Search Results (103)

This paper explores contemporary approaches to preserving and promoting cultural heritage by integrating game elements and advanced technologies, such as Virtual Reality (VR) and Augmented Reality (AR). In an era increasingly shaped by digital innovation, preserving cultural heritage demands new strategies to sustain engagement with historical narratives and artifacts. Emerging technologies like VR and AR offer immersive, interactive experiences that appeal to modern audiences, especially younger generations accustomed to digital environments (Bekele and Champion). Gamification—the use of game design principles in non-game contexts—has gained significant traction in education and cultural heritage, providing new methods for increasing user engagement and retention (Werbach and Hunter). By incorporating gamified features, heritage can be made more accessible, fostering emotional connections and deeper understanding (Huotari and Hamari; Zichermann and Cunningham). This aligns with the shift toward interactive digital storytelling as a tool to transform static heritage presentations into dynamic, participatory experiences (Champion and Rahaman). Central to this research is the conceptualization and development of a mobile application leveraging VR and AR to enhance user engagement and education around cultural heritage. Drawing on the principles of self-determination theory (Deci and Ryan) and empirical findings on gamified learning (Landers and Landers), the application combines educational content with interactive elements, creating an immersive learning environment. By addressing both content accessibility and interactive immersion, this application bridges the gap between traditional heritage preservation and the expectations of a digitally native audience. The recent literature underscores the potential of VR and AR in cultural preservation, emphasizing their ability to transcend physical boundaries, simulate historical environments, and promote active participation (Milgram and Kishino, Addison; Azuma). As virtual environments evolve, platforms like the metaverse expand possibilities for experiencing cultural heritage in spaces free of geographical limitations (Cipresso et al.; Radianti et al.). Such advancements have already demonstrated significant educational and experiential benefits (Wu et al.; Akçayır and Akçayır). This study employs both quantitative and qualitative methods to examine the target group’s attitudes toward gamified technologies for cultural heritage preservation. The initial results indicate substantial interest and willingness among users to engage with applications employing VR and AR. This aligns with findings in the literature that suggest immersive experiences can enhance learning outcomes and foster long-term engagement (Merchant et al.; Speicher et al.). The project has garnered significant recognition, receiving the Rector’s Award for the best scientific paper in the technical field at the University of Zagreb and earning bronze medals at the ARCA Innovation Fair and the INOVA Fair. These accolades underscore the project’s innovative approach and its potential for real-world application. By presenting a robust framework for integrating gamification and immersive technologies into cultural heritage preservation, this paper contributes to the growing discourse on utilizing advanced digital tools to ensure the sustainability and relevance of cultural heritage for future generations. Full article

Maritime transport plays a critical role in global logistics. Compared to road transport, the pace of research and development is much slower for maritime transport. It faces many major challenges, such as busy ports, long journeys, significant accidents, and greenhouse gas emissions. The problems have been exacerbated by recent regional conflicts and increasing international shipping demands. Maritime Autonomous Surface Ships (MASSs) are widely regarded as a promising solution to addressing maritime transport problems with improved safety and efficiency. With advanced sensing and path-planning technologies, MASSs can autonomously understand environments and navigate without human intervention. However, the complex traffic and water conditions and the corner cases are large barriers in the way of MASSs being practically deployed. In this paper, to address the above issues, we investigated the application of Large Language Models (LLMs), which have demonstrated strong generalization abilities. Given the substantial computational demands of LLMs, we propose a framework for LLM-assisted navigation in connected MASSs. In this framework, LLMs are deployed onshore or in remote clouds, to facilitate navigation and provide guidance services for MASSs. Additionally, certain large oceangoing vessels can deploy LLMs locally, to obtain real-time navigation recommendations. To the best of our knowledge, this is the first attempt to apply LLMs to assist with ship navigation. Specifically, MASSs transmit assistance requests to LLMs, which then process these requests and return assistance guidance. A crucial aspect, which has not been investigated in the literature, of this safety-critical LLM-assisted guidance system is the knowledge and safety performance of the LLMs, in regard to ship handling, navigation rules, and skills. To assess LLMs’ knowledge of navigation rules and their qualifications for navigation assistance systems, we designed and conducted navigation theory tests for LLMs, which consisted of more than 1500 multiple-choice questions. These questions were similar to the official theory exams that are used to award the Officer Of the Watch (OOW) certificate based on the Standards of Training, Certification, and Watchkeeping (STCW) for Seafarers. A wide range of LLMs were tested, which included commercial ones from OpenAI and Baidu and an open-source one called ChatGLM, from Tsinghua. Our experimental results indicated that among all the tested LLMs, only GPT-4o passed the tests, with an accuracy of 86%. This suggests that, while the current LLMs possess significant potential in regard to navigation and guidance systems for connected MASSs, further improvements are needed. Full article

The paper investigates and assesses the contract selection procedure used in Kuwait’s construction sector. The ideas and insights of engineers involved in significant Kuwaiti construction projects will be extensively considered. In the Kuwaiti building sector, various ways of choosing the contract form will be discussed. In order to gather pertinent data about the country’s primary construction projects, questionnaires will be used. This methodology ensures a first-hand account of the challenges and preferences within the industry. This data will be analyzed to determine the best ways to enhance the current system utilized for Kuwaiti building contracts for the choice of contract forms and payment terms that could benefit from the inclusion of measures to guarantee those types of task delivery systems and payment terms in the country’s future construction projects. This paper explores the typical contract types and payment mechanisms used in Kuwait, drawing on a thorough analysis of current literature, governmental regulations, and business practices. The Standard Forms of Contract, which offer fixed-priced payments, have become the most popular option. However, when used for extensive and technically challenging projects, this contract form’s simplicity presents difficulties. Additionally, the existing system encourages contract awards based on the lowest tender, which raises questions regarding appropriateness and proportionality. The study suggests an arsenal of criteria for improving the deal selection process, with an emphasis on diverse terms of payment inside the task delivery system, in order to allay these worries. It highlights the need for a more comprehensive approach for deal selection that takes project complexity, financial constraints, and long-term project interest into account. The proposed criteria additionally include adaptability to project complexity, ensuring flexibility for challenges in large projects. Financial considerations, aligning with budgetary requirements, are crucial. The emphasis is on long-term project success, and finding a balance between simplicity and adaptability in contracts is key. These comprehensive criteria aim to improve decision-making in selecting standard contract forms for construction projects, addressing challenges in large, technically demanding endeavors in Kuwait. This research contributes to the field by introducing a novel set of criteria for contract selection, tailored to the Kuwaiti construction context. The study’s originality lies in its approach to addressing the challenges posed by current practices and its focus on refining the system for future projects. Additionally, the study employs a rigorous questionnaire survey to extract firsthand insights from industry professionals, ensuring a robust and contextually relevant exploration of the contract selection landscape in Kuwait’s construction sector. Full article

When carrying out construction work, identifying the best contractor is a critical component of the project life cycle in the construction industry. The investor must use effective and efficient strategies to create a competitive bidding environment in public projects. The research presented in this paper was conducted to demonstrate the competitive nature of public procurements, where contractors compete to present the best bid and win the contract. To award the contract, the best offer must be selected. Based on different strategies and multi-criteria decision-making approaches this study proposes a method for identifying the most suitable strategy out of eight bidding strategies on four different lots, resulting in the most suitable one for landslide rehabilitation in the Brčko district. The results reveal the optimal approach to follow to minimize time and financial losses in the case of landslide rehabilitation during periods of market instability. Such research findings validate the efficiency of the bidding strategies-based decision-making support. The proposed method allows for compromise on both the completion date and the lowest bid made by the winning contractor. Full article

Despite being disproportionately impacted by health disparities, Black, Hispanic, Indigenous, and other underrepresented populations account for a significant minority of graduates in biomedical data science-related disciplines. Given their commitment to educating underrepresented students and trainees, minority serving institutions (MSIs) can play a significant role in enhancing diversity in the biomedical data science workforce. Little has been published about the reach, curricular breadth, and best practices for delivering these data science training programs. The purpose of this paper is to summarize six Research Centers in Minority Institutions (RCMIs) awarded funding from the National Institute of Minority Health Disparities (NIMHD) to develop new data science training programs. A cross-sectional survey was conducted to better understand the demographics of learners served, curricular topics covered, methods of instruction and assessment, challenges, and recommendations by program directors. Programs demonstrated overall success in reach and curricular diversity, serving a broad range of students and faculty, while also covering a broad range of topics. The main challenges highlighted were a lack of resources and infrastructure and teaching learners with varying levels of experience and knowledge. Further investments in MSIs are needed to sustain training efforts and develop pathways for diversifying the biomedical data science workforce. Full article

Measuring functional fitness (FF) to track the decline in physical abilities is important in order to maintain a healthy life in old age. This paper aims to develop an estimation model of FF variables, which represents strength, flexibility, and aerobic endurance, using easy-to-measure physical parameters for Korean older adults aged over 65 years old. The estimation models were developed using various machine learning techniques and were trained with the National Fitness Award datasets from 2015 to 2019 compiled by the Korea Sports Promotion Foundation. The machine-learning-based nonlinear regression models were employed to improve the performance of the previous linear regression models. To derive the optimal estimation model that showed the best estimation accuracy, we developed five different machine-learning-based estimation models and compares the estimation accuracy not only among the machine learning models, but also with the previous linear regression model. The coefficient of determination of the FF variables was used to compare the performance of each model; the mean absolute percentage error (MAPE) and standard error of estimation (SEE) were used to evaluate the model performance. The deep neural network (DNN) model presented the best performance among the regression models for the estimation of all of the FF variables. The coefficient of determination in the HGS test was 0.784, while those of the others were less than 0.5 meaning that the HGS of older adults can be reliably estimated using easy-to-measure independent variables. Full article

The design of existing machine-learning-based DoS detection systems in software-defined networking (SDN) suffers from two major problems. First, the proper time window for conducting network traffic analysis is unknown and has proven challenging to determine. Second, it is unable to detect unknown types of DoS saturation attacks. An unknown saturation attack is an attack that is not represented in the training data. In this paper, we evaluate three supervised classifiers for detecting a family of DDoS flooding attacks (UDP, TCP-SYN, IP-Spoofing, TCP-SARFU, and ICMP) and their combinations using different time windows. This work represents an extension of the runner-up best-paper award entitled ‘Detecting Saturation Attacks in SDN via Machine Learning’ published in the 2019 4th International Conference on Computing, Communications and Security (ICCCS). The results in this paper show that the trained supervised models fail in detecting unknown saturation attacks, and their overall detection performance decreases when the time window of the network traffic increases. Moreover, we investigate the performance of four semi-supervised classifiers in detecting unknown flooding attacks. The results indicate that semi-supervised classifiers outperform the supervised classifiers in the detection of unknown flooding attacks. Furthermore, to further increase the possibility of detecting the known and unknown flooding attacks, we propose an enhanced hybrid approach that combines two supervised and semi-supervised classifiers. The results demonstrate that the hybrid approach has outperformed individually supervised or semi-supervised classifiers in detecting the known and unknown flooding DoS attacks in SDN. Full article