Holistic Solution Research Articles

E-WASTE FACILITY LOCATOR

Integrated mobile application and website offer a holistic e-waste management solution with multilingual, chatbot-driven assistance. Users can efficiently locate the nearest e-waste facility using path optimization and, upon device input, instantly view the rewards they'll receive post-disposal, which may include cryptocurrency, vouchers, NFTs, or cashback points, based on valuable materials recovered. To enhance user awareness, our platform delivers educational pop-ups related to users' recent search history, educating them about harmful and valuable components in their old devices. Furthermore, users can utilize AI and ML for quick assessments by uploading device pictures. In the spirit of sustainability, the platform allows users to not only dispose of their old devices but also purchase materials recovered from e-waste disposal. We are committed to cost-effective development, ensuring an economical yet environmentally conscious service that encompasses both a mobile app and website.

Auto Encoder Driven Hybrid Pipelines for Image Deblurring using NAFNET

The project introduces an innovative solution to the persistent challenge of image blurring in the realm of Computer Vision. Leveraging the synergies between auto-encoder structures and Non-Linear Activation Free Networks (NAFNET), the proposed methodology aims to achieve superior image restoration results by effectively addressing diverse types of blur. This approach offers a holistic solution that combines the strengths of traditional methods and state-of-the-art deep learning techniques. Quantitative evaluation using metrics demonstrates the efficacy of the proposed methodology in achieving superior deblurring results compared to existing techniques. By pushing the boundaries alongside of image deblurring capabilities, the project contributes to the advancement of the field and holds promise for applications across various domains, including photography, medical imaging, and surveillance.

Redesign User Interface (UI) and User Experience (UX) of Ikan Giling Segar Website with Design Thinking Method to Increase User Interest

In this era, digital transformation in business is increasing. One of the businesses that participated in the transformation and was studied in this research is Sabrina Store, which sells groundfish through its website, Ikan Giling Segar. Even so, challenges arise related to website design that is not optimal. This research is included in the Research and Development (R&D) research type. It uses design thinking methods to improve the appearance of the website design so that user interest also increases. Data were collected through questionnaires and related literature. Pre-questionnaires and questionnaires were conducted after the redesign using the User Experience Questionnaire (UEQ) method with respondents from potential users in East Java, as well as usability testing of the redesigned prototype. Results showed that five aspects of UEQ were below average before the redesign. However, after the redesign, all aspects improved. Usability and UEQ testing also showed that the redesign improved user interest and experience. Thus, using design thinking and R&D methods can provide holistic solutions to improve the UI / UX of online business websites with positive results.

Be Cool: A Holistic and Innovative Approach to Rehabilitation in Multiple Sclerosis: Study Protocol for a Randomized Controlled Trial.

(1) Background: Individuals with multiple sclerosis (MS) have to deal with numerous symptoms that adversely impact their quality of life. While pharmaceutical treatments offer some relief, they often fall short of addressing the full spectrum of MS symptoms. To bridge this gap, we introduce the Be Cool rehabilitation program, a comprehensive protocol designed to enhance the well-being and life quality of MS individuals. (2) Methods: The Be Cool program is a multifaceted approach that combines exercise training, nutritional guidance, psychological support, and cooling strategies. Adapted to meet the unique needs of MS individuals, this program aims to mitigate symptoms, promote physical and mental health, and improve overall quality of life. The integration of these strategies addresses the complex challenges faced by MS individuals, offering a holistic solution beyond conventional medication. (3) Conclusions: The Be Cool rehabilitation protocol is designed to offer individuals with MS a comprehensive approach to symptom management, fostering improvements in their quality of life. By addressing the multifaceted nature of MS through an integrated strategy, the program holds promise for more effective management of the condition.

The Challenge of Integrating Seismic and Energy Retrofitting of Buildings: An Opportunity for Sustainable Materials?

Recent earthquakes and escalating energy demands are exposing building stock deficiencies, particularly in terms of seismic resilience and energy efficiency. Many aged constructions do not fulfil current regulations both in terms of seismic and thermal design principles, thus requiring suitable retrofitting solutions. Integrated approaches for concurrent seismic and energy renovation have emerged as promising strategies in recent years, offering holistic solutions that optimize interventions and maximize benefits. While these combined methods hold significant potential for practical applications, there remain opportunities for further research to enhance their advantages. Furthermore, addressing climate concerns requires concentrated effort within the construction sector, where synergetic refurbishments can serve a dual purpose by reducing emissions and promoting the use of more sustainable materials. This study discusses strategies proposed in the literature for integrated retrofitting, considering their environmental impact, both in terms of energy performance and embodied carbon. The overview shows the innovation potential for the development of materials and systems combining acceptable performance with eco-friendly attributes. Yet, their application in integrated retrofitting systems, either as structural components or insulators, is still limited, underscoring the need for continued investigation and advancement. This paper concludes with recommendations to inspire further research and advancements in this critical field.

Exploring Crop Choices: Benefits, Challenges, and Rationale Among Rwandan Farmers

Farming decisions on crop choices are guided by different factors including natural conditions, household needs, traditions, stakeholder recommendations, and productivity. The best decision varies for each farmer based on specific circumstances. There are both benefits and challenges in farmers’ crop growing experience. In Rwanda, agriculture employs 70% of the population, contributing 33% to the GDP across three main agricultural seasons. However, food and nutritional insecurity remain pressing issue affecting both human and economic progress. This study explored the rationale, benefits, and challenges of farmers’ choices. This study used a qualitative descriptive approach, conducting six focus group discussions (FGDs) in each participating district. Each FGD comprised 10 participants, ensuring gender balance. Recruitment was facilitated by local community health workers (CHWs), with participants providing informed consent. Trained data collectors utilized voice recorders to collect the data. The researchers transcribed the data verbatim, anonymized the data, and translated the same data into English. Data analysis revealed four key themes: reasons for cultivation, factors influencing crop choice, farmers’ livelihoods, and farming challenges. Findings highlight the need for holistic and context-specific solutions in Rwandan agricultural development, emphasizing stakeholder collaboration to support informed decision-making and sustainable agriculture.

Public Sector Transformation: Increased Efficiency and Innovation in the Digital Economy

Public sector transformation is a must in responding to new challenges and taking advantage of opportunities that arise in the digital economy era. The aim of this study is to understand the role of efficiency and innovation in public sector transformation, as well as its impact on public performance and services. The research background covers the ever-evolving global dynamics, where advances in digital technology have fundamentally changed the economic landscape, encouraging the public sector to improve efficiency, responsiveness, and innovation in providing public services to society. The research methods used include literature studies, secondary data analysis, and direct surveys to stakeholders related to public sector transformation. The results show that a large number of government agencies have adopted digital technology as part of their transformation efforts. However, there are still significant challenges associated with the digital divide and lack of technological skills among bureaucracy. It also found that innovation and collaboration across sectors play an important role in designing holistic and effective solutions. The result of this study is that the transformation of the public sector towards increased efficiency and innovation has a significant impact on public performance and services. Digital technology adoption, technology skills development, innovation promotion, and cross-sector collaboration are key in achieving transformation goals. By combining these efforts, the public sector can create significant added value for society and position themselves for long-term success in the ever-changing era of the digital economy.

Predictive modeling of 3D textile composites using realistic micromechanical representations

An innovative and efficient progressive damage model, informed by coupon tests, and using minimal, readily available material parameters, is proposed in this paper to comprehensively predict the tensile, compressive and shear stress–strain response and strengths of a 3D woven angle interlock composite within 5% of test data. Realistic, high-fidelity microgeometries, with process-induced defects, and devoid of geometric assumptions, are constructed using the digital element approach. Excellent correlations with test micrographs are observed for yarn cross-section areas, aspect ratios and crimp ratios, that are on average within 5.5%, 1.9% and 1% of the micrograph measurements respectively. The mesoscale predictions capture the detailed failure mechanisms under each loading condition. Analytical stiffness expressions are derived in terms of the constituent material elastic properties and architecture geometry, which also predict the stiffnesses within 10% error margin. A robust workflow is established that combines the high-fidelity microgeometry generation, automated conformal finite element model creation and comprehensive property prediction capabilities to provide a holistic solution.

A Novel Approach Integrating IoT and WSN with Predictive Modeling and Optimization for Enhancing Efficiency and Sustainability in Smart Cities

The development of smart city solutions is necessary for traffic management and environmental sustainability to meet the challenges of global urbanization. Based on this idea, the paper presents a new way to amalgamate IoT and Wireless Sensor Networks with predictive modeling and optimization methods for use in smart city management. To this end, we have installed traffic sensors (infrared, ultrasonic) and environmental sensors (air quality, humidity, gas) at strategic points throughout the city where they can collect real-time data on traffic and pollution. Then, this data is used together with machine learning models such as ANN (Artificial Neural Network), DT or Decision Trees, KNN (K-Nearest Neighbors), and RF or Random Forests. In practical terms, it is through iterative training processes that our models have achieved ever greater accuracy over time. Now they can actually learn and adapt to changing urban dynamics. The holistic solution of this approach pertains to informed decision-making in smart city infrastructure management. Urban stakeholders can make data-driven decisions by leveraging advanced sensor technologies and machine learning algorithms to deal with traffic congestion, reduce pollution and enhance the quality of life for smart cities. Urban planners, policymakers and technologists working on smart city solutions to improve urban mobility and environmental quality will all be interested in the outcomes of this study.

Globe glider

Globe glider Tours and Travels aims to revolutionize the travel industry by offering a comprehensive platform that integrates seamless booking experiences, personalized trip planning, and cutting-edge travel recommendations. With an emphasis on leveraging advanced AI algorithms and machine learning techniques, Globe glider seeks to redefine the way travelers discover, plan, and embark on their journeys. Our platform will provide users with a holistic travel solution, from browsing destinations to booking accommodations, transportation, and activities, all tailored to their preferences and interests. Globe glider’s mission is the commitment to enhancing the traveler's experience through innovative technology and curated content. By harnessing vast datasets and employing sophisticated algorithms, our platform will deliver personalized recommendations for destinations, activities, and experiences based on each user's unique profile and past travel behavior. Additionally, Globeglider will offer real-time assistance and support throughout the travel journey, ensuring a seamless and stress-free experience from start to finish. In an ever-evolving travel landscape, Globe glider stands poised to empower travelers with the tools and resources needed to make informed decisions and create unforgettable experiences. With a focus on user-centric design, technological innovation, and unparalleled customer service, Globeglider aspires to become the go-to platform for travelers worldwide, facilitating seamless exploration, discovery, and adventure across the globe. Globeglider sets itself apart by prioritizing sustainability and responsible tourism practices. In an era where environmental conservation and ethical travel are paramount concerns, our platform will actively promote eco-friendly travel options, support local communities, and advocate for the preservation of cultural heritage sites. Through partnerships with eco-conscious suppliers and community-based tourism initiatives, we aspire to minimize the carbon footprint of travel while maximizing positive socio-economic impacts on host destinations. At the heart of GlobeGlider is a commitment to fostering a sense of exploration and connection among travelers. The application incorporates a user-centric approach, allowing individuals to customize their journeys based on preferences, interests, and travel goals. GlobeGlider not only streamlines the logistics of travel but also enhances the overall experience by offering curated suggestions, insider tips, and interactive maps. By combining convenience with a touch of adventure, GlobeGlider aims to empower users to embark on journeys that are not just destinations but transformative moments in their lives.

Synergistic approaches to elevate indoor air quality: A holistic examination of classroom refinement, air exchange optimization, and flooring material impact

This research endeavors to elevate indoor air quality within aging school environments by concentrating on refining interior finishing materials and windows. Renovations, encompassing window and floor remodeling in classrooms, aim to mitigate particulate matter (PM) infiltration and enhance air exchange rates. Utilizing SPS30 sensors for the analysis of 0.3–2.5 μm particles, with a focus on their implications for human health, the study evaluated air exchange rates, deposition rates, infiltration rates, and particle generation during classroom activities. Post-renovation results demonstrated a noteworthy decrease in air exchange rates, indicating an enhancement in airtightness. The investigation delves into particle generation with various flooring materials, accentuating the importance of opting for durable and low-particle-generating alternatives. Health risk assessments, considering multiple exposure routes (inhalation, dermal contact, and ingestion), revealed reduced risks post-renovation, particularly for children. To further optimize indoor air quality, the study suggests the implementation of air purification systems. Examination of PM generation during student activities showcased a substantial reduction post-renovation. This study underscores the positive influence of architectural enhancements on indoor air quality while acknowledging the necessity for holistic solutions and continuous research.

Development of Fuzzy Logic Controller in Automatic Vehicle Navigation using IoT

The integration of fuzzy logic controllers in automatic vehicle navigation systems represents a significant advancement in intelligent transportation systems, especially when paired with Internet of Things (IoT) functionalities and optimized through genetic algorithms. This innovative fusion harnesses the precision of fuzzy logic, the connectivity of IoT, and the optimization capabilities of genetic algorithms to transform automatic vehicle navigation. Fuzzy logic controllers excel in managing uncertainty and imprecision, providing decision-making capabilities akin to human reasoning. By simultaneously assessing multiple inputs and determining actions based on degrees of truth, fuzzy logic enables safe and efficient navigation in dynamic driving environments with fluctuating variables like obstacle proximity and traffic flow. IoT integration enhances navigation systems by enabling real-time data collection and sharing among vehicles and infrastructure, fostering adaptive route planning and improving the overall navigation experience. Genetic algorithms further optimize system performance by iteratively adjusting fuzzy logic controller parameters, ensuring efficient decision-making tailored to specific performance criteria such as travel time and fuel consumption. This collaborative integration of fuzzy logic controllers, IoT, and genetic algorithms offers a holistic solution to the challenges of automatic vehicle navigation, enhancing safety, efficiency, and adaptability in complex driving scenarios. Beyond enhancing individual vehicle performance, this approach contributes to overall transportation system efficiency and safety by mitigating traffic congestion, reducing emissions, and minimizing accidents. Consequently, these integrated systems address crucial societal challenges and pave the way for widespread adoption of autonomous vehicles in the future.

Citizens’ deliberation on solutions to fight urban household food waste and nexus with growing urban gardens: The case of porto metropolitan area in Portugal

Food waste is currently acknowledged as a major societal challenge, including the food waste at the household level estimated to be responsible for the wastage of one third of the food produced for human consumption. Hence, tackling household food waste (HFW) is gaining a momentum in societal and policy agendas accompanied by an increasing effort of the scientific community to deliver evidence to address the research gaps on the causes and on the solutions to address this multidimensional societal problem. The proposed solutions by published literature to mitigate HFW can be unfolded into four major types, actions to raise people awareness, participatory actions, economic incentives, and collective actions. However, there is little evidence on the household's assessment of the different types of actions and its combination. This paper contributes to this research gap by adopting an innovative participatory approach, using deliberative focus groups (DFG), and analyzing the collected data through content analysis resorting to the software Maxqda. We had conducted six DGF in the Porto metropolitan area before and during the pandemic COVID-19 crisis. Our results highlight the citizen's option for more holistic actions in comparison to separate actions as a way to effectively fight food waste at household level. Another finding of our study is that citizen's growing urban gardens found it the more effective way to reduce household food waste. These results suggest that urban policies and underlying legal frameworks should favor holistic solutions to incentivise fighting HFW and account for the urban gardens as a relevant part of the solution. In addition, the study has shown that qualitative deliberative citizen-led approaches show insightful to understand how common people perceive as alternative or complementary the different types of actions to fight HFW proposed by the literature review.

An Updated Integrated Pest Management System: A Footprint for Modern-Day Sustainable Agricultural Practices

Integrated Pest Management (IPM) stands out as a multifaceted approach, offering efficient, socially acceptable, and environmentally friendly pest management solutions in agriculture. It not only maximizes farmers' financial returns but also fosters environmental and human health benefits, contributing significantly to sustainable food production. However, challenges persist due to farmers' insufficient engagement in IPM technology development and a lack of understanding of its ecological principles. To address these challenges, the traditional ecological perspective of pest management has evolved into a more comprehensive IPM model. This modern approach integrates recent advancements in agricultural technology, leveraging contemporary communication tools, and responding to shifting consumer trends towards sustainably produced food systems. Additionally, heightened awareness of global trade and travel impacts necessitates a reconfiguration of IPM strategies. The enhanced IPM model encompasses four key components: identifying pest management alternatives, synthesizing knowledge and resources to develop effective management strategies, facilitating timely decision-making through information management, and fostering the dissemination of crucial information through outreach efforts. Moreover, the new model recognizes the interplay of human, environmental, social, and economic factors in food production. It places emphasis on the business aspect, involving producers, customers, and sellers, while also addressing sustainability considerations such as economic viability, environmental safety, and social acceptability. By integrating these elements, the revamped IPM approach holds the promise of doubling farmers' income, ensuring food security for all, and promoting overall health and well-being. It represents a holistic solution to pest management challenges, aligning with the broader goals of sustainable agriculture and enhancing the quality of food production for generations to come.

A comprehensive deep learning geometric shape optimization framework with field prediction surrogate and reinforcement learning

The optimization of aerodynamic components' geometric shapes demands a novel technical approach for adaptive and efficient exploration and decision-making within the design space. In this study, we introduce an innovative shape optimization framework that leverages deep reinforcement learning with neural network surrogate models. The field prediction surrogate, realized by two distinct U-net architectures, can efficiently generate holistic field solutions based on the transformed mesh coordinates. Subsequently, an inference engine dynamically calculates the key metric of the flow fields, serving as the objective function for the subsequent geometry-aware Deep Q network (DQN)-based optimization. The framework's efficacy is validated using a rocket nozzle as an illustrative example. During surrogate validation, under both friction and frictionless conditions, the l1 errors of the entire flow field of both the U-net vision transformer (ViT) and U-net convolutional neural network (CNN) architectures are less than 0.4%. The proposed U-net ViT consistently outperforms U-net CNN, and the superiority is particularly evident in complex flow areas, outlet sections, and vacuum thrust prediction. Following training, the DQN model is employed to explore the design variable space. The B-spline defining profile successfully is optimized to a final expanding segment shape with improved thrust. Under frictionless conditions, it closely approaches the theoretical optimum. In the practical condition considering friction, the optimized shape gains a 2.96% thrust improvement. The results demonstrate that the proposed framework, especially when coupled with U-net ViT, exhibits enhanced accuracy and adaptability for shape optimization tasks.

AI-based tool for early detection of Alzheimer's disease

In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease. Employing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our model undergoes training enriched by advanced image preprocessing techniques, achieving outstanding accuracy (testing 98.17 %, validation 97.52 %, training 99.62 %). The strategic use of transfer learning fortifies our competitive edge, incorporating the hippocampus approach and, notably, a progressive data augmentation technique. This innovative augmentation strategy gradually introduces augmentation factors during training, significantly elevating accuracy and enhancing the model's generalization ability. The study emphasizes practical application with a user-friendly website, empowering radiologists to predict class probabilities, track disease progression, and visualize patient images in both 2D and 3D formats, contributing significantly to the advancement of early AD detection.

Rover with Enhanced Manipulation and Vision Capabilities

Abstract: From planetary exploration to terrestrial uses, rovers are essential in many sectors. This research aims to improve the adaptability and efficacy of the rover in a variety of contexts by presenting a revolutionary design that is outfitted with sophisticated manipulation and vision capabilities. Modern robotic grippers, arms, and vision systems are all integrated into the system architecture to provide a holistic solution for challenging jobs. The creation, integration, and coordination of these upgraded capabilities are described in depth in this work, with experimental data demonstrating improved performance and providing support. Case studies and real-world applications show the potential effect of the proposed rover, and talks about future work and problems offer ideas for additional improvements in rover technology. This work opens up new possibilities for improved capabilities in difficult circumstances and ads to the field of robotic exploration and manipulation

Holistic Solutions of Cubic Diophantine Equation with Four unknowns

The non-homogenous cubic Diophantine equation with four unknowns represented

Cell line studies in Ayurveda: Bridging ancient wisdom and modern science

Ayurveda, an ancient Indian system of medicine, offers holistic solutions to modern health challenges by addressing the root causes of illness. In recent years, there has been a growing interest in integrating Ayurveda with modern scientific approaches through interdisciplinary research to explore synergies and uncover novel insights for healthcare advancements. Cell line studies are emerging as a pivotal tool in this effort. These studies facilitate the exploration of the molecular mechanisms underlying Ayurvedic principles and formulations, offering valuable insights into their therapeutic efficacy. This review article evaluates the role of cell line studies in bridging the gap between ancient Ayurvedic wisdom and modern scientific understanding. It provides an overview of existing research on cell line studies of Ayurvedic medicines, exploring their effectiveness, identifying key herbs and formulations studied, discussing clinical and pharmacological findings, addressing challenges and future directions, and emphasizing the importance of scientific validation when integrating Ayurveda into conventional healthcare.

Enhancing Digital Accessibility and Learning Outcomes: Key2enable's Assistive Technology for Children with Disabilities

This paper showcases how Key2enable's assistive technology works as a holistic solution comprising hardware and software that can enable people with severe disabilities to communicate, learn, engage, and autonomously use technology. This is an exploratory study and will develop a pathway to new developments in the field of assistive technology and portray how motivation plays a key role. The sample consists of 10 children with motor impairments and on the neurodivergence spectrum. There is insufficient research on this relatively small population and this study aims to address how using the right technology for cognitive stimulation can significantly accelerate progress and bring equal opportunities to all in this constantly changing digital world.