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In this study, we investigate the (2 + 1)-dimensional Korteweg-De Vries (KdV) equation with the extension of time-dependent coefficients. A symbolic computational method, the simplified Hirota’s method, and a long-wave method are utilized to create various exact solutions to the suggested equation. The symbolic computational method is applied to create the Lump solutions and periodic lump waves. Hirota’s method and a long-wave method are implemented to explore single-, double- and triple-M-lump waves, and interaction physical phenomena such as an interaction of single-M-lump with one-, two-soliton solutions, as well as a collision of double-M-lump with single-soliton waves. Furthermore, the simplified Hirota’s method is employed to explore complex multi-soliton solutions. To realize dynamics, the gained solutions are drawn via utilizing different arbitrary variable coefficients.

To enhance the accuracy of object recognition, various combination of recognition algorithms are used in recent literature. In this paper coherence of Discrete Wavelet Transform (DWT) is combined with four different algorithms: error vector of principal component analysis (PCA), eigen vector of PCA, eigen vector of Linear Discriminant Analysis (LDA) and Convolutional Neural Network (CNN) then combination of four results are done using entropy of detection probability and Fuzzy system. From this research the accuracy of recognition is found dependent on image and diversity of database. The combined method of the paper provides recognition rate of 89.56% for the worst case and 93.34% for the best case both can be said better in comparison with the previous works where individual method has been implemented on a specific set of images.

Reducing toxic effects of pesticide residues in agricultural soils through organic amendments is an eco-friendly technique. Cypermethrin (CYP) and Chlorpyrifos (CPP) are widely used pesticides in peach growing orchards in Swat valley of Pakistan. The aim of the current study was to investigate the degradation behavior of CYP and CPP in soil by the application of different combination of organic/inorganic amendments. A total of 36 soil samples were used in the current incubation study which was collected from 4 peach orchards in district Swat, Khyber Pakhtunkhwa (KPK), Pakistan. Different amendments including urea, farm yard manure (FYM) and saprofil were applied alone and in various combinations. The initial concentrations of CYP and CPP in the tested soil was range from 0.94 to 4.8 mg kg−1 and 0.024 to 4.12 to mg kg−1. Soil samples were taken at 5, 15, 30 and 45 days after exposure to different treatments. The extraction of pesticides from soils was done through quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction method. Soils amended with urea, FYM and saprofil individually and in combinations significantly reduced the concentrations of CYP and CPP. However, the concentration of CYP (24.6) and CPP (27.0) in soil showed higher reduction through the application of FYM. While the concentrations of CYP and CPP were declined with the 5, 15, 30 and 45 days intervals, however, reduction at day 30 and 45 was faster for CYP (16.7 to 8.46) than CPP (20.2 to 12.3). At day 5 and 15, the CYP (42.5 to 30.7) was slightly lower than CPP (42.9 to 32.7).The highest half-life value (t ½) of CYP was in control treatment (32 days) and the shortest was soil amended with FYM (18.6 days). While the longest half-life value (t ½) of CPP was maximum in control treatment (42 days) and the minimum was in FYM (22 days). Based on our findings, it was concluded that soil application of FYM is recommended for the degradation of CYP and CPP.

Background Tuberculosis is a chronic infection caused by Mycobacterium tuberculosis (M.tb), which needs proper macrophage activation for control. It has been debated whether the co-infection with helminth will affect the immune response to mycobacterial infection. Objective To determine the effect of sequential co-infection of Heligmosomoides polygyrus (H.pg) nematodes and M.tb on T cell responses, macrophages polarization and lung histopathological changes. Method This study used 49 mice divided into 7 treatment groups, with different sequence of infection of M.tb via inhalation and H.pg via oral ingestion for 8 and 16 weeks. T cells response in the lung, intestine, and peripheral blood were determined by flow cytometry. Cytokines (IL-4, IFN-γ, TGB-β1, and IL-10) were measured in peripheral blood using ELISA. Lung macrophage polarization were determined by the expression of iNOS (M1) or Arginase 1 (M2). Mycobacterial count were done in lung tissue. Lung histopathology were measured using Dorman’s semiquantitative score assessing peribronchiolitis, perivasculitis, alveolitis, and granuloma formation. Result M.tb infection induced Th1 response and M1 macrophage polarization, while H.pg infection induced Th2 and M2 polarization. In sequential co-infection, the final polarization of macrophage was dictated by the sequence of co-infection. However, all groups with M.tb infection showed the same degree of mycobacterial count in lung tissues and lung tissue histopathological changes. Conclusion Sequential co-infection of H.pg and M.tb induces different T cell response which leads to different macrophage polarization in lung tissue. Helminth infection induced M2 lung macrophage polarization, but did not cause different mycobacterial count nor lung histopathological changes.

In this research article, we have designed a novel image encryption scheme based on Deoxyribonucleic acid (DNA) and chaotic sequencing. We have studied sequences of different genes consisting of four bases, Adenine (A), Cytosine (C), Thymine (T) and Guanine (G), also known as nucleotides which are the fundamental code of life. The purpose of DNA is to store, copy and transmit the genetic information of the living organism. The proposed image encryption scheme uses the chaotic system to generate random sequences and to choose between two or more options. The image is totally transformed by first encoding it into DNA nucleotides, shuffling to achieve diffusion and then substitution is performed to achieve confusion. Then later the DNA fusion operation is performed on the DNA image and the DNA complements are applied on the resulting DNA image, a random number of times to break the residual correlation between the pixels. The cipher image has been tested to fulfill all the standard benchmarks to be categorized as a good ciphered image which includes entropy, correlation coefficient, Pearson’s chi square, peak signal to noise ratio, mean square error, mean absolute error, structure similarity indexed measures, differential analysis and histogram uniformity test.

Android reflects a revolution in handhelds and mobile devices. It is a virtual machine based, an open source mobile platform that powers millions of smartphone and devices and even a larger no. of applications in its ecosystem. Surprisingly in a short lifespan, Android has also seen a colossal expansion in application malware with 99% of the total malware for smartphones being found in the Android ecosystem. Subsequently, quite a few techniques have been proposed in the literature for the analysis and detection of these malicious applications for the Android platform. The increasing and diversified nature of Android malware has immensely attenuated the usefulness of prevailing malware detectors, which leaves Android users susceptible to novel malware. Here in this paper, as a remedy to this problem, we propose an anti-malware system that uses customized learning models, which are sufficiently deep, and are ’End to End deep learning architectures which detect and attribute the Android malware via opcodes extracted from application bytecode’. Our results show that Bidirectional long short-term memory (BiLSTMs) neural networks can be used to detect static behavior of Android malware beating the state-of-the-art models without using handcrafted features. For our experiments in our system, we also choose to work with distinct and independent deep learning models leveraging sequence specialists like recurrent neural networks, Long Short Term Memory networks and its Bidirectional variation as well as those are more usual neural architectures like a network of all connected layers(fully connected), deep convnets, Diabolo network (autoencoders) and generative graphical models like deep belief networks for static malware analysis on Android. To test our system, we have also augmented a bytecode dataset from three open and independently maintained state-of-the-art datasets. Our bytecode dataset, which is on an order of magnitude large, essentially suffice for our experiments. Our results suggests that our proposed system can lead to better design of malware detectors as we report an accuracy of 0.999 and an F1-score of 0.996 on a large dataset of more than 1.8 million Android applications.

The extracted marine Chlorella sp. residue (EMCR) was used as a feedstock for biochar and bio-oil production. The biochar was prepared by slow pyrolysis at 450 °C for 60 min in a tube furnace and employed as a microwave absorber (MA). The bio-oil was produced by microwave pyrolysis (MWP) with investigation of temperature (350–450 °C), time (20–40 min) and MA loading (10–30 wt%) at fixed microwave power of 850 W. The pyrolysis condition was optimized to obtain maximum bio-oil yield using the Response Surface Methodology (RSM) based on Central Composite Design (CCD). The optimum condition was 350 °C, 15% MA loading and 40 min, which yielded 46% bio-oil. Characterization of Chlorella sp. biomass, EMCR, biochar and bio-oil was performed by proximate and ultimate analysis, FTIR, TGA and GC-MS. The higher heating values of biomass, EMCR and biochar were 22.43, 15.49 and 10.79 MJ/kg, respectively. The results showed a high feasibility of applying EMCR as the feedstock for biochar and bio-oil production. The EMCR derived biochar presented great efficiency as the MA with high surface area of 266 m2/g. The bio-oil consisted of a mixture of chemicals, which requires further processes before using in many applications.

Background Mycobacterium tuberculosis (MTB) is divided into ancient and modern lineage. Both lineages have different virulence property and therefore associated with different immune responses and disease severity in tuberculosis (TB) patients. The aim of this study was to determine the association between MTB lineages and the level of tumor necrosis factor alpha (TNF-α), and lung tissue damage among Indonesian TB patients. Methods Thirty new and active TB patients were recruited randomly from Dr. Soewandhie Hospital, Surabaya, Indonesia. MTB isolates were isolated from bronchoalveolar lavage fluid (BALF) and their lineages were determined by primer-specific PCR targeting TbD1 and RD9 region gene. The degree of lung tissue damage was assessed and classified using NICE Scoring System and TNF-α level of BALF was measured by ELISA. Results MTB were detected in all patients in which 17 and 13 isolates were classified as modern and ancient lineage, respectively. Modern lineage was associated with the degree of lung damage (odds ratio [OR]: 3.97; 95% CI: 1.57–6.37), p Conclusion Modern lineage is likely associated with high TNF-α level and increases the risk of having severe lung damage three times among TB patients from Indonesia. Therefore, laboratory test to determine MTB lineages might crucial to be integrated in National Tuberculosis Control Program in the country.

Present study deals with the synthesis of macromolecular prodrugs of aceclofenac (ACL) on to hydrophilic polysaccharide hydroxyethylcellulose (HEC). ACL prodrugs synthesized were thoroughly characterized by spectroscopic techniques and found organo-soluble. The esterified prodrugs showed self-assembly behaviour at solvent interface of DMSO/water and appeared as nanoparticles of 200–550 nm diameter. The HEC-ACL conjugates showed significant inhibition (43%) in the levels of interleukin 6 (IL-6) which indicated its immunomodulatory potential. The conjugates showed 81% inhibition in edema created after carrageenan injection. HEC-aceclofenac conjugates appeared biocompatible in cell viability studies. Bioavailability studies revealed that ACL from the conjugate shows plasma half-life of 7.90 h which is significantly higher than unmodified ACL (3.93 h). Moreover, tmax of 4.0 h with peak plasma concentration (Cmax) of 8.27 μg mL−1 for ACL from conjugate indicated its enhanced bioavailability.

A recently developed method for the thermal stability measurement of phase-change materials (PCMs) involves thermal cycling using a thermoelectric module as a heating and cooling element. However, the utility of this approach was found to have some limitations, mainly because the thermoelectric polarity is changed according to time rather than the actual sample temperature. A method for thermal cycling test, where the thermoelectric polarity is automatically changed according to the sample temperature was developed in this study. In addition, a new cartridge design in this device requires a small sample volume (1.53 cm3) and can be easily assembled and disassembled. This proposed device was tested on beeswax as a PCM sample. This is very important for savings PCMs material which usually expensive. The results showed that the apparatus had automatically cycled between the melting and cooling temperatures of beeswax. The thermal data showed that beeswax retains consistent melting and freezing temperatures after 1000 cycles, however, its heat of fusion degrades over repeated thermal cycling. This apparatus can be readily applied to study a wide range of PCMs for such as thermal energy storage materials for energy conservation. To our best knowledge, yet no study has been performed on this kind of equipment so far.

Cedrus deodara (deodar) is practically used, as insect repellent, in the northern areas of Pakistan but no data available therefore this study was conducted to evaluate the effectiveness of deodar oil as an alternate of conventional insecticides against the larval pest stage of mealworm beetle (Tenebrio molitor), by feeding method. The aim of the study was to investigate the effectiveness of deodar oil as an alternate of conventional insecticides against the larval pest stage of mealworm beetle (Tenebrio molitor), by feeding method. All tested chemicals showed efficacy against the pests. The LC50 was determined by probit analysis and was found to be 3.41, 0.086 and 0.023% of larvae treated with deodar oil, Carbosulfan and Imidacloprid respectively The LC50 treated larvae were subjected to the evaluation of protein activity, qualitatively and quantitatively. The protein level in tested insects was enhanced when treated with Imidacloprid, Carbosulfan and deodar oil. The electrophoretic profile of treated insects showed more bands in insects treated with Cedrus deodara oil. This electrophoretic profile appeared in 4, 5, 7 and 8 bands for tested chemicals including control. Antifeedant activity was observed for C. deodara as larvae were deterred to feed on the food found in the container. Keywords: Tenebrio, Mealworms, Cedrus deodara, Imidacloprid, Carbosulfan, Protein level, Toxicity

© 2018 Elsevier Ltd Development of phase-change material (PCM) as thermal energy storage for building envelopes is promising for energy utilization. However, there are two major drawbacks of PCM application, which are low thermal conductivity and high-volume reduction due to phase-change transition. One solution is to develop a shape-stabilized phase-change material (SSPCM) as a composite that is able to prevent leakage during the transition from solid to liquid. Therefore, the objective of this study is to prepare beeswax/multi-walled carbon nanotubes as form-stable nanocomposite phase-change material for thermal energy storage, based on previously unattempted methods. Beeswax was being used as PCM because of its high latent heat and multi-walled carbon nanotubes (MWCNTs) as supporting material with high thermal conductivity. There are three types of MWCNTs applied in this research: pristine MWCNTs, ball-milled MWCNTs and acid-treated MWCNTs. Beeswax/CNT composite samples were prepared with ratios of 5 and 20 wt%. Composite samples were tested from structure modification and thermal performance, including latent heat, sensible heat, melting point, solidifying point, thermal conductivity, and thermal-cycle testing for up to 300 cycles. Experimental results showed that thermal conductivity of novel shape-stable nanocomposite PCM increased by a factor of 2 and there was no significant phase transition in the melting or solidifying temperature. The high heat storage capability and thermal conductivity of nanocomposite PCM enable it to be a potential material for thermal energy storage in practical applications.