Large Magnetic Moments of Arsenic-Doped Mn Clusters and their Relevance to Mn-Doped III-V Semiconductor Ferromagnetism

We report electronic and magnetic structure of arsenic-doped manganese clusters from density-functional theory using generalized gradient approximation for the exchange-correlation energy. We find that arsenic stabilizes manganese clusters, though the ferromagnetic coupling between Mn atoms are found only in Mn$_2$As and Mn$_4$As clusters with magnetic moments 9 $\mu_B$ and 17 $\mu_B$, respectively. For all other sizes, $x=$ 3, 5-10, Mn$_x$As clusters show ferrimagnetic coupling. It is suggested that, if grown during the low temperature MBE, the giant magnetic moments due to ferromagnetic coupling in Mn$_2$As and Mn$_4$As clusters could play a role on the ferromagnetism and on the variation observed in the Curie temperature of Mn-doped III-V semiconductors.Comment: 4 Pages, 3 Figures[1 EPS and 2 JPG files], RevTeX

Concept and challenges of delivering preventive and care services in prehospital emergency medical service: A qualitative study

Background and purpose: Prehospital emergency medical service (EMS) is an important part of the health care system. Today, the need for integrated emergency care services and delivering qualified preventive and care services felt more than before in accidents and emergencies. This survey aimed to investigate the views of emergency medical personnel on the concept and nature of preventive and care services and current challenges in delivering these services in prehospital EMS centers in Golestan province, Iran. Materials and methods: A qualitative study was done with 16 emergency medical personnel working in EMS sites in Golestan province using purposeful sampling. Data was collected by semistructured interview guide and framework analysis was implemented to analyze the data. Results: Two general themes were identified including 1) the concept of preventive and care services in the EMS and 2) the challenges in providing qualified services in the EMS sites in Golestan province. Also, five sub-themes and 12 subclasses were determined. Conclusion: To enhance and promote services, the EMS system of Golestan province need reforms in organizational structure, laws and administrative regulations, training programs, the system of monitoring and evaluation of personnel, also provision of equipment and manpower and provision of personnel amenities to improve the staff performance. Furthermore, it is necessary to boost emergency services at the community level through training people and enhancing collaboration and participation with other organizations. © 2015, Mazandaran University of Medical Sciences. All rights reserved

Interpretable Multi Labeled Bengali Toxic Comments Classification using Deep Learning

This paper presents a deep learning-based pipeline for categorizing Bengali toxic comments, in which at first a binary classification model is used to determine whether a comment is toxic or not, and then a multi-label classifier is employed to determine which toxicity type the comment belongs to. For this purpose, we have prepared a manually labeled dataset consisting of 16,073 instances among which 8,488 are Toxic and any toxic comment may correspond to one or more of the six toxic categories - vulgar, hate, religious, threat, troll, and insult simultaneously. Long Short Term Memory (LSTM) with BERT Embedding achieved 89.42% accuracy for the binary classification task while as a multi-label classifier, a combination of Convolutional Neural Network and Bi-directional Long Short Term Memory (CNN-BiLSTM) with attention mechanism achieved 78.92% accuracy and 0.86 as weighted F1-score. To explain the predictions and interpret the word feature importance during classification by the proposed models, we utilized Local Interpretable Model-Agnostic Explanations (LIME) framework. We have made our dataset public and can be accessed at - https://github.com/deepu099cse/Multi-Labeled-Bengali-Toxic-Comments-Classificatio

The Role of Δ(1232)\Delta(1232) in Two-pion Exchange Three-nucleon Potential

In this paper we have studied the two-pion exchange three-nucleon potential $(2\pi E-3NP)$ using an approximate $SU(2) \times SU(2)$ chiral symmetry of the strong interaction. The off-shell pion-nucleon scattering amplitudes obtained from the Weinberg Lagangian are supplemented with contributions from the well-known $\sigma$-term and the $\Delta(1232)$ exchange. It is the role of the $\Delta$-resonance in $2\pi E-3NP$, which we have investigated in detail in the framework of the Lagrangian field theory. The $\Delta$-contribution is quite appreciable and, more significantly, it is dependent on a parameter Z which is arbitrary but has the empirical bounds $|Z| \leq 1/2$. We find that the $\Delta$-contribution to the important parameters of the $2\pi E-3NP$ depends on the choice of a value for Z, although the correction to the binding energy of triton is not expected to be very sensitive to the variation of Z within its bounds.Comment: 14 pages, LaTe

Reactivity of [Re\u3csub\u3e2\u3c/sub\u3e(CO)\u3csub\u3e8\u3c/sub\u3e(MeCN)\u3csub\u3e2\u3c/sub\u3e] with Thiazoles: Hydrido Bridged Dirhenium Compounds Bearing Thiazoles in Different Coordination Modes

Reactions of the labile compound [Re2(CO)8(MeCN)2] with thiazole and 4-methylthiazole in refluxing benzene afforded the new compounds [Re2(CO)7{μ-2,3-η2-C3H(R)NS}{η1-NC3H2(4-R)S}(μ-H)] (1, R = H; 2, R = CH3), [Re2(CO)6{μ-2,3-η2-C3H(R)NS}{η1-NC3H2(4-R)S}2(μ-H)] (3, R = H; 4, R = CH3) and fac-[Re(CO)3(Cl){η1-NC3H2(4-R)S}2] (5, R = H; 6, R = CH3). Compounds 1 and 2 contain two rhenium atoms, one bridging thiazolide ligand, coordinated through the C(2) and N atoms and a η1-thiazole ligand coordinated through the nitrogen atom to the same Re as the thiazolide nitrogen. Compounds 3 and 4 contain a Re2(CO)6 group with one bridging thiazolide ligand coordinated through the C(2) and N atoms and two N-coordinated η1-thiazole ligands, each coordinated to one Re atom. A hydride ligand, formed by oxidative-addition of C(2)–H bond of the ligand, bridges Re–Re bond opposite the thiazolide ligand in compounds 1–4. Compound 5 contains a single rhenium atom with three carbonyl ligands, two N-coordinated η1-thiazole ligands and a terminal Cl ligand. Treatment of both 1 and 2 with 5 equiv. of thiazole and 4-methylthiazole in the presence of Me3NO in refluxing benzene afforded 3 and 4, respectively. Further activation of the coordinated η1-thiazole ligands in 1–4 is, however, unsuccessful and results only nonspecific decomposition. The single-crystal XRD structures of 1–5 are reported

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

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