Transparent Tool Research Articles

Role of supply chain digitalization and global supply chain in decarbonization of natural resources sector supply chain

Environmental management and decarbonization are becoming increasingly critical as industries seek sustainable development paths that align with global climate targets. As businesses aim to reduce their carbon footprint, integrating effective environmental strategies with technological advancements is essential. This necessitates a clear understanding of digital technologies and environmental policies to drive decarbonization efforts. In this context, this study explores the role of digitalization in reducing carbon emissions in the natural resources sector supply chain. Furthermore, it examines the role of supply chain mapping as a mediator in the relationship between supply chain digitalization and supply chain decarbonization efforts, providing a novel perspective on how technology can intersect to foster environmental sustainability. This research targets the metal sector supply chain, a significant contributor to global carbon emissions. The data for the study was gathered through a questionnaire administered to 246 employees from various levels of the metal industry in China. We employed Covariance-based Structural Equation Modelling (CB-SEM) using AMOs for analysis. Our results show that level digitalization of supply chain processes in the metal sector has a profound and direct impact on its supply chain decarbonization. Digital technologies facilitate more efficient resource use, improved energy efficiency, and the adoption of cleaner production methods. Importantly, the study identifies supply chain mapping as a crucial mechanism through which digitalization exerts its positive influence on decarbonization. Through detailed mapping, organizations can gain visibility over their entire supply chain, identifying carbon hotspots and opportunities for improvement that digital solutions can address. The novelty of this study lies in its systematic exploration of how supply chain mapping serves not just as a tool for transparency and efficiency but as a strategic facilitator of the beneficial impacts of digitalization on decarbonization.

Blockchain technology as a tool for corporate governance and transparency

Blockchain technology as a tool for corporate governance and transparency

GRADE evidence to decision framework in genomics guidelines: systematic review & appraisal

Abstract Background Transparent development of clinical practice guidelines (CPGs) is important for genomic medicine, as this field promises to partially challenge the traditional hierarchies of evidence. The GRADE Evidence to Decision (EtD) framework has recently emerged as a transparent and rigorous tool to produce recommendations. Our aim was to survey the genomic CPGs that used the GRADE EtD and appraise the applicability of its criteria and the quality of produced CPGs. Methods We systematically searched PubMed, Scopus, Web of Science and the gray literature till 2023 for relevant CPGs. From each eligible CPG, we extracted the reporting of the GRADE EtD criteria: benefits, harms, resource use, cost-effectiveness, acceptability, feasibility, equity and values. The quality of the CPGs were appraised across various domains using the (AGREE) II instrument. Results Among 1,144 records, 15 CPGs met inclusion criteria between 2018 and 2023, comprising 66 recommendations. Reporting of GRADE EtD criteria varied. Benefits, harms and their balance were addressed in approximately half of the recommendations 48% and 52% respectively. Feasibility, acceptability and values were also 52%, 47% and 40% respectively, resource use 39%, cost-effectiveness 23%. Equity was addressed in 36% of the recommendations. Quality appraisal of the CPGs also varied, domains were reported as follows: scope and purpose, clarity of presentation, editorial independence 86%, 85% and 84% respectively, rigor of development 77%, stakeholder involvement 72%, while applicability was 59%. Conclusions GRADE EtD is applied heterogeneously in genomic medicine CPGs, with difficulty in applying all of its criteria, including benefits and harms. Quality varied across domains with applicability of the CPGs less demonstrated. Key messages • Transparent approaches are needed in developing CPGs for genomic medicine. • Current genomic CPGs fail to address crucial aspects for producing recommendations, including benefits and harms.

A New Computer-Aided Diagnosis System for Breast Cancer Detection from Thermograms Using Metaheuristic Algorithms and Explainable AI

Advances in the early detection of breast cancer and treatment improvements have significantly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study, a novel interpretable computer aided diagnosis (CAD) system for breast cancer detection is proposed, leveraging Explainable Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named the Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid Spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combined the continuous and binary representations of PSO and SMO to effectively manage trade-offs between accuracy, feature selection, and hyperparameter tuning. We evaluated several CAD models and investigated the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor Filters, and Edge Detection. We further shed light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. With HSMO, our models achieved an accuracy of 98.27% and F1-score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis.

Statistical Predictive Hybrid Choice Modeling: Exploring Embedded Neural Architecture

This study introduces an enhanced version of the discrete choice model combining embedded neural architecture to enhance predictive accuracy while preserving interpretability in choice modeling across temporal dimensions. Unlike the traditional architectures, which directly utilize raw data without intermediary transformations, this study introduces a modified approach incorporating temporal embeddings for improved predictive performance. Leveraging the Phones Accelerometer dataset, the model excels in predictive accuracy, discrimination capability and robustness, outperforming traditional benchmarks. With intricate parameter estimates capturing spatial orientations and user-specific patterns, the model offers enhanced interpretability. Additionally, the model exhibits remarkable computational efficiency, minimizing training time and memory usage while ensuring competitive inference speed. Domain-specific considerations affirm its predictive accuracy across different datasets. Overall, the subject model emerges as a transparent, comprehensible, and powerful tool for deciphering accelerometer data and predicting user activities in real-world applications.

A Fuzzy Logic Application to Manage Construction-Cost Escalation

In large-scale projects, such as transport infrastructures, the cost-planning phase stands out as one of the most crucial for the project’s success. Cost and time overruns, during the construction phase, are often the cause of project failure. A meticulous, conscious, and accurate ex-ante analysis of cost and time assessment can greatly contribute to the efficient and effective completion of a project. Various approaches have been developed to estimate and mitigate cost overruns. Such mitigation is subject to careful analysis of project risks, encompassing construction, environmental, social acceptance, and market risks. In this contribution, we propose the implementation of the risk-assessment tool, suggested by the National Anti-Corruption Authority (ANAC), using fuzzy logic to enhance its effectiveness. Thanks to the implementation of fuzzy prioritizations, the risk matrix and the associated risk levels, which are obtained by combining the event’s probability of occurrence and its expected impact on costs, have been clearly and structurally defined. This tool can facilitate risk ranking and, therefore, the implementation of their management strategies during the design and construction phases as well as the consensus-creation process. This application can therefore be used by public authorities as a transparent and manageable tool to assess expected risk during the design and implementation phases of the project.

An anatomical analysis of the occipital nerve complex: an essential tool for the application of occipital nerve blocks

BackgroundOccipital nerve blocks are essential in diagnosing and treating headache disorders such as migraine, cervicogenic headache, occipital neuralgia, and cluster headache. In this study, we aimed to investigate the potential compression points of the greater occipital nerve (GON), third occipital nerve (TON), and lesser occipital nerve (LON) which are targeted to block in occipital nerve blocks and to develop a method to detect these points easily.MethodsTo identify potential compression points of the GON, TON, and LON, we dissected 43, 41, and 26 cadavers, respectively. A rigid, transparent tool divided into 1 × 1 cm sections was placed on the external occipital protuberance to measure the determined points. The cadaveric head was viewed from above, vertically, and the coordinates corresponding to each point were noted separately.ResultsSix, four, and one potential entrapment points were detected for the GON, TON, and LON, respectively. The distances of the point where the GON arose from the lower border of the obliquus capitis inferior muscle and the emerging point of the TON from the C2-C3 vertebrae to the posterior midline were statistically significant in terms of the sides (p = 0.040). Similarly, there was a statistical significance between genders for the distance of the point where the LON arose from the posterior edge of the sternocleidomastoid muscle to the posterior midline (p = 0.002).ConclusionsWe believe that with the method developed, the GON, TON, and LON compression points can be easily localized and blocked in diagnosing and treating patients experiencing headaches such as migraines, cervicogenic headaches, occipital neuralgia, and cluster headache.

Identification and application of quality elements, indicators, and criteria for assessment of impact on habitats in marine Natura 2000 areas

The EU Habitat Directive adopted in 1992, requires member states of the European Union to protect species and habitats considered to be of ‘Community Interest’ and listed in annexes to the directive. The appropriate environmental assessment of “plans and projects” is an important part of the conservation process. Despite several amendments and guidelines supporting the implementation of the Habitat Directive, science based operational procedures, indicators, and impact criteria for assessing potential negative impacts on marine Natura 2000 areas are still lacking. The lack of a generic and operational methodology complicates the management of plans and projects with potential impact on marine Natura 2000 areas. In this study, generic methods for the assessment of marine aquaculture in the inner Danish waters in relation to Natura 2000 areas was developed and applied for assessment of nine existing marine fin fish farms, in accordance with the latest methodological guidance on the provisions of Article 6(3) and (4) of the Habitat Directive. The applied methodology is based on high resolution 3D hydrodynamic- and ecosystem modelling (MIKE by DHI), that describes the dynamical physical, chemical, and biogeochemical processes and changes of marine ecosystems in time and space. To our knowledge, this is the first study that formulates operational biological quality elements, key indicators, concrete and generic impact criteria, and assessment procedures for operational assessment across several distinct marine habitat types. The method represents a generic, operational, transparent, and science-based assessment tool, that simplifies management, and is widely applicable for quantification of environmental impacts from various marine activities and eutrophication related pressures across geographical zones and different marine habitat types in marine Natura 2000 areas.

Effect of Transparency Initiatives on Public Trust in Government in Kenya

Purpose: To aim of the study was to analyze the effect of transparency initiatives on public trust in government in Kenya. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Transparency initiatives in Kenya have shown a positive impact on public trust in government. Efforts such as open access to government information, transparent budgeting processes, and public participation in decision-making have increased accountability and reduced corruption. These initiatives have led to greater citizen engagement and trust, as the public perceives the government to be more accountable and responsive to their needs. However, challenges remain in fully implementing these initiatives, and inconsistencies in transparency efforts can still undermine public trust. Unique Contribution to Theory, Practice and Policy: Social exchange theory, institutional theory & information processing theory may be used to anchor future studies on the effect of transparency initiatives on public trust in government in Kenya. Continuously improve online transparency portals by enhancing user interfaces, ensuring data accuracy, and expanding the scope of information available. Develop sector-specific transparency policies tailored to the unique needs of different governmental sectors (e.g., healthcare, education). This approach ensures that transparency initiatives address sector-specific challenges and enhance sector-specific public trust. Invest in training programs for public officials on transparency practices and the effective use of transparency tools.

Publisher preferences for a journal transparency tool: A modified three-round Delphi study

Background We propose the creation of a journal transparency tool (JTT), which will allow users to obtain information about a given scholarly journal’s operations and policies. We are obtaining preferences from different stakeholders to inform the development of this tool. This study aimed to identify the publishing community’s preferences for the JTT. Methods We conducted a modified three-round Delphi survey. Representatives from publishing houses and journal publishers were recruited through purposeful and snowball sampling. The first two Delphi rounds involved an online survey with items about JTT metrics and user features. During the third round, participants discussed and voted on JTT metric items that did not reach consensus after round 2 within a virtual consensus meeting. We defined consensus as 80% agreement to include or exclude an item in the JTT. Results Eighty-six participants completed the round 1 survey, and 43 participants (50% of round 1) completed the round 2 survey. In both rounds, respondents voted on JTT user feature and JTT metric item preferences and answered open-ended survey questions regarding the JTT. In round 3, a total of 21 participants discussed and voted on JTT metric items that did not reach consensus after round 2 during an online consensus group meeting. Fifteen out of 30 JTT metric items and none of the four JTT user feature items reached the 80% consensus threshold after all rounds of voting. Analysis of the round 3 online consensus group transcript resulted in two themes: ‘factors impacting support for JTT metrics’ and ‘suggestions for user clarity.’ Conclusions Participants suggested that the publishing community’s primary concerns for a JTT are to ensure that the tool is relevant, user-friendly, accessible, and equitable. The outcomes of this research will contribute to developing and refining the tool in accordance with publishing preferences.

Employer Strategies for Health Care Price Transparency.

This paper describes hospital, insurance, and pharmaceutical price transparency policies and applications in the United States and in selected countries around the world. Many of these policies apply to self-insured employers. So far, the experience in the United States and elsewhere is clear that federal and state price transparency regulations have had little impact on whether employees or dependents search for low-cost or high-quality providers or on the cost and quality of their health care. This is because of weak regulatory oversight, conflicting federal and state reporting requirements, and few economic incentives for providers and insurance companies to supply easily readable or analyzable price information. However, price transparency requirements are here to stay. This paper therefore offers several recommendations to maximize the utility of price transparency tools provided for employees and other insureds, by their employers, providers, commercial insurance carriers, or technology firms. From a policy perspective, coupling reporting requirements with clearer technological guidance and much stronger regulatory oversight would increase the utility of price transparency efforts. For individual employers, the impact of price transparency efforts may increase by coupling price transparency tools with health plan network and design strategies, behavioral economic nudges, and programs designed to improve health, well-being, and quality of care. Many program vendor partners, consultants, and actuarial, technology, and research firms can help make these efforts useful.

Challenges and Opportunities in Transforming Rural Governance Through E-Government in Indonesia

General Background: The adoption of E-Government systems is increasingly important in improving public services, particularly at the local government level, to enhance transparency and accessibility. Specific Background: In Indonesia, Village Information Systems (SID) have been introduced to streamline communication, resource management, and service delivery in rural areas. Sugihwaras Village in Sidoarjo Regency is one such location where SID has been implemented. Knowledge Gap: Despite its potential, the effectiveness of SID implementation remains underexplored, particularly in terms of communication, resources, and its impact on rural governance. Aims: This study aims to analyze and describe the implementation of E-Government through SID in Sugihwaras Village, focusing on the four indicators: communication, resources, disposition, and bureaucratic structure, using Edward III's policy implementation model. Results: The findings indicate that communication and resource allocation are the primary issues hindering SID effectiveness. Inadequate operator training and a lack of budget for operator fees have led to outdated information and diminished motivation. However, strong commitment from local government and adherence to standard operating procedures (SOPs) have ensured that the system continues to function. Novelty: This study highlights the crucial role of consistent training and budgetary support in enhancing E-Government systems at the village level, particularly in rural Indonesia. Implications: Strengthening operator skills and ensuring adequate financial support are essential for optimizing SID as a tool for public information transparency and service delivery, contributing to improved rural governance and E-Government effectiveness. Highlights: Communication gaps limit the Village Information System's efficiency. Budget constraints reduce operator motivation and system updates. Government commitment keeps the system operational despite obstacles. Keywords: E-Government, Village Information System, rural governance, communication, resource allocation

Decisional value scores: A new family of metrics for ethical AI-ML

AbstractResearch in ethical AI has made strides in quantitative expression of ethical values such as fairness, transparency, and privacy. Here we contribute to this effort by proposing a new family of metrics called “decisional value scores” (DVS). DVSs are scores assigned to a system based on whether the decisions it makes meet or fail to meet a particular standard (either individually, in total, or as a ratio or average over decisions made). Advantages of DVS include greater discrimination capacity between types of ethically relevant decisions and facilitation of ethical comparisons between decisions and decision-making systems, including across different modalities (for instance: human, machine, or coupled human–machine systems). After clarifying ambiguities in the concept of “decision” itself, including the question of how to individuate the decisions made by a system, we discuss the role and meaning of “decision” in common AI and machine learning approaches such as decision trees, neural networks, SVMs, and unsupervised classifiers. We then show how DVSs may be defined for several ethical values of interest, with an extended discussion of transparency. Finally, we explore how such metrics can be applied to real decision-making systems through two case studies: evaluations of LLMs for transparency; and evaluations of criminal risk assessment tools for utility, rights violations, fairness, and transparency.

Exploring explainable AI features in the vocal biomarkers of lung disease

This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.

Development of hybrid gradient boosting models for predicting the compressive strength of high-volume fly ash self-compacting concrete with silica fume

In an effort to extend the service life of structures under harsh exposure conditions and reduce the carbon dioxide emissions from cement production, researchers are examining the performance of cement concretes with different mineral admixtures. This study focuses on self-compacting concrete (SCC) incorporating high-volume fly ash (HVFA) and silica fume (SF) as supplementary cementitious materials (SCMs). The aim is to investigate the fresh and hardened properties, particularly the compressive strength, of these combinations. SCMs were used to replace varying amounts of cement, with SF showing the greatest improvement in mechanical characteristics across all age groups. Advanced modelling techniques, including Gradient Boosting (GB), Gradient Boosting-Particle Swarm Optimization, Gradient Boosting-Bayesian Optimization, and Gradient Boosting-Differential Evolution (GB-DE), were employed to analyse the strength characteristics of HVFA-SCC. The GB-DE model emerged as the most accurate, with an R² value of 0.995029 and an RMSE of 0.023862. Additionally, an open-source GUI based on GB-DE is introduced to provide engineers with a transparent tool for optimizing concrete mix designs, addressing the opacity of traditional machine learning models.

Factors Influencing Community Decisions in Choosing Community Health Centers for Promotive and Preventive Health Services: Literature Review

Introduction: Minister of Health Regulation Number 43 of 2019 stated that Community Health Centers (Puskesmas) were health service facilities that provided community health efforts (UKM) and individual health efforts (UKP) at the first level, prioritizing promotive and preventive efforts in their working areas. The reason for composing this article was crucial, as Community Health Centers, as both general health services and providers of promotive and preventive services, were expected to attract public interest.. Objective: Analyzed factors influencing community choices of health centers for promotive and preventive services. Method: A qualitative research study was conducted, employing a literature review via electronic databases, particularly utilizing Scopus journal sources. Keywords like "Primary health care," "consumer attraction," and "decision to visit the health center" guided the search, Inclusion criteria set for articles published in English, freely accessible, and between 2019 and 2023. Result: Based on the literature search process through electronic databases, 7 articles were obtained. The selected articles met the predetermined criteria. These articles were related to the use of price transparency tools, community satisfaction and dissatisfaction, the Health Extension Program, the crucial role of good healthcare services, as well as the influence of income and grants. Conclusion: Factors influencing the community's choice of Community Health Centers as a health service included offering lower service costs through insurance or BPJS Kesehatan, the presence of the Health Extension Program (HEP), patient satisfaction with services, gender factors, income, access to grants, and contact with health education.

Hawking for everyone: commemorating half a century of an unfinished scientific revolution

This year marks half a century since Stephen Hawking made his greatest scientific discovery by theoretically proving that ‘black holes ain’t so black’, as they behave like hot bodies with an absolute temperature that depends inversely on their mass. This discovery is expressed by a simple and elegant equation known as the Hawking temperature. The best way to commemorate this great scientific event is by bringing it to a wide audience. The simplest and most transparent and intuitive tool to achieve this goal is dimensional analysis. The objective of this work is to use this tool to derive the Hawking equation, reveal its meaning, and explore its main physical consequences.

ImproRisk model, an open access risk assessment tool

Abstract ImproRisk is an open‐access Risk Assessment model, developed in Cyprus, which contributes to the harmonization, standardization and transparency of the Dietary Exposure Assessment methodologies across Europe. It is a simple and transparent tool, built in R, for conducting accurate dietary exposure assessments to chemical substances. Aiming to be established as a standardized Risk Assessment tool at European level, the model has been designed to accept occurrence and consumption data, coded according to the EFSA's food classification and description system version 2 (FoodEx2). ImproRisk users are able to estimate dietary exposure to a chemical substance under study at any level of the EFSA's FoodEx2, considering the FoodEx2 base term and the Process facet (F28). Dietary exposure is estimated at individual level for different population groups and exposure estimates are stratified by different demographic characteristics. ImproRisk is considered as a useful tool both for risk assessors and risk managers, as it facilitates decision making, by providing information regarding the percentage of the population exceeding the relevant health‐based guidance value or reference points for a specific chemical substance.

SinglePointRNA, an user-friendly application implementing single cell RNA-seq analysis software.

Single-cell transcriptomics techniques, such as scRNA-seq, attempt to characterize gene expression profiles in each cell of a heterogeneous sample individually. Due to growing amounts of data generated and the increasing complexity of the computational protocols needed to process the resulting datasets, the demand for dedicated training in mathematical and programming skills may preclude the use of these powerful techniques by many teams. In order to help close that gap between wet-lab and dry-lab capabilities we have developed SinglePointRNA, a shiny-based R application that provides a graphic interface for different publicly available tools to analyze single cell RNA-seq data. The aim of SinglePointRNA is to provide an accessible and transparent tool set to researchers that allows them to perform detailed and custom analysis of their data autonomously. SinglePointRNA is structured in a context-driven framework that prioritizes providing the user with solid qualitative guidance at each step of the analysis process and interpretation of the results. Additionally, the rich user guides accompanying the software are intended to serve as a point of entry for users to learn more about computational techniques applied to single cell data analysis. The SinglePointRNA app, as well as case datasets for the different tutorials are available at www.github.com/ScienceParkMadrid/SinglePointRNA.

CAManim: Animating end-to-end network activation maps.

Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to image-based applications is the development of Convolutional Neural Networks (CNNs), which possess the ability to automatically extract features from data. However, comprehending these complex models and their learned representations, which typically comprise millions of parameters and numerous layers, remains a challenge for both developers and end-users. This challenge arises due to the absence of interpretable and transparent tools to make sense of black-box models. There exists a growing body of Explainable Artificial Intelligence (XAI) literature, including a collection of methods denoted Class Activation Maps (CAMs), that seek to demystify what representations the model learns from the data, how it informs a given prediction, and why it, at times, performs poorly in certain tasks. We propose a novel XAI visualization method denoted CAManim that seeks to simultaneously broaden and focus end-user understanding of CNN predictions by animating the CAM-based network activation maps through all layers, effectively depicting from end-to-end how a model progressively arrives at the final layer activation. Herein, we demonstrate that CAManim works with any CAM-based method and various CNN architectures. Beyond qualitative model assessments, we additionally propose a novel quantitative assessment that expands upon the Remove and Debias (ROAD) metric, pairing the qualitative end-to-end network visual explanations assessment with our novel quantitative "yellow brick ROAD" assessment (ybROAD). This builds upon prior research to address the increasing demand for interpretable, robust, and transparent model assessment methodology, ultimately improving an end-user's trust in a given model's predictions. Examples and source code can be found at: https://omni-ml.github.io/pytorch-grad-cam-anim/.