Research on the spectral reconstruction of a low-dimensional filter array micro-spectrometer based on a truncated singular value decomposition-convex optimization algorithm

Currently, the engineering of miniature spectrometers mainly faces three problems: the mismatch between the number of filters at the front end of the detector and the spectral reconstruction accuracy; the lack of a stable spectral reconstruction algorithm; and the lack of a spectral reconstruction evaluation method suitable for engineering. Therefore, based on 20 sets of filters, this paper classifies and optimizes the filter array by the K-means algorithm and particle swarm algorithm, and obtains the optimal filter combination under different matrix dimensions. Then, the truncated singular value decomposition-convex optimization algorithm is used for high-precision spectral reconstruction, and the detailed spectral reconstruction process of two typical target spectra is described. In terms of spectral evaluation, due to the strong randomness of the target detected during the working process of the spectrometer, the standard value of the target spectrum cannot be obtained. Therefore, for the first time, we adopt the method of joint cross-validation of multiple sets of data for spectral evaluation. The results show that when the random error of positive or negative 2 code values is applied multiple times for reconstruction, the spectral angle cosine value between the reconstructed curves becomes more than 0.995, which proves that the spectral reconstruction under this algorithm has high stability. At the same time, the spectral angle cosine value of the spectral reconstruction curve and the standard curve can reach above 0.99, meaning that it realizes a high-precision spectral reconstruction effect. A high-precision spectral reconstruction algorithm based on truncated singular value-convex optimization, which is suitable for engineering applications, is established in this paper, providing important scientific research value for the engineering application of micro-spectrometers.Comment: 22pages 11figure

The Effect of Ozone-Electric Treatment on the Enrichment and Transfer of Heavy Metal Cu in Sludge

In order to explore a safe, effective way to use sludge as agricultural fertilizer it is necessary to effectively separate and remove the heavy metals embedded in sludge. In the study, the ozone-electric two-stage treatment was used to transform heavy metal copper in the sludge, and then the treated sludge was used for maize production and the transferring of Cu in cultivation medium and plants, and the enrichment effect of Cu in plant were investigated. According to composition of culture substance, five treatments were set in maize planting experiments: CK, Agricultural soil without addition; T1, Agricultural soil supplemented with raw sludge; T2, Agricultural soil treated with ozone sludge; T3, Agricultural soil with ozone treated and electric treated sludge; T4, Agricultural soil added with common organic fertilizer. The results showed that in different treatments, the Cu content of organs showed the order of root> stem> leaf> cob> grain. Comparing root Cu content, the lowest was in T1 treatment, which was 11.60 mg/kg, while the lowest of grain Cu content was found in CK treatment, which was 1.36 mg/kg. In the upper, middle and lower soil layers, the highest and lowest Cu content was in T4 and CK, respectively. In both middle and lower soil layers, the Cu content of T1, T2 and T3 sludge treatments had a trend of T1>T2>T3; the difference of the Cu enrichment ability between different organs is not significant in the same soil layer. From each treatment, the Cu enrichment ability of plant of CK was higher than that of other treatments. According to the ability of Cu transferring to the above-ground part of plant, treatments are ranked as CK>T3>T4>T1>T2. The transferring of Cu from soil to plant was mainly affected by fertilizer level and the transferring rate of Cu from soil to stem, leaf and root was relatively high, but it was hardly affected by sludge. In summary, after ozone-electro treatment, the application of sludge does not significantly affect the Cu content in maize, and the Cu content in each treatment does not exceed the limit value of agricultural production

1,3,5-Tris(N-phenyl­benzimidazol-2-yl)benzene methanol solvate

The main mol­ecule of the title compound, C45H30N6·CH3OH, has a non-planar core: the dihedral angles between the benzimidazole rings and the central benzene ring are 20.19 (10), 34.57 (8), and 44.59 (8)°, while the dihedral angles between the peripheral phenyl rings and the attached benzimidazole rings are 84.57 (7), 62.71 (6) and 51.73 (6)°. The tri-substituted benzene mol­ecule is connected to the methanol solvent mol­ecule through an O—H⋯N hydrogen bond, forming a 1:1 solvate. In the crystal structure, no significant π–π inter­actions are present, and the mol­ecules are associated through weak C—H⋯N and C—H⋯O(methanol) contacts

Studies on Anti-Depressant Activity of Four Flavonoids Isolated from Apocynum venetum Linn (Apocynaceae) Leaf in Mice

Purpose: To investigate the anti-depressant activity of kaempferol, quercetin, kaempferol-3-O-β-Dglucose and quercetin-3-O-β-D-glucose isolated from Apocynum venetum Linn. (Apocynaceae) leaf and their mechanisms of action.Methods: The four flavonoids were isolated from Apocynum venetum leaf by chromatography. Mice were divided into vehicle, fluoxetine, kaempferol, quercetin, kaempferol-3-O-β-D-glucose and quercetin- 3-O-β-D-glucose groups (n = 10). Forced swimming (FST), tail suspension (TST) and locomotor activity (LAT) tests were used to evaluate the effects of the four flavonoids (0.35 mM/kg) on immobility time, monoamine neurotransmitters, viz, norepinephrine (NE), dopamine (DA) and 5-hydroxytryptamine (5- HT), as well as on the metabolite (5-HIAA) in mice brain and central nervous system (CNS) with the aid of video camera, HPLC-ECD and activity-monitoring system.Results: The four flavonoids significantly (p < 0.05) reduced mice immobility time (72.58 - 90.24; 52.58 - 70.24 s), 5-HIAA levels (940.8 - 1244.7; 880.8 - 1164.1 ng/g) and 5-HIAA/5-HT ratio (1.77 - 4.76; 1.83 - 4.16), but increased NE, DA and 5-HT levels (238.7 - 405.7, 308.4 - 528.1, 261.4 - 531.9; 243.9 - 423.6, 296.7 - 534.9, 279.8 - 481.4 ng/g) in FST and TST, compared with control group (146.18, 126.18 s; 1363.4, 1240.9 ng/g; 7.43, 6.16; 138.4, 235.4, 183.4 and 143.7, 218.6, 201.4 ng/g). The effects of the four flavonoids on the above indices were significant (p < 0.05) and positively related to their polarity. They had no CNS-stimulating effects in LAT.Conclusion: The anti-depressant activities of the four flavonoids are positively related to their polarity, and the mechanisms may be due to increased NE, DA and 5-HT and reduced 5-HT metabolism.Keywords: Kaempferol, Quercetin, Forced swimming test, Tail suspension test, Locomotor activity test, Neurotransmitter

Differentiate Quality of Experience Scheduling for Deep Learning Inferences with Docker Containers in the Cloud

With the prevalence of big-data-driven applications, such as face recognition on smartphones and tailored recommendations from Google Ads, we are on the road to a lifestyle with significantly more intelligence than ever before. Various neural network powered models are running at the back end of their intelligence to enable quick responses to users. Supporting those models requires lots of cloud-based computational resources, e.g., CPUs and GPUs. The cloud providers charge their clients by the amount of resources that they occupy. Clients have to balance the budget and quality of experiences (e.g., response time). The budget leans on individual business owners, and the required Quality of Experience (QoE) depends on usage scenarios of different applications. For instance, an autonomous vehicle requires an real-time response, but unlocking your smartphone can tolerate delays. However, cloud providers fail to offer a QoE-based option to their clients. In this paper, we propose DQoES, differentiated quality of experience scheduler for deep learning inferences. DQoES accepts clients' specifications on targeted QoEs, and dynamically adjusts resources to approach their targets. Through the extensive cloud-based experiments, DQoES demonstrates that it can schedule multiple concurrent jobs with respect to various QoEs and achieve up to 8x times more satisfied models when compared to the existing syste