Tools For Makers Research Articles

Making & Accessibility: A Systematic Literature Review on the Multilayered Dimensions of Accessible Making

In recent years, the making phenomenon has shown great potential in personal design and fabrication capabilities using modern fabrication tools (e.g., 3D printers, laser cutters, CNC machines), personal electronics (e.g., Arduino, Raspberry Pi, sensors), and related crafting techniques. With this trend of making, people have also engaged in developing a wide variety of assistive devices (e.g., prostheses, orthotics), adaptations (e.g., cane-hanger / cup-holder for wheelchairs), and support systems (e.g., tactile braille maps). While there are some successful projects, substantive gaps remain between current traction and overall potential in the field of making and accessibility. To better understand the relationship between people with disabilities and making and identify the types of ecosystems involved in these overlapping areas of research, we conducted a systematic literature review on design-related ACM conferences between January 2010 and December 2023. Our analysis highlights the concentration of research across diverse communities, adaptations for accessible making, and the utilization of maker tools for the development of assistive devices. We also highlight trending design, development, and evaluation methodologies adopted to support collaboration between stakeholders with mixed abilities. Based on the findings of this systematic literature review, we critically reflect on the gaps and provide recommendations for future researchers and practitioners in this growing field.

Engaging with information beyond vision: hands-on approaches to computational thinking for blind and visually impaired learners

Introduction: This project develops accessible maker tools and activities to foster computational thinking (CT) skills in blind and visually impaired (BVI) learners, while investigating the experiences of two key groups: (1) BVI learners and (2) librarians and maker professionals who design and deliver accessible CT programs. Methods: The pilot phase designed and delivered an accessible electronics and coding curriculum to three BVI youth in a two-day summer camp. Data was collected through two debrief focus groups—one with BVI learners and one with the maker professionals who served as instructors. Analysis: All interviews were recorded and transcribed. The research team used a grounded theory approach to analyse the interview data. Results: Both learners and instructors highlighted the benefits of tactile and multi-sensory learning tools, though challenges emerged with the text-based coding platform. Learners self-reported increased confidence, autonomy, and interest in CT skills. Instructors adapted their approaches with detailed verbal descriptions and modifications to tools and lesson plans. Understanding the diverse needs of BVI learners and providing personalized assistance was crucial. Conclusion: Tactile and physical approaches to computational thinking show promise for previously marginalized learners, though challenges remain. Future research will explore how emerging technologies, including AI, can further enhance accessibility for BVI learners.

Marine and atmospheric transport modeling supporting nuclear preparedness in Norway: Recent achievements and remaining challenges.

Marine and atmospheric transport modeling supporting nuclear preparedness in Norway: Recent achievements and remaining challenges.

Mapping trade opportunities in Central and South Asia: an exploration through trade indices and metrics

Purpose This study aims to explore the possibilities for regional (intra and inter) trade between Central Asia and South Asia (CASA) by examining several frameworks that impact the economic integration of these regions. Design/methodology/approach The study uses five indices, specifically the export diversification index, export survival rates, export sophistication, revealed comparative advantage and intra-industry trade index in South and Central Asia, from 2005 to 2021. Findings The findings show a twofold increase in the potential trade value between the two regions compared to the actual level. The intra-regional trade in Central Asia accounts for less than 5% of trade with all countries, whereas the intra-regional trade in South Asia accounts for 1.5%, and the inter-regional trade accounts for between 0.2 and 4% of total trade to all destinations. The intensity of trade measurements shows that inter-regional trade flows are modest because they make up a relatively small percentage of the entire trade volume, which includes all destinations, ranging from 0.2 to 4 %. Research limitations/implications These findings have a significant impact on the successful implementation of trading-related measures, initiatives and institutional mechanisms for encouraging improved trade between and within both regions. Practical implications Export diversification initiatives aim to provide favourable outcomes for all parties involved, focusing on expanding the range of goods and services exchanged. CASA countries with higher diversification have shown more favourable outcomes than those relying on a restricted range of products. The authors expect policies promoting a broader array of exports to boost market shares, while maintaining a narrow focus may hinder new export prospects. In addition, regional value chains and policy initiatives promoting trade and investment could lead to increased value additions to exports, technological transfers and job creation. Social implications The study emphasises the significance of trade-related policies, programmes and institutional mechanisms in promoting trade between CASA. It emphasises the need for policies that support investment and trade, adopt new requirements of the Agreement on Trade Facilitation and promote diversity and latency. Originality/value Based on economic analysis, the study offers practical insights and strategies for businesses in the CASA regions. It also provides analytical tools for academics and decision makers, as well as policy and programme recommendations for government agencies, development partners, researchers and individuals interested in trade dynamics in these regions.

Public Mass Shootings: Counterfactual Trend Analysis of the Federal Assault Weapons Ban.

Assault weapon and large-capacity magazine bans are potential tools for policy makers to prevent public mass shootings. However, the efficacy of these bans is a continual source of debate. In an earlier study, we estimated the impact of the Federal Assault Weapons Ban (FAWB) on the number of public mass shooting events in the United States. This study provides an updated assessment with 3 additional years of firearm surveillance data to characterize the longer-term effects. This study aims to estimate the impact of the FAWB on trends in public mass shootings from 1966 to 2022. We used linear regression to estimate the impact of the FAWB on the 4-year simple moving average of annual public mass shootings, defined by events with 4 or more deaths in 24 hours, not including the perpetrator. The study period spans 1966 to 2022. The model includes indicator variables for both the FAWB period (1995-2004) and the period after its removal (2005-2022). These indicators were interacted with a linear time trend. Estimates were controlled for the national homicide rate. After estimation, the model provided counterfactual estimates of public mass shootings if the FAWB was never imposed and if the FAWB remained in place. The overall upward trajectory in the number of public mass shootings substantially fell while the FAWB was in place. These trends are specific to events in which the perpetrator used an assault weapon or large-capacity magazine. Point estimates suggest the FAWB prevented up to 5 public mass shootings while the ban was active. A continuation of the FAWB and large-capacity magazine ban would have prevented up to 38 public mass shootings, but the CIs become wider as time moves further away from the period of the FAWB. The FAWB, which included a ban on large-capacity magazines, was associated with fewer public mass shooting events, fatalities, and nonfatal gun injuries. Gun control legislation is an important public health tool in the prevention of public mass shootings.

Construction and Application of a College English Blended Teaching System Based on Multi-Source Data Fusion

By using modern data analysis techniques, this study aims to construct an innovative university English teaching effectiveness evaluation model based on particle swarm algorithm and support vector machine. The model is designed to improve assessment accuracy and personalization. The research process includes the methodology of data collection, preprocessing, model construction and evaluation. The experimental results show that the model can more accurately assess students' English learning effectiveness and provide customized suggestions for personalized education. This research is important for improving the quality of university English education, promoting personalized learning, and providing support tools for educational decision makers.

Hybrid black-box classification for customer churn prediction with segmented interpretability analysis

Hybrid black-box classification for customer churn prediction with segmented interpretability analysis

The Use of Financial Tools in Small-Scale Irrigated Crops to Assess Socioeconomic Sustainability: A Case Study in Tocantins-Araguaia Basin, Brazil

To expand financial knowledge and promote the socioeconomic sustainability of agricultural production systems, this work applied financial instruments to small-scale irrigated crops, making use of agricultural projects from the Bom Sucesso/Santa Cruz Settlement, located in the Tocantins-Araguaia basin, Brazil. The economic viability indicators of two irrigated okra production systems (furrow and drip) were calculated using the following indicators: Net Present Value, Modified Internal Rate of Return, Cost–Benefit Index, Discounted Payback, and Average Cost of Production. And a risk assessment through sensitivity analysis, a tornado chart, and Monte Carlo simulation was performed in a simplified financial model. The economic viability results showed that both irrigated systems were economically viable, although the okra drip system had lower indicators and reduced financial resilience, with a 58% probability of success, while the furrow system’s was 89%. The crops’ financial return was mainly influenced by okra’s sale price and productivity, followed by harvesting and planting costs. The financial tools applied in this study demonstrated some key factors to assess socioeconomic sustainability, such as the necessity to increase producers’ organizational capacity and to achieve technical improvements, which could result in market power, competitive sale prices, lower input costs, and greater productivity. The applied instruments increased financial management and have the potential to the build economic and social strength in the area of small-scale irrigated crops, and they can be replicated using simplified tools for producers, companies, and policy makers.

Assessing Groundwater Resources Vulnerability with the New DRASTIC-LP: A Case Study in Chiang Mai Province, Thailand

Groundwater vulnerability has gained widespread attention, particularly in Chiang Mai Province, one of Thailand’s fastest-growing cities, which is experiencing rapid development in both economic and social sectors. The expansion of urban communities and the industrial, tourism, and agriculture sectors has resulted in the overutilization of available resources, notably water resources. This overuse, coupled with the adoption of modern technology to boost productivity and meet market demands, has led to an increased reliance on groundwater to supplement surface water sources, providing benefits across all sectors. However, the economic and social growth plays a pivotal role in shaping the diversity of land use, encompassing residential, commercial, industrial, and agricultural activities. These activities, in turn, directly contribute to environmental pollution, particularly in terms of the risk of groundwater contamination in Chiang Mai Province. This study aims to predict the future vulnerabilities of groundwater resources under an ensemble of climate change scenarios and changes in land-use patterns. Chiang Mai Province in northern Thailand is one of the fastest-growing cities and therefore is experiencing rapid urbanization, as well as land-use pattern changes, which was important for the case study. The new DRASTIC model, namely the DRASTIC-LP model, combined with GIS-based techniques and overlay techniques, was used to generate the map of groundwater resource vulnerabilities. A point pollution source (P)-related land-use pattern (L) that represents contamination impacts was considered an additional new DRASTIC parameter. The study’s findings reveal the high reliability and maximum effectiveness of the new DRASTIC-LP model in assessing groundwater vulnerability and contamination-risk areas under a climate change scenario (by MIROC-ESM-CHEM model under RCP.8.5 scenario) and land-use pattern changes (by CA_Markov Chian Model) for both the current year (2020) and the next 50-year period (2021–2070). Furthermore, the new DRASTIC-LP model is employed to trace the movement of pollutants from high- to very high-risk areas based on the groundwater vulnerability and contamination-risk maps. The results highlight that waste disposal dumping sites pose a more critical distribution and movement of pollutants when compared to industrial sites. Additionally, unconsolidated aquifers and cracked consolidated rock aquifers show a potentially higher occurrence of pollutant distribution and movement when compared to consolidated aquifers. Consequently, the study’s outcomes are applied to formulate guidelines for the management and control of groundwater resource contamination. These guidelines serve as valuable tools for decision makers, aiding in pollution prevention and the effective management of contamination risks in groundwater resources.

The Pernicious Predictability of State-Mandated Tests of Academic Achievement in the United States

The purpose of this study was to determine the predictiveness of community and family demographic variables related to the development of student academic background knowledge on the percentage of students who pass a state-mandated, commercially prepared, standardized Algebra 1 test in the state of New Jersey, USA. This explanatory, cross-sectional study utilized quantitative methods through hierarchical regression analysis. The results suggest that family demographic variables found in the United States Census data related to the development of student academic background knowledge predicted 75 percent of schools in which students achieved a passing score on a state standardized high school assessment of Algebra 1. We can conclude that construct-irrelevant variance, influenced in part by student background knowledge, can be used to predict standardized test results. The results call into question the use of standardized tests as tools for policy makers and educational leaders to accurately judge student learning or school quality.

Characteristics and Range of Reviews About Technologies for Aging in Place: Scoping Review of Reviews.

It is a contemporary and global challenge that the increasing number of older people requiring care will surpass the available caregivers. Solutions are needed to help older people maintain their health, prevent disability, and delay or avoid dependency on others. Technology can enable older people to age in place while maintaining their dignity and quality of life. Literature reviews on this topic have become important tools for researchers, practitioners, policy makers, and decision makers who need to navigate and access the extensive available evidence. Due to the large number and diversity of existing reviews, there is a need for a review of reviews that provides an overview of the range and characteristics of the evidence on technology for aging in place. This study aimed to explore the characteristics and the range of evidence on technologies for aging in place by conducting a scoping review of reviews and presenting an evidence map that researchers, policy makers, and practitioners may use to identify gaps and reviews of interest. The review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Literature searches were conducted in Web of Science, PubMed, and Scopus using a search string that consisted of the terms "older people" and "technology for ageing in place," with alternate terms using Boolean operators and truncation, adapted to the rules for each database. A total of 5447 studies were screened, with 344 studies included after full-text screening. The number of reviews on this topic has increased dramatically over time, and the literature is scattered across a variety of journals. Vocabularies and approaches used to describe technology, populations, and problems are highly heterogeneous. We have identified 3 principal ways that reviews have dealt with populations, 5 strategies that the reviews draw on to conceptualize technology, and 4 principal types of problems that they have dealt with. These may be understood as methods that can inform future reviews on this topic. The relationships among populations, technologies, and problems studied in the reviews are presented in an evidence map that includes pertinent gaps. Redundancies and unexploited synergies between bodies of evidence on technology for aging in place are highly likely. These results can be used to decrease this risk if they are used to inform the design of future reviews on this topic. There is a need for an examination of the current state of the art in knowledge on technology for aging in place in low- and middle-income countries, especially in Africa.

Welcoming Newcomers to Makerspaces

This article provides several self-guided, simple, creative activities to help learners gain familiarity and create with maker tools. Activities center around four categories: low technology, paper circuitry, Sphero robotics, and Cricut cutting machines. Learners use maker resources to build animals, construct LED bracelets or lanterns, code sirens, design stickers, and more. Although the intended audience for this article was undergraduate preservice teachers, most activities have also been completed by students in grades 3-8. Because materials differ by activity, they are presented in each activity.

Medications For Opioid Use Disorder Increased Among Louisiana Medicaid Enrollees During Policy Reforms, 2018-21.

Increasing access to medications for opioid use disorder (MOUD) is a key strategy in addressing the opioid crisis. To increase MOUD access, state governments have pursued a combination of increased funding for MOUD and requirements that providers offer treatment. Louisiana has pursued multiple strategies, including a requirement that residential treatment programs offer MOUD as part of their licensure. Using Louisiana Medicaid claims data for enrollees with diagnosed OUD from the period 2018-21, we analyzed trends in MOUD between enrollees treated in residential and nonresidential settings and across demographic subgroups, and we compared trends by MOUD type. MOUD use more than tripled from 2018 to 2021 among Louisiana Medicaid enrollees diagnosed with OUD. Most of the increase in MOUD was attributable to buprenorphine use. Methadone uptake also contributed to greater MOUD use but was almost exclusively used by enrollees treated in nonresidential settings, whereas naltrexone was consistently more common in residential treatment. By 2021, differences persisted across demographic groups: MOUD use was highest among enrollees who were White, were older, had comorbidities, and lived in a metropolitan area. Policies that promote MOUD in substance use treatment programs, particularly residential programs, are critical tools for policy makers confronting a complex and unprecedented national overdose crisis.

Managing incomplete general hesitant linguistic preference relations and their application

<p>Hesitant linguistic preference relations (HLPRs) are useful tools for decision makers (DMs) to express their qualitative judgements. However, the traditional HLPRs have one prominent drawback, which is to sort the linguistic values in a hesitant linguistic set. This will distort the DMs' initial judgements. In the present paper, a revised definition of HLPR, called general HLPR (GHLPR), was proposed. A characterization was explored for LPRs. Then, the characterization was extended to GHLPRs. Based on the characterization, the estimation of unknown entries in incomplete GHLPRs were carried out by two algorithms. The group decision-making problems with incomplete GHLPRs were settled by another algorithm. Finally, a case study was illustrated, and comparisons showed that our methods were more reasonable than the existent methods.</p>

Analysis of enterobiasis in the Czech Republic in 2018-2022.

Pinworm infection (known as enterobiasis or oxyuriasis) is one of the most common parasitic diseases globally and in the Czech Republic (CZ). The aim of this study is to analyse the available epidemiological data on the incidence of enterobiasis in the CZ from 2018-2022. A descriptive analysis was done of enterobiasis (ICD-10 code B80) data reported to the electronic Infectious Disease Information System in the CZ from 2018 to 2022. Data processing and analysis were conducted using MS Excel 2016. Univariate and multivariate logistic regression analyses were performed to assess the association between the probability of hospitalization and categorical variables using STATA version 17. The ECDC Map Maker tool (EMMa) was used to create the incidence map. A total of 4,836 cases were reported during the study period, with an average annual incidence of 9.1 cases per 100,000 population. The highest number of cases occurred in 2019 (n = 1,174), and the lowest in 2021 (n = 780). The disease was most common in the paediatric population, with the highest average age-specific incidence rates observed in children aged 5-9 years (80.9 per 100,000 population) and 10-14 years (42.3 per 100,000 population). Of 14 administrative regions of the CZ, the Olomouc Region had the highest average annual incidence (28.7 per 100,000 population), while the Pilsen Region had the lowest (2.2 per 100,000 population). A total of 472 (9.8%) patients needed hospitalization, most of them in the categories 10-14 years (n = 200, 42.4%) and 5-9 years (n = 178, 38%). The highest hospitalization rate was found in the age group 75+ (36.4%). A significantly higher probability of hospitalization was found in the age groups 6-19 years and 65+ compared to working-age population with enterobiasis. A significantly lower probability of hospitalization was seen in 2020-2022 compared to 2019. No difference in the hospitalization rates was noted between genders. No enterobiasis-related death was reported during the study period. The disease occurs year-round. A decrease in reported cases was observed annually during the school summer holidays in July and August. Neither outbreak nor imported cases were noted. Given that enterobiasis is often asymptomatic, many cases are not captured in the surveillance system. The Czech prevalence data indicate that it mainly affects the paediatric population. Therefore, preventive measures and programs should primarily target children.

Are all bike lanes built equal? Using bike share GPS data to quantify cycling infrastructure investments’ ridership effects in Hamilton, Ontario

Are all bike lanes built equal? Using bike share GPS data to quantify cycling infrastructure investments’ ridership effects in Hamilton, Ontario

Potential of the satellite-based Dynamic Habitat Index (DHI) to capture changes in soil properties and drought conditions across Land Use/Land Cover types in a Central European landscape

This study evaluates the sensitivity of the Dynamic Habitat Index (DHI), utilizing multitemporal Normalized Difference Vegetation Index (NDVI) data, to changing environmental conditions across Land Use/Land Cover (LULC) types in a central European landscape (2017–2020). We observed distinct DHI characteristics for all LULC types, and the DHI responded to an extreme drought year in 2018 with no return to pre-drought conditions except for deciduous forests. The DHI also effectively captured spatio-temporal variability of pedo-climatic conditions. Thus, integrated with ancillary geodata, the DHI enhances traditional categorical LULC maps, offering applications in biodiversity and ecosystem research. Such integrated products could serve as valuable tools for decision makers to formulate sustainable land management strategies and contribute to Sustainable Develop Goal indicators related to land degradation, e.g. by identifying deviations from typical, context-specific DHI profiles as a response to disturbance and environmental stress.

Height-for-age and weight-for-age growth charts for Pakistani infants under six months: derived from a novel case selection method using multiple indicator cluster survey data

BackgroundIn the past two decades, there has been a growing recognition of the need to establish indigenous standards or reference growth charts, particularly following the WHO multicenter growth study in 2006. The availability of accurate and reliable growth charts is crucial for monitoring child health. The choice of an appropriate model for constructing growth charts depends on various data characteristics, including the distribution’s tails and peak. While Pakistan has reported some reference growth charts, there is a notable absence of indigenous charts for children under two years of age, especially for infants aged 0–6 months who are exclusively breastfed. Additionally, acquiring data poses a significant challenge, particularly for low-income countries, as it demands substantial resources such as finances, time, and expertise. The Multiple Indicator Cluster Survey (MICS) constitutes a large-scale national survey conducted periodically in low-income countries under the auspices of UNICEF. In this study, we propose methods for generating selection variables utilizing the “Novel Case Selection Method,“ as previously published. Further our approach enables to select and fit appropriate model to the MICS data, selected, and to develop the standard growth charts.MethodsOut of the 11,478 children under 6 months of age included in MICS-6 (Pakistan), 3,655 children (1,831 males and 1,824 females) met the specified criteria and were selected using the “Novel Case Selection Method”. The sample was distributed across provinces as follows: 841 (23.0%) from KPK, 1,464 (40.1%) from Punjab, 819 (22.4%) from Sindh, and 531 (14.5%) from Balochistan. This sample encompassed both rural (76.4%) and urban (23.6%) populations. Following data cleaning and outlier removal, a total of 3,540 records for weight (1,768 males and 1,772 females) and 3,515 records for height (1,759 males and 1,756 females) were ultimately available for the development of standard charts. The Bayesian Information Criterion (BIC) was employed to determine the optimal degrees of freedom for L, M, and S using RefCurv_0.4.2. Three families within the gamlss class—namely, Box Cox Cole and Green (BCCG), Box Cox T (BCT), and Box Cox Power Exponential (BCPE)—were applied, each with three smoothing techniques: penalized splines (ps), cubic splines (cs), and polynomial splines (poly). The best-fitted model was selected from these nine combinations based on the Akaike Information Criteria.ResultsThe Novel Case Selection Method yielded 3655 cases as per criteria. After cleaning the data, this method lead to selection of 3540 children for “weight for age” (W/A) and 3515 children for “height for age” (H/A). The “BCPE” family and “ps” as smoothing method proved to be best on AIC for all four curves, i.e. the W/A male, W/A female, H/A male, and H/A female. The optimum selected degrees of freedom for the curve “W/A”, for both genders were (M = 1, L = 0, S = 0). The optimum degrees of freedom for H/A male were again (M = 1, L = 0, S = 0), but for females the selected degrees of freedom were (M = 1, L = 1, S = 1). The indigenous fitted standard curves for Pakistan were on lower trajectory in comparison to WHO standards.ConclusionThis study uses the Novel Case Selection Method with introduced algorithms to construct tailored growth charts for lower and middle-income countries. Leveraging extensive MICS data, the methodology ensures representative national samples. The resulting charts hold practical value and await validation from established data sources, offering valuable tools for policy makers and clinicians in diverse global contexts.

Synthetic Data as a Proxy for Real-World Electronic Health Records in the Patient Length of Stay Prediction

While generative artificial intelligence has gained popularity, e.g., for the creation of images, it can also be used for the creation of synthetic tabular data. This bears great potential, especially for the healthcare industry, where data are often scarce and underlie privacy restrictions. For instance, the creation of synthetic electronic health records (EHR) promises to improve the usage of machine learning algorithms, which usually work with large amounts of data. This also applies for the prediction of the patient length of stay (LOS), a key measure for hospitals. Thereby, the LOS represents one of the core tools for decision makers to plan the allocation of resources. Thus, this paper aims to add to the still-young research concerning the application of generative adversarial nets (GAN) on tabular EHR. It does that with the intention to leverage the advantages of synthetic data for the prediction of the LOS in order to contribute to the efficiency-enhancing and cost-saving aspirations of hospitals and insurance companies. Therefore, the applicability of synthetic data that is generated using GANs as a proxy for scarce real-world EHR for the patient LOS multi-class classification task is examined. In this context, the Conditional Tabular GAN (CTGAN) and the Copula GAN are selected as the underlying models as they are state-of-the-art GAN architectures designed for generating synthetic tabular data. The CTGAN is found to be the superior model for the underlying use case. Nevertheless, the paper shows that there is still room for improvement when applying state-of-the-art GAN architectures to clinical healthcare data.

Proactive Resource Request for Disaster Response: A Deep Learning-Based Optimization Model

In the realm of disaster response operations, effective resource management is crucial. This research introduces an innovative approach that proactively determines the optimal quantities of resources that should be requested by local agencies. This determination is based on both current and anticipated demands, thereby ensuring a more efficient and effective response to disasters. The approach first utilizes a method that combines deep learning and temporal point process for predicting irregularly spaced future demands, and then, it formulates the resource allocation problem faced with randomly arrived demands as a stochastic optimization model. The superiority of this approach over existing resource allocation methods is demonstrated using both real-world data and simulated scenarios. The findings highlight the need for a shift from reactive to proactive strategies. Moreover, the research emphasizes the potential of advanced techniques, such as deep learning and stochastic optimization, in disaster management. These techniques can provide valuable tools for policy makers and practitioners in the field, enabling them to make more informed and effective decisions. Policies that encourage the adoption of such optimized resource allocation strategies could lead to more effective disaster response operations.