Investigation of the Thermal Degradation of Polyurea: The Effect of Ammonium Polyphosphate and Expandable Graphite

Polyurea was compounded with ammonium polyphosphate and expandable graphite and the morphology was studied by atomic force microscopy. The thermal degradation of polyurea and polyurea compounded with the additives has been investigated using thermogravimetry coupled with Fourier Transform infrared spectroscopy and mass spectrometry. The study of the thermal degradation and the parameters affecting the thermal stability of PU is essential in order to effectively design flame retarded polyurea. In general, thermal decomposition of polyurea occurs in two steps assigned to the degradation of the hard segment and soft segment, respectively. Adding these additives accelerates the decomposition reaction of polyurea. However, it is clear that more char is formed. This char is thermally more stable than the carbonaceous structure obtained from neat PU. The intumescent shield traps the polymer fragments and limits the evolution of small flammable molecules that are able to feed the flame

High capacity multiuser multiantenna communication techniques

One of the main issues involved in the development of future wireless communication systems is the multiple access technique used to efficiently share the available spectrum among users. In rich multipath environment, spatial dimension can be exploited to meet the increasing number of users and their demands without consuming extra bandwidth and power. Therefore, it is utilized in the multiple-input multiple-output (MIMO) technology to increase the spectral efficiency significantly. However, multiuser MIMO (MU-MIMO) systems are still challenging to be widely adopted in next generation standards. In this thesis, new techniques are proposed to increase the channel and user capacity and improve the error performance of MU-MIMO over Rayleigh fading channel environment. For realistic system design and performance evaluation, channel correlation is considered as one of the main channel impurities due its severe influence on capacity and reliability. Two simple methods called generalized successive coloring technique (GSCT) and generalized iterative coloring technique (GICT) are proposed for accurate generation of correlated Rayleigh fading channels (CRFC). They are designed to overcome the shortcomings of existing methods by avoiding factorization of desired covariance matrix of the Gaussian samples. The superiority of these techniques is demonstrated by extensive simulations of different practical system scenarios. To mitigate the effects of channel correlations, a novel constellation constrained MU-MIMO (CC-MU-MIMO) scheme is proposed using transmit signal design and maximum likelihood joint detection (MLJD) at the receiver. It is designed to maximize the channel capacity and error performance based on principles of maximizing the minimum Euclidean distance (dmin) of composite received signals. Two signal design methods named as unequal power allocation (UPA) and rotation constellation (RC) are utilized to resolve the detection ambiguity caused by correlation. Extensive analysis and simulations demonstrate the effectiveness of considered scheme compared with conventional MU-MIMO. Furthermore, significant gain in SNR is achieved particularly in moderate to high correlations which have direct impact to maintain high user capacity. A new efficient receive antenna selection (RAS) technique referred to as phase difference based selection (PDBS) is proposed for single and multiuser MIMO systems to maximize the capacity over CRFC. It utilizes the received signal constellation to select the subset of antennas with highest (dmin) constellations due to its direct impact on the capacity and BER performance. A low complexity algorithm is designed by employing the Euclidean norm of channel matrix rows with their corresponding phase differences. Capacity analysis and simulation results show that PDBS outperforms norm based selection (NBS) and near to optimal selection (OS) for all correlation and SNR values. This technique provides fast RAS to capture most of the gains promised by multiantenna systems over different channel conditions. Finally, novel group layered MU-MIMO (GL-MU-MIMO) scheme is introduced to exploit the available spectrum for higher user capacity with affordable complexity. It takes the advantages of spatial difference among users and power control at base station to increase the number of users beyond the available number of RF chains. It is achieved by dividing the users into two groups according to their received power, high power group (HPG) and low power group (LPG). Different configurations of low complexity group layered multiuser detection (GL-MUD) and group power allocation ratio (η) are utilized to provide a valuable tradeoff between complexity and overall system performance. Furthermore, RAS diversity is incorporated by using NBS and a new selection algorithm called HPG-PDBS to increase the channel capacity and enhance the error performance. Extensive analysis and simulations demonstrate the superiority of proposed scheme compared with conventional MU-MIMO. By using appropriate value of (η), it shows higher sum rate capacity and substantial increase in the user capacity up to two-fold at target BER and SNR values

Molecular detection of Treponema species organisms in foremilk and udder cleft skin of dairy cows with digital dermatitis

Identification of reservoirs and transmission routes of digital dermatitis (DD)-associated Treponema spp. is considered an effective means for controlling DD infection in dairy cows. The objective of this study is to identify and characterize the potential reservoir niches for DD-associated Treponema spp. from healthy udder cleft skin and foremilk in lactating dairy cows. A large dairy farm was visited weekly from March to July 2015. Clinical investigation revealed that a total of 25 lame cows had DD lesions located at the plantar aspect of the interdigital cleft. A total of 75 samples, three per cow, were collected including deep swabs from DD lesions (n = 25), non-aseptically collected foremilk samples (n = 25) and skin swabs from udder cleft (n = 25). Treponema spp. were identified using nested PCR assays and confirmed by DNA sequencing. Results revealed that Treponema phagedenis (T. phagedenis)-like was the most identified species in the foremilk 40% (10/25), in comparison with DD lesions and udder cleft skin samples with 32% (8/25) and 20% (5/25), respectively. On the other hand, Treponema pedis (T. pedis) was the most identified species in the udder cleft skin 80% (20/25), in comparison with DD lesions and foremilk samples with 68% (17/25) and 60% (15/25), respectively. None of the examined samples were identified by PCR as containing DNA from Treponema medium (T. medium) or Treponema vincentii (T. vincentii)-like. To the best of our knowledge, this is the first report for detection of T. phagedenis-like and T. pedis from healthy skin of udder cleft and foremilk samples. Detection of DD Treponema spp. from udder cleft skin and foremilk samples indicates that these sites could be potential reservoirs for spirochetes involved in DD. Udder cleft skin and foremilk may have a role in transmission routes of DD Treponema in dairy farms.info:eu-repo/semantics/acceptedVersio

Investigation of nanodispersion in polystyrene-montmorillonite nanocomposites by solid state NMR

Nanocomposites result from combinations of materials with vastly different properties in the nanometer scale. These materials exhibit many unique properties such as improved thermal stability, reduced flammability, and improved mechanical properties. Many of the properties associated with polymer–clay nanocomposites are a function of the extent of exfoliation of the individual clay sheets or the quality of the nanodispersion. This work demonstrates that solid-state NMR can be used to characterize, quantitatively, the nanodispersion of variously modified montmorillonite (MMT) clays in polystyrene (PS) matrices. The direct influence of the paramagnetic Fe3, embedded in the aluminosilicate layers of MMT, on polymer protons within about 1 nm from the clay surfaces creates relaxation sources, which, via spin diffusion, significantly shorten the overall proton longitudinal relaxation time (T1 H). Deoxygenated samples were used to avoid the particularly strong contribution to the T1 H of PS from paramagnetic molecular oxygen. We used T1 H as an indicator of the nanodispersion of the clay in PS. This approach correlated reasonably well with X-ray diffraction and transmission electron microscopy (TEM) data. A model for interpreting the saturation-recovery data is proposed such that two parameters relating to the dispersion can be extracted. The first parameter, f, is the fraction of the potentially available clay surface that has been transformed into polymer–clay interfaces. The second parameter is a relative measure of the homogeneity of the dispersion of these actual polymer–clay interfaces. Finally, a quick assay of T1 H is reported for samples equilibrated with atmospheric oxygen. Included are these samples as well as 28 PS/MMT nanocomposite samples prepared by extrusion. These measurements are related to the development of highthroughput characterization techniques. This approach gives qualitative indications about dispersion; however, the more time-consuming analysis, of a few deoxygenated samples from this latter set, offers significantly greater insight into the clay dispersion. A second, probably superior, rapid-analysis method, applicable to oxygen-containing samples, is also demonstrated that should yield a reasonable estimate of the f parameter. Thus, for PS/MMT nanocomposites, one has the choice of a less complete NMR assay of dispersion that is significantly faster than TEM analysis, versus a slower and more complete NMR analysis with sample times comparable to TEM, information rivaling that of TEM, and a substantial advantage that this is a bulk characterization method. © 2003 Wiley Periodicals, Inc.* J Polym Sci Part B: Polym Phys 41: 3188–3213, 200

Material properties of nanoclay PVC composites

Nanocomposites of poly(vinyl chloride) have been prepared using both hectorite- and bentonite-based organically-modified clays. The organic modification used is tallow-triethanol-ammonium ion. The morphology of the systems was investigated using X-ray diffraction and transmission electron microscopy and these systems show that true nanocomposites, both intercalated and exfoliated systems, are produced. The mechanical properties have been evaluated and the modulus increases upon nanocomposite formation without a significant decrease in tensile strength or elongation at break. Thermal analysis studies using thermogravimetric analysis, differential scanning calorimetry, and dynamic mechanical analysis were conducted. Thermal stability of the PVC systems was assessed using a standard thermal process evaluating the evolution of hydrogen chloride and by color development through the yellowness index. Cone calorimetry was used to measure the fire properties and especially to evaluate smoke evolution. The addition of an appropriately-modified bentonite or hectorite nanoclay leads to both a reduction in the total smoke that is evolved, and an increase in the length of time over which smoke is evolved. Along with this, a reduction in the peak heat release rate is seen. It is likely that the presence of the clay in some way interferes with the cyclization of the conjugated system formed upon HCl loss

Improving Resnet-9 Generalization Trained on Small Datasets

This paper presents our proposed approach that won the first prize at the ICLR competition on Hardware Aware Efficient Training. The challenge is to achieve the highest possible accuracy in an image classification task in less than 10 minutes. The training is done on a small dataset of 5000 images picked randomly from CIFAR-10 dataset. The evaluation is performed by the competition organizers on a secret dataset with 1000 images of the same size. Our approach includes applying a series of technique for improving the generalization of ResNet-9 including: sharpness aware optimization, label smoothing, gradient centralization, input patch whitening as well as metalearning based training. Our experiments show that the ResNet-9 can achieve the accuracy of 88% while trained only on a 10% subset of CIFAR-10 dataset in less than 10 minuet

Metabolic signaling directs the reciprocal lineage decisions of αβ and γδ T cells

Wiring metabolic signaling circuits in thymocytes Cell differentiation is often accompanied by metabolic changes. Yang et al. report that generation of double-positive (DP) thymocytes from double-negative (DN) cells coincides with dynamic regulation of glycolytic and oxidative metabolism. Given the central role of mechanistic target of rapamycin complex 1 (mTORC1) signaling in regulating metabolic changes, they examined the role of mTORC1 pathway in thymocyte development by conditionally deleting RAPTOR, the key component of the mTORC1 complex, in thymocytes. Loss of RAPTOR impaired the DN-to-DP transition, but unexpectedly also perturbed the balance between αβ and γδ T cells and promoted the generation of γδ T cells. Their studies highlight an unappreciated role for mTORC1-dependent metabolic changes in controlling thymocyte fates. The interaction between extrinsic factors and intrinsic signal strength governs thymocyte development, but the mechanisms linking them remain elusive. We report that mechanistic target of rapamycin complex 1 (mTORC1) couples microenvironmental cues with metabolic programs to orchestrate the reciprocal development of two fundamentally distinct T cell lineages, the αβ and γδ T cells. Developing thymocytes dynamically engage metabolic programs including glycolysis and oxidative phosphorylation, as well as mTORC1 signaling. Loss of RAPTOR-mediated mTORC1 activity impairs the development of αβ T cells but promotes γδ T cell generation, associated with disrupted metabolic remodeling of oxidative and glycolytic metabolism. Mechanistically, we identify mTORC1-dependent control of reactive oxygen species production as a key metabolic signal in mediating αβ and γδ T cell development, and perturbation of redox homeostasis impinges upon thymocyte fate decisions and mTORC1-associated phenotypes. Furthermore, single-cell RNA sequencing and genetic dissection reveal that mTORC1 links developmental signals from T cell receptors and NOTCH to coordinate metabolic activity and signal strength. Our results establish mTORC1-driven metabolic signaling as a decisive factor for reciprocal αβ and γδ T cell development and provide insight into metabolic control of cell signaling and fate decisions. Development of αβ and γδ T cells requires coupling of environmental signals with metabolic and redox regulation by mTORC1. Development of αβ and γδ T cells requires coupling of environmental signals with metabolic and redox regulation by mTORC1

GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values

Massive transformer-based models face several challenges, including slow and computationally intensive pre-training and over-parametrization. This paper addresses these challenges by proposing a versatile method called GQKVA, which generalizes query, key, and value grouping techniques. GQKVA is designed to speed up transformer pre-training while reducing the model size. Our experiments with various GQKVA variants highlight a clear trade-off between performance and model size, allowing for customized choices based on resource and time limitations. Our findings also indicate that the conventional multi-head attention approach is not always the best choice, as there are lighter and faster alternatives available. We tested our method on ViT, which achieved an approximate 0.3% increase in accuracy while reducing the model size by about 4% in the task of image classification. Additionally, our most aggressive model reduction experiment resulted in a reduction of approximately 15% in model size, with only around a 1% drop in accuracy

SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling

In this paper, we present SwiftLearn, a data-efficient approach to accelerate training of deep learning models using a subset of data samples selected during the warm-up stages of training. This subset is selected based on an importance criteria measured over the entire dataset during warm-up stages, aiming to preserve the model performance with fewer examples during the rest of training. The importance measure we propose could be updated during training every once in a while, to make sure that all of the data samples have a chance to return to the training loop if they show a higher importance. The model architecture is unchanged but since the number of data samples controls the number of forward and backward passes during training, we can reduce the training time by reducing the number of training samples used in each epoch of training. Experimental results on a variety of CV and NLP models during both pretraining and finetuning show that the model performance could be preserved while achieving a significant speed-up during training. More specifically, BERT finetuning on GLUE benchmark shows that almost 90% of the data can be dropped achieving an end-to-end average speedup of 3.36x while keeping the average accuracy drop less than 0.92%