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Nanoparticles as the grease additives play an important role in anti-wear and friction-reducing property during the mechanical operation. To improve the lubrication action of grease, the tribological behavior of lithium-based greases with single (nanometer Al₂O₃ or nanometer ZnO) and composite additives (Al₂O₃–ZnO nanoparticles) were investigated in this paper.

The morphology and microstructure of nanoparticles were characterized by means of transmission electron microscope and X-ray diffraction. Tribological properties of different nanoparticles as additives in lithium-based greases were evaluated using a universal friction and wear testing machine. In addition, the friction coefficient (COF) and wear scar diameter were analyzed. The surface morphology and element overlay of the worn steel surface were analyzed by scanning electron microscopy (SEM) and energy dispersive spectrometer (EDS), respectively.

The results show that the greases with nanometer Al₂O₃ or nanometer ZnO and the composite nanoparticles additives both exhibit lower COFs and wear scar diameters than those of base grease. And the grease with Al₂O₃–ZnO composite nanoparticles possesses much lower COF and shows much better wear resistance than greases with single additives. When the additives contents are 0.4 Wt.% Al₂O₃ and 0.6 Wt.% ZnO, the composite nanoparticles-based grease exhibits the lowest mean COF (0.04) and wear scar diameter (0.65 mm), which is about 160% and 28% lower than those of base grease, respectively.

The main innovative thought of this work lies in dealing with the grease using single or composite nanoparticles. And through a serial contrast experiments, the anti-wear and friction-reducing property with different nanoparticles additives in lithium grease are evaluated.

The purpose of this paper is to elucidate the influences of the animal fat and fatty acid type on the formation of polycyclic aromatic hydrocarbons (PAHs) and to propose a formation mechanism of PAHs in fat during electric roasting, which is a method of non-direct-contact-flame heating.

The effects of animal fats and model fat on the formation of PAHs were valued on the basis of the ultra high-performance liquid chromatography data. The corresponding products of the FAME pyrolysis were detected by TG-FTIR. The proposal formation mechanism of PAHs was based on the summary of the literature.

Contrary to the International Agency for Research on Cancer, DF had higher risk with 280.53 ng/g of concentration after being roasted than the others animal fats of red meat in terms of PAHs formation. This research also ensured the importance of fat on PAHs formation, the concentration of PAHs in pure fats was higher after being electric roasted than that in meat patties and juice which made from corresponding animal fat. What is more, during pure animal fats and meat products being processed, less PAHs formed in the fat with lower extent of unsaturation and lower content of linolenate. In the same way, methyl linolenate demonstrated the significant increasement to PAHs formation compared to the other fatty acids. And, the number of carbon atom and the extent of unsaturation in fatty acid affects the formation of PAHs during roasting. The detection of alkene and alkane allows to propose a formation mechanism of PAHs during model fat being heated. Further study is required to elucidate the confirm moleculars during the formation of PAHs.

This work studied the effect of the carbon atom number and the unsaturation extent of fats and model fats on the formation of PAHs. This work also assure the important of alkene and alkane on the pyrolysis of model fats. This study also researched the formation and distribution of PAHs in pure fats and meat products after being heated.

The focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues.

Based on complex network theory and machine learning method, this paper constructs the structure of “three-layer coupling network” (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data.

From the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis.

Building a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters.

This study aims to examine the stationarity characteristics of per capita GDP of 17 Asian countries and subpanels for South Asia, East Asia, and high income Asian countries in nonlinear framework.

The authors employed a recently developed nonlinear panel unit root test suggested by Ucar and Omaga in PESTAR framework for full panel and the subpanels.

The results indicate that per capita GDP for the full panel of Asian countries and panel of South Asian countries are linear nonstationary, whereas for the panel of East Asia and high income developed countries have a nonlinear data generating process and are stationary.

The use of newly developed nonlinear panel unit root test for Asian countries is the main contribution of the study. In that aspect, this is the first study to employ such a test in this area.

The following is an annotated list of materials that discuss the ways in which librarians can provide library users with orientation to facilities and services, and instruct them in library information and computer skills. This is RSR's 11th annual review of this literature, and covers publications from 1984. A few items from 1983 have been included because of their significance, and because they were not available for review last year. Several items were not annotated because the compiler was unable to secure them.

The study aims to highlight the behaviour of one-dimensional and two-dimensional fin models under the natural room conditions, considering the different values of dimensionless Biot number (Bi). The effect of convection and radiation on the heat transfer process has also been demonstrated using the meshless local Petrov–Galerkin (MLPG) approach.

It is true that MLPG method is time-consuming and expensive in terms of man-hours, as it is in the developing stage, but with the advent of computationally fast new-generation computers, there is a big possibility of the development of MLPG software, which will not only reduce the computational time and cost but also enhance the accuracy and precision in the results. Bi values of 0.01 and 0.10 have been taken for the experimental investigation of one-dimensional and two-dimensional rectangular fin models. The numerical simulation results obtained by the analytical method, benchmark numerical method and the MLPG method for both the models have been compared with that of the experimental investigation results for validation and found to be in good agreement. Performance of the fin has also been demonstrated.

The experimental and numerical investigations have been conducted for one-dimensional and two-dimensional linear and nonlinear fin models of rectangular shape. MLPG is used as a potential numerical method. Effect of radiation is also, implemented successfully. Results are found to be in good agreement with analytical solution, when one-dimensional steady problem is solved; however, two-dimensional results obtained by the MLPG method are compared with that of the finite element method and found that the proposed method is as accurate as the established method. It is also found that for higher Bi, the one-dimensional model is not appropriate, as it does not demonstrate the appreciated error; hence, a two-dimensional model is required to predict the performance of a fin. Radiative fin illustrates more heat transfer than the pure convective fin. The performance parameters show that as the Bi increases, the performance of fin decreases because of high thermal resistance.

Though, best of the efforts have been put to showcase the behaviour of one-dimensional and two-dimensional fins under nonlinear conditions, at different Bi values, yet lot more is to be demonstrated. Nonlinearity, in the present paper, is exhibited by using the thermal and material properties as the function of temperature, but can be further demonstrated with their dependency on the area. Additionally, this paper can be made more elaborative by extending the research for transient problems, with different fin profiles. Natural convection model is adopted in the present study but it can also be studied by using forced convection model.

Fins are the most commonly used medium to enhance heat transfer from a hot primary surface. Heat transfer in its natural condition is nonlinear and hence been demonstrated. The outcome is practically viable, as it is applicable at large to the broad areas like automobile, aerospace and electronic and electrical devices.

As per the literature survey, lot of work has been done on fins using different numerical methods; but to the best of authors’ knowledge, this study is first in the area of nonlinear heat transfer of fins using dimensionless Bi by the truly meshfree MLPG method.

This chapter investigated tendering in stealth construction, emphasising innovative approaches and methodologies that prioritise environmental protection, safety, efficiency, and aesthetics. It began with an overview of the construction industry’s tendering processes, followed by an in-depth examination of various tendering types, including competitive and negotiated methods. The study highlighted contemporary trends such as electronic tendering, Building Information Modelling (BIM), green and sustainable procurement, risk management, data analytics, artificial intelligence, lean construction practices, and blockchain technology. Moreover, with a specific focus on stealth construction, the chapter further analysed certain criteria, including building cross-section development, visibility, radio frequency emission, and countermeasures. It explored integrating functional construction systems, including environmental, safety, health, and quality management. Additionally, it discussed methods like green building, modular construction, and low-impact techniques. Lastly, the chapter emphasised the strategies to achieve environmental protection, safety, speed, economy, and aesthetics in tendering for stealth construction.

The purpose of this paper is to analyze topics as alternative features for sentiment analysis in Indonesian tweets.

Given Indonesian tweets, the processes of sentiment analysis start by extracting features from the tweets. The features are words or topics. The authors use non-negative matrix factorization to extract the topics and apply a support vector machine to classify the tweets into its sentiment class.

The authors analyze the accuracy using the two-class and three-class sentiment analysis data sets. Both data sets are about sentiments of candidates for Indonesian presidential election. The experiments show that the standard word features give better accuracies than the topics features for the two-class sentiment analysis. Moreover, the topic features can slightly improve the accuracy of the standard word features. The topic features can also improve the accuracy of the standard word features for the three-class sentiment analysis.

The standard textual data representation for sentiment analysis using machine learning is bag of word and its extensions mainly created by natural language processing. This paper applies topics as novel features for the machine learning-based sentiment analysis in Indonesian tweets.

The purpose of this paper is to investigate the pricing strategy and the impact of agents' risk preference in a dual-channel supply chain in which both agents are risk-averse.

The authors make use of the mean-variance (MV) method to measure the risk aversion of the agents and apply Stackelberg game to obtain the optimal strategies of the proposed models. Furthermore, the authors compare the optimal strategies with that in the benchmark model in which no agent is risk-averse.

The authors find that the pricing decisions can be divided into four categories according to the risk attitudes of the agents: the decisions that are independent of two agents' risk attitudes, the decisions that depend on only one agent’s risk attitude (i.e. depend on only manufacturer's risk attitude and depend on only retailer's risk attitude) and the decisions that depend on both agents' risk attitudes. In addition, the authors find that the retail price will be lower and the wholesale price in most cases will be lower than that in the benchmark when at least one agent's risk control is effective; the demand will be always increasing as long as one agent's risk control is effective. Furthermore, compared to the benchmark, a win-win strategy (i.e. Pareto improvement) for the supply chain members can be obtained in a certain range where the agents' risk controls are appropriate.

This research provides a theoretical reference for the managers to make the pricing decisions and the risk control in dual-channel supply chains with heterogeneous preference consumers.