Miracle-dz/UniFAR-FTUs huggingface dataset:
DGeye: Probabilistic Risk Perception and Prediction for Urban Dangerous Goods Management

Recent years have witnessed the emergence of worldwide megalopolises and the accompanying public safety events, making urban safety a top priority in modern urban management. Among various threats, dangerous goods such as gas and hazardous chemicals transported through cities have bred repeated tragedies and become the deadly “bomb” we sleep with every day. While tremendous research efforts have been devoted to dealing with dangerous goods transportation (DGT) issues, further study is still in great need to quantify this problem and explore its intrinsic dynamics from a big data perspective. In this article, we present a novel system called DGeye, to feature a fusion between DGT trajectory data and residential population data for dangers perception and prediction. Specifically, DGeye first develops a probabilistic graphical model-based approach to mine spatio-temporally adjacent risk patterns from population-aware risk trajectories. Then, DGeye builds the novel causality network among risk patterns for risk pain-point identification, risk source attribution, and online risky state prediction. Experiments on both Beijing and Tianjin cities demonstrate the effectiveness of DGeye in real-life DGT risk management. As a case in point, our report powered by DGeye successfully drove the government to lay down gas pipelines for the famous Guijie food street in Beijing.
The Research Framework and Evolution of Service Robots

ABSTRACT With the development of emerging science and technology, service robots have attracted more attention from scholars. However, it is still lack of comprehensive analysis of these studies from a quantitative perspective. This paper combines quantitative and qualitative methods to analyze the researches of service robots from the perspective of systematic literature review. Based on the clustering analysis of the co-citation network and the co-word network, this paper identifies the main topics and proposes a research framework of service robots which divides the existing researches into five levels. Then, through the bursts detection, this study analyzes hot spots and development of the researches on service robots. Based on the timeline analysis, this paper divides the evolution of the researches into three stages. Besides, this study analyzes the researches in the last three years to determine the recent research priorities and trends, so as to provide a reference for scholars and managers.
Sustainability, Compostability, and Specific Microbial Activity on Agricultural Mulch Films Prepared from Poly(lactic acid)

Agricultural mulch film of poly(lactic acid) (PLA) has been prepared under industrial conditions by the extrusion blown film method with modified properties using poly(hydroxibutyrate) (PHB) and reactive compatibilizer maleic anhydride (MA). Processing parameters and blend composition have been optimized based upon processability and mechanical properties of the final materials. Because PLA is a biopolymer, evaluation of service life period of the mulch film is very much important. Sustainability of the film has been analyzed by keeping the films in a weatherometer, which can create accelerated weather conditions, followed by mechanical testing at regular intervals. Similarly, variation in compostability has been analyzed as per the American standard for test method, ASTM D 5988, using vermi-compost. In addition, specific microbial action on the mulch films also has been analyzed using bacteria Berkholdaria cepacia (B. cepacia), which is selective in particular toward PLA degradation and in mixed fungal i.
Experimental Study on Reservoir Physical Properties and Formation Blockage Risk in Geothermal Water Reinjection in Xining Basin: Taking Well DR2018 as an Example

The Xining Basin in Qinghai Province, China, is rich in mid–low temperature geothermal resources, but the reinjection of geothermal water has not yet started. In this paper, the physical properties of rocks sampled from the newly drilled geothermal well DR2018 were analyzed, and a series of core flooding experiments was conducted to assess the formation blockage risk during water reinjection. The experimental results show that the geothermal reservoir has a low porosity of 1.64–18.68% and a low permeability of 0.04–7.23 md. The rocks are weakly consolidated, and the movable clay and sand particles account for 0.18–23.42 wt %, which results in a significant rate and salinity sensitivity. Even at low water flow velocity of 0.31–1.64 cm/min, the core permeability will drop by 35–53% after 25-144PV injection. An obvious fluctuation and decline in core permeability can also be observed as the injected water salinity decreases. The blockage risk induced by the invasion of low-content scaling and suspended particles in injected water can be covered up by the migration of movable particles in cores. The particle migration and blockage in the near-wellbore formation will be the main reason to cause the decline in the well’s geothermal reinjection capacity.
Formation of Glycerol as Byproduct and its Application During Biodiesel Production From Pongamia Pinnata

Pongamia Pinnata is the scientific name of Karanja. It is a medium sized tree that is plentily found alloverOdisha. There are a lot of research is going on regarding production of Biodiesel from Karanja oil but the main objective of the paper is the production and implementation of Glycerol from Karanja oil. Alcohol glycerol, a clear, colorless, viscous, sweet-tasting liquid belonging to the family of organic compounds; molecular formula HOCH 2 CHOHCH 2 OH. In this experiment we have produced Biodiesel as well as Glycerol as a byproduct but we have focused mainly on the formation of the Glycerol and its application. So in a different view point,if the production of value added glycerol can be increased within the same cost of biodiesel production, overall cost of biodiesel can be reduced to an optimum level. The effective utilization of crude glycerol will contribute to the viability of biodiesel. In this experiment, we have taken non edible Karanja oil for preparation of Glycerol by transesterification of crude oil with methanol in presence of NaOH/KOH as catalyst and yielded of approximately 11% (w/w) glycerol.
Motives matter: The relation between motives and interpartner involvement in nonprofit–business partnerships

This paper explores what motivates nonprofit and business managers to engage in nonprofit-business partnerships. We use a relational-based lens to look at how the motives of nonprofit and business managers influence the intensity of interpartner involvement and commitment. Using survey data collected from senior managers involved with nonprofit-business partnerships in the Netherlands, we find that the motive of joint problem solving is the primary motive for managers to engage in partnerships. Nonprofit managers are primarily motivated to acquire knowledge and financial resources, while business managers enter partnerships for Corporate Social Responsibility (CSR) and image improvement reasons. Our quantitative findings also refine the dichotomy of instrumental and moral motives by introducing a second dimension: the expected impact of motives (from low to high). Evidence shows that the motives help determine the nature of the partnership: low-social impact motives are associated with collaborations with a low degree of inter-partner involvement, while high-social impact motives lead to partnerships where there is a high degree of inter-partner involvement.
Challenges in Operating University Moodle E-Learning: A Case Study from Lecturers’ Perceptions

Abstrak: Penelitian bertujuan untuk menginvestigasi persepsi tentang kesulitan dosen Pendidikan Bahasa Inggris yang menggunakan E-learning Moodle Undiksha dalam mata kuliah pedagogi, literature, reading dan writing. Penelitian ini menggunakan metode mix dan lebih kepada pendekatan kualitatif. Peneliti menggunakan kuisioner dan panduan interview saat pengumpulan data. Dosen Pendidikan Bahasa Inggris merupakan partisipan dalam penelitian ini. Hasil penelitian menunjukan ada enam masalah yang ditemui oleh dosen saat menggunakan E-learning Moodle Undiksha yang di kategorikan menjadi internal dan external. Kesulitan internal yang ditemui berupa tidak adanya fitur auto-refresh, konferensi video, pesan suara dan mengedit secara berkesinambungan. Sedangkan untuk halangan external diantaranya sikap buruk plagiarism dan partisipasi pasif dari mahasiswa Abstract: This research aimed to investigate the perceptions about challenges that are encountered by English Language Education lecturers using the Undiksha Moodle e-learning in pedagogy, literature, reading, and writing course. This study used an embedded mixed-method case study with qualitative dominant. Questionnaire and semi-structured interviews were used by the researcher in collecting the data. The participants were the lecturers of English Language Education. The findings manifested that there were six challenges that were encountered by the lecturers during operating the Undiksha Moodle e-learning that could be categorized into internal and external. The internal challenges such as the absence of several features like auto-refresh, video conference, voice message recording, and editing synchronously. While the external challenges were the bad attitude of the plagiarism act and students’ passive participation
From FAIR data to fair data use: Methodological data fairness in health-related social media research

The paper problematises the reliability and ethics of using social media data, such as sourced from Twitter or Instagram, to carry out health-related research. As in many other domains, the opportunity to mine social media for information has been hailed as transformative for research on well-being and disease. Considerations around the fairness, responsibilities and accountabilities relating to using such data have often been set aside, on the understanding that as long as data were anonymised, no real ethical or scientific issue would arise. We first counter this perception by emphasising that the use of social media data in health research can yield problematic and unethical results. We then provide a conceptualisation of methodological data fairness that can complement data management principles such as FAIR by enhancing the actionability of social media data for future research. We highlight the forms that methodological data fairness can take at different stages of the research process and identify practical steps through which researchers can ensure that their practices and outcomes are scientifically sound as well as fair to society at large. We conclude that making research data fair as well as FAIR is inextricably linked to concerns around the adequacy of data practices. The failure to act on those concerns raises serious ethical, methodological and epistemic issues with the knowledge and evidence that are being produced.
Organizational responses to work-life balance issues: The adoption and use of family-friendly policies in Korean organizations

ABSTRACT Korea’s low birth rate raises the issue of work-life balance (WLB) in the country. Given this context, this study aims to explore the factors that contribute to the adoption and use of family-friendly policies (FFPs) as a result of companies’ adaptive responses to internal and external pressures. This study explores these factors by focusing on the organizational slack and internal constituents based on organizational adaptation theory. The findings indicate that internal constituents (i.e., labor unions and the proportion of female, older and professional employees) have an impact on the adoption and use of FFPs, while the impact of organizational slack is limited. The results suggest strengthening policies to facilitate communications between employees and employers as well as among employees to enhance the effectiveness of WLB policies.
Effect of bile salt hydrolase-active Lactobacillus plantarum Y15 on high cholesterol diet induced hypercholesterolemic mice

ABSTRACT This study aimed to elucidate the underlying mechanisms for the protective effect of Lactobacillus on hypercholesterolemia. Firstly, twenty Lactobacillus strains were screened for potential probiotics with high bile salt hydrolase (BSH) activity using in vitro methods. Secondly, hypercholesterolemic mice induced by a cholesterol-enriched diet were administrated by the probiotic candidate strain L. plantarum Y15, identified from the screen. Results in vitro showed that L. plantarum Y15 possessed high BSH activity. In vivo, L. plantarum Y15 supplementation decreased total cholesterol and triglyceride in serum and liver, increased total cholesterol and total bile acid in feces, and alleviated histopathological changes in the liver. Furthermore, L. plantarum Y15 supplementation modulated the gut microbiota to the control group’s pattern, leading to increase BSH activity, which further influence FXR and SHP signaling pathway to upregulate the expression level of CYP7A1 to enhance the cholesterol catabolism.
Mitigation of Ionospheric Noise in Azimuth Offset Based on the Split-Spectrum Method

The pixel-offset method has been utilized as a powerful tool to measure large ground movements. However, L-band spaceborne synthetic aperture radar (SAR) data are often affected by the ionosphere, which produces serious noises in the azimuth component of the pixel offset field, called as azimuth streaks. Here, we propose a new method to mitigate azimuth streaks based on physical modeling. Azimuth streaks cannot be removed by simply combining the known relationship between ionospheric azimuth offset and the ionospheric phase delay with the phase delay obtained by the split-spectrum method. Thus, taking into account that image matching (coregistration) affects the measurement of azimuth offsets, we formulate a theoretical correction formula of azimuth streaks by subtracting the coregistration-induced effects approximated by a polynomial function from ionospheric azimuth offsets modeled using the split-spectrum method. Applying the method to two pairs of Advanced Land Observing Satellite-2 (ALOS-2)/Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) stripmap images which are severely affected by the ionosphere, we demonstrate effective mitigation of azimuth streaks. In the application to the case that no significant ground movement is detected by Global Navigation Satellite System (GNSS), while the standard deviation of azimuth pixel offset before the correction is 87.2 cm, the value after the correction is 29.2 cm, which is comparable to the theoretical measurement accuracy of the azimuth pixel offset. In the application to the 2016 Kumamoto earthquake, we substantially reduce the azimuth streaks and successfully extract the ground movements from the azimuth offset fields within an accuracy of about 20 cm. The result suggests the proposed method enables more accurate and operational estimation of the 3-D ground displacement.
Effect of Urban Heat Island and Global Warming Countermeasures on Heat Release and Carbon Dioxide Emissions from a Detached House

Urban air temperature rises induced by the urban heat island (UHIE) effect or by global warming (GW) can be beneficial in winter but detrimental in summer. The SCIENCE-Outdoor model was used to simulate changes to sensible heat release and CO2 emissions from buildings yielded by four UHIE countermeasures and five GW countermeasures. This model can evaluate the thermal condition of building envelope surfaces, both inside and outside. The results showed that water-consuming UHIE countermeasures such as evaporative space cooling and roof water showering provided positive effects (decreasing sensible heat release and CO2 emissions related to space conditioning) in summer. Additionally, they had no negative (unwanted cooling) effects in winter since they can be turned off in the heating season. Roof greening can provide the greatest space- conditioning CO2 emissions reductions among four UHIE countermeasures, and it reduces the amount of heat release slightly in the heating season. Since the effect on reducing carbon dioxide (CO2) emissions by UHIE countermeasures is not very significant, it is desirable to introduce GW countermeasures in order to reduce CO2 emissions. The significance of this study is that it constructed the new simulation model SCIENCE-Outdoor and applied it to show the influence of countermeasures upon both heat release and CO2 emissions.
Vocal, visible and vulnerable: female politicians at the intersection of Islamophobia, sexism and liberal multiculturalism

ABSTRACT To understand the burdens of “representing diversity” in multicultural democracies, this paper empirically explores Twitter posts about two female Muslim Canadian politicians, Iqra Khalid and Maryam Monsef. Using a mixed method approach, the study reveals that the majority of the most retweeted posts referring to the two members of Parliament were negative in tone. Khalid received more abusive tweets than Monsef, even though she is less prominent, due to the former’s introduction of an anti-Islamophobia bill. When we examined all the tweets using digital methods, however, we found that the tweets are mostly neutral and that only a small fraction counted as abusive language. This analysis thus reveals that toxicity in the online chatter is manifested mainly in the most retweeted posts, main topics, and recurrent hashtags which audiences often engage with, and that the “long tail,” which comprises the majority of tweets, are mainly neutral. This highlights the disproportionate effects of social media amplification of certain attacks and raises questions regarding the status of individual voices. It also supports findings of intersectional discrimination: so-called visible minority women are attacked, regardless of their political prominence, especially if they seek to redress discrimination.
Deep learning super‐resolution electron microscopy based on deep residual attention network

Field‐emission scanning electron microscopy has become a fundamental research tool in the fields of medicine and materials science owing to its effectiveness. However, an inherent contradiction exists between the resolution of field‐emission scanning electron microscopy and its field‐of‐view. To solve this problem, we propose a deep learning‐based method for electron microscopy that can simultaneously obtain a large field‐of‐view and ultrahigh resolution. To solve the super‐resolution problem, a deep residual attention network is designed based on residual learning and the attention mechanism, wherein the backbone network employs several residual groups to effectively extract the features; moreover, attention groups append the backbone part to refine and fuse the features. Besides, a high‐frequency information retention module is added to acquire high‐frequency signals, acting as an effective complement to the deep residual attention network. Owing to the lack of super‐resolution datasets for electron microscopy, we created a microscopic butterfly wing dataset. In the experiments, MixUp was also applied to super‐resolution problem as a simple and effective data augmentation method, to provide more data to train the model. To evaluate the proposed method, we used standard and self‐made datasets with peak signal‐to‐noise ratio (PSNR) and structural similarity (SSIM) as criteria. The results show that the proposed model exhibits a state‐of‐the‐art performance with a PSNR of 25.12 dB and an SSIM of 0.64, and makes field‐emission scanning electron microscopy more practical and promising in the field of optical device optimization.
Bibliometric Analysis in the Studies of Speech Disorders of Preschoolers in Education between 1955 and 2019

Speech disorders have been more researched than ever by scientists from various academic disciplines: social sciences or psychology generally and medical areas of language therapy or special education for children with a language disorder. The bibliometric analysis was used in this research to examine how the publications of speech disorders have been published during the sixty and five years between 1955 and 2019. The data showed that if they were classified into three phases, most of these publications were the third phase from 1999 to 2019 with original countries in the United States of America, the United Kingdom, and Australia. However, the leaders of knowledge in this sector had their research papers published from 1993 to 1999. These results explain the significant implications for varied tendencies of researches in the future.
Tracking control of uncertain Euler–Lagrange systems with fading and saturating actuations: A low‐cost neuroadaptive proportional‐integral‐derivative approach

Many important engineering systems can be classified as Euler–Lagrange (EL) systems. In this work we develop a new Proportional‐Integral‐Derivative (PID) ‐based tracking control solution for uncertain EL systems subject to actuation failures and saturation. Two set of control algorithms are developed using robust adaptive and neuroadaptive methods, which are shown to exhibit several salient features: (1) the control schemes are of PID form, hence is characterized with simplicity in structure and intuition in concept; (2) the PID gains in the scheme are automatically updated by analytic algorithms with no need for manual tuning, rendering the control scheme more user‐friendly; and (3) the developed control algorithms are robust against nonvanishing disturbances, adaptive to unknown virtual parameter and immune to partial actuation effectiveness faults.
Radiomics and Digital Image Texture Analysis in Oncology (Review)

One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of “virtual biopsy” is of particular relevance in oncology. The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered. Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.