GAPSO-SVM: An IDSS-based Energy-Aware Clustering Routing Algorithm for IoT Perception Layer

Wireless Personal Communications - With the emergence of Internet of Things (IoT) having large scale and generating huge amount of data, Intelligent Decision Support Systems (IDSSs) have attracted...     

Emerging Immunotherapies against Novel Molecular Targets in Breast Cancer

Immunotherapy is a highly emerging form of breast cancer therapy that enables clinicians to target cancers with specific receptor expression profiles. Two popular immunotherapeutic approaches involve chimeric antigen receptor-T cells (CAR-T) and bispecific antibodies (BsAb). Briefly mentioned in this review as well is the mRNA vaccine technology recently popularized by the COVID-19 vaccine. These forms of immunotherapy can highly select for the tumor target of interest to generate specific tumor lysis. Along with improvements in CAR-T, bispecific antibody engineering, and therapeutic administration, much research has been done on novel molecular targets that can especially be useful for triple-negative breast cancer (TNBC) immunotherapy. Combining emerging immunotherapeutics with tumor marker discovery sets the stage for highly targeted immunotherapy to be the future of cancer treatments. This review highlights the principles of CAR-T and BsAb therapy, improvements in CAR and BsAb engineering, and recently identified human breast cancer markers in the context of in vitro or in vivo CAR-T or BsAb treatment.     

Investigation of tensile and in-plane shear properties of carbon fiber reinforced composites with and without piezoelectric patches for micro-crack propagation using extended finite element method

Microsystem Technologies - The smart materials are capable to integrate the remarkabale active functions into a traditional structure of the composites. There is a wide research possibility towards...     

Automatic detection of Moroccan coastal upwelling zones using sea surface temperature images

An efficient unsupervised method is developed for automatic segmentation of the area covered by upwelling waters in the coastal ocean of Morocco using the Sea Surface Temperature (SST) satellite images. The proposed approach first uses the two popular unsupervised clustering techniques, k-means and fuzzy c-means (FCM), to provide different possible classifications to each SST image. Then several cluster validity indices are combined in order to determine the optimal number of clusters, followed by a cluster fusion scheme, which merges consecutive clusters to produce a first segmentation of upwelling area. The region-growing algorithm is then used to filter noisy residuals and to extract the final upwelling region. The performance of our algorithm is compared to a popular algorithm used to detect upwelling regions and is validated by an oceanographer over a database of 92 SST images covering each week of the years 2006 and 2007. The results show that our proposed method outperforms the latter algorithm, in terms of segmentation accuracy and computational efficiency.     

A New Method for Sentiment Analysis Using Contextual Auto-Encoders

Sentiment analysis, a hot research topic, presents new challenges for understanding users’ opinions and judgments expressed online. They aim to classify the subjective texts by assigning them a polarity label. In this paper, we introduce a novel machine learning framework using auto-encoders network to predict the sentiment polarity label at the word level and the sentence level. Inspired by the dimensionality reduction and the feature extraction capabilities of the auto-encoders, we propose a new model for distributed word vector representation “PMI-SA” using as input pointwise-mutual-information “PMI” word vectors. The resulted continuous word vectors are combined to represent a sentence. An unsupervised sentence embedding method, called Contextual Recursive Auto-Encoders “CoRAE”, is also developed for learning sentence representation. Indeed, CoRAE follows the basic idea of the recursive auto-encoders to deeply compose the vectors of words constituting the sentence, but without relying on any syntactic parse tree. The CoRAE model consists in combining recursively each word with its context words (neighbors’ words: previous and next) by considering the word order. A support vector machine classifier with fine-tuning technique is also used to show that our deep compositional representation model CoRAE improves significantly the accuracy of sentiment analysis task. Experimental results demonstrate that CoRAE remarkably outperforms several competitive baseline methods on two databases, namely, Sanders twitter corpus and Facebook comments corpus. The CoRAE model achieves an efficiency of 83.28% with the Facebook dataset and 97.57% with the Sanders dataset.     

Electrosynthesis and structural characterization of a novel aryl ether trimer

The structure of a novel trimer formed by three p-tert-butyl anisole moieties via the controlled potential electrolysis of p-tert-butyl anisole was determined by X-ray diffraction method. By cyclic voltammetry, we report a multistep electron transfer, each followed by a chemical reaction, resulting in an ECECEC mechanism. Preparative scale oxidation of p-tert-butyl anisole in dry acetonitrile leads to its two first oligomers. The symmetrical dimer, 2,2′-dimethoxy-5,5′-di-tert-butylbiphenyl, shows that the favored coupling sites are those in the ortho position of the methoxy group. The title compound, namely the 1-methoxy-bis-2,3-(2′-methoxy-5′-tert-butylphenyl)-4-tert-butylbenzene crystallizes in triclinic space group P-1 with a = 10.571(3) Å, b = 11.739(1) Å, c = 12.733(2) Å, α = 74.64(1)°, β = 88.71(2)°, γ = 76.58(2)°, V = 1480.8(5) Å³, and Z = 2. The structural analysis reveals that two p-tert-butyl anisole moieties are linked in ortho position on a third p-tert-butyl anisole fragment.     

Failure Analysis of Tractor Wheel Axle

Journal of Failure Analysis and Prevention - A number of wheel axles of MF-240 Tractors, which had broken after unusually short times in the field (~ 2–4 months), were examined to...     

Novel derivatives of 3D woven T-shaped composites with improved performance

Conventional 3D woven T-shaped preforms were modified for improving joint/ peel off strength of associated T-shaped polymeric composites. Preforms were modified at weaving level by preferred yarns orientations for better performance in associated composites. Major modifications studied are; the addition of supporting layer, single or double-crossing in joint layers and crossing along with supporting layer (in a single sample). Novel derivatives were compared with conventionally used (on-loom and off-loom) T-shaped 3D woven composites. Microscopic analysis was also carried out to analyze the orientation of yarns and analysis of samples from failure point after testing. By combined variation of crossing and supporting layers improvement of 47.37% in peel off strength and 70.37% in impact strength was observed. Modified T shapes can serve to be a good replacement for conventional T-shaped stiffener (used to avoid folding under loads) and in joints.