Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
2,999
result(s) for
"Liu, Bowen"
Sort by:
Engineering trustworthy software systems : 4th International School, SETSS 2018, Chongqing, China, April 7-12, 2018, Tutorial Lectures
This volume contains lectures on leading-edge research in methods and tools for use in computer system engineering; at the 4th International School on Engineering Trustworthy Software Systems, SETSS 2018, held in April 2018 at Southwest University in Chongqing, China. The five chapters in this volume provide an overview of research in the frontier of theories, methods, and tools for software modelling, design, and verification. The topics covered in these chapter include Software Verification with Whiley, Learning Büchi Automata and Its Applications, Security in IoT Applications, Programming in Z3, and The Impact of Alan Turing: Formal Methods and Beyond. The volume provides a useful resource for postgraduate students, resarchers, academics, and engineers in industry, who are interested in theory, methods, and tools for the development of trustworthy software.
Book
Targeting STAT3 in Cancer Immunotherapy
As a point of convergence for numerous oncogenic signaling pathways, signal transducer and activator of transcription 3 (STAT3) is central in regulating the anti-tumor immune response. STAT3 is broadly hyperactivated both in cancer and non-cancerous cells within the tumor ecosystem and plays important roles in inhibiting the expression of crucial immune activation regulators and promoting the production of immunosuppressive factors. Therefore, targeting the STAT3 signaling pathway has emerged as a promising therapeutic strategy for numerous cancers. In this review, we outline the importance of STAT3 signaling pathway in tumorigenesis and its immune regulation, and highlight the current status for the development of STAT3-targeting therapeutic approaches. We also summarize and discuss recent advances in STAT3-based combination immunotherapy in detail. These endeavors provide new insights into the translational application of STAT3 in cancer and may contribute to the promotion of more effective treatments toward malignancies.
Journal Article
Intelligent Innovative Logistics Model of Rural E-commerce on Consumer Behaviors
BackgroundThe integration of intelligent logistics models in rural e-commerce has significantly enhanced the convenience of online shopping for consumers and has concurrently bolstered the sales volume of e-commerce platforms. However, the proliferation of excessive shopping behaviors, potentially indicative of consumer mania, raises concerns. Mania is a psychological vulnerability often triggered by anxiety. This study aims to investigate the influence of the intelligent logistics model in rural e-commerce on consumer mania.Subjects and MethodsThe study involved 80 consumers from rural areas within a specific province, selected based on statistically significant variations in their online shopping habits. The participants were divided equally into an experimental group and a control group. Prior to and post the three-month experiment, both groups were assessed using the SCL-90 self-test scale. During the experiment, the control group employed traditional logistics distribution, while the experimental group utilized intelligent logistics distribution.ResultsSignificant differences were observed in the scores of obsessive-compulsive disorder (OCD), depression, anxiety, hostility, and terror disorder before and after the experiment (P<0.01). Moreover, two factors, namely interpersonal relationships, and paranoia, exhibited significant differences (P<0.05). Notably, there was no considerable difference in somatization symptoms and psychiatric symptoms (P>0.05).ConclusionsIntelligent innovative logistics mode will have an impact on consumers’ mania. Convenient online shopping logistics mode not only aggravates consumers’ excessive shopping behavior but also aggravates consumers’ anxiety and mania.AcknowledgementThe Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. CXZZ13-0709).
Journal Article
Engineered exosomes: desirable target-tracking characteristics for cerebrovascular and neurodegenerative disease therapies
As extracellular vesicles secreted by cells, exosomes are intercellular signalosomes for cell communication and pharmacological effectors. Because of their special properties, including low toxicity and immunogenicity, biodegradability, ability to encapsulate endogenous biologically active molecules and cross the blood-brain barrier (BBB), exosomes have great therapeutic potential in cerebrovascular and neurodegenerative diseases. However, the poor targeting ability of natural exosomes greatly reduces the therapeutic effect. Using engineering technology, exosomes can obtain active targeting ability to accumulate in specific cell types and tissues by attaching targeting units to the membrane surface or loading them into cavities. In this review, we outline the improved targeting functions of bioengineered exosomes, tracing and imaging techniques, administration methods, internalization in the BBB, and therapeutic effects of exosomes in cerebrovascular and neurodegenerative diseases and further evaluate the clinical opportunities and challenges in this research field.
Journal Article
Deciphering the tRNA-derived small RNAs: origin, development, and future
Transfer RNA (tRNA)-derived small RNAs (tsRNAs), a novel category of small noncoding RNAs, are enzymatically cleaved from tRNAs. Previous reports have shed some light on the roles of tsRNAs in the development of human diseases. However, our knowledge about tsRNAs is still relatively lacking. In this paper, we review the biogenesis, classification, subcellular localization as well as action mechanism of tsRNAs, and discuss the association between chemical modifications of tRNAs and the production and functions of tsRNAs. Furthermore, using immunity, metabolism, and malignancy as examples, we summarize the molecular mechanisms of tsRNAs in diseases and evaluate the potential of tsRNAs as new biomarkers and therapeutic targets. At the same time, we compile and introduce several resource databases that are currently publicly available for analyzing tsRNAs. Finally, we discuss the challenges associated with research in this field and future directions.
Journal Article
The Impact of the Wuhan Covid-19 Lockdown on Air Pollution and Health: A Machine Learning and Augmented Synthetic Control Approach
We quantify the impact of the Wuhan Covid-19 lockdown on concentrations of four air pollutants using a two-step approach. First, we use machine learning to remove the confounding effects of weather conditions on pollution concentrations. Second, we use a new augmented synthetic control method (Ben-Michael et al. in The augmented synthetic control method. University of California Berkeley, Mimeo, 2019. https://arxiv.org/pdf/1811.04170.pdf) to estimate the impact of the lockdown on weather normalised pollution relative to a control group of cities that were not in lockdown. We find NO2 concentrations fell by as much as 24 μg/m3 during the lockdown (a reduction of 63% from the pre-lockdown level), while PM10 concentrations fell by a similar amount but for a shorter period. The lockdown had no discernible impact on concentrations of SO2 or CO. We calculate that the reduction of NO2 concentrations could have prevented as many as 496 deaths in Wuhan city, 3368 deaths in Hubei province and 10,822 deaths in China as a whole.
Journal Article
A Semantically Guided Deep Supervised Hashing Model for Multi-Label Remote Sensing Image Retrieval
With the rapid growth of remote sensing data, efficiently managing and retrieving large-scale remote sensing images has become a significant challenge. Specifically, for multi-label image retrieval, single-scale feature extraction methods often fail to capture the rich and complex information inherent in these images. Additionally, the sheer volume of data creates challenges in retrieval efficiency. Furthermore, leveraging semantic information for more accurate retrieval remains an open issue. In this paper, we propose a multi-label remote sensing image retrieval method based on an improved Swin Transformer, called Semantically Guided Deep Supervised Hashing (SGDSH). The method aims to enhance feature extraction capabilities and improve retrieval precision. By utilizing multi-scale information through an end-to-end learning approach with a multi-scale feature fusion module, SGDSH effectively integrates both shallow and deep features. A classification layer is introduced to assist in training the hash codes, incorporating RS image category information to improve retrieval accuracy. The model is optimized for multi-label retrieval through a novel loss function that combines classification loss, pairwise similarity loss, and hash code quantization loss. Experimental results on three publicly available remote sensing datasets, with varying sizes and label distributions, demonstrate that SGDSH outperforms state-of-the-art multi-label hashing methods in terms of average accuracy and weighted average precision. Moreover, SGDSH returns more relevant images with higher label similarity to query images. These findings confirm the effectiveness of SGDSH for large-scale remote sensing image retrieval tasks and provide new insights for future research on multi-label remote sensing image retrieval.
Journal Article
Grating coupler based on chalcogenide materials on a lithium niobate thin-film substrate
This investigation introduces a vertically aligned periodic grating structure employing a GeSbSn ternary compound integrated with a LiNbO 3 thin-film platform, systematically engineered through computational electromagnetic modeling and optimization protocols. To improve light coupling performance and broaden operational bandwidth, systematic optimization and analytical investigation were conducted on key structural parameters including the Ge-Sb-Sn film thickness, periodic grating dimensions, duty cycle configuration, fiber alignment position, and incident tilt angle. Experimental results demonstrate a peak coupling efficiency of -4.6 dB at 1550 nm wavelength along with a 73 nm spectral range maintaining -1 dB efficiency tolerance, quantitatively confirming its broadband compatibility for photonic applications.
Journal Article
A highly active oxygen evolution electrocatalyst： Ultrathin CoNi double hydroxide/CoO nanosheets synthesized via interface-directed assembly
A cost-efficient and stable oxygen evolution electrocatalyst is essential for improving energy storage and conversion efficiencies. Herein, 2D nanosheets with randomly cross-linked CoNi layered double hydroxide （LDH） and small CoO nanocrystals were designed and synthesized via in situ reduction and interface- directed assembly in air. The formation of CoNi LDH/CoO nanosheets was attributed to the strong extrusion of hydrated metal-oxide clusters driven by the interfacial tension. The obtained loose and porous nanosheets exhibited low crystallinity due to the presence of numerous defects. Owing to the orbital hybridization between metal 3d and O 2p orbitals, and electron transfer between metal atoms through Ni-O-Co, a number of Co and Ni atoms in the CoNi LDH present a high ＋3 valency. These unique characteristics result in a high density of oxygen evolution reaction （OER） active sites, improving the affinity between OH- and catalyst, and resulting in a large accessible surface area and permeable channels for ion adsorption and transport. Therefore, the resulting nanosheets exhibited high catalytic activity towards the OER. The CoNi LDH/CoO featured a low onset potential of 1.48 V in alkaline medium, and required an overpotential of only 300 mV at a current density of 10 mA.cm-2, while displaying good stability in accelerated durability tests.
Journal Article
Distributed coordinated motion control of multiple UAVs oriented to optimization of air-ground relay network
A novel adaptive model-based motion control method for multi-UAV communication relay is proposed, which aims at improving the networks connectivity and the communications performance among a fleet of ground unmanned vehicles. The method addresses the challenge of relay UAVs motion control through joint consideration with unknown multi-user mobility, environmental effects on channel characteristics, unavailable angle-of-arrival data of received signals, and coordination among multiple UAVs. The method consists of two parts: (1) Network connectivity is constructed and communication performance index is defined using the minimum spanning tree in graph theory, which considers both the communication link between ground node and UAV, and the communication link between ground nodes. (2) A multi-UAV motion control strategy is proposed that combines Improved Particle Swarm Optimization (IPSO) and Distributed Nonlinear Model Predictive Control (DNMPC), where the Kalman filter is utilised to estimate future positions of the mobile nodes. Simulation results in both single and complex environments show that the presented method can drive the UAVs to reach or track the optimal relay positions and improve network performance, while demonstrating the benefits of considering the impact of environments on channel characteristics.
Journal Article