ARTICLE | doi:10.20944/preprints202209.0309.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: machine learning; natural language processing; commit messages; change prediction model
Online: 20 September 2022 (14:52:49 CEST)
Version Control and Source Code Management Systems, such as GitHub, contain large amount ofunstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrieve information from very large collection of unstructured data. In this study, we have extended our previous study by increasing our datasets and ML and clustering techniques. Method: We have followed a complex methodology made up of various steps. Starting from the raw commit messages we have employed NLP techniques to build a structured database. We have extracted their main features and used as input of different clustering algorithms. Once labelled each entry, we have applied supervised machine learning techniques to build a prediction and classification model. Results: We have developed a machine learning-based model to automatically classify commit messages of a software project. Our model exploits a ground-truth dataset which includes commit messages obtained from various GitHub projects belonging to the HEP context. Conclusions: The contribution of this paper is two-fold: it proposes a ground-truth database; it provides a machine learning prediction model. They automatically identify the more change-proneness areas of code. Our model has obtained a very high average precision, recall and F1-score.
ARTICLE | doi:10.20944/preprints202011.0056.v1
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: COVID-19; Deep Learning; Natural Language Processing; Topic Modelling; Text Classification; Latent Dirichlet allocation (LDA); Non-negative matrix factorization (NMF)
Online: 2 November 2020 (15:24:20 CET)
Ongoing COVID-19 Pandemic has resulted into massive damage to various platforms of global economy which has caused disruption to human livelihood. Natural Language Processing has been extensively used in different organizations to categorize sentiments, perform recommendation, summarizing information and topic modelling. This research aims to understand the non-medical impact of COVID-19 on global economy by leveraging the natural language processing methodology. This methodology comprises of text classification which includes topic modelling on unstructured COVID-19 media articles dataset provided by Anacode. Like other Natural Language Processing algorithms, Latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) has been proposed to classify the media articles dataset in order to analyze COVID-19 pandemic impacts in the different sectors of global economy. Model Accuracy was examined based on the coherence and perplexity score which came out to be 0.51 and -10.90 using LDA algorithm. Both the LDA and NMF algorithm identified similar prevalent topics that was impacted by COVID-19 pandemic in multiple sectors of economy. Through intertopic distance map visualization produced by LDA algorithm, it can be reciprocated that general industries which includes children schooling, parental care, and family gatherings had the major impact followed by business sector and the financial industry.
ARTICLE | doi:10.20944/preprints202209.0324.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Insurance; natural language processing; topic modelling; text analysis; complex networks; risk ranking
Online: 21 September 2022 (10:25:26 CEST)
The ability to identify and rank risk is essential for efficient and effective supervision of financial service firms, such as banks and insurers. Risk ranking ensures limited resources are allocated where they are most needed. Today, automatic risk identification within insurance supervision primarily relies on quantitative metrics based on numerical data (e.g. returns). The purpose of this work is to assess whether Natural Language Processing (NLP) and cognitive networks can achieve similar automated risk ranking and identification by analysing textual data, i.e. NIDT=829 investor transcripts from Bloomberg. To this aim, this work explores and tunes 3 NLP techniques: (1) keyword extraction enhanced by cognitive network analysis; (2) valence/sentiment analysis; and (3) topic modelling. Results highlight that keyword analysis, enriched by term frequency-inverse document frequency scores and semantic framing through cognitive networks, could detect events of relevance for the insurance system like cyber-attacks or the COVID-19 pandemic. Cognitive networks were found to highlight events that related to specific financial transitions: The semantic frame of "climate" grew in size by +538% between 2018 and 2020 and outlined an increased awareness that agents and insurers expressed towards climate change. A lexicon-based sentiment analysis achieved a Pearson’s correlation of ρ=0.16 (p<0.001,N=829) between sentiment levels and daily share prices. Although relatively weak, this finding indicates that insurance jargon is insightful to support risk supervision. Topic modelling is considered less amenable to support supervision, because of a lack of results’ stability and an intrinsic difficulty to interpret risk patterns. We discuss how these automatic methods could complement existing supervisory tools in automated risk ranking.
ARTICLE | doi:10.20944/preprints202207.0090.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Text mining; natural language processing; sustainability; semantic similarity; corporate social responsibility; Machine Learning
Online: 6 July 2022 (08:53:02 CEST)
This paper investigates if Corporate Social Responsibility (CSR) reports published by a selected group of Nordic companies are aligned with the Global Reporting Initiative (GRI) standards. To achieve this goal, several natural language processing, and text mining techniques were implemented and tested. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for Word Representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a strong impact on the environment.
ARTICLE | doi:10.20944/preprints202005.0171.v2
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: COVID-19; coronavirus; case-based reasoning; ontology; natural language processing
Online: 15 June 2020 (11:16:23 CEST)
Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 1.6 million cases while more than 105,000 have died due to it, with these figures rising on a daily basis across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is therefore crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is an effective paradigm that allows for the utilization of cases’ specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study therefore aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in classification of suspected cases of Covid19. The CBR model leverages on a novel feature selection and semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories at an accuracy of 97.10%. The study found that the proposed model can support physicians to easily diagnose suspected cases of Covid19 base on their medical records without subjecting the specimen to laboratory test. As a result, there will be a global minimization of contagion rate occasioned by slow testing and as well reduce false positive rates of diagnosed cases as observed in some parts of the globe.
ARTICLE | doi:10.20944/preprints202202.0007.v1
Subject: Mathematics & Computer Science, Other Keywords: mental health; natural language processing; interdisciplinary research; mental health helpline
Online: 1 February 2022 (12:03:47 CET)
During the last two years the COVID-19 pandemic has affected the world population in several ways. An important increase in mental health problems is a consequence of this pandemic that is ubiquitous worldwide. In this work we study the effect of the pandemic on the mental health of a population of teenagers and youth based on the analysis of natural language processing, machine learning algorithms and expert knowledge. The data analysed was obtained from a chat helpline called Safe time from theIt Get’s Better Foundation in Chile. The data consists of 10,986 conversations gathered from 2018 until 2020 between volunteers from the foundation and users of the platform. We compared the conversationsbefore and during the pandemic in terms of their thematic content. Our analysis found: a significantdecrease in self-image appreciation during the pandemic; a significant decrease in the quality of personalrelationships during the pandemic, and a significant increase of performance appreciation.
ARTICLE | doi:10.20944/preprints201805.0102.v2
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: machine learning; algorithms; natural language processing, deep learning, vector space models, semantic similarity, distributional semantics, latent semantic analysis, word2vec
Online: 10 May 2018 (05:56:56 CEST)
“You should know the words by the company they keep!” has been one of the most famous slogans attributed to John Rubert Firth, 1957. This has ignited a whole school in linguistic research known as the British empiricist contextualism. Sixty years later, many un- or semi-supervised machine learning algorithms have been successfully designed and implemented aiming at extracting word meaning from within the context of a text corpus. These algorithms treat words, more or less, as vectors of real numbers representing frequencies of word occurrences within context and word meaning as positions of words in a high-dimensional vector space model. Word associations, in turn, are treated as calculated distances among them. With the rise of Deep Learning (DL) and other artificial neural networks based architectures, learning the positioning of words and extracting word associations as measured by their distances has further improved. In this paper, however, we revisited the main stream of algorithmic approaches and set the stage for a partly cross-disciplinary evaluation framework to judge about the nature of the extracted word associations by state-of-the-art machine learning algorithms. Our preliminary results are based on word associations extracted from the application of DL framework on a Google News text corpus, as well as on comparisons with human created word association lists such as word collocation dictionaries and psycholinguistic experiments. The results and conclusions provide some insights into the inherited limitations in interpreting the type of word associations and underpinning relations between words with inevitable consequences in other areas, such as extraction of knowledge graphs or image understanding.
ARTICLE | doi:10.20944/preprints202111.0070.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: COVID-19; university student; socio-demographic factors; satisfaction; perception; online learning; mental health; habits; institutions; continents; Natural Language processing; Swivel embedding; Words Cloud.
Online: 3 November 2021 (09:06:22 CET)
The review of previous works shows this study is the first attempt to analyse the lockdown effect using Natural Language Processing Techniques, particularly sentiment analysis methods applied at large scale. On the other hand, it is also the first of its kind to analyse the impact of COVID 19 on the university community jointly on staff and students and with a multi-country perspective. The main overall findings of this work show that the most often related words were family, anxiety, house and life. On another front, it has also been shown that staff have a slightly less negative perception of the consequences of COVID in their daily life. We have used artificial intelligence models like swivel embedding and the Multilayer Perceptron, as classification algorithms. The performance reached in terms of accuracy metric are 88.8% and 88.5%, for student and staff respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.
ARTICLE | doi:10.20944/preprints202211.0201.v1
Subject: Mathematics & Computer Science, Other Keywords: Natural Language Ontologies; Ontology Engineering; Ontology Development; Semantic Web
Online: 10 November 2022 (11:14:20 CET)
The goal of the next generation World Wide Web is machine readability through linked databases. To improve web search, integration, and mining in local languages like Urdu, there is a growing need to develop ontologies and vocabulary in these languages. The majority of people use the web in local languages for agriculture, social media interaction, news, etc. How to create agents for the integration of web data. In our country, the literacy ratio is very low and IT literacy is negligible. More comprehensive information for its target audience is only possible through the World Wide Web. Our first target is to improve and enhance the use of social media and the web in local languages. That will encourage its constructive use in Urdu for society and the economy. The Web in natural languages is the source of income for small and medium enterprises. The semantic web is concerned with linked databases and structured data. In this work, we are focused on some selected ontologies to be translated into natural languages. Expertise in Ontology Engineering helps us in job production. Ontology Engineering has extensive freelancing opportunities. Only if the web is correctly interpreted in regional languages is an economic boost achievable. A standardized foundation for data sharing and reuse on the internet is provided by the Semantic Web. In other words, a group of standards and technology that enables computers to comprehend the semantics (meaning) of material on the Web.
ARTICLE | doi:10.20944/preprints202210.0381.v1
Subject: Mathematics & Computer Science, Other Keywords: corporate social responsibility; natural language processing; RoBERTa; sustainable development goals
Online: 25 October 2022 (08:22:30 CEST)
There is a strong need and demand from the United Nations, public institutions, and private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by the community volunteers. Results demonstrate that especially RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.
ARTICLE | doi:10.20944/preprints202111.0344.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: pharmacological text corpus; automatic relation extraction; natural language processing; deep learning
Online: 19 November 2021 (10:40:10 CET)
Nowadays, an analysis of virtual media to predict society’s reaction to any events or processes is a task of great relevance. Especially it concerns meaningful information on healthcare problems. Internet sources contain a large amount of pharmacologically meaningful information useful for pharmacovigilance purposes and repurposing drug use. An analysis of such a scale of information demands developing the methods that require the creation of a corpus with labeled relations among entities. Before, there have been no such Russian language datasets. This paper considers the first Russian language dataset where labeled entity pairs are divided into multiple contexts within a single text (by used drugs, by different users, by the cases of use, etc.), and a method based on the XLM-RoBERTa language model, previously trained on medical texts to evaluate the state-of-the-art accuracy for the task of indication of the four types of relationships among entities: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. As shown based on the presented dataset from the Russian Drug Review Corpus, the developed method achieves the F1-score of 81.2% (obtained using cross-validation and averaged for the four types of relationships), which is 7.8% higher than the basic classifiers.
Subject: Earth Sciences, Environmental Sciences Keywords: Natural radioactivity; risk assessment; 210Pb and 210Po; radiological impact; polluted mine site.
Online: 25 August 2021 (14:55:03 CEST)
Since the exploitation of mineral resources results in the release of radionuclides, and consuming radionuclides affects public health in the short and long term. A case study of the environmental radiation impact from coal mining and germanium processing was carried out in southwest China. The coal mines contain germanium and uranium and have been exploited for more than 40 years. The farmlands around the site of coal mining and germanium processing have been contaminated by the solid waste and mine water in some extend since then. Samples of crops have been collected from contaminated farmlands in research area. The research area covers a radius of 5 km, in which there are 2 coal mines located. 210Pb and 210Po have been analyzed as the key radionuclides during monitoring program. The average activity concentrations of 210Pb and 210Po in the crops were 1.38 and 1.32 Bq/kg in cereals, 4.07 and 2.19 Bq/kg in leafy vegetables and 1.63 and 1.32 Bq/kg in root vegetables. The annual effective doses due to the ingestion of 210Pb and 210Po in consumed crops have been estimated for adult residents living in research area. The average annual effective dose was 0.336mSv/a, while the minimum was 0.171 mSv/a and the maximum was 0.948 mSv/a. The results show that crops grown on contaminated farmland contained an enhanced level of radioactivity concentration. Ingestion doses of local residents in research area were significantly higher than the China average level of 0.112 mSv/a, and the world average level of 0.042 mSv/a through 210Pb and 210Po in crops intake respectively.
ARTICLE | doi:10.20944/preprints202205.0238.v2
Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: COVID-19; SARS-CoV-2; Omicron; Twitter; tweets; sentiment analysis; big data; Natural Language Processing; Data Science; Data Analysis
Online: 7 July 2022 (08:36:40 CEST)
This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domains embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs. Finally, to support similar research and development in this field centered around the analysis of tweets, we have developed an open-access Twitter dataset that comprises tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.
REVIEW | doi:10.20944/preprints202102.0592.v1
Subject: Chemistry, Analytical Chemistry Keywords: gastrointestinal diseases; nutraceutical; natural anti-inflammatory; natural antioxidant; watercress
Online: 25 February 2021 (17:03:03 CET)
The incidence of gastrointestinal diseases (cancer in particular) has increased progressively with considerable morbidity, mortality, and a high economic impact on the healthcare system. Dietary intake of natural bioactive phytochemicals showed to have cancer-preventing and therapeutic effects. This includes the cruciferous vegetable derivative phenethyl isothiocyanate (PEITC), a bioactive compound from watercress. PEITC antioxidant, anti-inflammatory and anti-cancer properties are of particular importance. This review summarizes the current knowledge on the role of PEITC as a potential natural nutraceutical or an adjuvant against oxidative/inflammatory-related disorders in the gastrointestinal tract. We also discuss the safe and recommended dose of PEITC. Besides, we establish a framework to guide the research and development of sustainable methodologies for obtaining and stabilizing this natural nutraceutical for industrial use. This is a topic that still needs more scientific development, but with the potential to lead to a viable strategy in the prevention of cancer and other associated diseases of the gastrointestinal tract.
ARTICLE | doi:10.20944/preprints201810.0661.v1
Subject: Social Sciences, Geography Keywords: national parks; ecosystem service value; natural infrastructure; natural capital
Online: 29 October 2018 (07:11:30 CET)
The annual budget for the United States National Park Service was roughly three billion dollars in 2016. This is distributed amongst 405 National Parks, 23 national scenic and historic trails, and 60 wild and scenic rivers. Entrance fees and concessions generate millions of dollars in income for the National Park Service; however, this metric fails to account for the total value of the National Parks. In failing to consider the value of the ecosystem services provided by the National Parks we fail to quantify and appreciate the contributions our parks make to society. This oversight allows us to continue to underfund a valuable part of our natural capital and consequently damage our supporting environment, national heritage, monetary economy, and many of our diverse cultures. We explore a simple benefits transfer valuation of the United States national parks using National Land Cover Data from 2011 and ecosystem service values determined by Costanza (et al). This produces an estimate suggesting the parks provide $84 billion / year in ecosystem service value. If the natural infrastructure 'asset' that is our national park system had a budget comparable to a piece of commercial real estate of this value, the annual budget of the National Park Service would be roughly an order of magnitude larger at something closer to $30 billion rather than $3 billion.
REVIEW | doi:10.20944/preprints201705.0115.v1
Subject: Chemistry, Medicinal Chemistry Keywords: Palladium; Suzuki cross coupling; natural product; non-natural product
Online: 15 May 2017 (18:30:52 CEST)
New class of biologically active and non-active compounds can be synthesized via transition metal mediated Suzuki cross coupling reaction that has a great impact on the advancement of organic chemistry. These resulted products can lend a helping hand in pharmaceutical and polymer chemistry for the betterment of mankind. Suzuki-Miyaura cross coupling reaction is one of the best tools through which many natural and non-natural compounds can be synthesized.
REVIEW | doi:10.20944/preprints201912.0332.v1
Online: 25 December 2019 (03:24:53 CET)
Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.
ARTICLE | doi:10.20944/preprints201804.0271.v1
Online: 20 April 2018 (14:51:02 CEST)
A review of the concept of "fitness" as it is used in evolutionary theory.
ARTICLE | doi:10.20944/preprints201811.0494.v1
Subject: Earth Sciences, Other Keywords: soil stoichiometry; soil nutrient; nutrient limitations; natural grassland; natural forest
Online: 20 November 2018 (09:35:23 CET)
The Loess Plateau is an important region for vegetation restoration in China, however, changes in soil organic carbon (SOC), soil nutrients, and stoichiometry after restoration in this vulnerable ecoregion are not well understood. Typical restoration types, including orchardland (OL), grassland (GL), shrubland (SL), and forestland (FL) were chosen to examine changes in the stocks and stoichiometry of SOC, soil total nitrogen (TN), and soil total phosphorus (TP) at different soil depths and recovery times. Results showed that SOC stocks first increased and then stabilized in OL, GL, and SL at 0–30 cm depth, while in FL, stocks gradually increased. Soil TN stocks first increased and then decreased in OL, SL, and FL with vegetation age at 0–30 cm depth, while soil TP stocks showed little variation between restoration types. In the later stages of restoration, the stocks of SOC and soil TN at 0–30 cm soil depth were still lower than those in natural grassland (NG) and natural forest (NF). The overall C:N, C:P, and N:P ratios increased with vegetation age. Additionally, the SOC, soil TN and soil TP stocks, and C:N, C:P, and N:P ratios decreased with soil depth. The FL had the highest rate of change in SOC and soil TN stocks, at 0-10 cm soil depth. These results indicate a complex response of SOC, soil TN, and soil TP stocks and stoichiometry to vegetation restoration, which could have important implications for understanding C, N, and P changes and nutrient limitations after vegetation restoration.
REVIEW | doi:10.20944/preprints201707.0051.v1
Subject: Biology, Forestry Keywords: natural disturbance; advance regeneration; planting; natural regeneration; uneven-aged silviculture
Online: 18 July 2017 (13:22:12 CEST)
Forest managers are often required to restore forest stands following natural disturbances, a situation that may become more common and more challenging under global change. In parts of Central Europe, particularly in mountain regions dominated by mixed temperate forests, the use of relatively low intensity, uneven-aged silviculture is a common management approach. Because this type of management is based on mimicking less intense disturbances, the restoration of more severe disturbance patches within forested landscapes has received little attention within the context of uneven-aged silviculture in the region. The goal of this paper is to synthesize research on the restoration of forests damaged by disturbances in temperate forests of Slovenia and neighbouring regions of Central Europe, where uneven-aged silviculture is practiced. We place particular emphasis on the most important biotic and abiotic drivers of post-disturbance regeneration, and use this information to inform silvicultural decisions about applying natural or artificial regeneration in disturbed areas. We conclude with guidelines for restoration silviculture in uneven-aged forest landscapes.
CONCEPT PAPER | doi:10.20944/preprints202101.0083.v1
Subject: Earth Sciences, Atmospheric Science Keywords: Ecosystem services; Natural resource management; Natural capital; Ecosystem service provisioning; Cost-benefit ratio
Online: 5 January 2021 (11:27:39 CET)
Natural capital is the wealth of nations that give them the economic status they represent. Worldwide, vulnerable people depend on natural capital for employment, salaries, wealth, and livelihoods and, in turn, determine the developmental index of the nation to which they belong. The availability of ecological services is crucial for clean water and air, food and fodder, and agricultural development. In this short commentary, we have tried to sum up the ideas and discussions over natural capital's role in ascribing economic status to countries. We have discussed how the prosperity of humans is intertwined with the services ecosystems provide and how poor natural resource management (NRM) has adversely cost human well-being. The paper concludes that to ensure the current and future human well-being, an in-depth understanding of the services ecosystems provide, is essential.
REVIEW | doi:10.20944/preprints202110.0247.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: natural language; NLP; Korean; dataset
Online: 18 October 2021 (14:33:41 CEST)
English based datasets are commonly available from Kaggle, GitHub, or recently published papers. Although benchmark tests with English datasets are sufficient to show off the performances of new models and methods, still a researcher need to train and validate the models on Korean based datasets to produce a technology or product, suitable for Korean processing. This paper introduces 15 popular Korean based NLP datasets with summarized details such as volume, license, repositories, and other research results inspired by the datasets. Also, I provide high-resolution instructions with sample or statistics of datasets. The main characteristics of datasets are presented on a single table to provide a rapid summarization of datasets for researchers.
ARTICLE | doi:10.20944/preprints201906.0133.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Natural Language Processing, Sentiment Analysis
Online: 14 June 2019 (14:55:52 CEST)
The sentiment of a word varies based on its context of usage: the words used around it and the part-of-speech it is used as. This paper proposes a technique to suggest the sentiment of a word by combining its part-of-speech and the semantic similarities of its co-occurrences with both context-specific and pre-trained embeddings to achieve powerful and fast results. A study was conducted across domains and sub-domains to measure variance of sentiment by switching domains and switching context within the same domain. Re-scoring a commonly used polarity lexicon showed that 10% of words changed scores while switching domains and 8% changed scores within domains while switching context. Part of Speech analysis on 65,353 commonly used sentiment lexicons showed that 81% of sentiment bearing (non-neutral) lexicons were of the tags NN (Common Noun), JJ (Adjective) or NNS (Proper Noun).
REVIEW | doi:10.20944/preprints201810.0450.v1
Subject: Medicine & Pharmacology, Gastroenterology Keywords: hepatocellular carcinoma; natural killer cell
Online: 19 October 2018 (11:09:02 CEST)
Hepatocellular carcinoma (HCC) is currently the third leading cause of malignancy-related mortalities worldwide. Natural killer (NK) cells are involved in the critical role of first line immunological defense against cancer development. Defects in NK cell functions are recognized as important mechanisms for immune evasion of tumor cells. NK cell function appears to be attenuated in HCC, and many previous reports suggested that NK cells play a critical role in controlling HCC, suggesting that boosting the activity of dysfunctional NK cells can enhance tumor cell killing. However, the detailed mechanisms of NK cell dysfunction in tumor microenvironment of HCC remain largely unknown. A better understanding of the mechanisms of NK cell dysfunction in HCC will help in the NK cell-mediated eradication of cancer cells and prolong patient survival. In this review, we describe the various mechanisms underlying NK cell dysfunction in HCC. Further, we summarize current advances in the approaches to enhance endogenous NK cell function and in adoptive NK cell therapies, to cure this difficult-to-treat cancer.
ARTICLE | doi:10.20944/preprints201801.0285.v1
Online: 30 January 2018 (13:34:32 CET)
P2Y2 and P2Y4 receptors are physiologically activated by UTP and are widely expressed in many cell types in humans. They promote an increase in intracellular calcium via PLCβ/ IP3 and act on ion flux and water secretion. P2Y2 plays an important role in inflammation and proliferation of tumor cells, which could be attenuated with the use of antagonists. However, little is known about the physiological functions related to P2Y4 due to the lack of selective ligands for these receptors, which can be solved through the search of novel compounds with antagonistic activity. In the present study, we have applied a methodology of calcium measurement to identify new antagonist candidates for these receptors. Firstly, we established optimal conditions for calcium assay using J774.G8, a murine macrophage cell line, which expresses functional P2Y2 and P2Y4 receptors. J774.G8 cells were loaded with 2 μM of Fluo-4 to test its sensitivity in responding calcium stimuli. ATP and ionomycin, known as inductors of intracellular calcium rise, were used to stimulate cells. The EC50 obtained were 11 μM and 103 nM, respectively. Subsequently, investigation of P2Y2 and P2Y4 expression was performed. These cells responded with EC50 of 1.021 μM to the UTP stimulation. Screening assays were performed and a total of 100 extracts from Brazilian natural products were tested. JA2, RA3, and RB3 extracts stood out for their ability to inhibit UTP-induced responses without causing cytotoxicity and presented IC50 of 32.32 μg/mL, 14.99 μg/mL, and 12.98 μg/mL, respectively. Collectively, our results point to the discovery of potential antagonists candidates from natural products for UTP-activated receptors.
ARTICLE | doi:10.20944/preprints201608.0168.v1
Subject: Social Sciences, Economics Keywords: natural capital; human capital; economic growth; small economies; Vector Auto regression; natural resource curse
Online: 18 August 2016 (05:13:21 CEST)
The question of the relevance of human and natural capital, as well as the potential adverse effect of natural capital on economic growth, has gained increased attention in development economics. The aim of this paper is to theoretically and empirically assess the relevance of several forms of capital on economic growth in small economies that are dependent upon tourism or natural resources. The empirical framework is based on Impulse Response Functions obtained from Vector Autoregressive models in which we focus on the model where economic growth is the dependent variable for ten small economies that are dependent upon either tourism or natural resources. We find that there is evidence of the ‘’natural resource curse’’, especially in the economies that have a strong dependence on resources that are easily substitutable and whose prices constantly fluctuate. We further find that in the majority of observed cases the type of capital these small economies are most dependent on for their economic growth causes negative impulses in the majority of the observed periods. The main policy recommendation should be to assure that even these small economies should strive towards further diversification and avoid dependence on only one segment of their economy.
REVIEW | doi:10.20944/preprints202207.0245.v1
Subject: Life Sciences, Microbiology Keywords: bioferments; natural cosmetics; fermentation; bioactive compounds
Online: 18 July 2022 (03:38:33 CEST)
The cosmetics industry is currently looking for innovative ingredients with higher bioactivity and bioavailability for the masses of natural and organic cosmetics. Bioferments are innovative ingredients extracted from natural raw materials by carrying out a fermentation process with appropriate strains of microorganisms. The review was conducted using the SciFinder database with the keywords fermented plant, cosmetics, fermentation. Mainly bioferments are made from plant-based raw materials. The review covers a wide range of fermented raw materials from waste materials (whey with beet pulp) to plant oils (F-Shiunko, F-Artemisia, F-Glycyrrhiza). The spectrum of applications for bioferments is broad and includes properties such as skin whitening, antioxidant properties (blackberry, soybean, goji berry), anti-ageing, anti-aging (red ginseng, black ginseng, Citrus unshiu peel), hydrating and anti-allergic (aloe vera, skimmed milk). Fermentation increases the biochemical and physiological activity of the substrate by converting high-molecular compounds into low-molecular structures, this makes fermented raw materials more compatible compared to unfermented raw materials.
REVIEW | doi:10.20944/preprints202205.0197.v1
Subject: Life Sciences, Other Keywords: Natural product; bioactive compounds; antimicrobial; antioxidant
Online: 16 May 2022 (05:07:30 CEST)
Natural compounds have diverse structures and are present in different forms of life. Metabolites such as tannins, anthocyanins, and alkaloids, among others, serve as a defense mechanism in live organisms and are undoubtedly compounds of interest for the food, cosmetic and pharmaceutical industries. Plants, bacteria, and insects represent a source of biomolecules with diverse activities, poorly studied in many cases. To use these molecules for different applications, it is essential to know their structure, concentrations, and biological activity potential. In vitro techniques that evaluate the biological activity of the molecules of interest have been developed since the 1950s. Currently, different methodologies have emerged to overcome some of the limitations of these traditional techniques, mainly the reduction of time and costs. However, emerging technologies continue to appear due to the urgent need to expand the analysis capacity of a growing number of reported biomolecules and the lack of therapeutic options to treat various diseases. This review presents an updated summary of the conventional and current methods to evaluate natural compounds' biological activity, including a diagram that summarizes the minimum techniques essential for correctly assessing molecules with biological potential.
COMMUNICATION | doi:10.20944/preprints202105.0701.v1
Subject: Chemistry, Analytical Chemistry Keywords: Natural products; databases; dereplication; taxonomy; NMR
Online: 28 May 2021 (12:59:37 CEST)
The recent revival of the study of organic natural products as renewable sources of medicinal drugs, cosmetics, dyes, and materials motivated the creation of general-purpose structural databases. Dereplication, the efficient identification of already reported compounds, relies on the grouping of structural, taxonomic and spectroscopic databases that focus on a particular taxon (species, genus, family, order…). A set of freely available python scripts, CNMRPredict, is proposed for the quick supplementation of taxon-oriented search results from the LOTUS database (lotus.naturalproducts.net) with predicted carbon-13 NMR data from the ACD/Labs (acdlabs.com) CNMR predictor and DB software to provide easily searchable databases. The database construction process is illustrated using Brassica rapa as taxon example.
REVIEW | doi:10.20944/preprints202001.0230.v1
Online: 21 January 2020 (03:15:50 CET)
Acquired Immunodeficiency Syndrome (AIDS) which is chiefly originated by a retrovirus named Human Immunodeficiency Virus (HIV), has influenced about 70 million populations worldwide. Even though several advancements have been invented in the field of antiretroviral combination therapy, still HIV has become the dominant reason for death in South Africa, for example. The current antiretroviral therapies have achieved success in providing instant HIV suppression but with countless undesirable adverse effects. In the present day, the biodiversity of the plant kingdom is being explored by several researchers for the discovery of potent anti-HIV drugs with different mechanisms of action. The primary challenge is to afford a treatment that is free from any sort of risk of drug resistance and serious side effects. Hence, there is a strong demand to evaluate the drugs obtained from natural plants as well as the synthetic derivatives that have been derived from the natural compounds by various chemical reactions. Several plants such as Andrographis paniculata, Dioscorea bulbifera, Aegle marmelos, Wistaria floribunda, Lindera chunii, Xanthoceras sorbifolia and others have displayed significant anti-HIV activity showing more potent anti-HIV activity along with their structures, SARs & important key findings.
REVIEW | doi:10.20944/preprints201911.0392.v1
Subject: Chemistry, Medicinal Chemistry Keywords: fungal pathogens; antifungal agents; natural products
Online: 30 November 2019 (11:30:19 CET)
In this review, we discuss novel natural products discovered within the last decade that are reported to have antifungal activity against pathogenic species. Nearly a hundred natural products were identified that originate from bacteria, alga, fungi, sponges and plants. Fungi were the most prolific source of antifungal compounds discovered during the period of review. The structural diversity of these antifungal leads encompasses all the major classes of natural products including polyketides, shikimate metabolites, terpenoids, alkaloids and peptides.
ARTICLE | doi:10.20944/preprints201907.0159.v1
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: prime; contradiction; greater than; natural number
Online: 15 July 2019 (06:30:26 CEST)
A twin prime numbers are two prime numbers which have the difference of 2 exactly. In other words, twin primes is a pair of prime that has a prime gap of two. Sometimes the term twin prime is used for a pair of twin primes; an alternative name for this is prime twin or prime pair. Up to date there is no any valid proof/disproof for twin prime conjecture. Through this research paper, my attempt is to provide a valid disproof for twin prime conjecture.
ARTICLE | doi:10.20944/preprints201902.0176.v1
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: Hyper-Operations, arithmetical operations, natural numbers
Online: 19 February 2019 (10:58:09 CET)
We examine the extensions of the basic arithmetical operations of addition and multiplication on the natural numbers into higher-rank hyper-operations also on the natural numbers. We go on to define the concepts of prime and composite numbers under these hyper-operations and derive some results about factorisation, resulting in fundamental theorems analogous to the Fundamental Theorem of Arithmetic.
ARTICLE | doi:10.20944/preprints201703.0154.v2
Subject: Social Sciences, Geography Keywords: sustainability indicators; natural hazards; earthquake; ELECTRE
Online: 24 August 2017 (12:37:08 CEST)
Natural hazards such as earthquakes take place around the world and when combined with humans create natural disasters. Earthquakes, a form of natural hazard, have, in recent years, caused damage and destruction in many rural areas due to the lack of sustainability in political, economic, social, physical and operational criteria. Thus, to overcome the damage caused by earthquakes in rural areas, an assessment of sustainability status seems necessary to plan and strengthen in relation to the status of sustainability indicators. Data collection was performed through field methods and questionnaires. To test the hypothesis, T statistical methods, correlation method and F-test were performed using SPSS software (V22.0, IBM Corporation, Armonk, NY, USA). The results of the study showed that villages were at a low and undesirable level for all aspects, except social index in terms of sustainability. Comparisons showed that there was a significant mean difference among villages in terms of sustainability. The multi-criteria decision-making analysis has been considered and applied to a ranking of villages in terms of sustainability against the hazard of earthquakes. Finally, in order to improve the sustainability indicators of villages, some strategies have been presented.
ARTICLE | doi:10.20944/preprints202211.0348.v1
Subject: Biology, Agricultural Sciences & Agronomy Keywords: cropping system; rotation; tillage; natural rainfall; Greece
Online: 18 November 2022 (03:53:32 CET)
Soil erosion is one of the biggest problems in the agricultural sector that can affect ecosystems and human societies. A field of 50 slope was selected to study the runoff, soil and nutrients’ loss as well as crop productivity in different treatments (conventional tillage (CT) vs. no-tillage (NT), plant vs. no plant cover, contour cultivation (CC) vs. perpendicular to the contour cultivation, (PC) under natural rainfall. The experiment was conducted in central Greece in two cultivation periods. In autumn, the field was cultivated with intercropping Triticosecale and Pisum sativum and in spring with Sunflower. The total rainfall was 141.4 mm in the 1st year and 311 mm in the 2nd. We found that runoff in the treatment of no tillage with contour cultivation was 85% lower in both years compared to the no tillage-no plant control. Therefore, the contour cultivation-no tillage treatment had a positively effect in decreasing phosphorus and potassium concentrations lost from soil: indeed, there was a decrease by 55% and 62% in P and K, respectively, in the NT compared to the CC treatments. We conclude that the NT-CC treatment with plant cover was the most effective in reducing water runoff, soil nutrients’ loss and increasing yield.
ARTICLE | doi:10.20944/preprints202210.0086.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: retweet prediction; multilayer network; natural language processing
Online: 8 October 2022 (03:00:41 CEST)
Retweet prediction is an important task related to different problems such as information spreading analysis, the automatic detection of fake news, social media monitoring, etc. In this study we explore the possibilities of retweet prediction based on heterogeneous data sources. In order to classify the tweet according to the amount of retweets, we combine features extracted from the multilayer network and the text. More specifically, we introduce a multilayer framework that proposes the multilayer network representation of Twitter. This formalism captures different users' actions and complex relationships as well as other key properties of communication on Twitter. We select a set of local network measures from each layer and construct a set of multilayer network features. In addition, we adopt a BERT-based language model, namely Cro-CoV-cseBERT to capture high-level semantics and structure of tweets as a set of text features. Then, we train six machine learning (ML) algorithms: random forest, multilayer perceptron, light gradient boosting machine, category embedding model, neural oblivious decision ensembles and attentive interpretable tabular learning model in the task of retweet prediction. We compare the performance of all six algorithms in three different setups (i) using only text features, (ii) using only multilayer network features and (iii) using both sets of features. We evaluate all setups in terms of standard evaluation measures i.e. precision, recall, F1-score and accuracy. For this task, we first prepare and use an empirical dataset of 199,431 tweets in the Croatian language posted during the period between January 1, 2020 and May 31, 2021. Our results indicate that by integrating multilayer network features with text features the prediction model would perform better than using just one set of features.
ARTICLE | doi:10.20944/preprints202206.0188.v1
Subject: Chemistry, Other Keywords: Stigmatellin; Myxobacteria; Biosynthesis; Natural Products; Secondary Metabolites
Online: 13 June 2022 (12:58:42 CEST)
Myxobacteria generate natural products with unique chemical structures, which not only feature remarkable biological functions but also demonstrate unprecedented biosynthetic assembly strategies. The stigmatellins have been previously described as potent inhibitors of the mitochondrial and photosynthetic respiratory chain and originate from an unusual polyketide synthase assembly line. While previous biosynthetic investigations were focused on the formation of the 5,7-dimethoxy-8-hydroxychromone ring, side chain decoration of the hydrophobic alkenyl chain in position 2 was investigated less thoroughly. We report here the full structure elucidation as well as cytotoxic and antimicrobial activities of three new stigmatellins isolated from the myxobacterium Vitiosangium cumulatum MCy10943T with side chain decorations distinct from previously characterized members of this compound family. These findings provide further implications considering the side chain decoration of these aromatic myxobacterial polyketides and their underlying biosynthesis.
REVIEW | doi:10.20944/preprints202204.0100.v1
Subject: Life Sciences, Biochemistry Keywords: Diabetes; Hyperglycemia; Cancer; AMPK; TET2; Natural products
Online: 11 April 2022 (14:00:31 CEST)
Emerging evidence suggests that sustained diabetes-associated factors such as inflammation, hyperinsulinemia, and hyperglycemia are major contributors to aberrant cell proliferation and subsequent neoplastic transformation. Epidemiological studies have also highlighted that diabetes promoting a sedentary lifestyle, with or without the direct involvement of insulin, is frequently linked to cancer. However, our knowledge regarding the molecular mechanisms that correlate hyperglycemia to oncogenic transformations remains limited. In this regard, a recent study has proved that hyperglycemia inactivates AMPK, which results in the destabilization of the TET2 and its tumour-suppressive role ultimately predisposing diabetes mellitus patients to cancer. To the management of hyperglycemia associated with oncogenesis, we need to explore a reverse pharmacology-based ethnopharmacological approach. Botanical-derived natural products are structurally and functionally more diverse with fewer or no side effects on humans. The present review discusses the molecular link between hyperglycemia and cancer progression with the effect of natural products as therapeutic agents on the hyperglycemia-cancer associated signalling pathway.
ARTICLE | doi:10.20944/preprints202202.0146.v1
Subject: Social Sciences, Other Keywords: leisure; recreation; gardening; physical activity; natural landscapes
Online: 10 February 2022 (10:33:35 CET)
The purpose of this study is to investigate the perceived benefits of community gardening. A garden colony is a collection of adjacent plots of land primarily for the purpose of gardening. The methodology of this research consists of observations, interviews, questionnaires, and focus group. As a result of this research we found that gardening is a natural and safe way to meet the lifelong demands for a healthy life. We found specific mental and physical benefits as a result of this community gardening. We encourage communities to allow people to buy plots of land away from their homes to promote this healthy activity. The garden colony provides a place for individual renewal and restoration; this urban oasis offers a way to maintain and promote lifelong healthy living, and they are an important contribution to one’s life by adding a sense of meaning and purpose.
ARTICLE | doi:10.20944/preprints202109.0107.v1
Subject: Materials Science, Polymers & Plastics Keywords: Microorganisms; Natural Rubber; Hevea Brasiliensis; Surfactants; Pasteurization.
Online: 6 September 2021 (15:43:34 CEST)
This research was a study of the effect of addition linear alkylbenzene sulfonates (LAS), NaHCO3, and NaCl and pasteurization on the preservation of natural rubber (NR). The samples were collected from rubber plantations of Chiang Rai province which were added with three surfactants in samples already. Physical and chemical properties were evaluated using pH, deterioration, viscosity, color, and odor. Then, the samples were stored at 28-30°C periods times of 0, 15, 30, 45, and 60 days. The experiment found that the color, viscosity, odor, and texture of NR samples were not spoiled after being preserved for 30 days but after 45 and 60 days found some coagulation of NR. In the case of non-preserved NR was found that spoiled NR in every period time range of 15-60 days. The pH testing found that increasing period times affect decreased pH value and increased viscosity due to salt of sulfate, carbonate, chloride, and thermal treatment of pasteurization which kill microorganisms and evaporated water. It concluded that the reagents were the process of cosurfactants with heat and frozen for increased effectiveness of anti-acid-producing bacteria and can use as short and long-term preservation of NR under the planting area condition of Thailand.
ARTICLE | doi:10.20944/preprints202109.0062.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Machine Learning; Natural Language Processing; Deep Learning
Online: 3 September 2021 (12:53:42 CEST)
Documenting cultural heritage by using artificial intelligence (AI) is crucial for preserving the memory of the past and a key point for future knowledge. However, modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. If we want to rely on AI for these tasks, it is essential to understand what lies behind these models. Among the ways to discover AI there are the senses and the intellect. We could consider AI as an intelligence. Intelligence has an essence, but we do not know whether it can be considered “something” or “someone”. Important issues in the analysis of AI concern the structure of symbols -operations with which the intellectual solution is carried out- and the search for strategic reference points, aspiring to create models with human-like intelligence. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we propose KERMIT as a unit of investigation for a possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning and human knowledge.
REVIEW | doi:10.20944/preprints202106.0619.v1
Subject: Life Sciences, Biochemistry Keywords: Natural products; Madagascan active principles; Nano-delivery.
Online: 25 June 2021 (12:02:28 CEST)
Natural products endowed of biological activity represent a primary source of commodities ranging from nutrition to therapeutic agents, as well as cosmetic tools, and recreational principles. These natural means have been used by mankind since centuries if not millennia. They are commonly used all over the world and socio-economical contexts but are particularly attractive in disadvantaged area or economically emerging situations all over the world. This is very likely due to the relatively easy recovery of these bioactive principles from the environment, to the low if any cost as well as ease of administration and to the general popular compliance concerning their consumption/ingestion. In this concise review, we focus on some popular bioactive principles of botanical origin which find a wide use in the Madagascan populations. But, due to space limitations only some most common and largely diffused principles in this country are considered. Finally, a possible nanotechnological administration is discussed in the case where a potential therapeutic usage is envisaged.
REVIEW | doi:10.20944/preprints202106.0130.v1
Online: 4 June 2021 (10:00:12 CEST)
Many inflammatory mechanisms are involved in the pathophysiology of COVID-19 infection. COVID-19 inhibits IFN antiviral responses, so we should expect an out-of-control viral replication. “Cytokine storms” occur due to the over-production of pro-inflammatory cytokines after an influx of neutrophils and monocytes/macrophages and may be responsible for the immunopathology of the lung involvement. Several cascades have been reported in the activation process of NF-κB. In this paper, to find new therapeutic options for COVID-19 infection, we reviewed some natural products that could potentially inhibit the NF-κB pathway. We found that sevoflurane, quercetin, resveratrol, curcumin, KIOM-C, bergenin, garcinia kola, shenfu, piperlongumine, wogonin, oroxylin, plantamajoside, naringin, ginseng, kaempferol, allium sativum L, illicium henryi, isoliquiritigenin, lianhua qingwen, magnoflorine, and ma Huang Tang might be effective in inhibiting the NF-KB pathway. These natural products could be helpful in the control of COVID-19 infections. However, larger clinical trials are needed to ascertain the efficacy of these products fully.
REVIEW | doi:10.20944/preprints202105.0076.v1
Subject: Chemistry, Analytical Chemistry Keywords: inclusion complexes; carotenoids; cyclodextrins; natural colorants; encapsulation
Online: 6 May 2021 (12:22:38 CEST)
The use of natural carotenoids as food colorants is an important trend of innovation in the industry due to their low toxicity, their potential as bio-functional ingredients, and the increasing demand for natural and organic foods. Despite these benefits, their inclusion in food matrices presents multiple challenges related to their low stability and low water solubility. The present review covers the main concepts and background of carotenoid inclusion complex formation in cyclodextrins as a strategy for their stabilization, and subsequent inclusion in food products as color additives. The review includes the key aspects of the molecular and physicochemical properties of cyclodextrins as complexing agents, and a detailed review of the published evidence on complex formation with natural carotenoids from different sources in cyclodextrins, comparing complex formation methodologies, recovery, inclusion efficiency, and instrumental characterization techniques. Moreover, process flow diagrams (PFD), based on the most promising carotenoid-cyclodextrin complex formation methodologies, are proposed, and discussed as a potential tool for their future scale-up. This review shows that the inclusion of carotenoids in complexes with cyclodextrins constitutes a promising technology for the stabilization of these pigments, with possible advantages in terms of their stability in food matrices.
ARTICLE | doi:10.20944/preprints202103.0049.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: natural language processing; deep learning; biased models
Online: 2 March 2021 (09:17:15 CET)
Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as "pre-training"), versatile and performing models are released continuously for every new network design. But these networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. Also, available resources are identified and a strategy to deal with bias in deep NLP is proposed.
REVIEW | doi:10.20944/preprints202101.0025.v1
Subject: Engineering, Biomedical & Chemical Engineering Keywords: Astaxanthin; natural antioxidant; bacteriocins; hispidin; oxidative stress
Online: 4 January 2021 (12:15:33 CET)
Oxidative stress is an elevated intracellular level of free oxygen radicals that cause lipid peroxidation, protein denaturation, DNA hydroxylation, and apoptosis, ultimately negotiating cells viability. Antioxidants can scavenge such free radicals, thus reducing the oxidative stress and eventually prevent cellular damage. Medicinal plants, fruits, and spices remain the prioritized sources of antioxidants and antimicrobial properties since the time immemorial, but in contrast to plants, microorganisms can be grown at a faster rate under controlled conditions. They are non-toxic, non-carcinogenic, and biodegradable as compared to synthetic antioxidants. Microorganisms including actinomycetes, archaea, bacteria, protozoa, yeast, and fungi are auspicious source of vital bioactive compounds. The list comprises ample of bioactive components from microorganisms. One of them is bacteriocins, which are ribosomally synthesized antimicrobial peptides product of Eurotium sp., Streptomyces parvulus, S. thermophiles, Lactococcus lactis, etc. It has a great potential as next-generation antibiotics targeting the multiple-drug resistant pathogens. Pneumocandins are antifungal lipohexapeptides derived from the fungus Glarea lozoyensis, and inhibit 1,3-β-glucan synthase of the fungal cell wall and act as a precursor for the synthesis of caspofungin. It is widely used against invasive fungal infections and has been recently approved by the FDA. Taxol (paclitaxel), a chemotherapeutic drug derived from the bark of Taxus brevifolia can also be produced by endophytic fungi Taxomyces andreanae and Nodulisporium sylviforme. It is known to inhibit several fungi such as Pythium, Aphanomyces and Phytophthora. Hispidin and its derivate isolated from P. hispidus, reduce inducible nitric oxide synthase (iNOS) expression, obstruct the transcriptional activity of NF-κB, and also decrease the production of reactive oxygen species (ROS) in macrophages. Astaxanthin, known as an “aquatic” carotenoid produced by H. pluvialis, also has excellent ROS quenching activity. This study mainly focuses on fascinating antioxidant and antimicrobial compounds that have been scarcely investigated in microorganisms and discuss the promise and challenges of microorganisms as providers of health benefits.
ARTICLE | doi:10.20944/preprints202012.0280.v1
Subject: Keywords: Thaumatin, sweet protein, molecular farming, natural sweeteners
Online: 11 December 2020 (12:56:40 CET)
There are currently worldwide efforts to reduce sugar intake due to the various adverse health effects linked with the overconsumption of sugars. Artificial sweeteners have been used as an alternative to nutritive sugars in numerous applications; however, their long-term effects on human health remain controversial. This led to a shift in consumer preference towards non-caloric sweeteners from natural sources. Thaumatins are a class of intensely sweet proteins found in arils of the fruits of the West-African plant Thaumatococcus danielli. Thaumatins’ current production method through aqueous extraction from this plant and uncertainty of the harvest from tropical rainforests limits its supply while the demand is increasing. Despite successful recombinant expression of the protein in several organisms, no large-scale bioproduction facilities exist. We present preliminary process design, process simulation, and economic analysis for a large-scale (50 metric tons/year) production of thaumatin II variant by several different molecular farming platforms.
REVIEW | doi:10.20944/preprints202008.0313.v2
Subject: Biology, Anatomy & Morphology Keywords: drug resistance; natural diversity; C. elegans; anthelmintics
Online: 25 November 2020 (14:47:29 CET)
Anthelmintic drugs are the major line of defense against parasitic nematode infections, but the arsenal is limited and resistance threatens sustained efficacy of the available drugs. Discoveries of the modes of action of these drugs and mechanisms of resistance have predominantly come from studies of a related non-parasitic nematode species, Caenorhabditis elegans, and the parasitic nematode Haemonchus contortus. Here, we discuss how our understanding of anthelmintic resistance and modes of action came from the interplay of results from each of these species. We argue that this “cycle of discovery”, where results from one species inform the design of experiments in the other, can use the complementary strengths of both to understand anthelmintic modes of action and mechanisms of resistance.
Subject: Keywords: molecular engineering; natural conformation; polymeric biomaterials; biocompatibility
Online: 17 March 2020 (03:53:20 CET)
Molecular engineering research is the fundamental way and the only way for the development of biomaterials. Based on molecular engineering, the biocompatibility of natural conformation and polymer biomaterials was studied. In this paper, we discuss that natural conformation is the basis of protein biological function, and that the synergistic action of peptide chain and side group is the motive force for protein to construct natural conformation and complete biological function. On the basis of the influence of the adsorption of polymer biomaterials on the natural conformation of proteins, the relationship between biocompatibility of biomaterials and protein conformation is further explained. Studies have shown that bismuth molecular materials can only be applied in the market and have their functionality if they have good biocompatibility. Therefore, the biocompatibility evaluation of new materials has important practical significance.
ARTICLE | doi:10.20944/preprints201808.0278.v1
Subject: Engineering, Other Keywords: Flow friction, Pipeline networks, Waterworks, Natural gas
Online: 15 August 2018 (16:02:09 CEST)
Accent is on determination of appropriate friction factor of the pipes and on selection of the representative equation for water or natural gas flow which is valuable for existing conditions in the looped network of pipelines. Note that in a municipal gas pipeline, natural gas can be treated as incompressible fluid (liquid) i.e. as water or oil. Even under this circumstance, calculation of water pipelines cannot be literary copied and applied for calculation of gas pipelines. Inappropriate friction factor, equally as e.g. inappropriate usage of water flow equations for calculation of gas networks can lead to inaccurate final results. Few iterative methods for determining the optimal hydraulic solution of water- and gas- looped pipeline networks, such as, Hardy Cross, modified Hardy Cross, node-loop method, node and M.M. Andrijashev method, will be shown. Speed of convergence will be compared and discussed using a simple network with three loops.
REVIEW | doi:10.20944/preprints201808.0155.v1
Subject: Medicine & Pharmacology, Pathology & Pathobiology Keywords: amyloid diseases; biocomputing; drug design; natural antiamyloids
Online: 8 August 2018 (04:27:10 CEST)
Amyloids result from the aggregation of several unrelated proteins, due to either specific mutations or promoting intra- or extra-cellular conditions. Structurally, they are rich in intermolecular β-sheets and are the causative agents of several diseases, both neurodegenerative and systemic. It is believed that the most toxic species are small aggregates, referred to as oligomers, rather than the final fibrillar assemblies. Their mechanisms of toxicity are mostly mediated by aberrant interactions with the cell membranes, with resulting derangement of membrane-related functions. Much effort is being put in the search for natural antiamyloid agents, and/or in the development of synthetic molecules. Actually, it is well documented that the prevention of amyloid aggregation results in several cytoprotective effects. Here, we portray the state of the art in the field. Several natural compounds are effective antiamyloid agents, notably tetracyclines and polyphenols. They are generally non-specific, as documented by their partially overlapping mechanisms, and the capability to interfere with the aggregation of several unrelated proteins. Among rationally designed molecules, we mention the prominent examples of β-breakers peptides, whole antibodies and fragments thereof, and the special case of drugs contrasting transthyretin aggregation. In this framework, we stress the pivotal role of the computational approaches. When combined with biophysical methods, in several cases they have helped clarify in detail the protein/drug modes of interaction, which make it plausible that more effective drugs will be developed in the future.
ARTICLE | doi:10.20944/preprints201706.0055.v1
Subject: Arts & Humanities, Theory Of Art Keywords: aesthetics; mathematical structure; category theory; natural intelligence
Online: 12 June 2017 (13:26:59 CEST)
This paper proposes a new approach to investigation into the aesthetics. Specifically, it argues that it is possible to explain the aesthetic and its underlying dynamic relations with axiomatic structure (the octahedral axiom derived category) based on contemporary mathematics – namely, category theory – and through this argument suggests the possibility for discussion about the mathematical structure of the aesthetic. If there was a way to describe the structure of aesthetics with the language of mathematical structures and mathematical axioms – a language completely devoid of arbitrariness – then we would make possible a synthetical argument about the essential human activity of “the aesthetics”, and we would also gain a new method and viewpoint on the philosophy and meaning of the act of creating a work of art and artistic activities. This paper presents one hypothesis as a first step in constructing the science of dynamic generative aesthetics based on axiomatic functionalism, which is in turn based on a new interdisciplinary investigation into the functional structure of aesthetics.
REVIEW | doi:10.20944/preprints201703.0118.v1
Subject: Chemistry, Medicinal Chemistry Keywords: dengue; chikungunya; virus enzymes; antiviral; natural products
Online: 16 March 2017 (09:42:52 CET)
Dengue virus (DENV) and chikungunya virus (CHIKV) are reemergent arboviruses that are transmitted by mosquitoes of the Aedes genus. During the last several decades, these viruses have been responsible for millions of cases of infection and thousands of deaths worldwide. Therefore, several investigations were conducted over the past few years to find antiviral compounds for the treatment of DENV and CHIKV infections. One attractive strategy is the screening of compounds that target enzymes involved in the replication of both DENV and CHIKV. In this review, we describe advances in the evaluation of natural products targeting the enzymes involved in the replication of these viruses.
ARTICLE | doi:10.20944/preprints201611.0119.v1
Subject: Social Sciences, Economics Keywords: land use preference; ecosystem service; natural conservation
Online: 23 November 2016 (18:08:51 CET)
This paper aims to build up a preference function to evaluate the public benefits of the type of agricultural farming, biodiversity, water provisions, land use type, ecotourism modes, and a monetary attribute (environmental trust fund and willingness to contribute) associated with an ecosystem service and land use program in a forest park. This study used the choice experiments to build a random utility model, analyze the average preference for the above land use attributes based on the conditional logit and used a latent class model to test the resident’s heterogeneous preferences for land use planning in the forest park. We also estimated the welfare derived from various land use programs. The empirical result had shown that: (1) increasing organic farming area, maintaining the status quo of species biodiversity, increasing the surface water provision, increasing the area of custom flora, increasing the wetland area, and setting up an integrated framework for ecotourism increase the public’s preference for the land use program; (2) we found that farmer and non-farmer haven’t the same land use preferences; (3) the ecotourism development program incorporating biodiversity, organic farming, ethnobotany, and wetland area with integrated ecotourism are more preferred than other land use program scenarios.
REVIEW | doi:10.20944/preprints202102.0455.v1
Subject: Medicine & Pharmacology, Allergology Keywords: essential oils; water extracts; ethanol extracts; periodontal bacteria; Candida; natural antimicrobials; natural anti-inflammatory; Sardinian plants; pharmaceutical plants
Online: 22 February 2021 (10:53:13 CET)
There is an increasing interest in revisiting plants for drug discovery proving scientifically their role as remedies. Pistacia lentiscus (PL) is a wild-growing shrub rich in terpenoids, which are pharmacological appealing. The more recurrent components in the oil are represented by α-pinene, terpinene, caryophyllene, limonene, and myrcene. High concentration of polyphenols enriches the extracts. PL-extracts showed in vitro and in animal model strong anti-inflammatory and anti-oxidative activities. The anti-inflammatory activity mainly occurs due to inhibition of NF-kB pathway or directly toward the proinflammatory cytokines, or arachidonic acid cascade against COX-2 and LOX. The antimicrobial activity of PL essential oil and extracts includes among others Staphylococcus aureus, Escherichia coli, periodontal bacteria and Candida sp.. In conclusion, the biological properties, and particularly the anti-inflammatory and anti-microbial capacity, propose PL as a new safe pharmaceutical agent.
ARTICLE | doi:10.20944/preprints202209.0107.v1
Subject: Earth Sciences, Geophysics Keywords: sedimentation, natural hazard, flood, floodplain, Electromagnetic, water level
Online: 7 September 2022 (08:26:28 CEST)
Sediment thickness increases can cause floodplains and the water level increases. This has the potential to generate a flood. Using electromagnetic waves, Time Domain Electromagnetic (TDEM) detected resistivity or conductivity contrast of lithology in the subsurface. It is measured in the time domain. TDEM method has been developing for decades. Here we tried to develop a 1-D forward modelling program for central loop configuration in the water environment using the Adaptive Born Forward Mapping (ABFM) method. We simulated this program in several water environment conditions (such as freshwater, brackish water and saline water) to know its response. Preventing natural hazards, especially flood hazards which are caused by the floodplain increases is our motivation in this research. Our simulation shows that Central-Loop Configuration Time-Domain Electromagnetic Method is able for imaging the sediment thickness clearly. The response of this method is extremely sensitive in saline water to depth changing than in other water environments.
ARTICLE | doi:10.20944/preprints202110.0360.v2
Subject: Mathematics & Computer Science, Other Keywords: Household Disaster Preparation; Natural Hazards Mitigation; Prediction Model
Online: 2 November 2021 (12:57:04 CET)
Natural disasters are showing an increase in the magnitude, frequency, and geographic distribution. Studies have shown that individuals’ self-sufficiency, which largely depends on household preparedness, is very important for hazard mitigation in at least the first 72 hours following a disaster. However, for factors that influence a household’s disaster preparedness, though there are many studies trying to identify from different aspects, we still lack an integrative analysis on how these factors contribute to a household’s preparation. This paper aims to build a classification model to predict whether a household has prepared for a potential disaster based on their personal characteristics and the environment they located. We collect data from the Federal Emergency Management Agency’s National Household Survey in 2018 and train four classification models - logistic regression, decision trees, support vector machines, and multi-layer perceptron classifier models- to predict the impact of personal characteristics and the environment they located on household prepare for the potential natural disaster. Results show that the multi-layer perceptron classifier model outperforms others with the highest scoring on both recall (0.8531) and f1 measure (0.7386). In addition, feature selection results also show that among other factors, a household’s accessibility to disaster-related information is the most critical factor that impacts household disaster preparation. Though there is still room for further parameter optimization, the model gives a clue that we could support disaster management by gathering publicly accessible data.
REVIEW | doi:10.20944/preprints202108.0152.v1
Online: 6 August 2021 (08:15:01 CEST)
Increasing environmental concern and consumer demand for natural, sustainable and eco-friendly products have prompted the replacement of synthetic surfactants with their natural plant-based alternatives. Saponins are the plant based natural surfactants characterized by their foam forming properties in aqueous solution. Their natural origin makes them eco-friendly, bio-degradable and non-toxic. Further, they possess better physicochemical properties than the syn-thetic ones. They are also reported to exhibit a lot of useful biological activities such as anti-cancer, antifungal, anti-inflammatory, antimicrobial, antioxidant and cholesterol-lowering properties. Because of their excellent surface activity, biological activities and wide distribution in nature, saponin rich plants deserve deeper insight as a sustainable source of natural surfactants as they possess the potential to replace toxic synthetic surfactants abundant today. This review article is intended to provide a brief overview on the saponins with a special notion on their surface-active properties. It encourages further studies on development of commercial formulations based on saponins for the complete replacement of the synthetic counter parts, making better use of plants sources thereby contributing to global agenda of green environment.
ARTICLE | doi:10.20944/preprints202107.0123.v1
Subject: Biology, Anatomy & Morphology Keywords: Sitophilus oryzae; Natural insecticides; Pandanus amaryllifolius; Azadirachta indica
Online: 6 July 2021 (08:07:57 CEST)
The aim of this study was to determine the effect of Pandanus (Pandanus amaryllifolius 20 Roxb.) and Neem (Azadirachta indica) leaves powder on the repellency, mortality, and weight loss 21 of grains due to Sitophilus oryzae. The methodes of this study used a completely randomized design 22 (CRD) with 7 treatments and 4 replications. The results of this study indicate that the best treat- 23 ment in terms of causing repellency was the treatment of 10 grams of pandanus with a percentage 24 of 87.5%, while the best treatment in terms of causing pest mortality and was also able to reduce 25 the risk of rice weight loss due to Sitophilus oryzae was treatment 10 gram of neem with a mortality 26 percentage of 76.25% and weight loss of rice 3.14%. This research showed that neem leaf com- 27 pounds are better in terms of causing mortality, while Pandanus compounds are better in terms of 28 causing mortality of Sitophilus oryzae.
ARTICLE | doi:10.20944/preprints202106.0429.v1
Subject: Life Sciences, Biochemistry Keywords: Fusarium graminearum; mycotoxins; wheat; natural infection; epidemic year
Online: 16 June 2021 (09:34:11 CEST)
Fusarium graminearum is a dangerous pathogen of the cereals producing mycotoxins (trichothecene and zearalenone) harmful for human and animal health. There were evaluated sixteen winter wheat varieties for their response in conditions of natural infection with F. graminearum in the epidemic year 2019, being well known that accumulation of mycotoxins (DON, ZON and T-2) is induced by different biotic and abiotic factors. Field plot was organized in Latin rectangle randomized with three replicates. For all evaluated wheat varieties were collected field data (incidence, severity and infection degree of the fungus F. graminearum) and laboratory data (mycotoxins concentration in grains) that have been processed using the software JASP (Version 0.14) for descriptive statistics, and exploratory factor analysis (EFA). Microsoft Excel 2019 was used to calculate Pearson’s correlation coefficients. The results showed negative corelation between plants’ density and F. graminearum attack frequency. Positive correlations were found between DON and T-2 and between DON and fungus attack intensity. This work highlights that during a F. graminearum epidemic year some of the most influential factors in the contamination with harmful mycotoxins (DON, ZON and T-2) are: plants density, frequency of the attack on ear, diseased ears and attack intensity on ears.
REVIEW | doi:10.20944/preprints202106.0131.v1
Subject: Medicine & Pharmacology, Allergology Keywords: COVID-19; Cytokine storm; IL-6; Natural product
Online: 4 June 2021 (10:05:40 CEST)
Plant species with anti-inflammatory properties might play an essential role in combatting COVID-19 via reducing cytokine storms. We aimed to review the extant evidence of the potential therapeutic efficacy of natural products against cytokine storms by inhibiting interleukin-6 (IL-6) as a major pathological mediator. Data were collected following an electronic search in major databases (Pubmed, Scopus, Web of Science, Google Scholar) and also preprint articles on preprint and medRxiv servers by using a combination of relevant keywords. Seventeen active compounds and medicinal plants were found and reviewed in the present review. Results of both in-vivo and in-vitro experiments conducted on these compounds showed that Phillyrin, SMFM, Qiangzhi decoction, curcumin, Shen-Fu, Forsythia, and Alpha-Mangostin inhibit the production of IL-6. Andrographolide and Liu Shen Wan have an inhibitory effect on releasing this agent, while Ilex Asprella and Deoxy-11,12-didehydroandrographolide and naringin reduce the expression of IL-6. Theaflavin and Cholorogenic acid inhibit the secretion of IL-6, Xuebijing, and Chai-Hu-Gui-Zi-Gan-Jiang-Tang and Lipanpaidu prescription can reduce the serum level of IL-6. These agents also effectively improve infected lungs, increase survival rates, and minimize tissue damage. Medicinal plants and their phytochemical ingredients with down-regulatory effects on the expression of IL-6 have a potential influence on the inhibition of cytokine storms during viral infection caused by COVID-19. Therefore, phytochemicals could be regarded as promising candidates for managing cytokine storm inflammatory responses due to COVID-19 infection.
REVIEW | doi:10.20944/preprints202103.0719.v1
Subject: Engineering, Mechanical Engineering Keywords: biodiesel; engine performance; emissions; natural feedstocks; production method
Online: 30 March 2021 (09:42:11 CEST)
Biodiesel has caught the attention of many researchers because it has great potential to be sustainable fossil fuel substitute. Biodiesel has non-toxic and renewable nature and has been proven to emit less amount of environmentally harmful emissions such as hydrocarbons (HC), and carbon monoxide (CO), as well as smoke particles during combustion. Problems related to global warming caused by greenhouse gas (GHG) emissions could also be solved by utilizing biodiesel as a daily energy source. However, the expensive cost of biodiesel production, mainly because of the cost of natural feedstock, holds the potential of biodiesel commercialization. The selection of natural sources of biodiesel should be made with observations from economic, agricultural, and technical perspectives to obtain one feasible biodiesel with superior characteristics. This review paper presents a detailed overview of various natural sources, their physicochemical properties, as well as the performance, emission, and combustion characteristics of biodiesel when used in a diesel engine. The recent progress in studies about natural feedstocks and manufacturing methods used in biodiesel production were evaluated in detail. Finally, the findings of the present work reveal that transesterification is currently the most superior and commonly used biodiesel production method compared to other methods available.
Subject: Mathematics & Computer Science, Geometry & Topology Keywords: B-Lift; , Natural Lift; Slant helix; Darboux helix
Online: 25 March 2021 (17:22:06 CET)
In this study, we introduce a new type curve in 3-dimensional space which called B-lift curve and we obtain the Frenet operators of the B-lift curve. Moreover, we consider the correpondence of Frenet operators between the Blift curve and the natural lift curve. Finally, we investigate the B-lift curve according to the main curve is slant helix or darboux helix.
Subject: Social Sciences, Accounting Keywords: Governance; Livelihoods; Natural Resources; Resilience; Traditional Systems; Pastoralism
Online: 18 March 2021 (13:15:35 CET)
Kenya’s natural resource base has dwindled over years. The existence of many natural resource policies, some that are incompatible, has resulted in complex rangeland management regimes, giving rise to fragmented interventions and inadequate natural resource policies in relation to pastoralism. The majority of pastoral land resources held under a controlled access system by the national government that regulates management and utilization of resources. Pastoralists in Kenya have become among the most marginalized and disadvantaged minority groups. This is due to limited or under investment by government and other actors, and access to, or ownership of land, water and other resources, which are fundamental for pastoralism. This study examines significant obstacles for the establishment of a more inclusive ‘governance’ approach to natural resource management in northern Kenya, that characterize the customary Boran knowledge such as Deedha’s (traditional grazing unit) and formal institutions and seeks to address the tension between them through a legal framework that accommodates both. The results of the study established existence of the traditional structures and institutions in governance of natural resources within the pastoralist communities in Isiolo County. These institutions have evolved to cope with changing dynamics brought about by formalization of the natural resources governance. The resulted showed that various formal institutions from national government agencies to county government department were involved in management of the natural resources. However, the study established various operational divergence and links between informal and formal institutions involved in natural resources management. The study concluded that both informal institution such as Deedha and formal institutions constituted by national and county government did governance of natural resources among pastoralist communities in Isiolo County. The communities however have more trust in the informal structures and institutions because of their flexibility and inclusiveness.
ARTICLE | doi:10.20944/preprints202103.0255.v1
Subject: Earth Sciences, Atmospheric Science Keywords: Fagus sylvatica; emergy accounting; natural capital; ecosystem functions
Online: 9 March 2021 (10:07:16 CET)
Forest ecosystems are important providers of ecosystem functions and services belonging to four categories: supporting, provisioning, regulating, and cultural ecosystem services. Forest management, generally focused on timber production, has consequences on the ability of the system to keep providing services. Silviculture, in fact, may affect ecological structures and processes from which services arise. In particular, the removal of biomass causes a radical change in the stocks and flows of energy characterizing the system. Aiming at the assessment of differences in stored natural capital and ecosystem functions and services provision, three differently managed temperate forests of common beech (Fagus sylvatica) were considered: (1) a forest in semi-natural condition, (2) a forest carefully managed to get timber in a sustainable way and (3) a forest exploited without management. Natural capital and ecosystem functions and services are here accounted in biophysical terms. Specifically, all the resources used up to create the biomass (stock) and maintain the production (flow) of the different components of the forest system were calculated. Both stored emergy and empower decrease at increasing human pressure on the forest, resulting in a loss of natural capital and a diminished ability of the natural system to contribute to human well-being in terms of ecosystem services provision.
ARTICLE | doi:10.20944/preprints202102.0321.v1
Subject: Biology, Anatomy & Morphology Keywords: Sundarbans; Fisheries; Natural disasters; Occupational changes; Climate change
Online: 16 February 2021 (13:20:45 CET)
The climate of Bangladesh has changed drastically which may put considerable adverse impacts on mangrove fishers but very few studies focused on this professional group. An attempt was made to perceive the impact and adaptation measures of the Sundarbans mangrove resource users, employing interviews and focus group discussions. A total of 150 respondents were randomly selected from the Sundarbans west under Shyamnagar Upazila of Satkhira District. It was revealed that the abundance of fishes, fuel woods, honey, golpata (Nypa fruticans), and shrimp post-larvae (PL) was reduced considerably. The resource users have adapted themselves by changing their occupation and becoming jobless and depending on the other family members. PL collection, honey collection, shrimp culture, and wood collection were found professional adapting strategies to adopt cyclone, flood, salinity intrusion, river erosion, and drought. Several recommendations are elicited, the implementation of which is important to ensure livelihood sustainability of the mangrove communities.
ARTICLE | doi:10.20944/preprints202011.0611.v1
Subject: Life Sciences, Biochemistry Keywords: cyanobacteria; thermal mud; natural products; anti-inflammatory; bioactivity
Online: 24 November 2020 (10:53:33 CET)
Background: The Balaruc-les-Bains’ thermal mud was found to be colonized predominantly by microorganisms, with cyanobacteria constituting the primary organism in the microbial biofilm observed on the mud surface. The success of cyanobacteria in colonizing this specific ecological niche can be explained in part by their taxa-specific adaptation capacities, and also the diversity of bioactive natural products that they synthesize. This array of components has physiological and ecological properties that may be exploited for various applications.
ARTICLE | doi:10.20944/preprints202007.0239.v1
Online: 11 July 2020 (10:23:28 CEST)
Our understanding and theoretical interpretation of observations in astrophysics and cosmology depends on our knowledge of the fundamental constants and their possible dependence on time and space. Atomic spectroscopy and radio astronomy give important information on the validity and stability of the fundamental constants. The possible dependence of the fine structure constant alpha on time and spatial direction is an active topic of research.Period doubling is a universal property of nonlinear dynamical systems, and the doubling is exact in principle. The value of the elementary charge squared can be calculated by the period doubling process from the Planck charge and thereby the value of alpha.If ‘old’ and ‘new’ electrons are identical, then the Planck charge, i.e. a set of natural constants, has remained constant over time. In this article we show that the value of alpha calculated from the Planck charge is 0.007 % smaller than the current accepted value of alpha.
ARTICLE | doi:10.20944/preprints202005.0374.v2
Subject: Life Sciences, Other Keywords: natural cosmetics; organic cosmetics; green cosmetics; cosmetology; certification
Online: 27 May 2020 (05:02:20 CEST)
The market of natural and organic cosmetics has been growing in last decades. The increase in interest in this type of product is a consequence of the concern that consumers have been presenting in relation to the environment and health. In addition to the appreciation the use of sustainable ingredients in cosmetic formulations, the consumers are also concerned about pollution caused by the use of plastics, which leads industries to reinvent themselves and rethink about the composition of packaging. The factor that most drives the purchase of natural and organic cosmetics is the fact that the consumer, in addition to contributing to the preservation of the environment, is also using a sustainable product. The growing demand for natural and organic cosmetics results in a concern of the brands with the organic issue, with the decreased use of animal derived ingredients and with the updating the parameters required for certification of a cosmetic as natural or organic. Due to the few studies available in this area, the importance of clarifying the definitions and concepts of natural and organic cosmetics is evident, in order to contribute with accurate information for the cosmetic sector.
ARTICLE | doi:10.20944/preprints201910.0292.v1
Subject: Medicine & Pharmacology, Nursing & Health Studies Keywords: maternal death; marginalized community; flood; natural disaster; Bangladesh
Online: 27 October 2019 (03:23:48 CET)
The study explored the community perception of maternal deaths influenced by natural disaster, practice of maternal complications during natural disaster among the rural population in Bangladesh. It also explored the challenges faced by the community for providing health care and referring the complicated pregnant mothers during disaster. Three focus group discussions (FGDs) and eight in-depth interviews (IDIs) were conducted in the marginalized rural communities in the flood-prone Khaliajhuri sub-district, Netrakona district, Bangladesh. Flood is one of the major risk factors for influencing maternal death. Pregnant mothers seriously suffer from maternal complication, lack of antenatal checkup and even any doctor during flood. During the time of delivery, it is difficult to find even a skilled attendant and referring the patient with delivery complications to the healthcare facility. Boat is the only mode of transport. Majority maternal deaths occur on the boats during transfer from the community to the hospital. The rural people feel that the maternal deaths influenced by natural disaster are the natural phenomena. It needs some pre-preparation to support pregnant women during the disaster. There is unawareness of maternal health, related care and complications during disaster among the local health service providers and volunteers.
ARTICLE | doi:10.20944/preprints201902.0258.v2
Subject: Medicine & Pharmacology, Nutrition Keywords: alcohol; natural wine; blood alcohol content; breathalyzer; pesticides
Online: 4 April 2019 (11:23:33 CEST)
Different alcoholic beverages can have different effects on blood alcohol concentrations (BAC) and neurotoxicity even if equalized for alcohol content by volume. Anecdotal evidence suggested that natural wine is metabolized differently from conventional wines. This triple-blind study compared the BAC of 55 healthy male subjects after consuming the equivalent of 2 units of alcohol of a natural or conventional wine over 3 mins in two separate sessions one week apart. BAC was measured using a professional breathalyzer every 20 mins after consumption for 2 hrs. The BAC curves in response to the two wines diverged significantly at twenty minutes, at forty minutes and also at their maximum concentrations (peaks), with the natural wine inducing a lower BAC than the conventional wine (T20 0.40 vs. 0.46 [p<0.0002], T40 0.49 vs. 0.53 [p<0.0015], peak 0.52 vs. 0.56 [p<0.0002]). These differences are likely related to the development of different amino acids and antioxidants in the two wines during their production. This in turn may affect the kinetics of alcohol absorption and metabolism. Other contributing factors may also include pesticide residues, differences in dry extract content and the use of indigenous or selected yeasts. Further studies are needed to fully understand why natural wines are metabolized differently from conventional wines.
ARTICLE | doi:10.20944/preprints201811.0561.v1
Subject: Keywords: cheminformatics, drugs, drug-likeness, drug discovery, natural products
Online: 23 November 2018 (13:56:32 CET)
We discuss further details on the concepts of “drug-likeness”, “lead-likeness”, and “natural product-likeness”. The discussion will first focus on natural products as drugs, then a discussion of previous studies in which the complexities of the scaffolds and chemical space of naturally occurring compounds have been compared with synthetic, semi-synthetic compounds and FDA-approved drugs. This is followed by guiding principles for designing “drug-like” natural product libraries for lead compound discovery purposes. We end up by presenting a tool for measuring “natural product-likeness” of compounds and a brief presentation of machine learning approaches and a binary quantitative structure-activity relationship (QSAR) for classifying drugs from non-drugs and natural compounds from non-natural ones, respectively.
ARTICLE | doi:10.20944/preprints201808.0281.v1
Subject: Engineering, Other Keywords: natural gas; distribution; pipeline network; friction; hydraulics resistance
Online: 15 August 2018 (16:22:35 CEST)
Here is shown method for the hydraulic solution of a looped gas-pipeline networks. Calculation of presented network is done according to principles of Hardy Cross method. The optimization was carried out by iteration of the pipes diameters, node consumptions are known and flow velocities through pipes have to stand below certain values. Accent is on determination of appropriate friction factor, and on selection of representative equation for natural gas flow under presented conditions in the network. Inappropriate usage of friction factor, equally as inappropriate usage of gas flow equation can lead to inaccurate final results. Here is shown new facts in comparison to previous calculation of gas distribution network in Kragujevac, Serbia which is done in 1994. After the implementation, measurements in situ have performed, and real measured values deviate from calculated. Causes for these errors are investigated, and improved and more accurate procedure is shown.
REVIEW | doi:10.20944/preprints201807.0486.v1
Subject: Medicine & Pharmacology, Other Keywords: sepsis; dysregulation; adaptation; evolution; natural selection; medical reversal
Online: 25 July 2018 (13:19:58 CEST)
For decades, sepsis research has been motivated by the idea of a dangerous overreaction of the immune system in sepsis. But is it true that the response to sepsis is dysregulated? This review surveys the history of sepsis trials and found that evidence for dysregulation does not exist in many of the physiologic mechanisms of sepsis. It is time to consider the alternative hypothesis, that sepsis traits are often functional, and do more harm than good. This review discusses the implications of this perspective for the future of sepsis research
ARTICLE | doi:10.20944/preprints201807.0292.v1
Subject: Life Sciences, Other Keywords: bread wheat, biochar, grain yield, natural water extracts
Online: 16 July 2018 (14:38:03 CEST)
Bread wheat (Triticum aestivum L.) is staple of Pakistani people. However, its yield at farmer field is low as compared with its genetic potential. Integration of various crop and soil management strategies might be an option to enhance wheat productivity at farmer field. This 2-year experiment was conducted to check the influence of combine application of natural plant water extracts and biochar on the productvity of wheat during the winter season of 2015-16. The experiment consisted of seven treatment viz. (1) control (2) application of biochar (0.18 kg pot-1) alone, (3) application of sorghum water extract (SWE) alone, (4) application of moringa water extract (MWE) alone, (5) application of biochar + SWE, (6) application of biochar + MWE, (7) application of biochar + SWE+MWE. The results revealed that application of both crop water extracts in combination with biochar improved the growth and grain yield of wheat. Use of MWE in combination with biochar enhanced the grain weight, grain number and grain yield of wheat by 44, 14, and 24%, respectively than the control treatment. In crux, use of MWE in combination with biochar might be a viable option to improve the productivity of bread wheat.
ARTICLE | doi:10.20944/preprints201711.0065.v1
Subject: Chemistry, Food Chemistry Keywords: betanin; natural red; pigment; betalain; Opuntia; beet root
Online: 10 November 2017 (04:56:22 CET)
Sourced so far mostly from beet root juice, betanin is a red-violet natural colorant increasingly used by the food, beverage and nutraceutical industries. We provide an updated bioeconomy perspective into a valued betacyanin whose supply and applications, we argue in this study, will rapidly expand.
ARTICLE | doi:10.20944/preprints201710.0145.v1
Subject: Chemistry, Food Chemistry Keywords: Opuntia ficus-indica; nutraceutical; betanin; pectin; natural colorant
Online: 23 October 2017 (03:57:04 CEST)
The integral extraction via microwave-assisted hydrodiffusion and hydrodistillation of water-soluble bioproducts contained in the peel of Opuntia ficus-indica white and red cultivars harvested in Sicily, affords red and stable aqueous extracts mostly containing valued betanin, pectin and biophenols. Potentially useful as nutraceutical products, these aqueous extracts are a source of valued ingredients in high demand for a number of important food, cosmetic, beverage and nutraceutical applications.
ARTICLE | doi:10.20944/preprints201704.0180.v1
Subject: Arts & Humanities, Linguistics Keywords: natural language, unigram entropy, entropy rate, learnability, expressivity
Online: 27 April 2017 (15:54:13 CEST)
The choice associated with words is a fundamental property of natural languages. It lies at the heart of quantitative linguistics, computational linguistics, and language sciences more generally. Information-theory gives us tools at hand to measure precisely the average amount of choice associated with words—the word entropy. Here we use three parallel corpora—encompassing ca. 450 million words in 1916 texts and 1259 languages—to tackle some of the major conceptual and practical problems of word entropy estimation: dependence on text size, register, style and estimation method, as well as non-independence of words in co-text. We present three main results: 1) a text size of 50K tokens is sufficient for word entropies to stabilize throughout the text, 2) across languages of the world, word entropies display a unimodal distribution that is skewed to the right. This suggests that there is a trade-off between the learnability and expressivity of words across languages of the world. 3) There is a strong linear relationship between unigram entropies and entropy rates, suggesting that they are inherently linked. We discuss the implications of these results for studying the diversity and evolution of languages from an information-theoretic point of view.
REVIEW | doi:10.20944/preprints201607.0041.v1
Subject: Earth Sciences, Environmental Sciences Keywords: natural gas hydrate; five forces model; intuitional arrangement
Online: 15 July 2016 (11:33:39 CEST)
Natural gas hydrate, also known as combustible ice and mainly composed of methane, it is identified as the potential clean energy in the 21th century. Due to its large reserves, gas hydrate can ease problems caused by energy resource shortage and has gained attention around the world. In this paper, we focus on the exploration and development of gas hydrate as well as discussing its status and future development trend in China and abroad, then we analyze its opportunities and challenges in China from four aspects: resource, technology, economy and police with five forces model and PEST method. The results show, China has abundance gas hydrate resource; however the backward technologies and inadequate investment has seriously hindered the future development of gas hydrate, so China should establish relevant cooperation framework and intuitional arrangement to attract more investment as well as breaking through technical difficulties to make gas hydrate commercialization as soon as possible.
ARTICLE | doi:10.20944/preprints202209.0097.v1
Subject: Arts & Humanities, Other Keywords: conservation; sacred groves; natural sites; heritage tourism; local communities
Online: 7 September 2022 (03:30:51 CEST)
The purpose of this study is to examine the potential of the sacred groves and natural sites for tourism in local communities in Nigeria. Three communities in Inyi town were chosen as case studies: Umuome, Enugwu-Inyi, and Alum, using a purposive sampling technique. An ethnographic data collection method was adopted, using in-depth interviews and direct observation. Grove locations were located and mapped using a geographic information system (GIS) throughout the research area. The secondary data was obtained from scholarly journals. The findings showed that the sacred groves and natural sites had exceptional value for cultural and eco-tourism. There are not many works that have studied the tourism dimension of the sacred groves in the study domain. The implication of the study is that tourism aids in the preservation of the sites' integrity. The research helps the government and policymakers to adopt a policy that promotes tourism in the sacred groves. This research is important to researchers at the universities and research institutions that are seeking to carry out a related research. Conclusively, utilizing the area for tourism is a superior conservation alternative.
ARTICLE | doi:10.20944/preprints202208.0198.v1
Subject: Social Sciences, Education Studies Keywords: thematic analysis; Indonesia; physics education research; natural language processing
Online: 10 August 2022 (09:42:09 CEST)
Emergent physics education research (PER) literatures have been disseminated through academic publications within the community. The growing body of literatures over years challenge Indonesian PER scholars to understand how the research community has been progressed and what possible future work that should be emphasized. Nevertheless, previous traditional method of thematic analysis performed serious limitation when the number of PER literatures exponentially increased. Dealing with this large volume of publications, one of the machine learning studies namely natural language processing (NLP) was employed in this study to automate our thematic analysis among Indonesian PER literatures that are still limited to be explored. One of the well-known NLP algorithm, latent Dirichlet allocation (LDA), has been performed to extract Indonesian PER topics and their associated development between 2014 and 2021. A total of 852 papers (~ 4 to 8 pages each) were collectively downloaded from five international conference proceedings organized by Indonesian PER researchers. Before their topics were modeled through LDA algorithm, our data corpus should be previously preprocessed through several common procedures of established NLP studies. Findings revealed that LDA has thematically quantified Indonesian PER topics and described their distinct development over certain period. The identified topics from this study demonstrated that Indonesian PER community has established robust development in eight distinctive topics to the present. They begin with initial interest in focusing research on physics laboratory and following the research based instruction in the late 2015. Indonesian PER scholars sustained to study continuous topic on 21st century skill until 2019 which gave way to a focus on developing relevant educational technology to address several forms of students’ performance including scientific literacy and problem solving. There is still lack of Indonesian PER literatures that have been attempted to address qualitative aspects of physics teaching and learning.
ARTICLE | doi:10.20944/preprints202204.0016.v1
Subject: Engineering, Electrical & Electronic Engineering Keywords: natural language processing; risk management; transmission lines; unstructured data
Online: 4 April 2022 (11:26:15 CEST)
Risk management of electric power transmission lines requires knowledge from different areas such as environment, land, investors, regulations, and engineering. Despite the widespread availability of databases for most of those areas, integrating them into a single database or model is a challenging problem. Instead, in this paper, we use a single source, the Brazilian National Electric Energy Agency’s (ANEEL) weekly reports, which contains decisions about the electrical grid, comprising most of the areas. Since the data is unstructured (text), we employed NLP techniques such as stemming and tokenization to identify keywords related to common causes of risks provided by an expert group on energy transmission. Then, we used models to estimate the probability of each risk. Our results show that we were able to estimate the probability of 97 risks out of 233.
ARTICLE | doi:10.20944/preprints202111.0322.v1
Subject: Medicine & Pharmacology, Pharmacology & Toxicology Keywords: binding agent; disintegrating agent; natural polymer; mucilage; Coccinia grandis
Online: 18 November 2021 (11:14:32 CET)
Mucilage from Coccinia grandis was extracted, isolated by maceration technique and precipitated, accordingly. The mucilage was evaluated for its physicochemical, binding, and disintegrant properties in tablets using paracetamol as a model drug. The crucial physicochemical properties such as flow properties, solubility, swelling index, loss on drying, viscosity, pH, microbial load, cytotoxicity were evaluated and the compatibility was analysed using sophisticated instrumental methods (TGA, DTA, DSC, and FTIR). The binding properties of the mucilage were used at three different concentrations and compared with starch and PVP as standard binders. The disintegrant properties of mucilage were used at two different concentrations and compared with standard disintegrants MCCP, SSG, and CCS. The wet granulation technique was used for the preparation of granules and was evaluated for the flow properties. The tablets were punched and evaluated for their hardness, friability, assay, disintegration time, in vitro dissolution profiles. In vitro cytotoxicity study of the mucilage was performed in human embryonic kidney (HEK) cell line using cytotoxic assay by MTT method. The outcome of the study indicated that the mucilage had good performance when compared with starch and PVP. Further, the mucilage acts as a good disintegrant than MCCP, SSG and CCS to paracetamol tablets. Moreover, the in vitro cytotoxicity evaluation results demonstrated that the mucilage is non-cytotoxic to human cells and is safe.
REVIEW | doi:10.20944/preprints202111.0089.v1
Subject: Engineering, Industrial & Manufacturing Engineering Keywords: Latent Dirichlet Allocation; Natural Language Processing; Condition based maintenance
Online: 3 November 2021 (14:59:02 CET)
In the field of industrial process monitoring, more and more interest is being shown in specific process categories. These include time-varying processes, that is, those processes whereby the response one receives as output from the system depends on when the input signal is sent into it. There are many reasons for this process variability and such contexts are not always analyzed with this operational characteristic at their core. At the same time, interest in certain categories of techniques is also becoming more prominent, to meet certain application needs. Among these, clustering and unsupervised techniques in general are gaining ground. This is largely due to the difficulty of finding fault data with which to train, for example, supervised models. On the other hand, the clustering technique, on which this contribution focuses, also makes it possible to compensate for the lack of complete knowledge of the structure of the process itself. With these two considerations in mind, this contribution proposes a literature review on the topic of clustering applied in time-varying contexts, in the maintenance field. The aim is to present an overview of the main fields of study, the role of clustering in this context and the main clustering techniques used.
ARTICLE | doi:10.20944/preprints202111.0001.v1
Subject: Earth Sciences, Environmental Sciences Keywords: Life cycle analysis; biomethane; diesel; natural gas; SimaPro; Ecoinvent
Online: 1 November 2021 (10:14:08 CET)
The Life Cycle Analysis (LCA) was used to assess the impact of biomethane plant of the “La Católica” in Pedregal-Majes-Arequipa farm, fed with cow manure and holding a production of 60 Nm3/day of purified biogas. Life cycle inventory, impact assessment and interpretation were performed. The functional unit established was 1 MJ of energy produced; the study was modeled with SimaPro software, Ecoinvent Database and ReCiPe Midpoint (H) impact assessment methodology, according to the impact categories of climate change and fossil resource depletion. The impact analysis was limited to the Well to Tank (WTT) approach, which involves feedstock transport, substrate mixed, anaerobic digestion, biogas purification, storage and injection of the fuel into transport vehicles. The digestion process generated the highest amount of CO2 emissions (1.79E-02 kg CO2 eq/MJ-biomethane) and the highest depletion of fossil resources (6.58E-03 kg oil eq/MJ-biomethane), compared to the other fuel production, due to energy consumption and transport infrastructure. Biomethane was then compared to fossil fuels, resulting in natural gas generating the least amount of CO2 emissions, followed by diesel and finally biomethane. For the fossil resource depletion category, biomethane presented the lowest amount of fossil fuel consumption (1.37E-02 kg oil eq/MJ-biomethane), followed by natural gas and diesel.
ARTICLE | doi:10.20944/preprints202109.0199.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Big Five; Natural Language Processing; Personality Detection; Artificial Intelligence
Online: 13 September 2021 (09:59:25 CEST)
Personality is the most critical feature that tells us about an individual. It is the collection of the individual’s thoughts, opinions, emotions and more. Personality detection is an emerging field in research and Deep Learning models have only recently started being developed. There is a need for a larger dataset that is unbiased as the current dataset that is used is in the form of questionnaires that the individuals themselves answer, hence increasing the chance of unconscious bias. We have used the famous stream-of-consciousness essays collated by James Pennbaker and Laura King. We have used the Big Five Model often known as the five-factor model or OCEAN model. Document-level feature extraction has been performed using Google’s word2vec embeddings and Mairesse features. The processed data has been fed into a deep convolutional network and a binary classifier has been used to classify the presence or absence of the personality trait. Hold- out method has been used to evaluate the model, and the F1 score has been used as the performance metric.
ARTICLE | doi:10.20944/preprints202005.0007.v2
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Process Mining; Business Processes; Natural Language Processing; Machine Learning
Online: 3 September 2021 (10:25:25 CEST)
Communication is indispensable for today's lifestyle, and thanks to technology, millions of people can communicate as quickly as possible. The effect of this breakthrough has transformed organizations to the degree that they generate billions of emails daily to facilitate their operations. There is implicit information behind this vast corpus of human-generated content that can be mined and used for their benefit. This paper tries to address the opportunity that email logs can bring to organizations and propose an approach to discover process models by combining supervised text classification and process mining. This framework consists of two main steps, text classification, and process mining. First, Emails will be classified with supervised machine learning, and to mine, the processes fuzzy Miner is used. To further investigate the application of this framework, we also applied this framework over a real-life dataset from a case study organization.
REVIEW | doi:10.20944/preprints202108.0155.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: Cognitive Graph; Knowledge Graph; Knowledge Reasoning; Natural Language Generating
Online: 6 August 2021 (10:14:00 CEST)
The realization of the third-generation artificial intelligence (AI) requires the evolution from perceptual intelligence to cognitive intelligence, where knowledge graphs may not meet the practical needs anymore. Based on the dual channel theory, cognitive graphs are established and developed through coordinating the implicit extraction module and the explicit reasoning module as well as integrating knowledge graphs, cognitive reasoning and logical expressions, which have achieved successes in multi-hop question answering. It is desired for cognitive graphs to be widely used in advanced AI applications such as large-scale knowledge representations and intelligent responses, promoting the development of Al dramatically. This review discusses cognitive graphs systematically and elaborately, including basic concepts, generations, theories and technologies. Moreover, we try to predict the development of cognitive intelligence in the short-term future and further enlighten more researches and studies.
ARTICLE | doi:10.20944/preprints202107.0070.v1
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: sentiment analysis; deep learning; recommender system; natural language processing
Online: 2 July 2021 (15:45:36 CEST)
Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.
ARTICLE | doi:10.20944/preprints202106.0445.v1
Subject: Biology, Ecology Keywords: allelopathy; leaf litter; condensed tannins; mangrove forests; natural regeneration
Online: 16 June 2021 (12:43:27 CEST)
Kandelia obovata (Ko) and Aegiceras corniculatum (Ac) are common and dominant plant species in mangrove wetlands in South China, and distribute in the similar tidal zones along the coastline. The present study aimed to determine the allelopathic effects of leaf litter leachates (LLLs) from Ko and their purified condensed tannins (PCTs) on the germination and growth of Ac by mangrove microcosms. Replicate pots containing five different levels of LLLs and PCTs were separately prepared and propagules of Ac were placed in each treatment. Both LLLs and PCTs significantly inhibited the germination and growth of Ac, especially in high levels. The final germination rates of roots, stems, and the number of fine roots declined continuously while other growth indicators, including the lengths of fine roots, nutritive roots, the biomasses of roots, stems, leaves, increased firstly and then decreased with increasing levels. These results indicated that LLLs from the leaf litter of Ko, in particular, their PCTs exerted an inhibition effect on propagule germination and seedling growth of Ac, and the inhibitory effects were concentration dependent. This study suggested that condensed tannins from leaf litter, acting as allelochemicals, could regulate the natural regeneration of a mangrove forest.
ARTICLE | doi:10.20944/preprints202106.0180.v1
Subject: Life Sciences, Biochemistry Keywords: LAIV, Influenza, HA, IGIP, IgA, IgG, vaccine, natural adjuvant
Online: 7 June 2021 (13:03:43 CEST)
Live attenuated influenza virus (LAIV) vaccines elicit a combination of systemic and mucosal immunity by mimicking a natural infection. To further enhance protective mucosal responses, we incorporated the gene encoding the IgA-inducing protein (IGIP) into the LAIV genomes of the cold-adapted A/Leningrad/134/17/57 (H2N2) strain (caLen) and the experimental attenuated backbone A/turkey/Ohio/313053/04 (H3N2) (OH/04att). Incorporation of IGIP into the caLen background led to a virus that grew poorly in prototypical substrates. In contrast, IGIP in the OH/04att background (IGIP-H1att) virus grew to titers comparable to the isogenic backbone H1att (H1N1) without IGIP. IGIP-H1att- and H1caLen-vaccinated mice were protected against lethal challenge with a homologous virus. The IGIP-H1att vaccine generated robust serum HAI responses in naïve mice against the homologous virus, equal or better than those obtained with the H1caLen vaccine. Analyses of IgG and IgA responses using a protein microarray revealed qualitative differences in humoral and mucosal responses between vaccine groups. Overall, serum and bronchoalveolar lavage samples from the IGIP-H1att group showed trends towards increased stimulation of IgG and IgA responses compared to H1caLen samples. In summary, introduction of genes encoding immunomodulatory functions into a candidate LAIV that can serve as natural adjuvants to improve overall vaccine safety and efficacy.
ARTICLE | doi:10.20944/preprints202101.0006.v1
Subject: Social Sciences, Accounting Keywords: sustainable tourism; tourism sensitivity; tourism vulnerability; natural disaster; earthquake
Online: 4 January 2021 (10:21:51 CET)
Despite increased global interest in the impacts of natural disasters on tourism, less study executes exploring how tourism sensitivity is addressed at the destination level. Generating a link between tourism and natural disaster management is vital in places that rely heavily on tourism and are prone to natural hazards. Ranau, Sabah (Malaysia) is one of the disaster-prone tourists' destination area. Hence, this paper applies the case study of Ranau earthquake 2015 to explore tourism sensitivity towards natural disasters. A qualitative of in-depth interview is applied to acquire information needed from the Ranau tourism entrepreneurs and operators. To analyse the qualitative data, a thematic analysis is conducted. Overall findings show that tourism activity in Ranau are identified to be sensitive towards the 2015 earthquake with a significant percentage of sensitivity level on two elements. These elements are known as Source and Power. The Source element includes tourism products, size of business, development, and natural disasters management with a significant sensitivity compared to the Power element (social capital). This provides insight to the need of specific tourism system adaptation as response to the earthquake and considering the integration of natural disaster management into tourism development to enhance long term sustainability.
ARTICLE | doi:10.20944/preprints202008.0355.v1
Subject: Mathematics & Computer Science, Information Technology & Data Management Keywords: social media; unemployment; crowdsourcing; natural language processing; mental health
Online: 17 August 2020 (08:29:47 CEST)
Social media, traditionally reserved for social exchanges on the net, has been increasingly used by researchers to gain insight into different facets of human life. Unemployment is an area that has gained attention by researchers in various fields. Medical practitioners especially in the area of mental health have traditionally monitored the effects of involuntary unemployment with great interest. In this work, we compare the feedback gathered from social media using crowdsourcing techniques to results obtained prior to the advent of Big Data. We find that the results are consistent in terms of 1) financial strain is the biggest stressor and concern, 2) onslaught of depression is typical and 3) possible interventions including reemployment and support from friends and family is crucial in minimizing the effects of involuntary unemployment. Lastly, we could not find enough evidence to study effects on physical health and somatization in this work.
ARTICLE | doi:10.20944/preprints202004.0292.v1
Subject: Medicine & Pharmacology, Nutrition Keywords: Porphyra tenera; immune; clinical trial; natural killer cells; cytokines
Online: 17 April 2020 (02:15:58 CEST)
Objective: The purpose of this study was to determine if Porphyra tenera extract (PTE) has immune-enhancing effects and is safe in healthy adults. Methods: Subjects (3x103 ≤ peripheral blood leukocyte levels < 8x103 cells/μl) who met the inclusion criteria were recruited for this study. Enrolled subjects (n=120) were randomly assigned to either the PTE group (n=60) who were given 2.5 g/day of PTE (as Porphyra tenera extract) in capsule form or the placebo group (n=60) who were given crystal cellulose capsules with the identical appearance, weight, and flavor as the PTE capsules for 8 weeks. Outcomes were assessed by measuring natural killer cell (NK-cell) activity, cytokines, and upper respiratory infection (URI), and safety parameters were assessed at baseline and 8 weeks. Results: Compared to baseline, NK cell activity (%) increased for all effector cell to target cell ratios in the PTE group after 8 weeks, but there were no changes in the placebo group (p<0.1). Subgroup analysis of 101 subjects without an URI revealed that NK-cell activity in the PTE group tended to be increased for all E:T ratios (E:T=12.5:1 p=0.068; E:T=25:1 p=0.036; E:T=50:1 p=0.081) compared to the placebo group. There was a significant difference between these two groups for the E:T=25:1 ratio, which increased from 20.3±12.0% at baseline to 23.2±12.4% after 8 weeks in the PTE group (p=0.036). There was no significant difference in levels of cytokines between these two groups. Conclusions: PTE supplementation appears to enhance immune function by improving NK-cell activity without adverse effects in healthy adults.
ARTICLE | doi:10.20944/preprints202004.0214.v1
Subject: Mathematics & Computer Science, Artificial Intelligence & Robotics Keywords: bot detection; machine learning; natural language processing; computation linguistics
Online: 13 April 2020 (13:08:26 CEST)
In this experiment, an efficient and accurate network of detecting automatically disseminated (bot) content on social platforms is devised. Through the utilisation of parallel convolutional neural network (CNN) which processes variable n-grams of text 15, 20, and 25 tokens in length encoded by Byte Pair Encoding (BPE), the complexities of linguistic content on social platforms are effectively captured and analysed. With validation on two sets of previously unexposed data, the model was able to achieve an accuracy of around 96.6% and 97.4% respectively — meeting or exceeding the performance of other comparable supervised ML solutions to this problem. Through testing, it is concluded that this method of text processing and analysis proves to be an effective way of classifying potentially artificially synthesized user data — aiding the security and integrity of social platforms.
REVIEW | doi:10.20944/preprints202003.0427.v1
Online: 29 March 2020 (08:26:36 CEST)
The coronavirus COVID-19 epidemic has wreaked havoc on inhabitants of earth killing thousands of humans from more than 150 countries. The epidemic has put a number of countries under complete lockdown and the deadly situation is still prevailing around the globe. Vaccines have been long known as the most effective means of preventing viral infections. However, the lack of vaccines against COVID-19 has further worsened the situation. In this time of health crisis, it is the duty of scientific research community to provide alternative, effective and affordable strategies to vaccinate human bodies against viral infections-COVID-19 based on focused experimental approaches. Growing evidence suggests that certain natural foods and lifestyle changes have potential to optimize immune functions against viral infections including improving defense function, resistance towards invading pathogens, while maintaining self-tolerance. Boosting immune system gives an edge in fending off viruses and staying healthy. This review presents the six smart steps to add to your to-do list which let the inner work of immunity take place against viral infections-COVID-19 by dissolving the powers of disease and illness. Many of these factors are associated in their functions to improve or properly maintain the immune function such as promoting anti-inflammatory functions, inhibiting pro-inflammatory mediators, modulating cell-mediated immunity, altering the antigen-presenting cellular functions as well as promoting communication between the innate and adaptive immune responses. Thus, a scientific illustration of boosting the immune system by proper sleep, moderate exercise, avoiding stress, utilizing vitamins enriched foods, intake of more water and use of fruits and vegetables will hopefully help the community to deal with the coronavirus by vaccinating the human systems naturally.
Subject: Engineering, Industrial & Manufacturing Engineering Keywords: Hydrostatic; Blade guides; Bandsaw; Diamond blade; Natural stone; Sawing
Online: 24 February 2020 (12:23:56 CET)
In a bandsaw machine the blade guides provide additional stiffness and help to align the blade near the cutting region. Typically these are either in form of blocks made of carbide or ceramics or as sealed bearings. Abrasive particles, generated while cutting hard and brittle materials like natural stones, settle between the contact surfaces of the guides and the blade causing wear and premature failure. The hydrostatic guide system as presented in this work, is a contactless blade guiding method that uses force of several pressurized water jets to align the blade to the direction of the cut. For this investigation, cutting tests were performed on a marble block using a galvanic diamond coated bandsaw blade with the upper roller guides replaced by hydrostatic guides. The results show that the hydrostatic guides help to reduce the passive force while cutting to a constant near zero in contrast to the traditional guides. This also resulted in reduced surface roughness of the stone plates that were cut indicating a reduction in lateral vibration of the band. Additionally, it has also been shown that using hydrostatic guides the bandsaw blade can be tilted to counter the bandsaw drift opening opportunities for further research in active alignment control. This original research work has shown that the hydrostatic guide systems are capable of replacing and in fact perform better than state of the art bearing or block guides particularly for stone cutting applications.
REVIEW | doi:10.20944/preprints202001.0324.v1
Subject: Chemistry, Medicinal Chemistry Keywords: natural products; sirtuin; drug discovery; epigenetics; structure–activity relationship
Online: 27 January 2020 (09:21:29 CET)
Natural products have been used for the treatment of human diseases since ancient history. Over time, due to the lack of precise tools and techniques for the separation, purification, and structural elucidation of active constituents in natural resources there has been a decline in financial support and efforts in characterization of natural products. Advances in the design of chemical compounds and the understanding of their functions is of pharmacological importance for the biomedical field. However, natural products regained attention as sources of novel drug candidates upon recent developments and progress in technology. Natural compounds were shown to bear an inherent ability to bind to biomacromolecules and cover an unparalleled chemical space in comparison to most libraries used for high-throughput screening. Thus, natural products hold a great potential for the drug discovery of new scaffolds for therapeutic targets such as Sirtuins. Sirtuins are Class III histone deacetylases that have been linked to many diseases such as Parkinson`s disease, Alzheimer’s disease, type II diabetes, and cancer linked to aging. In this review, we examine the revitalization of interest in natural products for drug discovery and discuss natural product modulators of Sirtuins that could serve as a starting point for the development of isoform selective and highly potent drug-like compounds.
Subject: Engineering, Mechanical Engineering Keywords: energy-flux-vector; porous cavity; natural convection; wavy-wall
Online: 17 October 2019 (11:00:16 CEST)
The study utilizes the energy-flux-vector method to analyze the heat transfer characteristics of natural convection in a wavy-wall porous square cavity with a partially-heated bottom surface. The effects of the modified Darcy number and modified Rayleigh number on the energy-flux-vector distribution and mean Nusselt number are examined. The results show that when a low modified Darcy number with any value of modified Rayleigh number is given, the recirculation regions are not formed in the energy-flux-vector distribution within the porous cavity. Therefore, a low mean Nusselt number is obtained. The recirculation regions do still not form and thus the mean Nusselt number has a low value when a low modified Darcy number with a high modified Rayleigh number is given. However, when the values of the modified Darcy number and modified Rayleigh number are high, the energy flux vectors generate recirculation regions and thus a high mean Nusselt number is obtained.
REVIEW | doi:10.20944/preprints201906.0029.v1
Subject: Chemistry, Organic Chemistry Keywords: antitumour compounds; marine natural products; bioactivity; cytotoxicity; marine invertebrates
Online: 4 June 2019 (12:55:33 CEST)
Recent advances in sampling and novel techniques in drug synthesis and isolation have promoted the discovery of anticancer agents from marine organisms to combat this major threat to public health worldwide. Bryozoans, filter-feeding, sessile aquatic invertebrates often characterized by a calcified skeleton, are an excellent source of pharmacologically interesting compounds including well-known chemical classes such as alkaloids and polyketides. This review covers the literature for secondary metabolites isolated from marine cheilostome and ctenostome bryozoans that have shown potential as cancer drugs. Moreover, we highlight examples such as bryostatins, the most known class of marine-derived compounds from this animal phylum, which is advancing through anticancer clinical trials due to their low toxicity and antineoplastic activity. The bryozoan antitumour compounds discovered until now show a wide range of chemical diversity and biological activities. Therefore, more research focusing on the isolation of secondary metabolites with potential anticancer properties from bryozoans and other overlooked taxa covering wider geographic areas is needed for an efficient bioprospecting of natural products.