weak points of the beam, improving both stability and load-bearing capacity. However, the effectiveness of stiffeners largely depends on their location along the beam's span. Optimal stiffener placement can significantly improve the beam's flexural strength and buckling resistant. This study investigates the influence of stiffener location on the flexural capacity of built-up hot-rolled and cold-formed beams. By analyzing three different types of cross-section beams with stiffening sleeve channels at four distinct locations, the research reveals that stiffeners placed closer to the beam supports result in a greater increase in flexural capacity. These findings provide valuable insights into the strategic positioning of stiffeners to maximize performance and structural integrity in practical applications.

Adoption of Digital Learning Technology: An Empirical Analysis of the Determinants in Telecom Sector []


Technology has advanced significantly from the analogue period to the digital era. Digital Learning Technology (DLT) is a learning paradigm based on the use of ubiquitous latest technologies, by using smart devices. It can be described as a learning environment that is assisted in daily life by wireless networks, mobile, and embedded computers. It aims to offer content and interaction to students wherever they are, at any time. The learning process has advanced thanks to the technology revolution, which has also fundamentally altered how knowledge is shared and learned. At present, there exist other frameworks too, but they are centered towards different paradigms, and point of view pertaining to DLT with its emphasis on Telecommunication Sector has not been taken into consideration. As, existing frameworks are centered towards different environments hence there exists a need to add dimensions of Empowered Learner, Digital Citizen, Knowledge Curator, Innovative Designer, Computational Thinker and Creator, Communicator & Global Collaborator. These have not been integrated together in existing available research. The study will ascertain level of knowledge of DLT and examine factors which affect the adoption rate, use, and role of DLT in telecoms setups. The results of this research will help create a framework that, if used in any academic or learning setting in a technology-based firm.

Mitigating Risks through effective Management for Enhancing Organizational Performance []


The success of the organisation performance is dependent upon number of key parameters such as employee’s performance, system deployment and many others. However, it is analysed that parameters under the context of the organizational Performance are continuously increasing. This research work has been conducted to analyse the effect of risk management on organisational Performance. To gather participant opinions on how Risk Management and Organizational Performance intersect, this study will use a survey-based methodology. This method will make it possible to gauge how respondents feel about these factors. A clear strategy or approach is always required to ensure the efficient execution and total integration of risk management inside the company.

Enhancing Urban Air Quality: The Efficacy of Plant-Based Greenery Systems in Air Purification []


Air pollution significantly impacts human health and environmental quality, especially in urban settings. Globally, air pollution was responsible for approximately 4.2 million premature deaths in 2019, with a substantial portion resulting from cardiovascular and respiratory diseases. Plant-based greenery systems, such as green walls, have emerged as effective solutions for mitigating urban air pollution by leveraging natural processes like photosynthesis, transpiration, and particulate matter adsorption. These systems not only enhance air quality but also provide thermal regulation, increase biodiversity, and add aesthetic value to urban environments. The deployment of green walls in densely populated areas addresses the lack of space for traditional greenery, offering a sustainable method to enhance urban living conditions by purifying the air, reducing urban heat island effects, and improving psychological well-being among residents. This review endeavors to offer a detailed examination of air pollutants, how plants reduce these pollutants, and the function of urban greenery systems in air purification, emphasizing their classification, historical evolution, and proven advantages.

An Effective Fine-Tuned Multi-head Attention based VGG-16Net for Hand Gesture Recognition []


Sign language serves as a vital communication tool between hearing-impaired individuals and those who can hear. However, it is not commonly learned by the general population, leading to a need for alternative methods of interaction. Computer vision has significantly contributed to addressing this gap. Various approaches have been explored in recent studies for recognizing human hand gestures, but these methods often encounter challenges due to their limited effectiveness. The methodology proposed in this work seeks to develop a robust deep learning model for recognizing hand gestures from input images. The key stages of this approach include image acquisition, preprocessing, segmentation, feature extraction, and recognition. Initially, input images are sourced from publicly available datasets. Preprocessing steps involve image resizing, Gabor filtering, and RGB color conversion. The Extended Fuzzy C-Means (E-FuzCM) clustering method is employed to segment finger regions, thereby reducing complexity. Discriminative features are then extracted from the segmented images using the Hybrid Quaternion Wavelet Transform with Local Mesh Pattern (HQWT_LMP) method. These extracted features are used to accurately recognize hand gestures through a novel Fine-Tuned Multi-head Attention based VGG-16 Net (FT_MA-VGG-16) model. To ensure precise results, the model's parameters are optimized using an Adam Optimization algorithm. The proposed study was implemented using Python, and the results demonstrate that the developed model achieves a 97.57% accuracy rate in hand gesture recognition.

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