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Real-world experience with nucleos(t)ide analogue therapy and patient survival rates in chronic viral hepatitis B treatment centers in Eritrea
Sentiment analysis of tweets employing convolutional neural network optimized by enhanced gorilla troops optimization algorithm
Seroprevalence of bovine viral diarrhea virus infection and its associated risk factors in dairy cattle in and around Sebeta sub city, Ethiopia
Incidence of oral complications during endotracheal intubation in general anesthesia among hospitalized children
Shaping ability of NiTi reciprocating file systems R-motion and waveOne gold in mesial canals of mandibular molars; micro CT study
Indoor incense burning and impaired lung function in patients with diabetes
AbstractWhile recent studies have indicated a potential link between incense burning and respiratory diseases, there is a lack of data specifically focused on diabetic patients. To explore the relationship between indoor incense burning and impaired lung function among Chinese individuals with diabetes, a comprehensive cross-sectional study was undertaken, enrolling 431 adults diagnosed with diabetes. Information on incense burning and characteristics was collected using a structured questionnaire. The outcome of the study, impaired lung function, was assessed using spirometry. Multivariable logistic regression models were employed. In the fully adjusted model, participants exposed to indoor incense burning exhibited 130% higher odds of impaired lung function compared to those not exposed, as indicated by an odds ratio (OR) of 2.3 (95% confidence interval [CI]: 0.97, 5.16; P = 0.05). Notably, this association was statistically significant only in men (OR = 3.39; 95%CI: 1.07, 9.82; P = 0.03). Our study has elucidated an association between exposure to indoor incense burning and impaired lung function in individuals with diabetes, independently of demographic factors. These findings underscore the importance of considering indoor environmental factors, such as incense burning, in the comprehensive management and care of diabetic individuals.
Vacuum electrospray deposition for face-on orientation and interface preservation in organic photovoltaics
Scene categorization by Hessian-regularized active perceptual feature selection
AbstractDecoding the semantic categories of complex sceneries is fundamental to numerous artificial intelligence (AI) infrastructures. This work presents an advanced selection of multi-channel perceptual visual features for recognizing scenic images with elaborate spatial structures, focusing on developing a deep hierarchical model dedicated to learning human gaze behavior. Utilizing the BING objectness measure, we efficiently localize objects or their details across varying scales within scenes. To emulate humans observing semantically or visually significant areas within scenes, we propose a robust deep active learning (RDAL) strategy. This strategy progressively generates gaze shifting paths (GSP) and calculates deep GSP representations within a unified architecture. A notable advantage of RDAL is the robustness to label noise, which is implemented by a carefully-designed sparse penalty term. This mechanism ensures that irrelevant or misleading deep GSP features are intelligently discarded. Afterward, a novel Hessian-regularized Feature Selector (HFS) is proposed to select high-quality features from the deep GSP features, wherein (i) the spatial composition of scenic patches can be optimally maintained, and (ii) a linear SVM is learned simultaneously. Empirical evaluations across six standard scenic datasets demonstrated our method’s superior performance, highlighting its exceptional ability to differentiate various sophisticated scenery categories.