Tool Users Research Articles

Research on the qualification of TQL-5 COTS Tools in airborne software reliability verification process based on DO-330

Abstract The rapid development of airborne software is inseparable from the help of tools, especially the TQL-5 (Tool Qualification Level 5 based on DO-330) COTS (commercial off-the-shelf tools) tool, which is the most widely used. It is worth noting that to ensure software reliability and safety, the tool also needs to be qualified. Based on the DO-330 standard, this paper introduces the qualification methods for COTS tools, TQL-5 tools, and TQL-5 COTS tools, focusing on how to qualify TQL-5 COTS tools and the objectives that tool developers and users need to meet. It plays a guiding role in the airworthiness of airborne software tool users and tool developers.

Thought-Controlled Computer Applications: A Brain–Computer Interface System for Severe Disability Support

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.

An evaluation of the performance of stopping rules in AI-aided screening for psychological meta-analytical research.

Several AI-aided screening tools have emerged to tackle the ever-expanding body of literature. These tools employ active learning, where algorithms sort abstracts based on human feedback. However, researchers using these tools face a crucial dilemma: When should they stop screening without knowing the proportion of relevant studies? Although numerous stopping rules have been proposed to guide users in this decision, they have yet to undergo comprehensive evaluation. In this study, we evaluated the performance of three stopping rules: the knee method, a data-driven heuristic, and a prevalence estimation technique. We measured performance via sensitivity, specificity, and screening cost and explored the influence of the prevalence of relevant studies and the choice of the learning algorithm. We curated a dataset of abstract collections from meta-analyses across five psychological research domains. Our findings revealed performance differences between stopping rules regarding all performance measures and variations in the performance of stopping rules across different prevalence ratios. Moreover, despite the relatively minor impact of the learning algorithm, we found that specific combinations of stopping rules and learning algorithms were most effective for certain prevalence ratios of relevant abstracts. Based on these results, we derived practical recommendations for users of AI-aided screening tools. Furthermore, we discuss possible implications and offer suggestions for future research.

Making a Murderer

Abstract Algorithmic risk assessment tools, such as COMPAS, are increasingly used in criminal justice systems to predict the risk of defendants to reoffend in the future. This paper argues that these tools may not only predict recidivism, but may themselves causally induce recidivism through self-fulfilling predictions. We argue that such “performative” effects can yield severe harms both to individuals and to society at large, which raise epistemic-ethical responsibilities on the part of developers and users of risk assessment tools. To meet these responsibilities, we present a novel desideratum on algorithmic tools, called explainability-in-context, which requires clarifying how these tools causally interact with the social, technological, and institutional environments they are embedded in. Risk assessment practices are thus subject to high epistemic standards, which haven't been sufficiently appreciated to date. Explainability-in-context, we contend, is a crucial goal to pursue in addressing the ethical challenges surrounding risk assessment tools.

WebMRT: An online tool to predict summertime mean radiant temperature using machine learning

Mean Radiant Temperature (Tmrt) is the most critical atmospheric variable influencing outdoor human thermal exposure and comfort in hot, dry environments. However, accurately quantifying Tmrt requires time-consuming field measurements with expensive equipment or complex, resource-intensive computations. We introduce WebMRT, an online tool to predict Tmrt using a data-driven approach. It features an intuitive interface using air temperature, shading status, and built environment features as predictors of Tmrt for a user-selected summer day, time, and location. Utilizing a tree-based ensemble model, WebMRT is trained on state-of-the-art human-biometeorological data collected by MaRTy using LightGBM after evaluating its performance against several candidate machine learning regressors. Feature engineering was applied to the day and time input, and two additional temporal features were derived: ‘Solar Altitude’ and ‘Minutes-from-Sunrise’. These inputs are integrated into the user interface, emphasizing simplicity and easy access for users at the frontend. After training the regressor on MaRTy datasets and employing k-fold cross-validation with ten folds, the model demonstrated strong predictive power (R2=0.92) with acceptable error (RMSE=3.43, MAPE=5.33) and bias (MBE=0.20). WebMRT also features optional fisheye photo uploads, processed using transfer learning techniques for image segmentation, further enhancing the tool's predictive accuracy, user experience, and applications towards climate action decision-making processes.

HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools.

With the exponential growth of the life sciences literature, biomedical text mining (BTM) has become an essential technology for accelerating the extraction of insights from publications. The identification of entities in texts, such as diseases or genes, and their normalization, i.e. grounding them in knowledge base, are crucial steps in any BTM pipeline to enable information aggregation from multiple documents. However, tools for these two steps are rarely applied in the same context in which they were developed. Instead, they are applied "in the wild," i.e. on application-dependent text collections from moderately to extremely different from those used for training, varying, e.g. in focus, genre or text type. This raises the question whether the reported performance, usually obtained by training and evaluating on different partitions of the same corpus, can be trusted for downstream applications. Here, we report on the results of a carefully designed cross-corpus benchmark for entity recognition and normalization, where tools were applied systematically to corpora not used during their training. Based on a survey of 28 published systems, we selected five, based on predefined criteria like feature richness and availability, for an in-depth analysis on three publicly available corpora covering four entity types. Our results present a mixed picture and show that cross-corpus performance is significantly lower than the in-corpus performance. HunFlair2, the redesigned and extended successor of the HunFlair tool, showed the best performance on average, being closely followed by PubTator Central. Our results indicate that users of BTM tools should expect a lower performance than the original published one when applying tools in "the wild" and show that further research is necessary for more robust BTM tools. All our models are integrated into the Natural Language Processing (NLP) framework flair: https://github.com/flairNLP/flair. Code to reproduce our results is available at: https://github.com/hu-ner/hunflair2-experiments.

Practical Improvement of Noncontact Production Monitoring of Doping in SiC Wafers with Extended Epilayer Defects

We discuss two defect related practical improvements in the corona noncontact CV metrology, (CnCV) for SiC. The improvements are introduced in response to requests from industrial tool users. The first improvement quantifies mapping of electrically active defects with the QUAD technique (Quality, Uniformity, and Defects). It provides the capability of user selectable die grids directly comparable with Near UV-PL and optical defect mapping. This shall enhance understanding of the device killer defects and help to correlate epilayer defects and device yield. The second improvement introduces auto-remeasurement of outliers appearing in doping measurements on defective sites. This procedure is analogous to that used in the Hg probe technique and it provides a means for correcting defect related distortions in SPC doping monitoring charts.

Key Characteristics and Perception of Different Outbreak Surveillance Systems in Côte d'Ivoire: Cross-Sectional Survey Among Users.

Accurate and timely infectious disease surveillance is pivotal for effective public health responses. An important component of this is the disease surveillance tools used. Understanding views and experiences of users is crucial for informing policy decisions and ensuring the seamless functioning of surveillance systems. In this study, we aimed to assess the user perceptions of 3 disease surveillance tools used in Côte d'Ivoire, namely, MAGPI, District Health Information Software 2 (DHIS2), and Surveillance Outbreak Response Management and Analysis System (SORMAS), the latter was implemented in 2021 within a pilot scheme. We conducted interviews and a web-based survey distributed to users of the 3 surveillance tools. The survey assessed users' views of the surveillance tools' usefulness, ease of use, feelings toward the tool, conditions that may influence the use, and other characteristics. The descriptive analysis compared responses from SORMAS, MAGPI, and DHIS2 users, providing a comprehensive evaluation of their experiences. Among the 159 respondents who actively use one of the systems, MAGPI was the most widely used surveillance tool among respondents (n=127, 79.9%), followed by DHIS2 (n=108, 67.9%), and SORMAS (n=25, 15.7%). In terms of users' perceptions, SORMAS, despite its limited implementation, emerged as a tool that allows for data analysis and had the most comprehensive set of functionalities. DHIS2 was appreciated for its frequency of report provision, although users reported occasional IT system failures. MAGPI was recognized for its ease of use but was reported to lack certain functionalities offered by the other surveillance systems. This study offers valuable insights into the perceptions of disease surveillance tools users in Côte d'Ivoire. While all systems were positively regarded, each exhibited strengths and weaknesses addressing different needs and functionalities. Policy makers and health officials can use these findings to enhance existing tools or consider a unified approach for infectious disease surveillance systems. Understanding users' perspectives allows them to optimize the choice of surveillance tools, ultimately strengthening public health responses in Côte d'Ivoire and potentially serving as a model for other countries facing similar decisions in their health care systems.

Mentoring Professionals to Use Digital Tools in Home Care.

Nurses use electronic information systems daily, and digital devices have been developed to enable patient to live at home as long as possible. This study aimed to test reverse mentoring for professionals working with digital tools in home care. An electronic survey was sent twice to nurses to collect their opinions about the tools they use. Based on the results from the first survey (N=184), the mentoring content focused on the use of information systems and digital tools. Respondents' experiences as information system users were more abundant than their experiences as digital tool users. Tools supporting independent living were seldom used, but safety devices and alarm monitoring were used daily. The mentoring meetings induced changes and encouraged participants to acquire skills related to the use of digital tools and to evaluate their work critically.

Assessing the impact of information literacy on farmers’ decision-making processes: A mixed-methods approach

This article investigates the link between information literacy and farmers’ decision-making processes, highlighting the potential for improving information literacy to promote sustainable agricultural practices. This study employed a mixed-methods approach – that is, qualitative and quantitative data collection techniques. It was found that gender did not significantly influence the reported skills and behaviours. However, the use of digital technologies showed a significant positive correlation with seeking information on new agricultural practices ( r = .254, p < .01). This shows that proficient users of digital tools are more likely to seek new agricultural information actively. This research emphasizes the importance of collaboration and knowledge-sharing among farmers and stakeholders to effectively use agricultural information in decision-making, with implications for academic research and practical agricultural interventions.

Benchmarking short and long read polishing tools for nanopore assemblies: achieving near-perfect genomes for outbreak isolates

BackgroundOxford Nanopore provides high throughput sequencing platforms able to reconstruct complete bacterial genomes with 99.95% accuracy. However, even small levels of error can obscure the phylogenetic relationships between closely related isolates. Polishing tools have been developed to correct these errors, but it is uncertain if they obtain the accuracy needed for the high-resolution source tracking of foodborne illness outbreaks.ResultsWe tested 132 combinations of assembly and short- and long-read polishing tools to assess their accuracy for reconstructing the genome sequences of 15 highly similar Salmonella enterica serovar Newport isolates from a 2020 onion outbreak. While long-read polishing alone improved accuracy, near perfect accuracy (99.9999% accuracy or ~ 5 nucleotide errors across the 4.8 Mbp genome, excluding low confidence regions) was only obtained by pipelines that combined both long- and short-read polishing tools. Notably, medaka was a more accurate and efficient long-read polisher than Racon. Among short-read polishers, NextPolish showed the highest accuracy, but Pilon, Polypolish, and POLCA performed similarly. Among the 5 best performing pipelines, polishing with medaka followed by NextPolish was the most common combination. Importantly, the order of polishing tools mattered i.e., using less accurate tools after more accurate ones introduced errors. Indels in homopolymers and repetitive regions, where the short reads could not be uniquely mapped, remained the most challenging errors to correct.ConclusionsShort reads are still needed to correct errors in nanopore sequenced assemblies to obtain the accuracy required for source tracking investigations. Our granular assessment of the performance of the polishing pipelines allowed us to suggest best practices for tool users and areas for improvement for tool developers.

P-500 Digital support tools for fertility patients – a systematic review and meta-analysis of the effect of these tools on psychological outcomes and pregnancy rates

Abstract Study question Do digital support tools for fertility patients improve psychological outcomes and pregnancy rates? Summary answer Overall, digital support tools for fertility patients do not improve depression, anxiety or Fertility Problem Index scores but have a beneficial effect on pregnancy rates. What is known already There are numerous mobile and web-based digital health tools in reproductive medicine, but the research evidence supporting the effect on clinical outcomes and to mitigate the psychological effects of infertility is sparse. If existing digital tools have demonstrated effectiveness in their stated aims of providing psychological support to patients during the arduous process of fertility treatment, it is vital to identify this so they can be implemented and further developed to tackle this important problem. In addition, if any digital tools have shown a positive impact on pregnancy rates, this would be of great interest to patients and clinicians alike. Study design, size, duration The study was prospectively registered (PROSPERO-CRD42020156441). An initial systematic review identified digital support tools and their salient features. This study presents meta-analysis of the data from RCT studies of digital support tools identified. Meta-analysis was performed of pregnancy rates, FPI (Fertility Problem Index), Depression and Anxiety scores between the digital support tool user and waiting list control groups. Studies were assessed for risk of bias (Cochrane risk of bias, robvis tool) and graded for quality. Participants/materials, setting, methods All studies were published after 2006. The RCT studies were conducted in the USA, Switzerland, Germany, UK, and The Netherlands. The total number of patients involved was 1268 (1059 women and 209 men). The average age of participants was 34.3 years. All studies reported psychological outcome scores. In 5 of the 10 studies identified pregnancy rates were reported. Meta-analysis was performed using the ‘meta’ package in R using random effects model to generate forest plots. Main results and the role of chance Overall, digital tools have a small positive impact on pregnancy rates, suggesting a beneficial effect for users of digital tools compared to non-user controls. Comparing digital support tools users with non-users, the clinical pregnancy rate (CPR) odds ratio (OR, with confidence interval) was 1.715 (1.042:2.82). The meta-analysis of the effect sizes of digital interventions on psychological outcomes, measured using validated measures of anxiety, depression, or the fertility specific FPI, identified no significant effects of digital interventions on patients’ psychological wellbeing. There was no significant standardized mean difference seen with the FPI analysis, SMD -0.10 (95% CI -0.26:0.05). As a variety of depression and anxiety outcome scales were used, there was insufficient comparable data to perform a quantitative meta-analysis for these outcomes and so this data is presented narratively. No convincing benefit of the digital tools to reduce symptoms of depression during fertility treatment is demonstrated in these studies. The impact of digital support tools on anxiety scores is somewhat mixed, with most studies demonstrating no significant difference in the anxiety scores between intervention and control group and a small number of statistically and clinically significant reductions in anxiety severity. Limitations, reasons for caution Meta-analysis was limited by the range of psychological outcome scales used. There were issues with deviation from intended interventions and missing outcome data. There is a risk the results of this meta-analysis were affected by publication bias, particularly the reporting of pregnancy rate, which was not included in all studies. Wider implications of the findings Establishing an agreed framework or core outcome set for evaluation of tools aiming to ameliorate the psychological distress associated with fertility treatments would be useful. Further research to explore the potential of digital support tools to impact pregnancy rates, particularly within fixed time frames/ treatment cycles, is warranted. Trial registration number N/A

Enacting machine agency when AI makes one’s day: understanding how users relate to AI communication technologies for scheduling

Abstract AI Communication Technologies (AICTs) make decisions about users’ communication on their behalf. Users’ implementation of AICTs that autonomously act may enable and constrain how they accomplish their work and interact with others. Drawing on interviews with users of two AICTs with differing levels of autonomy designed for work-related scheduling, this study investigated how users enacted AICTs in practice. Users of both tools drew on AICTs’ autonomous capabilities to enact machine agency, a structure that assigns AICTs the power to allocate resources, which helped them increase scheduling efficiency and guide how others interacted with them. Users of the tool that autonomously implemented decisions described a process of enactment in which they used the tool to control their work, perceived the tool was exhibiting too much control, and acted to regain control. I present implications for understanding how people enact machine agency with AICTs that make decisions about their work.

The industrial degradation of the workplace that Thorstein Veblen overlooked

Abstract Thorstein Veblen gave special importance to work. He claimed that once humans became tool users, their work activity was driven by an instinct of workmanship. This instinct is ‘an object of attention and sentiment in its own right’ beyond providing provisioning and serving another instinct, that of parental bent, or society’s wellbeing. Given appropriate social institutions, ‘labor might… assume that character of nobility in the eyes of society at large’. Yet, Veblen took little notice that during his lifetime between 1857 and 1929, the rapid industrialisation of the American economy massively proletarianised workers, greatly degrading their work experience. In Europe, this proletarianisation and degradation of the work process had begun centuries earlier with the rise of capitalism, and greatly accelerated with rapid industrialisation during Veblen’s lifetime. Although Veblen addressed how capitalism, as the latest predatory society, exploited workers, he presented industrialisation of the work process as positive for workers. This article first surveys the process of proletarianisation, focussing on the American experience. It then explores Veblen’s understanding of the impact of industrialisation on workers and how he missed a far-reaching labour-degrading process that was important in provoking massive violent insurrection, and which had earlier been addressed by political economists as diverse as Adam Smith and Karl Marx. Because he failed to recognise the degradation of the work process that accompanied industrialisation, he missed its contributing role in the explosion of conspicuous consumption by Americans of all classes, the subject of his most renowned work, The Theory of the Leisure Class.

Compression algorithm for colored de Bruijn graphs

A colored de Bruijn graph (also called a set of k-mer sets), is a set of k-mers with every k-mer assigned a set of colors. Colored de Bruijn graphs are used in a variety of applications, including variant calling, genome assembly, and database search. However, their size has posed a scalability challenge to algorithm developers and users. There have been numerous indexing data structures proposed that allow to store the graph compactly while supporting fast query operations. However, disk compression algorithms, which do not need to support queries on the compressed data and can thus be more space-efficient, have received little attention. The dearth of specialized compression tools has been a detriment to tool developers, tool users, and reproducibility efforts. In this paper, we develop a new tool that compresses colored de Bruijn graphs to disk, building on previous ideas for compression of k-mer sets and indexing colored de Bruijn graphs. We test our tool, called ESS-color, on various datasets, including both sequencing data and whole genomes. ESS-color achieves better compression than all evaluated tools and all datasets, with no other tool able to consistently achieve less than 44% space overhead. The software is available at http://github.com/medvedevgroup/ESSColor.

Abstract PO2-10-08: Contemporary HER2-Positive MBC Treatment Recommendations Among Community Healthcare Professionals: Analysis of an Online Interactive Decision Support Tool

Abstract Background: The management of HER2-positive (HER2+) metastatic breast cancer (MBC) has continued to evolve, with multiple FDA approvals of novel HER2-targeted agents since late 2019. Considering the growing HER2+ MBC treatment armamentarium, we sought to identify current practice patterns among community healthcare professionals (HCPs) for HER2+ MBC by comparing HCP and key opinion leader (KOL) treatment recommendations using an online decision support tool. Methods: An online decision support tool was developed with input from 5 KOLs to provide therapy recommendations for 630 HER2+ MBC patient case scenarios based on conventional considerations including distribution of metastatic disease, treatment history, duration of response, and specific comorbidities. HCP tool users entered specific patient criteria to define a case along with their intended management for each case. The tool then showed 5 KOL recommendations for the user-entered patient case, and the users were asked to indicate if the expert recommendations confirmed or changed their intended approach. An analysis of expert recommendations and user-selected therapy was performed. Results: Between November 2022 and July 2023, 313 patient cases were entered by 242 participating HCPs. HCP and KOL treatment recommendations were frequently discordant (Table). For patients with de novo HER2+ MBC (n = 55), there was strong concordance (63%; P = .0001) between HCP and KOL recommendations. However, weak treatment selection concordance (53%; P < .0001) was observed for patients with disease progression after standard neoadjuvant/adjuvant therapy with chemotherapy plus HER2-targeted agents (n = 57). In the second-line setting, 43% concordance was observed for patients with non‒central nervous system (CNS) disease progression without CNS progression after first-line therapy with THP (n = 67; P < .0001), whereas 61% concordance was observed for patients with non-CNS and CNS disease progression after first-line therapy with THP (n = 61; P < .0001). In the third-line setting, 31% concordance was observed for patients with non-CNS disease progression without CNS progression (n = 32; P < .0001), and 47% concordance was observed for patients with non-CNS and CNS disease progression (n = 19; P = .0009). For HCPs whose treatment plan did not match KOL recommendations in these scenarios, 62% indicated that KOL recommendations confirmed or changed their intended therapy, but 19% indicated that there were barriers to implementing those recommendations. Conclusions: These data suggest ongoing challenges to optimally incorporate novel HER2-targeted therapies in the care of patients with HER2+ MBC, particularly in the second line and beyond, but an online tool providing KOL advice on specific patient scenarios confirmed or changed the clinical approach for a majority of HCPs. Thus, continued development of resources for HCPs, including online decision support tools, may be increasingly important given the rapidly evolving treatment landscape. Table. Summary of Treatment Choices by KOLs or HCPs for Select Patient Scenarios Citation Format: Kristen Rosenthal, Sara Hurvitz, Komal Jhaveri, Heather McArthur, Sara Tolaney, Timothy Quill. Contemporary HER2-Positive MBC Treatment Recommendations Among Community Healthcare Professionals: Analysis of an Online Interactive Decision Support Tool [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-10-08.

Hooded crows (Corvus cornix) manufacture objects relative to a mental template

It was recently found that not only tool-specialized New Caledonian crows, but also Goffin cockatoos can manufacture physical objects in accordance with a mental template. That is, they can emulate features of existing objects when they manufacture new items. Both species spontaneously ripped pieces of card into large strips if they had previously learned that a large template was rewarded, and small strips when they previously learned that a small template was rewarded. Among New Caledonian crows, this cognitive ability was suggested as a potential mechanism underlying the transmission of natural tool designs. Here, we tested for the same ability in another non-specialised tool user–Hooded crows (Corvus cornix). Crows were exposed to pre-made template objects, varying first in colour and then in size, and were rewarded only if they chose pre-made objects that matched the template. In subsequent tests, birds were given the opportunity to manufacture versions of these objects. All three crows ripped paper pieces from the same colour material as the rewarded template, and, crucially, also manufactured objects that were more similar in size to previously rewarded, than unrewarded, templates, despite the birds being rewarded at random in both tests. Therefore, we found the ability to manufacture physical objects relative to a mental template in yet another bird species not specialized in using or making foraging tools in the wild, but with a high level of brain and cognitive development.

Common challenges and suggestions for risk of bias tool development: a systematic review of methodological studies

ObjectivesTo review the findings of studies that have evaluated the design and/or usability of key risk of bias (RoB) tools for the assessment of RoB in primary studies, as categorized by the Library of Assessment Tools and InsTruments Used to assess Data validity in Evidence Synthesis Network (a searchable library of RoB tools for evidence synthesis): Prediction model Risk Of Bias ASessment Tool (PROBAST) , Risk of Bias-2 (RoB2), Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I), Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), Quality Assessment of Diagnostic Accuracy Studies-Comparative (QUADAS-C), Quality Assessment of Prognostic Accuracy Studies (QUAPAS), Risk Of Bias in Non-randomised Studies of Exposures (ROBINS-E), and the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) RoB checklist. Study Design and SettingSystematic review of methodological studies. We conducted a forward citation search from the primary report of each tool, to identify primary studies that aimed to evaluate the design and/or usability of the tool. Two reviewers assessed studies for inclusion. We extracted tool features into Microsoft Word and used NVivo for document analysis, comprising a mix of deductive and inductive approaches. We summarized findings within each tool and explored common findings across tools. ResultsWe identified 13 tool evaluations meeting our inclusion criteria: PROBAST (3), RoB2 (3), ROBINS-I (4), and QUADAS-2 (3). We identified no evaluations for the other tools. Evaluations varied in clinical topic area, methodology, approach to bias assessment, and tool user background. Some had limitations affecting generalizability. We identified common findings across tools for 6/14 themes: (1) challenging items (eg, RoB2/ROBINS-I “deviations from intended interventions” domain), (2) overall RoB judgment (concerns with overall risk calculation in PROBAST/ROBINS-I), (3) tool usability (concerns about complexity), (4) time to complete tool (varying demands on time, eg, depending on number of outcomes assessed), (5) user agreement (varied across tools), and (6) recommendations for future use (eg, piloting) and development (add intermediate domain answer to QUADAS-2/PROBAST; provide clearer guidance for all tools). Of the other eight themes, seven only had findings for the QUADAS-2 tool, limiting comparison across tools, and one (“reorganization of questions”) had no findings. ConclusionEvaluations of key RoB tools have posited common challenges and recommendations for tool use and development. These findings may be helpful to people who use or develop RoB tools. Guidance is necessary to support the design and implementation of future RoB tool evaluations.

Advancing energy efficiency in livestock building: Simplified building energy simulation tool for geometric design of pigsty

Concerns related to food security and animal welfare have led to increasing interest in analyzing the energy demands of heating, ventilation, and air conditioning (HVAC) systems in livestock buildings. However, building energy simulation (BES) model, which facilitates precise HVAC systems energy analysis, is difficult to use for non-experts, and particularly, its usability drops significantly in livestock buildings, which are often constructed without involvement of building engineers. This study, therefore, developed a tool that can analyze the energy demand of pigsties by changing geometric design variables with only several numerical inputs without separate 3D modeling. Pigsty geometric design types were classified by examining pigsty architectural design manual and web portal map search. A simplified tool, utilizing EnergyPlus and MATLAB graphical user interface, was developed to analyze energy demand in pigsties by leveraging simple user inputs for geometric design modeling. This tool simplifies exploring potential energy savings through geometric design changes in the early design stages of livestock buildings. The methodology introduced in this study lays the groundwork for future enhancements, including the integration of various design-phase variables beyond geometric design.

PERANCANGAN ALAT PEMBUAT PAVING BLOCK DARI LIMBAH SAMPAH PLASTIK DENGAN MENGGUNAKAN PENDEKATAN ERGONOMI DAN METODE KANSEI ENGINEERING

Plastic is a well-known product that is often found everywhere. The existence of plastic in Indonesia is very important for Indonesian people. However, increasing plastic consumption causes an increase in plastic waste produced. The resulting result is that the environment is dirty and of course unhealthy. One way to reduce plastic waste is to process it into paving blocks with high selling value. However, the current tools are not ergonomic and risk causing workers to experience work-related illnesses. Therefore, it is necessary to design tools that are more ergonomic. This study aims to design a tool for making plastic waste paving blocks using an ergonomic and Kansei Engineering approach. The ergonomics approach aims to obtain the dimensions of the tool that will be designed to suit the dimensions of the human body as the user. Kansei Engineering aims to get the wishes of potential tool users for the plastic waste paving block making tools they will use. The results obtained by the user's wishes are easy to use, easy to move, colorful, bright in color, striking, thick and the design is obtained according to the user's body dimensions. Offers of tools designed to increase the productivity of making paving blocks from plastic waste and user comfort in using them.