In this study, a real-time remote monitoring and fault diagnosis method has been developed based on the Internet of Things (IoT) frame perception, and successfully applied to a mine hoist system. The proposed method combines the sensor technology, online monitoring technology, wireless transmission technology, and fault diagnosis technology. The basic structure of the traditional IoT comprises a perception layer, a network layer, and an application layer, the proposed structure contains an additional middleware layer between the network layer and the application layer. This four-layer system is used in a mine hoist remote monitoring and fault diagnosis framework to process heterogeneous multi-source information. The sensors and parameters are connected in the perception layer, the characteristic parameters are obtained using the configuration software, and the mine local area network is saved to the data server, thereby synchronizing real-time data in the local area network. The network layer utilizes mature Internet and long-distance wireless transmission communication technologies, whereas the middleware layer comprises of a Service-Oriented Architecture (SOA)-based IoT data processing framework that integrates the multi-source heterogeneous data. Further, the fault diagnosis method is analyzed and verified based on the gray association rules. In the application layer, a human-computer interface is used for the remote monitoring and diagnosis of the mine hoist and to provide the diagnosis results as feedback to the user. The results using the aforementioned analyses are applied to the remote monitoring and diagnosis of a mine hoist system. In this study, experimental tests are conducted in this study to significantly improve the fault monitoring, diagnostic capabilities, and reliability of the mine hoist system, indicating the good application good prospects of the proposed method.
Abstract Objective A low incidence and mortality rate of cancer has been observed in high-altitude regions, suggesting a potential positive effect of low press and hypoxia (LPH) environment on cancer. Based on this finding, our study aimed to construct a pan-cancer prognosis risk model using a series of ADME genes intervened by low oxygen, to explore the impact of LPH environment on the overall survival (OS) of various kinds of cancers, and to provide new ideas and approaches for cancer prevention and treatment. Datasets and Measures The study used multiple sources of data to construct the pan-cancer prognosis risk model, including gene expression and survival data of 8,628 samples from the cancer genome atlas, and three gene expression omnibus databases were employed to validate the prediction efficiency of the prognostic model. The AltitudeOmics dataset was specifically used to validate the significant changes in model gene expression in LPH. To further identify the biomarkers and refine the model, various analytical approaches were employed such as single-gene prognostic analysis, weighted gene co-expression network analysis, and stepwise cox regression. And LINCS L1000, AutoDockTools, and STITCH were utilized to explore effective interacting drugs for model genes. Main Outcomes and Conclusions The study identified eight ADME genes with significant changes in the LPH environment to describe the prognostic features of pan-cancer. Lower risk scores calculated by the model were associated with better prognosis in 25 types of tumors, with a p-value of less than 0.05. The LPH environment was found to reduce the overall expression value of model genes, which could decrease the death risk of tumor prognosis. Additionally, it is found that the low-risk group had a higher degree of T cell infiltration based on immune infiltration analysis. Finally, drug exploration led to the identification of three potential model-regulating drugs. Overall, the study provided a new approach to construct a pan-cancer survival prognosis model based on ADME genes from the perspective of LPH and offered new ideas for future tumor prognosis research.
Dendronized amphiphilic block copolymers POEGMA-b-P(Gn)-b-PDEAEMA were synthesized, and their self-assembly behavior could be regulated by the dendron generation, the type of common solvent, and CO2-stimulus.
Abstract Objectives: To explore the performance of Multi-scale Fusion Attention U-net (MSFA-U-net) in thyroid gland segmentation on CT localization images for radiotherapy. Methods: CT localization images for radiotherapy of 80 patients with breast cancer or head and neck tumors were selected; label images were manually delineated by experienced radiologists. The data set was randomly divided into the training set (n=60), the validation set (n=10), and the test set (n=10). Data expansion was performed in the training set, and the performance of the MSFA-U-net model was evaluated using the evaluation indicators Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), positive predictive value (PPV), sensitivity (SE), and Hausdorff distance (HD). Results: With the MSFA-U-net model, the DSC, JSC, PPV, SE, and HD indexes of the segmented thyroid gland in the test set were 0.8967±0.0935, 0.8219±0.1115, 0.9065±0.0940, 0.8979±0.1104, and 2.3922±0.5423, respectively. Compared with U-net, HR-net, and Attention U-net, MSFA-U-net showed that DSC increased by 0.052, 0.0376, and 0.0346 respectively; JSC increased by 0.0569, 0.0805, and 0.0433, respectively; SE increased by 0.0361, 0.1091, and 0.0831, respectively; and HD increased by −0.208, −0.1952, and −0.0548, respectively. The test set image results showed that the thyroid edges segmented by the MSFA-U-net model were closer to the standard thyroid delineated by the experts, in comparison with those segmented by the other three models. Moreover, the edges were smoother, over-anti-noise interference was stronger, and oversegmentation and undersegmentation were reduced. Conclusion: The MSFA-U-net model can meet basic clinical requirements and improve the efficiency of physicians' clinical work.
As the links among various fields of human society continue to deepen, the ecological characteristics of political development become more and more obvious.And the ecological concept has infiltrated into the course of political development.As an important part of the political system, democratic politics has its own ecological characteristics.From the perspective of political ecology, it is mainly reflected in the systemic nature of democratic political construction, the adaptability in the development of democratic politics, and the sustainability of democratic political development.
The potential applications of cationic poly(ionic liquids) range from medicine to energy storage, and the development of efficient synthetic strategies to target innovative cationic building blocks is an important goal. A post-polymerization click reaction is reported that provides facile access to trisaminocyclopropenium (TAC) ion-functionalized macromolecules of various architectures, which are the first class of polyelectrolytes that bear a formal charge on carbon. Quantitative conversions of polymers comprising pendant or main-chain secondary amines were observed for an array of TAC derivatives in three hours using near equimolar quantities of cyclopropenium chlorides. The resulting TAC polymers are biocompatible and efficient transfection agents. This robust, efficient, and orthogonal click reaction of an ionic liquid, which we term ClickabIL, allows straightforward screening of polymeric TAC derivatives. This platform provides a modular route to synthesize and study various properties of novel TAC-based polymers.
The antimicrobial resistance of Salmonella strains is rapidly increasing worldwide, which poses significant threats to animal and public health. In this study, a total of 249 porcine Salmonella isolates collected in China during 2008-2015 were examined, including 155 clinical isolates from diseased pigs and 94 nonclinical isolates from healthy pigs. Based on the minimum inhibitory concentration of seven antimicrobial agents, 96.4% of the isolates were resistant to at least one of the tested antibiotics and 81.0% of them showed multidrug resistance. The highest antimicrobial resistance was observed for tetracycline (85.9%), and the lowest was found for cefotaxime (13.3%). The isolates from diseased pigs exhibited significantly higher levels of antimicrobial resistance than those from healthy pigs. Twenty-two isolates from healthy pigs were resistant to ciprofloxacin, which may inhibit the curative effectiveness of fluoroquinolones on bacterial food-borne poisoning and infections in humans caused by contaminated food. Moreover, cefotaxime resistance of the strains isolated from diseased pigs during 2013-2015 was significantly higher compared with the strains isolated during 2008-2010. Further study showed that the correlation between phenotypic and genotypic resistance varied among the isolates from different sources, and in many cases, the presence of resistance genes was not consistent with the resistance to the corresponding antimicrobials. These results are very significant for veterinary practice and public health.
Abstract In view of the problems such as the overall low utilization rate of resources, the insufficient level of industry informatization, the prominent problem of repeated construction, and the insufficient level of innovation in the manufacturing process design of fully mechanized coal mining equipment, the integration and scheduling method of intelligent cloud manufacturing resources for fully mechanized coal mining equipment was studied. First, the necessity and importance of building intelligent cloud manufacturing resources integration and scheduling systems for fully mechanized mining equipment under the current situation were analyzed. Then, based on the underlying services of cloud computing and intelligent cloud manufacturing, the overall architecture of intelligent cloud manufacturing resources integration and scheduling systems for fully mechanized coal mining equipment was proposed, and the intelligent cloud manufacturing resources integration platform suitable for general and special technical services was designed. Then, a could scheduling method based on an adaptive genetic algorithm was proposed to realize the scheduling of cloud manufacturing resources. Finally, the practicability and operability of the system were verified by the cases. This study has realized the integration and sharing of resources, such as the design, calculation, data, simulation, and servicing of fully mechanized coal mining equipment under the intelligent cloud manufacturing environment.
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