Texture and hydrodynamics of a thin slab of superfluid3He-A in a torsional oscillator. II. Experiment

Experiments on a torsional oscillator containing a slab of liquid³He of thickness 105 µm and diameter 8.38 mm are described. Normal phase experiments confirm the theory of the oscillator dynamics but an apparent descrase in the measured value of T ² at lowT casts doubt on the existing slip theory. Measurements for the uniform texture for the A phase give values of _s and ₄₄. The _s measurements suggest that the A-phase energy gap is enhanced by a factor 1.24 over the BCS value. Distortion of the uniform texture by a magnetic field perpendicular to the slab occurs at a threshold field slightly smaller than predicted. Measurements for fields at different angles to the slab enable accurate values to be deduced for the superfluid density anisotropy and two other viscosity coefficients. The remaining two viscosity coefficients are only poorly determined. Flow alignment of the orbital texture was achieved by increasing the oscillator amplitude. A different texture obtained on some occasions after warming from the B phase was investigated but is not understood.     

Graft Copolymerization of Acrylamide onto Oil Palm Empty Fruit Bunch (OPEFB) Fiber

Grafting of acrylamide (AAm) onto oil palm empty fruits bunch fiber using hydrogen peroxide as initiator and methyl acrylate as comonomer was investigated. The amount of comonomer needed to make grafting of acrylamide possible was determined. The percentage of poly(acrylamide) and the comonomer in the final graft copolymer was estimated by elemental analysis. Results obtained indicated that methyl acrylate facilitated the incorporation of acrylamide monomer onto OPEFB. The reactivity ratios for both monomers were determined by using Fineman–Ross plot. The effects of reaction temperature and period as well as amount of the initiator, solvent, monomer and comonomer on the percentage of grafting at fixed amount of comonomer (11 mmol) were studied. Maximum percentage of grafting was achieved when the amount of initiator and solvent 3.98×10⁻³ mol and 50 mL respectively. The optimum reaction temperature was 50 ^○C and the reaction period was 90 min. Highest percentage of grafting was 232% when 25.6 mmol of acrylamide was used under these optimum conditions. The presence of functional group in the grafted polymer is characterized by infrared spectroscopy and the surface morphology is observed by scanning electron microscopy. Thermoanalytic investigation on OPEFB and OPEFB-g-PAAM were carried out to evaluate the thermal stability and respective activation energy of the materials.     

Techniques for pipelined broadcast on ethernet switched clusters

By splitting a large broadcast message into segments and broadcasting the segments in a pipelined fashion, pipelined broadcast can achieve high performance in many systems. In this paper, we investigate techniques for efficient pipelined broadcast on clusters connected by multiple Ethernet switches. Specifically, we develop algorithms for computing various contention-free broadcast trees that are suitable for pipelined broadcast on Ethernet switched clusters, extend the parametrized LogP model for predicting appropriate segment sizes for pipelined broadcast, show that the segment sizes computed based on the model yield high performance, and evaluate various pipelined broadcast schemes through experimentation on Ethernet switched clusters with various topologies. The results demonstrate that our techniques are practical and efficient for contemporary fast Ethernet and Giga-bit Ethernet clusters.     

Low‐cost dye‐sensitized solar cells with ball‐milled tellurium‐doped graphene as counter electrodes and a natural sensitizer dye

This paper presents a facile and economic development of dye‐sensitized solar cells using a nonprecious counter electrode made from ball‐milled tellurium‐doped graphene (Te‐Gr) and a natural sensitizer extracted from Calotropis gigantea leaves. The prepared materials were characterized using various techniques, such as Raman spectroscopy, X‐ray diffraction (XRD), atomic force microscopy (AFM), impedance spectroscopy, and scanning electron microscopy with built‐in energy‐dispersive X‐ray spectroscopy (SEM with EDS). The electrochemical activity of the produced counter electrodes and the impedance of the fabricated cells were examined and discussed to devise plans for future enhancement of cell performance. A clear pattern of improvement was found when using cost‐effective Te‐Gr relative to the costly platinum counter electrodes, especially when compared with cells employing another natural sensitizer. The results show approximately 51% enhancement over chlorophyll‐based cells made from spinach, where the added advantage in our approach is the utilization of an abundant plant extract with little nutritional appeal.     

In Vitro and In Vivo Metabolism of the Radiolytic Compound 2-Dodecylcyclobutanone

ABSTRACT: Our knowledge about the metabolism of alkylcyclobutanones (2-ACBs) is limited, and the lack of literature on the metabolism of 2-ACBs causes consumers to doubt the safety of irradiated foods. The objectives of this study were to evaluate the metabolism of 2-dodecylcyclobutanone (2-DCB) and identify any possible metabolite. The 2-DCB was mixed with rat S9 (postmitochondrial supernatant fraction) and β-nicotinamide adenine dinucleotide phosphate (NADPH) in phosphate buffer (pH 7.4) and incubated for 2 h at 37 °C. Then, the incubation mixture was mixed with sodium sulfate and extracted with n-hexane by using a Soxhlet apparatus. The hexane extract was concentrated under nitrogen and injected into the gas chromatography-mass spectrometry (GC-MS) machine running in selective ion monitoring mode (SIM) to measure 2-DCB concentration. The hexane extract from the in vitro and in vivo studies was also derivatized with a silylation reagent and injected into a GC-MS running in full scan mode. The average percentage of 2-DCB recovered from the test incubations was 23%, compared with 50% from the controls. The GC-MS chromatograms of the derivatized samples showed a unique peak in the in vitro test incubations and in the hexane extract of the rat feces that were given 2-DCB. This peak was later identified as 2-doecylcyclobutanol.     

Soybean Isoflavones: Effects of Processing and Health Benefits

Isoflavones belong to a group of compounds known as flavanoids that share a basic structure consisting of two benzene rings linked through a heterocyclic pyrane C ring. Soybean provides the most abundant source of isoflavones, which have been linked to the prevention of several diseases, including cancer (prostate, colon, and breast cancer), hypercholesterolemia, cardiovascular disease, atherosclerosis, osteoporosis, and relief of menopausal symptoms in certain women. Currently, isoflavone use is targeted as a nutraceutical. In this article, we review the structural aspects and the effect of processing on the different forms of isoflavones in soy protein. We also discuss the impact of these changes on the health benefits of eating foods containing isoflavones. Isoflavones, as a class of phytoestrogens, are gaining a lot of interest in clinical nutrition.     

Superconducting properties of zinc substitution in Tl-2223 phase

The effect of partial replacement of copper by zinc in Tl₂Ba₂Ca₂Cu₃O_10−δ superconductor phase is studied. Superconducting samples of the nominal composition Tl₂Ba₂Ca₂Cu_3−xZn_xO_10−δ with x ranging from 0 to 0.6 are prepared under normal pressure by a one step of solid-state reaction technique. The samples are characterized by using X-ray powder diffraction, scanning electron microscope (SEM) and EDX. The X-ray data indicate that the partial replacement of Cu²⁺ions by Zn²⁺ions does not influence the tetragonal structure of the samples, and the lattice parameters a and c vary according to the difference in the ionic radii of Cu and Zn. The superconducting parameters, such as superconducting transition temperature T_c, critical current density J_c and irreversibility field B_ir are calculated from electrical resistivity and AC-magnetic susceptibility measurements.     

Systematic multiscale models to predict the compressive strength of self-compacting concretes modified with nanosilica at different curing ages

The evolution of nanotechnology brings materials with novel performance and during last year’s much attempt has been established to include nanoparticles especially nano-silica (NS) into the concrete to improve performance and develop concrete with enhanced characteristics. Generally, NS is incorporated into the self-compacting concrete (SCC) aiming to positively influence the fresh, mechanical, microstructure, and durability properties of the composite. The most important mechanical property for all types of concrete composites is compressive strength. Therefore, developing reliable models for predicting the compressive strength of SCC is crucial regarding saving time, energy, and cost-effectiveness. Moreover, it gives valuable information for scheduling the construction work and provides information about the correct time for removing the formwork. In this study, three different models including the linear relationship model (LR), nonlinear model (NLR), and multi-logistic model (MLR) were proposed to predict the compressive strength of SCC mixtures made with or without NS. In this regard, a comprehensive data set that consists of 450 samples were collected and analyzed to develop the models. In the modeling process, the most important variables affecting the compressive strength such as NS content, cement content, water to binder ratio, curing time from 1 to 180 days, superplasticizer content, fine aggregate content, and coarse aggregate content were considered as input variables. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the coefficient of determination (R²) were used to evaluate the performance of the proposed models. The results indicated that the MLR model performed better for forecasting the compression strength of SCC mixtures modified with NS compared to other models. The SI and OBJ values of the MLR model were 18.8% and 16.7% lower than the NLR model, indicating the superior performance of the MLR model. Moreover, the sensitivity analysis demonstrated that the curing time is the most affecting variable for forecasting the compressive strength of SCC modified with NS.

     

Manufacturing features: Verification interaction accessibility and machinability

A major problem in CAD/CAM integration lies in the difficulty in representing the component definition adequately for all applications. Features are considered as a main factor in the CAD and CAM link because various design, engineering and manufacturing data can be associated with a feature. However, tagging feature labels onto geometry does not guarantee the geometric correctness of the resultant feature; knowledge of the topology and analysis of the geometry is needed to correctly identify the validity of the resultant feature. This paper discusses a feature-based design system capable of representing 2.5D components in terms of manufacturing features such as holes, slots and pockets, which are associated with distinctive manufacturing processes. The system is capable of verifying all the defined features by comparing the definition of the resultant features against those of the applied features. Feature interactions are considered to investigate the effect of the interaction on the validity, accessibility and machinability of each feature. Individual features can be extracted from the product model, where all the information about the product is held, for analyses. Each volumetric feature corresponds to a solid that can be removed by one or more machining operations; as a consequence of applying volumetric features, surface features are generated. These surface features provide enough information to enable the validity and machinability of the individual features to be determined and to establish the possible routes in which the feature can be accessed, if any. The proposed approach has been explored in a rapid prototyping test bed consisting of product modelling environment coupled with a solid modeller.     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.