Authors - Febrian Nasrullah, Abdul Mukti Soma Abstract - This study examines the impact of risk perception, financial selfefficacy, and financial literacy on the actual usage behavior and intention to use "Buy Now Pay Later" (BNPL) services among Generation Z in Indonesia. Adopting a quantitative approach, the study surveyed 385 Gen Z individuals who use BNPL services such as Kredivo or Akulaku. Data were analyzed using SEM-PLS with the aid of SmartPLS 4. The results indicate that financial selfefficacy and financial literacy contribute to actual usage behavior and intention, whereas risk perception has a negative impact on both. Furthermore, intention contributes to actual usage behavior and mediates the effects of the other variables. These findings indicate that financial management skills, risk perception, and individual confidence in financial capability play a pivotal role in shaping BNPL usage behavior among Generation Z in Indonesia.
Authors - Byron Albuja-Sanchez, Miguel Angel Lema Carrera, Luis Antonio Ortiz Parra Abstract - This study focuses on evaluating the capabilities of different large language models chatbots in the task of designing a PID controller for a third-order transfer function of a real-world vehicle’s cruise control system. Chatbots received a detailed prompt containing the system’s transfer function and the design’s goals in the form of overshoot and settling time constraints. Chatbots only received simulation-response information as feedback during the tuning process to test their predisposition to fix the errors without being specifically asked to do so. Results showed that chatbots have a good level of knowledge regarding basic control theory and basic tuning methods for PID controllers. Preferred tuning methods involved pole placement with dominant second order dynamics, Ziegler-Nichols and heuristic methodologies. Simulation results compared the controllers designed by chatbots with a PID tuned with ant lion optimizer algorithm, none of the evaluated chatbots outperformed the optimization-based benchmark controller. However, Gemini 3 Flash designed a controller which performance was close to the ant lion optimizer results. Chatbots’ underperformance was attributed to the following facts: no expert feedback was given to them to fix the observed flaws in the proposed designs, no specific methodologies were asked to be used in order to improve the results, and no specific instructions to redesign the controllers were given to chatbots in order to test their disposition to fix their errors. Results suggest that LLMs can assist in preliminary controller design tasks, although their effectiveness remains limited without expert-guided iteration and explicit optimization-oriented prompting.
Authors - Aman Kumar, Kathan Nitin Patel, Aviral Sharma Abstract - Diabetes mellitus is perceived as a disease that significantly impacts a nation’s social, human, and financial expenditures. Concurrently, it is imperative to lower the prevalence rate and address the misunderstandings surrounding diabetes. An improved model that employs machine learning techniques to identify the behavior of diabetes in an individual. We have employed the parameters observed in the typical lifestyle, as well as the individual's emotional states and physical activities in the elderly age group. For a variety of test parameters, the proposed model implements a network classifier. It has been noted that this methodology yields effective results in the diagnosis of diabetes mellitus when the appropriate dataset is provided. The dataset utilized in this reseacrh study is the Indian PIMA dataset from the UCI Machine learning database. The detection of diabetes is contingent upon the presence of eight features in this dataset. The proposed Machine learning model has been implemented using a multilayer neural network that has been trained on backpropagation and feed-forward network simulation.
Authors - Nallappagari Venkatarami Reddy Abstract - Business Process Automation (BPA) has become an essential requirement of modern enterprise environments owing to the need for operational efficiency, process agility, and smart decision making. Traditional methods of BPA mostly depend on rules-based approaches which are not able to adapt to the needs of a dynamic environment as these methods do not incorporate the element of adaptive intelligence and autonomous orchestration. To overcome such limitations, this study aims to develop an intelligent orchestration framework named IWOF for Autonomous Business Process Automation. In the proposed solution, PIEL, AWOE, DRAM, and PDOU have been used. Also, two new algorithms named AWIO and PARDO are developed for optimizing the process sequencing, resource assignment, and decision support tasks respectively. Experimental evaluation was performed by applying the proposed framework to datasets consisting of processes related to employee onboarding, payroll management, procurement approvals, recruitment workflow, and finance transactions. With the help of the IWOF model, Process Automation Accuracy, Workflow Completion Rate, Resource Utilization Efficiency, and Autonomous Business Process Automation Score (ABPAS) were measured to be 98.7%, 98.2%, 97.1%, and 98.9%, respectively, surpassing all other available models such as OSMAS and BPA-SME.
Authors - Abeer Tag, Tahani Almarri, Abir Sidilemine, Rowaa Khaled, Loay Ismail Abstract - Medication non-adherence among elderly and chronically ill patients remains a critical global health challenge, leading to severe complications, hospital readmissions, and reduced quality of life. This paper presents REMEDI, a smart mobile medication dispensing robot that integrates autonomous indoor navigation, biometric patient authentication, automated pill dispensing, pill verification, and real-time adherence monitoring into a unified platform. The system combines a TurtleBot3 Waffle Pi mobile base with a custom-designed three-cylinder dispensing mechanism controlled using Raspberry Pi 5 and Arduino Nano. Patient verification is performed using facial recognition with MobileFaceNet embeddings and liveness detection, achieving an overall verification accuracy of 83.3% and zero false accepts during experimental testing. Autonomous navigation is implemented using LiDAR-based SLAM and A* path planning, enabling map-based movement between predefined indoor patient locations. Post-dispensing verification uses a custom-trained YOLOv11 object detection model integrated with OpenCV for pill detection and counting. A companion Android application allows caregivers to enroll patients, schedule medications, and monitor adherence in real time. Experimental results show successful integrated operation, including dispensing delays below 5 seconds, navigation success rates of 84–92%, 95% dispensing reliability, and functional multi-patient queue management. Although pill verification achieved only 69% real-world accuracy, the results demonstrate the feasibility of integrating mobility, secure authentication, dispensing, and monitoring in one user-centered prototype. REMEDI aims to bridge the gap between stationary home medication dispensers and large institutional delivery robots.
Authors - Simon Kloker, Alex Cedric Luyima, Matthew Bazanya Abstract - This paper presents WASHtsApp, a WhatsApp-based mHealth chatbot that supports clean water, sanitation, and hygiene (WASH) education in rural African settings. The chatbot uses Retrieval-Augmented Generation (RAG) to reduce out-of-context responses and improve answer relevance. Following a Design Science Research approach, we evaluated the artifact in two steps: expert content validation (four WASH experts) and community acceptance validation (n = 71). Expert ratings classified 86% of responses as perfect or sufficient, while community results showed high perceived usefulness, ease of use, and intention to use. The findings indicate that WhatsApp is a viable delivery channel for WASH education and that a constrained RAG setup can provide useful localized guidance. We also discuss privacy, safety, and future improvements, including local-language support.
