Authors - Monica Alonso, Hortensia Amaris, Maria Angeles Moreno, Farzaneh Abdollahi, Lucia Gauchia Abstract - This paper introduces a novel approach to applying artificial intelligence algorithms based on Reinforcement Learning (RL) for microgrid energy management. Two energy storage systems are considered: stationary battery storage and electric vehicle batteries with G2V/V2G capability. The proposed energy management algorithm considers (i) the uncertainty of photovoltaic energy production, (ii) fluctuations in electricity market prices, and (iii) driver anxiety concerning the vehicle’ range at departure time. The significance of specific parameters, such as time horizon selection and the constant value related to the electric vehicle driver’s anxiety, are examined to optimise the RL reward. Results demonstrate the algorithm's excellent performance under different scenarios.
Authors - Muhammad Al-Zafar Khan, Jamal Al-Karaki, Marwan Omar Abstract - In this paper, we present a multi-agent reinforcement learning (MARL) framework for optimizing tissue repair processes using engineered biological agents. Our approach integrates: (1) Stochastic reaction-diffusion systems modeling molecular signaling, (2) Neural-like electrochemical communication with Hebbian plasticity, and (3) A biologically informed reward function combining chemical gradient tracking, neural synchronization, and robust penalties. A curriculum learning scheme guides the agent through progressively complex repair scenarios. In silico experiments demonstrate emergent repair strategies, including dynamic secretion control and spatial coordination.
Authors - Arinta Athaya Kayana, Candiwan Candiwan Abstract - Rapid digital transformation in various sectors has changed how individuals interact with technology, including in essential services such as fuel purchases. While the MyPertamina app promises to revolutionize access to subsidized fuel, its adoption has fallen short of expectations, raising curiosity about the underlying factors influencing user uptake. This study aims to explore the factors that influence the adoption of the MyPertamina application using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The methodology used is a quantitative approach through distributing questionnaires to MyPertamina users. The analysis results showed a significant relationship between performance expectations, government regulations, and the reliability of institutional sources on behavioral intentions and adoption. However, factors such as effort expectancy, awareness, perceived financial cost, technological infrastructure support, government regulation and institutional privacy concerns did not show a significant impact towards behavioral intention and adoption. The findings highlight the importance of improving user perceptions of the app’s performance and ensuring the reliability of institutional sources for increase behavioral intention and adoption. Additionally, refining regulatory and enhancing app features for a smoother refueling experience are crucial in encouraging broader usage. By addressing these factors, stakeholders can implement more effective strategies to increase the adoption.
Authors - Gloria Virginia, Maria Amanda, Budi Susanto, Umi Proboyekti Abstract - The digital preservation of traditional culinary knowledge is essential to sustaining cultural identity amid rapid technological change. This study focuses on improving the usability of a semantic web-based portal for Indonesian traditional food through a user-centered design approach. The redesign involved card sorting, tree testing, and prototype usability evaluation. Key improvements include enhanced navigation structures, clearer labeling, and the application of interface patterns such as fat menus, breadcrumbs, and carousels. Usability testing showed notable increases in success rates and navigation efficiency, with a System Usability Scale (SUS) score of 85.5, indicating excellent usability. The integration of information architecture principles significantly improved user interaction and content discoverability. This research demonstrates that combining cognitive-oriented design with semantic data models creates more intuitive and accessible cultural heritage platforms.
Authors - Shahin Doroudian, Mohsen Dorodchi Abstract - Firefighting operations in hazardous environments demand agile and adaptive strategies to effectively combat fires while safeguarding the lives of firefighters and civilians. Traditional approaches often rely on predetermined protocols, which may not adapt well to dynamic and unpredictable situations. This paper proposes a novel framework utilizing Reinforcement Learning (RL) to optimize rescue mission strategies. By harnessing the power of RL, this approach enables AI agents to learn and adapt their behavior based on environmental cues and feedback, leading to more effective and responsive rescue operations. The framework integrates various factors such as terrain and path complexity and the presence of hazards, like fire and smoke, into the decision-making process. Through iterative learning, agents evolve their strategies, identifying optimal paths and rescue tactics. Furthermore, this proposal discusses the potential benefits of employing DRL in rescue missions, including enhanced adaptability, scalability, and robustness in diverse and challenging environments. The adoption of RL to optimize strategies for rescue missions represents a significant opportunity to progress in the disaster response domain. At the end, our results show that the RL-driven method enhances rescue operation outcomes and safety by reducing rescue times, increasing the number of people rescued, optimizing resource utilization, and boosting overall efficiency.
Authors - Yijun Shao, Ying Yin, Debby Tsuang, Phillip Ma, Edward Zamrini, Ali Ahmed, Charles Faselis, Katherine Wilson, Karl Brown, Qing Zeng-Treitler Abstract - This study involved the development and evaluation of a novel Deep Neural Network (DNN) model for Alzheimer's disease and related dementias (ADRD) phenotyping. The model was initially trained on a large cohort of 100,000 cases and controls and subsequently fine-tuned using a smaller, expert reviewed dataset of 1,200 individuals. The final fine-tuned model achieved an Area Under the Receiver Operating Characteristic curve (AUC) of 0.832. For further validation, the model's predictive capability was assessed in a separate randomly selected patient cohort comprising individuals without an ADRD diagnosis from 2009 to 2018. The survival analysis shows that patients with higher predicted ADRD risk scores exhibited a significantly increased incidence of developing ADRD after their index date within five years.
Authors - Bat-Erdene Batsukh, Ariunbold Tsoodol Abstract - Agentic AI significantly enhance the efficiency and quality of news writing by generating engaging headlines and concise summaries while maintaining the accuracy and objectivity of the original content. These tools leverage advanced algorithms and large language models (LLMs) to streamline the content creation process, ultimately improving audience engagement and journalistic productivity. The approaches range from using neural network algorithms to LLMs and question-answering systems, each with its unique strengths and challenges. Agent, like News writers, operate in a dynamic and fast-paced environment where staying updated with current global events is crucial. The digital era has transformed how journalists gather, verify, and disseminate information. The integration of Natural Language Processing (NLP) into a Flutter-based news app offers an amazing way to deliver personalized news feeds tailored to individual user experiences. By leveraging NLP techniques, the app can analyse user behaviour, understand content, provide relevant news recommendations, neural machine translations and news content summarizations. Developing a centralized AI-powered news app using Flutter presents a unique set of challenges that span technical, ethical, and operational domains. We conducted this research to discover ways to overcome these challenges and spectacles. In particular, we used artificial intelligence to automatically translate news into the reader's native language and to provide a summary view instead of a large amount of text when reading in detail.
Authors - Dmitry A. Zaitsev, Alistair A. McEwan, Alexander A. Kostikov Abstract - Real-time embedded applications are normally viewed as continuous processes and are often specified using Partial Differential Equations (PDEs) and with certain boundary conditions. In this paper we present techniques for fast mass-parallel numerical solving of PDEs. We compose specialized lattices based on the integer number approximation specified with Sleptsov nets to be implemented as dedicated hardware, which we prototype on an FPGA. For mass-parallel solving of PDEs, we employ ad-hoc finite-difference schemes and iteration methods that allow us to recalculate the lattice values in a single time cycle with appropriate accuracy suitable for control of hypersonic objects and thermonuclear reactions.
Authors - Md Amirul Islam, Giovanni Stea Abstract - Recent advancements in industry underscore the growing demand for systems that provide both high computational performance and real-time assurances, despite these objectives traditionally being seen as conflicting. To support the complex task of designing such Network-on-Chip (NoC) systems with both performance and Quality of Service (QoS) requirements, frameworks such as ARM MPAM envisage systems with hardware support for resource partitioning and the observation of its effects. It enables new application areas for traditional QoS techniques. Network Calculus (NC) uses transformation operations to model traffic profiles through network nodes. It enables the estimation of the minimum service guaranteed to a flow as it moves through flow-controlled nodes. This paper proposes using Network Calculus to derive insights from traces of traffic samples and to evaluate service curves for QoS validation and worst-case delay analysis in NoC architectures. The derived worst-case performance bound is compared with existing works based on Queueing Theory (QT) and Network Calculus (NC). This comparison demonstrates a significant improvement in the accuracy of the delay bounds over the existing QT and NC approaches.
Authors - Khalid Jaber Almalki Abstract - Blockchain technology offers a new solution to address IoT data accuracy, security, and speed issues in healthcare. This paper details a secure way to communicate, verify, and evaluate healthcare data from IoT devices. The technology protects medical data with strong encryption and agreement. The suggested solution outperforms bitcoin and conventional systems in data security, integrity, scalability, latency, speed, and interoperability. User identification, energy efficiency, network stability, and regulatory compliance top the framework's goals. It's ideal for healthcare applications. The strategy uses statistical analysis and machine learning to make verified data meaningful. This improves healthcare choices. The system improves patient care and operational efficiency, making it a major advance in healthcare data management. This study highlights how blockchain-enhanced IoT devices will transform healthcare data processing, improving patient outcomes and business practices.
Authors - Altaf Raja, AKhil Pandey, Vishal Shrivastava, Mohit Mishra, Sangeeta Sharma Abstract - Grocery inventory control is an essential thing of every day existence that often leads to inefficiencies such as over-buying, forgetting critical objects, or meals wastage. traditional methods rely on human reminiscence and guide tracking, which might be at risk of mistakes. This studies paper introduces mind field, a clever jar ready with AI and IoT technology, designed to automate grocery monitoring. mind field is available in two versions: kind-1, which specializes in weight-based totally tracking and notifications, and type-2, which includes superior sensors to screen freshness, temperature, and humidity. The machine integrates with a cellular software to offer real-time updates, personalized indicators, and buying reminders. by leveraging AI-pushed facts analysis and cloud storage, mind field objectives to enhance grocery control performance for households and small agencies. This paper discusses the trouble, proposed solution, machine structure, advantages, and future scope of the brain container machine. The brain container device leverages cutting edge AI and IoT technology to provide an sensible and automatic answer for grocery management. not like conventional stock monitoring methods, it no longer only detects low stock tiers however additionally analyzes person intake patterns to optimize grocery purchases. The seamless integration with a cellular software guarantees actual-time tracking and proactive indicators, making it a noticeably handy and efficient device. This research explores the gadget's architecture, functionalities, and its impact on lowering meals wastage, optimizing family and enterprise inventory, and promoting sustainable intake practices.
Authors - Aishwarya Reehl Abstract - Predicting Crime is an integral part of keeping the community safe and harmonious. It provides valuable information to the respective authorities to anticipate concerns, prevent victims from being potential targets, and allocate their resources in the best possible way. This paper determines the use of a Machine learning algorithm to predict Crime in Montgomery County. We propose a new model designed to enhance the accuracy of crime data. We cover how descriptive models help understand and demonstrate the next potential move for various crimes. This research also shows how we can pre-process information as needed for prediction algorithms.
Authors - Mikhail Ola Adisa, Sonny Rosenthal, Ifeoma Adaji, Shola Oyedeji, Jari Porras Abstract - Civic campaigns and technology interventions are increasingly recognized as powerful drivers of sustainable waste management behaviors. This study investigates how civic campaigns and persuasive technologies interact to promote sustainable waste practices in Finland. Drawing on a mixed-methods approach, the study combines survey data from 255 residents with interviews from civic organizations to explore the role of engagement levels, campaign effectiveness, and ICT interventions in shaping pro-environmental behavior. Findings reveal that while residents generally exhibit strong recycling habits, sustained participation depends on continued civic outreach, awareness of environmental concerns, and value-based motivation. Digital persuasive tools, such as mobile/web apps, social media, and innovative technologies, were found to be effective for raising waste awareness and supporting sustainable efforts but do not independently drive behavioral change. The study highlights the importance of integrating civic strategies with persuasive technologies to bridge the intention-action gap, scale engagement, and reinforce sustainability norms. The paper contributes to the literature on ICT for sustainability by demonstrating how tailored civic-tech can complement grassroots efforts to foster long-term sustainable behavior change.
Authors - Md.Raza Sheikh, Md.Farid Hossain, Tapu Rayhan, Ehashan Ahmed, Md Zahidul Islam Abstract - This study explores the use of machine learning models, particularly Deep Neural Networks (DNN), for crop prediction in Bangladesh’s diverse agricultural context. A comparison of five models—Gaussian Naive Bayes, Logistic Regression, Decision Trees, Random Forests, and DNN—was conducted using a comprehensive agricultural dataset. The results revealed that while all models had strengths, the DNN outperformed the others, achieving an accuracy of 97.98% in training and 97.95% in validation, with near-perfect precision, recall, and F1 scores. The DNN’s performance, despite its interpretability challenges, underscores its potential in accurately predicting crops from complex, high-dimensional data, crucial for Bangladesh’s varied cropping patterns. This research emphasizes the need for robust agricultural data and suggests that DNNs could significantly improve crop planning, management, and food security, contributing to a sustainable future for precision agriculture in Bangladesh.
Authors - Saravana Kumari Shanmuga Sundaram, Shyam A V Abstract - e-Sanjeevani is India's national telemedicine system. It had its modest origin during the COVID lockdown and is now the biggest recorded platform worldwide for primary healthcare, especially for underprivileged communities. For the design, implementation, and assessment, this article investigates the two service models of the platform: e-Sanjeevani OPD (patient-to-doctor) and e-Sanjeevani HWC (doctor-to-doctor). This study assesses e-Sanjeevani's scalability, usage patterns, and integration with the more extensive health system, such as the Ayushman Bharat Health Accounts (ABHA), and National Digital Health Mission (NDHM) frameworks, based on research literature, secondary data from official sources, and policy documents. For those who do not have access to primary healthcare, the platform has provided several advantages. However, there have been difficulties reaching the system's maximum efficiency. By early 2025, the platform had helped over 342 million individuals throughout India, supporting several aspects of healthcare delivery like diabetic foot, caries in elderly persons, and so on. The research suggests a conceptual framework for incorporating Large Language Models (LLMs) into e-Sanjeevani, addressing the existing challenges and extending the solution’s possibilities. The framework includes LLM-driven features, including clinical decision support, real-time translation, automated documentation, and individualized patient education. It comprises a layered architecture effortlessly incorporated into telehealth, supporting artificial intelligence augmentation at pre-consultation, consultation, and post-consultation phases. This integration can significantly increase provider efficiency, lower workload, and raise the general quality of treatment. The results underline present achievements and the transforming opportunities of LLM-enabled fair telemedicine for India and other low- and middle-income nations.
Authors - Fredy Gavilanes-Sagnay, Edison Loza-Aguirre, Henry N. Roa, Narcisa de Jesus Salazar Alvarez Abstract - This study investigates the performance of various channel estimation and signal detection techniques, including Kalman Filtering, Convolutional Neural Net-works (CNNs), and Recurrent Neural Networks (RNNs); with a focus on their application in 5G/6G networks. We evaluate these methods based on key metrics, including Bit Error Rate (BER), Mean Squared Error (MSE), and computational complexity, under different Signal-to-Noise Ratio conditions. Our results demonstrate that Deep Learning models (CNNs and RNN) significantly outperform traditional methods in terms of accuracy, achieving lower BER and MSE values. However, these improvements come at the cost of increased computational complexity, making them less feasible for real-time applications in resource-constrained environments. Reinforcement Learning models also show promise, offering real-time adaptability for dynamic spectrum management and beam tracking but they also face challenges regarding computational efficiency. Despite some limitations, Kalman Filtering remains valuable for applications where low latency and computational efficiency are critical. Our findings highlight the importance of optimizing these models to balance accuracy and computational load for large-scale 5G/6G networks.
Authors - Diego Pucuji, Angel Cornejo, Paul Velasteguí Abstract - The intensive use of fossil fuels has led to increasing environmental degradation, driving the search for sustainable solutions such as photovoltaic systems applied to electric mobility. In this context, electric motorcycles stand out for their energy efficiency, low maintenance, and ease of recharging. The parish of Calderón, in Quito, provides an ideal setting to evaluate these technologies due to its varied topography and climatic variability, which directly impact the efficiency of solar systems. This study, conducted at the Instituto Superior Tecnológico Vida Nueva, analyzed the performance of an off-grid photovoltaic system used to charge electric motorcycles under real operating conditions between March and April 2025. The installed system consisted of four 550W solar panels, a 2,560Wh lithium-ion battery, and a 3,000W inverter. A controlled load was applied using heat guns, and key energy parameters, including autonomy, consumption, and efficiency, were monitored. Despite the high cloud cover and rainfall during the study period, the system achieved an average daily generation of 5.46 kWh. The tested motorcycles achieved ranges of 22 km and 25 km with energy consumption of 1111 Wh and 1259 Wh, respectively. The results demonstrate the feasibility of using solar energy in urban areas for light electric mobility.
Authors - Valinho Antonio, Eric Umuhoza, Pierre Bakunzibake, Moise Busogi Abstract - Accurate crop discrimination is vital for effective agricultural planning and sustainability management, especially in regions like Sub-Saharan Africa (SSA), where small-scale farming predominates and ground data is scarce. Conducting field surveys in SSA is challenging due to labor and cost constraints, as well as logistical and political barriers. This paper proposes a framework design of cost-effective satellite-based machine learning for crop type classification in crop growth with limited reference data. So, we have identified the important satellite timeseries features and the machine learning model architecture to be used to timely and accurately identify crops in a small and intercropped farms. This study therefore has great role on agricultural data collection at large scale which is one of the ways to accomplish food security advocated by the sustainable development goal two, zero hunger.
Authors - Asini Silva, Thushani Weerasinghe Abstract - The rapidly evolving IT job market presents a significant challenge with the growing gap between academic curricula and industry needs. Our study addressed this misalignment with a data-driven approach. The study employed a constructive research approach to develop a user-friendly platform that extracts real-time data from LinkedIn job postings and creates a dataset of IT jobs and their top-demanding skills. The authors utilized tools such as Selenium and BeautifulSoup to extract job titles and descriptions, identifying the most sought-after skills within the IT industry. Testing and evaluation were conducted weekly for several months, resulting in a dataset consisting of more than 50 IT job positions, each listing 20 in-demand skills. This comprehensive dataset not only captures but also retains records over time, allowing for an in-depth examination of skills associated with historical postings. The analysis uncovers notable discrepancies between the skills highlighted in academic programs and those required by employers, revealing a critical barrier to graduate employability. Hence, this research provides valuable insights for educational institutions, policymakers, and job seekers, equipping them to align programs with industry expectations while empowering students to make informed decisions regarding skill development. Furthermore, the methodology utilized in this study is scalable. It can be applied to any region in the world, offering a robust framework for aligning education with the evolving demands of the job market.
Authors - Jose Luis Chavez Torres, Tyrone Alexander Guarderas Cabrera, Camila Nickole Fernandez Morocho, KunYong Zhang Abstract - This article presents a comparative structural analysis of two housing typologies: one composed of reinforced concrete and the other of steel frame structures. The structural design was carried out using the force-based design method (DBF), considering the seismic and geotechnical parameters of the study area, obtained through SPT tests and bibliographic sources. The fundamental period of both structures was determined, and the corresponding response spectrum was generated, enabling the structural design and subsequent load descent ac-cording to the NEC-2015 combinations. In the foundation stage, three viable alternatives were proposed: isolated footing, combined footing, and mat foundation. Using the specialized software SAFE 2016, the performance of each option was evaluated based on soil pressure, punching shear resistance, and settlement behavior. Finally, a cost-benefit analysis was performed, considering concrete volumes and steel quantities, to select the most technically and economically suitable structural and foundation system for the study area.
Authors - Taufik Iqbal Ramdhani, Riri Fitri Sari Abstract - Real-time and accurate road infrastructure monitoring is a major challenge in urban areas. Traditional methods, such as manual inspections by municipal staff or vehicular surveys using costly technologies like LiDAR or laser scanners, are prohibitively expensive, geographically constrained, and deployed infrequently. To address this, crowdsourcing has emerged as an effective approach for expanding both the coverage and frequency of infrastructure monitoring. Building on this concept, CrowPotChain introduces a novel platform that combines AI-driven pothole detection with secure blockchain-based report submission, ensuring tamper-proof and reliable crowdsourced data collection. The framework utilizes the YOLOv11s-seg model for semantic segmentation, combining convolutional neural networks (CNN) with transformer-based elements, which provides impressive detection metrics (Precision: 0.889, Recall: 0.894, mAP@0.5: 0.944). Every verified report includes geolocation, date/time, and pothole size, securely embedded in a Proof-of-Work (PoW) blockchain for verifiability and immutability. To examine the system's performance, a benchmark was performed on four setups: no AI & no blockchain, AI only, blockchain only, and AI + blockchain, using batches of transactions from 10 to 100. The findings show that the no AI & no blockchain deployment provides the most rapid per-transaction time (approximately 0.010s), followed by AI only (0.059s to 0.135s), blockchain only (0.075s to 0.162s), and AI + blockchain (0.145s to 0.696s). Although blockchain does incur substantial overhead, its combination with AI can still serve response needs for civic infrastructure crowdsource reporting. Future development will add gamification, NFT, and IPFS to enhance participation, encourage reporting, and provide scalable decentralized storage.
Authors - Jiwan N. Dehankar, Virendra K. Sharma Abstract - The advanced incorporation of Machine Learning (ML) in blockchain systems present special challenges related to security, scalability, and adversarial robustness. The traditional consensus protocols and aggregation techniques suffer from high latencies, susceptibility to Byzantine node attacks, and inefficiencies in communicating gradients that cripple real-time federated learning on the blockchain networks. On the other hand, existing solutions like PoW (Proof-of-Work) and centralized aggregation do not adapt dynamically to ML workloads and remain vulnerable to adversarial attacks, thus putting the model's integrity into jeopardy and causing grave computational overhead. To mitigate these issues, we present Blockchain-Federated Secure Learning Network (BFSL-Net), an infrastructural framework with a dual purpose of enhancing security and efficacy while providing scalability to blockchain-based ML systems. BFSL-Net is comprised of (1) the Multi-Tiered Hierarchical Consensus Framework (MHC-BCML), (2) Adaptive Byzantine-Resilient Aggregation (ABRA), (3) Secure Adversarial Gradient Masking (SAGM-MLBC) and (4) Hierarchical Graph Neural Network-Based Threat Intelligence (HGNN-TI). BFSL-Net, which brings all the above-mentioned methods into a unified system of real-time threat resistance federated learning. Proposed model shows an adversarial threat mitigation success of 99.6 %, marked 2.8 times improvement in efficiency of ML processing, and a 4.5-fold reduction in blockchain computational overhead, thereby promising secure, scalable ML production environments in the blockchain space.
Authors - Natalya Aleynikova, Anna Loskutova, Mikhail Matveev, Elena Sviridova Abstract - The paper solves the problem of controlling the states of a system that may exhibit prolonged transient processes. As an example, the learning process of students is considered. Control is carried out based on predicting the dynamics of student academic performance. Academic performance – one of the key indicators of learning quality – is typically measured on graded scales and represented, for instance, by time series of student grades (scores). Instead of traditional time series analysis of grades, the present paper proposes transitioning to a space of fuzzy states (categories): “Fail”, “Satisfactory”, “Good”, and “Excellent”. The dynamics of these fuzzy categories are described using a discrete-time Markov chain model with fuzzy states, analyzing not the current but the limiting (steady-state) distributions of a student’s states. The paper presents a recurrent algorithm for the transition from the space of numerical grades to the space of fuzzy states, constructing the stochastic matrix of the Markov chain. The properties of the stochastic matrix are investigated to determine the existence and uniqueness of the limiting state distributions. Additionally, an approach is proposed for identifying change points – the moments when a shift in a student’s performance trend occurs.
Authors - Jose Luis Chavez Torres, Camila Nickole Fernandez Morocho, Tyrone Alexander Guarderas Cabrera, Ulbio Fernando Mendoza Hidalgo, KunYong Zhang Abstract - This article analyzes a detailed lithological characterization of the Punzara area, located in Loja, Ecuador. Through a systematic field survey and spatial analysis through a geological map at a scale of 1:2500, six different lithological units were identified and delimited. These include heterogeneous alluvial deposits, clayey deposits characterized by a remarkable and anomalous presence of organic roots at depths between 1.0 and 1.5 meters, a massive sandy silt unit with outcrops up to 3 meters thick, a conglomerate with spheroidal clasts of 1 to 4 cm in a silt-sandy matrix, another conglomerate with intercalated lenses of sand and clay, and shale outcrops with intercalations of gray silts with thicknesses of up to 30 meters. The spatial distribution of these units, clearly visualized through the geological map, provided fundamental geological information for the understanding of the local geological framework and lays the foundations for evaluating their potential influence on slope stability in the area.
Authors - Gusti Anisa Wulandari, Dewi Susita, Mohammad Sofwan Effendi Abstract - This study investigates the mediating role of Innovative Work Behaviour (IWB) in the relationship between Knowledge Sharing (KS), Work Engagement (WE), and Job Performance (JP) among state civil apparatus (ASN) at the Directorate of Road Transport, Ministry of Transportation of the Republic of Indonesia. Using a mixed-methods approach, the research employs Structural Equation Modeling (SEM) with AMOS to analyze survey data from 203 respondents, followed by qualitative exploration to contextualize the statistical findings. Results show that KS and WE significantly influence IWB, and although they also directly impact JP, IWB fully mediates these effects. Descriptive analysis reveals gender-based differences in IWB, with female respondents showing higher scores than males, while males reported slightly higher scores in KS, WE, and JP. These findings highlight the strategic importance of fostering IWB to optimize job performance. The study recommends organizational focus on strengthening IWB through targeted interventions, and suggests future research include gender and educational background as potential moderating variables, supported by qualitative methods to deepen understanding.
Authors - Fhatuwani Mapande, Tranos Zuva, Kayode Oyetade Abstract - This paper presents a comprehensive and integrative review of key user perception models in the context of technology adoption. It critically examines five influential frameworks like Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovation (DOI), Innovation Adoption Lifecycle Model, and Technology Readiness Model (TRM) to uncover the multidimensional factors shaping user attitudes and behaviors toward emerging technologies. Through a structured comparative analysis, the study explores core constructs such as perceived usefulness, ease of use, social influence, user readiness, and psychological traits including optimism and discomfort. The novelty of this work lies in its synthesis of diverse theoretical perspectives, offering a holistic view that bridges cognitive, emotional, and sociocultural dimensions of technology adoption. The findings underscore the significance of integrated, user-centered approaches and highlight the role of contextual and sector-specific variables in influencing adoption outcomes. Practical recommendations are provided for researchers, developers, educators, and policymakers to design inclusive and adaptive strategies that enhance technology acceptance and sustained engagement. This study contributes to advancing theoretical understanding and guiding practical interventions in the evolving landscape of digital transformation.
Authors - Staphord Bengesi, Hoda El-Sayed, Md Kamruzzaman Sarker Abstract - Alzheimer's and Parkinson's diseases are two progressive neurodegenerative disorders that primarily affect senior citizens worldwide, and currently, there is no cure. In recent years, the number of diagnosed cases has been increasing. Since both diseases have an impact on the brain, MRI images are used as a crucial diagnostic tool. With advancements in AI, machine learning models are showing great promise in diagnosing and classifying MRI images. To explore this potential, we developed and tested five transformer models, such as ViT, Swin, DeiT, MedT, and Swin-ViT, using a Kaggle dataset containing MRI images from individuals with Alzheimer’s, Parkinson’s, and healthy controls. The models were evaluated on both a balanced dataset of over 2,900 samples and an unbalanced dataset of more than 7,000 samples. Our findings revealed that models trained on the unbalanced dataset outperformed those trained on the balanced dataset, highlighting the advantage of larger datasets in enhancing model performance.
Authors - Zuzana Trabalkova, Martin Stevik, Kamil Zelenak, Jakub Dandar, Zdenek Straka, Daniel Kvak, Karolina Kvakova, Petra Ovesna Abstract - The growing demand for chest radiography in healthcare, combined with radiologist shortages and increasing workloads, underscores the need for innovative diagnostic support tools. This crossover study evaluates the effect of commercially available deep learning-based automatic detection software (DLAD) on radiologists’ diagnostic performance in chest X-ray (CXR) interpretation. Five radiologists independently assessed a dataset of 540 anonymized CXRs, both independently and with DLAD assistance, in two phases separated by a 30-day washout period. DLAD assistance significantly improved diagnostic performance, with overall sensitivity (Se) increased from 0.762 (95% CI: 0.705–0.811) to 0.911 (0.870–0.941, p < 0.001), while specificity (Sp) remained unchanged at 0.850 (0.805–0.887, p = 0.331). The positive predictive value (PPV ) slightly improved from 0.810 (0.755–0.856) to 0.836 (0.788–0.876, p = 0.331), and the negative predictive value (NPV ) increased from 0.810 (0.763–0.850) to 0.941 (0.882–0.947, p < 0.001). These improvements were consistent across radiologists, with notable reductions in false-negative rates. The findings emphasize DLAD’s potential to standardize diagnostic accuracy, enhance sensitivity, and support radiologists in chest X-ray interpretation. These results highlight the clinical value of AI-assisted workflows in improving detection rates while maintaining specificity.
Authors - Prince Kelvin Owusu, Caleb Annan, Ruhiya Abubakar, Moses Aggor, Emelia Sarpong, Gibson Afriyie Owusu, Jefferson Oduro Asiamah, Martins Larweh Nuertey Abstract - This study explores the market feasibility, challenges, and prospects of integrating mini-grids in island communities on Ghana’s Volta Lake, utilizing an exploratory sequential mixed-method design. Purposive and census sampling techniques were employed for qualitative and quantitative research, respectively, with 65 participants. Thematic analysis was applied to qualitative data from semi-structured interviews, while the Analytic Hierarchy Process (AHP) assessed Likert scale-based questionnaire responses. Identifying 21 challenges categorized into economic, political, technical, environmental, and social classes, economic challenges ranked highest (38.64%), with access to nance as the most significant challenge (12.03%). Despite a viable market, the study highlights a potential decrease in donor funding for mini-grid development in Ghana. Significantly, it concludes that policy unsuitability has cascading effects, necessitating a modification in the approach to enhance minigrid development in Ghana, emphasizing policy, tari scheme, and business model adjustments for holistic improvement.
Authors - Prince Kelvin Owusu, Philimina Pomaah Ofori, Moses Aggor, Gibson Afriyie Owusu, Jefferson Oduro Asiamah, Martins Larweh Nuertey, Joseph Djossou Akwetey, Joel Nana Sarfo Konadu Abstract - The integration of immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR) is revolutionizing global marketing and advertising strategies, yet their application within Ghana’s communication landscape remains underexplored. This study investigates the pioneering role of AR and VR in transforming marketing practices in Ghana, with a focus on how these technologies influence consumer engagement, brand perception, and strategic communication. Employing a mixed-methods research design, the study combines qualitative interviews with 15 marketing professionals across major urban centers and quantitative survey data from 250 consumers who have interacted with immersive advertisements in retail, real estate, and tourism sectors. The findings reveal a rising trend in experimental AR/VR adoption among Ghanaian firms, driven by a desire to differentiate brands and deepen customer interaction. However, results also indicate significant barriers, including high implementation costs, limited technological infrastructure, and a lack of skilled personnel. Consumer responses demonstrated high engagement and positive emotional reactions to AR/VR content, particularly among younger demographics, though accessibility concerns persist. The study concludes that while AR and VR offer transformative potential for Ghana’s marketing sector, their long-term success depends on strategic investment in digital infrastructure, public-private partnerships, and targeted capacity-building programs. It recommends that policymakers support immersive technology adoption through subsidies and training initiatives, while marketers should focus on culturally relevant, mobile-optimized AR/VR campaigns to maximize reach and effectiveness. This research contributes to the growing discourse on digital innovation in emerging economies and provides a roadmap for integrating immersive technologies into Ghana’s evolving communication ecosystem.
Authors - Fawzy Alsharif, Irem Yildirim Abstract - Microwave imaging is a promising non-invasive technique for early stage cancer detection, leveraging its sensitivity to variations in the dielectric properties of biological tissues. In this work, a compact ultra-wideband (UWB) antenna specifically designed for lung cancer imaging is presented and analyzed through electromagnetic simulations. The antenna is fabricated on a Rogers RT5880 substrate (εr = 2.2, thickness = 1.65 mm) with overall dimensions of 23 × 21 × 1.58 mm³ and is impedance-matched to a 50 Ω feedline. Performance evaluations using CST and HFSS reveal operation across three frequency bands centered at 3.08 GHz, 6.04 GHz, and 9.54 GHz. The antenna achieves a peak gain of 4.52 dBi and a maximum radiation efficiency of 86% at the highest frequency. It offers a wide operational bandwidth from 2.58 GHz to 11.67 GHz. A realistic lung phantom modeled in CST demonstrates the antenna’s effectiveness in detecting signal changes caused by dielectric contrast in tissues, highlighting its potential for accurate and non-invasive lung cancer diagnosis.
Authors - Manideep Pendyala, Udit Goel, Jim Samuel, Pal Patel, Janki Kanakia, Alexander Pelaez, Neel Savalia, Tanya Khanna Abstract - Emojis have become an integral part of modern digital communication. Despite their widespread use, most sentiment analysis methods and models disregard emojis during preprocessing, leading to the loss of vital emotional cues. This paper introduces a curated dataset of sentence pairs, with and without emojis, each annotated across three sentiment categories, to assess the impact of emoji inclusion on sentiment classification. We evaluate emoji-inclusive and emoji-exclusive strategies against our human-determined gold standard, using a range of approaches, including the traditional lexicon-dictionary based methods, and also artificial intelligence (AI) methods including pre-trained machine learning (ML) based classifiers, and large language models (LLMs). Results show that retaining emojis significantly enhances the performance of all the LLMs we tested, with models such as Qwen, Deepseek, Bert and Mistral achieving accuracy improvements of over 25%, over an emoji-exclusive strategy. These findings highlight that emojis carry meaningful semantic and affective signals. We emphasize the limitations of current approaches to emoji handling, where emojis are often ignored or treated as irrelevant noise. Instead, we advocate for more thoughtful methods that recognize emojis as meaningful components of communication and incorporate them as valuable sources of information.
Authors - Diego Ricardo Salazar-Armijos, Hector Mauricio Revelo-Herrera, Holger Alfredo Zapata-Mayorga, Paul Diaz-Zuniga, Aida Noemy Bedon-Bedon, Nelson Fernando Vinueza-Escobar Abstract - This research, conducted within the framework of the project "Dropout in Higher Education – Early Warning Model with Emerging Technologies at the University of the Armed Forces ESPE", analyzed the factors influencing the dropout of Information Technology students at the Santo Domingo campus between 2017 and 2023. Socioeconomic and academic variables were considered, based on enrollment data in accordance with the regulations of higher education in Ecuador. Logistic regression and decision tree algorithms were applied due to their classification capabilities and statistical relevance. Additionally, an ANOVA-based comparison was performed. The study concluded that academic performance is the main factor associated with student dropout.
