Ashleybeyer9203

The study found that prison officers were ordered to fight at the forefront of pandemic control in prisons by working on shifts inside for an extended and indefinite period of time, which proved effective in terminating the spread of the virus, but placed a heavy burden on the personal lives of the officers. The findings also reveal new facets in the mobility and experience of frontline officers. While effective in terms of what the statistics have demonstrated, the Chinese measures have been less effective in adjusting to the needs of frontline staff and acknowledging the personal sacrifices demanded and made in this process.At present times, the drastic advancements in the 5G cellular and internet of things (IoT) technologies find useful in different applications of the healthcare sector. At the same time, COVID-19 is commonly spread from animals to persons, but today it is transmitting among persons by adapting the structure. It is a severe virus and inappropriately resulted in a global pandemic. Radiologists utilize X-ray or computed tomography (CT) images to diagnose COVID-19 disease. It is essential to identify and classify the disease through the use of image processing techniques. So, a new intelligent disease diagnosis model is in need to identify the COVID-19. In this view, this paper presents a novel IoT enabled Depthwise separable convolution neural network (DWS-CNN) with Deep support vector machine (DSVM) for COVID-19 diagnosis and classification. The proposed DWS-CNN model aims to detect both binary and multiple classes of COVID-19 by incorporating a set of processes namely data acquisition, Gaussian filtering (GF) based preprocessing, feature extraction, and classification. Initially, patient data will be collected in the data acquisition stage using IoT devices and sent to the cloud server. Besides, the GF technique is applied to remove the existence of noise that exists in the image. Then, the DWS-CNN model is employed for replacing default convolution for automatic feature extraction. Finally, the DSVM model is applied to determine the binary and multiple class labels of COVID-19. The diagnostic outcome of the DWS-CNN model is tested against Chest X-ray (CXR) image dataset and the results are investigated interms of distinct performance measures. The experimental results ensured the superior results of the DWS-CNN model by attaining maximum classification performance with the accuracy of 98.54% and 99.06% on binary and multiclass respectively.Electronic health records (EHRs) have been widely used to help physicians to make decisions by predicting medical events such as diseases, prescriptions, outcomes, and so on. How to represent patient longitudinal medical data is the key to making these predictions. Recurrent neural network (RNN) is a popular model for patient longitudinal medical data representation from the view of patient status sequences, but it cannot represent complex interactions among different types of medical information, i.e., temporal medical event graphs, which can be represented by graph neural network (GNN). In this paper, we propose a hybrid method of RNN and GNN, called RGNN, for next-period prescription prediction from two views, where RNN is used to represent patient status sequences, and GNN is used to represent temporal medical event graphs. Experiments conducted on the public MIMIC-III ICU data show that the proposed method is effective for next-period prescription prediction, and RNN and GNN are mutually complementary.The main objective of the article is to propose an advanced architecture and workflow based on Apache Hadoop and Apache Spark big data platforms. The primary purpose of the presented architecture is collecting, storing, processing, and analysing intensive data from social media streams. This paper presents how the proposed architecture and data workflow can be applied to analyse Tweets with a specific flood topic. The secondary objective, trying to describe the flood alert situation by using only Tweet messages and exploring the informative potential of such data is demonstrated as well. The predictive machine learning approach based on Bayes Theorem was utilized to classify flood and no flood messages. For this study, approximately 100,000 Twitter messages were processed and analysed. Messages were related to the flooding domain and collected over a period of 5 days (14 May - 18 May 2018). Spark application was developed to run data processing commands automatically and to generate the appropriate output data. Results confirmed the advantages of many well-known features of Spark and Hadoop in social media data processing. It was noted that such technologies are prepared to deal with social media data streams, but there are still challenges that one has to take into account. Based on the flood tweet analysis, it was observed that Twitter messages with some considerations are informative enough to be used to estimate general flood alert situations in particular regions. Text analysis techniques proved that Twitter messages contain valuable flood-spatial information.We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from clients in return for insuring them against intermittent, heavy-tailed risks. Firms manage their capital and pay dividends to their investors and use either reinsurance contracts or cat bonds to hedge their tail risk. The model generates plausible time series of profits and losses and recovers stylized facts, such as the insurance cycle and the emergence of asymmetric firm size distributions. We use the model to investigate the problem of risk model homogeneity. Under the European regulatory framework Solvency II, insurance companies are required to use only certified risk models. This has led to a situation in which only a few firms provide risk models, creating a systemic fragility to the errors in these models. We demonstrate that using too few models increases the risk of nonpayment and default while lowering profits for the industry as a whole. The presence of the reinsurance industry ameliorates the problem but does not remove it. Our results suggest that it would be valuable for regulators to incentivize model diversity. The framework we develop here provides a first step toward a simulation model of the insurance industry, which could be used to test policies and strategies for capital management.Two analogues of 3-(dimethylsulfonio)propanoate (DMSP), 3-(diallylsulfonio)propanoate (DAllSP), and 3-(allylmethylsulfonio)propanoate (AllMSP), were synthesized and fed to marine bacteria from the Roseobacter clade. These bacteria are able to degrade DMSP into dimethyl sulfide and methanethiol. The DMSP analogues were also degraded, resulting in the release of allylated sulfur volatiles known from garlic. For unknown compounds, structural suggestions were made based on their mass spectrometric fragmentation pattern and confirmed by the synthesis of reference compounds. The results of the feeding experiments allowed to conclude on the substrate tolerance of DMSP degrading enzymes in marine bacteria.Amino- and polyaminophthalazinones were synthesized by the palladium-catalyzed amination (alkyl- and arylamines, polyamines) of 4-bromophthalazinones in good yields. The coordinating properties of selected aminophthalazinones towards Cu(II) ions were investigated and the participation of the nitrogen atoms in the complexation of the metal ion was shown. A biological screening of the potential cytotoxicity of selected synthesized compounds on HT-29 and PC-3 cell lines, as well as on the L-929 cell line, proved that some amino derivatives of phthalazinone show interesting anticancer activities. The detailed synthesis, spectroscopic data, and biological assays are reported.The difunctionalization of alkenes involving a trifluoromethylthio group (SCF3) for the conversion of versatile and readily available olefins into structurally more complex molecules has been successfully studied. However, the disproportionate dithiolation of alkenes is unknown. Herein, a transition-metal-free protocol is presented for the vicinal trifluoromethylthio-thiolation of unactivated alkenes via a radical process under mild conditions with a broad substrate scope and excellent tolerance.Herein, we report the enantiospecific synthesis of two artificial glutamate analogs designed based on IKM-159, an antagonist selective to the AMPA-type ionotropic glutamate receptor. The synthesis features the chiral resolution of the carboxylic acid intermediate by the esterification with ʟ-menthol, followed by a configurational analysis by NMR, conformational calculation, and X-ray crystallography. A mice in vivo assay showed that (2R)-MC-27, with a six-membered oxacycle, is neuroactive, whereas the (2S)-counterpart is inactive. STAT inhibitor It was also found that TKM-38, with an eight-membered azacycle, is neuronally inactive, showing that the activity is controlled by the ring C.A highly modular method for the synthesis of (Z)-3-[amino(phenyl/methyl)methylidene]-1,3-dihydro-2H-indol-2-ones starting from easily available 3-bromooxindoles or (2-oxoindolin-3-yl)triflate and thioacetamides or thiobenzamides is described. A series of 49 compounds, several of which have previously been shown to possess significant tyrosin kinase inhibiting activity, was prepared in yields varying mostly from 70 to 97% and always surpassing those obtained by other published methods. The method includes an Eschenmoser coupling reaction, which is very feasible (even without using a thiophile except tertiary amides) and scalable. The (Z)-configuration of all products was confirmed by NMR techniques.A new heterogeneous catalytic system consisting of cobalt nanoparticles (CoNPs) supported on MgO and tert-butyl hydroperoxide (TBHP) as oxidant is presented. This CoNPs@MgO/t-BuOOH catalytic combination allowed the epoxidation of a variety of olefins with good to excellent yield and high selectivity. The catalyst preparation is simple and straightforward from commercially available starting materials and it could be recovered and reused maintaining its unaltered high activity.Ligand-targeted microbubbles are focusing interest for molecular imaging and delivery of chemotherapeutics. Lipid-peptide conjugates (lipopeptides) that feature alternating serine-glycine (SG) n segments rather than classical poly(oxyethylene) linkers between the lipid polar head and a targeting ligand were proposed for the liposome-mediated, selective delivery of anticancer drugs. Here, we report the synthesis of perfluoroalkylated lipopeptides (F-lipopeptides) bearing two hydrophobic chains (C n F2 n +1, n = 6, 7, 8, 1-3) grafted through a lysine moiety on a hydrophilic chain composed of a lysine-serine-serine (KSS) sequence followed by 5 SG sequences. These F-lipopeptides are precursors of targeting lipopeptide conjugates. A hydrocarbon counterpart with a C10H21 chain (4) was synthesized for comparison. The capacity for the F-lipopeptides to spontaneously adsorb at the air/water interface and form monolayers when combined with dipalmitoylphosphatidylcholine (DPPC) was investigated. The F-lipopeptides 1-3 demonstrated a markedly enhanced tendency to form monolayers at the air/water interface, with equilibrium surface pressures reaching ≈7-10 mN m-1 versus less than 1 mN m-1 only for their hydrocarbon analog 4.