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.

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.

Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.

Smart object recommendation based on topic learning and joint features in the social internet of things

With the extensive integration of the Internet,social networks and the internet of things,the social internet of things has increasingly become a significant research issue.In the social internet of things application scenario,one of the greatest challenges is how to accurately recommend or match smart objects for users with massive resources.Although a variety of recommendation algorithms have been employed in this field,they ignore the massive text resources in the social internet of things,which can effectively improve the effect of recommendation.In this paper,a smart object recommendation approach named object recommendation based on topic learning and joint features is proposed.The proposed approach extracts and calculates topics and service relevant features of texts related to smart objects and introduces the“thing-thing”relationship information in the internet of things to improve the effect of recommendation.Experiments show that the proposed approach enables higher accuracy compared to the existing recommendation methods.

Recommending Who to Follow on Twitter Based on Tweet Contents and Social Connections

In this paper, we examine methods that can provide accurate results in a form of a recommender system within a social networking framework. The social networking site of choice is Twitter, due to its interesting social graph connections and content characteristics. We built a recommender system which recommends potential users to follow by analyzing their tweets using the CRM114 regex engine as a basis for content classification. The evaluation of the recommender system was based on a dataset generated from real Twitter users created in late 2009.

Graph Convolutional Network-Based Repository Recommendation System

GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community informationand the fact that the scoring metrics used to calculate the matching degree between developers and repositoriesare developed manually and rely too much on human experience, leading to poor recommendation results. Toaddress these problems, we design a questionnaire to investigate which repository information developers focus onand propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solveinsufficient information utilization in open-source communities, we construct a Developer-Repository networkusing four types of behavioral data that best reflect developers’ programming preferences and extract features ofdevelopers and repositories from the repository content that developers focus on. Then, we design a repositoryrecommendation model based on a multi-layer graph convolutional network to avoid the manual formulation ofscoringmetrics. Thismodel takes the Developer-Repository network, developer features and repository features asinputs, and recommends the top-k repositories that developers are most likely to be interested in by learning theirpreferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-sourcerepository recommendation methods, GCNRec achieves higher precision and hit rate.

Towards machine-learning-driven effective mashup recommendations from big data in mobile networks and the Internet-of-Things

A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually limited.To enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a mashup.The emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of mashups.Such a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage data.Thus,the question of how to determine the most suitable mashups from big data has become a challenging problem.In this paper,we propose a mashup recommendation framework from big data in mobile networks and the IoT.The proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix factorization.We employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the mashups.We also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization algorithm.We then crawl through a real-world large mashup dataset and perform experiments.The experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.

Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.

Automatic Clustering of User Behaviour Profiles for Web Recommendation System

Web usage mining,content mining,and structure mining comprise the web mining process.Web-Page Recommendation(WPR)development by incor-porating Data Mining Techniques(DMT)did not include end-users with improved performance in the obtainedfiltering results.The cluster user profile-based clustering process is delayed when it has a low precision rate.Markov Chain Monte Carlo-Dynamic Clustering(MC2-DC)is based on the User Behavior Profile(UBP)model group’s similar user behavior on a dynamic update of UBP.The Reversible-Jump Concept(RJC)reviews the history with updated UBP and moves to appropriate clusters.Hamilton’s Filtering Framework(HFF)is designed tofilter user data based on personalised information on automatically updated UBP through the Search Engine(SE).The Hamilton Filtered Regime Switching User Query Probability(HFRSUQP)works forward the updated UBP for easy and accuratefiltering of users’interests and improves WPR.A Probabilistic User Result Feature Ranking based on Gaussian Distribution(PURFR-GD)has been developed to user rank results in a web mining process.PURFR-GD decreases the delay time in the end-to-end workflow for SE personalization in various meth-ods by using the Gaussian Distribution Function(GDF).The theoretical analysis and experiment results of the proposed MC2-DC method automatically increase the updated UBP accuracy by 18.78%.HFRSUQP enabled extensive Maximize Log-Likelihood(ML-L)increases to 15.28%of User Personalized Information Search Retrieval Rate(UPISRT).For feature ranking,the PURFR-GD model defines higher Classification Accuracy(CA)and Precision Ratio(PR)while uti-lising minimum Execution Time(ET).Furthermore,UPISRT's ranking perfor-mance has improved by 20%.

Automatic Crop Expert System Using Improved LSTM with Attention Block

Agriculture plays an important role in the economy of any country.Approximately half of the population of developing countries is directly or indirectly connected to the agriculture field.Many farmers do not choose the right crop for cultivation depending on their soil type,crop type,and climatic requirements like rainfall.This wrong decision of crop selection directly affects the production of the crops which leads to yield and economic loss in the country.Many parameters should be observed such as soil characteristics,type of crop,and environmental factors for the cultivation of the right crop.Manual decision-making is time-taking and requires extensive experience.Therefore,there should be an automated system for the right crop recommendation to reduce human efforts and loss.An automated crop recommender system should take these parameters as input and suggest the farmer’s right crop.Therefore,in this paper,a long short-term memory Network with an attention block has been proposed.The proposed model contains 27 layers,the first of which is a feature input layer.There exist 25 hidden layers between them,and an output layer completes the structure.Through these levels,the proposed model enables a successful recommendation of the crop.Additionally,the dropout layer’s regularization properties aids in reduction of overfitting of the model.In this paper,a customized novel long short-term memory(LSTM)model is proposed with a residual attention block that recommends the right crop to farmers.Evaluation metrics used for the proposed model include f1-score,recall,precision,and accuracy attaining values as 95.69%,96.56%,96.9%,and 97.26%respectively.

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Nowadays,commercial transactions and customer reviews are part of human life and various business applications.The technologies create a great impact on online user reviews and activities,affecting the business process.Customer reviews and ratings are more helpful to the new customer to purchase the product,but the fake reviews completely affect the business.The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information.Therefore,in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity.Here,Amazon Product Kaggle dataset information is utilized for investigating the customer review.The collected information is analyzed and processed by batch normalized capsule networks(NCN).The network explores the user reviews according to product details,time,price purchasing factors,etc.,ensuring product quality and ratings.Then effective recommendation system is developed using a butterfly optimized matrix factorizationfiltering approach.Then the system’s efficiency is evaluated using the Rand Index,Dunn index,accuracy,and error rate.

Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads.But how to alleviate sparsity and further enhance accuracy is still challenging.Addressing at these issues,we propose to fuse spatio-temporal contexts into deep factorization machine(STC_DeepFM)offline for pick-up area recommendation,and within the area to recommend pick-up points online using factorization machine(FM).Firstly,we divide the urban area into several grids with equal size.Spatio-temporal contexts are destilled from pick-up points or points-of-interest(POIs)belonged to the preceding grids.Secondly,the contexts are integrated into deep factorization machine(DeepFM)to mine high-order interaction relationships from grids.And a novel algorithm named STC_DeepFM is presented for offline pick-up area recommendation.Thirdly,we devise the architecture of offline-to-online(O2O)recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and efficiency.Some experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts,different recommendation models,and the O2O architecture.The results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods,and the O2O architecture achieves excellent real-time performance.

Gamified Learning Systems’Personalized Feedback Report Dashboards via Custom Machine Learning Algorithms and Recommendation Systems

Gamification in education enables for the holistic optimization of the learning process,empowering learners to ameliorate their digital,cognitive,emotional and social skills,via their active experimentation with game design elements,accompanying pertinent pedagogical objectives of interest.This paper focuses on a cross-platform,innovative,gamified,educational learning system product,funded by the Hellenic Republic Ministry of Development and Investments:howlearn.By applying gamification techniques,in 3D virtual environments,within which,learners fulfil STEAM(Science,Technology,Engineering,Arts and Mathematics)-related Experiments(Simulations,Virtual Labs,Interactive Storytelling Scenarios,Decision Making Case Studies),howlearn covers learners’subject material,while,simultaneously,functioning,as an Authoring Gamification Tool and as a Game Metrics Repository;users’metrics are being,dynamically,analyzed,through Machine Learning Algorithms.Consequently,the System learns from the data and learners receive Personalized Feedback Report Dashboards of their overall performance,weaknesses,interests and general class competency.A Custom Recommendation System(Collaborative Filtering,Content-Based Filtering)then supplies suggestions,representing the best matches between Experiments and learners,while also focusing on the reinforcement of the learning weaknesses of the latter.Ultimately,by optimizing the Accuracy,Performance and Predictive capability of the Personalized Feedback Report,we provide learners with scientifically valid performance assessments and educational recommendations,thence intensifying sustainable,learner-centered education.

Soil Fertility Properties of Tobacco Growing Area in Jiyuan

Soil fertility properties of the main tobacco growing area in Jiyuan （Shaoyuan,Wangwu,Xiaye and Daiyu） in west Henan Province were analyzed. Results showed that Jiyuan was one of the potential areas which could produce tobacco leaves with high quality. Its main properties of soli fertility were as follows：the content of total nitrogen,alkali-hydrolysable nitrogen,olsen-phosphorus and organic matters were suitable for high-quality tobacco production; especially,all the flue-cured tobacco growing areas in Henan Province were rich in available potassium; besides,the concentrate of water soluble chloride ion was at reasonable level. The problem was that the micro-elements such as Zn-DTPA and available boron content were at a low level in individual areas. Based on this survey,the recommendations for fertilization in Jiyuan such as stabilizing nitrogen rate,increasing phosphorus,stabilizing potassium,and applying boron commonly and supplementing zinc for the deficient soils were put forward.

Ontology-based framework for personalized recommendation in digital libraries

To promote information service ability of digital libraries, a browsing and searching personalized recommendation framework based on the use of ontology is described, where the advantages of ontology are exploited in different parts of the retrieval cycle including query-based relevance measures, semantic user preference representation and automatic update, and personalized result ranking. Both the usage and information resources can be exploited to extract useful knowledge from the way users interact with a digital library. Through combination and mapping between the extracted knowledge and domain ontology, semantic content retrieval between queries and documents can be utilized. Furthermore, ontology-based conceptual vector of user preference can be applied in personalized recommendation feedback.

Research on Economic Impact of Super Rice on Rice Growers and Contribution to Food Security

Super rice is an essendal part of China＇s rice production. Through survey on actual situation of 1568 households of rice growers in Heilongjiang, Hunan and Zhejiang provinces, this paper focused on influence of super rice development on increase of China＇s grain yield, influence on increase of rice growers＇ economic in- come, difference in production cost and profit between the North and the South, as well as profit percentage of super rice in production, processing, and sales. It obtained following results： rice price determines rice growers＇ income; expansion of super rice extension area plays a great role in increase of China＇s grain yield; by 2015 and 2020, keeping the yield of other crops not changed, merely the extension of super rice can increase grain for 5 million tons and 11 million tons separately; super rice significantly increases rice growers＇ economic income; for production cost of super rice, the South is higher than the North, and the profit ratio of cost is up to 35.54% on average; with respect of profit in production, processing, and sales, the ratio is 1：2：1.5; with the yield of other crops unchanged, every increase of 1% in area percentage of super rice to rice will additionally produce 1 million tons of grain for China, which is equivalent to saving the yield of 133 300 hm2 farmland and can additional feed 3.5 million people. In view of importance of super rice production, at the same time of strengthening research on super rice variety, it is required to accelerate expanding production area of super rice in suitable areas. Since the development of super rice can support China＇s ration demand of increasing population, China should make effort to realize ＂one yuan for one mu＂ financial subsidy for super rice of main grain production provinces and counties. Besides, China should establish special financial plan for extension of super rice.

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.