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High-performance computing (HPC) offers the computing power to continuously support the world's most important discoveries in various scientific and business domains such as chemistry, physics, biology, material science, drug discovery, and financial investment risk analysis. Researchers from across the HPC community have been developing software systems, tools, libraries, frameworks, application packages, and methods that can fully exploit these extremely powerful computing resources. The eleventh paper titled "A large scale parallel fluid-structure interaction computing platform for simulating structural responses to a detonation shock" by Yang et al.[10] presents a partitioned fluid-structure interaction computing platform designed for parallel simulating structural responses to a detonation shock. [Extracted from the article]

Introduction: Liver cancer presents a growing global health concern, necessitating advanced approaches for intervention. This review investigates the use and effectiveness of software navigation in interventional radiology for liver tumour procedures. Methods: In accordance with Preferred Reporting Items for Systematic reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐ScR) guidelines, a scoping review was conducted of the literature published between 2013 and 2023 sourcing articles through MEDLINE, Scopus, CINAHL and Embase. Eligible studies focused on liver cancer, utilised cone‐beam computed tomography (CBCT), and employed software for intervention. Twenty‐one articles were deemed eligible for data extraction and analysis. Results: Categorised by type, software applications yielded diverse benefits. Feeder detection software significantly enhanced vessel identification, reducing non‐target embolisation by up to 43%. Motion correction software demonstrated a 20% enhancement in image quality, effectively mitigating breathing‐induced motion artefacts. Liver perfusion software facilitated efficient tumour targeting while simultaneously reducing the occurrence of side effects. Needle guide software enabled precise radiofrequency ablation needle placement. Additionally, these software applications provided detailed anatomical simulations. Overall, software integration resulted in shorter procedures, reduced radiation exposure and decreased contrast media usage. Conclusion: This scoping review highlights the innovative yet relatively underexplored role of software navigation for liver tumour procedures. The integration of software applications not only enhances procedural efficiency but also bolsters operator confidence, and contributes to improved patient outcomes. Despite the current lack of uniformity and standardisation, these software‐driven advancements hold significant promise for transforming liver tumour interventions. To realise these benefits, further research is needed to explore the clinical impact and optimal utilisation of software navigation tools in interventional radiology. [ABSTRACT FROM AUTHOR]

Abtract: In this paper, a new cybernetic control technology that can be used in complex software systems will be introduced. In this approach, the software systems are governed by cybernetic control objects and the class libraries defining the types of these cybernetic control objects are produced by special meta‐programming platforms. In this approach, the requirements of the software to be developed are received from the programmer by meta‐programming systems before coding. Actually, the cybernetic control objects have standard design and properties and the programmers only determine the quantities and the locations of these properties before library production process. Then, the meta‐programming platforms build project‐specific class libraries based on previously determined code templates. By this way, the cybernetic control objects are constructed with optimal memory and they can receive feedback about ongoing operations on the process. With the help of the feedback coming from the process, the control objects steer the process in the line of the programmer directives. By this way, the control of the programmer on the software increases significantly. In addition, in this paper, a typical application of this approach to the multithread programming will be introduced. [ABSTRACT FROM AUTHOR]

Different quality grades of tea tend to have a high degree of similarity in appearance. Traditional image‐based identification methods have limited effects, while complex deep learning architectures require much data and long‐term training. In this paper, two tea quality identification methods based on deep convolutional neural networks and transfer learning are proposed. Different types and quality of tea images are collected by a self‐designed computer vision system to form a data set, which is small‐scale and of high inter‐ and intraclass similarity. The first method uses three simplified convolutional neural network (CNN) models with different image input sizes to identify the quality of tea. The second method performs transfer learning to identify the tea quality by fine‐tuning the mature AlexNet and ResNet50 architecture. Classification performance and model complexity are measured and compared. The related application software is also developed. The results show that the performance of the CNN models and the transfer learning models are close, and both can achieve high identification accuracy. However, the complexity of the CNN models is two to three orders of magnitude lower than that of the transfer learning models. The study shows that deep CNNs and transfer learning have great potential to be rapid and effective methods for automated tea quality identification tasks with high inter‐ and intrasimilarity. [ABSTRACT FROM AUTHOR]

Context: GitHub is the world's most prominent host of source code, with more than 327M repositories. However, most of these repositories are not labelled or inadequately, making it harder for users to find relevant projects. Various proposals for software application domain classification over the past years have been proposed. However, these several of those approaches suffer from multiple issues, called antipatterns of software classification, that reduce their usability. Objective: In this paper, we propose a new taxonomy in the GitHub ecosystem, called GitRanking, starting from a well‐structured data set, composed of curated repositories annotated with topics. The main objective is to create a baseline methodology for software classification that is expandable, hierarchical, grounded in a knowledge base, and free of antipatterns. Method: We collected 121K topics from GitHub and used GitRanking to create a taxonomy of 301 ranked application domains. GitRanking (1) uses active sampling to ensure a minimal number of annotations to create the ranking; and (2) links each topic to Wikidata, reducing ambiguities and improving the reusability of the taxonomy. Furthermore, we adopt the conceived taxonomy in a classification task by considering a state‐of‐the‐art classifier. Results: Our results show that GitRanking can effectively rank terms in a hierarchy according to how general or specific their meaning is. Furthermore, we show that GitRanking is a dynamically extensible method: it can currently accept further terms to be ranked, and with a minimum number of annotations (≈15$$ \approx 15 $$). Concerning the classification task, we show that the model achieves an F1‐score of 34%, with a precision of 54%. Conclusion: This paper is the first collective attempt at building a ground‐up taxonomy of software domains. Our vision is that our taxonomy, and its extensibility, can be used to better and more precisely label software projects. [ABSTRACT FROM AUTHOR]

In the present paper, we assess whether website rating systems are useful for selecting educational apps for preschool age children. We selected the 10 highest scoring and 10 lowest scoring apps for 2–4‐year‐olds from two widely used websites (Good App Guide; Common Sense Media). Apps rated highly by the two websites had a higher educational potential as assessed by a validated questionnaire for evaluating the educational potential of apps and were more likely to include a learning goal and feedback compared to low scoring apps. However, high scoring apps scored on average just 9/20 for indicators of educational potential, and both high and low scoring apps had poor language quality as determined by psycholinguistic and construction type analyses. We argue that website rating systems should also include quality of feedback, adjustable content, social interactions, storyline and a more fine‐grained analysis of language in their assessments. Practitioner notesWhat is already known about this topic Appropriately designed apps for preschool age children have the potential to teach early school readiness skills.Selecting high quality educational apps for preschool age children is challenging.The children's app marketplace is currently unregulated.What this paper adds We assess whether two leading app rating websites are useful for selecting educational apps for preschool age children.Children's apps rated highly by two app website rating systems had a higher educational potential than low rated apps as measured by a research informed app evaluation tool.In‐depth analysis of the language in apps shows that highly rated children's apps on app rating websites may not enrich a child's early language environment.Implications for practice and/or policy Children's app rating website assessments should include potential for feedback, language, adjustable content, storyline and social interactions.Policy should be implemented for app ratings in the app stores or on website app rating systems. [ABSTRACT FROM AUTHOR]

This paper introduces the Fish 3D Locomotion app (F3LA), a Python‐based, Graphical User Interface (GUI)‐equipped tool designed to automate behavioral endpoint extraction in zebrafish locomotion assays. Building on our previous work, which utilized a specialized aquatic tank with a mirror and a single camera for fish movement tracking in three dimensions, F3LA significantly enhances data processing efficiency. Its accuracy was tested by reanalyzing and comprehensively comparing the calculated data with the previously published data from prior publications. From the comparison results, 90% of endpoints showed a similar statistical difference result. These minor differences were due to the different starting points for the dataset and updated calculation formulas that are implemented in F3LA. In addition, shoaling area or shoaling volume calculations are also included in F3LA as a new feature that can serve as sensitive indicators of social cohesion, group dynamics, or stress responses, offering insights into neuropsychological conditions or the effects of pharmacological interventions. Furthermore, F3LA offers a marked improvement over manual operations, being at least five times faster, while maintaining consistent accuracy as it reduces human‐induced errors, ensuring a higher degree of reliability in the results. Finally, the potency of F3LA was tested to evaluate the toxicities of 14 rare earth elements (REEs) to the adult zebrafish behaviors. Based on the results, our findings suggested that each tested REE altered fish behaviors in different patterns and magnitudes to each other. However, among the tested light rare earth elements (LREEs), neodymium was demonstrated to cause more relatively severe behavior alterations than other LREEs, indicated by the statistically higher value of entropy (0.2695 ± 0.04977 (mean with a standard deviation)) than the control group (0.2352 ± 0.05896). Meanwhile, in terms of heavy rare earth elements (HREEs), erbium seemed to lead to more distinct behavior toxicities than other HREEs, which was shown by the statistically lower level of fractal dimension (2.022 ± 0.3412) than the untreated group (2.255 ± 0.1661). Taken together, F3LA's development marks a significant advance in high‐throughput toxicological and pharmacological assessments in zebrafish, leveraging three‐dimensional locomotion data for a more comprehensive analysis of fish behavior performance, providing a significant contribution to research in various fields. [ABSTRACT FROM AUTHOR]

Robotic Process Automation (RPA) is an emerging software technology for automating business processes. RPA uses software robots to perform repetitive and error‐prone tasks previously done by human actors quickly and accurately. These robots mimic humans by interacting with existing software applications through user interfaces (UI). The goal of RPA is to relieve employees from repetitive and tedious tasks to increase productivity and to provide better service quality. Yet, despite all the RPA benefits, most organizations fail to adopt RPA. One of the main reasons for the lack of adoption is that organizations are unable to effectively identify the processes that are suitable for RPA. This paper proposes a new method, called Rule‐based robotic process analysis (RRPA), that assists process automation practitioners to classify business processes according to their suitability for RPA. The RRPA method computes a suitability score for RPA using a combination of two RPA goals: (i) the RPA feasibility, which assesses the extent to which the process or the activity lends itself to automation with RPA and (ii) the RPA relevance, which assesses whether the RPA automation is worthwhile. We tested the RRPA method on a set of 13 processes. The results showed that the method is effective at 82.05% and efficient at 76.19%. [ABSTRACT FROM AUTHOR]

The paper presents a research on estimating the positioning accuracy of autonomous industrial vehicles, using an original sensor fusion combination, in specific polymer production factories environments. Mechanical and electrical subsystems designs are presented, together with the software application and testing results. Conclusions are drawn, serving for future positioning system design. [ABSTRACT FROM AUTHOR]

Background and purpose: Successful long‐term treatment of spasticity in people with multiple sclerosis (pwMS) is challenging. We investigated the effects of multidisciplinary inpatient rehabilitation (MIR) and an individualized self‐training program delivered by an app on spasticity in pwMS. Methods: First, we assessed the efficacy of 4‐week MIR in ambulatory pwMS (Expanded Disability Status Scale < 7.0) with moderate to severe lower limb spasticity (defined by ≥4 points on the Numeric Rating Scale for spasticity [NRSs]) in a cohort of 115 pwMS at seven rehabilitation centers in Austria. In the case of a clinically relevant improvement in spasticity of ≥20% on the NRSs following MIR (n = 94), pwMS were randomly allocated in a 1:1 ratio to either the newly designed MS‐Spasticity App or to a paper‐based self‐training program for 12 weeks. The primary outcome was change in NRSs (German Clinical Trials Registry DRKS00023960). Results: MIR led to a significant reduction of 2.0 points on the NRSs (95% confidence interval [CI] = 2.5–2.0, p < 0.000). MIR was further associated with a statistically significant improvement in spasticity on the Modified Ashworth Scale, strength, and all mobility outcomes. Following MIR, self‐training with the MS‐Spasticity App was associated with a sustained positive effect on the NRSs, whereas paper‐based self‐training led to a worsening in spasticity (median NRSs difference = 1.0, 95% CI = 1.7–0.3, p = 0.009). The MS‐Spasticity App was also associated with a significantly better adherence to self‐training (95% vs. 72% completion rate, p < 0.001). Conclusions: In pwMS, MIR is able to significantly improve lower limb spasticity, strength, and mobility. Following MIR, an individually tailored antispasticity program delivered by an app leads to sustained positive long‐term management. [ABSTRACT FROM AUTHOR]

The rapid expansion of smart devices leads to the increasing demand for vulnerability detection in the cyber security field. Writing secure source codes is crucial to protect applications and software. Recent vulnerability detection methods are mainly using machine learning and deep learning. However, there are still some challenges, how to learn accurate source code semantic embedding at the function level, how to effectively perform vulnerability detection using the learned semantic embedding of source code and how to solve the overfitting problem of learning‐based models. In this paper, we consider codes as various graphs with node features and propose a tensor‐based gated graph neural network called TensorGNN to produce code embedding for function‐level vulnerability detection. First, we propose a high‐dimensional tensor for combining different code graph representations. Second, inspired by the work of tensor technology, we propose the TensorGNN model to produce accurate code representations using the graph tensor. We evaluate our model on 7 C and C++ large open‐source code corpus (e.g. SARD&NVD, Debian, SATE IV, FFmpeg, libpng&LibTiff, Wireshark and Github datasets), which contains 13 types of vulnerabilities. Our TensorGNN model improves on existing state‐of‐the‐art works by 10%–30% on average in terms of vulnerability detection accuracy and F1, while our TensorGNN model needs less training time and model parameters. Specifically, compared with other existing works, our model reduces 25–47 times of the number of parameters and decreases 3–10 times of training time. Results of evaluations show that TensorGNN has better performance while using fewer training parameters and less training time. [ABSTRACT FROM AUTHOR]

Rigid solid body dynamics is a key element of the undergraduate mechanical engineering curriculum. In a context of reverse engineering and/or sustainable development, being able to analyze the mechanical and material properties of a system without damaging it is a required skill. In this work, two systems based on wheels rolling over horizontal paths without sliding are studied: an unbalanced hollow cylinder and a wheel with displaced mass assembly. Four generations of last year's bachelor's students in mechanical engineering, representing a hundred people a year, followed a total of 12 h of practical sessions working on such systems. Without focusing on the programming part, this paper aims at showing how computer tools can help and improve a rigid solid body dynamics course. The objectives were to develop, apply, and analyze this teaching sequence, allowing students to (i) perform and characterize experimental measurements, (ii) establish systems motion equations, and (iii) think about dynamics results through cross‐talks between different techniques such as geometrical and analytical methods as well as computer‐assisted design tools. Different analysis methods of these systems could be discussed in the context of the classes, successfully bringing pertinent reflections to the students. The possibility of transferring such discussions to other contexts proved to be wide, thanks to computer science, dynamics software, other analysis methods and applications. [ABSTRACT FROM AUTHOR]

Researchers recommend that parents look for five benchmarks as indicators of quality educational apps (ie, scaffolding, curriculum, development team, feedback, learning theory), yet results show that parents undervalue some of these benchmarks. The current study examined if a short video‐based intervention would enhance parents' value‐judgements of apps featuring the five educational benchmarks. In original and modified app experiments (n = 100; n = 101), parents of children 4–11 years old were randomly assigned to watch either a 9‐minute video that detailed how the five benchmarks augment learning, or a 2‐minute control video. Parents evaluated 10 simulated apps containing either benchmarks or buzzwords. The original app experiment shows that a brief intervention can help parents identify quality educational apps via the benchmarks, but the modified app experiment suggests it only works if developers are using specific keywords in app descriptions. Helping parents select quality educational apps is more complicated than simply telling them what to look for. Practitioner notesWhat is already known about this topic Parents have a main role in selecting apps and deciding on how often children use them; however, they have difficulty evaluating an app's educational potential in a market mixed with high‐ and low‐quality products that lacks a standard for including educational apps on the App Store.There are five research‐based benchmarks that are indicators of quality educational apps. These include apps created by an interdisciplinary development team, having a guiding curriculum with a clear purpose, including scaffolding and feedback, and being based on a learning theory. However, parents are not valuing all of these educational benchmarks equally.Educational videos disseminated via YouTube have become an established medium to enhance people's knowledge.Mayer's cognitive theory of multimedia learning, which suggests that people learn better when there is both auditory and visual information given together, is a framework used to design educational videos.What this paper adds This study leveraged a useful, accessible medium (ie, educational YouTube videos) to make research on educational apps accessible enough such that it could influence parents' app selection.Parents of children aged 4–11 years‐old viewed either an educational intervention‐ or control‐video and assessed educational apps through measures that replicate how consumers evaluate apps on the App Store (ie, their willingness to download the app, how much they would pay, their rating, and ranking).Original and modified app experiments demonstrate that a brief, educational video designed using key features from Mayer's multimedia theory can improve parents' app selection.In the original app experiment, parents in the intervention group are valuing the guiding curriculum and development team benchmarks over others, which may be due to the structure of the intervention video (ie, worked‐examples immediately after pre‐training of benchmarks).In the modified app experiment, parents in the intervention group did not differ from the control group in their evaluations of the different benchmarks apart from rating the app with learning theory higher and ranking the guiding curriculum benchmark lower. The higher rating of learning theory may be a result of the language change in the simulated app to include more user‐friendly terms. The unexpected ranking result may be due to a limitation with the measure.Building on prior research on the educational benchmarks, these experiments show that parents are valuing them over buzzwords; however, even when shown an educational video, they are not valuing them all equally.Implications for practice and/or policy An educational video based on the five educational benchmarks could be used as a tool to easily disseminate knowledge to enhance parents' educational app selection.The experiments shed light on the complexity of knowledge dissemination to the public. While there is evidence for the use of research‐based educational videos to disseminate knowledge to parents when selecting educational apps, the App Store descriptions should include accessible language that makes the research‐based benchmarks easy to select.This study also provides suggestions for improving the development of educational videos. [ABSTRACT FROM AUTHOR]

The Laplace Beltrami operator is one of the essential tools in geometric processing. It allows us to solve numerous partial differential equations on discrete surface meshes, which is a fundamental building block in many computer graphics applications. Discrete Laplacians are typically limited to standard elements like triangles or quadrilaterals, which severely constrains the tessellation of the mesh. But in recent years, several approaches were able to generalize the Laplace Beltrami and its closely related gradient and divergence operators to more general meshes. This allows artists and engineers to work with a wider range of elements which are sometimes required and beneficial in their field. This paper discusses the different constructions of these three ubiquitous differential operators on arbitrary polygons and analyzes their individual advantages and properties in common computer graphics applications. [ABSTRACT FROM AUTHOR]

This paper is one of the first to analyse the ethical implications of specific healthcare artificial intelligence (AI) applications, and the first to provide a detailed analysis of AI‐based systems for clinical decision support. AI is increasingly being deployed across multiple domains. In response, a plethora of ethical guidelines and principles for general AI use have been published, with some convergence about which ethical concepts are relevant to this new technology. However, few of these frameworks are healthcare‐specific, and there has been limited examination of actual AI applications in healthcare. Our ethical evaluation identifies context‐ and case‐specific healthcare ethical issues for two applications, and investigates the extent to which the general ethical principles for AI‐assisted healthcare expressed in existing frameworks capture what is most ethically relevant from the perspective of healthcare ethics. We provide a detailed description and analysis of two AI‐based systems for clinical decision support (Painchek® and IDx‐DR). Our results identify ethical challenges associated with potentially deceptive promissory claims, lack of patient and public involvement in healthcare AI development and deployment, and lack of attention to the impact of AIs on healthcare relationships. Our analysis also highlights the close connection between evaluation and technical development and reporting. Critical appraisal frameworks for healthcare AIs should include explicit ethical evaluation with benchmarks. However, each application will require scrutiny across the AI life‐cycle to identify ethical issues specific to healthcare. This level of analysis requires more attention to detail than is suggested by current ethical guidance or frameworks. [ABSTRACT FROM AUTHOR]

A systematic review is a type of literature review that aims to collect and analyse all available evidence from the literature on a particular topic. The process of screening and identifying eligible articles from the vast amounts of literature is a time‐consuming task. Specialised software has been developed to aid in the screening process and save significant time and labour. However, the most suitable software tools that are available often come with a cost or only offer either a limited or a trial version for free. In this paper, we report the release of a new software application, Catchii, which contains all the important features of a systematic review screening application while being completely free. It supports a user at different stages of screening, from detecting duplicates to creating the final flowchart for a publication. Catchii is designed to provide a good user experience and streamline the screening process through its clean and user‐friendly interface on both computers and mobile devices. All in all, Catchii is a valuable addition to the current selection of systematic review screening applications. It enables researchers without financial resources to access features found in the best paid tools, while also diminishing costs for those who have previously relied on paid applications. Catchii is available at https://catchii.org. [ABSTRACT FROM AUTHOR]

GLAM organizations have been digitizing their collections and making them available for the public for several decades. Recent methods for publishing digital collections such as "GLAM Labs" and "Collections as Data" provide guidelines for the application of computational methods to reuse the contents of cultural heritage institutions in innovative and creative ways. Jupyter Notebooks have become a powerful tool to foster use of these collections by digital humanities researchers. Based on previous approaches for quality assessment, which have been adapted for cultural heritage collections, this paper proposes a methodology for assessing the quality of projects based on Jupyter Notebooks published by relevant GLAM institutions. A list of projects based on Jupyter Notebooks using cultural heritage data has been evaluated. Common features and best practices have been identified. A detailed analysis, that can be useful for organizations interested in creating their own Jupyter Notebooks projects, has been provided. Open issues requiring further work and additional avenues for exploration are outlined. [ABSTRACT FROM AUTHOR]

Educational apps can be considered a dominant medium for providing educational content to children. Parents are major stakeholders and mediators in the selection of apps (Dias & Brito, 2021). It is critical to know how they choose apps for their children and understand what indicates a quality educational app, as well‐designed apps can support and enhance children's learning process. An online study with parents was conducted to identify parents' most dominant needs while selecting apps for their children. Parents' app selection behaviour was investigated leveraging Uses and Gratifications theory. Parents viewed 10 mock math apps that replicated the App Store presentation format. Five apps included educational benchmarks (eg, feedback) and five contained educational buzzwords (eg, interactive). Immediately following each app, parents provided value judgements of the app by stating whether they would download the app or not, rating it on a 5‐point‐scale, stating how much they would be willing to pay, and explaining why they chose to download the app or not. Results from paired‐samples t‐tests, and repeated‐measures ANOVAs indicated that parents value educational benchmarks over buzzwords suggesting that parents are primarily seeking apps that meet their children's educational needs. Parents' app needs seem to align with some of the research on what makes a good educational app. Practitioner notesWhat is already known about this topicTouch screen devices can enhance learning outcomes for children, if well designed educational applications are used (Camilleri & Camilleri, 2019; Cohen et al., 2011).Five educational benchmarks have been identified as indicators of app quality that parents can use to distinguish well designed apps (Dubé et al., 2020); having a development team that involves educators, possessing a guiding curriculum (Vaala et al., 2015), being based on a learning theory (Kebritchi & Hirumi, 2008), containing scaffolded learning, and providing feedback (Callaghan & Reich, 2018; Cayton‐Hodges et al., 2015).Uses and Gratifications theory suggests that people use the media to satisfy their psychological needs and to achieve their personal goals (Katz et al., 1973).What this paper addsThe study used Uses and Gratifications theory to identify parents' most dominant needs while selecting apps for their children. With the assumption that parents select apps based on their anticipated gratifications or parental need fulfilment (Broekman et al., 2016, 2018).Different features of the apps are presented in the forms of images and text descriptions in the App Store. The study investigated which features parents value when selecting apps from the App Store by including educational benchmarks and buzzwords in the images and text descriptions of the apps.Parents valued educational benchmarks over buzzwords. Thus, parents' app needs seem to align with the research‐based signifiers of app quality.Parents valued apps that feature development team, scaffolding, and guiding curriculums more than those with central learning theories and feedback.Development team had the highest download frequency and rating while learning theory had the lowest download frequency and rating.Parents were willing to pay more for the development team app and the least for ones containing feedback.The learning theory app was ranked the highest while the development team app received the lowest ranking from parents.Implications for practice and/or policyIncluding research‐based educational benchmarks in the apps and their app store descriptions promotes a research‐based framework for developing and identifying quality apps.Research‐based educational benchmarks could be used to determine a set of evidence‐based guidelines to assist app developers in the process of developing and presenting apps. [ABSTRACT FROM AUTHOR]

Thin‐walled sections distortional buckling phenomena is treated in EN1993 part:1‐3 (EC3‐1‐3) through the calculation of reduced thickness of stiffeners group, mainly by the application of the simplified method presented in this specification which comes with a limitation of applicability to non‐standard sections or with multiple stiffeners that can result in over conservative or insecure results. With the objective of facilitating de study/utilization of such kind of sections by designers, maintaining a safe set of results and without the overwhelming amount of calculations, the alternative method in clause 5.5.1(7) is used, applied only to the distortional analysis and correspondent effective thickness of stiffeners This alternative method allows the use of Linear Buckling Analysis (LBA), which, in this paper, are obtained through the Generalized Beam Theory (GBT) by the GBTUL computer application that applies this theory in the identification of distortional modes and buckling critical loads calculation. The results are then incorporated in EC3‐1‐3 algorithm as defined in clause 5.5.1(7). For the application of this alternative method the LBA results are integrated with EC3‐1‐3 using the Class4Section application, updated for this paper, which uses the critical buckling loads for the calculation of the critical stress of stiffeners group. This more generic approach by the alternative method is easily implemented in computational environment. Following previous work by the authors, the section moment capacity is analyzed in this paper by applying this alternative method and comparing it with available experimental results for selected thin‐walled sections mainly used in flexural problems, namely Sigma and Omega section types, as well as the most commonly known C and Z sections, but with multiple stiffeners. [ABSTRACT FROM AUTHOR]

Objectives: SlowMo therapy is a pioneering blended digital therapy for paranoia, augmenting face‐to‐face therapy with an interactive 'webapp' and a mobile app. A recent large‐scale trial demonstrated small–moderate effects on paranoia alongside improvements in self‐esteem, worry, well‐being and quality of life. This paper provides a comprehensive account of therapy personalisation within this targeted approach. Design: Case examples illustrate therapy delivery and descriptive data are presented on personalised thought content. Method: Thought content was extracted from the webapp (n = 140 participants) and coded using newly devised categories: Worries: (1) Persecutory, (2) Negative social evaluation, (3) Negative self‐concept, (4) Loss/life stresses, (5) Sensory‐perceptual experiences and (6) Health anxieties. Safer thoughts: (1) Safer alternative (specific alternatives to worries), (2) Second‐wave (generalised) coping, (3) Positive self‐concept, (4) Positive activities and (5) Third‐wave (mindfulness‐based) coping. Data on therapy fidelity are also presented. Results: Worries: 'Persecutory' (92.9% of people) and 'Negative social evaluation' (74.3%) were most common. 'General worries/ life stresses' (31.4%) and 'Negative self‐concept' (22.1%) were present in a significant minority; 'Health anxieties' (10%) and 'Sensory‐perceptual' (10%) were less common. Safer thoughts: 'Second‐wave (general) coping' (85%), 'Safer alternatives' (76.4%), 'Positive self‐concept' (65.7%) and 'Positive activities' (64.3%) were common with 'Third‐wave' (mindfulness) coping observed for 30%. Fidelity: Only three therapy withdrawals were therapy related. Session adherence was excellent (mean = 15.2/16; SD = 0.9). Behavioural work was conducted with 71% of people (119/168). Conclusion: SlowMo therapy delivers a targeted yet personalised approach. Potential mechanisms of action extend beyond reasoning. Implications for cognitive models of paranoia and causal interventionist approaches are discussed. [ABSTRACT FROM AUTHOR]

Purpose: Respiratory motion during positron emission tomography (PET) scans can be a major detriment to image quality in oncological imaging. The impact of motion on lesion quantification and detectability can be assessed using phantoms with realistic anatomy representation and motion modeling. In this work, we develop an anthropomorphic phantom for PET imaging that combines anatomic fidelity and a realistic breathing mechanism with deformable lungs. Methods: We start from a previously developed anatomically accurate but static phantom of a human torso, and add elastic lungs with a highly controllable actuation mechanism which replicates the physics of breathing. The space outside the lungs is filled with a radioactive water solution. To maintain anatomical accuracy and realistic gamma ray attenuation in the torso, all motion mechanisms and actuators are positioned outside of the phantom compartment. The actuation mechanism can produce custom respiratory waveforms with breathing rates up to 25 breaths per minute and tidal volumes up to 1200 mL. Results: Several tests were performed to validate the performance of the phantom assembly, in which the phantom was filled with water and given respiratory waveforms to execute. All parts demonstrated expected performance. Force requirements were not exceeded and no leaks were detected, although continued use of the phantom is required to evaluate wear. The motion of the lungs was determined to be within a reasonable realistic range. Conclusions: The full mechanical design is described in this paper, as well as a software application with graphical user interface which was developed to plan and visualize respiratory patterns. Both are available online as open source files. The developed phantom will facilitate future work in evaluating the impact of respiratory motion on lesion quantification and detectability in clinical practice. [ABSTRACT FROM AUTHOR]

Composite column design in accordance with EN 1994‐1‐1 can be computed by the general method or by simplified methods. For the general method, EN 1994‐1‐1 refers to a comprehensive FE‐Analysis. However, the recent Eurocode 4 does not provide sufficient guidance for the application of the general method and there is not a harmony in the literature on how to do it. Additional information may be found as non‐contradictory complementary information (NCI) in National Annexes (NA). This paper points out the procedure for the general method and refers to the changes and additional information provided by the draft of prEN 1994‐1‐1:2021. Furthermore, the modelling and computation by FE‐Analysis for several software applications are presented by implementing specified initial bow imperfections and predefined residual stresses for the steel section. The results obtained with the general method's approach are compared with that calculated by simplified method by the European buckling curves. In addition, the impact of different stress‐strain curves for concrete and strain limits introduced by Codes are investigated. [ABSTRACT FROM AUTHOR]

The HYDRUS codes have become standard tools for addressing many soil, agricultural, environmental, and hydrological problems requiring the evaluation of various subsurface physical, chemical, and biological processes. There are now many thousands of HYDRUS users worldwide, with thousands of applications of the HYDRUS models appearing in the peer‐reviewed literature. In this manuscript, we provide an overview of the capabilities of the most recent Version 5 of HYDRUS, focusing primarily on features implemented since 2016. We briefly describe the standard HYDRUS model and its standard and nonstandard specialized add‐on modules that significantly expand the capabilities of the software packages. The standard add‐on modules include HPx, UNSATCHEM, Wetland, Furrow, PFAS, COSMIC, DPU, SLOPE Cube, and Particle Tracking. Recent developments of the HYDRUS Package for MODFLOW are also described, along with additional capabilities incorporated into the graphical user interface supporting HYDRUS. Also discussed are new or improved options to simulate the fate and transport of environmental isotopes, multi‐cropping systems, compensated root water uptake, and hydraulic redistribution within the rootzone, which will be implemented in upcoming add‐on modules. Another objective is to review selected applications of the HYDRUS models, such as evaluations of various irrigation, low‐impact development (LID), and managed aquifer recharge (MAR) schemes. Core Ideas: An overview of the capabilities of the most recent Version 5 of HYDRUS.The description of the standard and nonstandard specialized HYDRUS add‐on modules.A review of selected popular applications of the HYDRUS models.Description of new or improved upcoming HYDRUS add‐on modules. [ABSTRACT FROM AUTHOR]

A cloud‐hosted web‐based software application, nmfMapping, for carrying out a non‐negative matrix factorization of a set of powder diffraction or atomic pair distribution function datasets is described. This application allows structure scientists to find trends rapidly in sets of related data such as from in situ and operando diffraction experiments. The application is easy to use and does not require any programming expertise. It is available at https://pdfitc.org/. [ABSTRACT FROM AUTHOR]

With technological developments, the resolution of the display systems is increasing, causing image data to increase. Several technologies are aiding in optimizing these data during transmission and storage while maintaining quality. Still, the internal data transmission for accessing the memory sub‐system during reading and writing cycles consumes significant power while requiring large memory for storage, which is inefficient, especially for portable displays. Hence, this work investigates the feasibility of reducing data through approximation for images in different formats and resolutions for human and computer vision based applications. Different image quality assessment metrics are utilized for performance evaluations with optimum image quality assessment selection according to application requirements. In addition, the feasibility analysis is conducted for three different applications as examples of computer vision. The experimental analysis highlights theoretical and industrial importance by showing that image data can be reduced from 25.0% to 62.5%, while satisfying requirements for HVS‐based and computer vision‐based applications. [ABSTRACT FROM AUTHOR]

Knowledge work is growing in today's marketplace, supported by "knowledge workers," a valuable asset in organizations. Traditional software applications are too rigid to support knowledge work or empower knowledge workers to perform their nonroutine tasks. Therefore, most knowledge work today is managed manually or using ad‐hoc technologies such as e‐mail and social media. Adaptive case management (ACM) is a new promising approach for effectively supporting knowledge work, empowering knowledge workers, and overcoming the limitations of traditional software applications. Nevertheless, ACM is an emerging field accompanied by confusion and debate among researchers and practitioners. This study aims at providing an overview of the key concepts and principles of ACM. Furthermore, this study outlines the major differences between traditional business process management and ACM. [ABSTRACT FROM AUTHOR]

The laboratories of control theory are often limited to simulations and a few or none experimental tests. As in other technical subjects, practical experiments really help to understand the theory but need much time and the experimental platforms are usually expensive. This paper describes a platform that implements a universal third‐order discrete controller based on field programmable gate array (FPGA). The controller is implemented using fixed‐point arithmetic, with 17‐bit coefficients, but students can reduce the number of bits to find the resolution problems that may arise. This low‐cost platform, based on a Xilinx Spartan FPGA, allows students to try controllers just by configuring the coefficients of the controller through a computer application and a USB port. It drastically reduces the implementation time, allowing more time for design and testing. Therefore, students can try many controllers in the same laboratory session so that they can check experimentally their behavior in real prototypes and see the differences between simulations and physical systems. This platform has been applied in a real course over two academic years. The student opinion survey shows that the survey respondents consider the platform useful for more deeply understanding the subject, with an average score of 4.47/5.00 on a Likert scale (with a margin of error of 15.56% with a 95% confidence level). Besides this, the platform records the usage statistics showing that there is a relationship between the application usage and the marks in both the theory and laboratory parts. [ABSTRACT FROM AUTHOR]

The one‐stage object detector has been widely applied in many computer vision applications due to its high detection efficiency and simple framework. However, one‐stage detectors heavily rely on Non‐maximum Suppression to remove the duplicated predictions for the same objects, and the detectors produce detection confidence to measure the quality of those predictions. The localisation quality is an important factor to evaluate the predicted bounding boxes, but its role has not been fully utilised in previous works. To alleviate the problem, the Quality Prediction Block (QPB), a lightweight sub‐network, is designed by the authors, which strengthens the effect of localisation quality evaluation on detection confidence by leveraging the features of predicted bounding boxes. The QPB is simple in structure and applies to different forms of detection confidence. Extensive experiments are conducted on the public benchmarks, MS COCO, PASCAL VOC and Berkeley DeepDrive. The results demonstrate the effectiveness of our method in the detectors with various forms of detection confidence. The proposed approach also achieves better performance in the stronger one‐stage detectors. [ABSTRACT FROM AUTHOR]

Rain removal is very important for many applications in computer vision, and it is a challenging problem due to its ill‐posed nature, especially for single‐image deraining. In order to remove rain streaks more thoroughly, as well as to retain more details, a progressive dilation dense residual fusion network is proposed. The entire network is designed in a cascade manner with multiple fusion blocks. The fusion block consists of a dilation dense residual block (DDRB) and a dense residual feature fusion block (DRFFB), where DDRB is created for feature extraction and DRFFB is mainly designed for feature fusion operation. Meanwhile, detail compensation memory mechanism (DCMM) is leveraged between each of two cascade modules to retain more background details. Compared with previous state‐of‐the‐art methods, extensive experiments show that the proposed method can achieve better results, in terms of rain streaks removal and background details preservation. Furthermore, the authors' network also shows its superiority for image noise removal. [ABSTRACT FROM AUTHOR]

Objective: To assess prevalence of elevated blood pressure readings, rates of established hypertension diagnosis, and blood pressure control medication prescription rates in a cohort of older people experiencing unsheltered homelessness presenting to a Street Medicine clinic in Miami‐Dade County, Florida. In addition, we will compare outcomes found in the study cohort to that of the general population. Methods: Demographic information, clinical history, blood pressure control medication prescription, and blood pressure measurements were taken by a Street Medicine team of medical providers. The team routinely provides medical evaluations and care for people experiencing unsheltered homelessness who reside in rough sleeper tent encampments located on the street. Clinical information and vitals were recorded in REDCap. De‐identified data from patients 65 years and above were downloaded and compared to a general population data set—the Centers for Disease Control National Health and Nutrition Examination Survey (CDC NHANES) 2017–2020 Pre‐pandemic cohort. Data analysis was performed using R Studio version 4.3.2. Results: Blood pressure was reported in 120 distinct interactions with older people experiencing homelessness. Compared to the age‐matched NHANES data, older people experiencing unsheltered homelessness were at significantly increased relative risk for elevated blood pressure within the range of Stage 1 Hypertension (RR: 3.914, 95% CI: 2.560–5.892, P < 0.001), and within range of Stage 2 Hypertension (RR: 5.550, 95% CI: 4.272–7.210, P < 0.001). According to NHANES, 49.6% of adults over 60 with diagnosed hypertension receive treatment. Of study participants, 69% of those with elevated blood pressure had previously received a diagnosis of hypertension and 15.9% on medication to control blood pressure. Conclusion: Our cohort of older people experiencing unsheltered homelessness had higher rates of elevated blood pressure and reduced rates of hypertension diagnosis and treatment as compared to the general population. Older people experiencing unsheltered homelessness are a growing population, and future research should seek to evaluate and understand older adult care vulnerabilities, including chronic disease management, to improve health outcomes for those who are aging, hypertensive, and unhoused. [ABSTRACT FROM AUTHOR]

Virtual patients (VPs) are widely used within quantitative systems pharmacology (QSP) modeling to explore the impact of variability and uncertainty on clinical responses. In one method of generating VPs, parameters are sampled randomly from a distribution, and possible VPs are accepted or rejected based on constraints on model output behavior. This approach works but can be inefficient (i.e., the vast majority of model runs typically do not result in valid VPs). Machine learning surrogate models offer an opportunity to improve the efficiency of VP creation significantly. In this approach, surrogate models are trained using the full QSP model and subsequently used to rapidly pre‐screen for parameter combinations that result in feasible VPs. The overwhelming majority of parameter combinations pre‐vetted using the surrogate models result in valid VPs when tested in the original QSP model. This tutorial presents this novel workflow and demonstrates how a surrogate model software application can be used to select and optimize the surrogate models in a case study. We then discuss the relative efficiency of the methods and scalability of the proposed method. [ABSTRACT FROM AUTHOR]

PBPK applications published in the literature support a greater adoption of non‐open source‐code (NOSC) software as opposed to open source‐code (OSC) alternatives. However, a significant number of PBPK modelers are still using OSC software, understanding the rationale for the use of this modality is important and may help those embarking on PBPK modeling. No previous analysis of PBPK modeling trends has included the rationale of the modeler. An in‐depth analysis of PBPK applications of OSC software is warranted to determine the true impact of OSC software on the rise of PBPK. Publications focusing on PBPK modeling applications, which used OSC software, were identified by systematically searching the scientific literature for original articles. A total of 171 articles were extracted from the narrowed subset. The rise in the use of OSC software for PBPK applications was greater than the general discipline of pharmacokinetics (9 vs. 4), but less than the overall growth of the PBPK area (9 vs. 43). Our report demonstrates conclusively that the surge in PBPK usage is primarily attributable to the availability and implementations of NOSC software. Modelers preferred not to share the reasons for their selection of certain modeling software and no 'explicit' rationale was given to support the use of OSC analysed by this study. As the preference for NOSC versus OSC software tools in the PBPK area continues to be divided, initiatives to add the rationale in using one form over another to every future PBPK modeling report will be a welcomed and informative addition. [ABSTRACT FROM AUTHOR]

Video data received for analysis often come in a variety of file formats and compression schemes. These data are often transcoded to a consistent file format for forensic examination and/or ingesting into a video analytic system. The file format often requested is an MP4 file format. The MP4 file format is a very common and a universally accepted file format. The practical application of this transcoding process, across the analytical community, has generated differences in video quality. This study sought to explore possible origins of the differences and assist the practitioner by defining minimum recommendations to ensure that quality of the video data is maintained through the transcoding process. This study sought to generate real world data by asking participants to transcode provided video files to an MP4 file format using programs they would typically utilize to perform this task. The transcoded results were evaluated based on measurable metrics of quality. As the results were analyzed, determining why these differences might have occurred became less about a particular software application and more about the settings employed by the practitioner or of the capabilities of the program. This study supports the need for any video examiner who is transcoding video data to be cognizant of the settings utilized by the programs employed for transcoding video data, as loss of video quality can affect analytics as well as further analysis. [ABSTRACT FROM AUTHOR]

One of the main factors leading to fatalities in construction sites is the inefficiency of safety training programs. This article presents an immersive and interactive virtual reality (VR) model for safety training, which is an advanced and more comprehensive version of previously developed VR models in the literature. The novelty of this research is present in the proposed general safety training model that is fully immersive and interactive, that is built based on the concepts of experiential learning; where users learn from their mistakes rather than being passive learners in a virtual environment. The model, which was developed using the Unity® platform and C#, contains 12 scenario‐based hazards with programmed interactions and responses to the actions of the trainees. The developed model was tested for two aspects: (1) the ability to achieve learning outcomes and supersede traditional training, and (2) user experience and usability. Using an experimental setting and statistical tests, the developed model has shown statistical significance when it comes to its efficiency in enhancing hazard identification and mitigation skills as opposed to traditional training. The model also performed well in terms of usability and user experience, and important lessons were extracted for future VR development in construction education applications. This research strengthens the utility of VR as an effective tool for experiential learning; which enables future research in exploring how computer applications can improve experiential learning even further. [ABSTRACT FROM AUTHOR]

Lattice structures are known for their superior performance in weight reduction, stiffness, and increased manufacturability, but when materials need to be adaptive, multifunctional, and with tunable properties, functionally graded lattice structures (FGLS) play a crucial role in innovative solutions. Recent digital manufacturing technologies offer ideal conditions for creating lattice structures with high geometrical complexity and functional gradients. However, the design and development of these inherently complex structures often require the use of specialized methods and software tools. As researchers venture from diverse areas into the research of FGLS in search of novel applications, it is important to choose adequate design tools. Herein, an overview of methods and software for the design of FGLS is presented, identifying geometrical parameters of common lattice topologies and ways to manipulate them to create functional gradients. Fifty software applications or methods are listed, dedicated to the design of single material lattices produced by additive manufacturing processes. Typical applications of the reviewed software are identified, and FGLS design methods are compared. Generative Design seems more suited as an explorative process, Topology Optimization is an established efficiency‐focused design process, and Machine Learning is considered a promising data‐driven approach for the design of innovative FGLS. [ABSTRACT FROM AUTHOR]

We present a Python package geared toward the intuitive analysis and visualization of paleoclimate timeseries, Pyleoclim. The code is open‐source, object‐oriented, and built upon the standard scientific Python stack, allowing users to take advantage of a large collection of existing and emerging techniques. We describe the code's philosophy, structure, and base functionalities and apply it to three paleoclimate problems: (a) orbital‐scale climate variability in a deep‐sea core, illustrating spectral, wavelet, and coherency analysis in the presence of age uncertainties; (b) correlating a high‐resolution speleothem to a climate field, illustrating correlation analysis in the presence of various statistical pitfalls (including age uncertainties); (c) model‐data confrontations in the frequency domain, illustrating the characterization of scaling behavior. We show how the package may be used for transparent and reproducible analysis of paleoclimate and paleoceanographic datasets, supporting Findable, Accessible, Interoperable, and Reusable software and an open science ethos. The package is supported by an extensive documentation and a growing library of tutorials shared publicly as videos and cloud‐executable Jupyter notebooks, to encourage adoption by new users. Plain Language Summary: This article describes a software application called Pyleoclim meant to help scientists analyze datasets of ordered observations, particularly applicable to the study of past climates, environments, and ecology. Pyleoclim is meant to be used by domain scientists as well as students interested in learning more about Earth's climate through examples provided in the documentation and online tutorials. Pyleoclim is intended to help scientists save time with their analyses, documenting the steps for better transparency, and as such, allows other scientists to reproduce results from previous studies. Key Points: Pyleoclim makes timeseries analysis tools accessible to practicing scientists, via a user‐friendly Python packageThree Jupyter Notebooks illustrate how Pyleoclim facilitates common and advanced tasksPyleoclim can enhance reproducibility and rigor of paleogeoscientific workflows involving timeseries [ABSTRACT FROM AUTHOR]

The number of malicious software applications, or malware programs, increases every year. Their development becomes more sophisticated as new techniques are used to bypass program scanning software applications, such as antiviruses. Thereby, deep learning‐based methods emerge as a new promising way to identify these threats. Our main purpose and contribution in this work is proposing and implementing a successful approach to tackle both binary and multiclass malware classification problems. We used unsupervised word embedding algorithms for representing software applications to be analyzed and long‐short term memory for classifying the software applications. For evaluating our pipeline, we introduce a new dataset for binary and multiclass malware classification because we could not find large datasets containing sufficient samples of cleanware and the various malware types for multiclass classification that could be used to evaluate classification models. Our experimental results reached an accuracy of 88.94% for binary classification and 75.13% for multiclass classification. These results suggest that the proposed dataset is challenging, and using it can help in the training of better malware classifiers, improving security. [ABSTRACT FROM AUTHOR]

Layered (2D) artificial (or synthetic) antiferromagnets are fabricated by atom deposition techniques and possess very thin, nanometer‐scale, magnetically ordered layers separated by a very thin nonmagnetic layer that antiferromagnetically couples the magnetic layers. Artificial antiferromagnets were crucial in the discovery of the giant magnetic effect (GMR), which had an incredible impact on the evolution of computer memory and its applications, and nucleated the dawn of spintronics (magnetoelectrics). The fundamental structural motif has been more recently achieved by using synthetic chemical methods that led to insulating artificial antiferromagnets. Examples of magnetically ordered layers that are antiferromagnetic coupled to form artificial antiferromagnets have been extended to isolated ions (0D) as well as extended chain (1D) and extended network 3D structures, and new phenomena and applications are anticipated as insulating antiferromagnets are more effective at propagating spin currents with respect to dielectric materials. [ABSTRACT FROM AUTHOR]

Grit, a personality trait characterized by having passion and perseverance for long‐term goals, has been linked to increased performance, retention, and well‐being in various fields. In the 15 years since the original grit scale was published, many studies have investigated factors that promote grit. However, a scale has not yet been developed measuring characteristics that can contribute to higher levels of grit. This study aimed to develop a novel scale to measure the psychological resources of grit. The Delphi technique was used to obtain consensus from a national and international panel of academics and practitioners who are experts in grit and related constructs. A total of 30 participants rated 100 scale items over three rounds of online surveys. Experts agreed that items selected for the final scale (n = 20) were essential, with 85% rating them as important or very important. The scale, called the Grit Psychological Resources Scale, has promise as a helpful tool for practitioners involved in staff development and building capabilities that contribute to goal achievement. [ABSTRACT FROM AUTHOR]

Image matting plays a vital role in a variety of computer vision tasks including video editing and image fusion. Previously presented image matting algorithms might fail in producing favorable results since most of them concentrate on the similarity between the neighboring pixels while neglecting the corresponding spatial relationship. To address this issue, an end‐to‐end image matting framework through leveraging deep learning mechanism and graph theory is proposed. The proposed pipeline is a concatenation of one deep feature extraction component and a Graph Convolutional Network (GCN). The former part takes an image and its corresponding trimap as inputs and can generate the pixel‐wise features, which are then exploited as the input of the GCN locating at the latter part of the proposed framework. The GCN would refine the features for every pixel and predict the alpha matte outcome of the image. The approach outperforms a group of state‐of‐the‐art matting techniques as shown by the theoretical analysis and experimental results in terms of both accuracy and visual effects. [ABSTRACT FROM AUTHOR]

One of the major challenge that organizations face in the present environment is having an efficient model for software cost estimation (SCE). In this article, the significance of the meta‐heuristic algorithm in addressing various optimization challenges faced in mathematical models and software applications is discussed. The proposed method uses the new evolutionism‐based self‐adaptive mutation operator to solve the multi‐objective optimization problems. This approach addresses the issues that exist in multi‐objective differential evolution algorithms. To improve diversity among candidate solutions, the Pareto optimality principle is integrated with the evolutionism‐based self‐adaptive mutation operator in a multi‐objective DE algorithm. To reduce the time complexity of Pareto dominance, we have adopted the non‐dominated sorting algorithm. We used eight benchmark test functions to evaluate the effectiveness of the proposed method, and it outperformed the most recent multi‐objective evolutionary algorithms (MOEAs). Furthermore, this article explores software engineering problems like SCE by using the proposed approach, where SCEs are accurately predicted by optimizing the tuning parameters of the multi‐objective constructive cost model. The proposed algorithm achieves better cost prediction as compared to the other standard benchmark algorithms for all objective problems in terms of prediction, mean absolute error, and root mean square error. [ABSTRACT FROM AUTHOR]

Summary: Fog computing has revolutionized the computing domain by enabling resource sharing, such as online storage, and providing applications and software as services in near vicinity to the edge nodes through the Internet. Small‐ to large‐sized companies, like Amazon, Google, Facebook, Twitter, and LinkedIn, have started switching to fog‐computing–enabled infrastructures. Fog computing being distributed in nature and in near vicinity gives rises to security and privacy issues. Although mostly now a days, user identification is adopted via single sign‐in process, such as simple password‐based authentications, which is not a secure process. Several multi‐tier authentication techniques are proposed to overcome single sign‐in process limitations. In this article, we go through state‐of‐the‐art schemes proposed over the period of 2011‐2018 for multi‐tier authentication, their weaknesses and security issues, and finally their solutions for fog‐computing environment. We performed the comparison of available multi‐tier authentication techniques based on three factors, ie, level of security, cost of deployment, and usability. Multi‐tier authentication techniques are classified into categories in accordance with the aspects that are concerned with the authentication process. We are optimistic that this work will provide useful information to the researchers about the architectures of fog enabled systems and the underlying authentication models in a consolidated form. [ABSTRACT FROM AUTHOR]

Foggy weather can cause such problems as blurred image information and the loss of image details, which may pose great challenges to road traffic target detection based on images and videos. In this study, we propose a domain‐adaptive road vehicle target detection method to implement domain adaptation for the real foggy scene. We firstly constructed a highway vehicle detection dataset with foggy images (HVFD), which contains normal weather images and foggy images and provides a complete data support for vehicle detection based on computer vision. Secondly, by improving CycleGAN we designed an improved generative confrontation network (CPGAN), which realised the style transfer between foggy images and normal weather images. Finally, we formulated a YOLOv4 target detection framework according to the domain adaptation based on the pre‐trained YOLOv4 fog vehicle detection model. The experimental results show that the method we put forward can effectively improve vehicle detection performance and reduce the work of manually labelling a large number of foggy image tags, which has a strong generalisation ability for computer vision‐based applications in low‐visibility weather. [ABSTRACT FROM AUTHOR]

Head pose estimation (HPE) notoriously represents a crucial task for many computer vision applications in robotics, biometry and video surveillance. While, in general, HPE can be performed on both still images and frames extracted from live video or captured footage, its functional approach and the related processing pipeline may have a significant impact on suitability to different application contexts. This implies that, for any real‐time application in which HPE is required, this information, namely the angular value of yaw, pitch and roll axes, should be provided in real‐time as well. Since, so far, the primary aim in HPE research has been on improving estimation accuracy, there are only a few works reporting the computing time of the proposed HPE method and even less explicitly addressing it. The present work stems from a previous Partitioned Iterated Function Systems‐based approach providing state‐of‐the‐art accuracy with high computing cost, and improve it by means of two regression models, namely Gradient Boosting Regressor and Extreme Gradient Boosting Regressor, achieving much faster response and an even lower mean absolute error on the yaw and roll axis, as shown by experiments conducted on the BIWI and AFLW2000 datasets. [ABSTRACT FROM AUTHOR]

Groundwater Spatiotemporal Data Analysis Tool (GWSDAT) is an open‐source, user‐friendly, software application for the visualization and interpretation of groundwater monitoring data. Since its first release, over 8 years ago, it has become increasingly well established and is now a globally adopted tool. Despite its widespread use, there is little published material considering the application of GWSDAT. The primary purpose of this article is to demonstrate the different benefits of using GWSDAT by considering a cross‐section of real case studies. The first case study is an example of using GWSDAT to provide evidence to support a proposal to reduce sampling frequency. The next case study discusses the use of GWSDAT to support the recommendation for site closure. The third case study considers how GWSDAT can be used to identify gaps in a monitoring network. The final case study reports on the application of the, newly introduced, well redundancy analysis feature in GWSDAT. Finally, the road map for future developments in the optimization of monitoring network and the motivation for doing this in a spatiotemporal framework is discussed [ABSTRACT FROM AUTHOR]

A correctly compiled asset register within an asset information management system (AIMS) provides the foundation for a successful asset data solution. The lack of correctly structured asset registers within organizations is acknowledged among research and communities of practice. A case study identified anomalies that emerge when using established standards, after which a comprehensive solution for hierarchies, naming conventions and attributes was offered. While standards such as BS ISO 1007 and ISO 14224 provide overarching solution principles, such provisions are not all‐encompassing and exist across several sources, which makes the task of developing asset registers error‐prone and laborious. Challenges associated with software applications were highlighted through combining personal industrial experience as well as consultations with the existing body of knowledge. Recommendations that enable successful deployment of AIMS, with emphasis on its accommodation of a reliable asset register were then proffered. Scalability was addressed which enables an asset register to expand. This study describes a novel and simplified approach embodied within a single document. Combining the prescriptions of this article with existing literature will ease the delivery of an asset register. [ABSTRACT FROM AUTHOR]

Motivated by the increasing number of embedded applications that make use of traffic‐intensive I/O devices, this work studies the memory contention generated by I/O devices and investigates on the regulation of the bus traffic they generate by means of COTS regulators, namely the QoS‐400 by Arm. To this purpose, the behavior of the QoS‐400 regulators is analytically characterized and then, taking the Xilinx Ultrascale+ as a reference modern heterogeneous platform, a software infrastructure to control such regulators from Linux is proposed. As an experience report, this article presents the results of an extensive experimental evaluation, based on both benchmarks and microbenchmarks, aimed at validating the effectiveness of QoS‐400 regulators in predictably controlling I/O‐related memory traffic, as well as assessing the impact of the regulation on software applications and I/O devices themselves. [ABSTRACT FROM AUTHOR]

The application of FactSage software in phosphogypsum resource utilization is described. Thermodynamic analysis is not only useful for predicting the amount and type of products including gases and solids under various conditions, but also for investigating the thermodynamic properties of related reactions. Statistical data analysis is conducted using the equilib module, reaction module, and phase diagram module of FactSage. The optimal conditions and mechanisms to utilize phosphogypsum for its products CaS, CaO, and CaCO3 are summarized. This comprehensive review on thermodynamic analysis in phosphogypsum resource utilization for the mentioned products is valuable for academicians, scientists, and researchers. [ABSTRACT FROM AUTHOR]

With the rapidly changing needs of the construction industry and higher education, Conceive—Design—Implement—Operate (CDIO) educational framework is essential in delivering basics and practical aspects to undergraduate students. This study presents an attempt to explore the use of hardware and software applications for geotechnical engineering education. An example for development of hardware (Arduino UNO) and software applications (Mobile applications) for estimating soil moisture and its regulation is presented. The study provides a new learning environment, where students from civil engineering (major) can work hand to hand with that from electronics (minor) to develop applications for soil moisture measurement. A collaborative workshop is proposed and executed. The workshop includes basic concepts related to hardware (Time domain reflectometry [TDR] probe, Arduino UNO, and Water flow measurement) and software (LabVIEW and Android studio programming platforms) followed by project implementation in a group of 5–6 students. Satisfaction and comprehension were found to increase significantly for subjects (electronics and optics) to students of civil engineering background. The adopted tools represent economical means for application in conventional classrooms as well as laboratories for undergraduate students with a civil engineering background. This integration of the digital and physical world is an attempt at approaching soil mechanics mathematics from technology and arts from a scientific perspective. The examples presented in this study can be useful for developing open‐source laboratories within the framework of the undergraduate curriculum in civil engineering. [ABSTRACT FROM AUTHOR]

Parametric 3D shape models are heavily utilized in computer graphics and vision applications to provide priors on the observed variability of an object's geometry (e.g., for faces). Original models were linear and operated on the entire shape at once. They were later enhanced to provide localized control on different shape parts separately. In deep shape models, nonlinearity was introduced via a sequence of fully‐connected layers and activation functions, and locality was introduced in recent models that use mesh convolution networks. As common limitations, these models often dictate, in one way or another, the allowed extent of spatial correlations and also require that a fixed mesh topology be specified ahead of time. To overcome these limitations, we present Shape Transformers, a new nonlinear parametric 3D shape model based on transformer architectures. A key benefit of this new model comes from using the transformer's self‐attention mechanism to automatically learn nonlinear spatial correlations for a class of 3D shapes. This is in contrast to global models that correlate everything and local models that dictate the correlation extent. Our transformer 3D shape autoencoder is a better alternative to mesh convolution models, which require specially‐crafted convolution, and down/up‐sampling operators that can be difficult to design. Our model is also topologically independent: it can be trained once and then evaluated on any mesh topology, unlike most previous methods. We demonstrate the application of our model to different datasets, including 3D faces, 3D hand shapes and full human bodies. Our experiments demonstrate the strong potential of our Shape Transformer model in several applications in computer graphics and vision. [ABSTRACT FROM AUTHOR]