Accessible Platform Research Articles

Simulation of container discharge dynamics using modified cellular automata

In the field of physical logistics, efficient and precise control of material flow during the emptying of containers such as silos and wagons is crucial. This study introduces a not conventional cellular automaton model to simulate spillage from containers. The model is implemented using Excel macros, providing a user-friendly and accessible platform for logistics professionals. By modifying the traditional cellular automaton framework, the simulation can accurately represents the dynamics of material flow during container emptying processes. The proposed method allows for fine-tuned control over the spillage by integrating specific constraints and parameters, enhancing operational efficiency and reducing material loss. The versatility of the model makes it applicable to a wide range of logistical scenarios, demonstrating its potential as a valuable tool in physical logistics management.

AirNet: predictive machine learning model for air quality forecasting using web interface

Air is one of the most significant elements of the environment. The increasing global air pollution crisis poses an unavoidable threat to human health, environmental sustainability, ecosystems, and the earth's climate. Air pollution has been referred to as a silent killer due to its insidious nature. Its indirect impact on human health further underscores its dangerous effects. Early detection of air quality can potentially save millions of lives globally. A unique and transformative approach can harness the power of machine learning to combat air pollution. This research presents a manual and web-based automatic prediction system that provides real-time alerts on air quality status and can help prevent premature deaths, chronic diseases, and other health problems. Air pollutants, including carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM 2.5), are used in this study for feature analysis and extraction. The system utilizes publicly available data from 23,463 different cities worldwide. Data preprocessing was performed before feeding the data into the machine learning models for feature correlation and evaluation. The proposed research uses various machine learning models to predict air quality, including Random Forest (100%), Logistic Regression (79%), Decision Tree (100%), Support Vector Machine (93%), Linear SVC (98%), K-Nearest Neighbor (99%), and Multinomial Naïve Bayes (52%). A user-friendly Django-based web interface offers an accessible platform for users to monitor air quality in real-time, based on the two best-performing models: Random Forest and Decision Tree techniques.

The –WIND RISK project: nowcast and simulation of thunderstorm outflows

–WIND RISK is a research project dealing with measurement and modelling of wind fields within thunderstorm cumulonimbus clouds and outflows at the ground (i.e., downbursts and gust fronts). The general objective of the project is to advance the knowledge about the processes occurring inside thunderstorm clouds responsible for strong outflows, and the identification of the environmental (synoptic and meso-scale) conditions favourable to their development. The area under investigation is Northwestern Italy and the Ligurian Sea, which are among the most exposed regions to strong thunderstorms in Europe. Two measurement campaigns using Doppler weather radars and remote-sensing ground-based anemometry, and ad-hoc simulations using the WRF model are carried out to build a database of thunderstorm wind fields with special focus on downdraft and downburst characterization. All measurements and simulations are shared within the scientific community after the end of the project through datasets published on open access platforms.

Design and Development of a 5-DOF SCARA Robot Arm for Robotics Education in a STEM Laboratory

The development of a 5-degree-of-freedom (DOF) SCARA robot arm was successfully achieved for educational use within the CSL Laboratory at the School of Applied STEM, Universitas Prasetiya Mulya. The design utilizes cost-effective, locally sourced materials and an open-source control system based on Processing Java and Arduino C. These features make the SCARA robot arm an accessible tool for students to learn robotics, particularly in the areas of kinematics, control, and programming. Extensive testing of the robot’s inverse kinematics algorithm showed promising results, with average error rates of 1.20% for the Inner Arm, 4.21% for the Outer Arm, and 3.39% for the Z-axis. These low error rates highlight the robot’s precision in movement. This research not only met its objective of creating an accessible platform for teaching robotics but also demonstrated potential for future development in robotics education and industrial applications.

Amazonian amphibians: diversity, spatial distribution patterns, conservation and sampling deficits.

The Amazon biome is home to the largest tropical forest on the planet and has the greatest global biodiversity on Earth. Despite this, several less charismatic taxonomic groups, such as amphibians, lack comprehensive studies on their species richness and spatial distribution in the Amazon Region. In this study, we investigated: i) patterns of richness and endemism of Amazonian amphibians across geopolitical and biogeographic divisions, ii) similarities between different Amazonian bioregions, iii) temporal trends in amphibian sampling, iv) conservation status of amphibians according to assessments of the IUCN and v) the importance of diverse data sources in building a robust database of amphibian occurrences. We aggregated data from four different sources: publicly accessible platforms, peer-reviewed articles, grey literature and fieldwork inventories spanning 15 years (2007-2021), ultimately compiling 160,643 records of 947 species across 7,418 sampled sites. The greatest diversity of species was found in Peru, Brazil and Ecuador, with notable amphibian diversity and endemism in regions such as the western basins and the Tapajós River Basin in the central-southern Amazon. Geographical analysis of species diversity revealed four distinct groups defined by latitudinal (the Amazon River) and longitudinal (the Juruá, Madeira and Tapajós Rivers) gradients, with low species similarity (< 40%), particularly in the basins of north-western Amazonia. Amphibian sampling in the Amazon has intensified since the 1950s with the establishment of important research centres such as INPA and the GOELD Museum in the Brazilian Amazon. Approximately 18% of Amazonian amphibian species face extinction risk, according to IUCN assessments, highlighting the need for comprehensive data sources to understand and conserve species in this megadiverse region. Our findings suggest that river systems likely influence Amazonian amphibian species composition due to biogeographic history, emphasising the need for robust taxonomic and spatial databases. This study, therefore, contributes a valuable large-scale dataset for Amazonian amphibians, guiding future research and strategies for amphibian conservation.

XQTLatlas: a comprehensive resource for human cellular-resolution multi-omics genetic regulatory landscape.

Understanding how genetic variants influence molecular phenotypes in different cellular contexts is crucial for elucidating the molecular and cellular mechanisms behind complex traits, which in turn has spurred significant advances in research into molecular quantitative trait locus (xQTL) at the cellular level. With the rapid proliferation of data, there is a critical need for a comprehensive and accessible platform to integrate this information. To meet this need, we developed xQTLatlas (http://www.hitxqtl.org.cn/), a database that provides a multi-omics genetic regulatory landscape at cellular resolution. xQTLatlas compiles xQTL summary statistics from 151 cell types and 339 cell states across 55 human tissues. It organizes these data into 20 xQTL types, based on four distinct discovery strategies, and spans 13 molecular phenotypes. Each entry in xQTLatlas is meticulously annotated with comprehensive metadata, including the origin of the tissue, cell type, cell state and the QTL discovery strategies utilized. Additionally, xQTLatlas features multiscale data exploration tools and a suite of interactive visualizations, facilitating in-depth analysis of cell-level xQTL. xQTLatlas provides a valuable resource for deepening our understanding of the impact of functional variants on molecular phenotypes in different cellular environments, thereby facilitating extensive research efforts.

Web-Based DICOM Viewers: A Survey and a Performance Classification.

The standard for managing image data in healthcare is the DICOM (Digital Imaging and Communications in Medicine) protocol. DICOM web-viewers provide flexible and accessible platforms for their users to view and analyze DICOM images remotely. This article presents a comprehensive evaluation of various web-based DICOM viewers, emphasizing their performance in different rendering scenarios, browsers, and operating systems. The study includes a total of 16 web-based viewers, of which 12 were surveyed, and 7 were compared performance-wise based on the availability of an online demo. The criteria for examination include accessibility features, such as available information or requirements for usage, interface features, such as loading capabilities or cloud storage, two-dimensional (2D) viewing features, such as the ability to perform measurements or alter the viewing window, and three-dimensional (3D) viewing features, such as volume rendering or secondary reconstruction. Only 4 of the viewers allow for the viewing of local DICOM files in 3D (other than MPR(Multiplanar reconstruction)). Premium software offers a large amount of features with overall good performance. One of the free alternatives demonstrated the best efficiency in both 2D and 3D rendering but faces challenges with missing 3D rendering features in its interface, which is still in development. Other free options exhibited slower performance, especially in 2D rendering but have more ready-to-use features on their web app. The evaluation also underscores the importance of browser choice, with some browsers performing much better than the competition, and highlights the significance of hardware when dealing with rendering tasks.

Students' Perception On Duolingo in Enhancing Vocabulary Mastery

The advent of technology has ushered in a plethora of engaging and innovative online learning tools for English, one of the them is Duolingo app standing out as an accessible and effective platform for language acquisition, particularly in English. This study aims to elucidate and observe students' perspectives on utilizing Duolingo software for vocabulary mastery at IAIN Lhokseumawe. Employing qualitative analysis techniques, the research employed a combination of tests, questionnaires, and interviews to gather data. The non-probability sampling method was utilized, with 20 students from the fifth semester selected as the sample. The study revealed that a majority of students were motivated to enhance their English vocabulary through the Duolingo application. The app provided an easy-to-understand platform, sparking interest and enthusiasm among students for learning new words. Furthermore, Duolingo proved the fact in preventing boredom during learning sessions and fostering novel approaches to learning. The research findings suggest a positive and highly beneficial perception among students regarding the use of Duolingo for enhancing vocabulary while learning English.

Pooled nanoparticle screening using a chemical barcoding approach.

We report the development of a small molecule-based barcoding platform for pooled screening of nanoparticle delivery. Using aryl halide-based tags (halocodes), we achieve high-sensitivity detection via gas chromatography coupled with mass spectrometry or electron capture. This enables barcoding and tracking of nanoparticles with minimal halocode concentrations and without altering their physicochemical properties. To demonstrate the utility of our platform for pooled screening, we synthesized a halocoded library of polylactide-co-glycolide (PLGA) nanoparticles and quantified uptake in ovarian cancer cells in a pooled manner. Our findings correlate with conventional fluorescence-based assays. Additionally, we demonstrate the potential of halocodes for spatial mapping of nanoparticles using mass spectrometry imaging (MSI). Halocoding presents an accessible and modular nanoparticle screening platform capable of quantifying delivery of pooled nanocarrier libraries in a range of biological settings.

Short-term plasticity and context-dependent circuit function: Insights from retinal circuitry

Changes in synaptic strength across timescales are integral to algorithmic operations of neural circuits. However, pinpointing synaptic loci that undergo plasticity in intact brain circuits and delineating contributions of synaptic plasticity to circuit function remain challenging. The whole-mount retina preparation provides an accessible platform for measuring plasticity at specific synapses while monitoring circuit-level behaviors during visual processing ex vivo. In this review, we discuss insights gained from retina studies into the versatile roles of short-term synaptic plasticity in context-dependent circuit functions. Plasticity at single synapse level greatly expands the algorithms of common microcircuit motifs and contributes to diverse circuit-level behaviors such as gain modulation, selective gating, and stimulus-dependent excitatory/inhibitory balance. Examples in retinal circuitry offer unequivocal support that synaptic plasticity increases the computational capacity of hardwired neural circuitry.

Socioecologies in shaping migrants and refugee youths’ sexual and reproductive health and rights: a participatory action research study

ObjectiveThis study explores socioecological factors facilitating the sexual and reproductive health and rights (SRHR) experiences of migrant and refugee youth (MRY) in Greater Western Sydney, Australia. MRY may be at higher risk for poorer SRH outcomes due to cultural, linguistic, and systemic barriers.MethodsUsing participatory action research, 17 focus groups were conducted with 87 MRY aged 15–29 from diverse cultural backgrounds. Data were analysed thematically, using socioecological framework.ResultsKey facilitators of MRY's SRHR were identified at the microsystem and exosystem levels, including (1) Peer dynamics and support, with friends serving as trusted confidants and sources of advice; (2) Safety and contraceptive choices, highlighting the importance of access to contraception and STI prevention; and (3) Digital platforms for SRHR information access, with online resources filling knowledge gaps.ConclusionFindings suggest the need for SRHR interventions to leverage peer support networks, expand access to contraceptive options, and develop culturally appropriate digital resources for MRY. Further research is needed to identify and enhance facilitators across all socioecological levels to comprehensively support MRY's SRHR needs.

ForzaETH Race Stack—Scaled Autonomous Head‐to‐Head Racing on Fully Commercial Off‐the‐Shelf Hardware

ABSTRACTAutonomous racing in robotics combines high‐speed dynamics with the necessity for reliability and real‐time decision‐making. While such racing pushes software and hardware to their limits, many existing full‐system solutions necessitate complex, custom hardware and software, and usually focus on Time‐TrIals rather than full unrestricted Head‐to‐head racing, due to financial and safety constraints. This limits their reproducibility, making advancements and replication feasible mostly for well‐resourced laboratories with comprehensive expertise in mechanical, electrical, and robotics fields. Researchers interested in the autonomy domain but with only partial experience in one of these fields, need to spend significant time with familiarization and integration. The ForzaETH Race Stack addresses this gap by providing an autonomous racing software platform designed for F1TENTH, a 1:10 scaled Head‐to‐Head autonomous racing competition, which simplifies replication by using commercial off‐the‐shelf hardware. This approach enhances the competitive aspect of autonomous racing and provides an accessible platform for research and development in the field. The ForzaETH Race Stack is designed with modularity and operational ease of use in mind, allowing customization and adaptability to various environmental conditions, such as track friction and layout, which is exemplified by the various modularly implemented state estimation and control systems. Capable of handling both Time‐Trials and Head‐to‐Head racing, the stack has demonstrated its effectiveness, robustness, and adaptability in the field by winning the official F1TENTH international competition multiple times. Furthermore, the stack demonstrated its reliability and performance at unprecedented scales, up to over on tracks up to 150 m in length.

MolPlay: Democratizing Interactive Molecular Simulations and Analyses with a Portable, Turnkey Platform.

Computer-based tools for visualizing and manipulating molecular structures in real-time hold immense potential for accelerating research and improving education, but are only used to a limited extent. This paper explores the possibilities of these powerful techniques and presents a classification of common interactive modeling tasks, such as assembly, deformation, sampling of rare events, along with relevant use cases, especially for the study of membranes and membrane proteins. I introduce MolPlay─a platform that provides a ready-to-use software environment with a curated set of hands-on examples to democratize access to interactive molecular simulations and analyses (IMSA). These research tools allow researchers to visualize, manipulate, and study digital models of molecules in real-time and can be started directly from a small, portable storage device. The portable, self-contained nature of MolPlay enables versatile deployment scenarios ranging from outreach to teaching in computer laboratories to facilitating the distribution of research software. Initial tests at an international workshop have shown that MolPlay appeals to different target groups and lowers the access barriers to IMSA. By consolidating more than a decade of expertise into one accessible platform, MolPlay represents a significant step toward broader adoption of these underutilized but powerful computational techniques in education and research.

SCRATCH: A programmable, open-hardware, benchtop robot that automatically scratches cultured tissues to investigate cell migration, healing, and tissue sculpting.

Despite the widespread popularity of the ' scratch assay', where a pipette is dragged through cultured tissue to create an injury gap to study cell migration and healing, the manual nature of the assay carries significant drawbacks. So much of the process depends on individual manual technique, which can complicate quantification, reduce throughput, and limit the versatility and reproducibility of the approach. Here, we present a truly open-source, low-cost, accessible, and robotic scratching platform that addresses all of the core issues. Compatible with nearly all standard cell culture dishes and usable directly in a sterile culture hood, our robot makes highly reproducible scratches in a variety of complex cultured tissues with high throughput. Moreover, we demonstrate how scratching can be programmed to precisely remove areas of tissue to sculpt arbitrary tissue and wound shapes, as well as enable truly complex co-culture experiments. This system significantly improves the usefulness of the conventional scratch assay, and opens up new possibilities in complex tissue engineering and cell biological assays for realistic wound healing and migration research.

Impact of Learning Mediums: A Comparative Analysis of Telephone and Virtual Education on Women's Childbearing Attitudes

Background: Improving women's attitudes towards childbearing plays a key role in promoting childbearing policies. Objectives: The present study aimed to compare the effectiveness of two educational methods-telephone-based and virtual training-on women's attitudes towards childbearing. Methods: This interventional study was conducted on 174 married women aged 18-35, without children, living in Babol city. The participants were randomly divided into three groups of 58: Two intervention groups and one control group. The first group received telephone-based training, while the second group received virtual training via WhatsApp video calls, both on the topic of childbearing over a four-week period. Data collection tools included a demographic questionnaire and a fertility and childbearing attitude scale, completed before, immediately after, and three months post-intervention. Data was analyzed using SPSS version 22 with chi-square tests, analysis of variance, and a linear mixed model. Results: The mean attitude scores towards childbearing increased significantly in both the telephone training group (P = 0.008) and the virtual training group (P = 0.038) compared to the control group three months later. Although telephone-based training had a greater effect on attitude scores during the follow-up phase compared to virtual training, this difference was not statistically significant. Conclusions: Both educational methods had a lasting impact on women's attitudes towards childbearing. This underscores the value of using accessible and cost-effective platforms to support childbearing policies.

Impact of a social media-delivered distance learning program on mhGAP training among primary care providers in Jalisco, Mexico

BackgroundThe World Health Organization’s (WHO) Mental Health Gap Action Programme (mhGAP) aims to provide evidence-based guidelines for the management of mental, neurological, and substance use disorders in non-specialized healthcare settings. However, implementing these guidelines remains a challenge due to various factors, including limited training opportunities for primary care providers. This study con the effectiveness of a social media-delivered distance education program on the mhGAP intervention guide, to overcome barriers of technology access and digital literacy, providing a familiar and accessible platform for primary care providers in Jalisco.MethodsA quasi-experimental study with a pre-test/post-test design was conducted. Primary care providers from Jalisco were invited to participate in a distance education program on the mhGAP intervention guide. The program consisted of online modules, webinars, and discussion forums facilitated by mental health experts. Knowledge assessments were conducted before and after the intervention using a standardized questionnaire. Participant satisfaction and perceived utility were also evaluated through surveys and focus group discussions.ResultsA total of 1,096 primary care providers completed the program. The mean knowledge score significantly improved from 58.2% (SD = 12.8%) in the pre-test to 81.4% (SD = 9.6%) in the post-test (p < 0.001), with a large effect size (Cohen’s d = 2.04). Subgroup analyses revealed consistent knowledge gains across different demographic and professional characteristics. Participant satisfaction was high, with 92% rating the program’s overall quality as “good” or “excellent.” Qualitative findings highlighted the benefits of accessibility, flexibility, interactivity, and practical applicability of the distance education approach.ConclusionsThe social media-delivered distance education program on the mhGAP intervention guide effectively improved the knowledge of primary care providers in Jalisco, Mexico. Participants reported high levels of satisfaction and perceived utility. This study demonstrates the potential of distance education strategies to disseminate evidence-based guidelines and enhance mental health service delivery in primary care settings, particularly in resource-limited areas.

Poetry unveiled: Multimodality and aesthetic responses as a fresh approach to teaching and reading verse

AbstractIn secondary English classrooms, poetry is often a text that is least liked because it is viewed as being “inaccessible,” reserved for the elite, and/or too abstract. Part of the reason for this also lies in the traditional, colonial structures of introducing poetry such as relying on canonical texts and close reading analysis. Yet, outside of the classroom poetry is used in more accessible and engaging manners. With the advancements of technology, there also includes multimodal ways in which to read and write poetry that could be much more interesting for both educators and youth. This paper opens a discussion to consider multimodal and aesthetic responses to including poetry such as using digital apps like PhoneMe, a free accessible platform that allows users to post their written poems, record themselves reciting poems, and pin their poems directly on to an interactive digital map. The uniqueness of PhoneMe—a layered multimodal approach—can provide a more engaging way to teach and learn poetry.

Data Access Platform for Marine and Freshwater Applications

Data Access Platform for Marine and Freshwater Applications

Development of robotic arm control using Arduino controller

The advance of Arduino-based technology has spurred innovation in the realm of robotic arm control, offering a cost-effective and accessible platform for enthusiasts and professionals alike. This paper presents the development of robot arm control using an Arduino controller. The work involves the integration of Arduino microcontrollers and sensors to enable precise and dynamic control of a robotic arm. The proposed robot is controlled by 4 servo motors, the motors rotate left, right, front, and back. The paper discusses the challenges encountered during the development process and proposes solutions, paving the way for further advancements in this burgeoning field. With Arduino's widespread availability and affordability, the presented robotic arm control system holds promise for expanding the accessibility of robotics education and fostering innovation in automation technologies. This paper provides a glimpse into the promising synergy between Arduino and robotic arm control, highlighting the contributions and implications of this technology in shaping the future of automation.

An integrated dataset of near-surface Eulerian fields and Lagrangian trajectories from an ocean model

A dataset consisting of numerically simulated oceanic velocities and sea surface height changes, provided conjointly from Eulerian and Lagrangian points of view, is made available as cloud-optimized archives on a cloud storage platform for unrestricted access. The Eulerian component of the dataset comprises oceanic velocity components at 0 m and 15 m depth, as well as total and steric sea surface height changes, obtained at hourly time steps for one year, with an approximate horizontal resolution of 1/25 degree on an irregular global geographical spatial grid, from the HYbrid Coordinate Ocean Model. The Lagrangian component of the dataset comprises the trajectories of particles advected in the Eulerian velocity field of the model. The particles were advected forward and backward for 30 days from a regular 1/4 degree grid in order to achieve 60-day long trajectories at 0 m and 15 m depths, with start times separated by 30 days, in 11 releases. This integrated dataset may help to link Eulerian and Lagrangian observational perspectives.