The deep neural network - BERT model (Bidirectional Encoder Representations from Transformers) and the stocks cumulative abnormal return is used in this article to analyze the sentiment of financial texts. The proposed approach, unlike those used so far, does not require the creation of dictionaries, takes into account the broad context of words and their meaning in financial texts, eliminates the problem of ambiguity of words in various contexts, does not require manual labelling of data and is free from the subjective assessment of the researcher. The sentiment of financial texts in the meaning presented in this paper is directly related to the market reaction to the information contained in these texts. For texts belonging to one of the two classes (positive or negative) with the highest probability the BERT model gives the results of predictions with a precision level of 62.38% for the positive class and 55% for the negative class. The results at this level can be used in event study, market efficiency research, investment strategy development or support of investment analysts using fundamental analysis.

Texas ranks first in number of natural hazard fatalities in the United States (U.S.). Based on data culled from the National Climatic Data Center databases from 1959 to 2016, the number of hydrometeorological fatalities in Texas have increased over the 58-year study period, but the per capita fatalities have significantly decreased. Spatial review found that flooding is the predominant hydrometeorological disaster in a majority of the Texas counties located in “Flash Flood Alley” and accounts for 43% of all hydrometeorological fatalities in the state. Flooding fatalities are highest on “Transportation Routes” followed by heat fatalities in “Permanent Residences”. Seasonal and monthly stratification identifies Spring and Summer as the deadliest seasons, with the month of May registering the highest number of total fatalities dominated by flooding and tornado fatalities. Demographic trends of hydrometeorological disaster fatalities indicated that approximately twice as many male fatalities occurred during the study period than female fatalities, but with decreasing gender disparity over time. Adults are the highest fatality risk group overall, children most at risk to die in flooding, and the elderly at greatest risk of heat-related death.

Flavonoids are widely recognized as natural polydrugs, given their anti-inflammatory, antioxidant, sedative and antineoplastic activities. Recently, different studies have shown that flavonoid have the potential to inhibit BET bromodomains. Previous reports suggest that flavonoids are putative inhibitors of the ZA channel due to their orientation and interactions with P86, V87, L92, L94 and N140. Herein, a comprehensive characterization of the binding mode of the biflavonoid amentoflavone and fisetin is discussed. To this end, both compounds were docked with BRD4 using four docking programs. Results were post-processed with protein-ligand interaction fingerprints. To gain further insights into the binding mode of the two natural products, docking results were further analyzed with molecular dynamics. Results showed that amentoflavone makes numerous contacts in the ZA channel, as previously described for flavonoids and kinase inhibitors. It was also found that amentoflavone can potentially make contacts with non-canonical residues for BET inhibition. Most of these contacts were not observed with fisetin. Based on these results, amentoflavone was tested for BRD4 inhibition, showing activity in the micromolar range. This work may serve as basis for scaffold optimization and further characterization of flavonoids as BET inhibitors.

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents is growing enormously in recent years through instant messaging, social networking posts, blogs, online portals, and other digital platforms. Unfortunately, the misapplication of technologies has boosted with this rapid growth of online content which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier `tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

The objective is to study the local perceptions on the conservation of wild chimpanzees (Pan troglodytes verus) in the Réserve Naturelle Communautaire de Dindéfélo (RNCD), southeast of Senegal, to design specific actions to improve conservation management. We conducted 338 semi-structured interviews in three main villages of RNCD. Three-fourths of the population were farmers. Of those interviewed, 29% received elementary education. Two of the three villages participated in a project to plant trees as fences. On average, 66% of the respondents were animists. Of the respondents who were afraid to see a chimpanzee, 68% answered because they attack. Seventy-seven percent washed their clothes in the forest river because there was more water than in the village wells. Of the interviewees who threw the old clothes into the forest, 50% did it due to tradition. Ninety-six percent of respondents stated that chimpanzees do not feed from their crops. The main problems of the locals with the Reserve were lack of water and basic resources and not been allow to cut trees in protected areas. There were significant relationships between education (1 relationship), environmental project (4 relationships) and animism (11 relationships) with local perceptions. The 93% of the respondents who had the traditional belief that “if the old clothes were burnt, children would become sick” feel fear of chimpanzees, while those who did not have this animistic belief the 6% are afraid (χ² = 1.57, P < 0.02). These local perceptions allow us to design specific course of action to improve chimpanzee conservation and sustainable coexistence in this complex period of the Anthropocene.

Increased gut permeability (leaky gut) with increased translocation of Gram-negative bacteria plays a role in the gut-brain axis through effects on systemic immune-inflammatory processes. Deficit schizophrenia is characterized by an immune-inflammatory response combined with a deficit in natural IgM antibodies to oxidative specific epitopes (OSEs), which are a first line defense against bacterial infections. This study measured plasma IgA/IgM responses to 5 Gram-negative bacteria in association with IgM responses to malondialdehyde (MDA) and azelaic acid in 80 schizophrenia patients (40 with the deficit syndrome and 40 without) and in 38 healthy controls.Deficit schizophrenia was characterized by significantly increased IgA responses to Hafnei alvei, Pseudomonas aeruginosa, Morganella morganii and Klebsiella pneumoniae as compared with non-deficit schizophrenia. The presence of deficit schizophrenia was highly predicted by increased IgA responses to Pseudomonas putida and IgM responses to all 5 Gram-negative bacteria and lowered natural IgM to MDA and azelaic acid with a bootstrap area under the ROC curve of 0.960 (2000 random curves). A large proportion of the variance (41.5%) in the PANSS negative score was explained by the regression on IgA responses to K. pneumoniae and IgM responses to the 5 enterobacteria coupled with lowered IgM antibodies to azelaic acid. There were significant associations between IgA levels to Gram-negative bacteria and Mini Mental State Examination, Boston naming test, Verbal Fluency and Word List Memory test scores.These findings provide further evidence that deficit schizophrenia is a distinct phenotype of schizophrenia, which is characterized by an increased impact of Gram-negative commensal bacteria coupled with a deficit in natural IgM, pointing to aberrations in B1 cells. It is concluded that increased bacterial translocation and deficits in the compensatory immune-regulatory system (CIRS) may drive negative symptoms and neurocognitive impairments, which are hallmarks of deficit schizophrenia.

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.

Abstract: The promotion of sustainable packaging is part of the European Green Deal and plays a key role in the EU’s social and political strategy. One option is the use of renewable resources and biomass waste as raw materials for polymer production. Lignocellulose biomass from annual and perennial industrial crops and agricultural residues are a major source of polysaccharides, proteins, and lignin, and can also be used to obtain plant-based extracts and essential oils. Therefore, these biomasses are considered as potential substitute for fossil-based resources. Here, the status quo of bio-based polymers is discussed and evaluated in terms of properties related to packaging applications such as gas and water vapor permeability as well as mechanical properties. So far, their practical use is still restricted due to lower performance in fundamental packaging functions that directly influence food quality and safety, the length of shelf life and thus the amount of food waste. Besides bio-based polymers, this review focuses on plant extracts as active packaging agents. Incorporating extracts of herbs, flowers, trees, and their fruits is inevitable to achieve desired material properties that are capable to prolong the food shelf life. Finally, the adoption potential of packaging based on polymers from renewable resources is discussed from a bioeconomy perspective.

Aiming to improve the flame retardancy performance of natural rubber (NR), we developed a novel flame retardant synergistic agent through grafting of MCM-41 to graphene oxide (GO)，named as GO-NH-MCM-41，as an assistant of intumescent flame retardants (IFR). The structure of GO-NH-MCM-41 was characterized by FTIR, TEM and SEM tests, which confirmed that a fine grafting had been applied between GO and MCM-41. The flame retardancy of NR/IFR/GO-NH-MCM-41 composites was evaluated by limited oxygen index (LOI), UL-94 and cone calorimeter test. The LOI value of NR/IFR/GO-NH-MCM-41 reached to 26.3%; the UL-94 ratings improved to V-0 rating. Moreover, the addition of GO-NH-MCM-41 obviously decreased the peak heat release rate (PHRR) and the total heat release (THR) of the natural rubber composites. And the addition of GO-NH-MCM-41 increased the thickness of char residue. The images of SEM indicated the char residue were more compact and continuous. The degradation of GO-NH-MCM-41 based NR composites completed with a mass loss of 35.57% at 600 ℃. The tensile strength and the elongation at break of NR/IFR/GO-NH-MCM-41 composites were 13.9 MPa and 496.7%, respectively. The results of rubber process analyzer (RPA) reached the maximum value, probably due to a better network of the fillers in the matrix.

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.

In December 2019, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in the province of Wuhan, China. Since then, it has spread worldwide with new mutations being reported. We performed genomic analysis to identify the changes in genetic diversity of SARS-CoV-2 between December 2019 and November 2020, and through molecular surveillance, we monitored the mutations that could be involved in viral fitness. We analyzed 2,213 complete genomes from 6 geographical regions worldwide, which were downloaded from GenBank and GISAID databases. Although SARS-CoV-2 presented low genetic diversity, there has been an increase over time, with the presence of several hotspot mutations throughout its genome. We identified 7 frequent mutations that resulted in non-synonymous substitutions (dN). Two of them, C14408T>P323L and A23403G>D614G, located in the nsp12 and Spike protein, respectively, emerged early in the pandemic and showed a considerable increase in frequency over time. Two other mutations, A1163T>I120F in nsp2 and G22992A>S477N in the Spike protein emerged recently and have spread in Oceania and Europe. Continuous molecular surveillance of SARS-CoV-2 will be necessary to detect and describe the transmission dynamics of new variants of the virus with clinical relevance. This information is important to improve programs to control the virus.

Social media gives researchers an invaluable opportunity to gain insight into different facets of human life.Researchers put a great emphasis on categorizing the socioeconomic status (SES) of individuals to help predict various findings of interest. Forum uses, hashtags and so on are common tools of conversations grouping. On the other hand, crowdsourcing is a concept that involves gathering intelligence to group online user community based on common interest. This paper provides a mechanism to look at writings on social media and group them based on their academic background. We build upon earlier work where we analyzed online forum posts from various geographical regions in the USA and Canada and characterized the readability scores of such users. Specifically, we collected 1000 tweets from the members of the US Senate and computed the Flesch-Kincaid readability score for the Senators. Comparing the Senators’ tweets to the ones from average citizens, we note the following. 1) US Senators’ readability based on their tweets rate is much higher affirming the gap between the academic performance of US Senators and their average citizen, and 2) the immense difference among average citizen’s score compared to those of US Senators is attributed to the wide spectrum of academic attainment.

Though innovations for sustainable management of natural resources have emerged over time, the rising demand for nature based health solutions and integration of endemic flora into global value chains could have adverse impacts on ecosystems. The ecological risks in the exploitation of wild medicinal plant resources are exacerbated by a myriad of agrotechnological risks and challenges that highly constrain their domestication. Successful exploitation and commercialization of medicinal plants thus require a clear understanding of their demand and production systems or value chain analysis. Accordingly there is need for innovative approaches towards their integration into global value chains. Since quality and safety, traceability, certification, as well as, consumer tastes and preferences are critical drivers in purchasing decisions by global consumers, they are inadvertently exploited to weaken Indigenous knowledge (IK), undermine common property rights and entrench value chains that favour a few elite buyers. This tend to create pervasive incentives for overexploitation of medicinal plant resources and environmental degradation. Potential solution lies in the recognition of drivers of vulnerability to environmental degradation and the innovative use of policy bricolage, feedback loops and interactions between knowledge, power and agency on one hand, and collective action and property rights institutions on the other hand. We conceptualise a framework that can mediate a transformational agenda and enhance systematic understanding of sustainability lenses in endemic medicinal plant resources value chains. This could in turn strengthen IK, enhance collective action and promote participation of local actors with positive impact on the utilisation and integration of endemic medicinal plant resources into global value chains.

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.

Hydrogen gas is seeping from the sedimentary basin of São Franciso, Brazil. The seepages of H₂ are accompanied by helium whose isotopes reveal a strong crustal signature. Geophysical data indicates that this intra-cratonic basin is characterized by i) a relatively high geothermal gradient, ii) deep faults delineating a horst and graben structure and affecting the entire sedimentary sequence, iii) an archean to paleoproterozoïc basements enriched in radiogenic elements and displaying mafic and ultramafic units, and iv) a possible karstic reservoir located 400 m below the surface. The high geothermal gradient could be due to a thin lithosphere enriched in radiogenic elements, which can also contribute to a massive radiolysis process of water at depth, releasing an important amount of H₂. Alternatively, ultramafic rocks that may have generated H₂ during their serpentinization are also documented in the basement. The seismic profiles show that the faults seen at the surface are deeply rooted in the basement, and can drain deep fluids to shallow depths in a short time scale. The carbonate reservoirs within the Bambuí group which forms the main part of the sedimentary layers are crossed by the fault system and represent good candidates for temporary H₂ accumulation zones. The formation by chemical dissolution of sinkholes located at 400 m depth might explain the presence of sub-circular depressions seen at the surface. These sinkholes might control the migration of gas from temporary storage reservoirs in the upper layer of the Bambuí formation to the surface. The very high fluxes of H₂ escaping out of these structures which have been recently documented are, however, in disagreement with the newly developed H₂ production model in the Precambrian continental crust. They either question the validity of these models or the measurement methodology.

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

The main restriction of the Semantic Web is the difficult of the SPARQL language, that is necessary to extract information from the Knowledge Representation also known as ontology. Making the Semantic Web accessible for people who do not know SPARQL, is essential the use of friendlier interfaces and a good alternative is Natural Language. This paper shows the implementation of a friendly prototype interface to query and retrieve, by voice, information from website building with the Semantic Web tools. In that way, the end users avoid the complicated SPARQL language. To achieve this, the interface recognizes a speech query and converts it into text, it processes the text through a java program and identifies keywords, generates a SPARQL query, extracts the information from the website and read it in voice, for the user. In our work Google Cloud Speech API makes Speech-to-Text conversions and Text-to Speech conversions are made with SVOX Pico. As results, we have measured three variables: The success rate in queries, the response time of query and a usability survey. The values of the variables allows the evaluation of our prototype. Finally the interface proposed provides us a new approach in the problem, using the Cloud like a Service, reducing barriers of access to the Semantic Web for people without technical knowledge of Semantic Web technologies.

The enantioselective alpha-amination of 1,3-dicarbonyl compounds has been performed using a catalytic system based on deep eutectic solvents (DES) and chiral 2-amino benzimidazole-derived organocatalysts. This procedure avoids the use of toxic volatile organic compounds (VOCs) as a reaction medium, providing access to highly functionalized chiral molecules, which are important intermediates for the natural product synthesis, in a selective and efficient manner. Furthermore, the reaction can be performed on a large scale and recycling the catalytic system is possible for at least five times, leading to a clean, cheap, simple, and scalable procedure that meets most of the criteria required to be a green and sustainable process.

The disaster response operational strategies requires tool that a may provide time bound clear picture of potential spots. Natural disaster sometimes comes slowly like increment of water level resulting flood or early warning of tsunami whereas in manmade disasters like 1CBRNE attack or chemical accident or industrial structure collapse or leakage through a pipeline of some toxic hazardous gas requires very accurate and quick response strategies. 2Unmanned Aerial System is commercially used tools present in the market and also used by military organization for various operations throughout the world. The accuracy and time bound pictorial representation of the spots by these tow tools makes them very useful for providing clear picture of potential disaster sites and enabling decision makers to take decision on these representation. Apart of these the potential to deliver important material faster than any other mode also makes these equipment and technology a very important asset for making disaster risk reduction strategies. This paper elaborates the efficacy and utility of Unmanned Aerial System or Drones in developing robust and time bound strategies for disaster risk reduction. This paper also evaluates some experiences and describes some initiatives using Unmanned Aerial System to support disaster risk reduction strategies and post disaster decision making efforts.

Crowdsourced Data (CSD) generated by citizens is becoming more popular as its potential utilisation in many applications is increasing due to its currency and availability. However, the quality of CSD, including its relevance, is often questioned as the data is not generated by professionals nor follows standard data collection procedures. The quality of CSD can be assessed according to a range of attributes including its relevance. Information relevance has been explored through using in Geographic Information Retrieval (GIR) techniques to identify relevant information. This research tested a relevance assessment approach for CSD by adapting relevance assessment techniques available in the GIR domain. The thematic and geographic relevance were assessed using the Term Frequency-Inverse Document Frequency (TF-IDF), Vector Space Model (VSM) and Natural Language Processing (NLP) techniques. The thematic and geographic specificities of the queries were calculated as 0.44 and 0.67 respectively, which indicates the queries used were more geographically specific than thematically specific. The Spearman's rho value of 0.62 indicated that the final ranked relevance lists showed reasonable agreement with a manually classified list and confirmed the potential of the approach for CSD relevance assessment for other possible crowdsourced data analysis.

Beer is a fermented beverage with a history as old as human civilization and its productive process has been spread all around the world becoming unique in every country and iconic of entire populations. Ales and lagers are by far the most common beers; however, the combination of raw materials, manufacture techniques and aroma profiles are almost infinite, so it is not surprising to notice that there is a large amount of different beer styles, each of them with unique characteristics. Nowadays, diversification is becoming increasingly important in the brewing market and the brewers are continuously interested in improving and extending the already wide range of products, especially in craft brewery. One of the major components that can have a deep impact on the final product is yeast, since it is able to convert carbohydrates in wort, especially maltose and maltotriose, into ethanol, carbon dioxide and other minor aroma-active compounds. Saccharomyces cerevisiae (top‐fermenting yeasts used to produce ales) and Saccharomyces pastorianus (cryotolerant bottom‐fermenting hybrids between S. cerevisiae and Saccharomyces eubayanus responsible for the fermentation of lagers) are most used in breweries. However, an increasing number of different yeast starter cultures are commercially available, to improve the production efficiency also at relative low temperatures and to obtain desirable and diversified aroma profiles avoiding undesired compounds. Four main genetic engineering-free trends are becoming popular in craft brewing yeast development: 1) the research for novel reservoirs as source of new performant S. cerevisiae yeasts; 2) the creation of synthetic hybrids between S. cerevisiae and Saccharomyces non-cerevisiae in order to mimic lager yeasts by expanding their genetic background; 3) the exploitation of evolutionary engineering approaches; 4) the usage of non-Saccharomyces yeasts either in co-coculture or in sequential fermentation with S. cerevisiae. In the present work we summarized pro and contra of these approaches and provided an overview on the most recent advances on how brewing yeast genome evolved and domestication took place. Finally, we delineated how the correlations maps between genotypes and relevant brewing phenotypes can assist and further improve the search for novel craft beer starter yeasts.

In this review, we summarize the current state-of-the-art in the utilization of close-range sensing in forest monitoring. We include technologies, such as terrestrial and mobile laser scanning as well as unmanned aerial vehicles, which are mainly used for collecting detailed information from single trees, forest patches or small forested landscapes. Based on the current published scientific literature, the capacity to characterize changes in forest ecosystems using close-range sensing has clearly been recognized. Forest growth has been the most investigated cause for changes and terrestrial laser scanner the most applied sensor for capturing forest structural changes. Unmanned aerial vehicles, on the other hand, have been used to acquire aerial imagery for detecting tree height growth and monitoring forest health. Mobile laser scanning has not yet been used in forest change monitoring except for a few early investigations. Considering the length of the forest growth process, investigated time spans have been rather short, less than 10 years. In addition, data from only two time points have been used in many of the studies, which has further been limiting the capability of understanding dynamics related to forest growth. In general, method development and quantification of changes have been the main interests so far regardless of the driver of change. This shows that the close-range remote sensing community has just started to explore the time dimension and its possibilities for forest characterization.

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.

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.

The present paper reviews evidence for ecological evolution of Montiaceae. Montiaceae (Portulacineae) comprise a family of ca. 275 species and ca. 25 subspecific taxa of flowering plants distributed mainly in extreme western America, with additional endemism elsewhere, including other continents and islands. They have diversified repeatedly across steep ecological gradients. Based on narrative analysis, I argue that phylogenetic transitions from annual to perennial life history have been more frequent than suggested by computational phylogenetic reconstructions. I suggest that a reported phylogenetic correlation between the evolution of life history and temperature niche is coincidental and not causal. I demonstrate how statistical phylogenetic comparative analysis (PhCA) missed evidence for marked moisture niche diversification among Montiaceae. I discount PhCA evidence for the relation between Montiaceae genome duplication and ecological diversification. Based on the present analysis of Montiaceae evolution, I criticize the premise of the prevalent statistical approach to PhCA, which tests Darwinian deterministic hypotheses against stochastic evolutionary null models. I discuss theoretical/empirical evidence that evolution is neither stochastic, nor Darwinistically-determined, but idiosyncratic. Idiosyncraticity describes the outcome of a stochastically perturbed nonlinear chaos-like process. The Principle of Evolutionary Idiosyncraticity (PEI) is based on the evolutionary theory of Natural Drift, which maintains that determinism in evolution is a property of the organism and not, as maintained by the theory of Natural Selection, its traits or its milieu. This determinism is characteristic of chaotic functions, which are absolutely determinate, generate self-similarity, but remain absolutely unpredictable. PEI explains precisely observations that evolution proceeds not linearly, but chaotically, producing both quasi-linear fractal-like patterns and non-linear jumps. PEI has ramifications for all areas of macroevolutionary research. In particular, it demonstrates both the fallacy and futility of the statistical PhCA approach that interprets evolutionary causes in terms of evolutionary correlations. However, statistical methods of PhCA can be applied heuristically and fruitfully to reveal idiosyncraticity and discover evolutionary novelty. This, in turn, is demonstrated by the emergence of statistical anomalies in evolutionary analyses of Montiaceae.

Fungal infections impact the lives of at least 12 million people every year, killing over 1.5 million. Wide-spread use of fungicides and prophylactic antifungal therapy have driven resistance in many serious fungal pathogens, and there is an urgent need to expand the current antifungal ar-senal. Recent research has focused on improving azoles, our most successful class of antifungals, by looking for synergistic interactions with secondary compounds. Synergists can co-operate with azoles by targeting steps in related pathways, or they may act on mechanisms related to re-sistance like active efflux or on totally disparate pathways or processes. A variety of sources of potential synergists have been explored, including pre-existing antimicrobials, pharmaceuticals approved for other uses, bioactive natural compounds and phytochemicals, and novel synthetic compounds. Synergy can successfully widen the antifungal spectrum, decrease inhibitory dosag-es, reduce toxicity, and prevent the development of resistance. This review highlights the diversity of mechanisms that have been exploited for the purposes of azole synergy and demonstrates that synergy remains a promising approach for meeting the urgent need for novel antifungal strate-gies.

A global revolution in information and communication technologies (ICT) has occurred over the past few decades, emerging first in industrialized countries and then in developing countries. While researchers have examined many facets of the ICT revolution, relatively little work has systematically examined the degree to which ICT has reduced natural disaster vulnerability. In this article we use cross-country data over the 1980-2013 period to estimate the relationship between newly-emerging cell phone access/use and disaster-induced fatalities. Our estimates suggest that a one-standard-deviation increase in cell phone usage reduces disaster fatalities by nearly one half. The estimated effect increases to almost three quarters for geologic events where people are typically afflicted without warning. The largest marginal benefit from cell phones in terms of saving lives come from events where there is typically no warning, thus many are caught by surprise; cell phones are used to call for help and coordinate assistance.

Formation of Fe- and Mg-rich smectite and zeolite under alkaline conditions is concerned as secondary minerals after alkaline alteration of bentonite in a repository of radioactive wastes. It might be crucial for safety assessment whether smectite will be formed or not as secondary minerals after alkaline alteration of bentonite. In present paper, Fe- and Mg-rich smectite which are currently interacting with hyperalkaline groundwater was found at Narra in Palawan, Philippines. Mineralogical and geochemical investigation was conducted to understand formation process of smectite and factors determined secondary mineral species. Our study revealed a certain amount of smectite may have been produced under hyperalkaline conditions, altered from amorphous or poorly crystalline material such as M-S-H and F-S-H. Key factor which decides smectite or zeolite as secondary minerals after alkaline alteration of bentonite might be whether nuclei of M-S-H and/or F-S-H will be formed or not. This might be decided by the presence of dissolved Mg2+ and Fe2+ in the system. Our suggested formation process of smectite under alkaline conditions is analogue with generally-accepted model of smectite formation that might have been occurred on early Mars.

The incidence of lung cancer has increased in recent years and causes major mortalities across the globe. Besides, the availability of the several chemotherapeutics modalities in the management, there is still a challenge to find out an efficient remedy with lesser or no toxic effects. Hence, there is a necessity to employ complementary research to establish effective management for lung cancer. In this study, we have implemented a novel herbal informatics model to find out the alternative remedy in the treatment of lung cancer. This model utilizes five major steps of the bioprospection process based on the classical surge followed by the binary index and rationale-based selection of herbal products targeting the cancer-causing factors which are explained in detail in the methodology section of this model. This study revealed 07 herbals such as Withania somnifera (Ws), Berberis vulgaris(Bv), Glycyrrhiza glabra(Gg), Andrographis paniculate(Ap), Azadirachta indica(Ai), Cinnamomum Verum(Cv), Piper longum(Pl) based on the fuzzy set optimization scoring(0.6-1) that could be further studied in vitro and in vivo level for utilization in the management of lung cancer.

Tree-killing bark beetles are the most economically important insects in conifer forests worldwide. Yet despite >200 years of research, the drivers of population eruptions or crashes are still not fully understood, precluding reliable predictions of the effects of global change on beetle population dynamics and impacts on ecosystems and humans. We critically analyze potential biotic and abiotic drivers of population dynamics of the European spruce bark beetle (Ips typographus) and present a novel ecological framework that integrates the multiple drivers governing this bark beetle system. We call for large-scale collaborative research efforts to improve our understanding of the population dynamics of this important pest; an approach that might serve as a blueprint for other eruptive forest insects.

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.

One of the principal hallmarks of Alzheimer’s disease (AD) is related to the aggregation of amyloid-β fibrils in an insoluble form in the brain, also known as amyloidosis. Therefore, a prominent therapeutic strategy against AD consists either in blocking the amyloid aggregation and/or destroying the already formed aggregates. Natural products have shown significant therapeutic potential as amyloid inhibitors from in vitro studies as well as in vivo animal tests. In this study, the interaction of five natural biophenols (curcumin, dopamine, (-)-Epigallocatechin-3-gallate, Quercetin, and Rosmarinic acid) with the amyloid-β(1-40) fibrils has been studied through computational simulations. The results allowed the identification and characterization of the different binding modalities of each compounds and their consequences on fibril dynamics and aggregation. It emerges that the lateral aggregation of the fibrils is strongly influenced by the intercalation of the ligands, which modulate the double-layered structure stability.

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.

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.

What proportions should a thermally massive building have? How should the thermal mass be distributed? Should the "massing" change with the choice of material? This paper shows how to optimize the physical proportions of a building so that it synchronizes ambient heat exchanges in a natural feedback cycle. The internal mass is thermally coupled with buoyancy ventilation; the cycle is driven by the daily swing of outdoor temperature. Tripling up functions in this way—so that structural materials can reliably cool and power the ventilation for buildings—could help decarbonize the construction industry and provide an effective strategy for adapting to life-threatening heatwaves. Based on harmonic analysis, the method allows designers to thermally tune the form and mass of a building to meet chosen targets for temperature and ventilation in free-running mode. Once the optimal balance of exchange rates is known, design teams can proportionally vary the building height and ventilation openings against the surface area and thickness of an internal thermal mass. The possible permutations are infinite but parametrically constrained, allowing teams to fairly compare the functional and environmental credentials of different construction materials while they produce and evaluate preliminary options for organizing the exterior form and interior spaces of a building. An example study suggests that thin-shell structures of minimum weight, and even timber buildings, may be optimally tuned to produce ample ventilation and temperature attenuation.

A frequent problem in Geotechnics is the soils with inadequate physical-mechanical properties before the efforts to which they will be subjected by the constructions, incurring cost overruns caused by their engineering improvement. The need to improve the engineering properties of soils is not recent, currently observing that the most common alternatives are binders such as cement and lime. The climate change observed in recent decades and the uncontrolled emission of greenhouse gases have motivated Geotechnical and Geoenvironmental researchers to seek mechanisms for soil reinforcement from a more sustainable and environmentally friendly approach by proposing the use of recycled and waste materials. An alternative is natural fibers, which can be obtained as waste from many agro-industrial processes, implying their high availability and low cost. Sawdust as a by-product of wood processing has a rough texture that can generate high friction between the fiber and the matrix of the soils, leading to a significant increase in its shearing strength and bearing capacity. This concept of improving the properties of soils using natural fibers distributed randomly is inspired by the natural phenomenon of grass and/or plants that when growing on a slope can effectively stabilize a said slope.

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.

The UK Government is committed to reducing greenhouse gas (GHG) emissions by 80% by 2050. Buildings are responsible for 37% of the total GHG emissions in the UK and the need to reduce their emissions has resulted in more stringent building regulations in the recent past. The regulations, energy rating systems and voluntary guidelines — all are primarily aimed at reducing the need for heating and associated energy use by increasing insulation and air-tightness. However, future climates are projected to be warmer than the present day. Internal gains dominated non-domestic buildings will likely overheat, the adaptation to which will require energy-intensive cooling solutions, thus defeating the purpose of heating-focused regulations. This research investigated the effects of warming climate on overheating, and energy use and resulting emissions in representative urban office spaces in London in the present-day and future climates using hourly dynamic thermal simulations. Findings suggest that more airtight and highly—insulated office buildings designed for heating—dominated temperate UK climate will overheat in the 2050s. Heating demand reduces but electricity consumption increases by 121% when hybrid cooling is adopted to ameliorate overheating. Despite the rise, adopting a mixed-mode ventilation strategy was one of the ways of achieving overall energy efficiency while meeting benchmark overheating and carbon emissions target in present and future climatic contradictions. Current heating-focused legislations need to be urgently re-evaluated to account for the effects of climatic variability and overheating risks.

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.

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.

The establishment of a well characterized non-human primate model of Zika virus (ZIKV) infection is critical for the development of medical interventions. In this study, challenging Indian rhesus macaques (IRMs) with ZIKV strains of the Asian lineage resulted in dose dependent peak viral loads between days 2 and 5 post infection; and a robust immune response which protected the animals from homologous and heterologous re-challenge. In contrast, viremia in IRMs challenged with an African lineage strain was below the assays lower limit of quantitation and the immune response was insufficient to protect from re-challenge. These results corroborate previous observations but are contrary to reports using other African strains obviating the need for additional studies to elucidate the variables contributing to the disparities. Nonetheless, the utility of an Asian lineage ZIKV IRM model for countermeasures development was verified by vaccinating animals with a formalin inactivated reference vaccine and demonstrating sterilizing immunity against a subsequent subcutaneous challenge.

Smenamides are an intriguing class of peptide/polyketide molecules of marine origin showing antiproliferative activity against lung cancer Calu-1 cells at nanomolar concentrations through a clear pro-apoptotic mechanism. To probe the role of the activity-determining structural features, the 16-epi-analogue of smenamide A and eight simplified analogues in the 16-epi series were prepared using a flexible synthetic route. The synthetic analogues were tested on multiple myeloma (MM) cell lines showing that the configuration at C-16 slightly affects the activity, since the 16-epi-derivative is still active at nanomolar concentrations. Interestingly, it was found that the truncated compound 8, mainly composed of the pyrrolinone terminus, was not active while compound 17, essentially lacking the pyrrolinone moiety, was 1000-fold less active than the intact substance and was the most active among all the synthesized compounds.

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on mobile data science and intelligent apps in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

In the recent years, tissue engineering researchers have exploited a variety of biomaterials that can potentially mimic extracellular matrix (ECM) for tissue regeneration. Natural cellulose, mainly obtained from bacterial (BC) and plant-based (PC) sources, can serve as a high potential scaffold material for different regenerative purposes. Natural cellulose has drawn the attention of researchers due to its advantage over synthetic cellulose in terms of availability, cost-effectiveness, perfusablility, biocompatibility, negligible toxicity, mild immune response and due to imitating native tissues. In this article, we will review the recent in vivo and in vitro studies aimed to assess the potentials of natural cellulose for the purpose of soft (skin, heart, veins, nerve, among others) and hard (bone and tooth) tissue engineering.

The dominance of social media has added to the channels of bullying to perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in today’s cyber world and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social media platforms to manage the impact in our society. Several machine learning (ML) algorithms have been proposed for this purpose. However, their performances are not consistent due to high class imbalance issue and generalisation. In recent years, large language models (LLM) like BERT and RoBERTa have achieved state of the art (SOTA) results in several natural language processing (NLP) tasks. Unfortunately, the LLMs have not been applied extensively. In our paper, we explored the use of these models for cyberbullying (CB) detection. We have prepared a new dataset (D2) from existing studies (Formspring and Twitter). Our experimental results for dataset D1 and D2 showed RoBERTa outperformed other models.

Herpes simplex virus (HSV) infections can be treated with direct acting antivirals like acyclovir and foscarnet, but long-term use can lead to drug resistance, which motivates research into broadly-acting antivirals that can provide a greater genetic barrier to resistance. Photodynamic inactivation (PDI) employs a photosensitizer, light, and oxygen to create a local burst of reactive oxygen species that inactivate microorganisms. The botanical plant extract Orthoquin^TM is a powerful photosensitizer with antimicrobial properties. Here we report that Orthoquin also has antiviral properties. Photoactivated Orthoquin inhibited herpes simplex virus type 1 (HSV-1) and herpes simplex virus type 2 (HSV-2) infection of target cells in a dose-dependent manner, across a broad range of sub-cytotoxic concentrations. HSV inactivation required direct contact between Orthoquin and the inoculum, whereas pre-treatment of target cells had no effect. Orthoquin did not cause appreciable damage to viral capsids or pre-mature release of viral genomes as measured by qPCR for the HSV-1 genome. By contrast, immunoblotting for HSV-1 antigens in purified virion preparations suggested that higher doses of Orthoquin had a physical impact on certain HSV-1 proteins that altered protein mobility or antigen detection. Orthoquin PDI also inhibited the non-enveloped adenovirus (AdV) in a dose-dependent manner, whereas Orthoquin-mediated inhibition of the enveloped vesicular stomatitis virus (VSV) was light-independent. Together, these findings suggest that broad antiviral effects of Orthoquin-mediated PDI may stem from damage to viral attachment proteins.

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.

Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. For deriving any further conclusion, first, it is necessary to know how to compare users. In this paper, a hybrid model has been proposed to measure Twitter profiles’ similarity and quantifies the likeness degree of profiles by calculating features considering users’ behavioral habits. For this, first, the timeline of each profile has been extracted using the official TwitterAPI. Then, in parallel, three aspects of a profile are deliberated. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized to compare the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

Distributional word vector representation orword embedding has become an essential ingredient in many natural language processing (NLP) tasks such as machine translation, document classification, information retrieval andquestion answering. Investigation of embedding model helps to reduce the feature space and improves textual semantic as well as syntactic relations.This paper presents three embedding techniques (such as Word2Vec, GloVe, and FastText) with different hyperparameters implemented on a Bengali corpusconsists of180 million words. The performance of the embedding techniques is evaluated with extrinsic and intrinsic ways. Extrinsic performance evaluated by text classification, which achieved a maximum of 96.48% accuracy. Intrinsic performance evaluatedby word similarity (e.g., semantic, syntactic and relatedness) and analogy tasks. The maximum Pearson (ˆr) correlation accuracy of 60.66% (Ssˆr) achieved for semantic similarities and 71.64% (Syˆr) for syntactic similarities whereas the relatedness obtained 79.80% (Rsˆr). The semantic word analogy tasks achieved 44.00% of accuracy while syntactic word analogy tasks obtained 36.00%

As a holistic medical school, Iranian traditional medicine (ITM) considers the human body as a dynamic and intricate network of interconnecting processes. Currently, systems biology and more precisely systems medicine and pharmacology can be an aid in providing rationalizations for many traditional medications and treatments and elucidating a great deal of knowledge they can offer to guide future research in medicine. Therefore, re-organization and standardization of traditional medicine data are requested more than ever before. To address this issue, we have constructed UNaProd, a Universal Natural Product database for materia medica of ITM. Primarily based on Makhzan al-Advieh, which is the most recent encyclopedia of materia medica in ITM with the largest number of monographs, this database was created using both text mining methods and manual editing. UNaProd is currently hosting to 2696 monographs from herbal to animal to mineral compounds in 16 diverse attributes such as origin and scientific name. In the current version, UNaProd is hyperlinked to IrGO and CMAUP databases for Mizaj and molecular features respectively and it is freely available at http://jafarilab.com/unaprod/.

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.

Changes in extreme weather patterns are expected under climate change. In this study, a risk assessment was conducted using 4 building damage history datasets and 33 weather datasets (precipitation, wind speed, snow, and temperature) from 230 regions in South Korea to quantitatively analyze and predict building damage caused by potential future natural disasters. Decision tree analysis was used to evaluate building damage risk in 230 regions. The decision tree model to determine the risk of flood, gale, and typhoon was generated, which excluded gales, with less damage. The weight (variable importance) and limit value (damage limit) of the weather variables ware derived using the decision tree model. Using these two factors, we assessed the building damage risk in 230 regions in South Korea until 2100. The number of regions at risk of flood damage increased by more than 30% in average. Conversely, regions at risk of snowfall damage decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, and the number of regions at high risk of typhoon damage increased by up to 62.5% in RCP 8.5. These results can be used as objective data to minimize future building damage throughout South Korea, representing the first step towards sustainable development in the region with respect to disaster response.

Background: Both natural immunity and vaccine-induced immunity to COVID-19 may be useful to reduce the mortality/morbidity of this disease, but still a lot of controversy exists. Aims: This narrative review analyzes the literature about: a) the duration of natural immunity; b) cellular immunity; c) cross-reactivity; d) the duration of post-vaccination immune protection; e) the probability of reinfection and its clinical manifestations in the recovered patients; f) comparisons between vaccinated and unvaccinated in the possible reinfections; g) the role of hybrid immunity; h) the effectiveness of natural and vaccine-induced immunity against Omicron variant; i) comparative incidence of adverse effects after vaccination in recovered individuals vs. COVID-19-naïve subjects. Material and Methods: through multiple search engines we investigated COVID-19 literature related to the aims of the review, published since April 2020 through July 2022, including also the previous articles pertinent to the investigated topics. Results: nearly 900 studies were collected and 238 pertinent articles were included. It was highlighted that the vast majority of individuals after COVID-19 develop a natural immunity both of cell-mediated and humoral type, which is effective over time and provides protection against both reinfection and serious illness. Vaccine-induced immunity was shown to decay faster than natural immunity. In general, the severity of the symptoms of reinfection is significantly lower than in the primary infection, with a lower degree of hospitalizations (0.06%) and an extremely low mortality. Conclusions: this narrative review regarding a vast number of articles highlighted the valuable protection induced by the natural immunity after COVID-19, which seems comparable or superior to the one induced by anti-SARS-CoV-2 vaccination. Vaccination of the unvaccinated COVID-19-recovered subjects may not be indicated. Further research is needed in order to: a) measure the durability of immunity over time; b) evaluate both the impacts of Omicron-5 on vaccinated and healed subjects and of hybrid immunity.

Historical analysis of Australian bushfire data spanning 170 years addresses whether the strength of recent fire events has been exacerbated by human-induced climate change. The question of “cause” looks at the characteristics of a wider range of natural hazards. Fire characteristics are compared with earthquake hazard characteristics: (1) energy – termed “magnitude”; (2) severity – termed “intensity”; and (3) resultant damage to people and structures – termed “impact”.Published global, Northern and Southern hemisphere temperature data are shown to vary consistently in phase over 170 years, but vary in amplitude with statistical significance. CO₂ levels north and south of the Equator have tracked quite consistently. Thus, Southern Hemisphere bushfire magnitude and intensity is compared with the Southern Hemisphere climate record, rather than a global data set.28 major bushfires and associated droughts since 1850 show neither apparent drought extent, nor area burned, nor bushfire intensity, correlates with changes in Southern Hemisphere climate. Average rainfall from 1900 shows a wetter, rather than drier trend. Cyclone energy shows no significant trend with climate. Planet-wide “greening”, through CO₂ fertilisation, is an insignificant contributor to bushfire magnitude. Combustion theory shows recorded “global warming” could have had no significant influence on bushfire magnitude or intensity. Any increase in Australian bushfire impact, as judged by lives lost, similarly, shows no correlation with bushfire magnitude, nor indeed, any observed Southern Hemisphere global warming.Thus, bushfire magnitude seems much more likely driven by fuel load and any anomalous bushfire intensity is likely driven by anomalous ground level fuel load. The evidence suggests that any CO₂ emissions reduction will have no impact on future bushfire “severity.

The demographic history of anatomically modern humans (AMH) involves multiple migration events, population extinctions and genetic adaptations. As genome-wide data from complete genome sequencing becomes increasingly abundant and available even from extinct hominins, new insights of the evolutionary history of our species are discovered. It is currently known that AMH introgressed with archaic hominins once they left the African continent. Current out of African human genomes carry fragments of archaic origin. This review focuses on the fitness consequences of archaic interbreeding in current human populations. We discuss new insights and challenges that researchers face when interpreting the potential impact of introgression on fitness and testing hypotheses about the role of selection within the context of health and disease.

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.

Objectives: Studies evaluating machine learning (ML) algorithms on cross-lingual RadLex® mappings for developing context-sensitive radiological reporting tools are lacking. Therefore, we investigated whether ML-based approaches can be utilized to assist radiologists in providing key imaging biomarkers – such as The Alberta stroke programme early CT score (APECTS). Material and Methods: A stratified random sample (age, gender, year) of CT reports (n=206) with suspected ischemic stroke was generated out of 3997 reports signed off between 2015-2019. Three independent, blinded readers assessed these reports and manually annotated clinico-radiologically relevant key features. The primary outcome was whether ASPECTS should have been provided (yes/no: 154/52). For all reports, both the findings and impressions underwent cross-lingual (German to English) RadLex®-mappings using natural language processing. Well-established ML-algorithms including classification trees, random forests, elastic net, support vector machines (SVMs) and boosted trees were evaluated in a 5 x 5-fold nested cross-validation framework. Further, a linear classifier (fastText) was directly fitted on the German reports. Ensemble learning was used to provide robust importance rankings of these ML-algorithms. Performance was evaluated using derivates of the confusion matrix and metrics of calibration including AUC, brier score and log loss as well as visually by calibration plots. Results: On this imbalanced classification task SVMs showed the highest accuracies both on human-extracted- (87%) and fully automated RadLex® features (findings: 82.5%; impressions: 85.4%). FastText without pre-trained language model showed the highest accuracy (89.3%) and AUC (92%) on the impressions. Ensemble learner revealed that boosted trees, fastText and SVMs are the most important ML-classifiers. Boosted trees fitted on the findings showed the best overall calibration curve. Conclusions: Contextual ML-based assistance suggesting ASPECTS while reporting neuroradiological emergencies is feasible, even if ML-models are restricted to be developed on limited and highly imbalanced data sets.

Technological advancements and innovations have profoundly changed the lives of people giving rise to smart environments, cities, and societies. As homes are the building block of cities and societies, smart homes are critical to establishing smart living and are expected to play a key role in enabling smart cities and societies. The current academic literature and commercial advancements on smart homes have mainly focused on developing and providing smart functions for homes to provide security management and facilitate the residents in their various activities such as ambiance management. Homes are much more than physical structures, buildings, appliances, operational machines, and systems. Homes are composed of families and are inherently complex phenomena underlined by humans and their relationships with each other, subject to individual, intragroup, intergroup, and intercommunity goals. There is a clear need to understand, define, consolidate existing research, and actualize the overarching roles of smart homes, the roles of smart homes that would serve the needs of future smart cities and societies. This paper introduces our data-driven parameter discovery methodology and uses it to provide, for the first time, an extensive, rather fairly comprehensive, analysis of the families and homes landscape seen through the eyes of academics and the public using over a hundred thousand research papers and nearly a million tweets. We develop a methodology using deep learning, natural language processing (NLP), and big data analytics methods and apply it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The 66 discovered parameters and the knowledge space comprising 100s of dimensions are explained by reviewing and referencing over 300 articles from the academic literature and tweets. The knowledge and parameters discovered in this paper can be used to develop a holistic understanding of matters related to families and homes facilitating the development of better, community-specific, policies, technologies, solutions, and industries for families and homes, leading to strengthening families and homes, and in turn, empowering sustainable societies across the globe.

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.

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.

Fire behavior of natural rubber latex foam under different thickness conditions(d=1,2and 5cm)were explored though a little of experiments by using the self-built small scale experimental platform. It can be shown that the flame spread law of thermally thin and thermally thickness are different. Natural rubber latex foam with thickness of 2cm show higher fire risk, which value of flame spread rate, maximum flame height, maximum mass loss rate and maximum temperature is 0.00293m/s, 851.875mm, 1.83g/s, 948K,repectively.That may because the thickness of residue formed of thermally thick materials is larger than the thin one, obstructing the contact of the natural rubber latex foam with fresh air .In addition, a special phenomenon is noticed that during the second stage, the bottom unburned zone located in the four edges(thermally thin material) and middle player(thermally thick material).

Argulus is an ectoparasite that frequently infects goldfish, with severe effects and a high death rate. The use of plant extracts has been widely reported to have antiparasitic activities, including one of the solasodine compounds in sour eggplant extracts. This research evaluated the antiparasitic activities of Compositions A (Solanum ferox and Zingiber zerumbet extract at 400 and 200 ppm, respectively), B (SFE and Boesenbergia pandurata at 900 ppm), and C (single Solanum ferox at 400 ppm). The research stages comprised 1) isolation and analysis of solasodine content in sour eggplant extracts and its extract composition, 2) in vitro antiparasitic activities on Argulus sp. at doses of 50 and 100 ppm with a testing time of 60–240 minutes, and 3) in vivo antiparasitic activities on Argulus sp. on goldfish at a dose of 100 ppm and observation for 2–12 hours of medication. The results showed that sour eggplant extracts had a solasodine concentration of 7.151 mg/L, whereas the derivative compositions A, B, and C were 656, 485, and 295 ppm, respectively. The in vitro testing demonstrated that composition A was effective for killing approximately 80–100% of Argulus, whereas compositions B and C killed 80–90% and 60–70%, respectively. Viewed from the effectiveness for killing parasites, the result was excellent (above 50%). The in vivo medication test was continued using three extract compositions at a dose of 100 ppm. Compositions A, B, and C were found to be capable of releasing 81.33, 75.67, and 71.00 arguli, respectively, per fish. We concluded that the single SFE extract and a concoction with BPE and ZZE had reasonable antiparasitic activity, whereas the concoction of SFE and ZZE killed more Argulus parasites at a higher rate.

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.

The aim of the research was a comprehensive evaluation of concrete using the EIPI method. In the evaluation the compressive strength of concrete and its durability properties represented by sorptivity and air permeability were taken into account. Since copper slag waste with increased natural radioactivity was used in the assessed concrete, additional evaluation was carried out taking into account the influence of natural radioactivity within the performance index. Also the reference concrete, which was made without the use of copper slag waste, was evaluated for comparative purposes. In order to make the evaluation as comprehensive as possible, the concrete made with the use of three types of cement was subjected to the assessment: CEM I, CEM II and CEM III. The results show that in both approaches the best result was achieved by concrete with CEM III cement. If natural radioactivity is not taken into account in the evaluation, the best result is obtained by concrete made with copper slag waste, and if radioactivity is considered, the best result is obtained with concrete without addition of the waste. It follows from the above that although natural radioactivity has a significant impact on the EIPI evaluation result, the decisive factor is still the type of cement.

This review seeks to explain four features of viral respiratory illnesses that have perplexed many generations of microbiologists: (1) the seasonal occurrence of viral respiratory illness; (2) the occurrence of respiratory illness year-round in the Tropics; (3) the rapid response of illness to temperature drops in temperate regions; (4) the explosive arrival and rapid termination of epidemics caused by influenza and other respiratory viruses. I discuss the inadequacy of the popular explanations of seasonality, and propose a simple hypothesis, called Temperature Dependent Viral Tropism (TD-VT), that is compatible with the above and other features of respiratory illness. TD-VT notes that viruses can often transmit themselves more effectively if they moderate their pathogenicity (thereby maintaining the mobility of their hosts) and suggests that most endemic respiratory viruses accomplish this by developing thermal sensitivity, in the sense that they normally replicate rapidly only at temperatures below normal body temperature. This allows them to confine themselves to the upper respiratory tract and to avoid infecting the lungs, heart, gut etc. I review biochemical and tissue-culture studies that found that “wild” respiratory viruses often show natural thermal sensitivity within a range that supports organ-specific tropism within the human body, and I discuss the evident tendency for viral strains to adapt their thermal sensitivity to their local climate and season. I also explore the possible misinterpretation of early experiments where volunteers were inoculated nasally with viral samples and then chilled. Next, I discuss the practical implications of the TD-VT hypothesis for preventing and treating respiratory illness. Finally, I note that the hypothesis is very testable and make suggestions for the most important experiments to increase our understanding of the seasonality and pathogenicity of viral respiratory illness.

Solar reflective index (SRI) is one of the important parameters in the analysis of a building’s energetic and thermal performance, especially for “cool” reflective paints or coatings. However, there exists less information on the typical performance of the cool materials exposed for long term in the Middle East and more specifically in the United Arab Emirates (UAE). In this study, we determined values of reflectance, emittance, solar reflective index (SRI), and color difference, for three different high SRI white paints exposed naturally on low and high sloped racks for three years. After 3 years, reflectance values of exposed paint panels significantly decreased with an increased color difference in comparison with original unexposed paint surfaces. Emissivity of the paint changed very little for all of the exposed samples, but SRI, determined from both the reflectance and emittance, was reduced from between 95 and 110 to between 60 and 90 after three years. This appeared to be related to exposure to high temperatures, UV radiation, and adhesion of airborne contaminants in conjunction with low precipitation. Macroscopically, panels exposed on a low slope were the most discolored with the greatest presence of dust infusion and reduction in increase in surface temperatures which was demonstrated through thermal imaging. Such natural weathering research study is necessary for the development of standard exposure tests and determination of various control elements to increase the durability of cool materials in hot and arid climatic conditions of UAE.

Human language is a prime example of a complex system characterized by multiple scales of description. Understanding its origins and distinctiveness has sparked investigations with very different approaches, ranging from the Universal Grammar to statistical analyses of word usage, all of which highlight, from different angles, the potential existence of universal patterns shared by all languages. Yet, a cohesive perspective remains elusive. In this paper we address this challenge. First, we provide a basic structure of universality, and define recursion as a special case thereof. We cast generative grammars of formal languages, the Universal Grammar and the Greenberg Universals in our basic structure of universality, and compare their mathematical properties. We then define universality for writing systems and show that only those using the rebus principle are universal. Finally, we examine Zipf's law for the statistics of word usage, explain its role as a complexity attractor, and explore its relation to universal writing systems as well as its similarities with universal Turing machines. Overall, we find that there are two main kinds of universality, termed {\it mechanistic} and {\it emergent}, and unveil some connections between them.

A revitalized practice of natural philosophy can help people to live a better life and promote a flourishing ecosystem. Such a philosophy is natural in two senses. First, it is natural by seeking to understand the whole of nature, including mental phenomena, In particular, a comprehensive natural philosophy should address the phenomena of sentience by embracing first- and second-person methods of investigation. Moreover, to expand our understanding of the world, natural philosophy should embrace a full panoply of explanations, similar to Aristotle’s four causes. Second, such a philosophy is natural by being grounded in human nature, taking full account of human capacities and limitations. Future natural philosophers should also make use of all human capacities, including emotion and intuition as well as reason and perception, to investigate nature. Finally, since the majority of our brain's activities are unconscious, natural philosophy should explore the unconscious mind with the aim of deepening our relation to the rest of nature and enhancing well-being.

The article presents a literature review of three Chinese manuscripts containing natural dyes recipes and three containing synthetic dyes from the Ming Dynasty to the Republican Period. The manuscripts mentioned were exclusively used as references and sources. Four databases were used to access the original documents, then, a translation from old mandarin to Spanish was made. For natural dyes, the results show four dyestuffs were commonly used to produce colors, sappanwood (Caesalpinia sappan L.), followed by safflower (Carthamus tinctorius L.), indigo (Indigofera tinctoria L.), and sophora (S. japonicum L.). While the synthetic dyes exported to or produced in China were mainly bluish, greenish, and violet probably because they were difficult to obtain from natural ones. The literature review of the dyes and their recipes can be a key factor in any art history or conservation study, improving the understanding of how the colors were achieved and as complementary information for analytical analyses. It provides useful details that contribute to the preservation of Chinese dye history on silk heritage.

The risk related to embankment dam breaches needs to be evaluated in order to prepare emergency action plans. The physical and hydrodynamic parameters of the flood wave generated from dam-failure event correspond to various breach parameters such as width, slope and formation time. This study aimed to simulate dam-breach failure scenario of Yabous dam (NE Algeria) and analyze its influence on areas (urban and natural environments) downstream the dam. The simulation was completed using the sensitivity analysis method in order to assess the impact of breach parameters on the dam-break scenario. The propagation of flood wave associated to dam-break was simulated using the one-dimensional HEC-RAS hydraulic model. This study ap-plied a sensitivity analysis of three breach parameters (slope, width, and formation time) in five sites selected downstream the embankment dam. The simulation showed that the maximum flow of the flood wave recorded at the level of the breach was 8768 m3/s, which gradually attenuated along the river course to reach 1579.2m3/s at about 8.5km downstream the dam. This study estab-lished the map of flood-prone areas that illustrated zones threatened with the flooding wave trig-gered by the dam failure due to extreme rainfall events. The sensitivity analysis showed that flood wave flow, height and width revealed positive and similar changes for the increase in adjustments (±25% and ±50%) of breach width and slope in the 5 sites. However, flood wave parameters of breach formation time showed significant trends that changed in the opposite direction compared to breach slope and width.

Nowadays, the imidazolium-based ionic liquids containing acetate counter-ions are attracting much attention as both highly selective absorbents of the acidic gases and CO₂ carriers in the supported ionic liquid membranes. In this regard, the investigation of the gas transport properties of such membranes may be appropriate for better understanding of various factors affecting the separation performance and the selection of the optimal operating conditions. In this work, we have tested CH₄, CO₂ and H₂S permeability across the SILM impregnated by 1-butyl-3-methylimidazolium acetate (bmim[ace]) with the following determination of the ideal selectivity in order to compare the facilitated transport membrane performance with the SILM that dissolves acidic gases physically, namely, containing 1-butyl-3-methylimidazolium tetrafluoroborate (bmim[BF₄]). Both SILMs have showed modest individual gases permeability and ideal selectivity of CO₂/CH₄ and H₂S/CH₄ separation that achieves values up to 15 and 32, respectively. The effect of the feed gas mixture composition on the permeability of acidic gases and permeselectivity of the gas pair was investigated. It turned out that the permeation behavior for the bmim[ace]-based SILM toward the binary CO₂/CH₄, H₂S/CH₄ and ternary CO₂/H₂S/CH₄ mixtures was featured with high acidic gases selectivity due to the relatively low methane penetration through the liquid phase saturated by acidic gases.

In the past few decades, the use of agricultural waste has been increasing due to the increasing demand for sustainable materials. This paper aims to review the significance of agricultural waste as a type of wealth and its potential as an alternative material in the composite field. It was found that many agricultural wastes had been utilized in various production of ecological products. The review discusses the physical, mechanical, and chemical properties of the natural fibers made from agricultural waste and the use of Pineapple Leaf Fiber Reinforced Composite to manufacture UAVs (Unmanned Aerial Vehicles). The extracted waste fibers’ physical, mechanical, and chemical properties show that these agricultural wastes can produce better advanced composite materials suitable for structural applications. The use of pineapple leaf fiber in Unmanned Aerial Vehicles manufacturing has the potential to revolutionize this industry and contribute to a more sustainable future.

The cosmological relevance of emptiness—that is, space without bodies—is not yet sufficiently appreciated in natural philosophy. This paper addresses two aspects of cosmic emptiness from the perspective of natural philosophy: the distances to the stars in the closer cosmic environment and the expansion of space as a result of the accelerated expansion of the universe. Both aspects will be discussed from both a historical and a systematic perspective. Emptiness can be interpreted as “coming” in a two-fold sense: Whereas in the past knowledge of emptiness as it were came to human beings, in the future it is coming insofar as its relevance in the cosmos will increase.The longer and more closely emptiness was studied since the beginning of modernity, the larger became the spaces over which it was found to extend. From a systematic perspective, I will show with regard to the closer cosmic environment that the earth may be separated from the perhaps habitable planets of other stars by an emptiness that is inimical to life and cannot be traversed by humans. This assumption is a result of the discussion of the constraints and possibilities of interstellar space travel as defined by the known natural laws and technical means. With the accelerated expansion of the universe, the distances to other galaxies (outside of the so-called local group) are increasing. According to the current standard model of cosmology and assuming that the acceleration will remain constant, in the distant future this expansion will lead first to a substantial change in the epistemic conditions of cosmological knowledge and finally to the completion of the cosmic emptiness and of its relevance, respectively. Imagining the postulated completely empty last state leads human thought to the very limits of what is conceivable.

Protective coatings, in recent years also from nanocomposite formulations, are commonly applied onto architectural stone and stone artefacts, mainly to prevent absorption into the porous stone structure of condensed water and dissolved atmospheric pollutants. While standard protocols are available to assess a coating’s performance, understanding the response of the coating-stone system is a complex task, due to the interplay of various factors determining the overall behaviour. Characterization techniques allowing to correlate the extent and nature of surface modification upon treatment with the most relevant physical properties (i.e water absorption and surface wettability) are thus of great interest. Electrokinetic analysis based on streaming current measurements, thanks to its sensitivity towards even minor changes in the surface chemical composition, may fulfil such requirement. Indeed, by involving the interaction with a testing aqueous electrolyte solution, this technique allows to probe not only the outer surface but also the outermost layer of the pore network, which plays a crucial role in the interaction of the stone with condensed atmospheric water. In this work a correlation was found between the extent of surface modification, as determined by streaming current measurements, surface wettability and capillary water absorption of 6 coating-lithotype combinations (3 lithotypes and 2 nanocomposites).

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.

The excessive use of synthetic insecticides in modern agriculture has led to the contamination of the environment and the development of insect resistance. In this study, we evaluated the potential of tangerine (Citrus reticulata L.) peel essential oil (EO) as a natural insecticide against greenhouse whitefly (Trialeurodes vaporariorum W. (Homoptera: Aleyrodidae)), a common pest in greenhouse production. Petroleum ether (PET) and n-hexane (HEX) were used as solvents to extract essential oil (EO) from tangerine peels. The yield of EO was 1.59 % and 2.00 % (m/m) for PET and HEX, respectively. The insecticidal activity of EO was evaluated by measuring the mortality rate (MR) of greenhouse whiteflies at different time intervals. The results showed that PET and HEX extracts of tangerine EO effectively controlled the greenhouse whitefly. With both solvents, it was observed that with doses of 12.5% (v/v), similar results were achieved as with the positive control (corresponding to the commercial insecticide imidacloprid). Additionally, the FTIR analysis found that the EO contained d-limonene, which may be the source of its insecticidal properties. Therefore, tangerine peel essential oil is an excellent natural insecticide candidate for controlling greenhouse whiteflies effectively and sustainably.

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.

Weeds are a permanent component of anthropogenic ecosystems, which requires strict control to avoid the accumulation of their long–lasting seeds in the soil. With high crop infestation, many elements of crop production technologies (fertilization, productive varieties, growth stimulators, etc.) turn out to be practically meaningless due to high yield losses. Intensive use of chemical herbicides (CH) has led to undesirable consequences: contamination of soil and wastewater, accumulation of their residues in the crop, the emergence of CH-resistant populations of weeds. In this regard, the development of environmentally friendly CH with new mechanisms of action is relevant. Natural phytotoxins of plant or microbial origin may be directly explored in herbicidal formulations (biorational CH) or indirectly as scaffolds for nature-derived CH. This review considers: 1) the main current trends in the development of CH that may be important for enhancement of biorational herbicides; 2) advances in the development and practical application of natural compounds for weed control; 3) the use of phytotoxins as prototypes of synthetic herbicides. Some modern approaches such as computational methods of virtual screening and design herbicidal molecules, development of modern formulations, determination of molecular targets are stressed as important to make exploration of natural compounds more effective.

The concept of green energy is now at the forefront of development discourse, with the United Nations Sustainable Development Goals (SDGs) 7, 11, and 12 all aimed at promoting green energy consumption to combat the three planetary crises: climate change, biodiversity loss, and pollution. Similarly, issues regarding Africa’s natural resource curse have caused a stir in the growth and development literature for some time now and there is no sign that it will die out. This study, the first of its kind, simultaneously assesses the impact of green energy consumption and Africa’s natural resources rents on economic growth by applying the Feasible Generalized Least Square (FGLS) estimator and the dynamic panel models of the Pooled Mean Group (PMG) and Mean Group (MG) estimators on data from 1990 to 2020 for 24 selected African countries. The results show that green energy consumption has a short-run growth-limiting effect and a long-run growth-enhancing effect in Africa. The study also found evidence of the natural resource curse phenomenon in Africa. The study, therefore, calls for the advancement and usage of green energy for both domestic and industrial production in Africa. The study further calls for a revamp in the global tax policy to curb illicit financial activities and strengthening institutional quality for transparency and accountability in the entire value chain of natural resource management in Africa.

The structures of recently discovered primarolides A and B suggest their non-enzymatic formation from a common 2-formylbenzophenone precursor. This hypothesis is based on the experimentally proven facile conversion of pestalone (also a 2-formyl-benzophenone) either into the isomeric lactone pestalalactone or the structurally related isoindolinone pestalachloride A. In a related fashion, the racemic isoindolinone natural product mariline A is supposed to biosynthetically originate from the corresponding keto-aldehyde and an aniline, as experimentally supported by model studies. Due to the close structural relationship with known systems, it appears highly probable that primarolides A and B were generated under the fermentation conditions from a massarinin-related 2-formylbenzophenone (proprimarolide) by reaction either with aniline or a nucleophilic catalyst, respectively. Suberoylanilide hydroxamic acid (SAHA), used as an additive during the fermentation, is supposed to act both as a source of aniline and as a nucleophilic catalyst.

Computational Fluid Dynamics (CFD) is utilized to study entropy generation for the rarefied steady state laminar 2-D flow of air-Al2O3 nanofluid in square cavity equipped with two solid fins at the hot wall. Such flows are of great importance in industrial applications, such cooling of the electronic equipment’s and nuclear reactors. In the current study, effects of Knudsen number (Kn), Rayleigh number (Ra) and the nano solid particles volume fraction (ϕ) on the entropy generation are investigated. The values of parameters considered in this work are as follows: 0≤Kn≤0.1, 〖10〗^3≤Ra≤〖10〗^6,0≤ϕ≤0.2. Length of the fins (LF) is considered to be fixed and equals to 0.5 m, whereas the location of the fins with respect to the lower wall (HF) is set to 0.25 and 0.75 m. Simulations demonstrate that there is an inverse direct effect of Kn on the entropy generation. Moreover, it is found that when Ra is less than 104, the entropy generation, due to the flow, increases as ϕ increases. In addition, the entropy generation due to the flow will decrease at Ra greater than 104 as ϕ increases. Moreover, the entropy generation due to heat will increase as both the ϕ and Ra increase. In addition, a correlation model of the total entropy generation as a function of all of the investigated parameters in this study is proposed. Finally, an optimization technique is adapted to find out the conditions at which the total entropy generation is minimized

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.

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).

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.

The search for new chemical compounds with antitumor pharmacological activity is a necessary process for creating more effective drugs for each specific malignancy type. This review presents the outcomes of screening studies of natural compounds with high anti-glioma activity. Despite significant advances in cancer therapy, there are still some tumors currently considered completely incurable including brain gliomas. This review covers the main problems of the glioma chemotherapy including drug resistance, side effects of common anti-glioma drugs, and genetic diversity of brain tumors. The main emphasis is made on the characterization of natural compounds isolated from marine organisms because taxonomic diversity of organisms in seawaters significantly exceeds that of terrestrial species. Thus, we should expect greater chemical diversity of marine compounds and greater likelihood of finding effective molecules with antiglioma activity. The review covers at least 15 classes of organic compounds with their chemical formulas provided as well as semi-inhibitory concentrations, mechanisms of action, and pharmacokinetic profiles. In conclusion, the analysis of the taxonomic diversity of marine species containing bioactives with antiglioma activity is performed noting cytotoxicity indicators and to the tumor cells in comparison with similar indicators of antitumor agents approved for clinical use as antiglioblastoma chemotherapeutics.

The article is devoted to biological models using recurrence sequences, which are connected with the harmonic progression 1, 1/2, …, 1/n, and some cooperative properties of genomes. The harmonic progression is itself one of the recurrence sequences based on the harmonic mean. This progression appears in the hyperbolic rules of oligomer cooperative organization in eukaryotic and prokaryotic genomes. This allows thinking that the harmonic progression is also related to inherited physiological systems, which must be structurally consistent with the genetic coding system for their transmission to descendants and survival in evolution. The harmonic progression is one of historically known mathematical series, whose features were studied by Pythagoras, Leibniz, Newton, Euler, Fourier, Dirichlet, Riemann. It is widely used in many known algorithms and is closely related to some other important mathematical objects, for example, the function of the natural logarithm and harmonic numbers. Accordingly, the article describes the possibilities of using these interrelated mathematical objects to model biological structures, including logarithmic spirals and some other. Modeling inherited spiral configurations seems to be a particularly urgent task, since they are extremely common at all levels of organization of living bodies and, according to Goethe, are lines of life. The principle of a recurrence similarity, that is a special similarity of parts and transformations presented in recurrence sequences of numbers and matrix operators (the scale similarity and scale transformations are only particular cases of such similarity), is considered as one of the key principles of structural organization of living bodies.

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.

This data descriptor summarizes the process applied and data gathered from 50 publications/papers reporting on the use of photography generated by tourists, tour operators and members of the public, with a particular focus on the crowdsourcing of photographs through online platforms and social networking sites (SNSs) as a method of research for wildlife conservation and ecotourism. The papers were collected in a systematic literature review to inform a pilot study of the feasibility of using SNSs to crowdsource georeferenced photographs of endangered Bornean Pygmy Elephants (Elephas maximus borneensis) taken by ecotourists along the Lower Kinabatangan River region of Sabah, Malaysia. Papers were sourced using the Murdoch University Findit online-search tool to search over 100 databases, including Proquest, Scopus and Web of Science. The criteria for a paper to be included in the review (and shared via the dataset attached to this this data descriptor) were that it was peer-reviewed, published in English, between 1997 and the 31 December 2017, had the full text accessible online and reported on a study or studies that utilized photographs that tourists, tour operators and/or members of the public generated and shared via SNSs or online platforms.

Streptomyces, being one of the most promising genera due to its ability to synthesize a variety of bioactive secondary metabolites of pharmaceutical interest, here studied in relation to its genomic and metabolomic potential. Coinciding with the increase in sequenced data, mining of bacterial genomes for biosynthetic gene clusters (BGCs) has become a routine component of natural product discovery. Herein, we describe the isolation and characterization of a Streptomyces tendae VITAKN with quorum sensing inhibitory activity (QSI) that was isolated from southern coastal parts of India. The nearly complete genome consists of 8,621,231bp with a GC content of 72.2%. Utilizing the BiG-SCAPE-CORASON platform, a sequence similarity network predicted from this strain was evaluated through sequence similarity analysis with the MIBiG database and existing 3,365 BGCs predicted by antiSMASH analysis of publicly available complete Streptomyces genomes. Crude extract analyzed on LC-HRMS/MS and Global Natural Product Social Molecular Networking (GNPS) online workflow using dereplication resulted in the identification of cyclic dipeptides (2,5-diketopiperazines, DKPs) in the extract, which are known to possess QSI activity. Our results highlight the potential use of genomic mining coupled with LC-HRMS/MS and bionformatic tools (GNPS) as a potent approach for metabolome studies in discovering novel QSI lead compounds. This study also provides the biosynthetic diversity of these BGCs and an assessment of the predicted chemical space yet to be discovered.

It is desirable to upgrade previous evolutionary theories, which have remained incomplete and controversial for decades. Here we employ the concept of carbon-based entities (CBEs), which include methane, amino acids, proteins, organisms, and other entities containing relatively many carbon atoms. We deduce the driving force, mechanisms, steps, modes, tempos of CBE evolution, through integration of biology, physics, and chemistry using logics for complex issues. We hence establish the Carbon-Based Evolutionary Theory (CBET). The CBET suggests that evolution is the increase in hierarchy, diversity, fitness of CBEs under natural selection and driven by thermodynamics due to the chemical effect of the thermodynamic features of the Earth on CBEs. It provides better explanations for life origin, macroevolution events, natural selection, sympatric speciation, and evolution tempos than previous evolutionary theories. It reveals the evolutionary basis of multiple important social notions, including diversity, collaboration, altruism, obeying rules, and proper increase in freedom. It refutes some wrong notions in thermodynamics, including negative entropy (negentropy) and that biological order is equal to thermodynamic order, which have misled many people. The CBET is supported by its deduction and application. It could be a rare bridge linking laws of thermodynamics, evolution of life, and development of human society, and could have great significance in various sciences.

The ketogenic diet and walking exercise training activity are two key public health lifestyle factors. The potential of combined lifestyle factors interventions focused on getting to compliance in forced exercise. A balanced ketogenic diet and regular exercise activity is a key modifiable factor to the prevention and management of chronic diseases. Influence health across the lifespan and reduction of the risk of premature death through several biological mechanisms. Community older group’s lifestyle factors interventions contribute identity in their natural living environment. While the older health benefits of walking exercise training strategies are commonly to study, combined ketogenic diet and walking exercise interventions have induced greater benefits in community older groups.

Recent maritime legislations demand the transformation of the sector to greener and more energy efficient transportation. Liquified Natural Gas (LNG) seems a promising alternative fuel solution that could replace the conventional fuel sources. Various studies have been focused on the prediction of LNG price, however, no previous work has been made on the forecast of spot charter rate of LNG carrier ships. An important knowledge for the maritime industries and companies when it comes to decision-making. Therefore, this study is focused on the development of a machine learning pipeline to address the aforementioned problem by: (i) forming a dataset with variables relevant to LNG; (ii) identifying the variables that impact on the freight price of LNG carrier; (iii) developing and evaluating regression models for short and mid-term forecast. The results showed that the General Regression Neural Network presented a stable overall performance for 2, 4 and 6 months forecast.

The violation of privacy, others people or personal, is a very current problem, which concerns not only on the web but also in private life. In the years 1990 it was expected that nowadays, that any routine operation was carried out "manually", and it would be performed through mobile phones or personal computers. The problem pertains the distribution network that allows to share and bring together information and as result the network becomes unsafe, if subjected to attacks. Nowaday we put personal information on web because otherwise we are seen as “weak”. This work aims to measure and analyze how much information are shared by users of a pre-established social network and it is carried out through a set of algorithms techniques of machine learning.

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.

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.

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.

Drug resistance has become an extremely serious problem worldwide. Antibiotic resistance genes (ARGs) entering the environment with wastewaters promote replenishment of the resistome of natural microbioms. Distribution of several clinically significant ARGs in wastewaters of Rostov-on-Don (Southern Russia), lower reaches of the Don River and natural waters of the neighboring region was investigated. Metagenomic DNA samples isolated from 250 ml of wastewaters or natural waters and 200 mg of surface sediments were used for the study. Identification of the ARGs was carried out with end-point detection PCR. Presence of NDM, OXA-48, CTX-M, VanA, VanB, ErmB, and TetM/TetO genes was detected in urban wastewaters. Samples of wastewater treatment plant (WWTP) sewage were enriched with ARGs in contrast to non-treated wastewaters from the sewage collector. NDM, VanA, ErmB, TetM/TetO genes were found only in wastewaters and were absent in samples of natural waters and surface sediments. Only OXA-48, VanB and CTXM genes were found in natural waters and surface sediments. The described ARGs are quite typical for urban and hospital wastewaters. The target ARGs were detected in the samples connected to the anthropogenous sources of pollution such as Rostov municipal WWTP or livestock enterprise effluents.

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.

Wound healing is a process through which skin maintains itself. Once a wound occurs, the inflammatory and proliferative stages are instigated in reaction to injury. It is established that wound restorative comprises four stages including haemostasis, inflammation, proliferation, and remodeling. The amelioration of wound healing is very challenging as tumors can develop at the site of chronic injury. There are numerous plants, plant extracts and plant based natural products were widely used by tribal communities from ancient times for the treatment of cuts, burns, scars, burns and wounds. The therapeutic potential of these plants is recognized due to the presence of phytomolecules such as phenolic compounds, flavonoids, triterpenoids, saponins, tannins, alkaloids and glycosides. The plant used for the treatments of wound healing includes Achillea millefolium, Andrographis paniculata, Boswellia sacra, Calendula officinalis, Crocus sativus, Curcuma longa, Ehretia laevis, Ehretia microphylla, Glycyrrhiza glabra, Malva sylvestris, Rosmarinus officinalis and Salvia officinalis. This assemblage comprises the structures of phytomolecules isolated from the different extracts of these plants, mechanistic insights and important key findings responsible for wound healing. The mechanistic insights involved in wound healing are similar to cytotoxic, anti-inflammatory and antioxidant agents such as ROS generation, DNA fragmentation and western blotting. This review article is an effort to bridge the gaps in the prevailing literature and thus offers gigantic scope for researchers and academicians betrothed in validation of the customary claims and development of safer and efficient and worldwide recognized natural potential candidates as drugs for healing of wounds, burns and cuts.

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.