Based on the demographic shift and the related challenges resulting from the growing number of elderly and persons with chronic diseases, the idea of smart home that supports its inhabitants in the daily life, gains importance. The purpose of this paper was to examine in a prototypic Ambient Assisted Living environment if users after interaction with different health-supporting applications intend to use such in the future. Two experimental studies exemplary show possible applications of home-integrated technology that can support, assist and accompany the target group in different contexts, and examine to what extent participants are willing to future use such sophisticated technology at home. The results show that people in general, but especially the old and chronically ill ones are quite fascinated of health-supporting ambient technology and the majority intends to use such ambient assistance in the future (study I). Moreover, serious games for healthcare are shown as a hedonic use of technology in smart homes that have a great potential to retain or improve the physical health, mobility and the overall well-being of the inhabitants (study II). The article provides two examples of ambient technology to leverage the demographic change and presents important user factors for facilitating high user acceptance.

When users interact with mobile platforms in an augmented reality environment, cognitive and emotional engagements change through different stimuli cues that respond to users’ behavioral intentions. The effects of those engagements through augmented reality, considering user-platform interactions, are unexplored. This study investigated a nuanced understanding of how stimuli cues in augmented reality affect sense of immersion and sense of presence, followed by an interaction-engagement-intention (I-E-I) model. A quantitative method was used to validate the proposed model. Based on an online survey with 886 responses, product fit, network quality, and artificial intelligence-driven recommendation influences were assessed for cognitive engagements. This study examined the importance of engaging satisfaction and trust as emotional engagements, influencing users’ continuance intention. The findings showed that sources of information, especially online reviews, positively affect the perception of subjective norms. Also, trust has a more significant influence on the continuance intention to use AR mobile platforms. Also, the results explored that the generation of users significantly impacts continuance intention. This could enhance the capabilities of information system designers, researchers, marketing professionals, and solution providers to attain sustainable user retention.

The current surge in the deployment of web applications underscores the need to consider users' individual preferences in order to enhance their experience. In response to this, an innovative approach is emerging that focuses on the detailed analysis of interaction data captured by web browsers. This data, which includes metrics such as the number of mouse clicks, keystrokes, and navigation patterns, offers insights into user behaviour and preferences. By leveraging this information, developers can achieve a higher degree of personalization in web applications, particularly in the context of interactive elements such as online games. This paper presents the WebTraceSense project, which aims to pioneer this approach by developing a framework that encompasses a backend and frontend, advanced visualization modules, a DevOps cycle, and the integration of AI and statistical methods. The backend of this framework will be responsible for securely collecting, storing, and processing vast amounts of interaction data from various websites. The frontend will provide a user-friendly interface that allows developers to easily access and utilize the platform’s capabilities. One of the key components of this framework is the visualization modules, which will enable developers to monitor, analyse, and interpret user interactions in real-time, facilitating more informed decisions about user interface design and functionality. Furthermore, the WebTraceSense framework incorporates a DevOps cycle to ensure continuous integration and delivery, thereby promoting agile development practices and enhancing the overall efficiency of the development process. Moreover, the integration of AI methods and statistical techniques will be a cornerstone of this framework. By applying machine learning algorithms and statistical analysis, the platform will not only personalize user experiences based on historical interaction data but also infer new user behaviours and predict future preferences. In order to validate the proposed components, a case study was conducted which demonstrated the usefulness of the WebTraceSense framework in the creation of visualizations based on an existing dataset.

In recent years, with the rapid development of computer vision technology and the popularization of intelligent hardware, as well as people’s increasing demand for intelligent products for human-computer interaction, visual grounding technology can help machines and humans identify and locate objects, thereby promoting human-computer interaction and intelligent manufacturing. At the same time, human-computer interaction is constantly evolving and improving, becoming increasingly intelligent, humane, and efficient. This paper proposes a new VG model and designs a language verification module that uses language information as the main information to increase the model’s interactivity. Additionally, we propose the combination of visual grounding and human-computer interaction, aiming to explore the research status and development trends of visual grounding and human-computer interaction technology, as well as their application in practical scenarios and optimization directions, to provide references and guidance for relevant researchers and promote the development and application of visual grounding and human-computer interaction technology.

Human-Computer Interaction (HCI) has evolved significantly with advances in Artificial Intelligence (AI) and Computer Vision (CV), paving the way for more intelligent and adaptive user interfaces. This paper explores the design and implementation of user interfaces that leverage AI and CV to create more intuitive, responsive, and personalized experiences. By integrating techniques such as facial recognition, gesture control, and eye tracking, interfaces can adapt in real-time to users’ actions and intentions, thus enhancing accessibility, efficiency, and engagement across various applications, from healthcare to gaming. The study reviews current advancements and challenges in using AI and CV for HCI, including data privacy, model interpretability, and computational efficiency. Experimental results demonstrate the potential of these technologies to improve interaction quality and user satisfaction. This work aims to contribute to the growing field of intelligent HCI design, offering insights into practical implementations and future research directions in AI-driven interface development.

A sustainable smart tourism ecosystem relies on building digital networks that link tourists to destinations. This study explores how web and immersive technologies, such as the VRRO platform, can improve tourism by offering interactive experiences to augment traditional travel. The study underscores the role of VRRO's design in enhancing user interaction and engagement, thereby encouraging the generation of user content and the development of a virtual tour network. An empirical analysis investigates user engagement, revealing that the platform is accessible to users regardless of their technological familiarity. It suggests a high likelihood of sustained use and recommendations. In conclusion, VRRO demonstrates how accessible and innovative technologies in tourism modernize travel experiences and significantly contribute to the evolution of the broader tourism ecosystem.

Understanding visual engagement in marketing is crucial for optimizing user experience and enhancing campaign effectiveness. This study introduces the Visual Interaction Analysis (VIA) methodology, utilizing advanced eye-tracking technology within immersive virtual reality (VR) environments to examine generational and gender differences in visual engagement after exposure to advertising materials. Using the Cognitive 3D platform for precise data capture, this research analyzed fixation patterns—total fixations, duration of fixations, and average duration per fixation—across Millennial and Generation Z cohorts. The analysis involved 44 participants, who were exposed to three posters in controlled VR settings. Results revealed significant generational differences: Generation Z demonstrated more focused attention, with higher fixation counts and longer total fixation durations compared to Millennials, suggesting that differences in media consumption and technological adaptation influence engagement strategies. However, both generations showed a similar depth of engagement, as indicated by the average duration per fixation. Gender analysis within Generation Z further revealed that female participants engaged for longer periods per fixation, indicating deeper processing of visual information. This methodology offers a robust framework for quantifying visual engagement, providing actionable insights that can help marketers tailor strategies and optimize campaign designs before launch.

In the rapidly evolving landscape of mobile applications, the demand for efficient and user-friendly platforms for services like motorcycle rentals is rising, particularly in urban areas. This paper presents the development of a Motorcycle Rent Application using Agile Methodology, tailored for the Android platform and developed with Flutter. Agile is iterative and adaptive approach, emphasizing continuous improvement and customer feedback, was crucial in managing the project's dynamic requirements. Flutter, with its single code base for multiple platforms and hot reload feature, significantly expedited development and enhanced the application's responsiveness and user interface. The project successfully implemented features such as user registration, motorcycle listing, rental booking, and payment integration, resulting in a seamless user experience. This study demonstrates the effectiveness of combining Agile Methodology with Flutter to create high-quality, responsive mobile applications that meet modern user expectations.

This study explores the impact of multi-user interaction, group work, and simulations within virtual worlds on language learning and the development of intergroup empathy. Two distinct research projects were conducted, involving a total of 241 participants aged 19-45. The language learning study engaged 116 participants in diverse interactive experiences, while the intercultural study had 125 participants collaborating in multicultural groups and participating in perspective-taking simulations. Both studies employed qualitative data collection methods, including surveys, interviews, and observations. The findings suggest that the combination of networking strategies, collaborative learning, and simulations within virtual worlds contributes to improvements in learners' language proficiency, confidence, and empathy towards diverse social groups. Participants reported increased motivation and engagement, attributed to the immersive and interactive nature of the virtual environments. The studies highlight the importance of collaboration and reflection in facilitating language acquisition and intercultural understanding. Technical challenges were identified as potential barriers to implementation. The results demonstrate the potential of virtual worlds to enhance language education and foster empathy in diverse societies, offering valuable insights for educators and researchers. However, the findings may be limited by the specific contexts and sample sizes of the studies, warranting further research to explore the generalizability and long-term impact of virtual world interventions. not exaggerate the main conclusions.

Design plays a critical role and creates a positive user experience, establishing a solid brand identity and staying competitive in the digital world. This research investigates the impact of user data collecting and analysis in improving user experience. The Data-driven User interface design can create an optimized and relevant UI with individual preferences, which leads to increasing user engagement and usability. The proposal explores methodologies to implement data collection and user research for tailoring digital products.

The obligation to keep a distance from other people due to the pandemic has changed the pattern of human life, especially in shopping for their primary needs, namely food. The food marketplace's presence presents new hope in maintaining health and food availability without having to crowd with other people while shopping. The main problem that is often a concern for shopping online is the security of transactions and the ease of using this food marketplace application. This research is the intensity of using the Food Marketplace in terms of interest, transaction security, and ease of use of this application. Researchers analyzed the relationship between variables with the Structural equation model. Respondents who became this sample were 300 application users spread across various major cities in Indonesia. This study's results provide a view that the intensity of the use of the food marketplace has increased significantly during the new normal life. This paper's main value lies in providing a better understanding of consumer behavior in the Indonesian food marketplace that has not been previously researched. By investigating the factors that influence the intensity of use of the food marketplace, it is hoped that it will provide a basis for further research

Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (aged 18-35) with high and low gaming skills participated in this study. Participants completed the TMT-VR using three interaction modes: eye-tracking, head movement, and controller. Performance metrics included task completion time and accuracy. User experience, usability, and acceptability of TMT-VR were also examined. Results showed that both eye-tracking and head movement modes significantly outperformed the controller in terms of task completion time and accuracy. No significant differences were found between eye-tracking and head movement modes. Gaming skills did not significantly influence task performance using any interaction mode. Participants reported high usability, acceptability, and user experience for the TMT-VR. Eye-tracking facilitated higher accuracy, while head movement higher speed. The TMT-VR offers high usability and user experience and is well accepted by the participants. The findings suggest that VR-based assessments can effectively measure cognitive performance without being influenced by prior gaming skills, indicating potential applicability for diverse populations.

With the recent developments in augmented reality (AR) technologies comes an increased interest in the use of smart glasses for hands-on training. Whether this interest is turned into market success or not depends at the least on whether the interaction with smart AR glasses satisfies users, an aspect of AR use that so far has received little attention. With this contribution, we seek to change this. The objective of the article, therefore, is to investigate user satisfaction in AR applied to three cases of practical use. User satisfaction of AR can be broken down into satisfaction with the interaction and satisfaction with the delivery device. A total of 142 participants from three different industrial sectors contributed to this study, namely, aeronautics, medicine, and astronautics. In our analysis, we investigated the influence of different factors, such as age, gender, level of education, level of Internet knowledge, and the roles of the participants in the different sectors. Even though users were not familiar with the smart glasses, results show that general computer knowledge has a positive effect on user satisfaction. Further analysis using two-factor interactions shows that there is no significant interaction between the different factors and user satisfaction. The results of the study affirm that the questionnaires developed for user satisfaction of smart glasses and the AR application performed well, but leave room for improvement.

This study provides an evaluation of a web-based platform for game management and sales, developed using PHP, MySQL, and SASS. The development process utilized the SCRUM methodology, concentrating on essential modules such as user management, game catalog, payment processing, and technical support. The platform's performance was assessed using Google's Lighthouse tool, which evaluates four critical areas: performance, accessibility, best practices, and SEO. The results demonstrated exceptional performance with a score of 99/100 and perfect adherence to best practices with a score of 100/100, reflecting high efficiency and compliance with web development standards. Accessibility received a score of 83/100, indicating potential areas for improvement, while SEO achieved a score of 91/100, suggesting effective search engine optimization. The findings reveal that the platform exhibits high levels of optimization and operational efficiency. However, there remains an opportunity to enhance accessibility features. This study highlights the significance of rigorous web development practices and optimization efforts in achieving superior performance and user experience in web-based systems.

With the view toward enlivening the Chinese automobile market, this work seeks to develop a framework to reveal the tastes of Chinese car users from online user-generated content (UGC) and identify future product improvement orientation. We construct an importance-satisfaction gap analysis (ISGA) model based on sentiment analysis technique. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users' satisfaction with different attributes based on the rules, and weigh the importance of attributes using TF-IDF method, which lay the statistic foundation for our ISGA model. We also take time into account and further analyze the ISGA model, aiming to provide new insight into the changing tastes of Chinese car users so as to suggest correct design positioning and resource allocation. The final evaluation results show that the method proposed in this paper, which integrates Chinese word-boundary-identified algorithm and sentiment lexicon matching rules, is well supported.

As we approach the 135th anniversary of the automobile, two industry trends, automation and digitalization, are rapidly revolutionizing the thus far, relatively unchanged automotive user experience. This paper describes the development of the Valeo MyMobius user interface concept. The goal of this project was to explore how to achieve an intuitive driving experience as the automotive industry undergoes transition from primarily analog to primarily digital interfaces and from physical buttons to multimodal interactions. To achieve the perception of intuitiveness, designers must understand their users, find and reduce physical and cognitive friction points, and bridge knowledge gaps with interface designs that facilitate discovery and learnability. The Valeo MyMobius concept featured steering wheel touch displays that supported quick, frequent menu selections using swiping gestures (common in smartphone interactions) and reinforcing icons (to facilitate learnability). Learning algorithms personalized the experience by tailoring suggestions, while more complex interactions were handled with a conversational voice assistant, which also served as a driving copilot, capable of contextually suggesting when Advanced Driving Assistance System (ADAS) features such as ACC could be utilized. The visual design aesthetic embodied Kenya Hara’s design philosophy of “Emptiness,” reducing visual clutter and creating spaces that are ready to receive inspiration and information. Altogether, the Valeo MyMobius concept demonstrated an attainable future where the perception of intuitiveness can be achieved with today’s technologies.

We introduce Get Free Copy (getfreecopy.com), a web-based platform designed to streamline the search for biomedical literature across major repositories like arXiv, bioRxiv, medRxiv, and PubMed Central (PMC). Addressing challenges posed by paywalls and fragmented databases, it offers a unified interface for efficient retrieval of free, legitimate copies of biomedical literature. The platform's implementation involves a Node.js backend and dynamic front-end display, enhancing accessibility and research efficiency. As an open-source project, Get Free Copy represents a significant contribution to the open-access movement, inviting global researcher collaboration for further development.

This research discusses the business model challenges faced by cag aggregator platform models in developing countries. Technology-based platforms have been disrupting traditional businesses and rewriting the rules of the game. Ola cabs has been instrumental in shaping the Indian platform business landscape in a big way. Using case methodology the author analyses the situation and business model to present the salient features of Ola’s model. However, this paper also puts the spotlight on the inherent challenges in this model and warns the need for continuous business model innovation and service quality.

Diagnosing melanocytic lesions is among the most challenging problems in the practice of pathology. The difficulty of physically masking melanin pigment and the similarity of its color to commonly used chromogens often complicate examination of the cytomorphology and immunohistochemical staining results for tumor cells. Melanin bleach can be very helpful for histopathological diagnosis of heavily pigmented melanocytic lesions. Although various depigmentation methods have been reported, no standardized methods have been developed. This study developed a fully automated platform that incorporates hydrogen peroxide-based melanin depigmentation in an automated immunohistochemical analysis. The utility of the method was tested in one cell block of malignant melanoma cells in pleural effusion, ten ocular melanoma tissue samples, and ten cutaneous melanoma tissue samples. Our results demonstrated that the proposed method, which can be performed in only 3 hours, effectively preserves cell cytomorphology and immunoreactivity. The method is particularly effective for removing melanin pigment to facilitate histopathological examination of cytomorphology and for obtaining an unmasked tissue section for immunohistochemical analysis.

Low-Code Development Platforms (LCDPs) empower users to create and deploy custom software with little to no programming. These platforms streamline development, offering benefits like faster time-to-market, reduced technical barriers, and broader participation in software creation, even for those without traditional coding skills. This study explores the application of LCDPs in Precision Agriculture (PA) through a systematic literature review (SLR). By analyzing the general characteristics and challenges of LCDPs, alongside insights from existing PA research, we assess their feasibility and potential impact in agricultural contexts. Our findings suggest that LCDPs can enable farmers and agricultural professionals to create tailored applications for real-time monitoring, data analysis, and automation, enhancing farming efficiency. However, challenges such as scalability, extensibility, data security, and integration with complex IoT systems must be addressed to fully realize the benefits of LCDPs in PA. This study contributes to the growing knowledge base in agricultural technology, offering valuable insights for researchers, practitioners, and policymakers looking to leverage LCDPs for sustainable and efficient farming practices.

The extensive collection of paper documents and books stored in the archives of universities worldwide is a hidden cultural heritage that is frequently inaccessible. To overcome this problem, the University of Genoa, Italy, seeks to collect, store, and digitize a wide variety of items, encompassing books, manuscripts, archival materials, and documents related to museum artifacts, which together form a cultural heritage of great importance and historical significance. To make such a cultural heritage accessible to both humans and machines, images and videos must be provided with alternate descriptions, metadata, and speech-to-text transcriptions while ancient texts, for which OCR techniques are often not effective, must be accompanied by word-for-word transcripts. This work presents the design of a transcription system for the “University Museum System” at the University of Genoa, Italy (SMA-UniGe), including user interface elements and users’ engagement techniques. The goal is to create an accessible digital heritage that can be enjoyed by all, facilitated by a community of digital volunteers who are eager to dedicate their time, have a great experience, socialize, and interact on the proposed transcription system. The system exploits gamification theory to transform the typically monotonous task of transcription into a captivating experience. This activity is in line with the so-called University third mission, i.e., the activity of public engagement that aims at generating knowledge outside the academic environment to the benefit of the social, cultural, and economic development.

Visual Patient Avatar ICU is an innovative approach to patient monitoring enhancing the user's situation awareness in intensive care settings. It dynamically displays the patient's current vital signs through changes in color, shape and animation. The technology can also indicate patient-inserted devices, such as arterial lines, central lines and urinary catheters, along with their insertion locations. We conducted an international, multi-center study using a sequential qualitative-quantitative design to evaluate users' perception of Visual Patient Avatar ICU among physicians and nurses. Twenty-five nurses and twenty-five physicians from the ICU participated in the structured interviews. Forty of them completed the online survey. Overall, ICU professionals expressed a positive outlook on Visual Patient Avatar ICU. They described Visual Patient Avatar ICU as a simple and intuitive tool that improved information retention and facilitated problem identification. However, a subset of participants expressed concerns about potential information overload and a sense of incompleteness due to missing exact numerical values. These findings provide valuable insights into user perceptions of Visual Patient Avatar ICU and encourage further technology development before clinical implementation.

User experience and user requirements are two independent approaches. User requirements address the customer's requirements and expectations for the solution, whereas user experience encompasses all aspects of how the user interacts with and uses the software. The software product should be easy to use and has usable features. Moreover, the additional value for the software is if the product has an attractive design or working environment that is in line with user behaviors, it can occur if integrate software requirements and user experience. Integration escalates developer productivity by focusing on features that meet the user's needs and expectations. That integration improves efficiency in software development by identifying and addressing problems that may arise during the development process, saving developer time and effort in developing software. The usage context of integration of user experience and user requirements in UX Journey contributes increase developer productivity and self-efficacy in developing software by focusing development on features that match the user needs, as well as increasing efficiency in overcoming problems that arise during the development process. UX Journey makes developers feel more confident in their ability to develop quality software.

Medical Device Design process carries a high responsibility when defining the characteristics of the object for its correct interaction with users. This study presents a proposal for the improvement of the medical device design processes, in order to increase user acceptance, considering two key factors: the hierarchy of users and the relationship with the patient's health status. The goal of the study seeks to address this research gap and raise design factors with practical suggestions for the design of new medical devices. The results obtained will help medical device designers in the development stage to make more informed decisions about the functions and features required in the final product; As well, in the design process didactics, demonstrating the importance of the correct execution of the process, and how the factors considered can generate an impact on the final product. An experiment was carried out with Forty design engineering students, who designed birthing beds, with two design processes, the traditional product design process, and the new design process based on hierarchies (proposed in this study). The results showed that there is a significant increase in user acceptance with the new birthing bed that was developed with the hierarchical based design process.

This study reports on empirical findings of participatory design workshops for the development of a supportive user experience design system in the automotive. Identifying and addressing this area with traditional research methods is problematic due to the different UX design perspectives that might be conflicting and the related automotive domain limitations. To help resolve this problem, we conducted research with 12 User Experience (UX) designers through individual participatory prototyping activities to gain insights on their explicit, observable, tacit and latent needs. These activities allowed us to explore their motivation to use different technologies; the system's architecture; detailed features of interactivity and describe user needs including Efficiency, Effectiveness, Engagement, Naturalness, Ease of Use, Information retrieval, Self-Image awareness, Politeness, and Flexibility. Our analysis led us to design implications that translate participants' needs into UX design goals, informing practitioners on how to develop relevant systems further.

Entering University is an important transition for students. Requires adaptation and can influence academic success. Psychological disorders might emerge when difficulties like anxiety occur due to insecurity about emotions in a student's life. We are currently living in post-pandemic conditions where pressure is increasing. The project "Toolbox: University Student – Web-interactive Platform", a platform for learning anxiety management, was developed to address this problem among university students. The main objective is to understand whether using the platform (based on cognitive behavioral theory) has effectively managed anxiety among students. It is a case study with a sample of 31 students from two Portuguese Universities, aged between 19 and 47 years. They were divided into two groups: intervention (n=17) and control (n=13). The results show that participants subjected to intervention with the interactive platform decreased their anxiety.

The change of tire groove depth will have a huge impact on tire performance, and the use of excessively worn tires is not conducive to the driving safety of automobiles. Tire groove depth detection has become one of the annual inspection items of automobiles, but the research on its related detection technology is still relatively backward. Based on the principle of monocular vision ranging (MVR), image processing technology and cloud platform technology, this paper develops a tire groove depth detection system, which realizes non-destructive detection of tire groove depth. In addition, the system uses the cloud platform to store the test results, and builds a multi-level data management system, allowing car owners to keep track of the tire wear status and historical changes, which is of great significance to ensuring driving safety.

The studied area (the Ionian Islands: Paxoi, Lefkas, Kefalonia and Zakynthos), is situated in the western ends of Ionian Basin (IB) in contact with the Apulian Platform (AP) and named as Apulian Platform Margins (APM). The proposed model is based on fieldwork, previously published data and balanced geologic cross-sections. Late Jurassic characterizes Ionian Basin to Early Eocene NNW–SSE extension followed by Middle Eocene to Middle Miocene compression (NNW–SSE directed). The space availability, the distance of Ionian Thrust from the Kefalonia strike-slip fault (KSSF) and the altitude between Apulian platform and Ionian basin that produced during extensional regime were the main factors for the produced structures due to inversion tectonic. In Zakynthos Island, the space availability (far from KFT), and the reactivation of normal bounding faults formed an open geometry anticline (Vrachionas anticline) and a foreland basin (Kalamaki thrust foreland basin). In Kefalonia Island, the space from FKT was limited and the tectonic inversion formed anticline geometries (Aenos Mountain), nappes (within the Aenos Mountain) and small foreland basins (Argostoli gulf), all within the margins (APM). In Lefkas Island, the lack of space, very close to KFT, lead to the movement of the Ionian basin over the margins, attempting to overthrust the Apulian platform. Because the obstacle between basin and platform was very large, the moving part of Ionian Basin strongly deformed producing nappes and anticlines in the external part of the Ionian basin, and a very narrow foreland basin (Ionian Thrust foreland basin).

Cybercrime Threat Intelligence (CTI) allows us to change our actions from reactive to proactive in the fight against threat actors, and to make more informed, data-driven security decisions. Thus this study proposes the practical implementation of CTI in the Arab world. This study focuses on strengthening CTI by integrating Indicators of Compromise (IoCs) and collecting realistic security alerts from honeypot systems and open source intelligence. The collected data is stored in the Malware Information Sharing Platform (MISP), an open source platform that allows users to create and share IoCs with other organizations while staying informed about new threats. It features an intuitive interface for data analysis and threat identification, facilitating sharing, storage, and correlation of IoCs. Therefore, we leveraged MISP to generate IoCs based on the collected data and analyzed the results to identify potential cyber threats. The implemented platform aims to provide organizations with actionable information to prevent, detect and respond to cybercrime. This study presents a practical approach to strengthening CTI in the Arab world and provides an opportunity to strengthen the region’s security posture.

One of the most crucial aspects of the user experience in virtual reality (VR) is the sense of presence. To evaluate this, both subjective and objective methods can be employed. While subjective methods are easy to implement and interpret, they may not fully capture user feedback, and the results can sometimes lack consistency. In contrast, using objective methods, such as electroencephalography (EEG), can provide more reliable insights. To investigate the influence of realism on the sense of presence, we conducted an EEG study with 21 participants who experienced two VR environments—one realistic and one non-realistic. During the study, we continuously measured their brain activity using an EEG device. Our findings showed that alteration in the level of realism in an environment can be detected through changes in brain activity. Notably, we observed that users take longer to adapt to a non-realistic environment when transitioning from a realistic scene, compared to the reverse. Although our study has limitations, such as the total number of participants, we gained valuable initial insights into how realism may influence brain activity. These findings suggest that higher realism may lead to reduced cognitive load, increased attention, improved decision-making, and suppression of irrelevant information.

This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.

The Smart Angel connected medical device allows ambulatory surgery patients to monitor their health by taking their own blood pressure and oxygen levels and by answering a health questionnaire from home. This preventive device must necessarily be "usable" by patients with different profiles. The objective of this article is, therefore, to better understand the links between certain characteristics of potential patients and usability. We conducted an experimental study involving thirty-six participants, investigating the effects of four patient characteristics (i.e. age, education, technophilia and health literacy) on usability measured in terms of effectiveness, efficiency, and satisfaction. The results show a moderate correlation between age, health literacy and usability. However, there is a weak correlation between technophilia and usability and no relationship between the level of education and usability. This study provides theoretical insights into the effects of user characteristics by means of personas in usability (ISO 9241-11).

People with Intellectual Disability (ID) encounter several problems in their daily living regarding their needs, activities, interrelationships, and communication. On this concept, an interactive platform is proposed, aiming to provide personalized recommendations for information and entertainment, including creative and educational activities, tailored to the special users’ needs of this population. Furthermore, the proposed platform integrates capabilities of automatic recognition of health related emergencies, such as fever, oxygen saturation decline and tachycardia, as well as location tracking and detection of wandering behavior based on smartwatch/smartphone sensors, while providing automated assistance and appropriate alerts and notifications to the caregivers.

This study focuses on the creation and implementation of a platform to improve personal financial management for scholarship students of the National Program of Scholarships and Educational Credit (Pronabec) in Peru. Using a mixed methodology that combines qualitative and quantitative techniques, the effectiveness and user satisfaction with the platform were evaluated. The results indicated that 50\% of users found the tool useful, 55\% considered it easy to use, 70\% reported a positive impact on their personal budget planning, and 85\% expressed high overall satisfaction. In addition to providing tools for financial recording and analysis, the platform promotes financial education among its users, significantly contributing to improving their financial management skills.

The generation of platform chemicals and hydrochar is of great interest because they reduce dependence on fossil resources and contribute to climate change mitigation by reducing carbon emissions. The main objective of this study was to evaluate the effect of biomass particle size and biomass water ratio in a hydrothermal conversion system for the generation of value-added products obtained from sugarcane bagasse. Biomass characterization was performed using proximal, elemental, and structural analysis; hydrothermal carbonization was carried out at 220 to 260 °C for one hour; and conversion was monitored using pH, conductivity, and IR spectroscopy. Platform chemicals were quantified by HPLC-IR. Hydrochars were characterized by scanning electron microscopy and energy dispersive spectroscopy. The results show that with a biomass:water ratio of 1:50 and a particle size of 212 μm at 220 °C, a yield of 31.07 % of platform chemicals was obtained on a dry basis. Likewise, the hydrochar with the highest carbon content and a higher porosity appearance was obtained with a biomass-to-water ratio of 1:50 and a particle size of 600 μm at 260 °C.

Background: South Africa’s public healthcare sector is overburdened, especially its under-resourced primary healthcare delivery system. This burden could be relieved by alleviating the population’s ill-health, focusing on the social determinants of health. These include living conditions and levels of social cohesion. In an attempt to address the aforementioned ‘challenge landscape’, this article considers socio-economic empowerment of those marginalised members of society living at the base of the pyramid (BOP) to improve factors contributing to poor health. We propose that Innovation Platforms (IPs) offer opportunities to achieve this by drawing diverse stakeholders together, which should include marginalised individuals, to pool resources and knowledge and collaborate around a specific set of challenges. Method: A Grounded Theory approach is utilised to develop the framework comprising concept definition from a systemized literature review. It is evaluated through various progressive stages through three phases of evaluation: 1) the initial framework was subjected to scrutiny in a theoretical case study, 2) a first-pass semi-structured interview and later four more semi-structured interviews with subject matter experts, and 3) an instrumental case study to refine the framework and to understand its application in a particular situation (this included four stakeholder interviews and a workshop and feedback session with the project champion). Results: This article contributes to the extant literature by addressing the lack of guidance on stakeholder engagement practices critical to the proper functioning of IPs in the context of overcoming the complex challenges associated with social determinants of health. The final output of the study is a refined management tool for stakeholder engagement in IPs. The tool provides practical recommendations to support policy makers, researchers and practitioners in 1) establishing IPs, 2) identifying areas for improvement and 3) identifying reasons for an IP’s failure and lessons to learn.

The commercial equipment that carries out the measurement of temperature has a high cost. Therefore, this article describes the development of a temperature measurement equipment, which uses a microcontrolled platform, responsible for managing the data of the collected temperature signals and making available the acquired information, so that they can be verified in real time at the measurement site, or remotely. The construction of the temperature measurement equipment was performed using open platform hardware / software, where performance tests were carried out with the objective of developing a temperature measurement equipment that has measurement quality and low cost.

The topic of technology development and its disruptive effects has been the subject of much debate over the last 20 years with numerous theories at both macro and micro scale offering potential models of technology progression and disruption. This paper focuses on how the potential theories of technology progression can be integrated and considers whether suitable indicators of this progression and any subsequent disruptive effects (particularly considering these geographically) might be derived, based on the use of big data analytic techniques. Given the magnitude of the economic, social and political implications of many disruptive technologies, the ability to quantify disruptive change at the earliest possible stage could deliver major returns by reducing uncertainty, assisting public policy intervention and managing the technology transition through disruption into deployment. However, determining when this stage has been reached is problematic because small random effects in the timing, direction of development, the availability of essential supportive technologies or “platform” technologies, market response or government policy can all result in failure of a technology, its form of adoption or optimality of implementation. This paper reviews some of the key models of technology evolution and their disruptive effect including, in particular, the geographical spread of disruption. It suggests a methodology for utilising the recent explosion of open and web-discoverable data to determine a methodology to achieve this earlier determination and considers the potential exploitation of big data modelling and predictive analytical techniques to achieve this goal.

As virtual reality (VR) games appeal to increasingly large audiences, many newcomers need convenient and flexible onboarding sessions. Traditional methods of creating tutorial levels can be time-consuming and require additional effort from the development team. In this paper, we present a toolkit for computer-assisted tutorial generation in VR. We conducted an interview with developers and a between-subjects user study to evaluate our toolkit compared to a manually designed onboarding experience by measuring user performance in actual gameplay. The results indicate that our toolkit performs equivalently to manual tutorials in terms of user success. Most users subjectively prefer a manually designed tutorial primarily for its aesthetic value. These results are encouraging, as they prove the feasibility of semi-automated onboarding.

In the field of visualization, understanding users’ analytical process is important to determine the effectiveness of a designed visualization application. To understand the users’ analytical process, numerous studies have been performed to capture and analyze user interactions. Although many studies have emphasized the importance of analyzing user interactions to understand users’ analytical reasoning processes, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that bridges this gap by correlating semantic user interactions with analysis decisions through an interactive wire transaction analysis system and a visual state transition matrix. With the designed analysis system, interactive analysis can be performed to evaluate financial fraud in wire transactions. It also allows the mapping of captured user interactions and analytical decisions back onto the visualization, revealing users’ distinct results. The visual state transition matrix further aids to help understanding users’ analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to assess the effectiveness of our approach in understanding analysts’ strategies by connecting them to their decisions. From the study, we observed an average of 72% accuracy in clearly classifying the semantic user interactions. For classifying individual decisions, we observed an average of 70% classification accuracy. This emphasizes the importance of capturing semantic user interactions to understand users’ analytical processes. Overall, it is determined that the proposed approach not only enhances the understanding of analytical behaviors but also offers a robust approach for evaluating user interactions in visualization tools.

The SARS coronavirus 2 pandemic affected many areas of public life and industry. This also applies to research particularly that which relies on user test studies. In order to minimize the risk of infection, several aspects of experiment design including the setting might require alteration. In order to develop a step-by-step approach to plan a study with regard to infection protection, an extensive review of the latest research involving the SARS coronavirus 2 pandemic as a blueprint for dealing with other health situations. As a result, a six-step concept was developed that is applicable for user test studies in both stationary rooms and vehicles which can be adapted to the respective circumstances. The infection protection measures determined through research were implemented in the individual sub-steps from study planning to execution. They allow a step-by-step approach to prevent infections in user test studies during a pandemic and in situations where increased hygiene measures are required, in order to continue to carry out user test studies.

From ancient time, electric grid system developed as one way direction in which users get the electricity from generators to far end. However, it is not the consumer centric as its one way process and consumer have no way to communicate to the server. Thus, with the development of digital revolution, the grid converted to smart grid and meter converted to smart meter. In smart grid, the protocol follows the bidirectional way of communication with support of consumers in the system. Recently in 2016, Jo et al. proposed the scheme for smart grid system using privacy preserving model and claimed to be efficient and secure. However, in this paper we have analyzed the scheme of Jo et al. and proved that the scheme is vulnerable to Replay attack and afterwards shows the change in protocol to withstand against this attack.

This research explores the transformation of learning methodologies from traditional to modern approaches, focusing on the development of a gamified educational application with an Artificial Intelligence (AI) mentor for secondary school graduates preparing for Croatian state graduation exams. Traditionally, education has been marked by a passive student role and teacher-centric transmission of knowledge. In contrast, modern learning tools, such as gamified applications, introduce interactive and engaging elements. The purpose of this study is to identify user-centric learning approaches through a comprehensive survey of graduating students, assessing their readiness for exams, learning styles, and preferences. The research explores issues such as financial burdens on parents for preparatory courses and copyright concerns with external educational materials. In summary, the learning application should prioritise sustained knowledge retention, motivation, and stress reduction, offering an alternative to external preparatory courses. Conclusions underscore the importance of fostering an engaging learning environment, incorporating teacher-like elements, and aligning with user expectations. As digital technology positively impacts the learning experience, understanding user preferences is crucial in designing educational apps that cater to learners' needs. The study provides insights for developing a gamified learning application that not only prepares students for exams but also enhances long-term knowledge acquisition and engagement.

Approximately 20% of the world's population resides in coastal areas at risk of rising sea levels due to climate change. This could lead to severe weather events and ecological impact on critical infrastructure. To combat the effects of water-based environmental forces on the land, hard engineering methods such as seawalls have been utilized. However, a more sustainable solution is emerging in nature-based engineering, which involves growing vegetation. This approach can help extend the lifespan of existing technology and provide climate change adaptation and resilience for coastal and riverine communities. The following paper will review seaweed farms as an advanced nature-based mitigation approach. The paper also outlines the findings of experiments at RWTH Aachen University's hydraulic laboratory. These tests involved using seaweed as a natural solution to dampen waves in a model system to test a hypothesis. One result involving a system with two lines of seaweed revealed 15 percent wave damping. Using seaweed as a nature-based solution, a gentle engineering approach to designing future vegetated protection systems can extend the design life of existing coastal infrastructure. This can also provide protection against climate-induced sea level rise, promote coastal risk mitigation, restore ecosystems, and foster blue bio-economic development.

The MQTT IoT connection technology is widely used, becoming the primary means for IoT devices to communicate with servers amid the vigorous development of IoT technology. On the OneNET cloud platform, MQTT protocol connection and data communication, message sending, and subscription are established, one-to-many message transmission is solved, and the reverse control from server to device is realized in big data environment. This technology can provide fast and secure messaging services for remote devices with a small code capacity and limited bandwidth. Experiments indicated that the communication mode has low cost, small bandwidth, and slight delay, which meets the requirements of device data acquisition and control in a large area and has broad application prospects and reference significance in the field of mobile applications.

The cultivation of pea pods spans across various regions and climates with a global production of around 20 million tons. The pea peel wastes, which make up 30–40% of the total weight of the peas, are freely available in large quantities. The biomass used was characterized via ultimate, proximate, and structural analysis obtaining a 20,2 %w of cellulose and 17,4 %w of hemicellulose which via valorization processes can be transformed into platform chemicals. Hydrothermal valorization presents itself as a clean form of treatment of said wastes, ranging from 120 – 180 ºC (LHW) to 180 to 260 ºC (HTC). The use of LHW can lead to the production of sugars (up to 70%w yield) and levulinic acid (4 %w yield) while the use of HTC leads to Formic acid (40 %w) and levulinic acid (4 %w yield). The use of LHW for longer times favors the production of HMF and Furfural. The use of homogeneous catalysts (H2SO4, CH3COOH, KOH and NaHCO3) was implemented and their selectivity described. Solid fractions of LHW and HTC were characterized via FTIR and elemental analysis and the change in their structure described as they shift from biomass to biochar. Optimal conditions for each platform chemical were reported for best utilization of the peapod waste.

Short-form video has taken over social media and attracted attention from advertisers. As brands shift their advertising spending to short-form video social platforms, doubts remain about the advertising efficacy on these platforms, especially given the skyrocketing advertising costs in recent years. In a large-scale randomized experiment on ByteDance in collaboration with an automobile brand, we show a significant effect of the advertising campaign on ByteDance, compared with effects reported in other studies. Exposed users are eight times more likely to convert from outside than from within ByteDance, which raises the importance of information sharing between the platform and brands. When considering conversions outside ByteDance, the average cost per conversion, which brands commonly use to evaluate the cost of campaigns, shrinks by 5 or 25 times, depending on the methods used to calculate it. Information sharing can also affect a brand’s targeting strategy. While commonly used demographic variables by the automobile brand are effective for target marketing with only platform data, they are not when considering conversions outside ByteDance. Instead, a behavioral variable proposed herein (prior brand home page visits) effectively moderates the advertising effect but has no effect when including only platform data in the analysis.

One-fifth of the world’s population and critical infrastructures are near the coast and regions at high risk of sea level elevation. Climate change is expected to increase coastal extreme events, rising sea levels, and impact on ecosystem. This paper reviews coastal physical processes, wave and impacts, and the introduction of nature-based models for the mitigation of flooding, erosion, and recovery. Hard engineering like seawalls has been used to prevent, protect, and control water-based environmental forces with an extended impact on the land. A nature-based engineering solution, such as growing vegetation, is being adopted as a sustainable solution to help make existing technology live its design life and provide climate change adaptation and resilience for coastal and riverine communities. This paper presents applications of seaweed farms as an advanced nature-based mitigation approach. The result of the experiments conducted at RWTH Aachen University on wave damping of seaweed types and farming structures to validate the hypothesis will be presented in part B. A soft engineering approach to designing future vegetated protection systems using seaweed as a nature-based solution can help existing coastal infrastructure design life and protect against climate-induced SLR rise and adaptation, coastal risk mitigation, ecosystem restoration, and blue bio-economic development.

Background: The aim of the study is to present the process of monitoring the changes in the effi-ciency of equilibrium reactions in the children with postural disorders, in the course of the thera-peutic process. Methods: The shape of spine and the efficiency of equilibrium reactions in standing posture and during gait were assessed in all the subjects. The subjects were put in 2 groups: with the shape of spine disorder and without one. The recommended set of therapeutic activities in home conditions lasted about 20 minutes and was performed by the child with parent’s supervision. The therapeutic program was based on elements of the PNF and Vojta methods. The following parameters were measured: maximum movement of the center of presure (CoP) in the frontal plane during gate, maximum movement of the CoP in the sagittal plane, movement of the CoP in the frontal plane in static conditions, movement of the CoP in the sagittal plane in static conditions. Results: Six statistically significant differences were recorded, all of them were related to measurement I. The Friedman test result was statistically significant for all the indexes. Post-hoc analyzes were performed using the Dunn-Bonferroni test. Conclusions: 1. The children with shape of spine disorders are featured by lower efficiency of equivalent reactions in relation to the children without disorders. 2. The therapy with the application of neurophysiological methods in the treatment of shape of spine disorders improves equilibrium reactions in these patients. 3. Long-lasting and thorough observations of the therapeutic process in the children with shape of spine disorders should include the monitoring of the efficiency of equilibrium reactions

This paper present a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source [1], is designed for researchers, students and makers for a broad range of their exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system are illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperatures in a lake.

With the rapid consumer adoption of mobile devices such as tablets and smart phones, the tele-traffic has experienced a tremendous growth, low power technologies are highly desirable for future communication networks. In this paper, we consider an ambient backscatter (AB) based user cooperation (UC) scheme for mmWave wireless powered communication networks (WPCN) with lens antenna arrays. Firstly, we formulate an optimization problem to maximize the minimum rate of two users by jointly designing power and time allocation. Then we introduce auxiliary variables and transform the original problem into a convex form. Finally, we propose an efficient algorithm to solve the transformed problem. Simulation results demonstrate that the proposed AB based UC scheme outperforms the competing schemes, thus improve the fairness performance of throughput in WPCN.

Previous research demonstrates that the interruption of immersive experiences may lead to a bias in the results of questionnaires. Thus, the traditional way of presenting questionnaires, paper-based or web-based, may not be compatible with evaluating VR experiences. Recent research has shown the positive impact of embedding questionnaires contextually into the virtual environment. However, a comprehensive overview of the available VR questionnaire solutions is currently missing. Furthermore, no clear taxonomy exists for these different solutions in the literature. To address this, we present a literature review of VR questionnaire user interfaces (UI) following PRISMA guidelines. Our search returned 1.109 initial results, which were screened for eligibility, resulting in a corpus of 25 papers. This paper contributes to HCI and games research with a literature review of embedded questionnaires in VR, discussing the advantages and disadvantages and introducing a taxonomy of in-VR questionnaire UIs.

Incorporating AI tools like ChatGPT into higher educational settings has been beneficial for education, yet the extent of user satisfaction with the information provided by these tools, known as user information satisfaction (UIS), remains underexplored. Our study introduces a UIS model specifically designed for ChatGPT's application in the educational sector. Drawing from established UIS theory, we crafted a model centered around seven essential factors that influence the effective use of ChatGPT, aiming to guide both educators and learners in overcoming common challenges such as plagiarism and ensuring the ethical use of AI. We gathered data from Indonesian university participants and applied Structural Equation Modeling using Smart-PLS 4.0 for our data analysis. The results reveal that completeness, precision, timeliness, convenience, and the format of information as the most influential factors driving user satisfaction with ChatGPT. Interestingly, our research indicated that the accuracy and reliability of the information, typically deemed paramount, were not the primary concerns in the academic use of ChatGPT. Our findings lead to a recommendation for a cautious approach to the integration of ChatGPT in higher education. We advocate for a strategic use that recognizes its innovative potential while also acknowledging its limitations, thus ensuring a responsible and effective application in educational contexts. This balanced perspective is crucial for integrating AI tools into the academic fabric without compromising educational integrity or quality.

The Odunpazarı Modern Museum (OMM), conceptualized by Architect Kengo Kuma and inaugurated in 2019 in Eskişehir, stands as a testament to the historical significance of the timber trade in the region. The deliberate choice of wood as the primary construction material serves as a tribute to this historical narrative. The conspicuous incorporation of wooden lath materials into both the external and internal spaces signifies a conscious reference to the region's historical heritage and aligns with sustainability principles in design. The timber used to construct OMM was sourced from exhausted industrial pine forests. The creation of exhibition spaces at diverse scales involved the assembly of boxes of varying dimensions, illustrating OMM's inclination towards a formative structure characterized by “Addition and Integration.” This architectural methodology involves juxtaposing two or more primary forms to achieve a partial amalgamation. Factors such as form characteristics, dimensional distinctions, spatial arrangements, and extent of surface interactions collectively contribute to the compelling effect of this integrative approach. Within the confines of the museum, unconventional partitioning is implemented, and the strategic arrangement of masses results in multiple façades, even in the absence of overt wooden surfaces. The discernible visibility of the building's façade from an internal vantage point underscores the pivotal role played by wood in the architectural design of the OMM. The exterior impact of the wooden shell complements its interior application. The voluminous void at the heart of the structure allows for the infiltration of natural light into the internal spaces, and the wooden facade facilitates the transmission of light. The use of glass to segregate the cafeteria area from the museum entrance preserves a visual continuity between these spaces. The fundamental research inquiry in this study revolves around the user experience of wood, a material that actively influences diverse facets, including structural elements, contextual considerations, interior arrangements, and external aesthetics. To address this inquiry, a comprehensive case study and survey were conducted with the objective of elucidating the functional, sensory, perceptual, and psychological impacts of wood, the primary material in the museum's spatial configuration, on the overarching user experience.

With young adults (18-30 years) exhibiting the lowest vegetable intake, primarily due to in-adequate cooking skills, this study aims to explore the usability and desirability of educational tools such as written recipes and cooking videos to foster sustainable eating habits. Employing a descriptive, qualitative, and user-centered design, three focus groups were held at a major urban university campus in New Zealand, engaging students, and staff within the target age group. Discussions with 15 participants identified key facilitators for vegetable-based cooking, including access to ingredients, ease of recipe visualization, and cost-effectiveness. Conse-quently, 13 vegetable-centric recipes and instructional videos were created, incorporating nu-tritional value, affordability, and preparation efficiency. These resources, tailored to young adults' preferences, were designed to mitigate the identified barriers to vegetable use, con-tributing to sustainable food practices. The application of a user-centered approach in devel-oping educational content yielded a set of recipes and videos that not only address the gaps in cooking skills among young adults but also promote the broader goals of sustainability in food consumption patterns. This approach offers actionable insights for practitioners and food marketers to enhance vegetable utilization and sustainable eating behaviors in this demo-graphic.

Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user association in massive multiple-input multiple-output (M-MIMO) communication networks. Our approach tackles a multi-objective optimization problem aiming to maximize network utility while meeting stringent quality of service requirements in M-MIMO networks. To address the non-convex and nonlinear nature of this problem, we introduce a novel multi-agent DQN framework. This framework defines a large action space, state space, and reward functions, enabling us to learn a near-optimal policy. Simulation results demonstrate the superiority of our Parameterized Deep DQN (PD-DQN) approach when compared to traditional DQN and RL methods. Specifically, we show that our approach outperforms traditional DQN methods in terms of convergence speed and final performance. Additionally, our approach shows 72.2 % and 108.5 % improvement over DQN methods and RL method respectively in handling large-scale multi-agent problems in M-MIMO networks.

We report on an exploratory study conducted at a graduate school in Sweden with a humanoid robot, Baxter. First, we describe a list of potentially useful capabilities for a robot teaching assistant derived from brainstorming and interviews with faculty members, teachers, and students. These selected capabilities consist of reading, greeting, alerting, remote operation, clarification, and motion. Second, we present feedback on how the robot's capabilities, demonstrated in part with the Wizard of Oz approach, were perceived, and iteratively adapted over the course of several lectures, using the \EP tool. Third, we discuss observations and findings regarding the capabilities and the development process. Our findings suggest that using a social robot as a teaching assistant is promising using the chosen capabilities. We find that personalizing the capabilities and the role of embodiment are important topics to be considered in future work.

Wireless sensor networks are widely used in many applications such as environmental monitoring, health care, smart grid and surveillance. Many security protocols have been proposed and intensively studied due to the inherent nature of wireless networks. In particular, Wu et al. proposed a promising authentication scheme which is sufficiently robust against various attacks. However, according to our analysis, Wu et al.'s scheme has two serious security weaknesses against malicious outsiders. First, their scheme can lead to user impersonation attacks. Second, user anonymity is not preserved in their scheme. In this paper, we present these vulnerabilities of Wu et al.'s scheme in detail. We also propose a new scheme by fixing such vulnerabilities and improving the performance of the protocol.

Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some on-line resource, new information is added to the user profile. Exposing private data does not only reveal information about single users' preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident on \emph{social networks} where users receive suggestions based on their friends' activity. We propose an information theoretic approach to measure the differential update of the anonymity risk for time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third party services get to know about the users when a new activity is shared. We use real Facebook data to show how our model can be applied on a real world scenario.

The skyrocketing growth in the number of Internet of Things (IoT) devices will certainly pose a huge traffic demand for fifth-generation (5G) wireless networks and beyond. In-band full-duplex (IBFD), which is theoretically expected to double the spectral efficiency of a half-duplex (HD) wireless channel and to connect more devices, has been considered as a promising technology to accelerate the development of IoT. To exploit the full potential of IBFD, the key challenge is how to handle network interference (including self-interference, co-channel interference and multiuser interference) more effectively. In this paper, we propose a simple yet efficient user grouping method, where a base station (BS) serves strong downlink users and weak uplink users and vice versa in different frequency bands, mitigating severe network interference. We aim to maximize a minimum rate among all users subject to bandwidth and power constraints, which is formulated as a highly nonconvex optimization problem. By leveraging inner approximation framework, we develop a very efficient iterative algorithm to solve this problem, which guarantees at least a local optimal solution. Numerical results are provided to show not only the benefit of using full-duplex raido at BS, but also the advantage of the proposed user grouping method.

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. However, capturing the shrub biomass accurately by satellite observations is challenging due to the short and sparse features of shrubs. In this study, we presented a framework to estimate shrub biomass at a 10m spatial resolution by integrating ground in-situ data, unmanned aerial vehicle (UAV), Sentinel-2, and Sentinel-1 observations. The pilot study was conducted in the Helan mountains of Ningxia province, China. First, the spatial distribution map of shrubland and other land cover types was generated in 2023. Then, a prediction model of shrub biomass was developed in a Random Forest Regression (RFR) approach driven by different predicted variable datasets based on in-situ measurements, UAV, and satellite images. Finally, the developed model was used to produce the biomass map of shrubland in 2023. The uncertainty was characterized by creating a standard deviation (SD) map using the Leave-One-Out Cross-Validation (LOOCV) method in the shrub biomass estimates. The resultant shrubland distribution map in 2023 has an F1 score of 0.92. The shrub biomass model driven by spectral bands and vegetation indices (R2= 0.62) demonstrated superior performance than that only driven by spectral bands (R2=0.33) or vegetation indices (R2=0.55). The uncertainty of the estimated biomass was lower than 4%, with the lowest values (<2%) happening in the high shrub coverage (>30%) and biomass production (>30kg/m2) regions. Furthermore, the findings show that the magnitude of shrub biomass was affected by the aridity and precipitation significantly in the study region. This study provides a workflow to accurately monitor the biomass of shrublands in a complicated environment based on remote sensing images from multi-platform observations.

The popularity of online retail is followed by decreasing transactions at shopping centres. Leisure and companionship trips to restaurants and cafés are replacing the frequency of intra-urban shopping trips. Several studies demonstrated a notable trend in urban commercial areas, wherein functionality has shifted from shopping centres to primarily focused on restaurants and cafes. Leisure and companionship places in the urban south have greater diversity than formal restaurants and cafés. This article employs a comprehensive framework to examine the categorization of restaurants and cafés in the urban south, referred to as food services. This study aims to investigate the spatial pattern of food service in Surabaya and ascertain whether there is a concentration of all food service variants around existing shopping centres. This research employs the POI of food service units gathered through web scraping from Google Maps. Applying the k-means cluster analysis demonstrates that the spatial pattern of food services is categorized into several distinct clusters. This article discloses five types of urban spaces characterized by varying compositions of food service variants within them. These spaces include the city centre, sub-city centre, areas along main roads, residential neighbourhoods, and peri-urban areas.

Considering that consumers are more willing to buy products online, companies are increasingly selling remanufactured products online through e-commerce platforms. Notwithstanding the high attention it elicits from researchers and companies, the study about the remanufacturing trading platform is in its infancy. Thus, we investigate 20 remanufacturing trading platforms related nowadays and make a gap analysis among them in terms of: (i) business model, (ii) product display, (iii) delivery products, (iv) quality assurance and after-sales service, (v) product review and star rate and (vi) transaction and payment. On this basis, we analyze features and trends for the development of remanufacturing trading platforms and propose six key applications aimed at filling the identified gaps. The consortium blockchain has the characteristics of security and transparency, high credibility, traceability and unfalsifiability, low cost, and strong scalability, which can provide effective support for the six key applications. Then, we construct the technical framework and the model of a consortium blockchain-supported remanufacturing trading platform. Further, we analyze the coupling mechanism between consortium blockchain and remanufacturing trading platform to explain how the remanufacturing trading platform supported by consortium blockchain achieves development characteristics. This study provides important guidance for the development, construction, and operation management of remanufacturing trading platforms.

Reference evapotranspiration (ET0) is the first step in calculating crop irrigation demand, and numerous methods have been proposed to estimate this parameter. FAO-56 Penman-Monteith (PM) is the only standard method for defining and calculating ET0. However, it requires radiation, air temperature, atmospheric humidity, and wind speed data, limiting its application in regions where these data are unavailable; therefore, new alternatives are required. This study compared the accuracy of ET0 calculated with the Blaney-Criddle (BC) and Hargreaves-Samani (HS) methods versus PM using information from an automated weather station (AWS) and the NASA-POWER platform (NP) for different periods of time. The information collected corresponding at Module XII of the Lagunera Region Irrigation District 017, a semi-arid region in the North of Mexico. The HS method underestimated by 5.5 % the reference evapotranspiration (ET0) compared to the PM method during the period from March to August, and yielded the best fit in the different evaluation periods: daily, average, and 5-day cumulative; the latter showed the best values of inferential parameters. The information about maximum and minimum temperatures from the NP platform was suitable for estimating ET0 using the HS equation. This data source is a timely alternative, particularly in semi-arid regions where no data from weather stations are available.

Today, the digital economy has pursued the required social and economic development, and enterprises can achieve long-term sustainability by relying on the digital platform ecosystem. However, the development of the digital economy and the rapid growth of platform ecosystems have also generated some problems. To study effective digital platform ecosystem governance, this study used the tripartite evolutionary game method and analyzed the changes and effects of platform regulation under different development stages from the perspectives of government, platforms, and enterprises. The results indicate that in the early stages, digital platforms primarily relied on the government’s obligation to regulate. With the digital platform ecosystem’s evolution, the platform organization and settled enterprises’ governance effects gradually appeared, establishing the three common constraints of governance mode. Finally, when the digital platform ecosystem is in its self-renewal period, the government effect is reduced, with the platform itself and settled enterprises taking primary supervisory roles. This study provides reasonable methods for effective platform governance at different stages and enriches the theoretical basis and management practices of platform ecosystems.

Adaption of laser communication terminals to airborne and lean-satellite platforms is now a vogue, made possible due to the progressing advancements in lightweight components and compactness of onboard electro-optical transceivers and control systems. This enables highly secured and superior data-transmission rates beyond multiple Gigabit/second on CubeSats and drones compared to Megabit/second rates offered by similar radio-transceivers form factors. However, laser-transmission links require a very stringent beam-pointing stability because they are easily perturbed by attitude variations and micro-vibrations generated by the host platform’s propulsion system or other mechanically active subsystems in proximity with the transmitter’s optical head. Severe line-of-sight jitter causes the downlink laser beam to drift from the targeted receiving system’s field-of-view, inducing pointing errors, increasing signal outage probability and information loss. We experimentally examine the platform jitter generated by the propellers of an hexacopter drone during ground operation and the attitude-control unit’s reaction wheels in a 6U CubeSat structure. We determined the vibration spectrum unique to these platforms and accordingly prescribe requirements for applicable optical fine pointing and disturbance isolation or suppression systems needed to achieve a high-fidelity laser-communication link.

Research has shown the multitude of applications that IoT, cloud computing and forecast technologies present in every sector. In agriculture, one application is the monitoring of factors that influence crop development to assist in making crop management decisions. Research on the application of such technologies in agriculture has been mainly conducted at small experimental sites or under controlled conditions. This research has provided relevant insights and guidelines for the use of different types of sensors, application of a multitude of algorithms to forecast relevant parameters as well as architectural approaches of IoT platforms. However, research on the implementation of IoT platforms at the commercial scale is needed to identify platform requirements to properly function under such conditions. This article evaluates an IoT platform (IRRISENS) based on fully replicable microservices used to sense soil, crop and atmosphere parameters, interact with third party cloud services, planning and scheduling irrigation as well as control of irrigation water control devices. The proposed IoT platform was evaluated during one growing season at four commercial scale farms on two different broadacre irrigated crops with very different water management requirements (rice and cotton). Five main requirements for IoT platforms to be used in agriculture at commercial scale were identified from implementing IRRISENS in rice and cotton production: scalability, flexibility, heterogeneity, robustness to failure and security. The platform addressed all these requirements. The results showed that the microservice approach followed in the platform is robust against both intermittent and critical failures in the field that could occur in any of the monitored sites. Further, processing or storage overload caused for any reason at one farm did not affect the performance of the platform regarding the other monitored farms. This paper also discusses how the microservice approach can address the data heterogeneity issue when crops with different management requirements are monitored. Since there are no shared microservices among farms, the IoT platform proposed here also provides data isolation maintaining data confidentiality for each user, which is relevant in a commercial farm scenario.

We propose a concept of hybrid nanoelectronic-magnetic device for logic integrated platforms made of magnetic core-shell nanoparticles deposited onto prepatterned Si (111) substrate with basic logic circuitry made of metallic conductive lines. The synthesis of magnetic material and the creation of nanoelectronic prepatterned interdigitated die is reported and its capabilities are demonstrated in terms of magnetotransport properties. The laser pyrolysis method is employed in order to synthesize magnetic core-shell Fe / FeC nanoparticles with sizes between 12 – 15 nm. E-beam lithography has been used in order to design and execute two different layouts of interdigitated die, prepatterned with logic capacity, one with two pads and 50 microns thick conductive metallic lines, another one with 4 pads and parallel 5 microns thick conductive lines separated by 5 microns thick spacer. The as-obtained structures are morphologically characterized by means of optical, scanning and transmission electron microscopies. As-synthesized core-shell nanoparticles have been magnetically characterized inasmuch as the hybrid device obtained by depositing centrifugated and dispersed core-shell nanoparticles from liquid carrier solutions. For the first time, a significant giant magnetoresistive (GMR) effect has been observed and measured for the hybrid architectured device made of Fe / FeC nanosized materials on pre-patterned interdigitated die. A ∆R/R of 8% at 4.2 K has been measured from conductivity-in-plane electron transport measurements. This opens possibilities for the use of such devices as arrays of nanosensors and in spintronic applications.

The Android-based IoT platform just like the existing Android provides an environment that makes it easy to utilize Google's infrastructure services including development tools and APIs through which it helps to control the sensors of IoT devices. Applications running on the Android-based IoT platform are often UI free and are used without the user’s consent to registered permissions. It is difficult to respond to the misuse of permissions as well as to check them when they are registered indiscriminately while updating applications. This paper analyzes the versions of before and after an application the update running on the Android-based IoT platform and the collected permission lists. It aims to identify the same permissions before and after the update, and deleted and newly added permissions after the update were identified, and thereby respond to security threats that can arise from the permissions that is not needed for IoT devices to perform certain functions.

Background: In recent years, the integration of robotic systems into various aspects of daily life has become increasingly common. As these technologies continue to advance, ensuring user-friendly interfaces and seamless interactions becomes more essential. For social robots to genuinely provide lasting value to humans, a favourable user experience (UX) emerges as an essential prerequisite. Aim: This article aimed to evaluate the usability of the MINI robot, highlighting its strengths and areas for improvement based on user feedback and performance. Method: In a controlled lab setting, a mixed-method qualitative study was conducted with ten individuals aged 65 and above diagnosed with mild dementia (MD) and mild cognitive impairment (MCI). Participants engaged in individual MINI robot interaction sessions, completing cognitive tasks as per written instructions. Video and audio recordings documented interactions, while post-session System Usability Scale (SUS) questionnaires quantified usability perception. Ethical guidelines were followed, ensuring informed consent, and the data underwent qualitative and quantitative analyses, contributing insights into the MINI robot's usability for this demographic. Results: The study addresses the ongoing challenges that tasks present, especially for MD individuals, emphasizing the importance of user support. Most tasks require both verbal and physical interactions, indicating that MD individuals face challenges when switching response methods within subtasks. These complexities originate from the selection and use of response methods, including difficulties with voice recognition, tablet touch, and tactile sensors. These challenges persist across tasks, with individuals with MD struggling to comprehend task instructions and provide correct answers and individuals with MCI struggling to use response devices, often due to the limitations of the robot's speech recognition. Technical shortcomings have been identified. The results of the System Usability Scale (SUS) indicate positive perceptions, although there are lower ratings for instructor assistance and pre-use learning. The average SUS score of 68.3 places device usability in the "good" category. Conclusion: Our study examines the usability of the MINI robot, revealing strengths in quick learning, simple system and operation, and integration of features while also highlighting areas for improvement. Careful design and modifications are essential for meaningful engagement with people with dementia. The robot could better benefit people with MD and MCI if clear, detailed instructions and instructor assistance are available.

This article explores the specific impact of artificial intelligence (AI) -driven data visualisation technologies in the user experience of the Internet industry. Through the introduction of machine learning and data mining technologies, AI has significantly improved the intelligence and automation of data visualization tools, promoting innovation in visualization methods and improving the quality and efficiency of data cleaning, processing, and analysis. In addition, AI has also promoted the development of personalised data visualization, providing users with a richer and more intuitive way to present data. This paper analyses the applications of AI-driven data visualization in machine learning, natural language processing, and computer vision. It demonstrates the positive effects of AI data visualization on user understanding and optimisation of models through practical feedback from Yellowbrick tools.

Despite inclusive design focusing on improving environmental accessibility for mobility aid (MA) users, it often fails to ensure true inclusivity due to mismatches between interventions and user perceptions. Therefore, understanding mobility aids users' perceptions of inclusivity is essential for advancing disability studies and design, as it highlights key interventions for more effective practices. This study aims to explore perceived inclusivity in MAs usage and identifies gaps in users' needs, classifying their needs and offering recommendations to meet them. First, the DARE-Inclusive Design Framework was used to develop interview guidelines and interpret results. Next, semi-structured, in-depth interviews were conducted with 12 experienced physiotherapists in Iran, and finally, an inductive thematic analysis was used to identify and present themes. Four themes were identified: 1. Perceived Financial Value: Assessing Worth, 2. Objective Enhancements: Optimizing Environments and MAs, 3. Subjective Enhancements: Trustworthiness, Support, and Hope, and 4. Contextual Factors: Interpretations and Representations. The four interconnected themes provide guidelines for inclusivity-oriented interventions, emphasizing financial assessment, high-tech integration, and aesthetic and symbolic considerations in mobility aid design. Physiotherapists can also mediate emotional responses and enhance inclusion during rehabilitation. Additionally, social context and disability etiology impact users' acceptance and use of MAs.

Technology can mediate the gap between observation methods and scientific experience. Intuition—as a form of unconscious knowledge—is one of the most important elements in the emergence of disruptive technology, since the new ideas do not work only on the technical level, but also on social, psychological, political, and economic ones. Its origin is influenced by the creators’ background, the current scientific and technical possibilities, and also by deep aspirations, illusions, motivations, and human values of researchers.However, there is an epistemological breakthrough between scientific experience and intuition. Owing to the idea that intuitions are not individual, this article explores the relation between intuition and innovation since different perspectives with the aim to provide researchers more pathways for creating collective disruptive innovations in the future.

This study investigates the implementation and evaluation of a university bot designed to improve communication and management of academic events in educational environments. With the rapid evolution of digital technology, bots have emerged as powerful tools with applications in various sectors such as e-commerce, education, and healthcare. In particular, the use of bots in customer service and event management has demonstrated significant benefits by handling frequent inquiries, providing real-time information and assisting with administrative tasks, freeing up human resources for more strategic functions. This project adopted a mixed methodological approach combining qualitative and quantitative methods to assess usability and user satisfaction with the bot. The Usability Scale System (SUS) and the User Experience Questionnaire (UEQ) were used as standard tools to collect data on user perception. The results highlighted a high acceptance of the bot, with an acceptance rate of 90\%, indicating high user interest in using the application. This high acceptance underscores the bot's ability to effectively present the information users are looking for. The bot provides a list of academic events categorized by type, making it easy to find and manage information about grants, conferences, courses and other events. Its design focuses on a friendly and accessible interface that allows direct consultation without the need for conversational interaction. This project contributes to operational optimization in educational environments by providing a practical solution for the management of academic events and offers valuable insights on how to efficiently present information in similar applications. These findings are fundamental for future research and development in the creation of information systems in the educational environment.

The rising importance of Big Data in modern information analysis is supported by vast quantities of user data, but it is only possible to collect sufficient data for all tasks within certain data-gathering contexts. There are many cases where a domain is too novel, too niche, or too sparsely collected to adequately support Big Data tasks. To remedy this, we have created ADG Engine that allows for the generation of additional data that follows the trends and patterns of the data that’s already been collected. Using a database structure that tracks users across different activity types, ADG Engine can use all available information to maximize the authenticity of the generated data. Our efforts are particularly geared towards data analytics by identifying abnormalities in the data and allowing the user to generate normal and abnormal data at custom ratios. In situations where it would be impractical or impossible to expand the available dataset by collecting more data, it can still be possible to move forward with algorithmically expanded data datasets.

Children with Autism Spectrum Disorder have deficits in social interaction and expressing and understanding emotions. Many robots for children with ASD have been proposed. However, few studies were found about how to design a social robot for children with ASD. Studies reviewed employed non-experimental studies to evaluate a social robot, but the methodology to design a social robot is rare. This study proposes a design path for a social robot for emotional communi-cation for children with ASD, following User-Centered Design approach. This design path is ap-plied with a study case, evaluated with an expert group on psychologist, human-robot interaction, human-computer interaction, and parents of children with ASD. Results show than following the design path proposed to design a social robot to communication emotions for children ASD is favorable.

Background: Mental health difficulties during teenage years e are common and are a risk factor for later mental and physical health problems. Rural young people are at greater risk for mental health difficulties and have less access to services than their urban counterparts. The purpose of this study was to explore young people and their carers’ experiences of mental health support provided by a rural mobile service, and to identify access enablers from the service users’ perspective. Methods: A qualitative descriptive approach was used to analyse twelve interviews with current service users and eight interviews with family members of young people who had accessed the service.Results: Three main themes were identified: (a) Access and flexibility, (b) Clinicians’ qualities and strategies, and (c) Experiences of change. The mobile service was perceived to be effective in producing positive change in mental health, relationships and attainment of life goals. Key enablers to access included the flexibility of the mobile service, the variety of service delivery modes and therapeutic methods offered, the ease of access facilitated by location in schools, and young people’s autonomy in how they chose to utilise the service. Conclusion: This study provides information about what is important to rural young people and their families in mental health service provision. The findings have implications for changing the way services are organized and operated. Healthcare policy and services could support user-led model design that incorporates the access and use enablers and removes the barriers to rural mental health support.

(1) Background: Existing research has demonstrated the potential of mHealth apps in improving the caregiving outcomes of stroke. Since several apps were published in commercially available app stores without explaining their design and evaluation processes, it is necessary to identify the usability and user experience issues to promote long-term adherence and usage; (2) Methods: User reviews were extracted from the 47 previously identified apps that support stroke caregiving needs using a python-scraper. The reviews were pre-processed and filtered using python scripts. The final corpus was classified based on usability and user experience dimensions to highlight issues within the app; (3) Results: A total of 162,095 were extracted from the two app stores. After filtration, 15,818 reviews were included and classified based on the usability and user experience dimensions. Findings highlight critical issues related to the errors/effectiveness, efficiency and support that contribute to decreased satisfaction, emotion and frustration in using the app; (4) Conclusion: The study identified several usability and user experience issues due to the inability of the app developers to understand the needs of the user. Further, the study describes the inclusion of a participatory design approach to promote an improved understanding of user needs; therefore, limiting any issues and ensuring continued use.

We report the experiences of the bike sharing system in Ningbo, China. To analyze the impact of the system, system log data are collected for the whole month of May 2014 (with more than 1.7 million usage records), supplemented by questionnaire data from 1,856 respondents. Our results show that public bike usage follows the same pattern as road traffic, with morning and evening peaks on week days. In terms of mode substitution, 16% of public bike trips would have been made by private cars. In average, each public bike is used 3.7 times per day, with each trip lasting around 23 minutes and covering around 3.5 kilometre. This translates into a reduction of car travel by more than 11 million kilometre per year, a significant contribution to the sustainability of urban transport in Ningbo.

Abstract: Imaging devices of less than 300,000 pixels are mostly used for sewage conduit exploration due to the petty nature of the survey industry in Korea. P articular ly , devices of less than 100,000 pixels are still widely used, and the environment for image processing is very bitter . Since the sewage conduit image s covered in this study ha ve a very low resolution (240 × 320 = 76,800 pixels), it is very difficult to detect cracks. Because most of the resolution of the sewe r conduit images are very low in Korea, this problem of low resolution was selected as the subject of study. Cracks were detected through a total of six steps of improving the crack in Step 2, finding the optimal threshold value in Step 3, and applying an algorithm to detect cracks in Step 5. Cracks were effectively detected by the optimal parameters in Steps 2 and 3 and the user algorithm in Step 5. Desp ite the very low resolution, the cracked image s showed 96.4% accuracy of detection, and the non cracked image s showed 94.5% accuracy . Moreover, the analysis was excellent in quality , also . It is believed that the findings of this study can be effectively u sed for crack detection with low resolution images.

Identifying or authenticating a computer user are necessary steps to keep systems secure on the network and to prevent fraudulent users from accessing accounts. Keystroke dynamics authentication can be used as an additional authentication method. Keystroke dynamics involves in-depth analysis of how you type on the keyboard, analysis of how long a key is pressed or the time between two consecutive keys. This field has seen a continuous growth in scientific research. In the last five years alone, about 10,000 scientific researches in this field have been published. One of the main problems facing researchers is the small number of public data sets that include how users type on the keyboard. This paper aims to provide researchers with a data set that includes how to type free text on the keyboard by 80 users. The data were collected in a single session via a web platform. The dataset contains 410,633 key-events collected in a total time interval of almost 24 hours. In similar research, most datasets are with texts written by users in English. The language in which the users wrote for this research is Romanian. This paper also provides an extensive analysis of the data set collected and presents relevant information for the analysis of the data set in future research.

Crowdsourcing software design (CSD) is the completion of specific software design tasks on behalf of a client by a large, unspecified group of external individuals who have the specialized knowledge required by an open call. Although current CSD platforms have provided features to improve coordination in the CSD process (such as email notifications, chat, and announcements), these features are insufficient to solve the coordination limitations. A lack of appropriate coordination support in CSD activities may cause delays and missed opportunities for participants, and thus the best quality of design contest results may not be guaranteed. This research aims to support the effective management of the CSD process through identifying the key activity dependencies among participants in CSD platforms and designing a set of process models to provide coordination support through managing this activity. In order to do this, a five-stage approach is used: First, the current CSD process is investigated by reviewing 14 CSD platforms. Second, the review resulted in the identification of 17 possible suggestions to improve CSD. These suggestions were evaluated in stage 3 through distributing a survey to 41 participants who have experience of using platforms in the field of CSD. In stage 4, we designed ten process models that can meet the requirements of suggestions, while in stage 5, we evaluated these process models through interviews with domain experts. The result shows that coordination support in the activities of the CSD can add valuable contribution to the development of CSD platforms.

This article reports on the development of a big data processing platform that uses hot words（politics, economy, military, culture and society） reflecting the national image as the search object and the key time points as the time domain of data resources. The platform crawls (in real time), continuously expands and dynamically monitors a corpus of mainstream media covering China in developed countries (including the United States, Britain, Germany, France and Japan) and in developing countries (such as Brazil, India and South Africa). In this study, the principles of platform development and operation and the software and hardware operating environment were determined.

Due to the influence of factors such as user consumption habits and manufacturer service quality, e-commerce platforms cannot push personalized product information to any consumer. This project studies the push of product information on e-commerce platforms based on deep neural networks. It mines the data of users' online shopping behaviors such as browsing, collecting, and adding to cart on e-commerce platforms, and pre-processes the mined user behavior data by cleaning, integrating, and normalizing them. It builds a deep bidirectional Transformer model to learn users' historical behavior data and predict the most probable products that meet users' behavior needs, thereby realizing the automatic push of product information on e-commerce platforms. The experimental results show that the F1 value of the push result of product information on e-commerce platforms under the method designed by this algorithm is 0.97, which confirms the effectiveness and superiority of this method.

The study focused on assessing passenger risks specifically at the platform-train interface (PTI). The primary aim of this investigation is to analyze how various factors, including station design, passenger flow, and weather conditions, impact the frequency and types of incidents occurring at this critical interaction point. To achieve this, the study utilized a comprehensive data analysis approach, examining records from the Valparaiso Metro for the period of 2022-2023. During this timeframe, a total of 361 incidents were documented, providing a substantial dataset for evaluating incident patterns and their correlations with different influencing factors. The analysis revealed that incidents are notably influenced by peak hour conditions and weekdays. Specifically, the study found that crowded conditions inside the train during peak hours, which typically occur in the morning and evening rush periods, contribute significantly to the occurrence of incidents. When examining incidents occurring on the platform, the study identified that these are closely related to the type of station and the presence of stairs for station access. Conversely, stations designed with more accessible features might experience fewer such incidents. Future studies will focus on incorporating additional factors and analysing new data to provide a more comprehensive understanding of incident dynamics.

In this study, we have designed a promising platform based on metal-organic frameworks (MOFs) to develop paper-based analytical devices (PADs) to immobilize biomolecules for application in biosensors. PADs have become increasingly popular in sensing applications in recent years due to their portability, low cost, simplicity, efficiency, fast detection capability, excellent sensitivity, and selectivity. In addition, MOFs are an excellent choice for developing highly sensitive and selective sensors. Their ability to reversibly and reproducibly adsorb and desorb specific molecules allows using these materials as sensors for various applications. They also offer the possibility to modify their structure and properties, making them highly versatile and adaptable to different environments and sensing needs. In this research, we synthesized and characterized three different amino-functionalized MOFs: UiO66-NH2 (Zr), MIL125-NH2 (Ti), and MIL101-NH2 (Fe). These MOFs were used to fabricate PADs capable of sensitive and portable monitoring of alkaline phosphatase (ALP) enzyme activity by laser-induced fluorescence (LIF). Overall, amino-derivated MOF platforms demonstrate significant potential for integration into biosensor PADs, offering key properties that enhance their performance and applicability in analytical chemistry and diagnostics.

Temporomandibular disorders (TMDs) refer to a group of conditions that affect the temporomandibular joint, causing pain and dysfunction in the jaw joint and related muscles. The diagnosis of TMDs typically involved clinical assessment through operator-based physical examination, self-reported questionnaire and imaging studies. To objectivize the measurement of TMD, this study aims at investigating on the feasibility to use machine-learning algorithms fed with data gathered from low-cost and portable instruments to discriminate the presence of TMD in adult subjects. Through this aim, the experimental protocol involved fifty participants, equally distributed between TMD and healthy subjects, acting as control group. The diagnosis of TMD has been performed by a skilled operator through the typical clinical scale. Participants underwent to a baropodometric analysis by using a pressure matrix and the evaluation of the cervical mobility through inertial sensors. Nine machine-learning algorithms belonging to support vector machine, k-nearest neighbours and decision tree were compared. The k-nearest neighbours based on cosine distance was found as the best performing, achieving performance of 0.94, 0.94 and 0.08 for the accuracy, F1-score and G-index, respectively. These findings open the possibility to use such methodology to support the diagnosis of TMDs in clinical environments.

The Gram-negative, strictly anaerobic bacterium Megasphaera elsdenii was first isolated from the rumen in 1953 and is common in the mammalian gastrointestinal tract. Its ability to use either lactate or glucose as its major energy source for growth has been well documented, although it can also ferment amino acids to ammonia and branched-chain fatty acids, which are growth factors for other bacteria. The ruminal abundance of M. elsdenii usually increases in animals fed grain-based diets due to its ability to use lactate (the product of rapid ruminal sugar fermentation), especially at low ruminal pH (&lt; 5.5). M. elsdenii has been proposed as a potential dietary probiotic to prevent ruminal acidosis in feedlot cattle and high-producing dairy cows. However, this bacterium has also been associated with milk fat depression (MFD) in dairy cows, although proving a causative role has remained elusive. This review summarizes the unique physiology of this intriguing bacterium, and its role of in ruminal community function and in the health and productivity of the host animal. In addition to its effects in the rumen, the ability of M. elsdenii to produce C2-C7 carboxylic acids – potential precursors for industrial fuel and chemical production, is examined.

(1) Background: Mobile health (mHealth) solutions can become means of improving functional recovery and reducing the peri-operative burden and costs associated with arthroplasty procedures. Aim of this study is to explore the objectives, functionalities, and outcomes of a platform designed to provide personalized surgical experiences to qualified patients, along with the associated problems and opportunities. (2) Methods: A survey-based analysis was conducted on patients who were prescribed the use of a specific care management platform and underwent primary Robotic total knee arthroplasty (rTKA) between January 2021 and February 2023. (3) Results: Patients registered on the platform who have undergone primary Robotic TKA (rTKA) were considered. The mean age of registered patients is 68,6 years. The male (M) / female (F) ratio is 45.1% / 54.9%. The patients interviewed were at an average distance of 485 days from the intervention, with a standard deviation of 187.5. The survey highlighted appreciation for the App and its features but limits in its use and in its perception from the patients. All these data were evaluated according to the Wald principles and strategies to improve patient recruitment, enhance adherence, and create a comprehensive patient journey for optimized surgical experiences were analyzed. (4) Conclusions: This patient care platform may have the potential to impact surgical experiences by increasing patient engagement, facilitating remote monitoring, and providing personalized care. There is the need to emphasizes the importance of integrating the recruiting process, improving adherence strategies, and creating a comprehensive patient journey within the platform

Colombia is the world's leading producer of mild washed arabica coffee and produces 12.6 million bags of green coffee, but at the same time 784,000 tons of waste biomass are dumped in open fields, of which only 5% is recovered or used. The objective of this project was to evaluate the production of platform chemicals from these coffee wastes. To achieve this, biomass characterization was carried out using proximate analysis, ultimate analysis and structural analysis. Hydrothermal valorization was carried out at a temperature range of 120-180 °C (LHW) and 180-260 °C (HTC) for one hour, the platform chemicals obtained were quantified by HPLC-IR, monitored by pH and conductivity, and the solid fraction was characterized by monitoring the functional groups in IR spectroscopy, and elemental analysis. Hydrolysis processes were obtained from 150 °C, production of platform chemicals from 180 °C and maximum concentration at 180 °C-4h, over 200 °C degradation of the products in the liquid fraction starts to take place. Homogeneous basic and acid catalysts were used to improve the yields of the reaction. The kinetics of the hydrolysis of lignocellulosic structures to sugars were also analyzed and described, and reaction orders of 1 (LHW), 3 (HTC) and their respective reaction rate equations were reported.

The increase in online orders following the COVID-19 pandemic has heightened consumer ex-pectations regarding prompt deliveries. However, this phenomenon has subsequently triggered a substantial increase in the number of vehicles navigating through urban areas, highly affecting the overall quality of life within cities. Capitalizing on the complexities posed by urban logistics, this paper proposes an innovative digital business model for logistics services. In this context, we introduce “Urban Logistics as a Service (ULaaS), platform”, a concept rooted in the principals of circular economy and harnessed through the application of the Canvas methodology. The con-ceptual framework outlines the ULaaS ecosystem and explores potential user-functionality com-binations. The proposed platform serves as both an on-demand service provider and a control hub for stakeholders such as traffic authorities, policymakers, logistics providers, carriers, and shippers. This platform streamlines citywide logistics to enhance movement efficiency, ease congestion, and reduce emissions. The ULaaS concept was validated through analysis of data collected from interviews in the unique social context of Campinas City, Brazil. The key value-generating mod-ules within the ULaaS ecosystem include parking reservations, shared warehousing, e-commerce deliveries, and comprehensive logistics solutions. However, implementing Urban Logistics as a Service has challenges. These include defining public and private sector roles, creating effective pricing models, and fairly distributing respon-sibilities among service providers. The paper also elucidates various monetization models and required investments, crucial for fostering potential private sector involvement.

A global existential and interlocking environmental, climate and cooperation/equity polycrisis is being faced, which increasingly impacts and is impacted by water and land systems. As a result, transformations in response are gaining increasing traction. Advances in approaches to visualising and communicating how innovations and changes in landscape features enable shifts, transformations and transitions are more crucial than ever before. Visions help focus actions, collaboration and alignment of multiple actors in working towards a common purpose, whilst also entering people’s consciousness at the deep level of values, transforming beliefs and consequently, thinking and action. They give direction on effort and pull on transformational innovation. The evidence-based ‘Water Visions Visualisation Platform’ presents an innovative and accessible way to illustrate, communicate and support future water visioning and strategizing at the landscape scale based on composite paradigms, scenarios, horizons and concepts. Plausible visions of the future are envisioned, illustrated, narratively described and qualitatively assessed, as well as connected with real-world examples and resources through the interactive Platform. This paper outlines the co-creation methodology, the architecture and the co-development of the Platform, as well as a preliminary evaluation through literature-based criteria and the sharing of the Platform with a number of audiences.

The Internet of Things (IoT) is still a relatively new field of research, and its potential for use in the healthcare and medical sectors is enormous. In the last five years, IoT has been a go-to option for various applications such as using sensors for different features, machine-to-machine communication, etc., but precisely, in the medical sector, it is still lagging far behind compared to other sectors. Hence, this study emphasizes the IoT applications in medical fields, Medical IoT sensors and devices, IoT platforms for data visualization, and artificial intelligence in medical applica-tions. A systematic review considering PRISMA guidelines on research articles as well as the websites on IoMT sensors and devices have been carried out. A total of 986 articles after the year 2001 have been initially selected, and by applying the inclusion-exclusion criterion, a total of 597 articles have been identified. From these, 23 studies have been finally selected for this systematic review. This review analyzes different sensor monitoring circuits in detail, considering an Inten-sive Care Unit (ICU) scenario, device applications, and the data management system, including IoT platforms for the patients. Lastly, detailed discussion and challenges have been outlined, and possible prospects have been presented.

The obligation to keep a distance from other people due to the pandemic has changed human life patterns, especially in shopping for their primary needs, namely food. The presence of the food marketplace presents new hope in maintaining health and food availability without crowding with other people while shopping. The main problem that is often a concern of the public when shopping online is transaction security and the Ease of use of this food marketplace application. This research is the intensity of using the Food Marketplace in terms of Interest, transaction security, and Ease of use of this application. Researchers analyzed the relationship between variables with the Structural equation model. Respondents who became this sample were 300 application users spread across various major cities in Indonesia. This study's results provide a view that the intensity of the food marketplace's use has increased significantly during the new normal life. This paper's primary value lies in providing a better understanding of consumer behavior in the Indonesian food marketplace that has not been previously researched. By examining the determinants of the intensity of marketplace use in the food sector, it is hoped that this can become the basis for further research.

Improvements in web platforms for data exchange and trading are creating more opportunities for users to obtain data from data providers of different domains. However, the current data exchange platforms are limited to unilateral information provision from data providers to users. In contrast, there are insufficient means for data providers to learn what kinds of data users desire and for what purposes. In this paper, we propose and discuss the description items for sharing users’ call for data as data requests in the data marketplace. We also discuss structural differences in data requests and providable data using variables, as well as possibilities of data matching. In the study, we developed an interactive platform, treasuring every encounter of data affairs (TEEDA), to facilitate matching and interactions between data providers and users. The basic features of TEEDA are described in this paper. From experiments, we found the same distributions of the frequency of variables but different distributions of the number of variables in each piece of data, which are important factors to consider in the discussion of data matching in the data marketplace.

Most of the structures in flowing water are a challenge to their stability and sustainable with different flow conditions. Recent, renewable energy research and development covers ocean and river energy platform in which flow of water drag considered in various conversion devices towards the offshore and onshore establishment. Various energy platforms have been suggested for offshore development. However, the stability of these platforms in water is a serious concern. To study the water interaction over circular and square cross-section cascade system under the water has been carried out. Water flow around the pillars or column of the energy platform are analyzed through simulation software. Very low velocity 0.5 m/s has been considered to analyze the system. Total fifteen numbers of cascade pillars having circular and square cross-section area were considered. K-ε turbulence model is adopted to calculate the flow interaction to the column. A velocity, pressure, and energy fields are found around the column.

This study analyzes the modeling and dynamics of a novel passive in Multi-Degree-of-Freedom (MDOF) vibration isolation platform which can achieve significant isolation effect. Symmetrical Scissor-Like structures (SLSs) are utilized in the proposed MDOF isolation platform as the supporting and isolation elastic components. Based on the mathematical modeling and theoretical analysis of the MDOF vibration isolation system with SLSs, the effect of structural parameter and joint friction on stiffness and damping properties is investigated. It is shown that due to geometric relations within the SLSs, the natural frequencies can be reduced via adjusting structural parameters of the SLS for different direction vibration isolation. Theoretical and experimental results show that the SLS isolation platform can achieve much better loading capacity and vibration isolation performance simultaneously by only using linear passive components because of the MDOF adjustable stiffness property. Therefore, with low costing and energy consumption, the proposed novel isolation platform can provide the improvement of vibration suppression in various engineering practices.

Developing effective cognitive training tools for older adults, specifically addressing executive functions such as planning, is a challenging task. It is of paramount importance to ensure the implementation of engaging activities, which must be tailored to the specific needs and expectations of older adults. Furthermore, it is essential to provide the appropriate level of complexity for the planning task. A human-centred approach was employed to address the issues identified in the design of the tool. Two pilot studies were conducted with older adults to fine-tune the training task and optimise its suitability for them. This also led to an enhancement of the underlying planning engine, transitioning from simple fast-forward planner (PDDL4J) to an advanced heuristic search planner (ENHSP). The results show that conducting user studies enabled the development of a cognitive training system that gradually increases the proposed difficulty levels of the planning task while maintaining usability and satisfaction among older adults. This highlights the importance of conducting user studies when implementing cognitive training tools for older adults.

This study investigated how different gamification elements in mobile apps influence users' basic psychological needs (autonomy, competence, relatedness) and subsequent engagement outcomes. Survey data was analyzed using PLS-SEM. Social gamification elements enhanced autonomy and competence needs, while immersive elements boosted autonomy and relatedness. Achievement/progression components increased competence and relatedness perceptions. Interestingly, personal traits did not significantly impact psychological needs. Crucially, satisfying autonomy and relatedness predicted higher user satisfaction and continued usage intentions, highlighting their importance for sustained engagement. Competence influenced satisfaction but not continuance intention. The findings demonstrate the motivational roles of distinct gamification features in fostering need-supportive experiences. Designers should incorporate varied social, immersive, and achievement components to address users' psychological requirements holistically. Prioritizing autonomy and relatedness support appears vital for achieving enjoyable, self-motivated, and persistent user engagement across gamified systems and domains. The study provides actionable insights for optimizing motivational affordances through psychologically-informed gamification implementations.

Virtual tours with 360 photos present a quick, easy and accessible way to create immersive experiences. The visualization in diverse devices, provide a user experience that can be different. A virtual tour with 360 photos of the Chapel of Dolores (Tenerife), has been created. Whose access is limited to specific days, so the creation of a virtual tour allows you to visit it at any time, in addition, a pilot test has been carried out with students of the degree of restoration and conservation of cultural heritage in order to measure the user experience of the virtual tour depending on the device used. The participants took a virtual tour of the Dolores Chapel, first with a cell phone and then with the virtual reality headset, and then completed a user experience questionnaire. The results obtained comparing both visualization technologies are detailed, highlighting that when using the virtual reality headset no better results are obtained in the immersion subscale, in addition, the visualization on a Smartphone is more comfortable and obtains better results in consequences such as dizziness and vertigo. Therefore, it may be an interesting strategy to have simple versions that work quickly on the cell phones.