Search

In order to improve the surface performance of engineering components, a Ni60-Ti-B4C hard coating was prepared in-situ on the surface of Q235 using laser cladding method based on Ni60-Ti-B4C powder. The effect of process parameters (laser power and scanning speed) on the microstructure, dilution rate, microhardness and wear resistance of the coatings was investigated in detail. The highest microhardness and lower dilution rate coatings were obtained by process optimization. A laser power of 1000 W and scanning speed of 6 mm/s were found to be the optimum parameters. The optimized Ni60-Ti-B4C coating mainly consists of in-situ rod-like TiB2 and block-like TiC of different sizes with TiB2-TiC eutectic structure and small amounts of FeNi, Ni3Cr and NiSi phases. The optimized Ni60-Ti-B4C coating has a dilution rate of 27.1% and a wear loss of 2.1 mg. The microhardness is 1278 HV0.3, which is about 7 times of the microhardness of Q235 steel substrate, and 2.5 times the microhardness of laser cladding coated Ni60 coating. The abrasion of the Ni60-Ti-B4C coating is mainly adhesive abrasion. This study can provide a reference value for the use of surface technology to improve the overall performance of metal parts. [ABSTRACT FROM AUTHOR]

Aims: Our objective was to examine short-term responses of A.sinensis agronomic, soil productivity to agricultural management (i.e. amino acid (AAs) and nitrogen-fixing bacteria (NFB) as alone or co-applicationed exogenous nitrogen (N) after cover planting). Methods: A field experiment was established with four fertilization treatments, including NFB (Y1), NFB and AAs (Y2), AAs (Y3), and sterile water (CK). The plant yield, biomass, N and C content, photosynthesis and the soil chemical properties, microbial community and metabolite spectrum were evaluated. Results: Y2 improved the crop biomass by 32–66% than CK, Y1 and Y3. It also decreased soil C/N by 24.29% than CK. 16S rRNA and ITS genes revealed that Y2 markedly enhanced the growth of copiotrophic taxa (Alloprevotella, Bactriodies and Alistipes) while inhibiting the growth of oligotrophic taxa (Sphingomonas and Ellin6055), among the dominant bacteria. Y2 reduced the growth of pathogenic taxa (Thanatephorus and Fusarium) that are encouraged by AAs. Further, AAs and NFB addition directly affected the diversity of bacteria and fungi and indirectly affected fungal diversity by altering soil total organic carbon (TOC), total nitrogen (TN), C/N. Additionally, soil metabolomics analysis revealed that Y3 elevated the concentration of chemical compounds that impede microbial growth, while decreasing fructose, N-Acetylglutamic acid, and arginine involved in C and N metabolism. Y2 augmented the content of disease-resistant compounds and lysine in N metabolism. Conclusion: Inoculating AAs with NFB (Bacillus amyloliquefaciae and Pseudomonas fluorescence) is a strategy with immense benefits as a biofertilizer for sustainable A.sinensis production, which also renders soil ecological services. [ABSTRACT FROM AUTHOR]

Distance measures have been scorching issues in solving problems of multi-attribute decision making (MADM). Though kinds of pioneered distance measures of hesitant fuzzy sets (HFS) have been proposed, several existing ones are still risking in achieving reasonable ranking results as which mainly are constructed on processes of arrangement and extension through adding certain membership degrees. Furthermore, in most distance measures, there are no influences of membership values as hesitant characteristics, which only take lengths of HFS elements (HFEs) into account. From such perspective, we establish the concept of hesitant index to express such hesitance with direction-senses, including positive hesitant index and negative hesitant index. Hesitant index is more supple and applicable in describing hesitant situation towards alternatives containing influences of both different directions of hesitance. Via utilizing positive hesitant index and negative hesitant index, we propose virous distance measures to make up inadequacies of some existing distance measures from the view of combing direct and indirect calculations. Furthermore, on the foundation of hesitant index, hesitant information scale is elaborated to describe characteristics of HFEs. Via taking hesitant information scales as important parts, new distance measures are presented from the view of indirect calculation. Proposed distance measures prompted from hesitant index satisfy better triangle inequality than compared existing ones, and proposed distance measures prompted from hesitant information scales not only satisfy triangle inequality, but also can avoid arrangement and extension processes towards different lengths of HFEs. Finally, relevant experiments demonstrate effectiveness and advantages of proposed distance measures in MADM. [ABSTRACT FROM AUTHOR]

The hesitant fuzzy set has been an important tool to address problems of decision making. There are several various improved hesitant fuzzy sets, such as dual hesitant fuzzy set, hesitant interval-valued fuzzy set, and intuitionistic hesitant fuzzy set, however, no one kind of improved fuzzy sets could reflect attitude characteristics of decision makers on time-sequences. In reality, time-sequence is one important sector to reflect hesitant situations as decision makers might have different knowledges of the same alternative at different moments. To perfect the description of such hesitant situations and obtain more reasonable results of decision making, we define a new kind of hesitant fuzzy set, namely, time-sequential hesitant fuzzy set. Meanwhile, its corresponding basic operators, score function and distance measures are proposed. We also propose the concept of fluctuated hesitant information to describe hesitant degrees of decision makers on time-sequences. By comprehensively utilizing the score function, fluctuated hesitant information and distance measures under time-sequential hesitant fuzzy set, a synthetic decision model is proposed. Two illustrated examples and one real-application are utilized to illustrate the effectiveness and advantage of the synthetic decision model under time-sequential hesitant fuzzy set. [ABSTRACT FROM AUTHOR]

A T-S fuzzy model identification method based on physical membership function is proposed for maneuvering target tracking. T-S fuzzy model is a good tool for fitting complex and nonlinear systems conducted by separating inputs–outputs spaces of systems and identifying corresponding parameters. Usually membership degrees play important roles in fusing local fuzzy models for reflecting nonlinear property of T-S fuzzy model, however, usual membership degrees are obtained by Gaussian function, which not only lacks interpretability and meanings but also is complex to be used. In this paper, a physical membership function with interpretability and physical meanings is proposed. To identify T-S fuzzy model based on the proposed method, first, a hyper-planed FSC algorithm as the separating method is utilized. Then UKF is used to identify consequent parameters. Finally, the proposed physical membership function is used to fuse local models and estimate final states. We apply the proposed T-S fuzzy model algorithms to maneuvering target tracking, and comparisons with several classical methods on both simulated data and real data demonstrate effectiveness and advantages of the proposed methods in tracking accuracy. [ABSTRACT FROM AUTHOR]

The battery management system (BMS) is the main safeguard of a battery system for electric propulsion and machine electrification. It is tasked to ensure reliable and safe operation of battery cells connected to provide high currents at high voltage levels. In addition to effectively monitoring all the electrical parameters of a battery pack system, such as the voltage, current, and temperature, the BMS is also used to improve the battery performance with proper safety measures within the system. With growing acceptance of lithium-ion batteries, major industry sectors such as the automotive, renewable energy, manufacturing, construction, and even some in the mining industry have brought forward the mass transition from fossil fuel dependency to electric powered machinery and redefined the world of energy storage. Hence, the functional safety considerations, which are those relating to automatic protection, in battery management for battery pack technologies are particularly important to ensure that the overall electrical system, regardless of whether it is for electric transportation or stationary energy storage, is in accordance with high standards of safety, reliability, and quality. If the system or product fails to meet functional and other safety requirements on account of faulty design or a sequence of failure events, then the environment, people, and property could be endangered. This paper analyzed the details of BMS for electric transportation and large-scale energy storage systems, particularly in areas concerned with hazardous environment. The analysis covers the aspect of functional safety that applies to BMS and is in accordance with the relevant industrial standards. A comprehensive evaluation of the components, architecture, risk reduction techniques, and failure mode analysis applicable to BMS operation was also presented. The article further provided recommendations on safety design and performance optimization in relation to the overall BMS integration. [ABSTRACT FROM AUTHOR]

This study considered the role of coal as China’s basic energy source and examines the development of the coal industry. We focused on the intelligent development of coal mines, and introduced the “Chinese mode” of intelligent mining in underground coal mines, which uses complete sets of technical equipment to propose classification and grading standards. In view of the basic characteristics and technical requirements of intelligent coal mine systems, we established a digital logic model and propose an information entity and knowledge map construction method. This involves an active information push strategy based on a knowledge demand model and an intelligent portfolio modeling and distribution method for collaborative control of coal mines. The top-level architecture of 5G+ intelligent coal mine systems combines intelligent applications such as autonomous intelligent mining, human–machine collaborative rapid tunneling, unmanned auxiliary transportation, closed-loop safety control, lean collaborative operation, and intelligent ecology. Progress in intelligent mining technology was described in terms of a dynamic modified geological model, underground 5G network and positioning technology, intelligent control of the mining height and straightness of the longwall working face, and intelligent mining equipment. The development of intelligent coal mines was analyzed in terms of its imbalances, bottlenecks, and the compatibility of large-scale systems. Implementation ideas for promoting the development of intelligent coal mines were proposed, such as establishing construction standards and technical specifications, implementing classification and grading standards according to mining policy, accelerating key technology research, and building a new management and control model. [ABSTRACT FROM AUTHOR]

Social media has become an essential channel for posting disaster-related information, which provides governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient attention, and extracting useful information is still challenging. This paper aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes toward disaster response and public demands for targeted relief supplies during different types of disasters. We focus on different natural disasters based on properties such as types, durations, and damages, which contains a total of 41,993 tweets. In this paper, public perception is assessed qualitatively by manually classified tweets, which contain information like the demand for targeted relief supplies, satisfactions of disaster response, and public fear. Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models. To better provide decision-makers with the appropriate model, the comparison of machine learning models based on computational time and prediction accuracy is conducted. The change of public opinion during different natural disasters and the evolution of peoples' behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are studied. The results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster management. [ABSTRACT FROM AUTHOR]