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In order to realize real-time and multi-dimensional control of substation risk early warning, a substation risk early warning and control system based on edge computing is designed. The sensing layer collects the operation data of substation equipment through the Internet of Things equipment, and transmits the data to the edge layer through the communication between edge equipment and external equipment. After receiving data, edge layer parses the data through the communication protocol to complete data consistency, and packages cloud services downstream data sending to the cloud computing layer. The layer uses cloud computing to compute and analyze substation equipment data and transmit it to the application layer. The application layer adopts support vector machine (SVM) algorithm to establish a risk early warning mechanism, divide the risk data attribute, and obtain warning value. Based on this, the risk situation corresponding to the early warning value is predicted by the logistic regression risk prediction model, and the probability of failure is obtained to complete the substation risk warning. Meanwhile, the early warning results are presented on the visual interface, and the corresponding risk warning is controlled and controlled by the control channel. The test results show that the communication jitter results of the system are all below 15 ms, which can realize the calculation of risk probability, obtain the number of various risk warnings of the substation, have good work energy efficiency, and realize the multi-dimensional risk warning and control of the substation.

Nasal administration is a non-invasive method of drug delivery that offers several advantages, including rapid onset of action, ease of use, no first-pass effect, and fewer side effects. On this basis, nose-to-brain delivery technology offers a new method for drug delivery to the brain and central nervous system, which has attracted widespread attention. In this paper, the development status and trends of nasal drug delivery and nose-to-brain delivery technology are deeply analyzed through multiple dimensions: literature research, questionnaire surveys, and patent analysis. First, FDA-approved nasal formulations for nose-to-brain delivery were combed. Second, we collected a large amount of relevant information about nasal drug delivery through a questionnaire survey of 165 pharmaceutical industry practitioners in 28 provinces and 161 different organizations in China. Third, and most importantly, we conducted a patent analysis of approximately 700+ patents related to nose-to-brain delivery, both domestically and internationally. This analysis was conducted in terms of patent application trends, technology life cycle, technology composition, and technology evolution. The LDA topic model was employed to identify technological topics in each time window (1990–2023), and the five key major evolution paths were extracted. The research results in this paper will provide useful references for relevant researchers and enterprises in the pharmaceutical industry, promoting the further development and application of nasal drug delivery and nose-to-brain delivery technology. [ABSTRACT FROM AUTHOR]

Powdery mildew significantly impacts the yield of natural rubber by being one of the predominant diseases that affect rubber trees. Accurate, non-destructive recognition of powdery mildew in the early stage is essential for the cultivation management of rubber trees. The objective of this study is to establish a technique for the early detection of powdery mildew in rubber trees by combining spectral and physicochemical parameter features. At three field experiment sites and in the laboratory, a spectroradiometer and a hand-held optical leaf-clip meter were utilized, respectively, to measure the hyperspectral reflectance data (350–2500 nm) and physicochemical parameter data of both healthy and early-stage powdery-mildew-infected leaves. Initially, vegetation indices were extracted from hyperspectral reflectance data, and wavelet energy coefficients were obtained through continuous wavelet transform (CWT). Subsequently, significant vegetation indices (VIs) were selected using the ReliefF algorithm, and the optimal wavelengths (OWs) were chosen via competitive adaptive reweighted sampling. Principal component analysis was used for the dimensionality reduction of significant wavelet energy coefficients, resulting in wavelet features (WFs). To evaluate the detection capability of the aforementioned features, the three spectral features extracted above, along with their combinations with physicochemical parameter features (PFs) (VIs + PFs, OWs + PFs, WFs + PFs), were used to construct six classes of features. In turn, these features were input into support vector machine (SVM), random forest (RF), and logistic regression (LR), respectively, to build early detection models for powdery mildew in rubber trees. The results revealed that models based on WFs perform well, markedly outperforming those constructed using VIs and OWs as inputs. Moreover, models incorporating combined features surpass those relying on single features, with an overall accuracy (OA) improvement of over 1.9% and an increase in F1-Score of over 0.012. The model that combines WFs and PFs shows superior performance over all the other models, achieving OAs of 94.3%, 90.6%, and 93.4%, and F1-Scores of 0.952, 0.917, and 0.941 on SVM, RF, and LR, respectively. Compared to using WFs alone, the OAs improved by 1.9%, 2.8%, and 1.9%, and the F1-Scores increased by 0.017, 0.017, and 0.016, respectively. This study showcases the viability of early detection of powdery mildew in rubber trees. [ABSTRACT FROM AUTHOR]

Based on the high attitude platform of building slabstone-installing robot with independent development,research was carried out about multi-DIM vibration. The paper firstly constructs the system with two degrees of freedom parallel mechanism as the main body and rotating MR damper as actuator. The dynamics equation of the vibration system is established by using Lagrange equation. The paper analyzes the vibration characteristics and designs a kind of rotary MR damper based on a shear mode of magneto rheological fluid. The mechanical characteristics of MR damper is analyzed. The kinematics and kinetic analysis of parallel mechanism is done. The multi-DIM vibration suppression works quickly based on the position of platform according to the kinematics equation. Finally the simulation experiment of this system of multi-DIM vibration suppression is introduced with the aid of to calculate and analysis whether the model is effective and feasible.

Powdery mildew is one of the most significant rubber tree diseases, with a substantial impact on the yield of natural rubber. This study aims to establish a detection approach that coupled continuous wavelet transform (CWT) and machine learning for the accurate assessment of powdery mildew severity in rubber trees. In this study, hyperspectral reflectance data (350–2500 nm) of healthy and powdery mildew-infected leaves were measured with a spectroradiometer in a laboratory. Subsequently, three types of wavelet features (WFs) were extracted using CWT. They were as follows: WFs dimensionally reduced by the principal component analysis (PCA) of significant wavelet energy coefficients (PCA-WFs); WFs extracted from the top 1% of the determination coefficient between wavelet energy coefficients and the powdery mildew disease class (1%R2-WFs); and all WFs at a single decomposition scale (SS-WFs). To assess the detection capability of the WFs, the three types of WFs were input into the random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN), respectively. As a control, 13 optimal traditional spectral features (SFs) were extracted and combined with the same classification methods. The results revealed that the WF-based models all performed well and outperformed those based on SFs. The models constructed based on PCA-WFs had a higher accuracy and more stable performance than other models. The model combined PCA-WFs with RF exhibited the optimal performance among all models, with an overall accuracy (OA) of 92.0% and a kappa coefficient of 0.90. This study demonstrates the feasibility of combining CWT with machine learning in rubber tree powdery mildew detection. [ABSTRACT FROM AUTHOR]

Release of genetically modified (GM) plants has sparked off intensive debates worldwide partly because of concerns about potential adverse unintended effects of GM plants to the agro system and the safety of foods. In this study, with the aim of revealing the molecular basis for unintended effects of a single site insertion GM Kemingdao (KMD) rice transformed with a synthetic cry1Ab gene, and bridging unintended effects of KMD rice through clues of differentially expressed genes, comparative transcriptome analyses were performed for GM KMD rice and its parent rice of Xiushui11 (XS11). The results showed that 680 differentially expressed transcripts were identified from 30-day old seedlings of GM KMD rice. The absolute majority of these changed expression transcripts dispersed and located over all rice chromosomes, and existed physical distance on chromosome from the insertion site, while only two transcripts were found to be differentially expressed within the 21 genes located within 100 kb up and down-stream of the insertion site. Pathway and biology function analyses further revealed that differentially expressed transcripts of KMD rice were involved in certain biological processes, and mainly implicated in two types of pathways. One type was pathways implicated in plant stress/defense responses, which were considerably in coordination with the reported unintended effects of KMD rice, which were more susceptible to rice diseases compared to its parent rice XS11; the other type was pathways associated with amino acids metabolism. With this clue, new unintended effects for changes in amino acids synthesis of KMD rice leaves were successfully revealed. Such that an actual case was firstly provided for identification of unintended effects in GM plants by comparative transciptome analysis.

Objective: A comprehensive strategy for microbial identification and contamination investigation during sterile drug manufacturing was innovatively established in this study, mainly based on MALDI-TOF MS for the identification and complemented by sequencing technology on strain typing., Methods: It was implemented to monitor the bacterial contamination of a sterile drug manufacturing facility, including its bacterial distribution features and patterns. In three months, two hundred ninety-two samples were collected covering multiple critical components of raw materials, personnel, environment, and production water., Results: Based on our strategy, the bacterial profile across the production process was determined: 241/292 bacterial identities were obtained, and Staphylococcus spp. (40.25%), Micrococcus spp.(11.20%), Bacillus spp. (8.30%), Actinobacteria (5.81%), and Paenibacillus spp. (4.56%) are shown to be the most dominant microbial contaminants. With 75.8% species-level and 95.4% genus-level identification capability, MALDI-TOF MS was promising to be a first-line tool for environmental monitoring routine. Furthermore, to determine the source of the most frequently occurring Staphylococcus cohnii , which evidenced a widespread presence in the entire process, a more discriminating S. cohnii whole-genome SNP typing method was developed to track the transmission routes. Phylogenetic analysis based on SNP results indicated critical environment contamination is highly relevant to personnel flow in this case. The strain typing results provide robust and accurate information for the following risk assessment step and support effective preventive and corrective measures., Conclusion: In general, the strategy presented in this research will facilitate the development of improved production and environmental control processes for the pharmaceutical industry, and give insights about how to provide more sound and reliable evidence for the optimization of its control program., Competing Interests: CZL was employed by Hangzhou Digital-Micro Biotech Co., Ltd. LCZ was employed by Shanghai SPH New Asia Pharmaceutical Co., Ltd. YLF, GLC, and MCY were employed by China State Institute of Pharmaceutical Industry, Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Song, Li, Liu, Wang, Qin, Zhang, Fan, Shao, Chen and Yang.)

The antitumor drug candidate X-05 is being developed as an innovative anti-lung cancer drug candidate due to its excellent antitumour activity. A Caco-2 cell permeability study and solubility study confirmed that X-05 belonged to BCS class or compounds. Therefore, the main challenge is to develop appropriate preparations for preclinical studies and further clinical phase research. By evaluating the preliminary results of kinetic solubility in biorelevant media and the structural analysis of X-05 and polymers, three polymers PVP K30, PVP VA 64 and HPMCAS, which may have intermolecular interactions with X-05, were chosen to select the optimal carrier for X-05 to prepare amorphous solid dispersions (ASDs). ASD X-05-PVP VA 64 was selected as the optimal polymer by evaluating its kinetic solubility in biorelevant media and solid stability. The physical and chemical properties of ASD X-05-PVP VA 64 remain stable when the drug loading is as high as 50%. The drug-polymer interactions of ASD X-05-PVP VA 64 were studied by ultraviolet spectrophotometry, nuclear magnetic resonance spectrometry, infrared and Raman spectrophotometry, and the results indicated that the intermolecular hydrogen bond interaction between the drug and polymer was the foundation of the solubilization and stabilization of X-05 in PVP VA 64.