Search Results (8,646)

Promoting behavioral change in mobility is essential for sustainable urban development. This study evaluates the effectiveness of gamified challenges in fostering sustainable travel behaviors among high school students and teachers within the High School Challenge (HSC) 2024 campaign in Lecco, Italy. Over a 13-week period, participants tracked their commuting habits via gamified mobile application, Play&Go , that awarded points for sustainable mobility choices and introduced weekly challenges. Using behavioral (GPS-based tracking) and self-report data, we assessed the influence of challenge types, player characteristics (HEXAD Player Types, Big Five traits), and user experience evaluations on participation, retention, and behavior change. The results show that challenges, particularly those based on walking distances and framed as intra-team goals, significantly enhanced user engagement and contributed to improved mobility behaviors during participants’ free time. Compared to the 2023 edition without challenges, the 2024 campaign achieved better retention. HEXAD Player Types were more predictive of user appreciation than Personality Traits, though these effects were more evident in subjective evaluations than actual behavior. Overall, findings highlight the importance of tailoring gamified interventions to users’ motivational profiles and structuring challenges around SMART principles. This study contributes to the design of behaviorally informed, scalable solutions for sustainable mobility transitions. Full article

To address the limitations of existing knowledge graph-based recommendation algorithms, including insufficient utilization of semantic information and inadequate modeling of user behavior motivations, we propose SKGRec, a novel recommendation model that integrates knowledge graph and semantic features. The model constructs a semantic interaction graph (USIG) of user behaviors and employs a self-attention mechanism and a ranked optimization loss function to mine user interactions in fine-grained semantic associations. A relationship-aware aggregation module is designed to dynamically integrate higher-order relational features in the knowledge graph through the attention scoring function. In addition, a multi-hop relational path inference mechanism is introduced to capture long-distance dependencies to improve the depth of user interest modeling. Experiments on the Amazon-Book and Last-FM datasets show that SKGRec significantly outperforms several state-of-the-art recommendation algorithms on the Recall@20 and NDCG@20 metrics. Comparison experiments validate the effectiveness of semantic analysis of user behavior and multi-hop path inference, while cold-start experiments further confirm the robustness of the model in sparse-data scenarios. This study provides a new optimization approach for knowledge graph and semantic-driven recommendation systems, enabling more accurate capture of user preferences and alleviating the problem of noise interference. Full article

The evolution of 3GPP non-terrestrial networks (NTNs) is enabling new avenues for broadband connectivity via satellite, especially within the scope of 5G. The parallel rise in satellite mega-constellations has further fueled efforts toward ubiquitous global Internet access. This convergence has fostered collaboration between mobile network operators and satellite providers, allowing the former to leverage mature space infrastructure and the latter to integrate with terrestrial mobile standards. However, integrating these technologies presents significant architectural challenges. This study investigates 5G NTN architectures using satellite mega-constellations, focusing on transparent architectures where Starlink is employed to relay the backhaul, midhaul, and new radio (NR) links. The performance of these architectures is assessed through a testbed utilizing OpenAirInterface (OAI) and Open5GS, which collects key user-experience metrics such as round-trip time (RTT) and jitter when pinging the User Plane Function (UPF) in the 5G core (5GC). Results show that backhaul and midhaul relays maintain delays of 50–60 ms, while NR relays incur delays exceeding one second due to traffic overload introduced by the RFSimulator tool, which is indispensable to transmit the NR signal over Starlink. These findings suggest that while transparent architectures provide valuable insights and utility, regenerative architectures are essential for addressing current time issues and fully realizing the capabilities of space-based broadband services. Full article

Emergency preparedness training in organizations frequently involves simple evacuation drills triggered by fire alarms, limiting the opportunities for broader skill development. Digital technologies, particularly virtual reality (VR), offer promising methods to enhance learning for handling incidents and evacuations. However, implementing VR-based training remains challenging due to unclear integration strategies within organizational practices and a lack of empirical evidence of VR’s effectiveness. This paper explores how VR-based training tools can be implemented in schools to enhance emergency preparedness among students, teachers, and staff. Following a design science research process, data were collected from a questionnaire-based study involving 12 participants and an exploratory study with 13 participants. The questionnaire-based study investigates initial attitudes and willingness to adopt VR training, while the exploratory study assesses the VR prototype’s usability, realism, and perceived effectiveness for emergency preparedness training. Despite a limited sample size and technical constraints of the early prototype, findings indicate strong student enthusiasm for gamified and immersive learning experiences. Teachers emphasized the need for technical and instructional support to regularly utilize VR training modules, while firefighters acknowledged the potential of VR tools, but also highlighted the critical importance of regular drills and professional validation. The relevance of the results of utilizing VR in this context is further discussed in terms of how it can be integrated into university curricula and aligned with other accessible digital preparedness tools. Full article

Background: prostate fusion biopsies are key in the diagnosis of prostate cancer (PCa); however, the fusion imaging system is not always user-friendly or reliable. The aim of this study was to assess the feasibility, accuracy, and effectiveness of transperineal fusion biopsies performed with a novel fusion imaging device equipped with AI-driven auto-contouring. Methods: data from 148 patients who underwent MRI-targeted and systematic prostate fusion biopsy with UroFusion (Esaote) were prospectively collected. All biopsies were performed in-office, under local anaesthesia. Results: cancer detection rate was 64% overall and 56% for clinically significant PCa (csPCa, ISUP ≥ 2). PCa was detected in 35%, 65% and 84% of lesions scored as PI-RADS 3, 4 and 5, respectively. Outfield positive systematic cores were found in the contralateral lobe in one third of cases. Median device-time to obtain fusion imaging was 5 min and median biopsy duration was 15 min. Median difference in volume estimation between ultrasound and MRI auto-contouring was only 1 cc. Detection rate did not differ between experienced and novice, supervised users. Conclusions: in this initial prospective experience, fusion biopsies performed with UroFusion AI-driven auto-contouring system appeared time-efficient, accurate, well tolerated, and user-friendly, with comparable outcomes between experienced and novice users. Systematic biopsies remain highly recommended given the non-negligible rates of positive outfield cores. Full article

Background and Objectives: Three-dimensional (3D) scanning is increasingly utilized in medical practice, from orthotics to surgical planning. However, traditional hand measurement techniques remain inconsistent and prone to human error and are often time-consuming. This research evaluates the practicality of a commercial 3D scanning method by comparing the accuracy of scans conducted by two user groups. Materials and Methods: This study evaluated the following two groups: an experimental group (n = 45) and a control group (n = 42). A total of 261 hand scans were captured using the Structure Sensor Pro 3D scanner for iPad (Structure, Boulder, CO, USA). The scans were then evaluated using Meshmixer software (version 3.5.474), analyzing key parameters, such as surface area, volume, number of vertices, and triangles, etc. Furthermore, a digital literacy test and a user experience survey were conducted to support a more comprehensive evaluation of participant performance within the study. Results: The experimental group outperformed the control group on all measured parameters, including surface area, volume, vertices, triangle, and gap count, with large effect sizes observed. User experience data revealed that participants in the experimental group rated the 3D scanner significantly higher across all dimensions, particularly in ease of use, excitement, supportiveness, and practicality. Conclusions: A short 15 min training session can promote scan reliability, demonstrating that even minimal instruction improves users’ proficiency in 3D scanning, fundamental for supporting clinical accuracy in diagnosis, surgical planning, and personalized device manufacturing Full article

In recent decades, university dormitories have gradually evolved from traditional residential spaces into educationally meaningful venues that support informal learning. However, limited research has explored how supportive environmental factors within dormitories influence students’ informal learning experiences. This study aims to evaluate key environmental factors that affect students’ satisfaction with informal learning in dormitory settings. Based on a comprehensive literature review, two types of informal learning behaviors—individual and collaborative—were defined, and a multi-dimensional evaluation framework comprising five categories and 26 environmental indicators was established. Field observations and structured questionnaires were employed to assess students’ satisfaction with each environmental factor and their overall informal learning experiences. Quantitative analyses were conducted to examine the relationships between environmental conditions and learning satisfaction. Results show that all five-factor categories—spatial designs, natural environments, physical settings, social aspects, and resources—positively influence informal learning, with resources being the most impactful. While environmental influences on individual and collaborative learning exhibit minor differences, the overall patterns are consistent. Compared to other informal learning spaces on campus, dormitory users place greater emphasis on spatial controllability. This study further demonstrates the cognitive and emotional value of dormitory environments and proposes targeted directions for optimizing them as informal learning spaces. Full article

Biophilic design has emerged as a multidimensional response to growing concerns about health, well-being, and ecological balance in the built environment. Despite its rising prominence, research on the topic remains fragmented across building typologies, user groups, and geographic contexts. This study presents a comprehensive review of the biophilic design literature, employing a hybrid methodology combining structured content analysis and bibliometric mapping. All peer-reviewed studies indexed in the Web of Science and Scopus were manually screened for architectural relevance and systematically coded. A total of 435 studies were analysed to identify key trends, thematic patterns, and research gaps in the biophilic design discipline. This review categorises the literature by methodological strategies, building typologies, spatial scales, population groups, and specific biophilic design parameters. It also examines geographic and cultural dimensions, including climate responsiveness, heritage buildings, policy frameworks, theory development, pedagogy, and COVID-19-related research. The findings show a strong emphasis on institutional contexts, particularly workplaces, schools, and healthcare, and a reliance on perception-based methods such as surveys and experiments. In contrast, advanced tools like artificial intelligence, simulation, and VR are notably underused. Few studies engage with neuroarchitecture or neuroscience-informed approaches, despite growing recognition of how spatial design can influence cognitive and emotional responses. Experimental and biometric methods remain scarce among the few relevant contributions, revealing a missed opportunity to connect biophilic strategies with empirical evidence. Regarding biophilic parameters, greenery, daylight, and sensory experience are the most studied parameters, while psychological parameters remain underexplored. Cultural and climate-specific considerations appear in relatively few studies, and many fail to define a user group or building typology. This review highlights the need for more inclusive, context-responsive, and methodologically diverse research. By bridging macro-scale bibliometric patterns with fine-grained thematic insights, this study provides a replicable review model and valuable reference for advancing biophilic design as an evidence-based, adaptable, and human-centred approach to sustainable architecture. Full article

Advanced three-dimensional extended reality (XR) technologies are highly suitable for cultural heritage research and education. XR tools enable the creation of realistic virtual or augmented reality applications for curating and disseminating information about cultural artifacts and sites. Developing XR applications for cultural heritage requires interdisciplinary collaboration involving strong teamwork and soft skills to manage user requirements, system specifications, and design cycles. Given the diverse end-users, achieving high precision, accuracy, and efficiency in information management and user experience is crucial. Human–computer interaction (HCI) design and evaluation methods are essential for ensuring usability and return on investment. This article presents ten case studies of cultural heritage software projects, illustrating the interdisciplinary work between computer science and HCI design. Students from institutions such as the State University of New York (USA), Glasgow School of Art (UK), University of Granada (Spain), University of Málaga (Spain), Duy Tan University (Vietnam), Imperial College London (UK), Research University Institute of Communication & Computer Systems (Greece), Technical University of Košice (Slovakia), and Indiana University (USA) contributed to creating, assessing, and improving the usability of these diverse cultural heritage applications. The results include a structured typology of CH XR application scenarios, detailed insights into design and evaluation practices across ten international use cases, and a development framework that supports interdisciplinary collaboration and stakeholder integration in phygital cultural heritage projects. Full article

Nowadays, the Metaverse is facing many challenges. In this context, Virtual Reality (VR) applications allowing voice-based human–3D object interactions are limited due to the current hardware/software limitations. In fact, adopting Automated Speech Recognition (ASR) systems to interact with 3D objects in VR applications through users’ voice commands presents significant challenges due to the hardware and software limitations of headset devices. This paper aims to bridge this gap by proposing a methodology to address these issues. In particular, starting from a Mel-Frequency Cepstral Coefficient (MFCC) extraction algorithm able to capture the unique characteristics of the user’s voice, we pass it as input to a Convolutional Neural Network (CNN) model. After that, in order to integrate the CNN model with a VR application running on a standalone headset, such as Oculus Quest, we converted it into an Open Neural Network Exchange (ONNX) format, i.e., a Machine Learning (ML) interoperability open standard format. The proposed system demonstrates good performance and represents a foundation for the development of user-centric, effective computing systems, enhancing accessibility to VR environments through voice-based commands. Experiments demonstrate that a native CNN model developed through TensorFlow presents comparable performances with respect to the corresponding CNN model converted into the ONNX format, paving the way towards the development of VR applications running in headsets controlled through the user’s voice. Full article

The cognitive cycle has been studied via cognitive architectures and by analyzing cognitive experiments. An emerging theoretical approach suggests that several automatic cognitive processes retrieve information, making it available to an internal agent, which in turn decides which information to access. Derived from this view, four phases of the cognitive cycle can be formulated and reproduced within a cognitive monitoring system. This exploratory work presents a new theory, Attention as Internal Action, and proposes a hypothesis about the relationship between an iteration of the cognitive cycle and a conscious motor action. The design of a continuous reaction time task is presented as a tool for quick cognitive evaluation. Via continuously provided user responses, the computational system behind the task adapts triggering stimuli based on the suggested hypothesis. Its software implementation was employed to assess whether a previously conducted simulation of the cognitive cycle’s time range aligned with empirical data. A control group was assigned to perform a separate simple reaction time task in a sequence of five days. The analysis showed that the experimental cognitive monitoring system produced results more closely aligned with the established understanding of the timing of the cognitive cycle than the control task did. Full article

Federated learning is a distributed machine learning technique that allows multiple devices to collaborate on learning a shared model without exchanging data. It can be used to improve model accuracy while protecting user privacy. However, traditional federated learning is vulnerable to attacks from generative adversarial networks (GANs). As a new privacy protection method, differential privacy enhances privacy protection capabilities by sacrificing some data accuracy. To optimize the privacy budget allocation scheme in traditional differential privacy, we propose a differential privacy method called ADP-FL, which dynamically adjusts the privacy budget based on Newton’s Law of Cooling. While maintaining the overall privacy budget, it dynamically tunes adaptive parameters to improve training accuracy. Additionally, we propose an asynchronous federated learning aggregation scheme that combines privacy budget with data freshness, thereby reducing the impact of differential privacy on accuracy. We conducted extensive experiments on differential privacy algorithms based on Gaussian mechanisms and Laplace mechanisms. The experimental results show that, under the same privacy budget, our algorithm achieves higher accuracy and lower communication overhead compared to the baseline algorithm. Full article

In this post-digital age, writing assessment has been markedly influenced by advancements in artificial intelligence (AI), emphasizing the role of automated formative feedback in supporting second language (L2) writing. This study investigates how Norwegian teachers use an AI-driven automated feedback tool, the Essay Assessment Technology (EAT), in process writing for the first time. Framed by the second and third-order barriers framework, we looked at teachers’ beliefs and the design level changes that they made in their teaching. Data were collected in Autumn 2022, during the testing of EAT’s first prototype. Teachers were first introduced to EAT in a workshop. A total of 3 English as a second language teachers from different schools were informants in this study. Teachers then used EAT in the classroom with their 9th-grade students (13 years old). Through individual teacher interviews, this descriptive qualitative study explores teachers’ perceptions, user experiences, and pedagogical decisions when incorporating EAT into their practices. The findings indicate that teachers’ beliefs about technology and its role in student learning, as well as their views on students’ responsibilities in task completion, significantly influence their instructional choices. Additionally, teachers not only adopt AI-driven tools but are also able to reflect and solve complex teaching and learning activities in the classroom, which demonstrates that these teachers have applied design thinking processes in integrating technology in their teaching. Based on the results in this study, we suggest the need for targeted professional development to support effective technology integration. Full article

We present a miniaturized, inexpensive, and user-friendly microfluidic platform to support biological applications. The system integrates a mini-incubator providing controlled environmental conditions and housing a microfluidic device for long-term cell culture experiments. The incubator is designed to be compatible with standard inverted optical microscopes and Raman spectrometers, allowing for the non-invasive imaging and spectroscopic analysis of cell cultures in vitro. The microfluidic device, which reproduces a dynamic environment, was optimized to sustain a passive, gravity-driven flow of medium, eliminating the need for an external pumping system and reducing mechanical stress on the cells. The platform was tested using Raman analysis and adherent tumoral cells to assess proliferation prior and subsequent to hydrogen peroxide treatment for oxidative stress induction. The results demonstrated a successful adhesion of cells onto the substrate and their proliferation. Furthermore, the platform is suitable for carrying out optical monitoring of cultures and Raman analysis. In fact, it was possible to discriminate spectra deriving from control and hydrogen peroxide-treated cells in terms of DNA backbone and cellular membrane modification effects provoked by reactive oxygen species (ROS) activity. The 800–1100 cm⁻¹ band highlights the destructive effects of ROS on the DNA backbone’s structure, as its rupture modifies its vibration; moreover, unpaired nucleotides are increased in treated sample, as shown in the 1154–1185 cm⁻¹ band. Protein synthesis deterioration, led by DNA structure damage, is highlighted in the 1257–1341 cm⁻¹, 1440–1450 cm⁻¹, and 1640–1670 cm⁻¹ bands. Furthermore, membrane damage is emphasized in changes in the 1270, 1301, and 1738 cm⁻¹ frequencies, as phospholipid synthesis is accelerated in an attempt to compensate for the membrane damage brought about by the ROS attack. This study highlights the potential use of this platform as an alternative to conventional culturing and analysis procedures, considering that cell culturing, optical imaging, and Raman spectroscopy can be performed simultaneously on living cells with minimal cellular stress and without the need for labeling or fixation. Full article

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article