Four high-entropy metal diborides have been synthesized and densified by borocarbothermal reduction of metal oxides with boron carbide and graphite and subsequent spark plasma sintering. Three of ...them, (Hf0.2Zr0.2Ti0.2Ta0.2Nb0.2)B2, (Hf0.2Zr0.2Ti0.2Ta0.2Mo0.2)B2, and (Hf0.2Zr0.2Ti0.2Ta0.2Cr0.2)B2, possess single high-entropy phases and have been sintered to >99% of the theoretical densities. The fourth (Hf0.2Zr0.2Ti0.2Mo0.2W0.2)B2 specimen contained a Ti–Mo–W rich secondary phase in addition to the primary metal diboride phase. The specimens made by borocarbothermal reduction exhibit improved hardnesses in comparison with those samples previously fabricated via high energy ball milling and spark plasma sintering. Interestingly, the single-phase (Hf0.2Zr0.2Ti0.2Ta0.2Mo0.2)B2 and (Hf0.2Zr0.2Ti0.2Ta0.2Cr0.2)B2 (both of which have Vickers hardness values of ~25 GPa) are substantially harder than (Hf0.2Zr0.2Ti0.2Ta0.2Nb0.2)B2 (20.5 GPa), despite MoB2 and CrB2 being typically considered as softer components. These single-phase high-entropy metal diborides were found to have low thermal conductivities of 12–25 W/mK, which are ~1/10 to ~1/5 of the reported values of HfB2 and ZrB2.
In this paper, starting from exercise prescription and health promotion of hypertension, the hyperparameters of the XGBoost model are optimized using the Bayesian optimization algorithm, and then the ...BO-XGBoost blood pressure detection model is constructed. The model can estimate blood pressure from the ECG pulse wave signal of hypertensive patients and understand the patient’s blood pressure by PPG signal and ECG signal. For the effect of low-intensity exercise therapy combined with health education on the quality of life and blood pressure of hypertensive patients, experiments of the model and comparative test analysis of quality of life scores were conducted in this paper. From the model detection, the systolic and diastolic blood pressure detected by the BO-XGBoost blood pressure detection model was 118.6 mmHg and 65.2 mmHg, respectively, with an error of 3.03% and 3.83%, respectively, compared with the standard value of blood pressure. In terms of quality of life, the quality of life scores of the control and experimental groups improved by about 14% to 20% after the intervention compared with those before the intervention. This indicates that low-intensity exercise therapy combined with health education can help hypertensive patients improve their blood pressure control and quality of life.
•High-entropy boride-carbide two-phase ultrahigh temperature ceramics are made.•This work further extends the emerging field of high-entropy ceramics.•A novel reactive spark plasma sintering route ...produces > ∼99 % dense specimens.•A thermodynamic relation governing the equilibrium phase compositions is discovered.•The hardness is higher than the weighted average of the two high-entropy phases.
A series of dual-phase high-entropy ultra-high temperature ceramics (DPHE-UHTCs) are fabricated starting from N binary borides and (5-N) binary carbides powders. > ∼99 % relative densities have been achieved with virtually no native oxides. These DPHE-UHTCs consist of a hexagonal high-entropy boride (HEB) phase and a cubic high-entropy carbide (HEC) phase. A thermodynamic relation that governs the compositions of the HEB and HEC phases in equilibrium is discovered and a thermodynamic model is proposed. These DPHE-UHTCs exhibit tunable grain size, Vickers microhardness, Young’s and shear moduli, and thermal conductivity. The DPHE-UHTCs have higher hardness than the weighted linear average of the two single-phase HEB and HEC, which are already harder than the rule-of-mixture averages of individual binary borides and carbides. This study extends the state of the art by introducing dual-phase high-entropy ceramics (DPHECs), which provide a new platform to tailor various properties via changing the phase fraction and microstructure.
Recent advances in computer vision have allowed broad applications in every area of life, and agriculture is not left out. For the agri-food industry, the use of advanced technology is essential. ...Owing to deep learning’s capability to learn robust features from images, it has witnessed enormous application in several fields. Fruit detection and classification remains challenging due to the form, color, and texture of different fruit species. While studying the impact of computer vision on fruit detection and classification, we pointed out that till 2018 many conventional machine learning methods were utilized while a few methods exploited the application of deep learning methods for fruit detection and classification. This has prompted us to pursue an extensive study on surveying and implementing deep learning models for fruit detection and classification. In this article, we intensively discussed the datasets used by many scholars, the practical descriptors, the model’s implementation, and the challenges of using deep learning to detect and categorize fruits. Lastly, we summarized the results of different deep learning methods applied in previous studies for the purpose of fruit detection and classification. This review covers the study of recently published articles that utilized deep learning models for fruit identification and classification. Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset “Fruit 360” to make it easier for beginner researchers in the field of agriculture to understand the role of deep learning in the agriculture domain.
Hypernatremia is an uncommon but important electrolyte abnormality in intensive care unit patients. Sepsis is one of the most common causes of intensive care unit admission, but few studies about the ...role of hypernatremia in sepsis has been published yet. In this study, we aimed to explore the risk factors for developing hypernatremia in patients with sepsis, and the prognosis of patients with sepsis with or without hypernatremia was also assessed.
In this retrospective cohort study of 51 septic intensive care unit patients at a single center, we examined the risk factors for the development of hypernatremia and the association of hypernatremia with clinical outcomes using univariate and multivariable analyses. Clinical outcomes such as mortality and hospital duration of patients with or without hypernatremia were also compared.
Acute Physiology and Chronic Health Evaluation II score (odds ratio = 1.15; 95% CI: 1.022-1.294) was found to be the only independent risk factor for hypernatremia in patients with sepsis. Moreover, patients developing hypernatremia during hospitalization showed significantly higher morbidity and mortality.
Acute Physiology and Chronic Health Evaluation II score may be an independent risk factor for hypernatremia in patients with sepsis. Moreover, hypernatremia is strongly associated with worse outcome in sepsis.
Abstract Background & aim Esophageal squamous cell carcinoma (ESCC), a common disease in China, is mainly treated surgically. We established a prospective database of patients with esophageal cancer ...between January 2000 and December 2010, including 486 subjects with ESCC who underwent surgical treatment. In this study, we explored the prognostic significance of the expressions of HOXC6 and HOXC8, responsible for embryonic development, by studying the specimens collected from clinical subjects during strict follow-up periods. Materials & methods Immunohistochemistry was used to detect the expressions of HOXC6 and HOXC8 in 274 ESCC subjects including 138 ESCC subjects treated with surgery alone and 136 ESCC subjects treated with neoadjuvant chemotherapy. Survival analysis was performed from the day of surgery to August 2013. Results The 5-y survival rate of the 274 ESCC subjects was 44.2%, with a median survival time of 44.12 mo. For the 274 ESCC subjects involved in the investigation of HOXC6 and HOXC8 expressions, the median survival time of subjects with high-level expressions of HOXC6 and HOXC8 was shorter than that for subjects with low-level expressions ( P = 0.001, P < 0.001, respectively). Similar results were obtained from the analysis of the prognostic value of HOXC6 and HOXC8 in the group treated with surgery alone and the group treated with neoadjuvant chemotherapy. Multivariate analysis demonstrated that HOXC6 and HOXC8 expressions were independent prognostic factors in patients with ESCC. Conclusions The HOXC6 and HOXC8 genes can be used as prognostic markers in patients with ESCC, but prospective studies are still needed to confirm.
Purpose
Despite the extant literature on purchasing behaviour, little is known about why and how consumers purchase and use pharmaceutical products in Malaysia. This paper aims to develop a holistic ...model that articulates the factors underlying pharmaceutical product purchase and use and their relationships.
Design/methodology/approach
Drawing upon the grounded theory approach, in-depth interviews and analysis on the basis of theoretical saturation were conducted. Enumerators, trained to perform interviews, the transcription and content analysis, sampled 120 Malaysian consumers in the first phase and 40 in the second. ATLAS.ti was used to assist the coding and analytical process.
Findings
The findings largely support extant literature on purchasing decisions. Particularly, the evaluation of product attributes was found to be an immediate determinant of purchase and use. The effect, in turn, was moderated by personal factors and facilitating conditions. The need for healthcare products and the knowledge about them were constructed as antecedents in the model. The study also highlights that consumers’ purchase and use of pharmaceutical products could be better understood and managed with a holistic view of the decision-making process.
Originality/value
This study proposes the conbe-hepro model to explicate the antecedents and determinants of consumers’ purchase and use of pharmaceutical products in the Malaysian context. It provides a theoretical grounding that enriches our knowledge of the changing behaviour of consumers in healthcare literature. It also yields practical insights to the government bodies and the healthcare providers in Malaysia and other developing countries.
Summary Background Major depressive disorder is one of the most common mental disorders in children and adolescents. However, whether to use pharmacological interventions in this population and which ...drug should be preferred are still matters of controversy. Consequently, we aimed to compare and rank antidepressants and placebo for major depressive disorder in young people. Methods We did a network meta-analysis to identify both direct and indirect evidence from relevant trials. We searched PubMed, the Cochrane Library, Web of Science, Embase, CINAHL, PsycINFO, LiLACS, regulatory agencies' websites, and international registers for published and unpublished, double-blind randomised controlled trials up to May 31, 2015, for the acute treatment of major depressive disorder in children and adolescents. We included trials of amitriptyline, citalopram, clomipramine, desipramine, duloxetine, escitalopram, fluoxetine, imipramine, mirtazapine, nefazodone, nortriptyline, paroxetine, sertraline, and venlafaxine. Trials recruiting participants with treatment-resistant depression, treatment duration of less than 4 weeks, or an overall sample size of less than ten patients were excluded. We extracted the relevant information from the published reports with a predefined data extraction sheet, and assessed the risk of bias with the Cochrane risk of bias tool. The primary outcomes were efficacy (change in depressive symptoms) and tolerability (discontinuations due to adverse events). We did pair-wise meta-analyses using the random-effects model and then did a random-effects network meta-analysis within a Bayesian framework. We assessed the quality of evidence contributing to each network estimate using the GRADE framework. This study is registered with PROSPERO, number CRD42015016023. Findings We deemed 34 trials eligible, including 5260 participants and 14 antidepressant treatments. The quality of evidence was rated as very low in most comparisons. For efficacy, only fluoxetine was statistically significantly more effective than placebo (standardised mean difference −0·51, 95% credible interval CrI −0·99 to −0·03). In terms of tolerability, fluoxetine was also better than duloxetine (odds ratio OR 0·31, 95% CrI 0·13 to 0·95) and imipramine (0·23, 0·04 to 0·78). Patients given imipramine, venlafaxine, and duloxetine had more discontinuations due to adverse events than did those given placebo (5·49, 1·96 to 20·86; 3·19, 1·01 to 18·70; and 2·80, 1·20 to 9·42, respectively). In terms of heterogeneity, the global I2 values were 33·21% for efficacy and 0% for tolerability. Interpretation When considering the risk–benefit profile of antidepressants in the acute treatment of major depressive disorder, these drugs do not seem to offer a clear advantage for children and adolescents. Fluoxetine is probably the best option to consider when a pharmacological treatment is indicated. Funding National Basic Research Program of China (973 Program).
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•A new hybrid explainable AI pneumonia identification framework is proposed based on the ensemble Transformer Strategy.•The multi-head attention mechanism is used to address the ...relationships of the distant pixels among the chest X-ray images.•A comprehensive and robust binary and multi-class pneumonia classification study is conducted using two Chest X-ray datasets.•The XAI framework improves the performance by 2.05% (binary) and 1.3% (multiclass) against the individual ensemble scenarios.
Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging is the most well-known screening approach used for detecting pneumonia in the early stages. While chest-Xray images are mostly blurry with low illumination, a strong feature extraction approach is required for promising identification performance.
A new hybrid explainable deep learning framework is proposed for accurate pneumonia disease identification using chest X-ray images.
The proposed hybrid workflow is developed by fusing the capabilities of both ensemble convolutional networks and the Transformer Encoder mechanism. The ensemble learning backbone is used to extract strong features from the raw input X-ray images in two different scenarios: ensemble A (i.e., DenseNet201, VGG16, and GoogleNet) and ensemble B (i.e., DenseNet201, InceptionResNetV2, and Xception). Whereas, the Transformer Encoder is built based on the self-attention mechanism with multilayer perceptron (MLP) for accurate disease identification. The visual explainable saliency maps are derived to emphasize the crucial predicted regions on the input X-ray images. The end-to-end training process of the proposed deep learning models over all scenarios is performed for binary and multi-class classification scenarios.
The proposed hybrid deep learning model recorded 99.21% classification performance in terms of overall accuracy and F1-score for the binary classification task, while it achieved 98.19% accuracy and 97.29% F1-score for multi-classification task. For the ensemble binary identification scenario, ensemble A recorded 97.22% accuracy and 97.14% F1-score, while ensemble B achieved 96.44% for both accuracy and F1-score. For the ensemble multiclass identification scenario, ensemble A recorded 97.2% accuracy and 95.8% F1-score, while ensemble B recorded 96.4% accuracy and 94.9% F1-score.
The proposed hybrid deep learning framework could provide promising and encouraging explainable identification performance comparing with the individual, ensemble models, or even the latest AI models in the literature. The code is available here: https://github.com/chiagoziemchima/Pneumonia_Identificaton.
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