Smart Cup for In-Situ 3D Measurement of Wall-Mounted Debris via 2D Sensing Grid in Production Pipelines.

The accumulation of separated out impurities from pipeline transported medium onto the pipe wall is a major cause of downtime maintenance of oil and gas production systems. To regularly scrub off wall-mounted debris and probe the severity, pipeline inspection gauges (PIG) are the state-of-the-art tools developed for the task, using the pressure differential across the device as the driving force, and tag-along sensing equipment for wall defects measurement. Currently, the PIG propulsion and sensing tasks are realized by separate compartments, limited to large diameter operations. In this work, a soft solution for medium to small diameter pipelines has been demonstrated. The smart cup with integrated sensing grid is proposed to achieve integrated wall-mounted debris dimensional measurement, without the need of additional sensors. To achieve the goal, this work starts from the mathematical modelling of the geometric problem, to new fabrication procedures, experimental setup, and finally finishes with validation results. Initial results have shown that using the proposed smart cup, the wall-mounted debris can be detected, with modelling error maxed at 5.1%, and deformation detection accuracy between 1.18% and 1.92% with respect to the outer diameter.

Graph neural network for integrated water network partitioning and dynamic district metered areas.

Water distribution systems (WDSs) are used to transmit and distribute water resources in cities. Water distribution networks (WDNs) are partitioned into district metered areas (DMAs) by water network partitioning (WNP), which can be used for leak control, pollution monitoring, and pressure optimization in WDS management. In order to overcome the limitations of optimal search range and the decrease of recovery ability caused by two-step WNP and fixed DMAs in previous studies, this study developed a new method combining a graph neural network to realize integrated WNP and dynamic DMAs to optimize WDS management and respond to emergencies. The proposed method was tested in a practical case study; the results showed that good hydraulic performance of the WDN was maintained and that dynamic DMAs demonstrated excellent stability in emergency situations, which proves the effectiveness of the method in WNP.

Gravitational Wave-Signal Recognition Model Based on Fourier Transform and Convolutional Neural Network.

The recent detection of gravitational waves is a remarkable milestone in the history of astrophysics. With the further development of gravitational wave detection technology, traditional filter-matching methods no longer meet the needs of signal recognition. Thus, it is imperative that we develop new methods. In this study, we apply a gravitational wave signal recognition model based on Fourier transformation and a convolutional neural network (CNN). The gravitational wave time-domain signal is transformed into a 2D frequency-domain signal graph for feature recognition using a CNN model. Experimental results reveal that the frequency-domain signal graph provides a better feature description of the gravitational wave signal than that provided by the time-domain signal. Our method takes advantage of the CNN's convolution computation to improve the accuracy of signal recognition. The impact of the training set size and image filtering on the performance of the developed model is also evaluated. Additionally, the Resnet101 model, developed on the Baidu EasyDL platform, is adopted as a comparative model. Our average recognition accuracy performs approximately 4% better than the Resnet101 model. Based on the excellent performance of convolutional neural network in the field of image recognition, this paper studies the characteristics of gravitational wave signals and obtains a more appropriate recognition model after training and tuning, in order to achieve the purpose of automatic recognition of whether the signal data contain real gravitational wave signals.

Structure of microprismatic Fresnel lenses for creating uniform focal images.

Flat Fresnel lenses are known to form a point image in the focal plane. However, several practical applications require transforming lens to concentrate a parallel light beam into a uniformly illuminated light circle. We previously proposed a novel algorithm for simulating such a transforming Fresnel concentrator. In this study, we applied this method to the diamond-cutting technique to create prismatic refractive surfaces of high optical quality. To reduce the discreteness of formed images, each refractive lens zone was fabricated from several small identical microprisms in the simulation. The new fabricated circular light beam concentrators were investigated by computer modelling and experimentally with a collimated laser beam.

Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction.

The atmospheric particulate matter (PM) with a diameter of 2.5 µm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.

Curcumol inhibits the viability and invasion of colorectal cancer cells via miR-30a-5p and Hippo signaling pathway.

MicroRNA-30a-5p (miR-30a-5p), which functions as a tumor suppressor, has been reported to be downregulated in colorectal cancer (CRC) tissues and to be associated with cancer invasion. However, the detailed regulatory mechanism of curcumol in the malignant progression of CRC remains unknown. MTT, Transwell, scratch, western blotting and reverse transcription-quantitative PCR assays were performed to examine how curcumol inhibited CRC cell viability, invasion and migration, and to detect the role of miR-30a-5p and curcumol in the invasion and Hippo signaling pathways of CRC cells. The present study revealed that miR-30a-5p expression was downregulated in human CRC tissues and cells. The results demonstrated that miR-30a-5p downregulation was accompanied by the inactivation of the Hippo signaling pathway, which was demonstrated to promote CRC cell viability, invasion and migration. Curcumol treatment was identified to increase miR-30a-5p expression and to activate the Hippo signaling pathway, which in turn inhibited the invasion and migration of CRC cells. Overexpression of miR-30a-5p enhanced the effects of curcumol on cell invasion and migration, and the Hippo signaling pathway in CRC cells. Furthermore, downregulation of miR-30a-5p reversed the effects of curcumol on cell invasion and migration, and the Hippo signaling pathway in CRC cells. These findings identified novel signaling pathways associated with miR-30a-5p and revealed the effects of curcumol on miR-30a-5p expression. Therefore, curcumol may serve as a potential therapeutic strategy to delay CRC progression.

Osthole induces necroptosis via ROS overproduction in glioma cells.

Glioma is a common primary malignant tumor that has a poor prognosis and often develops drug resistance. The coumarin derivative osthole has previously been reported to induce cancer cell apoptosis. Recently, we found that it could also trigger glioma cell necroptosis, a type of cell death that is usually accompanied with reactive oxygen species (ROS) production. However, the relationship between ROS production and necroptosis induced by osthole has not been fully elucidated. In this study, we found that osthole could induce necroptosis of glioma cell lines U87 and C6; such cell death was distinct from apoptosis induced by MG-132. Expression of necroptosis inhibitor caspase-8 was decreased, and levels of necroptosis proteins receptor-interacting protein 1 (RIP1), RIP3 and mixed lineage kinase domain-like protein were increased in U87 and C6 cells after treatment with osthole, whereas levels of apoptosis-related proteins caspase-3, caspase-7, and caspase-9 were not increased. Lactate dehydrogenase release and flow cytometry assays confirmed that cell death induced by osthole was primarily necrosis. In addition, necroptosis induced by osthole was accompanied by excessive production of ROS, as observed for other necroptosis-inducing reagents. Pretreatment with the RIP1 inhibitor necrostatin-1 attenuated both osthole-induced necroptosis and the production of ROS in U87 cells. Furthermore, the ROS inhibitor N-acetylcysteine decreased osthole-induced necroptosis and growth inhibition. Overall, these findings suggest that osthole induces necroptosis of glioma cells via ROS production and thus may have potential for development into a therapeutic drug for glioma therapy.

PTEN/AKT/mTOR signaling mediates anticancer effects of epigallocatechin­3­gallate in ovarian cancer.

Epigallocatechin­3­gallate (EGCG), a polyphenol present in green tea, exhibits anticancer effects in various types of cancer. A number of studies have focused on the effects of EGCG on lung cancer, but not ovarian cancer. Previous reports have implicated that EGCG suppressed ovarian cancer cell proliferation and induced apoptosis, but its potential anticancer mechanisms and signaling pathways remain unclear. Thus, it is necessary to determine the anti­ovarian cancer effects of EGCG and explore the underlying mechanisms. In the present study, EGCG exerted stronger proliferation inhibition on SKOV3 cells compared with A549 cells and induced apoptosis in SKOV3 cells, as well as upregulated PTEN expression and downregulated the expression of phosphoinositide­dependent kinase­1 (PDK1), phosphor (p)­AKT and p­mTOR. These effects were reversed by the PTEN inhibitor VO­Ohpic trihydrate. The results of the mouse xenograft experiment demonstrated that 50 mg/kg EGCG exhibited increased tumor growth inhibition compared with 5 mg/kg paclitaxel. In addition, PTEN expression was upregulated, whereas the expression levels of PDK1, p­AKT and p­mTOR were downregulated in the EGCG treatment group compared with those in untreated mice in vivo. In conclusion, the results of the present study provided a new underlying mechanism of the effect of EGCG on ovarian cancer and may lead to the development of EGCG as a candidate drug for ovarian cancer therapy.

Dispersion of speckle suppression efficiency for binary DOE structures: spectral domain and coherent matrix approaches.

We present the first general theoretical description of speckle suppression efficiency based on an active diffractive optical element (DOE). The approach is based on spectral analysis of diffracted beams and a coherent matrix. Analytical formulae are obtained for the dispersion of speckle suppression efficiency using different DOE structures and different DOE activation methods. We show that a one-sided 2D DOE structure has smaller speckle suppression range than a two-sided 1D DOE structure. Both DOE structures have sufficient speckle suppression range to suppress low-order speckles in the entire visible range, but only the two-sided 1D DOE can suppress higher-order speckles. We also show that a linear shift 2D DOE in a laser projector with a large numerical aperture has higher effective speckle suppression efficiency than the method using switching or step-wise shift DOE structures. The generalized theoretical models elucidate the mechanism and practical realization of speckle suppression.