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

FOXO-regulated DEAF1 controls muscle regeneration through autophagy

The commonality between various muscle diseases is the loss of muscle mass, function, and regeneration, which severely restricts mobility and impairs the quality of life. With muscle stem cells (MuSCs) playing a key role in facilitating muscle repair, targeting regulators of muscle regeneration has been shown to be a promising therapeutic approach to repair muscles. However, the underlying molecular mechanisms driving muscle regeneration are complex and poorly understood. Here, we identified a new regulator of muscle regeneration, Deaf1 (Deformed epidermal autoregulatory factor-1) – a transcriptional factor downstream of foxo signaling. We showed that Deaf1 is transcriptionally repressed by FOXOs and that DEAF1 targets to Pik3c3 and Atg16l1 promoter regions and suppresses their expression. Deaf1 depletion therefore induces macroautophagy/autophagy, which in turn blocks MuSC survival and differentiation. In contrast, Deaf1 overexpression inactivates autophagy in MuSCs, leading to increased protein aggregation and cell death. The fact that Deaf1 depletion and its overexpression both lead to defects in muscle regeneration highlights the importance of fine tuning DEAF1-regulated autophagy during muscle regeneration. We further showed that Deaf1 expression is altered in aging and cachectic MuSCs. Manipulation of Deaf1 expression can attenuate muscle atrophy and restore muscle regeneration in aged mice or mice with cachectic cancers. Together, our findings unveil an evolutionarily conserved role for DEAF1 in muscle regeneration, providing insights into the development of new therapeutic strategies against muscle atrophy

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

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

Prediction model of ocular metastases in gastric adenocarcinoma: Machine learning-based development and interpretation study

Background: Although gastric adenocarcinoma (GA) related ocular metastasis (OM) is rare, its occurrence indicates a more severe disease. We aimed to utilize machine learning (ML) to analyze the risk factors of GA-related OM and predict its risks. Methods: This is a retrospective cohort study. The clinical data of 3532 GA patients were collected and randomly classified into training and validation sets in a ratio of 7:3. Those with or without OM were classified into OM and non-OM (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator were conducted. We integrated the variables identified through feature importance ranking and further refined the selection process using forward sequential feature selection based on random forest (RF) algorithm before incorporating them into the ML model. We applied six ML algorithms to construct the predictive GA model. The area under the receiver operating characteristic (ROC) curve indicated the model's predictive ability. Also, we established a network risk calculator based on the best performance model. We used Shapley additive interpretation (SHAP) to identify risk factors and to confirm the interpretability of the black box model. We have de-identified all patient details. Results: The ML model, consisting of 13 variables, achieved an optimal predictive performance using the gradient boosting machine (GBM) model, with an impressive area under the curve (AUC) of 0.997 in the test set. Utilizing the SHAP method, we identified crucial factors for OM in GA patients, including LDL, CA724, CEA, AFP, CA125, Hb, CA153, and Ca2+. Additionally, we validated the model's reliability through an analysis of two patient cases and developed a functional online web prediction calculator based on the GBM model. Conclusion: We used the ML method to establish a risk prediction model for GA-related OM and showed that GBM performed best among the six ML models. The model may identify patients with GA-related OM to provide early and timely treatment

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