Zhuhai Institute of Advanced Technology

Combined with backstepping techniques, an observer-based adaptive consensus tracking control strategy is developed for a class of high-order nonlinear multiagent systems, of which each follower agent is modeled in a semi-strict-feedback form. By constructing the neural network-based state observer for each follower, the proposed consensus control method solves the unmeasurable state problem of high-order nonlinear multiagent systems. The control algorithm can guarantee that all signals of the multiagent system are semi-globally uniformly ultimately bounded and all outputs can synchronously track a reference signal to a desired accuracy. A simulation example is carried out to further demonstrate the effectiveness of the proposed consensus control method.

Research results on the effects of land cover change on water resources vary greatly and the topic remains controversial. Here we use published data worldwide to examine the validity of Fuh's equation, which relates annual water yield (R) to a wetness index (precipitation/potential evapotranspiration; P/PET) and watershed characteristics (m). We identify two critical values at P/PET=1 and m=2. m plays a more important role than P/PET when m<2, and a lesser role when m>2. When P/PET<1, the relative water yield (R/P) is more responsive to changes in m than it is when P/PET>1, suggesting that any land cover changes in non-humid regions (P/PET<1) or in watersheds of low water retention capacity (m<2) can lead to greater hydrological responses. m significantly correlates with forest coverage, watershed slope and watershed area. This global pattern has far-reaching significance in studying and managing hydrological responses to land cover and climate changes.

Compared with the existing neural network (NN) or fuzzy logic system (FLS) based adaptive consensus methods, the proposed approach can greatly alleviate the computation burden because it needs only to update a few adaptive parameters online. In the multiagent agreement control, the system uncertainties derive from the unknown nonlinear dynamics are counteracted by employing the adaptive NNs; the state delays are compensated by designing a Lyapunov-Krasovskii functional. Finally, based on Lyapunov stability theory, it is demonstrated that the proposed consensus scheme can steer a multiagent system synchronizing to the predefined reference signals. Two simulation examples, a numerical multiagent system and a practical multimanipulator system, are carried out to further verify and testify the effectiveness of the proposed agreement approach.

Internet of Things has been one of the catalysts in revolutionizing conventional healthcare services. With the growing society, traditional healthcare systems reach their capacity in providing sufficient and high-quality services. The world is facing the aging population and the inherent need for assisted-living environments for senior citizens. There is also a commitment by national healthcare organizations to increase support for personalized, integrated care to prevent and manage chronic conditions. Many applications related to In-Home Health Monitoring have been introduced over the last few decades, thanks to the advances in mobile and Internet of Things technologies and services. Such advances include improvements in optimized network architecture, indoor networks coverage, increased device reliability and performance, ultra-low device cost, low device power consumption, and improved device and network security and privacy. Current studies of in-home health monitoring systems presented many benefits including improved safety, quality of life and reduction in hospitalization and cost. However, many challenges of such a paradigm shift still exist, that need to be addressed to support scale-up and wide uptake of such systems, including technology acceptance and adoption by patients, healthcare providers and policymakers. The aim of this paper is three folds: First, review of key factors that drove the adoption and growth of the IoT-based in-home remote monitoring; Second, present the latest advances of IoT based in-home remote monitoring system architecture and key building blocks; Third, discuss future outlook and our recommendations of the in-home remote monitoring applications going forward.

Abstract Ferroptosis is a new iron-dependent form of programmed cell death characterized by iron accumulation and lipid peroxidation. In recent years, ferroptosis has garnered enormous interest in disease treatment research communities in pursuit to reveal the mechanism and key targets of ferroptosis because ferroptosis is closely related to the pathophysiological processes of many diseases. Recent studies have shown some key targets, such as glutathione peroxidase 4 (GPX4) and System Xc − , and several inducers and inhibitors have been developed to regulate these key targets. With the emergence of new ferroptosis targets, studies on inducers and inhibitors have made new developments. The selection and use of inducers and inhibitors are very important for related work. This paper briefly introduces important regulatory targets in the ferroptosis metabolic pathway, lists and categorizes commonly used and recently developed inducers and inhibitors, and discusses their medical application. The paper ends of with potential future research direction for ferroptosis.

MOTIVATION: Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction. RESULTS: We present MusiteDeep, the first deep-learning framework for predicting general and kinase-specific phosphorylation sites. MusiteDeep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimensional attention mechanism. It achieves over a 50% relative improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data. AVAILABILITY AND IMPLEMENTATION: MusiteDeep is provided as an open-source tool available at https://github.com/duolinwang/MusiteDeep. CONTACT: xudong@missouri.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nature has always inspired robotic designs and concepts. It is conceivable that biomimic nanorobots will soon play a prominent role in medicine. The "Terminator" in the science fiction film is a cybernetic organism with living tissue over a metal endoskeleton, which inspired us to develop natural-killer-cell-mimic nanorobots with aggregation-induced emission (AIE) characteristics (NK@AIEdots) by coating a natural kill cell membrane on an AIE-active polymeric endoskeleton, PBPTV, a highly bright NIR-II AIE-active conjugated polymer. Owing to the AIE and soft-matter characteristics of PBPTV, as-prepared NK@AIEdots maintained a superior NIR-II brightness (quantum yield ∼7.9% in water) and good biocompatibility. Besides, they can serve as a tight junction (TJ) modulator to trigger an intracellular signaling cascade, causing TJ disruption and actin cytoskeleton reorganization to form an intercellular "green channel" to help them to cross the blood-brain barrier (BBB) silently. Furthermore, they can initiatively accumulate in glioblastoma cells in the complex brain matrix for high-contrast and through-skull tumor imaging. The tumor growth was also greatly inhibited by these NK@AIEdots under the NIR light illumination. As far as we know, the quantum yield of PBPTV is the highest among the existing NIR-II luminescent conjugated polymers. Besides, the NK-cell biomimetic nanorobots showed great potential for BBB-crossing active delivery.

In this study, a novel adaptive neural network (NN)‐based leader‐following consensus approach is proposed for a class of non‐linear second‐order multi‐agent systems. For the existing NN consensus approaches, to obtain the desired approximation accuracy, the NN‐based adaptive consensus algorithms require the number of NN nodes to must be large enough, and thus the online computation burden often are very heavy. However, the proposed adaptive consensus scheme can greatly reduce the online computation burden, because the adaptive adjusting parameters are designed in scalar form, which is the norm of the estimation of the optimal NN weight matrix. According to Lyapunov stability theory, the proposed approach can guarantee the leader‐following consensus behaviour of non‐linear second‐order multi‐agent systems to be obtained. Finally, a numerical simulation and a multi‐manipulator simulation are carried out to further demonstrate the effectiveness of the proposed consensus approach.

Epidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include closing borders, schools, suspending community services and commuters. Resuming such curfews depends on the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty involved and the complex societal-political factors that influence the widespread of the new virus, any forecast is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model which offers forecast at the best efforts is a big challenge in machine learning. In the past, three popular methods have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the best forecasting model from several models, and 3) fine-tuning the parameters of an individual forecasting model for the highest possible accuracy. In this paper, a methodology that embraces these three virtues of data mining from a small dataset is proposed. An experiment that is based on the recent coronavirus outbreak originated from Wuhan is conducted by applying this methodology. It is shown that an optimized forecasting model that is constructed from a new algorithm, namely polynomial neural network with corrective feedback (PNN+cf) is able to make a forecast that has relatively the lowest prediction error. The results showcase that the newly proposed methodology and PNN+cf are useful in generating acceptable forecast upon the critical time of disease outbreak when the samples are far from abundant.

The history of text can be traced back over thousands of years. Rich and precise semantic information carried by text is important in a wide range of vision-based application scenarios. Therefore, text recognition in natural scenes has been an active research topic in computer vision and pattern recognition. In recent years, with the rise and development of deep learning, numerous methods have shown promising results in terms of innovation, practicality, and efficiency. This article aims to (1) summarize the fundamental problems and the state-of-the-art associated with scene text recognition, (2) introduce new insights and ideas, (3) provide a comprehensive review of publicly available resources, and (4) point out directions for future work. In summary, this literature review attempts to present an entire picture of the field of scene text recognition. It provides a comprehensive reference for people entering this field and could be helpful in inspiring future research. Related resources are available at our GitHub repository: https://github.com/HCIILAB/Scene-Text-Recognition.

Abstract Developing lightweight and broadband microwave absorbers for dealing with serious electromagnetic radiation pollution is a great challenge. Here, a novel Fe–Co/N-doped carbon/reduced graphene oxide (Fe–Co/NC/rGO) composite with hierarchically porous structure was designed and synthetized by in situ growth of Fe-doped Co-based metal organic frameworks (Co-MOF) on the sheets of porous cocoon-like rGO followed by calcination. The Fe–Co/NC composites are homogeneously distributed on the sheets of porous rGO. The Fe–Co/NC/rGO composite with multiple components (Fe/Co/NC/rGO) causes magnetic loss, dielectric loss, resistance loss, interfacial polarization, and good impedance matching. The hierarchically porous structure of the Fe–Co/NC/rGO enhances the multiple reflections and scattering of microwaves. Compared with the Co/NC and Fe–Co/NC, the hierarchically porous Fe–Co/NC/rGO composite exhibits much better microwave absorption performances due to the rational composition and porous structural design. Its minimum reflection loss (RL min ) reaches − 43.26 dB at 11.28 GHz with a thickness of 2.5 mm, and the effective absorption frequency (RL ≤ − 10 dB) is up to 9.12 GHz (8.88–18 GHz) with the same thickness of 2.5 mm. Moreover, the widest effective bandwidth of 9.29 GHz occurs at a thickness of 2.63 mm. This work provides a lightweight and broadband microwave absorbing material while offering a new idea to design excellent microwave absorbers with multicomponent and hierarchically porous structures.

Extracellular vesicles (EVs) are released by many cell types and distributed within various biofluids. EVs have a lipid membrane-confined structure that allows for carrying unique molecular information originating from their parent cells. The species and quantity of EV cargo molecules, including nucleic acids, proteins, lipids, and metabolites, may vary largely owing to their parent cell types and the pathophysiologic status. Such heterogeneity in EV populations provides immense challenges to researchers, yet allows for the possibility to prognosticate the pathogenesis of a particular tissue from unique molecular signatures of dispersing EVs within biofluids. However, the inherent nature of EV's small size requires advanced methods for EV purification and evaluation from the complex biofluid. Recently, the interdisciplinary significance of EV research has attracted growing interests, and the EV analytical platforms for their diagnostic prospect have markedly progressed. This review summarizes the recent advances in these EV detection techniques and methods with the intention of translating an EV-based liquid biopsy into clinical practice. This article aims to present an overview of current EV assessment techniques, with a focus on their progress and limitations, as well as an outlook on the clinical translation of an EV-based liquid biopsy that may augment current paradigms for the diagnosis, prognosis, and monitoring the response to therapy in a variety of disease settings.

Network pharmacology can ascertain the therapeutic mechanism of drugs for treating diseases at the level of biological targets and pathways. The effective mechanism study of traditional Chinese medicine (TCM) characterized by multi-component, multi-targeted, and integrative efficacy, perfectly corresponds to the application of network pharmacology. Currently, network pharmacology has been widely utilized to clarify the mechanism of the physiological activity of TCM. In this review, we comprehensively summarize the application of network pharmacology in TCM to reveal its potential of verifying the phenotype and underlying causes of diseases, realizing the personalized and accurate application of TCM. We searched the literature using "TCM network pharmacology" and "network pharmacology" as keywords from Web of Science, PubMed, Google Scholar, as well as Chinese National Knowledge Infrastructure in the last decade. The origins, development, and application of network pharmacology are closely correlated with the study of TCM which has been applied in China for thousands of years. Network pharmacology and TCM have the same core idea and promote each other. A well-defined research strategy for network pharmacology has been utilized in several aspects of TCM research, including the elucidation of the biological basis of diseases and syndromes, the prediction of TCM targets, the screening of TCM active compounds, and the decipherment of mechanisms of TCM in treating diseases. However, several factors limit its application, such as the selection of databases and algorithms, the unstable quality of the research results, and the lack of standardization. This review aims to provide references and ideas for the research of TCM and to encourage the personalized and precise use of Chinese medicine.

Abstract Two-dimensional metal-organic frameworks (MOFs) have been explored as effective electrocatalysts for hydrogen evolution reaction (HER). However, the sluggish water activation kinetics and structural instability under ultrahigh-current density hinder their large-scale industrial applications. Herein, we develop a universal ligand regulation strategy to build well-aligned Ni-benzenedicarboxylic acid (BDC)-based MOF nanosheet arrays with S introducing (S-NiBDC). Benefiting from the closer p -band center to the Fermi level with strong electron transferability, S-NiBDC array exhibits a low overpotential of 310 mV to attain 1.0 A cm −2 with high stability in alkaline electrolyte. We speculate the newly-constructed triangular “Ni 2 -S 1 ” motif as the improved HER active region based on detailed mechanism analysis and structural characterization, and the enhanced covalency of Ni-O bonds by S introducing stabilizes S-NiBDC structure. Experimental observations and theoretical calculations elucidate that such Ni sites in “Ni 2 -S 1 ” center distinctly accelerate the water activation kinetics, while the S site readily captures the H atom as the optimal HER active site, boosting the whole HER activity.

The growth and development of black soldier fly, Hermetia illucens (L.), larvae fed chicken manure inoculated with bacteria isolated from black soldier fly larvae and associated larval feed was evaluated. Four strains of Bacillus subtilis were evaluated. B. subtilis strains S15, S16, S19, were isolated from the gut of black soldier fly larvae. B. natto strain D1 was isolated from the diet fed to black soldier fly larvae. These bacteria were added individually into nonsterile 200 g fresh hen manure at 10(6) cfu/g and homogenized. Treated manure was then inoculated with 4-d old black soldier fly larvae. Prepupal weight ranged from 0.0606 g in the control to 0.0946 g in manure treated with the S15 strain. Larval survivorship to the prepupal stage in all treatments ranged from 98.00 ± 2.65% to 99.33 ± 1.15%. Prepupal survivorship to the pupal stage ranged from 91.92 ± 1.87% to 97.95 ± 1.03%. Adult emergence from the pupal stage did not significantly (P < 0.05) differ across treatments and ranged from 98.95 ± 1.82% to 100.00 ± 0.00%. Adult body length resulting from the larvae in each of the treatments was significantly greater than those from the control. Longevity of adults did not differ significantly between treatments. Time from hatching to the development of the first pupa did not differ significantly across treatments; however, development time from hatching to 90% reaching the prepupual stage was significantly different between treatments and ranged from 29.00 ± 1.00 d to 34.33 ± 3.51 d. Development time from hatching to 90% reaching the adult stages was significantly different between treatments. Our results demonstrate that inoculating poultry manure with bacteria from black soldier fly larvae influences the growth and development of conspecific larvae feeding on the manure.

Abstract Current nonlinear optical materials face a conventional limitation in the trade‐off between the band gap and birefringence, especially in the deep UV spectral region. To circumvent this dilemma, we propose a general principle, π‐conjugated confinement, to partially decouple the interunit π‐conjugated interactions by the separation of non‐π‐conjugated units. The goal is to further enlarge the band gap to a value larger than that of the singular π‐conjugated counterpart and to maintain a suitable density of π‐conjugated units to gain a large optical anisotropy. We reveal that π‐conjugated confinement is a shared structural feature for all DUV NLO materials known to date, and thus, it provides a novel and essential design criterion for future design synthesis. Guided by this principle, the carbonophosphates are predicted to be a new promising DUV candidate system. Sr 3 Y[PO 4 ][CO 3 ] 3 ( 1 ) and Na 3 X[PO 4 ][CO 3 ] (X=Ba, Sr, Ca, Mg, 2 – 5 ) exhibit not only greatly enhanced birefringence that is 3–24 times larger than that of singular phosphates but also enhanced band gaps that are 0.2–1.7 eV wider than those of singular carbonates.

Wide-bandgap (WBG) absorbers in tandem configurations suffer from poor crystallinity and weak texture, which leads to severe mixed halide-cation ion migration and phase segregation during practical operation. We control WBG film growth insensitive to compositions by nucleating the 3C phase before any formation of bromine-rich aggregates and 2H phases. The resultant WBG absorbers show improved crystallinity and strong texture with suppressed nonradiative recombination and enhanced resistance to various aging stresses. Perovskite/silicon tandem solar cells achieve power conversion efficiencies of 29.4% (28.8% assessed by a third party) in a 25-square centimeter active area and 32.5% in a 1-square centimeter active area. These solar cells retained 98.3 and 90% of the original efficiency after 1301 and 800 hours of operation at 25° and 50°C, respectively, at the maximum power point (AM 1.5G illumination, full spectrum, 1-sun) when encapsulated.

Photodynamic therapy (PDT) is a promising alternative approach for effective cancer treatment that is associated with an antitumor immune response. However, immunosuppression of the tumor microenvironment limits the immune response induced by PDT. Stimulation and proliferation of T cells is a critical step for generating immune responses and depends on the efficient presentation of tumor antigens and co-stimulatory molecules by antigen-presenting cells (APCs). Here, biomimetic aggregation-induced emission (AIE) photosensitizers with antigen-presenting and hitchhiking abilities (DC@AIEdots) are developed by coating dendritic cell (DC) membranes on the nanoaggregates of the AIEgens. Notably, the inner AIE molecules can selectively accumulate in lipid droplets of tumor cells, and the outer cell membrane can facilitate the hitchhiking of DC@AIEdots onto the endogenous T cells and enhance the tumor delivery efficiency by about 1.6 times. Furthermore, DC@AIEdots can stimulate the in vivo proliferation and activation of T cells and trigger the immune system. The potential applications of therapeutic agents targeting lipid droplets for immunotherapy are indicated and a new hitchhiking approach for drug delivery is provided. Lastly, the study presents a photoactive and artificial antigen-presenting platform for effective T cell stimulation and cancer photodynamic immunotherapy.

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction.

Abstract Lithium‐metal batteries (LMBs) using lithium‐metal anodes and high‐voltage cathodes have been deemed as one of the most promising high‐energy‐density battery technology. However, its practical application is largely hindered by the notorious dendrite growth of lithium‐metal anodes, the fast structure degradation of the cathode, and insufficient electrode–electrolyte interphase kinetics. Here, a dual‐anion regulated electrolyte is developed for LMBs using lithium bis(trifluoromethylsulfonyl)imide (LiTFSI) and lithium difluoro(bisoxalato)phosphate (LiDFBOP) as anion regulators. The incorporation of TFSI − in the solvation sheath reduces the desolvation energy of Li + , and DFBOP − promotes the formation of highly ion‐conductive and sustainable inorganic‐rich interphases on the electrodes. Significantly enhanced performance is demonstrated on Li||LiNi 0.83 Co 0.11 Mn 0.06 O 2 pouch cells, with 84.6% capacity retention after 150 cycles in 6.0 Ah pouch cells and an ultrahigh rate capability up to 5 C in 2.0 Ah pouch cells. Furthermore, a pouch cell with an ultralarge capacity of 39.0 Ah is fabricated and achieves an ultrahigh energy density of 521.3 Wh kg −1 . The findings provide a facile electrolyte design strategy for promoting the practical utilization of high‐energy‐density LMBs.