Water quality prediction based on multi-task learning

Water pollution seriously endangers people’s lives and restricts the sustainable development of the economy. Water quality prediction is essential for early warning and prevention of water pollution. However, the nonlinear characteristics of water quality data make it challenging to accurately predicted by traditional methods. Recently, the methods based on deep learning can better deal with nonlinear characteristics, which improves the prediction performance. Still, they rarely consider the relationship between multiple prediction indicators of water quality. The relationship between multiple indicators is crucial for the prediction because they can provide more associated auxiliary information

Artificial Intelligent Diagnosis and Monitoring in Manufacturing

The manufacturing sector is heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. It has been reported that 35% of US manufacturers are currently collecting data from sensors for manufacturing processes enhancement. Nevertheless, many are still struggling to achieve the 'Industry 4.0', which aims to achieve nearly 50% reduction in maintenance cost and total machine downtime by proper health management. For increasing productivity and reducing operating costs, a central challenge lies in the detection of faults or wearing parts in machining operations. Here we propose a data-driven, end-to-end framework for monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to extract hidden degradation features from noisy data. We demonstrate the proposed framework on several representative experimental manufacturing datasets drawn from a wide variety of applications, ranging from mechanical to electrical systems. Results reveal that the framework performs well in all benchmark applications examined and can be applied in diverse contexts, indicating its potential for use as a critical corner stone in smart manufacturing

Correlation Between Thymus Radiology and Myasthenia Gravis in Clinical Practice

Background: The ability to distinguish between a normal thymus, thymic hyperplasia, and thymoma should aid in clinical management and decision making for patients with myasthenia gravis (MG). We sought to determine the accuracy of routine radiological examinations in predicting thymic pathology.Methods: We retrospectively analyzed the records of patients with MG who had undergone thymectomy from the Second Affiliated Hospital of Zhengzhou University. Each patient received at least one initial radiological diagnosis and one histological diagnosis, and the patients were classified into the all-patient, CT, contrast CT, and MRI groups. The sensitivity, accuracy and specificity of each group were calculated for different histological types.Results: This study included 114 patients. All sensitivity, specificity and accuracy values except for sensitivity to hyperplasia in each group for different histological types were satisfactory. MRI had higher sensitivity (68.4, 95% CI: 43.5–87.4%) to histological hyperplasia than did CT (14.3, 95% CI: 0.4–57.9%) and contrast CT (26.7, 95% CI: 7.8–55.1%). Contrast CT had higher specificity (97.9, 95% CI: 88.9–99.95%) for histological hyperplasia than did MRI (88.5, 95% CI: 69.9–97.6%).Discussion: For patients with MG, CT, contrast CT, and MRI examinations can effectively identify thymoma. Additionally, compared with CT or contrast CT, MRI may have a stronger ability to distinguish thymoma and detect hyperplasia

Strengthened proximity effect at grain boundaries to enhance inter-grain supercurrent in Ba1-xKxFe2As2 superconductors

Iron-based superconductors have great potential for high-power applications due to their prominent high-field properties. One of the central issues in enhancing the critical current density of iron-based superconducting wires is to reveal the roles and limitations of grain boundaries in supercurrent transport. Here, we finely tuned the electronic properties of grain boundaries by doping Ba1-xKxFe2As2 superconductors in a wide range (0.25<x<0.598). It is found that the intra-grain Jcintra peaks near x~0.287, while the inter-grain Jcinter has a maximum at about x~0.458. Remarkably, the grain boundary transparency parameter defined as Jcinter/Jcintra rises monotonically with doping. Through detailed microscopic analysis, we suggest that the FeAs segregation phase commonly existing at grain boundaries and the adjacent grains constitute superconductor-normal metal-superconductor (SNS) Josephson junctions which play a key role in transporting supercurrent. A sandwich model based on the proximity effect and the SNS junction is proposed to well interpret our data. It is found that overdoping in superconducting grains largely strengthens the proximity effect and consequently enhances the intergrain supercurrent. Our results will shed new insights and inspirations for improving the application parameters of iron-based superconductors by grain boundary engineering.Comment: 22 pages, 6 figure

Oil palm waste: An abundant and promising feedstock for microwave pyrolysis conversion into good quality biochar with potential multi-applications

Oil palm waste (OPW), comprising mainly of empty fruit bunch, mesocarp fiber, frond, trunk, and palm kernel shell generated from palm oil industry, was collected, characterized, and then pyrolyzed to evaluate their potential to be converted into biochar with desirable properties for use in multi-applications. The OPW was detected to have considerable amounts of carbon (43–51 wt%) and fixed carbon (30–39 wt%), showing potential to be converted into carbon-rich biochar. Microwave pyrolysis of palm kernel shell as the selected OPW produced a biochar with zero sulphur content and high heating value (23–26 MJ/kg) that is nearly comparable to conventional coal, thus indicating its potential as an eco-friendly solid fuel. The biochar obtained was also showed low moisture (<3 wt%) and ash (3 wt%), and a highly porous structure with high BET surface area (210 m2/g), indicating the presence of many adsorption sites and thus showing desirable characteristics for potential use as pollutant adsorbent in wastewater treatment, or bio-fertilizer to absorb nutrient and promote plant growth. Our results demonstrate that OPW is a biowaste that shows exceptional promise to be transformed into high-grade biochar rather than simply disposed by landfilling or burned as low-grade fuel in boiler

Interdecadal variation of precipitation over Yunnan, China in summer and its possible causes

In recent decades, severe drought conditions have become increasingly frequent in Yunnan, Southwest China. The extreme drought events cause huge losses to agricultural economy, ecological security and human health. To uncover the reasons behind the worsening drought conditions, this study investigates the interdecadal variability (IDV) of summer precipitation in Yunnan during 1961–2019 and its association with the Indo-Pacific Sea surface temperature (SST) configuration based on gauge observation and reanalysis data. The dominant mode of summer precipitation IDV in Yunnan shows a uniform pattern characterizing the alternations of flood and drought. Specifically, a relatively wet period persists from the early 1990s to the early 2000s, followed by a relatively dry period from the early 2000s to the late 2010s. The IDV of precipitation is consistent with the IDV of the column-integrated water vapor flux divergence, where the wind anomalies play a major role in modulating the moisture supply. The main SST forcings of the IDV of precipitation include the sea surface temperature anomalies (SSTAs) over the Bay of Bengal (BOB), the Western Pacific Warm Pool (WPWP), and the western North Pacific (WNP). The negative SSTAs over the BOB and the WPWP trigger a Gill-Matsuno-type response that enhances the cyclonic curvature over Yunnan. The SSTAs over the WNP show a tripole pattern that weakens the WNP subtropical high and further enhances the cyclonic anomaly over Yunnan. The above SST configuration also favors moisture transport to Yunnan. Numerical experiments verify the key physical processes