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Using Shared Decision-Making Approach in Workout Adjustment Guidance:A Case into Running Training Apps
No full textCurrent running apps use advanced algorithms to analyse performance data and recommend workouts. Yet, it is unclear how interfaces can support recreational runners' autonomy and decision-making during runners' making adjustments in their training plans. We addressed this challenge by conducting a three-step study. We first conducted semi-structured interviews to explore runners' needs and training plan and workout decision strategies, then adopted Shared Decision-Making (SDM) principles to redesign a running app interface and finally carried out a followg-'up evaluation study to understand the dynamics of technology-mediated workout adjustments. We found that technology-mediated workout adjustments approach is promising, but requires clear assistanceg-'selection control and interface simplicity. From these results, we distil a "Threeg-'Step Interaction Model"that balances user autonomy with system guidance. Our findings offer SportsHCI design guidelines for interfaces that deliver personalised support while preserving athlete independence.</p
Attack-Defense Trees with Offensive and Defensive Attributes
No full textEffective risk management in cybersecurity requires a thorough understanding of the interplay between attacker capabilities and defense strategies. Attack-Defense Trees (ADTs) are a commonly used methodology for representing this interplay; however, previous work in this domain has only focused on analyzing metrics such as cost, damage, or time from the perspective of the attacker. This approach provides an incomplete view of the system, as it neglects to model defender attributes: in real-world scenarios, defenders have finite resources for countermeasures and are similarly constrained. In this paper, we propose a novel framework that incorporates defense metrics into ADTs, and we present efficient algorithms for computing the Pareto front between defense and attack metrics. Our methods encode both attacker and defender metrics as semirings, allowing our methods to be used for many metrics such as cost, damage, and skill. We analyze tree-structured ADTs using a bottom-up approach and general ADTs by translating them into binary decision diagrams. Experiments on randomly generated ADTS demonstrate that both approaches effectively handle ADTs with several hundred nodes.</p
Deviating from mechanical alignment in total knee arthroplastyinterchangeable targets?:are osteophyte-free knee kinematic and ligament strain restoration
No full textHow Can Cryptography Secure Online Assessments Against Academic Dishonesty?
No full textPopular learning platforms like Canvas LMS (Learning Management System), Moodle, and Google Forms have become widespread among university students. This research focuses on these platforms because of their significant role in modern online education. However, there are concerns about data integrity, especially regarding online assessments. During and after the COVID-19 pandemic, there was a notable increase in online cheating incidents, including unauthorized access, content sharing, plagiarism, and the use of external resources during exams. These issues highlight the vulnerabilities of these educational platforms. This study aims to identify the gaps that facilitate online cheating in Canvas LMS, Moodle, and Google Forms. After pinpointing these gaps, it proposes solutions for each issue and integrates them into a prototype. The prototype uses cryptographic protocols for each stage of the examination, employing encryption and digital signatures to ensure the integrity of exam data. Additionally, it features Two-Factor Authentication (2FA), a Safe Exam Browser (SEB), and restrictions on copy-pasting to help reduce academic dishonesty among students. The developed prototype is designed to secure online assessments and maintain the integrity of exam data while protecting student privacy
Personal recovery in the general population:Comparison of psychometric properties of the Brief INSPIRE-O in those with and without common mental disorders
No full textThe 5-item Brief INSPIRE-O instrument, based on the Connectedness, Hope, Identity, Meaning in Life, and Empowerment framework, is a novel tool to assess personal recovery. Although initially developed for clinical populations, its conceptual alignment with core dimensions of psychological well-being suggests its potential applicability to a broader audience. The current study aimed to examine its validity, reliability, and measurement invariance across people with and without common mental disorders (CMDs). The scale was administered in a Dutch general population sample (n = 5,451). Confirmatory factor analyses supported a unidimensional structure with robust factor loadings and scalar invariance across individuals with and without CMDs in the past year. In addition, the Brief INPSIRE-O showed acceptable reliability (ω = .71–.78) and the expected pattern of correlations with other health indicators supported its construct validity. In conclusion, the Brief INSPIRE-O appears to be a psychometrically sound measure of positive psychological functioning that can be validly used and compared across people with and without CMDs.</p
Reduction of RF Heating Near Bilateral Deep Brain Stimulation Leads Using Two-Channel RF Shimming at 3T
No full textThe use of 3T MRI in patients with bilateral deep brain stimulation (DBS) leads is limited by safety concerns due to radiofrequency (RF) heating. A promising strategy to overcome this problem involves RF shimming using low-specific absorption rate (SAR) calibration scans to estimate the RF-induced currents based on image artifacts near the leads. Although clinically available two-channel RF shimming can suppress RF heating in a single lead configuration, complete nulling is not possible when more than one lead is involved. This study aims to develop a method to minimize rather than null RF heating and optimize imaging performance during 3T MRI in a bilateral DBS lead configuration by using two-channel RF shimming. An anthropomorphic phantom equipped with bilateral DBS leads and fiber-optic temperature sensors was constructed. Optimal RF shim settings were determined in multiple phantom orientations using a low-SAR calibration protocol. These settings were evaluated and compared with the quadrature mode by measuring local RF heating during a high-SAR imaging sequence and inspecting residual image artifacts. Measured heating curves and imaging data confirmed that tailored RF shim settings minimized RF heating and image artifacts for both leads simultaneously in all orientations studied. Two-channel RF shimming on a clinical 3T MRI scanner can thus be optimized in a bilateral DBS lead configuration to minimize RF heating and maximize imaging performance. This workflow could potentially enable a patient-specific workflow for safe imaging in patients with bilateral DBS leads at 3T.</p
Behavior Nets:Context-Aware Behavior Modeling for Code Injection-Based Windows Malware
No full textDespite significant effort put into research and development of defense mechanisms, new malware is continuously developed rapidly, making it still one of the major threats on the Internet. For malware to be successful, it is in the developer’s best interest to evade detection as long as possible. One method in achieving this is using Code Injection, where malicious code is injected into another benign process, making it do something it was not intended to do.Automated detection and characterization of Code Injection is difficult. Many injection techniques depend solely on system calls that in isolation look benign and can easily be confused with other background system activity. There is therefore a need for models that can consider the context in which a single system event resides, such that relevant activity can be distinguished easily.In previous work, we conducted the first systematic study on code injection to gain more insights into the different techniques available to malware developers on the Windows platform. This paper extends this work by introducing and formalizing Behavior Nets: A novel, reusable, context-aware modeling language that expresses malicious software behavior in observable events and their general interdependence. This allows for matching on system calls, even if those system calls are typically used in a benign context. We evaluate Behavior Nets and experimentally confirm that introducing event context into behavioral signatures yields better results in characterizing malicious behavior than the state of the art. We conclude with valuable insights on how future malware research based on dynamic analysis should be conducted
Automated scan-vs-BIM registration using columns segmented by deep learning for construction progress monitoring:“This paper is based on the MSc thesis of the first author.”
No full textIn construction automation applications, coarse registration between 3D Building Information Modelling (BIM) and the as-built point cloud is vital for the monitoring of construction progress. This can be achieved by extracting highly distinct geometric features in both datasets to speed up the correspondence search. However, the existing geometric feature-based coarse registration methods have limitations in the Architecture, Engineering, Construction & Facility Management (AEC/FM) context because building designs often contain a considerable self-similarity, symmetry, and lack of texture. In this work, we propose an automatic coarse registration method that is motivated by the Random Sample Consensus (RANSAC) algorithm to estimate the transformation parameters that best align the as-built point cloud in the coordinate frame of the BIM model by matching the corresponding columns. The method is based on the extraction of columns from the as-built point cloud and the as-planned BIM model. For the point cloud data, fully automated column extraction techniques are used by applying deep learning, whereas the BIM model columns are extracted from the available semantic information. Experiments are carried out on real-life datasets from the building construction site to validate the proposed method. The results show that our proposed column-based registration method achieved an RMSE of 2 centimeters, and the cloud-to-cloud mean distance of 1.6cm ± 1.8cm after fine registration. The accuracy of the co-registration result shows that our proposed approach contributes to automating the registration between the as-built point cloud and the as-planned BIM model for construction progress monitoring.</p
Exergy-Based Sustainability Assessment of Gold Mining in Colombia:A Comparative Analysis of Open-Pit and Alluvial Mining
No full textThermodynamic methods such as exergy analysis enable the evaluation of environmental load (environmental impacts) by quantifying entropy generation and exergy destruction associated with using renewable and non-renewable resources throughout a production system. Based on the principle that environmental impacts occur when exergy is dissipated into the environment, this study applies exergy analysis as a tool for assessing the sustainability of gold mining in Colombia. Two extraction technologies—open-pit and alluvial mining—are evaluated by calculating exergy efficiencies, cumulative exergy demand (CExD), and associated environmental impacts. The results reveal significant differences between the two methods: open-pit mining is heavily dependent on fossil fuels (53% of input exergy), with 99.62% of total exergy destroyed, resulting in an exergy efficiency of just 0.37% and a sustainability index (SI) of 1.00. In contrast, alluvial mining relies predominantly on water (94%), with 69% of input exergy destroyed, an exergy efficiency of 31%, and an SI of 1.46. Four strategies are proposed to reduce environmental burdens: improving efficiency, minimizing exergy losses, integrating renewable energy, and adopting circular economy principles. This study presents the first application of exergy analysis to comprehensively assess the exergy cost of gold production, from extraction through refining, casting, and molding, highlighting critical exergy hotspots and offering a thermodynamic foundation for optimizing resource use in mineral processing.<p/