Transparent Decision Research Articles

Residents’ behavioral support for tourism in a burgeoning rural destination

This study aimed to test a model derived from the social exchange theory and the theory of planned behavior in determining the role that antecedents play in explaining residents' behavioral support for tourism in a rapidly developing destination. Data were collected from 511 residents of McCurtain County in Oklahoma, USA, using onsite interception and online sampling strategies. Based on a two-step structural equation modeling analysis, eight out of the 10 proposed hypotheses were supported. Of the four potential predictors of residents’ attitudinal support for tourism, perceived benefits, perceived costs, and trust in local government were significant, explaining 63% of the variance in the construct. Attitudinal support, subjective norms, and perceived behavioral control then significantly explained 84% of the variance in behavioral intentions to support tourism. These intentions, in turn, significantly explained 44% of the variance in behavioral support for tourism. Implications, limitations, and future research suggestions are discussed at the close of the article. Management ImplicationsResidents' support for tourism within McCurtain County in Oklahoma, USA, is not very strong mostly because residents do not feel that they can trust the government in making decisions regarding tourism and that they have limited power to contribute to tourism planning and management. To address the lack of trust in government, efforts should be made to increase transparency in tourism decision-making. This could occur through the greater inclusion of residents in planning sessions and sharing reports with the public (e.g., sharing minutes and reports via websites, radios, newspapers, etc.). The government should find ways to include more residents in tourism-related decision-making that will affect not only tourism businesses but also the community as a whole. This could look like a regular assessment of residents’ perspectives through conducting surveys, holding focus group interviews, or hosting town hall meetings.

Integrated Deep Learning Architectures for Perception, Control, and Decision-making in Robotics: a Framework for Sensing, Cognition, and Transparent Decision-making

The increasing complexity of robotic applications demands innovative approaches for addressing problems that lack analytical solutions, with deep learning (DL) emerging as a key tool for enabling robots to learn and adapt in dynamic environments. This survey reviews existing DL techniques in robotics, categorizing the major challenges and exploring successful solutions that leverage DL for perception, control, and decision-making. We discuss the use of modular versus end-to-end DL architectures, providing guidelines for selecting appropriate model structures and training strategies. The review also examines advancements in neuro-robotics systems (NRS), where the convergence of neuroscience and robotics is driving the development of robots with embodied intelligence, enabling more natural human-robot interactions. Recent progress in neural mechanisms for perception, cognition, learning, and control is highlighted, offering insights into creating future neuro-robots. Furthermore, we address the challenge of uncertainty estimation in neural networks, crucial for reliable robotic decision-making, and evaluate existing frameworks such as Bayesian belief networks and Monte Carlo sampling that improve uncertainty modeling without requiring architectural changes. Lastly, we explore efforts to enhance transparency in robotic systems through integrated reasoning and learning methods, focusing on architectures that combine logical reasoning with deep learning to provide explainable decision-making. This survey aims to offer a structured overview of current research and guide future developments in neuro-robotics.

Using the WHO-INTEGRATE framework to develop a COVID-19 guideline for schools, Germany.

At the beginning of the coronavirus disease 2019 (COVID-19) pandemic, reliable, globally applicable recommendations for safe and continuous school operations were lacking. In October 2020, the German Association of Scientific Medical Societies' task force for COVID-19 guidelines and public health researchers at Ludwig-Maximilians-Universität München initiated the rapid development of a living evidence- and consensus-based guideline to reduce severe acute respiratory syndrome coronavirus 2 transmission in schools. To facilitate transparent, structured and comprehensive decision-making with a whole-of-society perspective, they applied the WHO-INTEGRATE evidence-to-decision framework. This framework supported a broad, multisectoral composition of the guideline panel. The panel used newly synthesized evidence on nine school measures. Participating medical societies or the guideline secretariat completed evidence-to-decision tables. They also drafted recommendations for the guideline panel, who discussed and revised them during moderated consensus conferences. In Germany, each state is responsible for organizing schooling. The German Association of Scientific Medical Societies coordinates development of evidence- and consensus-based guidelines. The first version of the guideline was published in February 2021, and the guideline dissemination created much media attention. Of the 16 state education ministries, almost all knew about the guideline, nine recognized it as a relevant source of information and five used it to check existing directives. The WHO-INTEGRATE framework facilitated a comprehensive assessment of school measures from the start of guideline development, considering the broad societal impact of the measures. Using the framework in rapid mode was feasible, but it fell short of its potential.

Kuasa Eksklusi dalam Pembangunan Geothermal di Desa Ulubelu, Kecamatan Golewa, Kabupaten Ngada

The research with the title Power Of Exclusion in Geothermal Development in Ulubelu Vilage,Golewa District, Ngada Regerency is motivated by the development of the Mataloko Georhermal Power Plant (PLTPB) with a formulation of How the power of exclusion is Particed Regarding Regulation,coercion, markets and legitimacy in the dimensions of power John Gaventa with the aim of exsplaning the practice of exclusionary power,in order to differentiate this proposal, the researcher used Tania Muray Lii research theory of exclusionary power by elaborating on John Gaventa three dimensional theory of power. The research method used in writing this proposal is descriptive qualitative. The research method used in this research is in the form of excluded local communities in the case limiting community access to land and natural. Resources project regulations energy as a central government initiative and regional government support and coercive power as an exclusionary strategy as well as the market as e vehicle for exclusionand supported by legitimasi which is used as a mode of geothermal development. Resources project regulations energy as a central government initiative and regional government support and coercive power as an axclusionary strategy as well as the market as a vehicle for exclusion and supported by legitiasi which is used as a mode of the geothermal development. The complexcity of the power dynamics the occur and the interactions between various kinds of intersests in the power dynamics the occur in the context of geothermal development. The analysis uses lii (2011) social exclusion theory framework and John Gaventa (2006) 3 dimensional theory of power to provide an In depth understanding of exclusion that exixts in society. Not a few people have lost access to land and land and to agricultural land as well as the implementation of authotharian regulations without considering the local context. This research shows that even though geothermal energy is clean, the development procces can create or worsen social injustice if it is not managed well. The implicatins of these findings are the need for WKP policy reform that takes into account the right of indigenous peoples, fair compensation mechanisms and transparent decision making processes that involve the entire community as well as aducational programs to increase public understanding of community rights.

Development of Machine Learning Model for Predicting Prolonged Operation Time in Lumbar Stenosis undergoing Posterior Lumbar Interbody Fusion: A Multi-center study

Background ContextLonger posterior lumbar interbody fusion (PLIF) surgeries for individuals with lumbar spinal stenosis are linked to more complications and negatively affect recovery after the operation. Therefore, there is a critical need for a method to accurately predict patients who are at risk for prolonged operation times. PurposeThis research aimed to develop a clinical model to predict prolonged operation time for patients undergoing PLIF procedures. Study Design/SettingThis study employs a machine-learning approach to analyze data retrospectively collected. Patient Sample3233 patients diagnosed with lumbar spinal stenosis (LSS) had posterior lumbar interbody fusion (PLIF) at 22 hospitals in China from January 2015 to December 2022. Outcome MeasuresThe primary outcome was operation time. Prolonged operation time defined as exceeded 75% of the overall surgical duration, which mean exceeding 240 minutes. MethodsA total of 3233 patients who underwent PLIF surgery with lumbar spinal stenosis (LSS) were divided into one training group and four test groups based on different district areas. The training group included 1569 patients, while Test1 had 541, Test2 had 403, Test3 had 351, and Test4 had 369 patients. Variables consisted of demographics, perioperative details, preoperative laboratory examinations and other Additional factors. Six algorithms were employed for variable screening, and variables identified by more than two screening methods were incorporated into the final model. In the training cohort, a 10-fold cross-validation (CV) and Bayesian hyperparameter optimization techniques were utilized to construct a model using eleven machine learning algorithms. Following this, the model was evaluated using four separate external test sets, and the mean Area Under the Curve (AUC) was computed to determine the best-performing model. Further performance metrics of the best model were evaluated, and SHapley Additive exPlanations(SHAP) were used for interpretability analysis to enhance decision-making transparency. Ultimately, an online calculator was created. ResultsAmong the various machine learning models, the Random Forest achieved the highest performance in the validation set, with AUROC scores of 0.832 in Test1, 0.834 in Test2, 0.816 inTest3, 0.822 in Test4) compared with other machine learning models. The top contributing variables were number of levels fusion, pre-APTT, weight and age. The predictive model was further refined by developing a web-based calculator for clinical application. (https://wenle.shinyapps.io/PPOT_LSS/) ConclusionsThis predictive model can facilitate identification of risk for prolonged operation time following PLIF surgery. Predictive calculators are expected to improve preoperative planning, identify patients with high risk factors, and help clinicians facilitating the improvement of treatment plans and the implementation of clinical intervention.

Explainable AI (XAI) in healthcare: Enhancing trust and transparency in critical decision-making

The integration of artificial intelligence (AI) in healthcare is revolutionizing diagnostic and treatment procedures, offering unprecedented accuracy and efficiency. However, the opacity of many advanced AI models, often described as "black boxes," creates challenges in adoption due to concerns around trust, transparency, and interpretability, particularly in high-stakes environments like healthcare. Explainable AI (XAI) addresses these concerns by providing a framework that not only achieves high performance but also offers insight into how decisions are made. This research explores the application of XAI techniques in healthcare, focusing on critical areas such as disease diagnostics, predictive analytics, and personalized treatment recommendations. The study will analyse various XAI methods, including model-agnostic approaches (LIME, SHAP), interpretable deep learning models, and domain-specific applications of XAI. It also evaluates the ethical implications, such as accountability and bias mitigation, and how XAI can foster collaboration between clinicians and AI systems. Ultimately, the goal is to create AI systems that are both powerful and trustworthy, promoting broader adoption in the healthcare sector while ensuring ethical and safe outcomes for patients.

Generative Artificial Intelligence: Challenges and Opportunities for Systems Developers: A Systematic Mapping of Literature

Generative Artificial Intelligence tools have gained increasing prominence in recent years. However, the increasing use of these technologies and the functionalities they offer has sparked discussions about their impact and even raised concerns about the potential replacement of human work by automation carried out by machines. This study proposes a Systematic Literature Review to evaluate the opportunities and challenges that these technologies present to system developers in the current and future technological scenario. Aiming at state-of-the-art research to identify how Generative AIs are being applied in the context of software development and what are the latest trends and innovations in this field and how these innovations affect the opportunities and challenges for system developers. As a result, several studies were found that highlight how Generative AI has provided productivity and systems development optimized solutions in the industry, as well as promoting innovations. Studies also emphasize the need for a balance between the use of AI tools and development carried out by human participation, which must be mediated by common sense. Furthermore, the review will explore the ethical implications associated with the widespread adoption of AI technologies, addressing issues such as data privacy, decision-making transparency, and the responsibility of developers in ensuring that AI applications are used in a way that benefits society. The findings of this review will contribute to a better understanding of how generative AI is reshaping the software development landscape and provide insights for future research and development in this rapidly evolving field.

The impact of people’s participation on public policy satisfaction in Vietnam

People’s participation in public decision-making not only helps ensure transparency and accountability but also contributes to improving their satisfaction with government policies and operations. This study examines whether transparency in decision-making, trust in public agencies, access to information, and people’s awareness of their rights and responsibilities play an important role in promoting their participation. The study also explores whether this participation directly affects people’s satisfaction with public decisions and policies. This study uses mixed method approach through the SEM linear regression model. The analysis of the results shows that transparency, trust, information accessibility and citizen awareness have a positive and significant impact on the level of their participation in public activities. However, increased participation does not necessarily lead to higher levels of satisfaction with public policies, especially when the participation process does not meet people’s expectations or lacks transparency and efficiency. The study concludes that, to improve people’s satisfaction, it is necessary not only to promote participation, but also to improve the quality of this process, ensuring that participation is substantive and has a clear impact on public decisions. As such, specific recommendations are made to improve public policy and enhance people’s participation and satisfaction in the context of modern public governance.

Data-driven classification and explainable-AI in the field of lung imaging.

Detecting lung diseases in medical images can be quite challenging for radiologists. In some cases, even experienced experts may struggle with accurately diagnosing chest diseases, leading to potential inaccuracies due to complex or unseen biomarkers. This review paper delves into various datasets and machine learning techniques employed in recent research for lung disease classification, focusing on pneumonia analysis using chest X-ray images. We explore conventional machine learning methods, pretrained deep learning models, customized convolutional neural networks (CNNs), and ensemble methods. A comprehensive comparison of different classification approaches is presented, encompassing data acquisition, preprocessing, feature extraction, and classification using machine vision, machine and deep learning, and explainable-AI (XAI). Our analysis highlights the superior performance of transfer learning-based methods using CNNs and ensemble models/features for lung disease classification. In addition, our comprehensive review offers insights for researchers in other medical domains too who utilize radiological images. By providing a thorough overview of various techniques, our work enables the establishment of effective strategies and identification of suitable methods for a wide range of challenges. Currently, beyond traditional evaluation metrics, researchers emphasize the importance of XAI techniques in machine and deep learning models and their applications in classification tasks. This incorporation helps in gaining a deeper understanding of their decision-making processes, leading to improved trust, transparency, and overall clinical decision-making. Our comprehensive review serves as a valuable resource for researchers and practitioners seeking not only to advance the field of lung disease detection using machine learning and XAI but also from other diverse domains.

Guiding trust: The role of labeling information in the market for gluten-free pasta (2019–2023)

Labeling transparency is crucial in today's consumer landscape, ensuring informed choices and trust. Food labels are the first piece of information consumers have and become critical when foods must meet dietary restrictions. With its widespread consumption, gluten-free pasta is a fitting model to evaluate labels’ contribution to food literacy. The scope of this study is to examine the clean status and transparency of gluten-free pasta labels in the global marketplace analyzing by ingredient composition, nutritional facts, nutritional claims, and the implementation of quantitative ingredient declarations (QUID). To achieve this, Mintel Global New Product Database (GNPD) was used to retrieve a total of 1188 products sold from 2019 to 2023. Findings revealed variations in gluten-free pasta ingredients, with fresh and dry types varying in nutrients. Branded and private-label pasta differed in salt content, while pulses-based pasta was fiber and protein-rich but pricier. Organic pasta did not consistently show superior qualities, and whole grains did not change nutrition or price. Vegan options were similar in nutrition and price to vegetarian choices. While nutritional claims generally complied with regulations, inconsistent use of QUID highlighted the need for stricter labeling standards to ensure product transparency and support consumer decision-making. Our findings underline the critical role of clear and accurate labeling in empowering consumers and advocating for enhanced regulatory oversight.

Innovation portfolio management for the public non-profit research and development sector: What can we learn from the private sector?

ABSTRACT Innovation Portfolio Management refers to the systems, processes and mechanisms to intentionally manage innovation investments and decisions within an organization against its mission or strategy. This approach to managing and optimizing innovation and scaling investments is rarely used by public non-profit R&D organizations. This study draws lessons from innovation portfolio management in private sector organizations. Private sector invests in innovation portfolio management to: (i) enhance transparent and evidence-based decision-making, (ii) strengthen organizational agility and mission-orientedness, (iii) facilitate monitoring, risk management and prioritization and (iv) improve overall organizational performance and learning. These innovation portfolio management benefits are equally relevant for the public non-profit R&D sector, where innovation performance and outcomes are for societal impact. Public non-profit R&D can and should manage their innovation portfolios more intentionally, and can learn from private sector successes and challenges. Embracing the opportunities, whilst managing the challenges and risks requires investment in methods, mechanisms and mindsets for effective innovation portfolio management.

Vaccination governance in protracted conflict settings: the case of northwest Syria

BackgroundEffective vaccination governance in conflict-affected regions poses unique challenges. This study evaluates the governance of vaccination programs in northwest Syria, focusing on effectiveness, efficiency, inclusiveness, data availability, vision, transparency, accountability, and sustainability.MethodsUsing a mixed-methods approach, and adapting Siddiqi’s framework for health governance, data were collected through 14 key informant interviews (KIIs), a validating workshop, and ethnographic observations. Findings were triangulated to provide a comprehensive understanding of vaccination governance.ResultsThe study highlights innovative approaches used to navigate the complex health governance landscape to deliver vaccination interventions, which strengthened sub-national vaccination structures such as The Syria Immunisation Group (SIG). The analysis revealed several key themes. Effectiveness and efficiency were demonstrated through cold-chain reliability and extensive outreach activities, though formal reports lacked detailed analysis of vaccine losses and linkage between disease outbreak data and coverage statistics. Key informants and workshop participants rated the vaccination strategy positively but identified inefficiencies due to irregular funding and bureaucracy. Inclusiveness and data availability were prioritised, with outreach activities targeting vulnerable groups. However, significant gaps in demographic data and reliance on paper-based systems hindered comprehensive coverage analysis. Digitalisation efforts were noted but require further support. The SIG demonstrated a clear strategic vision supported by international organizations such as the World Health Organization, yet limited partner participation in strategic planning raised concerns about broader ownership and engagement. While the SIG was perceived as approachable, the lack of public documentation and financial disclosure limited transparency. Internal information sharing was prevalent, but public communication strategies were insufficient. Accountability and sustainability faced challenges due to a decentralized structure and reliance on diverse donors. Despite stabilizing factors such as decentralization and financial continuity, fragmented oversight and reliance on donor funding remained significant concerns.DiscussionThe study highlights the complexities of vaccination governance in conflict-affected areas. Comparisons with other conflict zones underscore the importance of local organisations and international support. The SIG’s role is pivotal, but its legitimacy, transparency, and inclusivity require improvement. The potential transition to early recovery in Syria poses additional challenges to SIG’s sustainability and integration into national programs.ConclusionThe governance of vaccination in northwest Syria is multifaceted, involving multiple stakeholders and lacking a legitimate government. Enhancing transparency, local ownership, and participatory decision-making are crucial for improving governance. The role of international bodies is essential, emphasising the need for structured feedback mechanisms and transparent monitoring processes to ensure the program’s success and sustainability.

Securing online integrity: a hybrid approach to deepfake detection and removal using Explainable AI and Adversarial Robustness Training

As deepfake technology becomes increasingly sophisticated, the proliferation of manipulated images presents a significant threat to online integrity, requiring advanced detection and mitigation strategies. Addressing this critical challenge, our study introduces a pioneering approach that integrates Explainable AI (XAI) with Adversarial Robustness Training (ART) to enhance the detection and removal of deepfake content. The proposed methodology, termed XAI-ART, begins with the creation of a diverse dataset that includes both authentic and manipulated images, followed by comprehensive preprocessing and augmentation. We then employ Adversarial Robustness Training to fortify the deep learning model against adversarial manipulations. By incorporating Explainable AI techniques, our approach not only improves detection accuracy but also provides transparency in model decision-making, offering clear insights into how deepfake content is identified. Our experimental results underscore the effectiveness of XAI-ART, with the model achieving an impressive accuracy of 97.5% in distinguishing between genuine and manipulated images. The recall rate of 96.8% indicates that our model effectively captures the majority of deepfake instances, while the F1-Score of 97.5% demonstrates a well-balanced performance in precision and recall. Importantly, the model maintains high robustness against adversarial attacks, with a minimal accuracy reduction to 96.7% under perturbations.

Clinician voices on ethics of LLM integration in healthcare: a thematic analysis of ethical concerns and implications

ObjectivesThis study aimed to explain and categorize key ethical concerns about integrating large language models (LLMs) in healthcare, drawing particularly from the perspectives of clinicians in online discussions.Materials and methodsWe analyzed 3049 posts and comments extracted from a self-identified clinician subreddit using unsupervised machine learning via Latent Dirichlet Allocation and a structured qualitative analysis methodology.ResultsAnalysis uncovered 14 salient themes of ethical implications, which we further consolidated into 4 overarching domains reflecting ethical issues around various clinical applications of LLM in healthcare, LLM coding, algorithm, and data governance, LLM’s role in health equity and the distribution of public health services, and the relationship between users (human) and LLM systems (machine).DiscussionMapping themes to ethical frameworks in literature illustrated multifaceted issues covering transparent LLM decisions, fairness, privacy, access disparities, user experiences, and reliability.ConclusionThis study emphasizes the need for ongoing ethical review from stakeholders to ensure responsible innovation and advocates for tailored governance to enhance LLM use in healthcare, aiming to improve clinical outcomes ethically and effectively.

From tradition to technological advancement: embracing blockchain technology in family businesses

PurposeDespite the rapid advancement of blockchain technology across various sectors, scholarly research on its application within family businesses remains significantly underdeveloped. This study aims to address this gap by examining the application of blockchain technology within family businesses to identify key application domains, benefits and implementation challenges.Design/methodology/approachThe study employs a conceptual approach, drawing on existing literature on family businesses and blockchain technology. This review aimed to identify the unique characteristics of family businesses, their challenges and the distinctive features of blockchain technology that can potentially be mapped to each other. Based on the literature review, we develop a conceptual framework exploring blockchain technology applications in family businesses. Real-world case studies of family businesses that have implemented blockchain technology were identified to provide practical insights and implementation challenges.FindingsBlockchain technology possesses transformative potential for family businesses across several critical domains. It includes enhancing trust and transparency in operations, improving governance and decision-making and facilitating succession planning and intergenerational wealth management. Case study evidence illustrates the tangible benefits of blockchain, including enhanced supply chain transparency, optimized business processes, increased customer trust and resultant business sustainability. Blockchain technology implementation challenges include data privacy concerns, integration with legacy systems, regulatory uncertainty and change management issues.Research limitations/implicationsThis study is limited by its reliance on existing literature and case studies. It may not capture the full spectrum of challenges and opportunities associated with blockchain applications in family businesses. Future research should focus on longitudinal and empirical research to provide a deeper understanding of the impact of blockchain technology application in family businesses.Originality/valueThis study contributes to the literature by exploring the intersection of family businesses and blockchain technology, an area that has received limited academic attention. It identifies potential application domains of blockchain technology in family businesses and develops a conceptual framework based on existing literature. Through case studies, the research provides practical insights and valuable lessons for family businesses considering blockchain implementation. It also addresses key considerations and challenges, providing a clear roadmap for blockchain technology integration in family businesses. The study lays the groundwork for further research and exploration in blockchain technology and family businesses.

Development of an multicriteria decision analysis framework for rare disease reimbursement prioritization in Malaysia.

Rare diseases (RD)-related policies have received significant attention due to the pressing medical requirements associated with these medical conditions and the substantial impact and treatments they may have on healthcare budgets. Nevertheless, policymakers frequently encounter difficulties in managing issues concerning resource allocation and prioritization within this population. Realizing the need to address such problems, this study was conducted to develop a framework based on the multicriteria decision analysis to improve RD reimbursement prioritization in Malaysia. Primarily, a scoping review was performed to identify the methods and criteria used for the reimbursement of RD treatment, followed by strategic stakeholder engagement and a deliberative process on determining the best approach for the framework, including criteria identification, elicitation of weights, and a pilot assessment using the framework. The findings reflected the priorities and perspectives of the stakeholders, which identified eight key criteria and their associated weights, namely effectiveness (19.6 percent), disease severity (15.6 percent), safety (14.2 percent), access to treatment (12.6 percent), economic consideration (12.2 percent), type of therapeutic treatment (11.5 percent), availability of alternatives (8.3 percent), and population group (6 percent). In summary, the developed framework was well-accepted by the Rare Disease Committee, which will be applied as part of the committee deliberation for transparent and equitable decision making on fund allocation and reimbursement of orphan and RD treatment in Malaysia.

Real-time Sorting of Broiler Chicken Meat with Robotic arm: XAI-enhanced Deep Learning and LIME Framework for Freshness Detection

Ensuring food safety in poultry processing is crucial due to the risk of contamination, which can lead to health hazards. Traditional methods for sorting chicken meat based on freshness are often manual and prone to error, necessitating the development of an automated, accurate sorting system. This research focuses on integrating Convolutional Neural Networks (CNNs) with Local Interpretable Model-agnostic Explanations (LIME) to enhance the accuracy and efficiency of sorting broiler chicken meat into fresh and rotten categories. This study employed the InceptionV3 CNN model combined with LIME for transparent decision-making. The model was trained and tested on a dataset of chicken meat images to guide a robotic arm for real-time sorting. The system achieved an impressive sorting accuracy of 96.3 %, outperforming traditional manual methods. With the guidance of the CNN-LIME integration, the robotic arm successfully processed 1000 samples of fresh and 300 samples of rotten chicken meat, achieving a precision of 94.19% for fresh meat and 97.24% for rotten meat. This integration significantly improved sorting accuracy and efficiency in poultry processing, providing a reliable method for distinguishing between fresh and rotten chicken meat. This system improves food safety and operational efficiency in automated food processing. It shows the effectiveness of combining explainable AI with robotics, reducing human exposure to harmful contaminants ethically. It also contributes to public health by ensuring safer food products. This research presents a new approach to automated food sorting by integrating CNNs with LIME for transparency and accuracy. This can be beneficial for food processing industries and researchers in the field of AI and robotics.

Evaluating models of expert judgment to inform assessment of ecosystem viability and collapse.

Expert judgment underpins assessment of threatened ecosystems. However, experts are often narrowly defined, and variability in their judgments may be substantial. Models built from structured elicitation with large diverse expert panels can contribute to more consistent and transparent decision-making. We conducted a structured elicitation under a broad definition of expertise to examine variation in judgments of ecosystem viability and collapse in a critically endangered ecosystem. We explored whether variation in judgments among 83 experts was related to affiliation and management expertise and assessed performance of an average model based on common ecosystem indicators. There were systematic differences among individuals, much of which were not explained by affiliation or expertise. However, of the individuals affiliated with government, those in conservation and environmental departments were more likely to determine a patch was viable than those in agriculture and rural land management. Classification errors from an average model, in which all individuals were weighted equally, were highest among government agriculture experts (27%) and lowest among government conservation experts (12%). Differences were mostly cases in which the average model predicted a patch was viable but the individual thought it was not. These differences arose primarily for areas that were grazed or cleared of mature trees. These areas are often the target of restoration, but they are also valuable for agriculture. These results highlight the potential for conflicting advice and disagreement about policies and actions for conserving and restoring threatened ecosystems. Although adoption of an average model can improve consistency of ecosystem assessment, it can fail to capture and convey diverse opinions held by experts. Structured elicitation and models of ecosystem viability play an important role in providing data-driven evidence of where differences arise among experts to support engagement and discussion among stakeholders and decision makers and to improve the management of threatened ecosystems.

Kepemimpinan Kepala Madrasah dalam Konteks Manajemen Berbasis Sekolah (MBS) pada MAN 13 Jakarta

This study aims to explore and analyze the role of school leadership in the implementation of School-Based Management (SBM) at MAN 13 Jakarta. In the era of educational decentralization, SBM is a model applied to improve educational quality through increased participation and accountability at the school level. The research method used is a qualitative approach with in-depth interviews, observations, and document analysis. The findings reveal that leadership at MAN 13 Jakarta plays a crucial role in mobilizing the participation of all stakeholders, including teachers, students, and parents, as well as in creating a conducive learning environment. Effective leadership at MAN 13 Jakarta is characterized by open communication, transparency in decision-making, and staff empowerment. Challenges faced include resistance to change and resource limitations, but these can be overcome through adaptive and collaborative leadership strategies.

How to build and operationalise a hospital command centre

In the ever-evolving healthcare landscape, hospitals routinely grapple with daily disruptions and unforeseen events affecting resources and capacity. This paper explores the implementation and impact of the Rochester Hospital Operations Command Center (RHOCC) as a strategic response to challenges faced by the Mayo Clinic Hospital campuses in Rochester. The RHOCC, developed with an expedited timeline in response to capacity issues, exacerbated by COVID-19, serves as a centralised hub for real-time monitoring, coordination and decision-making related to patient flow. The process of establishing the command centre involved role identification, skill set determination, location of important personnel in an organised central space and vendor selection to develop the necessary infrastructure. Dashboards were developed to provide real-time actionable insights into external transfers, surgical and other planned admissions, emergency department status, patient throughput and discharges. These dashboards include colour-coded keys highlighting thresholds related to patient flow metrics and are used to facilitate daily huddles and enhance transparency and data-driven decision-making among hospital stakeholders. The outcomes of implementing the RHOCC include improved efficiency and collaboration, as well as a significantly enhanced ability to proactively respond to factors affecting census. The development of a command centre — driven by leadership endorsement and a commitment to continuous improvement — emerges as a transformative strategy in healthcare innovation. The RHOCC’s approach of building capability, fostering inclusion and articulating a vision of providing superior care while driving impactful improvements, positions it as a leading force in creating operational efficiencies to define healthcare delivery.