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With the rapid development and stable operated application of lithium-ion batteries used in uninterruptible power supply (UPS), the prediction of remaining useful life (RUL) for lithium-ion battery played an important role. More and more researchers paid more attentions on the reliability and safety for lithium-ion batteries based on prediction of RUL. The purpose of this paper is to predict the life of lithium-ion battery based on auto regression and particle filter method.

In this paper, a simple and effective RUL prediction method based on the combination method of auto-regression (AR) time-series model and particle filter (PF) was proposed for lithium-ion battery. The proposed method deformed the double-exponential empirical degradation model and reduced the number of parameters for such model to improve the efficiency of training. By using the PF algorithm to track the process of lithium-ion battery capacity decline and modified observations of the state space equations, the proposed PF + AR model fully considered the declined process of batteries to meet more accurate prediction of RUL.

Experiments on CALCE dataset have fully compared the conventional PF algorithm and the AR + PF algorithm both on original exponential empirical degradation model and the deformed double-exponential one. Experimental results have shown that the proposed PF + AR method improved the prediction accuracy, decreases the error rate and reduces the uncertainty ranges of RUL, which was more suitable for the deformed double-exponential empirical degradation model.

In the running of UPS device based on lithium-ion battery, the proposed AR + PF combination algorithm will quickly, accurately and robustly predict the RUL of lithium-ion batteries, which had a strong application value in the stable operation of laboratory and other application scenarios.

In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination, background, occlusion and other factors, we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern (CS-LBP) and deep residual network (DRN) model.

The algorithm first extracts the block CSP-LBP features of the face image, then incorporates the extracted features into the DRN model, and gives the face recognition results by using a well-trained DRN model. The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination.

Compared with the direct usage of the original image, the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency. Experimental results on the face datasets of FERET, YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms.

The proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment, and it is particularly robust to the change of illumination, which proves its superiority.

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.

First, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.

In the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.

The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.

Crop price insurance can reduce losses from price volatility for crop producers, but pricing is one paramount important issue. The objective of this research is to provide a tentative method for pricing of crop price insurance with data from the futures market.

With weekly settlement price of January corn futures from the second week of January 2009 to the fourth week of April 2020 in China, we assume that corn futures price follows fractional Brownian motion, and apply an improved Black model.

Our results reveal that the proposed model can be used to yield a fair premium and crop price insurance with varying insured prices and gradient coverage ratios can be used to meet the divergent needs of farmers at an affordable cost for price risk management purposes.

Results can be fine-tuned by extending research to crops such as wheat and rice, and by modeling price volatility with data from options market.

This study offers one plausible way to rate crop price insurance using data from the futures market, and thus adds to this thread of literature by incorporating fractional Brownian motion into an improved Black model.

This study investigates the different effects of the (mis)alignment between servitization strategies (i.e. product-oriented and customer-oriented services) and inter-organizational information technology (IT) capabilities (i.e. IT reconfiguration and integration) concerning the environmental performance of servitized manufacturing firms.

Using hierarchical regression analysis, this study examines the theoretical model based on multi-respondent survey data from 1,149 senior managers of 383 manufacturers in China.

Product-oriented services have a non-significant effect on environmental performance, whereas customer-oriented services exert a significantly positive effect. The alignment between product-oriented services and IT reconfiguration, and that between customer-oriented services and IT integration, improve environmental performance. In contrast, the misalignment between product-oriented services and IT integration, and that between customer-oriented services and IT reconfiguration, hurt environmental performance.

This study elucidates the different effects of the (mis)alignment between servitization strategies and inter-organizational IT capabilities on environmental performance. It also resolves the inconsistency regarding the implications of servitization on environmental performance.

Granular lubrication can solve some lubrication problems under many extreme operating conditions. Meanwhile, the flow constitutive relation is one of its unsolved problems in fully understanding its rheological mechanism.

In this paper, a plane shear cell under granular lubrication is established by the discrete element method to study the flow constitutive relation and its mechanical mechanism of the hard granular lubricants.

Research results show that the flow regimes in granular flow lubrication strongly rely on the dimensionless parameter I, in which it is called the inertial coefficient. When the inertial coefficient I increase, the flow regimes of the granular lubricants also evolve from a quasi-static state to a collisional state accordingly. Comparing to the influence of the restitution coefficient, the friction coefficient of the hard granular lubricants has a strong influence on its constitutive relation of the granular flow lubrication. Finally, it is shows that the dimensionless parameter I has strong influence on the contacts and flow states of this granular lubrication system than the influence of the dimensionless parameter R.

These findings reveal the constitutive relation and mechanical mechanism of granular lubrication and can also offer the helpful reference for the design of the new granular lubrication bearing.

This study aims to improve the lubrication performance of molybdenum disulfide powders at textured surface of cemented carbide materials, a squeeze motion of vibration assistance method was introduced and investigated.

Surface texture was fabricated on YT15 cemented carbide samples using a laser marking machine. After that, a tribological experiment was carried out on a self-built friction testing machine under different amplitude and frequency of squeeze motion conditions. Moreover, a simulation model was also established to verify the principle of squeeze motion on the lubrication performance improving of MoS₂ particles at textured interfaces.

Analysis results indicated that surface texture on test sample can increase the storage ability of solid lubrication particles, and the lubrication film at the contact interface is more easily formed due to the reciprocating action. Squeeze motion can improve the storage ability of it due to an intermittent contact, which provides an opportunity for MoS₂ particles infiltration, and then a more uniform distribution and load-bearing properties of force chain are also established and formed simultaneously. Thus, a better tribological performance at the contact interface is obtained.

The main contribution of this work is to provide a reference for the molybdenum disulfide powder lubrication with textured surface of cemented carbide materials.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0166/

The purpose of this study is to reveal the friction reduction performance and mechanism of granular flow lubrication during the milling of difficult-to-machining materials and provide a high-performance lubrication method for the precision cutting of nickel-based alloys.

The milling tests for Inconel 718 superalloy under dry cutting, flood lubrication and granular flow lubrication were carried out, and the milling force and machined surface quality were used to evaluate their friction reduction effect. Furthermore, based on the energy dispersive spectrometer (EDS) spectrums and the topographical features of machined surface, the lubrication mechanism of different granular mediums was explored during granular flow lubrication.

Compared with flood lubrication, the granular flow lubrication had a significant force reduction effect, and the maximum milling force was reduced by about 30%. At the same time, the granular flow lubrication was more conducive to reducing the tool trace size, repressing surface damage and thus achieving better surface quality. The soft particles had better friction reduction performance than the hard particles with the same particle size, and the friction reduction performance of nanoscale hard particles was superior to that of microscale hard particles. The friction reduction mechanism of MoS₂ and WS₂ soft particles is the mending effect and adsorption film effect, whereas that of SiO₂ and Al₂O₃ hard particles is mainly manifested as the rolling and polishing effect.

Granular flow lubrication was applied in the precision milling of Inconel 718 superalloy, and a comparative study was conducted on the friction reduction performance of soft particles (MoS₂, WS₂) and hard particles (SiO₂, Al₂O₃). Based on the EDS spectrums and topographical features of machined surface, the friction reduction mechanism of soft and hard particles was explored.

The purpose of this paper is to study a new liquid-phase assisted texture treatment method to improve the tribological properties of 304 stainless steel.

Three groups of textured type (KY, KJ and YJ) were prepared on 304 stainless steel surface using laser circular and cross scanning method in air and liquid assisted condition. The surface morphology and element content of test samples were measured with scanning electron microscope, energy spectrum. Then, the tribological test was carried out using MWF-500 reciprocating friction and wear testing machine under dry and oil lubrication condition.

The experimental results showed that the textured surface of laser processing in air was obviously blackened, and the oxygen content was increased from 16.9% to 24%. These cases did not occur on liquid-assisted laser textured surface, which induced a better wettability and surface texture processing quality. For friction test, the friction coefficient of cross-scanning textured surface prepared in assisted liquid (YJ) was the smallest. It is reduced by 55% in oil lubrication case compared to the original surface (YS). The cross-scanning textured surface prepared in air (KJ) was a little worse in friction coefficient and a little better in wear quantity than the cross-scanning textured surface prepared in assisted liquid (YJ). It is indicated that the laser processing surface with assisted liquid has obvious advantages in surface texture quality and interfacial tribological property. The main reason is that the assisted liquid plays a role in cooling and protecting action of the machined surface. The bubbles, generated at the solid–liquid interface because of the laser heat effect, scatter the laser beam and carry out the processed melt meanwhile. The lubricating medium is easier to penetrate and store in the contact interfaces because of the higher surface textured performance and wettability.

The main contribution of this work is in providing a new surface texture processing method that has a better surface micropits quality and interfacial tribology regulation ability.

S. Xavier Alphonse is a PhD in English literature. He is the Founder Director of the Indian Centre for Research and Development of Community Education (ICRDCE), Chennai, India and former Principal of Loyola College, Chennai and former Vice Principal of St. Joseph's College, Trichy. He was a member of the University Grants Commission (UGC), New Delhi for two terms (2006-2012). Dr. Alphonse was also a member of the Distance Education Council (DEC), New Delhi. He is the chairman of National committee on Community Colleges appointed by the UGC to discuss the concept and methodology for establishment and functioning of the Community Colleges. Dr. Alphonse is a consultant to the state government of Tamilnadu for the Community College Project. He was also the consultant to the Honourable Prime Minister of Papua New Guinea for the creation of 13 community colleges in 2008. He is an authority on college autonomy in India, and pioneered the formation of community colleges as an alternative system of education for the poor and the downtrodden. He has written 73 articles and authored and edited 37 books on the Community College system and its implementation.