Optimized forecast components-SVM-based fault diagnosis with applications for wastewater treatment

Process monitoring of wastewater treatment plant (WWTP) is a challenging industrial problem, due to its exposure to the hostile working environment and significant disturbances. This paper proposed a novel fault diagnosis method, termed as optimization forecast components-support vector machine (OFC-SVM). The method firstly improved the forecastable component analysis (ForeCA) for feature extraction. Secondly, in order to further enhance the method, the quadratic Grid Search (GS) algorithm is utilized to optimize the parameters of the proposed method. Thirdly, to properly evaluate the method performance, a new evaluation index is proposed, named Pre Alarm Rate (PAR), aiming to achieve the quantitative trade-off between false alarm rate (FAR) and missed alarm rate(MAR). Then, the new ROC curve can be further derived by PAR. Finally, the performance of OFC-SVM is strictly compared with other five methods as well as validated by a Monte Carlo model and a full-scale WWTP

Motion synchronization for the SHA/EMA hybrid actuation system by using an optimization algorithm

The current research develops a mathematical model and control strategies to address two major problems force fighting and precise position tracking for a hybrid actuation system composed of servo-hydraulic actuator and electro-mechanical actuator (SHA/EMA). The force fighting and desired position tracking are two essential problems of the SHA/EMA actuation system for a large civil aircraft. The trajectory-based fractional order proportional integral derivative (FOPID) control for the SHA/EMA actuation system is proposed, tuned with the help of the particle swarm optimization (PSO) technique and implemented with the support of the FOMCON toolbox in Matlab. The experiments are performed under different external aerodynamic loads that the aircraft usually experiences during flight operations. The results show that the proposed method shows better results for tracking performance, force fighting and load rejection ability

Modeling of adaptive multi-output soft-sensors with applications in wastewater treatments

Given the multivariable coupling, strong nonlinearity and time-varying features in the wastewater treatment processes, adaptive strategies, including just-in-time learning (JITL), time difference (TD), and moving window (MW) methods have been chosen in this paper to enhance multi-output soft-sensor models to ensure online prediction for a variety of hard-to-measure variables simultaneously. In the proposed adaptive multi-output soft-sensors, multi-output partial least squares (MPLS), multi-output relevant vector machine (MRVM) and multi-output Gaussian process regression (MGPR) served as the multi-output models. The integration of adaptive strategies and multi-output models not only provides a solution for multi-output prediction, but also offers a potential to alleviate the degradation of multi-output soft-sensors. To further improve the adaptive ability, four adaptive soft-sensors, termed TD-MW, TD-JIT, JIT-MW, and TD-JIT-MW, have been proposed by mixing the three aforementioned adaptive strategies to upgrade multi-output softsensors. All the adaptive multi-output soft-sensors are analyzed and compared in terms of simulation data and practical industrial data, which exhibit stationary and nonstationary behaviors, respectively

Dietary exposure assessment of dibutyl phthalate in edible vegetable oil in Shanghai

Objective This paper aim to investigate dibutyl phthalate (DBP) concentration level in edible vegetable oil sold in Shanghai, and to evaluate the dietary exposure risk of local residents. Methods By combining monitoring data of DBP in edible vegetable oil sold in Shanghai from 2015 to 2019 with the dietary consumption data of residents, the dietary exposure of DBP in edible vegetable oil was assessed via point assessment method. Results A total of 1 248 DBP samples in edible vegetable oil were tested from 2015 to 2019, the overall unqualified rate was 3.4% (43/1 248), and the mean concentration was (0.34±2.15) mg/kg. According to the annual statistics, the unqualified rate showed an upward trend of fluctuation and reached 4.4% (13/295) in 2019. According to the statistics of edible vegetable oil varieties, DBP contamination levels in walnut oil, sesame oil and rapeseed oil were relatively serious, with the unqualified rate of 28.6% (6/21), 10.5% (20/190) and 9.2% (8/87), respectively. The mean and 97.5 percentile daily DBP intake from edible vegetable oil in general population were 0.23 and 0.40 μg/kg BW, accounting for 2.3% and 4.0% of tolerable daily intake (TDI, 10 μg/kg BW), respectively. Conclusion The health risk of DBP intake from edible vegetable oil was relatively low and acceptable for Shanghai residents

Shifts in soil ammonia-oxidizing community maintain the nitrogen stimulation of nitrification across climatic conditions

Anthropogenic nitrogen (N) loading alters soil ammonia-oxidizing archaea (AOA) and bacteria (AOB) abundances, likely leading to substantial changes in soil nitrification. However, the factors and mechanisms determining the responses of soil AOA:AOB and nitrification to N loading are still unclear, making it difficult to predict future changes in soil nitrification. Herein, we synthesize 68 field studies around the world to evaluate the impacts of N loading on soil ammonia oxidizers and nitrification. Across a wide range of biotic and abiotic factors, climate is the most important driver of the responses of AOA:AOB to N loading. Climate does not directly affect the N-stimulation of nitrification, but does so via climate-related shifts in AOA:AOB. Specifically, climate modulates the responses of AOA:AOB to N loading by affecting soil pH, N-availability and moisture. AOB play a dominant role in affecting nitrification in dry climates, while the impacts from AOA can exceed AOB in humid climates. Together, these results suggest that climate-related shifts in soil ammonia-oxidizing community maintain the N-stimulation of nitrification, highlighting the importance of microbial community composition in mediating the responses of the soil N cycle to N loading

Targeting matrix metalloproteases in diabetic wound healing

Chronic inflammation participates in the progression of multiple chronic diseases, including obesity, diabetes mellitus (DM), and DM related complications. Diabetic ulcer, characterized by chronic wounds that are recalcitrant to healing, is a serious complication of DM tremendously affecting the quality of life of patients and imposing a costly medical burden on society. Matrix metalloproteases (MMPs) are a family of zinc endopeptidases with the capacity of degrading all the components of the extracellular matrix, which play a pivotal part in healing process under various conditions including DM. During diabetic wound healing, the dynamic changes of MMPs in the serum, skin tissues, and wound fluid of patients are in connection with the degree of wound recovery, suggesting that MMPs can function as essential biomarkers for the diagnosis of diabetic ulcer. MMPs participate in various biological processes relevant to diabetic ulcer, such as ECM secretion, granulation tissue configuration, angiogenesis, collagen growth, re-epithelization, inflammatory response, as well as oxidative stress, thus, seeking and developing agents targeting MMPs has emerged as a potential way to treat diabetic ulcer. Natural products especially flavonoids, polysaccharides, alkaloids, polypeptides, and estrogens extracted from herbs, vegetables, as well as animals that have been extensively illustrated to treat diabetic ulcer through targeting MMPs-mediated signaling pathways, are discussed in this review and may contribute to the development of functional foods or drug candidates for diabetic ulcer therapy. This review highlights the regulation of MMPs in diabetic wound healing, and the potential therapeutic ability of natural products for diabetic wound healing by targeting MMPs

Suicide rates among patients with first and second primary cancer

Abstract Aims With advancements in cancer treatments, the survival rates of patients with their first primary cancer (FPC) have increased, resulting in a rise in the number of patients with second primary cancer (SPC). However, there has been no assessment on the incidence of suicide among patients with SPC. This study assessed the occurrence of suicide among patients with SPC and compared them with that in patients with FPC. Methods This was a retrospective, population-based cohort study that followed patients with FPC and SPC diagnosed from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) 17 registries database between 1 January 2000 and 31 December 2019. Results For patients with SPC, an age of 85+ years at diagnosis was associated with a higher incidence of suicide death (HR, 1.727; 95% CI, 1.075–2.774), while the suicide death was not considerably different in the chemotherapy group (P > 0.05). Female genital system cancers (HR, 3.042; 95% CI, 1.819–6.361) accounted for the highest suicide death among patients with SPC. The suicide death distribution of patients with SPC over time indicated that suicide events mainly occurred within 5 to 15 years of diagnosis. Compared with patients with FPC, patients with SPC in general had a lower risk of suicide, but increased year by year. Conclusion The risk of suicide was reduced in patients with SPC compared with patients with FPC, but increased year by year. Therefore, oncologists and related health professionals need to provide continuous psychological support to reduce the incidence of suicide. The highest suicide death was found among patients with female genital system cancer

Giant pressure-enhancement of multiferroicity in CuBr2

Type-II multiferroic materials, in which ferroelectric polarization is induced by inversion non-symmetric magnetic order, promise new and highly efficient multifunctional applications based on the mutual control of magnetic and electric properties. Although this phenomenon has to date been limited to low temperatures, here we report a giant pressure-dependence of the multiferroic critical temperature in CuBr$_2$. At 4.5 GPa, $T_\mathrm{C}$ is enhanced from 73.5 to 162 K, to our knowledge the highest value yet reported for a non-oxide type-II multiferroic. This growth shows no sign of saturating and the dielectric loss remains small under these high pressures. We establish the structure under pressure and demonstrate a 60\% increase in the two-magnon Raman energy scale up to 3.6 GPa. First-principles structural and magnetic energy calculations provide a quantitative explanation in terms of dramatically pressure-enhanced interactions between CuBr$_2$ chains. These large, pressure-tuned magnetic interactions motivate structural control in cuprous halides as a route to applied high-temperature multiferroicity.Comment: 10 pages, 6 figure