Predicting the schedule and cost performance in public school building projects in Taiwan

The Ministry of Education (MOE) of Taiwan invests about NTD 30 billion a year in Public School Building Projects (PSBPs). However, 95% of the PSBPs have been extended and have incurred increased costs. A PSBP performance evaluation and prediction system was established by using the Fuzzy Delphi Method (FDM), association rules and an Artificial Neural Network (ANN). Sixty-two Taiwanese PSBPs were used as the samples, while eleven high correlation factors that influence the project performance of PSBPs were defined, and the reasons leading to the poor project performance were discussed in this study. Moreover, the results of the test cases operated by ANN showed that the accuracy rate for schedule and cost variability predictions can reach 84%. The high accuracy rate indicated the reliability of priority control for high-risk projects in the future. The proposed approach can be provided to clients, design and construction firms, and project managers to understand the project performance in real time and to establish a dynamic tracking review and response measures for improving the overall project satisfaction. First published online 20 December 202

Application of Fungi as Meat Alternatives in Industry: Mini Review

Human consumption has outpaced meat production and manufacturing due to the rising human population and limited land for livestock agriculture. Meat consumption can have negative effects on human health, but meat production can negatively affect the environment by causing global warming and water pollution. Hence, this study produces the idea of using fungus as an alternative to replacing meat. Fungus is an ideal choice as a meat replacement because it has high nutritional content and a fast growth rate. The main objective of this review was to assess the nutritional potential of nine fungal species namely Fusarium venenatum, Neurospora intermedia, Tuber sp., Xerocomus badius, Ganoderma lucidum, Pleurotuseryngii, Agaricus bisporus, Pleurotus sajor-caju and Lentinula edodes and to determine which species is the best candidate for meat replacement. The nutritional values, toxicity, and growth rate of each fungus were assessed. Comparative data analysis suggests that F. venenatum, N. intermedia, P. eryngii, A. bisporus, P. sajor-caju, and L. edodes are found suitable for producing fungi-based meat

Decoupling Optimization for Complex PDN Structures using Deep Reinforcement Learning

This Article Presents a New Optimization Method for Complex Power Distribution Networks (PDNs) with Irregular Shapes and Multilayer Structures using Deep Reinforcement Learning (DRL), Which Has Not Been Considered Before. a Fast Boundary Integration Method is Applied to Compute the Impedance Matrix of a PDN Structure. Subsequently, a New DRL Algorithm based on Proximal Policy Optimization (PPO) is Proposed to Optimize the Decoupling Capacitor (Decap) Placement by Minimizing the Number of Decaps While Satisfying the Desired Target Impedance. in the Proposed Approach, the PDN Structure Information is Encoded into Matrices and Serves as the Input of the DRL Algorithm, Which Increases the Flexibility of the Method to Be Extended and Generalized to Different PDN Configurations. Also, the Output of the Algorithm Determines the Selection of Decap Types and Locations Collaboratively, Making It Easier to Find the Optimal Solution in a Huge Search Space. the Proposed Method is Compared with the State-Of-The-Art Approaches and Shows Consistent Advantages in Reducing the Number of Decaps in Different Testing Cases

The ErbB2ΔEx16 splice variant is a major oncogenic driver in breast cancer that promotes a pro-metastatic tumor microenvironment.

Amplification and overexpression of erbB2/neu proto-oncogene is observed in 20-30% human breast cancer and is inversely correlated with the survival of the patient. Despite this, somatic activating mutations within erbB2 in human breast cancers are rare. However, we have previously reported that a splice isoform of erbB2, containing an in-frame deletion of exon 16 (herein referred to as ErbB2ΔEx16), results in oncogenic activation of erbB2 because of constitutive dimerization of the ErbB2 receptor. Here, we demonstrate that the ErbB2ΔEx16 is a major oncogenic driver in breast cancer that constitutively signals from the cell surface. We further show that inducible expression of the ErbB2ΔEx16 variant in mammary gland of transgenic mice results in the rapid development of metastatic multifocal mammary tumors. Genetic and biochemical characterization of the ErbB2ΔEx16-derived mammary tumors exhibit several unique features that distinguish this model from the conventional ErbB2 ones expressing the erbB2 proto-oncogene in mammary epithelium. Unlike the wild-type ErbB2-derived tumors that express luminal keratins, ErbB2ΔEx16-derived tumors exhibit high degree of intratumoral heterogeneity co-expressing both basal and luminal keratins. Consistent with these distinct pathological features, the ErbB2ΔEx16 tumors exhibit distinct signaling and gene expression profiles that correlate with activation of number of key transcription factors implicated in breast cancer metastasis and cancer stem cell renewal

Microstructure and Mechanical Properties of a-CN x Films Prepared by Bias Voltage Assisted PLD with Carbon Nitride Target

International audienceAmorphous carbon nitride (a-CN x) films were deposited on silicon substrates using pulsed laser deposition technique (PLD) with a carbon nitride target and a negative bias voltage up to -120 V. The microstructure, chemical composition, bonding configuration and mechanical properties of the films were characterized by using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, nanoindentation and ball-on-disc abrasion test. The results show that the negative bias voltage promotes the formation of sp 3 hybridization bonding and leads to a great improvement of nitrogen content (up to 38 at.%) in the films. With an increasing bias voltage from -40 V to -120 V, the nitrogen content and the fraction of sp 3 hybridization bonding Corresponding author

Effect of Garlic and Turmeric Powders on In Vitro Digestibility of the Cooked Rice

Consumers nowadays are interested in foods with low glycemic index (GI) and high indigestible carbohydrate content. In Asia, white rice is the staple food and is generally considered as a readily digestible and high GI food. Occasionally, white rice is cooked by mixing with other ingredients such as herbs and spices. This study is carried out to determine the total phenolic content and the effect of in vitro digestibility on cooked white rice with added garlic and turmeric powders. Rice cooked with addition of turmeric powder (3 % w/w) showed the highest total phenolic content (92.02 mg GAE/100 g) among all the cooked rice samples. The effect of incorporating garlic powder (3 % w/w) and turmeric powder (3 % w/w) into the rice preparation was determined using an in vitro digestion protocol. Results show that by incorporating either garlic or turmeric powder into the rice, starch digestibility was significantly reduced. Rice with added turmeric powder showed a greater reduction in digestibility with significantly lower fraction of rapidly digestible starch (41.5 %; white rice 57.6 %) beside higher fraction of slowly digestible starch (36.1 %; white rice 28.4 %) and resistant starch fraction (22.6 %; white rice 14%). Overall, both spices were able to inhibit starch digestion which can be considered as a potential ingredient in lowering starch digestibility in the cooked rice

a mixed-method approach

Background Sedentary behaviours (SB) can be characterized by low energy expenditure in a reclining position (e.g., sitting) often associated with work and transport. Prolonged SB is associated with increased risk for chronic conditions, and due to technological advances, the working population is in office settings with high occupational exposure to SB. This study aims to assess SB among office workers, as well as barriers and strategies towards reducing SB in the work setting. Methods Using a mixed-methods approach guided by the socio-ecological framework, non-academic office workers from a professional school in a large public university were recruited. Of 180 eligible office workers, 40 enrolled and completed all assessments. Self- reported and objectively measured SB and activity levels were captured. Focus group discussion (FGD) were conducted to further understand perceptions, barriers, and strategies to reducing workplace SB. Environmental factors were systematically evaluated by trained research staff using an adapted version of the Checklist for Health Promotion Environments at Worksites (CHEW). Thematic analysis of FGD was conducted and descriptive analysis of quantitative data was performed. Results The sample was mostly Chinese (n = 33, 80 %) with a total of 24 (60 %) female participants. Most participants worked five days a week for about 9.5(0.5) hrs/day. Accelerometer data show that participants spend the majority of their days in sedentary activities both on workdays (76.9 %) and non-workdays (69.5 %). Self-report data confirm these findings with median sitting time of 420(180) minutes at work. From qualitative analyses, major barriers to reducing SB emerged, including the following themes: workplace social and cultural norms, personal factors, job scope, and physical building/office infrastructure. CHEW results confirm a lack of support from the physical infrastructure and information environment to reducing SB. Conclusions There is high SB among office workers in this sample. We identified multiple levels of influence for prolonged occupational SB, with a particular emphasis on workplace norms and infrastructure as important barriers to reducing SB and increasing PA. A larger, representative sample of the Singaporean population is needed to confirm our findings but it seems that any intervention aimed at reducing SB in the workplace should target individual, environmental, and organizational levels

A Dnn-Ensemble Method for Error Reduction and Training Data Selection in Dnn based Modeling

Deep neural networks (DNNs) have been widely adopted in modeling electromagnetic compatibility (EMC) problems, but the training data acquisition is usually time-consuming through various simulators. This paper presents a powerful approach using an ensemble of DNN s to effectively reduce the training data size in DNN-based modeling problems. A batch of training data with the largest uncertainties is selected using active learning through the variance among the ensemble of DNNs. Subsequently, a greedy sampling algorithm is applied to select a data subset using diversity. Thus, the proposed method can achieve both uncertainty and diversity in data selection. By averaging the outputs of the DNN ensemble, the prediction error can be further reduced. Simple mathematical functions are used to validate the proposed method, and a high-dimensional strip line modeling problem also demonstrates the effectiveness of this DNN-ensemble approach. The proposed method is task agnostic and can be used in other surrogate modeling problems with DNN s

Effects of caesarean section on maternal health in low risk nulliparous women: a prospective matched cohort study in Shanghai, China

<p>Abstract</p> <p>Background</p> <p>Rates of caesarean section are progressively increasing in many parts of the world. As a result of psychosocial factors there has been an increasing tendency for pregnant women without justifiable medical indications for caesarean section to ask for this procedure in China. A critical examination of this issue in relation to maternal outcomes is important. At present there are no clinical trials to help assess the risks and benefits of caesarean section in low risk women. To fill the gap left by trials, this indication-matched cohort study was carried out to examine prospectively the outcomes of caesarean section on women with no absolute obstetric indication compared with similar women who had vaginal delivery.</p> <p>Methods</p> <p>An indication-matched cohort study was undertaken to compare maternal outcomes following caesarean section with those undergoing vaginal delivery, in which the two groups were matched for non-absolute indications. 301 nulliparous women with caesarean section were matched successfully with 301 women who delivered vaginally in the Maternal and Children's Hospitals (MCHs) in Shanghai, China. Logistic regression model or binomial regression model was used to estimate the relative risk (RR) directly. Adjusted RRs were calculated adjusting for propensity score and medical indications.</p> <p>Results</p> <p>The incidence of total complications was 2.2 times higher in the caesarean section group during hospitalization post-partum, compared with the vaginal delivery group (RR = 2.2; 95% CI: 1.1-4.4). The risk of haemorrhage from the start of labour until 2 hours post-partum was significantly higher in the caesarean group (RR = 5.6; 95% CI: 1.2-26.9). The risk of chronic abdominal pain was significantly higher for the caesarean section group (RR = 3.6; 95% CI: 1.2-10.9) than for the vaginal delivery group within 12 months post-partum. The two groups had similar incidences of anaemia and complicating infections such as wound complications or urinary tract infection.</p> <p>Conclusions</p> <p>In nulliparous women who were at low risk, caesarean section was associated with a higher rate of post-partum morbidity. Those requesting the surgical procedure with no conventional medical indication, should be advised of the potential risks.</p