Enhancing AW/EP tribological characteristics of biolubricant synthesized from chemically modified cotton methyl-esters by using nanoparticle as additives

Purpose The purpose of this study is to improve the tribological characteristics of cotton-biolubricant by adding nanoparticles at extreme pressure (EP) conditions in comparison with commercial lubricant SAE-40. Design/methodology/approach This research involved the synthesis of cotton-biolubricant by transesterification process and then the addition of nanoparticles in it to improve anti wear (AW)/EP tribological behavior. SAE-40 was studied as a reference commercial lubricant. AW/EP characteristics of all samples were estimated by the four-ball tribo-tester according to the American Society for Testing and Materials D2783 standard. Findings The addition of 1-Wt.% TiO2 and Al2O3 with oleic acid surfactant in cotton-biolubricant decreased wear scar diameter effectively and enhanced the lubricity, load-wear-index, weld-load and flash-temperature-parameters. This investigation revealed that cotton-biolubricant with TiO2 nano-particle additive is more effective and will help in developing new efficient biolubricant to replace petroleum-based lubricants. Research limitations/implications Cotton biolubricant with TiO2 nano-particles appeared as an optimistic solution for the global bio-lubricant market. Originality/value No one has not studied the cotton biolubricant with nanoparticles for internal combustion engine applications at high temperature and EP conditions.

On Optimistic and Pessimistic Bilevel Optimization Models for Demand Response Management

This paper investigates bilevel optimization models for demand response management, and highlights the often overlooked consequences of a common modeling assumption in the field. That is, the overwhelming majority of existing research deals with the so-called optimistic variant of the problem where, in case of multiple optimal consumption schedules for a consumer (follower), the consumer chooses an optimal schedule that is the most favorable for the electricity retailer (leader). However, this assumption is usually illegitimate in practice; as a result, consumers may easily deviate from their expected behavior during realization, and the retailer suffers significant losses. One way out is to solve the pessimistic variant instead, where the retailer prepares for the least favorable optimal responses from the consumers. The main contribution of the paper is an exact procedure for solving the pessimistic variant of the problem. First, key properties of optimal solutions are formally proven and efficiently solvable special cases are identified. Then, a detailed investigation of the optimistic and pessimistic variants of the problem is presented. It is demonstrated that the set of optimal consumption schedules typically contains various responses that are equal for the follower, but bring radically different profits for the leader. The main procedure for solving the pessimistic variant reduces the problem to solving the optimistic variant with slightly perturbed problem data. A numerical case study shows that the optimistic solution may perform poorly in practice, while the pessimistic solution gives very close to the highest profit that can be achieved theoretically. To the best of the authors’ knowledge, this paper is the first to propose an exact solution approach for the pessimistic variant of the problem.

The Core of Uncertain Coalitional Game Based on Hurwicz Criterion with Application to Cyber Security Information Sharing

The coalitional game focuses on how people share the payoffs of collaboration when they form coalitions. However, we cannot accurately obtain the different coalitions’ payoffs because of the actual situation’s limitation or economic and technical factors. Therefore, we have to rely on experts in the field to estimate the likelihood of various events and give their belief degrees. To deal with the belief degrees, scholars of uncertainty theory suppose the transferable payoffs to be uncertain variables and propose the uncertain coalitional game. Many expected and optimistic solution concepts have been put forward in the literature, even though the expected value criterion does not consider the decision-maker’s attitude to risk, and the optimistic value criterion is too extreme. To better describe the different subjective judgments of decision-makers, the Hurwicz criterion is applied to the uncertain coalitional game, in which the players intend to maximize their Hurwicz payoffs. Besides, the method to discover the Hurwicz-core is provided, and the condition that the core is nonempty is proved. What’s more, the Hurwicz–Shapley value is mathematically proven to be in the Hurwicz-core in a convex uncertain coalitional game. An application in cyber security information sharing is provided.

DEN-DIS: “May Get Life in Future” - Hybridized Data Stream Clustering Framework in Market Research Arena

Data streams pose several computational challenges due to their large volume of massive data arriving at a very fast rate. Data streams are gaining the attention of today’s research community for their utility in almost all fields. In turn, organizing the data into groups enables the researchers to derive with many useful and valuable information and conclusions based on the categories that were discovered. Clustering makes this organization or grouping easier and plays an important role in exploratory data analysis. This paper focuses on the amalgamation of two very important algorithms namely Density Based clustering used to group the data and the dissimilarity matrix algorithm used to find the outlier among the data. Before feeding the data, the algorithm filters out the sparse data and a continuous monitoring system provides the frequent outlier and inlier checks on the live stream data using buffer timer. This approach provides an optimistic solution in recognizing the outlier data which may later get reverted as inlier based on certain criteria. The concept of DenDis approach will pave a new innovation world of considering every data which “May Get Life in Future”.

An Investigation of the Optimistic Solution to the Linear Trilevel Programming Problem

In this paper, we consider a general version of a linear trilevel programming problem. Three different types of optimistic optimal solutions for a special trilevel programming problem have formerly been suggested. This paper presents the mathematical formulation of all of the three types of optimistic optimal solutions for the given linear trilevel programming problem. Moreover, some properties of the inducible region (the feasible region for the trilevel programming problem) corresponding to each optimistic optimal solution are investigated. Finally, a numerical example is presented to compare the different types of optimistic optimal solutions.

A Review on the Evolution of Eigenvalue Based Spectrum Sensing Algorithms for Cognitive Radio

Spectrum scarcity has been encountered as a leading problem when launching new wireless services. To overcome this problem, cognitive radio is an optimistic solution. Spectrum sensing is a prominent task of cognitive radio. Over the past decade, numerous spectrum sensing algorithms have been proposed. In this paper, we present a comprehensive survey ofevolutionary achievements of eigenvalue based spectrum sensing algorithms. The correlation between signal samples due to oversampling, multipath or multiple receivers gets reflected on the eigenvalues of the covariance matrix. It has been observed that different combinations ofeigenvalues are used as test statistics and the distribution of eigenvalues and derivation of probability of detection is based on RMT (Random Matrix Theory). The main advantage offered by these algorithms is their robustness to noise uncertainty which severely affect other methods. Furthermore, they do not require accurate synchronization. These detections can be used for different signal detection applications without any prior information of signal or noise. To evaluate the performance of eigenvalue based spectrum sensing techniques under fading channels, we have simulated maximum to minimum eigenvalue based Detection(MME) and maximum eigenvalue based detection (MED) estimation for Rician fading channel. Simulation results shows that MME is much better than MED.

Deciding Optimal Noise Monitoring Sites with Matrix Gray Absolute Relation Degree Theory

Noise maps are applied to assess noise level in cities all around the world. There are mainly two ways of producing noise maps: one way is producing noise maps through theoretical simulations with the surrounding conditions, such as traffic flow, building distribution, etc.; the other one is calculating noise level with actual measurement data from noise monitors. Currently literature mainly focuses on considering more factors that affect sound traveling during theoretical simulations and interpolation methods in producing noise maps based on measurements of noise. Although many factors were considered during simulation, noise maps have to be calibrated by actual noise measurements. Therefore, the way of obtaining noise data is significant to both producing and calibrating a noise map. However, there is little literature mentioned about rules of deciding the right monitoring sites when placed the specified number of noise sensors and given the deviation of a noise map produced with data from them. In this work, by utilizing matrix Gray Absolute Relation Degree Theory, we calculated the relation degrees between the most precise noise surface and those interpolated with different combinations of noise data with specified number. We found that surfaces plotted with different combinations of noise data produced different relation degrees with the most precise one. Then we decided the least significant one among the total and calculated the corresponding deviation when it was excluded in making a noise surface. Processing the left noise data in the same way, we found out the least significant datum among the left data one by one. With this method, we optimized the noise sensor’s distribution in an area about 2[Formula: see text]km2. And we also calculated the bias of surfaces with the least significant data removed. Our practice provides an optimistic solution to the situation faced by most governments that there is limited financial budget available for noise monitoring, especially in the undeveloped regions.