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This paper aims to overcome the defect that the traditional clustering method is excessively dependent on initial clustering radius and also provide new technical measures for detecting the component content of lubricating oil based on the fuzzy neural system model.

According to the layers model of the fuzzy neural system model for the given sample data pair, the new clustering method can be implemented, and through the fuzzy system model, the detection method for the selected oil samples is given. By applying this method, the composition contents of 30 kinds of oil samples in lubricating oil are checked, and the actual composition contents of oil samples are compared.

Through the detection of 21 mineral elements in 30 oil samples, it can be known that the four mineral elements such as Zn, P, Ca and Mg have largest contribution rate to the lubricating oil, and they can be regarded as the main factors for classification of lubricating oil. The results show that the fuzzy system to be established based on sample data clustering has better performance in detection lubricant component content.

In spite of lots of methods for detecting the component of lubricating oil at the present, there is still no detection of the component of lubricating oil through clustering method based on sample data pair. The new nearest clustering method is proposed in this paper, and it can be more effectively used to detect the content of lubricating oil.

In order to find the relationship between operation machine status and oil monitoring information, the oil monitoring information characteristics abstraction and fault diagnostic system is established. The purpose of this paper is to find an effective method to monitor and diagnose the machine running status, and consequently, serve the industry.

The operation status information of equipments is obtained through applying the methods of statistical, trend, entropy and clustering characteristics as a whole; and the multi‐characteristic integration method is established based on the existing literature, industry practices and oil characteristic analysis.

Using multi‐characteristic integration method, an oil monitoring and diagnostic system is established based on the above status information. This multi‐characteristic integration method is applied to D‐100/8 air compressor sets in the status monitoring project of a shipbuilding company. The analysis conclusions of the operation status can be obtained promptly and accurately by the method, and can provide guidance for the equipment maintenance.

A novel comprehensive oil monitoring data processing method are presented in this paper, which can scientifically distill latent laws among the monitoring information and detect accurately the measurement index of the fault states and abnormity data.

The main purpose of this paper is to propose a model of economic development able to generate a cross‐border sustainable economic development, in regions in conflict. The Italian industrial district model implements a community industry synergy process led by the authorities according to a top‐down approach. The cluster model implements a clustering specialization process led, in the American version, by a bottom‐up approach and in the European version by a top‐down approach. The regional innovation system (RIS) provides the regional and international innovation networking required for both models in order to confront the global competition. The proposed progressive model creates the industrial specialization (industrial district) required for the development of the clustering process supported by the RIS.

The authors have selected, from the list of producers (growers and producers of olive oil), a random sample of 103 growers of olives and producers of olive oil from both groups from the Northern regions (Galilee in Israel and the Northern West Bank): 26 Palestinian growers, 25 Palestinian producers, 13 Israeli growers and 39 Israeli producers of olive oil, and interviewed them.

The results show that the community‐industry synergy of the industrial district model is supported by the economic actors from both sides of the border but refused for political reasons by the regional authorities and professional associations. The raw material (olives), the human capital and the knowledge required in order to start the clustering process exist.

The study has evaluated the Israeli‐Jewish and Arab and the Palestinian olive sector, and clearly indicates that bottom‐up decision‐making process is the only way for the moment for initiating the cluster and RIS models in the olive sector. The intervention of a third party is required in order to start the bottom‐up implementation of the industrial district model and launch the clustering process.

The main contribution of this paper lies in organizing the industrial district in such a way that it will generate a cluster in the long run. Thus, it is called progressive model.

The purpose of this paper is to develop a maintenance decision support system (DSS) framework using in-service lubricant data for fault diagnosis. The DSS reveals embedded patterns in the data (knowledge discovery) and automatically quantifies the influence of lubricant parameters on the unhealthy state of the machine using alternative classifiers. The classifiers are compared for robustness from which decision-makers select an appropriate classifier given a specific lubricant data set.

The DSS embeds a framework integrating cluster and principal component analysis, for feature extraction, and eight classifiers among them extreme gradient boosting (XGB), random forest (RF), decision trees (DT) and logistic regression (LR). A qualitative and quantitative criterion is developed in conjunction with practitioners for comparing the classifier models.

The results show the importance of embedded knowledge, explored via a knowledge discovery approach. Moreover, the efficacy of the embedded knowledge on maintenance DSS is emphasized. Importantly, the proposed framework is demonstrated as plausible for decision support due to its high accuracy and consideration of practitioners needs.

The proposed framework will potentially assist maintenance managers in accurately exploiting lubricant data for maintenance DSS, while offering insights with reduced time and errors.

Advances in lubricant-based intelligent approach for fault diagnosis is seldom utilized in practice, however, may be incorporated in the information management systems offering high predictive accuracy. The classification models' comparison approach, will inevitably assist the industry in selecting amongst divergent models' for DSS.

Palm oil is widely used in the food industry; however, there are two main controversies connected to its use, namely, its nutritional value and the environmental consequences deriving from its crop. In Italy, the use of palm oil has recently been criticized, insomuch that some important bakery companies decided to substitute it, creating a real food marketing case. Through a focus on biscuits, this study is aimed at profiling consumers with regard to palm oil issue to better comprehend if the presence of this ingredient truly influences their food purchases and if they care about the nutritional and environmental aspects, highlighting the impact of the Covid-19 pandemic on consumers' consumption.

A questionnaire was administered to 243 subjects in Italy, in order to apply a cluster analysis.

The findings show the presence of three main kinds of consumers: (1) compromise finders (sensitive to cost savings but trying to privilege palm-oil free food), (2) brand-loyal consumers (palm oil does not influence their preferences) and (3) healthsensitives (the presence of palm oil profoundly affects their choices), who represent the majority of our sample. The results and implications are discussed.

Research on palm oil is essentially focused on chemistry, natural sciences or on its industrial uses: this study analyzes the consumer point of view by applying a different methodology compared to existing studies.

When facing a clouded global economy, many countries would increase their gold reserves. On the other hand, oil supply and demand depends on the political and economic situations of oil producing countries and their production technologies. Both oil and gold reserve play important roles in the economic development of a country. The paper aims to discuss this issue.

This paper uses the historical data of oil and gold prices as research data, and uses the historical price tendency charts of oil and gold, as well as cluster analysis, to discuss the correlation between the historical data of oil and gold prices. By referring to the technical index equation of stocks, the technical indices of oil and gold prices are calculated as the independent variable and the closing price as the dependent variable of the forecasting model.

The findings indicate that there is no obvious correlation between the price tendencies of oil and gold. According to five evaluating indicators, the MFOAGRNN forecast model has better forecast ability than the other three forecasting models.

This paper explored the correlation between oil and gold prices, and built oil and gold prices forecasting models. In addition, this paper proposes a modified FOA (MFOA), where an escape parameter Δ is added to Si. The findings showed that the forecasting model that combines MFOA and GRNN has the best ability to forecast the closing price of oil and gold.

The purpose of this paper is to guide the choice of consumers, putting on the label an additional claim, which can provide more information on the sensory profile and the nutritional and preservation features of the marketed extra virgin olive oil.

In order to define the concept of global quality, the following parameters were considered: fruity, bitter, pungency, 1-penten-3-one, phenolic substances, tocopherols, peroxide value, free acidity, palmitic acid, stearic acid oleic acid linoleic acid, and the campesterol/stigmasterol ratio. The study was carried out on 143 commercial extra virgin olive oils.

The Global Quality Index was calculated as the square root of the sum of the squares of the individual local indices, according to three different algorithms. The computation obtained was recognised by chemometric analysis.

A legislative amendment on the labelling could be proposed through an additional claim that safeguards the consumers on the health profile, inducing them to a more targeted purchase.

Three different global quality levels “excellent”, “good”, and “sufficient” have been determined. This clustering has also been recognised with a statistical approach. Since in the market, consumers can find extra virgin olive oils of different overall quality levels, it is possible to guide the choice of customers through an additional claim on the label, able to give more information on the sensory profile and the nutritional and preservation features of the product.

The price clustering behavior is becoming a core part of the market efficiency theory especially with the development of trading strategies and the occurrence of major and unprecedented shocks which have led to severe inquiry regarding asset price dynamics and their distribution. However, research on emerging stock market is scant. The study contributes to the literature on price clustering by investigating an active emerging stock market, the Muscat stock market one of the Arabian Gulf Markets.

This research adopts the artificial intelligence technique and other statistical estimation procedure in understanding the price clustering patterns in Muscat stock market and their main determinants.

The findings reveal that stock prices are marked by clustering behavior as commonly highlighted in the previous studies. However, we found strong evidence of price preferences to cluster on numbers closer to zero than to one. We also show that the nature of firm’s activity matters for price clustering behavior. In addition, firms with traded bonds in Oman market experienced a substantial less stock price clustering than other firms. Clustered stock prices are more likely to have higher prices and higher volatility of price. Finally, clustering raised when the market became highly uncertain during the Covid-19 crisis especially for the financial firms.

This study provides novel results on price clustering literature especially for an active emerging market and during the Covid-19 pandemic crisis.

Electrical characteristics of transformer oil (TO) have been studied during normal and thermal aging conditions. In this paper, breakdown voltage (BDV), partial discharge (PD), heat transfer results and the physical mechanisms considering the impact of varying the diameter of Al₂O₃ nanoparticles (NPs) have been investigated. Different quantities of the two sizes of Al₂O₃ were added to the oil using a two-step method to determine the positive effect of NPs on the electrical and thermal properties of TO. Finally, the physical mechanisms related to the obtained experimental results have been performed.

The implementation of nanoparticles in this paper was provided by US Research Nanomaterials, Inc., USA. The provided Al₂O₃ NPs have an average particle size of 20–80 nm and a specific surface area of 138 and 58 m²/g, respectively, which have a purity of over 99%. Thermal aging has been done. The IEC 60156 standard has been implemented to calculate the BDV, and a 500-mL volume test cell (Apar TO 1020) has been used. PD test is performed according to Standard IEC 60343, and a JDEVS-PDMA 300 device was used for this test.

BDV tests indicate that 20 nm Al₂O₃ is more effective at improving BDV than 80 nm Al₂O₃, with an improvement of 113% compared to 99% for the latter. The analysis of Weibull probability at BDV indicates that 20 nm Al₂O₃ performs better, with improvements of 141%, 125% and 112% at probabilities of 1, 10 and 50%, respectively. The results of the PD tests using the PDPR pattern also show that 20 nm Al₂O₃ is superior. For the heat transfer test, 0.05 g/L of both diameters were used to ensure fair conditions, and again, the advantage was with 20 nm Al₂O₃ (23% vs 18%).

The effect of Al₂O₃ NP diameter (20 and 80 nm) on various properties of virgin and aged TO has been investigated experimentally in this paper to examine the effect of proposed NP on electrical improvement of TO.