This study aims to reveal the long-term station-based characteristics of precipitation in Istanbul, a mega city located on the continents of Europe and Asia, with complex topography and coastline along the Marmara and Black Seas. Using data from five different stations, three located in the European continent and two in the Asian continent, with measurement periods ranging from 72 to 93 years, wet and dry days have been identified, statistics on precipitation conditions during the warm and cold seasons have been generated, categorization based on precipitation intensities has been performed, and analyses have been conducted using extreme precipitation indices. At stations located in the northern part of the city, higher annual total precipitation has been observed compared to those in the south. A similar situation applies to the number of wet days. While during the cold season, the wet and dry day counts are nearly the same across all stations, this condition exhibits significant differences in favor of dry days during the warm season. Apart from dry conditions, “moderate” precipitation is the most frequently observed type across all stations. However, “extreme” events occur significantly more often (6%) during the warm season compared to the cold season (2%). Long-term anomalies in terms of annual precipitation totals have shown similarity between stations in the north and south, which has also been observed in longitudinally close stations. Despite the longer duration of the cold season and stronger temperature gradients, extreme rainfall events are more frequent during the warm season, primarily due to thunderstorm activity. While trend analyses revealed limited significant trends in precipitation intensity categories and extreme indices, the study highlights the importance of comprehensive examination of extreme rainfall events on both station-based and regional levels, shedding light on potential implications for regional climate change. Lastly, during the cold season, the inter-station correlation in terms of annual total precipitation amounts has been considerably higher compared to the warm season. Full article

Biaxial compression tests based on an elliptical tunnel were conducted to study the failure characteristics and the meso-crack evolution mechanism of tunnels with different cross-sections constructed in sandstone. The progressive crack propagation process around the elliptical tunnel was investigated using a real-time digital image correlation (DIC) system. Numerical simulations were performed on egg-shaped, U-shaped, and straight-walled arched tunnels based on the mesoscopic parameters of the elliptical tunnel and following the principle of an equal cross-sectional area. The meso-crack evolution and stress conditions of the four types of tunnels were compared. The results show that (1) fractures around an elliptical tunnel were mainly distributed at the end of the long axis and mainly induce slabbing failure, and the failure mode is similar to a V-shaped notch; (2) strain localization is an important characteristic of rock fracturing, which forebodes the initiation, propagation, and coalescence paths of macro-cracks; and (3) the peak loads of tunnels with egg-shaped, U-shaped, and straight-walled arched cross-sections are 98.76%, 97.56%, and 90.57% that of an elliptical cross-section. The elliptical cross-section shows the optimal bearing capacity. Full article

Oil/air two-phase flow distribution in the bearings is the basis for bearing lubrication status identification and precise thermal analysis of the bearing. In order to understand the fluid behavior inside the under-race lubrication ball bearing and obtain an accurate oil volume fraction prediction model. A numerical study of ball bearing with under-race lubrication is carried out to study oil-gas two-phase distribution inside the bearing, and the influence of several parameters is quantified, like bearing rotating speed, oil flow rate, oil viscosity, and oil density. The results indicate that the oil fraction in the bearing cavity between the inner and outer ring shows a periodic distribution along the circumference direction, and the period is the same as the number of under-race oil supply holes. Oil distribution alone radial direction is affected by the outer-ring-guiding cage and centrifugal force, leading to oil accumulation near the outer ring. Different bearing running conditions and oil characteristics do not change the oil distribution trend alone in circumference and radial direction, but the difference ratio. Finally, based on the numerical simulation results, a formula for the average oil volume fraction prediction in the bearing ring cavity is constructed. Full article

The separation of rare earth ions (RE³⁺) from aqueous solutions poses a significant challenge due to their similar chemical and physical characteristics. This study presents a method for synthesizing hematite nanoparticles (Fe₂O₃ NPs) through the high-temperature phase transition of natural pyrite for adsorbing RE³⁺ from mine wastewater. The characteristics of Fe₂O₃ NPs were studied using XRD, SEM, BET, XPS, FTIR, and Zeta potential. The optimal condition for RE³⁺ adsorption by Fe₂O₃ NPs was determined to be at pH 6.0 with an adsorption time of 60 min. The maximum adsorption capacities of Fe₂O₃ NPs for La³⁺, Ce³⁺, Pr³⁺, Nd³⁺, Sm³⁺, Gd³⁺, Dy³⁺, and Y³⁺ were 12.80, 14.02, 14.67, 15.52, 17.66, 19.16, 19.94, and 11.82 mg·g⁻¹, respectively. The experimental data fitted well with the Langmuir isotherm and pseudo-second-order models, suggesting that the adsorption process was dominated by monolayer chemisorption. Thermodynamic analysis revealed the endothermic nature of the adsorption process. At room temperature, the adsorption of RE³⁺ in most cases (La³⁺, Ce³⁺, Pr³⁺, Nd³⁺, Sm³⁺, and Y³⁺) onto Fe₂O₃ NPs was non-spontaneous, except for the adsorption of Gd³⁺ and Dy³⁺, which was spontaneous. The higher separation selectivity of Fe₂O₃ NPs for Gd³⁺ and Dy³⁺ was confirmed by the separation factor. Moreover, Fe₂O₃ NPs exhibited excellent stability, with an RE³⁺ removal efficiency exceeding 94.70% after five adsorption–desorption cycles, demonstrating its potential for the recovery of RE³⁺ from mine wastewater. Full article

Geogenic arsenic (As)-rich groundwater poses a significant environmental challenge worldwide, yet our understanding of the interplay between dissolved organic matter (DOM) transformation and arsenic mobilization during microbial sulfate reduction remains limited. This study involved microcosm experiments using As-rich aquifer sediments from the Singe Tsangpo River basin (STR) and Jianghan Plain (JHP), respectively. The findings revealed that microbial sulfate reduction remarkably increased arsenic mobilization in both STR and JHP sediments compared to that in unamended sediments. Moreover, the mobilization of As during microbial sulfate reduction coincided with increases in the fluorescence intensity of two humic-like substances, C2 and C3 (R = 0.87/0.87 and R = 0.73/0.66 in the STR and JHP sediments, respectively; p < 0.05), suggesting competitive desorption between DOM and As during incubation. Moreover, the transformations in the DOM molecular characteristics showed significant increases in CHOS molecular and low-O/C-value molecular intensities corresponding to the enhancement of microbial sulfate reduction and the possible occurrence of methanogenesis processes, which suggests a substantial bioproduction contribution to DOM components that is conducive to As mobilization during the microbial sulfate reduction. The present results thus provide new insights into the co-evolution between As mobilization and DOM transformations in alluvial aquifer systems under strong microbial sulfate reduction conditions. Full article

Brain tumors necessitate swift detection and classification for optimal patient outcomes. Deep learning has been extensively utilized to recognize complex tumor patterns in magnetic resonance imaging (MRI) images, aiding in tumor diagnosis, treatment, and prognostication. However, model complexity and limited generalizability with unfamiliar data hinder appropriate clinical integration. The objective of this study is to develop a clean-energy cloud-based deep learning platform to classify brain tumors. Three datasets of a total of 2611 axial MRI images were used to train our multi-layer convolutional neural network (CNN). Our platform automatically optimized every transfer learning and data augmentation feature combination to provide the highest predictive accuracy for our classification task. Our proposed system identified and classified brain tumors successfully and efficiently with an overall precision value of 96.8% [95% CI; 93.8–97.6]. Using clean energy supercomputing resources and cloud platforms cut our workflow to 103 min, $0 in total cost, and a negligible carbon footprint (0.0014 kg eq CO₂). By leveraging automated optimized learning, we developed a cost-effective deep learning (DL) platform that accurately classified brain tumors from axial MRI images of different levels. Although studies have identified machine learning tools to overcome these obstacles, only some are cost-effective, generalizable, and usable regardless of experience. Full article

Sarcoidosis is a multisystem granulomatous disease of an unknown aetiology. It can exist in many organs. Pulmonary and intrathoracic lymph nodes are most commonly involved. Lung sarcoidosis is uncommon in Asia. However, due to the large population of our country and the development of bronchoscopy, percutaneous lung puncture, and other medical technologies, the number of pulmonary sarcoidosis patients is on the rise. Pulmonary sarcoidosis patients have no obvious symptoms in the early stage, and the clinical manifestations in the later stage may vary from person to person. Eventually, the disease progresses to life-threatening pulmonary fibrosis. Therefore, patients with pulmonary sarcoidosis should receive a timely diagnosis. In recent years, the imaging features and serologic biomarkers of pulmonary sarcoidosis have been continuously studied. The diagnostic value of imaging and serologic biomarkers for pulmonary sarcoidosis is summarized below. Full article

Surgical aortic valve replacement (SAVR) with a biological heart valve prosthesis (BHV) is often used as a treatment in elderly patients with symptomatic aortic valve disease. This age group is also at risk for the development of dementia in the years following SAVR. The research question is “what are the predictors for the development of dementia?”. In 1500 patients undergoing SAVR with or without an associated procedure, preoperative (demographic, cardiac and non-cardiac comorbid conditions), perioperative (associated procedures, cross-clamp and cardiopulmonary bypass time) and postoperative 30-day adverse events (bleeding, thromboembolism, heart failure, conduction defects, arrhythmias, delirium, renal and pulmonary complications) were investigated for their effect on the occurrence of dementia by univariate analyses. Significant factors were entered in a multivariate analysis. The sum of the individual follow-up of the patients was 10,182 patient-years, with a mean follow-up of 6.8 years. Data for the development of dementia could be obtained in 1233 of the 1406 patients who left the hospital alive. Dementia during long-term follow-up developed in 216/1233 (17.2%) of the patients at 70 ± 37 months. Development of dementia reduced the mean survival from 123 (119–128) to 109 (102–116) months (p < 0.001). Postoperative delirium was the dominant predictor (OR = 3.55 with a 95%CI of 2.41–4.93; p < 0.00), followed by age > 80 years (2.38; 1.78–3.18; p < 0.001); preoperative atrial fibrillation (1.47; 1.07–2.01; p = 0.018); cardiopulmonary bypass time > 120 min (1.34; 1.02–1.78; p = 0.039) and postoperative thromboembolism (1.94; 1.02–3.70; p = 0.044). Postoperative delirium, as a marker for poor condition, and an age of 80 or more were the dominant predictors. Full article

This article examines the emergence of new representations of Byzantium in early Arabic literature, with a focus on the Sīra, the biography of the Prophet Muḥammad. This historical investigation leads to a dual conclusions that the Arab perception of Byzantium not only forged an “imaginary Byzantium” but also marked the emergence of Arab self-consciousness. This process significantly influenced the Arab historical and cultural narratives, framing them within the context of the Arabic identity that emerged in late antiquity. Nevertheless, this relationship between the early Islamic community and Byzantium does little to confirm accurate knowledge about Byzantium, rendering the emerging representations as not truly reflective of “reality”, but rather presenting us with an “imaginary Byzantium”. This applies whether related to events in the 1st/7th century or the transition from oral to written texts during the 2nd/8th and 3rd/9th centuries. Furthermore, these representations reveal more about the creators of this imaginary than the other itself, shedding light on the motives of early Muslim writers who used the Sīra as a vehicle for these imaginaries. Ultimately, the article identifies, through the textual analysis and historical contextualization of Sīra, two narrative layers therein that are related to the imaginary Byzantium. The first layer reflected a pervasive fear of Byzantium, while the second layer represented an attitude of challenge and rivalry. Full article

Myocardial remodeling is developed by increased stress in acute or chronic pathophysiologies. Stressed heart morphology (SHM) is a new description representing basal septal hypertrophy (BSH) caused by emotional stress and chronic stress due to increased afterload in hypertension. Acute stress cardiomyopathy (ASC) and hypertension could be together in clinical practice. Therefore, there are some geometric and functional aspects regarding this specific location, septal base under acute and chronic stress stimuli. The findings by our and the other research groups support that hypertension-mediated myocardial involvement could be pre-existed in ASC cases. Beyond a frequently seen predominant base, hyperkinetic tissue response is detected in both hypertension and ASC. Furthermore, hypertension is the responsible factor in recurrent ASC. The most supportive prospective finding is BSH in which a hypercontractile base takes a longer time to exist morphologically than an acutely developed syndrome under both physiologic exercise and pressure overload by transaortic binding in small animals using microimaging. However, cardiac decompensation with apical ballooning could mask the possible underlying hypertensive disease. In fact, enough time for the assessment of previous hypertension history or segmental analysis could not be provided in an emergency unit, since ASC is accepted as an acute coronary syndrome during an acute episode. Additional supportive findings for SHM are increased stress scores in hypertensive BSH and the existence of similar tissue aspects in excessive sympathetic overdrive like pheochromocytoma which could result in both hypertensive disease and ASC. Exercise hypertension as the typical form of blood pressure variability is the sum of physiologic exercise and pathologic increased blood pressure and results in increased mortality. Hypertension is not rare in patients with a high stress score and leads to repetitive attacks in ASC supporting the important role of an emotional component as well as the potential danger due to multiple stressors at the same time. In the current review, the impact of multiple stressors on segmental or global myocardial remodeling and the hazardous potential of multiple stressors at the same time are discussed. As a result, incidentally determined segmental remodeling could be recalled in patients with multiple stressors and contribute to the early and combined management of both hypertension and chronic stress in the prevention of global remodeling and heart failure. Full article

Steel-fiber reinforced polymer (FRP) composite bars (SFCBs) can enhance the controllability of damage in concrete structures; thus, studying the interfacial bonding between them is fundamental and a prerequisite for achieving deformation coordination and collaboration. However, research on the interfacial bonding performance between SFCBs and concrete remains inadequate. This study conducted central pullout tests on SFCB-concrete specimens with different concrete strengths (C30, C50, and C70), bar diameters (12, 16 and 20 mm), and hoop reinforcement constraints, analyzing variations in failure modes, bond-slip curves, bond strength, etc. Additionally, finite element simulations were performed using ABAQUS software to further validate the bonding mechanism of SFCB-concrete. The results showed that the failure mode of the specimens was related to the confinement effect on the bars. Insufficient concrete cover and lack of hoop restraint led to splitting failure, whereas pullout failure occurred otherwise. For the specimens with pullout failure, the interfacial damage between the SFCB and concrete was mainly caused by the surface fibers wear of the bar and the shear of the concrete lugs, which indicated that the bond of the SFCB-concrete interface consisted mainly of mechanical interlocking forces. In addition, the variation of concrete strength as well as bar diameter did not affect the bond-slip relationship of SFCB-concrete. However, the bond strength of SFCB-concrete increased with the increase of concrete strength. For example, compared with C30 concrete, when the concrete strength was increased to C70, the bond strength of the specimens under the same conditions was increased to 50–101.6%. In contrast, the bond strength of the specimens decreased by 13.29–28.71% when the bar diameter was increased from 12 to 14 mm. These discoveries serve as valuable references for the implementation of sustainable SFCB-reinforced concrete structures. Full article

Datasets used in AI applications for human health require careful selection. In healthcare, machine learning (ML) models are fine-tuned to reduce errors, and our study focuses on minimizing errors by generating code snippets for cost-sensitive learning using water potability datasets. Water potability ensures safe drinking water through various scientific methods, with our approach using ML algorithms for prediction. We preprocess data with ChatGPT-generated code snippets and aim to demonstrate how zero-shot learning prompts in ChatGPT can produce reliable code snippets that cater to cost-sensitive learning. Our dataset is sourced from Kaggle. We compare model performance metrics of logistic regressors and gradient boosting classifiers without additional code fine-tuning to check the accuracy. Other classifier performance metrics are compared with results of the top 5 code authors on the Kaggle scoreboard. Cost-sensitive learning is crucial in domains like healthcare to prevent misclassifications with serious consequences, such as type II errors in water potability assessment. Full article

The integration of immigrants in a host society must consider aspects related to the labour field, as well as other factors including their differences. The existence of 97 unemployed Venezuelan migrant women living in Cúcuta, Los Patios and La Parada, border cities of Norte de Santander, Colombia with the state of Táchira, places them at a disadvantage in terms of integration; hence, this study set out to propose strategies to guide governance officials and actors in managing their integration. By means of a multidimensional analysis, three profiles of these unemployed migrant women were obtained for their diversity, generating strategies for each profile in structural, social and cultural contexts; through this, it became evident that the characteristics of those who settle as immigrants can be considered in order to establish integration strategies in line with these characteristics. Thus, the methodology of the study could be useful in other areas of migration for the design of integration strategies that consider the heterogeneity of immigrants to facilitate their contribution to the society and economy of the country that has hosted them. Full article

This study presents a rigorous investigation into the mathematical and physical properties inherent in the Fourier phase spectrum of earthquake ground motions. This exploration includes a detailed examination of the probability distribution of phase angles and differences, elucidated through two novel numerical experiments utilizing the reduction ad absurdum approach. Moreover, the study scrutinizes the physical attributes of earthquake ground motion’s phase spectrum, employing the circular frequency-dependent phase derivative as a key analytical factor. In a novel approach, the research delves into the relationship between circular frequency-dependent phase derivatives and Fourier amplitudes, shedding light on essential connections within earthquake phenomena, particularly addressing non-stationarity. Expanding the scope, the study comprehensively examines the influence of source, propagation path, and site on both the phase spectrum and accelerogram. Employing the control variate technique facilitates this analysis, providing valuable insights into the underlying physical mechanisms governing earthquake wave behavior. The findings highlight the temporal properties of the phase spectrum, attributing its complexity to the temporal heterogeneity in energy release during the fault rupture and dispersion of earthquake waves. This novel approach not only enhances the understanding of earthquake dynamics, but also underscores the significance of considering temporal variations in earthquake events. Full article

Oriented object detection (OOD) can precisely detect objects with arbitrary direction in remote sensing images (RSIs). Up to now, the two-stage OOD methods have attracted more attention because of their high detection accuracy. However, the two-stage methods only rely on the features of each proposal for object recognition, which leads to the misclassification problem because of the intra-class diversity, inter-class similarity and clutter backgrounds in RSIs. To address the above problem, an OOD model combining scene classification is proposed. Considering the fact that each foreground object has a strong contextual relationship with the scene of the RSI, a scene classification branch is added to the baseline OOD model, and the scene classification result of input RSI is used to exclude the impossible categories. To focus on the hard instances and enhance the consistency between classification and regression, a task-aligned focal loss (TFL) which combines the classification difficulty with the regression loss is proposed, and TFL assigns lager weights to the hard instances and optimizes the classification and regression branches simultaneously. The ablation study proves the effectiveness of scene classification branch, TFL and their combination. The comparisons with 15 and 14 OOD methods on the DOTA and DIOR-R datasets validate the superiority of our method. Full article

Bartonella is an intracellular parasitic zoonotic pathogen that can infect animals and cause a variety of human diseases. This study investigates Bartonella infection in small mammals in Yunnan Province, China, focusing on tissue tropism. A total of 333 small mammals were sampled from thirteen species, three orders, four families, and four genera in Heqing and Gongshan Counties. Conventional PCR and real-time quantitative PCR (qPCR) were utilized for detection and quantification, followed by bioinformatic analysis of obtained DNA sequences. Results show a 31.5% detection rate, varying across species. Notably, Apodemus chevrieri, Eothenomys eleusis, Niviventer fulvescens, Rattus tanezumi, Episoriculus leucops, Anourosorex squamipes, and Ochotona Thibetana exhibited infection rates of 44.4%, 27.7%, 100.0%, 6.3%, 60.0%, 23.5%, and 22.2%, respectively. Genetic analysis identified thirty, ten, and five strains based on ssrA, rpoB, and gltA genes, with nucleotide identities ranging from 92.1% to 100.0%. Bartonella strains were assigned to B. grahamii, B. rochalimae, B. sendai, B. koshimizu, B. phoceensis, B. taylorii, and a new species identified in Episoriculus leucops (GS136). Analysis of the different tissues naturally infected by Bartonella species revealed varied copy numbers across different tissues, with the highest load in spleen tissue. These findings underscore Bartonella’s diverse species and host range in Yunnan Province, highlighting the presence of extensive tissue tropism in Bartonella species naturally infecting small mammalian tissues. Full article

In environments with a low signal-to-reverberation ratio (SRR) characterized by fluctuations in clutter number and distribution, particle filter-based tracking methods may experience significant fluctuations in the posterior probability of existence. This can lead to interruptions or even loss of the target trajectory. To address this issue, an adaptive PF-based tracking method (APF) with joint reverberation suppression is proposed. This method establishes the state space model under the Bayesian framework and implements it through particle filtering. To keep the weak target echoes, all the non-zero entries contained in the sparse matrix processed by the low-rank and sparsity decomposition (LRSD) are treated as the measurements. The prominent feature of this approach is introducing an adaptive measurement likelihood ratio (AMLR) into the posterior update step, which solves the problem of unstable tracking due to the strong fluctuation in the number of point measurements per frame. The proposed method is verified by four shallow water experimental datasets obtained by an active sonar with a uniform horizontal linear array. The results demonstrate that the tracking frame success ratio of the proposed method improved by over 14% compared with the conventional PF tracking method. Full article

Surface runoff is a major component of the hydrological cycle, and it is essential for supporting the ecosystem services provided by grassland and forest ecosystems. It is of practical importance to understand the mechanisms and the dynamic processes of runoff in a river’s basin, and in this study, we focused on the restored montane Pailugou Basin in the Qilian Mountains, Gansu Province, China, since its water status is extremely important for the large arid area and local economies therein. Our purpose was to determine the annual variation in the surface runoff in the Pailugou Basin because it is important to understand the influence of climate fluctuations on surface water resources and the economy of the basin. In addition, little is known about the annual variations in precipitation and runoff in this region of the world. Daily atmospheric precipitation, air temperature and runoff data from 2000 to 2019 were analyzed by the calculation of the uneven annual distribution of surface runoff, the calculation of the complete adjustment coefficient, and the vector accumulation expressed by the concentration degree. We also used the cumulative anomaly approach to determine the interannual variation trend of runoff, while the change trend was quantified by the sliding average method. Finally, we used the Mann–Kendall mutation test method and regression analysis to establish the time-series trend for precipitation and runoff and to determine the period of abrupt runoff changes. The results indicated concentrated and positive distributions of surface runoff on an annual basis, with a small degree of dispersion, and an explicit concentration of extreme flows. The relative variation ranges exhibited a decreasing trend, and the distribution of the surface runoff gradually was uniform over the year. The runoff was highest from July to September (85% of the annual total). We also determined that annual surface runoff in the basin fluctuated over the 20-year period but showed an overall increasing trend, increasing by 3.94 × 10⁵ m³, with an average increase rate of 0.42 × 10⁵ m³ every ten years. From 2005 to 2014, the annual runoff and the proportion of runoff in the flood season (July to September) to the annual runoff fluctuated greatly. The correlation between the runoff and precipitation was significant (r = 0.839, p < 0.05), whereas the correlation between air temperature and surface runoff was low (r = 0.421, p < 0.05). Full article

Bans on single-use plastic shopping bags (SUPBs) are a popular policy to tackle plastic pollution. However, their success has been evaluated solely based on reduced SUPBs consumption, ignoring the impacts of substitutes. This article addresses this gap by analyzing the Chilean plastic bag ban law. Results show a reduction of ~249 kilotons of SUPBs consumed and a change in the materiality of shopping bags (mainly toward paper), but also an increase of more than 50% of bin liners after the enactment of the ban. Despite some undesired effects, an improvement in the environmental performance of the bag market is obtained in fifteen of the eighteen categories studied. The environmental impacts are on average 38% lower than in the counterfactual scenario. This suggests that the law is being effective in protecting the environment. The strictness of the ban and its rapid enforcement were positive aspects of its design, but ignoring the end-of-life of the bags could be limiting its impact. To reduce the environmental impact of substitutes, it is recommended to create design guidelines for shopping bags and bin liners. Full article

This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models—an artificial neural network (ANN), a multiple linear regression, a support vector machine, and a regression tree—are employed and compared for performance, using evaluation metrics such as mean absolute deviation, root mean square error, coefficient of correlation, and mean absolute percentage error. After preprocessing 1030 samples, the dataset is split into two subsets: 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process. Full article

A rare case of an anomalous location of the orifice of the coronary artery was found in a 99-year-old male cadaver undergoing routine dissection. The presence of the right coronary artery (RCA), left coronary artery (LCA), and conus artery (conus branch) originating from the right Valsalva sinus are the characteristic findings of this case. Then, the LCA passed through the aorta and the pulmonary artery. The LCA and RCA branches were normal. These findings are useful for future surgical procedures, including cardiac catheterization. Full article

This paper addresses the critical need for precise thermal modeling in electronics, where temperature significantly impacts system reliability. We emphasize the necessity of accurate temperature measurement and uncertainty quantification in thermal imaging, a vital tool across multiple industries. Current mathematical models and uncertainty measures, such as Rényi and Shannon entropies, are inadequate for the detailed informational content required in thermal images. Our work introduces a novel entropy that effectively captures the informational content of thermal images by combining local and global data, surpassing existing metrics. Validated by rigorous experimentation, this method enhances thermal images’ reliability and information preservation. We also present two enhancement frameworks that integrate an optimized genetic algorithm and image fusion techniques, improving image quality by reducing artifacts and enhancing contrast. These advancements offer significant contributions to thermal imaging and uncertainty quantification, with broad applications in various sectors. Full article