Tracking moisture contents in the pollution layer on a composite insulator surface using hyperspectral imaging technology.

Electrical insulators used in transmission lines and outdoor substations are exposed to severe environmental pollution, which significantly increases the risk of power system failure, especially when the pollution layer is highly humid due to adverse weather conditions. The focus of this paper is to establish an effective method for assessing the moisture content (MC) in pollution layers as it serves as a crucial indicator for evaluating the risk of failure in insulators. Hyperspectral imaging (HSI) technology with a spectral range of 371.08-1037.89 nm was applied to determine significant changes in reflectance spectral characteristics in insulators during dynamic wetting and drying periods. Partial least squares regression (PLSR) models were utilized to evaluate the data presentation enhancement abilities of spectral transformation models and the data dimensionality reduction abilities of characteristic band selection methods. Furthermore, PLSR models were developed to calculate the MC along the pixel dimension to visually retrieve the dynamic wetting and drying processes of the pollution layer. The R-squared and root-mean-square error (RMSE) results in the cross-verification set and prediction set of the RE-RF(70%)-PLSR model with two characteristic bands with a wavelength of 543.28 nm and 848.01 nm were as follows: RCV2 = 0.9824, RMSECV = 0.0367, RP2 = 0.9818, RMSEP = 0.0369, respectively. This research contributes towards the visualization retrieval of the MC and offers an important technique for analyzing flashover evolution, optimizing insulator design, and preparing coating materials for insulators.

Testing facility location and dynamic capacity planning for pandemics with demand uncertainty.

The outbreak of coronavirus disease 2019 (COVID-19) has seriously affected the whole world, and epidemic research has attracted increasing amounts of scholarly attention. Critical facilities such as warehouses to store emergency supplies and testing or vaccination sites could help to control the spread of COVID-19. This paper focuses on how to locate the testing facilities to satisfy the varying demand, i.e., test kits, caused by pandemics. We propose a two-phase optimization framework to locate facilities and adjust capacity during large-scale emergencies. During the first phase, the initial prepositioning strategies are determined to meet predetermined fill-rate requirements using the sample average approximation formulation. We develop an online convex optimization-based Lagrangian relaxation approach to solve the problem. Specifically, to overcome the difficulty that all scenarios should be addressed simultaneously in each iteration, we adopt an online gradient descent algorithm, in which a near-optimal approximation for a given Lagrangian dual multiplier is constructed. During the second phase, the capacity to deal with varying demand is adjusted dynamically. To overcome the inaccuracy of long-term prediction, we design a dynamic allocation policy and adaptive dynamic allocation policy to adjust the policy to meet the varying demand with only one day's prediction. A comprehensive case study with the threat of COVID-19 is conducted. Numerical results have verified that the proposed two-phase framework is effective in meeting the varying demand caused by pandemics. Specifically, our adaptive policy can achieve a solution with only a 3.3% gap from the optimal solution with perfect information.

The therapeutic prospects of N-acetylgalactosamine-siRNA conjugates.

RNA interference has become increasingly used for genetic therapy following the rapid development of oligonucleotide drugs. Significant progress has been made in its delivery system and implementation in the treatment of target organs. After a brief introduction of RNA interference technology and siRNA, the efficiency and stability of GalNAc-siRNA conjugates are highlighted since several oligonucleotide drugs of GalNAc have been approved for clinical use in recent years. The structure and features of GalNAc-siRNA conjugates are studied and the clinical efficiency and limitations of oligonucleotide-based drugs are summarized and investigated. Furthermore, another delivery system, lipid nanoparticles, that confer many advantages, is concluded, includ-ing stability and mass production, compared with GalNAc-siRNA conjugates. Importantly, developing new approaches for the use of oligonucleotide drugs brings hope to genetic therapy.

Insight Into the Prospects for RNAi Therapy of Cancer.

RNA interference (RNAi), also known as gene silencing, is a biological process that prevents gene expression in certain diseases such as cancer. It can be used to improve the accuracy, efficiency, and stability of treatments, particularly genetic therapies. However, challenges such as delivery of oligonucleotide drug to less accessible parts of the body and the high incidence of toxic side effects are encountered. It is therefore imperative to improve their delivery to target sites and reduce their harmful effects on noncancerous cells to harness their full potential. In this study, the role of RNAi in the treatment of COVID-19, the novel coronavirus disease plaguing many countries, has been discussed. This review aims to ascertain the mechanism and application of RNAi and explore the current challenges of RNAi therapy by identifying some of the cancer delivery systems and providing drug information for their improvement. It is worth mentioning that delivery systems such as lipid-based delivery systems and exosomes have revolutionized RNAi therapy by reducing their immunogenicity and improving their cellular affinity. A deeper understanding of the mechanism and challenges associated with RNAi in cancer therapy can provide new insights into RNAi drug development.

Regulation of the osteoblastic and chondrocytic differentiation of stem cells by the extracellular matrix and subsequent bone formation modes.

While various factors have been reported to direct stem cell differentiation lineage, little is known about how nature orchestrates the mesenchymal stem cell (MSC) differentiation and bone morphogenesis during skeleton development and bone regeneration. The present study reports that the matrix has a critical regulating effect on MSC differentiation and the subsequent bone formation modes. A simply combined hydroxyapatite (HA)-collagen matrix stimulates the MSC differentiation into the osteoblastic lineage and leads to a straightforward intramembranous bone formation mode, in contrast to the chondrocytic differentiation and endochondral mode observed on HA-synthetic hydrogel matrix. The accelerated MSC condensation and robust MSC-matrix and MSC-MSC interactions on collagen-based matrix might be the critical factors contributing to such events, likely through the orchestrated signal cascades and cellular events modulated by the extracellular matrix. The results demonstrate that matrix plays critical role in modulating the stem cell differentiation lineage and bone formation mode, which has been largely overlooked.