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Optimising Visual User Interfaces to Reduce CognitiveFatigue and Enhance Mental Well-being
No full textUser interface design is a key priority in modern computer systems, especially when the users are non-technical. Due to the importance of designing more user-friendly interfaces, the focus has been increased on designing human-centred systems over functional-centred systems of the past. Any human-computer interface can cause different levels of cognitive fatigue in the user, which can cause significant mental stress, which is not healthy for the users. This study has used the critical literature review method and reviewed six theories/concepts related to the design of visual user interfaces which could potentially reduce user cognitive fatigue. The reviewed theories are attention restoration theory, cognitive load theory, Gestalt principles, Fitts's law, progressive disclosure and UX honeycomb. The current commercial purposes of interface design do not seem to consider the user's mental health or well-being when designing user interfaces and user experience. They only try to maximise user retention and engagement. The study findings advocate for a paradigm shift towards designing visual interfaces that prioritize human-centric principles, with a primary emphasis on promoting user mental health and well-being over commercial objectives of constant user retention and engagement. For example, attention restoration theory can be considered as one of the key theories which is helpful to design better interfaces which consider user health and well-being. However, there are challenges to the 2 designers to find the right equilibrium between user engagement and user well-being. Designers can use the findings, subject to further empirical validations
Investigating the Implementation of Community-Based Stroke Telerehabilitation in England; A Realist Synthesis Study Protocol
Get PDFTelerehabilitation (TR) shows promise as a method of remote service delivery, yet there is little guidance to inform implementation in the context of the National Health Service (NHS) in England. This paper presents the protocol for a realist synthesis study aiming to investigate how TR can be implemented to support the provision of high-quality, equitable community-based stroke rehabilitation, and under what conditions. Using a realist approach, we will synthesise information from (1) an evidence review, (2) qualitative interviews with clinicians (n ≤ 30), and patient–family carer dyads (n ≤ 60) from three purposively selected community stroke rehabilitation services in England. Working groups including rehabilitation professionals, service-users and policy-makers will co-develop actionable recommendations. Insights from the review and the interviews will be synthesised to test and refine programme theories that explain how TR works and for whom in clinical practice, and draw key messages for service implementation. This protocol highlights the need to improve our understanding of TR implementation in the context of multidisciplinary, community-based stroke service provision. We suggest the use of a realist methodology and co-production to inform evidence-based recommendations that consider the needs and priorities of clinicians and people affected by stroke
Agreement between equation-derived body fat estimator and bioelectrical impedance analysis for body fat measurement in middle-aged southern Indians
Get PDFExcess body fat (BF) contributes to metabolic syndrome (MetS). The Clínica Universidad de Navarra—Body Adiposity Estimator (CUN- BAE) is an equation-derived body fat estimator proposed to assess BF. However, its efficiency compared to the standard method is unknown. We aimed to compare the efficacy of CUN-BAE with the standard method in estimating BF in southern Indians. We included 351 subjects, with 166 MetS patients and 185 non-MetS subjects. BF was obtained from the standard bioelectrical impedance analysis (BIA) method and measured by CUN- BAE in the same subjects. We compared the efficacy of CUN- BAE in estimating BF with that of BIA via Bland–Altman plots, intraclass correlation coefficients, concordance correlation coefficients and the kappa index. The mean body fat percentage (BF%) values measured by BIA and CUN- BAE in all the subjects were 28.91 ± 8.94 and 29.22 ± 8.63, respectively. We observed significant absolute agreement between CUN- BAE and BIA for BF%. BIA and CUN- BAE showed good reproducibility for BF%. CUN- BAE had accuracy comparable to BIA for detecting MetS using BF%. Our findings indicate that CUN- BAE provides precise BF estimates similar to the BIA method, making it suitable for routine clinical practice when access to BF measurement devices is limited
Corrigendum: Stimulation of hair regrowth in an animal model of androgenic alopecia using 2-deoxy-D-ribose
Get PDFMagnetic field of the roAp star KIC~10685175: observations versus theory
Get PDFContext. KIC 10685175 is a roAp star whose polar magnetic field is predicted to be 6 kG through a nonadiabatic axisymmetric pulsation theoretical model.
Aims. In this work, we aim to measure the magnetic field strength of KIC 10685175 using high-resolution spectropolarimetric observations, and compare it with the one predicted by the theoretical model.
Methods. Two high-resolution unpolarized spectra have been analyzed to ascertain the presence of magnetically split lines and derive the iron abundance of this star through equivalent width measurements of 10 Fe lines. One polarized spectrum has been used to measure the mean longitudinal magnetic field with the least-squares deconvolution technique. Further, to examine the presence of chemical spots on the stellar surface, we have measured the mean longitudinal magnetic fields using different lines belonging to different elements.
Results. From the study of two high-resolution unpolarized spectra, we obtained the spectroscopic atmospheric parameters including the effective temperature (Teff), surface gravity (lo
A Novel Approach for Solving N-Queen Problem Using Non-Sequential Conflict Resolution Algorithm
Get PDFThe N-Queens problem is a fundamental challenge in combinatorial optimization, commonly used as a benchmark for assessing the efficiency of algorithms. Traditional algorithms, such as Backtracking with Forward Checking (BFC), constraint satisfaction problem (CSP) techniques, Lookahead algorithms, and heuristic-based methods, often face challenges with exponential time complexity, making them less practical for large-scale instances. This paper introduces a novel algorithm, non-sequential conflict resolution (NSCR), which improves performance over traditional algorithms through dynamic conflict resolution. The NSCR algorithm iteratively resolves conflicts among queens by adjusting their positions, aiming to optimize both time complexity and memory usage. While NSCR also operates within exponential time bounds, it demonstrates improved scalability and efficiency compared to traditional methods. A significant strength of the NSCR algorithm lies in its space complexity, which is O(n), and a time complexity that, while typically lower than traditional methods, can reach O(n3) in the worst-case scenario. This linear space complexity is highly advantageous, particularly when dealing with large problem sizes, as it ensures efficient use of memory resources. Comparative analysis with the aforementioned algorithms shows that NSCR offers superior resource management, using up to 60% less memory and reducing runtime by approximately 50%, making it an efficient option for large-scale instances of the N-Queens problem. The algorithm’s performance, evaluated on problem sizes ranging from 8 to 1000 queens, highlights its ability to manage computational resources effectively, despite the inherent challenges of exponential time complexity
Progress Visualisation, Competition, and Collaboration in Digital Game-based Learning as Audience (DGBL-AA)
Get PDFThis study builds on previous work investigating the efficacy of a novel framework for the design of interactive learning activities based on Digital Game-Based Learning (DGBL). The work presented here provides further explanation of the setup from the previous experiment with the novel framework, DGBL-AA, by considering the provision of a game that a learner can ‘observe’ as an audience, with parameters related to a linked learning activity. Using a quasi-experimental approach, we analyse children’s reactions and perceptions of a game they observe as spectators. While the previous work was grounded in the same principles, this paper finalises the name DGBL-AA and elaborates on the details of the framework. It articulates the underlying principles that facilitate the framework's generalisation across traditional classroom education, GBL, DGBL, and life scenarios, typically phenomena seen in sports. The paper identifies the primary challenges in DGBL and sets specific goals for the framework to address these issues. The experiments and the Frogs game, developed specifically for this study, illustrate the framework's adaptability and offer insights into its potential for widespread implementation. This study evaluates the framework's impact on learning outcomes and student engagement. Our findings suggest that the framework significantly enhances the educational experience, as evidenced by quantitative and qualitative data from the experiments. The experimental group demonstrated higher engagement and better performance compared to the control group. However, there was no significant difference between the competition and collaboration modes of the Frogs game. Future research directions include developing additional games with various progress visualisations to further assess the framework's efficacy. We also aim to explore the long-term effects of the framework on student achievement and motivation. Eventually, a virtual world that allows people to observe and motivate their learning could be created. This could involve implementing the framework in different subjects, age groups, and educational settings to validate its versatility and robustness
A Proactive Model for Intrusion Detection Using Image Representation of Network Flows
Get PDFMany interconnected IoT devices driven by imperatives of efficiency and convenience often lack adequate security measures, making them susceptible to exploitation by cyber-criminals. Effective network security necessitates meticulous intrusion detection, which typically involves scrutinizing the network traffic using deep packet or stateful protocol inspection techniques. However, traditional inspection methods often require manual feature engineering, which can result in loss of payload information and thus, false alarms. In this study, a controlled testbed environment is established to capture botnet traffic. The paper introduces a detection approach that involves converting raw NetFlow data to IDX, short for ‘Index,’ image representations. A hybrid deep learning architecture is designed, integrating VGG19 and GRU structures to learn the spatial and temporal features, respectively. The detection results show that the proposed solution achieves 98.883% true positives rate and 0.9% false negatives rate, surpassing conventional anomaly detection. In addition, an adaptive sliding window technique is introduced for live intrusion detection and prevention. Through iterative testing and refinement, a runtime of 0.041ms per image and 0.00171ms per packet is achieved, confirming the robust nature of the proposed method