Project management: a simulation-based optimization method for dynamic time-cost tradeoff decisions

Project managers face difficult decisions with regard to completing projects on time and within the project budget. A successful project manager not only needs to assure that the project is completed, but also desires to make optimal use of resources and maximize the profitability of the project. The goal of this research is to address the time-cost tradeoff problem associated with selecting from among project activity alternatives under uncertainty. Specifically, activities that make up a project may have several alternatives each with an associated cost and stochastic duration. The final project cost is a result of the time and cost required to complete each activity and lateness penalties that may be assessed if the project is not completed by the specified completion time. In an effort to optimize the project time-cost tradeoff, a dynamic, simulation-based optimization method is presented. In particular, the method minimizes the expected project cost due to lateness penalties and the activity alternatives selected. The method is designed to be implemented in two phases. The first phase, referred to as the static phase, is implemented prior to the start of the project. The static phase results in the expected cost for the recommended project configuration including the alternative selected for each activity and the distributions of the project completion and total project cost. The second phase, referred to as the dynamic phase, is implemented as the project progresses. The dynamic phase allows the project manager to reevaluate the remaining project and activity alternatives to dynamically minimize the expected total project cost. The method provides an optimal solution under the assumptions of traditional crashing implementations and a heuristic solution for the generalized problem. An experimental performance evaluation shows the effectiveness of the method for making project management decisions. Finally, the method is fully implemented in computer software and integrated into a commercially available project management tool

Enhanced Manhattan-based Clustering using Fuzzy C-Means Algorithm for High Dimensional Datasets

The problem of mining a high dimensional data includes a high computational cost, a high dimensional dataset composed of thousands of attribute and or instances. The efficiency of an algorithm, specifically, its speed is oftentimes sacrificed when this kind of dataset is supplied to the algorithm. Fuzzy C-Means algorithm is one which suffers from this problem. This clustering algorithm requires high computational resources as it processes whether low or high dimensional data. Netflix data rating, small round blue cell tumors (SRBCTs) and Colon Cancer (52,308, and 2,000 of attributes and 1500, 83 and 62 of instances respectively) dataset were identified as a high dimensional dataset. As such, the Manhattan distance measure employing the trigonometric function was used to enhance the fuzzy c-means algorithm. Results show an increase on the efficiency of processing large amount of data using the Netflix ,Colon cancer and SRCBT an (39,296, 38,952 and 85,774 milliseconds to complete the different clusters, respectively) average of 54,674 milliseconds while Manhattan distance measure took an average of (36,858, 36,501 and 82,86 milliseconds, respectively)  52,703 milliseconds for the entire dataset to cluster. On the other hand, the enhanced Manhattan distance measure took (33,216, 32,368 and 81,125 milliseconds, respectively) 48,903 seconds on clustering the datasets. Given the said result, the enhanced Manhattan distance measure is 11% more efficient compared to Euclidean distance measure and 7% more efficient than the Manhattan distance measure respectively

COVIDetect: A Desktop Application as a Diagnostic Tool for Novel Coronavirus (COVID-19) Pneumonia in Chest X-ray Images Using Convolutional Neural Network

The COVID-19 pandemic has heavily affected the well-being of people worldwide. Current diagnostic tools, like the RT-PCR, are expensive and time-consuming; thus, there is a need for cheaper and faster means of COVID-19 detection. This study proposes using a desktop application with a convolutional neural network (CNN) and visual analysis as a supplementary diagnostic tool for detecting COVID-19 pneumonia in chest X-ray images. The CNN used is a sequential Keras model that was trained and tested through eight epochs using an augmented dataset. Random data augmentation techniques applied were rotation and horizontal flipping, which increased the total images used to 13,584. Visual analysis was created using the Grad-CAM algorithm to determine patterns in chest X-ray images. These were implemented in a desktop application and evaluated by a professional pulmonologist. Results showed that the CNN achieved an average accuracy rate of 97.96% among the three classes, which was superior among related studies. The CNN also achieved a precision, recall, and F1-score of 99.67%, 99.62%, and 99.64% respectively for COVID-19 pneumonia, 99.26%, 94.83%, and 96.99% respectively for viral pneumonia, and 95.12%, 99.42%, and 97.22% respectively for normal chest X-ray images. Meanwhile, the visual analysis was also accurate, as evaluated by a professional pulmonologist, where patterns of haziness were determined. Hence, this could serve as an effective supplementary diagnostic tool for healthcare professionals for faster and more accurate diagnosis of COVID-19 and viral pneumonia patients

Role of STAT3 in Transformation and Drug Resistance in CML

Chronic myeloid leukemia (CML) is initially driven by the bcr–abl fusion oncoprotein. The identification of bcr–abl led to the discovery and rapid translation into the clinic of bcr–abl kinase inhibitors. Although, bcr–abl inhibitors are efficacious, experimental evidence indicates that targeting bcr–abl is not sufficient for elimination of minimal residual disease found within the bone marrow (BM). Experimental evidence indicates that the failure to eliminate the leukemic stem cell contributes to persistent minimal residual disease. Thus curative strategies will likely need to focus on strategies where bcr–abl inhibitors are given in combination with agents that specifically target the leukemic stem cell or the leukemic stem cell niche. One potential target to be exploited is the Janus kinase (JAK)/signal transducers and activators of transcription 3 (STAT3) pathway. Recently using STAT3 conditional knock-out mice it was shown that STAT3 is critical for initiating the disease. Interestingly, in the absence of treatment, STAT3 was not shown to be required for maintenance of the disease, suggesting that STAT3 is required only in the tumor initiating stem cell population (Hoelbl et al., 2010). In the context of the BM microenvironment, STAT3 is activated in a bcr–abl independent manner by the cytokine milieu. Activation of JAK/STAT3 was shown to contribute to cell survival even in the event of complete inhibition of bcr–abl activity within the BM compartment. Taken together, these studies suggest that JAK/STAT3 is an attractive therapeutic target for developing strategies for targeting the JAK–STAT3 pathway in combination with bcr–abl kinase inhibitors and may represent a viable strategy for eliminating or reducing minimal residual disease located in the BM in CML

BrExit and foreign investment in the UK

We explore the likely effect of Brexit on inward foreign direct investment (FDI) through its possible effect on the benchmark variables that characterize the macroeconomy. For this we propose the use of a Markov regime switching structural vector auto-regression to distinguish between the volatile and stable states of the economy and account, among other effects, for the contemporaneous effects that the frequency of FDI innately generates. Our findings suggest that, if Brexit triggers a sterling depreciation in the current economic climate, this will fuel a prolonged negative effect on FDI. FDI flows may be positively affected (at most) by a sterling depreciation after Brexit only if this event drives the UK economy to a period of highly volatile growth, inflation, interest and exchange rates: a scenario that is rather unlikely. And, even then, the sterling depreciation benefits would last for only a short period of time

Spin-orbit induced mixed-spin ground state in RRNiO3_3 perovskites probed by XAS: new insight into the metal to insulator transition

We report on a Ni L$_{2,3}$ edges x-ray absorption spectroscopy (XAS) study in $R$NiO$_3$ perovskites. These compounds exhibit a metal to insulator ($MI$) transition as temperature decreases. The L$_{3}$ edge presents a clear splitting in the insulating state, associated to a less hybridized ground state. Using charge transfer multiplet calculations, we establish the importance of the crystal field and 3d spin-orbit coupling to create a mixed-spin ground state. We explain the $MI$ transition in $R$NiO$_3$ perovskites in terms of modifications in the Ni$^{3+}$ crystal field splitting that induces a spin transition from an essentially low-spin (LS) to a mixed-spin state.Comment: 4 pages, 4 figures, accepted as PRB - Rapid Comm. Dez. 200

U.S. Philanthropic Commitments For HIV/AIDS

This report produced by the Funders Concerned About AIDS (FCAA) organisation covers 2003 HIV/AIDS grant commitments from 170 grantmaking organisations in all sectors of US philanthropy. The report presents and analyses data on total and top grantmaking, changes in giving pattern, geographic distribution and intended use of HIV/AIDS grants. The appendices list related resources for further reference

Gender differences in V˙O2 and HR kinetics at the onset of moderate and heavy exercise intensity in adolescents

The majority of the studies on (V)over dotO(2) kinetics in pediatric populations investigated gender differences in prepubertal children during submaximal intensity exercise, but studies are lacking in adolescents. The purpose of this study was to test the hypothesis that gender differences exist in the (V)over dotO(2) and heart rate (HR) kinetic responses to moderate (M) and heavy (H) intensity exercise in adolescents. Twenty-one healthy African-American adolescents (9 males, 15.8 +/- 1.1 year; 12 females, 15.7 +/- 1 year) performed constant work load exercise on a cycle ergometer at M and H. The (V)over dotO(2) kinetics of the male group was previously analyzed (Lai et al., Appl. Physiol. Nutr. Metab. 33:107-117, 2008b). For both genders, (V)over dotO(2) and HR kinetics were described with a single exponential at M and a double exponential at H. The fundamental time constant (tau(1)) of (V)over dotO(2) was significantly higher in female than male at M (45 +/- 7 vs. 36 +/- 11 sec, P < 0.01) and H (41 +/- 8 vs. 29 +/- 9 sec, P < 0.01), respectively. The functional gain (G(1)) was not statistically different between gender at M and statistically higher in females than males at H: 9.7 +/- 1.2 versus 10.9 +/- 1.3 mL min(-1) W-1, respectively. The amplitude of the slow component was not significantly different between genders. The HR kinetics were significantly (tau(1), P < 0.01) slower in females than males at M (61 +/- 16 sec vs. 45 +/- 20 sec, P < 0.01) and H (42 +/- 10 sec vs. 30 +/- 8 sec, P = 0.03). The G(1) of HR was higher in females than males at M: 0.53 +/- 0.11 versus 0.98 +/- 0.2 bpm W-1 and H: 0.40 +/- 0.11 versus 0.73 +/- 0.23 bpm W-1, respectively. Gender differences in the (V)over dotO(2) and HR kinetics suggest that oxygen delivery and utilization kinetics of female adolescents differ from those in male adolescents