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.

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.

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.

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.

Enhancing Multicriteria-Based Recommendations by Alleviating Scalability and Sparsity Issues Using Collaborative Denoising Autoencoder

A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.

Recommendation Method for Contrastive Enhancement of Neighborhood Information

Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.

Enhancing personalized exercise recommendation with student and exercise portraits

The exercise recommendation system is emerging as a promising application in online learning scenarios,providing personalized recommendations to assist students with explicit learning directions.Existing solutions generally follow a collaborative filtering paradigm,while the implicit connections between students(exercises)have been largely ignored.In this study,we aim to propose an exercise recommendation paradigm that can reveal the latent connections between student-student(exercise-exercise).Specifically,a new framework was proposed,namely personalized exercise recommendation with student and exercise portraits(PERP).It consists of three sequential and interdependent modules:Collaborative student exercise graph(CSEG)construction,joint random walk,and recommendation list optimization.Technically,CSEG is created as a unified heterogeneous graph with students’response behaviors and student(exercise)relationships.Then,a joint random walk to take full advantage of the spectral properties of nearly uncoupled Markov chains is performed on CSEG,which allows for full exploration of both similar exercises that students have finished and connections between students(exercises)with similar portraits.Finally,we propose to optimize the recommendation list to obtain different exercise suggestions.After analyses of two public datasets,the results demonstrated that PERP can satisfy novelty,accuracy,and diversity.

The use of oral human immunodeficiency virus pre-exposure prophylaxis in pregnant and lactating women in sub-Saharan Africa:considerations,barriers,and recommendations Author links open overlay panel

In sub-Saharan Africa(SSA),63%of new human immunodeficiency virus(HIV)infections in 2021 were among women,particularly adolescent girls,and young women.There is a high incidence of HIV among pregnant and lactating women(PLW)in SSA.It is estimated that the risk of HIV-acquisition during pregnancy and the postpartum period more than doubles.In this article,we discuss the safety and effectiveness of drugs used for oral HIV pre-exposure prophylaxis(PrEP),considerations for initiating PrEP in PLW,the barriers to initiating and adhering to PrEP among them and suggest recommendations to address these barriers.Tenofovir/emtricitabine,the most widely used combination in SSA,is safe,clinically effective,and cost-effective among PLW.Any PLW who requests PrEP and has no medical contraindications should receive it.PrEP users who are pregnant or lactating may experience barriers to starting and adhering for a variety of reasons,including personal,pill-related,and healthcare facility-related issues.To address the barriers,we recommend an increased provision of information on PrEP to the women and the communities,increasing and/or facilitating access to PrEP among the PLW,and developing strategies to increase adherence.

Levels of evidence and grades of recommendation supporting European society for medical oncology clinical practice guidelines

Background:The European Society for Medical Oncology(ESMO)guidelines are among the most comprehensive and widely used clinical practice guidelines(CPGs)globally.However,the level of scientific evidence supporting ESMO CPG recommendations has not been systematically investigated.This study assessed ESMO CPG levels of evidence(LOE)and grades of recommendations(GOR),as well as their trends over time across various cancer settings.Methods:We manually extracted every recommendation with the Infectious Diseases Society of America(IDSA)classification from each CPG.We examined the distribution of LOE and GOR in all available ESMO CPG guidelines across different topics and cancer types.Results:Among the 1,823 recommendations in the current CPG,30%were classified as LOEⅠ,and 43%were classified as GOR A.Overall,there was a slight decrease in LOEⅠ(−2%)and an increase in the proportion of GOR A(+1%)in the current CPG compared to previous versions.The proportion of GOR A recommendations based on higher levels of evidence such as randomized trials(LOEⅠ–Ⅱ)shows a decrease(71%vs.63%,p=0.009)while recommendations based on lower levels of evidence(LOEⅢ–Ⅴ)show an increase(29%vs.37%,p=0.01)between previous and current version.In the current versions,the highest proportion of LOEⅠ(42%)was found in recommendations related to pharmacotherapy,while the highest proportion of GOR A recommendations was found in the areas of pathology(50%)and diagnostic(50%)recommendations.Significant variability in LOEⅠand GOR A recommendations and their changes over time was observed across different cancer types.Conclusion:One-third of the current ESMO CPG recommendations are supported by the highest level of evidence.More well-designed randomized clinical trials are needed to increase the proportion of LOEⅠand GOR A recommendations,ultimately leading to improved outcomes for cancer patients.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Adherence to Advisory Committee on Immunization Practices in diabetes mellitus patients in Saudi Arabia:A multicenter retrospective study

BACKGROUND Patients with diabetes mellitus(DM)are predisposed to an increased risk of infection signifying the importance of vaccination to protect against its potentially severe complications.The Centers for Disease Control and Prevention/Advisory Committee on Immunization Practices(CDC/ACIP)issued immunization recommendations to protect this patient population.AIM To assess the adherence of patients with DM to the CDC/ACIP immunization recommendations in Saudi Arabia and to identify the factors associated with the vaccine adherence rate.METHODS An observational retrospective study conducted in 2023 was used to collect data on the vaccination records from 13 diabetes care centers in Saudi Arabia with 1000 eligible patients in phase I with data collected through chart review and 709 patients in phase II through online survey.RESULTS Among participants,10.01%(n=71)had never received any vaccine,while 85.89%(n=609)received at least one dose of the coronavirus disease 2019(COVID-19)vaccine,and 34.83%(n=247)had received the annual influenza vaccine.Only 2.96%(n=21),2.11%(n=15),and 1.12%(n=8)received herpes zoster,tetanus,diphtheria,and pertussis(Tdap),and human papillomavirus(HPV)vaccines,respectively.For patients with DM in Saudi Arabia,the rate of vaccination for annual influenza and COVID-19 vaccines was higher compared to other vaccinations such as herpes zoster,Tdap,pneumococcal,and HPV.Factors such as vaccine recommendations provided by family physicians or specialists,site of care,income level,DM-related hospitalization history,residency site,hemoglobin A1c(HbA1c)level,and health sector type can significantly influence the vaccination rate in patients with DM.Among non-vaccinated patients with DM,the most reported barriers were lack of knowledge and fear of side effects.This signifies the need for large-scale research in this area to identify additional factors that might facilitate adherence to CDC/ACIP vaccine recommendations in patients with DM.CONCLUSION In Saudi Arabia,patients with DM showed higher vaccination rates for annual influenza and COVID-19 vaccines compared to other vaccinations such as herpes zoster,Tdap,pneumococcal,and HPV.Factors such as vaccine recommendations provided by family physicians or specialists,the site of care,income level,DM-related hospitalization history,residency site,HbA1c level,and health sector type can significantly influence the vaccination rate in patients with DM.

Research and design of matrix operation accelerator based on reconfigurable array

In the case of massive data,matrix operations are very computationally intensive,and the memory limitation in standalone mode leads to the system inefficiencies.At the same time,it is difficult for matrix operations to achieve flexible switching between different requirements when implemented in hardware.To address this problem,this paper proposes a matrix operation accelerator based on reconfigurable arrays in the context of the application of recommender systems(RS).Based on the reconfigurable array processor(APR-16)with reconfiguration,a parallelized design of matrix operations on processing element(PE)array is realized with flexibility.The experimental results show that,compared with the proposed central processing unit(CPU)and graphics processing unit(GPU)hybrid implementation matrix multiplication framework,the energy efficiency ratio of the accelerator proposed in this paper is improved by about 35×.Compared with blocked alternating least squares(BALS),its the energy efficiency ratio has been accelerated by about 1×,and the switching of matrix factorization(MF)schemes suitable for different sparsity can be realized.

Integrated Development of Tea and Tourism in Taishan Mountain Tea Valley in the Context of Rural Revitalization

In recent years,the integrated development of the tea and tourism has become an important way to promote the rural revitalization of Tai'an tea-producing areas,but the state of industrial upgrading still needs to be improved.In order to better integrate tea and tourism in Tea Valley,this paper uses questionnaire and SWOT analysis to study.It found the following problems:(i)45.7%of tourists were interested in tea culture,but they know litle about it,and the integration of tea culture and tea tourism products is low.(i)50.54%of the tourists were interested in the tea picking experience project and wanted to add the tea food experience item.(ii)The acceptance of 45.7%of tourists to scenic spots was 1-2 h,which shows that the quality of transportation service facilities and personnel needs to be improved.Combined with the SWOT analysis,it is found that the scenic spot is rich in resources,convenient transportation,profound cultural heritage,and good govern-ment policy support conditions,which is suitable for the SO strategy(growth development strategy),so the scenic spot relies on internal ad-vantages and seizes external opportunities to develop the integration of tea and tourism.Based on the analysis results,it came up with the perti-nent recommendations for the key problems:(i)The infrastructure service is not well established.It is suggested that scenic spots should in-crease investment,regularly train service personnel to improve their quality,and improve transportation services,accommodation,catering and public service hardware facilities.(i)Tea tourism products are insufficient.It is suggested that tea tourism products should be innovatively designed and tea tourism experiential products and tourism commodities should be planned.(i)Tea culture and tourism lack organic combi-nation.It is recommended to deeply excavate tea culture,and take consideration of both spiritual and material aspects.The specific methods include building teahouses,Taishan Mountain(Mount Tai)tea culture throughout the whole process of tea production,development and pro-cessing,and AR tea culture experience hall can be built to make tourists feel tea culture from dfferent perspectives.(iv)The publicity is in-sufficient.Strategies should be made in three aspects:improving the Internet media,taking into account the traditional media,and expanding the market based on the surrounding areas.(v)The tea industry chain is short.It is recommended to add four new industrial chain designs of"tea+accommodation,tea+catering,tea+Intemet,tea+training",hoping to bring some inspiration and ideas to the managers of scenic spots.

澜湄开放教育及柬埔寨远程教育的未来

This journal article delves into the future of distance learning in Cambodia and the potential for collaboration with the Mekong-Lancang Open Education initiative. It explores how distance learning can successfully address the current issues the Cambodian educational system is currently facing. The article discusses the goals and opportunities for collaboration that the Mekong-Lancang Open Education initiative presents, as well as the difficulties and potential solutions involved in implementing distance learning in Cambodia. Moreover,the article also offers insightful case studies and best practices from other countries, offering valuable insights and lessons for Cambodia. Lastly, the article concludes with policy recommendations for the future of distance learning in Cambodia. Future research and studies should concentrate on continually evaluating and improving the Mekong-Lancang Open Education Initiative to ensure that it effectively meets the educational needs of students and educators.

Analysis of the Impact of Road Traffic Safety Facilities on Traffic Safety

The development of the national economy is closely tied to infrastructure construction.In recent years,China has seen a significant increase in the number and scale of road construction projects,aimed at facilitating the flow of goods and enabling convenient travel for the masses.However,this surge in road construction also raises concerns about road traffic safety.Road traffic safety facilities play a crucial role in warning and protecting against traffic accidents.To ensure their effective implementation,this paper analyzes the essence of road traffic facilities and their impact on traffic safety.By identifying challenges in the application of traffic safety facilities and adhering to safety facility application principles,suggestions are proposed to enhance traffic safety management.

Chest Radiography: General Practitioners’ Compliance with Recommendations

Introduction: Chest radiography is the most frequently prescribed imaging test in general practice in France. We aimed to assess the extent to which general practitioners follow the recommendations of the French National Authority for Health in prescribing chest radiography. Methodology: We conducted a retrospective analysis study, in two radiology centers belonging to the same group in Saint-Omer and Aire-sur-la-Lys, of requests for chest radiography sent by general practitioners over the winter period between December 22, 2013, and March 21, 2014, for patients aged over 18 years. Results: One hundred and seventy-seven requests for chest X-rays were analyzed, 71.75% of which complied with recommendations. The most frequent reason was the search for bronchopulmonary infection, accounting for 70.08% of prescriptions, followed by 11.2% for requests to rule out pulmonary neoplasia, whereas the latter reason did not comply with recommendations. Chest X-rays contributed to a positive diagnosis in 28.81% of cases. The positive diagnosis was given by 36.22% of the recommended chest X-rays, versus 10% for those not recommended. Conclusion: In most cases, general practitioners follow HAS recommendations for prescribing chest X-rays. Non-recommended chest X-rays do not appear to make a major contribution to diagnosis or patient management, confirming the value of following the recommendations of the French National Authority for Health.

Hybrid Scalable Researcher Recommendation System Using Azure Data Lake Analytics

This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].

Panel Recommends 12 Steps for Cutting Cancer Deaths

If more Americans quit smoking,lost a little weight and started eating bet-ter,at least 60,000 cancer deaths could be avoided each year,according tO a re-port issued Monday by a government advisory panel. 假如有更多的美国人戒烟,适当减肥,并开始吃得更健康些,至少每年可以减少60 000死于癌症的病例。政府顾问团周一发表的一则报告如是说。