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The expansive scientific software ecosystem, characterized by millions of titles across various platforms and formats, poses significant challenges in maintaining reproducibility and provenance in scientific research. The diversity of independently developed applications, evolving versions and heterogeneous components highlights the need for rigorous methodologies to navigate these complexities. In response to these challenges, the SBGrid team builds, installs and configures over 530 specialized software applications for use in the on‐premises and cloud‐based computing environments of SBGrid Consortium members. To address the intricacies of supporting this diverse application collection, the team has developed the Capsule Software Execution Environment, generally referred to as Capsules. Capsules rely on a collection of programmatically generated bash scripts that work together to isolate the runtime environment of one application from all other applications, thereby providing a transparent cross‐platform solution without requiring specialized tools or elevated account privileges for researchers. Capsules facilitate modular, secure software distribution while maintaining a centralized, conflict‐free environment. The SBGrid platform, which combines Capsules with the SBGrid collection of structural biology applications, aligns with FAIR goals by enhancing the findability, accessibility, interoperability and reusability of scientific software, ensuring seamless functionality across diverse computing environments. Its adaptability enables application beyond structural biology into other scientific fields. [ABSTRACT FROM AUTHOR]

This study includes the development of a farm advisory framework known as the "AquaProFAN framework," which is concerned with the study of various requirements by the aquafarmers and stakeholder community in fisheries. AquaProFAN (Aqua Professional Farm Advisory Network) advisory committee provided careful consideration to the aquafarmers' inquiries and proper clarification with many innovative ideas, and the feasibility study on chatbot implementation was also performed. The end product, called AquaGent, is an AI‐based chatbot designed for shrimp aquafarmers and support services. It was developed and deployed in social media platforms like Facebook Messenger and Telegram for research and dissemination purposes. The effectiveness of the chatbot was analysed and the findings indicated that: (i) informational requests are more satisfactory than emotional requests, (ii) the performance of chatbot is comparatively better than other software applications concerning informational requests, (iii) participants perceive 'AquaGent' chatbot to be more user friendly and time saving. It can be concluded that the implementation of this state‐of‐art technology in aquaculture sector will improve stakeholders' understanding for future efficient and profitable production. [ABSTRACT FROM AUTHOR]

This study was conducted to assess the disparities in camel activities such as eating, drinking, sitting, standing, and sleeping between primiparous and multiparous females before parturition using computer vision. Also, any extraordinary behaviours during the final 2 h before parturition and the necessary manual interventions were meticulously recorded. Five primiparous (age: 4.5–7 years) and 7 multiparous (age: 8–14 years; parity: 2.1 ± 1.5) dromedary camels, were included in this study. Pre‐partum females were housed double in a parturition pen provided with two Reolink RLC‐810A cameras and the data were collected and recorded for each female. Two primiparous and 1 multiparous female required assistance in pulling the calf from both forelimbs to complete their parturition (27.3%). The drinking and sleeping activities were similar in primiparous and multiparous females during the recorded 32 h leading up to calving. Only eating activity exhibited a longer period in primiparous females compared to multiparous females specifically during the 12‐h before calving. Sitting activity was longer, and standing activity was shorter in multiparous than in primiparous females during the 24, 12, and 6 h before calving. All parturient camels, whether primiparous or multiparous, exhibited signs of distress. Some extraordinary behaviours were observed, such as two multiparous females attempting to deter their primiparous counterparts from eating. Additionally, three females displayed a distinctive standing position on their knees while their hind limbs were in a complete standing position for 3–5 min before transitioning to sitting or standing positions. Furthermore, one primiparous female stood while the head and forelimbs of the calf partially protruded from her vulva. In conclusion, the application of computer vision and deep learning technology proves valuable for observing prepartum camels under farm conditions, potentially reducing economic losses stemming from delayed human intervention in dystocia cases. [ABSTRACT FROM AUTHOR]

Security is an essential attribute of high‐quality software. However, effectively incorporating security practices into different phases of the software development life cycle (SDLC) remains challenging. Owing to less mature secure testing processes, organizations are prone to ineffective testing practices for defect detection, including severe security‐related failures. Thus, in this study, we present a maturity model for secure software testing (MMSST) to assist software development organizations in improving the secure testing of software applications. We conducted a multivocal literature review and identified 68 primary studies from the formal and gray literature. Then, based on the available evidence, 27 process areas were identified to develop the proposed MMSST. The MMSST includes five main categories: governance, contrive and design, execution, deployment and configuration, and mature. The MMSST was subsequently evaluated using case studies related to practical environments. Results demonstrate that the proposed MMSST is useful for estimating the maturity level of an organization with respect to the secure testing phase of the SDLC. The participants of the case studies also agreed that the proposed MMSST is useful in terms of structure, user satisfaction, and ease of use. We believe that the proposed MMSST can help organizations evaluate and improve software security testing practices. In addition, the proposed MMSST is expected to provide researchers and industry practitioners with an effective foundation for developing new secure testing approaches and tools. [ABSTRACT FROM AUTHOR]

Web search is a crucial technology for the digital economy. Dominated by a few gatekeepers focused on commercial success, however, web publishers have to optimize their content for these gatekeepers, resulting in a closed ecosystem of search engines as well as the risk of publishers sacrificing quality. To encourage an open search ecosystem and offer users genuine choice among alternative search engines, we propose the development of an Open Web Index (OWI). We outline six core principles for developing and maintaining an open index, based on open data principles, legal compliance, and collaborative technology development. The combination of an open index with what we call declarative search engines will facilitate the development of vertical search engines and innovative web data products (including, e.g., large language models), enabling a fair and open information space. This framework underpins the EU‐funded project OpenWebSearch.EU, marking the first step towards realizing an Open Web Index. [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]

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]

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]

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]

Inputs and Outputs: The Strike‐a‐Match Function, written in JavaScript version ES6+, accepts the input of two datasets (one dataset defining eligibility criteria for research studies or clinical decision support, and one dataset defining characteristics for an individual patient). It returns an output signaling whether the patient characteristics are a match for the eligibility criteria. Purpose: Ultimately, such a system will play a "matchmaker" role in facilitating point‐of‐care recognition of patient‐specific clinical decision support. Specifications: The eligibility criteria are defined in HL7 FHIR (version R5) EvidenceVariable Resource JSON structure. The patient characteristics are provided in an FHIR Bundle Resource JSON including one Patient Resource and one or more Observation and Condition Resources which could be obtained from the patient's electronic health record. Application: The Strike‐a‐Match Function determines whether or not the patient is a match to the eligibility criteria and an Eligibility Criteria Matching Software Demonstration interface provides a human‐readable display of matching results by criteria for the clinician or patient to consider. This is the first software application, serving as proof of principle, that compares patient characteristics and eligibility criteria with all data exchanged using HL7 FHIR JSON. An Eligibility Criteria Matching Software Library at https://fevir.net/110192 provides a method for sharing functions using the same information model. [ABSTRACT FROM AUTHOR]

GRASP is a scientific software application designed for the graphical inspection, reduction and analysis of multidetector data produced by the small-angle neutron scattering (SANS) instruments at the Institut Laue-Langevin and other neutron sources around the world. The first developments of GRASP began more than 20 years ago and were written in MATLAB, allowing rapid development of scientific code, with much of the data handling, matrix manipulation, mathematical tools, user interface and graphical tools integrated at a high level in the underlying MATLAB platform. By their very nature, multidimensional data are often best appreciated in graphical form. GRASP deals with many of the diverse requirements for data reduction and analysis of SANS data using a general set of tools and reduction algorithms suited to 2D multidetector data. A further fundamental architectural inclusion is a third dimension of data manipulation, thereby easily allowing parametric analysis and cross referencing of series data such as composition, kinetic measurements, temperature, magnetic field, angle or time of flight, often considered as a single 'measurement'. This article serves as a reference document for users of the software, and outlines the architecture and strategy of the program. An overview of some of the features, capabilities, peripheral user modules and neutron scattering tools is presented. [ABSTRACT FROM AUTHOR]

In thinking back on Richard Lerner's impact on our lab's trajectory at Scripps Research, we see his fingerprints on all the efforts we have undertaken over the last 40 years. Immunology and virology have been a continuing driving focus in our software development and application within the Molecular Graphics Lab (now the Center for Computational Structural Biology – CCSB), spurred on by the marriage of chemistry with biology that Richard enabled at Scripps. Many of our earliest and continuing computational docking targets have been focused on HIV with collaborations with Chi‐Huey Wong and Barry Sharpless. Along with subsequent computational work with Jeff Kelly, Reza Ghadiri, Udi Keinan and Ben Cravatt we have been able to sustain continuous efforts in developing and applying docking and other computational technologies to structural chemistry problems. In this remembrance, we give our reflections of Richard Lerner's character, vision, and his inspiration in our scientific lives and in the environment that he created at Scripps Research. [ABSTRACT FROM AUTHOR]

Comparison of software applications for the construction of the special model subsoil In the context of Building Information Modeling (BIM), the subsurface is receiving increasing attention. The so‐called special model subsoil, which has to be constructed by a geotechnical ex‐pert, must contain not only the geometrical representation of the relevant soil layers, but also the associated data of the accompanying geotechnical report. In addition to the difficulty of more often than not insufficient specifications regarding information depth and structure by the client, limitations can arise from the software application used for modelling. This work provides a comparison of the construction process of the special model subsoil for a fictional test project using four different and well‐established software applications. The differences found regarding the effort required to obtain a working model, the modelling functionalities and the usefulness of the final special model subsoil as part of the parent BIM‐model are presented. Furthermore, more general aspects such as the level of software support, training materials and integration with other software applications are described and compared. The key differences in the geometrical modelling results are compared using cross‐sections. Based on the experienced gained by this comparison, areas are identified where current software applications are lacking and further development is required. [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]

Introduction: Pregnant women are vulnerable to severe acute respiratory syndrome coronavirus (SARS‐CoV‐2) infection. Neutralizing antibodies against the SARS‐CoV‐2 spike (S) protein protect from severe disease. This study analyzes the antibody titers to SARS‐CoV‐2 S protein in pregnant women and their newborns at delivery, and six months later. Methods: We conducted a prospective study on pregnant women with confirmed SARS‐CoV‐2 infection and newborns. Antibody (IgG, IgM, and IgA) titers were determined using immunoassays in serum and milk samples. An angiotensin‐converting enzyme 2 (ACE2) receptor‐binding inhibition assay to the S protein was performed on the same serum and milk samples. Results: At birth, antibodies to SARS‐CoV‐2 spike protein were detected in 81.9% of mothers' sera, 78.9% of cord blood samples, and 63.2% of milk samples. Symptomatic women had higher antibody titers (IgG, IgM, and IgA) than the asymptomatic ones (P < 0.05). At six months postpartum, IgG levels decreased drastically in children's serum (P < 0.001) but remained high in mothers' serum. Antibody titers correlated positively with its capacity to inhibit the ACE2–spike protein interaction at baseline in maternal sera (R2 = 0.203; P < 0.001), cord sera (R2 = 0.378; P < 0.001), and milk (R2 = 0.564; P < 0.001), and at six months in maternal sera (R2 = 0.600; P < 0.001). Conclusions: High antibody levels against SARS‐CoV‐2 spike protein were found in most pregnant women. Due to the efficient transfer of IgG to cord blood and high IgA titers in breast milk, neonates may be passively immunized to SARS‐CoV‐2 infection. Our findings could guide newborn management and maternal vaccination policies. [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]

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]

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]

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]

The pharmacokinetics (PKs) and safety of medications in particular groups can be predicted using the physiologically‐based pharmacokinetic (PBPK) model. Using the PBPK model may enable safe pediatric clinical trials and speed up the process of new drug research and development, especially for children, a population in which it is relatively difficult to conduct clinical trials. This review summarizes the role of pediatric PBPK (P‐PBPK) modeling software in dose prediction over the past 6 years and briefly introduces the process of general P‐PBPK modeling. We summarized the theories and applications of this software and discussed the application trends and future perspectives in the area. The modeling software's extensive use will undoubtedly make it easier to predict dose prediction for young patients. [ABSTRACT FROM AUTHOR]

Purpose: To develop an automated image recognition software for the objective quantification of choroidal vascularity index (CVI) and choroidal thickness (CT) at different choroidal locations on images obtained from enhanced depth imaging optical coherence tomography (EDI‐OCT), and to validate its reliability and investigate the difference and correlation between measurements made by manual and software. Methods: A total of 390 EDI‐OCT scans, captured from 130 eligible emmetropic or myopic subjects, were categorized into four grades in terms of their accessibility to identify the choroidal‐scleral interface (CSI) and were further assessed for CT and CVI at five locations (subfoveal, nasal, temporal, superior and inferior) by the newly developed Choroidal Vascularity Index Software (CVIS) and three ophthalmologists. Choroidal parameters acquired from CVIS were evaluated for its reliability and correlation with ocular factors, in comparison to manual measurements. Distribution of difference and correlation coefficient between CVIS and manual measurements were also analysed. Results: Choroidal Vascularity Index Software (CVIS) demonstrated excellent intra‐session reliability for CT (ICC: 0.992) and CVI (ICC: 0.978) measurements, compared to the relatively lower intra‐ and inter‐observer reliability of manual measurements. Choroidal Vascularity Index Software (CVIS) and manual assessments had the highest correlation at nasal choroid (CT: r = 0.829, p < 0.001; CVI: r = 0.665, p < 0.001). Choroidal parameters identified with CVIS showed stronger correlations with axial length than manual measurements. Conclusion: This automated software, CVIS, exhibited excellent reliability compared to manual measurements, which are subject to image quality and clinical experience. With its validated clinical relevance, CVIS holds promise to serve as a flexible and robust tool in future vitreoretinal and chorioretinal studies. [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]

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]

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]

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]

Pre‐dimensioning of ventilation openings in double façades Double façades improve sound insulation while natural ventilation and offer the shading in the space between the façades, protection from higher wind loads. With regard to natural ventilation, however, it should be noted that the air flows must pass through two façade layers and the air exchange rate cannot be planned with the existing assumptions for single‐skin façades. The potentially higher temperatures in the intermediate façades space also require special consideration when planning summer heat protection. Climate data were recorded, and air exchange measurements were taken on 10 buildings in Hamburg with corridor double façades and box window double façades. Based on these data, a calculation approach of the outdoor air exchange was created and applied in transient thermal building simulations. A method was created with which the required size of the ventilation openings can be determined quickly and without software application in early design phases. [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]

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]

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]

In the present study, the experience of fear in various stimulus situations (involving animals, accidents, and violence) and its regulation by the cognitive reappraisal strategy in 46 psychology students was analyzed. The students self‐assessed their experience of fear via questionnaires and a computerized task. In the questionnaires, the intensity of the experience of fear in everyday situations and the frequency of cognitive reappraisal strategy use were measured. In the computer application, photos eliciting fear were presented under two conditions: the first involved experiencing the photos and the continuation of this experience, while the second involved experiencing the photos followed by the use of the cognitive reappraisal strategy. The results confirm differences between the various stimulus situations. When measured with the questionnaires, the most intense fear was reported in situations involving accidents, whereas violence triggered the most intense fear while experiencing the photos. When the students continued to experience a photo, their experience of fear intensified significantly, but when they regulated their experience using cognitive reappraisal, their fear decreased. However, the intensity of the regulated fear was not connected to the frequency of the everyday use of cognitive reappraisal, as reported in the questionnaire. The study confirms the importance of contextual factors when experiencing and regulating fear. [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]

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]

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]

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]

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]

Automatic and accurate 3D face expression categorization has been a challenging task in many computer vision applications. This article presents a novel approach named 3D face expression recognition with ensemble deep learning (3D‐FER‐EDL). The framework comprises three levels. Each of the first two levels gives preference to a subset of labels, considering the congruent features among expressions. Each 3D expressive face is represented as a CDS features vector, which combines corner, distance, and slope features. CDS feature vector is given as input to the first level learners. Expression probability vectors from the first level learners are jointly given as the input to the second level learners. The probability vectors from level one and two are given as the input to the third level, which is the meta classifier. The experiments are conducted on the Bosphorus database and the CASIA 3D face database to evaluate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In the field of animal behavior and behavioral ecology, many standardized methods to observe animal behavior were established in the last decades. While the protocols remained similar, behavioral researchers can take advantage of technological advancements to enter observations directly onto a handheld computer (phone, tablet, etc.), saving time and potentially increasing fidelity of recordings. However, we now have the choice between many different platforms for recording behavioral observations. Our challenge is choosing the most appropriate platform that fits a particular study question, research design, budget, and desired amount of preparatory time. Here, we review six low‐cost software applications for handheld computers that are available for real‐time entry of behavioral observations: Animal Behaviour Pro, Animal Observer, BORIS, CyberTracker, Prim8, and ZooMonitor. We discuss the preliminary decisions that have to be made about the study design, and we assess the six applications by providing the advantages and disadvantages of each platform and an overall application comparison. In our supplemental material, we illustrate the setup and data collection routines, and how to customize certain platforms so they will work more effectively for particular study aims or sampling methods. Our goal is to help researchers make calculated decisions about what behavioral observation platform is best for their study system and question. [ABSTRACT FROM AUTHOR]

Wikipedia is the largest web‐based open encyclopedia covering more than 300 languages. Different language editions of Wikipedia differ significantly in terms of their information coverage. In this article, we compare the information coverage in English Wikipedia (most exhaustive) and Wikipedias in 8 other widely spoken languages, namely Arabic, German, Hindi, Korean, Portuguese, Russian, Spanish, and Turkish. We analyze variations in different language editions of Wikipedia in terms of the number of topics covered as well as the amount of information discussed about different topics. Further, as a step towards bridging the information gap, we present WikiCompare—a browser plugin that allows Wikipedia readers to have a comprehensive overview of topics by incorporating missing information from Wikipedia page in other language. [ABSTRACT FROM AUTHOR]

Purpose: Organ‐at‐risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time‐savings resulting from the use of an interactive‐machine‐learning method for an organ‐at‐risk contouring task. Methods: We implement an open‐source interactive‐machine‐learning software application that facilitates corrective‐annotation for deep‐learning generated contours on X‐ray CT images. A trained‐physician contoured 933 hearts using our software by delineating the first image, starting model training, and then correcting the model predictions for all subsequent images. These corrections were added into the training data, which was used for continuously training the assisting model. From the 933 hearts, the same physician also contoured the first 10 and last 10 in Eclipse (Varian) to enable comparison in terms of accuracy and duration. Results: We find strong agreement with manual delineations, with a dice score of 0.95. The annotations created using corrective‐annotation also take less time to create as more images are annotated, resulting in substantial time savings compared to manual methods. After 923 images had been delineated, hearts took 2 min and 2 s to delineate on average, which includes time to evaluate the initial model prediction and assign the needed corrections, compared to 7 min and 1 s when delineating manually. Conclusions: Our experiment demonstrates that interactive‐machine‐learning with corrective‐annotation provides a fast and accessible way for non computer‐scientists to train deep‐learning models to segment their own structures of interest as part of routine clinical workflows. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore the challenges and strengths that child welfare professionals experience in non‐metro areas, with a secondary focus on how they encounter and respond to substance use in families and communities. We conducted three focus groups in non‐metro counties (n = 35). We used thematic analysis supported by Dedoose, beginning with line by line coding, then thematic identification and mapping. Emergent themes focused on challenges and strengths experienced in non‐metro communities, including (Community context)—unique community attributes; the call for treatment; (Challenges)—uphill battles; reporting; access to appropriate care; funding; interacting with state systems; and (Strength)—finding strengths and creative solutions. Child welfare professionals in non‐metro communities embody and respond to both challenges and strengths. They feel frustrated by standards that do not take available resources into account, yet they effectively leverage social capital to complete their work. Giving voice to child welfare professionals can enhance policy making and create more sustainable systems for child welfare practice in non‐metro settings. [ABSTRACT FROM AUTHOR]

A visual prosthesis is an auxiliary device for patients with blinding diseases that cannot be treated with conventional surgery or drugs. It converts captured images into corresponding electrical stimulation patterns, according to which phosphenes are generated through the action of internal electrodes on the visual pathway to form visual perception. However, due to some restrictions such as the few implantable electrodes that the biological tissue can accommodate, the induced perception is far from ideal. Therefore, an important issue in visual prosthesis research is how to detect and present useful information in low‐resolution prosthetic vision to improve the visual function of the wearer. In recent years, with the development and broad application of computer vision methods, researchers have investigated the possibility of their utilization in visual prostheses by simulating prosthetic visual percepts. Through the optimization of visual perception by image processing, the efficiency of visual prosthesis devices can be further improved to better meet the needs of prosthesis wearers. In this article, recent works on prosthetic vision centering on implementing computer vision methods are reviewed. Differences, strengths, and weaknesses of the mentioned methods are discussed. The development directions of optimizing prosthetic vision and improving methods of visual perception are analyzed. [ABSTRACT FROM AUTHOR]

In what has been termed the global era, individuals from diverse linguistic and cultural backgrounds frequently participate in joint activities that require intercultural communication. Concomitantly, research on communication in contexts involving mobile technologies is nascent, and investigations addressing pragmatics in particular are few. In this article, we examine place‐based mobile augmented reality (AR) apps, which have been shown to provide learners with valuable opportunities for location‐situated social and collaborative interaction and embodied experience of place. We focus on cataloguing social actions, specifically directives, in 4 groups of mobile AR game players using English as a lingua franca (ELF) to communicate. We uncovered a variety of directive strategies, both verbal and nonverbal, that are rarely discussed in pragmatics teaching and learning yet were critical to the unfolding communicative action. Implications of the study findings for pragmatics instruction are provided within the broader recommendations for ELF pragmatics. [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]

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]

In Denmark, remote monitoring and management of inflammatory bowel disease (IBD) started two decades ago with the web application Constant Care. The disease monitoring in Constant Care consists of simple disease activity questionnaires and home measurement of faecal calprotectin, a stool biomarker for inflammation. It has now been implemented in clinical practice at North Zealand University Hospital in Denmark. Digital health care solutions facilitate remote contact between patients and healthcare providers and have been shown to reduce time to remission, outpatient visits and hospital admissions, and increase adherence to medical therapy, quality of life and disease and treatment knowledge. In Australia, E‐Health is an area of increasing interest, particularly given the significant distances travelled by rural patients to access specialist care. There are several foreseeable benefits to incorporating E‐Health/remote monitoring into Australian IBD management, including reduced burdens of time and cost on rural patients, and more efficient management of well outpatients, thereby increasing clinic availability for acutely unwell patients. The significant portion of IBD patients managed in private practice in Australia, and the infrastructure within private practice that is well suited to implementation of E‐Health makes Australia a viable setting for an E‐Health IBD management model like Denmark's Constant Care model. One pilot study is currently underway investigating the feasibility of rapid and remote IBD monitoring and E‐Health in an Australian IBD population. The current COVID‐19 pandemic has further illustrated the importance of telehealth as a means of maintaining health services to patients in geographic, or social, isolation. [ABSTRACT FROM AUTHOR]

The goal of this paper is to define a new Bézier‐like curve, which has many applications in computer graphics and related areas. By applying an integral approach, we construct a new Bézier‐like basis in the space Γm=span1,t,t2,...,tm−2,et,e−t. Using Bézier‐like basis, we define Bézier‐like curves. We then compare the Bezie‐like basis and curves with the classical Bézier basis and curves, respectively. [ABSTRACT FROM AUTHOR]

BlueScientific distributes the Axia ChemiSEM, a user-friendly and accessible technology from Thermo Scientific. The Axia ChemiSEM system simplifies workflows, saves time, and enhances laboratory sustainability without compromising space. It is capable of accommodating samples weighing up to 10kg and offers functionality and versatility in sample positioning. The Axia ChemiSEM system delivers high-quality imaging performance and can handle a wide range of samples, including insulating items. It is suitable for various fields such as process and quality control, failure analysis, materials science analysis, and aerospace materials development. The system also provides access to a range of application software options and supports advanced system automation. [Extracted from the article]