A study of manpower issues in project management

This article is about factors of manpower issue in project management that affect the project running.Lacking of manpower will delay or suspend the progress of a project.The cause of lacking manpower can be ranged from base workers until the top management board of a company.Thus we need to minimize project delays and make recommendations for effective projects.We distributed 25 sets of survey questionnaires for data collection and do data analysis by calculating Relative Importance Index (RI).The result shown that contractor factor occupied the largest capacity among all factors

Recent advances in the field of carbon-based cathode electrocatalysts for Zn-air batteries

Carbon-based catalysts are widely regarded as one of the most promising materials for energy storage and conversion technologies due to their high electrical conductivity as well as tunable micro- and nanostructures. Developing efficient, low-cost, and durable bifunctional carbon-based electrocatalysts remains challenging. In this review, the recent advances in the field of carbon-based oxygen reduction reaction/oxygen evolution reaction electrocatalysts for Zn-air batteries are briefly reviewed, focusing on the fabrication strategies of carbon-based electrocatalysts. Finally, the present challenges and perspectives in developing advanced bifunctional carbon-based electrocatalysts are outlined. This journal i

Effect of exogenous emulsifier on growth performance, fat digestibility, apparent metabolisable energy in broiler chickens

This research was done to evaluate the effect of a commercial exogenous emulsifier (polyethylene glycol ricinoleate (PEGR)) with high hydrophilic-to-lipophilic balance (HLB) supplementation to broiler chicken diets on growth performance, digestibility of fat and apparent metabolisable energy (AME) content in week 1, 3 and 5. A total of 360 one-day-old male Cobb broiler chicks were assigned in groups of 30 to 12 battery cages. The chicks were randomly assigned to two dietary treatments, with 6 replicate cages per treatment. The diets were either standard broiler starter and finisher, with rice bran oil (RBO) as supplemented fat source or similar diets + 0.05% emulsifier (RBOV). Feed intakes of RBOV groups significantly increased compared to those of RBO groups from week 2 till 4 while body weights of RBOV diets significantly increased in week 4 and 5. Both RBOV and RBO groups had similar FCR except for week 5. Addition of this strongly hydrophilic emulsifier showed no significant difference in fat digestibility of both RC and RV groups but higher AME was noted for the treatment than for the control groups at week 5. Therefore, supplementing the exogenous emulsifier into a broiler diet enriched with rice bran oil improved body weight and AME content at week 5 with minimal effect on fat digestibility

Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals.Comment: 16 pages, 8 figure

Normal Values of Myocardial Deformation Assessed by Cardiovascular Magnetic Resonance Feature Tracking in a Healthy Chinese Population: A Multicenter Study

Reference values on atrial and ventricular strain from cardiovascular magnetic resonance (CMR) are essential in identifying patients with impaired atrial and ventricular function. However, reference values have not been established for Chinese subjects. One hundred and fifty healthy volunteers (75 Males/75 Females; 18–82 years) were recruited. All underwent CMR scans with images acceptable for further strain analysis. Subjects were stratified by age: Group 1, 18–44 years; Group 2, 45–59 years; Group 3, ≥60 years. Feature tracking of CMR cine imaging was used to obtain left atrial global longitudinal (LA Ell) and circumferential strains (LA Ecc) and respective systolic strain rates, left ventricular longitudinal (LV Ell), circumferential (LV Ecc) and radial strains (LV Err) and their respective strain rates, and right ventricular longitudinal strain (RV Ell) and strain rate. LA Ell and LA Ecc were 32.8 ± 9.2% and 40.3 ± 13.4%, respectively, and RV Ell was −29.3 ± 6.0%. LV Ell, LV Ecc and LV Err were −22.4 ± 2.9%, −24.3 ± 3.1%, and 79.0 ± 19.4%, respectively. LV Ell and LV Ecc were higher in females than males (P < 0.05). LA Ell, LA Ecc, and LV Ecc decreased, while LV Err increased with age (P < 0.05). LV Ell and RV Ell were not shown to be associated with age. Normal ranges for atrial and ventricular strain and strain rates are provided using CMR feature tracking in Chinese subjects

The ARIC predictive model reliably predicted risk of type II diabetes in Asian populations

10.1186/1471-2288-12-48BMC Medical Research Methodology12

Machine learning versus classical electrocardiographic criteria for echocardiographic left ventricular hypertrophy in a pre-participation cohort

Background: Classical electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH) are well studied in older populations and patients with hypertension. Their utility in young pre-participation cohorts is unclear.Aims: We aimed to develop machine learning models for detection of echocardiogram-diagnosed LVH from ECG, and compare these models with classical criteria.Methods: Between November 2009 and December 2014, pre-participation screening ECG and subsequent echocardiographic data was collected from 17 310 males aged 16 to 23, who reported for medical screening prior to military conscription. A final diagnosis of LVH was made during echocardiography, defined by a left ventricular mass index >115 g/m2. The continuous and threshold forms of classical ECG criteria (Sokolow–Lyon, Romhilt–Estes, Modified Cornell, Cornell Product, and Cornell) were compared against machine learning models (Logistic Regression, GLMNet, Random Forests, Gradient Boosting Machines) using receiver-operating characteristics curve analysis. We also compared the important variables identified by machine learning models with the input variables of classical criteria.Results: Prevalence of echocardiographic LVH in this population was 0.82% (143/17310). Classical ECG criteria had poor performance in predicting LVH. Machine learning methods achieved superior performance: Logistic Regression (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.738–0.884), GLMNet (AUC, 0.873; 95% CI, 0.817–0.929), Random Forest (AUC, 0.824; 95% CI, 0.749–0.898), Gradient Boosting Machines (AUC, 0.800; 95% CI, 0.738–0.862).Conclusions: Machine learning methods are superior to classical ECG criteria in diagnosing echocardiographic LVH in the context of pre-participation screening

Prediction model of ocular metastasis from primary liver cancer: Machine learning‐based development and interpretation study

Background: Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML). Methods: We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non‐ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10‐fold cross‐validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM. Results: Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA. Conclusion: We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients

Prognostic Value of Leucocyte Telomere Length in Acute Myocardial Infarction

Introduction: Leucocyte telomere length (LTL) has been described as a marker of biological age, endothelial dysfunction and atherosclerosis. The association between LTL and clinical characteristics of Asian patients, and their outcomes following acute myocardial infarction (AMI) have been inconclusive. Objective: To investigate the relationship between LTL and developing AMI, the association of LTL with inpatient and 30-day mortality, and the comparison to LTL with established AMI risk scores in predicting these outcomes. Methodology: 100 patients aged 30-70 years admitted with an AMI to a tertiary referral center between May-Oct 2017 were enrolled; these were matched with 100 non-AMI ('healthy') controls for gender and age (+/- 1 year). Clinical data was obtained prospectively; inpatient and 30-day outcomes documented. LTL was reflected by a well described variable called a tis ratio (TSR). The TSR was measured at enrolment using a quantitative PCR-based methods (qPCR) and results blinded to the clinician