Search Results (734)

Shift organizations in automotive manufacturing often rely on manual task allocation, resulting in inefficiencies, human error, and increased workload for supervisors. This research introduces an automated solution using the Kuhn-Munkres algorithm, integrated with the Moodle learning management system, to optimize task assignments based on operator qualifications and task complexity. Simulations conducted with real industrial data demonstrate that the proposed method meets operational requirements, both logically and mathematically. The system improves the start of shifts by assigning simpler tasks initially, enhancing operator confidence and reducing the need for assistance. It also ensures that task assignments align with required training levels, improving quality and process reliability. For industrial practitioners, the approach provides a practical tool to reduce planning time, human error, and supervisory burden, while increasing shift productivity. From an academic perspective, the study contributes to applied operations research and workforce optimization, offering a replicable model grounded in real-world applications. The integration of algorithmic task allocation with training systems enables a more accurate matching of workforce capabilities to production demands. This study aims to support data-driven decision-making in shift management, with the potential to enhance operational efficiency and encourage timely start of work, thereby possibly contributing to smoother production flow and improved organizational performance. Full article

Aims/Background: Polypharmacy, or the concurrent intake of five or more medications, is a significant issue in clinical practice, particularly in multimorbid elderly individuals. Despite its importance for patient safety, medical education often lacks systematic training in recognising and managing polypharmacy within the framework of patient-centred care. We investigated the impact of a structured learning intervention introducing polypharmacy as a chronic condition, assessing whether it enhances medical students’ diagnostic competence, confidence, and interprofessional collaboration. Methods: A prospective cohort study was conducted with 50 final-year medical students who received a three-phase educational intervention. Phase 1 was interactive workshops on the principles of polypharmacy, its dangers, and diagnostic tools. Phase 2 involved simulated patient consultations and medication review exercises with pharmacists. Phase 3 involved reflection through debriefing sessions, reflective diaries, and standardised patient feedback. Student knowledge, confidence, and attitudes towards polypharmacy management were assessed using pre- and post-intervention questionnaires. Quantitative data were analysed through paired t-tests, and qualitative data were analysed thematically from reflective diaries. Results: Students demonstrated considerable improvement after the intervention in identifying symptoms of polypharmacy, suggesting deprescribing strategies, and working in multidisciplinary teams. Confidence in prioritising polypharmacy as a primary diagnostic problem increased from 32% to 86% (p < 0.01), and knowledge of diagnostic tools increased from 3.1 ± 0.6 to 4.7 ± 0.3 (p < 0.01). Standardised patients felt communication and patient-centredness had improved, with satisfaction scores increasing from 3.5 ± 0.8 to 4.8 ± 0.4 (p < 0.01). Reflective diaries indicated a shift towards more holistic thinking regarding medication burden. The small sample size limits the generalisability of the results. Conclusions: Teaching polypharmacy as a chronic condition in medical school enhances diagnostic competence, interprofessional teamwork, and patient safety. Education is a structured way of integrating the management of polypharmacy into routine clinical practice. This model provides valuable insights for designing medical curricula. Future research must assess the impact of such training on patient outcomes and clinical decision-making in the long term. Full article

A reliable prediction of the remaining useful life of critical electronic components, such as insulated gate bipolar transistors, is necessary for preventing failures in many industrial applications. Recently, diverse machine-learning techniques have been used for this task. However, they are generally focused on capturing the temporal dependencies or on representing the probabilistic nature of the degradation of the device. This work proposes a neural network architecture that combines long short-term memory and mixture density networks to address both targets simultaneously when modeling the remaining useful life. The proposed model was trained and evaluated using a real dataset of insulated gate bipolar transistors, demonstrating a high capacity for predicting the remaining useful life of the validation devices. The proposed model outperformed the other algorithms considered in the study in terms of root mean squared error and coefficient of determination. In general terms, an average reduction of at least 18% of the root mean squared error was obtained when compared with the second-best model among those considered in this work, but in some specific cases, the root mean squared error during the prediction of remaining useful life decreased up to 21%. In addition to the high performance obtained, the characteristics of the network output also facilitated the creation of confidence intervals, which are more informative than solely exact values for decision-making. Full article

Background/Objectives: End-of-life care is a vital part of nursing education that has been overlooked until recent years. Advance care planning should be incorporated into the prelicensure nursing curriculum to build student nurses’ confidence in aiding patients and families with their preferred future care plans. Advance care planning tools, such as the Advance Health Care Directive (AHCD) and Five Wishes, provide experiential learning opportunities that bridge theoretical knowledge with real-world patient advocacy. In this study, students were asked to complete either the AHCD or Five Wishes document as though planning for their own end-of-life care, encouraging personal reflection and professional insight. Embedding these assignments into nursing education strengthens students’ confidence in facilitating end-of-life discussions. This study applied Kolb’s experiential learning theory, including concrete experience, reflective observation, abstract conceptualization, and active experimentation, to explore student nurses’ perspectives on the Advance Health Care Directive and Five Wishes assignments, as well as their understanding of end-of-life care. Methods: This study used an exploratory–descriptive qualitative design featuring one open-ended question to collect students’ views on the assignments. Results: The final sample comprised 67 prelicensure student nurses from Bachelor of Science and Entry-Level Master’s programs. The Advance Health Care Directive and/or Five Wishes assignment enhanced students’ understanding of end-of-life decision-making. Conclusions: It is essential to complete the assignment and immerse oneself in an end-of-life situation to grasp patients’ perspectives and concerns regarding when to engage in difficult conversations with their patients. Full article

Background/Objectives: This study shows a lightweight hybrid framework based on a feedforward neural network using a single frontopolar electroencephalography channel, which is a practical configuration for wearable systems combining memory and a sleep stage transition probability matrix. Methods: Motivated by autocorrelation analysis, revealing strong temporal dependencies across sleep stages, we incorporate prior epoch information as additional features. To capture temporal context without requiring long input sequences, we introduce a transition-aware feature derived from the softmax output of the previous epoch, weighted by a learned stage transition matrix. The model combines predictions from memory-based and no-memory networks using a confidence-driven fallback strategy. Results: The proposed model achieves up to 85.4% accuracy and 0.79 Cohen’s kappa, despite using only a single 30 s epoch per prediction. Compared to other models that use a single frontopolar channel, our method outperforms convolutional neural networks, recurrent neural networks, and decision tree approaches. Additionally, confidence-based rejection of low-certainty predictions enhances reliability, since most of the epochs with low confidence in the sleep stage classification contain transitions between sleep stages. Conclusions: These results demonstrate that the proposed method balances performance, interpretability, and computational efficiency, making it well-suited for real-time clinical and wearable sleep staging applications using battery-powered computing devices. Full article

The article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density estimation, projection pursuit, log-spline estimation, and wavelet-based estimation. The study is extended with modified versions of these methods, where the sample is first clustered using the EM algorithm based on Gaussian mixture components prior to density estimation. Estimation accuracy is quantitatively evaluated using MAE and MAPE criteria, with simulation experiments conducted over 100,000 replications for various sample sizes. The results show that estimation accuracy strongly depends on the density structure, sample size, and degree of component overlap. Clustering before density estimation significantly improves accuracy for multimodal and asymmetric densities. Although no formal statistical tests are conducted, the performance improvement is validated through non-overlapping confidence intervals obtained from 100,000 simulation replications. In addition, several decision-making systems are compared for automatically selecting the most appropriate estimation method based on the sample’s statistical features. Among the tested systems, kernel discriminant analysis yielded the lowest error rates, while neural networks and hybrid methods showed competitive but more variable performance depending on the evaluation criterion. The findings highlight the importance of using structurally adaptive estimators and automation of method selection in nonparametric statistics. The article concludes with recommendations for method selection based on sample characteristics and outlines future research directions, including extensions to multivariate settings and real-time decision-making systems. Full article

Background: To assess the accuracy of ChatGPT-4o and OpenAI-o1 in answering refractive surgery questions from the AAO BCSC Self-Assessment Program and to evaluate whether their performance could meaningfully support clinical decision making, we compared the models with 1983 ophthalmology residents and clinicians. Methods: A randomized, questionnaire-based study was conducted with 228 text-only questions from the Refractive Surgery section of the BCSC Self-Assessment Program. Each model received the prompt, “Please provide an answer to the following questions.” Accuracy was measured as the proportion of correct answers and reported with 95 percent confidence intervals. Differences between groups were assessed with the chi-squared test for independence and pairwise comparisons. Results: OpenAI-o1 achieved the highest score (91.2%, 95% CI 87.6–95.0%), followed by ChatGPT-4o (86.4%, 95% CI 81.9–90.9%) and the average score from 1983 users of the Refractive Surgery section of the BCSC Self-Assessment Program (77%, 95% CI 75.2–78.8%). Both language models significantly outperformed human users. The five-point margin of OpenAI-o1 over ChatGPT-4o did not reach statistical significance (p = 0.1045) but could represent one additional correct decision in twenty clinically relevant scenarios. Conclusions: Both ChatGPT-4o and OpenAI-o1 significantly outperformed BCSC Program users, demonstrating a level of accuracy that could augment medical decision making. Although OpenAI-o1 scored higher than ChatGPT-4o, the difference did not reach statistical significance. These findings indicate that the “advanced reasoning” architecture of OpenAI-o1 offers only incremental gains and underscores the need for prospective studies linking LLM recommendations to concrete clinical outcomes before routine deployment in refractive-surgery practice. Full article

Much U.S. research supports the effectiveness of parenting education delivered via the home visiting method. Home visitors are essential to reaching fathers in this context, but not all have favorable attitudes toward father engagement or feel confident working with fathers. Given that father involvement is important for a wide range of child and adolescent outcomes and that fathers benefit from parenting education, it is important to better understand the forces that shape home visitors’ attitudes toward fathers, and thus their subsequent efforts to include them in publicly funded programming. Using survey data from 95 home visitors in Tennessee, this study explores whether home visitors’ beliefs about fathers and attitudes toward father engagement vary as a function of home visitor or agency characteristics. Results suggest training in social work, reporting father-friendly organizational attitudes and behaviors at one’s agency, and reporting supervisor support specifically for father engagement relate to various favorable fathering attitudes. Home visitors’ strategies to engage fathers in home visiting are presented, including strategies for before, during, and after the home visit. Overall, family service agency administrators are in key positions to make decisions that can improve agency father-friendliness, home visitor attitudes toward fathers, and subsequent outcomes for fathers, mothers, and children. Full article

Random Forests are powerful machine learning models widely applied in classification and regression tasks due to their robust predictive performance. Nevertheless, traditional Random Forests face computational challenges during tree construction, particularly in high-dimensional data or on resource-constrained devices. In this paper, a novel node-splitting algorithm, BayesSplit, is proposed to accelerate decision tree construction via a Bayesian-based impurity estimation framework. BayesSplit treats impurity reduction as a Bernoulli event with Beta-conjugate priors for each split point and incorporates two main strategies. First, Dynamic Posterior Parameter Refinement updates the Beta parameters based on observed impurity reductions in batch iterations. Second, Posterior-Derived Confidence Bounding establishes statistical confidence intervals, efficiently filtering out suboptimal splits. Theoretical analysis demonstrates that BayesSplit converges to optimal splits with high probability, while experimental results show up to a 95% reduction in training time compared to baselines and maintains or exceeds generalization performance. Compared to the state-of-the-art MABSplit, BayesSplit achieves similar accuracy on classification tasks and reduces regression training time by 20–70% with lower MSEs. Furthermore, BayesSplit enhances feature importance stability by up to 40%, making it particularly suitable for deployment in computationally constrained environments. Full article

Antibiotic resistance presents a global threat, making the swift development of alternative treatments essential. Phage therapy, which employs bacterial viruses that specifically target bacteria, shows promise. Although this method has been utilized for over a century, primarily in Eastern Europe, its use in the US remains limited. This study aimed to assess the awareness and willingness of US healthcare providers to adopt phage therapy in response to the growing issue of antibiotic resistance. A survey of 196 healthcare providers, primarily MDs and DOs, found that while 99% were aware of antimicrobial resistance, only 49% were knowledgeable about phage therapy as a treatment for resistant bacterial infections. Nonetheless, 56% were open to considering phage therapy, and this willingness was associated with prior knowledge, concerns about antibiotic resistance, previous training, and confidence in recommending it (p < 0.05). Our study of U.S. healthcare providers revealed key findings about their views on phage therapy as a potential alternative for treating bacterial infections. Credible information is essential to promoting phage therapy use among U.S. providers via educational initiatives, clinical guidance, and research dissemination to promote phage therapy use among U.S. providers. Evidence-based education and clinical guidance help providers make sound decisions on the appropriate and safe use of phage therapy. Full article

Background and Objectives: Orthodontic treatment, whether fixed or removable, offers several benefits, including improved aesthetics, enhanced oral function, and increased self-confidence. However, it may also cause discomfort and pain, particularly following adjustment visits. This study aimed to assess pain characteristics (latency and continuity), food impairment, weight loss, and analgesic use in relation to treatment duration and appliance type. Methods: This observational study included 160 orthodontic patients who completed a structured questionnaire comprising 13 single-choice items. The questionnaire assessed age, gender, residential environment, educational status, type and duration of orthodontic treatment, pain characteristics (duration, latency, continuity), food impairment, and analgesic use. Inclusion criteria specified patients with moderate anterior crowding undergoing fixed orthodontic treatment or treatment with clear aligners on both arches, for at least one month. All fixed appliance cases involved 0.022-inch-slot Roth prescription brackets. Results: Patients undergoing fixed orthodontic treatment reported a higher frequency of pain (91.4%), greater need for analgesics (95.2%), and more food impairment compared to those with clear aligners. Patients treated for less than 6 months more frequently reported pain lasting 1 week (57.1%), while those treated for 1–2 years more commonly reported pain lasting several days (43.8%). Conclusions: Fixed orthodontic appliances are associated with greater discomfort, longer pain latency, more frequent analgesic use, and higher dietary impact compared to clear aligners. These findings emphasize the importance of personalized patient counseling and proactive pain management to improve compliance, enhance quality of life, and support informed decision-making in orthodontic care. Full article

In high-stakes decision-making environments, predictive models must deliver not only high accuracy but also reliable uncertainty estimations and transparent explanations. This study explores the integration of probability calibration techniques with Conformal Prediction (CP) within a predictive process monitoring (PPM) framework tailored to healthcare analytics. CP is renowned for its distribution-free prediction regions and formal coverage guarantees under minimal assumptions; however, its practical utility critically depends on well-calibrated probability estimates. We compare a range of post-hoc calibration methods—including parametric approaches like Platt scaling and Beta calibration, as well as non-parametric techniques such as Isotonic Regression and Spline calibration—to assess their impact on aligning raw model outputs with observed outcomes. By incorporating these calibrated probabilities into the CP framework, our multilayer analysis evaluates improvements in prediction region validity, including tighter coverage gaps and reduced minority error contributions. Furthermore, we employ SHAP-based explainability to explain how calibration influences feature attribution for both high-confidence and ambiguous predictions. Experimental results on process-driven healthcare data indicate that the integration of calibration with CP not only enhances the statistical robustness of uncertainty estimates but also improves the interpretability of predictions, thereby supporting safer and robust clinical decision-making. Full article

Background: Early identification of sepsis is critical for improving outcomes, particularly in low-resource emergency settings. In Thai community hospitals, where physicians may not always be available, triage is often nurse-led. Selecting accurate and practical sepsis screening tools is essential not only for timely clinical decision-making but also for timely diagnosis and promoting appropriate antibiotic use. Methods: This cross-sectional study analyzed 475 adult patients with suspected sepsis who presented to the emergency department of a Thai community hospital, using retrospective data from January 2021 to December 2022. Six screening tools were evaluated: Systemic Inflammatory Response Syndrome (SIRS), Quick Sequential Organ Failure Assessment (qSOFA), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), National Early Warning Score version 2 (NEWS2), and Search Out Severity (SOS). Diagnostic accuracy was assessed using International Classification of Diseases, Tenth Revision (ICD-10) codes as the reference standard. Performance metrics included sensitivity, specificity, predictive values, likelihood ratios, and the area under the receiver operating characteristic (AUROC) curve, all reported with 95% confidence intervals. Results: SIRS had the highest sensitivity (84%), while qSOFA demonstrated the highest specificity (91%). NEWS2, NEWS, and MEWS showed moderate and balanced diagnostic accuracy. SOS also demonstrated moderate accuracy. Conclusions: A two-step screening approach—using SIRS for initial triage followed by NEWS2 for confirmation—is recommended. This strategy enhances nurse-led screening and optimizes limited resources in emergency care. Early sepsis detection through accurate screening tools constitutes a feasible public health intervention to support appropriate antibiotic use and mitigate antimicrobial resistance, especially in resource-limited community hospital settings. Full article

Background: Our study aimed to evaluate the impact of EVT on stroke outcomes in patients with LVO with a National Institute of Health Stroke Scale (NIHSS) score of ≤5, exhibiting primarily cortical signs. Methods: We conducted a multicenter registry-based analysis of patients with acute ischemic stroke with LVO who arrived within 12 h of onset. Among these, patients with low NIHSS scores and prominent cortical signs (Items 2, 3, 9, or 11) were included. Patients were divided into two groups: those who underwent EVT and those treated with the best medical therapy (BMT), which included intravenous thrombolysis where appropriate. The primary outcome measure was a modified Rankin scale (mRS) score of 0–1 at 3 months and symptomatic hemorrhagic transformation (SHT). We performed logistic regression analysis to evaluate the impact of EVT on the outcomes. Results: Of the 970 patients with LVO, 291 met the inclusion criteria, with 95 and 196 undergoing EVT and BMT, respectively. The EVT group demonstrated a significantly higher rate of 3-month mRS score of 0–1 (65.3% vs. 39.3%, p < 0.001) and a lower incidence of SHT than the BMT group (3.2% vs. 12.8%, p = 0.01). Multivariate analysis confirmed that EVT was associated with improved functional recovery (mRS score, 0–1; odds ratio [OR], 3.61; 95% confidence interval [CI], 1.82–7.06; p < 0.001) and reduced risk of SHT (OR, 0.19; 95% CI, 0.05–0.74; p = 0.02). Notably, patients with specific cortical signs, such as aphasia and spatial neglect, exhibited better outcomes with EVT. Conclusions: EVT may significantly improve the functional outcomes in patients with mild LVO stroke who present with cortical signs, despite low NIHSS scores. These findings suggest that cortical signs should be a key factor in EVT decision-making for mild stroke cases, thereby advocating for a more individualized approach in acute stroke management. Full article

A simple and fast analytical method was developed and applied to assess the effect of two forms of zinc fertilization on a pecan tree cultivar in Uruguay: fertigation and foliar application with a specially formulated fertilizer. Zinc content was determined in 36 leaf samples from two crop cycles: 2020–2021 and 2021–2022. Fresh samples were dried, ground, and sieved. Analytical determinations were performed by flame atomic absorption spectrometry (FAAS, considered a standard method) and energy dispersive X-ray spectrometry (EDXRF, the proposed method). In the first case, sample preparation was carried out by microwave-assisted digestion using 4.5 mol L⁻¹ HNO₃. In the second case, pellets (Φ 13 mm, 2–3 mm thick) were prepared by direct mechanical pressing. Figures of merit of both methodologies were adequate for the purpose of zinc monitoring. The results obtained from both methodologies were statistically compared and found to be equivalent (95% confidence level). Based on the principles of Green Analytical Chemistry, both procedures were evaluated using the Analytical Greenness Metric Approach (AGREE and AGREEprep) tools. It was concluded that EDXRF was notably greener than FAAS and can be postulated as an alternative to the standard method. The information emerging from the analyses aided decision-making at the agronomic level. Full article