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.

(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.

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.

With the growing popularity of smartphones, user identification has become an essential component of maintaining security and privacy. This study investigates how smartphone accelerometer data can be used to identify users, and it makes recommendations for the ideal application parts. Accelerometer data from the HMOG public dataset was used to train deep learning, conventional classifiers, and voting classifiers, which were then utilized to identify users. To enhance performance, feature selection and pre-processing techniques were researched. The results show that RFE feature selection outperforms other approaches and that LSTM followed by XGBoost has the best identification performance as indicated by a relatively large number of machine learning performance measures. The proposed identification system nevertheless performed well and outperformed existing methods, which were principally created and tested on the same HMOG public smartphone dataset, even with a larger number of users. Further work would be necessary for such an application to reach its full potential, though.

Data Meshes is an approach to data architecture and organization that treats data as a product and focuses on decentralizing data ownership and access. It has recently emerged as a field that presents quite a few challenges related to data ownership, governance, security, monitoring, and observability. To address these challenges, this paper introduces an innovative algorithmic framework leveraging data blueprints to enable the dynamic creation of Data Meshes and Data Products in response to user requests, ensuring that stakeholders will have access to specific portions of the Data Mesh as needed. Ownership and governance concerns are addressed through a unique mechanism involving Blockchain and Non-Fungible Tokens (NFTs). This facilitates secure and transparent transfer of data ownership, with the ability to mint time-based NFTs. By combining these advancements with the fundamental tenets of Data Meshes, this research offers a comprehensive solution to the challenges surrounding data ownership and governance. It empowers stakeholders to navigate the complexities of data management within a decentralized architecture, ensuring a secure, efficient, and user-centric approach to data utilization. The proposed framework is demonstrated using real-world data from a poultry meat production factory.

Information overload is the biggest challenges nowadays for any website, especially the e-commerce website. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations like as data sparsity, low accuracy, cold-start and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the aim of the paper is to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes on twenty-four trust metrics in terms of the methodology, trust properties & measurement, validation approaches, and the experimented dataset.

This comprehensive review digs into the complex landscape of the Metaverse, focusing on user perceptions and the critical roles of usability, social influence, and interoperability. The paper integrates various scholarly insights, underscoring the Metaverse's evolving significance in the digital realm and its implications across diverse sectors. Central to the findings is the importance of usability. The influence of social factors is also prominent, shaping user behavior and decision-making within these virtual spaces. Interoperability becomes a big problem, showing how important it is to have standard protocols and technologies so that experiences on different Metaverse platforms are smooth and seamless. The review further reveals insights into user experience and accessibility, emphasizing the need for inclusive, ergonomic, and user-friendly design to enhance engagement and reduce barriers. Moreover, the paper discusses the ethical and societal implications of the Metaverse, including issues like digital harassment, aggressive marketing, and privacy concerns, which call for responsible development and robust regulatory frameworks. Additionally, the review identifies gaps in current research, particularly in user experience in healthcare applications, the impact of the Metaverse on educational outcomes, and the need for more empirical studies on the long-term effects of Metaverse interactions on user behavior and psychology. This synthesis offers a detailed overview of current user perceptions and challenges within the Metaverse and sets the stage for future research directions, aiming to foster a secure, inclusive, and interoperable Metaverse environment that aligns with ethical standards and societal needs.

Background: The Davidson Airway Function & Nasal Evaluation (DAFNE) Scoring System was developed as an intuitive and research-based scoring system that could be validated through beta testing and easily introduced to healthcare providers of several subspecialties who treat nasal obstruction and breathing disorders (MDs, PAs, PTs, APRNs, DDSs, and DCs). This scoring system was shown to increase the knowledge of airway function, nasal measurement parameters, and identification of proper treatment options for sleep and breathing disorders. The basis for the DAFNE score was developed from a systematic review of nasal measurement data. Methods: Electronic searches of PubMed, MEDLINE, EMBASE Cochrane Library, and Scopus of publications between 1988-2022 were used to identify studies validating nasal function measurement parameters to create the algorithm for the DAFNE Score™. The systematic review was accomplished using the 2020 ‘Preferred Reporting Items for Systematic Reviews’ (PRISMA) guidelines. Results: Twenty studies met the inclusion criteria for systematic review. Primary outcomes measurements demonstrated reliability, repeatability and validity of the DAFNE measurement technologies, data and output. Conclusions: The data analysis and systematic review uncovered a need and framework to develop and validate a web-based software algorithm for global access to improve the understanding of data interpretation of nasal measurements from three nasal measurement technologies. DAFNE Scoring should be used as an adjunct tool in routine clinical practice and research to further understand the technology data output and how to collaborate with other healthcare providers to improve patient outcomes.

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.

This article presents the graphical user interface of the ACUX-R mobile recommendation system, tailored for the cultural tourism domain. ACUX-R offers personalized recommendations based on visiting preferences, augmenting the overall user experience. Building upon a comprehensive methodology and recommendation algorithms from previous work, this contribution focuses on the user interface aspects of the ACUX-R by highlighting key design considerations, user interface elements and functionalities that contribute to an effective and engaging user experience. In an effort to evaluate the proposed interface, an initial case study with a dataset consisting of points of interest from the City of Athens, Greece has been performed. In the framework of the aforementioned study, the proposed user interface attained high ratings with respect to inspiring, exciting, interesting, and enthusiastic user experiences.

Since the COVID-19 pandemic, urban forest mountains have become important restorative environmental spaces, and demand-customized management based on users’ experiences is needed. We collected 21,557 data points from blogs from January 2020 to December 2021. For data analysis, keyword frequency, term frequency-inverse document frequency, and sentiment analyses were conducted using TEXTOM 4.0, and a semantic linkage network was established and analyzed using Gephi 0.92. In the analyses, the restorative environment components of “being away” “fascination,” “extent,” and “compatibility” were derived from users’ experiences. Fascination, which stems from natural objects such as rocks, valleys, and trails, was derived the most frequently, and being away and compatibility, representing leisure activities such as climbing and walking, formed the largest cluster in cluster analysis. Sentiment analysis revealed a high positive word rate of 91.6%, with favorable feelings accounting for 87.5%, whereas the proportions of joy and interest (12.5%) were relatively low. This study showed that leisure activities related to nature objects had a positive impact. However, joy and interest, which correspond to vitality, were relatively lacking in the restorative experience. To improve restorative experience quality, diversification of leisure activities should be promoted and customized management plans corresponding to restorative environment components developed.

Stairs are among the key elements in architectural composition, both aesthetically and spatially. They are also one of the main innovations in architecture and building, allowing pedestrians to bridge considerable height differences with relative efficiency. It is therefore surprising that, in spite of all stair regulations in building codes, stairs are responsible for a huge number of accidents – second only to motorcars. The extent of safety failures suggests that user interaction with stairs is poorly understood by designers and policy makers. This is not unrelated to the lack of research into the design and use of stairs. Templer’s seminal work is the exception but it dates from 1992 and since then little has been done to understand the relation between architectural design and stair performance, including safety. To redress this, we can build on affordance-based analyses of stair climbability, which establish a clear connection between the form of a stair and the perception of action possibilities and dangers by all kinds of users. By doing so affordances establish a comprehensive and consistent framework for the analysis of architectural designs, which utilizes both domain and psychological knowledge, including as a foundation for computational applications.

Although various vaccines are now commercially available, they have not been able to stop the spread of COVID-19 infection completely. An excellent strategy to quickly get safe, effective, and affordable COVID-19 treatment is to repurpose drugs that are already approved for other diseases as adjuvants along with the ongoing vaccine regime. The process of developing an accurate and standardized drug repurposing dataset requires a considerable level of resources and expertise due to the commercial availability of an extensive array of drugs that could be potentially used to address the SARS-CoV-2 infection. To address this bottleneck, we created the CoviRx platform. CoviRx is a user-friendly interface that provides access to the data, which is manually curated for COVID-19 drug repurposing data. Through CoviRx, the data curated has been made open-source to help advance drug repurposing research. CoviRx also encourages users to submit their findings after thoroughly validating the data, followed by merging it by enforcing uniformity and integ-rity-preserving constraints. This article discusses the various features of CoviRx and its design principles. CoviRx has been designed so that its functionality is independent of the data it dis-plays. Thus, in the future, this platform can be extended to include any other disease X beyond COVID-19. CoviRx can be accessed at www.covirx.org.

The advancement of mass spectrometry technologies has revolutionised plant metabolomics research by enabling the acquisition of raw metabolomics data. However, the identification, analysis, and visualisation of these data require specialised tools. Existing solutions lack a dedicated plant-specific metabolite database and pose usability challenges. To address these limitations, we developed PlantMetSuite, a web-based tool for comprehensive metabolomics analysis and visualisation. PlantMetSuite encompasses interactive bioinformatics tools and databases specifically tailored for plant metabolomics data, facilitating upstream-to-downstream analysis in metabolomics and supporting integrative multi-omics investigations. PlantMetSuite can be accessed directly through a user's browser without the need for installation or programming skills. The tool is freely available at https://plantmetsuite.verygenome.com/ and will undergo regular updates and expansions to incorporate additional libraries and newly published metabolomics analysis methods. The tool's significance lies in empowering researchers with an accessible and customisable platform for unlocking plant metabolomics insights.

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e. professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies. This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

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.

Artificial Intelligence (AI) has many advantages over humans in that it can detect incredibly subtle patterns within large quantities of data. This study suggests using AI algorithms in user research tasks for mining variables ranging from the tone of voice, image banks, historical records, and product use to determine where brands sit and where there could be competitive advantages. This study reviews the current design processes of User Experience (UX) designers and the advancements in Deep Learning (DL). It identifies potential areas of automation in the Preparation, Incubation and Illumination stages of the design process. It recognises the possibility of using Long Short-Term Memory (LSTM) models to automate design feedback and the creation of alternate designs with Generative Adversarial Networks (GANs).

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.

Poor medication adherence among older adults is a widespread problem worldwide. As the population ages, the design of smartphone medication management apps is critical to improving medication adherence among older adults. Taking the design of an elderly medication reminder APP as an example, this study proposes a sustainable design research method that integrates the KANO model, Analytic Hierarchy Process (AHP), Quality Function Deployment (QFD), and PUGH decision matrix. The method collects user demands through in-depth interviews, and applies the KANO model to classify these demands. The hierarchical structure of user needs is established by using AHP, and the priority is sorted according to the weight and importance determined by the judgment matrix. QFD is used to translate user needs into design requirements, and the house of quality matrix identifies key design requirements. Finally, design alternatives are evaluated using Pugh's concept selection method. The results of this study demonstrate that the integration of KANO-AHP-QFD-PUGH can be effective as a sustainable optimal design approach for the user experience of a medication reminder application for the elderly. This integrated method not only facilitates innovative optimization and sustainability of application design methods but also provides valuable theoretical and practical insights for future drug-assisted design for elderly users.

The energy-intensive sector of the cement industry needs to find technologically advanced methods to produce cement with as little energy as possible, without polluting the environment and without sacrificing the quality (fineness) of the cement. In addition, the stress on the machines changes the behavior of the data. By combining the k-means method, two (2) feature selection methods, nine (9) machine learning methods, and the differential evolution (DE) method, a dynamic system is created where the user determines the key performance indicators (KPIs) each time. The user has the potential to control the distribution of the data, the input of machine learning models, and the ability to switch the system to the suggested values without damage. The selection of the best-performing models is based on the normalized root mean squared error (NRMSE). The DE method optimizes the dynamic objective function. The optimal values of the manipulated variables are evaluated compared to the value of the objective function from the actual values of the variables. In all experiments, the recommendations are acceptable. The final results show an improvement in the operation of both the cement mill and kiln compared to the current operating condition.

The Internet of Things (IoT) has become one of the most attractive domains nowadays. It works by creating a special network between physical devices such as vehicles, home equipment, and other items. In recent days, the common technologies of communication such as Wi-Fi and 2G/3G/4G cellular are insufficient for the IoT networks because they are designed to serve appliances with immense processing capabilities such as laptops and PCs. Moreover, most of these technologies are centralized and use an existing infrastructure. Currently, the new communication technologies such as Z-Wave, 6LowPAN, and Thread are dedicated to the IoT and have been developed to meet its requirements. These technologies can handle many factors such as range, data requirements, security, power demands, and battery life. Nevertheless, the security issues in IoT systems have major concerns and matters because vulnerabilities in such systems may result in fatal catastrophes. In this paper, an enhanced IoT security framework for authentication and authorization is proposed and implemented to protect the IoT protocols from different types of attacks such as man-in-the-middle attack, reply attack, and brute force attack. The proposed framework combines an enhanced token authentication that has identity verification capabilities and a new sender verification mechanism on the IoT device side based on time stamp, which in turn can mitigate the need for local identity verification methods in IoT devices. The proposed IoT security framework is tested using security analysis with different types of attacks compared with previous related frameworks. The analysis shows the high capability of the proposed framework to protect IoT networks against many types of attacks compared with current available security frameworks. Finally, the proposed framework is developed using Windows application to simulate the framework phases, check its validity through the real network, and calculate the payload time is adds.

In an effort to upgrade and provide a unified and improved instrument control and data acquisition system for the ORNL SANS instrument suite (Bio-SANS, EQ-SANS, GP-SANS), beamline scientists and developers teamed up and worked closely together to design and develop a new system. We began with an in-depth analysis of user needs and requirements, covering all perspectives of control and data acquisition based on previous usage data and user feedback. Our design and implementation were guided by the principles from the latest user experience and design research and based on effective practices from our previous projects. In this article, we share details of our design process as well as prominent features of the new instrument control and data acquisition system. The new system provides a sophisticated Q-Range Planner to help scientists and users plan and execute instrument configurations easily and efficiently. The system also provides different user operation interfaces, such as wizard-type tool Panel Scan, a Scripting Tool based on Python Language, and Table Scan, all of which are tailored to different user needs. The new system further captures all the metadata to enable post-experiment data reduction and possibly automatic reduction and provides users with enhanced live displays and additional feedback at the run time. We hope our results will serve as a good example for developing a user-friendly instrument control and data acquisition system at large user facilities.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

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.

A significant body of research has examined the accuracy and diversity of several types of recommendation systems. However, little is known about the psychological mechanisms underlying the effects of the different types of recommendation systems on user satisfaction. The findings of our experiment showed that user satisfaction is influenced by shopping goal. Specifically, when a user’s shopping goal aligns with the type of recommendation system, the user satisfaction is enhanced. Our study evaluated the mediating role of feeling right and psychological reactance for a better understanding of this relationship. We further tested the moderated mediation effect of feeling right and psychological reactance moderated by the user shopping goal. For goal-directed users, accurate recommendations trigger the activation of feeling right, consequently increasing the user satisfaction. Conversely, when exploratory users face accurate recommendations, they activate psychological reactance, which leads to a reduction in user satisfaction.

Quality of Service (QoS) metrics deal with network quantities, e.g. latency and loss, whereas Quality of Experience (QoE) provides a proxy metric for end-user experience. Many papers in the literature have proposed mappings between various QoS metrics and QoE. This paper goes further in providing analysis for QoE versus bandwidth cost. We measure QoE using the widely accepted Mean Opinion Score (MOS) rating. Our results naturally show that increasing bandwidth increases MOS. However, we extend this understanding by providing analysis for internet access scenarios, using TCP, and varying the number of TCP sources multiplexed together. For these target scenarios our analysis indicates what MOS increase you get by further expenditure on bandwidth. We anticipate that this will be of considerable value to commercial organizations responsible for bandwidth purchase and allocation.

The following article indicates the determinants of “Internet User Skills” among European countries based on the application of the database deriving from the DESI-Index. The data were analyzed using the following econometric models, namely: Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, WLS corrected for heteroskedasticity. The Elbow method and the Silouette coefficient method were compared for the optimization of the number of clusters obtained by the k-Means algorithm. The result shows the presence of 5 clusters. A network analysis was carried out using the Euclidean distance with the result of identifying two network structures between some analyzed countries. subsequently a comparison was made between six different machine learning algorithms for the prediction of the future value of the variable of interest. The result shows that the best predictor algorithm is Gradient Boosted Tree Regression with an expected value of the predicted variable increasing by a value of 1.75%. Later a further comparison was made by comparing 6 algorithms with the increased data. The result shows that the best predictor is Simple Regression Tree. The interest variable is predicted to decrease by an amount equal to -6.099%. Statistical errors improve on average by 32.43% in the transition between the original data and the increased data.

Organ-on-a-Chip (OoC) systems bring together cell biology, engineering, and material science for creating systems that recapitulate the in vivo microenvironment of tissues and organs. The versatility of OoC systems enables in vitro models for studying physiological processes, drug development, and testing in both academia and industry. This paper evaluates current platforms from the end-user perspective, elaborating on usability, complexity, and robustness. We surveyed 165 peers in over 30 countries and grouped the responses according to preliminary knowledge and the source of the OoC systems that are used. The survey clearly shows that current commercial OoC platforms provide a substantial level of robustness and usability - which is also indicated by an increasing adaptation of the pharmaceutical industry but a lack of complexity can challenge their use as a predictive platform. Self-made systems on the other hand are less robust and standardized but provide the opportunity to develop customized and more complex models which are often needed for human disease modeling. This review serves as a guide for researchers in the OoC field and encourages the development of next-generation OoCs.

Medical Self-service Terminals (MST) offer potential for process optimization and enhanced patient convenience in hospitals, including the elderly. However, the behavioral inclination (BI) of elderly Chinese individuals towards MST adoption remains suboptimal. This study aims to elucidate BI determinants for MST among the aging demographic, focusing on User Interface (UI) attributes within the extended Unified Technology Acceptance and Use Theory (UTAUT) Model. The empirical inquiry examines seven factors, comprising three key UI attributes and four fundamental UTAUT factors. Results reveal significant positive associations of Performance Expectancy (β=0.40, p<0.001), Effort Expectancy (β=0.50, p<0.001), and Social Influence (β=0.25, p<0.05) with BI. Notably, User Interface Design positively correlates with Performance Expectancy (β=0.89, p<0.001) and Effort Expectancy (β=0.81, p<0.001). These findings emphasize the interplay between objective UI attributes and subjective UTAUT determinants. This research enhances understanding of UI's impact on MST BI and acceptance, particularly within the Chinese elderly population, urging MST researchers to prioritize elderly attitudes for effective technological integration in healthcare.

The development of low-cost, open-source Remotely Operated Vehicle (ROV) systems has provided almost unrestricted access for researchers looking to monitor the marine environment in ever greater resolution. Sampling microbial communities from the marine environment, however, still usually relies on Niskin-bottle sampling (ROV or CTD based), a method which introduces an inaccuracy and variability that is incompatible with metatranscriptomic analysis. Here, we describe a versatile, easily-replicated platform which achieves in situ mRNA preservation, via the addition of RNAlater to filtered microbial cells, to enhance ROV or CTD functionality.

The aim of this study was to identify factors, that influence satisfaction and dissatisfaction in the use of public e-procurement. The use of IT solutions promotes sustainable development. Hence, there is a need to conduct research that will allow for the best possible adjustment of e-procurement solutions in order to implement them as widely as possible in business practice. This study used a quantitative exploratory survey-based approach. The obtained research results were analyzed using statistical methods, including factor analysis PCA. Factor analysis identified three main satisfaction factors and two dissatisfaction factors in e-procurement use, highlighting the importance of support, intuitiveness, and security in user satisfaction. This study contributes to the existing literature by providing a nuanced understanding of the factors influencing user satisfaction and dissatisfaction within e-procurement systems, specifically highlighting the role of intuitive design and technical support. It offers a unique perspective by integrating these insights with the specific requirements of public procurement, thereby filling a gap in research on enhancing user experience in the context of sustainable purchasing practices.

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.

This study explores the public library service evaluation domain through user-generated content on Google Maps, highlighting digital feedback's significant yet underexplored potential in understanding public library patronage across Taiwan's six major cities. Utilizing a mixed-methods research design, this study integrates Google Maps review content analysis with Social Network Analysis to delineate public perceptions and identify areas for service enhancement in public libraries. It innovatively leverages personal experiences extracted from over 60,000 Google Maps reviews to evaluate public library services in cities such as Taipei, New Taipei, Taoyuan, Taichung, Tainan, and Kaohsiung. The research taps into the National Library of Taiwan's National Library Statistics System to provide a robust analysis of library performance and user satisfaction, offering a novel perspective by emphasizing user-centric feedback from Google Maps as a primary data source. This approach provides quantitative data on library usage and geographic distribution and enriches our understanding of the qualitative experiences of library users. In analyzing the keywords from Google Maps reviews of public libraries, we categorize and interpret these under the three core LibQUAL+ dimensions—Affect of Service, Information Control, and Library as Place. The findings expose variances in perceived service quality among the cities, with Kaohsiung and Taichung receiving the highest accolades for service satisfaction. Simultaneously, the study identifies potential areas for improvement, particularly in cities with lower satisfaction ratings like Taipei. This personalized feedback illustrates the intimate relationship between public libraries and their communities, offering invaluable insights for policymakers and library management to enhance service delivery and user experience.

Wellness is a multidimensional concept that touches upon the various physical, mental, emotional, spiritual, social and environmental facets of health. Interest towards and importance of wellness have been growing constantly for the past two decades and thus makes it crucial to understand which factors affect public engagement with wellness information for multiple stakeholders. The Instagram account of New York Times (NYT) specifically for sharing wellness content with the handle nyt_well was selected as the object of study. 773 posts from this account between March of 2019 and December of 2022 were collected and analyzed to answer the research question of which factors are most influential to public engagement with wellness content. Two negative binomial regressions were run on features including the type of post, length, word count, sentiment score and topic with number of likes and comments as the dependent variables for each of those regression models. Results indicated that the type of post and its sentiment score were the two most influential determinants of public engagement with p-values smaller than 0.05. While the effects of some of these factors aligned with findings from previous studies conducted on social media content not related to wellness (e.g., marketing), some others affected the two separate public engagement metrics in opposite directions, warranting future studies to investigate further on the cause of this phenomenon.

This study investigates the cultural dimension in sustainable lighting design to create inclusive environments. India being one of the most culturally and ethnically diverse nations in the world, with a population of almost 18% of the world population, requires attention to include cultural dimension in the design of sustainable environments. With the changing lifestyle and growth in organized retailing, the Indian retail market needs an upgrade to create inclusive environments for shared retail experiences. Lighting is among most influencing atmospheric attribute to create simulating environment for a holistic shopping experience. Preference of lighting conditions vary across the store profiles and users’ cultural background. Very little research has been carried out to understand the lighting preferences of retail customers in India. This study investigated the effects of correlated colour temperature and illuminance levels on spatial impressions and user preferences in mid-range store profile. This study involved ninety-three participants in evaluating high definition visualisations of the sixteen lighting conditions. The observations from this study emphasizes the necessity of similar studies across various states of India to identify the lighting preferences for other functional spaces and cultural backgrounds within the country. The findings may contribute towards providing recommended guidelines in lighting design and include a cultural dimension in the design of sustainable store environments.

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.

Technological advances in game-mediated robotics provide an opportunity to engage children with cerebral palsy (CP) and other neuromotor disabilities in more frequent and intensive therapy by making personalized, programmed interventions available 24/7 in children’s homes. Though shown to be clinically effective and feasible to produce, little is known of the subjective factors impacting acceptance of what we term assistive/rehabilitative (A/R) gamebots by their target populations. This research describes the conceptualization phase of an effort to develop a valid and reliable instrument to guide the design of A/R gamebots. We conducted in-depth interviews with 8 children with CP and their families who had trialed an exemplar A/R gamebot, PedBotHome, for 28 days in their homes. The goal was to understand how existing theories and instruments were either appropriate or inappropriate for measuring the subjective experience of A/R gamebots. Key findings were the importance of differentiating the use case of therapy from that of assistance in rehabilitative technology assessment, the need to incorporate the differing perspectives of children with CP and those of their parents into A/R gamebot evaluation, and the potential conflict between the goals of preserving the quality of the experience of game play for the child while also optimizing the intensity and duration of therapy provided during play.

Plant cells' ability to withstand abiotic stress is strongly linked to modifications in their mechanical characteristics. Nevertheless, the lack of a workable method for consistently tracking plant cell mechanical properties severely restricts our comprehension of the mechanical alterations in plant cells under stress. With Polyethylene Glycol (PEG6000), we created a drought-like environment, and we used the Double Resonator Piezoelectric Cytometry (DRPC) method to dynamically and non-invasively track changes in the stress (ΔS) created and viscoelasticity (storage modulus G' and loss modulus G") of protoplasts and suspension cells of rice during drought stress. The findings demonstrate that, rice suspension cells and protoplasts react mechanically differently to 5%–15% PEG6000 stress, implying distinct resistance mechanisms. However, neither of them can withstand 25% PEG6000 stress, they respond mechanically similarly to 25% PEG6000 stress. The results of DRPC are further corroborated by the morphological alterations of rice cells and protoplasts observed under an optical microscope. To sum up, the DRPC technique functions as a precise cellular mechanical sensor and offers novel research tools for the evaluation of plant cell adversity and differentiating between the mechanical reactions of cells and protoplasts under abiotic stress.

Poor social skills in autism spectrum disorder (ASD) are associated with reduced independence in daily life. Current interventions for improving the social skills of individuals with ASD fail to represent the complexity of real-life social settings and situations. Virtual reality (VR) may facilitate social skills training in social environments and situations proximal to real life, however, more research is needed for elucidating aspects such as the acceptability, usability, and user experience of VR systems in ASD. Twenty-five participants with ASD attended a neuropsychological evaluation and three sessions of VR social skills training, incorporating 5 social scenarios with three difficulty levels for each. Participants reported high acceptability, system usability, and user experience. Significant correlations were observed between performance in social scenarios, self-reports, and executive functions. Working memory and planning ability were significant predictors of functionality level in ASD and the VR system’s perceived usability respectively. Yet, performance in social scenarios was the best predictor of usability, acceptability, and functionality level in ASD. Planning ability substantially predicted performance in social scenarios, postulating an implication in social skills. Immersive VR social skills training appears effective in individuals with ASD, yet an error-less approach, which is adaptive to the individual’s needs, should be preferred.

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.

Blood gas analysis plays a central role in modern medicine. Advances in technology have expanded the range of available parameters and increased the complexity of their interpretation. By applying user-centered design principles, it is possible to reduce the cognitive load associated with interpreting blood gas analysis. In this international, multicenter study, we explored anesthesiologists' perspectives on Visual Blood, a novel visualization technique for presenting blood gas analysis results. We conducted interviews with participants following two computer-based simulation studies: the first utilizing Virtual Reality (VR) (50 participants) and the second without VR (70 participants). Employing the template approach, we identified key themes in the interview responses and formulated six statements, which were rated using Likert scales: from 1 (strongly disagree) to 5 (strongly agree) in an online questionnaire. The most frequently mentioned theme was the positive usability features of Visual Blood. The online survey revealed that participants found Visual Blood to be an intuitive method for interpreting blood gas analysis (median 4, IQR 4-4, p<0.001). Participants noted that minimal training was required to effectively learn how to interpret Visual Blood (median 4, IQR 4-4, p<0.001). However, adjustments are necessary to reduce visual overload (median 4, IQR 2-4, p<0.001). Overall, Visual Blood received a favorable response. The strengths and weaknesses derived from these data will help optimize future versions of Visual Blood to improve the presentation of blood gas analysis results.

Falls are extremely damaging to the elderly. The number of elderly who have experienced falls has increased over the years, several of the elderly stay alone or in in badly maintained elderly homes. This makes a low-cost fall detection system a necessity. There has been huge improvements in terms of IoT systems, ML algorithms. Varied data sets have been collected across the world for fall detection. These data sets have a very little in common among them, in terms of user demographics, sensors used, the ADL and Fall activities Hence in this paper we present a data set that has wide user demographics, we used various sensors – such as accelerometer, gyroscope, magnetometer and hear rate. We used wrist worn sensors to collect data. In this paper we present a detailed analysis of the data set we collected using common ML algorithms such as – Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM). We analyzed the performance of these algorithms for variations in accuracy with respect to age, gender, height, weight and health issues and we have identified outliers by analyzing each incorrect prediction. This paper provides the complete details of the data collection methodology, The methods used for analysis and presents the results of analysis in complete detail.

Data from Earth Observation (EO) satellites are increasingly used to monitor the environment, understand variability and change, inform evaluations of climate model forecasts and manage natural resources. Policy makers are progressively relying on the information derived from these datasets to make decisions on mitigating and adapting to climate change. These decisions should be evidence based, which requires confidence in derived products as well as the reference measurements used to calibrate, validate or inform product development. In support of the European Union’s Earth Observation Programmes Copernicus Climate Change Service, the Quality Assurance for Essential Climate Variables (QA4ECV) project fulfilled a gap in the delivery of climate quality satellite derived datasets by prototyping a robust, generic system for the implementation and evaluation of Quality Assurance (QA) measures for satellite-derived ECV climate data record products. The project demonstrated the QA system on six new long-term, climate quality ECV data records for surface Albedo, Leaf Area Index, FAPAR, NO₂, HCHO and CO. Provision of standardized QA information provides data users with evidence-based confidence in the products and enables judgement on the fitness-for-purpose of various ECV data products their specific applications.

Abstract: With the continuous integration of various energy systems and the reform of energy trading marketization, the traditional power demand response can no longer meet the needs of multi-energy coupling. For the joint response problem of multi-energy, this paper proposes a comprehensive demand response optimization incentive model considering the user response characteristics, with the complete energy service providers and multiple users as the participants of the comprehensive demand response, to solve the response strategy for the optimal goal of all parties. The implementation architecture of integrated demand response and the electrothermal energy hub model is introduced first. Secondly, the integrated demand response collaborative optimization strategy of the integrated energy service providers and users is established, and the existence and uniqueness of the optimal solution are proved. Finally, the example analysis demonstrates that the proposed strategy can reduce the integrated energy service providers' response cost and user dissatisfaction under multiple scenarios.

The collaborative economy comprises resource circulation systems where consumers can act as both obtainers and providers of products and services. Despite considerable research on collaborative economies, there is a dearth of understanding of how individuals switch from being an obtainer to a provider. We address this void by drawing on 31 in-depth semi-structured interviews with collaborative economy obtainers. The findings suggest that personal values, learning experience, social benefits, mutuality, and peer influence drive obtainers to become providers. In contrast, distrusting strangers, a sense of intimacy, a lack of resources to share, and a lack of skills inhibit the switchover process. Our findings contextualize the drivers and inhibitors idiosyncratically to convert obtainers into providers, offer important implications for managers, contribute to the collaborative economy and sharing economy literature, and suggest compelling avenues for future research.

In this paper, we propose a novel approach of video skimming by exploiting the fusion of video temporal information and keyword information representation extracted from multi-model video information including audio, text and visual indices. In addition, we introduce the brand-safe filtering and sentiment analysis in order to only reserve the user-friendly content in the video skim. In the experiment by using the videos from YouTube-8M dataset, we have proved that the semantic conservation in the video skim from the proposed approach highly outperforms the approaches by only partial information of the video in conserving the semantic content of the video.

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.

The Chat Generative Pre-training Transformer (GPT), also known as ChatGPT, is a powerful generative AI model that can simulate human-like dialogues across a variety of domains. However, this popularity has attracted the attention of malicious actors who exploit ChatGPT to launch cyberattacks. This paper examines the tactics that adversaries use to leverage ChatGPT in a variety of cyberattacks. Attackers pose as regular users and manipulate ChatGPT’s vulnerability to malicious interactions, particularly in the context of cyber assault. The paper presents illustrative examples of cyberattacks that are possible with ChatGPT and discusses the realm of ChatGPT-fueled cybersecurity threats. The paper also investigates the extent of user awareness of the relationship between ChatGPT and cyberattacks. A survey of 253 participants was conducted, and their responses were measured on a three-point Likert scale. The results provide a comprehensive understanding of how ChatGPT can be used to improve business processes and identify areas for improvement. Over 80% of the participants agreed that cyber criminals use ChatGPT for malicious purposes. This finding underscores the importance of improving the security of this novel model. Organizations must take steps to protect their computational infrastructure. This analysis also highlights opportunities for streamlining processes, improving service quality, and increasing efficiency. Finally, the paper provides recommendations for using ChatGPT in a secure manner, outlining ways to mitigate potential cyberattacks and strengthen defenses against adversaries.

With the widespread use of mobile phones, increasing use of mobile applications in different areas of life have gained a large place. Between the reasons of the preference of the rapid increasing number of apps in the application markets of the different platforms, the aesthetic appereance of the icons that apps have perhaps the most important one. In this study, the visual symmetrical side of the icons of the most downloaded apss which are developed for preparing to Public Servant Exam in Turkey is emphasized. Two different types of data are obtained with working on the icons with image processing technique by using Mathworks Matlab program and survey method which is applied on Korkut Ata University students. By comparing the obtained data with the binary logistic regression method, it was determined that the visual symmetry in the apps’ icons partially contributed to the aesthetic appreciation.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

: The objective of this work is to elucidate the role of partnerships and the number of partners necessary for lead user innovation outcomes. Lead user theory identifies these users as being ahead of market trends and experiencing significant benefits from innovating. So far, most lead user research has concentrated on firms and not individual lead users’ perspective. Data were obtained using a quantitative design during which 321 respondents participated. The SPSS software was used to analyze the data and determine significance. These findings indicate a connection between number of partners and lead user innovation success. This demonstrates that number of partners is significant in determining the success or failure of an innovation. This study provides empirical evidence that the number of partners is an essential factor that influences the success of innovation, and establishes the absolute numbers for achievement of successful innovation. Future studies must investigate the possible implications of integrating the partner variables with other characteristics of the lead user.

One of the main challenges for autonomous learning in virtual environments is finding the right material that fits students’ needs and supports their learning process. Personalized recommender systems partially solve this problem by suggesting online educational resources to students based on their preferences. However, in educational environments (which need a proper characterization of both users and educational resources), most existing recommendation algorithms either fail to include all the available information or use hybrid processes that do not exploit possible relationships between users and item features. This article presents a personalized recommender system for educational resources aimed at combining user and item information into a single mathematical model based on matrix factorization. As a result, estimated latent factors can provide insight into possible interactions between users and item features, improving the quality of the information retrieval process. We validated the proposed model on a real dataset that contains the ratings assigned by students from Universidad Nacional de Colombia and Universidade Feevale to educational resources in the Colombian Federation of Learning Object Repositories (FROAC in Spanish). User characterization included learning style and educational level, whereas item characterization (obtained from the objects’ metadata), included interactivity level, aggregation level and type, and resource format. These results, compared to those obtained when not all the available information is included, show that our method can improve the recommendation process.

The Covid-19 pandemic has resulted in the closure of schools at every level, globally, forcing education to move online. Meeting the needs of students online for Science Lab classes, in particular, is a challenge since the physical labs are not available to the teachers or students. OLabs is a virtual Science Lab providing a complete learning environment of theory, experimental procedures, videos, animations, simulations, and assessments that capture real lab experiences with the relevant pedagogy. This study looks at the acquisition and behaviors of users, on the OLabs platform, during pre and Covid-19 times. Using Google Analytics, we observe that, during the pandemic time, users increasingly adopted OLabs as a new learning pedagogy for performing experiments as indicated by parameters like the number of users; the number of unique pages viewed per session; time spent on viewing content; bounce rate; and preference for content types such as theory, simulations, videos, and animations.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

Pedestrians, bicyclists, scooterists are Vulnerable Road Users (VRU) in traffic accidents. The number of fatalities and injuries in traffic accidents involving vulnerable road users has been steadily increasing in the last two decades in the U.S. even though road vehicles now have perception sensors like camera to detect risk and issue collision warnings or apply emergency braking. Perception sensors like camera are highly affected by lighting and weather conditions. Camera, radar and lidar cannot detect vulnerable road users in partially occluded and occluded situations. This paper proposes the use of Vehicle-to-VRU communication to inform nearby vehicles of VRUs having trajectories with potential collision risk. An Android smartphone app with low energy Bluetooth (BLE) advertising is developed and used for this communication. The same app is also used to collect motion data of VRUs for training. VRU motion data is smoothed using a Kalman filter and an LSTM neural network is used for future motion prediction. This information is used in an algorithm comparing Time-To-collision-Zone (TTZ) for the vehicle and VRU and issue a driver warning with different severity levels. The warning severity level is based on analysis of real data from a smart intersection for close vehicle and VRU interactions. The resulting driver warning system is demonstrated using proof-of-concept experiments. The method can easily be extended to a VRU collision mitigation system.

Data is present in the data sources like Files and Data bases, Retrieval of information from that data sources is one of the important issue nowadays. So for retrieval information from the data sources clustering is used. In the present market different types of clustering algorithms were available. But opting of the clustering is based on user requirements. This paper focuses on the study of hierarchical clustering approach on different conditions or measures or with customer choices like clustering process number of clusters generated at each level, number of levels, attributes range for performing the clustering on the given data set. In brief overview we discuss the hierarchical approach for clustering algorithm with the user opting choices.

This paper presents a modular approach to generic exergame design that combines custom physical exercises in a meaningful and motivating story. This aims to provide a tool that can be individually tailored and adapted to people with different needs, making it applicable to different diseases and states of disease. The game is based on motion capturing and integrates four example exercises that can be configured via our therapeutic web platform "Blexer-med". To prove the feasibility for a wide range of different users, evaluation tests were performed on 14 patients with various types and degrees of neuromuscular disorders, classified into three groups based on strength and autonomy. Users were free to choose their schedule and frequency. Game scores and three surveys (before, during, and after the intervention) showed similar experiences for all groups, with the most vulnerable having the most fun and satisfaction. The players were motivated by the story and by achieving high scores. The average usage time was 2.5 times per week, 20 minutes per session. Pure exercise time was about half the game time. The concept has proven feasible and forms a reasonable basis for further developments. The full 3D exercise needs further fine-tuning to enhance fun and motivation.

Due to the tremendous progress in the automotive industry, the growth of the urban population, the number of vehicles is increasing and this creates parking challenges. Intelligent parking management systems offer an optimal solution for finding empty parking space so that drivers can quickly find their car parking space. To solve these problems, it is necessary to design an intelligent parking system, in addition to providing comfort to drivers, which is also economically viable. This paper proposes an intelligent multi-storey car parking system with the help of RFID technology and examining user preferences that can effectively solve car parking problems. The proposed method is a multi-objective decision-making method to reduce the problem of car parking, which is called MODM-RPCP. Therefore, the proposed MODM-RPCP method can allocate the best space for their stopping place by using the decision-making system and based on the priorities considered by the users. The simulation results show that the MODM-RPCP reduces the average booking time more than 19.2% and 27.1%, and decreases the response time of central parking management server more than 20.1% and 29.78% compared to MOGWOLA and ODPP approaches.

Contact tracing has become an indispensable tool of various extensive measures to control the spread of COVID-19 pandemic due to novel coronavirus. This essential tool helps to identify, isolate and quarantine the contacted persons of a COVID-19 patient. However, the existing contact tracing applications developed by various countries, health organizations to trace down the contacts after identifying a COVID-19 patient suffers from several security and privacy concerns. In this work, we have identified those security and privacy issues of several leading contact tracing applications and proposed a blockchain-based framework to overcome the major security and privacy challenges imposed by the applications. We have discussed the security and privacy measures that are achieved by the proposed framework to show the effectiveness against the security and privacy issues raised by the existing mobile contact tracing applications.

The main moto of this study is to examine and study on behavior of Software Quality Assurance (SQA) issues of project stakeholders in a Scrum environment and their consequences. This inductive case study identifies SQA principles relevant to Meeting User Expectations. The Stakeholders in the Scrum project having lack of Concrete Guidance on Scrum’s SQA approaches, methods, and techniques. The insufficiency of concrete guidelines in Scrum needs a management squad to develop concepts that can include implementing practices from other methodologies and wisely modifying the system structure to incorporate the practices adopted, ensuring improvement in the processes, and creating a shared ownership environment. Through explaining the incompleteness of Agile approaches with special attention to the lack of concrete instructions in Scrum, the study uses techniques to customize literature and advocate for Scrum’s versatility. The study uses strategies to adapt literature and argue for Scrum’s simplicity by illustrating the incompleteness of Agile approaches with special attention to the lack of concrete instructions in Scrum methodology.

A tabletop system can facilitate multi-user collaboration in a variety of settings including small meetings, group work, and education and training exercises. The ability of identifying the users touching the table and their positions can promote collaborative work among participants, so methods have been studied that involve the attaching of sensors to the table or chairs or to the users themselves. An effective method of recognizing user actions without placing a burden on the user would be some type of visual process, so the development of a method that processes multi-touch gestures by visual means is desired. This paper describes the development of a multi-touch tabletop system using infrared image recognition for user position identification and presents the results of touch-gesture recognition experiments and a system usability evaluation. Using an FTIR touch panel and infrared light, this system picks up the shadow area of the user’s hand by infrared camera in relation to user touch operations and estimates user position by image recognition. The multi-touch gestures prepared for this system include an operation to change the direction of an object to face the user and a copy operation in which two users generate duplicates of an object. The average recognition rate of the change-direction gesture and copy gesture were found to be 96% and 85%, respectively. In addition, the system usability evaluation revealed that prior learning was easy and that system operations could be easily performed.

Investors are currently obliged to take ESG (Environment, Social, Governance) issues into consideration as part of their fiduciary duty. As such, it becomes increasingly important to identify sustainable investments that hold financial value as well. A sector where this is especially underdeveloped is real estate. This has a lot to do with the obfuscated conceptualization of ESG. The article identifies key gaps in literature and practice, and provides a framework to further the understanding of how ESG factors can add societal and financial value in the real estate sector. A key premise of the article is that the user in the building is grossly overlooked. Drawing on insights from behavioral social science and environmental psychology, the paper explains the role of the user in improving buildings’ ESG, also taking into account the investment value. To conclude, the article makes the case that the transition to user-centered smart real estate is the solution to improving both the environmental (E) and social (S) sustainability of buildings, as well as their investment value. Therefore, practitioners and academics are encouraged to critically evaluate and contextualize the ESG framework they are using, as well as the extent to which users are considered and smart technology is employed.

User experience-related challenges play a significant part in user-technology interactions. However, design processes have not always kept up with the evolving needs of users in terms of technology interactions. This paper aims to present the design, structure and evaluation of an integrated mental health intervention platform (AMGR-I), emphasizing the importance of user-centred design and engagement in addressing the evolving needs of users in technology interactions, specifically focusing on individuals with mental health disorders. The AMGR-I pilot platform integrates community, e-learning, and care sub-platforms to provide a holistic approach to mental health intervention. The study utilizes participatory design research with targeted users to gather insights into user preferences and expectations. By involving the targeted audience in the design process, the platform’s user-centered design can be improved effectively. The platform recognizes the changing view of users, who now seek products that deliver pleasure, enjoyment, engagement, and embodiment. This research contributes to the field by highlighting the importance of user-centered design in the development of a comprehensive platform for mental health support.

The proliferation of physiological sensors opens new opportunities to explore interactions, conduct experiments and evaluate the user experience with continuous monitoring of bodily functions. Commercial devices, however, can be costly or limit access to raw waveform data, while low-cost sensors are efforts-intensive to setup. To address these challenges, we introduce PhysioKit, an open-source, low-cost physiological computing toolkit. PhysioKit consists of (i) a sensor and hardware layer that can be configured in a modular manner per research needs, (ii) a software application layer that enables data acquisition, real-time visualization and machine learning (ML)-enabled signal quality assessment. This also supports basic visual biofeedback configurations and synchronized acquisition for co-located or remote multi-user settings. In a validation study for photo-plethysmography (PPG) sensor with 15 participants, PhysioKit shows strong agreement with research-grade PPG sensors. Furthermore, we report usability survey results from 10 micro-project teams (44 individual members in total) who use PhysioKit for 4-6 weeks, providing insights into its use cases and research benefits. We conclude by discussing extensibility and potential impact of the toolkit on the research community.

This paper describes the formulations for viscoplasticity of metals based on the Chaboche and Delobelle model. The implementations of the viscoplastic models were detailed herein and then implemented via user subroutines for material models (UMAT) in ABAQUS. Two typical metals, i.e., 316 Stainless Steel and Zircaloy-4, were chosen as examples and their viscoplastic behaviors were captured. Numerical simulations are compared to reported experiments to validate the models and the UMAT codes. Typical viscoplastic behaviors of both metals, such as stress relaxation and creep, were captured well against available experiments. We publicize all the data and codes via: https://github.com/XJTU-Zhou-group/ABAQUS_UMAT_316ss_Zr4.

A number of decision support tools facilitating the use of Electric Vehicles (EVs) have been recently developed. Due to the EVs’ limited autonomy, routing and path planning are the main challenges treated in such tools. Specifically, determining at which Charging Stations (CSs) to stop, and how much the EV should charge at them is complex. This complexity is further compounded by the fact that charging times depend on the CS technology, the EV characteristics, and follow a nonlinear function. Considering these factors, we propose a path planning methodology for EVs with user preferences, where charging is performed at public CSs. To achieve this, we introduce the Electric Vehicle Shortest Path Problem with time windows and user preferences (EVSPPWP) and propose an efficient solution algorithm for it. Given an origin and a destination, the algorithm prioritizes CSs close to Points of Interest (POIs) that match user inputted preferences, and user-defined time windows are considered for activities such as lunch and spending the night at hotels. The algorithm produces flexible solutions by considering clusters of charging points (CPs) as separate CSs. Furthermore, the algorithm yields resilient paths by ensuring that recommended paths have a minimum number of CSs in their vicinity. The main contributions of our methodology are: modeling user-defined time windows, including user-defined weights for different POI categories, creating CSs based on clusters of CPs with sufficient proximity, using resilient paths, and proposing an efficient algorithm for solving the EVSPPWP. To facilitate the use of our methodology, the algorithm was integrated into a web interface. We illustrate the use of the web interface, giving usage examples and comparing different settings.

Analysis of immediate effects of the COVID-19 pandemic on SMEs in Rwanda aims to assess and quantify the economic and financial impacts of the pandemic during the lockdown and presents options adopted by SMEs to reopen their business opera-tions. It also seeks to recommend options for sustainable and resilient economic recovery from the scars of COVID-19. This analysis used data collected from nearly 220 SMEs located across the country and applied an Exploratory Data Analysis (EDA) and multivariate linear regression models to measure the pandemic effects. The study revealed that firms downsized employment by 36.4 per cent, but data depict significant deviation within SMEs by their size. The COVID-19 pandemic has had a greater magni-tude of negative effects on sales levels among small businesses. Tax amounts were reduced amidst the crisis however, data do not show any significant variations among SMEs. Additionally, SMEs owners applied various coping mechanisms during re-opening. The study used firm-level data, providing detailed and reliable effects of the shocks on business performance. This analysis recommends establishing the medium-term financing and technical support for SMEs to ensure steady and sustainable recovery from pandemic effects as well as strengthening their resilience to any other socio-economics shocks.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

This study presents a comprehensive approach to trim optimization as a energy efficiency improvement measure, focusing on reducing fuel consumption for one RO-RO car carrier. Utilizing Computational Fluid Dynamics (CFD) software, the methodology incorporates Artificial Neural Networks (ANN) to develop a mathematical model for estimating key parameters such as brake power, daily fuel oil consumption (DFOC) and propeller speed. The complex ANN model is then integrated into a user-friendly software tool for practical engineering applications. The research outlines a seven-phase trim optimization process and discusses its potential extension to other types of ships, aiming to establish a universal methodology for CFD-based engineering analyses. Based on trim optimization results, the biggest DFOC goes up to 10.5% at 7.5m draft and up to 8% for higher drafts. Generally, in every considered case, it is recommended to sail with the trim towards the bow, meaning that the ship’s weight (ballast) should be adjusted to tilt slightly forward.

With the escalation of size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long-Short Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built, that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.

The transportation industry is rapidly transitioning from Internal Combustion Engine (ICE) based vehicles to Electric Vehicles (EVs) to promote clean energy. However, large-scale adoption of EVs can compromise the reliability of the power grids by introducing large uncertainty in the demand. Demand response with a controlled charge scheduling strategy for EVs can mitigate such issues. In this paper, a deep reinforcement learning- based charge scheduling strategy is developed for individual EVs by considering user’s dynamic driving behavior and charging preferences. The temporal dynamics of user’s anxiety about charging the EV battery is rigorously addressed. A dynamic weight allocation technique is applied to continuously tune user’s priority for charging and cost-saving with respect to charging duration. The sequential charging control problem is formulated as a Markov decision process, and an episodic approach to the deep deterministic policy gradient (DDPG) algorithm with target policy smoothing and delayed policy update techniques is applied to develop the optimal charge scheduling strategy. A real-world dataset that captures user’s driving behavior, such as arrival time, departure time, and charging duration, is utilized in this study. The extensive simulation results reveal the effectiveness of the proposed algorithm in minimizing energy cost while satisfying user’s charging requirements.

News recommendation schemes utilize features of the news itself and information about users to suggest and recommend relevant news items to the users towards the interest they have. However, the effectiveness of the existing news recommendation scheme is limited in the occurrence of new user cold start problems. Therefore, we designed a news recommender system using hybrid approaches to address new user cold start problems to ease and suggest more related news articles for new users. To achieve the objective mentioned above, user demographic data with a hybrid recommendation system that contains the scheme of both content-based and collaborative filtering approaches is proposed. To evaluate the effectiveness of the proposed model, an extensive experiment is conducted using a dataset of news articles with user rating value and user demographic data. The performance of the proposed model is done by two ways of experiment. So, the performance of the proposed model performs around 68.05% of Precision, 42.46% of Recall and 52.1% of the average F1 score for the experiment based on individual user similarity in the system. And also performs around 93.75% of precision, 40.25% of recall and 56.31% F1-score for the similarity of users based on the similarity of users within the same category which is better than the first experiment.

With quick improvement in the field of network, everything becomes online. Remote user authentication is a mechanism in which remote server verify the correctness of user over common or public channel. Remote user authentication can be Single server authentication or Multi server authentication. The disadvantage of single server authentication is that the user needs to recall user id and password for each service, he/she need to utilize, however it can overcome by Multi-server authentication in which user needs to register himself with Registration Center (RC) for the first run through and after that onwards he can utilize each service (which are given by servers, associated with RC) by recollecting only one user id and password. In this paper, we analyze Chen's authentication scheme (based on multi server authentication) and show that this scheme is vulnerable to password guessing attack, replay attack, RC spoofing attack, session key verification attack and perfect forward secrecy attack. In this Paper, we propose a biometric based remote user authentication scheme in multi-server environment. Proposed scheme is more secure and efficient as compared to chen's scheme\cite{23}.

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 commonly accepted definition of sustainability considers the availability of relevant resources to make an activity feasible and durable while also recognizing users' support as essential part of the social side of sustainability. IoT represents a disruption in the general scenario of computing for both users and professionals, The real expansion and integration of applications based on IoT depend on our capacity of exploring the necessary skills and professional profiles that are essential for implementation of IoT projects, but also on the perception of relevant aspects for users, e.g., on privacy, legal, IPR and security issues. Our participation in several EU-funded projects with focus in this area has enabled the collection of information on both sides of IoT sustainability through surveys but also collecting data from a variety of sources. Thanks to these varied and complementary sources of information, this article will explore the user and professional aspects for sustainability of Internet of Things in practice.

Electric vehicles offer a means to reduce greenhouse gas emissions in passenger transport. The availability of reliable charging infrastructure is crucial for the successful uptake of electric vehicles in dense urban areas. In a pilot project in the city of Hamburg, Germany, public charging infrastructure is equipped with a reservation option providing exclusive access for local residents and businesses. The present paper combines quantitative and qualitative methods to investigate the effects of the newly introduced neighborhood charging concept. We use a methodology combining a quantitative questionnaire survey and qualitative focus group discussions as well as the analyses of charging infrastructure utilization data. Results show that inner-city charging and parking options are of key importance for (potential) users of electric vehicles. Hence, the neighborhood concept is rated very positively. Providing guaranteed charging and parking facilities are therefore likely to increase the stock of EVs. On the other hand, these could to a large extent be additional cars with consequential disadvantages. The study shows that openly accessible infrastructure is presently utilized much more intense than the exclusive option. Consequentially, the concept evaluated should be part of an integrated approach managing parking and supporting efficient concepts like car sharing.

This study explores the Experience Economy dimensions within fishing tourism, adopting a data science perspective by employing web crawling for extensive data gathering on reviewer profiles from TripAdvisor. Utilizing Natural Language Processing (NLP) techniques, it scrutinizes the relationship between Experience Economy dimensions and user profiling aspects. Findings reveal "Entertainment" as the predominant dimension in tourist reviews, followed by "Aesthetic," "Educational," and "Escapist." Particularly notable are the frequent co-occurrences of "Entertainment"-"Aesthetic" and "Educational"-"Entertainment" pairs. The practical implications underscore the potential socioeconomic benefits of fishing tourism for local communities and fishermen, emphasizing the necessity for governmental support in terms of infrastructure, leadership, legislation, and financial backing to foster sustainable development in this sector. Notably, this research stands as a pioneering effort in its focus on Experience Economy dimensions and user profiling within the context of fishing tourism, drawing data from business pages and user profiles.

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

In this study, we investigate the growth of EV ownership among Swedish households. Through detailed socio-economic data and the Swedish vehicle register, we are able compare characteristics of households that bought an EV in 2016 with those that became EV owners in 2020. We are thus able to describe and analyse the transition from the early EV owners to the early majority. Unlike many prior studies in the field, which are based on surveys or interviews, this study relies on registry data, thereby encompassing all car owners in Sweden. In this study, we have also scrutinized the significance of ownership mode by distinguishing between households that have chosen to purchase a car and those that have chosen to lease one. The results show that the demand for EVs has reached new consumer groups, e.g. both younger and older car buyers, and that the option to lease a car privately has broadened the market for new cars in general, and for EV’s in particular.

Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO

Researchers have asserted that, as patients rely on external powerful sources to control their disorders, in addition to their own internal powers and virtues, examining their coping mechanisms can lead to a better understanding of the initiation and progression of chronic diseases and uncover potential opportunities to balance and enhance their psycho-spiritual well-being and possibly their treatment and recovery. This review serves as a user’s manual for investigators who choose to use the CSI-SF to conduct their research on coping behaviors. The CSI-SF that measures four coping strategies based on 16 items was first assessed using the Jackson Heart Study (JHS) cohort in 2007. The reliability and construct validity of the CSI-SF was also later assessed among hemodialysis patients across 13 countries. In this study, the CSI-SF was assessed to be a reliable and valid instrument for measuring coping strategies. The CSI-SF serves the purpose of developing an inner voice that can assist with understanding how people cope with everyday life. The information gathered from administration of the CSI-SF can inform investigators about environmental cues and triggers that can also impact individual health.

This study analyzes the evolution of Corporate and Social Responsibility (CSR) and Environmental, Social, and Governance (ESG) concepts in social media, specifically on Twitter, from 2007 to 2022. The research aims to understand how society perceives organizational practices related to these aspects. Twitter provides an authentic environment for capturing user perceptions. The methodology involved collecting Tweets using Python and analyzing them in English, Spanish, and Portuguese with RStudio. The study's main findings highlight significant contributions and concerns within the CSR and ESG debates. While CSR's popularity showed a downward trend from 2010 to 2019, ESG grew exponentially during the pandemic and post-pandemic periods. Word analysis revealed the impact of specific hashtags: RSC in Spanish, #CSR in English, and #ESG in Portuguese. Society's increasing concerns concern environmental issues for combating global warming and ensuring a better quality of life, alongside financial transparency and ethical practices in companies. However, the social aspect of ESG, represented by the letter "S," didn't garner the same attention as environmental and governance matters. This discrepancy might stem from a lack of awareness or lower priority in public discussions on social media, overshadowing the social dimension in the ESG framework.

Previous high throughput screening studies indicated trans-eQTLs tend to be tissue specific. In this study, I probed if this feature can be used to identify tissue-specific gene regulatory networks. eQTL data (P<5e-8) for 16,259 genes were identified and their summary association statistics were obtained from the eQTLGene database. Next, eQTLs that display both cis and trans regulatory effects were selected and the association between their corresponding genes were examined using Mendelian randomization. A total of 169 genes that exerted trans-regulatory impacts on 692 genes were identified. 90% of the genes (N=749) aggregated into a gene regulatory network significantly enriched in hemo-immune processes. The robustness of finding was confirmed through simulation. The identified network displayed the scale-free topology. This provided the reason to examine the association of the network’s hub genes with the phenome. The outcome of analyses revealed GSDMB and ORMDL3 impact several disorders of immune origin and ALDH2 overexpression contributes to obesity. This study reports trans-eQTLs can be used to identify tissue-specific gene regulatory networks and describes a workflow to achieve this purpose. The network identified in this study showed scale-free topology indicating the hub genes of a GRN could be targeted to prevent disease outcomes.

Regulation of the diet in terms of the content of soluble and insoluble fibre is of great importance from the point of view of dietetics, microbiology of the gastrointestinal tract and immunology of the organism. Dietary fibre plays an important role in maintaining the balance between the con-tinuity of gut mucus production (intestinal mucosa), the stability of the intestinal microbiome composition, the regeneration of the intestinal epithelium and the immune status within the gut-associated lymphoid tissue (GALT) are played by pectin and their metabolic products in the form of pectin oligosaccharides. The main purpose of this review was to focus on pectin, their functions, their impact on the human's health and on some of the most promising prebiotics due to their solubility in the gastrointestinal tract in the form of pectin oligosaccharides. They have the properties of pectin, but have also additional features and postbiotics obtained in fermentation process can affect the intestinal microbiome and GALT-related immunity. They can be obtained from plant agro-wastes.

Researchers are always looking to find ways to use energy more efficiently while keeping people comfortable at home. They've tried different methods like fuzzy logic and genetic algorithms to do this. In this paper, we introduce a novel model that combines different techniques like data smoothing, optimization, machine learning, and control algorithms to reach this goal in smart homes. The proposed model is made up of three main parts: smoothing, optimization, and control. The smoothing part uses the Kalman filter to get rid of any unnecessary noise and make sure the data used to predict energy use is clean and smooth. The optimization part uses both genetic with firefly algorithms and artificial neural networks to minimize the gap between the actual environmental conditions and the conditions users find comfortable. It adjusts the comfort settings in real-time using machine learning. The control part uses Mamdani fuzzy logic to make sure energy is delivered in the best way possible to the systems that control temperature, lighting, etc., making the home comfortable and saving energy at the same time. Comparative analyses demonstrate the superiority of the proposed model over existing algorithms, including the Particle Swarm Optimization (PSO) algorithm. Additionally, the paper discusses the importance of adaptive controllers in addressing issues associated with incorrect PID controller selection and highlights the benefits of using the FA-GA in energy optimization and comfort improvement. Overall, the proposed model offers an effective solution for efficient energy consumption optimization and enhanced user comfort in diverse settings through the integration of advanced algorithms and optimization modules.

To achieve user retention through multifactor synergy, Internet enterprises must reduce costs and increase efficiency and sustainable development. In response to the dilemma that Internet compa-nies are experiencing increasingly high user acquisition costs and serious user churn, this paper uses a sample of 46,695 user reviews of nine product series from Xiaomi Ecological Chain. Case studies and qualitative comparative analysis are used to explore the influence mechanisms of quality of experience, brand trust, and brand attachment on users' retention intentions. Our find-ings are as follows. (1) Brand attachment alone is not necessary for high user retention intention, but user perception, cognition, and brand trust are necessary. (2) Quality of experience positively impacts brand trust, attachment, and user retention intention. Therefore, improving user perception and cognition is critical in generating high user retention intention. (3) Five configuration paths can achieve high user retention intention, three configuration paths lead to low user retention intention, and there is an asymmetric relationship between these paths. Among them, the role of quality of experience-driven configuration paths in generating user retention intention is the most prominent. (4) In the substitution relationship between configuration paths, user perception, and cognition can substitute with brand trust and attachment. Our findings have important theoretical and practical implications for revealing the realization paths of high user retention intention in Internet companies and provide a new perspective for future research.

Gene regulation is vital for cellular function and homeostasis, involving diverse mechanisms controlling specific gene products and contributing to tissue-specific gene expression. Dysregulation leads to diseases, emphasizing the need for understanding these mechanisms. Non-coding regions, notably enhancers and super-enhancers (SEs), act as crucial gene regulators, orchestrating transcriptional activity via transcription factors (TFs). SEs, with high activity, are pivotal in cell identity and disease progression. Current computational approaches focus on individual regulators like TFs and miRNAs, neglecting SE interactions. We emphasize incorporating SEs into models to enhance understanding. Experimental studies have recently linked miRNAs and TFs to SEs. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) provides valuable SE information. In this review, we categorize computational methods leveraging TF and miRNA data into three areas and explore challenges integrating enhancers/SEs. These include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators, including SEs. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in identifying therapeutic targets and disease biomarkers. We believe that understanding the role of ChIP-Seq data in predicting the effect of miRNA on gene regulation is crucial to construct statistical/computational model that would tackle these challenges.

Space syntax can be potentially applied for the evaluation of quality of user flows particularly in solving problems of sustainable projects in spaces intended for users to move around. This study aims to analyze the concepts of pedestrian flows (open and closed) by space syntax based bibli-ographical approach on a global scale, demonstrating the capability of improvements in Sus-tainable Development Goals (SDGs) as applied to the architecture of sustainable flows. Scopus theoretical reference bases were used, which are directly related to the theme of space syntax in open and closed spaces. Frequency analyzes were carried out, applying the content analysis, to identify words with a degree of similarity, related to “space syntax: flow in urban environ-ments” and “space syntax in closed built systems”, in relations to the SDGs. The results show that pedestrian flows identified in the literature aid to understand the global production on space syntax in open and closed spaces, directed to user flows in the built environment, where many environments of flows in the built space analyzed become unsustainable because they do not present full flow efficiency. In our study on space syntax, the following central terms were identified: pedestrian movements (open urban systems) and space (closed built systems), which allows a better understanding of the flows, highlighting the importance of the urbanist architect on the functionality of user flows in sustainable architectural projects on a global scale. The most frequent categories for open environments were the terms space and form and for closed envi-ronments were space and flow.

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.

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.

Voice search technology has gained popularity in recent years, transforming the way individuals interact with digital devices. This study aims to explore the sustainable use intention of voice search, focusing on the psychological factors that influence new technology adoption. Using the Technology Acceptance Model (TAM) and anthropomorphism theory, it provides insights for developers, marketers, and policymakers interested in voice search technology. Through an online survey targeting experienced voice search users in the U.S., the study examines the impact of TAM factors (perceived usefulness, ease of use, quality satisfaction) and anthropomorphism traits (narcissism, Machiavellianism) on sustainable use intention of voice search. Results from hierarchical regression analysis show that perceived usefulness, ease of use, and quality satisfaction positively affect sustainable intention of voice search. Additionally, narcissism and Machiavellianism predict sustainable intention of voice search. By merging TAM and anthropomorphism theory, this study enhances our understanding of voice search behavior and technology adoption.

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.

Background: Facebook represents a new dimension for global information sharing. Suicidal behaviours and attempts are growingly reported on Facebook. This scoping review explores the various aspects of suicidal behaviours associated with Facebook, discussing the challenges and preventive measures. Methods: PubMed, Google Scholar, and Scopus were searched for related articles published in English up to October 2021, using different combinations of "Facebook" and "suicide". A group of consultant psychiatrists screened the records and read the full-text articles to extract relevant data. Results: Facebook impacts suicidal behaviours in different aspects. Announcing suicides through sharing notes or personal information, which can be helpful in prediction of suicide, and harmful since negatively affects audience. Live-streaming videos of suicide is another aspect, which questions the ability of Facebook to monitor shared contents that can negatively affect the audience. Helping bereaved families to share feelings and seek support online, commemorating the lost person by sharing their photos is another positive impact. Moreover, it can provide real-world details of everyday user behaviours, which are helpful to predict suicide risk, primarily through novel machine-learning techniques, and provide early warning and valuable help to prevent it. It can also provide a timeline of the user's activities and state of mind before suicide. Conclusions: Social media can detect suicidal tendencies, provide support for those seeking help, comfort family and friends with their grief, and provide insights via timelining the users' activities leading to their suicide. The lack of quantitative studies on evaluating preventative efforts on Facebook was one of the limitations. The creators' commitment and the users' social responsibility will be required to create a mentally healthy Facebook environment.

Battery electric vehicles provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. In particular, state-of-charge assumptions and representing fast charging drive curtailment of renewable energy feed-in and required gas power plant flexibility. A detailed representation of fleet charge connection is less important. Peak load can be significantly reduced by 5% and 3% in both scenarios, respectively. Shifted load is very robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

The introduction of ChatGPT in academia has raised significant concerns, particularly regarding issues like plagiarism and the ethical use of generative text for academic purposes. Existing literature has predominantly focused on ChatGPT adoption, leaving a notable gap in addressing the strategies users employ to mitigate these emerging challenges. To bridge this gap, this research utilizes the Uncertainty Reduction Theory (URT) to formulate user strategies for reducing uncertainty. These strategies include both interactive and passive approaches. Concurrently, the study identifies key sources of uncertainty, which include concerns related to transparency, information accuracy, and privacy. Additionally, it introduces the concepts of seeking clarification and consulting peer feedback as mediating roles to facilitate reduced uncertainty. We tested these hypotheses with a sample of Indonesian users (N = 566) using structural equation modeling via Smart-PLS 4.0 software. The results confirm that interactive Uncertainty Reduction Strategies (URS) are more effective in reducing uncertainty when using ChatGPT compared to passive URS. Transparency concerns, information accuracy, and privacy concerns are identified as factors that increase the level of uncertainty. In contrast, consulting peer feedback is the most effective strategy for reducing uncertainty compared to seeking clarification at the individual-system level. Insights from the mediating effects confirm that consulting peer feedback significantly mediates uncertainty sources to reduce uncertainty. The study discusses various strategies for the ethical use of ChatGPT by users in the educational context and contributes significantly to theoretical development.

: Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America, and to enhance the utility of drought monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impacts information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and the colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and the warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and indicators are changing over time as the climate warms, and by recommending ways to strengthen the use of indices and indicators in drought decision-making.

Methamphetamine (METH) has the potential to disrupt the activities of neurotransmitters in the Central Nervous System (CNS) and cause neurotoxicity through various pathways. These pathways include increased production of reactive nitrogen and oxygen species, hypothermia, and induction of mitochondrial apoptosis. In this study, we investigated the long-term effects of METH addiction on the structural changes in the amygdala of postmortem human brains, as well as the involvement of the CREB/BDNF and Akt-1/GSK3 signaling pathways. We examined ten male postmortem brains, comparing control subjects with chronic METH users, using immunohistochemistry, Real-time PCR (to measure levels of CREB, BDNF, Akt-1, GSK3, and TNF-α), Tunnel assay, stereology, and assays for ROS, GSSG, and GPX. The findings revealed that METH significantly reduced the expression of BDNF, CREB, Akt-1, and GPX, while increasing the levels of GSSG, ROS, RIPK3, GSK3, and TNF-α. Furthermore, METH was found to induce inflammation and neurodegeneration in the amygdala, with ROS production mediated by the CREB/BDNF and Akt-1/GSK3 signaling pathways.

Precision medicine approaches often relies on complex and integrative analysis of multiple biomarkers from “omics” data to generate insights that can help either diagnostics, prognostics or therapeutical decisions. Such insights are often made using Machine learning (ML) models that make sample classification for a particular phenotype (yes/no). Building such models is a challenge and time-consuming, requiring advanced coding skills and mathematical modelling expertise. Artificial intelligence (AI) is a methodological solution that has the potential to facilitate, optimize and scale model development. In this work, we developed an AI-based, user-friendly and code-free platform (https://digitalphenomics.com) that fully automates the development of predictive models from quantitative “omics” data. Here, we show the application of this tool with the development of cancer survival prognostics models using real-life data from breast, lung and renal cancer transcriptomes. We report and compare their sensitivity, specificity, accuracy and Receiver Operating Characteristic (ROC) curve Area Under the Curve (AUC). Further, we report the associated sets of genes (biomarkers) and their expression pattern that are predictive of cancer survival. Moreover, we made our models available as online tools to generate prognostic predictions based on the gene expression of the biomarkers. In conclusion, we demonstrated that our tool is a robust user-friendly solution to develop bespoke predictive tools from “omics” data which facilitate precision medicine introduction to the point-of-care.