Miracle-dz/UniFAR-FTUs huggingface dataset:
KETG: A K NOWLEDGE E NHANCED T EXT G ENERA - TION F RAMEWORK

Embedding logical knowledge information into text generation is a challenging NLP task. In this paper, we propose a knowledge enhanced text generation (KETG) framework, which incorporates both the knowledge and associated text corpus to address logicality and diversity in text generation. Specifically, we validate our framework on rhetorical text generation from our newly built rhetoric knowledge graph. Experiments show that our framework outperforms baseline models such as Transformer and GPT-2, on rhetorical type control, semantic comprehensibility and diversity.
Silver Nanoparticles Stabilized with Phosphorus-Containing Heterocyclic Surfactants: Synthesis, Physico-Chemical Properties, and Biological Activity Determination

Phosphorus-containing heterocyclic cationic surfactants alkyldimethylphenylphospholium bromides with the alkyl chain length 14 to 18 carbon atoms were used for the stabilization of silver nanodispersions. Zeta potential of silver nanodispersions ranges from +35 to +70 mV, which indicates the formation of stable silver nanoparticles (AgNPs). Long-chain heptadecyl and octadecyl homologs of the surfactants series provided the most intensive stabilizing effect to AgNPs, resulting in high positive zeta potential values and smaller diameter of AgNPs in the range 50–60 nm. A comparison with non-heterocyclic alkyltrimethylphosphonium surfactants of the same alkyl chain length showed better stability and more positive zeta potential values for silver nanodispersions stabilized with heterocyclic phospholium surfactants. Investigations of biological activity of phospholium-capped AgNPs are represented by the studies of antimicrobial activity and cytotoxicity. While cytotoxicity results revealed an increased level of HepG2 cell growth inhibition as compared with the cytotoxicity level of silver-free surfactant solutions, no enhanced antimicrobial action of phospholium-capped AgNPs against microbial pathogens was observed. The comparison of cytotoxicity of AgNPs stabilized with various non-heterocyclic ammonium and phosphonium surfactants shows that AgNPs capped with heterocyclic alkyldimethylphenylphospholium and non-heterocyclic triphenyl-substituted phosphonium surfactants have the highest cytotoxicity among silver nanodispersions stabilized by the series of ammonium and phosphonium surfactants.
Syntheses of benzo[c]coumarin carboxylic acids and fluorescence properties in the solid state

Abstract 7-Hydroxy-3-methoxy-6-oxo-6H-benzo[c]- chromene-8-carboxylic acid (3a), 3,7-dihydroxy-6-oxo-6H- benzo[c]chromene-8-carboxylic acid (3b), 1,7-dihydroxy-6-oxo-6H-benzo[c]chromene-8-carboxylic acid (3c) and 2-bromo-7-hydroxy-6-oxo-6H-benzo[c]chromene-8-carboxylic acid (3d) were synthesized and characterized by the method of single crystal X-ray diffraction. Owing to bigger conjugated system and various H-bonds in 7-hydroxy-6-oxo-6H-benzo[c]chromene-8-carboxylic skeleton, 3a–3c have excellent fluorescence in ethanol solution and the solid state. The synthons involved in each crystal are identified, and the fluorescence emission differences leading to molecular recognition are determined.
Multiple-time measurements of multidimensional psychiatric states from immediately before the COVID-19 pandemic to one year later: A longitudinal online survey of the Japanese population

The coronavirus disease 2019 (COVID-19) pandemic has profoundly affected the mental health of both infected and uninfected people. Although most psychiatric disorders have highly overlapping genetic and pathogenic backgrounds, most studies investigating the impact of the pandemic have examined only single psychiatric disorders. It is necessary to examine longitudinal trajectories of factors that modulate psychiatric states across multiple dimensions. 2274 Japanese citizens participated in online surveys presented in December 2019 (before the pandemic), August 2020, Dec 2020, and April 2021. These surveys included nine questionnaires on psychiatric symptoms, such as depression and anxiety. Multi-dimensional psychiatric time series data were then decomposed into four principal components. We used generalized linear models to identify modulating factors for effects of the pandemic on these components. The four principal components can be interpreted as general psychiatric burden, social withdrawal, alcohol-related problems, and depression/anxiety. Principal components associated with general psychiatric burden and depression/anxiety peaked during the initial phase of the pandemic. They were further exacerbated by the economic burden of the pandemic. In contrast, principal components associated with social withdrawal showed a delayed peak, with human relationships as an important risk modulating factor. In addition, being elderly and female were risk factors shared across all components. Our results show that COVID-19 has imposed a large and varied burden on the Japanese population since the commencement of the pandemic. Although components related to the general psychiatric burden remained elevated, peak intensities differed between components related to depression/anxiety and those related to social anxiety. These results underline the importance of using flexible monitoring and mitigation strategies for mental problems, according to the phase of the pandemic.
The Role of Mass Media in Tackling COVID-19 in Pakistan

At the end of 2019, in December, the Huanan Seafood Wholesale Market appeared to be linked with the massive outbreak of pneumonia in Wuhan. It was identified that a virus later called the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was responsible for this condition. The disease was named COVID-19 and declared a pandemic as it affected the entire world and claimed many lives. News pertaining to the spread of COVID-19 became the hot talk in all forms of media ranging from print and television to social media platforms. This study aims to provide an analysis of the role of all types of media in the crisis and emergency that has arisen worldwide due to the outbreak of COVID-19. The role of media in disseminating information, particularly within Pakistan, is discussed in detail regarding the spread of coronavirus and the media's active part in sensationalizing or controlling the situation.
Applying Natural Language Processing techniques to analyze HIV-related discussions on Social Media

Nowadays social media are being used to monitor the progress of viruses and share important prevention and treatment information. This has also allowed the creation of a community of people united by the same disease, to give themselves strength, comfort and advice. The objective of this work is to extract and understand discussions about HIV on a popular social media platform: Twitter, a microblogging application. Tweets with the hashtag #HIV were collected in the date range of one year, starting from November 12th 2018 to November 12th 2019. They were then filtered and cleaned using NLP techniques, which allowed the removal of duplicates, non-english texts and useless information, such as tweets only containing urls, mentions or hashtags. After the cleaning phase, the main analyzes carried out were sentiment analysis and content analysis which, using data mining and text mining algorithms were able to reveal their emotions and the most influential topics written about HIV. This study illustrates the potential of using social media to analyse the spread of viruses and health conditions using two types of analyses for the same topic and dataset: sentimental analysis and content analysis. HIV-related messages were used by organisations and credible sources to disseminate information about treatment and prevention, but also by individual users to share their thoughts, emotions and experiences of living with HIV. Twitter is also used by celebrities and health authorities to respond to public concerns. This work shows that many tweets are written for the purpose of giving information and emotional support with assistance from the online 5 community and also for health care professionals who support individuals living with HIV/AIDS. The algorithms and notions covered in this work can subsequently be used by the public health community or data scientists to analyze tweets regarding other viruses or diseases, showing how social media can be used to identify, detect and study outbreaks in a specific geographical area and in a specific period of time.
Implant Positioning in Total Hip Arthroplasty within a Target Zone: A Comparison between Conventional vs Computer Assisted Navigated Surgery

Methods: A review of 176 (86 CANS/90 CON) primary THA cases was performed. Acetabular component inclination (target range: 30° 50°) and anteversion (target range: 15° 35°) were measured. Limb length discrepancy (LLD) was measured on post-operative radiographs. Ninety-day complications and assessments of hip strength, gait, and range of motion (ROM) were recorded. Chi-square was used to compare %outside the target ranges between groups and complication frequencies. A t-test was used to compare distance from the center of the target range and operative time. A generalized linear model was used to compare post-operative assessments. Type-I error, α = 0.05.
(Ir)Reproducible Machine Learning: Supplement

We focus on reproducing the out-of-sample results reported by Muchlinski et al. (2016). Table 1 provides the comparisons between the results reported in Muchlinski et al. (2016), our reproductions of their reported (incorrect) results, as well as the corrected version of their results. Muchlinski et al. (2016) re-use the dataset from Hegre and Sambanis (2006) when training their models, and provide a separate out-of-sample test set for evaluation. To address missing values, they use a Random Forests based imputation method in R called rfImpute. However, the training and test sets are imputed together, which leads to a data leakage. This results in overoptimistic performance claims. Below, we detail the steps we take to correct their results, provide a visualization of the data leakage, and provide a simulation showcasing how the data leakage can result in overoptimistic claims of performance.
N ON -I NHERENT F EATURE C OMPATIBLE L EARNING

The need of Feature Compatible Learning (FCL) arises from many large scale retrieval-based applications, where updating the entire library of embedding vectors is expensive. When an upgraded embedding model shows potential, it is desired to transform the benefit of the new model without refreshing the library. While progresses have been made along this new direction, existing approaches for feature compatible learning mostly rely on old training data and classifiers, which are not available in many industry settings. In this work, we introduce an approach for feature compatible learning without inheriting old classifier and training data, i.e., Non-Inherent Feature Compatible Learning. Our approach requires only features extracted by old model’s backbone and new training data, and makes no assumption about the overlap between old and new training data. We propose a unified framework for FCL, and extend it to handle the case where the old model is a black-box. Specifically, we learn a simple pseudo classifier in lieu of the old model, and further enhance it with a random walk algorithm. As a result, the embedding features produced by the new model can be matched with those from the old model without sacrificing performance. Experiments on ImageNet ILSVRC 2012 and Places365 data proved the efficacy of the proposed approach.
Interpretable Medical Image Classiﬁcation with Self-Supervised Anatomical Embedding and Prior Knowledge

In medical image analysis tasks, it is important to make machine learning models focus on correct anatomical locations, so as to improve interpretability and robustness of the model. We adopt a latest algorithm called self-supervised anatomical embedding (SAM) to locate point of interest (POI) on computed tomography (CT) scans. SAM can detect arbitrary POI with only one labeled sample needed. Then, we can extract targeted features from the POIs to train a simple prediction model guided by clinical prior knowledge. This approach mimics the practice of human radiologists, thus is interpretable, controllable, and robust. We illustrate our approach on the application of CT contrast phase classiﬁcation and it outperforms an existing deep learning based method trained on the whole image.
Examining the Formation of Entrepreneurial Activities through Cognitive Approach*

There have been questions how entrepreneurs think, act and why individuals become entrepreneurs. The trait-based explanation of entrepreneurial activities has been main stream. However, the trait-based theory has been criticized because it assumes that entrepreneurial traits are inherited, stable and enduring over time. This research accepts the cognitive theory to see how entrepreneurs learn or accept others’ values, how entrepreneurial perceptions of opportunity impact entrepreneurial actions and how individuals acquire the social legitimation of the formation of entrepreneurial activities. In order to capture the attitudes, activities and motivations of people who are involved in entrepreneurial activities, the author uses the GEM Korea 2016 data. The data from the Global Entrepreneurship Monitor(GEM) has been well known for the data to capture individuals early-stage entrepreneurial activities. This paper used the sample from the APS(Adult Population Survey) of the GEM which was completed by a representative sample of two thousand adults in Korea by the qualified survey vendor, with strict procedures and oversight by the GEM central data team. The hypotheses are tested with logit regression analysis to estimate the probability of the influence of perceptual variables such as individual perception in social learning, the opportunity recognition in the environment, and social legitimation in the entrepreneurial activities. Based on the results, individuals tend to have high entrepreneurial activities if individuals have high self-efficacy. Also, the existence of role models around the entrepreneurs encourages the individuals involve in entrepreneurial activities more however the perception of opportunity in the environment is not strongly associated with entrepreneurial activities. The media exposure of successful entrepreneurs is more important than others’ perception of entrepreneurs on the desirable career option or respect from communities. This paper can contribute to the cognitive processes, particular perception about oneself, as well as perception which is impacted by a community or a society.
Preliminary design of simple IoT-based Smart Home trainer for kids

In the Industrial 4.0 era, all household appliances have been connected to the internet, i.e., lights, curtains, fans, and so on. Users can control it via a smartphone as a remote. The competency to master the concepts and practices related to IoT are useful. The basic concept of IoT can be introduced to students since they are in primary school. In this paper, we design an Internet-of-Things trainer to demonstrate one of a simple IoT application to children. A smart home system is selected as a use case; we select simple household appliances to represent a smart home environment. The trainer is divided into two parts: Android app as a remote system and the end-device as the node. The node is then divided into three types: mechanical-based (door-lock), electrical-based (mini DC fan, generic power switch), and sensor (temperature & humidity sensor). We used a 4-D research and development model to develop this trainer. However, this paper is limited to the “Define,” “Design,” and “Development” stages. All nodes are tested their performance, and according to the functional test, it can work properly as expected: the nodes can be controlled (OFF to ON, or vice versa) and monitored (temperature and humidity) by using a smartphone. This trainer kit can be taught for 6th grade of elementary school students (about 12 – 14 years old).
Yield Determinants of Some Sweet Potato (Ipomoea batatas L) Clones Using Principal Component Analysis

This study aims to determine the factors influencing the yield of sweet potatoes using the Principal Component Analysis (PCA). Meanwhile, this tool was used to form new set of smaller and mutually independent variables (no longer correlated) based on the research variables which includes Plant Length (PL), Number of Leaves (NL), Number of Shoots (NS), Leaf area (LA), Total Fresh Weight (TFW), Number of Tubers (NT), Fresh Weight of Tuber (FWT), Dry Weight of Tuber (DWT), and Harvest Index (HI). The results showed that the combined effect of TFW, NT, FWT, and DWT, as the main determinants of sweet potatoes yield was 71.048% as indicated by the total variance (cumulative percent of variance). Therefore, treatments in line with these values are important for improved crop yield.
Analysis of Customer Needs for Food Products Using Kano Model, A Case Study of Steamed Brownies

Recently, the increased interest in the food business has led to fierce competition among players in the sector. Due to this condition, the quality of food produced needs to be evaluated and systematically designed. Conventionally, the development of products such as ingredient formulation, re-engineering process, installation of new, and more sophisticated type of equipment is driven by technology. However, in a competitive business environment, customer satisfaction becomes the major theme, and the development of products that relies only on technology push may result in failure as the developed products may not meet the customer expectations. Therefore, controlling products that the customer needs is an important issue to be addressed. This research aimed to analyze the quality of a food product that met the customer’s needs. Moreover, the product was steamed brownies and was produced by small-medium enterprises (SMEs) in Surakarta (Indonesia). Kano model was applied to ascertain the customer satisfaction attributes and the result showed that there were twelve quality attributes that were identified as the ‘voice’ of the brownie customers. The Kano diagram categorized two attributes into Attractive elements, eight into One Dimensional group and the other two into Indifferent attributes. In addition, the quality attribute classification was beneficial for the brownie producers to determine the direction of product development that brought about a higher level of customer satisfaction.
Development of interactive learning multimedia for mathematics subjects for grade 5 elementary schools

The learning process during a pandemic requires teachers to be more interactive in making online learning media. The government has designed the Covid Emergency Curriculum, which is that educational units in their current condition can choose one of the several components needed in the learning process, namely still referring to the national curriculum, using the emergency curriculum, or making curriculum simplification independently. For curriculum simplification, only the main material in learning activities needs to be conveyed, while for practice it can be independently or through an application media. This study aims to create an augmented reality-based learning media on geometry in mathematics subjects for grade 5 elementary school. This media can make it easier for students to understand the shapes with augmented reality and there is a learning video feature that also explains the material presented by the teacher. With the black box white box method, the application is developed by adjusting the curriculum simplification planned by the government. Features in the application include objectives and materials, learning videos, augmented reality simulations and integrated with Google Classroom as a learning evaluation platform. The results of this research are digital applications that can be accessed from the web or android applications.
Antibacterial Activity of Metallic Nanoparticles

Metallic nanoparticles (NPs) for medical applications have been documented since ear- lier times. Advancement in modern medicines results in an increase in utilization of NPs for medical purposes due to their antibacterial, antiviral, antifungal, anti-inflammatory, and antiangiogenic properties. In this chapter, three metallic NPs are studied extensively as powerful nanoweapons for the destruction of bacteria. Recent research gives evidence that metallic NPs are very effective in supporting antimicrobial activities. The chemi cally and laser-ablated silver, gold, and copper NPs exhibited enhanced antibacterial activity than previously reported. The antibacterial mechanism was found dose- and size-dependent and was more profound for Gram-negative bacterium as compared to Gram-positive ones. The dose calculations of minimum inhibitory concentration (MIC) with NPs have been calculated for both Gram-positive and Gram-negative bacteria. The maximum zone of inhibition by disk diffusion was also experimented against various bacteria. These NPs exhibit excellent performance physically, catalytically, and chemi - cally. Present study will be beneficial in areas of environment, information technology, health, cosmetics, and food department. This chapter will cover the details of fabrication and antibacterial activity results of silver, gold, and copper NPs. This chapter endeavors to demonstrate the use of metallic NPs as an alternative antibacterial nanobiotics.
Emissions projections for US utilities through 2050

Decarbonization is an urgent global policy priority, with increasing movement towards zero-carbon targets in the United States and elsewhere. Given the joint decarbonization strategies of electrifying fossil fuel-based energy uses and decarbonizing the electricity supply, understanding how electricity emissions might change over time is of particular value in evaluating policy sequencing strategies. For example, is the electricity system likely to decarbonize quickly enough to motivate electrification even on relatively carbon-intensive systems? Although electricity sector decarbonization has been widely studied, limited research has focused on evaluating emissions factors at the utility level, which is where the impact of electrification strategies is operationalized. Given the existing fleet of electricity generators, ownership structures, and generator lifespans, committed emissions can be modeled at the utility level. Generator lifespans are modeled using capacity-weighted mean age-on-retirement for similar units over the last two decades, a simple empirical outcome variable reflecting the length of time the unit might reasonably be expected to operate. By also evaluating generators in wholesale power markets and designing scenarios for new-build generation, first-order annual average emissions factors can be projected forward on a multidecadal time scale at the utility level. This letter presents a new model of utility-specific annual average emissions projections (greenhouse gases and air pollutants) through 2050 for the United States, using a 2019 base year to define existing asset characteristics. Enabling the creation and evaluation of scenario-based projections for dynamic environmental intensity metrics in a decarbonizing electricity sector can inform life cycle and other environmental assessment studies that evaluate impact over time, in addition to highlighting particular opportunities and risks associated with the timing and location of long-lived capital investments as the fossil fuel electricity generator fleet turns over. Model results can also be used to contextualize utilities’ decarbonization commitments and timelines against their asset bases.