Miracle-dz/UniFAR-FTUs huggingface dataset:
Surplus food redistribution and healthy, sustainable diets: Exploring the contradictions of charitable food provisioning

A substantial body of literature points to the necessity of a ‘Great Food Transformation’ requiring an urgent shift towards sustainable food systems across multiple levels. A key part of this transition is the need to reduce food waste and food loss by 50 percent and where charitable surplus food redistribution is regarded as making an important contribution to this target. Surplus driven charitable food provisioning is now part of the food environment in many countries and is influencing the diets of a significant number of people. Its proponents argue that such work contributes to a more sustainable food system by reducing food waste and food insecurity. However, few studies have examined the factors influencing the governance of food within the charitable food system. This paper seeks to fill this gap in the literature through an examination of recent developments in charitable food provisioning in the Republic of Ireland. Using Cork city as a case study we explore Ireland’s charitable food system by examining the motivations, ideas, and practices of key organisations. The paper highlights the growing role of surplus-driven charitable food systems and argues that the redistribution of surplus products for the purpose of reducing food waste and improving economic efficiency requires re-evaluation within a wider appreciation of sustainable diets, and, ultimately, with regard to strengthening the right to food for all.
Azo-linked porous organic polymers: robust and time-efficient synthesis via NaBH4-mediated reductive homocoupling on polynitro monomers and adsorption capacity towards aniline in water

Time-efficient synthetic methods of porous organic polymers are searched in order to extend the applications of these materials. In this work, we show a robust and time-efficient synthetic method of azo-linked porous organic polymers named Azo-POPs based on a NaBH4-mediated reductive coupling polymerization on well-known polynitro monomers. Azo-POPs were found to have a high Brunauer–Emmett–Teller (BET) surface area and potential for aniline adsorption. Interestingly, Azo-POP-1 showed adsorption capacity towards aniline as high as 1059.68 mg g−1 at 293 K, which surpassed that of adsorbent materials reported in the literature.
OTT_A_236475 4559..4567

1Department of Hepatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, People’s Republic of China; 2Department of Hepatic Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, People’s Republic of China; 3The Liver Center of Fujian Province, Fujian Medical University, Fuzhou 350025, People’s Republic of China; 4Southeast Big Data Institute of Hepatobiliary Health, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, People’s Republic of China; 5The Third Department of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai 200438, People’s Republic of China Objective: The aim of this study was to investigate predictive and prognostic significance of elevated carbohydrate antigen 125 (CA125) serum level preoperatively. Methods: A total of 3440 HCC patients were retrospectively enrolled into this study, and all
The Relationships among Corporate Culture and Employee Performance

Related studies about the mechanism and its impact on the organizational culture are not clear. Based on the literature research, this study established a theoretical model in the impact on the employees’ performance and organizational culture. The authors take 421 effective samples in Chinese situation as the research object and use statistical analysis software SPSS and SEM and take empirical methods such as the reliability analysis, reliability analysis, confirmatory factor analysis and path analysis. Results show that clan culture, adhocracy culture and market culture have significant positive influence on employees’ job satisfaction, organizational citizenship behavior, has significant negative impact on employee’s workplace deviation behavior. Hierarchical culture has negative influence on employees’ job satisfaction, organizational citizenship behavior, has positive influence on employee’s workplace deviation behavior. The results have positive significance for the construction of organizational culture and effective management of employees’ performance.
Morphological Alterations and Virulence Gene Expression of Listeria Monocytogenes Cells in Response to Gamma Irradiation Treatments

Gamma irradiation is one of the most popular ray treatments in food industry. It's used to control micro-organisms proliferation in a wide range of products however the response of bacteria to low doses is still unknown. In this study we mainly focus on morphological alteration and virulence gene expression in Listeria monocytogenes after gamma irradiation treatments. The atomic force micrographs (AFM) showed that 0.5 kGy dose has no effect on the membrane morphology of L. monocytogenes. However, after 0.7 kGy treatment, the cells lost their typical shape and smooth membrane and 1 kGy dose was totally destructive. Moreover, membrane fatty acid composition was analyzed by the chromatographic method after different gamma doses. Significant modifications on fatty acids composition were detected in the irradiated strain showing a novo synthesis of membrane lipids: C12:0; C14:0; C15:0; C16:0 and C18:0. In addition, we reported an increase of the saturated fatty acid, essential for membrane adaptation under stress conditions. The expression levels of three virulence genes (hlyA, fri and prfA) were studied in the same conditions using real-time PCR technique. The analysis revealed that both prfA and fri genes were up-regulated after gamma treatment. The induction of prfA, which is a regulator gene, may affect the expression other genes controlling the adaptive form in the treated strain. This study open prospects for further researches to explain the regulatory mechanisms of the adaptive response in Listeria monocytogenes when exposed to sublethal irradiation-stress.
Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES

Abstract. Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be used to make local decisions, then a full quantification of uncertainty is necessary, but the computational cost of running just one simulation at high resolution can hinder proper analysis. Statistical emulation is an increasingly common technique for developing fast approximate models in a way that maintains accuracy but also provides comprehensive uncertainty bounds for the approximation. In this work, we develop a statistical emulation framework for land surface models which acknowledges the forcing data fed into the model, providing predictions at a high resolution. We use The Joint UK Land Environment Simulator (JULES) as a case study for this strategy, and perform initial sensitivity analysis and parameter tuning to showcase its capabilities. JULES is perhaps one of the most complex land surface models, and so our success here suggests incredible gains can be made for all types of land surface model.
Phosgene distribution derived from MIPAS ESA v8 data: intercomparisons and trends

Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) measured the middle-infrared limb emission spectrum of the atmosphere from 2002 to 2012 on board ENVISAT, a polar-orbiting satellite. Recently, the European Space Agency (ESA) completed the ﬁnal reprocessing of MIPAS measurements, using Version 8 of the Level 1 and Level 2 processors, which include more accurate models, processing strategies and auxiliary data. The list of retrieved gases has been extended, it now includes a number of new species with weak emission features in the MIPAS spectral range. The new retrieved trace species include carbonyl chloride (COCl2), also called phosgene. Due to its toxicity, its use has been reduced over the years, however it is still used by chemical industries for sevaeral applications. Besides its direct injection in the troposphere, stratospheric phosgene is mainly produced from the photolysis of CCl4, a molecule present in the atmosphere because of human activity. Since phosgene has a long stratospheric lifetime, it must be carefully monitored as it is involved in the ozone destruction cycles, especially over the winter polar regions. In this paper we exploit the ESA MIPAS Version 8 data in order to discuss the phosgene distribution, variability and trends in the middle and lower stratosphere and in the upper troposphere. The zonal averages show that phosgene volume mixing ratio is larger in the stratosphere, with a peak of 40 pptv between 50 and 30 hPa at equatorial latitudes, while at middle and polar latitudes it varies from 10 to 25 pptv. A moderate seasonal variability is observed in polar regions, mostly between 80 and 50 hPa. The comparison of MIPAS/ENVISAT COCl2 v.8 proﬁles with the ones retrieved from MIPAS/balloon and ACE-FTS measurements highlights a negative bias of about 2 pptv, mainly in polar and mid-latitude regions. Part of this bias is attributed to the fact that the ESA Level 2 v.8 processor uses an updated spectroscopic database. For the trend computation, a ﬁxed pressure grid is used to interpolate the phosgene proﬁles and, for each pressure level, VMR monthly averages are computed in pre-deﬁned 10°-wide latitude bins. Then, for each latitudinal bin and pressure level, a regression model has been ﬁtted to the resulting time-series in order to derive the atmospheric trends. We ﬁnd that the phosgene trends are different in the two hemispheres. The analysis shows that the stratosphere of the Northern Hemisphere is characterised by a negative trend, of about −7 pptv/decade, while in the Southern Hemisphere phosgene mixing ratios increase with a rate of the order of +4 pptv/decade. In the upper troposphere a positive trend is found in both hemispheres.
Technische Universität Berlin Fakultät I-Fachgebiet Audiokommunikation Master’s Thesis Automatic Chord Recognition with Fully Convolutional Neural Networks

This thesis proposes a new, fully convolutional neural network architecture for frame-wise automatic chord recognition and, by doing so, expands the deep learning vocabulary of the field. The architecture is based on the MultiResUnet, which is then extended with spatial dropout, image pyramids, and a structured training approach. In the first step, three data sets are merged to obtain a data collection of 361 songs with a total of up to 20 hours, 27 minutes, and 16 seconds of real-world music. Furthermore, pitch-shifting data augmentation is applied where each song is shifted up or down by one to six discrete semitones resulting in 12 deformations for each audio file. Moreover, all original and pitch-shifted audio files are transformed into a three-dimensional representation via the harmonic constant-Q transform. This representation is used as an input to the model mentioned above. The model is then trained in a supervised manner and predicts for each frame, the root pitch class, the active pitch classes, and the bass pitch class from the spatial dropout’s output layer. Afterward, results are evaluated using 15 different comparison methods implemented by the mir_eval package. In addition, it is also shown that the proposed model exceeds the baseline over every single chord recognition metric, while training time per epoch is reduced by half, and fewer training parameters are required. The thesis continues with a detailed root confusion and strict quality-wise error analysis. Finally, it concludes with a two-track analysis where the reference annotations and the models’ estimation are visualized and compared against each other.
Analysis of the social network of the Instance of Governance of the Caminho Novo Tourist Circuit, MG: a complex and systemic perspectivei,ii

i Research funded by the Coordination for the Improvement of Higher Education Personnel Brazil (CAPES) Funding Code 001, summarizing the Master's dissertation presented at the Stricto Sensu Graduate Program in Tourism of the Fluminense Federal University (UFF). The authors also received financial support form FAPERJ for the English language version of the article. ii Awarded paper in the Master Highlight Award ANPTUR 2018 (3rd place) at the XV Seminar of the National Association for Research and Postgraduate Tourism (ANPTUR), held between September 19 and 21, 2018, at Anhembi Morumbi University (UAM), São Paulo, SP.
Combining Sentiment Lexicons and Content-Based Features for Depression Detection

Numerous studies on mental depression have found that tweets posted by users with major depressive disorder could be utilized for depression detection. The potential of sentiment analysis for detecting depression through an analysis of social media messages has brought increasing attention to this field. In this article, we propose 90 unique features as input to a machine learning classifier framework for detecting depression using social media texts. Derived from a combination of feature extraction approaches using sentiment lexicons and textual contents, these features are able to provide impressive results in terms of depression detection. While the performance of different feature groups varied, the combination of all features resulted in accuracies greater than 96% for all standard single classifiers, and the best accuracy of over 98% with Gradient Boosting, an ensemble classifier.
Assessing the Impact of Traffic Emissions on Fine Particulate Matter and Carbon Monoxide Levels in Hanoi through COVID-19 Social Distancing Periods

ABSTRACT Hanoi, Vietnam, is usually ranked as one of the most polluted capital cities in terms of air quality, particularly PM2.5. However, there has not been enough data to determine the main source of this pollution. In this study, we utilized the rare opportunity of the COVID-19 social distancing to assess the contribution of traffic emission to PM2.5 and CO levels when traffic volume was reduced significantly in Hanoi. Hourly PM2.5 and CO concentrations were measured from nine urban and traffic monitoring stations during pre-, soft, hard, and post-social distancing periods. As a result, we observed large reductions in both PM2.5 and CO levels during social distancing periods. PM2.5 concentrations were 14-18% lower during the social distancing than before this period, while CO concentrations had a more considerable drop by 28-41%. It is known that meteorological conditions can have significant effects on the ambient levels of air pollutants. To overcome this challenge, weather normalized concentrations of those pollutants were estimated using the random forest model, a machine learning technique. The normalized weather concentrations showed smaller reductions by 7-10% for PM2.5 and 5-11% for CO, indicating the presence of favorable weather conditions for better air quality during the social distancing period. In further analysis, the apparent improvement of air quality in Hanoi during the social distancing period could be attributed mainly to reducing traffic emissions while emissions from coal-fired power plants remained relatively stable.
Integrating two Metaprogramming Environments: An Explorative Case Study

The automated generation of source code, often referred to as metaprogramming, has been pursued for decades in computer programming. Though many such metaprogramming environments have been proposed and implemented, scalable collaboration within and between such environments remains challenging. It has been argued in previous work that a metacircular metaprogramming architecture, where the the metaprogramming code (re)generates itself, enables a more scalable collaboration and easier integration. In this contribution, an explorative case study is performed to integrate this meta-circular architecture with another metaprogramming environment. Some preliminary results from applying this approach in practice are presented and discussed.
ASSESSMENT OF THE ALLELOPATHIC POTENTIAL AND IDENTIFICATION OF THE PHYTOTOXIC SUBSTANCES FROM THE STRAW OF BANGLADESHI INDIGENOUS RICE VARIETY ‘GORIA’

Straw from Bangladeshi indigenous rice (Oryza sativa L. ssp. indica) variety ‘Goria’ was incorporated into a gray soil to observe the phytotoxic effects on Echinochloa oryzicola. The residues of ‘Goria’ caused inhibition on the growth and dry weight of E. oryzicola. The rate of ‘Goria’ straw incorporation was sequentially evaluated to observe autotoxicity in the cultivation of the same rice variety. An improvement was found on the growth and yield parameters of rice due to the straw incorporation into the soil. Aqueous methanol extracts of ‘Goria’ straw inhibited the seedling growth of Lepidium sativum L. and E. oryzicola, which suggested that this variety might contain phytotoxic substance(s). Two biologically active compounds, (-)-loliolide and 3β-hydroxy-5α,6α-epoxy-7megastigmen-9-one, were isolated using several chromatographic steps. The phytotoxic potential of these two compounds was assayed in vitro on the seedling growth of test flora to validate it. The inhibitory activity of 3β-hydroxy-5α,6α-epoxy-7-megastigmen-9-one was greater than that of (-)-loliolide, as demonstrated by comparison of the I50 values. However, the two compounds synergistically suppressed the growth of L. sativum and E. oryzicola more strongly than the individual compounds. The results suggest that incorporation of allelopathic rice Goria-straw could help suppress E. oryzicola in rice to achieve non-herbicidal weed control.
PRIMING WITH MORINGA (MORINGA OLEIFERA LAM.) LEAF EXTRACT BOOSTS THE GROWTH AND PHYSIO-BIOCHEMICAL ATTRIBUTES OF LEAD-STRESSED FENUGREEK (TRIGONELLA FOENUM-GRAECUM L.) SEEDLINGS

Lead (Pb) is a potentially toxic environmental concern, affecting all forms of life on the planet. However, moringaleaf extract (MLE) attained enormous attention owing to its high phytohormones, antioxidants and essential nutrient contents. To evaluate the potential of MLE in alleviating the phytotoxicity of Pb on fenugreek seedlings, a pot experimentwas conducted at the laboratory of Biological Sciences Department, Jeddah University. The uptake of Pb by fenugreek seedlings negatively affected growth rate, chlorophyll content, soluble sugars, enzymatic and non-enzymatic antioxidants, availability of the macronutrients (N, P, Ca, K), as well as K/Na ratio. Contrastingly, H2O2, MDA, soluble proteins and Mg content were increased. Seed priming in the aqueous MLE significantly ameliorated the ionic and osmotic deteriorations resulting fromPb uptake by fenugreek seedlings.MLE potentially retrieved seedling growth, balanced carbon and nitrogen metabolism, decreased the ROS-induced oxidative damage, in addition to sustaining ionic and osmotic homeostasis in Pb-stressed fenugreek seedlings. To sum up, the present study proved MLE as a potent eco-friendly approach to counteract Pb-phytotoxicity.
Sirodom Mongkhonthanaphon and Yachai Limpiyakorn ADeep Neural Network for Pixel-Wise Classification of Titanium Microstructure

In recent decades, deep learning has been widely used for automatically classifying images with high accuracy. Semantic segmentation is a type of deep learning that focuses on classifying every pixel into classes. In Metallurgy domain, Titanium and its alloys are considered an ideal biomaterial that suit for implant material. In this research, deep learning is thus introduced as a means to automate the pixel-wise classification of Titanium microstructure in order to reduce the time and uncertainties during human inspection for quality control. The method applies a semantic segmentation technique, fully convolutional neural network, implemented with the U-net architecture. The proposed approach has investigated the integration of Fine-tuning to the U-net architecture for improving the model performance. The dataset of Ti-6A1-4V microstructure images is augmented using elastic deformations. The results report slightly increase of accuracy, while the training time is much faster than training from scratch.
Intent Identification and Analysis for User-centered Chatbot Design: A Case Study on the Example of Recruiting Chatbots in Germany

Chatbots are text-based dialogue systems that automate communication processes. Instead of communicating with a person, the user communicates with a computer system. Due to the use of Artificial Intelligence (AI) methods, such systems have become increasingly powerful in recent years and allow for more realistic dialogue processes. In particular, methods from the field of machine learning have contributed to an improved understanding of natural language. Nevertheless, such systems are not yet able to acquire the knowledge required to answer user queries independently. Dialogue structures and elements need to be defined as the conversational design of the chatbot. Herein, an user intent describes an information need or a goal that the user aims to achieve by entering text. For a usercentered chatbot design, a relevant set of intents must be identified and structured. In addition, training questions are required in order train the AI models for matching user input with the defined set of user intents. This article describes the procedure for developing chatbots using the example of an application in recruiting. The focus is on the appropriate identification and analysis of user intents. In our case study, the procedure for usercentered intent identification is described as well as approaches for the analysis and consolidation of intents. Furthermore, it is shown how corresponding measures affect the quality of intention identification. Keywords–Chatbots; Conversational Design; Prototyping; User Intent Analysis; User-centered Design; Machine Learning.
Alzheimer’s Disease Diagnosis using Deep Learning Techniques

Deep learning is one of the machine learning approach which has shown promising results and performance as compare to traditional algorithms of machine learning in terms of high dimensional data of MRI brain image. In this article the application of deep learning in medical field is addressed. A thorough review of various algorithms of deep learning for diagnosis of Alzheimer’s disease is done, in which this disease is a progressive brain disorder that destroy the brain memory gradually, it is a common disease in older adults which is caused by dementia. It has been obtained in most research papers that the most widely used and represented algorithm is Convolutional Neural Networks (CNN) when it deals with brain image analysis. After study of various related papers for diagnosing of AD, we have come to this point and suggested that the AD prediction at earlier stages can be increased by using an advance deep learning techniques in different dataset (ADNI, OASIS) combining to one.
Brain MR Image Enhancement using Average Intensity Replacement Based on GWOHE Algorithm

The most important task in MR Image Enhancement is to obtain a high resolution optimized visual image using advanced image processing techniques. Most of the life photographs and various images such as aerial, medical and satellite are associated with noise and low grade intensity. To improve the quality for better visual appearance, noise has to be suppressed and contrast has to be enhanced. Traditional contrast improvement techniques do best for various images. But for MRI of brain images, there are chances of misrecognization of WMH (White Matter Hyperintensities) as Cerebrospinal fluid (CSF) in traditional enhancement techniques. To overcome this ambiguity and enhance WMH regions of MRI brain images, a novel algorithm has been proposed in this paper. This algorithm is called as Mean Intensity replacement based on Grey Wolf Optimization Histogram Equalization (GWOHE). This technique is applied on FLAIR images and comparison is tabulated along with existing technique for parameters such as PSNR, AMBE.