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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]

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]

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]

Model performance has been significantly enhanced by channel attention. The average pooling procedure creates skewness, lowering the performance of the network architecture. In the channel attention approach, average pooling is used to collect feature information to provide representative values. By leveraging the central limit theorem, we hypothesize that the strip-shaped average pooling operation will generate a one-dimensional tensor by considering the spatial position information of the feature map. The resulting tensor, obtained through average pooling, serves as the representative value for the features, mitigating skewness during the process. By incorporating the concept of the central limit theorem into the channel attention operation process, this study introduces a novel attention mechanism known as the "Central Attention Mechanism (CAM)." Instead of directly using average pooling to generate channel representative values, the central attention approach employs star-stripe average pooling to normalize multiple feature representative values into a single representative value. In this way, strip-shaped average pooling can be utilized to collect data and generate a one-dimensional tensor, while star-stripe average pooling can provide feature representative values based on different spatial directions. To generate channel attention for the complementary input features, the activation of the feature representation value is performed for each channel. Our attention approach is flexible and can be seamlessly incorporated into various traditional network structures. Through rigorous testing, we demonstrate the effectiveness of our attention strategy, which can be applied to a wide range of computer vision applications and outperforms previous attention techniques. [ABSTRACT FROM AUTHOR]

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The paper analyzes how to improve the teaching of Operational Research, focusing on distance learning courses where professors and students are separated through space and time. This case study was done in a public Industrial Engineering undergraduate course, and the work structure is divided into three main parts: an exploratory‐descriptive documentary analysis, application of free software, and evaluation of learning. The authors showed the feasibility of using the SageMath tool in the teaching‐learning process. The study revealed the importance of developing alternative solutions to educational realities marked by economic and financial constraints, where structure teaching with free software is a ruling factor once Engineering education is not a similar global event everywhere. Distance learning is a phenomenon that has been growing over the last 20 years in Brazil, but this was one of the first experiences with the use of SageMath. So, it can serve as a reference for countries with socioeconomic conditions similar to Brazil. Also, this case study can help other professors to enhance their teaching in a distance learning context even in scarcity scenarios of educational resources. The software implementation would be justified in part of the groups studied. [ABSTRACT FROM AUTHOR]

Thin elastic ribbons represent a class of intermediary objects lying in-between thin elastic plates and thin elastic rods. Although the two latter families of thin structures have received much interest from the Computer Graphics community over the last decades, ribbons have seldom been considered and modelled numerically so far, in spite of a growing number of applications in Computer Design. In this article, starting from the reduced developable ribbon models [Sadowsky 1929; Wunderlich 1962] recently popularised in Soft Matter Physics, we propose a both accurate and efficient algorithm for computing the statics of thin elastic ribbons. Inspired by the super-clothoid model for thin elastic rods, our method relies on compact ribbon elements whose normal curvature varies linearly with respect to arc length s, while their geodesic torsion is quadratic in s. In contrast, however, for the sake of efficiency, our algorithm avoids building a fully reduced kinematic chain and instead treats each element independently, gluing them only at the final solving stage through well-chosen bilateral constraints. Thanks to this mixed variational strategy, which yields a banded Hessian, our algorithm recovers the linear complexity of low-order models while preserving the high-order convergence of curvature-based models. As a result, our approach is scalable to a large number of elements, and suitable for various boundary conditions and unilateral contact constraints, making it possible to handle challenging scenarios such as confined buckling experiments or Möbius bands with contact. Remarkably, our mixed algorithm proves an order of magnitude faster compared to Discrete Elastic Ribbon models of the literature while achieving, in a few seconds only, high accuracy levels that remain out of reach for such low-order models. Additionally, our numerical model can incorporate various ribbon energies, including the RibExt model for quasi-developable ribbons recently introduced in Physics [Audoly and Neukirch 2021], which allows to transition smoothly between a rectangular Kirchhoff rod and a (developable) Sadowsky ribbon. Our numerical scheme is carefully validated against demanding experiments of the Physics literature, which demonstrates its accuracy, efficiency, robustness, and versatility. Our Merci code is publicly available at https://gitlab.inria.fr/elan-public-code/merci for the sake of reproducibility and future benchmarking. [ABSTRACT FROM AUTHOR]

Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the self-attention mechanism. Our feature-sampling structure integrates multiple attention mechanisms and bidirectional feature aggregation to enhance the discriminative power of the detection model. Specifically, we introduce the multi-head attention for multi-scale feature fusion module to capture multi-scale contextual information effectively. Moreover, we incorporate the bidirectional aggregation mechanism to facilitate information exchange between different layers of the detection network. Experimental results on a benchmark dataset (VLD-45 dataset) demonstrate that our proposed method outperforms baseline models in terms of both detection accuracy and efficiency. Our experimental evaluation using the VLD-45 dataset achieves a state-of-the-art result of 90.3% mAP. Our method has also improved AP by 10% for difficult samples, such as HAVAL and LAND ROVER. Our method provides a new detection framework for small-size objects, with potential applications in various fields. [ABSTRACT FROM AUTHOR]

Multi-focus image fusion has wide applications in the field of computer vision. This research work presents a two-fold multi-focus image fusion framework in spatial domain. Firstly, the salient features of the focused regions in multi-focus images are detected using Gaussian curvature filter (GCF) and range filtering. The initial focus detection map is then refined using morphological filters. Experiments have shown that the proposed method is fast and robust when compared with latest multi-focus fusion schemes. [ABSTRACT FROM AUTHOR]

Objectives. This study aimed to compare postural assessment and postural awareness using the artificial intelligence posture evaluation and correction system (APECS) in individuals receiving posture training. Methods. Participants’ physical characteristics were recorded. The participants’ posture was evaluated with APECS and the New York posture rating chart test (NYPR), and their body and postural awareness was evaluated with the body awareness questionnaire (BAQ), postural awareness form (PAF) and postural habits and awareness scale (PHAS). Results. In the study, results of the PAF (p = 0.000), BAQ (p = 0.013) and PHAS (p = 0.033) were found to be different between the groups. While the groups were similar in the PHAS sub-dimension of postural habit (p = 0.331), there was a significant difference between the groups in postural awareness (p = 0.04). NYPR results of the participants in the group receiving posture training were similar to those in the group not receiving posture training (p = 0.45). Conclusion. Postural deviations measured by digital posture assessment in individuals receiving posture training were significantly different in the group receiving posture training. Postural awareness and body awareness were better in individuals who received posture training than in individuals who did not receive posture training. [ABSTRACT FROM AUTHOR]

The rapid uprising technologies of smartphone applications and software introduced a new era for analytical detection techniques. It has transformed bench-top laboratory methods into simpler ones depending on cost-effective, portable, and widely accessible devices. In this work, two high performance thin layer chromatographic (HPTLC) methods were developed based on smartphone's camera detection and either ImageJ desktop software or Color-Picker smartphone's application as alternative techniques to conventional densitometric detection. A mixture of Naltrexone hydrochloride (NAL) and Bupropion hydrochloride (BUP) was chromatographed on HPTLC- plates using ethyl acetate, methanol, acetone, and glacial acetic acid (3:6:1:0.5, by volume) as a developing system. The developed plates were scanned at 203 nm for the densitometric analysis, then visualized by modified Dragendorff's reagent and shot by a smartphone's camera. The captured images were uploaded to either ImageJ software or Color-Picker application to detect the separated spots. The results derived from the three detection methods were compared over the concentration range of 0.4–24 & 0.6–18 µg/band for the densitometric method, 0.4–24 & 2–24 µg/band for ImageJ built method and 0.8–20 & 5–20 µg/band for Color Picker built method for NAL and BUP, respectively. The methods were found to be appropriate for assaying both active drug substances in pure forms and combined in marketed pharmaceutical formulations. The excellent sustainability of densitometric and ImageJ-based methods enabled also the assessment of their dosage form content uniformity. The greenness and sustainability of the methods were assessed by three metric tools, namely Green Analytical Procedure Index (GAPI), Analytical GREEnness Metric Approach (AGREE), and White Analytical Chemistry (WAC). The assessments results confirmed the sustainability and superiority of the proposed methods in terms of sample treatment, waste mount, energy consumption, cost, and number of analyzed samples per an hour. [ABSTRACT FROM AUTHOR]

While interactive simulations have been mostly limited to Computer Graphics applications, new generations of Graphics Processing Units (GPUs) allow the realization of industrial-grade interactive 3D physics simulations. By combining an immersed boundary method with efficient GPU-based MINRES and CG solvers using a GPU-based geometric multigrid preconditioner, we demonstrate a fast industrial 3D computational mechanics solver. The various implementation aspects - specifically how they differ from similar concepts used in the Computer Graphics community - are discussed in detail. The proposed concept opens up new classes of industrial simulation applications allowing a democratization beyond today's expert users, from designer centric simulation to operational and service decisions based on 3D simulations. To support this, we provide various benchmark cases including a real-world study of a simulation-based service decision for a damaged gear-box mount. [ABSTRACT FROM AUTHOR]

Envisioning trajectories towards sustainability encompasses enacting significant changes in multiple spheres (i.e., infrastructure, policy, practices, behaviors). These changes unfold within the intricate landscapes of wicked problems, where diverse perspectives and potential solutions intersect and often clash. Advancing more equitable and sustainable trajectories demands recognition of and collaboration with diverse voices to uncover meaningful synergies among groups striving to catalyze substantial change. Projects of this nature necessitate the exploration of varied tools and methodologies to elicit, convey, and integrate ideas effectively. Creating spaces for reflexivity is essential for catalyzing more meaningful impact as individuals engage in discussions aimed at sharing and questioning the coherence of their projects while forging synergies, identifying common objectives, and planning long-term outcomes. We present the initial phase of an endeavor in which we developed a software that elicits causal networks based on mapping relations between projects' actions and outcomes. To illustrate our approach, we describe the results of using this software within collaborative workshops with groups spearheading projects initiated by a government entity in Mexico City. By adapting elements of the Theory of Change model, this software transcends the dominant linear project logic by guiding participants in designing causation networks that unveil how different projects can articulate to identify potential common elements and find new possibilities for coordination among initiatives. We discuss the potential of such software application as a dynamic tool to guide and promote reflection and coherence when crafting projects that aim to more meaningfully address sustainability problems. [ABSTRACT FROM AUTHOR]

Multiple object tracking (MOT), as a typical application scenario of computer vision, has attracted significant attention from both academic and industrial communities. With its rapid development, MOT has becomes an hot topic. However, maintaining robust MOT in complex scenarios still faces significant challenges, such as irregular motion patterns, similar appearances, and frequent occlusions. Based on an extensive investigation into the state-of-the-art MOT, this survey has made the following efforts: 1) listing down preceding MOT approaches and current classifications; 2) surveying the MOT metrics and benchmark databases; 3) evaluating the MOT approaches frequently employed; 4) discussing the main challenges for MOT; and 5) putting forward potential directions for the development of future MOT approaches. By doing so, it strives to provide a systematic and comprehensive overview of existing MOT methods from SDE to TBA perspectives, thereby promoting further research into this emerging and important field. [ABSTRACT FROM AUTHOR]

Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites. Using laparoscopic cholecystectomy and semantic segmentation models for predicting safe/dangerous ("Go"/"No-Go") zones of dissection as an example use case, this study aimed to develop and test the performance of a novel data pipeline linked to a web-platform that enables real-time deployment from any edge device. To test this infrastructure and demonstrate its scalability and generalizability, lightweight U-Net and SegFormer models were trained on annotated frames from a large and diverse multicenter dataset from 136 institutions, and then tested on a separate prospectively collected dataset. A web-platform was created to enable real-time inference on any surgical video stream, and performance was tested on and optimized for a range of network speeds. The U-Net and SegFormer models respectively achieved mean Dice scores of 57% and 60%, precision 45% and 53%, and recall 82% and 75% for predicting the Go zone, and mean Dice scores of 76% and 76%, precision 68% and 68%, and recall 92% and 92% for predicting the No-Go zone. After optimization of the client-server interaction over the network, we deliver a prediction stream of at least 60 fps and with a maximum round-trip delay of 70 ms for speeds above 8 Mbps. Clinical deployment of machine learning models for surgical guidance is feasible and cost-effective using a generalizable, scalable and equipment-agnostic framework that lacks dependency on hardware with high computing performance or ultra-fast internet connection speed. [ABSTRACT FROM AUTHOR]

Once upon a time the statistics of quantal release were fashionable: "n" available vesicles (fusion sites), each with probability "p" of releasing a quantum. The story was not so simple, a nice paradigm to be abandoned. Biophysicists, experimenting with "black films," explained the astonishing rapidity of spike- induced release: calcium can trigger the fusion of lipidic vesicles with a lipid bilayer, by masking the negative charges of the membranes. The idea passed away, buried by the discovery of NSF, SNAPs, SNARE proteins and synaptotagmin, Munc, RIM, complexin. Electrophysiology used to be a field for few adepts. Then came patch clamp, and multielectrode arrays and everybody became electrophysiologists. Now, optogenetics have blossomed, and the whole field has changed again. Nice surprise for me, when Alvarez de Toledo demonstrated that release of transmitters could occur through the transient opening of a pore between the vesicle and the plasma-membrane, no collapse of the vesicle in the membrane needed: my mentor Bruno Ceccarelli had cherished this idea ("kiss and run") and tried to prove it for 20 years. The most impressive developments have probably regarded IT, computers and all their applications; machine learning, AI, and the truly spectacular innovations in brain imaging, especially functional ones, have transformed cognitive neurosciences into a new extraordinarily prolific field, and certainly let us imagine that we may finally understand what is going on in our brains. Cellular neuroscience, on the other hand, though the large public has been much less aware of the incredible amount of information the scientific community has acquired on the cellular aspects of neuronal function, may indeed help us to eventually understand the mechanistic detail of how the brain work. But this is no more in the past, this is the future. [ABSTRACT FROM AUTHOR]

A new computer application (app) that creates interactive checklists is described. NaturaList is a progressive web app that is simple to implement, requiring only minimal skills in information technology from the developer, while also providing the possibility of an abundance of filter/search tools and links to other resources. The data are supplied in a simple spreadsheet. Once the resulting checklist is established on a webserver, it can be easily modified and updated. After implementation by the developer, it is initially accessed by the user online (looking like a standard webpage), after which it can function offline as a mobile app on any device (laptop, tablet, smartphone, etc.). Two floristic examples (from Vanuatu and Niue) are used to illustrate some of the features of NaturaList, but the app could just as easily be applied to any group of organisms, or to folk taxonomies, and allows for multilingual content. [ABSTRACT FROM AUTHOR]

Background: Photon-counting computed tomography (PCCT) enables new ways of image reconstruction, e.g. material decomposition and creation of virtual non-contrast (VNC) series with higher resolution and lower radiation dose than standard computed tomography (CT). Clinical experiences of this are limited. Purpose: To compare true non-contrast (TNC) series with VNC series derived from non-enhanced (VNCu), arterial phase (VNCa) and portal venous phase (VNCv) in clinically approved PCCT. Material and Methods: A total of 45 clinical, tri-phasic abdominal CT scans from the PCCT Naetom Alpha, between February 2022 and November 2022, were retrospectively assessed. Placing a region of interest in six different locations in each VNC series – right liver parenchyma, left liver parenchyma, spleen, aorta, erector spinae muscle, and in the subcutaneous fat – absolute Hounsfield values (HU) and standard deviations (SD) were collected. Differences in HU (ΔHU) were compared and statistically analyzed. Results: Statistically significant differences between VNC and TNC were seen in all measurements, with the largest difference in the subcutaneous fat and the smallest difference in the erector spinae muscle. Only small differences were seen between VNCa and VNCv, where the largest differences were seen in the left and right liver lobes. Conclusion: VNC images from the first-generation clinically approved PCCT showed a significant difference between VNC and TNC images. The differences vary with the type of tissue. Only small differences were seen depending from which contrast phase the VNC was derived. [ABSTRACT FROM AUTHOR]

The Good Behavior Game (GBG) is an interdependent group-oriented contingency management system successfully used in school settings to promote positive student behaviors. As a classroom management intervention, there is a large body of evidence for it increasing desirable classroom behaviors and decreasing problem behaviors across a range of student populations. Recent studies have also demonstrated that a positive reinforcement-focused version of the GBG can be successfully implemented using a software application. The purpose of this article is to provide a brief overview of the GBG and its evidence base and describe the steps for implementing the GBG with a freely available software application for classroom management. [ABSTRACT FROM AUTHOR]