A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model

The Journal of Supercomputing - Deep learning algorithms have yielded remarkable results in medical diagnosis and image analysis, besides their contribution to improvements in a number of fields...     

N,N-dimethylglycyl-amido derivative of minocycline and 6-demethyl-6-desoxytetracycline, two new glycylcyclines highly effective against tetracycline-resistant gram-positive cocci

The in vitro activities of the N,N-dimethylglycyl-amino derivative of minocycline (DMG-MINO) and 6-dimethyl-6-dexoxytetracycline (DMG-DMDOT), members of a new generation of tetracyclines, were evaluated by an agar dilution method and were compared with those of tetracycline and minocycline against 224 tetracycline-resistant and 73 tetracycline-susceptible recent clinical isolates of gram-positive cocci, including multiple-antibiotic-resistant methicillin-resistant Staphylococcus aureus and penicillin-resistant Streptococcus pneumoniae. The MICs of DMG-MINO and DMG-DMDOT were up to 500- to 2,000-fold lower than those of tetracycline against methicillin-resistant S. aureus and Streptococcus pneumoniae (MIC for 50% of strains tested [MIC50], < 0.06 microgram/ml). Against Streptococcus groups A, B, C, and G and Enterococcus faecalis, the MIC50 was 0.5 microgram/ml. MIC50s were greater only for coagulase-negative staphylococci (2 micrograms/ml). These data indicate that DMG-MINO and DMG-DMDOT are very potent drugs, and further in vitro and in vivo studies are warranted.     

Methacrylate-functionalized POSS influence on cross-linking and mechanical properties of styrene-butadiene rubber

Iranian Polymer Journal - The addition of methacrylate-functional polyhedral oligomeric silsesquioxane (MA-POSS) nanoparticles to styrene-butadiene rubber (SBR) composites was evaluated in terms of...     

Extracting lexical and phrasal paraphrases: a review of the literature

Recent advances in natural language processing have increased the popularity of paraphrase extraction. Most of the attention, however, has been focused on the extraction methods only without taking the resource factor into the consideration. Unknowingly, there is a strong relationship between them and the resource factor also plays an equally important role in paraphrase extraction. In addition, almost all of the previous studies have been focused on corpus-based methods that extract paraphrases from corpora based solely on syntactic similarity. Despite the popularity of corpus-based methods, a considerable amount of research has consistently shown that these methods are vulnerable to several types of erroneous paraphrases. For these reasons, it is necessary to evaluate whether the trend is moving in a positive direction. This paper reviews the major research on paraphrase extraction methods in detail. It begins by exploring the definition of paraphrase from different perspectives to provide a better understanding of the concept of paraphrase extraction. It then studies the characteristics and potential uses of different types of paraphrase resources. After that, it divides paraphrase extraction methods into four main categories: heuristic-based, knowledge-based, corpus-based and hybrid-based and summarizes their strengths and weaknesses. This paper concludes with some potential open research issues for future directions.     

Use of ultrasonic energy in reactive dyeing of cellulosic fabrics

The use of ultrasonic energy in dyeing cotton fabrics with monochlorotriazine and vinylsulphone reactive dyes has been studied spectrophotometrically. The dyeings carried out conventionally and by the use of ultrasonic techniques were compared in terms of percentage exhaustion, total amount of dye transferred to the washing bath after dyeing, fastness properties and colour values of the dyed materials. Overall results have shown that the use of ultrasound in reactive dyeing of cellulosic fabrics can result in energy savings, less water consumption, increased depth of colour and better process conditions.     

Preparation Polyelectrolyte Complexes of Chitosan-Polyacrylic Acid-Modified Iron Sand Leachate for Proton Exchange Membranes

ABSTRACT

Polyelectrolyte complex (PEC) of chitosan (Chi) and poly (acrylic acid) (PAA)-modified iron sand leachate were prepared and considered for applicability as a proton exchange membrane in fuel cells. Chi-PAA-hematite blended in different weight ratios and the resulting membranes were treated to enable the formation of the polyelectrolyte. The membranes of Chi-PAA polyblend were treated using iron sand leachate and reveal high ion exchange capacity (IEC), proton conductivity, water uptake, and good mechanical stability. The result of research indicated that the membrane with 40 wt% of Chi and 60 wt% of PAA blend which its conductivity of 6.10 × 10^?2 S cm^?1 was potentially for a proton exchange membrane in fuel cell applications.     

Unsupervised Multiway Data Analysis: A Literature Survey

Two-way arrays or matrices are often not enough to represent all the information in the data and standard two-way analysis techniques commonly applied on matrices may fail to find the underlying structures in multi-modal datasets. Multiway data analysis has recently become popular as an exploratory analysis tool in discovering the structures in higher-order datasets, where data have more than two modes. We provide a review of significant contributions in the literature on multiway models, algorithms as well as their applications in diverse disciplines including chemometrics, neuroscience, social network analysis, text mining and computer vision.     

Approximate probabilistic optimization using exact-capacity-approximate-response-distribution (ECARD)

Probabilistic structural design deals with uncertainties in response (e.g. stresses) and capacity (e.g. failure stresses). The calculation of the structural response is typically expensive (e.g., finite element simulations), while the capacity is usually available from tests. Furthermore, the random variables that influence response and capacity are often disjoint. In previous work we have shown that this disjoint property can be used to reduce the cost of obtaining the probability of failure via Monte Carlo simulations. In this paper we propose to use this property for an approximate probabilistic optimization based on exact capacity and approximate response distributions (ECARD). In Approximate Probabilistic Optimization Using ECARD, the change in response distribution is approximated as the structure is re-designed while the capacity distribution is kept exact, thus significantly reducing the number of expensive response simulations. ECARD may be viewed as an extension of SORA (Sequential Optimization and Reliability Assessment), which proceeds with deterministic optimization iterations. In contrast, ECARD has probabilistic optimization iterations, but in each iteration, the response distribution is approximated so as not to require additional response calculations. The use of inexpensive probabilistic optimization allows easy incorporation of system reliability constraints and optimal allocation of risk between failure modes. The method is demonstrated using a beam problem and a ten-bar truss problem. The former allocates risk between two different failure modes, while the latter allocates risk between members. It is shown that ECARD provides most of the improvement from risk re-allocation that can be obtained from full probabilistic optimization.