Enlightenment and Suggestions on the Construction of Information Platform and Processing Mechanism of Drug Shortages in Developed Countries to China

Objective To study how to improve and perfect the information platform and processing mechanism of drug shortages in China.Methods By searching the relevant policies from official websites of FDA,European Medicines Agency(EMA),Health Canada(HC)and National Health Commission,the good experience of the United States,the European Union and Canada in the construction of information platform and processing mechanism of drug shortages was summarized for reference in China.Results and Conclusion China has initially established the processing mechanism of drug shortages,but the platform construction should be improved,and the information disclosure of drug shortages varies from province to province.We should improve the information platform of drug shortages,strengthen the disclosure and communication of information,enrich the processing tools and measures after the drug shortages occurs,and strengthen the cooperation with relevant associations and other non-governmental departments.

A framework of force of information influence and application for C4KISR system

The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

Dynamic Update Scheme of Spectrum Information Based on Spectrum Opportunity Incentive in the Database-Assisted Dynamic Spectrum Management

To solve the problem of delayed update of spectrum information(SI) in the database assisted dynamic spectrum management(DB-DSM), this paper studies a novel dynamic update scheme of SI in DB-DSM. Firstly, a dynamic update mechanism of SI based on spectrum opportunity incentive is established, in which spectrum users are encouraged to actively assist the database to update SI in real time. Secondly, the information update contribution(IUC) of spectrum opportunity is defined to describe the cost of accessing spectrum opportunity for heterogeneous spectrum users, and the profit of SI update obtained by the database from spectrum allocation. The process that the database determines the IUC of spectrum opportunity and spectrum user selects spectrum opportunity is mapped to a Hotelling model. Thirdly, the process of determining the IUC of spectrum opportunities is further modelled as a Stackelberg game by establishing multiple virtual spectrum resource providers(VSRPs) in the database. It is proved that there is a Nash Equilibrium in the game of determining the IUC of spectrum opportunities by VSRPs. Finally, an algorithm of determining the IUC based on a genetic algorithm is designed to achieve the optimal IUC. The-oretical analysis and simulation results show that the proposed method can quickly find the optimal solution of the IUC, and ensure that the spectrum resource provider can obtain the optimal profit of SI update.

Characterization of Small-Scale Farmers and Assessment of Their Access to Crop Production Information in Selected Counties of Kenya

Small-scale farming accounts for 78% of total agricultural production in Kenya and contributes to 23.5% of the country’s GDP. Their crop production activities are mostly rainfed subsistence with any surplus being sold to bring in some income. Timely decisions on farm practices such as farm preparation and planting are critical determinants of the seasonal outcomes. In Kenya, most small-scale farmers have no reliable source of information that would help them make timely and accurate decisions. County governments have extension officers who are mandated with giving farmers advisory services to farmers but they are not able to reach most farmers due to facilitation constraints. The mode and format of sharing information is also critical since it’s important to ensure that it’s timely, well-understood and usable. This study sought to assess access to geospatial derived and other crop production information by farmers in four selected counties of Kenya. Specific objectives were to determine the profile of small-scale farmers in terms of age, education and farm size;to determine the type of information that is made available to them by County and Sub-County extension officers including the format and mode of provision;and to determine if the information provided was useful in terms of accuracy, timeliness and adequacy. The results indicated that over 80% of the farmers were over 35 years of age and over 56% were male. Majority had attained primary education (34%) or secondary education (29%) and most farmers in all the counties grew maize (71%). Notably, fellow farmers were a source of information (71%) with the frequency of sharing information being mostly seasonal (37%) and when information was available (43%). Over 66% of interviewed farmers indicating that they faced challenges while using provided information. The results from the study are insightful and helpful in determining effective ways of providing farmers with useful information to ensure maximum benefits.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

AMulti-Constraint Path Optimization Scheme Based on Information Fusion in Software Defined Network

The existingmultipath routing in Software Defined Network (SDN) is relatively blind and inefficient, and there is alack of cooperation between the terminal and network sides, making it difficult to achieve dynamic adaptationof service requirements and network resources. To address these issues, we propose a multi-constraint pathoptimization scheme based on information fusion in SDN. The proposed scheme collects network topology andnetwork state information on the network side and computes disjoint paths between end hosts. It uses the FuzzyAnalytic Hierarchy Process (FAHP) to calculate the weight coefficients of multiple constrained parameters andconstructs a composite quality evaluation function for the paths to determine the priority of the disjoint paths. TheSDN controller extracts the service attributes by analyzing the packet header and selects the optimal path for flowrule forwarding. Furthermore, the service attributes are fed back to the path composite quality evaluation function,and the path priority is dynamically adjusted to achieve dynamic adaptation between service requirements andnetwork status. By continuously monitoring and analyzing the service attributes, the scheme can ensure optimalrouting decisions in response to varying network conditions and evolving service demands. The experimentalresults demonstrated that the proposed scheme can effectively improve average throughput and link utilizationwhile meeting the Quality of Service (QoS) requirements of various applications.

Impacts of information about COVID-19 on pig farmers'production willingness and behavior:Evidence from China

This paper examines the impacts of information about COVID-19 on pig farmers'production willingness by using endorsement experiments and follow-up surveys conducted in 2020 and 2021 in China.Our results show that,first,farmers were less willing to scale up production when they received information about COVID-19.The information in 2020 that the second wave of COVID-19 might occur without a vaccine reduced farmers'willingness to scale up by 13.4%,while the information in 2021 that COVID-19 might continue to spread despite the introduction of vaccine reduced farmers'willingness by 4.4%.Second,farmers whose production was affected by COVID-19 were considerably less willing to scale up,given the access to COVID-19 information.Third,farmers'production willingness can predict their actual production behavior.

The Impact of Health Information Technology on Hospital Performance: A Systematic Integrative Literature Review

Objective: To review, categorise, and synthesise findings from literature on health information technology (HIT) functionalities, HIT use, and the impact of HIT on hospital performance. Materials and Methods: We conducted a systematic integrative literature review based on a compre-hensive database search. To organise, categorise and synthesise the ex-isting literature, we adopted the affordance actualization theory. To align the literature with our research framework, we used four categories: 1) the functionalities of HIT and how these functionalities are measured;2) use and immediate outcomes of HIT functionalities;3) different perfor-mance indicators and how HIT functionalities affect them;and 4) what hospital characteristics influence the outcome of hospital performance. Results: Fifty-two studies were included. We identified four types of HIT. Only ten studies (19.2%) define the use of HIT by explicitly meas-uring the use rate of HIT. We identified five dimensions of hospital per-formance indicators. Every dimension showed mixed results;however, in general, HIT has a positive impact on mortality and patient readmis-sions. We found several hospital characteristics that may affect the rela-tionship between HIT and hospital-level outcomes. Discussion: Further efforts should focus on embedded research on HIT functionalities, use and effects of HIT implementations with more performance indicators and adjusted for hospital characteristics. Conclusion: The proposed framework could help hospitals and researchers make decisions regard-ing the functionalities, use and effects of HIT implementation in hospi-tals. Given our research outcomes, we suggest future research opportuni-ties to improve understanding of how HIT affects hospital performance. .

Research on Driver’s Fatigue Detection Based on Information Fusion

Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.

Mutual Information Maximization via Joint Power Allocation in Integrated Sensing and Communications System

In this paper, we focus on the power allocation of Integrated Sensing and Communication(ISAC) with orthogonal frequency division multiplexing(OFDM) waveform. In order to improve the spectrum utilization efficiency in ISAC, we propose a design scheme based on spectrum sharing, that is,to maximize the mutual information(MI) of radar sensing while ensuring certain communication rate and transmission power constraints. In the proposed scheme, three cases are considered for the scattering off the target due to the communication signals,as negligible signal, beneficial signal, and interference signal to radar sensing, respectively, thus requiring three power allocation schemes. However,the corresponding power allocation schemes are nonconvex and their closed-form solutions are unavailable as a consequence. Motivated by this, alternating optimization(AO), sequence convex programming(SCP) and Lagrange multiplier are individually combined for three suboptimal solutions corresponding with three power allocation schemes. By combining the three algorithms, we transform the non-convex problem which is difficult to deal with into a convex problem which is easy to solve and obtain the suboptimal solution of the corresponding optimization problem. Numerical results show that, compared with the allocation results of the existing algorithms, the proposed joint design algorithm significantly improves the radar performance.

Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

Relationship between clinical belonging,professional identity,and nursing information ability among nursing interns:Model construction

BACKGROUND Clinical belonging refers to the feeling that clinical medical staff feel recognized and accepted by others or groups.The level of clinical belonging of nursing interns affects students’learning motivation and confidence,which in turn affects their clinical practice behavior.AIM To explore the effects of professional identity and nursing information ability on clinical belonging among nursing interns and establish a relationship model for these factors.METHODS The researchers used the convenience sampling method to select 682 nursing interns from China.The survey was conducted using a general information questionnaire,clinical sense of belonging scale,nursing information ability self-assessment scale,and a nursing student professional identity questionnaire.The mediating effect of nursing information ability between their professional identity and clinical sense of belonging was analyzed using SPSS 21.0 and the path analysis in structural equation modeling.RESULTS The total scores of clinical belonging,professional identity,and nursing information ability of nursing interns were(104.29±13.11)points,(57.89±7.16)points,and(70.29±6.20)points,respectively.Nursing information ability had a direct effect on the clinical sense of belonging(effect value=0.46,P<0.05).Occupational identity had a direct effect(effect value=0.52,P<0.05)and an indirect effect(effect value=0.21,P<0.05)on clinical belonging.CONCLUSION Nursing administrators in nursing colleges and hospitals should take effective measures to improve the professional identity and nursing information ability of nursing interns,as well as the clinical sense of belonging among nursing interns.

Dynamics of information diffusion and disease transmission in time-varying multiplex networks with asymmetric activity levels

While the interaction between information and disease in static networks has been extensively investigated,many studies have ignored the characteristics of network evolution.In this study,we construct a new two-layer coupling model to explore the interactions between information and disease.The upper layer describes the diffusion of disease-related information,and the lower layer represents the disease transmission.We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation,revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network.Subsequently,we derive the disease outbreak threshold by using the microscopic Markov-chain approach(MMCA).Finally,we perform extensive Monte Carlo(MC)numerical simulations to verify the accuracy of our theoretical results.Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer.Moreover,reducing the damping factor can delay disease outbreaks and suppress disease transmission,while improving individual quarantine measures can contribute positively to disease control.This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.

基于Arduino与Processing的心率检测计设计研究

随着社会经济的高速发展,人们的物质生活也有了极大的提高,但同时也伴随着各种疾病的到来,身体健康已经成为人们普遍关注的焦点,因此,心率检测仪、血压计、血糖仪等各种家用医疗监测仪器已经逐渐融入日常生活。心脏病是人们难以预防的突发致命疾病之一,本文介绍的是一款基于Arduino【是基于易用硬件和软件的原型开源平台,包由可编程的电路板(简称微控制器),以及集成开发环境(称为Arduino IDE)的现成软件组成】Processing(开源编程语言,包括编辑器、编译器、展示器)的简易心率检测计系统,其功能实用、操作简单,可以测量心率,当超出正常心率范围时及时预警,是一款便携的实时心率测试仪。

Data Component:An Innovative Framework for Information Value Metrics in the Digital Economy

The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.

Decentralized Optimal Control and Stabilization of Interconnected Systems With Asymmetric Information

The paper addresses the decentralized optimal control and stabilization problems for interconnected systems subject to asymmetric information.Compared with previous work,a closed-loop optimal solution to the control problem and sufficient and necessary conditions for the stabilization problem of the interconnected systems are given for the first time.The main challenge lies in three aspects:Firstly,the asymmetric information results in coupling between control and estimation and failure of the separation principle.Secondly,two extra unknown variables are generated by asymmetric information(different information filtration)when solving forward-backward stochastic difference equations.Thirdly,the existence of additive noise makes the study of mean-square boundedness an obstacle.The adopted technique is proving and assuming the linear form of controllers and establishing the equivalence between the two systems with and without additive noise.A dual-motor parallel drive system is presented to demonstrate the validity of the proposed algorithm.

Investigation on taste characteristics and sensory perception of soft-boiled chicken during oral processing based on electronic tongue and electronic nose

The sensory perception of food is a dynamic process,which is closely related to the release of flavor substances during oral processing.It’s not only affected by the food material,but also subjected to the individual oral environment.To explore the oral processing characteristics of soft-boiled chicken,the sensory properties,texture,particle size,viscosity,characteristic values of electronic nose and tongue of different chicken samples were investigated.The correlation analysis showed that the physical characteristics especially the cohesiveness,springiness,resilience of the sample determined oral processing behavior.The addition of chicken skin played a role in lubrication during oral processing.The particle size of the bolus was heightened at the early stage,and the fluidity was enhanced in the end,which reduced the chewing time to the swallowing point and raised the aromatic compounds signal of electronic nose.But the effect of chicken skin on chicken thigh with relatively high fat content,was opposite in electronic nose,which had a certain masking effect on the perception of umami and sweet taste.In conclusion,fat played a critical role in chicken oral processing and chicken thigh had obvious advantages in comprehensive evaluation of soft-boiled chicken,which was more popular among people.

Age of Information for Short-Packet Covert Communication with Time Modulated Retrodirective Array

In this paper,the covert age of information(CAoI),which characterizes the timeliness and covertness performance of communication,is first investigated in the short-packet covert communication with time modulated retrodirective array(TMRDA).Specifically,the TMRDA is designed to maximize the antenna gain in the target direction while the side lobe is sufficiently suppressed.On this basis,the covertness constraint and CAoI are derived in closed form.To facilitate the covert transmission design,the transmit power and block-length are jointly optimized to minimize the CAoI,which demonstrates the trade-off between covertness and timelessness.Our results illustrate that there exists an optimal block-length that yields the minimum CAoI,and the presented optimization results can achieve enhanced performance compared with the fixed block-length case.Additionally,we observe that smaller beam pointing error at Bob leads to improvements in CAoI.

Literature classification and its applications in condensed matter physics and materials science by natural language processing

The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

Implantable Electrochemical Microsensors for In Vivo Monitoring of Animal Physiological Information

In vivo monitoring of animal physiological information plays a crucial role in promptly alerting humans to potential diseases in animals and aiding in the exploration of mechanisms underlying human diseases.Currently,implantable electrochemical microsensors have emerged as a prominent area of research.These microsensors not only fulfill the technical requirements for monitoring animal physiological information but also offer an ideal platform for integration.They have been extensively studied for their ability to monitor animal physiological information in a minimally invasive manner,characterized by their bloodless,painless features,and exceptional performance.The development of implantable electrochemical microsensors for in vivo monitoring of animal physiological information has witnessed significant scientific and technological advancements through dedicated efforts.This review commenced with a comprehensive discussion of the construction of microsensors,including the materials utilized and the methods employed for fabrication.Following this,we proceeded to explore the various implantation technologies employed for electrochemical microsensors.In addition,a comprehensive overview was provided of the various applications of implantable electrochemical microsensors,specifically in the monitoring of diseases and the investigation of disease mechanisms.Lastly,a concise conclusion was conducted on the recent advancements and significant obstacles pertaining to the practical implementation of implantable electrochemical microsensors.