ROLE OF KATI BASTI AND PATRA POTTALI SWEDA IN THE MANAGEMENT OF GRIDHRASI- A CASE STUDY

The modern busy lifestyle of these days leads to several lifestyle disorders. Sciatica is one of the locomotory disorder occurs due to improper sitting posture, heavy weight lifting, stress injury and trauma to the spine. This disease became a great concern to the working people. Sciatica can be correlated with Gridhrasi in Ayurveda which is characterized as Ruk (pain), Stambha (stiffness), tingling sensation and numbness of lower limb. Gridhrasi is a type of Vatavyadhi mentioned in Ayurveda classics. Ayurveda through its advance treatment i.e. Panchakarma (Bio purification) eliminates the causative factors for disease. A male patient presented with signs and symptoms of Gridhrasi (sciatica) and MRI report suggesting of disc diffuse and disc protrusion and mild thecal compression. So In the present single case study patient was treated with Patra pottali sveda and Kati basti with some oral medicine. Both the therapies were effective in relieving symptoms like pain, stiffness, pricking sensation and numbness of leg

A Fundamental Study on Drying Characteristics of Oil-Palm Fronds for Gasification

Due to the major concern on the decreasing of fossil fuels nowadays, research and development (R&D) has been established to find alternative sources of fuels that are environmental friendly, sustainable and economical. With response towards this matter, renewable energy has been considered as the primary option of energy sustainability after fossil fuel. The most promising types of renewable energy are biomass. This report basically discusses the preliminary research done and basic understanding of the chosen topic. Gasification is the conversion of biomass into a fuel gas which can be used as a renewable energy. Oil palm fronds are one of the most abundant agricultural byproducts in Malaysia with an estimated availability of 30 million tons annually. There are some research has been proposed to investigate the potential of oil palm fronds as a major and unsuitable to be fed into gasification process. This is because high water content will reduce the possibility of ignition in the process and reduces the heating value of the product gas due to needed to evaporate the additional moisture before combustion or gasification. The objective of this project is to identify the optimum and economical method of drying the oil-palm fronds, especially in terms of minimum temperature for drying, humidity, drying time (shorter drying time is preferred) and the form of oil-palm fronds itself. The challenge in this project is to investigate the drying characteristic of the fronds from the most suitable drying condition to the least suitable condition. The drying test will be done using the oven in the lab under various conditions. The outcome of the project will be useful in determining the drying method for large-scale biomass gas production

SALSA-TEXT : self attentive latent space based adversarial text generation

Inspired by the success of self attention mechanism and Transformer architecture in sequence transduction and image generation applications, we propose novel self attention-based architectures to improve the performance of adversarial latent code- based schemes in text generation. Adversarial latent code-based text generation has recently gained a lot of attention due to their promising results. In this paper, we take a step to fortify the architectures used in these setups, specifically AAE and ARAE. We benchmark two latent code-based methods (AAE and ARAE) designed based on adversarial setups. In our experiments, the Google sentence compression dataset is utilized to compare our method with these methods using various objective and subjective measures. The experiments demonstrate the proposed (self) attention-based models outperform the state-of-the-art in adversarial code-based text generation.Comment: 10 pages, 3 figures, under review at ICLR 201

The Effect of COVID-19 Uncertainty on Corporate Default Risk: International evidence

This paper investigates the effect of COVID-19 uncertainty on corporate default risk using an international sample of firms from 71 countries. We document that corporate default risk increases with higher COVID-19 uncertainty, even after controlling for a wide range of firmlevel and country-level characteristics. The effect is weaker for firms in highly religious adherence countries, stronger for firms in developed countries, and for firms geographically closer to China and Italy. Further, the effect is weaker for highly innovative firms and less financially constrained firms. Our findings are robust to propensity score matching and entropy balancing methods to address selection bias, diagnostic tests regarding omitted variable bias, and alternative measures of COVID-19 uncertainty and default risk

Design and analysis of a boosted pierce oscillator using MEMS SAW resonators

This paper highlights the design and analysis of a pierce oscillator circuit for CMOS MEMS surface acoustic wave resonators. The boosted pierce topology using two, three-stage cascode amplifiers provides sufficient gain to counteract the high insertion losses of - 65 dB at 1.3 GHz of the SAW resonator. For accurate prediction of the oscillator’s performance before fabrication, circuit design utilized touchstone S2P measurement results of the MEMS SAW resonator, which provides better results compared to the conventional method of using equivalent circuit simulations. This circuit was designed using Silterra’s 0.13 lm CMOS process. It has low power consumption of 1.52 mW with high voltage swing 0.10–0.99 V. All simulations were conducted using Cadence Design Systems and results indicate that phase noise of 92.63 dBc at 1 MHz

CNN-ViT Supported Weakly-Supervised Video Segment Level Anomaly Detection

Video anomaly event detection (VAED) is one of the key technologies in computer vision for smart surveillance systems. With the advent of deep learning, contemporary advances in VAED have achieved substantial success. Recently, weakly supervised VAED (WVAED) has become a popular VAED technical route of research. WVAED methods do not depend on a supplementary self-supervised substitute task, yet they can assess anomaly scores straightway. However, the performance of WVAED methods depends on pretrained feature extractors. In this paper, we first address taking advantage of two pretrained feature extractors for CNN (e.g., C3D and I3D) and ViT (e.g., CLIP), for effectively extracting discerning representations. We then consider long-range and short-range temporal dependencies and put forward video snippets of interest by leveraging our proposed temporal self-attention network (TSAN). We design a multiple instance learning (MIL)-based generalized architecture named CNN-ViT-TSAN, by using CNN- and/or ViT-extracted features and TSAN to specify a series of models for the WVAED problem. Experimental results on publicly available popular crowd datasets demonstrated the effectiveness of our CNN-ViT-TSAN.publishedVersio