Jamming Assisted Covert Wireless Communication in Multi-Channel Systems

This paper investigates the jammerassisted multi-channel covert wireless communication(CWC)by exploiting the randomness of sub-channel selection to confuse the warden.In particular,we propose two sub-channel selection transmission schemes,named random sub-channel selection(RSS)scheme and maximum sub-channel selection(MSS)scheme,to enhance communication covertness.For each proposed scheme,we first derive closed-form expressions of the transmission outage probability(TOP),the average effective rate,and the minimum average detection error probability(DEP).Then,the average effective covert rate(ECR)is maximized by jointly optimizing the transmit power at the transmitter and the number of sub-channels.Numerical results show that there is an optimal value of the number of sub-channels that maximizes the average ECR.We also find that to achieve the maximum average ECR,a larger number of subchannels are needed facing a stricter covertness constraint.

A novel multi-channel porous structure facilitating mass transport towards highly efficient alkaline water electrolysis

An advantageous porous architecture of electrodes is pivotal in significantly enhancing alkaline water electrolysis(AWE)efficiency by optimizing the mass transport mechanisms.This effect becomes even more pronounced when aiming to achieve elevated current densities.Herein,we employed a rapid and scalable laser texturing process to craft novel multi-channel porous electrodes.Particularly,the obtained electrodes exhibit the lowest Tafel slope of 79 mV dec^(-1)(HER)and 49 mV dec^(-1)(OER).As anticipated,the alkaline electrolyzer(AEL)cell incorporating multi-channel porous electrodes(NP-LT30)exhibited a remarkable improvement in cell efficiency,with voltage drops(from 2.28 to 1.97 V)exceeding 300 mV under 1 A cm^(-1),compared to conventional perforated Ni plate electrodes.This enhancement mainly stemmed from the employed multi-channel porous structure,facilitating mass transport and bubble dynamics through an innovative convection mode,surpassing the traditional convection mode.Furthermore,the NP-LT30-based AEL cell demonstrated exceptional durability for 300 h under 1.0 A cm^(-2).This study underscores the capability of the novel multi-channel porous electrodes to expedite mass transport in practical AWE applications.

Omni-Channel Retailing and Firm Performance from a Customer Lifetime Value Perspective

In the digital era,retailers are keen to find out whether omni-channel retailing helps improve long-term firm performance.In this paper,we employ machine learning techniques on a large consumption data set in order to measure customer lifetime value(CLV)as the basis for determining long-term firm performance,and we provide an empirical analysis of the relationship between omni-channel retailing and CLV.The results suggest that omni-channel retailing may effectively enhance CLV.Further analysis reveals that this process is influenced by heterogeneous consumer requirements and that significant differences exist in the extent to which the omni-channel transition may influence CLV depending on consumer preferences for diversity of commodities,sensitivity to the cost of contract performance,and sensitivity to warehousing costs.Hence,retailers should provide consumers with a complete portfolio of goods and services based on target consumers’heterogeneous requirements in order to increase omni-channel efficiency.

The Interrelationship between Culture and Capital Structure:Evidence from International Retailers

The present study investigates the influence of cultural factors in 2022 on the capital structure of global retailers.There are sixteen retailers from eight countries in the sample.In recent times,numerous academicians have taken an interest in examining the capital structure and business model of retailers,owing to their swift and consistent growth.However,the fact that the majority of research originates from the retailers’host country gives rise to debate regarding the applicability of the capital structure of said retailers to countries with distinct cultural environments.Consequently,academics have begun to investigate whether the capital structure of multinational retailers is impacted by the diversity of national cultures.

Design of Cold-Junction Compensation and Disconnection Detection Circuits of Various Thermocouples and Implementation of Multi-Channel Interfaces Using Them-A Secondary Publication

Cold-junction compensation(CJC)and disconnection detection circuit design of various thermocouples(TC)and multi-channel TC interface circuits were designed.The CJC and disconnection detection circuit consists of a CJC semiconductor device,an instrumentation amplifier(IA),two resistors,and a diode for disconnection detection.Based on the basic circuit,a multi-channel interface circuit was also implemented.The CJC was implemented using compensation semiconductor and IA,and disconnection detection was detected by using two resistors and a diode so that IA input voltage became-0.42 V.As a result of the experiment using R-type TC,the error of the designed circuit was reduced from 0.14 mV to 3μV after CJC in the temperature range of 0°C to 1400°C.In addition,it was confirmed that the output voltage of IA was saturated from 88 mV to-14.2 V when TC was disconnected from normal.The output voltage of the designed circuit was 0 V to 10 V in the temperature range of 0°C to 1400°C.The results of the 4-channel interface experiment using R-type TC were almost identical to the CJC and disconnection detection results for each channel.The implemented multi-channel interface has a feature that can be applied equally to E,J,K,T,R,and S-type TCs by changing the terminals of CJC semiconductor devices and adjusting the IA gain.

Development of multi-channel observation and inversion for IP electrical sounding method

In order to improve the exploration effect of deep non-ferrous mineral resources, multi-channel observation methods for induced polarization （IP） electrical sounding data and their inversion imaging technology are studied. First of all, four multi-channel observation methods are developed based on conventional IP electrical method, namely three-electrode and four-electrode arrays of unilateral and bilateral current transmitting. Then the maximum smoothness constrained inversion method of the least squares sense for IP electrical sounding data is proposed, and the inversion software is programmed. Finally, the simulation and inversion results of geo-electrical model for the proposed observation methods are analyzed. And the comparison results show that three-electrode array of bilateral current transmitting gives the best result, but the intensity in field work is larger than others; unilateral three-electrode and four-electrode arrays give the better results. Taking detection results and convenience of field exploration work into consideration, these two methods are more suitable for practical application; bilateral observation method of four-electrode array is not suitable for the detection of the steep ore bodies.

Design of multi-channel data acquisition system based on LabVIEW and NI PXI-5105

A data acquisition system based on LabVIEW and NI PXI-5105 is presented for multi-channel data acquisition. It can realize the functions of parameter setting, data acquisition and storage, waveform display and data analysis using LabVIEW and NI-SCOPE device driver. The advantages of the system are that the setting is convenient, the operation is easy, the interface is friendly and the functions are practical. The experiment results show that the system has good stability and high reliability and is a powerful tool for multi-channel data acquisition.

A maximum-independent-set-based channel allocation algorithm for multi-channel wireless networks

A channel allocation algorithm based on the maximum independent set is proposed to decrease network conflict and improve network performance. First, a channel allocation model is formulated and a series of the maximum independent sets （MISs） are obtained from a contention graph by the proposed approximation algorithm with low complexity. Then, a weighted contention graph is obtained using the number of contention vertices between two MISs as a weighted value. Links are allocated to channels by the weighted contention graph to minimize conflicts between independent sets. Finally, after channel allocation, each node allocates network interface cards （NICs） to links that are allocated channels according to the queue lengths of NICs. Simulations are conducted to evaluate the proposed algorithm. The results show that the proposed algorithm significantly improves the network throughput and decreases the end to end delay.

基于Multi-Radio Multi-Channel传感器网络无冲突信道分配算法的研究

为降低通信冲突和信道干扰,对Mult-i Radio Mult-i Channel传感器网络无冲突信道进行研究,结果证实在网络通信半径大于3倍的网络最大功率通信半径的前提下,Sensor节点规模满足2倍网络功率级数加1的环境下,网络无冲突信道分配的信道数达到网络信道冲突图的最大值。文章通过对无冲突信道算法的运用,最终证实其可以有效地提高传感器网络的工作效率,大幅度提升网络的吞吐量。

Genotypic Characterization of Methicillin-resistant Staphylococcus aureus Isolated from Pigs and Retail Foods in China

Objective To investigate the genotypic diversity of Methicillin-resistant Staphylococcus aureus （MRSA） isolated from pigs and retail foods from different geographical areas in China and further to study the routes and rates of transmission of this pathogen from animals to food. Methods Seventy-one MRSA isolates were obtained from pigs and retail foods and then characterized by multi-locus sequencing typing （MLST）, spa typing, multiple-locus variable number of tandem repeat analysis （MLVA）, pulsed-field gel electrophoresis （PFGE）, and antimicrobial susceptibility testing. Results All isolated MRSA exhibited multi-drug resistance （MDR）. Greater diversity was found in food-associated MRSA （7 STs, 8 spa types, and 10 MLVA patterns） compared to pig-associated MRSA （3 STs, 1 spa type, and 6 MLVA patterns）. PFGE patterns were more diverse for pig-associated MRSA than those of food-associated isolates （40 vs. 11 pulse types）. Among the pig-associated isolates, CC9-ST9-t899-MC2236 was the most prevalent clone （96.4%）, and CC9-ST9-t437-MC621 （20.0%） was the predominant clone among the food-associated isolates. The CC9-ST9 isolates showed significantly higher antimicrobial resistance than other clones. Interestingly, CC398-ST398-t034 clone was identified from both pig- and food-associated isolates. Of note, some community- and hospital-associated MRSA strains （t030, t172, t1244, and t4549） were also identified as food-associated isolates. Conclusion CC9-ST9-t899-MC2236-MDR was the most predominant clone in pigs, but significant genetic diversity was observed in food-associated MRSA. Our results demonstrate the great need for improved surveillance of MRSA in livestock and food and effective prevention strategies to limit MDR-MRSA infections in China.

A Novel Design of Efficient Multi-channel UART Controller Based on FPGA

In traditional universal asynchronous receiver transmitter （UART） controller, the data transmission is inefficient and the data bus utilization ratio is low. A novel design is provided to solve these problems. The architecture of the system is introduced, the flow charts of data processing as well as the implementation state machine are also presented in detail. This paper is concluded by comparing the performance of this design, which is realized on field programmable gate army （FPGA） using Verilog hardware description language （HDL）, with other traditional UART controllers.

Prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network

This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in China.In the process of determining the structure of the chaotic neural network,the number of input layer nodes of the network is calculated by reconstructing phase space and computing its saturated embedding dimension,and then the number of hidden layer nodes is estimated by trial and error.Finally,this model is applied to predict the retail prices of eggs and compared with ARIMA.The result shows that the chaotic neural network has better nonlinear fitting ability and higher precision in the prediction of weekly retail price of eggs.The empirical result also shows that the chaotic neural network can be widely used in the field of short-term prediction of agricultural prices.

Performance analysis of multi-channel order statistics detector for range-spread target

The problem of two order statistics detection schemes for the detection of a spatially distributed target in white Gaussian noise are studied.When the number of strong scattering cells is known,we first show an optimal detector,which requires many processing channels.The structure of such optimal detector is complex.Therefore,a simpler quasi-optimal detector is then introduced.The quasi-optimal detector,called the strong scattering cells’ number dependent order statistics（SND-OS） detector,takes the form of an average of maximum strong scattering cells with a known number.If the number of strong scattering cells is unknown in real situation,the multi-channel order statistics（MC-OS） detector is used.In each channel,a various number of maximums scattered from target are averaged.Then,the false alarm probability analysis and thresholds sets for each channel are given,following the detection results presented by means of Monte Carlo simulation strategy based on simulated target model and three measured targets.In particular,the theoretical analysis and simulation results highlight that the MC-OS detector can efficiently detect range-spread targets in white Gaussian noise.

Downtown Retailing Development Under Suburbanization——A Case Study of Beijing

The role of retailing has diminished in the center of cities of Western countries since its heyday in the 1920s. This trend was caused by violent transformations in the economic, social and locational contexts which involve resi- dential suburbanization, as well as new forms of commercial development out of town. As far as transforming China is concerned, the downtown retailing center has also come to turning point of development in the context of suburbaniza- tion. Based on the survey of Beijing residents’ shopping behavior, the present research tends to making clear the actual change and development problem of downtown retailing center. Combining with the macro retailing environment, re- lated influencing factors are further discussed too. Study results show that downtown retailing still plays an important part in resident’s shopping activity. However, with rapid urban sprawl, retailing in urban center has been greatly threatened by booming out-of-town commerce. In the course of such spatial change, the suburbanization of resident, the prevalence of new commercial forms and the change of consumers’ life style all have exerted their own pressure on downtown retailing. But for the special socioeconomic background of development and the support of government policy, it will still keep its vitality of retailing for a long period of time.

Impact of Shipping Distance on Online Retailers' Sales:A Case Study of Maiyang on Tmall

Many studies have qualitatively explained that information and communication technology(ICT) has loosened the restrictions of distance and space on retailers' sales. Few empirical studies, however, have explored the impact of shipping distance on online retailers' sales. This study examined the Maiyang(M-Y) store on Tmall in China as a case study to investigate the relationship between shipping distance and sales. The results showed that sales volume in 2014 at the county level did not strictly obey the distance decay law. The shipped distance of high-priced commodities may not be much longer than that of low-priced commodities. Within the scope of investigation, the relationships between income, cost, and net profit curves do not follow central place theory. Goods have neither thresholds nor ranges. The key factor in the spatial discrepancy of sales is the size of market. The impact of shipping distance on sales is not as strong as that of traditional retailers in Information Era.

A time-sharing multi-channel pulse amplitude analyzer

A conventional multi-channel pulse amplitude analyzer acquires single energy spectrum,but provides no information on its tendency with time.To address the limitation,we propose a scheme of time-sharing multichannel pulse amplitude analyzer(TSMCA).A dual-port random access memory is divided into two storage spaces,one for current energy spectrum data acquisition and another for previous energy spectrum data storage.The two tasks can be performed simultaneously,and the time-related variation tendency of energy spectrum can be obtained.A prototype system of TSMCA is designed.It performs nicely,with maximum channel number of 4096 in capacity of 2^(32)/Ch,minimal time-sharing slice of 25 ms,the differential nonlinearity of <1.5%,and the integral nonlinearity of <0.3%.

Receding Horizon Estimation for Linear Discrete-time Systems with Multi-channel Observation Delays

This paper investigates the receding horizon state estimation for the linear discrete-time system with multi-channel observation delays. The receding horizon estimation is designed by the reorganized observation technique and the linear unbiased estimation method. The estimation gains are developed by solving a set of Riccati equations, and a stability result about the state estimation is shown. Finally, an example is given to illustrate the efficiency of the receding horizon state estimation.

The impact of FinTech start-ups on incumbent retail banks’share prices

Background:This study aims to clarify the role of FinTech digital banking start-ups in the financial industry.We examine the impact of the funding of such start-ups on the stock returns of 47 incumbent US retail banks for 2010 to 2016.Methods:To capture the importance of FinTech start-ups,we use data on both the dollar-volume of funding and number of deals.We relate these to the stock returns with panel data regression methods.Results:Our results indicate a positive relationship exists between the growth in FinTech funding or deals and the contemporaneous stock returns of incumbent retail banks.Conclusions:Although these results suggest complementarity between FinTech and traditional banking,we note that our results at the banking industry level are not statistically significant,and that the coefficient signs for about one-third of the banks are negative,but not statistically significant.Since the FinTech industry is young and our sample period short,we cannot rule out that our findings are spurious.

Multi-Channel Iterative FDE for Single Carrier Block Transmission over Underwater Acoustic Channels

Recently, single carrier block transmission(SCBT) has received much attention in high-rate phase-coherent underwater acoustic communication.However,minimum-mean-square-error(MMSE) linear FDE may suffer performance loss in the severely time dispersive underwater acoustic channel. To combat the channel distortion, a novel multi-channel receiver with maximum ratio combining and a low complex T/4 fractional iterative frequency domain equalization(FDE) is investigated to improve diversity gain and the bit error rate(BER) performance. The proposed method has been verified by the real data from a lake underwater acoustic communication test in November 2011. At 1.8 km, the useful data rates are around 1500 and 3000 bits/s for BPSK and QPSK respectively. The results show the improvements of system performance. Compared with MMSE FDE system, the output SNR improvement is 6.9 d B, and the BER is from 10-3 to no error bits for BPSK. The output SNR improvement is 5.3 d B, and the BER is from 1.91×10-2 to 2.2×10-4for QPSK.

A Hybrid Deep Learning Scheme for Multi-Channel Sleep Stage Classification

Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases.This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography(EEG),electrocardiogram(ECG),electromyogram(EMG),and electrooculogram(EOG).Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods.Traditional hand-crafted feature extraction methods choose features manually from raw data,which is tedious,and these features are limited in their ability to balance efficiency and accuracy.Moreover,most of the existing works on sleep staging are either single channel(a single-lead EEG may not contain enough information)or only EEG signal based which can not reveal more complicated physical features for reliable classification of various sleep stages.This study proposes an approach to combine Convolutional Neural Networks(CNNs)and Gated Recurrent Units(GRUs)that can discover hidden features from multi-biological signal data to recognize the different sleep stages efficiently.In the proposed scheme,the CNN is designed to extract concealed features from the multi-biological signals,and the GRU is employed to automatically learn the transition rules among different sleep stages.After that,the softmax layers are used to classify various sleep stages.The proposed method was tested on two publicly available databases:Sleep Heart Health Study(SHHS)and St.Vincent’s University Hospital/University College Dublin Sleep Apnoea(UCDDB).The experimental results reveal that the proposed model yields better performance compared to state-of-the-art works.Our proposed scheme will assist in building a new system to deal with multi-channel or multi-modal signal processing tasks in various applications.