Search Results (2,029)

This study examined the effectiveness of an Artificial Intelligence (AI)-supported self-coaching system designed to improve preschool teachers’ implementation of embedded instruction (EI) for young children with autism in inclusive early childhood classrooms. Using a multiple-probe across participants single-case design with four teacher–child dyads, the study evaluated changes in teacher fidelity, child learning outcomes, maintenance, generalization, and teacher perceptions. Following baseline and an initial EI training, teachers engaged in weekly AI-supported self-coaching cycles that included planning, data entry, reflection, and AI-generated individualized feedback. Results demonstrated clear functional relations between the introduction of the AI-supported system and increases in teachers’ EI fidelity. All teachers reached high levels of accurate implementation, maintained their performance after AI supports were withdrawn, and generalized EI procedures to non-targeted routines. Correspondingly, children showed substantial improvements in unprompted correct responding on individualized goals, with gains sustained across maintenance and generalization probes. Social validity data indicated that teachers found both EI and AI-supported self-coaching highly acceptable, feasible, and helpful for guiding instructional decision-making. Findings provide promising initial evidence that AI-supported self-coaching can serve as a scalable, cost-effective professional development approach that strengthens teacher practice and enhances learning outcomes for young children with autism in inclusive preschool settings. Full article

Walkability for non-motorized users is crucial for fostering inclusive, healthy, and sustainable communities. By prioritizing modern human-centered design principles, social equality is promoted for all categories of users, regardless of physical abilities or socio-economic status. Despite the importance of this indicator, a series of inconsistencies that produce inadequate and inaccessible urban space can still be observed in cities. The aim of this paper is to present the methodology for the calculation of the walkability index at the local level. This new methodological procedure considers walkability for pedestrians, with a special focus on people with reduced mobility. Based on specifically defined criteria, initial calculations were performed and integrated into the dynamic walkability index (DWI). One of the main advantages of this index is that it includes the dynamic component of the share of different categories of users in the total sample, which enables simple time modification without repeating the entire procedure. The developed methodology can be used as a tool for ranking existing street segments according to the urgency of reconstruction, while on the other hand promoting equality and inclusion of all categories of users in decision-making processes, thus creating more comfortable and safer environments. Full article

The advancement of automated driving technologies offers potential safety and efficiency gains, yet safety remains the primary barrier to higher-level deployment. Failures in automated driving systems rarely result from a single technical malfunction. Instead, they emerge from coupled organizational, technical, human, and environmental factors, particularly in partial and conditional automation where human supervision and intervention remain critical. This study systematically identifies safety failures in automated driving systems and analyzes how they propagate across system layers and human–machine interactions. A qualitative case-based analytical approach is adopted by integrating the Swiss Cheese model and the SHELL model. The Swiss Cheese model is used to represent multilayer defensive structures, including governance and policy, perception, planning and decision-making, control and actuation, and human–machine interfaces. The SHELL model structures interaction failures between liveware and software, hardware, environment, and other liveware. The results reveal recurrent cross-layer failure pathways in which interface-level mismatches, such as low-salience alerts, sensor miscalibration, adverse environmental conditions, and inadequate handover communication, align with latent system weaknesses to produce unsafe outcomes. These findings demonstrate that autonomous driving safety failures are predominantly socio-technical in nature rather than purely technological. The proposed hybrid framework provides actionable insights for system designers, operators, and regulators by identifying critical intervention points for improving interface design, operational procedures, and policy-level safeguards in autonomous driving systems. Full article

Background: The objective of this work was to support the implementation of the European Health Technology Assessment Regulation (EU HTAR) and optimize performance of the evolving EU HTA system. Therefore, an inclusive multi-stakeholder framework of key performance indicators (KPI) for success measurement was developed. Methods: A modified Delphi-procedure was applied as follows: (1) development of a generic KPI pool at the Fall Convention 2024 of the European Access Academy (EAA); (2) review of initial pool and identification of additional KPIs; (3) development of prioritized KPIs covering patient, clinician, Health Technology Developer (HTD), and System/Member State (MS) perspectives, and (4) consolidation of the stakeholder-centric KPIs after EAA’s Spring Convention 2025. Results: Steps 1 and 2 of the Delphi procedure revealed 14 generic KPI domains. Steps 3 and 4 resulted in four prioritized KPIs for patients (patient input; utilization of patient-centric outcome measures; time to access; equity); six for clinicians (population/intervention/comparator/outcomes (PICO); addressing uncertainty; clinician involvement; transparency; equity and time to access); four for HTDs (PICO; joint scientific consultation (JSC) process; joint clinical assessment (JCA) process; time to national decision making); five from a system/MS perspective (PICO; learning and training the health system; reducing duplication; equity and time to access). The scope of, e.g., the PICO-related KPI, differed between stakeholder groups. Also, several KPIs intentionally reached beyond the remit of EU HTA as they are also dependent on MS-specific factors including national health systems and budgets. Discussion and Conclusions: The KPI framework developed here presents a step towards the generation of systematic multi-stakeholder evidence to support a successful implementation of the EU HTAR. The relevance of the identified stakeholder-centric KPIs is confirmed by their alignment with the Health System Goals suggested in the context of “Performance measurement for health improvement” by the World Health Organisation. Implementation of the framework, i.e., measurement of KPIs, is envisioned to provide evidence to inform the 2028 revision of the EU HTAR. Full article

Background: Effective preoperative patient counseling is essential to shared decision-making. In thyroid surgery, patient communication can be complicated by the complex anatomy and variable surgical approaches, which may not be fully conveyed through conventional verbal explanations or schematic drawings. Virtual three-dimensional (3D) thyroid models may provide an intuitive tool to enhance patient comprehension. Methods: We conducted a randomized controlled trial at Peking Union Medical College Hospital with 94 newly-diagnosed thyroid cancer patients scheduled for thyroidectomy. Participants were assigned to either the control group (n = 47), which received preoperative drawing-based counseling, or the intervention group (n = 47), which utilized a virtual 3D model for communication. The Thyroid Navigator app, developed by Kuma Hospital, was used to provide dynamic 3D representation of the thyroid gland, surrounding structures, and potential surgical procedures. After standardized preoperative consultations, patients were surveyed to assess their understanding in pertinent anatomy and postoperative complications. Results: Patients in the 3D model group demonstrated similar correct response rates in lesion localization (p = 0.536) or parathyroid gland recognition (p = 0.071), but significantly higher accuracy in identifying the recurrent laryngeal nerve and the extent of lymph node dissection compared with the control group (p < 0.05). Moreover, comprehension of the causes of major postoperative complications—including hoarseness (recurrent laryngeal nerve injury, p = 0.004), hypocalcemia (parathyroid gland impairment, p = 0.015), and bleeding (inadequate hemostasis, p = 0.008)—was significantly improved in the 3D model group. Conclusions: Use of a virtual 3D thyroid model significantly improves patient comprehension of thyroid anatomy, surgical procedures, and potential complications, thereby enhancing clinician–patient communication. Virtual 3D models represent a practical and cost-effective supplement to conventional counseling in thyroid surgery, offering clear benefits in patient education and shared decision-making. Full article

Background/Objectives: Preterm birth remains a major cause of neonatal morbidity and mortality, particularly in multiple pregnancies and in cases of cervical shortening. While cervical cerclage is established in singleton pregnancies, its efficacy in multiple gestations remains uncertain. This study compares pregnancy and neonatal outcomes following second-trimester cerclage in singleton and multiple pregnancies with a short cervix. Methods: In this retrospective cohort study, 96 women underwent second-trimester cerclage at a tertiary perinatal center between 2020 and 2024. All had a cervical length ≤ 25 mm or prolapsed membranes without infection or premature rupture. Primary outcomes included term delivery rate, gestational age, mode of delivery, and neonatal outcomes; secondary outcomes comprised surgical complications and rehospitalization, defined as the need for renewed inpatient care due to threatened preterm labor or procedure-related complications. Results: In total, 79 singleton and 17 multiple pregnancies were analyzed. Term delivery occurred more often in singletons (54%) than multiples (18%, p = 0.006). Mean gestational age at birth was 258 ± 25 days in singletons versus 228 ± 28 days in multiples (p < 0.001). Birth weight was significantly lower in multiples (1985 g vs. 2943 g; p < 0.001), and neonatal infections were more frequent (53% vs. 26%; p = 0.008). Caesarean delivery was more common in multiples (82% vs. 33%; p < 0.001). Apart from increased postoperative contractions in multiples (24% vs. 5%; p = 0.031), complication rates and rehospitalization (27% vs. 29%; p = 0.8) were similar. Conclusions: Second-trimester cerclage is less effective in preventing preterm birth in multiple pregnancies compared to singleton pregnancies; however, it appears to be associated with a stabilizing clinical course and may facilitate outpatient management in selected high-risk cases. These findings support individualized counseling and shared decision-making, particularly in multifetal gestations. Full article

Background/Objectives: Neonatal Intensive Care Units (NICUs) constitute a highly complex clinical environment characterized by patient fragility and frequent ethically sensitive decisions. To date, systematic studies investigating how Italian NICUs address these challenges and what forms of ethics support are effectively available are lacking. The aim of this study is therefore to assess how ethical issues are managed in Italian NICUs, with particular attention to the availability, use, and perceived usefulness of clinical ethics support in everyday practice. Methods: A 25-item questionnaire was developed by adapting an existing tool for investigating clinical ethics activities to the neonatal context. Following expert review by the GIBCE (Gruppo Interdisciplinare di Bioetica Clinica e Consulenza Etica in ambito sanitario), the final instrument covered four areas (general data, experience with ethical dilemmas, tools and procedures, opinions and training needs). A manual web search identified all Italian NICUs and their clinical directors, who were asked to disseminate the survey among staff. Participation was voluntary and anonymous. Data collection was conducted via Google Forms and analyzed through qualitative thematic analysis. Results: A total of 217 questionnaires were collected. The most frequent ethical dilemmas concern quality of life with anticipated multiple or severe disabilities (72.4%) and decisions to withdraw or withhold life-sustaining treatments (64.5%). Major challenges include fear of medico-legal repercussions (57.6%) and communication divergences between physicians and nurses (49.8%). More than half of respondents (52.1%) reported no formal training in clinical ethics, and 68.7% had never developed a Shared Care Plan (Shared Document for healthcare ethics planning) as defined by the Italian Law 219/2017. Conclusions: Findings highlight marked fragmentation in ethical practices across Italian NICUs. On this basis, establishing structured and accessible CEC services could help promote consistency, reinforce shared ethical standards, and support transparent and equitable decision-making in critical neonatal care. Full article

This paper develops a procedure for assessing both supply chain processes and strategic criteria in the context of platform-based supply chains, addressing the problem that organizations often invest in digital platforms without a clear understanding of how process effectiveness, process dysfunction, and strategic platform priorities jointly influence implementation success. The main research objective is to evaluate how effective and dysfunctional supply chain processes, together with prioritized strategic platform criteria, shape performance, productivity, and resilience outcomes in platform-based supply chain integration. The paper further discusses how identified dysfunctional processes and prioritized strategic criteria relate to operational vulnerabilities and resilience-building measures. The research adopts a multi-criteria decision-making (MCDM) approach to address the challenges of digital transformation and platform integration. An exploratory study was conducted applying the analytic hierarchy process (AHP) to evaluate functional and dysfunctional processes, complemented by the best worst method (BWM) to prioritize critical strategic criteria. The combined assessment highlights effective and dysfunctional processes while also identifying the most influential factors driving platform-based adoption and their potential implications for operational vulnerability and resilience. The results demonstrate how platform integration contributes to performance improvement, process alignment, and productivity gains across supply chain operations. The study contributes to both theory and practice by integrating MCDM techniques to support structured decision-making, enhancing responsiveness, resilience, and alignment with platform-oriented strategies. The primary contribution lies in providing a dual-level framework that enables supply chain managers to diagnose weaknesses, leverage strengths, and strategically guide the transition toward platform-based supply chain operations, with a measurable impact on organizational performance and productivity development. Full article

Artificial intelligence (AI) is rapidly transforming medical diagnostics by allowing for early, accurate, and data-driven clinical decision-making. This review provides an overview of how machine learning (ML), deep learning, and emerging multimodal foundation models have been used in diagnostic procedures across imaging, pathology, molecular analysis, physiological monitoring, and electronic health record (EHR)-integrated decision-support systems. We have discussed the basic computational foundations of supervised, unsupervised, and reinforcement learning and have also shown the importance of data curation, validation metrics, interpretability methods, and feature engineering. The use of AI in many different applications has shown that it can find abnormalities and integrate some features from multi-omics and imaging, which has shown improvements in prognostic modeling. However, concerns about data heterogeneity, model drift, bias, and strict regulatory guidelines still remain and are yet to be addressed in this field. Looking forward, future advancements in federated learning, generative AI, and low-resource diagnostics will pave the way for adaptable and globally accessible AI-assisted diagnostics. Full article

Scaling Agile in industrial software projects is a process control problem that must balance governance, scalability, and adaptability while keeping decisions auditable. We present a hybrid fuzzy multi-criteria decision-making (MCDM) framework that combines Fuzzy Analytic Hierarchy Process (FAHP) for uncertainty-aware weighting with a tunable VIKOR–PROMETHEE ranking stage. Weighting and ranking are kept distinct to support traceability and parameter sensitivity. A three-layer hierarchy organizes twenty-two criteria across organizational, project, group, and framework levels. In a single-enterprise validation with two independent expert panels (n = 10 practitioners), the tuned hybrid achieved lower rank error than single-method baselines (mean absolute error, $\mathrm{MAE}$ = 1.03; Spearman $\rho$ = 0.53) using pre-specified thresholds and a transparent $\alpha +\beta$ = 1 control. The procedure is practical for process governance: elicit priorities, derive fuzzy weights, apply the hybrid ranking, and verify stability with sensitivity analysis. The framework operationalizes modeling, optimization, control, and monitoring of scaling decisions, making trade-offs explicit and reproducible in industrial settings. Full article

The acceptance or rejection of a measurement is determined based on its associated measurement uncertainty. In this procedure, there is a risk of making incorrect decisions, including the potential rejection of compliant measurements or the acceptance of non-conforming ones. This study introduces a mathematical model for the spatial evaluation of the global producer’s and global consumer’s risk, predicated on Bayes’ theorem and a decision rule that includes a guard band. The proposed model is appropriate for risk assessment within the framework of multivariate linear regression. Its applicability is demonstrated through an example involving the flatness of the workbench table surface of a coordinate measuring machine. The least-risk direction on the workbench was identified, and risks were quantified under varying selections of reference planes and differing measurement uncertainties anticipated in future measurement processes. Model evaluation was performed using confusion matrix-based metrics. The spaces of the commonly used metrics, constrained by the dimensions of the coordinate measuring machine workbench, were constructed. Using the evaluated metrics, the optimal guard band width was specified to ensure the minimum values of both the global producer’s and the global consumer’s risk. Full article

Multi-criteria decision-making (MCDM) models are moving toward being data-oriented. Meanwhile, MCDM models’ totalitarian reliance on experts’ preferences may reduce the accuracy of results in real-world challenges. Therefore, there is a huge gap in refining MCDM models to be data-structured rather than relying on experts’ and decision-makers’ ideas. In this research article, the primary indifference threshold values of the Indifference Threshold-based Attribute Ratio Analysis (ITARA) model, which is one of the popular objective weighting MCDM techniques, have been investigated and improved to achieve the goal of a full-objective MCDM model. ITARA utilizes decision-makers’ and experts’ opinions to set the indifference threshold values, which are integral to obtaining criteria weights, and since this step is not data-based, unlike the whole technique, it is prone to deficiencies. Three critical frameworks based on the minimum value, standard deviation, and max–min distance are designed to assess the sensitivity of the indifference threshold values and optimize the initialization values to start the model. Two case studies based on actual data are considered in this research to observe the frameworks’ outcomes and the rank reversal phenomenon. The results demonstrated that the assigning weights procedure is deeply sensitive to a max–min framework, while the standard deviation framework illustrated more stable results and a slight change in criteria rankings. The min framework moderately fluctuated between the max–min and standard deviation frameworks. Full article

Road technical condition assessment and maintenance decision-making rely heavily on technical standards whose clauses, computational formulas, and decision logic are often expressed in unstructured formats, leading to fragmented knowledge representation, isolated indicator calculation procedures, and limited interpretability of decision outcomes. To address these challenges, a semantic framework with executable reasoning and computation components, Road Performance and Maintenance Ontology (RPMO), was developed, composed of a core ontology, an assessment ontology, and a maintenance ontology. The framework formalized clauses, computational formulas, and decision rules from standards and integrated semantic web rule language (SWRL) rules with external computational programs to automate distress identification and the computation and write-back of performance indicators. Validation through three use case scenarios conducted on eleven expressway asphalt pavement segments demonstrated that the framework produced distress severity inference, indicator computation, performance rating, and maintenance recommendations that were highly consistent with technical standards and expert judgment, with all reasoning results traceable to specific clauses and rule instances. This research established a methodological foundation for semantic transformation of road technical standards and automated execution of assessment and decision logic, enhancing the efficiency, transparency, and consistency of maintenance decision-making to support explicit, reliable, and knowledge-driven intelligent systems. Full article

Background: Variants in the GBA gene represent the most common genetic risk factor for Parkinson’s disease and are associated with a more aggressive disease course. Deep brain stimulation is an established therapy for advanced Parkinson’s disease, yet the influence of GBA status on postoperative outcomes remains incompletely defined. This review aims to summarize the clinical relevance of GBA genotyping prior to DBS and to evaluate its potential contribution to decision-making, risk stratification, and long-term management. Methods: A structured narrative review was conducted. The literature on sequencing methodology, variant interpretation, and postoperative outcomes in GBA-positive and GBA-negative patients was examined. Particular focus was placed on motor, cognitive, and neuropsychiatric outcomes, and on studies comparing trajectories across variant classes. Results: Across all study designs, patients with GBA-associated Parkinson’s disease demonstrated robust motor improvement after DBS, with outcomes comparable to those in non-carriers. Cognitive and neuropsychiatric decline occurred more rapidly in GBA carriers. Recent evidence indicates that cognitive and neuropsychiatric decline is influenced more by the genetic profile than the stimulation procedure. Variant severity appears to influence postoperative trajectories. Long-read sequencing improves detection of recombinant alleles and may refine genotype–phenotype associations. Genotyping provides additional value in counseling, expectation management, and postoperative planning. Conclusions: DBS remains an effective motor therapy for patients with GBA-associated Parkinson’s disease. Current findings indicate GBA genotyping should inform, and not limit, candidate selection. Integration of clinical, cognitive and genetic data supports more individualized management. Methodological advances in sequencing and the development of prediction models may further enhance personalized DBS planning. Full article

In recent years, long-term vehicle rental has gained importance as a flexible and cost-effective mobility solution. This model reduces the high initial costs associated with vehicle purchases, ensures predictable expenses through fixed monthly payments, reduces the risk of depreciation, and enables systematic fleet renewal, supporting its adaptation to changing environmental regulations and technological advancements. This paper proposes a tool to support the process of selecting propulsion technologies in long-term rental fleets, taking into account their economic, technical, environmental, and social implications for sustainable fleet management. The developed procedure combines secondary fleet data analysis, expert research conducted among service providers, and multi-criteria analysis conducted using the Analytic Hierarchy Process method. The results indicate that under current conditions in Poland, combustion vehicles remain the optimal solution for fleet operators, while electric vehicles—despite their environmental benefits and additional benefits—remain the least competitive. The proposed approach is comprehensive, adaptable, and easy to implement, providing a practical tool for fleet operators and end users. The results also provide guidance for public decision-makers on strengthening the market position of low- and zero-emission vehicles. Full article