Life Cycle Analysis of Integrated Gasification Combined Cycle Power Generation in the Context of Southeast Asia

Coal remains a major source of electricity production even under the current state of developments in climate policies due to national energy priorities. Coal remains the most attractive option, especially to the developing economies in Southeast Asia, due to its abundance and affordability in the region, despite the heavily polluting nature of this energy source. Gasification of coal running on an integration gasification combined cycle (IGCC) power generation with carbon capture and storage (CCS) represents an option to reduce the environmental impacts of power generation from coal, but the decarbonization potential and suitability of IGCC in the context of Southeast Asia remain unclear. Using Singapore as an example, this paper presents a study on the life cycle analysis (LCA) of IGCC power generation with and without CCS based on a generic process-driven analysis method. We further evaluate the suitability of IGCC with and without CCS as an option to address the energy and climate objectives for the developing economies in Southeast Asia. Findings suggest that the current IGCC technology is a much less attractive option in the context of Southeast Asia when compared to other available power generation technologies, such as solar photovoltaic systems, coal with CCS, and potentially nuclear power technologies

Hierarchically Structured Matrix Recovery-Based Channel Estimation for RIS-Aided Communications

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for improving capacity and extending coverage of wireless networks. In this work, we consider RIS-aided millimeter wave (mmWave) multiple-input and multiple-output (MIMO) communications, where acquiring accurate channel state information is challenging due to the high dimensionality of channels. To achieve efficient channel estimation, fully exploiting the structures of the channels is crucial. To this end, we formulate the channel estimation as a hierarchically structured matrix recovery problem, and design a low-complexity message passing algorithm to solve it, leveraging the unitary approximate message passing. Simulation results demonstrate the superiority of the proposed algorithm with performance close to the oracle bound

Up-Regulation of miR-130b-3p Activates the PTEN/PI3K/AKT/NF-κB Pathway to Defense against Mycoplasma gallisepticum (HS Strain) Infection of Chicken

Mycoplasma gallisepticum (MG) is the pathogen of chronic respiratory disease (CRD), hallmarked by vigorous inflammation in chickens, causing the poultry industry enormous losses. miRNAs have emerged as important regulators of animal diseases. Previous miRNA sequencing data has demonstrated that miR-130b-3p is up-regulated in MG-infected chicken embryo lungs. Therefore, we aimed to investigate the function of miR-130b-3p in MG infection of chickens. RT-qPCR results confirmed that miR-130b-3p was up-regulated both in MG-infected chicken embryo lungs and chicken embryonic fibroblast cells (DF-1 cells). Furthermore, functional studies showed that overexpression of miR-130b-3p promoted MG-infected DF-1 cell proliferation and cell cycle, whereas inhibition of miR-130b-3p weakened these cellular processes. Luciferase reporter assay combined with gene expression data supported that phosphatase and tensin homolog deleted on chromosome ten (PTEN) was a direct target of miR-130b-3p. Additionally, overexpression of miR-130b-3p resulted in up-regulations of phosphatidylinositol-3 kinase (PI3K), serine/threonine kinase (AKT), and nuclear factor-κB (NF-κB), whereas inhibition of miR-130b-3p led to the opposite results. Altogether, upon MG infection, up-regulation of miR-130b-3p activates the PI3K/AKT/NF-κB pathway, facilitates cell proliferation and cell cycle via down-regulating PTEN. This study helps to understand the mechanism of host response to MG infection

Water-Body Type Classification in Dual PolSAR Imagery Using a Two-Step Deep-Learning Method

Water-body type problems classification plays a vital role in ecological conservation, water resource management, and urban planning. Accurate classification can aid decision-makers in understanding the functions of different water-body types, providing key information for urban planning and promoting harmony between human activities and the natural environment. Despite extensive research in the field of water-body segmentation, exploration in the water-body type classification community is not as widespread. Therefore, this article proposes a novel water-body type classification method based on a two-step deep-learning model, decomposing water-body type classification into water-body segmentation and water-body type identification. Especially, this method constructs a unique data strategy by organically integrating backscatter features, polarimetric features, and DEM features, providing the model with rich and comprehensive information. In the first step, the segmentation network uses the fused feature to extract all water-body from synthetic aperture radar images. Subsequently, the extracted water-body are combined with the input data, forming a multifeature input for the identification network to distinguish between natural and artificial water-body. During this process, a swarm intelligence optimization algorithm is employed to explore the optimal hyperparameters of the network, including those of the segmentation and identification networks. Finally, the proposed method is assessed using extensive experiments on water-body segmentation tasks, water-body type identification tasks, and joint water-body type classification tasks. This article not only provides a new perspective in the field of water-body type classification but also demonstrates the immense potential of deep-learning network hyperparameter optimization and feature fusion in solving such

Cyproterone Acetate Mediates IRE1α Signaling Pathway to Alleviate Pyroptosis of Ovarian Granulosa Cells Induced by Hyperandrogen

Objective: Hyperandrogenemia (HA) is the main pathophysiological change that takes place in polycystic ovary syndrome (PCOS). Cyproterone acetate (CYA) is a drug commonly used to reduce androgen in patients with PCOS. Long-term and continuous exposure to HA can cause ovarian granulosa cells (GCs), pyroptotic death, and follicular dysfunction in PCOS mice. The aim of this study was to investigate whether CYA could ameliorate the hyperandrogenemia-induced pyroptosis of PCOS ovarian GCs by alleviating the activation of the IRE1α signaling pathway. Methods: Firstly, thirty PCOS patients with HA as their main clinical manifestation were selected as the study group, and thirty non-PCOS patients were selected as the control group. The GCs and follicular fluid of the patients were collected, and the expression of pyroptosis-related proteins was detected. Secondly, a PCOS mouse model induced by dehydroepiandrosterone (DHEA) was constructed, and the treatment group model was constructed with the subcutaneous injection of cyproterone acetate in PCOS mice. The expression of pyroptosis-related protein in ovarian GCs was detected to explore the alleviating effect of CYA on the pyroptosis of ovarian GCs in PCOS mice. Thirdly, KGN cells-i.e., from the human GC line-were cultured with dihydrotestosterone, CYA, and ERN1 (IRE1α gene) small interfering RNA in vitro to explore whether CYA can alleviate the activation of the IRE1α signaling pathway and ameliorate the hyperandrogenemia-induced pyroptosis of PCOS ovarian GCs. Results: The expression of pyroptosis-related proteins was significantly increased in ovarian GCs of PCOS patients with HA as the main clinical manifestation, and in the PCOS mouse model induced by DHEA. After treatment with CYA, the expression of pyroptosis-related proteins in the ovarian GCs of mice was significantly lower than that in PCOS mice. In vitro experiments showed that CYA could ameliorate KGN cells’ pyroptosis by alleviating the activation of the IRE1α signaling pathway. Conclusion: This study showed that CYA could ameliorate the activation of the IRE1α signaling pathway in mouse GCs and KGN cells, and also alleviate pyroptosis in ovarian GCs. This study provides a new mechanism and evidential support for CYA in the treatment of PCOS patients

A Research Study on Protocol Stack of Space-Based Optical Backbone Network

Facing the growing high data rate and large communication capacity demands, optical communications are widely recognized to be used to implement satellite communications. For a space-based optical backbone network, an appropriately designed protocol stack is important. In this paper, we proposed a protocol stack that is suitable for a space-based optical backbone network. Following this, we then used software to simulate this stack, built a hardware platform to test it, and finally, analyzed the results. The results showed that the proposed protocol stack was well designed to provide efficient control and management of the space-based optical backbone network. It could improve management efficiency by collecting resources and automatically calculating and building route paths. It could also facilitate data forwarding in intermediate satellite nodes with limited source and power, by using an advanced orbiting systems (AOS) frame switching scheme to avoid unnecessary processes, such as unpacking, upper-layer processing and repacking for passing services. The protocol stack could also support the use of unidirectional links to improve the link resource utilization. Finally, it could also provide transparent transmission for different kinds of services

Tunable Spin–Orbit Interaction in Trilayer Graphene Exemplified in Electric-Double-Layer Transistors

Taking advantage of ultrahigh electric field generated in electric-double-layer transistors (EDLTs), we investigated spin–orbit interaction (SOI) and its modulation in epitaxial trilayer graphene. It was found in magnetotransport that the dephasing length <i>L</i>_ϕ and spin relaxation length <i>L</i>_so of carriers can be effectively modulated with gate bias. As a direct result, SOI-induced weak antilocalization (WAL), together with a crossover from WAL to weak localization (WL), was observed at near-zero magnetic field. Interestingly, among existing localization models, only the Iordanskii–Lyanda-Geller–Pikus theory can successfully reproduce the obtained magnetoconductance well, serving as evidence for gate tuning of the weak but distinct SOI in graphene. Realization of SOI and its large tunability in the trilayer graphene EDLTs provides us with a possibility to electrically manipulate spin precession in graphene systems without ferromagnetics