Recommended Concentration Limits of Typical Indoor Air Contaminants

From the view of both objective and subjective factors,the indoor air quality(IAQ)evaluation was considered.Carbon dioxide(CO2)and formaldehyde(HCHO)were selected as the typical contaminants of indoor air,and the evaluation method of logarithmic index was adopted as the evaluation means of IAQ.Then the recommended limits(RL)of typical contaminants CO2 and HCHO were given through analysis and calculation.The limits of CO2 and HCHO in Indoor Air Quality Standard of China or other existing standards probably correspond to the level of PD=25(%).The result shows that the existing standards fail to meet the requirement of the definition of "acceptable indoor air quality",that is to say,less than 20% of the people express dissatisfaction.When PD=20%,RL of CO2 and HCHO are 728×10-6 and 0.068×10-6 respectively,which are stricter than the limits in the existing standards.The method proposed in this paper is applicable to 13.1%≤PD≤86.7%.

Network Pharmacology Approach to Investigate the Preventive Mechanism of Hunan Expert Group Recommended Chinese Medicine Prevention No.2 Prescription Against COVID-19

Objective To explore the possible preventive mechanism of Hunan expert group recommended Chinese medicine prescription of No.2(Pre-No.2)against coronavirus disease 2019(COVID-19)by network pharmacology method.Methods The target proteins of effective components and active compounds in Pre-No.2 were screened by searching the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP).A component-target-disease interaction network of Pre-No.2 was constructed by Cytoscape 3.7.2,gene ontology(GO)analysis,and Kyoto encyclopedia of genes and genomes(KEGG)analysis of target protein pathway by DAVID.Results A total of 163 compounds and 278 target protein targets in Pre-No.2 were collected from the TCMSP database.Kaempferol,wogonin,7-methoxy-2-methyl isoflavone,formononetin,isorhamnetin,and licochalcone A were the most frequent targets in the regulatory network.GO enrichment analysis showed that Pre-No.2 regulated response to virus,viral processes,humoral immune responses,defense responses to virus and viral entry into host cells.KEGG enrichment analysis showed that the formula regulated the NF-κB signaling pathway,B cell receptor signaling pathway,viral carcinogenesis,T cell signaling pathway and FcγR-mediated phagocytosis signaling pathway.Conclusions Pre-No.2 may play a preventive role against COVID-19 through regulation of the Toll-like signaling,T cell signaling,B cell signaling and other signaling pathways.It may regulate the immune system to protect against anti-influenza virus.

Recommended Books

Book 1: (Editor-in-Chief: Shi Yafeng; Published by Elsevier and Science Press Beijing in 2008, 539 pages) Glaciers and Related Environments in China Since the professional institution for glaciology attached to the Chinese Academy of Sciences was established in 1958, studies of glaciers in alpine regions, and of Quaternary glaciations throughout

First Batch of Anhui Brandname Products Recommended by Anhui Provincial People's Government

Product NameMagang brand high quality carbon steel,common low carbon steel-made cold/hotrolling coil rod(without twisting control)Magang brand integral roll steel-madepassenger train wheel(φ915mm) forrailway useJingjing brand electrolytic

Top Enterprises Recommended to Africa

Closer sino-African relations have encouraged more Chinese enterprises to invest in African countries.Statistics show that more than 2,000 Chinese enterprises had invested in the continent by 2012.

Evaluation of a model recommended for N fertilizer application in irrigated rice

The response of rice to N fertilizer applicationhas shown that high rates of N application donot always ensure a proportional increase inyield due to high N losses. A model, ORYZA-0 was developed by ten Berge for designingoptimum N fertilizer management strategy inrice. We evaluated the performance ofORYZA-0 in Jinhua, Zhejiang Province. ORYZA-0 includes N uptakes, partition-ing of N among the organs, and utilization ofleaf N in converting solar energy to dry mat-ter. It can predict the amount and time of Nfertilizer application to achieve a maximumbiomass or yield combining with Price algo-rithm optimization procedure.

Remaining issues of recommended management in current guidelines for asymptomatic common bile duct stones

Current guidelines for treating asymptomatic common bile duct stones(CBDS)recommend stone removal,with endoscopic retrograde cholangiopan-creatography(ERCP)being the first treatment choice.When deciding on ERCP treatment for asymptomatic CBDS,the risk of ERCP-related complications and outcome of natural history of asymptomatic CBDS should be compared.The incidence rate of ERCP-related complications,particularly of post-ERCP pancreatitis for asymptomatic CBDS,was reportedly higher than that of symptomatic CBDS,increasing the risk of ERCP-related complications for asymptomatic CBDS compared with that previously reported for biliopancreatic diseases.Although studies have reported short-to middle-term outcomes of natural history of asymptomatic CBDS,its long-term natural history is not well known.Till date,there are no prospective studies that determined whether ERCP has a better outcome than no treatment in patients with asymptomatic CBDS or not.No randomized controlled trial has evaluated the risk of early and late ERCP-related complications vs the risk of biliary complications in the wait-and-see approach,suggesting that a change is needed in our perspective on endoscopic treatment for asymptomatic CBDS.Further studies examining long-term complication risks of ERCP and wait-and-see groups for asymptomatic CBDS are warranted to discuss whether routine endoscopic treatment for asymptomatic CBDS is justified or not.

Benign Strategy for Recommended Location Service Based on Trajectory Data

A new collaborative filtered recommendation strategy oriented to trajectory data is proposed for communication bottlenecks and vulnerability in centralized system structure location services. In the strategy based on distributed system architecture, individual user information profiles were established using daily trajectory information and neighboring user groups were established using density measure. Then the trajectory similarity and profile similarity were calculated to recommend appropriate location services using collaborative filtering recommendation method. The strategy was verified on real position data set. The proposed strategy provides higher quality location services to ensure the privacy of user position information.

COVID-19 Measures Recommended for Revenue Authorities

1.Introduction The COVID-19 pandemic is affecting the lives of the world population in various ways and has resulted in an unforeseen scale of disruption of activities across the globe.Its emergence has health and economic implications that impact individuals,organizations and sovereign states which is inclusive of the stakeholders in a tax system.Thus,revenue authorities need to take actions to protect and ease the burden on its external and internal stakeholders.

A multilayer network diffusion-based model for reviewer recommendation

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

A review of public health guidelines for postpartum physical activity and sedentary behavior from around the world

Background:The period following pregnancy is a critical time window when future habits with respect to physical activity(PA) and sedentary behavior(SB) are established;therefore,it warrants guidance.The purpose of this scoping review was to summarize public health-oriented country-specific postpartum PA and SB guidelines worldwide.Methods:To identity guidelines published since 2010,we performed a(a) systematic search of 4 databases(CINAHL,Global Health,PubMed,and SPORTDiscus),(b) structured repeatable web-based search separately for 194 countries,and(c) separate web-based search.Only the most recent guideline was included for each country.Results:We identified 22 countries with public health-oriented postpartum guidelines for PA and 11 countries with SB guidelines.The continents with guidelines included Europe(n=12),Asia(n=5),Oceania(n=2),Africa(n=1),North America(n=1),and South America(n=1).The most common benefits recorded for PA included weight control/management(n=10),reducing the risk of postpartum depression or depressive symptoms(n=9),and improving mood/well-being(n=8).Postpartum guidelines specified exercises to engage in,including pelvic floor exercises(n=17);muscle strengthening,weight training,or resistance exercises(n=13);aerobics/general aerobic activity(n=13);walking(n=11);cycling(n=9);and swimming(n=9).Eleven guidelines remarked on the interaction between PA and breastfeeding;several guidelines stated that PA did not impact breast milk quantity(n=7),breast milk quality(n=6),or infant growth(n=3).For SB,suggestions included limiting long-term sitting and interrupting sitting with PA.Conclusion:Country-specific postpartum guidelines for PA and SB can help promote healthy behaviors using a culturally appropriate context while providing specific guidance to public health practitioners.

An Adaptive Program Recommendation System for Multi-User Sharing Environment

More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and they cannot make effective use of the mixed information generated by multi-user when exploring users’potential interests.To solve these problems,this paper proposes an adaptive program recommendation system for multi-user sharing environment.Specifically,we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions,which can separate the profile of target user from mixed log records.Subsequently,an online recommendation module with adaptive timevarying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module.On one hand,to learn the dynamic changes in user-interest,a time-varying linear upper confidence bound(LinUCB)based on personal information is designed.On the other hand,to reduce the risk of exploration,a timeinvariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user,which is integrated into the time-varying LinUCB by cross-weighting strategy.Finally,experimental results validate the efficiency of the proposed scheme.

Urban Traffic Control Meets Decision Recommendation System:A Survey and Perspective

Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems.Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and expertise.However,this process is cumbersome,labor-intensive,and cannot be applied on a large network scale.Recent studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and scalability.Such a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been conducted.In this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field.

Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.

Improving Diversity with Multi-Loss Adversarial Training in Personalized News Recommendation

Users’interests are often diverse and multi-grained,with their underlying intents even more so.Effectively captur-ing users’interests and uncovering the relationships between diverse interests are key to news recommendation.Meanwhile,diversity is an important metric for evaluating news recommendation algorithms,as users tend to reject excessive homogeneous information in their recommendation lists.However,recommendation models themselves lack diversity awareness,making it challenging to achieve a good balance between the accuracy and diversity of news recommendations.In this paper,we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity.Unlike most existing works that solely optimize accuracy or employ more features to meet diversity,the proposed algorithm leverages the diversity-aware capability of the model.First,we introduce an augmented user model to fully capture user intent and the behavioral guidance they might undergo as a result.Specifically,we focus on the relationship between the original clicked news and the augmented clicked news.Moreover,we propose an effective adversarial training method for diversity(AT4D),which is a pluggable component that can enhance both the accuracy and diversity of news recommendation results.Extensive experiments on real-world datasets confirm the efficacy of the proposed algorithm in improving both the accuracy and diversity of news recommendations.

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

Recommendation System Based on Perceptron and Graph Convolution Network

The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data.This paper presents a new approach to address such issues,utilizing the graph convolution network to extract association relations.The proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction layer.The embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation layer.The forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph convolution.Furthermore,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction layer.The score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,respectively.Finally,the prediction score of users to items is obtained.The recall rate and normalized discounted cumulative gain were used as evaluation indexes.The proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms.

Diversified and compatible web APIs recommendation based on game theory in IoT

With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can be accessible remotely.In this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile applications.However,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs selected.Considering this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this paper.First of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation subgraphs.Afterwards,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search problem.At last,a set of experiments are designed and implemented on a real dataset crawled from www.programmableweb.com.Experimental results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.

Enhancing ChatGPT’s Querying Capability with Voice-Based Interaction and CNN-Based Impair Vision Detection Model

This paper presents an innovative approach to enhance the querying capability of ChatGPT,a conversational artificial intelligence model,by incorporating voice-based interaction and a convolutional neural network(CNN)-based impaired vision detection model.The proposed system aims to improve user experience and accessibility by allowing users to interact with ChatGPT using voice commands.Additionally,a CNN-based model is employed to detect impairments in user vision,enabling the system to adapt its responses and provide appropriate assistance.This research tackles head-on the challenges of user experience and inclusivity in artificial intelligence(AI).It underscores our commitment to overcoming these obstacles,making ChatGPT more accessible and valuable for a broader audience.The integration of voice-based interaction and impaired vision detection represents a novel approach to conversational AI.Notably,this innovation transcends novelty;it carries the potential to profoundly impact the lives of users,particularly those with visual impairments.The modular approach to system design ensures adaptability and scalability,critical for the practical implementation of these advancements.Crucially,the solution places the user at its core.Customizing responses for those with visual impairments demonstrates AI’s potential to not only understand but also accommodate individual needs and preferences.