Purpose:**Background Hospital fire emergencies require fast, coordinatedstaff responses to protect vulnerable patients. Traditional training often lacksrealism and engagement, limiting retention and preparedness. Immersive VirtualReality (VR) offers a promising solution by enabling lifelike, repeatable trainingin a safe environment. This study evaluates a VR-based fire safety trainingprogram developed for a Jordanian hospital.Methods: A serious VR game was custom-designed to replicate hospital wards,fire protocols (R.A.C.E., P.A.S.S.), and emergency conditions. Developed incollaboration with clinical staff and safety experts, the simulation emphasizedrealism, interactivity, and procedural fidelity. Content validity (CVI ≈ 0.94) andface realism were established through expert review and pilot testing. Forty hospitalstaff were randomly assigned to VR or traditional training. Knowledge,self-efficacy, and practical fire drill performance were assessed pre-training, posttraining,and at 4-week follow-up. Post-training interviews provided qualitativeinsights.Results: The VR simulation provided a highly interactive, immersive environmentthat closely mirrored the actual hospital setting, offering participants alifelike emergency experience. The VR group demonstrated significantly greaterknowledge gains (mean +3.3 vs. +1.3; p ¡ 0.001) and better retention (8.1 vs.16.2; p ¡ 0.001). Self-efficacy improved more in the VR group (+1.3 vs. +0.5; p =0.004). In drills, 90% of VR participants completed all required actions correctly,vs. 65% of controls. VR-trained staff responded 35 seconds faster on average.Participants highlighted the realism and contextual relevance as key learningadvantages.Conclusion: Immersive VR training significantly enhances hospital staff’s firesafety knowledge, confidence, and real-world emergency performance.
Acceptance sampling techniques play a pivotal role in industries by determining whether to accept or reject lots through the inspection of samples. To mitigate the risk of defective outgoing products and minimize production costs, acceptance sampling doesn't guarantee defect-free items. Instead, it involves scrutinizing a sample from a batch to make decisions regarding the overall lot quality. Most acceptance sampling plans are traditionally designed without an economic basis to meet both producer and consumer quality and risk requirements. This study focuses on constructing an economic model of the group chain sampling plan (GChSP) for minimizing the producer's total cost, encompassing inspection and failure costs. The study explores various lifetime distributions such as inverse Rayleigh, Log-logistic, and Generalized Pareto distributions. The research unfolds in four stages: first, identifying design parameters; second, developing procedures to minimize total costs; third, obtaining the operating characteristic (OC) function using lifetime distributions; and lastly, measuring performance using numerical data. The minimized total cost is calculated for different distributions and design parameters, including the minimum number of groups and the acceptance number. Results indicate that the minimized total cost tends to increase with decreasing consumer risk and increasing termination time and pre-specified testing time.
The current condition of churches in Surakarta City does not yet consider facilities that can accommodate the presence of people with disabilities in churches. This study aims to evaluate the accessibility level of church facilities and provide design proposals for accessible facilities for church congregants, including people with disabilities. The objects of this study include St. Antonius Purbayan Catholic Church, Margoyudan Christian Church of Java, and Bethel Indonesia Keluarga Allah Church. The accessible congregation approach was used to audit the accessibility level of the churches. Priority church facilities were then selected as objects for improvement proposals based on the accessibility index scores. Following this, church facility designs were created based on the Regulation of the Minister of Public Works and Public Housing of the Republic of Indonesia No. 14/PRT/M/2017. The results of the accessibility index assessment using the accessible congregation approach showed that the three churches in Surakarta City were categorized as less accessible. The proposed facility improvements for the three churches include providing designated parking spaces, improving ramps, adding components to toilets, lifts, lactation rooms, signs, and evacuation facilities.
Rice is a strategic commodity in supporting national food security. However, its productivity remains hindered by manual growth monitoring processes, climate change challenges, and limited human resources. This final project develops a seedling detection and counting system using the YOLO (You Only Look Once) algorithm, with aerial imagery input acquired from UAV (Unmanned Aerial Vehicle), presented through an interactive web-based dashboard. The dataset is enhanced with MIRV (mirror vertical) and MIRH (mirror horizontal) augmentation techniques to improve training data diversity. All experiments were conducted on three models: YOLO11n, YOLOv10n, and YO-LOv8n. Evaluation shows that the YOLO11n configuration using AdamW and a learning rate of 0.01 achieves mAP@50 of 0.592 and precision of 0.852. The system supports data-driven agronomic decision-making to anticipate crop failure risks, thus assisting large-scale rice field owners in monitoring seedling effectively and efficiently.
The increase in annual demand for sugar products in Indonesia is facing challenges, particularly in terms of sugarcane supply, the raw material for the sugar agro-industry. Sustainability assessment has been widely conducted using various approaches; however, it has not accounted uncertainty of future conditions in the analyses. This study aims to develop a model for assessing the sustainability of sugarcane supply using a Multiaspect Sustainability Analysis (MSA) approach. It involves analyzing the situation and conditions of raw material supply, determining the key aspects and factors for the sustainability assessment model, and implementing the model to assess the sustainability of sugarcane supply. The model included three primary aspects (economic, social, and environmental) comprising 22 factors. Twoyear data from a West Javan sugar mill, along with potential future conditions, were used for the analysis. The findings show that the sugarcane supply sustainability has improved from being "Moderate sustainable" to "Sustainable". The economic aspect is suggested as the priority for improvement, with five sensitive leverage factors. This study effectively integrated both current and possible future conditions into the analysis. The simulation resulted in Scenario 2 showing the best improvement. These findings serve as a basis for developing strategic plans to improve the sustainability.
The supply level is a stock management policy in the Republic of Korea Army that expresses inventory levels in terms of days. The purpose of operating the supply level system is to ensure continuous, cost-effective, and efficient supply operations. However, the current supply level determination lacks a clear basis for calculation. To address this issue, previous studies have proposed various methods for determining supply levels, such as simulations and meta-modeling. However, these studies fail to account for the dynamics of demand and lead time, which can fluctuate over time during wartime. This study aims to overcome these limitations by applying Rolling-Horizon optimization in system dynamics. To achieve this, we construct a supply chain model for the Army and incorporate real-world supply operation data from the Republic of Korea Army into the model. Through the application of system dynamics-based Rolling-Horizon optimization, we determine the optimal supply level while considering time-varying demand and lead time. Furthermore, this research not only establishes a theoretical framework for supply level determination but also provides practical insights for military logistics, enabling supply officers to adapt inventory management strategies in response to uncertain and evolving wartime conditions.
Internet of Things (IoT) technology is crucial for advancing sustainable development in industrial production. This study employs bibliometric methods, utilizing tools such as CiteSpace, VOSviewer, and Bibliometrix, to conduct a systematic analysis of 1,047 relevant publications from the Web of Science Core Collection between 2012 and 2025. The aim is to map the knowledge structure, research theme evolution, and future development pathways within this field. Findings indicate the field's development unfolds in two distinct phases: an exploratory period (2012-2018) and a developmental phase (2019-2025). Research focus has shifted from early-stage energy efficiency improvements and environmental monitoring toward intelligent manufacturing systems and their integration with cutting-edge technologies such as artificial intelligence, big data, and digital twins. Core research themes encompass the Industrial Internet of Things (IIoT), blockchain technology, and industrial environmental and lifecycle management. Keyword co-occurrence analysis identified three major research clusters: 1) Technology-Support Cluster featuring IIoT, block-chain, 5G, and cloud computing; 2) Value Strategy Cluster focusing on life cycle assessment, circular economy, and supply chain management; 3) New Technology Intervention Cluster involving artificial intelligence and Industry 5.0. Furthermore, citation analysis reveals that current research frontiers concentrate on implementing circular economy strategies, yet face multiple challenges including data interoperability, high system integration costs, and cybersecurity. This study employs systematic bibliometric analysis for the first time to clearly delineate the knowledge landscape of IoT-driven industrial sustainability. It identifies two evolutionary stages in this field-from exploration to development-and highlights research clusters centered on industrial IoT, blockchain, and lifecycle management. The findings provide scholars, industry practitioners, and policymakers with an objective research panorama and development roadmap, pointing to key future research directions such as technology convergence, data interoperability, and economic viability. Subsequent research should focus on addressing these challenges and exploring synergies between IoT, artificial intelligence, and sustainable development practices to foster more resilient industrial growth.
This study examined how switch type (Clicky, Tactile, Linear) and keycap height (Low, Middle, High) affect mechanical-keyboard usability across performance (typing speed, accuracy), satisfaction (overall, ease of use, tactile feedback, pleasure, preference), discomfort (overall, finger, wrist), and noise. Nine switch-keycap combinations were tested. Switch type significantly influenced typing speed, tactile feedback, and noise; Clicky switches yielded faster typing and stronger tactile feedback but the highest noise. Keycap height significantly affected satisfaction and discomfort; Low keycaps increased satisfaction, whereas High keycaps increased discomfort. These findings inform mechanical-keyboard design to enhance usability.
The increasing number of older adults in Malaysia motivates researchers to develop the Multifunctional Stepladder, a chair-like ladder that adopts the flexibility and independence concept. On the other hand, the number of older adults in Indonesia is increasing, as is the number of nursing homes . Unfortunately, those nursing homes are facing inadequate facilities problem. These inadequate facilities lead to health and safety problems for older adults who stay in nursing homes. Thus, this research aimed to develop the Smart Multifunctional Stepladder as an improvement of the previously developed Multifunctional Stepladder for nursing home usage. The Smart Multifunctional Stepladder was developed by integrating it with a monitoring application. The methods used are a combination of Design Thinking and Ergonomic Function Deployment (EFD). The monitoring application development process is detailed in this paper. Three nursing homes participated in this study. The in-depth interview and questionnaire survey were executed. The results are the Smart Multifunctional Stepladder integrated with older adult monitoring application called SIMOLAN. SIMOLAN is used to record the health and safety conditions of older adults who use the Smart Multifunctional Stepladder. The Multifunctional Stepladder design is also improved and equipped with an emergency button.
Stock investors seek to minimize risk and continually evaluate whether their target companies are suitable. To achieve this, it is crucial to understand the trends in corporate valuation. This study aims to gain valuable insights into the evaluation of portfolio companies through a factor analysis of stock price fluctuations. In this study, text data were used to analyze the factors behind stock price fluctuations. To model the data, it was necessary to quantify the text using methods such as bag-of-words. This made the specific information vague. Therefore, it was impossible to examine the specific factors that caused stock price fluctuations at any given time. For example, financial statements (e.g., investor relations materials) were provided as textual information to disclose information about how companies perform. As these contexts contain various content, each had a different influence on stock price fluctuations. However, if the data were used in model learning, differences in content might not be represented and might be considered to have the same effect as financial data. We focus on this issue of past studies and propose a new method to analyze the detailed information of the stock price for each time.. We first constructed a model using information other than textual data. Second, we focused on the model residuals to clarify the textual information to be focused on. The proposed model was based on a time-varying coefficient model that used numerical data such as economic trend indicators. In addition, we validated the proposed model by analyzing real-world data.
In the post-COVID times, supply chain resilience is receiving significant attention in academic research. Most of literature emphasizes enhancing supply chain resilience rather than designing a resilient supply chain. The aim of this paper is to design a resilient Supply Chain Network (SCN) while maximizing profits. A multi-objective SCN design model is introduced maximizing node dispersion as well as the flow complexity, serving as dual objectives to enhance resilience in the supply chain network. Weighted sum of objectives optimization method is adopted for solving the multi-objective model. The design model yields SCN structures with variant SC densities and flow complexities relevant to the set weight for each objective. Selection of weights depends on whether the severity of anticipated disruption would be more vulnerable to the geographic proximity of facilities and/or the number of transportation links between facilities. The SCN with maximum SC profit is achieved in comparison with the profit loss in case of disruption.
This study examines the impact of gamification elements, task complexity, and individual differences on cognitive responses and performance during virtual assembly training. The interaction between cognitive and emotional responses and gamification during virtual assembly training remains underexplored. Eleven male participants, with a mean age of 20 +/- 1.35 years, completed engine assembly tasks using ARCarEngine, a custom-built assembly simulator, in a 2x2x2 factorial design that varied the presence of points, badges, and task complexity. Performance was assessed using assembly time, assembly score, response time, and error rate. Results indicated that higher task complexity was associated with elevated cognitive load and lower cognitive engagement, yet was also associated with shorter assembly times, likely reflecting task familiarity or learning effects. The combination of points and badges was linked to improved both speed and accuracy, whereas badges alone were associated with lower performance. Cognitive load was negatively associated with performance; however, higher cognitive and emotional engagement unexpectedly correlated with slower response times and more errors. Rather than a decline in interest, this likely reflects a trade-off where high engagement manifests as cautious behavior, distinct from the efficiency gains achieved through repetitive practice. Individual differences, particularly game preferences and personality traits, were associated with cognitive-emotional responses and performance. These findings highlight the value of personalized gamification approaches and suggest that incorporating task complexity and adaptive task sequencing may enhance the effectiveness of virtual assembly training.
The article presents an analysis of dispersion control charts commonly used in process monitoring, namely R,S and S_p charts. These charts are utilized to track the variation (sigma) in non-normal distributions, which is frequently encountered in real-world scenarios. The research also focuses on situations where the process's in-control dispersion is unknown and control limits are estimated based on preliminary Phase I data. Moreover, the study investigates the performance of the proposed dispersion monitoring scheme by comparing it with existing methods, employing the probability of signal as a performance metric. The efficacy of the proposed approach is further demonstrated through the examination of two medical datasets, showcasing its practical application. The adjusted constants proposed in this work are more generalized and result in the correct decision regarding the actual state of control. The IC robustness comes at the cost of a deterioration (increase) in the unconditional OOCARL, but this effect is negligible for large values of m or significant changes in variability. When applying these limits, prior knowledge of the underlying distribution is crucial, which poses a practical limitation on the proposed strategy.
The rich diversity of Indonesia in geography, culture, ethnicity, and race can result in stereotypes, negatively affecting credit decision-making in the banking sector. Such stereotyping can result in biased credit evaluations for loan proposals, restricting access to financial services for particular groups, even when their applications are valid. This initial study investigated the impact of stereotype bias on credit decisions in the Indonesian banking system via experiments involving groups of Indonesian bankers, each comprising two bankers of equal rank from different divisions (the business and risk unit). Both bankers were authorized to make credit decisions for small and medium enterprises. During group discussions, they reviewed modified loan proposals that contained stereotypes, decided on loan approval, and rated their confidence in the loan's potential success. The findings revealed that stereotype bias affected decisions in two of the three experiments. In one instance, an objectively approved loan was rejected due to the applicant's ethnic background. Additionally, the results suggested that social conformity within the groups above masked the impact of stereotype bias.
The whale optimization algorithm (WOA) is one of the most powerful swarm-based, nature-inspired metaheuristic algorithms, developed by mimicking the bubble-net hunting maneuver technique of humpback whales to solve complex optimization problems. The WOA has been widely adopted in various real-world optimization fields due to its simple structure, minimal parameter requirements, and fast convergence rate. This article proposes a novel modified version of the original WOA, named the random-flight whale optimization algorithm (RFWOA), which incorporates three types of random-flight mechanisms, i.e., uniform distribution, Rayleigh flights, and L & eacute;vy flights. These mechanisms are employed to generate elite solutions and enhance the search performance. The proposed RFWOA helps obtaining a better tradeoff between the exploration and exploitation properties of the original WOA. To evaluate its effectiveness, the RFWOA is tested against ten benchmark functions and applied to solve ten multi-depot multi-vehicle routing problems (MDMVRP) for global optimization. The results obtained by RFWOA are compared with those obtained by other well-known nature-inspired algorithms, including the original WOA, particle swarm optimization (PSO), and genetic algorithm (GA). The experimental results demonstrated that the proposed RFWOA significantly outperformed the competing algorithms in solving both the benchmark functions and the MDMVRP optimization tasks. These results confirm the superiority of the RFWOA over the original WOA, PSO, and GA.
This study investigates governance and operational challenges within Indonesia's halal ecosystem, focusing on sustainable value creation for halal-certified Micro, Small, and Medium Enterprises (MSMEs). Using a qualitative case study approach, the research involved 15 semi-structured interviews and field observations with MSME owners, certification bodies, local government officials, and religious leaders. Thematic analysis revealed three core findings: (1) institutional fragmentation and role ambiguity, (2) diverse interpretations of halal and tayyib principles, and (3) limited digital infrastructure for traceability and compliance. The study recommends an integrated halal governance platform to bridge regulatory, cultural, and technological gaps. Practical policy suggestions include localized certification support, digital capacity building, and inclusive stakeholder forums. These insight Halal ecosystem; MSMEs; governance; qualitative case study; Indonesia; sustainability; digital infrastructure; policy integrations contribute to halal value chain literature in emerging economies and propose pathways to strengthen sustainable halal development in Indonesia.
