Project dispute prediction by hybrid machine learning techniques

This study compares several well-known machine learning techniques for public-private partnership (PPP) project dispute problems. Single and hybrid classification techniques are applied to construct models for PPP project dispute prediction. The single classification techniques utilized are multilayer perceptron (MLP) neural networks, decision trees (DTs), support vector machines, the naïve Bayes classifier, and k-nearest neighbor. Two types of hybrid learning models are developed. One combines clustering and classification techniques and the other combines multiple classification techniques. Experimental results indicate that hybrid models outperform single models in prediction accuracy, Type I and II errors, and the receiver operating characteristic curve. Additionally, the hybrid model combining multiple classification techniques perform better than that combining clustering and classification techniques. Particularly, the MLP+MLP and DT+DT models perform best and second best, achieving prediction accuracies of 97.08% and 95.77%, respectively. This study demonstrates the efficiency and effectiveness of hybrid machine learning techniques for early prediction of dispute occurrence using conceptual project information as model input. The models provide a proactive warning and decision-support information needed to select the appropriate resolution strategy before a dispute occurs

Enhanced Therapeutic Epidermal Growth Factor Receptor (EGFR) Antibody Delivery via Pulsed Ultrasound with Targeting Microbubbles for Glioma Treatment

Pulsed-mode ultrasound (pUS) in combination with intravenously (IV) administered microbubbles (MBs) can enhance local drug delivery by temporarily enhancing capillary permeability. This study evaluates the use of epidermal growth factor receptor (EGFR)-targeting MBs after pUS treatment to enhance the effects of therapeutic-EGFR antibody delivery to glioma tumor cells in mice. Three animal groups were compared: (1) IV-injected non-targeting MBs, (2) IV-injected targeting MBs, and (3) IV-injected targeting MBs combined with pUS treatment. All animals were analyzed using high-frequency small-animal US imaging. The mean halftime of circulating targeting MBs was significantly increased from 3.13 min of targeting bubble alone to 5.86 min by targeting MBs combined with pUS treatment, compared to 2.34 min for non-targeting MBs. Compared to targeting bubble administration alone, pUS exposure prior to injection of targeting MBs was also significantly better at suppressing tumor growth when monitored for up to 35 days (p < 0.05). The final relative tumor volumes were 2664, 700, and 188 mm(3) for non-targeting MBs, targeting MBs, and targeting MBs combined with pUS treatment, respectively. pUS treatment prolonged the mean circulatory halftime of targeting MBs and enhanced the anti-tumor effect of EGFR antibodies in a human glioma model in mice. Targeting MBs combined with pUS treatment thus has potential for enhanced therapeutic antibody delivery for facilitating anti-glioma treatment

DeepMachining: Online Prediction of Machining Errors of Lathe Machines

We describe DeepMachining, a deep learning-based AI system for online prediction of machining errors of lathe machine operations. We have built and evaluated DeepMachining based on manufacturing data from factories. Specifically, we first pretrain a deep learning model for a given lathe machine's operations to learn the salient features of machining states. Then, we fine-tune the pretrained model to adapt to specific machining tasks. We demonstrate that DeepMachining achieves high prediction accuracy for multiple tasks that involve different workpieces and cutting tools. To the best of our knowledge, this work is one of the first factory experiments using pre-trained deep-learning models to predict machining errors of lathe machines

Detecting Effects of Low Levels of FCCP on Stem Cell Micromotion and Wound-Healing Migration by Time-Series Capacitance Measurement.

Electric cell-substrate impedance sensing (ECIS) has been used as a real-time impedance-based method to quantify cell behavior in tissue culture. The method is capable of measuring both the resistance and capacitance of a cell-covered microelectrode at various AC frequencies. In this study, we demonstrate the application of high-frequency capacitance measurement (f = 40 or 64 kHz) for the sensitive detection of both the micromotion and wound-healing migration of human mesenchymal stem cells (hMSCs). Impedance measurements of cell-covered electrodes upon the challenge of various concentrations of carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP), from 0.1 to 30 μM, were conducted using ECIS. FCCP is an uncoupler of mitochondrial oxidative phosphorylation (OXPHOS), thereby reducing mitochondrial ATP production. By numerically analyzing the time-series capacitance data, a dose-dependent decrease in hMSC micromotion and wound-healing migration was observed, and the effect was significantly detected at levels as low as 0.1 μM. While most reported works with ECIS use the resistance/impedance time series, our results suggest the potential use of high-frequency capacitance time series for assessing migratory cell behavior such as micromotion and wound-healing migration