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Cyberbullying is one of the latest threats in the online world, affecting millions of people worldwide. The detection of cyberbullying became a challenging task due to the complexity involved. In this paper, we propose a novel approach for cyberbullying detection using CR* which concatenates the deep learning model’s features such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) which helps in detecting text, audio, and emoji. Initially, we collect a dataset of cyberbullying messages from social media platforms. Then we integrate convolutional neural networks and recurrent neural networks to build our deep learning model entitled Convolutional Recurrent (CR*) to extract features from a combination of the text data, emoji, and audio. The multimodal features were then concatenated and passed through fully connected layers for classification. The proposed approach can be useful for detecting cyberbullying on various online platforms and can help prevent the spread of cyberbullying.

A sort of digital circuit used to multiply two binary values is called a Baugh-Wooley multiplier. It is renowned for being more effective than other kinds of multipliers in terms of speed and gate count. You would normally start by creating a simple version of the circuit utilizing logic gates like AND gates, XOR gates, and half adders to simulate the Baugh-Wooley multiplier. The design would then be optimized utilizing methods like parallel processing, pipelining, and optimization algorithms to lower the overall gate count and boost the functionality of the circuit. Because of this, estimating a Baugh-Wooley multiplier would require a thorough knowledge of these elements as well as the specifications of the application. Verilog HDL is used to implement this design, and Modelsim 6.4 c is used to simulate it. The synthesis process tool from Xilinx measures performance.

Background: Lymphovascular invasion (LVI) is a major route of metastatic dissemination and recent studies indicate its value as an independent prognostic indicator for advanced breast, colorectal, squamous cell, prostate, brain cancers. LVI is a clinicopathological hallmark of inflammatory breast cancer (IBC), an understudied and most lethal breast cancer. IBC is often misdiagnosed due to an absence of a solid mass and its unique presentation of diffuse tumor cell clusters/emboli in the dermal lymphatics. Widely used mammary tumor implantation models coupled with bioluminescence or fluorescence imaging to monitor tumor growth kinetics are ineffective for evaluating spatial and temporal changes in growth and migration patterns of individual tumor cells and clusters within their microenvironmental context. The goal of this study was to develop a murine model to simulate the unique clinicopathological features of IBC patients and to assess both qualitatively and quantitatively local tumor growth, motility, and LVI. Methods: To specifically facilitate visualization of lymphatic and endothelial vessels along with tumor-vessel interactions, we generated a transgenic nude mice model (ProxTom RFP Nu/Nu) wherein, the mice exhibit red, fluorescent lymphatics [tdTomato fluorophore under control of a Prox1 promoter, which encodes a transcription factor (prospero-related homeobox 1) necessary for the formation and maintenance of lymphatic vessels]. Next, we employed a surgical technique, wherein a window chamber is placed on the dorsal skinfold of mice, which allows for microscopic examination of implanted tumor cells and ability to track dynamic changes of the tumor in its local microenvironment from the time of implantation up to 10 days. Patient-derived IBC or PDX stably transfected to express green, red fluorescent and/or dual tagged with luciferase reporters were transplanted in mice bearing window chambers. Intravital fluorescence microscopy and IVIS imaging were used to serially quantify local tumor growth, motility, length density of lymph and blood vessels, and degree of tumor cell lymphatic invasion over 0-140h. Results: Multichannel optical imaging of the window chamber in the ProxTom RFP Nu/Nu mice demonstrated co-localization of IBC tumor cells and lymphatics. Diffuse tumor cells were observed along regions of lymphatic vessels both proximal and distal to the primary tumor site. However, measurement of blood and lymph vessel density showed no significant change over time. Next, these datasets were used for quantitative analysis by setting the tumor cell channel (GFP) at a threshold to count any clusters greater than 50 pixels2 (~0.0013mm2) and with greater than the mean + 2 standard deviations of the background signal while avoiding noise/artifacts from very small regions ( Citation Format: Gayathri R Devi, Dorababu Sannareddy, Alexandra Bennion, Ashlyn Rickard, Douglas C Rouse, Mark W Dewhirst, Gregory M Palmer. Monitoring Lymphovascular Invasion and Tumor Growth of Inflammatory Breast Cancer in a Murine Lymphatic Reporter Window Chamber Model [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-08-06.

Electronic word of mouth (EWoM) is similar to the standard word of mouth- the only difference is, the use of Internet as a base in EWoM. By understanding the personality antecedents of EWoM, the consumer buyer beahviour could be analysed more specifically. The current study has the objectives of understanding the influence of the personality antecedents and EWoM, and of the consumer towards consumer purchase behaviour. The data were collected from 200 respondents in different districts of TamilNadu through a structured questionnaire. The data is analyzed by using the structural equation model in determining the relationship towards the purchase behaviour. It is evident from the study that EWoM determinant influent purchase behaviour with significant R square value of 0.41, and equally the personality of the customer influence with R square value 0.58. The model fit well and has greater alignment of fit also. The personality antecedents of EWoM have become an integral part of the current generation while making a purchase decision.

For the rapid evaluation of any anti-psoriatic preparations require a credible and scientifically acceptable model that should at least partially resemble the skin. The skin is composed by cholesterol, proteins like elastin, keratin etc. Therefore a semi-permeable membrane composed on most of such agents may help us to evaluate how the absorption and associated permeation of the drug happens through such membrane. We have developed a surrogate skin model using the essential chemical composition of the skin and have evaluated the absorption kinetics of Psorolin B and details are presented in the article. Keywords: Psorolin B, lipo-hydro balance, xerotic skin, psoriasis

In the present paper we have investigated Psorolin B ointment for improving the bio-mechanical property of the skin. Psorolin B ointment is proprietary Siddha formulation composed of Boswellia serrata, Wrightia tinctoria, Cynodon dactylon, Hydnocarpus laurifolia and red ochre. The bio-mechanical property/elasticity of the skin is evaluated in volar forearm region by four different methods such as applying uniaxial force, centripetal force, vacuum suction and instant retraction by pinch-pull test. The findings clearly showed that Psorolin B ointment improved the skin elasticity and dexterity of the cutaneous tissue. In our previous study we have already established the elastase and collagenase inhibitory effect of Psorolin B and it also possess strong antioxidant effect. The findings are discussed in the article.

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT.

The design of the present investigation was to prepare Piroxicam bounded pharmacosomes to improve the water solubility, bioavailability and to minimize the gastrointestinal toxicity of Piroxicam. Pharmacosome is a potential approach in vesicular drug delivery systems which exhibits several advantages over conventional vesicular drug delivery systems. Pharmacosomes are phospholipid complexes with a potential to improve bioavailability of poorly water soluble as well as poorly lipophilic drugs. Piroxicam was complexed with soya phosphatidylcholine in various ratios using conventional solvent evaporation technique. In our study, Pharmacosomes thus prepared were subjected to drug solubility, drug content evaluation, surface morphology (by scanning electron microscopy), crystallinity (by X- ray powder diffraction), quality control tests for capsules and in-vitro dissolution study. Solubility profile of the prepared complexes was found to be much better than Piroxicam pure drug. This improvement in solubility of the prepared pharmacosomes may result in improved dissolution and lower gastrointestinal toxicity. Drug content was found to be 96.84%w /W for the optimized Piroxicam phospholipid complex, F1 (Piroxicam: lecithin ratio of 1:1). The pharmacosomes were found to be disc shaped in scanning electron microscopy. X-ray powder diffraction data confirmed the formation of phospholipid complexes. The formulation F1 showed 94.69% drug release while the free Piroxicam showed a total of only 60.42% at the end of 10-hour dissolution study. Thus the solubility and hence the bioavailability of Piroxicam can be increased to a greater extent by complexing it with soya phosphatidylcholine.