ABSTRACT Forest planning faces many uncertainties, yet existing decision support systems (DSS) seldom incorporate techniques to address them. This study explores how stochastic programming (SP) functionality could be added to forest DSSs to account for data uncertainty, aiming to investigate the added value of such functionality. An SP model was applied to a traditional long-term forest planning problem, and its quantitative performance was compared to deterministic optimisation. The user value of the DSS integration was explored in a workshop with potential users. The findings indicate that incorporating SP in a DSS is feasible both user-wise and for quantitatively improving the decisions, even if computational time and model complexity increase. Quantitatively, SP increased the total expected NPV by at least 2% compared to deterministic optimisation. Users involved in evaluating the SP integration acknowledged the benefits of using SP in forest planning, but expressed concerns about the increased complexity in problem specification and results interpretation. To enhance user adoption, the presentation of SP settings and outcomes should be done in a user-friendly manner, including intuitive visualisations and simplified summary statistics. This study underscores the potential of integrating SP in DSSs to improve long-term forest planning under data uncertainty.

This paper examines the cultural economy of everyday micro-transactions in platform labour in the Philippines. It proposes sachet capital as a conceptual lens for understanding the mechanisms through which platform capital expands in Asia (and beyond). The analysis builds on enduring sachet logics of ‘just the right unit’, ‘just-in-time’ exchange and transactional interdependency that have long organized the cultural economy of the economic margins. It highlights how platforms transform these practices by blurring labour, consumption and finance into sachetized transactional units that capitalize on informal relations and infrastructures of survival. Sachet capital is a strategy of accumulation attuned to social economies, embedding itself within and restructuring the transactional interdependencies that dominate everyday life. The paper advances three core arguments: (1) sachet transactions are key sites of capital accumulation, both sustaining and being reshaped by platform labour; (2) labour platformization leverages and legitimizes familiar sachet economies, making gig work culturally legible and materially viable despite precarity; and (3) these socio-technical relations of production are organized through a dynamic tension between care and extraction, embedding workers in systems of support that also deepen dependency. By examining the mutually reinforcing operations of labour platforms and financial super-apps like GCash, it argues that the ambitions of scale in platform capitalism are realized through the facilitation and capitalization of countless micro-exchanges grafted onto situated transactional cultures and precarious livelihoods.

To ensure the structural integrity of underground structures during fires, effective fire safety measures must be incorporated into their design. The fire performance of tunnels depends heavily on their support systems, with shotcrete being a cost-efficient and widely adopted tunnel lining solution. Its ability to be sprayed directly onto various surfaces makes it adaptable to diverse tunnel cross-sections and applications. However, during a fire, shotcrete is susceptible to fire-induced spalling, the sudden ejection of surface layers, which can reduce cross-sectional area, lower fire resistance, and cause severe structural damage. Spalling is generally attributed to two primary mechanisms: thermal stresses and vapour pressure build-up. Understanding the interplay between these mechanisms is essential for developing mitigation strategies. This study employed laboratory-scale fire tests to assess the performance of shotcrete under unrestrained and unloaded conditions, considering different sample sizes and moisture contents. A mathematical framework was developed to quantify the contributions of thermal stress and vapour pressure to spalling and to estimate the associated energy release, enabling the calculation of spalled particle velocities. Advanced imaging techniques were used to observe spalling progression and validate the velocity estimates. The results identified vapour pressure build-up as the dominant energy source during spalling events, with thermal stress promoting micro-crack formation that facilitates vapour escape and particle ejection. Heated surface area and moisture content were also found to be significant factors influencing spalling severity. Future research should examine shotcrete behaviour under mechanical load to evaluate how compressive stresses from restrained thermal expansion interact with vapour pressure and thermal stress mechanisms.

The longwall gateroad entries are typically subjected to significant stress and ground deformations which are induced by mining activities, rendering them vulnerable to extensive roof collapses, floor heaves, and sidewall spalling. The Support Technology Optimization Program (STOP), with site-specific Ground Reaction Curves (GRCs), has been extensively utilized to evaluate the performance of standing support systems in gateroad entries. However, measuring GRCs in-situ in underground mines remains impractical, and only a few GRC samples are available in the STOP program. In this research, an entry-scale modeling approach was developed, based on the numerical program of the FLAC 3D , in order to enhance the application of the STOP program with the local geology- and stress-dependent GRC. A case study at a Northern Appalachian coal mine was used in this study to illustrate the whole modeling procedure for site-specific GRC development and standing support performance assessment. Modeling results were compared to field measurements, and the obtained results were in good agreement with the measured data on roof deformation and support load. The R 2 value was 0.91 and the root mean square error was around 0.99 for the roof deformation validation, while the relative error averaged at around 11.8% for the support load verification. The STOP program results also show that the loading density on wooden cribs ranged from 250 kN/m using the weakest crib to 850 kN/m using the default crib with 2 rows spaced at 238 cm under the tailgate stress state. It is concluded that the proposed modeling approach, together with support system database from STOP, could simulate the ground response of a gateroad entry and generate a site-specific GRC for standing support system performance evaluation. The developed geology- and stress-dependent GRC can be integrated into STOP to help optimize the standing support design. The research can provide insights into the optimized support design of gateroad entries of underground excavations in the mining engineering field.

Academic buoyancy in EFL learning: A mixed-methods study among Chinese private university students.

Stem11 score: toward rapid clinical prognostication for acute myeloid leukemia.

Artificial intelligence for marine oil spill management: Recent advances and future directions.

Governance Framework for Safe and Ethical Implementation of Artificial Intelligence in Surgery: A Modified Delphi Consensus.

IPASDU: A DSS based on MAUT to evaluate intervention programs based on physical activity and sport for the prevention of drug use.

Loneliness among fathers of infants: Actual conditions and its relationship with social support.

Unlocking the potential of clinical decision support in cardiovascular care: A mixed-methods systematic review of implementation barriers and enablers.

Injectable anticipatory medications are routinely prescribed ahead of need in many countries to help manage distressing end-of-life symptoms. However, little is known about the lived experience of patients and informal caregivers as they navigate their prescription, supply and use. To explore and map patient journeys in navigating anticipatory medication care, and to identify healthcare interactions with the greatest potential for enhancing patient and informal caregiver experiences of care. Qualitative secondary analysis of longitudinal interview data using framework analysis and patient journey mapping techniques. Adults (18+) prescribed anticipatory medications (n = 6), informal caregivers (n = 9) and health care professionals involved in their care (n = 5). Visually mapping journeys highlighted that patients and informal caregivers' experiences of anticipatory medication processes varied greatly and were influenced by the context of care. All participants appreciated access to injectable medications for future symptom control. However, journeys repeatedly highlighted suboptimal information exchange between patients, informal caregivers and healthcare professionals, regarding their purpose and threshold for use. Navigating unfamiliar and complex end-of-life medication support systems was more challenging when patients lived alone or experienced communication difficulties. Patient and informal caregiver experiences of timely symptom control could be improved by healthcare professionals having open and ongoing conversations about the role of anticipatory medications. Simplified and well-signposted routes for accessing healthcare professional advice and medication input are needed. Using journey mapping offers a novel way to visually illustrate different patient and informal caregivers lived experience and can be adapted for researching experiences of various care pathways.

Decision support systems (DSS) for predicting hypertensive events using real-world telemonitoring data.

Predicting gait kinematics in youth with cerebral palsy using clinically informed machine learning algorithms.

Architectural and translational perspectives on clinical decision support systems for rare disease diagnosis: a scoping review.

Technology-Enabled Crisis Care for Youth: Bridging the Gap.

Efficacy and safety of a digital clinical decision support system using a C-reactive protein-based algorithm and evidence-based stopping rules to guide antibiotic therapy duration: A single-center, open-label randomized controlled trial in a tertiary care hospital.

Continuous renal replacement therapy (CRRT) is a complex, high-risk life-sustaining intervention in intensive care units (ICUs). Despite its widespread use, understanding of how nurses navigate 'human-machine' interactions to develop professional competence remains limited. To describe the clinical experiences of critical care nurses in caring for patients receiving CRRT and to explore their professional growth trajectory from technical anxiety to autonomy. A descriptive qualitative study design was employed. Ten registered nurses with at least 1 year of ICU experience were recruited from a tertiary medical centre using purposeful sampling. Data from semi-structured interviews were analysed using inductive content analysis. The study was reported in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ). Methodological rigour was ensured using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research. A dynamic growth trajectory from 'technical anxiety' to 'professional mastery' emerged, consisting of four core themes: (1) navigating uncertainty, (2) battling the machine, (3) beyond the numbers: developing clinical judgement and (4) the safety net of interprofessional collaboration. A critical turning point occurred when nurses integrated machine data with physiological responses to see the 'patient behind the machine'. Caring for patients on CRRT involves a complex psychological and professional maturation process. Through accumulated practice and interprofessional support, nurses overcome initial fears and develop 'technological competency as caring'. Healthcare institutions should implement simulation-based education focusing on clinical troubleshooting and establish robust interprofessional support systems to reduce cognitive load and foster professional resilience among nurses.

Large language models have emerged as potential tools to support hypertension care, including diagnosis, treatment decision-making, and patient education. However, evidence regarding their validity, performance, and clinical applicability remains limited. The objective is to map current applications of large language models in hypertension care, with emphasis on model optimization strategies, evaluation approaches, and reported limitations. We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews-compliant scoping review of primary studies published between 2023 and 2025 evaluating large language models in hypertension. Thirty-three studies were included. Data were charted on clinical use cases, model optimization techniques, evaluation metrics, data sets, and limitations. Applications were categorized into clinical decision support systems, patient education, medical education, research support, and administrative functions. GPT-based models predominated (82%). Model optimization was limited: 89% relied exclusively on prompt engineering. Most applications focused on patient education (52%) and clinical decision support systems (24%). In clinical decision support systems, reported accuracy ranged from 65% to 100%, reaching 87% to 91% for ambulatory blood pressure monitoring interpretation. Patient education applications showed accuracy between 80% and 90%, but frequent issues included excessive language complexity and occasional unsafe outputs. Across domains, evaluation methods were heterogeneous, reproducibility was inconsistently assessed, and safety concerns, including hallucinations and outdated knowledge, were commonly reported. Current evidence suggests that large language models may support selected tasks in hypertension care; however, their clinical reliability remains uncertain. The limited methodological rigor, minimal use of advanced optimization techniques, and narrow scope of evaluated applications preclude conclusions regarding routine clinical use. Further rigorously designed studies are required before broader implementation can be considered.

Artificial intelligence (AI)-enabled decision support systems are increasingly used in emergency departments and intensive care units to support triage, prediction of deterioration, sepsis recognition and escalation decisions. Although these tools may enhance patient safety, they also introduce new challenges for nurses who remain professionally accountable for clinical judgement in high-acuity settings. To explore emergency and critical care nurses' experiences of using AI-enabled decision support systems, with a focus on clinical judgement, escalation decisions, professional responsibility and ethical considerations across the emergency department-to-intensive care unit continuum. A qualitative study informed by Husserl's descriptive phenomenology was conducted across three emergency departments and three intensive care units in three public hospitals in northern Saudi Arabia. Semi-structured interviews were conducted with 16 nurses who had direct experience using AI-enabled decision support systems. Data were analysed using Braun and Clarke's reflexive thematic analysis. Reporting followed the Standards for Reporting Qualitative Research. Three themes were identified. 'Judgment under algorithmic pressure' reflected nurses' efforts to interpret AI alerts alongside bedside assessment, clinical uncertainty and concerns about over-reliance. 'Navigating escalation and responsibility' highlighted heightened accountability during patient deterioration and transfer from the emergency department to intensive care. 'Professional identity and ethical framing' captured nurses' concerns about role changes, moral agency and ethical discomfort when AI recommendations conflicted with patient-centred judgement. Emergency and critical care Nurses experienced AI-enabled decision support as both helpful and burdensome. Rather than reducing responsibility, AI intensified interpretive work, accountability and ethical tension. Human-centred governance, AI literacy and clear accountability frameworks are needed to support safe AI integration in acute care. Nurses require organisational support, ethical guidance and AI-focused education to critically engage with decision support systems while preserving professional judgement and moral agency.