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

This paper investigates the creation of multimodal web interfaces to foster inclusive services, aiming to empower individuals with disabilities. This paper details the development of SEU (Services to Empower yoU), an online platform designed to improve access to offline services for persons with disabilities through personalised multimodal web interfaces. Developed using a participatory design approach, SEU involved collaboration with Human-Computer Interaction experts, occupational therapists, accessibility experts, and persons with disabilities. The platform supports multiple user needs across cognitive, visual, motor, and hearing impairments. SEU was evaluated in a two-phase usability study, which included expert assessment and testing by persons with disabilities, supplemented with interviews, observations, and questionnaires. Feedback highlighted SEU's effective design and usability, emphasising its utility in enhancing service accessibility. The study indicates that both experts and persons with disabilities consider the platform an added value. Although the platform was well-received, suggestions for better cognitive support and the addition of a mobile application were noted, acknowledging the regular smartphone usage among the target users. [ABSTRACT FROM AUTHOR]

Along with their physical health, modern people also need to manage the health of their scalp and hair due to changes in lifestyle habits, job stress, and environmental pollution. In this study, a machine learning model was developed to diagnose scalp conditions such as fine dandruff and perifollicular erythema. Then, transfer learning was conducted using EfficientNet-B0. A web platform that allows users to easily diagnose the condition of their scalp was also proposed. The results showed that the accuracy of the diagnosis model for fine dandruff and perifollicular erythema was 75% and 82%, respectively. It showed good performance in classifying normal, mild, moderate, and severe cases compared to previous studies. Finally, a fast and convenient web platform was developed where users can upload an image and immediately visualize their scalp condition, receive diagnostic results, and see similar cases and solutions. The analysis of user satisfaction indicates that this web application has achieved exceptional outcomes in terms of user satisfaction, garnering high evaluations for its usability, design effectiveness, and overall user experience. This setup enables users to easily check their scalp condition and is accessible to everyone, which is a significant advantage. This is expected to play a crucial role in contributing to global scalp health by advocating the benefits of the early detection and treatment of scalp-related conditions. [ABSTRACT FROM AUTHOR]

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

AbstractExploration of demographic variations is required to develop dynamic web platforms that cater to the varying preferences of diverse users. Hence, this study evaluated instructors’ demographic variations on a web-based platform for connecting with practitioners for student development. Both objective and subjective measures were adopted to investigate age- and gender-related differences in gaze behavior, task completion time, perceived cognitive load, perceived usability, and trust. Compared to male instructors, female instructors had higher fixation counts, longer task completion times, and statistically significant longer fixation duration. Female instructors gave higher usability and trust ratings but reported a higher cognitive workload. Compared to Generation Y instructors, Generation X instructors had longer fixation duration, higher fixation count, and statistically longer task completion time. Generation X instructors reported high cognitive load, lower usability, and trust ratings. The study also reveals demographic differences in parameters that instructors focused on while connecting with practitioners via a web platform. [ABSTRACT FROM AUTHOR]

Computational agents support humans in many areas of life and are therefore found in heterogeneous contexts. This means that agents operate in rapidly changing environments and can be confronted with huge state and action spaces. In order to perform services and carry out activities satisfactorily, i.e. in a goal-oriented manner, agents require prior knowledge and therefore have to develop and pursue context-dependent policies. The problem here is that prescribing policies in advance is limited and inflexible, especially in dynamically changing environments. Moreover, the context (i.e. the external and internal state) of an agent determines its choice of actions. Since the environments in which agents operate can be stochastic and complex in terms of the number of states and feasible actions, activities are usually modelled in a simplified way by Markov decision processes so that, for example, agents with reinforcement learning are able to learn policies, i.e. state-action pairs, that help to capture the context and act accordingly to optimally perform activities. However, training policies for all possible contexts using reinforcement learning is time-consuming. A requirement and challenge for agents is to learn strategies quickly and respond immediately in cross-context environments and applications, e.g., the Internet, service robotics, cyber-physical systems. In this work, we propose a novel simulation-based approach that enables a) the representation of heterogeneous contexts through knowledge graphs and entity embeddings and b) the context-aware composition of policies on demand by ensembles of agents running in parallel. The evaluation we conducted with the "Virtual Home" dataset indicates that agents with a need to switch seamlessly between different contexts, e.g. in a home environment, can request on-demand composed policies that lead to the successful completion of context-appropriate activities without having to learn these policies in lengthy training steps and episodes, in contrast to agents that use reinforcement learning. The presented approach enables both context-aware and cross-context applicability of untrained computational agents. Furthermore, the source code of the approach as well as the generated data, i.e. the trained embeddings and the semantic representation of domestic activities, is open source and openly accessible on Github and Figshare. [ABSTRACT FROM AUTHOR]

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Objective: HealthPathways is a web-based platform designed for use during a consultation to offer health professionals locally agreed information to support clinical and referral decision making. This study aimed to investigate whether access to specific HealthPathways pages in the North Coast New South Wales (NSW) region increased during specific critical events. High pageviews is used as a proxy for platform usefulness, and/or trust as a source of up-to-date information. Methods: Data were extracted from Google Analytics from December 2015 to December 2021. Descriptive statistics were generated for the total number of pageviews for all pages by month and year (2015–2021); for the top 15 most viewed pages in 2019, February–March 2020 inclusive (early COVID-19 pandemic period), 10–24 March 2021 (North Coast NSW region local disaster declaration period) and 26 June–11 October 2021 (NSW COVID-19 lockdown period); and for monthly pageviews for the Influenza Immunisation pathway (2016–2021). Results: Access to specific pages in HealthPathways increased alongside the occurrence of critical events affecting the region. Spikes in access to specific pages were seen during COVID-19 lockdown periods, during natural disasters, as well as during the annual influenza season. Conclusions: HealthPathways is viewed as a useful and trusted source of information for health professionals in the North Coast NSW region. HealthPathways provides an opportunity for timely dissemination of information during critical events, including natural disasters and emergencies. What is known about the topic? Evidence from New Zealand suggests that HealthPathways is viewed as a useful and trusted source of information by health professionals during critical and emergency events, and when information needs are subject to frequent change. What does this paper add? This paper provides evidence in the Australian context that health professionals access specific pages in HealthPathways at higher rates during critical and emergency events. What are the implications for practitioners? HealthPathways has the capacity to provide timely and accurate information to health professionals during critical and emergency events, as well as to identify their emerging information needs. [ABSTRACT FROM AUTHOR]

In the contemporary agricultural landscape, agriculture faces four pressing demands: competitiveness, ensuring food security for a growing population, environmental sustainability, and providing farmers with acceptable living conditions. To meet this global challenge, digital technologies represent a major avenue for innovation and development towards modernized digital agriculture. In this context, irrigation advisory platforms have proven to be transformational tools for both farmers and policymakers, offering insights into the appropriate crop water requirements. This article presents a benchmarking analysis of around 20 professional irrigation advisory platforms. The methodology involves selecting 20 platforms based on accessibility ensuring geographical characteristic diversity. Our findings highlight key criteria shaping the ecosystem of such platforms, including the services offered and their objectives, the types of covered crops, the target users, the form, as well as the source, the availability of the platform, and the variety of data utilized. Lastly, we discuss the main conclusions drawn from our analysis and provide insights into the challenges and future perspectives of irrigation advisory platforms in enhancing agricultural practices and sustainability. [ABSTRACT FROM AUTHOR]

Abstract Since the onset of conflict in Syria in 2011, several heritage sites have suffered partial or complete destruction. The ancient city of Palmyra, a UNESCO World Heritage Site since 1980, was placed on the List of World Heritage in Danger in 2013, alongside five other Syrian heritage sites. Between 2015 and 2017, Palmyra experienced the loss of nearly 40% of its historic buildings and landmarks. The site’s remote location and ongoing armed conflict pose additional challenges in accessing and preserving it. This paper introduces an interactive online platform employing 360° media to document and disseminate information about the world heritage site of Palmyra. The platform contributes to the safeguarding process of Palmyra, facilitating public engagement and interaction with the site. It also has potential applications for other sites in Syria and beyond. The platform allows users to explore both archival and current information of damaged historical monuments and buildings, revealing their layered histories before and after demolition. Employing interactive 360° media, similar to applications like Google Street View, the platform offers a reality-based digital environment. Users can virtually move into different buildings or follow specific pathways within the site, accessing archival information and documentation about particular monuments and buildings along their journey. This project serves as a unique informational resource for decision-makers and policy planners, aiding in the understanding of the site and the development of relevant strategies and recommendations. It also proves beneficial as an educational tool. The collective understanding fostered by this platform is expected to positively influence post-conflict restoration and reconstruction plans, making it an invaluable asset for research and educational purposes.

In the contemporary agricultural landscape, agriculture faces four pressing demands: competitiveness, ensuring food security for a growing population, environmental sustainability, and providing farmers with acceptable living conditions. To meet this global challenge, digital technologies represent a major avenue for innovation and development towards modernized digital agriculture. In this context, irrigation advisory platforms have proven to be transformational tools for both farmers and policymakers, offering insights into the appropriate crop water requirements. This article presents a benchmarking analysis of around 20 professional irrigation advisory platforms. The methodology involves selecting 20 platforms based on accessibility ensuring geographical characteristic diversity. Our findings highlight key criteria shaping the ecosystem of such platforms, including the services offered and their objectives, the types of covered crops, the target users, the form, as well as the source, the availability of the platform, and the variety of data utilized. Lastly, we discuss the main conclusions drawn from our analysis and provide insights into the challenges and future perspectives of irrigation advisory platforms in enhancing agricultural practices and sustainability.

Since the onset of conflict in Syria in 2011, several heritage sites have suffered partial or complete destruction. The ancient city of Palmyra, a UNESCO World Heritage Site since 1980, was placed on the List of World Heritage in Danger in 2013, alongside five other Syrian heritage sites. Between 2015 and 2017, Palmyra experienced the loss of nearly 40% of its historic buildings and landmarks. The site's remote location and ongoing armed conflict pose additional challenges in accessing and preserving it. This paper introduces an interactive online platform employing 360° media to document and disseminate information about the world heritage site of Palmyra. The platform contributes to the safeguarding process of Palmyra, facilitating public engagement and interaction with the site. It also has potential applications for other sites in Syria and beyond. The platform allows users to explore both archival and current information of damaged historical monuments and buildings, revealing their layered histories before and after demolition. Employing interactive 360° media, similar to applications like Google Street View, the platform offers a reality-based digital environment. Users can virtually move into different buildings or follow specific pathways within the site, accessing archival information and documentation about particular monuments and buildings along their journey. This project serves as a unique informational resource for decision-makers and policy planners, aiding in the understanding of the site and the development of relevant strategies and recommendations. It also proves beneficial as an educational tool. The collective understanding fostered by this platform is expected to positively influence post-conflict restoration and reconstruction plans, making it an invaluable asset for research and educational purposes. [ABSTRACT FROM AUTHOR]

Powder bed fusion—laser beam (PBF-LB), a prevalent and rapidly advancing additive manufacturing (AM) technology nowadays, serves the industry by producing thin, complex, and lightweight components for various sectors, including healthcare, automotive, defence, and aerospace. However, this technology encounters challenges related to the construction of critical parts and the high overall process costs. Equally significant is the role of support structures in metal laser powder bed fusion (PBF-LB/M). The absence of supports can lead to defective and collapsed parts, while the incorrect selection of a support type or the addition of unnecessary supports results in increased material usage and additional post-processing efforts. Therefore, there is a pressing need for advanced software capable of generating appropriate support structures and predicting the thermomechanical behaviour of a part under PBF-LB/M printing conditions. Such software would be beneficial for the industry to avoid printing defects caused by high thermal stresses, minimise material usage, reduce printing time, and ensure high-quality prints. In this study, we introduce a web-based support generation and optimisation platform for PBF-LB/M. Through this platform, among other features, users can import three-dimensional (3D) parts and generate block-type support structures with diamond perforations based on the PySLM library, all within a user-friendly web environment. The first release of the platform (v0.6) is fully interactive and accessible online at no cost. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0 (I4.0), a significant challenge hindering digital transformation is the lack of mutual understanding between academia—particularly within engineering and computer science—and the industrial sector, especially small to medium-sized enterprises (SMEs). This gap can result in industries missing out on the potential benefits of cutting-edge scientific research and innovations that can address their daily concerns. At the same time, academics may struggle to identify real-world application areas for their emerging technological solutions. Moreover, the ever-increasing complexity of industrial challenges and technologies has widened the hiatus. To address this issue, our study introduces a comprehensive taxonomy, developed through a transparent, iterative process and presented via a user-centric web platform. Distinct from existing taxonomies, ours emphasizes practical applicability by categorizing and connecting industrial challenges with I4.0 technologies using articles, best practices, and use cases from academic and grey literature, thereby effectively bridging the academic-industrial communication gap. Its effectiveness and practical utility were validated in a workshop as part of the Erasmus+ project PLANET4, where industry professionals provided positive feedback after applying it to real-world challenges. Future work will include expanding the taxonomy, developing an Industry 4.0 ontology, and further enhancing the usability and maintainability of the developed web platform.

Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets--gene expression, DNA methylation, and DNA accessibility--while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer's superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification. [ABSTRACT FROM AUTHOR]

The blockchain technology can become a solution in data privacy and protection, being considered a public and decentralized database, in which the identity of a user is masked in the form of an address. Based on this technology, several decentralized protocols such as Bitcoin or Ethereum have been created, with the help of which decentralized applications can be created. In this article, we examine how these technologies can be used to ensure the transparency of a software application, but also the privacy and protection of user data. Other facilities like Non-Fungible Token - NFT and Smart Contract are used. The paper proposes the design and the implementation (including the source code) of a web platform for education / courses incorporating blockchain technology. The results and conclusions are showed. Finally, the contributions of the paper, the limitations and future works are also highlighted. [ABSTRACT FROM AUTHOR]

Along with their physical health, modern people also need to manage the health of their scalp and hair due to changes in lifestyle habits, job stress, and environmental pollution. In this study, a machine learning model was developed to diagnose scalp conditions such as fine dandruff and perifollicular erythema. Then, transfer learning was conducted using EfficientNet-B0. A web platform that allows users to easily diagnose the condition of their scalp was also proposed. The results showed that the accuracy of the diagnosis model for fine dandruff and perifollicular erythema was 75% and 82%, respectively. It showed good performance in classifying normal, mild, moderate, and severe cases compared to previous studies. Finally, a fast and convenient web platform was developed where users can upload an image and immediately visualize their scalp condition, receive diagnostic results, and see similar cases and solutions. The analysis of user satisfaction indicates that this web application has achieved exceptional outcomes in terms of user satisfaction, garnering high evaluations for its usability, design effectiveness, and overall user experience. This setup enables users to easily check their scalp condition and is accessible to everyone, which is a significant advantage. This is expected to play a crucial role in contributing to global scalp health by advocating the benefits of the early detection and treatment of scalp-related conditions.

With the rapid development of networks and information technology, the form of mass communication is changing day by day, and the efficient and convenient communication of information also brings challenges to the dissemination of traditional culture. This paper proposes a deep recommendation algorithm based on user-adaptive networks after examining the functional advantages of adaptive network platforms and optimizing the drawbacks of current network platform recommendations. It contains two parts: the main network and the strategy network, which combine the null convolutional neural network and the residual network, which is capable of better modeling the user’s historical behavior sequence. On this basis, the algorithm is combined with the development of the traditional culture dissemination-oriented network platform system, constructed by the user layer, testing layer, learning layer, support layer, and management layer composed of a traditional culture adaptive network platform. Then, the performance of the system is tested. The adaptive network platform system of this paper has the lowest number of nodes with latency distribution of [1, 3.1] ms and [4, 10] ms. The lowest load reaches 0.05, and the proportion of users with a continuity index less than 0.9 is only about 36%. Meanwhile, the average playback volume of the platform video is 6,275,800, and the average number of likes reaches 207,906, which is much higher than the average value of 8800 of the number of points stepped on, indicating that the system in this paper is optimal in all aspects of the performance and that the dissemination of traditional culture is excellent. This study presents an effective communication method for traditional culture that is widely applicable and makes a significant contribution to the promotion of traditional culture.

Information technology is of great significance in building a new college student education management model. This paper first presents the steps of constructing a hierarchical analysis method, which orders the combination of each index affecting the evaluation object to constitute a hierarchical and clear system, analyzes each element layer by layer, and calculates the weight value of each index. Then the process of the fuzzy comprehensive evaluation method is introduced, decomposing the total objective of the evaluation into a fuzzy set composed of several indicators, determining the affiliation function, and establishing the fuzzy matrix. Then the hierarchical analysis method and fuzzy comprehensive evaluation method are combined to evaluate the ability of college student education management. Finally, a new model of college student education management is proposed. Among the existing problems, 78.3% of students think that the construction of online platforms is lagging, and 89.8% think the current Internet platform lacks effective supervision. The weights of “students, education management team, family education, school management system, social environment, and education carrier” are 0.4323, 0.203, 0.08212, 0.13233, 0.10345, and 0.0687, respectively. It is not the sole responsibility of the school but requires the concerted efforts of the school, society, and family.

This paper first analyzes the effective application of web platforms and information technology in college teaching and constructs a web-based teaching aid platform for college courses based on the open Web. Secondly, the A3C algorithm is used to implement adaptive coding decisions to improve the performance of the platform by adaptive network coding. Finally, the performance test analysis is conducted for the web teaching aid platform built in this paper. The results show that the average response time of the system using the communication function with 2000 concurrent users is 1.807s, and the CPU and memory usage are 1146MHz and 1486MB, respectively, after running for 25s. This shows that adaptive network coding can effectively improve the performance of the supplementary teaching platform.

This paper aims to describe the outcomes of the Main10ance project, which focused on developing an integrated HBIM-GIS platform to support the maintenance and conservation plans for the historic built heritage. The pilot case is the UNESCO complex of the Sacri Monti, located in northern Italy and Switzerland, which consists of groups of chapels and architectural artifacts holding significant historical and cultural value. Given their importance, specific maintenance plans involving multiple stakeholders and specialists are required. This study focuses on creating a unified system that semantically and spatially describes the architectural elements of the case study and the surrounding context and indoor assets. After a 3D integrated metric survey and the related data processing, parametric 3D models were created in a BIM environment, and a spatial database was developed to incorporate both geometric and alphanumeric entities. The database enables interoperability among different actors and domains, gathering heritage-related information crucial for restoration and conservation purposes. Additionally, the custom 4MAIN10ANCE web platform was developed with different levels of details (LODs), enabling the retrieval of both 2D and 3D data and establishing connections between the BIM models of the chapels and associated information. [ABSTRACT FROM AUTHOR]

Cracking the entangling code of protein–ligand interaction (PLI) is of great importance to structure-based drug design and discovery. Different physical and biochemical representations can be used to describe PLI such as energy terms and interaction fingerprints, which can be analyzed by machine learning (ML) algorithms to create ML-based scoring functions (MLSFs). Here, we propose the ML-based PLI capturer (ML-PLIC), a web platform that automatically characterizes PLI and generates MLSFs to identify the potential binders of a specific protein target through virtual screening (VS). ML-PLIC comprises five modules, including Docking for ligand docking, Descriptors for PLI generation, Modeling for MLSF training, Screening for VS and Pipeline for the integration of the aforementioned functions. We validated the MLSFs constructed by ML-PLIC in three benchmark datasets (Directory of Useful Decoys-Enhanced, Active as Decoys and TocoDecoy), demonstrating accuracy outperforming traditional docking tools and competitive performance to the deep learning-based SF, and provided a case study of the Serine/threonine-protein kinase WEE1 in which MLSFs were developed by using the ML-based VS pipeline in ML-PLIC. Underpinning the latest version of ML-PLIC is a powerful platform that incorporates physical and biological knowledge about PLI, leveraging PLI characterization and MLSF generation into the design of structure-based VS pipeline. The ML-PLIC web platform is now freely available at http://cadd.zju.edu.cn/plic/. [ABSTRACT FROM AUTHOR]

Stevia rebaudiana Bertoni Morita II, a perennial plant native to Paraguay and Brazil, is also widely cultivated in the state of Colima, Mexico, for its use as a sweetener in food and beverages. The optimization of soil parameters is crucial for maximizing biomass production and stevioside levels in stevia crops. This research presents the development and implementation of a monitoring system to track essential soil parameters, including pH, temperature, humidity, electrical conductivity, nitrogen, phosphorus, and potassium. The system employs a wireless sensor network to collect quasi-real-time data, which are transmitted and stored in a web-based platform. A Mamdani-type fuzzy logic model is utilized to process the collected data and provide farmers an integrated assessment of soil quality. By comparing the quality data output of the fuzzy logic model with a linear regression model, the system demonstrated acceptable performance, with a determination coefficient of 0.532 for random data and 0.906 for gathered measurements. The system enables farmers to gain insights into the soil quality of their stevia crops and empowers them to take preventive and corrective actions to improve the soil quality specifically for stevia crops. [ABSTRACT FROM AUTHOR]

This study explores whether and how peer pressure influenced students' participation in web-based peer learning (WPL). Fifteen students enrolled in a university reading course were followed over the course of one semester, and interviews with them along with the researcher's observational notes on their learning activities were qualitatively analyzed. Peer pressure slowly and steadily occurred during the students' WPL, with the students feeling differently about it at different levels. All of the students became somewhat used to it later in the semester. The peer pressure was mainly influenced by technological factors (e.g., the openness of the web platform) and non-technological factors (e.g., the academic gaps between students), along with other accompanying reasons (e.g., self-motivation for looking academically decent). The study concludes that students' experiences of peer pressure in relation to WPL involved a gradual process, had multiple causes, and ultimately positively impacted the students, although in the process, peer pressure exerted either negative or positive power on the students. [ABSTRACT FROM AUTHOR]

Youth unemployment tends to be higher than in other age groups mainly because young people represent an inexperienced labour force. Some authors also believe it is due to an inability to match the qualified workforce to vacant positions. The importance of enabling young people to participate equally in the labour market is of the essence for economic growth. One of the attempts to tackle this issue was addressed through the Erasmus+ project CUVID, aimed at providing young people a way to present themselves in video-based form. This paper analyses quality standards for video CV platforms. For this purpose, 19 expert interviews with HR managers were conducted to identify their expectations towards video CVs. Based on the analysis of expert interviews, a recommendations summary with technical and user-oriented prerequisites was created, along with requirements and a transferability model for video CVs in the form of the CUVID platform.

Background: Circular RNAs (circRNAs) are covalently closed-loop RNAs with critical regulatory roles in cells. Tens of thousands of circRNAs have been unveiled due to the recent advances in high throughput RNA sequencing technologies and bioinformatic tools development. At the same time, polymerase chain reaction (PCR) cross-validation for circRNAs predicted by bioinformatic tools remains an essential part of any circRNA study before publication. Results: Here, we present the CircPrime web-based platform, providing a user-friendly solution for DNA primer design and thermocycling conditions for circRNA identification with routine PCR methods. Conclusions: User-friendly CircPrime web platform (http://circprime.elgene.net/) works with outputs of the most popular bioinformatic predictors of circRNAs to design specific circular RNA primers. CircPrime works with circRNA coordinates and any reference genome from the National Center for Biotechnology Information database). [ABSTRACT FROM AUTHOR]

Youth unemployment tends to be higher than in other age groups mainly because young people represent an inexperienced labour force. Some authors also believe it is due to an inability to match the qualified workforce to vacant positions. The importance of enabling young people to participate equally in the labour market is of the essence for economic growth. One of the attempts to tackle this issue was addressed through the Erasmus+ project CUVID, aimed at providing young people a way to present themselves in video-based form. This paper analyses quality standards for video CV platforms. For this purpose, 19 expert interviews with HR managers were conducted to identify their expectations towards video CVs. Based on the analysis of expert interviews, a recommendations summary with technical and user-oriented prerequisites was created, along with requirements and a transferability model for video CVs in the form of the CUVID platform. [ABSTRACT FROM AUTHOR]

Remote sensing can help evolution of agricultural practices by providing periodic information about the status of a given crop over the harvest season, at different scales, and for different segments. Applications in precision agriculture use remote sensing practices based on multispectral images to measure plants’ parameters throughout their development cycle. The aim of the current research is to develop a computational module capable of integrating and providing remote sensing data for the AgDataBox precision agriculture platform. The herein developed application enabled the persistence of a new field, the search for raster images generated by orbital satellites, the selection of vegetation indices, as well as the vectorization and insertion of images of interest in the AgDataBox platform.

Abstract Natural products (NPs) are a valuable source for anti-inflammatory drug discovery. However, they are limited by the unpredictability of the structures and functions. Therefore, computational and data-driven pre-evaluation could enable more efficient NP-inspired drug development. Since NPs possess structural features that differ from synthetic compounds, models trained with synthetic compounds may not perform well with NPs. There is also an urgent demand for well-curated databases and user-friendly predictive tools. We presented a comprehensive online web platform (InflamNat, http://www.inflamnat.com/ or http://39.104.56.4/ ) for anti-inflammatory natural product research. InflamNat is a database containing the physicochemical properties, cellular anti-inflammatory bioactivities, and molecular targets of 1351 NPs that tested on their anti-inflammatory activities. InflamNat provides two machine learning-based predictive tools specifically designed for NPs that (a) predict the anti-inflammatory activity of NPs, and (b) predict the compound-target relationship for compounds and targets collected in the database but lacking existing relationship data. A novel multi-tokenization transformer model (MTT) was proposed as the sequential encoder for both predictive tools to obtain a high-quality representation of sequential data. The experimental results showed that the proposed predictive tools achieved an AUC value of 0.842 and 0.872 in the prediction of anti-inflammatory activity and compound-target interactions, respectively.

Loneliness and social isolation are triggers for unfavorable changes in older adults' health and well-being. Information and communication technology (ICT) can be used by older adults to mitigate the negative effects of loneliness and social isolation. However, ICT needs to be customized to the specific needs and conditions of older adults. The aim of this study was to explore older adults' use of a new, co-designed and research-based web platform for social interaction from the perspectives of older adults, researchers, and social services personnel. The study is an intervention study with a multimethod approach in which 20 older adults used the web platform for social interaction "the Fik@ room" for eight weeks. Quantitative and qualitative data were collected pretest, during the test, and posttest. The Fik@ room met the expectations of those older adults who completed the study. It enabled them to expand their social network and develop new friendships, but their experiences of loneliness were not reduced. The involvement of social services personnel in recruitment and support was important in facilitating older adults' use of the Fik@ room. Our study contributes knowledge about a new, co-designed and research-based web platform, customized specifically for older adults, which is valuable in guiding the design and delivery of future web platforms for social interaction among older adults. [ABSTRACT FROM AUTHOR]

Background: Innovative approaches are needed to understand barriers to and facilitators of physical activity among insufficiently active adults. Although social comparison processes (ie, self-evaluations relative to others) are often used to motivate physical activity in digital environments, user preferences and responses to comparison information are poorly understood. Objective: We used an iterative approach to better understand users' selection of comparison targets, how they interacted with their selected targets, and how they responded to these targets. Methods: Across 3 studies, different samples of insufficiently active college students used the Fitbit system (Fitbit LLC) to track their steps per day as well as a separate, adaptive web platform each day for 7 to 9 days (N=112). The adaptive platform was designed with different layouts for each study; each allowed participants to select their preferred comparison target from various sets of options, view the desired amount of information about their selected target, and rate their physical activity motivation before and after viewing information about their selected target. Targets were presented as achieving physical activity at various levels below and above their own, which were accessed via the Fitbit system each day. We examined the types of comparison target selections, time spent viewing and number of elements viewed for each type of target, and day-level associations between comparison selections and physical activity outcomes (motivation and behavior). Results: Study 1 (n=5) demonstrated that the new web platform could be used as intended and that participants' interactions with the platform (ie, the type of target selected, the time spent viewing the selected target's profile, and the number of profile elements viewed) varied across the days. Studies 2 (n=53) and 3 (n=54) replicated these findings; in both studies, age was positively associated with time spent viewing the selected target's profile and the number of profile elements viewed. Across all studies, upward targets (who had more steps per day than the participant) were selected more often than downward targets (who had fewer steps per day than the participant), although only a subset of either type of target selection was associated with benefits for physical activity motivation or behavior. Conclusions: Capturing physical activity-based social comparison preferences is feasible in an adaptive digital environment, and day-to-day differences in preferences for social comparison targets are associated with day-to-day changes in physical activity motivation and behavior. Findings show that participants only sometimes focus on the comparison opportunities that support their physical activity motivation or behavior, which helps explain previous, equivocal findings regarding the benefits of physical activity-based comparisons. Additional investigation of day-level determinants of comparison selections and responses is needed to fully understand how best to harness comparison processes in digital tools to promote physical activity. [ABSTRACT FROM AUTHOR]

The emergence of collective intelligence has been studied in much greater detail in small groups than in larger ones. Nevertheless, in groups of several hundreds or thousands of members, it is well-known that the social environment exerts a considerable influence on individual behavior. A few recent papers have dealt with some aspects of large group situations, but have not provided an in-depth analysis of the role of interactions among the members of a group in the creation of ideas, as well as the group's overall performance. In this study, we report an experiment where a large set of individuals, i.e., 789 high-school students, cooperated online in real time to solve two different examinations on a specifically designed platform (Thinkhub). Our goal of this paper 6 to describe the specific mechanisms of idea creation we were able to observe and to measure the group's performance as a whole. When we deal with communication networks featuring a large number of interacting entities, it seems natural to model the set as a complex system by resorting to the tools of statistical mechanics. Our experiment shows how an interaction in small groups that increase in size over several phases, leading to a final phase where the students are confronted with the most popular answers of the previous phases, is capable of producing high-quality answers to all examination questions, whereby the last phase plays a crucial role. Our experiment likewise shows that a group's performance in such a task progresses in a linear manner in parallel with the size of the group. Finally, we show that the controlled interaction and dynamics foreseen in the system can reduce the spread of "fake news" within the group. [ABSTRACT FROM AUTHOR]