Numerical and experimental investigation on the thermochemical gasification potential of Cocoa pod husk (Theobroma Cacoa) in an open-core gasifier

Clean Technologies and Environmental Policy - Thermochemical conversion is a promising technology to generate producer gas (PG) from different types of agroforestry biomass residues. To use an...     

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.     

Controlled release of drugs from gradient hydrogels for high-throughput analysis of cell-drug interactions

In this paper, we report a method to fabricate microengineered hydrogels that contain a concentration gradient of a drug for high-throughput analysis of cell-drug interactions. A microfluidic gradient generator was used to create a concentration gradient of okadaic acid (OA) as a model drug within poly(ethylene glycol) diacrylate hydrogels. These hydrogels were then incubated with MC3T3-E1 cell seeded glass slides to investigate the cell viability through the spatially controlled release of OA. The drug was released from the hydrogel in a gradient manner and induced a gradient of the cell viability. The drug concentration gradient containing hydrogels developed in this study have the potential to be used for drug discovery and diagnostics applications due to their ability to simultaneously test the effects of different concentrations of various chemicals.     

Silver nanoparticle/r-graphene oxide deposited mesoporous-manganese oxide nanocomposite for pollutant removal and supercapacitor applications

Mesoporous manganese oxide was prepared by a non-ionic surfactant route using Triton X-100, followed by Ag nanoparticle (NP) and graphene oxide incorporation by an ultra-sonication-assisted process. Fine Ag NPs were incorporated into the tubular texture of mesoporous manganese oxide. The crystalline phase, particle size, and morphology of the prepared materials were characterized by X-ray diffraction (XRD), Barrett–Joyner–Halenda–Brunauer–Emmett–Teller analysis, scanning electron microscopy–energy dispersive X-ray analysis, and high-resolution transmission electron microscopy (HR-TEM). The XRD results confirmed the formation of the Mn₂O₃ phase for the as-prepared mesoporous manganese oxide and its nanocomposite. Very fine Ag NPs (<5–10 nm) were obtained. The mesoporous MnO₂ and graphene-incorporated Ag NPs/meso-MnO₂ had a tubular structure and “flaky pastry”-type morphology for the synthesized nanocomposites. HR-TEM images further confirmed the beautiful structural formation upon graphene addition and spherical/dot-shaped NP incorporation into the matrix of MnO₂. Improved surface area was obtained for the Ag NPs and graphene-incorporated mesoporous MnO₂ as compared to bulk MnO₂. The Cr(VI) removal analysis was performed using a batch technique, and enhanced removal of Cr(VI) was achieved (>98% removal of Cr(VI) within 1–2 h of reaction time) for Ag NP-incorporated mesoporous MnO₂. Efficient activity was observed because of the fine Ag NPs present in mesoporous manganese oxide, as opposed to the case of graphene oxide-doped meso-MnO₂ and pristine mesoporous meso-MnO₂.     

Optimization of hybrid laser–TIG welding of 316LN stainless steel using genetic algorithm

Determination of optimum hybrid laser–TIG welding process variables for achieving the maximum depth of penetration (DOP) in type 316LN stainless steel has been carried out using a genetic algorithm (GA). Nd:YAG pulsed laser and the TIG heat source were coupled at the weld pool to carry out hybrid welding. Design of experiments approach was used to generate the experimental design matrix. Bead-on-plate welds were carried out based on the design matrix. The input variables considered were laser power, pulse frequency, pulse duration, and TIG current. The response variable considered was the DOP. Multiple-regression model was developed correlating the process variables with the DOP using the generated data. The regression model was used for evaluating the objective function in GA. GA-based model was developed and it produced a set of solutions. Tournament and roulette wheel selection methods were used during the execution of GA. It was found that both the selection methods identified similar welding process parameters for achieving the maximum DOP. Excellent agreement was observed between the target DOP and the DOP values obtained in the validation experiments during hybrid laser–TIG welding.     

Unsupervised learning-based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Human-made/developed algorithms provide automatic identification and segmentation of the tissues, lesions and tumor regions available in brain magnetic resonance scan images, which invocates predicaments such as high computational cost and low accuracy rate. Such hassles are reconciled with the utilization of an unsupervised approach in combination with clustering techniques. Initially, static features are chosen from the input image, which is fed to the self-organizing map (SOM), where the algorithm employs the dimensionality reduction of input images. Consecutively, the reduced SOM prototype of data is clustered by the modified fuzzy K-means (MFKM) algorithm. The MFKM algorithm can be modified in terms of membership variables because it operates with spatial information and converges quickly, and this would be of greater benefit to radiologists as they reduce the wrong predictions and voluminous time that normally occur owing to human involvement. The proposed algorithm provides 98.77% sensitivity and 97.5% specificity, which are better than any other traditional algorithms mentioned in this article.