Wear and Morphological Analysis on Basalt/Sisal Hybrid Fiber Reinforced Poly lactic acid Composites

The investigation was undertaken to evaluate the wear behavior of basalt fiber and sisal fiber reinforced polylactic acid PLA composites. Basalt saline-treated chopped fiber and treated sisal chopped fiber were alloyed with polylactic acid and the samples were obtained using an injection mold in a twin-screw extruder. Three weight fraction samples were prepared, namely PBSi-1 (90% by weight polylactic acid, 5% by weight basalt and 5% by weight sisal), PBSi-2 (85% by weight polylactic acid, 7.5% by weight basalt and 7.5% by weight sisal) and PBSi-3 (80% by weight polylactic acid, 10% by weight basalt and 10% by weight sisal). The wear behavior of the prepared specimen were determined using a Pin-on-disc. The wear loss was measured at four different loads (10 N, 20 N, 30 N and 40 N) and four different sliding speeds (100 rpm, 150 rpm, 200 rpm and 250). The wear mechanism map was generated based on the wear regime nature using the Fuzzy Cluster C-means algorithm. The PBSi-3 composite showed a more mild wear regime than the severe and ultra-severe wear, due to the increase in the basalt and sisal fiber content within the composite that results in an increase of hardness and wear resistance. The predominant mechanism observed in the SEM image of PBSi-3 composite is ironing, which indicates the lesser wear occurrence in the composite.

Fuzzy Cluster Analysis: Application to Determining Metallicities for Very Metal-poor Stars

This work presents a first attempt to apply fuzzy cluster analysis (FCA) to analyzing stellar spectra. FCA is adopted to categorize line indices measured from LAMOST low-resolution spectra, and automatically remove the least metallicity-sensitive indices. The FCA-processed indices are then transferred to the artificial neural network (ANN) to derive metallicities for 147 very metal-poor (VMP) stars that have been analyzed by high-resolution spectroscopy. The FCA-ANN method could derive robust metallicities for VMP stars, with a precision of ∼0.2 dex compared with high-resolution analysis. The recommended FCA threshold value λ for this test is between 0.9965 and 0.9975. After reducing the dimension of the line indices through FCA, the derived metallicities are still robust, with no loss of accuracy, and the FCA-ANN method performs stably for different spectral quality from [Fe/H] ∼ −1.8 down to −3.5. Compared with traditional classification methods, FCA considers ambiguity in groupings and noncontinuity of data, and is thus more suitable for observational data analysis. Though this early test uses FCA to analyze low-resolution spectra, and feeds the input to the ANN method to derive metallicities, FCA should be able to, in the large data era, also analyze slitless spectroscopy and multiband photometry, and prepare the input for methods not limited to ANN, in the field of stellar physics for other studies, e.g., stellar classification, identification of peculiar objects. The literature-collected high-resolution sample can help improve pipelines to derive stellar metallicities, and systematic offsets in metallicities for VMP stars for three published LAMOST catalogs have been discussed.

High-Performance Computational Early Warning Analysis of Agricultural Economy Relying on Binary Fuzzy Cluster Analysis Algorithm

In this paper, a binary fuzzy cluster analysis algorithm is used for an in-depth study and analysis of high-performance computational early warning in the agricultural economy. The definition of interval type-two fuzzy set and its operation are summarized. Considering the uncertain information that will be encountered in the evaluation process, this paper constructs the evaluation model of rural informatization construction performance based on interval type-two fuzzy numbers, which improves the accuracy of the evaluation results. The fuzzy clustering centres are modified according to the nature of the fuzzy clustering centre matrix, and the optimal fuzzy clustering centres and optimal fuzzy clustering division matrix with consistent order are solved. Using the level eigenvalues to find out in the abundant water period, there are 4 monitoring sections of water quality evaluation results for still clean and 5 monitoring sections of water quality evaluation results for slight pollution. In the flat-water period, there are 2 monitoring sections of the water quality evaluation results that are still clean and 7 monitoring sections of the water quality evaluation results are slightly polluted. In the dry water period, the water quality evaluation results of the 9 monitoring sections are slightly polluted. The results and the use of integrated pollutant index evaluation method results are consistent, indicating that the use of fuzzy clustering model for water quality evaluation is practical and effective. Its functional structure includes management business, innovation business, service business, production business, and operation business functions, in addition to the design of the collection and storage scheme based on Hadoop data warehouse, to achieve accurate matching of information of science and technology agricultural services in the whole agricultural industry chain. Finally, the implementation and maintenance of the cloud-based science and technology agricultural service information system are deployed, and the operational effects of its information system-related functions are demonstrated using prototype design. This paper constructs a comprehensive information system for science and technology agricultural services based on cloud technology that integrates management, innovation, service, production, and operation, which meets not only the needs of traditional science and technology agricultural service information systems, such as expert consultation and training demonstration, but also other needs spawned by the development of agricultural modernization.

Benefit Evaluation Model of Prefabricated Buildings in Seasonally Frozen Regions

In order to effectively develop the benefit evaluation model of prefabricated houses in seasonal frozen soil areas, and improve the comprehensive benefits of prefabricated buildings, this paper proposes a life cycle benefit evaluation model for prefabricated buildings in seasonally frozen regions. According to the climatic characteristics of the area, the impact of the seasonally frozen regions is listed as an evaluation index in the construction stage for comprehensive analysis. The 16 indicators that affect the comprehensive benefits of prefabricated buildings are grouped by the nearest neighbor element analysis method. Fuzzy cluster analysis and analytic hierarchy process are used to filter out the most influential index group to calculate the index weight. Then the model proposed in this paper is compared with the existing model to test the validity of the model. The research results show that research and development costs weight is 0.23, design cost weight is 0.10, construction cost weight is 0.22, resource consumption weight is 0.25, building demolition cost weight is 0.04, and seasonal freezing effect weight is 0.16. The calculation result passed the consistency test and the expert scoring result conformed to the normal distribution, which proved the accuracy of the conclusion. It is proposed that the calculation result of the comprehensive benefit score of the model is 1.8% lower than the previous results, which proves the validity of the model. The model can speed up the efficiency of comprehensive benefit evaluation of prefabricated buildings thereby improving the development level of prefabricated buildings.

Research on the Mental Health of College Students Based on Fuzzy Clustering Algorithm

The mental health of young college students has always been a social concern. Strengthening the supervision of college students’ mental health problems is an important research content. In this regard, this paper proposes to apply fuzzy cluster analysis to the health analysis of college students and explore college students through fuzzy clustering. Explore the potential relationship between the factors that affect the health of college students, and this will provide a reference for the early prevention and intervention of college students’ mental health problems. In view of this, an improved fuzzy clustering method based on the firefly algorithm is proposed. First, the Chebyshev diagram is introduced into the firefly algorithm to initialize the population distribution. Then, an adaptive step size method is proposed to balance exploration and development capabilities. Finally, in the local search process, a Gaussian perturbation strategy is added to the optimal individual in each iteration to make it jump out of the local optimal. The process has good optimization capabilities and is easy to obtain the global optimal value. It can be used as the initial center of the fuzzy C-means clustering algorithm for clustering, which can effectively enhance the robustness of the algorithm and improve the global optimization ability. In order to evaluate the effectiveness of the algorithm, comparative experiments were carried out on four datasets, and the experimental results show that the algorithm is better than the comparison algorithm in clustering accuracy and robustness.

Multi-Criteria Decision-based Path Planning for Data Collection in Fuzzy-Cluster based Large Sensor Networks

Wireless sensor networks (WSN) are widely used to gather information using wireless communication, but due to the confined power of sensor nodes, it is a predominant task to make WSN energy-efficient. Generally, a sensor node finds routes towards the base station (BS) to transmit the data. The sensor node transmits information at once or via neighbor nodes in a multi-hop manner. The nodes close to the BS transmit more data than other nodes. The nodes close to the BS tend to deplete their power quicker than different nodes in the network. This issue is known as the hotspot problem, leading to network separation and reducing the lifetime of the sensor network. The mobile sink as a better strategy to dispose of the hotspot problem in the course of information transmission, but the most critical challenges are finding sojourn points and path planning. This paper makes use of a cluster head (CH) as a sojourn point and multi-criteria decision strategy for path planning by the mobile sink at some stage in information collection. This paper contributes a fuzzy-based clustering algorithm that uses fuzzy logic to decide on cluster heads (CHs). The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach used to figure out the mobile sink's route. Based on this route, the mobile sink will move to collect data from every CH in the network. This scheme receives an order of CHs to visit and collect the data with the mobile sink. The simulation outcomes exhibit that this strategy performs better than the other protocols, which uses multi-hop information transmission for extending the lifetime of wireless sensor networks and one parameter for route planning.

Research on music teaching practice based on fuzzy intelligent interactive tablet

With the development of economy and the progress of science and technology, China, as a powerful country in science and technology and education, has been constantly improving the level of educational informatization and perfecting the basic teaching equipment, which has realized informatization and modernization to a certain extent. This research takes intelligent interactive tablet as interactive means and combines the theories of aesthetics, psychology, pedagogy and fuzzy teaching to research the appreciation, singing and creation aspects of music teaching practice. After three months of teaching practice, the students are evaluated and construct fuzzy cluster evaluation model, investigation and analysis and other methods, which verified that the fuzzy intelligent interactive tablet can provide rich content for music teaching, improve the teaching efficiency of students’ participation, and prove that the fuzzy intelligent interactive tablet has a great auxiliary effect on music teaching.