Crack Spread and its forecasting analysis. An empirical analysis using VAR and ARIMA model.

This thesis strives to describe crack spread which refers to the difference between the price of crude oil and the price of refined products extracted from it. It has a stable price movement throughout the last decade. But last year, there was a huge price hike. This paper showed the relationship between crack spread and WTI crude oil price movement. Also, it analyzed the price forecasting for both cases using two different models, namely ARIMA and VAR

[6]-Gingerol, from Zingiber officinale, potentiates GLP-1 mediated glucose-stimulated insulin secretion pathway in pancreatic β-cells and increases RAB8/RAB10-regulated membrane presentation of GLUT4 transporters in skeletal muscle to improve hyperglycemia in Leprdb/db type 2 diabetic mice

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Abstract Background [6]-Gingerol, a major component of Zingiber officinale, was previously reported to ameliorate hyperglycemia in type 2 diabetic mice. Endocrine signaling is involved in insulin secretion and is perturbed in db/db Type-2 diabetic mice. [6]-Gingerol was reported to restore the disrupted endocrine signaling in rodents. In this current study on Leprdb/db diabetic mice, we investigated the involvement of endocrine pathway in the insulin secretagogue activity of [6]-Gingerol and the mechanism(s) through which [6]-Gingerol ameliorates hyperglycemia. Methods Leprdb/db type 2 diabetic mice were orally administered a daily dose of [6]-Gingerol (200 mg/kg) for 28 days. We measured the plasma levels of different endocrine hormones in fasting and fed conditions. GLP-1 levels were modulated using pharmacological approaches, and cAMP/PKA pathway for insulin secretion was assessed by qRT-PCR and ELISA in isolated pancreatic islets. Total skeletal muscle and its membrane fractions were used to measure glycogen synthase 1 level and Glut4 expression and protein levels. Results 4-weeks treatment of [6]-Gingerol dramatically increased glucose-stimulated insulin secretion and improved glucose tolerance. Plasma GLP-1 was found to be significantly elevated in the treated mice. Pharmacological intervention of GLP-1 levels regulated the effect of [6]-Gingerol on insulin secretion. Mechanistically, [6]-Gingerol treatment upregulated and activated cAMP, PKA, and CREB in the pancreatic islets, which are critical components of GLP-1-mediated insulin secretion pathway. [6]-Gingerol upregulated both Rab27a GTPase and its effector protein Slp4-a expression in isolated islets, which regulates the exocytosis of insulin-containing dense-core granules. [6]-Gingerol treatment improved skeletal glycogen storage by increased glycogen synthase 1 activity. Additionally, GLUT4 transporters were highly abundant in the membrane of the skeletal myocytes, which could be explained by the increased expression of Rab8 and Rab10 GTPases that are responsible for GLUT4 vesicle fusion to the membrane. Conclusions Collectively, our study reports that GLP-1 mediates the insulinotropic activity of [6]-Gingerol, and [6]-Gingerol treatment facilitates glucose disposal in skeletal muscles through increased activity of glycogen synthase 1 and enhanced cell surface presentation of GLUT4 transporters

Patient Satisfaction in Chamber Setting in Bangladesh measured by Patient-Doctor Relationship Questionnaire (PDRQ-9 Bangla)

Background: Assessment of patient satisfaction is crucial but there is significant lagging in this sector. Patient satisfaction is an important indicator of health care quality as well as a predictor of treatment adherence. The Good patient-doctor relationship is considered as an integral part of the patient satisfaction. In Bangladesh, this domain is yet to be explored in a large scale. Aim: It was aimed to look into the patient satisfaction level in chamber setting in Bangladesh measured using the patient-doctor relationship questionnaire (PDRQ-9 Bangla). Methods: PDRQ-9 is a short yet excellent tool for assessing the patient-doctor relationship. The data collection was done in private chamber setting by the PDRQ-9 and analyzed. Results: Though the result was not completely in line with the existing literature, the PDRQ-9 was found to be a useful and brief measurement tool in the context of the patient-doctor relationship. Conclusion: Large-scale research in this particular aspect of patient satisfaction in future may provide a more succinct resul

Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic

Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues

Machine learning enabled IoT system for soil nutrients monitoring and crop recommendation

Agriculture plays a vital role in feeding the growing global population. But optimizing crop production and resource management remains a significant challenge for farmers. This research paper proposes an innovative ML-enabled IoT device to monitor soil nutrients and provide accurate crop recommendations. The device utilizes the FC-28 sensor, DHT11 sensor, and JXBS-3001 sensor to collect real-time data on soil composition, moisture, humidity, temperature, and for nutrient levels. The collected data is transmitted to a server using the MQTT protocol. Machine learning algorithms are employed to analyze the collected data and generate customized recommendations, including a possible high-yielding crop list, fertilizer names, and its amount based on crop requirements and soil nutrients. Furthermore, the applied fertilizers and treatments to the field during production are stored in the database. As a result, it has become possible to determine the quality of the produce at the consumer level through the mobile app. The system's effectiveness is evaluated through field experiments, comparing its performance with traditional methods. The results demonstrate the device's ability to enhance crop productivity and optimize resource utilization, promoting sustainable agricultural practices and food security. The research contributes to IoT-enabled agriculture, demonstrating the potential of ML techniques in improving soil nutrient management, facilitating informed decision-making about crop fertilizers, and assessing the quality of produced crops at the consumer level

Detection of COVID-19, pneumonia, and tuberculosis from radiographs using AI-driven knowledge distillation

Chest radiography is an essential diagnostic tool for respiratory diseases such as COVID-19, pneumonia, and tuberculosis because it accurately depicts the structures of the chest. However, accurate detection of these diseases from radiographs is a complex task that requires the availability of medical imaging equipment and trained personnel. Conventional deep learning models offer a viable automated solution for this task. However, the high complexity of these models often poses a significant obstacle to their practical deployment within automated medical applications, including mobile apps, web apps, and cloud-based platforms. This study addresses and resolves this dilemma by reducing the complexity of neural networks using knowledge distillation techniques (KDT). The proposed technique trains a neural network on an extensive collection of chest X-ray images and propagates the knowledge to a smaller network capable of real-time detection. To create a comprehensive dataset, we have integrated three popular chest radiograph datasets with chest radiographs for COVID-19, pneumonia, and tuberculosis. Our experiments show that this knowledge distillation approach outperforms conventional deep learning methods in terms of computational complexity and performance for real-time respiratory disease detection. Specifically, our system achieves an impressive average accuracy of 0.97, precision of 0.94, and recall of 0.97

Toward Trustworthy Metaverse: Advancements and Challenges

The Metaverse, a transformative digital realm, holds immense promise for reshaping industries and human interactions while potentially addressing global challenges and democratizing opportunities. However, it also introduces a spectrum of complexities that demand careful navigation. To establish trustworthiness within the Metaverse ecosystem, gaining a deep understanding of its applications, challenges, and existing solutions is imperative. In this comprehensive survey, we first delve into Metaverse applications, drawing insights from existing literature. Subsequently, we explore the diverse challenges the Metaverse presents, analyzing them through the lens of existing research. We then scrutinize the overall trustworthiness of the Metaverse environment and investigate existing solutions to previously identified challenges through a thorough review and analysis of pertinent literature. Lastly, we discussed future research directions aimed at fostering a trustworthy Metaverse environment. This comprehensive review can provide an overview of the Metaverse, its application domains, challenges, existing solutions and research directions for many multidisciplinary studies

G-BERT: An Efficient Method for Identifying Hate Speech in Bengali Texts on Social Media

The rapid increase in Internet users has increased online concerns such as hate speech, abusive texts, and harassment. In Bangladesh, hate text in Bengali is frequently used on various social media platforms to condemn and abuse individuals. However, Research on recognizing hate speech in Bengali texts is lacking. The pervasive negative impact of hate speech on individuals’ well-being and the urgent need for effective measures to address hate speech in Bengali texts have created a significant research gap in the Bengali hate speech detection field. This study suggests a technique for identifying hate speech in Bengali social media posts that may harm individuals’ sentiments. Our approach utilizes the Bidirectional Encoder Representations from Transformers (BERT) architecture to extract Bengali text properties, whereas hate speech is categorized using a Gated Recurrent Units (GRU) model with a Softmax activation function. We propose a new model, G-BERT, that combines both models. We compared our model’s performance with several other algorithms and achieved an accuracy, precision, recall, and F1-score of 95.56%, 95.07%, 93.63%, and 92.15%, respectively. Our proposed model outperformed all other classification algorithms tested. Our findings show that the strategy we have suggested is successful in locating hate speech in Bengali texts posted on social media platforms, which can aid in mitigating online hate speech and promoting a more respectful online environment

Self-Writer: Clusterable Embedding Based Self-Supervised Writer Recognition from Unlabeled Data

Writer recognition based on a small amount of handwritten text is one of the most challenging deep learning problems because of the implicit characteristics of handwriting styles. In a deep convolutional neural network, writer recognition based on supervised learning has shown great success. These supervised methods typically require a lot of annotated data. However, collecting annotated data is expensive. Although unsupervised writer recognition methods may address data annotation issues significantly, they often fail to capture sufficient feature relationships and usually perform less efficiently than supervised learning methods. Self-supervised learning may solve the unlabeled dataset issue and train the unsupervised datasets in a supervised manner. This paper introduces Self-Writer, a self-supervised writer recognition approach dealing with unlabeled data. The proposed scheme generates clusterable embeddings from a small fixed-length image frame such as a text block. The training strategy presumes that a small image frame of handwritten text should include the writer’s handwriting characteristics. We construct pairwise constraints and nongenerative augmentation to train Siamese architecture to generate embeddings depending on such an assumption. Self-Writer is evaluated on the two most widely used datasets, IAM and CVL, on pairwise and triplet architecture. We find Self-Writer to be convincing in achieving satisfactory performance using pairwise architectures