Flame Retardancy of Polystyrene Nanocomposites Based on an Oligomeric Organically-Modified Clay Containing Phosphate

Novel modified clays, which may enable the formation of flame retarded polystyrene nanocomposites by melt or solution blending, have been prepared using an ammonium salt which contains an oligomeric material consisting of vinylbenzyl chloride, styrene and vinyl phosphate reacting with dimethylhexadecylamine. These nanocomposites have been characterized by X-ray diffraction, transmission electron microscopy, thermogravimetric analysis, cone calorimetry and the evaluation of mechanical properties. Melt blending is an effective, economical way to produce intercalated nanocomposites with greatly reduced peak heat release rate and a decreased total heat release; the polymer does not all undergo thermal degradation

Nanocomposites Based on Poly (ε-Caprolactone) (PCL)/Clay Hybrid: Polystyrene, High Impact Polystyrene, ABS, Polypropylene and Polyethylene

Nanocomposites of polystyrene, high impact polystyrene, acrylonitrile–butadiene–styrene terploymer, polypropylene and polyethylene have been prepared using an organically-modified clay that contains polycaprolactone—PCL-modified clay. Depending upon the mode of preparation of the PCL-modified clay, all three types of nanocomposites, immiscible, intercalated and exfoliated, may be produced. The materials have been characterized by X-ray diffraction, transmission electron microscopy, cone calorimetry, thermogravimetric analysis, and the evaluation of mechanical properties

Methyl Methacrylate Oligomerically-Modified Clay and its Poly (Methyl Methacrylate) Nanocomposites

A methyl methacrylate oligomerically-modified clay was used to prepare poly(methyl methacrylate) clay nanocomposites by melt blending and the effect of the clay loading level on the modified clay and corresponding nanocomposite was studied. These nanocomposites were characterized by X-ray diffraction, transmission electron microscopy, thermogravimetric analysis and cone calorimetry. The results show a mixed intercalated/delaminated morphology with good nanodispersion. The compatibility between the methylacrylate-subsituted clay and poly(methyl methacrylate) (PMMA) are greatly improved compared to other oligomerically-modified clays

Flammability of styrenic polymer clay nanocomposites based on a methyl methacrylate oligomerically-modified clay

Nanocomposites of polystyrene, high impact polystyrene, acrylonitrile–butadiene–styrene terpolymer, polypropylene, and polyethylene were prepared using a methyl methacrylate oligomerically-modified clay by melt blending and the thermal stability and fire retardancy were studied. These nanocomposites were characterized by X-ray diffraction, transmission electron microscopy, thermogravimetric analysis and cone calorimetry. The results show a mixed morphology, depending on the polymer

Optimal Resource Allocation for U-Shaped Parallel Split Learning

Split learning (SL) has emerged as a promising approach for model training without revealing the raw data samples from the data owners. However, traditional SL inevitably leaks label privacy as the tail model (with the last layers) should be placed on the server. To overcome this limitation, one promising solution is to utilize U-shaped architecture to leave both early layers and last layers on the user side. In this paper, we develop a novel parallel U-shaped split learning and devise the optimal resource optimization scheme to improve the performance of edge networks. In the proposed framework, multiple users communicate with an edge server for SL. We analyze the end-to-end delay of each client during the training process and design an efficient resource allocation algorithm, called LSCRA, which finds the optimal computing resource allocation and split layers. Our experimental results show the effectiveness of LSCRA and that U-shaped PSL can achieve a similar performance with other SL baselines while preserving label privacy. Index Terms: U-shaped network, split learning, label privacy, resource allocation, 5G/6G edge networks.Comment: 6 pages, 6 figure

Altered brain functional networks in Internet gaming disorder: independent component and graph theoretical analysis under a probability-discounting task

Internet gaming disorder (IGD) is becoming a matter of concern around the world. However, the neural mechanism underlying IGD remains unclear. In present study, we used independent component analysis (ICA) and graph theoretical analysis (GTA) to explore the potential changed networks in IGD subjects compared to recreational game user (RGU) under a probability-discounting task. Imaging and behavioral data were collected from 18 IGD and 20 RGU subjects. Behavioral results showed the IGD subjects, comparing to RGU, prefer risky options to the fixed ones and spent less time in making risky decisions. In imaging results, the ICA analysis revealed that the IGD showed stronger functional connectivity (FC) in reward circuits and executive control network, as well as lower FC in anterior salience network (ASN) than RGU; for the GTA results, the IGD showed impaired FC in reward circuits and ASN compared to RGU. Taken all, these results suggest that IGD subjects were more sensitive to rewards and, at the same time, they usually neglect the potential punishment under a risky circumstance. Besides this, they were more impulsive in decision-making as they could not control their impulsivity effectively. This might explain why IGD subjects cannot stop their gaming behaviors even facing serve negative consequences

APPLICATION OF THE RESIDUE THEOREM TO BILATERAL HYPERGEOMETRIC SERIES

The application of the residue theorem to bilateral hypergeometric series identities is systematically reviewed by exemplifying three classes of summation theorems due to Dougall (1907), Jackson (1949, 1952) and Slater-Lakin (1953).The application of the residue theorem to bilateral hypergeometric series identities is systematically reviewed by exemplifying three classes of summation theorems due to Dougall (1907), Jackson (1949, 1952) and Slater-Lakin (1953)

ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers

Quantization techniques are pivotal in reducing the memory and computational demands of deep neural network inference. Existing solutions, such as ZeroQuant, offer dynamic quantization for models like BERT and GPT but overlook crucial memory-bounded operators and the complexities of per-token quantization. Addressing these gaps, we present a novel, fully hardware-enhanced robust optimized post-training W8A8 quantization framework, ZeroQuant-HERO. This framework uniquely integrates both memory bandwidth and compute-intensive operators, aiming for optimal hardware performance. Additionally, it offers flexibility by allowing specific INT8 modules to switch to FP16/BF16 mode, enhancing accuracy.Comment: 8 pages, 2 figure