Search Results (3,993)

Background/Objectives: Dementia is a leading cause of cognitive decline, with significant challenges for early detection and timely intervention. The lack of effective, user-centred technologies further limits clinical response, particularly in underserved areas. This study aimed to develop and describe a co-design process for creating a Diagnostic and Statistical Manual of Mental Disorders (DSM-5)-compliant, AI-powered Smart Assistant (SmartApp) to monitor neurocognitive decline, while ensuring accessibility, clinical relevance, and responsible AI integration. Methods: A co-design framework was applied using a novel combination of Agile principles and the Double Diamond Model (DDM). More than twenty iterative Scrum sprints were conducted, involving key stakeholders such as clinicians (psychiatrist, psychologist, physician), designers, students, and academic researchers. Prototype testing and design workshops were organised to gather structured feedback. Feedback was systematically incorporated into subsequent iterations to refine functionality, usability, and clinical applicability. Results: The iterative process resulted in a SmartApp that integrates a DSM-5-based screening tool with 24 items across key cognitive domains. Key features include longitudinal tracking of cognitive performance, comparative visual graphs, predictive analytics using a regression-based machine learning module, and adaptive user interfaces. Workshop participants reported high satisfaction with features such as simplified navigation, notification reminders, and clinician-focused reporting modules. Conclusions: The findings suggest that combining co-design methods with Agile/DDM frameworks provides an effective pathway for developing AI-powered clinical tools as per responsible AI standards. The SmartApp offers a clinically relevant, user-friendly platform for dementia screening and monitoring, with potential to support vulnerable populations through scalable, responsible digital health solutions. Full article

Relativistic jets from AGN are an important driver of feedback in galaxies. They interact with their environments over a wide range of physical scales during their lifetime, and an understanding of these interactions is crucial for unraveling the role of supermassive black holes in shaping galaxy evolution. The impact of such jets has been traditionally considered in the context of heating large-scale environments. However, in the last few decades, there has been additional focus on the immediate impact of jet feedback on the host galaxy itself. In this review, we outline the development of various numerical simulations from the onset of research on jets to the present day, where sophisticated numerical techniques have been employed to study jet feedback, including a range of physical processes. The jets can act as important agents of energy injection into a host’s ISM, as confirmed in both observations of multi-phase gas as well as in simulations. Such interactions have the potential to impact the kinematics of the gas as well as star formation. We summarize recent results from simulations of jet feedback on kpc scales and outline the broader implications for observations and galaxy evolution. Full article

Traditional assessment methods in physical education often emphasize final grades, lacking real-time monitoring and feedback during the learning process. To address this limitation and enhance the formative evaluation of student performance, this study proposes a real-time assessment system for Baduanjin instruction in physical education, utilizing a commercially available inertial measurement unit-based motion capture device. The system was developed in four stages. First, a dataset was created by recruiting 20 university students and one expert physical education instructor. Participants were asked to perform standardized Baduanjin routines while wearing wireless inertial measurement unit sensors on key body joints. The collected kinematic data, sampled at 100 Hz, included joint angles and movement trajectories. Second, preprocessing and feature extraction techniques were applied to the raw data to construct a labeled dataset for training. Third, supervised machine learning algorithms were used to build models for motion type recognition and motion accuracy evaluation. Model performance was assessed using cross-validation and compared with expert evaluations. Finally, a user-facing formative assessment system was developed and tested in a controlled classroom environment. The system demonstrated a high motion recognition accuracy of 99.77%, and the correlation coefficient between system-assessed motion accuracy and expert ratings exceeded 0.80, indicating strong validity. The results demonstrate that the formative assessment system built on inertial measurement unit is appropriate for the Baduanjin physical education. Full article

Assessing e-learning students’ satisfaction with lecturers’ interactions in asynchronous forums is essential for enhancing teaching and learning processes. The discussion forum allows students to share comments and ideas with peers or lecturers, stimulating diverse perspectives and improving learning efficacy. However, lecturers’ responses are often similar or redundant to previous students’ comments, limiting feedback depth and potentially reducing students’ perceived value of the interaction. Machine learning classifiers have been widely used to assess satisfaction based on sentiment or semantic similarity. However, integrating sentiment and semantic similarity between students’ comments or opinions and lecturers’ responses in asynchronous online discussion forums has received limited attention and may be improved. Through this research, we propose a novel model called E-learning Satisfaction Assessment using Textual Neural Network (E-SATNet). The E-SATNet model has two main sub-networks. The first sub-network employs a Convolutional Neural Network (CNN) to extract sentiment-related features from students’ reactions to lecturers’ responses. The second sub-network utilizes a Bidirectional Long Short-Term Memory (BiLSTM) to extract semantic features from lecturers’ responses and compute their similarity with the overall discussion content. Evaluation results show that E-SATNet effectively assesses satisfaction, achieving an average F1-score of 88.12. Full article

With industrial transformation and the rise in the 15 min community life circle, optimizing walkability and preserving industrial heritage are key to revitalizing former industrial areas. This study, focusing on Shijingshan District in Beijing, proposes a walkability evaluation framework integrating multi-source big data and street-level perception. Using Points of Interest (POI) classification, which refers to the categorization of key urban amenities, pedestrian network modeling, and street view image data, a Walkability Friendliness Index is developed across four dimensions: accessibility, convenience, diversity, and safety. POI data provide insights into the spatial distribution of essential services, while pedestrian network data, derived from OpenStreetMap, model the walkable road network. Street view image data, processed through semantic segmentation, are used to assess the quality and safety of pedestrian pathways. Results indicate that core communities exhibit higher Walkability Friendliness Index scores due to better connectivity and land use diversity, while older and newly developed areas face challenges such as street discontinuity and service gaps. Accordingly, targeted optimization strategies are proposed: enhancing accessibility by repairing fragmented alleys and improving network connectivity; promoting functional diversity through infill commercial and service facilities; upgrading lighting, greenery, and barrier-free infrastructure to ensure safety; and delineating priority zones and balanced enhancement zones for differentiated improvement. This study presents a replicable technical framework encompassing data acquisition, model evaluation, and strategy development for enhancing walkability, providing valuable insights for the revitalization of industrial districts worldwide. Future research will incorporate virtual reality and subjective user feedback to further enhance the adaptability of the model to dynamic spatiotemporal changes. Full article

Background: As means of addressing ongoing challenges in accessing publicly funded specialist care, new models of care have been trialled. One such approach is using physiotherapists in advance practice roles, who in collaboration with other health professionals, act as an initial orthopedic point of contact and coordinate care. This research investigated the impact of a model of care, the Spinal Virtual Clinic Model, implemented for the first time in South Australia, using advanced practice physiotherapists in a large metropolitan hospital in South Australia. Although formally named the “Spinal Virtual Clinic” by the health service, this model does not involve direct patient contact and differs from traditional virtual or telehealth clinics. Instead, it is best understood as a physiotherapy-led referral triage and management service. Methods: This research was conducted in two stages. Stage 1 was a retrospective clinical audit of sequential patients triaged to the Spinal Virtual Clinic, as well as a follow up audit to capture any subsequent engagement with the Orthopaedic Spinal Service following the initial Spinal Virtual Clinic correspondence. Data were descriptively analysed. In Stage 2, semi-structured interviews were conducted with patients from the Spinal Virtual Clinic to explore their perspectives on this model of care. The interviews were transcribed verbatim and independently analysed using thematic analysis. The sequential use of quantitative and qualitative approaches enabled us to both describe engagement with this model of care and better understand the underlying perspectives. Results: Three hundred and nine referrals were triaged to the physiotherapy-led spinal virtual clinic over a six-month period from 1 January 2021 to 30 June 2021. Majority of referrals were triaged as low acuity did not need formal spinal specialist review and could be managed safely in primary care. Therapist-led active management strategies (80.8%), trial of neuropathic medication (35.6%) closely followed by advice regarding targeted spinal injections (foraminal and epidural), were the most common conservative management strategies recommended. Only a small proportion needed surgical review. Interviews with eleven patients revealed that while many valued the convenience, timely advice, and reassurance offered by the service, others expressed confusion about the referral process and disappointment at not seeing a specialist. A key recommendation identified was improved communication, including providing patients with direct feedback alongside general practitioner correspondence. Conclusions: This research, underpinned by quantitative and qualitative research, has showcased the potential of this model of care, the spinal virtual clinic, to have a positive impact on improving access and reducing the burden on the health system for low acuity patients. As historical models of care become unsustainable and obsolete, alternative models of care can be implemented in health care settings where outpatient demand significantly exceeds capacity. Full article

In today’s competitive digital landscape, application usability plays a critical role in user satisfaction and retention. Negative user reviews offer valuable insights into real-world usability issues, yet traditional analysis methods often fall short in scalability and contextual understanding. This paper proposes an intelligent framework that utilizes large language models (LLMs), including GPT-4, Gemini, and BLOOM, to automate the extraction of actionable usability recommendations from negative app reviews. By applying prompting and fine-tuning techniques, the framework transforms unstructured feedback into meaningful suggestions aligned with three core usability dimensions: correctness, completeness, and satisfaction. A manually annotated dataset of Instagram negative reviews was used to evaluate model performance. Results show that GPT-4 consistently outperformed other models, achieving BLEU scores up to 0.64, ROUGE scores up to 0.80, and METEOR scores up to 0.90—demonstrating high semantic accuracy and contextual relevance in generated recommendations. Gemini and BLOOM, while improved through fine-tuning, showed significantly lower performance. This study also introduces a practical, web-based tool that enables real-time review analysis and recommendation generation, supporting data-driven, user-centered software development. These findings illustrate the potential of LLM-based frameworks to enhance software usability analysis and accelerate feedback-driven design processes. Full article

The present study examines the application of interactive software models for training on the topic of “Cyclic Codes” in order to increase the success rate and engagement of students in technical disciplines. Two models have been developed—based on the polynomial method and the LFSR approach—through an established methodology adapted to the specifics of the content. A pedagogical experiment with a control and experimental group was conducted, and ANCOVA analysis was applied to eliminate the influence of initial grades. The results show a statistically significant advantage of the experimental group in terms of final grades, which confirms the positive effect of using interactive models. The analysis of engagement and solved tasks reveals that the polynomial model is used more widely and contributes to the systematic application of algorithmic steps, while the LFSR model has an illustrative nature and supports intuitive understanding through visualization of processes. The feedback received from students shows high satisfaction and points to improvements in the interface and functionality. In conclusion, interactive models prove their effectiveness as complementary tools for learning complex technical concepts, and prospects for future development through the integration of artificial intelligence and enhanced gamification are also discussed. Full article

Despite rapid progress in natural language processing, current interactive AI systems continue to struggle with interpreting ambiguous, idiomatic, and contextually rich human language, a barrier to natural human–computer interaction. Many deployed applications, such as language games or educational tools, showcase surface-level adaptation but do not systematically probe or advance the deeper semantic understanding of user intent in open-ended, creative settings. In this paper, we present Semantrix, a web-based semantic word-guessing platform, not merely as a game but as a living testbed for evaluating and extending the semantic capabilities of state-of-the-art Transformer models in human-facing contexts. Semantrix challenges models to both assess the nuanced meaning of user guesses and generate dynamic, context-sensitive hints in real time, exposing the system to the diversity, ambiguity, and unpredictability of genuine human interaction. To empirically investigate how advanced semantic representations and adaptive language feedback affect user experience, we conducted a preregistered 2 × 2 factorial study (N = 42), independently manipulating embedding depth (Transformers vs. Word2Vec) and feedback adaptivity (dynamic hints vs. minimal feedback). Our findings revealed that only the combination of Transformer-based semantic modelling and adaptive hint generation sustained user engagement, motivation, and enjoyment; conditions lacking either component led to pronounced attrition, highlighting the limitations of shallow or static approaches. Beyond benchmarking game performance, we argue that the methodologies applied in platforms like Semantrix are helpful for improving machine understanding of natural language, paving the way for more robust, intuitive, and human-aligned AI approaches. Full article

The performance and beam quality of the new fourth-generation synchrotron light source with ultra-low emittance are highly susceptible to coupled-bunch instabilities. These instabilities arise from the interaction between the bunched electron beam and the surrounding vacuum chamber installations. To mitigate these effects, the installation of a transverse bunch-by-bunch feedback system is planned. This system will comprise a button-type beam position monitor (BPM) for beam signal detection, a digital feedback controller, a broadband power amplifier, and a broadband stripline kicker as the primary actuator. One of the critical challenges lies in the development of the stripline kicker, which must be optimized for high shunt impedance and wide bandwidth while minimizing beam-coupling impedance. This work focuses on the comprehensive design of the stripline kicker intended for transverse (horizontal and vertical) bunch-by-bunch feedback in the Siam Photon Source II (SPS-II) storage ring. The stripline kicker design also incorporates features to enable its use for beam excitation in the SPS-II tune measurement system. The optimization process involves analytical approximations and detailed numerical electromagnetic field analysis of the stripline’s 3D geometry, focusing on impedance matching, field homogeneity, power transmission, and beam-coupling impedance. The details of engineering design are discussed to ensure that it meets the fabrication possibilities and stringent requirements of the SPS-II accelerator. Full article

This work presents a high-common-mode-rejection-ratio (CMRR) and high-gain FinFET-based bio-potential amplifier with a novel CMRR reduction technique. In this paper, a feedback buffer is used alongside a capacitively coupled chopper-stabilized circuit to reduce the common-mode signal gain, thus boosting the overall CMRR of the circuit. The conventional pseudoresistor in the feedback circuit is replaced with a tunable parallel-cell configuration of pseudoresistors to achieve high linearity. A chopper spike filter is used to mitigate spikes generated by switching activity. The mid-band gain of the chopper-stabilized amplifier is 42.6 dB, with a bandwidth in the range of 6.96 Hz to 621 Hz. The noise efficiency factor (NEF) of the chopper-stabilized amplifier is 6.1, and its power dissipation is 0.92 µW. The linearity of the parallel pseudoresistor cell is tested for different tuning voltages (V_tune) and various numbers of parallel pseudoresistor cells. The simulation results also demonstrate the pseudoresistor cell performance for different process corners and temperature changes. The low cut-off frequency is adjusted by varying the parameters of the parallel pseudoresistor cell. The CMRR of the chopper-stabilized amplifier, with and without the feedback buffer, is 106.9 dB and 100.3 dB, respectively. The feedback buffer also reduces the low cut-off frequency, demonstrating its multi-utility. The proposed circuit is compatible with bio-signal acquisition and processing. Additionally, a machine learning-based arrhythmia diagnosis model is presented using a convolutional neural network (CNN) + Long Short-Term Memory (LSTM) algorithm. For arrhythmia diagnosis using the CNN+LSTM algorithm, an accuracy of 99.12% and a mean square error (MSE) of 0.0273 were achieved. Full article

Runoff prediction plays a critical role in water resource management and flood mitigation. Traditional runoff prediction methods often rely on single-layer optimization frameworks that process the data without decomposition and employ relatively simple prediction models, leading to suboptimal performance. In this study, a novel two-layer optimization framework is proposed that integrates data decomposition techniques with multi-model combination strategies, establishing a closed-loop feedback mechanism between decomposition and prediction processes. The framework employs the Snow Ablation Optimizer (SAO) to optimize combination weights across both layers. Its adaptive fitness function incorporates three evaluation metrics—Mean Absolute Percentage Error (MAPE), Relative Root Mean Square Error (RRMSE), and Nash–Sutcliffe Efficiency (NSE)—to enable adaptive data processing and intelligent model selection. We validated the framework using observational data from Huangzhuang Hydrological Station in the Hanjiang River Basin. The results demonstrate that, at the decomposition layer, optimal performance was achieved by combining non-decomposition, Singular Spectrum Analysis (SSA), and Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) (with coefficients 0.4061, 0.6115, and −0.0063), paired with the long short-term memory (LSTM) model. At the prediction layer, the proposed algorithm achieved a 32.84% improvement over the best single decomposition method and a 30.21% improvement over the best single combination optimization approach. These findings confirm the framework’s effectiveness in enhancing runoff data decomposition and optimizing multi-model selection. Full article

This paper applied an adapted design sprint approach, called a design summit, to educational prototyping. Design sprints provide a structure for applying design thinking and capturing user requirements that can be adapted to the needs of varied design contexts. However, finding a format that meets project requirements and brings in diverse stakeholders while also considering their availability can be difficult to construct using a traditional design sprint approach. Through four adapted stages of understanding, defining, iterating and prototyping towards a problem space, this paper presents a case study of a design summit applied to education/instructional design, specifically towards the problem of designing teacher professional development on the topic of the high ability (HA) student. A key feature in our applied approach is using concurrent prototyping, over many months, to achieve project outcomes. The case study presents the process and challenges of developing educational resources suited to the professional development of teachers and school leaders that need to support HA students. Through iteration, the results show how diverse stakeholders engaged and provided feedback to inform prototyping outcomes. Our design summit case study demonstrates how careful planning, focused elicitation of user requirements and an elongated and concurrent prototyping process results in outcomes that meet education stakeholder expectations and align with project requirements. Full article

Gout is a chronic inflammatory disease characterized by the deposition of monosodium urate (MSU) crystals within joints, leading to recurrent acute flares and long-term tissue damage. While various hypotheses have been proposed to explain the self-limiting nature of acute gout attacks, we posit that aggregated neutrophil extracellular traps (aggNETs) play a central role in this process. This review focuses on the mechanisms underlying MSU crystal-induced formation of neutrophil extracellular traps (NETs) and explores their dual role in the clinical progression of gout. During the initial phase of acute flares, massive NET formation is accompanied by the release of preformed inflammatory mediators, which is a condition that amplifies inflammatory cascades. As neutrophil recruitment reaches a critical threshold, the NETs tend to form high-order aggregates (aggNETs). The latter encapsulate MSU crystals and further pro-inflammatory mediators within their three-dimensional scaffold. High concentrations of neutrophil serine proteases (NSPs) within the aggNETs facilitate the degradation of soluble inflammatory mediators and eventually promote the resolution of inflammation in a kind of negative inflammatory feedback loop. In advanced stages of gout, MSU crystal deposits are often visible via dual-energy computed tomography (DECT), and the formation of palpable tophi is frequently observed. Based on the mechanisms of resolution of inflammation and the clinical course of the disease, building on the traditional static model of “central crystal–peripheral fibrous encapsulation,” we have expanded the NETs component and refined the overall concept, proposing a more dynamic, multilayered, multicentric, and heterogeneous model of tophus maturation. Notably, in patients with late-stage gout, tophi exist in a stable state, referred to as “silent” tophi. However, during clinical tophus removal, the disruption of the structural or functional stability of “silent” tophi often leads to the explosive reactivation of inflammation. Considering these findings, we propose that future therapeutic strategies should focus on the precise modulation of NET dynamics, aiming to maintain immune equilibrium and prevent the recurrence of gout flares. Full article

Vehicular edge computing (VEC) represents a concrete application of mobile edge computing (MEC) in the field of intelligent transportation, with task offloading serving as one of its core components. The design of efficient task offloading strategies poses significant challenges due to the dynamic network topology, stringent low-latency requirements, and massive data processing demands. This paper proposes a digital twin (DT)-assisted intelligent task offloading approach, which establishes a dynamic interaction and mapping between the virtual and physical worlds to enable real-time monitoring of VEC network states, thereby optimizing offloading decisions. First, to meet diverse user service requirements, an optimization model is formulated with the objective of minimizing task processing latency and energy consumption. Next, a gravity model-based vehicle clustering algorithm is integrated with digital twin technology to find the optimal offloading space and ensure link stability among vehicles within aggregated clusters. Furthermore, to minimize overall system costs, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is utilized to train the offloading policy, enabling automatic optimization of both latency and energy consumption. consumption. Finally, a feedback mechanism is introduced to dynamically adjust parameters and enhance the robustness of the clustering process. Simulation results demonstrate that the proposed approach significantly outperforms baseline methods in terms of task completion cost, energy consumption, delay, and success rate, thereby validating its potential and superior performance in dynamic vehicular network environments. Full article