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The printed circuit board (PCB) serves as a fundamental component across all electronic devices, and the assurance of its fabrication quality exerts a major influence on the final pricing of the product. Consequently, research and development (R&D) focused on optimizing inspection techniques has emerged as a expanding area of global interest. Among the various paradigms utilized for PCB evaluation, visual analysis constitutes the preponderance of contemporary methodologies. To that end, this survey aggregates the most influential investigations conducted over the past quarter-century, outlining their operational frameworks and historical progression up to the present day, with a particular emphasis on the integration of artificial intelligence (AI) methodologies to enhance functional efficacy and minimize operational expenditures. Additionally, this study underscores the vital role that open-access PCB datasets play in facilitating the advancement of these techniques and establishing baseline metrics for benchmarking. Lastly, the current difficulties and obstacles confronting the discipline are delineated and analyzed.

At present, the majority of healthcare spending is driven by chronic patients who experience multiple readmissions due to their underlying conditions. Implementing individualized prevention frameworks could optimize the clinical management of this demographic. This study aimed to generate localized predictive models utilizing interpretable machine learning approaches to provide early identification of individual unscheduled hospital readmissions. To achieve this, a retrospective case-control investigation was performed, utilizing patient readmission data spanning 2018–2019. Following the cleaning and curation of the initial data repository (n = 76,210), the final sample size comprised n = 29,026 participants. A machine learning analysis evaluated multiple algorithms, employing unscheduled hospital readmission as the dependent variable, alongside local model-agnostic interpretability techniques.The results demonstrated a 13% rate of unscheduled hospital readmissions. Statistically significant variations were identified regarding patient age and length of hospital stay (p < 0.001 in both instances). A logistic regression framework highlighted chronic therapy (odds ratio: 3.75), a history of diabetes mellitus (odds ratio: 1.14), and length of stay (odds ratio: 1.02) as statistically meaningful predictors. Machine learning algorithms demonstrated superior performance concerning sensitivity and complementary evaluation metrics. Within this analytical framework, length of stay and patient age emerged as the primary determinants for forecasting unscheduled readmissions. Notably, other clinical indicators, such as documented allergies and histories of adverse drug reactions, also demonstrated predictive relevance. Individualized prognostic models exhibited high sensitivity. In conclusion, this investigation identified critical determinants governing unscheduled hospital readmissions, highlighting the prominence of age and hospitalization duration. A personalized risk assessment framework capable of predicting readmissions with high accuracy was established. Future investigations should incorporate a broader array of clinical parameters to further optimize the predictive capacity of this model.

Global emergency departments and urgent care facilities regularly experience prolonged waiting periods that surpass patient expectations, primarily driven by unexpected spikes in patient volumes. The resulting delays caused by insufficient staffing during these high-demand intervals have been associated with detrimental clinical consequences. Prior investigations into predicting patient volumes have predominantly relied on traditional statistical approaches. Furthermore, these efforts have mainly focused on short-range predictions, which offer minimal utility for the long-term scheduling of medical staff. This paper contributes to a growing domain of research aimed at investigating the capacity of machine learning frameworks to produce precise patient volume projections. Utilizing a decade's worth of data from two major urgent care centers, this study constructs long-range patient flow predictions extending up to one quarter in advance by evaluating a selection of modern, high-performing algorithms. A unique aspect of this research is the integration of eXplainable Artificial Intelligence (XAI) frameworks, specifically LIME and Shapley, to conduct a comprehensive assessment of internal model dynamics that are typically obscured. These diagnostic instruments allowed us to examine how effectively the predictive frameworks adjusted to volatile demand shifts induced by COVID-19 pandemic lockdowns, while also determining the most significant predictor variables to gain deep operational insights. The empirical findings indicate that an innovative ensemble combining gradient boosting techniques with advanced univariate frameworks like Prophet yielded the most precise and dependable performance overall. On average, this methodology produced a 16%–30% improvement over the current internal forecasting techniques utilized for predicting daily patient volumes 90 days into the future.