Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning

Cooking recipes allow individuals to exchange culinary ideas and provide food preparation instructions. Due to a lack of adequate labeled data, categorizing raw recipes found online to the appropriate food genres is a challenging task in this domain. Utilizing the knowledge of domain experts to categorize recipes could be a solution. In this study, we present a novel dataset of two million culinary recipes labeled in respective categories leveraging the knowledge of food experts and an active learning technique. To construct the dataset, we collect the recipes from the RecipeNLG dataset. Then, we employ three human experts whose trustworthiness score is higher than 86.667% to categorize 300K recipe by their Named Entity Recognition (NER) and assign it to one of the nine categories: bakery, drinks, non-veg, vegetables, fast food, cereals, meals, sides and fusion. Finally, we categorize the remaining 1900K recipes using Active Learning method with a blend of Query-by-Committee and Human In The Loop (HITL) approaches. There are more than two million recipes in our dataset, each of which is categorized and has a confidence score linked with it. For the 9 genres, the Fleiss Kappa score of this massive dataset is roughly 0.56026. We believe that the research community can use this dataset to perform various machine learning tasks such as recipe genre classification, recipe generation of a specific genre, new recipe creation, etc. The dataset can also be used to train and evaluate the performance of various NLP tasks such as named entity recognition, part-of-speech tagging, semantic role labeling, and so on. The dataset will be available upon publication: https://tinyurl.com/3zu4778y

Contrastive Learning for API Aspect Analysis

We present a novel approach - CLAA - for API aspect detection in API reviews that utilizes transformer models trained with a supervised contrastive loss objective function. We evaluate CLAA using performance and impact analysis. For performance analysis, we utilized a benchmark dataset on developer discussions collected from Stack Overflow and compare the results to those obtained using state-of-the-art transformer models. Our experiments show that contrastive learning can significantly improve the performance of transformer models in detecting aspects such as Performance, Security, Usability, and Documentation. For impact analysis, we performed empirical and developer study. On a randomly selected and manually labeled 200 online reviews, CLAA achieved 92% accuracy while the SOTA baseline achieved 81.5%. According to our developer study involving 10 participants, the use of 'Stack Overflow + CLAA' resulted in increased accuracy and confidence during API selection. Replication package: https://github.com/shahariar-shibli/Contrastive-Learning-for-API-Aspect-AnalysisComment: Accepted in the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE2023

Towards Automated Recipe Genre Classification using Semi-Supervised Learning

Sharing cooking recipes is a great way to exchange culinary ideas and provide instructions for food preparation. However, categorizing raw recipes found online into appropriate food genres can be challenging due to a lack of adequate labeled data. In this study, we present a dataset named the ``Assorted, Archetypal, and Annotated Two Million Extended (3A2M+) Cooking Recipe Dataset" that contains two million culinary recipes labeled in respective categories with extended named entities extracted from recipe descriptions. This collection of data includes various features such as title, NER, directions, and extended NER, as well as nine different labels representing genres including bakery, drinks, non-veg, vegetables, fast food, cereals, meals, sides, and fusions. The proposed pipeline named 3A2M+ extends the size of the Named Entity Recognition (NER) list to address missing named entities like heat, time or process from the recipe directions using two NER extraction tools. 3A2M+ dataset provides a comprehensive solution to the various challenging recipe-related tasks, including classification, named entity recognition, and recipe generation. Furthermore, we have demonstrated traditional machine learning, deep learning and pre-trained language models to classify the recipes into their corresponding genre and achieved an overall accuracy of 98.6\%. Our investigation indicates that the title feature played a more significant role in classifying the genre

Review on Smart Electro-Clothing Systems (SeCSs)

This review paper presents an overview of the smart electro-clothing systems (SeCSs) targeted at health monitoring, sports benefits, fitness tracking, and social activities. Technical features of the available SeCSs, covering both textile and electronic components, are thoroughly discussed and their applications in the industry and research purposes are highlighted. In addition, it also presents the developments in the associated areas of wearable sensor systems and textile-based dry sensors. As became evident during the literature research, such a review on SeCSs covering all relevant issues has not been presented before. This paper will be particularly helpful for new generation researchers who are and will be investigating the design, development, function, and comforts of the sensor integrated clothing materials

Safety Measures of Journalists during Corona Pandemic in Bangladesh

Among other frontline fighters journalists have been the first responders to the pandemic of the COVID-19 virus Because of following professional responsibilities they have become highly vulnerable to get exposed to the risk As a result providing safety measures to them has received the highest priority at this time It has been urged by national and international organizations and associations to media employers to provide safety measures to their respective journalists This study aims to examine the management of media employers of Bangladesh in providing safety measures to journalists The study interviews 48 journalists of 12 newspapers and 12 television channels selecting one reporter and one copy editor from each media The results reveal that the majority of journalists received inadequate nonstandard irregular imbalanced and improper safety measures while the rest got nothing because of the employer s total negligence and financial crisis The study also shows that the media employers failed to distribute safety measures between reporters and copy editors equally Based on the findings the study concludes by calling for a proper safety plan to protect journalists from health risk

Impact of Sensor Networks on Aquatic Biodiversity in Wetland: An Innovative Approach

Aquatic biodiversity is in the central field of environmental conservation issues in a wetland. Yet it determinately faced aquatic conservation authorities the loss of biodiversity as a very important global issue for several years due to misuse wireless sensor technology. The study attempts to re-look at the sensor networks that affect the aquatic biodiversity within and around the Tanguar Haor- wetland study at Sunamganj district in Bangladesh. Key aquatic conservation tools provided at the Tanguar Haor and its challenges with gaps in policies for wetland management practices are highlighted. The study shows the aquatic biodiversity-related rules and regulations amended were apex in Bangladesh from 2010 to 2018. The study represents the impact of processed sensor networks on aquatic biodiversity in a wetland to be compared to larger, medium, and smaller animals in a bright, dark and optimum environment, facilitating the design and misuse of wireless sensor networks within GPS locations. Approximately 64% of the respondents agreed on the development of aquatic biodiversity for managing the wetland at Sunamganj with secure peripheral sensor networks. The research also found that the Tanguar Haor is at risk due to misuse of wireless sensor networks compared to other wetlands in the Sylhet Division. Scientific knowledge is indispensable in wetland resource management but it poorly identified such knowledge while various performances are still below par. The research is unique and represents the innovative idea to improve the existing wetland policy linking with the appropriateness for the Ramsar Wetland Conservation Strateg

Development of techniques for quantitative biofilm assay and isolation and characterization of an E. Coli gene involved in biofilm maturation

In their natural habitat bacteria predominantly exist as biofilms in which bacteria differ physiologically and metabolically from free-living cells. The aim of the study was to identify and characterize genes that are involved in biofilm maturation. The crystal violet (CV) assay is widely used to identify early stage biofilm mutants. However, it is not sufficiently sensitive to isolate late stage mutants that fail to develop mature biofilms. Using tetrazolium salt XTT (sodium 3'-{ l-[(phenylamino)-carbonyl]-3,4tetrazolium}- bis (4- methoxy-6-nitro) benzenesulfonic acid hydrate) this study therefore developed a more sensitive assay, named the XTT assay, to identify smaller changes in the quantity of biofilm. Using a combination of the CV and the XTT assays, a pool of transposon mediated mutant cells was screened and four genes (yhjN, adiA, bglX and glpX) were identified as potentially being involved in biofilm maturation. glpX was selected for further study. GlpX is a largely uncharacterised protein that had been shown to have FBPase activity in vitro. FBPase activity is required for the synthesis of colanic acid, a polysaccharide previously shown to playa key role in the development of the three-dimensional structure of the fully formed biofilm. The findings of this study 4 indicate that glpX-encoded FBPase activity is involved in colanic acid biosy~thesis in E. coli. A preliminary molecular analysis was made of the expression of glpX.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Real-time driving emissions of on-road diesel vehicles

The impact of driver behaviour, traffic conditions, and route features on emissions and fuel consumption of diesel vehicles were investigated in Australian conditions. A diverse range of drivers took part in the study (sample size: 30) where no training was provided which enabled the capture of actual real-world driving behaviour and emissions. It was found that driving behaviour is significantly more influential on emissions than traffic conditions. A method of identifying emission hotspots due to certain route features was developed by analyzing high instantaneous emissions. Such hotspots may have an impact on air-quality degradation in surrounding areas

Design and development of vertical axis wind turbine

A wind turbine is a device that converts kinetic energy from the wind into electrical power. Vertical Axis Wind Turbine (VAWT) is one type of wind turbine where the main rotor shaft is set vertically and it can capture wind from any direction. The aim of this work is to develop a theoretical model for the design and performance of Darrieus type vertical axis wind turbine for small scale energy applications. A small 3 bladed turbine (prototype) is constructed and investigated the performance for low wind velocity. The model is based on NACA 0018 airfoil & light wood is used as blade material. The full scale Vertical Axis Wind Turbine is made for 36 inch height, 24 inch diameter, blade cord length is 3.937 inch & blade height is 24 inch. A 100 watt 24 volt brushless DC motor is used to measure output power. The whirling speed of blade & electric power output for the corresponding speed is measured through Tachometer & Wattmeter. The power curves show the relation between the rotational wind speed of the turbine and the power produced for a range of wind speeds. This approach indicates to develop vertical axis wind turbine with better performance to meet the increasing power demand

A Study on Urea-Water Solution Spray-Wall Impingement Process and Solid Deposit Formation in Urea-SCR de-NOx System

Selective catalytic reduction (SCR) has been exhibited as a promising method of NOx abatement from diesel engine emissions. Long-term durability is one of the key requirements for the automotive SCR system. A high NOx conversion, droplet distribution and mixing, and fluid film and solid deposit formation are the major challenges to the successful implementation of the SCR system. The current study is therefore three-fold. Firstly, high-speed images disclose detailed information of the spray impingement on the heated impingement surface. The spray impingement investigation took place in a specially-designed optically-accessible visualization chamber where the Z-type shadowgraph technique was used to capture the high-speed images. Wall temperature has a great influence on the film formation and wall wetting. A higher wall temperature can significantly increase the droplet evaporation, and consequently, wall wetting decreases. The numerical analysis was performed based on the Eulerian-Lagrangian approach using STAR CCM+ CFD code. Secondly, the resultant phenomena due to spray-wall impingement such as fluid film generation and transport, solid deposit formation, and thermal decomposition were recorded using a high-speed camera operating at a low frame rate. Infrared thermal imaging was used to observe the spray cooling effect after impingement. Spray impingement caused local cooling, which led to wall film formation, which introduced urea crystallization. Finally, solid deposits were analyzed and characterized using Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA). FTIR analysis revealed that urea decomposition products vary based on the temperature, and undecomposed urea, biuret, cyanuric acid, ammeline, and melamine can be formed at different temperatures. TGA analysis showed that accumulated deposits were hard to remove. Moreover, complete thermal decomposition of deposits is not possible at the regular exhaust temperature, as it requires a comparatively long time span