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The purposes of this paper are optimization of high speed reducer in electric vehicles based on the analysis of lubrication and verification of simulation accuracy and optimization results.

The traditional CFD method presents poor applicability to complex geometric problems due to grid deformity. Therefore, moving particle semi-implicit (MPS) method is applied in this study to simulate lubrication of the reducer and analyze the influence of input speed and lubrication system design on the distribution. According to the results, the reducer is optimized. Meanwhile, the experiments for lubrication and churning power loss is carried out to verify the accuracy of simulation and optimization effects.

The flow field of lubricant inside the reducer is obtained. The lubrication system of reducer needs to be improved. Simulation and experiment show that the optimization is sufficient and efficient.

According to the simulation of lubrication, the reducer is optimized. The lubrication experimental setup is established. The conclusion of paper can provide the method and tool for reducer in electric vehicle.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0123/

This article aims to analyze the incorporation of harm reduction health workers with lived experience in all services focused on the care for drug users in Belo Horizonte’s mental health network, one of the largest Brazilian Psychosocial Care Networks (Rede de Atenção Psicossocial [RAPS]), and reports the findings of a qualitative study on the life stories, recovery journeys and the professional practice of harm reducers in Belo Horizonte.

For the development of this research, the authors chose the following methodological tools: documentary research; follow-up of harm reduction interventions; access to harm reducers’ field diaries; joint writing of the life stories of the harm reducers.

It indicates that these professionals, as harm reducers empowered by their life experience, have developed various elements and active ingredients of peer support. Their practices and the results of their actions have consolidated their inclusion as Belo Horizonte RAPS’ health workers and highlight that recovery-oriented care and peer support are a real possibility in Brazil.

In this research, harm reducers are coauthors and report their own life experience. In this article, they also talk about the impact of this experience on their work and on the lives of users of mental health services, emphasizing the protagonism and exercise of citizenship by harm reducers.

The lubrication of the high-speed reducer of an electric vehicle is investigated. The specific contents include visualization of the flow field inside reducer, lubrication evaluation of bearings and efficiency experiment.

The flow field inside reducer at five working conditions: straight, uphill, downhill, left lean and right lean is simulated by smoothed particle hydrodynamics (SPH). According to the instantaneous number of particles through bearings, the lubrication states of bearings are evaluated. The test platform is set up to measure the efficiency of the reducer.

The flow field inside the reducer is obtained, the lubrication of bearings needs to be improved, the efficiency of the electric vehicle reducer meets the requirement.

The SPH method is used to simulate lubrication instead of using the traditional grid-based finite volume method. A novel method to evaluate the lubrication of bearings is proposed. The method and conclusions can guide electric vehicle reducer design.

The scalability of similarity joins is threatened by the unexpected data characteristic of data skewness. This is a pervasive problem in scientific data. Due to skewness, the uneven distribution of attributes occurs, and it can cause a severe load imbalance problem. When database join operations are applied to these datasets, skewness occurs exponentially. All the algorithms developed to date for the implementation of database joins are highly skew sensitive. This paper presents a new approach for handling data-skewness in a character- based string similarity join using the MapReduce framework. In the literature, no such work exists to handle data skewness in character-based string similarity join, although work for set based string similarity joins exists. Proposed work has been divided into three stages, and every stage is further divided into mapper and reducer phases, which are dedicated to a specific task. The first stage is dedicated to finding the length of strings from a dataset. For valid candidate pair generation, MR-Pass Join framework has been suggested in the second stage. MRFA concepts are incorporated for string similarity join, which is named as “MRFA-SSJ” (MapReduce Frequency Adaptive – String Similarity Join) in the third stage which is further divided into four MapReduce phases. Hence, MRFA-SSJ has been proposed to handle skewness in the string similarity join. The experiments have been implemented on three different datasets namely: DBLP, Query log and a real dataset of IP addresses & Cookies by deploying Hadoop framework. The proposed algorithm has been compared with three known algorithms and it has been noticed that all these algorithms fail when data is highly skewed, whereas our proposed method handles highly skewed data without any problem. A set-up of the 15-node cluster has been used in this experiment, and we are following the Zipf distribution law for the analysis of skewness factor. Also, a comparison among existing and proposed techniques has been shown. Existing techniques survived till Zipf factor 0.5 whereas the proposed algorithm survives up to Zipf factor 1. Hence the proposed algorithm is skew insensitive and ensures scalability with a reasonable query processing time for string similarity database join. It also ensures the even distribution of attributes.

The purpose of this paper is to analyze the force and basic rating life of angular contact ball bearings of RV reducer under the actual operating condition.

Force analysis of angular contact ball bearing under the actual operating condition, calculate the axial, radial load and internal load distribution, calculate the basic rating life of angular contact ball bearing under variable load conditions.

The external load has a great influence on the radial load of angular contact ball bearing, further affecting the basic rating life of angular contact ball bearing, which is a great influence on the overall life of RV reducer under the condition of high frequency and heavy load.

This paper provides important ideas for the design and manufacture of RV reducer in theory and experiment technology.

This paper aims to solve the lubrication failures in the turning arm bearing of RV reducer, give some help in perfecting the bearing structure design and provide theoretical basis for the reducer’s performance improvement.

The paper establishes a mixed lubrication analysis model to study performance parameters. According to the discretization of parameters and iteration of equations, numerical simulation and theoretical analysis are achieved in computational process.

Considering influences of contact load, real rough surface and realistic geometry of RV reducer turning arm roller bearing, the mixed lubrication analysis model is established to study the ratio of oil film thickness, pressure distribution and maximum von Mises stress in different speeds, temperatures and fillets. The results of mixed lubrication show that reasonable round corner modification, increase in temperature and speed, decrease of surface roughness and lubricant types can improve the lubrication performance.

The mixed lubrication analysis model is established to study the influences of contact load, real rough surface and realistic geometry of RV reducer turning arm roller bearing. Different speed, temperature, lubricant and fillet modification are also considered in the research to analyze oil film thickness, pressure distribution and maximum von Mises stress. These studies can optimize structural design of bearing and direct engineer operations.

The innovation in big data is increasing day by day in such a way that the conventional software tools face several problems in managing the big data. Moreover, the occurrence of the imbalance data in the massive data sets is a major constraint to the research industry.

The purpose of the paper is to introduce a big data classification technique using the MapReduce framework based on an optimization algorithm. The big data classification is enabled using the MapReduce framework, which utilizes the proposed optimization algorithm, named chicken-based bacterial foraging (CBF) algorithm. The proposed algorithm is generated by integrating the bacterial foraging optimization (BFO) algorithm with the cat swarm optimization (CSO) algorithm. The proposed model executes the process in two stages, namely, training and testing phases. In the training phase, the big data that is produced from different distributed sources is subjected to parallel processing using the mappers in the mapper phase, which perform the preprocessing and feature selection based on the proposed CBF algorithm. The preprocessing step eliminates the redundant and inconsistent data, whereas the feature section step is done on the preprocessed data for extracting the significant features from the data, to provide improved classification accuracy. The selected features are fed into the reducer for data classification using the deep belief network (DBN) classifier, which is trained using the proposed CBF algorithm such that the data are classified into various classes, and finally, at the end of the training process, the individual reducers present the trained models. Thus, the incremental data are handled effectively based on the training model in the training phase. In the testing phase, the incremental data are taken and split into different subsets and fed into the different mappers for the classification. Each mapper contains a trained model which is obtained from the training phase. The trained model is utilized for classifying the incremental data. After classification, the output obtained from each mapper is fused and fed into the reducer for the classification.

The maximum accuracy and Jaccard coefficient are obtained using the epileptic seizure recognition database. The proposed CBF-DBN produces a maximal accuracy value of 91.129%, whereas the accuracy values of the existing neural network (NN), DBN, naive Bayes classifier-term frequency–inverse document frequency (NBC-TFIDF) are 82.894%, 86.184% and 86.512%, respectively. The Jaccard coefficient of the proposed CBF-DBN produces a maximal Jaccard coefficient value of 88.928%, whereas the Jaccard coefficient values of the existing NN, DBN, NBC-TFIDF are 75.891%, 79.850% and 81.103%, respectively.

In this paper, a big data classification method is proposed for categorizing massive data sets for meeting the constraints of huge data. The big data classification is performed on the MapReduce framework based on training and testing phases in such a way that the data are handled in parallel at the same time. In the training phase, the big data is obtained and partitioned into different subsets of data and fed into the mapper. In the mapper, the features extraction step is performed for extracting the significant features. The obtained features are subjected to the reducers for classifying the data using the obtained features. The DBN classifier is utilized for the classification wherein the DBN is trained using the proposed CBF algorithm. The trained model is obtained as an output after the classification. In the testing phase, the incremental data are considered for the classification. New data are first split into subsets and fed into the mapper for classification. The trained models obtained from the training phase are used for the classification. The classified results from each mapper are fused and fed into the reducer for the classification of big data.

The purpose of this study is to systematically analyze the wear of cylindrical needle bearings in rotary vector reducers under temperature rise and identify the influencing factors.

Based on the dynamic characteristics of the RV-20E reducer, the time-varying contact force of the cylindrical needle bearing and the entrainment speed of the inner and outer raceways were calculated. A mixed elastohydrodynamic lubrication model of the needle bearing, considering friction and temperature rise, was established using a dynamic rough tooth surface model. The model solved for the oil film thickness, contact stress and wear conditions of the bearing raceway contact area. The effects of the number of rolling needles, the diameter of rolling needles and surface strength on the wear characteristics were analyzed.

The results of this study show that the oil film thickness, oil film pressure and surface scratches of cylindrical needle bearings exhibit an uneven, patchy distribution under the combined effects of friction and temperature rise. When the radius of the rolling needle is less than 1.44 mm, inner ring wear is less than outer ring wear. Conversely, when the radius exceeds 1.44 mm, inner ring wear is greater. The optimal rolling needle radius is 1.6 mm. Increasing the number of rolling needles and enhancing the yield strength of the contact surface significantly extend bearing life.

This study provides valuable recommendations for optimizing bearing structural parameters and material characteristics in the design of rotary vector reducers.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0242/

The present study aims to use the qualitative approach of soft laddering to gather insight into both risk perception and subsequent marketing strategies of medical tourism. Medical tourism has recently started to spark interest of marketing researchers and has been a frequently discussed topic by both journalists and physicians. Nevertheless, there are only few marketing studies offering in-depth research on the phenomenon of medical tourism. In the present article, we will concentrate on the central notion of risk inherent to any medical procedure and a fortiori to surgery in the so-called Third World countries. Because medical tourism is a product to be sold to costumers, the perceived risk needs to be dealt with on the marketing level.

In the present study, the qualitative approach of soft laddering was used with 23 participants from France and 22 participants from Quebec to gather insight into both risk perception and subsequent marketing strategies of medical tourism.

The results show significant differences in consumer behavior between the two nationalities involved. We will see how these differences are related to underlying values as well as different reasons and constraints to purchase.

The results could help developed countries’ government to implement health policy.

Qualitative research does not often use soft laddering, which was used in this paper. Moreover, this qualitative approach has some advantages as the number of chains, as well as the frequencies of selection and the diversity of elements quoted by consumers (kinds of risks, values and consequences of the choices made).

The purpose of the paper is to provide information on wear debris on oil and vibration analysis as predictive maintenance techniques in reducer.

The estate of a reducer is verified by analyzing the vibration and oil conditions of a test rig under well‐designed conditions utilizing some predictive variables.

According to the vibration and oil analysis it is found out what it was happening into the reducer without disassembling it.

This paper demonstrates the use of oil debris analysis and vibration analysis as a technique that enhances preventive maintenance practices. The paper helps practitioners to utilize these techniques more effectively.

This paper gives information about two predictive maintenance techniques with a test rig.