Radar Sensing via OTFS Signaling

By multiplexing information symbols in the delay-Doppler(DD)domain,orthogonal time frequency space(OTFS)is a promising candidate for future wireless communication in high-mobility scenarios.In addition to the superior communication performance,OTFS is also a natural choice for radar sensing since the primary parameters(range and velocity of targets)in radar signal processing can be inferred directly from the delay and Doppler shifts.Though there are several works on OTFS radar sensing,most of them consider the integer parameter estimation only,while the delay and Doppler shifts are usually fractional in the real world.In this paper,we propose a two-step method to estimate the fractional delay and Doppler shifts.We first perform the two-dimensional(2D)correlation between the received and transmitted DD domain symbols to obtain the integer parts of the parameters.Then a difference-based method is implemented to estimate the fractional parts of delay and Doppler indices.Meanwhile,we implement a target detection method based on a generalized likelihood ratio test since the number of potential targets in the sensing scenario is usually unknown.The simulation results show that the proposed method can obtain the delay and Doppler shifts accurately and get the number of sensing targets with a high detection probability.

Effects of Stocking Density on Water Quality,Growth and Economic Benefits of Chinese Softshelled Turtle Pelodiscus sinensis in Ponds

The Chinese soft-shelled turtle Pelodiscus sinensis is a high-valued freshwater species cultured in China.This study investigated the effects of stocking density on water quality,growth performance and economic return of Pelodiscus sinensis cultured in ponds.P.sinensis were stocked at densities of 1 ind./m^2(LD:low stocking density) and 2 ind./m^2(HD:high stocking density) in 3 000-m^2 ponds,with three replicate ponds for each density.P.sinensis juveniles were fed with a commercial dry pellet feed of 46% crude protein and minced fillet of silver carp ans cultured for 122 days.The results showed that the levels of total nitrogen(TN),total phosphorous(TP),Chlorophyll-a(Chl.a) and turbidity in LD treatment were significantly lower than those in HD treatment(P<0.05).The mean TN and TP concentration in LD treatment was 29.3% and 35.7% lower compared to the HD treatment at the end of the experiment,respectively.Mean survival rates,final weight,average growth rates and PER were significantly higher in LD treatment compared with the HD treatment(P<0.05),respectively.Production was significantly affected by stocking density,which was higher in HD treatment,but the net income was higher in LD treatment.The results suggest that turtles with mean weight 55.6 g rearing at a low stocking density(1 ind./m^2) in ponds had a positive effect on overall economic return and was effective at improving turtle growth performance and water quality.

A Self‑Powered,Highly Embedded and Sensitive Tribo‑Label‑Sensor for the Fast and Stable Label Printer

Label-sensor is an essential component of the label printer which is becoming a most significant tool for the development of Internet of Things(IoT).However,some drawbacks of the traditional infrared label-sensor make the printer fail to realize the high-speed recognition of labels as well as stable printing.Herein,we propose a selfpowered and highly sensitive tribo-label-sensor(TLS)for accurate label identification,positioning and counting by embedding triboelectric nanogenerator into the indispensable roller structure of a label printer.The sensing mechanism,device parameters and deep comparison with infrared sensor are systematically studied both in theory and experiment.As the results,TLS delivers 6 times higher signal magnitude than traditional one.Moreover,TLS is immune to label jitter and temperature variation during fast printing and can also be used for transparent label directly and shows long-term robustness.This work may provide an alternative toolkit with outstanding advantages to improve current label printer and further promote the development of IoT.

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular networks.In this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler.We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed algorithm.To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)algorithm.Finally,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

应急状态下新冠流行曲线预测的方法学研究——基于北京市百度搜索和传统流感样病例监测

Surveillance is an essential work on infectious diseases prevention and control.When the pandemic occurred,the inadequacy of traditional surveillance was exposed,but it also provided a valuable opportunity to explore new surveillance methods.This study aimed to estimate the transmission dynamics and epidemic curve of severe acute respiratory syndrome coronavirus 2(SARS-Co V-2)Omicron BF.7 in Beijing under the emergent situation using Baidu index and influenza-like illness(ILI)surveillance.A novel hybrid model(multiattention bidirectional gated recurrent unit(MABG)-susceptible-exposed-infected-removed(SEIR))was developed,which leveraged a deep learning algorithm(MABG)to scrutinize the past records of ILI occurrences and the Baidu index of diverse symptoms such as fever,pyrexia,cough,sore throat,anti-fever medicine,and runny nose.By considering the current Baidu index and the correlation between ILI cases and coronavirus disease 2019(COVID-19)cases,a transmission dynamics model(SEIR)was formulated to estimate the transmission dynamics and epidemic curve of SARS-Co V-2.During the COVID-19 pandemic,when conventional surveillance measures have been suspended temporarily,cases of ILI can serve as a useful indicator for estimating the epidemiological trends of COVID-19.In the specific case of Beijing,it has been ascertained that cumulative infection attack rate surpass 80.25%(95%confidence interval(95%CI):77.51%-82.99%)since December 17,2022,with the apex of the outbreak projected to transpire on December 12.The culmination of existing patients is expected to occur three days subsequent to this peak.Effective reproduction number(Rt)represents the average number of secondary infections generated from a single infected individual at a specific point in time during an epidemic,remained below 1 since December 17,2022.The traditional disease surveillance systems should be complemented with information from modern surveillance data such as online data sources with advanced technical support.Modern surveillance channels should be used primarily in emerging infectious and disease outbreaks.Syndrome surveillance on COVID-19 should be established to following on the epidemic,clinical severity,and medical resource demand.

Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness-China,2014-2019

Introduction:This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage(ILI%)across regions in China.The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics.Methods:In this study,data on ILI%and Baidu Index were collected from 30 provincial-level administrative divisions(PLADs)spanning April 2014 to March 2019.The Baidu Index was categorized into Overall Index,Ordinary Index,Prevention Index,Symptom Index,and Treatment Index based on search query themes.The lagged correlation between the Baidu Index and ILI%was examined through the cross-correlation function(CCF)method.Results:Correlating the Baidu Overall Index of 30 PLADs with ILI%revealed CCF values ranging from 0.46 to 0.86,with a median lag of 0.5 days.Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%,with median lags of−9 and−0.5 days,respectively,compared to 0 and 3 days for the Ordinary and Treatment Indexes.The median lag days between the Baidu Index and the ILI%were earlier in the northern PLADs compared to the southern PLADs.Discussion:The Prevention and Symptom Indexes show promising predictive capabilities for influenzalike illness epidemics.

Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases-Weifang City,Shandong Province,China,2020-2023

Introduction:Respiratory infectious diseases,such as influenza and coronavirus disease 2019(COVID-19),present significant global public health challenges.The emergence of artificial intelligence(AI)and big data offers opportunities to improve traditional disease surveillance and early warning systems.Methods:The study analyzed data from January 2020 to May 2023,comprising influenza-like illness(ILI)statistics,Baidu index,and clinical data from Weifang.Three methodologies were evaluated:the adaptive dynamic threshold method(ADTM)for dynamic threshold adjustments,the machine learning supervised method(MLSM),and the machine learning unsupervised method(MLUM)utilizing anomaly detection.The comparison focused on sensitivity,specificity,timeliness,and warning consistency.Results:ADTM issued 37 warnings with a sensitivity of 71%and a specificity of 85%.MLSM generated 35 warnings,with a sensitivity of 82%and a specificity of 87%.MLUM produced 63 warnings with a sensitivity of 100%and specificity of 80%.The initial warnings from ADTM and MLUM preceded those from MLSM by five days.The Kappa coefficient indicated moderate agreement between the methods,with values ranging from 0.52 to 0.62(P<0.05).Discussion:The study explores the comparison between traditional methods and two machine learning approaches for early warning systems.It emphasizes the validation of machine learning’s reliability and underscores the unique advantages of each method.Furthermore,it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response,alongside acknowledging limitations and the need for broader validation.

A Large-Scale Online Survey on Clinical Severity and Associated Risk Factors for SARS-CoV-2 Omicron Infection — China, April–May 2023

What is already known about this topic?Individuals who initially contract severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)lack significant mixed immunity.Therefore,it is crucial to monitor the clinical characteristics and associated factors of these individuals in order to inform policymaking.What is added by this report?The common symptoms reported were fever,cough,and sore throat.Reinfections and receiving four vaccination doses within a 6-month period were found to be associated with a shorter duration of virus shedding,decreased hospitalization rate,and reduced risk of pneumonia.Individuals aged 60 years and older,as well as those with underlying medical conditions,had a higher risk of developing pneumonia.What are the implication for public health practices?Online surveys conducted through social media platforms have the potential to complement disease surveillance and data collection efforts.In terms of vaccination prioritization,it is recommended to prioritize older individuals and those with underlying diseases.

Microstructural evolution and mechanical properties of binary Mg-xBi(x=2,5,and 8 wt%)alloys

The microstructures and mechanical properties of Mg-x Bi(x=2,5,and 8 wt%)were investigated compared with pure Mg.The ascast Mg-Bi billets consist of dendriticα-Mg grain matrix,divorced eutectic Mg_(3)Bi_(2) phase and secondary precipitated Mg_(3)Bi_(2) phase.After homogenization,secondary precipitated Mg_(3)Bi_(2) particles were all dissolved into the matrix,while most of the divorced eutectic Mg_(3)Bi_(2) intermetallic compounds retained.All the as-extruded samples exhibit fully dynamic recrystallization(DRX)and the average grain size decreases and the amount of nano-scale Mg_(3)Bi_(2) precipitates increases with increasing Bi content.Additionally,Bi alloying has little influence on the texture of the as-extruded pure Mg,with all the samples showing typical basal texture.As the results of grain refinement and precipitation hardening,the strengths of the as-extruded samples increase under both tensile and compressive tests.Besides,the yield asymmetry significantly decreases with Bi content.Moreover,all the as-extruded samples represent similar ductility under compression,while the tensile elongation first increases and then decreases with more Bi added.The as-extruded Mg-5Bi alloy demonstrates good combination of strength and ductility.However,Mg-8Bi alloy displays lower tensile ductility than that of Mg-5Bi alloy due to the presence of abundant undissolved coarse Mg_(3)Bi_(2) particles,which act as cracking sources during tensile test.Furthermore,the strain hardening rate,strain hardening exponent n,and hardening capacity Hc are greatly reduced by Bi addition,mainly owing to the enhanced synergy effects of grain refinement and numerous Mg_(3)Bi_(2) precipitates.

Multi-Task Deep Learning Based Hybrid Precoding for mmWave Massive MIMO System

Due to the different signal-to-noise ratio(SNR)of each subchannel,the bit error rate(BER)of hybrid precoding based on singular value decomposition(SVD)decreases.In this paper,we propose a multi-task learning based precoding network(PN)model to solve the BER loss problem caused by SVD based hybrid precoding under imperfect channel state information(CSI).Specifically,we firstly generate a dataset including imcomplete CSI input channel matrix and corresponding output labels to train the PN model.The output labels are designed based on uniform channel decomposition(UCD)which decomposes the channel into multiple subchannels with same gain,while the vertical-bell layered space-time structure(V-BLAST)signal processing technology is combined to eliminate the inner interference of the subchannels.Then,the PN model is trained to design the analog and digital precoding/combining matrix simultaneous.Simulation results show that the proposed scheme has only negligible gap in spectrum efficiency compared with the fully digital precoding,while achieves better BER performance than SVD based hybrid precoding.

EEG Emotion Recognition Using an Attention Mechanism Based on an Optimized Hybrid Model

Emotions serve various functions.The traditional emotion recognition methods are based primarily on readily accessible facial expressions,gestures,and voice signals.However,it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications.Electroencephalogram(EEG)signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage.Although EEG signals are commonly used in current emotional recognition research,the accuracy is low when using traditional methods.Therefore,this study presented an optimized hybrid pattern with an attention mechanism(FFT_CLA)for EEG emotional recognition.First,the EEG signal was processed via the fast fourier transform(FFT),after which the convolutional neural network(CNN),long short-term memory(LSTM),and CNN-LSTM-attention(CLA)methods were used to extract and classify the EEG features.Finally,the experiments compared and analyzed the recognition results obtained via three DEAP dataset models,namely FFT_CNN,FFT_LSTM,and FFT_CLA.The final experimental results indicated that the recognition rates of the FFT_CNN,FFT_LSTM,and FFT_CLA models within the DEAP dataset were 87.39%,88.30%,and 92.38%,respectively.The FFT_CLA model improved the accuracy of EEG emotion recognition and used the attention mechanism to address the often-ignored importance of different channels and samples when extracting EEG features.

Experts’Consensus on the Management of Respiratory Disease Syndemic

The global burden of respiratory diseases is a significant and increasing threat to individuals worldwide.In 2017,there were 544.9 million cases of chronic respiratory diseases,a 39.8%increase since 1990(1).These diseases were the third leading cause of global mortality in 2017,accounting for 7.0%of all deaths,an 18.0%increase compared to 1990.

Progress of additives for morphology control in organic photovoltaics

The microstructure of the active layer in organic photovoltaics(OPVs),such as the size of phase separation,purity of the phases,and molecular packing within each phase,plays a crucial role in influencing the behavior of excitons and charge carriers within the active layer.It is also a key determinant of the photovoltaic performance of the device.During the optimization of OPV devices,the use of additives has been demonstrated to be an effective strategy in microstructure control,leading to enhanced performance.Therefore,the quest for stable and efficient novel additives,along with an exploration and summarization of the mechanisms underlying additive-induced microstructure control,is essential for a better understanding of the developmental trends of high-performance additives.In this review,we categorize additives based on their chemical structures and discuss their effects on the microstructure of the active layer from both thermodynamic and kinetic perspectives.Furthermore,we elaborate on the working mechanisms and their impact on the photovoltaic performance of the devices.This review provides an overview of recent advances in additives for OPVs,offering potential guidance for the future development of additives and further optimization of the active layer in photovoltaic devices.

Standardized Volume Power Density Boost in Frequency-Up Converted Contact-Separation Mode Triboelectric Nanogenerators

The influence of a mechanical structure's volume increment on the volume power density (VPD) of triboelectric nanogenerators (TENGs) is often neglected when considering surface charge density and surface power density. This paper aims to address this gap by introducing a standardized VPD metric for a more comprehensive evaluation of TENG performance. The study specifically focuses on 2 frequency-up mechanisms, namely, the integration of planetary gears (PG-TENG) and the implementation of a double-cantilever structure (DC-TENG), to investigate their impact on VPD. The study reveals that the PG-TENG achieves the highest volume average power density, measuring at 0.92 W/m^(3). This value surpasses the DC-TENG by 1.26 times and the counterpart TENG by a magnitude of 69.9 times. Additionally, the PG-TENG demonstrates superior average power output. These findings introduce a new approach for enhancing TENGs by incorporating frequency-up mechanisms, and highlight the importance of VPD as a key performance metric for evaluating TENGs.

Mapping the Characteristics of Respiratory Infectious Disease Epidemics in China Based on the Baidu Index from November 2022 to January 2023

Introduction:Infectious diseases pose a significant global health and economic burden,underscoring the critical need for precise predictive models.The Baidu index provides enhanced real-time surveillance capabilities that augment traditional systems.Methods:Baidu search engine data on the keyword“fever”were extracted from 255 cities in China from November 2022 to January 2023.Onset and peak dates for influenza epidemics were identified by testing various criteria that combined thresholds and consecutive days.Results:The most effective scenario for indicating epidemic commencement involved a 90th percentile threshold exceeded for seven consecutive days,minimizing false starts.Peak detection was optimized using a 7-day moving average,balancing stability and precision.Discussion:The use of internet search data,such as the Baidu index,significantly improves the timeliness and accuracy of disease surveillance models.This innovative approach supports faster public health interventions and demonstrates its potential for enhancing epidemic monitoring and response efforts.

Physicochemical water quality parameters in typical rice-crayfish integrated systems(RCIS)in China

To determine the variation of water quality in rice-crayfish(Procambarus clarkii)integrated systems(RCIS)in China,eleven water quality parameters were measured monthly in a typical RCIS located in Qianjiang City(Hubei Province)from July 2014 to June 2015,the parameters were analyzed with principal component analysis(PCA)and compared between the trenches and rice areas during the rice fallow period(Nov-May).The trench and rice area comprehensive results showed that pH(7.48-8.68),NH_(4)^(+)-N(0.2-1.09 mg/L),NO_(2)^(-)-N(<0.052 mg/L)and conductivity(435-951μS/cm)were within the suitable ranges for P.clarkii and that turbidity(TU)was high during the crayfish harvesting and rice planting season.Annual averages of total nitrogen(TN),total phosphorus(TP),permanganate index(CODMn),and chlorophyll a(Chl.a)content were<2(except in Nov-Dec),0.25,10 mg/L,and 50 mg/m^(3)(especially in Nov-May,<10 mg/m^(3)),respectively.Dissolved oxygen(DO)was below 4 mg/L in Mar-Sep,with a minimum of~1 mg/L,and much higher in Oct-Feb.The maximum and minimum monthly average water temperature(WT)were 31.4°C in July and 5.7°C in December,while the maximum and minimum instantaneous WT were 39.7°C and 2.5°C,respectively.PCA analysis showed that the first three axes,which were mainly correlated with DO,WT and nutrient level,described most information of the parameters,and parameters showed seasonal changes.Some differences were observed in water parameters between the trenches and rice areas,i.e.,trenches generally had higher TU,WT and DO,and lower TN,TP and CODMn,although no significant differences were found in some months and some parameters.The study revealed relatively low water nutrient level,probable extreme WT and DO level in some seasons,and certain differences between the trenches and rice areas in typical CRIS in China.Accordingly,some measures should be taken to improve the negative parameters:1)enhance the water fertility;2)increase DO,especially in Mar-Sep;3)increase the trench and water depth to avoid extreme WT.And water quality management should be addressed in both trenches and rice areas.

COVID-19 Outbreak Caused by Contaminated Packaging of Imported Cold-Chain Products—Liaoning Province,China,July 2020

Summary What is known about this topic?Few major outbreaks of coronavirus disease 2019(COVID-19)have occurred in China after major nonpharmaceutical interventions and vaccines have been deployed and implemented.However,sporadic outbreaks that had high possibility to be linked to cold chain products were reported in several cities of China..What is added by this report?In July 2020,a COVID-19 outbreak occurred in Dalian,China.The investigations of this outbreak strongly suggested that the infection source was from COVID-19 virus-contaminated packaging of frozen seafood during inbound unloading personnel contact.What are the implications for public health practice?Virus contaminated paper surfaces could maintain infectivity for at least 17–24 days at-25℃.Exposure to COVID-19 virus-contaminated surfaces is a potential route for introducing the virus to a susceptible population.Countries with no domestic transmission of COVID-19 should consider introducing prevention strategies for both inbound travellers and imported goods.Several measures to prevent the introduction of the virus via cold-chain goods can be implemented.

Characteristics ofβ-type Ti-41Nb alloy produced by laser powder bed fusion:Microstructure,mechanical properties and in vitro biocompatibility

Aβ-type Ti-41 Nb alloy with high relative density has been successfully fabricated by laser powder bed fusion(L-PBF)using pre-alloyed powders for potential implant application.The homogeneous microstructure can be achieved in L-PBF fabricated(L-PBFed)Ti-41 Nb alloy but slight composition segregation was detected along molten pool boundaries.The L-PBFed alloy was dominated by typical epitaxial columnar grains with strong(001)grain orientation along building direction(BD),and cellular structure was distinguished within the columnar grains.The main reasons for this microstructure can be attributed to effective thermal gradient and epitaxial growth.L-PBFed alloy exhibited higher mechanical strength compared with cold rolling plus annealing(CRA)alloy due to the finer grains,dislocations accumulation and different TRIP behaviors,accompanied by good ductility.It also exhibited much lower thermal conductivity and better hydrophilic feature than those of CP-Ti.Besides,the L-PBFed alloy exhibited slightly better cell spread and cell proliferation rates compared with CP-Ti.Moreover,L-PBFed alloy presented better alkaline phosphatase(ALP)activities and extracellular matrix(ECM)mineralization,which suggests that the L-PBFed alloy can stimulate the osteogenic differentiation of rat bone mesenchymal stem cells.The Ti-41 Nb alloy,fabricated by L-PBF,reveals a good combination of mechanical properties,physicochemical properties and biocompatibility,exhibiting the great potential as the dental implant.

Effects and interaction of temperature and relative humidity on the trend of influenza prevalence:A multi-central study based on 30 provinces in China's Mainland from 2013 to 2018

Background:Evidence is inefficient about how meteorological factors influence the trends of influenza transmission in different regions of China.Methods:We estimated the time-varying reproduction number(Rt)of influenza and explored the impact of temperature and relative humidity on Rt using generalized additive quasi-Poisson regression models combined with the distribution lag non-linear model(DLNM).The effect of temperature and humidity interaction on Rt of influenza was explored.The multiple random-meta analysis was used to evaluate region-specific association.The excess risk(ER)index was defined to investigate the correlation between Rt and each meteorological factor with the modification of seasonal and regional characteristics.Results:Low temperature and low relative humidity contributed to influenza epidemics on the national level,while shapes of merged cumulative effect plots were different across regions.Compared to that of median temperature,the merged RR(95%CI)of low tem-perature in northern and southern regions were 1.40(1.24,1.45)and 1.20(1.14,1.27),respectively,while those of high temperature were 1.10(1.03,1.17)and 1.00(0.95,1.04),respectively.There were negative interactions between temperature and relative humidity on national(SI=0.59,95%CI:0.57e0.61),southern(SI=0.49,95%CI:0.17e0.80),and northern regions(SI=0.59,95%CI:0.56,0.62).In general,with the increase of the change of the two meteorological factors,the ER of Rt also gradually increased.Conclusions:Temperature and relative humidity have an effect on the influenza epidemics in China,and there is an interaction between the two meteorological factors,but the effect of each factor is heterogeneous among regions.Meteorological factors may be considered to predict the trend of influenza epidemic.

A Nosocomial COVID-19 Outbreak Initiated by an Infected Dockworker at Qingdao City Port-Shandong Province, China, October, 2020

On October 10,2020,3 cases of coronavirus disease 2019(COVID-19)were detected via routine preadmission nucleic acid screening of inpatients and their visitors at Qingdao Central Hospital.The 3 cases were all related to the Qingdao Chest Hospital.An additional 9 cases were discovered and confirmed through tracing and testing contacts of the 3 initial cases.Viral isolates from all 12 cases were found to have high degrees of genetic homology with the viral isolate from a dockworker at Qingdao Port,referred to as Patient A in this report.A joint investigation team was formed to determine the source of infection and the scale of the outbreak,and to prevent further spread of the virus.