Curcumin has shown pharmacological properties against different phenotypes of various disease models. Different synthetic routes have been employed to develop its numerous derivatives for diverse and improved therapeutic roles. In this study, we have synthesized curcumin derivatives containing isoxazole, pyrazoles, and pyrimidines and then the synthesized molecules were evaluated for their anti-inflammatory and antinociceptive activities in experimental animal models. Acute toxicity of synthesized molecules was evaluated in albino mice by oral administration. Any behavioral and neurological changes were observed at dose of 10 mg/kg body weight.Additionally, cyclooxygenase-2 (COX-2) enzyme inhibition studies were performed through in vitro assays. In vivo anti-inflammatory studies showed that curcumin with pyrimidines was the most potent anti-inflammatory agent which inhibited induced edema from 74.7% to 75.9%. Compounds 7, 9, and 12 exhibited relatively higher prevention of writhing episodes than any other compound with antinociceptive activity of 73.2%, 74.9%, and 71.8%, respectively. This was better than diclofenac sodium (reference drug, 67.1% inhibition). Similarly, COX-2 in vitro inhibition assays results revealed that compound 12 (75.3% inhibition) was the most potent compound.Molecular docking studies of 10, 11, and 12 compounds in human COX-2 binding site revealed the similar binding modes as that of other COX-2-selective inhibitors. K E Y W O R D Santi-inflammatory, COX-2 inhibition, curcumin derivatives, molecular modeling Chemically curcumin is diferuloylmethane which has attracted much attention of medicinal chemists for various diseases and therapeutic agents development. It has shown its pharmacological safety and wide range of biological activities such as antibacterial to anticancer agent [1][2][3][4] . Currently, curcumin is acclaimed to be one of the most widely researched naturally occurring chemopreventive agent which is cytoprotective to healthy human cells [5][6][7] . In spite of important therapeutic application, limited therapeutic utility concerns are associated with curcumin because of its poor absorption and fast metabolism under physiological conditions [8] . Active methylene and keto moiety are believed to be responsible for its rapid metabolism. In order to circumvent the problem of rapid metabolism and to improve its pharmacokinetics profile, several synthetic modifications have been studied on carbonyl and active methylene moiety [9] . In present study, isoxazole, N-substituted pyrazoles, and pyrimidine ring were incorporated in this focused segment of curcumin. Nitrogen heterocyclic moieties such as pyrazoles and pyrimidines containing derivatives gained considerable attention in medicinal chemistry for

Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.

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Inorganic perovskite materials are possible candidates for conversion of solar energy to electrical energy due to their high absorption coefficient. Perovskite solar cells (PSCs) introduced a new type of device structure that has attention due to better efficiencies and interest in PSCs that has been increasing in recent years. Halide perovskite materials such as CsPbIBr2 show remarkable optical and structural performance with their better physical properties. Perovskite solar cells are a possible candidate to replace conventional silicon solar panels. In the present study, CsPbIBr2 perovskite materials’ thin films were prepared for light-absorbing application. Five thin films were deposited on the glass substrates by subsequent spin-coating of CsI and PbBr2 solutions, subsequently annealed at different temperature values (as-deposited, 100, 150, 200 and 250 °C) to get CsPbIBr2 thin films with a better crystal structure. Structural characterizations were made by using X-ray diffraction. CsPbIBr2 thin films were found to be polycrystalline in nature. With increasing annealing temperature, the crystallinity was improved, and the crystalline size was increased. Optical properties were studied by using transmission data, and by increasing annealing temperature, a small variation in optical band gap energy was observed in the range of 1.70–1.83 eV. The conductivity of CsPbIBr2 thin films was determined by a hot probe technique and was found to have little fluctuating response toward p-type conductivity, which may be due to intrinsic defects or presence of CsI phase, but a stable intrinsic nature was observed. The obtained physical properties of CsPbIBr2 thin films suggest them as a suitable candidate as a light-harvesting layer. These thin films could be an especially good partner with Si or other lower band gap energy materials in tandem solar cells (TSC). CsPbIBr2 material will harvest light having energy of ∼1.7 eV or higher, while a lower energy part of the solar spectrum will be absorbed in the partner part of the TSC.

Kunitz-type trypsin inhibitor was first characterized from Enterolobium contortisiliquum (EcTI) (Batista et al., 1996). Plant-based KTIs have shown inhibition of trypsin or chymotrypsin along other serine proteinases such as subtilisin and elastase (Revina et al., 2004; Sumikawa et al., 2006). Some individual KTIs typically have shown more specific activities in comparison to those that inhibit cysteine or aspartic proteinases (Heibges et al., 2003). Kunitz-type trypsin inhibitors are also found in

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Reliability measurement and estimation of an industrial system is a difficult and essential problematic task for control engineers. In this context reliability can be described as the probability that machine network will implement its proposed functions under the observing condition throughout a specified time period of running machine system network. In this study single sensor method is applied for fault diagnosis depending on identification of single parameter. At early stages it is hard to diagnose machine fault due to ambiguities in modeling environment. Due to these uncertainties and ambiguities in modeling, decision making become difficult and lead to high financial loss. To overcome these issues between the machine fault symptoms and estimating the severity of the fault; a new method of artificial intelligence fault diagnosis based approach Dempster-Shafer theory has been proposed in this paper. This theory will help in making accurate decision of the machine condition by fusing information from different sensors. The experimental results demonstrate the efficient performance of this theory which can be easily compared between unsurpassed discrete classifiers with the single sensor source data.

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