Miracle-dz/UniFAR-FTUs huggingface dataset:
Networking Universities and Hospitals: A Case Study of Research and Commercialization in the Taiwanese Herbal Medicine Sector

Abstract This paper provides an empirical account of the case of Taiwan in her innovative efforts in the herbal medicine sector through scientific and technological development. This study aims to propose a network analysis approach, which is typically found in innovation studies, to illustrate the sociological dimensions of actor network theory. The sector is of interest from a social studies of science perspective due to the collision between traditional knowledge philosophies, which are well accepted in East Asian communities, and scientific regulatory standards, that have raised issues regarding the legitimacy and safety of the products. This study considers the actor network linkages between universities, government and industry which act as indicators of knowledge diffusion and collaboration. Relevant records were captured using a heuristic search string and was used to visualize: (i) the number of researchers (agglomeration denoted by the size of bubbles), (ii) organizational linkages through co-authorship (connectedness indicated by the presence of lines between organizations), and (iii) position of the organization (centrality in relation to other organizations). The case presented in this study takes a snapshot of how an advanced economy such as Taiwan, has developed a productive innovation system for herbal medicine. The development of the actor network has evolved mainly from productive working relationships in a close-knit community of researchers that mainly interact through the research organizations in Taipei.
PEDAGOJİK SEVGİ EĞİLİMLERİ ÖLÇEĞİNİN UYARLANMASI: GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI

Bu arastirmanin amaci Yin ve arkadaslari (2019) tarafindan gelistirilen pedagojik sevgi egilimleri olceginin Turk kulturune uyarlanmasidir. Orijinal formu 6 boyut ve 29 madde olan olcegin uyarlama calismalarinin ilk asamasinda dilsel esdegerlik calismasi yapilmistir. Arastirmanin verilerini toplamak icin Firat Universitesi Bilimsel Arastirma ve Yayin Etigi Kurulu’ndan etik uygunluk raporu (Tarih: 29/12/2020, Sayi: 432967) alinmistir. Olcegin gecerlik ve guvenirlik calismalari icin 291 sinif ogretmeninden veri toplanmistir. Yapilan acimlayici faktor analizinde, 7 madde elenerek toplam varyansin %67,71’ ini aciklayan 6 boyutlu olcek elde edilmistir. Yapilan dogrulayici faktor analizinde iyi ve mukemmel uyumu gosteren (ꭓ 2 /sd = 1.77, CFI= . 95, TLI= . 95, RMSEA= . 050, RMR= . 017, SRMR= . 049 uyum indeksi degerleri elde edilmistir. Olusan faktorler olcegin orijinal hali dikkate alinarak bag ve adanmislik, cesitliligin kabulu, samimiyet, empati, sinif toplulugunun kabulu ve nezaket, bagislama olarak adlandirilmistir. Bu sonuclar olcme aracinin yapisal gecerliginin oldugunu ortaya koymustur. Olcegin guvenirlik analizleri icin Cronbach Alpha ic tutarlilik katsayisi hesaplanmistir. Cronbach Alpha ic tutarlilik katsayilari, bag ve adanmislik boyutu icin . 85, cesitliligin kabulu boyutu icin . 73, samimiyet boyutu icin . 85, empati boyutu icin . 92, sinif toplulugunun kabulu ve nezaket boyutu icin . 83, bagislama icin . 75, olcegin geneli icin ise . 92 olarak hesaplanmistir. Arastirma sonuclari uyarlanan pedagojik sevgi egilimleri olceginin ogretmenlerin pedagojik sevgi duzeylerini olcebilecek gecerli ve guvenilir bir olcme araci oldugunu gostermektedir.
Effect of silicic acid on the aggregation properties of goethite

Aggregation properties of iron oxide particles have received much attention because of their environmental importance in soil and aquatic environments. The effects of silicic acid on the pH‐dependent surface charges that govern the aggregation of goethite have received little attention. In this study we determined the effect of silicic acid as a function of pH and ionic strength (IS) on the aggregation of a synthetic goethite by a combination of particle‐size analysis using dynamic light scattering and test tube experiments to quantify particles in suspension. We investigated silicic acid adsorption on goethite quantitatively by batch adsorption isotherms, and the changes in surface charge on the adsorption of silicic acid were identified by the zeta potential (ζ). Our results revealed that silicic acid can lead to a significant decrease in ζ. A change in the pHiep (iep: isoelectric point) from 8.5 to lower pH values, typically pH 4–5.5, in the presence of silicic acid suggests enhanced aggregation of goethite in the acidic pH range. At alkaline pH, both goethite and silicic acid were negatively charged because of strong deprotonation, and silicic acid adsorption promoted goethite dispersion. Ionic strength affected the aggregation of goethite by shifting ζ towards the iep and it can also obscure the role of silicic acid.
Molecular level structural analysis of natural organic matter and of humic substances by NMR spectroscopy

Natural organic matter (NOM) represents an ubiquitous, very complex mixture of organic and (a few) inorganic constituents and occurs in terrestrial, limnic and marine ecosystems. NOM plays immensely important roles in the natural world, and it is a key refractory constituent of the global carbon and other element cycles. The characterization and structural analysis of NOM, which features a substantial extent of both polydispersity and molecular heterogeneity, is most demanding with respect of methodology and concepts and requires the use of complementary techniques capable of delivering molecular-level precision data. NMR spectroscopy combines far reaching, isotope-specific definition of chemical environments and unsurpassed detailed information concerning the short range molecular order with the most reliable quantification of any organic structural spectroscopy method. Mass spectra provide unsurpassed resolution and provide thousands of molecular formulae in a single experiment. By use of multinuclear one and two dimensional NMR experiments, in-depth information concerning carbon backbone, functional groups and interactions of NOM of various origin became available. These findings considerably advance the understanding of NOM functions in these ecosystems. Four key examples were covered in-depth: (1) the binding of cadmium to NOM from an aquatic source, (2) the molecular-level precision analysis of fulvic and humic acids in soil, (3), the computation of aromatic substitution patterns of NOM from NMR data, and (4) the characterization of a major novel key constituent of NOM, namely CRAM (carboxylic-rich alicyclic molecules) which is most likely derived from terpenoids.
Map of Return of Shares and Volume Activity Trading of Companies in Indonesia on The Pandemic Time of Covid-19

This research was conducted to determine the impact of Covid-19 on the Company’s stock returns and trading volume activity. The covid-19 pandemic event is important for research because it includes investor’s assessment of the information generated in the capital market. This study was conducted to test the following hypotheses: before and after the Covid-19 pandemic was declared a national non-natural disaster, (1) there was a significant average change in the average abnormal return; (2) it is a significant average change in average trading volume activity. This research was conducted using event research methods. The sample for this study comes from 45 companies in the JII index. The analysis tool used is a regression with the SPSS. The descriptive statistic can be confirmed by calculating the standard deviation value. The result shows that the standard deviation range is 0.0002 to 0.03, so the research tool could be described as data obtained is suitable to the measurement variable. The conclusions explain that the events before and after the declaration of the Covid-19 outbreak as a national non-natural disaster have positive and a significant impact on the average abnormal return rate of stock activity and changes in trading volume activity.
NLP-MeTaxa: A Natural Language Processing approach for Metagenomic Taxonomic Binning based on deep learning

Metagenomics is the study of genomic content in mass from an environment of interest such as the human gut or soil. Taxonomy is one of the most important fields of metagenomics, which is the science of defining and naming groups of microbial organisms that share the same characteristics. The problem of taxonomy classification is the identification and quantification of microbial species or higher-level taxa sampled by high throughput sequencing. Although many methods exist to deal with the taxonomic classification problem, assignment to low taxonomic ranks remains an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. In this paper, we introduce NLP-MeTaxa, a novel composition-based method for taxonomic binning, which relies on the use of words embeddings and deep learning architecture. The new proposed approach is word-based, where the metagenomic DNA fragments are processed as a set of overlapping words by using the word2vec model to vectorize them in order to feed the deep learning model. NLP-MeTaxa output is visualized as NCBI taxonomy tree, this representation helps to show the connection between the predicted taxonomic identifiers. NLP-MeTaxa was trained on large-scale data from the NCBI RefSeq, more than 14,000 complete microbial genomes. The NLP-MeTaxa code is available at the website: https://github.com/padriba/NLP_MeTaxa/ We evaluated NLP-MeTaxa with a real and simulated metagenomic dataset and compared our results to other tools' results. The experimental results have shown that our method outperforms the other methods especially for the classification of low-ranking taxonomic class such as species and genus. In summary, our new method might provide novel insight for understanding the microbial community through the identification of the organisms it might contain.
SECOND LANGUAGE IDENTITY FORMATION AS EXPERIENCED BY INTERNATIONAL STUDENTS IN STUDY ABROAD COUNTRIES

Language and cultural identity are things that can summarized in terms of language identity. The construct of second language identity has strong relation with study abroad. Second language identity tend to appears in continuous classical conditioning through study abroad. However, no further investigation about this issue has been researched in Indonesian international student context. This study purposed to explore how is the second language identity constructed through the interactions during study abroad. Through narrative interviews, this study will explore more about how second language identity of two international students constructed in their study abroad countries. The result of this study is second language identity construction is more powerful based on self-desire of participants and who the interlocutors are. Further, by knowing how the second language identity is constructed when international students are in native and non-native English-speaking countries, this study will give an empirical contribution about how English language learning patterns depend on the environment. Thus, the English teacher or language expert later could adapt English learning patterns through the construction of other people's language identity.
Can media synchronize our physiological responses? Skin conductance synchrony as a function of message valence, arousal, and emotional change rate

ABSTRACT Guided by nonlinear dynamical systems theory, this study examined the degree to which media can synchronize individuals’ emotional arousal responses (as indicated by skin conductance) during video viewing as a function of message valence, arousal, and emotional change rate. Data from 490 paired dyads created from 45 participants were analyzed. We used cross recurrence analysis (a nonlinear dynamical analysis) to capture the dynamics of physiological synchrony. Results showed that calm compared to arousing messages and negative arousing compared to positive arousing messages generated stronger, more deterministically structured, and more stable skin conductance synchrony. Fast compared to slow changes in emotion generated stronger but not necessarily more deterministic and stable skin conductance synchrony. Theoretical and practical implications are discussed.
Identifying community values related to heat: recommendations for forecast and health risk communication

Abstract. Effective communication of heat risk to public audiences is critical to promoting behavioral changes that reduce susceptibility to heat-related illness. The U.S. National Weather Service (NWS) provides heat-related information to the public using social media platforms such as Facebook. We applied a novel rhetorical framework to evaluate five years (2015–2019) of public responses to NWS heat-related Facebook posts for the Phoenix (Arizona) County Warning Area, one of the hottest regions in North America and the tenth largest metropolitan area in the U.S., to identify “commonplaces” or community norms, beliefs, and values that may present challenges to the effectiveness of heat risk communication. We found two key commonplaces: 1) the normalization of heat, and 2) heat as a marker of community identify. These commonplaces imply that local audiences may be resistant to behavioral change, but they can also be harnessed in an effort to promote protective action. We also found that public responses to NWS posts declined over the heat season, further suggesting the normalization of heat and highlighting the need to maintain engagement. This work provides a readily generalizable framework for other messengers of high-impact weather events to improve the effectiveness of their communication with receiver audiences.
Preliminary Phenotypic and SNP-Based Molecular Characterization of Maize (Zea mays L.)-Mexicana (Zea mays SSP. Mexicana) Introgression Lines under Inbred Background of 48-2

Wild relatives possess potential genetic diversity for maize (Zea mays L.) improvement. Characterization of maize-mexicana introgression lines (ILs) is of great value to diversify the genetic base and improve the maize germplasm. Four maize-mexicana IL generations, i.e. BC1, BC2, BC3, and RIL, were constructed under the elite inbred background of 48-2, elite inbred line that is widely used in maize breeding in Southwestern China, and were phenotyped in different years and genotyped with 56110 SNPs. The results indicated that 48-2 had higher phenotypic performances than all the characterized ILs on most of the agronomic traits. Compared with other ILs, BC2 individuals exhibited more similar performance to 48-2 on most traits and possessed the highest kernel ratio (66.5%). Population structure and principal component analysis indicated that BC3 individuals gathered closer to 48-2 and exhibited the lowest mexicana-introgression frequency (0.50%), while BC2 (29.06%) and RIL (18.52%) showed higher introgression frequency. The high level of genetic diversity observed in the maize-mexicana ILs demonstrated that Z. mays ssp. mexicana can serve as a potential source for the enrichment of maize germplasm.
Pathogen identification and control of sooty spot caused by Cladosporium ramotenellum, appearing on fresh easy peeler mandarins from Perú

During the 2018 season, superficial dry and firm black spots, where sometimes an aerial mycelium developed, appeared on the rind of easy peeler mandarins causing high economic losses in fresh citrus exports from Perú. In this work, we have identified the causal agent, a species of Cladosporium not previously reported as a citrus pathogen. The pathogen was isolated from rind lesions of affected fruit and was identified by sequencing as Cladosporium ramotenellum; and fulfilment of Koch postulates was proven. This species was present on the surface of immature fruit in the groves, indicating that the infection is likely initiated before harvest. Cladosporium ramotenellum is resistant to the postharvest fungicides imazalil, pyrimethanil, and thiabendazole, but sensitive to propiconazole, prochloraz, and ortho-phenylphenol. We designed a postharvest industrial treatment to decrease the Cladosporium sp. load on the fruit surface that limited the incidence of infection and reduced the postharvest losses caused by the fungus. Although this species is quite ubiquitous, this is the first description of C. ramotenellum causing decay of citrus fruit, being the symptoms of this disease similar to the ones described previously and caused by Cladosporium cladosporoides in cv. Satsuma mandarins from Japan.
Health Query Expansion based on Graph Matching between DBpedia and UMLS

Information Retrieval (IR) in the medical domain is considered as a challenging task for many reasons. Short health queries tend to lack information on user's intent, and the target corpus may not have sufficient information for Relevance Feedbacks. And even, if the user obtains relevant documents to his/her queries, it is difficult for him/her to understand the technical terms. In contrast, in this paper, we propose an approach for health queries reformulation based on graph matching between two external linked data sources: DBpedia and Unified Medical Language System (UMLS). DBpedia has a broad coverage of topics and less noise compared to Wikipedia articles, and UMLS is specific to the medical domain. We also introduced the degree centrality to measure the graph connectivity and to select the most efficient candidate terms for query expansion. Experimental results on MEDLINE collection using Okapi BM25 as a retrieval model showed that our approach outperformed related methods, and the two sources achieved very good retrieval results. They helped in the diversification of the retrieved documents and the improvement of the recall.
Algorithmically Exploiting the Knowledge Accumulated in Textual Domains for Technical Support

Processing natural language and extract relevant information in deep technical engineering domain remains an open challenge. On the other side, manufacturers of high-value assets which often deliver product services through the equipment life, supporting maintenance, spare parts management and remote monitoring and diagnostics for issues resolution, have availability of a good amount of textual data containing technical cases with a certain engineering depth. This paper presents a case study in which various Artificial Intelligence algorithms were applied to historical technical cases to extract know-how useful to help technicians in approaching new cases. Initially the work process and available data are presented; the focus is on the outbound communication delivered from the technical team to the site operators, that is structured in 3 main paragraphs: event description, technical assessment, recommended actions. The work proceeded in two parallel streams: the first concerned the analysis of event descriptions and technical assessments, aiming to detect recurring topics; the second concerned the analysis of recommended actions that technical support delivered trough years to site operators in order to create a library, which can help for enabling statistical data analysis, quality check review and being the starting point for further AI/NLP developments. A text preprocessing was applied to both streams, consisted in defining standard and domain entities / stopwords and identifying / removing them, creating acronyms and synonyms maps in order to make context disambiguation, sentence splitting for the recommended actions, and finally text lemmatization. For every text the output of the preprocess was a series of keywords. Then, unsupervised learning algorithms were applied. For this purpose, firstly, we applied feature extraction, bag of words (TF-IDF) and word embeddings (W2V, D2V, BERT), in order to transform our data from language domain into points in a n-features domain. Afterwards, different combinations of unsupervised algorithms were applied to split data into homogeneous groups, such as: LDA, K-means, Spectral, Affinity Propagation and HDBSCAN. The combinations between language modeling and clustering were evaluated using the Silhouette score and visual analysis. To validate the effectiveness, the developed NLP algorithms have been implemented into the current SW application used by technical support to perform the service. Moreover, a dedicated app to show trending topics and retrieve insightful information has been developed. An outlook of the open technical challenges and on the future perspective of NLP applications in the work process are finally delivered.
Present Tectonic Dynamics of the Geological Structural Setting of the Eastern Part of the Adriatic Region Obtained from Geodetic and Geological Data

The Adriatic microplate has always attracted scientific attention, and various studies on the geodynamics of this area have been performed over the years. With the development of global navigation satellite system (GNSS) technology in the last 30 years, most significant research in this field has used it as the primary source of data on geodynamic movements. However, apart from a few global positioning system (GPS) campaigns conducted in the 1990s, the measurements had a low spatiotemporal density. Therefore, the eastern side of the Adria region or the territory of the Republic of Croatia was usually omitted from the results presented in the various published papers. A study of this literature concluded that the territory of Croatia represents a kind of scientific gap and that denser measurement data from GPS/GNSS stations could be used to supplement the geodynamic picture of the area in question. Thus, GPS/GNSS measurements from 83 stations (geodynamic, reference, and POS’ GPS/GNSS) all over Croatia and neighboring countries for a period of almost 20 years (1994–2013) were collected and processed with Bernese software to obtain a unique database of relative velocities. From the geological perspective, the most important and latest insights on the recent geological structural setting, tectonic movements, most active faults, and relationships and movements of structures were taken into account. It was important to compare the geodetic and geological data, observe the present tectonic dynamics of the geological structural setting, and determine the causes of the obtained directions of movement. The research presented in this paper, based on a combination of geodetic and geological data, was conducted to broaden the current knowledge of the present tectonic dynamics of the geological structural setting of the eastern part of the Adriatic region.