Relationship between pore coarsening and mass loss during supersolidus liquid phase sintering of alpha brass

ABSTRACT

The effect of sintering variables on the zinc loss during sintering of brass was investigated. Zinc loss, evident through mass change, starts during heating of the powder at approximately 700°C. By supersolidus liquid phase sintering of the cold-pressed brass powder, the maximum amount of zinc loss occurred at lower temperatures and prolonged sintering times. At higher temperatures, especially near the zinc boiling point, the rate of zinc loss from the surface is surprisingly moderate since closing of the pores causes zinc to be trapped in the samples. At lower temperatures, in contrast, when the pores in the surface are open and there is also a lower volume fraction of liquid phase, zinc can easily escape from the samples. Generally, it was thus concluded that during sintering, zinc loss depends more strongly on the pore connectivity and the specific surface than just on the nominal vapour pressure.     

Low temperature preparation and characterization of nanospherical anatase TiO2 and its photocatalytic activity on Congo red degradation under sunlight

Nanospherical titanium oxide with anatase structure was obtained at low temperature and normal pressure using a simple method of preparation. The dilute titanium (III) chloride solution was stirred at room temperature for several hours, and then ammonium hydroxide with hydrogen peroxide was added. A yellow gel TiO₂·xH₂O was obtained with nanodot shape and amorphous structure. Heated at 300 °C, it crystallized to pure anatase structure with nanospherical shape. The anatase particles prepared show good adsorption and photodegradation of Congo red solution under the sunlight. Materials were characterized by powder X-ray diffraction, thermal analysis, transmission electron microscope and UV spectroscopy.     

Plant Responses to Herbivory,Wounding, and Infection

Plants have various self-defense mechanisms against biotic attacks, involving both physical and chemical barriers. Physical barriers include spines, trichomes, and cuticle layers, whereas chemical barriers include secondary metabolites (SMs) and volatile organic compounds (VOCs). Complex interactions between plants and herbivores occur. Plant responses to insect herbivory begin with the perception of physical stimuli, chemical compounds (orally secreted by insects and herbivore-induced VOCs) during feeding. Plant cell membranes then generate ion fluxes that create differences in plasma membrane potential (Vm), which provokes the initiation of signal transduction, the activation of various hormones (e.g., jasmonic acid, salicylic acid, and ethylene), and the release of VOCs and SMs. This review of recent studies of plant–herbivore–infection interactions focuses on early and late plant responses, including physical barriers, signal transduction, SM production as well as epigenetic regulation, and phytohormone responses.     

Antimicrobial silicone rubbers based on photocatalytically active additives

The modification of plastics to generate germ-reducing surface materials is a promising strategy to decrease nosocomial infections in hygiene-sensitive areas. In this paper, photocatalytically active nanoparticles were incorporated as additives, not as a coating, into silicone rubber matrix material to produce elastic antibacterial bulk materials. Samples with 5 wt% and 10 wt% of two different types of TiO₂ and ZnO were prepared and investigated. The thermal analysis of the developed materials showed a complete vulcanization of the developed materials and slight modifications of mechanical properties were found. Investigations of the surface of the materials indicated no changes in the wettability of the surfaces or in their fourier transform infrared spectrometer (FT-IR) spectra, suggesting no degradation of the developed material. The photocatalytic activity on the surface of the test samples was investigated by microbial tests with Escherichia coli, Pseudomonas fluorescens, and Staphylococcus aureus bacteria. Depending on the additive type and the test germs, the samples showed different intensities of a germ-reducing effect (up to >99,999%).     

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.     

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.