Multi-platform Software Research Articles

Unlocking inclusive education: A quality assessment of software design in applications for children with autism

Digital technologies are an essential resource for maximizing education opportunities, yet the COVID-19 pandemic has exposed learning inequities, particularly among underrepresented groups such as children with autism. In this study, we have evaluated the quality of a distinctive dataset of multiplatform software applications, encompassing both assistive and mainstream software, first-hand acquired from special education professionals and families of children with autism. Through a heuristic evaluation based on a system indicators, and an in-depth analysis, we aimed to (1) assess the quality and effectiveness of assistive technologies in supporting the education of children with autism; (2) determine the adaptability of mainstream applications to the unique educational needs of children with autism; and (3) explore the features and constraints of applications targeting children with ASD, categorized according to the needs they cover. The resulting quality ranking, organized by cognitive domains, provides insights into the engagement and effectiveness of applications supporting the learning of children with autism. Furthermore, the findings delineating the functionalities and limitations of these applications contribute to the identification of necessary software engineering best practices. These practices align with user-centered design principles and drive the development of accessible software, thereby fostering high-quality inclusive education for children with autism.

PyFaceWipe: a new defacing tool for almost any MRI contrast.

Defacing research MRI brain scans is often a mandatory step. With current defacing software, there are issues with Windows compatibility and researcher doubt regarding the adequacy of preservation of brain voxels in non-T1w scans. To address this, we developed PyFaceWipe, a multiplatform software for multiple MRI contrasts, which was evaluated based on its anonymisation ability and effect on downstream processing. Multiple MRI brain scan contrasts from the OASIS-3 dataset were defaced with PyFaceWipe and PyDeface and manually assessed for brain voxel preservation, remnant facial features and effect on automated face detection. Original and PyFaceWipe-defaced data from locally acquired T1w structural scans underwent volumetry with FastSurfer and brain atlas generation with ANTS. 214 MRI scans of several contrasts from OASIS-3 were successfully processed with both PyFaceWipe and PyDeface. PyFaceWipe maintained complete brain voxel preservation in all tested contrasts except ASL (45%) and DWI (90%), and PyDeface in all tested contrasts except ASL (95%), BOLD (25%), DWI (40%) and T2* (25%). Manual review of PyFaceWipe showed no failures of facial feature removal. Pinna removal was less successful (6% of T1 scans showed residual complete pinna). PyDeface achieved 5.1% failure rate. Automated detection found no faces in PyFaceWipe-defaced scans, 19 faces in PyDeface scans compared with 78 from the 224 original scans. Brain atlas generation showed no significant difference between atlases created from original and defaced data in both young adulthood and late elderly cohorts. Structural volumetry dice scores were ≥ 0.98 for all structures except for grey matter which had 0.93. PyFaceWipe output was identical across the tested operating systems. PyFaceWipe is a promising multiplatform defacing tool, demonstrating excellent brain voxel preservation and competitive defacing in multiple MRI contrasts, performing favourably against PyDeface. ASL, BOLD, DWI and T2* scans did not produce recognisable 3D renders and hence should not require defacing. Structural volumetry dice scores (≥ 0.98) were higher than previously published FreeSurfer results, except for grey matter which were comparable. The effect is measurable and care should be exercised during studies. ANTS atlas creation showed no significant effect from PyFaceWipe defacing.

ProSeq4: A user-friendly multiplatform program for preparation and analysis of large-scale DNA polymorphism datasets.

Preparation of DNA polymorphism datasets for analysis is an important step in evolutionary genetic and molecular ecology studies. Ever-growing dataset sizes make this step time consuming, but few convenient software tools are available to facilitate processing of large-scale datasets including thousands of sequence alignments. Here I report "processor of sequences v4" (proSeq4)-a user-friendly multiplatform software for preparation and evolutionary genetic analyses of genome- or transcriptome-scale sequence polymorphism datasets. The program has an easy-to-use graphic user interface and is designed to process and analyse many thousands of datasets. It supports over two dozen file formats, includes a flexible sequence editor and various tools for data visualization, quality control and most commonly used evolutionary genetic analyses, such as NJ-phylogeny reconstruction, DNA polymorphism analyses and coalescent simulations. Command line tools (e.g. vcf2fasta) are also provided for easier integration into bioinformatic pipelines. Apart of molecular ecology and evolution research, proSeq4 may be useful for teaching, e.g. for visual illustration of different shapes of phylogenies generated with coalescent simulations in different scenarios. ProSeq4 source code and binaries for Windows, MacOS and Ubuntu are available from https://sourceforge.net/projects/proseq/.

PyHiM: a new open-source, multi-platform software package for spatial genomics based on multiplexed DNA-FISH imaging

Genome-wide ensemble sequencing methods improved our understanding of chromatin organization in eukaryotes but lack the ability to capture single-cell heterogeneity and spatial organization. To overcome these limitations, new imaging-based methods have emerged, giving rise to the field of spatial genomics. Here, we present pyHiM, a user-friendly python toolbox specifically designed for the analysis of multiplexed DNA-FISH data and the reconstruction of chromatin traces in individual cells. pyHiM employs a modular architecture, allowing independent execution of analysis steps and customization according to sample specificity and computing resources. pyHiM aims to facilitate the democratization and standardization of spatial genomics analysis.

MetaWin 3: open-source software for meta-analysis.

The rise of research synthesis and systematic reviews over the last 25years has been aided by a series of software packages providing simple and accessible GUI interfaces which are intuitively easy to use by novice analysts and users. Development of many of these packages has been abandoned over time due to a variety of factors, leaving a gap in the software infrastructure available for meta-analysis. To fulfill the continued demand for a GUI-based meta-analytic system, we have now released MetaWin 3 as free, open-source, multi-platform software. MetaWin3 is written in Python and developed from scratch relative to earlier versions. The codebase is available on Github, with pre-compiled executables for both Windows and macOS available from the MetaWin website. MetaWin includes standardized effect size calculations, exploratory and publication bias analyses, and allows for both simple and complex explanatory models of variation within a meta-analytic framework, including meta-regression, using traditional least-squares/moments estimation.

Prediction of thermal infrared radiation using an artificial neural network applied to the projection and design of processes in renewable energies

This work aims to predict thermal infrared radiation in geographical areas where the necessary measurement devices are not available, through the design of an artificial neural network (RNA). The RNA uses the following variables as input data: specific humidity, relative humidity, ambient temperature, wind speed, and atmospheric pressure, it is important to mention that the sample of space of time is from, (1990 - 2019), they are data from Mexico City, as it is a metropolis with an extensive air quality database, which are obtained from two online tools developed by the National Aeronautics and Space Administration (NASA). In addition, thermal infrared radiation data from NASA are included, to validate the prediction made by the algorithm. Matlab was used to implement RNA, a multiplatform software that offers an integrated development environment with its own programming language. It is recognized for its computational ability and is considered a suitable tool for this purpose.

Precise Prediction of Transitional Occlusion Using Reverse Engineering in Surgery-first Orthognathic Surgery Case

With surgery-first approach, the skeletal discrepancy is corrected with orthognathic surgery at the start of treatment, but the teeth remain in their original compensated positions. This change in treatment sequence means clinicians have to determine what the occlusion immediately after the surgery, which is called transitional occlusion, should look like during the planning process. This report describes the surgical and orthodontic planning for a surgery-first case that corrected a large Cl III malocclusion. Virtual planning was used to reverse engineer the transitional postsurgical occlusion from the final ideal position. A multiplatform software can be a powerful tool for analyzing problems in finishing a surgery-first case, predicting outcomes, and improving the accuracy in planning surgery-first approach.

ADOBE AFTER EFFECTS SOFTWARE VIDEO EDITING DAN MOTION GRAPHICS YANG POPULER

Adobe After Effects is a popular software for video editing and motion graphics that was developed by Adobe Inc and was first released in 1993 by CoSA. After Adobe Systems acquired CoSA in 1994, Adobe After Effects became a multi-platform software and has continued to evolve over the years with more advanced features. This software is widely used in Hollywood films and by many companies in the production of promotional videos, advertisements, and other videos. Adobe After Effects is used to create special effects in videos such as animation, compositing, and color grading. It offers up to 50 different standard effects to modify and animate objects, and can even use Expressions to produce more dynamic movement. The software has several functions in the field of multimedia, including creating animations and motion graphics, adding special effects, compositing, color grading, and integration with other software. The main purpose of Adobe After Effects is to help users create high-quality videos with complex and detailed special effects. It allows users to explore their creativity by using various tools and features available in the software. Adobe After Effects is very important in video production because it can produce very complex and detailed visual effects such as explosion effects, fire effects, and special effects in Hollywood films.

DRR: An open-source multi-platform package for the damped rank-reduction method and its applications in seismology

We present an open-source multi-platform package for the damped rank-reduction (DRR) method, that has been widely used in various problems in seismology. The DRR method is an optimally improved rank-reduction (RR) method by damping the singular values using an analytically derived threshold. The DRR method can separate the useful signal and unwanted noise in multi-channel seismic data better than the standard RR method, resulting in spatially more coherent seismic events without damaging the critical subtleties. There have been some advanced methodological improvements or ingenious implementations of the DRR method in the past decade, as well as dozens of new applications in different areas of seismology, most of which will be included in the presented package. The DRR package is composed of two parts: MATdrr and Pydrr, corresponding to the Matlab and Python versions, respectively. The current DRR package includes localized DRR, optimally damped rank-reduction (ODRR), off-the-grid reconstruction, de-aliased reconstruction, and many reproducible examples in different areas of seismology. The DRR package will be continuously developed to include more functionalities in the future.

Evaluation of a Telehealth-Based Pharmacist Led Chronic Care Management Program

Background: Clinical pharmacy services improve several patient chronic disease outcomes. This review evaluates a pharmacist-led chronic care management (CCM) program partnered with a health system for patient outcomes and sustainability. Methods: A mixed methods evaluation based on the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework was completed. Patient A1c and blood pressure readings were retrospectively collected from the electronic health record from August 2018-April 2022. Patients that completed >4 CCM visits with a diagnosis of diabetes and/or hypertension were included. Results: 557 patients enrolled, 53 had uncontrolled systolic blood pressure (SBP), SBP >130 mmHg. Average SBP at baseline was 141.0 mmHg and average SBP at 6 months was 130.2 mmHg, (P < .001). 76 patients had uncontrolled diabetes, A1c > 7%. Average A1c at baseline = 9.1% and average A1c at 6 months = 8.3%, (P < .001). 4464 CCM visits with 247 disease-state targeted patients were completed over 44-month with a 100% adoption rate across clinic locations. Implementation facilitators included patient medication cost concerns, disease burden, provider revenue generation, CCM dedicated software, streamlined call process, and remote EMR access. Implementation barriers included provider discomfort “selling the program,” potential patient costs, unclear need from patient, pharmacists not considered providers, pharmacist cost, multi-platform software, reprioritized stakeholder support, and lack of partner site diversification. Program maintenance showed revenue generation was $5925.31-$8879.89 from August 2021-May 2022 and profitability was $3385.61-$1614.23. Conclusion: This study provides lessons learned, strategies for implementation, and ideas for process efficiencies leading to maintenance of a telehealth pharmacist-led CCM service.

Intelligent Multi-Platform Software Practice of Accounting Teaching Innovation under the Background of High-Frequency Data Clustering

Intelligent Multi-Platform Software Practice of Accounting Teaching Innovation under the Background of High-Frequency Data Clustering

MosquIoT: A System Based on IoT and Machine Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae)

Millions of people around the world are infected with mosquito-borne diseases each year. One of the most dangerous species is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aedes aegypti</i> , the main vector of viruses such as dengue, yellow fever, chikungunya, and Zika, among others. Mosquito prevention and eradication campaigns are essential to avoid major public health consequences. In this respect, entomological surveillance is an important tool. At present, this traditional monitoring tool is executed manually and requires digital transformation to help authorities make better decisions, improve their planning efforts, speed up execution, and better manage available resources. Therefore, new technological tools based on proven techniques need to be designed and developed. However, such tools should also be cost-effective, autonomous, reliable, and easy to implement, and should be enabled by connectivity and multi-platform software applications. This paper presents the design, development, and testing of an innovative system named “MosquIoT”. It is based on traditional ovitraps with embedded Internet of Things (IoT) and Tiny Machine Learning (TinyML) technologies, which enable the detection and quantification of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ae</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aegypti eggs</i> . This innovative and promising solution may help dynamically understand the behavior of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ae</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aegypti</i> populations in cities, shifting from the current reactive entomological monitoring model to a proactive and predictive digital one.

POMONA: a multiplatform software for modeling seed physiology.

Seed physiology is related to functional and metabolic traits of the seed-seedling transition. In this sense, modeling the kinetics, uniformity and capacity of a seed sample plays a central role in designing strategies for trade, food, and environmental security. Thus, POMONA is presented as an easy-to-use multiplatform software designed to bring several logistic and linearized models into a single package, allowing for convenient and fast assessment of seed germination and or longevity, even if the data has a non-Normal distribution. POMONA is implemented in JavaScript using the Quasar framework and can run in the Microsoft Windows operating system, GNU/Linux, and Android-powered mobile hardware or on a web server as a service. The capabilities of POMONA are showcased through a series of examples with diaspores of corn and soybean, evidencing its robustness, accuracy, and performance. POMONA can be the first step for the creation of an automatic multiplatform that will benefit laboratory users, including those focused on image analysis.

Formative research, POL and the influence of the teacher-researcher in the production of science and technology: Integrative project cases

In a context of active and constructive teaching, the role of the teacher-researcher takes on crucial meaning, as a mediator in the formalization and promotion of research skills in university students, as demonstrated in this research project. From didactics and pedagogy, the Project Oriented Learning (AOP) strategy was used to articulate scientific theoretical knowledge with practice, which provides solutions to needs and problems of various kinds. In this sense, a compilation of integrative projects is presented as a training strategy within the framework of the training research of the Technological University of Jalisco in the periods 2020 and 2021, where the students integrated theoretical, conceptual and methodological knowledge, as well as own skills of their discipline – Maintenance Industrial area, Mechatronics Automation area and Information Technologies Multiplatform Software Development area, which help to strengthen technical, scientific, communicative, teamwork and cognitive skills, which materialized in the construction of technological prototypes and reports scientists where the processes and results achieved are made explicit as evidence of their investigative skills and, in turn, allowed a better evaluation of the academic performance of the students.

The g3mclass is a practical software for multiclass classification on biomarkers

The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification. This software automates the validated multiclass classifier applicable to single analyte tests and multiplexing assays. The g3mclass achieves automation using the original semi-constrained expectation–maximization (EM) algorithm that allows inference from the test, control, and query data that human experts cannot interpret. In this study, we used real-world clinical data and gene expression datasets (ERBB2, ESR1, PGR) to provide examples of how g3mclass may help overcome the problems of over-/underdiagnosis and equivocal results in diagnostic tests for breast cancer. We showed the g3mclass output’s accuracy, robustness, scalability, and interpretability. The user-friendly interface and free dissemination of this multi-platform software aim to ease its use by research laboratories, biomedical pharma, companion diagnostic developers, and healthcare regulators. Furthermore, the g3mclass automatic extracting information through probabilistic modeling is adaptable for blending with machine learning and artificial intelligence.

The Implementation of REST API in Multi-Platform Software Development for Food and Beverage Learning Application

This website-based food and beverage learning application is an educational application about food and drink. The problem was that information was more often accessed on smartphone devices, such as Android. The research method used was quantitative, so this research provides a concrete value for the optimal implementation of the REST API. The result obtained was that the website-based REST API application worked on multi-platform devices. With the implementation of this REST API, various devices can communicate with each other with the same source and data, so it does not cause multiple databases to store information.

Interactive and Fast Low-Light Image Enhancement Algo-rithm and Application

Abstract To obtain personalized outcomes for the low-light image enhancement, a novel interactive algorithm based on the well-designed Gamma Curve is proposed to enrich the enhancement techniques. Different from the previous works trying to enhance the image in solely brightness or naturalness by a specific designed deep network, the proposed method is capable of controlling the output results according to the user’s preferences by the same framework with different parameters. There would be three main advantages brought by the proposed network: 1) Interactivity, which allows to generate enhancements results according to users’ preferences in human-interactive manners; 2) Convenience, wherein the model only needs to train for once without using any reference images, and then can obtain results with different brightness during testing by adjusting the hyper-parameter. 3) Fastness, which results from the lightweight network and the excellent properties of the Gamma Curve to make the network operate in extraordinary high speed. Experiments demonstrate the superiority of our algorithm relative to the previous work. In addition, a multi-platform low-illumination enhancement software is explored to facilitate its application for the public.

Textile structures for limiting the effects of maritime and fluvial disasters

Water is essential for the lives of humans, animals and plants, while representing an indispensable resource for the economy, and its management goes beyond national borders. Water pollution with oil residues affects both surface water and groundwater; its consequences on the organoleptic properties of water, aquatic fauna and flora are particularly damaging for all ecosystems and their biodiversity. Mechanical recovery of the pollutant provides, according to the international norms, the limitation of the polluted surface and the concentration of the pollutant in order to recover and store the water-hydrocarbon mixture, with limits related to the agitation of the marine/fluvial environment and distance from the intervention base to the polluted area. The choice of blocking/storage systems must take into account several factors, such as type and amount of pollutant recovered, the flow of recovery units, area of use, hydro-weather conditions in the field, mode of transport and mode of location in terrain. This study presents the constructive solutions for the dams made of textile materials, used in case of disasters in the maritime and fluvial area. Post-processing of data, performed with the help of IBM multiplatform software suite, highlighted the variation intervals of the structural parameters for 7 composite materials differentiated by: location, mass, raw material, thickness and thermal resistance. These composites were the basis for computer-aided designs of 14 experimental models (EM), differentiated by the dimensions used, the materials of the floats, the skirt and the area of use (maritime from 4bf to 10 bf or fluvial).

SpectroGLY: A Low-Cost IoT-Based Ecosystem for the Detection of Glyphosate Residues in Waters

Glyphosate contamination in waters is becoming a major health problem that needs to be urgently addressed, as accidental spraying, drift or leakage of this highly water-soluble herbicide can impact aquatic ecosystems. Researchers are increasingly concerned about exposure to glyphosate and the risks its poses to human health, since it may cause substantial damage, even in small doses. The detection of glyphosate residues in waters is not a simple task, as it requires complex and expensive equipment and qualified personnel. New technological tools need to be designed and developed, based on proven, but also cost-efficient, agile and user-friendly, analytical techniques, which can be used in the field and in the lab, enabled by connectivity and multi-platform software applications. This paper presents the design, development and testing of an innovative low-cost VIS-NIR (Visible and Near-Infrared) spectrometer (called SpectroGLY), based on IoT (Internet of Things) technologies, which allows potential glyphosate contamination in waters to be detected. SpectroGLY combines the functional concept of a traditional lab spectrometer with the IoT technological concept, enabling the integration of several connectivity options for rural and urban settings and digital visualization and monitoring platforms (Mobile App and Dashboard Web). Thanks to its portability, it can be used in any context and provides results in 10 minutes. Additionally, it is unnecessary to transfer the sample to a laboratory (optimizing time, costs and the capacity for corrective actions by the authorities). In short, this paper proposes an innovative, low-cost, agile and highly promising solution to avoid potential intoxications that may occur due to ingestion of water contaminated by this herbicide.

Technical Note on the production of 3D anatomical models using an open access software from medical imaging

Introduction: 3D modelling and intra-operative guides are rapidly increasing in use. They have been shown in meta-analyses to increase surgical accuracy, reduce operative time and improve teaching(1, 2). Despite the growing availability of 3D printers, access to 3D models and intra-operative guides is limited by the cost and availability of software and engineers. Methods: This technical note shows how to use 3Dslicer, a free open source multiplatform software available to download from the internet. It can be used to create 3D models from medical imaging such as CT and MRI. Results: 3D objects can be manipulated digitally or printed using 3D printers. They can be used to aid teaching, surgical planning and patient demonstrations. Conclusions: 3Dslicer is an easy to use software which should be in the surgeon's armamentarium. Awareness of the software and this technical note will allow its ubiquitous use by surgeons. This can help in surgical teaching, appreciation of surgical planning and anatomy. It has the potential to reduce cost and production time compared with outsourcing to outside companies. This software has been already been shown to be used successfully in construction of in-house cutting guides for maxillofacial reconstruction (4). This will also create a next generation of surgeons who are familiar with software technology. Reducing the gap between surgeon and technology will help promote innovation and faster transition of technological advances into the operating room. Introduction: 3D modelling and intra-operative guides are rapidly increasing in use. They have been shown in meta-analyses to increase surgical accuracy, reduce operative time and improve teaching(1, 2). Despite the growing availability of 3D printers, access to 3D models and intra-operative guides is limited by the cost and availability of software and engineers. Methods: This technical note shows how to use 3Dslicer, a free open source multiplatform software available to download from the internet. It can be used to create 3D models from medical imaging such as CT and MRI. Results: 3D objects can be manipulated digitally or printed using 3D printers. They can be used to aid teaching, surgical planning and patient demonstrations. Conclusions: 3Dslicer is an easy to use software which should be in the surgeon's armamentarium. Awareness of the software and this technical note will allow its ubiquitous use by surgeons. This can help in surgical teaching, appreciation of surgical planning and anatomy. It has the potential to reduce cost and production time compared with outsourcing to outside companies. This software has been already been shown to be used successfully in construction of in-house cutting guides for maxillofacial reconstruction (4). This will also create a next generation of surgeons who are familiar with software technology. Reducing the gap between surgeon and technology will help promote innovation and faster transition of technological advances into the operating room.