A Spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems

Water pollution incidents have occurred frequently in recent years, causing severe damages, economic loss and long-lasting society impact. A viable solution is to install water quality monitoring sensors in water supply networks (WSNs) for real-time pollution detection, thereby mitigating the risk of catastrophic contamination incidents. Given the significant cost of placing sensors at all locations in a network, a critical issue is where to deploy sensors within WSNs, while achieving rapid detection of contaminant events. Existing studies have mainly focused on sensor placement in water distribution systems (WDSs). However, the problem is still not adequately addressed, especially for large scale WSNs. In this paper, we investigate the sensor placement problem in large scale WDSs with the objective of minimizing the impact of contamination events. Specifically, we propose a two-phase Spark-based genetic algorithm (SGA). Experimental results show that SGA outperforms other traditional algorithms in both accuracy and efficiency, which validates the feasibility and effectiveness of our proposed approach

Screening and Growth Characteristics of Probiotics for High Efficient Degradation of Corn Gluten Meal

The strains with high protease production were isolated and screened from various sources to provide strain resources for microbial fermentation of corn gluten meal to produce corn peptide, also to provide strain resources for the value-added and comprehensive utilization of corn gluten meal.The strains were screened by corn gluten meal medium and Folinol method, and were identified by 16S rDNA sequences.The activity and growth characteristics of the target strain protease were further studied.The result showed that 405 bacteria strains were isolated and 58 strains had transparent hydrolytic circles from corn gluten meal medium plates.The strains were classified into 4 families, 5 genera, 17 species and 2 subspecies, including members of Bacillus, Lactobacillus and Planomicrobium.And the Bacillus spp.was the most abundant.The target strains can secrete alkaline, acidic and neutral proteases, and most of the strains show higher alkaline protease activity.Eight strains were selected and their growth characteristics were studied.The optimal growth temperature and pH were 30-40 ℃ and 7～9, respectively.The protease activity and growth characteristics showed the strain specificity

Unlocking Low-Light-Rainy Image Restoration by Pairwise Degradation Feature Vector Guidance

Rain in the dark is a common natural phenomenon. Photos captured in such a condition significantly impact the performance of various nighttime activities, such as autonomous driving, surveillance systems, and night photography. While existing methods designed for low-light enhancement or deraining show promising performance, they have limitations in simultaneously addressing the task of brightening low light and removing rain. Furthermore, using a cascade approach, such as ``deraining followed by low-light enhancement'' or vice versa, may lead to difficult-to-handle rain patterns or excessively blurred and overexposed images. To overcome these limitations, we propose an end-to-end network called $L^{2}RIRNet$ which can jointly handle low-light enhancement and deraining. Our network mainly includes a Pairwise Degradation Feature Vector Extraction Network (P-Net) and a Restoration Network (R-Net). P-Net can learn degradation feature vectors on the dark and light areas separately, using contrastive learning to guide the image restoration process. The R-Net is responsible for restoring the image. We also introduce an effective Fast Fourier - ResNet Detail Guidance Module (FFR-DG) that initially guides image restoration using detail image that do not contain degradation information but focus on texture detail information. Additionally, we contribute a dataset containing synthetic and real-world low-light-rainy images. Extensive experiments demonstrate that our $L^{2}RIRNet$ outperforms existing methods in both synthetic and complex real-world scenarios

Electronic Structure of KFe2_2Se2_2 from First Principles Calculations

Electronic structure and magnetic properties for iron-selenide KFe$_2$Se$_2$ are studied by first-principles calculations. The ground state is stripe-like antiferromagnetic with calculated 2.26 $\mu_B$ magnetic moment on Fe atoms; and the $J_1$, $J_2$ coupling strengths are calculated to be 0.038 eV and 0.029 eV. The states around $E_F$ are dominated by the Fe-3d orbitals which hybridize noticeably to the Se-4p orbitals. While the band structure of KFe$_2$Se$_2$ is similar to a heavily electron-doped BaFe$_2$As$_2$ or FeSe system, the Fermi surface of KFe$_2$Se$_2$ is much closer to \fs11 system since the electron sheets around $M$ is symmetric with respect to $x$-$y$ exchange. These features, as well as the absence of Fermi surface nesting, suggest that the parental KFe$_2$Se$_2$ could be regarded as an electron over-doped 11 system with possible local moment magnetism.Comment: accepted by Chinese Physics Letter, to appear as Chinese Physics Letter, Vol 28, page 057402 (2011

Research on the Law of Garlic Price Based on Big Data

In view of the frequent fluctuation of garlic price under the market economy and the current situation of garlic price, the fluctuation of garlic price in the circulation link of garlic industry chain is analyzed, and the application mode of multidisciplinary in the agricultural industry is discussed. On the basis of the big data platform of garlic industry chain, this paper constructs a Garch model to analyze the fluctuation law of garlic price in the circulation link and provides the garlic industry service from the angle of price fluctuation combined with the economic analysis. The research shows that the average price rate of the price of garlic shows “agglomeration” and cyclical phenomenon, which has the characteristics of fragility, left and a non-normal distribution and the fitting value of the GARCH model is very close to the true value. Finally, it looks into the industrial service form from the perspective of garlic price fluctuation

Role of TRPM8 in dorsal root ganglion in nerve injury-induced chronic pain

<p>Abstract</p> <p>Background</p> <p>Chronic neuropathic pain is an intractable pain with few effective treatments. Moderate cold stimulation can relieve pain, and this may be a novel train of thought for exploring new methods of analgesia. Transient receptor potential melastatin 8 (TRPM8) ion channel has been proposed to be an important molecular sensor for cold. Here we investigate the role of TRPM8 in the mechanism of chronic neuropathic pain using a rat model of chronic constriction injury (CCI) to the sciatic nerve.</p> <p>Results</p> <p>Mechanical allodynia, cold and thermal hyperalgesia of CCI rats began on the 4th day following surgery and maintained at the peak during the period from the 10th to 14th day after operation. The level of TRPM8 protein in L5 dorsal root ganglion (DRG) ipsilateral to nerve injury was significantly increased on the 4th day after CCI, and reached the peak on the 10th day, and remained elevated on the 14th day following CCI. This time course of the alteration of TRPM8 expression was consistent with that of CCI-induced hyperalgesic response of the operated hind paw. Besides, activation of cold receptor TRPM8 of CCI rats by intrathecal application of menthol resulted in the inhibition of mechanical allodynia and thermal hyperalgesia and the enhancement of cold hyperalgesia. In contrast, downregulation of TRPM8 protein in ipsilateral L5 DRG of CCI rats by intrathecal TRPM8 antisense oligonucleotide attenuated cold hyperalgesia, but it had no effect on CCI-induced mechanical allodynia and thermal hyperalgesia.</p> <p>Conclusions</p> <p>TRPM8 may play different roles in mechanical allodynia, cold and thermal hyperalgesia that develop after nerve injury, and it is a very promising research direction for the development of new therapies for chronic neuroapthic pain.</p