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We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software application to design custom coarse-grained (CG) models, as well as pre-defined models of polyelectrolytes, peptides, and globular proteins in the Extensible Simulation Package for Research on Soft Matter (ESPResSo). The Python interface of ESPResSo offers a flexible framework, capable of building custom CG models from scratch. As a downside, building CG models from scratch is prone to mistakes, especially for newcomers in the field of CG modeling, or for molecules with complex architectures. The pyMBE module builds CG models in ESPResSo using a hierarchical bottom-up approach, providing a robust tool to automate the setup of CG models and helping new users prevent common mistakes. ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which have been designed to study chemical reaction equilibria in macromolecular systems with many reactive species. However, setting up these methods for systems, which contain several types of reactive groups, is an error-prone task, especially for beginners. The pyMBE module enables the automatic setup of cpH and G-RxMC simulations in ESPResSo, lowering the barrier for newcomers and opening the door to investigate complex systems not studied with these methods yet. To demonstrate some of the applications of pyMBE, we showcase several case studies where we successfully reproduce previously published simulations of charge-regulating peptides and globular proteins in bulk solution and weak polyelectrolytes in dialysis. The pyMBE module is publicly available as a GitHub repository (https://github.com/pyMBE-dev/pyMBE), which includes its source code and various sample and test scripts, including the ones that we used to generate the data presented in this article. [ABSTRACT FROM AUTHOR]

Simulating mixed-state evolution in open quantum systems is crucial for various chemical physics, quantum optics, and computer science applications. These simulations typically follow the Lindblad master equation dynamics. An alternative approach known as quantum state diffusion unraveling is based on the trajectories of pure states generated by random wave functions, which evolve according to a nonlinear Itô–Schrödinger equation (ISE). This study introduces weak first-order and second-order solvers for the ISE based on directly applying the Itô–Taylor expansion with exact derivatives in the interaction picture. We tested the method on free and driven Morse oscillators coupled to a thermal environment and found that both orders allowed practical estimation with a few dozen iterations. The variance was relatively small compared to the linear unraveling and did not grow with time. The second-order solver delivers a much higher accuracy and stability with bigger time steps than the first-order scheme, with a small additional workload. However, the second-order algorithm has quadratic complexity with the number of Lindblad operators as opposed to the linear complexity of the first-order algorithm. [ABSTRACT FROM AUTHOR]

In 2020, Libraries and Cultural Resources (LCR) at the University of Calgary used Qualtrics XM to design and pilot a point-of-use survey to collect user feedback on the usability of our implementation of Primo, Ex Libris's web-scale discovery service. Over a two-week period, users were presented with the pop-up survey while searching and asked to provide feedback. This article summarizes how we designed and implemented this point-of-use survey and the lessons learned from this project. [ABSTRACT FROM AUTHOR]

The many-body expansion lies at the heart of numerous fragment-based methods that are intended to sidestep the nonlinear scaling of ab initio quantum chemistry, making electronic structure calculations feasible in large systems. In principle, inclusion of higher-order n-body terms ought to improve the accuracy in a controllable way, but unfavorable combinatorics often defeats this in practice and applications with n ≥ 4 are rare. Here, we outline an algorithm to overcome this combinatorial bottleneck, based on a bottom-up approach to energy-based screening. This is implemented within a new open-source software application ("Fragme∩t"), which is integrated with a lightweight semi-empirical method that is used to cull subsystems, attenuating the combinatorial growth of higher-order terms in the graph that is used to manage the calculations. This facilitates applications of unprecedented size, and we report four-body calculations in ( H 2 O) 64 clusters that afford relative energies within 0.1 kcal/mol/monomer of the supersystem result using less than 10% of the unique subsystems. We also report n-body calculations in ( H 2 O) 20 clusters up to n = 8, at which point the expansion terminates naturally due to screening. These are the largest n-body calculations reported to date using ab initio electronic structure theory, and they confirm that high-order n-body terms are mostly artifacts of basis-set superposition error. [ABSTRACT FROM AUTHOR]

Gaussian Approximation Potentials (GAPs) are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software implementation provides the means for both fitting models using ab initio data and using the resulting potentials in atomic simulations. Details of the GAP theory, algorithms and software are presented, together with detailed usage examples to help new and existing users. We review some recent developments to the GAP framework, including Message Passing Interface parallelisation of the fitting code enabling its use on thousands of central processing unit cores and compression of descriptors to eliminate the poor scaling with the number of different chemical elements. [ABSTRACT FROM AUTHOR]

Background: The use of computer‐assisted reading comprehension is of critical importance in the context of promoting effective and engaging literacy education in the digital age. It provides students with the opportunity to work at their own pace and convenience, thereby facilitating self‐directed learning and accommodating various learning preferences and schedules. Objectives: The objective of the study was to investigate the impact of computer‐assisted and direct strategy teaching on reading comprehension, reading comprehension self‐efficacy and reading comprehension metacognitive awareness. An experimental application based on the SQ4R strategy was conducted in the direct strategy teaching (DST) and computer‐assisted strategy teaching (CAST) programmes. In the DST group, the implementation of the SQ4R strategy was conducted through direct strategy teaching, whereas in the CAST group, the same activities were carried out with the assistance of computer‐based resources. The principal objective of the study was to evaluate the efficacy of computer‐assisted strategy instruction. Methods: This study employed a combined sequential descriptive design, integrating qualitative and quantitative research models. The study was conducted with 61 fourth‐grade students, enrolled in three classrooms of a public school situated in the central district of Kastamonu, Turkey, which is characterised by a moderate socioeconomic level. The quantitative component of the study was designed as a pre‐test–post‐test control group experimental study. The qualitative component of the study comprised focus group interviews and observation. Two experimental groups and one control group were established in the course of this study. In the quantitative dimension of the combined sequential descriptive model, the Reading Comprehension Test, Metacognitive Reading Comprehension Scale, and Reading Comprehension Self‐Efficacy Scale were employed as data collection instruments. In order to collect data in the qualitative dimension, semi‐structured interview and observation forms were employed. Furthermore, the researcher's diaries, maintained throughout the research process, were employed as a data source. The quantitative data were analysed using the following techniques: arithmetic mean, frequency, percentage, standard deviation, ANOVA and covariance analysis (ANCOVA). A descriptive analysis was employed for the evaluation of the qualitative data. Results and Conclusions: Upon examination of the post‐test scores of the CAST, DST and control group students in the Reading Comprehension Test, it was observed that the CAST group exhibited the highest average. A statistically significant difference was also identified between the CAST group and the control group. Nevertheless, no statistically significant difference was identified between the DST group and the control group. Nevertheless, an examination of the mean scores reveals that the DST group exhibited considerably higher Reading Comprehension Test scores. Lay Description: Computer aided tools can be used to teachvarious reading strategies.CAST can help students improve their readingcomprehension.CAST can contribute to students' reading andcomprehension motivation.CAST can offer customised learning materialsaccording to the individual needs of the students. [ABSTRACT FROM AUTHOR]

The wastewater management sector is grappling with a multitude of intricate challenges, including but not limited to insufficient infrastructure, financial constraints, rapid urbanization, population booms, and emerging environmental contaminants. These issues necessitate an urgent pivot toward sustainable wastewater management strategies that harmonize ecological, economic, and societal imperatives—a vision aligned with the United Nations' sustainable development goals. To extend the reach of wastewater networks globally, software tools play a pivotal role in the efficient design of sewer systems, given the inherent complexity of manual design processes. This research paper presents a comprehensive review of the available literature on sewer network design software and selects the most used options—Bentley SewerGEMS, Urbano CANALIS, and Mike+ SWMM—for in-depth analysis. Furthermore, a novel software solution, INSINCE, based on machine learning principles, is included, and evaluated. The assessment and comparison of results encompass critical parameters such as network length, velocity distribution, depth profiling, and hydraulic design obtained from these four software applications. Qualitative aspects including user-friendliness, cross-platform integration, and software capabilities are also discussed. This study underscores the pivotal role of software selection in achieving optimal sewer system designs, offering invaluable insights to enhance wastewater management practices. Practical Applications: The research on sewer network design software, including Bentley SewerGEMS, Urbano CANALIS, Mike+ SWMM, and the novel InSINCE, offers crucial insights for practitioners and engineers in the wastewater management sector. These findings underscore the pivotal role of software selection in achieving optimal sewer system designs, with InSINCE showing promise in streamlining design processes through machine learning and optimization techniques. The study highlights the importance of considering critical parameters such as network length, velocity distribution, and hydraulic design in software evaluation. Practitioners can leverage this research to make informed decisions on software choices, ultimately enhancing wastewater management practices. Furthermore, the identified areas for improvement, such as optimal layout selection and life cycle cost analysis, provide valuable guidance for future software development, emphasizing the potential for integrating advanced machine learning algorithms to elevate software functionalities. Overall, this research serves as a valuable resource for stakeholders seeking sustainable solutions in urban wastewater management. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging technology incorporating various hardware devices and software applications to exchange, analyze, and process a huge amount of data. IoT uses cloud and fog infrastructures, comprising different hardware and software components like computing machines, networking components, storage, and virtualization elements. They can receive, process, store, and exchange data in real time. A cloud is a centralized system containing large data centres that are far from client devices. However, as IoT generates massive amounts of data, issues like latency, response time, execution of tasks within their deadline, and bandwidth arise when data is sent to the cloud for processing. Compared to the cloud, fog computing is vital as a distributed system consisting of millions of devices located at the minimum distance from the client devices. In addition, fog infrastructure reduces bandwidth and latency because it is closer to the end-user. However, maximizing utilization of resources, minimizing response time, and ensuring the completion of deadline-constrained tasks within their deadline are important research problems in fog computing. This research proposes a task scheduling technique called Resource Aware Prioritized Task Scheduling (RAPTS) in a heterogeneous fog computing environment. The aim is to execute deadline-constrained tasks within their deadlines, minimize response time and cost, as well as makespan, and maximize resource utilization of the fog layer. The RAPTS is implemented using iFogSim and its performance is evaluated regarding response time, resource utilization, task deadlines, cost, and makespan. The results have been compared with state-of-the-art fog schedulers like RACE (CFP) and RACE (FOP). The results reveal that the RAPTS have shown up to 29%, 53%, 15%, 11%, and 43% improvement in terms of resource utilization, response time, makespan, cost, and meeting task deadlines, respectively. [ABSTRACT FROM AUTHOR]

In this work, we consider the degenerate Frobenius-Euler-Genocchi polynomials utilizing the degenerate exponential function and the degenerate Changhee-Frobenius-Euler-Genocchi polynomials utilizing the degenerate logarithm function. Then, we analyze some summation and addition formulas for these polynomials. In addition, we derive some correlations with degenerate Stirling numbers of both kinds and degenerate Frobenius-Euler polynomials. Moreover, we present difference and derivative operator rules for the generalized degenerate Frobenius-Euler-Genocchi polynomials. Lastly, we show certain zeros of both the degenerate Frobenius-Euler-Genocchi polynomials and the degenerate Changhee-Frobenius-Euler-Genocchi polynomials and provide their beautifully graphical representations. [ABSTRACT FROM AUTHOR]

We present a hard-real-time multi-resolution mesh shape deformation technique for skeleton-driven soft-body characters. Producing mesh deformations at multiple levels of detail is very important in many applications in computer graphics. Our work targets applications where the multi-resolution shapes must be generated at fast speeds ("hard-real-time", e.g., a few milliseconds at most and preferably under 1 millisecond), as commonly needed in computer games, virtual reality and Metaverse applications. We assume that the character mesh is driven by a skeleton, and that high-quality character shapes are available in a set of training poses originating from a high-quality (slow) rig such as volumetric FEM simulation. Our method combines multi-resolution analysis, mesh partition of unity, and neural networks, to learn the pre-skinning shape deformations in an arbitrary character pose. Combined with linear blend skinning, this makes it possible to reconstruct the training shapes, as well as interpolate and extrapolate them. Crucially, we simultaneously achieve this at hard real-time rates and at multiple mesh resolution levels. Our technique makes it possible to trade deformation quality for memory and computation speed, to accommodate the strict requirements of modern real-time systems. Furthermore, we propose memory layout and code improvements to boost computation speeds. Previous methods for realtime approximations of quality shape deformations did not focus on hard real-time, or did not investigate the multi-resolution aspect of the problem. Compared to a "naive" approach of separately processing each hierarchical level of detail, our method offers a substantial memory reduction as well as computational speedups. It also makes it possible to construct the shape progressively level by level and interrupt the computation at any time, enabling graceful degradation of the deformation detail. We demonstrate our technique on several examples, including a stylized human character, human hands, and an inverse-kinematics-driven quadruped animal. [ABSTRACT FROM AUTHOR]

Image translation has various applications in computer graphics and computer vision, aiming to transfer images from one domain to another. Thanks to the excellent generation capability of diffusion models, recent single-view image translation methods achieve realistic results. However, directly applying diffusion models for multi-view image translation remains challenging for two major obstacles: the need for paired training data and the limited view consistency. To overcome the obstacles, we present a first unified multi-view image to multi-view image translation framework based on diffusion models, called MV2MV. Firstly, we propose a novel self-supervised training strategy that exploits the success of off-the-shelf single-view image translators and the 3D Gaussian Splatting (3DGS) technique to generate pseudo ground truths as supervisory signals, leading to enhanced consistency and fine details. Additionally, we propose a latent multi-view consistency block, which utilizes the latent-3DGS as the underlying 3D representation to facilitate information exchange across multi-view images and inject 3D prior into the diffusion model to enforce consistency. Finally, our approach simultaneously optimizes the diffusion model and 3DGS to achieve a better trade-off between consistency and realism. Extensive experiments across various translation tasks demonstrate that MV2MV outperforms task-specific specialists in both quantitative and qualitative. [ABSTRACT FROM AUTHOR]

Particle-based representations of radiance fields such as 3D Gaussian Splatting have found great success for reconstructing and re-rendering of complex scenes. Most existing methods render particles via rasterization, projecting them to screen space tiles for processing in a sorted order. This work instead considers ray tracing the particles, building a bounding volume hierarchy and casting a ray for each pixel using high-performance GPU ray tracing hardware. To efficiently handle large numbers of semi-transparent particles, we describe a specialized rendering algorithm which encapsulates particles with bounding meshes to leverage fast ray-triangle intersections, and shades batches of intersections in depth-order. The benefits of ray tracing are well-known in computer graphics: processing incoherent rays for secondary lighting effects such as shadows and reflections, rendering from highly-distorted cameras common in robotics, stochastically sampling rays, and more. With our renderer, this flexibility comes at little cost compared to rasterization. Experiments demonstrate the speed and accuracy of our approach, as well as several applications in computer graphics and vision. We further propose related improvements to the basic Gaussian representation, including a simple use of generalized kernel functions which significantly reduces particle hit counts. [ABSTRACT FROM AUTHOR]

Real-time rendering of human head avatars is a cornerstone of many computer graphics applications, such as augmented reality, video games, and films, to name a few. Recent approaches address this challenge with computationally efficient geometry primitives in a carefully calibrated multi-view setup. Albeit producing photorealistic head renderings, they often fail to represent complex motion changes, such as the mouth interior and strongly varying head poses. We propose a new method to generate highly dynamic and deformable human head avatars from multi-view imagery in real time. At the core of our method is a hierarchical representation of head models that can capture the complex dynamics of facial expressions and head movements. First, with rich facial features extracted from raw input frames, we learn to deform the coarse facial geometry of the template mesh. We then initialize 3D Gaussians on the deformed surface and refine their positions in a fine step. We train this coarse-to-fine facial avatar model along with the head pose as learnable parameters in an end-to-end framework. This enables not only controllable facial animation via video inputs but also high-fidelity novel view synthesis of challenging facial expressions, such as tongue deformations and fine-grained teeth structure under large motion changes. Moreover, it encourages the learned head avatar to generalize towards new facial expressions and head poses at inference time. We demonstrate the performance of our method with comparisons against the related methods on different datasets, spanning challenging facial expression sequences across multiple identities. We also show the potential application of our approach by demonstrating a cross-identity facial performance transfer application. We make the code available on our project page. [ABSTRACT FROM AUTHOR]

Precisely understanding how objects move in 3D is essential for broad scenarios such as video editing, gaming, driving, and athletics. With screen-displayed computer graphics content, users only perceive limited cues to judge the object motion from the on-screen optical flow. Conventionally, visual perception is studied with stationary settings and singular objects. However, in practical applications, we---the observer---also move within complex scenes. Therefore, we must extract object motion from a combined optical flow displayed on screen, which can often lead to mis-estimations due to perceptual ambiguities. We measure and model observers' perceptual accuracy of object motions in dynamic 3D environments, a universal but under-investigated scenario in computer graphics applications. We design and employ a crowdsourcing-based psychophysical study, quantifying the relationships among patterns of scene dynamics and content, and the resulting perceptual judgments of object motion direction. The acquired psychophysical data underpins a model for generalized conditions. We then demonstrate the model's guidance ability to significantly enhance users' understanding of task object motion in gaming and animation design. With applications in measuring and compensating for object motion errors in video and rendering, we hope the research establishes a new frontier for understanding and mitigating perceptual errors caused by the gap between screen-displayed graphics and the physical world. [ABSTRACT FROM AUTHOR]

Summary: The unmanned aircraft system (UAS) is rapidly gaining popularity in civil and military domains. A UAS consists of an application software that is responsible for defining a UAS mission and its expected behavior. A UAS during its mission experiences changes (or interruptions) that require the unmanned aerial vehicle (UAV) in a UAS to self‐adapt, that is, to adjust both its behavior and position in real‐time, particularly for maintaining formation in the case of a UAS swarm. This adaptation is critical as the UAS operates in an open environment, interacting with humans, buildings, and neighboring UAVs. To verify if a UAS correctly makes an adaptation, it is important to test it. The current industrial practice for testing the self‐adaptive behaviors in UAS is to carry out testing activities manually. This is particularly true for existing UAS rather than newly developed ones. Manual testing is time‐consuming and allows the execution of a limited set of test cases. To address this problem, we propose an automated model‐based approach to test the self‐adaptive behavior of UAS application software. The work is conducted in collaboration with an industrial partner and demonstrated through a case study of UAS swarm formation flight application software. Further, the approach is verified on various self‐adaptive behaviors for three open‐source autopilots (i.e., Ardu‐Copter, Ardu‐Plane, and Quad‐Plane). Using the proposed model‐based testing approach we are able to test sixty unique self‐adaptive behaviors. The testing results show that around 80% of the behavior adaptations are correctly executed by UAS application software. [ABSTRACT FROM AUTHOR]

Abstract\nIMPLICATIONS FOR REHABILITATIONThis study aimed to explore the effect of new software targeting naming deficits in Turkish-speaking people with aphasia. The study included seven Turkish people with aphasia. These participants took the Aphasia Language Assessment Test (ADD), Turkish Picture-Naming Test (T-RAT), and Technology Familiarity Assessment, and the words for therapy were determined. The selection of categories and words for inclusion in the application was based on the frequency and typicality features. The 12-week speech-language treatment sessions were divided into two sections. During the first six weeks, a speech-language pathologist (SLP) used the software to train people. After the first part, the SLP applied the T-RAT. Participants utilized the software at home in the second part. All participants took the ADD, T-RAT, and Software Evaluation Questionnaire following the second part. Participants’ naming abilities improved in all tests. Both therapy sessions with the SLP and those in which participants used the program independently demonstrated this improvement. Participants also reported high levels of treatment satisfaction. Cueing hierarchy approaches used in the software helped to improve naming skills in people with aphasia (PWA), independent of type, stage, or severity of aphasia. The findings of this study indicate that both sessions directed by an SLP and independent use of the software by participants can positively impact the therapy process with the developed software.Cueing hierarchy approach is commonly used to treat word-retrieval impairments in people with aphasia (PWA). Several studies in the literature report that integrating semantic and phonological approaches is beneficial in therapeutic interventions.Different levels of improvement were observed in the naming skills of PWA when the cueing hierarchy methods were presented via software. This study has shown that many PWA could improve their naming skills via software-based therapy.It was determined that working at home with the application could effectively contribute to the therapy process. Consequently, enhanced accessibility to therapeutic interventions will be facilitated, resulting in an increased frequency of treatment sessions.With rapid technological development, the use of software applications in speech and language therapies is increasing. An application that included cueing hierarchy techniques was created for naming therapy with Turkish PWA in this study. The PWAs’ naming abilities showed improvement, and they stated satisfaction with using the software in their therapeutic processes.Cueing hierarchy approach is commonly used to treat word-retrieval impairments in people with aphasia (PWA). Several studies in the literature report that integrating semantic and phonological approaches is beneficial in therapeutic interventions.Different levels of improvement were observed in the naming skills of PWA when the cueing hierarchy methods were presented via software. This study has shown that many PWA could improve their naming skills via software-based therapy.It was determined that working at home with the application could effectively contribute to the therapy process. Consequently, enhanced accessibility to therapeutic interventions will be facilitated, resulting in an increased frequency of treatment sessions.With rapid technological development, the use of software applications in speech and language therapies is increasing. An application that included cueing hierarchy techniques was created for naming therapy with Turkish PWA in this study. The PWAs’ naming abilities showed improvement, and they stated satisfaction with using the software in their therapeutic processes. [ABSTRACT FROM AUTHOR]

Virtual microscopy is becoming increasingly common in both medical education and routine clinical practice. Virtual microscopy software is typically designed either for (1) training students in anatomy, histology, and histopathology, or (2) quantitative analysis—but not both simultaneously. QuPath is one of the most widely used software applications for histopathology image analysis in research and provides a comprehensive set of computational tools to evaluate histology slides. We have enhanced QuPath by developing a new extension, QuPath Edu, which adapts the software to function as an intuitive microanatomy learning environment. Additionally, we have created an entirely new, complementary software platform called OpenMicroanatomy, which provides an alternative way to access QuPath Edu teaching content through a web interface. These tools have been used in teaching of first year medical and dentistry students at the University of Oulu Medical Faculty, and we conducted a user survey for the Class of 2023 to assess the usability and student experience. In general, the introduced annotation and quiz features were appreciated by the students and the system usability of OpenMicroanatomy was considered excellent (SUS score 84.8). Together, these freely available tools enable teachers to develop and deploy innovative training material for anatomy, histopathology, quantitative analysis, and artificial intelligence in a wide range of contexts. This unique combination can provide the next generation of students with essential multidisciplinary skills. [ABSTRACT FROM AUTHOR]

Images taken on rainy days have rain streaks of varying degrees of intensity, which seriously affect the visibility of the background scene. Aiming at the above problems, we propose a rain mark removal algorithm based on the combination of dual-attention mechanism U-Net and multi-convolution. First, we add a double attention mechanism to the encoder of U-Net. It can give different weights to the rain mark features that need to be extracted in different channels and spaces so that sufficient rain mark features can be obtained. With different dilation factors, we can obtain rain mark characteristics of different depths. Secondly, the multi-convolutional channel integrates the characteristics of rain streaks and prepares sufficient rain mark information for the task of clearing rain streaks. By introducing a cyclic rain streaks detection and removal mechanism into the network architecture, it can achieve gradual removal of rain streaks. Even in the case of heavy rain, our algorithm can get good results. Finally, we tested on both synthetic and real datasets to obtain subjective results and objective evaluations. Experimental results show that for the rainy day image de-rain task with different intensities of rain streaks, our algorithm is more robust. Moreover, the ability of our algorithm to remove rain streaks is better than that of the other five different classical algorithms. The de-raining images produced by our algorithm are visually sharper, and its visibility enhancements are effective for computer vision applications (Google Vision API). [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 National Fatality Review Case Reporting System (NFR-CRS) is a web-based data collection tool for child death review and fetal and infant mortality review teams. The NFR-CRS captures information from the multidisciplinary review, including the social and community risk factors that may have impacted the death. The NFR-CRS is a nimble data system that has evolved throughout the past 20 years. The most recent enhancements include a life stressors section focused on collecting contextual information such as racism and poverty, revised cause of death sections to better align data collected with risk factors identified in the research, and enhanced data visualizations. The NFR-CRS has improved data quality since the launch of a data quality initiative in 2015. As a result of the data quality initiative, the completeness, consistency, accuracy, and timeliness of the NFR-CRS data have improved. Limitations for the NFR-CRS include the fact that data entered are not population-based, data fields have evolved over time, and there are high percentages of missing and unknown data. Despite its limitations, the NFR-CRS remains a valuable research tool, especially when paired with other data sources. [ABSTRACT FROM AUTHOR]

Over the past few decades, the use of functional magnetic resonance imaging (fMRI) on neonates and very young children has increased dramatically in research and clinical settings. However, the specific characteristics of this population and the MRI standards largely derived from adult studies, pose serious practical challenges. The current study aims to provide general methodological guidelines for customized neonatal fMRI by assessing the performance of various fMRI hardware and software applications. Specifically, this article focuses on MR equipment (head coils) and MR sequences (singleband vs. multiband). We computed and compared the signal-to-noise ratio (SNR) and the temporal SNR (tSNR) in different fMRI protocols using a small-size spherical phantom in three different commercial receiver-only head-neck coils. Our findings highlight the importance of coil selection and fMRI sequence planning in optimizing neonatal fMRI. For SNR, the prescan normalize filter resulted in significantly higher values overall, while in general there was no difference between the different sequences. In terms of head coil performance, the 20-channel head coil showed slightly but significantly higher values compared to the others. For tSNR, there was no difference in the usage of the prescan normalize filter, but the values were significantly higher in the singleband EPI sequences compared to the multiband. In contrast to the SNR, the pediatric head coil seems to have an advantage for tSNR. We provide five practical guidelines to assist researchers and clinicians in developing fMRI studies in neonates and young infants. These recommendations are especially relevant considering ethical constraints and exogenous challenges of neonatal fMRI. [ABSTRACT FROM AUTHOR]

Background: Diaries have been used regularly in international settings as an evidence‐based and easily applicable intervention following a person‐centred approach in the intensive care unit (ICU). In addition, a diary web application known as 'Post‐ICU' has been implemented. Aim: To explore the usability of an innovative digital diary from the perspectives of intensive care patients' relatives. Study Design: A cross‐sectional online survey study was applied among a convenience sample of relatives in the ICUs of two hospitals in the Netherlands. The investigator‐developed questionnaire included, among other things, items with the appreciation of the layout, user friendliness and functionality of the diary. Relevance and applicability were rated between 1 and 10. Data were analysed with SPSS© software, version 27, and reported as the means (±standard deviation [SD]) and percentages. Results: Sixty‐three relatives with an average age of 51 years (SD ± 14.3) participated in the study; there was a slight predominance of women (57%). All but one participant found using the digital diary easy and were able to upload photos to the diary. The participants had invited other relatives (75%) and nurses (61%) to write in the diary, which they viewed as easily feasible (89%). The relevance and applicability of the diary were rated with mean scores of 8.1 (SD ± 1.9) and 8.3 (SD ± 1.6), respectively. Conclusions: The participants found the Post‐ICU diary web application highly valuable and easily feasible. Perceived ease of use, perceived usefulness and technophobia were not found to influence the usability of the digital diary. Relevance to Clinical Practice: The implementation of this new digital tool supports a person‐centred ICU policy because of its focus on the personal diary entries of the patient and the collaborative writing process featuring relatives and professionals. [ABSTRACT FROM AUTHOR]

For caregivers of people living with major neurocognitive disorders (MNCD), adapting and applying methods optimizing learning (MOL) to a specific situation, based on preserved cognitive skills, can be challenging. This study aimed to 1) co-develop workshops, a web application and accompanying materials to support the operationalization of MOL; 2) better understand the factors influencing caregivers' use of MOL, and 3) evaluate the perceived effects of the workshops. A participatory action research study was conducted in collaboration with family caregivers of people living with MNCD and healthcare and community organization workers (HCOW). Qualitative questionnaires and interviews were conducted and transcribed in verbatim. Thematic content analysis was conducted. Workshops and accompanying materials were co-developed and continuously improved based on conceptual framework and feedback from the participants. The final version of the workshops consisted of seven two-hour sessions structured around the type of cognitive deficits affecting the realization of activities. Facilitators (e.g. help to identify the person's capabilities) and barriers (e.g. fluctuation of MNCD symptoms) for caregivers' use of the MOL were reported. The identification of the cause of reactive behavioral expressions could be challenging for some family caregivers, reducing the use of MOL. Caregivers mentioned their increased preparedness to support and relationship with the person living with MNCD and feeling of competence for analyzing the reactive behavioral expressions and to use MOL. This participatory action research has shown that caregivers can acquire abilities to adapt and apply MOL in specific situations with people living with MNCD. [ABSTRACT FROM AUTHOR]

At-home respite services seem too rigid to meet the needs of older adults and their caregivers. It is critical to develop service flexibility, as it allows for personalized care, adapted to health conditions, preferences, and evolving needs. While no prior studies used coconstruction methods to increase flexibility, this study could offer a better understanding of flexible respite for stakeholders. Using a living lab approach, this article aimed to (1) empirically determine the characteristics of this type of respite model and (2) document the levers and obstacles to consider for its implementation. Starting from a pre-existing flexible respite model named ANAAIS, the research team led workshops and interviews. First, the team carried out 2 workshops (TRIAGE and persona-scenario) with a total of 3 caregivers and 8 homecare professionals or managers. Second, a team member conducted interviews with 3 caregivers and 6 homecare professionals or managers. Content analysis was used on the data. The stakeholders coconstructed a Québec version of ANAAIS, a web application allowing caregivers to request an affordable respite, at the time wanted, and offered by a qualified care worker, through a simple application. Levers and barriers to its deployment are linked to the model's characteristics as well as its internal and external context. For example, relations and connexions were perceived as a lever to deployment, while the lack of resources was considered an obstacle. This living lab project showed the feasibility and pragmatism of coconstructing a flexible and applicable respite service model. [ABSTRACT FROM AUTHOR]

Aim: Systematically evaluate the depth and quality of play recommendations provided in popular sources for parents of infants in the first year of life. Methods: This represents the second stage of a larger analysis of educational content available to parents. Two coders (>90% agreement) extracted and coded play activities from popular websites, applications, and books screened from a systematic online search. Depth of instruction variables were extracted. Activity quality was rated based on opportunities for child-initiated movement, problem-solving with objects, and responsive communication. Results: 4370 play activities from 214 sources were analyzed. Activities were likely to suggest specific ages for infants and that a caregiver be present. Less than half of the activities incorporated toys or provided guidance about how to position or physically support infants. Activity quality was low; most activities did not explicitly encourage parents to provide opportunities for child-initiated movement, problem-solving with objects, or quality communication. Conclusions: Parents may encounter a large number of play activities in popular sources, but the depth of instruction and quality of those activities could be improved. Provision of higher-quality education to parents may enhance parent-child play interactions to positively impact parent and child outcomes, especially for children at risk for delays. [ABSTRACT FROM AUTHOR]

Executive dysfunction is most often caused by post-traumatic or post-stroke damage to the prefrontal regions of the brain. The aim of this study was to compare the efficacy of two computer-assisted therapy programs for executive dysfunctions in patients with acquired brain injury. Patients were trained using either a newly developed application ExeSystem (designed to help improve the ability to manage and control one's own behavior by performing tasks imitating natural, everyday situations) or a combination of two commercial applications RehaCom and CogniPlus. Data collected after a three-week period of therapy conducted in two 15-person groups of participants indicated comparable efficacy of both therapy programs in improving quality of daily functioning, executive attention, as well as planning and problem-solving but not memory. The improvement in social competence (p =.028) was the only advantage of therapy with the ExeSystem. Therapeutic interactions using computer programs were shown to be positively evaluated by patients (p <.01). This study confirmed at least equal efficacy of computer-based executive function therapy using ExeSystem compared to RehaCom and CogniPlus. However, despite the implementation of a more ecological and comprehensive approach to the content of a new application, the benefits of this approach were limited. [ABSTRACT FROM AUTHOR]

Developing deep learning network models for computer vision applications in concrete damage detection is a challenging task due to the shortage of training images. To address this issue, this study proposes a novel style-controlled image synthesis method for concrete damages based on the fusion of a convolutional encoder and an attention-enhanced conditional generative adversarial network. This makes it possible to generate effective images that can improve the damage detection performance of deep learning networks. To achieve this, a network architecture for concrete damage image synthesis, named DamageGAN-AE, was designed by fusing a convolutional encoder and an attention-enhanced conditional generative adversarial network. The DamageGAN-AE networks with different attention modules were trained, and the training results show that the well-trained DamageGAN-AE enhanced by coordinate attention is the best model for concrete damage image synthesis. The well-trained DamageGAN-AE was compared with the current competing methods to verify its performance. The DamageGAN-AE with image encoder was trained to implement the style-controlled image synthesis. Finally, the generated concrete damage images with diverse styles by the DamageGAN-AE model with image encoder were used to train deep learning networks. The results indicate that the generated style-controlled concrete damage images by the proposed method can effectively improve the concrete damage detection performance of deep learning networks. [ABSTRACT FROM AUTHOR]

The hardware and software of a computer are controlled by its operating system (OS), which performs essential tasks such as input and output processing, file and memory management, and the management of peripheral devices such as disk drives and printers. Application software refers to programs designed for specific purposes, these applications, often freely available and open source, contribute to the rising number of downloads. In the third quarter of 2022, combined downloads from the Apple App Store and Google Play Reached an estimated 35.3 billion. However, the prevalence of insecurity in these applications and technologies heightens the potential for cybercrimes. Protection against unauthorized intruders is crucial in identifying malicious applications. Machine learning (ML) serves as a promising avenue for detecting malware attacks, offering potential solutions to bolster cybersecurity measures. We propose a novel approach utilizing ML to enhance malware detection accuracy by segmenting datasets into distinct groups. Our research employs supervised ML techniques on the CICMaldroid2020 dataset, which includes comprehensive information such as intent actions, permissions, and sensitive APIs. The dataset was partitioned into four groups, each containing 150 features, and analyzed across four experiments to distinguish between attack and benign classes. Our proposed model demonstrated exceptional performance, with the random forest algorithm achieving an accuracy of 98.6% and a precision of 98.75%. These results highlight the effectiveness of our segmentation approach and its significant contribution to advancing malware detection in Android applications, offering a promising direction for future cybersecurity solutions. [ABSTRACT FROM AUTHOR]

In multi-regional clinical trials, planning the sample size for participating regions is essential for the evaluation of the treatment effect consistency across regions. Based on the MRCT design and sample size allocation to regions, consistency probability is usually used to predict the consistent trend between regions and the overall population, while preserving a certain proportion of the overall treatment effect. Specific enrollment characteristics in a region of interest should also be considered during the time of the sample size planning. To facilitate efficient and harmonized regional sample size planning, we have developed RegionSizeR, a comprehensive and user-friendly interactive web-based R shiny application that can be obtained from https://github.com/rsr-ss/RegionSizeR. This simulation-based app can serve as an initial point for discussions on sample size allocation plans, following preservation of treatment effect method in ICH E17. The app accommodates various types of endpoints and designs, including continuous, binary, and time-to-event endpoints, for superiority, non-inferiority, and MCP-Mod designs. To ensure the validity of this app, independent testing is conducted allowing a discrepancy of no more than 1% across all results considering various scenarios. [ABSTRACT FROM AUTHOR]

Shadow detection is a fundamental and challenging task in many computer vision applications. Intuitively, most shadows come from the occlusion of light by the object itself, resulting in the object and its shadow being contiguous (referred to as the adjacent shadow in this article). In this case, when the color of the object is similar to that of the shadow, existing methods struggle to achieve accurate detection. To address this problem, we present SwinShadow, a transformer-based architecture that fully utilizes the powerful shifted window mechanism for detecting adjacent shadows. The mechanism operates in two steps. Initially, it applies local self-attention within a single window, enabling the network to focus on local details. Subsequently, it shifts the attention windows to facilitate inter-window attention, enabling the capture of a broader range of adjacent information. These combined steps significantly improve the network's capacity to distinguish shadows from nearby objects. And the whole process can be divided into three parts: encoder, decoder, and feature integration. During encoding, we adopt Swin Transformer to acquire hierarchical features. Then during decoding, for shallow layers, we propose a deep supervision (DS) module to suppress the false positives and boost the representation capability of shadow features for subsequent processing, while for deep layers, we leverage a double attention (DA) module to integrate local and shifted window in one stage to achieve a larger receptive field and enhance the continuity of information. Ultimately, a new multi-level aggregation (MLA) mechanism is applied to fuse the decoded features for mask prediction. Extensive experiments on three shadow detection benchmark datasets, SBU, UCF, and ISTD, demonstrate that our network achieves good performance in terms of balance error rate (BER). The source code and results are now publicly available at https://github.com/harrytea/SwinShadow. [ABSTRACT FROM AUTHOR]

Mathematical modeling and numerical simulation have a considerable presence in the vast universe of engineering disciplines, given their usefulness in explaining, comprehending, and simulating phenomena and processes with which engineers are in contact in their daily creative and problem‐solving work. For this reason, engineering study programs have at least one course dedicated to dealing with the mathematical modeling of dynamic systems as an essential complement to subsequent courses such as automatic control, structural dynamics, and mechanical vibrations. Nowadays, many technological tools illustrate the applications of mathematical modeling interactively through experiments that offer an incomparable motivation to the students to corroborate with real‐world examples, the utility and veracity of the theory presented to them in the classroom and that in many occasions seems lacking utility and direct relation with the world in which they develop. Based on those mentioned above, this paper presents an example of applying the Laplace transform in modeling physical systems, using a second‐order circuit attached to an Arduino Due board in conjunction with the Jupyter Notebook environment. The numerical and experimental results can be obtained through three optional kernels: Python, Octave, or MATLAB®. For educational purposes, the resulting computer application was presented to undergraduate students of Mechatronics Engineering as an illustrative complement to two courses entitled Signals and Systems Analysis, part of the second semester, and Mathematical Modeling, part of the fifth semester. [ABSTRACT FROM AUTHOR]

Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its use of a standardized query language, SQL. However, with the rapid increase in both the volume and complexity of data, relational databases have recently encountered challenges in effectively modeling this expanding information. To address performance challenges, new database systems have emerged, offering alternative approaches to data modeling—these are known as NoSQL databases. In this paper, we present an indoor navigation application designed to operate on both a relational database, Microsoft SQL Server, and a graph-based NoSQL database, Neo4j. We describe the algorithms implemented for testing and the performance metrics analyzed to draw our conclusions. The results revealed Neo4j's strength in managing data with complex relationships but also exposed its limitations in handling concurrent access, where SQL Server demonstrated significantly greater stability. [ABSTRACT FROM AUTHOR]

This paper explores the concept of zip quines, which are zip files that contain themselves upon extraction, extending the idea of computational self-reference. While only two individuals, Russ Cox and Erling Ellingsen, have created such entities, this study focuses on Cox's method to develop a generator for these files. Overcoming the initial limitations, the generator allows for the inclusion of additional files within the zip quine. Additionally, this research explores the concept of looped zip files, wherein a zip archive contains another archive. This archive then contains the initial zip file. By offering practical methodologies and insights, this study advances the understanding and application of quines in computer science. [ABSTRACT FROM AUTHOR]

Big Data (i.e., social big data, vehicular big data, healthcare big data etc) points to massive and complex data, that require special technologies and approaches for storage, processing, and analysis. Similarly, big data applications are software and systems utilizing large and complex datasets to extract insights, support decision-making, and address diverse business and societal challenges. Recently, the significance of big data applications has grown immensely for organizations across diverse sectors as they increasingly rely on insights derived from data. The increasing reliance on data insights has rendered traditional technologies and platforms inefficient due to scalability limitations and performance issues. This study contributes by identifying key domains impacted by big data, examining its effect on decision-making, addressing inherent complexities and opportunities, exploring core technologies, and offering solutions for potential concerns. Additionally, it conducts a comparative analysis to demonstrate the superiority of this research. These contributions provide valuable insights into the evolving landscape shaped by big data applications. [ABSTRACT FROM AUTHOR]

Background: Biomarkers of Alzheimer's disease (AD) and mild cognitive impairment (MCI, or prodromal AD) are highly significant for early diagnosis, clinical trials and treatment outcome evaluations. Electroencephalography (EEG), being noninvasive and easily accessible, has recently been the center of focus. However, a comprehensive understanding of EEG in dementia is still needed. A primary objective of this study is to investigate which of the many EEG characteristics could effectively differentiate between individuals with AD or prodromal AD and healthy individuals. Methods: We collected resting state EEG data from individuals with AD, prodromal AD, and normal cognition. Two distinct preprocessing pipelines were employed to study the reliability of the extracted measures across different datasets. We extracted 41 different EEG features. We have also developed a stand-alone software application package, Feature Analyzer, as a comprehensive toolbox for EEG analysis. This tool allows users to extract 41 EEG features spanning various domains, including complexity measures, wavelet features, spectral power ratios, and entropy measures. We performed statistical tests to investigate the differences in AD or prodromal AD from age-matched cognitively normal individuals based on the extracted EEG features, power spectral density (PSD), and EEG functional connectivity. Results: Spectral power ratio measures such as theta/alpha and theta/beta power ratios showed significant differences between cognitively normal and AD individuals. Theta power was higher in AD, suggesting a slowing of oscillations in AD; however, the functional connectivity of the theta band was decreased in AD individuals. In contrast, we observed increased gamma/alpha power ratio, gamma power, and gamma functional connectivity in prodromal AD. Entropy and complexity measures after correcting for multiple electrode comparisons did not show differences in AD or prodromal AD groups. We thus catalogued AD and prodromal AD-specific EEG features. Conclusions: Our findings reveal that the changes in power and connectivity in certain frequency bands of EEG differ in prodromal AD and AD. The spectral power, power ratios, and the functional connectivity of theta and gamma could be biomarkers for diagnosis of AD and prodromal AD, measure the treatment outcome, and possibly a target for brain stimulation. [ABSTRACT FROM AUTHOR]

Designing and modeling tunnels are a complex and labor‐intensive process, exacerbated by the involvement of multiple designers and software applications to accommodate diverse geometric and subsurface conditions. This often results in reduced efficiency, productivity, and increased potential for errors. To address these challenges, the authors implemented a strategy integrating building information modeling and mathematical relationships governing tunnel geometries and rock support elements. Using Autodesk Revit, they developed a prototype that allows designers to model tunnels aligned with ground conditions without reliance on multiple software applications. This streamlined approach enhances productivity, simplifies complexities, and improves accuracy in tunnel design and review processes, including quantity take‐offs. A case study comparing the prototype with conventional methods demonstrated significant improvements in efficiency, productivity, and design accuracy. This research offers practical benefits to stakeholders in tunnel projects, optimizing work quality, and outcomes in tunnel design and construction. [ABSTRACT FROM AUTHOR]

This study introduces an integrated computational environment that leverages Brain–Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human–Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system's Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods. [ABSTRACT FROM AUTHOR]

Based on a diesel hydrogenation unit in Middle East, INPLANT software is for hydraulic calculations of the two-phase flow pipeline in the reaction system. The effects of pipeline diameter, gas-liquid ratio and fluid load on the transition of two-phase flow patterns in horizontal pipelines are analyzed. The results show that the unsteady slug flow can be transformed into stable annular flow by increasing the gas-liquid ratio of the fluid; changing the pipe diameter and fluid load cannot transform the two-phase flow pattern of the pipeline studied in this paper from slug flow to annular flow; however, when the liquid phase velocity is reduced to a sufficiently low, the flow pattern can be transformed into wavy flow. The calculation results are used to guide the operation adjustment of the unit, improving the gas-liquid ratio of the fluid can eliminate the pressure fluctuations of the reaction system. It shows that the flow pattern transformation trend simulated by the software is close to the actual production, and the accuracy is high. [ABSTRACT FROM AUTHOR]

In the past decade, substantial global efforts have been devoted to the development of reliable and efficient solutions for early flood warning and monitoring. One of the most common strategies for tackling this challenge involves the application of computer vision techniques to images obtained from the numerous surveillance cameras present in urban settings today. While there are various datasets available for training and testing these techniques, none of them specifically addresses the issue of out-of-distribution (OoD) behavior. This issue becomes particularly critical when evaluating the reliability of these methods under challenging environmental conditions. Our work stands as the first attempt to bridge this gap by introducing a new, highly controlled synthetic dataset that encompasses the essential attributes required for analyzing OoD behavior. The very high correlation between the accuracy of artificial intelligence (AI) models trained on our synthetic dataset and models trained on real-world data proves our dataset's ability to predict real-world OoD behavior reliably. [ABSTRACT FROM AUTHOR]

Atomic Force Microscopy (AFM), High‐Speed AFM (HS‐AFM) simulation AFM, and Localization AFM (LAFM) enable the study of molecules and surfaces with increasingly higher spatiotemporal resolution. However, effective and rapid analysis of the images and movies produced by these techniques can be challenging, often requiring the use of multiple image processing software applications and scripts. Here, NanoLocz, an open‐source solution that offers advanced analysis capabilities for the AFM community, is presented. Integration and continued development of AFM analysis tools is essential to improve access to data, increase throughput, and open new analysis opportunities. NanoLocz efficiently leverages the rich data AFM has to offer by incorporating and combining existing and newly developed analysis methods for AFM, HS‐AFM, simulation AFM, and LAFM seamlessly. It facilitates and streamlines AFM analysis workflows from import of raw data, through to various analysis workflows. Here, the study demonstrates the capabilities of NanoLocz and the new methods it enables including single‐molecule LAFM, time‐resolved LAFM, and simulation LAFM. [ABSTRACT FROM AUTHOR]

Human lives and property are threatened by Flash floods (FF) worldwide and as a result of the unprecedented conditions of the climate change effects the losses are predicted to increase in the future. As it seems difficult to avoid and prevent them, real-time flash flood detections could be an appropriate solution for damage reduction and better management. Currently, the development of computer vision applications such as deep learning and AI has been advanced. Although AI models have been developed for applications in many fields, their implementations for geosciences are limited based on large amounts of training data and the highly required computational infrastructure. Hence, this work aims to train the latest YOLOv8 model and apply it to real-time flash flood detection for regions of Korea and possibly for other nations. To overcome the shortage of training data, we created small on-site flash flood models and took pictures and footage of them. More than 1500 photos of FF were used for model trains and validations gaining a model mean average precision of above 60% of all training depths (25, 50, 75, and 100 epochs). Despite some model false positives and missed false positive detections using the Korean FF test dataset, the YOLOv8 best model generated bounding boxes (BB) with high confidence values in most FF events. Furthermore, the robustness of the model is highlighted by its ability to smoothly detect the precise positions of the FF areas with high confidence values (best 0.86) when applied for input footage and webcam streams. It is highly encouraged to establish a real-time FF warning system to reduce their negative effects. Although YOLO is effective and fast, like other deep learning models, it requires large input data to ensure higher accuracy and confidence. Future works might explore this aspect, particularly the data acquired in light inefficiency to improve the model detections at night time. [ABSTRACT FROM AUTHOR]

Currently, people spend a lot of time using their mobile devices. With such ubiquity of mobile devices in our daily life, battery capacity and quality are of utmost importance. Running software applications (called apps) are one of the major factors influencing the power consumption in mobile devices. In order to meet user needs, mobile apps are becoming inherently complex and resource greedy. Therefore, fulfilling these requirements at the pace imposed by the market may degrade software construction quality and insert so-called energy code smells: bad patterns in the source code of an app that indicate a deeper problem and adversely affect power consumption. This work proposes a developer-oriented framework for identifying and fixing patterns via analyzing different application code flavors in a user-driven test scenario. A special app was designed in order to validate the Android implementation of the proposed methodology. The study results have shown significant improvement regarding energy efficiency after correcting one or more energy code smells, with a 4 to 30 percent decrease in battery drain. Additionally, the power consumption signature term is defined in the context of mobile applications. This paper presents a developer-oriented framework for assessing power consumption in mobile applications. Our key contributions include identifying significant energy code smells, demonstrating their impact on power consumption, and providing a toolset for developers to improve energy efficiency. [ABSTRACT FROM AUTHOR]

Motivation The Peter Clark (PC) algorithm is a popular causal discovery method to learn causal graphs in a data-driven way. Until recently, existing PC algorithm implementations in R had important limitations regarding missing values, temporal structure or mixed measurement scales (categorical/continuous), which are all common features of cohort data. The new R packages presented here, micd and tpc , fill these gaps. Implementation micd and tpc packages are R packages. General features The micd package provides add-on functionality for dealing with missing values to the existing pcalg R package, including methods for multiple imputations relying on the Missing At Random assumption. Also, micd allows for mixed measurement scales assuming conditional Gaussianity. The tpc package efficiently exploits temporal information in a way that results in a more informative output that is less prone to statistical errors. Availability The tpc and micd packages are freely available on the Comprehensive R Archive Network (CRAN). Their source code is also available on GitHub (https://github.com/bips-hb/micd ; https://github.com/bips-hb/tpc). [ABSTRACT FROM AUTHOR]

Aims and Objectives: (I) To identify the opinion and practices of nursing professionals regarding the presence of family members during invasive procedures in hospitalised children; (II) to determine the knowledge of nursing professionals about the patient‐and family‐centred care model. Background: Family presence in invasive procedures benefits the patient and their relatives, but varied attitudes exist among healthcare personnel, with some being favourable and others unfavourable toward family presence. Design: Observational, descriptive, cross‐sectional study. Methods: Study population: Nurses from paediatric critical care services, emergency services, hospital wards, day hospitals and outpatient clinics at a Catalan tertiary hospital who participated voluntarily between September 2021 and July 2022. Data collection instrument: A questionnaire prepared by the researchers, based on the literature and reviewed by experts. REDCap link with access to the questionnaire was sent out to potential respondents through the institutional email. Bivariate analysis was performed with the R 4.2 program. The study was approved by the hospital's Clinical Research Committee and participants gave informed consent before responding to the questionnaire. Results: A total of 172 nurses participated, and 155 valid responses were obtained. All respondents consider the family as a key element in paediatric care and report inviting family members to participate in the care given to their child. However, 12.0% of nurses do not invite the family to be present in invasive procedures. Almost all respondents note the need for training to acquire communication skills and improve the management of emotions. Conclusions: The results show a favourable opinion towards the presence of family members and highlight the need to train nurses to develop communication skills. Relevance to clinical practice: The data provided can favour the design of measures to improve and promote the presence of parents during invasive procedures, reinforcing the patient‐and family‐centred care model and improving the quality of care provided. One example is the creation of family care protocols where the inclusion of parents and the roles of each individual involved in the care process appears. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in synthetic data for several computer vision applications, such as automotive, detection and tracking, surveillance, medical image analysis and robotics. Early use of synthetic data was aimed at performing controlled experiments under the analysis by synthesis approach. Currently, synthetic data are mainly used for training computer vision models, especially deep learning ones, to address well-known issues of real data, such as manual annotation effort, data imbalance and bias, and privacy-related restrictions. In this work, we survey the use of synthetic training data focusing on applications related to video surveillance, whose relevance has rapidly increased in the past few years due to their connection to security: crowd counting, object and pedestrian detection and tracking, behaviour analysis, person re-identification and face recognition. Synthetic training data are even more interesting in this kind of application, to address further, specific issues arising, e.g., from typically unconstrained image or video acquisition conditions and cross-scene application scenarios. We categorise and discuss the existing methods for creating synthetic data, analyse the synthetic data sets proposed in the literature for each of the considered applications, and provide an overview of their effectiveness as training data. We finally discuss whether and to what extent the existing synthetic data sets mitigate the issues of real data, highlight existing open issues, and suggest future research directions in this field. [ABSTRACT FROM AUTHOR]

Transparency in teamwork and across team members' status is one of the main challenges in remote work, and using online collaborative whiteboards (OCW) is a potential solution for more transparent teamwork. We explore the experience of transparency of three design teams who used an OCW called Miro in remote work during the COVID-19 pandemic. Our aim was to gain in-depth understanding on what constitutes transparency in this teamwork context. We conducted a qualitative interview study with 11 participants who are user experience and service designers, and who actively use Miro in their daily work. We adopted sociomateriality as our research lens and thematically analysed the data, finding that transparency at work is a dynamic experience ranging between positive and negative. Rather than being merely users' or organisations' choices or the result of the tool affordances, the experience of transparency at work is an outcome of sociomaterial entanglements between the user, tool, and organisation. Furthermore, the occupational and organisational factors not only affect the experience of transparency, but they also actively constitute it. [ABSTRACT FROM AUTHOR]

Purpose: This study explores the feasibility, acceptability, and treatment outcomes of Empower@Home, a digital cognitive behavioral therapy intervention for geriatric depression. Method: Participants with depressive symptoms (PHQ-9 ≥ 5) underwent a nine-session remote intervention supported by a lay coach (N = 103). Results: Most participants (91%) completed all nine sessions (mean = 8.5). According to the Treatment Evaluation Inventory, participant attitudes toward the program were largely positive. A medium effect in depression reduction was observed following the intervention (Cohen's d = 0.75) and at a 10-week follow-up (Cohen's d = 0.60). This reduction was large (Cohen's d = 1.31 at posttest and Cohen's d = 1.18 at 10-week follow-up) among those who presented with moderate depression at baseline (PHQ-9 ≥ 10). Significant improvements were also reported in anxiety, social isolation, loneliness, and behavioral activation. Discussion: Empower@Home is a promising, acceptable digital mental health intervention for treating depression in older adults. [ABSTRACT FROM AUTHOR]

The Eleveld pharmacokinetic–pharmacodynamic model for propofol predicts bispectral index (BIS) processed electroencephalogram values from estimated effect-site concentrations. We investigated agreement between measured and predicted BIS values during total intravenous anaesthesia (TIVA). Forty participants undergoing lower limb surgery received TIVA using remifentanil target-controlled infusions and propofol by manually controlled, target-guided infusions based upon the Eleveld model and directed by two pharmacokinetic computer simulation applications: PKPD Tools and StelSim. We evaluated the predictive performance of the Eleveld model by calculating median prediction errors (BIS units) and by Bland–Altman analyses. We also performed |Bland-Altman analysis of supplementary data provided by the authors of the Eleveld model. Whereas median prediction errors were small (MDPE –1.9, MDAPE 10), the ranges were wide (–18.5 to 24.3 and 1.7 to 24.3). The proportion of MDAPE >10 BIS units was 47.8%. Bland–Altman analysis showed a small mean bias (–0.52 BIS units) with wide limits of agreement (–27.7 to 26.2). Each participant's limits of agreement did not meet the requirements for declaring interchangeability between the two measurements. The measurement differences depended on the BIS values, as indicated by the positive slopes of the differences vs BIS values. Bland–Altman analysis of the Eleveld model supplementary data revealed similar results. BIS predictions by the Eleveld model should be interpreted with caution. In spite of the acceptable MDPE and MDAPE, there are unacceptable degrees of both within-subject and between-subject variation during propofol target-controlled infusions. This limits the use of adjusting targeted concentrations to achieve desired simulated BIS values with confidence. [ABSTRACT FROM AUTHOR]