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Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese mainland

Infectious Diseases of Poverty 2024年第1期13卷 27-41页

作者： Xiaohan Si Liping Wang Kerrie Mengersen Wenbiao Hu Ecosystem Change and Population Health Research Group School of Public Health and Social WorkQueensland University of TechnologyBrisbaneQLD 4059Australia Information Center Chinese Center for Disease Control and PreventionBeijing 102206China School of Mathematical sciences Queensland University of TechnologyBrisbaneQLD 4000Australia

Background Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes.However,research on seasonal influenza activities based on climate zones are still in lack.This study aims to utilize the ecological-based Koppen Geiger climate zones classif-cation system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese mainland and assess the feasibility of developing an early warning system.Methods Weekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System.Epidemic temporal indices,time series seasonality decomposition,spatial modelling theories including Moran's/and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission.Results All climate zones had peaks in Winter-Spring season.Arid,desert,cold(BWk)showed up the first peak.Only Tropical,savannah(Aw)and Temperate,dry winter with hot summer(Cwa)zones had unique summer peak.Temper-ate,no dry season and hot summer(Cfa)zone had highest average incidence rate(IR)at 1.047/100,000.The Global Moran's/showed that average IR had significant clustered trend(z=53.69,P<0.001),with local Moran's/identi-fied high-high cluster in Cfa and Cwa.IR differed among three age groups between climate zones(O-14 years old:F=26.80,P<0.001;15-64 years old:F=25.04,P<0.001;Above 65 years old:F=5.27,P<0.001).Age group 0-14 years had highest average IR in Cwa and Cfa(IR=6.23 and 6.21)with unique dual peaks in winter and spring season showed by seasonality decomposition.Conclusions Seasonal influenza exhibited distinct spatial and temporal patterns in different climate zones.Seasonal influenza primarily emerged in BWk,subsequently in Cfa and Cwa.Cfa,Cwa and BSk pose high risk for seasonal influ-enza epidemics.The research finds will provide scientific evidence for developing seasonal infl

关键词： Seasonal influenza Koppen Geiger climate zones classification system Chinese mainland Seasonality decomposition Local indicators of spatial association

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On Robust Cross-view Consistency in Self-supervised Monocular Depth Estimation

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Machine Intelligence Research 2024年第3期21卷 495-513页

作者： Haimei Zhao Jing Zhang Zhuo Chen Bo Yuan Dacheng Tao School of Computer science University of SydneySydney2008Australia Shenzhen International Graduate School Tsinghua UniversityShenzhen518055China School of Information Technology&Electrical Engineering University of QueenslandBrisbane4072Australia

Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric consistency and 3D point cloud consistency. However, they are very vulnerable to illumination variance, occlusions, texture-less regions, as well as moving objects, making them not robust enough to deal with various scenes. To address this challenge, we study two kinds of robust cross-view consistency in this paper. Firstly, the spatial offset field between adjacent frames is obtained by reconstructing the reference frame from its neighbors via deformable alignment, which is used to align the temporal depth features via a depth feature alignment (DFA) loss. Secondly, the 3D point clouds of each reference frame and its nearby frames are calculated and transformed into voxel space, where the point density in each voxel is calculated and aligned via a voxel density alignment (VDA) loss. In this way, we exploit the temporal coherence in both depth feature space and 3D voxel space for SS-MDE, shifting the “point-to-point” alignment paradigm to the “region-to-region” one. Compared with the photometric consistency loss as well as the rigid point cloud alignment loss, the proposed DFA and VDA losses are more robust owing to the strong representation power of deep features as well as the high tolerance of voxel density to the aforementioned challenges. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques. Extensive ablation study and analysis validate the effectiveness of the proposed losses, especially in challenging scenes. The code and models are available at https://***/sunnyHelen/RCVC-depth.

关键词： 3D vision depth estimation cross-view consistency self-supervised learning monocular perception.

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Artificial intelligence optimization and controllable slow-release iron sulfide realizes efficient separation of copper and arsenic in strongly acidic wastewater

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Journal of Environmental sciences 2024年第5期139卷 293-307页

作者： Xingfei Zhang Chenglong Lu Jia Tian Liqiang Zeng Yufeng Wang Wei Sun Haisheng Han Jianhua Kang School of Minerals Processing and Bioengineering Central South UniversityChangsha 410083China Centre for Mined Land Rehabilitation Sustainable Minerals InstituteThe University of QueenslandBrisbane 4072Australia

Iron sulfide(FeS)is a promising material for separating copper and arsenic from strongly acidic wastewater due to its S^(2-)slow-release effect.However,uncertainties arise because of the constant changes in wastewater composition,affecting the selection of operating parameters and FeS types.In this study,the aging method was first used to prepare various controllable FeS nanoparticles to weaken the arsenic removal ability without affecting the copper removal.Orthogonal experiments were conducted,and the results identified the Cu/As ratio,H_(2)SO_(4) concentration,and FeS dosage as the three main factors influencing the separation efficiency.The backpropagation artificial neural network(BP-ANN)model was established to determine the relationship between the influencing factors and the separation efficiency.The correlation coefficient(R)of overall model was 0.9923 after optimizing using genetic algorithm(GA).The BP-GA model was also solved using GA under specific constraints,predicting the best solution for the separation process in real-time.The predicted results show that the high temperature and long aging time of FeS were necessary to gain high separation efficiency,and the maximum separation factor can reached 1,400.This study provides a suitable sulfurizing material and a set of methods and models with robust flexibility that can successfully predict the separation efficiency of copper and arsenic from highly acidic environments.

关键词： FeS nanoparticles Aging Sulfide precipitation Orthogonal experiment Artificial neural network Prediction

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Attack-Resilient Distributed Cooperative Control of Virtually Coupled High-Speed Trains via Topology Reconfiguration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1066-1068页

作者： Shunyuan Xiao Xiaohua Ge Qing Wu the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China the School of science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the School of Engineering and Technology Central Queensland UniversityRockhampton QLD 4701Australia

Dear Editor,This letter addresses the resilient distributed cooperative control problem of a virtually coupled train convoy under stochastic disturbances and cyber attacks.The main purpose is to achieve distributed coordination of virtually coupled high-speed trains with the prescribed inter-train distance and same cruise velocity.

关键词： prescribed Speed trains

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Achieving high carrier mobility and low lattice thermal conductivity in GeTe-based alloys by cationic/anionic co-doping

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Rare Metals 2024年第6期43卷 2784-2795页

作者： Xiao-Qiang Wang Xiao-Quan Hu Jun-Yan Lin Chu-Bin Li Xiao-Tong Yu Qi-Yong Chen Li-Li Xi Qi-Shuo Yang Han Li Ji-Ye Zhang Shuan-Kui Li Kai Guo School of Physics and Materials science Guangzhou University Research Center for Advanced Information Materials (CAIM) Huangpu Research & Graduate School of Guangzhou University School of Materials science and Engineering Shanghai University Materials Genome Institute Shanghai University School of Mechanical and Mining Engineering University of Queensland Key Laboratory of Si-Based Information Materials & Devices and Integrated Circuits Design Department of Education of Guangdong Province

The Ⅳ-Ⅵ compound GeTe is considered as a promising alternative to the toxic PbTe for high-efficiency mid-temperature thermoelectric applications.However,pristine GeTe suffers from a high concentration of Ge vacancies,resulting in an excessively high hole concentration(> 1 × 1021cm-3),which greatly limits its thermoelectric enhancement.To address this issue,CuBiTe2alloying is introduced to increase the formation energy of Ge vacancies in GeTe,thereby inhibiting the high carrier concentration.The carrier scattering caused by the electronegativity difference between different elements is suppressed due to the similar electronegativity of Cu and Ge atoms.A relatively high hole mobility is obtained,which ultimately leads to a high power factor.Additionally,by introducing Se as an alloying element at the anionic site in GeTe,dense point defects with mass/strainfield fluctuations are induced.This contributes to the strengthening of phonon scattering,thereby reducing the lattice thermal conductivity from 1.44 W·m-1·K-1for pristine GeTe to 0.28 W·m-1·K-1for Ge0.95Cu0.05Bi0.05Te0.9Se0.15compound at 623 K.

关键词： GeTe Carrier mobility CuBiTe 2 alloying Lattice thermal conductivity Thermoelectric properties

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Suppressive effects of exercise-conditioned serum on cancer cells:A narrative review of the influence of exercise mode,volume,and intensity

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Journal of Sport and Health science 2024年第4期13卷 484-498,F0003页

作者： Francesco Bettariga Dennis R.Taaffe Daniel A.Galvao Chris Bishop Jin-Soo Kim Robert U.Newton Exercise Medicine Research Institute Edith Cowan UniversityJoondalupWA 6027Australia School of Medical and Health sciences Edith Cowan UniversityJoondalupWA 6027Australia London Sport Institute School of Science and TechnologyMiddlesex UniversityLondonNW44BTUK School of Human Movement and Nutrition sciences University of QueenslandSt.LuciaQLD 4072Australia

Cancer is a major cause of morbidity and mortality worldwide,and the incidence is increasing,highlighting the need for effective strategies to treat this disease.Exercise has emerged as fundamental therapeutic medicine in the management of cancer,associated with a lower risk of recur-rence and increased survival.Several avenues of research demonstrate reduction in growth,proliferation,and increased apoptosis of cancer cells,including breast,prostate,colorectal,and lung cancer,when cultured by serum collected after exercise in vitro(i.e.,the cultivation of cancer cell lines in an experimental setting,which simpliﬁes the biological system and provides mechanistic insight into cell responses).The underlying mechanisms of exercise-induced cancer suppressive effects may be attributed to the alteration in circulating factors,such as skeletal muscle-induced cytokines(i.e.,myokines)and hormones.However,exercise-induced tumor suppressive effects and detailed information about training interventions are not well investigated,constraining more precise application of exercise medicine within clinical oncology.To date,it remains unclear what role different training modes(i.e.,resistance and aerobic training)as well as volume and intensity have on exercise-condi-tioned serum and its effects on cancer cells.Nevertheless,the available evidence is that a single bout of aerobic training at moderate to vigorous intensity has cancer suppressive effects,while for chronic training interventions,exercise volume appears to be an inﬂuential candidate driving cancer inhibitory effects regardless of training mode.Insights for future research investigating training modes,volume and intensity are provided to further our understanding of the effects of exercise-conditioned serum on cancer cells.

关键词： Cancer cells High intensity interval training Moderate intensity continuous training Resistance training Myokines

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ZIF-8-functionalized polymer electrolyte with enhanced performance for high-temperature solid-state lithium metal batteries

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Rare Metals 2024年第3期43卷 984-994页

作者： Jin-Fang Zhang Yuan-Yuan Wang Xiao-Feng Li Gen-Yan Zhang Ying Li Rong Liu Sheng-Liang Hu Tuo-Ping Hu Ruth Knibbe Qing-Bing Xia School of Materials science and Engineering North University of ChinaTaiyuan030051China School of Energy and Power Engineering North University of ChinaTaiyuan030051China School of Chemistry and Chemical Engineering North University of ChinaTaiyuan030051China School of Mechanical and Mining Engineering The University of QueenslandBrisbaneQLD4072Australia

Solid polymer electrolytes(SPEs)with high ionic conductivity are desirable for solid-state lithium metal batteries(SSLMBs)to achieve enhanced safety and energy density.Incorporating nanofillers into a polymeric matrix to develop nanocomposite solid electrolytes(NCSEs)has become a promising method for improving the ionic conductivity of the SPEs.Here,a novel ZIF-8-functionalized NCSE was prepared for high-temperature S SLMB s using an in situ radical polymerization method.It is found that the ZIF-8 nanoparticles could reduce the crystallinity of polymer segments and offer a Lewis acid surface that promotes the dissociation of lithium bis(trifluoromethanesulfonyl)imide(LiTFSI)and stabilizes the TFSI^(-) anion movement.Thus,the as-prepared NCSE exhibits an outstanding ionic conductivity of 1.63×10^(-3)S·cm^(-1),an electrochem ical stability window of 5.0 V at 80℃,and excellent interface compatibility with lithium metal anode with a stable polarization over 2000 h.Furthermore,the assembled SSLMBs with LiFePO_(4)cathode show dendrite-free Li-metal surface,good rate capability,and stable cycling stability with a capacity retention of 70%over 1000 cycles at a high temperature of 80℃.This work provides valuable insights into promoting the ionic conductivity of SPEs.

关键词： Lithium metal batteries Metal-organic frameworks Solid polymer electrolytes Lithium metal anode Interfacial compatibility

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Gamma oscillations and their role in orchestrating balance and communication following stroke

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Neural Regeneration Research 2025年第2期20卷 477-478页

作者： Montana Samantzis Cong Wang Matilde Balbi queensland Brain Institute The University of QueenslandBrisbaneQLDAustralia Engineering Research Centre of Traditional Chinese Medicine Intelligent Rehabilitation Ministry of EducationShanghaiChina

Induced brain oscillations in the gamma range have recently garnered attention due to their reported neuroprotective effects in the treatment of Alzheimer’s disease.This method differs from pharmacological approaches by tapping into the neuronal population dynamics that underlie the homeostatic processes in the brain that are crucial for the recovery of function.Recently,induced gamma-range oscillations have been used to improve cerebral blood flow,motor function,and synaptic plasticity in a mouse model of focal stroke,highlighting the broad potential of recruiting intrinsic recovery processes for the treatment of neurological conditions.Addressing open questions,such as the frequency specificity of the benefits,will shed light on the intrinsic processes involved and allow clinicians to optimize recovery after stroke.

关键词： protective specificity Alzheimer

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Inflation Transmission Diagnostics via a Bayesian Graph Vector Autoregressive Model with Markov Switching

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Journal of Systems science & Complexity 2024年

作者： FU Jiali CAI Fengjing WU Jinran ZHAO Shangrui WANG You-Gan 1.College of Mathematics and Physics, Wenzhou University 2.Institute for Positive Psychology and Education, Australian Catholic University 3.School of science, Wuhan University of Technology 4.School of Mathematics and Physics, The University of queensland College of Mathematics and Physics Wenzhou University Institute for Positive Psychology and Education Australian Catholic University School of science Wuhan University of Technology School of Mathematics and Physics The University of Queensland

The transmission of inflation is a widespread occurrence, and managing inflationary pressures is a crucial macroeconomic challenge. Although inflation is a typical macroeconomic variable,its contemporaneous and lagged causal relationships have not been thoroughly investigated, which could result in missing important policy insights. The Bayesian graph vector autoregression(BGVAR)model can identify contemporaneous and lagged causal relationships among economic variables, but it lacks practical research on inflationary inflation. To account for the structural transformation in the inflation transmission process, the authors propose a Bayesian graph vector autoregressive model with Markov switching(MS-BGVAR), which considers both regime switching and contemporaneous causality among macroeconomic variables. The proposed study focuses on analyzing the dynamics of inflation transmission relationships among G7 countries under different regimes, as these countries represent developed nations. The authors use inflation data from 1971–2019, which shows two distinct inflation regimes within the sample period. The authors conduct simulation experiments to generate moderately dimensional simulated data for both regimes and indicators, demonstrating the theoretical reliability of the proposed model in accurately identifying graph structures. Finally, the authors apply the proposed model to identify structural breaks and causal transmission relationships in the inflation transmission process of G7 economies, demonstrating that the proposed model has significant economic significance and good explanatory power in the selected target countries.

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Clinically adaptable machine learning model to identify early appreciable features of diabetes

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Intelligent Medicine 2024年第1期4卷 22-32页

作者： Nurjahan Nipa Mahmudul Hasan Riyad Shahriare Satu Walliullah Koushik Chandra Howlader Mohammad Ali Moni Department of Information and Communication Technology Bangabandhu Sheikh Mujibur Rahman Digital UniversityBangladeshKaliakairGazipur1750 Bangladesh Department of Applied Mathematics Noakhali Science and Technology UniversityNoakhali3814Bangladesh Department of Management Information Systems Noakhali Science and Technology UniversityNoakhali3814Bangladesh Department of Computer science and Telecommunication Engineering Noakhali Science and Technology UniversityNoakhali3814Bangladesh Department of Computer science North Dakota State UniversityFargo58105NDUnited States School of Health and Rehabilitation sciences Faculty of Health and Behavioural SciencesThe University of QueenslandSt LuciaQLD 4072Australia

Objective Diabetes mellitus is a serious disease where the body of affected patients are failed to produce enough insulin that causes an abnormality of blood sugar.This disease happens for a number of reasons including modern lifestyle,lethargic attitude,unhealthy food consumption,family history,age,overweight,etc.The aim of this study was to propose a machine learning based prediction model that detected diabetes at the beginning.Methods In this work,we collected 520 patients records from the University of California,Irvine(UCI)machine learning repository of Sylhet Diabetes Hospital,Sylhet.Then,a similar questionnaire of that hospital was followed and assembled 558 patients records from all over Bangladesh through this questionnaire.However,we accumulated patient records of these two datasets.In the next step,these datasets were cleaned and applied thirty five state-of-arts classifiers such as logistic regression(LR),K nearest neighbors(KNN),support vector classifier(SVC),Nave Byes(NB),decision tree(DT),random forest(RF),stochastic gradient descent(SGD),Perceptron,AdaBoost,XGBoost,passive aggressive classifier(PAC),ridge classifier(RC),Nu-support vector classifier(NuSVC),linear support vector classifier(LSVC),calibrated classifier CV(CCCV),nearest centroid(NC),Gaussian process classifier(GPC),multinomial NB(MNB),complement NB,Bernoulli NB(BNB),categorical NB,Bagging,extra tree(ET),gradiant boosting classifier(GBC),Hist gradiant boosting classifier(HGBC),one vs rest classifier(OVsRC),multi-layer perceptron(MLP),label propagation(LP),label spreading(LS),stacking,ridge classifier CV(RCCV),logistic regression CV(LRCV),linear discriminant analysis(LDA),quadratic discriminant analysis(QDA),and light gradient boosting machine(LGBM)to explore best stable predictive model.The performance of the classifiers has been measured using five metrics such as accuracy,precision,recall,F1-score,and area under the receiver operating characteristic.Finally,these outcomes were interpreted using Shapley additive explanations methods a

关键词： Diabetes Early features Machine learning Classification

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