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The mediating role of mental well-being in the effect of local food experience on destination loyalty
No full textON MILNE-TYPE INEQUALITIES VIA KATUGAMPOLA FRACTIONAL MULTIPLICATIVE INTEGRALS
No full textThis work focuses on the study of Milne’s inequality in the framework of Katugampola fractional multiplicative integrals, inspired by recent progress in non-Newtonian fractional calculus. We develop a new integral identity related to these operators and employ it to derive Milne-type inequalities for the class of multiplicative differentiable (s,P)-convex mappings. Our analysis is further enriched through the application of Hölders inequality and the power mean inequality, which allow us to establish tighter bounds and extended results. To illustrate the validity and applicability of our theoretical approach, we include numerical examples that demonstrate the consistency and importance of the obtained estimates. Finally, we highlight several potential research directions intended to promote future investigations into this evolving domain of mathematical analysis
Doğum Sonrası Dönemde Telepsikiyatrik Takiple Algılanan Sosyal Destek , Anne Bebek Bağlanma Ve Postpartum Depresyon İlişkisi: Birinci Ve İkinci Ay Verileriyle Karşılaştırılması
No full textAntarktika Yüzey Sularında Bulunan Yarı Uçucu Organik Bileşiklerin Manyetik Karıştırma Çubuğu Ekstraksiyonu GC-MS/MS Analiz Sistemi ile Tespiti: Dağılım, Olası Kaynakların Tespiti ve Risk Değerlendirmesi
No full textA quantum calculus approach to Boole’s formula type inequalities with computational analysis and applications
No full textThe present study derives novel Boole’s formula–type inequalities that address convex functions in the framework of quantum calculus, a growing domain that broadens classical analysis via a parameter. We first formulate a unique quantum integral identity for -differentiable convex functions, which operates as a fundamental result for deriving an extensive class of Boole’s formula–type integral inequalities in the quantum calculus setting. These inequalities not only generalize classical findings but also reveal unique fundamental aspects of convex functions via quantum differentiation and integration, thus improving the theoretical basis of both convex analysis and quantum calculus. In addition to theoretical results, we demonstrate the practical relevance of the proposed inequalities by integrating them special means of real numbers and the Mittag-Leffler function which is pivotal in generalizing the exponential functions. In order to verify the theoretical findings, we provide several numerical examples with computational and graphical insights. The interaction between abstract theory and computational practice highlights the wider significance of our results in numerical analysis and inequality theory. These examples endorse the accuracy and validity of the presented results, highlighting their potential applicability in tackling real-world problems where quantum calculus offers a more adequate platform than traditional calculus. This work strengthens the understanding of Boole-type inequalities in quantum and traditional calculus settings and provides avenues for their application in modern computational mathematics research
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No full textExploring the correlation between strain localization and carbide fracture using high-resolution digital image correlation
No full textBy integrating an enhanced speckle pattern gold remodeling technique for high-resolution strain mapping of carbides with scanning electron microscopy, we have successfully mapped the strain localization of carbides with varying characteristics (size, aspect ratio, and roundness) during loading over numerous incremental strain steps. This study pioneers the use of high-resolution digital image correlation to track the strain evolutions within and outside carbides, providing qualitative and quantitative insights into the carbide fracture of the most widely used M50 aero-bearing steel at the microstructural level, revealing the relationship between the complex carbide characteristics and the fracture strains. The strain localization within the carbides (Type I), from the matrix (Type II), and at the interface (Type III) was found to govern distinct types of fracture modes, and a Type IV fracture was found to develop instantaneously without significant prior strain localization. Statistically, these four types of carbide fractures are dominated by the pre-existing defects, size, roundness, and aspect ratio, respectively. Nonetheless, these carbide characteristics factors, individually, fail to explain the distribution of the critical carbide fracture strain. We propose a composite factor that considers the combined effect of these individual influences, revealing a log-linear relationship with the applied strain to carbide fracture that is mechanistically meaningful: a small carbide that is round with smooth interface fractures at higher strain, whereas larger carbides that are elongated with sharp interfaces may fracture at lower strain. This paper suggests a robust quantitative in-situ approach to analyze the fracture of carbides, and the results may provide valuable insights for process optimization and carbide tuning for advanced bearing steel and other multi-phase alloys
Explainable reinforcement learning for glucose monitoring based on shapley value analysis
No full textBackground and Objective: Effective diabetes management requires continuous regulation of blood glucose in response to complex factors such as diet, activity, stress, and medication. Advances in continuous glucose monitoring and machine learning have improved short-term glucose prediction. However, preprocessing of signals like insulin, carbohydrate intake, heart rate, and activity to better capture metabolic dynamics remains underexplored. Similarly, the integration of predictive models with preventive strategies for guiding interventions is still limited. Methods: We propose a research-only decision-support framework combining signal preprocessing, CNN-based glucose prediction, Shapley Additive Explanations (SHAP) values attribution, and an Actor–Critic Reinforcement Learning (RL) agent. Exponential decay models preprocess inputs, a compact CNN forecasts short-term glucose levels, and SHAP values highlights the most influential input features; however, these attributions reflect associative patterns in the data and do not establish or map to causal clinical mechanisms. These SHAP-derived attributions guide the RL agent, which issues bounded one-step behavioral adjustments. Because SHAP-guided RL remains stochastic and uncertain, the proposed system is exploratory and not clinically safe, serving solely as a simulation framework. Results: Using the OhioT1DM dataset, the model achieved state-of-the-art RMSE across prediction horizons with a compact size of 7̃4 KB per patient and training under one minute for 1000 epochs. Over 98% of predictions fell within Clarke Error Grid Zones A and B, confirming safe 5–20 min forecasts. The preventive component corrected hyper- and hypoglycemia in 2̃5% of cases within 10 min when predictions were near 80–120 mg/dL (±10 mg/dL). When deviations exceed ±10 mg/dL, the RL agent is unable to fully restore blood glucose to the target range within 10 min but can bring it as close as possible to the defined interval. Conclusions: This study presents a significant innovation by bridging predictive accuracy, adaptability, and transparency in diabetes management. The integration of a predictive model with Reinforcement Learning (RL) guided by SHAP values, which are typically used for interpretability but here are employed in the learning process, delivers a powerful decision support framework. This approach advances the field toward next-generation, personalized digital health tools