A Novel Image Categorization Strategy Based on Salp Swarm Algorithm to Enhance Efficiency of MRI Images

The main target of this paper is presentation of an efficient method for MRI images classification so that it can be used to diagnose patients and non-patients. Image classification is one of the prominent subset topics of machine learning and data mining that the most important image technique is the auto-categorization of images. MRI images with high resolution and appropriate accuracy allow physicians to decide on the diagnosis of various diseases and treat them. The auto categorization of MRI images toward diagnosing brain diseases has been being used to accurately diagnose hospitals, clinics, physicians and medical research centers. In this paper, an effective method is proposed for categorizing MRI images, which emphasizes the classification stage. In this method, images have been firstly collected and tagged, and then the discrete wavelet transform method has been implemented to extract the relevant properties. All the ready features in a matrix will be subsequently held, and PCA method has been applied to reduce the features dimension. Furthermore, a new model using support vector machine classifier with radial basis function kernel i.e. SVM+RBF has been performed. The SVM Algorithm must bimanually initialized, while, these values have been automatically entered into the SVM classifier by Salp Swarm Algorithm (SSA): Due to high performance of SSA in fast and accurate solution of nonlinear problem as compared to other optimization algorithms, it has been applied to optimally solve the designed problem. Finally, after applying the optimal parameters and SVM classification training, the test data has been utilized and evaluated. The results have transparently suggested the effectiveness of the proposed method in the Accuracy criteria with 0.9833, the Sensitivity with 1, Specificity with 0.9818 and Error with 0.0167 in best iteration as compared to the conventional SVM method

Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines

Residual useful life (RUL) prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost. Owing to various failure mechanism and operating environment, the application of classical models in RUL prediction of aircraft engines is fairly difficult. In this study, a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed. First of all, sensor data obtained from the aircraft engines are preprocessed to eliminate singular values, reduce random fluctuation and preserve degradation trend of the raw sensor data. Secondly, three kinds of recurrent neural networks (RNN), including ordinary RNN, long short-term memory (LSTM), and gated recurrent unit (GRU), are individually constructed. Thirdly, ensemble learning mechanism is designed to merge the above RNNs for producing a more accurate RUL prediction. The effectiveness of the proposed method is validated using two characteristically different turbofan engine datasets. Experimental results show a competitive performance of the proposed method in comparison with typical methods reported in literatures

Leaves Based Lubricant Additive Towards Improving Tribological Properties

Three kinds of crop leaf-surface waxes were extracted from wheat, corn and broomcorn leaves, respectively. The crop leaf-surface waxes as lubricant additives were added to synthetic ester and the friction and wear properties of prepared lubricants for steel-aluminum and steel-copper friction pair were investigated in detail. The scanning electron microscopy (SEM) and secondary ion mass spectrometry (SIMS) were employed to explore the friction mechanisms. The results show that crop leaf-surface waxes could successfully reduce the friction and wear of steel-aluminum and steel-copper sliding friction pairs as compared with pure synthetic ester. For example, when the concentration of wheat leaf-surface wax as additive was 2%, the COFs was decreased by 58%; the four additives can be ranked by the anti-wear capability as follows: Corn > Wheat > Glycerol >Broomcorn to steel-aluminum sliding friction pairs. The SIMS spectra of positive and negative ions on the worn surfaces have reduced the exposure of Al and increased short chain ions counts. The good friction reduction and antiwear abilities are attributed to the adsorption or reaction films formed by leafsurface wax on worn surface

Clean Production for Chrome Free Leather by Using a Novel Triazine Compound

Based on a novel triazine compound, the properties of tanned leather and commercial feasibility in pilot scale have been investigated. Then this novel approach tanning was compared with conventional chrome tanning: in the condition of less-salt pickling and chrome free, the physicochemical properties including thermal stability and mechanical strength were analyzed. Meanwhile, the surface roughness and fiber dispersion were evaluated as well. The results show that the thermal stability and mechanical strength of the triazine compound tanned leather are similar to conventional chrome tanned leather, the fiber bundle is well-dispersed and much evener than that of chrome treating. The optimized tanning approach has obvious reduction in environmental impact and leads an excellent biodegradability of tanning liquor. In industrial application, the cost of materials and water treatment are reduced effectively. The production of chrome free leather can encourage the sustainable development of leather industry and protects ecological environment in some extent

LncRNA LINC01772 promotes metastasis and EMT process in cervical cancer by sponging miR-3611 to relieve ZEB1

Cervical cancer (CC), has been identified as one of the most frequent malignant tumors all over the world, with high mortality in females. A growing number of investigations have confirmed that long noncoding RNAs (lncRNAs) play a crucial role in the progression of multiple cancers. Nonetheless, the biological function and regulatory mechanism of LINC01772 in CC haven’t been explored so far. In this study, LINC01772 expression was found to be upregulated in tissues and cells of CC. Knocking down LINC01772 suppressed CC cell proliferation, migration and epithelial-mesenchymal transition (EMT) process. Through molecular mechanism assays, LINC01772 was verified to be bound with miR-3611 and LINC01772 acted as a sponge for miR-3611. Zinc finger E-box binding homeobox 1 (ZEB1) was a downstream target gene of miR-3611. ZEB1 was negatively regulated by miR-3611 but positively regulated by LINC01772. Rescue assays confirmed that miR-3611 inhibitor or ZEB1 overexpression offset the inhibitive effect of LINC01772 depletion on cell proliferation, migration and EMT process in CC. In a word, our study validated that LINC01772 promoted cell metastasis and EMT process in CC by sponging miR-3611 to upregulate ZEB1 expression, indicating that LINC01772 could serve as a new therapeutic target for patients with CC

A New Anti-Quantum Proxy Blind Signature for Blockchain-Enabled Internet of Things

Blockchain technology has become a research hotspot in recent years with the prominent characteristics as public, distributed and decentration. And blockchain-enabled internet of things (BIoT) has a tendency to make a revolutionary change for the internet of things (IoT) which requires distributed trustless consensus. However, the scalability and security issues become particularly important with the dramatically increasing number of IoT devices. Especially, with the development of quantum computing, many extant cryptographic algorithms applied in blockchain or BIoT systems are vulnerable to the quantum attacks. In this paper, an anti-quantum proxy blind signature scheme based on the lattice cryptography has been proposed, which can provide user anonymity and untraceability in the distributed applications of BIoT. Then, the security proof of the proposed scheme can derive that it is secure in random oracle model, and the efficiency analysis can indicate it is efficient than other similar literatures

Trichoderma spp. and Bacillus spp. as growth promoters in maize (Zea mays L.)

Microbes that are beneficial to plants are used to enhance the crop growth, yield and are alternatives to chemical fertilizers. Trichoderma and Bacillus are the predominant plant growth-promoting fungi and bacteria. The objective of this study was select, characterize, and evaluate isolates of Trichoderma spp. and Bacillus spp. native from the northern region of Sinaloa, Mexico, and assess their effect on growth promotion in maize (Zea mays L.). In greenhouse conditions, four Trichoderma isolates and twenty Bacillus isolates, as well as two controls, were tested in a completely randomized design with three replicates. We selected the two best strains of Trichoderma and Bacillus: TB = Trichoderma asperellum, TF = Trichoderma virens, B14 = Bacillus cereus sensu lato and B17 = Bacillus cereus, which were evaluated in the field in a completely randomized blocks in factorial arrangement design with three replicates applying different rates of nitrogen fertilizer (0, 150 kg N/ha, and 300 kg N/ha). Treatments 5 (B17 = B. cereus) and 11 (TF = T. virens) both fertilized with 150 kg N/ha showed similar yields and they did not reveal significant differences from the treatments fertilized with 300 kg N/ha. This indicated that treatment 5 (B17= B. cereus with 150 kg N/ha) and treatment 11 (TF= T. virens with 150 kg N/ha) were efficient as growth promoters, by not showing significant differences in root volume and dry weight of foliage. The results indicated a reduction of 50% in the rate of nitrogen to fertilizer required for maize (Zea mays L.) crops. These microorganisms Trichoderma and Bacillus could be an alternative to reduce the use of chemical fertilizers in maize

Influences of ascorbic acid and gibberellic acid in alleviating effects of salinity in Petunia under in vitro

Salinity is one of the abiotic stresses that limits the growth and productivity of many crops. A possible survival strategy for plant under saline conditions is to use compounds that could minimize the harmful effects of salt stress on the plant development. The objective of the presented study was to investigate the effect of exogenous ascorbic acid (ASA) with or without gibberellic acid (GA3) on key growth and biochemical parameters in two petunia cultivars ‘Prism Rose’ and ‘Prism White’ under saline (150 mM NaCl) and non-saline in vitro condition. Nodal cutting with an axillary buds were used as explants. Application of 1 mM ascorbic acid with or without 0.05 mM gibberellic acid into the MS medium stimulated the length of shoots and the number of new shoots of ‘Prism Rose’; whereas, it decreased the root length and the number of roots of both ‘Prism Rose’ and ‘Prism White’ under non-saline condition. The addition of ascorbic acid with or without gibberellic acid into the MS medium under saline condition, increased the length of plants and the number of new shoots, but did not affect their root number and length. NaCl treatments increased the proline content and lipid peroxidation which was indicated by the accumulation of malondialdehyde (MDA). The study revealed a correlation between chlorophylls a and b content and the leaf pigmentation intensity – parameter a*. Addition of 1 mM ascorbic acid with 0.05 mM gibberellic acid into the MS medium plays a protective role in salinity tolerance by improving the shoot growth and the development as well as increasing the activities of the antioxidant enzymes and other antioxidant substances

Morphometric and Biochemical Changes in <i>Agave americana</i> L. Plantlets Induced By Ethyl Methanesulfonate

A. americana L. is a crop with very little genetic variability. In order to evaluate the effect of ethyl methanesulfonate (EMS) to induce variability in in vitro plantlets of A. americana, different explants (meristems, leaves and roots) were evaluated for the production of callus. MS medium supplemented with ANA (2.68 μM) and BAP (2.68 μM) was used. Callus obtained from apical meristem were treated with 15 mM EMS for two hours after which shoot formation was induced using 2,4-D (0.11 μM) and BAP (44 μM). The EMS induced variations in the morphometric and morphological parameters of the plantlets obtained, with 60% of the plantlets presenting differences such as dwarfism and different leaf forms, without the presence of spines, as well as an increase in fructan content of 30% with respect to the control plantlets. PAL was increased and this activity is related with higher anthocyanins concentration in A. americana L. plantlets

Design of Intelligent Drunk Driving Detection System Based on Internet of Things

In recent years, with the rapid development of China’s economy and the continuous improvement of people’s living standards, the number of motor vehicles and the number of drivers in the country have grown rapidly. Due to the increase in the number of vehicles and the number of motorists, the traffic accident rate is increasing, causing serious economic losses to society. According to the traffic accident statistics of the Ministry of Communications of China in 2009, more than 300,000 car accidents occurred in the year, most of which were caused by drunk driving. Therefore, this paper proposes a design scheme based on the Internet of Things-based vehicle alcohol detection system. The system uses STM8S003F3 single-chip microcomputer as the main control chip of the system, combined with alcohol sensor MQ-3 circuit, LCD1602 liquid crystal display circuit, buzzer alarm circuit and button circuit to form a complete alcohol detection module hardware system. The main functions of the system are as follows: the alcohol sensor in the car detects the driver’s alcohol concentration value, and displays the value on the LCD screen. The buzzer alarm is exceeded and the information is sent to the traffic police department and the family’s mobile phone through the GPRS module. The system can effectively make up for the shortcomings of traffic police detection, which has certain research significance