In recent years, the high-frequency abnormal price fluctuations of small-scale agricultural products have been detrimental to market stability. The traditional method based on statistical data of production factors can no longer accurately predict abnormal price fluctuations. Taking scallions as a case, the paper identifies price influencing factors from massive network information, and analyzes the causes and changing characteristics of price fluctuations from four perspectives: market supply, economic environment and market sentiment, and attention. Initially, the study employs an LDA topic model to extract factors from online sentiment data over the past four years, including market supply and demand, economic conditions, related agricultural prices, and market attention. Subsequently, using the SO-PMI algorithm to expand domain-specific lexicons and employing semantic and sentiment analysis with syntactic parsing, the study aims to improve the accuracy of sentiment quantification in text analysis. Furthermore, cointegration and Granger causality tests validate the significant impact of these factors on scallion price fluctuations, mitigating spurious regression issues. Finally, by employing a TVP-VAR model, the study compares the impulse responses and shock magnitudes of various factors in different time and spatial dimensions. It also delves into the mechanisms of heterogeneous impacts and trends in scallion prices, considering the corresponding socio-economic context and public sentiment events within specific time windows, thereby providing valuable decision-making insights for the healthy development of agricultural markets.

Gas turbine power plants have important roles in the global power generation market. The examines thermodynamically the impact of steam injection for a combined cycle including a gas turbine cycle with a two-stage turbine and carbon dioxide recompression. The combined cycle is compared with the simple case without steam injection. Steam injection’s impact is observed on important parameters such as energy efficiency, exergy efficiency, and output power. It is revealed that steam injection reduces exergy destruction in components compared to the simple case. The efficiencies for both cases are obtained. The energy and exergy efficiencies respectively are found to be 30.4% and 29.4% for the simple case, and 35.3% and 34.1% for the case with steam injection. Also, incorporating steam injection reduces emissions of carbon dioxide.

Lubricated tribosystems such as main-shaft bearings in gas turbines have been successfully diagnosed by oil sampling for many years. In practice, the interpretation of wear debris analysis results can pose a challenge due to the intricate structure of power transmission systems and the varying degrees of sensitivity among test methods. In this work, oil samples acquired from the fleet of M601T turboprop engines were tested with optical emission spectrometry. Two-way analysis of variance (ANOVA) with interaction analysis and post hoc tests were carried out on measurement data from the whole fleet of the M-601T turboprop engines to study the impact of aluminum and zinc concentration on iron concentration. Finally, the developed model was used to evaluate the oil testing results for two specific engines of this type. Thanks to ANOVA, the assessment of engine health is based on a statistically proven correlation between the values of the dependent variable and the classifying factors.

Topic discovery is finding the main idea of large amounts of textual data. It indicates the recurring topics in the documents, allowing an overview of the texts. Current topic discovery models receive the texts, with or without pre-processing of Natural Language Processing. The processing consists of stopwords removal, text cleaning and normalization (lowercase conversion). A topic discovery model that receives texts with or without processing generates general topics since the input data is many uncategorized texts. The general topics do not offer a detailed overview of the input texts, and manual text categorization is a time-consuming and tedious task. Accordingly, it is necessary to integrate an automatic text classification task in the topic discovery process to obtain specific topics with their top words that contain relevant relationships based on belonging to a class. Text classification performs a word analysis that makes up a document to decide what class or category is being identified; then, integrating the text classification before a topic discovery process will provide latent topics depicted by top words with a high coherence in each topic based on the previously obtained classes. Therefore, this paper exposes a approach that integrates text classification into topic discovery from large amounts of English textual data, such as 20-Newsgroup and Reuters corpora. The text classification is accomplished with a Convolutional Neural Network(CNN) incorporating three embedding models based on semantic relationships. The topic discovery over categorized texts is realized with Latent Dirichlet Analysis(LDA), Probabilistic Latent Semantic Analysis(PLSA), and Latent Semantic Analysis(LSA) algorithms. An evaluation process was performed based on the normalized topic coherence metric. The 20-Newsgroup corpus was classified, and twenty topics with ten top words were discovered for each class, obtaining 0.1723 normalized topic coherence when applying LDA, 0.1622 with LSA, and 0.1716 with PLSA. The Reuters corpus was also classified, obtaining 0.1441 normalized topic coherence when applying the LDA algorithm to obtain 20 topics for each class.

Accurate analysis of continuous-wave electron spin resonance (cw ESR) spectra of biological or organic free-radicals and paramagnetic metal complexes is key to understand their structure-function relationships and electrochemical properties. Current method of analysis based on simulations often fail to extract the spectral information accurately. In addition, such analyses are highly sensitive to spectral resolution and artifacts, users’ defined input parameters and spectral complexity. We introduce a simulation-independent spectral analysis approach that enables broader application of ESR. We use a wavelet packet transform-based method for extracting g values and hyperfine (A) constants directly from cw ESR spectra. We show that our method overcomes the challenges associated with simulation-based methods for analyzing poorly / partially resolved and unresolved spectra, which is a common in most cases. The accuracy and consistency of the method are demonstrated on a series of experimental spectra of organic radicals and copper-nitrogen complexes. We showed that for a two-component system, the method identifies their individual spectral features even at a relative concentration of 5% for the minor component.

This article addresses the question how the religious narrative identity and subjective religiosity change over the course of 15 years. The cases portrayed are deconverts who have changed their religious affiliations multiple times. It will be carved out what led to their deconversion and what remains as a core of their faith after they have turned away from organized religion for good. Interviews have been conducted at three time points and are analyzed using content analysis. It will become clear that the needs and expectations of the two individuals differ highly, as well as the reasons for turning away from a religious community; yet what is a common core in this joint faithful journey is their need to live their religiosity, now in a private setting.

In the present manuscript, we report new insights into the concept of (a)synchronicity in Diels-Alder (DA) reactions in the framework of the reaction force analysis, in conjunction with natural population calculations and atomic resolution of energy derivatives along the intrinsic reaction coordinate (IRC) path. Our findings suggest that the DA reaction transitions between a preferentially concerted mechanism to a stepwise one in a 0.10 Å window of synchronicity indices, ranging from 0.90 to 1.00 Å. We have also shown that the relative position of the global minimum of the reaction force constant with respect to the TS is an alternative and quantifiable indicator of the (a)synchronicity in DA reactions. Moreover, the atomic resolution of energy derivatives reveals that the mechanism of the DA reaction involves two inner elementary processes associated with the formation of each of the two C-C bonds. This resolution goes on to indicate that, in asynchronous reactions, the driving and retarding components of the reaction force are mostly due the fast and slow-forming C-C bonds (elementary process) respectively, while in synchronous ones both elementary processes retard and drive the process concomitantly and equivalently.

The economy is a complex system, and the interactions between different agents are still not easy to quickly see-through. This complexity should reflect in a spatial dimension; in this way, tracking the tradeoffs opens a new window to the nexus of place and flow. Due to the fact, the economic systems often go through transitions and end up in another state, and this evolution is embedded in cities as the new motor of paradigm shift. To adequately represent and study these dynamics, we aim to develop an integrated method based on network analysis science and geographic economy synthesis to detect a multiscale navigator to track the transition from regional to the local level. This paper seeks to explore the specialization of regional clusters and their innovative behaviour in a particular lagging region, hence unfolding the innovation ecosystem to the smallest granularity then simulating the emergence phase of this complex system. First, our findings reveal that the local scale is relevant to start a bottom-up planning approach on policy implementation. Second, the global challenges could be addressed on a regional scale if we investigate the local complexity to unfold the innovation flow over its complex ecosystem and lead the knowledge as a critical element for inclusive transition, most probably into cities. Finally, the innovation network is an existing fact which can translate as a host for prosperity; In this line of reasoning, we intend to spatialize the track of the innovation flow to achieve transition hotspots and respond adequately to upcoming world concerns.

The doctoral defense is an important step towards obtaining the doctoral degree. As such, preparing for the event is necessary. Anecdotal evidence highlights that there is a wide variety of ways in which doctoral candidates prepare for the defense. In this work, I want to explore if there is a relation between the way in which a doctoral candidate prepares for the defense and two important aspects of the defense: the outcome of the defense, and the student perception during and after the defense. For this purpose, I first reviewed the literature on the topic of the preparation for the doctoral defense. Then, I carried out an international survey on the doctoral defense and analyzed the data of the 204 completed surveys with respect to the preparation for the defense using quantitative and qualitative methods. The methods I used included the statistical tests of the correlation between on one hand the preparation and on the other hand the defense outcome and student perception. I used inductive thematic analysis of the open-ended survey questions to gain deeper insight in the way candidates prepared for their defense. I found that candidates most often prepare by making their presentation, reading their thesis, and practicing. The most effective measure is the mock defense, followed by a preparatory course. Reading blogs, books, and chapters is a less effective preparation measure. The conclusion of this work is that doctoral candidates need to understand the format of their defense in order to be able to prepare properly, and that universities should explore either individual pathways to the defense or pilots using a mock defense and/or preparatory course to give their doctoral candidates the necessary tools to prepare for their doctoral defense.

The Sei Dareh Bridge is a cable-stayed bridge located in West Sumatra Province, Indonesia. The bridge, has a main span of 123 meters length and 9 meters wide, crosses the Batanghari River. Traffic load is transmitted through 4 prestressed cables to a 42.4 meter high pylon made of concrete. Bridge deck and traffic loads are directly supported by steel box girders as main beams that are reinforced laterally with cross beams IWF 800.300.16.24 and stringers IWF 350.350.12.19. This paper discusses static and dynamic testing on the bridge which aims to assess the feasibility before it is opened for public. Based on the test, it was concluded that the 73% static load could not be achieved because the deflection that occurred was beyond the allowable deflection. This is exacerbated by the sound of a loud clanging sound on the ST2-X1 prestressed cable when loading to 240 tons or 58% of the targeted load. In addition, this bridge is included in the "lazy bridge" category because it takes 24 hours to return to an undeformed condition after loading. As a recommendation for this bridge, it is necessary to carry out a structural health monitoring system (SHMS) regularly on the vehicle floor and cables.

Background & Objectives: The global pandemic caused by novel coronavirus SARS-CoV-2 has claimed several lives worldwide. With the virus gathering rapid spread, the world has witnessed increasing number of confirmed cases and mortality rate, India is not far behind with approximately 37,000 affected individuals as on May 2, 2020. The ongoing pandemic has raised several questions which need to be answered by analysis of transmission of the infection. The data has been collected on daily basis from WHO and other sites. We have represented the data collated graphically using statistical packages, R and other online softwares. The present study provides a holistic overview of the spread of COVID-19 infection in India. Methods: Real-time data query was done based on daily observations using publicly available data from reference websites for COVID-19 and other government official reports for the period (15^th February, 2020 to April 28^th, 2020). Statistical analysis was performed to draw important inferences regarding COVID-19 trend in India. Results: A decrease in growth rate of cases due to COVID-19 in India post lockdown and improvement in recovery rate during the month of April was identified. The case fatality rate was estimated to be 3.22% of the total reported cases. State-wise analysis revealed a deteriorating situation in states of Maharashtra and Gujarat among others as cases continued to increase rapidly there. A positive linear correlation between the number of deaths and total cases and exponential relation between population density and number of cases reported per square km was established. Interpretation & Conclusions: Despite early preventive measures taken up by the Government of India, the increasing number of cases in India is a concern. This study compiles state-wise and district-wise data to report the daily confirmed cases, case fatalities and strategies adopted in the form of case studies. Understanding the transmission spread of SARS-CoV-2 in a diverse and populated country like India will be crucial in assessing the effectiveness of control policies towards the spread of COVID-19 infection.

Process (or business process) management systems fulfill defining, executing, monitoring and managing process models deployed on process-aware enterprises. Accordingly, the functional formation of the systems is made up of three subsystems such as modeling subsystem, enacting subsystem and mining subsystem. In recent times, the mining subsystem has been becoming an essential subsystem. Many enterprises have successfully completed the introduction and application of the process automation technology through the modeling subsystem and the enacting subsystem. According as the time has come to the phase of redesigning and reengineering the deployed process models, from now on it is important for the mining subsystem to cooperate with the analyzing subsystem; the essential cooperation capability is to provide seamless integrations between the designing works with the modeling subsystem and the redesigning work with the mining subsystem. In other words, we need to seamlessly integrate the discovery functionality of the mining subsystem and the analyzing functionality of the modeling subsystem. This integrated approach might be suitable very well when those deployed process models discovered by the mining subsystem are complex and very large-scaled, in particular. In this paper, we propose an integrated approach for seamlessly as well as effectively providing the mining and the analyzing functionalities to the redesigning work on very large-scale and massively parallel process models that are discovered from their enactment event logs. The integrated approach especially aims at analyzing not only their structural complexity and correctness but also their animation-based behavioral properness, and becomes concretized to a sophisticated analyzer. The core function of the analyzer is to discover a very large-scale and massively parallel process model from a process log dataset and to validate the structural complexity and the syntactical and behavioral properness of the discovered process model. Finally, this paper writes up the detailed description of the system architecture with its functional integration of process mining and process analyzing. And more precisely, we excogitate a series of functional algorithms for extracting the structural constructs as well as for visualizing the behavioral properness on those discovered very large-scale and massively parallel process models. As experimental validation, we apply the proposed approach and analyzer to a couple of process enactment event log datasets available on the website of the 4TU.Centre for Research Data.

Serious outbreaks of foodborne disease have been caused by Listeria monocytogenes found in retail delicatessens and the severity of disease is significant, with high hospitalization and mortality rates. Little is understood about the formidable public health threat of L. monocytogenesin all four niches, humans, animals, food and environment in Egypt. This study analyzed the presence of L. monocytogenes collected from the four environmental niches and bioinformatic analysis was implemented to analyze and compare the data. PCR was used to detect virulence genes encoded by pathogenicity island (LIPI-1). prfA amino acid substation that causes constitutive expression of virulence was common in 77.7% of isolates. BLAST analysis did not match other isolates in the NCBI database suggesting this may be a characteristic of the region associated with these isolates. A second group included the NH1 isolate originating in China, and BLAST analysis showed this prfA allele was shared with isolates from other global locations such as Europe and North America. Identification of possible links and transmission pathways between the four niches, helps to decrease the risk of disease in humans, to take more specific control measures in the context of disease prevention, to limit economic losses associated with food recalls and highlightens the need to treatment options.

The driving force behind adopting the idea of ​​sustainable development are producers and retailers. Unfortunately, when preparing their product range for consumers, many of them still only pay attention to the size of the customers’ earnings, how often they shop and how much they buy when shopping. In consumer segmentation, sustainable values ​​that consumers apply when making their purchasing decisions are rarely taken into account. The aim of this article is to show that values ​​such as environmental protection, the ethical behaviour of producers, fair trade or maximising the utility function of consumption are so important in the purchasing process in the coffee market that they can be used as segmentation variables. The Polish coffee market was selected to carry out segmentation taking into account the consumers' sustainable values. The main source of the article is the results of a standardized online survey conducted on a nationwide sample of 800 coffee consumers in July 2018. Multi-dimensional analyses such as extrapolative factor analysis (EFA) and cluster analysis (CA) were used to describe the obtained results. As a result, six segments of coffee consumers were identified and described: “responsible, aspiring to be connoisseurs”, “loyal coffee enthusiasts”, “pragmatic users”, “coffee laypersons”, “sophisticated connoisseurs”, “consumerists, connoisseurs, but not at any price”. Among the identified segments, the sustainable consumption values ​​most often mentioned refer to "responsible, aspiring to be connoisseurs", and the least often - ”consumerists, connoisseurs, but not at any price”. The conclusions presented in the last part of the article may be used by manufacturing and trade enterprises, operating on the coffee market, in order to respond to the identified needs and expectations of consumers and by governmental and social organisations so as to determine the directions of pro-ecological education of consumers.

An important moment in the establishment of forestry seed orchards is the formation of their optimal composition, avoiding inbreeding depression in the outgoing seed material which occurs due to crossbreeding between closely related plus trees, the clones of which comprise the orchards. It is possible to minimize the negative effect of inbreeding by considering the hereditary aspect of the heterogeneous seed orchard material. The purpose of our work is to provide a comparative assessment of the formation and lignification of xylem in annual shoots of the plus trees of Scots pine. We investigated the formation and lignification of xylem in the annual shoots of the plus trees of Scots pine (Pinus sylvestris L.), found in natural forests in the Nizhny Novgorod region of the Russian Federation. Their vegetative progeny were cultivated in the clone archive stationed in the same region. Analyzing the one-type of the 1-year shoots, the time that the shoots were cut from the branches corresponded to the presence of plants in the characteristic phenological phases of seasonal development. A histochemical study of xylem was conducted, using the qualitative reactions of phloroglucinol to lignin. The power of xylem development was estimated by counting, in the radial direction, the cell rows from the core to the cambium in the microscope’s field of view. A significant differentiation of plus trees was revealed in a complex of signs characterizing the level of xylem development and the degree of xylem cell lignification in the tissues of annual shoots. Phenotypic differences in the physiological state of plus trees appeared given a leveled ecological background, indicating their genotypic determinism. This was confirmed by an analysis of variance. The share of the influence of differences between the proper plus trees was between 25.16% ± 8.91% and 53.98% ± 5.48% of the total phenotypic variance of the signs of the seasonal state of xylem. Factor analysis was used to reduce the number of considered indicators of xylem physiological state. The results allowed a cluster analysis to be carried out on the basis of the normalized values of the initial xylem features as well as the principal components derived from them. The association of plus trees was done on the basis of the similarity of the multidimensional estimates of xylem seasonal condition. The objects that were the most remote from the others were identified. This information provides a reasonable approach to the formation of an assortment of Scots pine seed orchards.

Based on the geologic conditions of natural gas hydrate (NGH) accumulations in the Shenhu area, northern slope of the South China Sea, this paper used a method of combining reservoir stimulation technique (RST) with depressurization to investigate the conversion efficiency of marine NGH reservoirs in different intrinsic permeability and initial NGH saturation conditions, and analyze the influence of variably-stimulation effect on marine NGH conversion efficiency in different accumulation conditions, provided a reference scheme for improving the NGH conversion efficiency in the Shenhu area. In this work, we performed calculations for the variations in CH4 production rate and cumulative volume of CH4 in different initial NGH saturation, intrinsic permeability and stimulation effect conditions, variance analysis and range analysis methods were used to analyze the significance of these key factors and their interaction, and investigated the sensitivity of stimulation effect on NGH conversion efficiency, respectively. The simulation results showed that stimulation effect have a significant influence on NGH conversion efficiency, and the influence of interaction between these three factors were not obvious. Possibly most important, we clarified an optimum stimulation effect for higher NGH conversion efficiency under different accumulation conditions, especially in low-permeability and high-saturation, which corresponding stimulation effect were different.

Flash flood is one of the most significant natural disasters in China, particularly in mountainous area, causing heavy economic damage and casualties of life. Accurate risk assessment is critical to an efficient flash flood management. There are more than 530,000 small watersheds in 2058 counties in China where flash flood should be prevented. In practice, with limited fund and different risk levels, the priorities of each small watershed for flash flood prevention and control are also needed for an efficient flash flood management. This paper, take Licheng county in China as an example, aims to give out these priorities for management. First, sensitive indexes are identified among index system, which includes 9 indexes based on underlying surface characteristics of small watershed in hilly region. Second, the range of each index and the rank division of each index for evaluation are determined. Based on the rank divisions for evaluation, the flash flood risk grade eigenvalue (H) is calculated by Variable Fuzzy Method (VFM ) using 1000 samplings generated by Latin hypercube sampling method. Third, the key sensitivity factors that affect flash flood risk grade eigenvalue (H) are assessed by two different global sensitivity analysis methods -- stepwise regression analysis and mutual entropy. Both results indicate that watershed slope (S) is the most sensitive factor; the second is antecedent precipitation index (CN); while other factors are slightly different sensitive in sequence. This study shows that stepwise regression analysis and mutual information analysis are appropriate for the sensitivity analysis of mountain flash flood risk. Finally, based on watershed slope (S), the priorities of flash flood prevention and control of 119 small watersheds in Licheng county are given out.

Retrospective information about the climate system and plant ecophysiology are key inputs in climate and Earth system modelling. Dendrochronology provides such information with large spatiotemporal coverage, and stable carbon isotope (¹³C/¹²C) analysis across tree-ring series is among the most advanced dendrochronological tools. For the past seventy years, this analysis was performed on whole molecules, and, to this day, ¹³C/¹²C variation is attributed to ¹³C discrimination during CO₂ diffusion into leaves and assimilation by rubisco. By contrast, ¹³C discrimination by post-rubisco processes is presumed constant. Here, recently reported results on the first dataset of intramolecular ¹³C discrimination in tree rings were synthesised by variance component analysis. The emerging picture is not consistent with the classical (DR-discrimination-centred) concepts and practices of carbon-isotope dendrochronology. Specifically, leaf and stem post-rubisco discrimination each account for more variation in the data than diffusion-rubisco discrimination, i.e., post-rubisco discrimination is not constant. Furthermore, diffusion-rubisco discrimination is used widely as proxy of leaf intrinsic water-use efficiency (iWUE), a key determinant in the responses of global biogeochemical cycles to climate change. However, since diffusion-rubisco discrimination is a small component of the total data variance, whole-molecule analysis yields confounded iWUE estimates, yet intramolecular analysis will likely offer solutions. Lastly, all currently observed ¹³C/¹²C-climate relationships are attributed to diffusion-rubisco discrimination. However, here, relationships with temperature and radiation derive from leaf-level post-rubisco discrimination while relationships with precipitation derive from stem-level post-rubisco discrimination. That said, advances in mass spectrometry may soon make intramolecular ¹³C/¹²C analysis broadly available taking carbon-isotope dendrochronology to the next level.

Maintaining a healthy cyber society is a big challenge due to the users’ freedom of expression and behaving. It can be solved by monitoring and analyzing the users’ behavior and taking proper actions towards them. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using Graph Analysis methods. Then the users’ behavioral patterns are analyzed by applying Metadata Analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then in the Abnormal Behavior Detection Filtering component, the interesting profiles are selected for further examinations. Finally, in the Contextual Analysis component, the contents will be analyzed using natural language processing techniques; A binary text classification model (SVM + TF-IDF with 88.89% accuracy) for detecting if the tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN with 80% accuracy); because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (Police) suggestions to control hate speech or terrorist propaganda.

Despite being stored at 113 K and atmospheric pressure, LNG cold potential is not exploited to reduce green ships’ energy needs. An innovative system based on three Organic Ranking Cycles integrated into the regasification equipment is proposed to produce additional power and recover cooling energy from condensers. A first-law analysis identified ethylene and ethane as suitable working fluids for the first and the second ORC making available freshwater and ice. Propane, ammonia and propylene could be arbitrarily employed in the third ORC for air-conditioning. An environmental analysis that combines exergy efficiency, ecological indices and hazard aspects for marine environment and ship’s passengers, indicated propylene as safer and more environmentally friendly. The exergy analysis confirms more than 20% of the LNG potential can be recovered from every cycle to produce a net clean power of 76 kW, whereas 270 kW can be saved by recovering condensers' cooling power to satisfy some ship needs. Assuming the sailing mode, a limitation of 162 kg in LNG consumptions was determined, avoiding the emission of 1584 kg of CO2 per day. Marine thermal pollution is reduced by 3.5 times by recovering the working fluids' condensation heat for the LNG pre-heating.

Machine learning has become more prevalent as a tool used for biogeochemical analysis in agricultural management. However, a common drawback of machine learning models is the lack of interpretability, as they are black boxes that provide little insight into agricultural management. To overcome this limitation, we compared three tree-based models (decision tree, random forest, and gradient boosting) to explain soil organic matter content through Shapley additive explanations (SHAP). Here, we used nationwide data on field crops, soil, terrain, and climate across South Korea (n = 9,584). Using the SHAP method, we identified common primary controls of the models, for example, regions with precipitation levels above 1400 mm and exchangeable potassium levels exceeding 1 cmol+ kg-1, which favor enhanced organic matter in the soil. Different models identified different impacts of nutrients on the organic matter level in the soil. The SHAP method is practical for assessing whether different machine learning models yield consistent findings in addressing these inquiries. Increasing the explainability of these models means determining essential variables related to soil organic matter management and understanding their associations for specific instances.

The Sei Dareh Bridge is a cable-stayed bridge located in West Sumatra Province, Indonesia. The bridge, which crosses the Batanghari River, has a main span of 123 meters long and 9 meters wide. Traffic load is transmitted through 24 prestressed cables to a 42.4-meter-high pylon made of concrete. The bridge deck and traffic loads are directly supported by steel box girders as main beams that are reinforced laterally with cross beams IWF 800.300.16.24 and stringers IWF 350.350.12.19. This paper discusses static and dynamic testing on the bridge which aims to assess its feasibility before it is opened to the public. Based on the test, it was concluded that a static load of 73% could not be achieved because the deflection that occurred was beyond the allowable deflection. This is exacerbated by a loud clanging sound on the ST2-X1 prestressed cable when loading to 240 tons or 58% of the targeted load. In addition, this bridge is included in the "lazy bridge" category because it takes 24 hours to return to an undeformed condition after loading. As a recommendation for this bridge, it is necessary to carry out a structural health monitoring system (SHMS) regularly on bridge slabs and cables.

This research presents a comprehensive bibliometric and sentiment analysis of nanocomposite literature from 1990 to 2024. Employing cutting-edge computational methods, the study delves deep into the progression of microstructural characterization, electrical properties, and mechanical behaviors of nanocomposites. The analysis utilizes advanced Boolean search strategies from the Scopus database, ensuring a thorough extraction of thematic content. The results explore various themes and insights, shedding light on trends evident in the Scopus database, particularly the prominence of research on nanocomposite microstructure, electrical attributes, and mechanical performance. The paper also offers textual analytics and bibliometric data, showcasing critical collaborative efforts and influential studies. Significant discoveries encompass the evolution of research language, shifts in thematic focus, and global contributions, providing a comprehensive perspective on the current landscape of nanocomposite research and its dynamic evolution. Moving forward, the "State-of-the-Art and Gaps Extracted from Results and Discussions" section of the paper delves into the most recent advancements in nanocomposite research. It delves into various types of nanocomposites, with a particular emphasis on their microstructural characteristics, electrical properties, mechanical dynamics, and the application of nanocomposite films. This section identifies key research themes, traces historical progress, and highlights emerging trends while underscoring the significance of collaboration and the influence of pivotal studies that have shaped the field. Lastly, in the "Literature Review Guided by Artificial Intelligence" section, the paper introduces a revised approach for researching nanocomposites through AI-guided techniques. This approach prioritizes articles published in 2023 based on citation frequency. Here, the focus is on exploring the relationship between nanocomposites and their electrical properties, emphasizing their fundamental interactions and impact on electrical characteristics. Various nanocomposite systems are covered, highlighting their composition, structure, and functionality. Findings from recent studies are integrated to provide a comprehensive overview of the current state of knowledge in this area. Notably, the sentiment analysis, anchored by an average sentiment score of 0.638771, underscores a positive trajectory in academic discourse, emphasizing the growing recognition of the potential of nanocomposites. The bibliometric exploration maps the intellectual domain, emphasizing pivotal research themes and the influence of crosslinking time on nanocomposite attributes. While this study is thorough, it acknowledges its limitations and advocates for broader database inclusion in future research endeavors. This work elucidates the prevailing trends in nanocomposite research, emphasizing the indispensable role of computational tools in comprehending this vast wealth of information.

Seeds can maintain their quality for a limited time; after that, they will lose their germination ability and vigor. Some physiological and physicochemical changes in the structure of the seeds during storage can decrease the quality of the seeds which is known as aging. Therefore, detection of the strong young seeds from the old ones is a vital issue in the modern agriculture. Conventional methods of detection of the seed viability and germination are destructive, time-consuming and costly. In this research, two peanut cultivars, namely North Carolina 2 (NC-2) and Florispan were selected and three artificial aging levels were induced to them. Hyperspectral images (HSI) of the samples were acquired and the seed viability was evaluated using two pre-trained convolutional neural network (CNN) image processing models, AlexNet and VGGNet. The noise of the reflection spectra of the samples was relatively resolved and modified by combining Preprocessing techniques of moving average (MA) and standard normal variate (SNV). Using principal component analysis (PCA), the dimensions were declined and three principal components (PC) were extracted. These PCs were then used as variables in the classification of support vector machine (SVM) and linear discriminant analysis (LDA). The results showed the high capability of CNN architectures such as AlexNet and VGGNet in detection of the seed viability based on the HIS with no pre-processing and feature extraction. The mentioned architectures reached the accuracy of 0.985 and 0.986, respectively. The combination of feature extraction method of PCA with LDA and SVM classifiers showed that the use of a limited number of PCs instead of all wavelengths can decrease the complexity of modeling, while enhancing the efficiency of the models such that LDA and SVM classifiers achieved the accuracy of 0.983 and 0.986 in classification of peanut sees, respectively.

Qualitative comparative analysis (QCA) was developed by Charles Ragin in 1987 for the comparative analysis of small data sets. The method has become widespread in sociological and economic research. There are examples of successful use of QCA in the field of medicine and epidemiology. The purpose of this review is to describe the key stages of QCA with a discussion the application of this method to the analysis of medical and epidemiological data.

Waterford Blaa is one of only seven Irish Protected Geographical Indication (PGI) products and the only Irish PGI bread. Currently, there are few published data characterising these products. This study aimed to determine whether any compositional or sensory markers could be used to differentiate between the three authorised producers of Waterford Blaa. Compositional testing was completed using AOAC standard methods; colour of top crust, bottom crust and crumb were identified with the Minolta Hunter lab colorimeter and texture profile analysis (TPA) was completed with an 80% compression test using the Instron 5544 to establish hardness, gumminess, and springiness. The fatty acid and aroma profiles were established in triplicate testing for the three authorised Blaa products using gas chromatography-flame ionisation detection and headspace-solid phase micro extraction-gas chromatography-mass spectrometry, respectively. As expected, results showed little variation in the overall composition of Waterford Blaa products. However, this study identified a unique fatty acid profile/volatile aroma profile for each Blaa. Specifically, the volatile profile showed potential differentiation between Blaa’s in terms of pentanal, furfural, 2-furanmethanol, 2-ethyl-1-hexanol and 2-pentyl furan. In addition, levels of hexanal and ethyl acetate were significantly different (p<0.05) in the crumb section of all three Blaa’s. This unique fingerprint has the potential to be used to identify fraudulent products claiming to be Waterford Blaa.

Using graph theory, computer aided motion, static force and power flow analysis of planetary gear mechanism is realized. The motion, static force and efficiency calculations of 6HP26 automatic transmission are carried out. Based on kinematic and static force analysis model, related matrices are acquired. Hence, kinematic and static force analysis of planetary gear mechanism is achieved. The link power can be determined by the link speed and torque. Power flow diagrams of each gear are acquired. The efficiency is calculated by transmission ratio method.

This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By analyzing data from the Scopus database, 533 relevant articles were identified for analysis. The findings reveal the prominent publication venues, influential authors, and countries contributing to ChatGPT research. Collaborative networks among researchers and institutions are visualized, highlighting patterns of co-authorship. The application domains of ChatGPT, such as customer support and content generation, are examined. Moreover, the study identifies emerging keywords and potential research areas for future exploration. The methodology employed includes data extraction, bibliometric analysis using various indicators, and visualization techniques such as Sankey diagrams. The analysis provides valuable insights into ChatGPT's influence in academia and offers researchers guidance for further advancements. This study stimulates discussions, collaborations and innovations to enhance ChatGPT's capabilities and impact across domains.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96\% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

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.

The determination of crack propagation velocities can provide valuable information for a better understanding of damage processes of concrete. The spatio-temporal analysis of crack patterns developing at a speed of several hundred meters per second is a rather challenging task. In the paper, a photogrammetric procedure for the determination of crack propagation velocities in concrete specimens using high-speed camera image sequences is presented. A cascaded image sequence processing which starts with the computation of displacement vector fields for a dense pattern of points on the specimen’s surface between consecutive time steps of the image sequence chain has been developed. These surface points are triangulated into a mesh, and as representations of cracks, discontinuities in the displacement vector fields are found by a deformation analysis applied to all triangles of the mesh. Connected components of the deformed triangles are computed using region-growing techniques. Then, the crack tips are determined using principal component analysis. The tips are tracked in the image sequence and the velocities between the time stamps of the images are derived. A major advantage of this method as compared to established techniques is in the fact of its allowing for spatio-temporally resolved, full-field measurements rather than point-wise measurements and that information on crack width can be obtained simultaneously. To validate the experimentation, the authors processed image sequences of tests on four compact-tension specimens performed on a split-Hopkinson tension bar. The images were taken by a high-speed camera at a frame rate of 160,000 images per second. By applying to these datasets the image sequence processing procedure as developed, crack propagation velocities of about 800 m/s were determined with a precision in the order of 50 m/s.

Near-fault ground motions can cause severe damage to civil structures, including bridges. Safety assessment of these structures for near fault ground motion is usually performed through Non-Linear Dynamic Analyses, while faster methods are often used. IMPAb (Incremental Modal Pushover Analysis for Bridges) permits to investigate the seismic response of a bridge by considering the effects of higher modes, which are often relevant for bridges. In this work, IMPAb is applied to a bridge case study considering near-fault pulse-like ground motion records. The records were analyzed and selected from the European Strong Motion Database and the pulse parameters were evaluated. In the paper results from standard pushover procedures and IMPAb are compared with nonlinear Response-History Analysis (NRHA), considering also the vertical component of the motion, as benchmark solutions and incremental dynamic analysis (IDA). Results from the case study demonstrate that the vertical seismic action has a minor influence on the structural response of the bridge. Therefore IMPAb, which can be applied considering vertical motion, remains very effective conserving the original formulation of the procedure, and can be considered a well performing procedure also for near-fault events.

This paper presents an approach to the Euler-Bernoulli beam theory (EBBT) using the finite difference method (FDM). The EBBT covers the case of small deflections, and shear deformations are not considered. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected points of the structure. Generally, the finite difference approximations are derived based on fourth-order polynomial hypothesis (FOPH) and second-order polynomial hypothesis (SOPH) for the deflection curve; the FOPH is made for the fourth and third derivative of the deflection curve while the SOPH is made for its second and first derivative. In addition, the boundary conditions and not the governing equations are applied at the beam’s ends. In this paper, the FOPH was made for all of the derivatives of the deflection curve, and additional points were introduced at the beam’s ends and positions of discontinuity (concentrated loads or moments, supports, hinges, springs, etc.). The introduction of additional points allowed us to apply the governing equations at the beam’s ends and to satisfy the boundary and continuity conditions. Moreover, grid points with variable spacing were also considered, the grid being uniform within beam segments. First-order analysis, second-order analysis, and vibration analysis of structures were conducted with this model. Furthermore, tapered beams were analyzed (element stiffness matrix, second-order analysis). Finally, a direct time integration method (DTIM) was presented. The FDM-based DTIM enabled the analysis of forced vibration of structures, with damping taken into account. The results obtained in this paper showed good agreement with those of other studies, and the accuracy was increased through a grid refinement. Especially in the first-order analysis of uniform beams, the results were exact for uniformly distributed and concentrated loads regardless of the grid. Further research will be needed to investigate polynomial refinements (higher-order polynomials such as fifth-order, sixth-order…) of the deflection curve; the polynomial refinements aimed to increase the accuracy, whereby non-centered finite difference approximations at beam’s ends and positions of discontinuity would be used.

This research article describes the creation of a mock-up for a Smart Insole prototype that employs Force Sensitive Resistors (FSRs) and an ADXL345 accelerometer. The mock-up is an early step towards realising the concept and functionality of the planned Smart Insole system. The key factors in the mock-up's design, integration, and testing are highlighted, with a focus on the iterative refining and optimisation process. The mock-up, which was carefully planned and implemented, highlights the practicality and possible applications of Smart Insole technology in healthcare, sports performance measurement, and rehabilitation. The paper outlines the development process and implementation of a "Smart Insole" utilizing Force Sensitive Resistors (FSR) and the ADXL345 accelerometer. The project aims to create an innovative wearable technology solution for monitoring foot biomechanics and enhancing human performance. The Smart Insole prototype integrates FSRs strategically placed on the insole to capture pressure distribution across the foot, along with an ADXL345 accelerometer to track foot orientation and movement. Through a combination of hardware integration, firmware development using Arduino IDE, and visualization using Processing application, the Smart Insole offers real-time monitoring and analysis of gait dynamics, pressure distribution, and foot movement.

This article investigates the vibration analysis of the mast on the bridge top of a 28-meter tugboat, in the quasi-static regime. The study aims to determine the natural frequencies using an equivalent dynamic model of the mast and considers the accelerations of different degrees of freedom of the vessel. Practical design and reinforcement strategies for the local bridge top structure are proposed, utilizing numerical and analytical tools. The investigation explores various alternatives to optimize the structural arrangement. By analyzing the modal behavior, insights are provided for efficient design and reinforcement. The results contribute to understand the mast's vibration characteristics, offering guidance for similar structures. The study employs numerical and analytical methods, enhancing the optimization of the mast's behavior. It determines natural frequencies, proposes practical design strategies, and explores alternatives for structural optimization. The findings provide insights for shipbuilding and mast design applications in other vessels and industries such as radar mast, compass deck and guyed mast.

The development of transversal competences, in interaction with professional competences, is essential for individuals to adapt to change and live meaningful and productive lives. The aim of this study was to explore the mutual interaction of six transversal competences—civic, digital, entrepreneurial, global, innovation, and research, as well as their respective sub-competences—and propose possible approaches to how this dynamic of interaction can be applied to facilitate the development of transversal competences. A sample of students (N = 1575) from 21 universities in Latvia completed a self-assessment survey evaluating multiple behavioural indicators for each transversal competence and sub-competence. The results revealed that civic competence was evaluated significantly lower than other competences, and, for most transversal competences, sub-competences associated with active and systematic collaboration and networking received lower evaluations. A network analysis showed that the community involvement sub-competence of the civic competence and the initiative and critical thinking sub-competences of the innovation competence had the strongest links to the other measured sub-competences on average. The results suggest that the development of initiative and cooperation-and-collaboration-related and network-building skills are likely to have a systematic positive effect on the development of a wider set of transversal competences and the professional autonomy of students in the long term.

The flux of opinions in newsletters and social media platforms significantly influences market perceptions regarding corporate entities and their financial instruments. This paper introduces an advanced model, the Sentiment Dynamics Analyzer (SDA), which leverages a refined hierarchical architecture of Transformers to effectively discern the sentiment embedded within financial texts, ranging from news headlines to microblogs. We have enhanced a RoBERTa model with specialized sentiment lexicons and an augmented layer of Transformer models tailored for nuanced sentence-level sentiment detection, aiming to assign a sentiment score from -1 to +1. Our evaluations demonstrate that SDA not only surpasses the previous best methods but also exhibits superior performance over robust baseline models. This success underscores the utility of integrating tailored contextual analyses with sector-specific sentiment insights in improving predictive accuracy.

-- This paper examines the critical role of business analysis in shaping small and medium-sized enterprises’ strategies for better odds of success in the ever-evolving Nigerian economy and within the competitive markets. Business analysis ability, as a knowledge-supporting factor, focuses on understanding consumer needs, planning, data collection, analysis, and presentation to decision-makers. The process begins with strategic planning and meticulous data collection, facilitating a deep understanding of the business and consumer behavior and process potential. Analytical skills unearth the true potential of organizational processes, particularly in product creation, resulting in distinctive and highly valued products. Such products align with theories like the resource-based view or VRIO theory, driving increased sales volume—a key performance metric. By leveraging analytical capabilities, Nigerian SMEs can navigate complex market dynamics, create value-driven businesses, and differentiate themselves from competitors thus increasing the odds of success. This paper sets the stage for further research into the impact of business analysis on organizational success within dynamic market landscapes.

This investigation aimed to explore the impact of acute and regular physical exercise on Brain-Derived Neurotrophic Factor (BDNF) levels in individuals with obesity. Using the Population, Intervention, Comparator, Outcome, and Study design (PICOS) approach, the inclusion criteria were defined as follows: i) population - individuals with overweight or obesity of any age or gender; ii) intervention - acute or chronic interventions involving aerobic or anaerobic methodologies; iii) comparator - for acute effect studies, pre- and post-evaluations served as comparators, while interventions included at least one experimental group or the inclusion of a control group; iv) outcome - BDNF variability concerning the applied methodology; v) study design - restricted to experimental studies exclusively. Conducted in August 2022, the searches were carried out on PubMed, Academic Search Complete, and Web of Science. The risk of bias was assessed using the Risk of Bias in Non-randomized Studies (RoBANS) tool. Out of 98 potentially relevant articles, 16 met the eligibility criteria, encompassing 23 trials. Substantial heterogeneity was observed for both acute (I2 = 80.4%) and long-term effects (I2 = 88.7%). Additionally, RoBANS analysis indicated a low risk of bias for the included studies. The study's findings revealed a significant increase in the concentration of circulating BDNF in individuals with obesity compared to the control group following acute exercise. However, such an effect was not evident in the context of long-term physical exercise, with a small effect size (ES = 0.49) recorded in the latter scenario. The observed significant increase in BDNF levels post-acute exercise suggests a potential acute response mechanism contributing to the intricate interplay between exercise and neurotrophic factors in this population. Nevertheless, the absence of a similar effect in the context of long-term physical exercise implies a potential adaptation or ceiling effect. These findings underscore the importance of considering both the duration and nature of exercise interventions when exploring the neurobiological responses associated with obesity and exercise. The study provides valuable insights for future research and clinical implications in understanding the nuanced relationship between exercise, neurotrophic factors, and obesity.

Quantitative analysis of human gait is critical for early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson’s Disease, stroke, and Cerebral Palsy. Gait analysis typically involves estimating gait characteristics, such as spatio-temporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates, but are limited in operational requirements when applied in daily life - such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing footstep-induced floor vibrations during walking using vibration sensors mounted on the floor surface. Our approach is non-intrusive, scalable, and perceived as more privacy-friendly, making it suitable for continuous gait health monitoring in daily life. We developed a floor vibration-based gait analysis framework to estimate various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step, stride, stance, swing, double-support, single-support time) and spatial parameters (step length, width, angle, stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, initial contact types). The main challenge is that floor vibrations are distinct across different floor types. To address this challenge, our approach develops floor-adaptive algorithms to extract floor-specific features and is designed to be generalizable to various settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

Elevated circulating triglyceride levels have been linked to an increased risk of diabetes, although the precise mechanisms remain unclear. This study aimed to investigate whether low-density lipoprotein (LDL) cholesterol, homeostatic model assessment (HOMA) for insulin resistance, and C-reactive protein served as mediators in this association across a sample of 18,435 US adults. Mediation analysis was conducted using the PROCESS Version 4.3 Macro for SPSS. Simple medi-ation analysis revealed that all three potential mediators played a role in mediating the associa-tion. However, in the parallel mediation analysis, where all three mediators were simultaneously included, HOMA for insulin resistance remained a significant mediator (indirect effect coefficient, 0.47; 95% confidence interval [CI], 0.43—0.52; p < 0.05) after adjusting for all tested confounding factors. Conversely, LDL cholesterol (indirect effect coefficient, -0.13; 95% CI, -0.31—0.05; p > 0.05) and C-reactive protein (indirect effect coefficient, 0.01; 95% CI, -0.003—0.02; p > 0.05) ceased to be significant mediators. HOMA for insulin resistance accounted for 49% of the association between triglycerides and diabetes. In conclusion, HOMA for insulin resistance was the dominant mediator underlying the association between triglycerides and diabetes. Therefore, reducing triglyceride levels may hold promise for improving insulin sensitivity in diabetic patients.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.

This study aimed to identify the most beneficial attitudes adopted by enterprise participants during their engagement in open-source software (OSS) activities. For this purpose, we selected repositories from GitHub, featuring the top 10 with the highest stars and forks, classified the participants according to their sentiment states, and compared their productivity. The results indicated that participants with positive sentiments were more productive than those with negative sentiments. Consequently, it can be inferred that encouraging organizations to express positive sentiments towards their developers publicly may stimulate OSS community activities and enhance productivity, thereby potentially leading to increased open innovation and sustainability.

The article focuses on breast cancer, mammography, and artificial intelligence. First, breast cancer is a widespread health problem that affects millions of people worldwide, and mammography is a widely adopted screening method. Then it introduced the advantages of mammography after AI participation, and introduced the importance of early detection of breast cancer and the application of artificial intelligence in breast cancer detection and treatment. From these aspects extend to the entire medical field of artificial intelligence issues. Several times throughout the paper, ethical issues that could arise from applying AI to healthcare are highlighted. At the end of the article, the paper describes the continued development of AI-based mammography analysis over the next period of time.

This paper introduces a novel graph polynomial approach for differentiating tree structures in dependency grammar. Utilizing this polynomial representation, we develop a metric to assess the similarity in syntax. This approach offers a detailed and inclusive analysis of the dependency structures and relationships in sentence construction. We employ this polynomial method to examine sentence structures across various languages in the Parallel Universal Dependencies treebanks. Our analysis includes comparing the syntax of original sentences and their translated counterparts in diverse languages, alongside a comprehensive study of syntactic typologies within these treebanks. Additionally, we explore the application of our methodology in evaluating the syntactic diversity within language corpora.

The process of Targeted Opinion Word Extraction (TOWE), a critical component of aspect-based sentiment analysis (ABSA), revolves around identifying opinionated words linked to specific aspect-terms within sentences. Existing deep learning approaches, while effective, often overlook the syntactic structure of sentences, a factor that previous studies have identified as beneficial for TOWE. In this study, we introduce the Syntactic-Enhanced Deep Learning Model (SEDLM) that integrates syntactic structures into deep learning frameworks for TOWE. Our approach leverages syntax-driven opinion potential scores and syntactic inter-word connections, enhancing model performance. Additionally, we introduce an innovative regularization strategy aimed at distinguishing word representations in TOWE tasks. Our comprehensive analysis reveals that SEDLM sets new benchmarks in performance across multiple standard datasets.

The analysis of drugs is a priority in public health, particularly antibiotics which develop resistance due, among other things, to bad dosages or poor quality of the active substance. In this study, we evaluated eight antibiotics available in health centers in Djibouti through qualitative and quantitative analysis. The tests were based on high-purity reference products. In qualitative analysis by HPLC, only amoxicillin presented two impurity peaks. This observation is found in the dosage of this active ingredient with a value of 83 ±0.6 mg instead of the 500 mg indicated on the label. It will be necessary to diversify the sample’s provenance to seek the origin of this degradation.

: With the increase in demand for non-dairy starter cultures and probiotic bacteria as carriers, the use of legumes (also called pulses) as an alternative has gained momentum. In this study, we investigated the diversity of bacterial communities in samples of pigeon pea (Cajanus cajan L. Millsp.) soaked in water for 12 h and 24 h. We soaked 500 g of pigeon pea in sterile distilled water at room temperature (± 25 °C) for 12 h and 24 h; 10 mL of the soaking water was then collected to measure the bacterial diversity using a metataxonomic analysis. The V1–V9 regions on the 16S ribosomal RNA gene were amplified using 27F and 1492R primers under specific polymerase chain reaction conditions for the bacterial identification. Genomic DNA (130 ng) was sequenced on a R9.4 flow cell by Oxford Nanopore Technologies using a GridION sequencer. Library preparations were initiated using a Native Barcoding Kit 24 V14 (SQK-NBD114.24). Primary data were acquired using MinKNOW version 22.05.7. A total of 13 bacterial families and 89 genera were identified in the pigeon pea sample soaked for 12 h; 26 families and 90 genera were identified in the pigeon pea sample soaked for 24 h. Among the bacterial families identified, the five predominant families in both samples were Enterobacteriaceae, Erwiniaceae, Yersiniaceae, Pectobacteriaceae, and Lactobacillaceae. According to the relative abundance of the identified bacterial genera, the following nine genera were predominant in both samples: Enterobacter, Klebsiella, Citrobacter, Pantoea, Kosakonia, Pseudoenterobacter, Pluralibacter, Leclercia, and Kluyvera. At a genus level, a slight increase in the abundance of Klebsiella, Kosakonia, and Pluralibacter and a slight decrease in the abundance of Citrobacter were observed after prolonged incubation from 12 h to 24 h. The values of five diversity indices revealed that the sample soaked in water for 24 h had a richer bacterial abundance and diversity than the 12 h sample. Shannon and Simpson values revealed a higher bacterial diversity in the sample collected at 24 h than the sample collected at 12 h. Species observations and abundance-based coverage estimator (ACE) values demonstrated that the sample collected at 24 h harbored a higher bacterial richness than the sample collected at 12 h. These findings indicated that the bacterial diversity in the pigeon pea samples increased with the soaking time. The bacterial communities during the soaking of the pigeon pea samples were dominated by the Enterobacteriaceae family and Enterobacter genus. The presence of bacterial genera such as Lacticaseibacillus, Lentilactobacillus, and Secundilactobacillus was notable because of their importance as starter cultures for fermented plant-based milk products, including pigeon pea beverages for lactose-intolerant individuals or individuals with malnutrition.

This study proposes a cephalometric analysis that can be used for biomechanical simulation by considering the important and complex relationship between craniofacial morphology and masseter muscle (MM) size and inclination and by including muscle values. The MM line of action drawn between the Gonion (Go) and Orbital (Or) point relative to dental and skeletal references (occlusal and Frankfort planes) were analyzed. A total of 510 pre-treatment lateral cephalometric tracings (217 males, 293 females, 6-50 years) and lateral Bolton standard tracings (6-18 years) were analyzed. The study examined the following: a) skeletal-cutaneous class (linear distance in mm between the projections of points A' and B' on the occlusal plane), b) the angle between the perpendicular line to the occlusal plane and the Go-Or line at the molar occlusal point, and c) the angle between the Go-Or line and the Frankfort plane. The skeletal classes of the patients ranged from -14.5 to 15.5 mm and did not differ by sex or age; the angle between the Go-Or line and the normal to the occlusal plane averaged 39° (range 15° to 53°) and decreased with age, while the angle between the Go-Or line and the Frankfort plane averaged 42° (range 30° to 54°), increased in older patients. No effect of gender was observed. The two angles were also significantly correlated with each other, but not with sagittal jaw discrepancy. Similar results were obtained with Bolton tracings; the positional relationship of the Go-Or line to the teeth and skeletal landmarks is partially consistent with the literature. The present cephalometric analysis can be usefully used to estimate the mechanical advantage of MM in biomechanical simulations of masticatory muscle performance.

Using annual panel data of 46 listed commercial banks in China from 2012 to 2021 and constructing a two-way fixed-effects model, this study empirically analyzes the competition and technology spillover effects of fintech on the profitability of commercial banks. The results show that (1) in the early stages of fintech development, the competition effect is larger than the technology spillover effect; thus, it is negatively correlated with commercial banks’ profitability. However, with the spread of innovative fintech, the technology spillover effects and commercial bank profitability will gradually improve. (2) The influence of fintech on the profitability of commercial banks differs. Compared to large commercial banks, fintech has more significant negative effects on small- and medium-sized commercial banks in the short run. However, the role of fintech for such banks will also grow in the future. The results of this study provide practical guidance for how commercial banks can respond to the fintech wave. To realize sustainable development of the banking industry, commercial banks should change their business philosophy and revenue model, vigorously improve their fintech innovation capability, differentiate their choice of fintech development routes, develop personalized customization with a focus on users, and ultimately realize digital transformation and upgrading.

The purpose of the investigation was to determine the relationship between rotary kiln liner loss and steel structure ovality. As measurement apparatus, a terrestrial laser scanner was used. The interior and exterior of the rotary kiln were measured. The primary focus object was inner-lining loss and the geometric characteristics of cylindrical shells. The research uncovered significant disparities in inner lining loss between sections. A correlation was found between ovality and elimination of inner lining. Due to the hypothesis of constant inner linig loss from the middle of the rotary kiln, the investigation found that the loss of brick lining was less than the value reported from the boreholes. The study offers significant information on maintenance and repair strategies for rotary kilns, which have the potential to increase their efficiency and useful life.

The objective of the study was to analyse the antimicrobial susceptibility trends of Spanish porcine bacteria to quinolones, cephalosporins and polymyxins. Isolates of Actinobacillus pleuropneumoniae, Pasteurella multocida, and Escherichia coli were isolated from sick pigs from 2019 to 2022. Antimicrobial susceptibility test was determined by minimal inhibitory concentration (MIC) following internationally accepted methodology. MIC categorization was based on distributing the range of MIC values in four categories being category one the most susceptible (lowest MIC value) and four the less susceptible one (highest MIC value). Moreover, clinical susceptibility (susceptible/resistant) was also determined according to CLSI and EUCAST clinical breakpoints. A logistic and multinomial logistic regression model was used to analyze the susceptibility data for dichotomized and categorized MIC data, respectively for any pair of antimicrobial/microorganism. In general terms, the antimicrobial susceptibility of pig bacteria to these antimicrobials remained stable or increased in the last four years in Spain. In the case of A. pleuropneumoniae and quinolones, it was observed a significant temporal trend where isolates from 2020 had significantly increased odds of being more susceptible than isolates from 2019. In the case of E. coli and polymyxins, a significant temporal trend was observed where isolates from 2020 and 2021 had significantly increased odds of being more susceptible than isolates from 2019 and 2020, respectively. Finally, a significant odd of being less susceptible was only observed for cephalosporins and E. coli for 2020 versus 2019, stagnating for the rest of study period. These results provide sound data on critical important antimicrobials in swine medicine.

In the present work we consider two modified Chevallier-Polarski-Linder (CPL) models in the background of homogeneous and isotropic FLRW space-time, namely (i) the generalized CPL model (Model I) and (ii) the logarithmic form of the the equation of state for the dark energy. From the observational data sets ((Pantheon+)+BAO+HST), we find that at the present epoch (redshift $z=0$), the equation of state for the dark energy converges almost at the same value $\omega_{fld}\approx -0.86$ and the variation of $\omega_{fld}(z)$ with respect to the redshift parameter is very small for both models. we also find that the present value of the Hubble parameter $H_0$ is almost same for both the models. Finally, we compare the models in the light of Akaike, Bayesian Information Criterion (BIC) and Bayesian Evidence. However, we find that Model II is better compared to the Model I from the estimated value of the deceleration parameter.

Objective: To verify SPRR2A is a hub gene for endometrial cancer (EC). Methods: Bioinformatics analysis was performed on the high-throughput sequencing dataset of 587 EC cases in TCGA. RT-qPCR verified the differential expression of the SPRR2A gene in EC cell lines. Results: Enrichment of differentially expressed genes in comparison with normal tissues has been found in several biological process pathways, including keratinization and epidermal growth. The signalling pathways of differentially expressed genes were mainly focused on the receptor-ligand pathway and passive transmembrane transport pathway. High SPRR2A expression was a poor prognostic indicator for EC, according to a one-way Cox analysis of TCGA EC data. Compared with normal tissues, EC tissues had considerably elevated SPRR2A expression according to the differential expressed genetic analysis of TCGA-UCEC data (P &lt; 0.05). RT-qPCR was used to experimentally confirm that the Ishikawa EC cell line expressed SPRR2A mRNA at a greater level than the human endometrial epithelial cell line and that this difference was statistically obvious (P &lt; 0.01). Conclusion: SPRR2A has been identified as hub gene for EC and potentially become a new marker for early diagnosis, precise treatment and prognosis predictor of EC.

Wastewater management in Bogor, Indonesia, faces significant challenges to achieving a sustainable state. The growth of self-help settlements which are sporadic, unplanned, unorganized, and very dense, has an impact on the government's difficulties in providing sustainable wastewater management services. The research aims to examine fast-growing areas of self-help settlements, identify critical elements as a basis for strategy, and map the roles and influence of stakeholders in managing fast-growing areas in self-help settlements based on domestic wastewater management. This research uses a case study approach. Data were collected through observation, interviews, and focus group discussions. Data in this study were analyzed using Mactor software. The result showed knowledge and understanding of the correct management of domestic wastewater are the basis for achieving the actors' goals. In an implementation, social capital is needed to increase community participation by providing incentives that can affect the management of domestic wastewater, and it is necessary to increase the capacity of actors to assist the community in managing domestic wastewater. Ease of implementation at the community level must be based on community-based planning with government assistance so that the community itself carries out the decision on the type of domestic wastewater management by considering the advantages and disadvantages of each wastewater management system.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions.

The study was carried out to evaluate the microbiological quality of raw milk produced in Supare-Akoko of Ondo State, Nigeria. Two study areas (Supare and Akungba Villages) both in the Akoko South-West L.G.A. of Ondo State, in Nigeria's South-West geopolitical zone, were selected for the study. The study area served as the raw milk collection points. Raw milk samples were collected with the aid sterile container into the milking bucket and then transported to the laboratory of the Adekunle Ajasin University's department of microbiology in Akungba-Akoko, Ondo state, Nigeria, for further analysis. Several tests such as test for serial dilution, P_H measurement, gram staining, and biochemical analysis, to identify and evaluate the microbe species in the raw milk selected for the study.The findings showed that numerous microbe species, including Bacillus, Staphylococcus, Pseudomonas, Alcaligens, Actinomycetes, Enterobacteria, Licheniformis, Listeria, and Streptococcus species, were present in all of the raw milk samples. Therefore, the study recommended that there should be adequate awareness & education on handling of milk in the most hygienic way possible, as well as the establishment of bulk milk tanks and milk processing plants in key locations that could enable appropriate test and production of quality milk before the product is being made available to the general public for consumption.

The research analysed the publications metric of literature regarding the impact of socially responsible investing (SRI) and environmental, social, and governance (ESG) on portfolio and financial performance. The study aimed to determine the latest trends, key themes, authors, and influential journals. Data from the SCOPUS database was used in this study. A total of 1377 research articles from academic journals related to this topic and published from 2013 to 2022, they data was cleaned to remove duplicate, and documents that do not meet the criteria of this study. For this bibliometric analysis, a total of 260 articles were chosen in order to summarise the collective knowledge and emerging patterns in ESG, SRI, ethical, and impact investing, a bibliometric analysis was employed. The thematic map classified the ESG, SRI, and performance relationship themes into four categories of themes: niche themes, motor themes (corporate financial performance, corporate social performance, ESG, ESG factors, sustainability, performance, integrated reporting, gender diversity, and board size), emerging or declining themes (social responsibility, environmental performance, socially responsible investment, ethical investment, and SRI), and basic or transversal themes (financial performance, corporate social performance, ESG performance, environmental, social, and governance). Social responsible investing, engagement, and ESG, implies a position between niche themes and highly developed topic/emerging or decreasing theme, and covid, ROA, while, tobin’s Q, implies a position between highly developed topic/emerging or decreasing theme and basic theme. The findings contribute to the enhanced understanding of ESG, SRI, ethical, and impact investing, which are crucial for an efficient capital market in promoting sustainability and sustainable development. The study offers vital practical implications and future research directions.

Ketamine, a racemic mixture of esketamine (S-ketamine) and arketamine (R-ketamine), has re-ceived particular attention for its rapid antidepressant and antisuicidal effects. NMDA receptor inhibition has been indicated as one of the main mechanisms of action of the racemic mixture, but other pharmacological targets have also been proposed. This study aimed at exploring the possible multiple targets of ketamine enantiomers related to their antidepressant and antisuicidal effects. To this end, targets were predicted in SwissTargetPrediction software for each ketamine enan-tiomer. The targets related to depression and suicide were collected by GeneCards database. The intersections of targets were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Network pharmacology analysis was performed in GeneMania and Cy-toscape softwares. Molecular docking was used to predict the main targets of the network. The results indicated that esketamine and arketamine share some biological targets, particularly NMDA receptor and phosphodiesterases 3A, 7A and 5A, but have specific molecular targets. While esketamine is predicted to interact with the GABAergic system, arketamine may interact with macrophage migration inhibitory factor (MIF). Both ketamine enantiomers activate neuroplas-ticity-related signaling pathways and show addiction potential. Our results identified novel poorly explored molecular targets that may be related to the beneficial effects of esketamine and arketamine against depression and suicide.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with high heterogeneity and has a prevalence of 0.7% to 3.2% in children. Gut microbiota are a collection of microorganisms that inhabit in human guts, which can produce various metabolites that affect the homeostasis and functions of nervous and endocrine systems. There are many studies on the relationship between various gut microbiota and ASD, but the alteration pattern of microbial profiles in ASD children is not conclusive. In order to more robustly describe the deregulations of gut microbiota compositions in ASD, a meta-analysis was performed. The related investigations from PubMed, Embase and Web of Science were collected and manually reviewed. By procedure, 26 eligible studies until 2023, with a total of 1021 ASD and 951 typically developed children and adolescents, were included for the meta-analysis. RevMan5.4 was used to assess the overall effect of 8 microbes at the phylum level and 19 microbes at the genus level. Results demonstrated relatively up-regulated abundance of Bacteroidetes, Verrucomicrobia, Bacteroides, Clostridium, Dorea and Sutterella, and down-regulated abundance of Proteobacteria, Bifidobacterium, Coprococcus, and Akkermansia in ASD children, indicating partly agreement in the ASD-associated microbes, albeit the heterogeneity of ASD.

In this perspective, we give an overview of several less explored structural and optical characterisation techniques useful for biomaterials. New insights into the structure of natural fibres such as spider silk can be gained with minimal sample preparation. Electromagnetic radiation (EMR) over a broad range of wavelengths (from X-ray to THz) provides information of the structure of the material at cor- respondingly different length scales (nm-to-mm). When the sample features, such as the alignment of certain fibres, cannot be characterised optically, polarisation analysis of the optical images can provide further information on feature alignment. The 3D complexity of biological samples necessitates that there be feature measure- ments and characterisation over a large range of length scales. We discuss the issue of characterising complex shapes by analysis of the link between the color and struc- ture of spider scales and silk. For example, it is shown that the green-blue color of a spider scale is dominated by the chitin slab’s Fabry-P ́erot type reflectivity rather than the surface nanostructure. The use of a chromaticity plot simplifies complex spectra and enables quantification of the apparent colors. All the experimental data presented herein are used to support the discussion on the structure-color link in the characterisation of materials.

The study was geared toward isolating, characterizing and identifying bacterial pathogen in sachet water in addition to determines the bacterial count of the sachet water sold in Obi Local Government Area. Twelve sachet water samples from different companies were analyzed using standard microbiological methods. The microorganism found are Escherichia Coli, Staphylococcus aures, Salmonella spp, with Staphlococuss aureus showing the highest incidence (50%), observed by Escherichia coli (29.17%) and Salmonella spp with the lowest occurrence (20.8%). The presence of these microbes in drinking water possesses a threat to life. The findings consequently suggest that some of the sachet water sold in obi L.G. A are not healthy for human intake and are risky to health. Hence there's need for strict concern and routine monitoring by regulatory agencies to make sure that appropriate treatment is applied inside the manufacturing of exceptional and safe sachet drinking water. This study is aimed to analyze the bacteriological quality of some selected sachet water in Obi Local Government Area of Benue State, Nigeria.

In Functional Analysis as well as Topology, we frequently encounter sets, say $X$, that contain elements close to each other. These sets display a defining "finite-ness" property: for all open cover $\mathcal{O}$ of $X$, there exists a finite subcollection $\mathcal{U}\subseteq\mathcal{O}$ such that $\mathcal{U}$ covers $X$. Such spaces $X$ are called $\textbf{compact}$, and the above "finite-ness" property afford us great convenience, because we can always investigate $X$ by investigating its finite open cover $\mathcal{U}$Arzela-

An essential step in solving any topology optimization problem is determining the sensitivities of the objective function and optimization constraints. Unfortunately, these sensitivities are usually derived and implemented manually. Nontrivial objective functions and constraints, especially with the involvement of material and geometric nonlinearities, need strenuous mathematical derivation, leading to error-prone implementation. Another intriguing approach to finding sensitivities is automatic differentiation. This paper uses the automatic differentiation and adjoint method to find the sensitivities for two multiphysics topology optimization problems: 1) thermoelasticity and 2) piezoelectricity. This approach is not limited to these examples and can be easily extended to other single- or multi-physics topology optimization problems.

A modern binary executable is a composition of various networks. Control flow graphs are commonly used to represent an executable program in labeled datasets used for classification tasks. Control flow and term representations are widely adopted, but provide only a partial view of program semantics. This study is an empirical analysis of the networks composing malicious binaries in order to provide a complete representation of the structural properties of a program. This is accomplished by the measurement of structural properties of program networks in a malicious binary executable dataset. We demonstrate the presence of Scale-Free properties of network structure for program data dependency and control flow graphs, and show that data dependency graphs also have Small-World structural properties. We show that program data dependency graphs have a degree correlation that is structurally disassortative, and that control flow graphs have a neutral degree assortativity, indicating the use of random graphs to model the structural properties of program control flow graphs would show increased accuracy. By providing an increase in feature resolution within labeled datasets of executable programs we provide a quantitative basis to interpret the results of classifiers trained on CFG graph features. An increase in feature resolution allows for the structural properties of program classes to be analyzed for patterns as well as their component parts. By capturing a complete picture of program graphs we can enable theoretical solutions for the mapping a program's operational semantics to its structure.

Different soil nutrients affect plant metabolites accumulation characteristics. The main soil nutrients and their correlation with Pepino metabolites were investigated in this study to evaluate differences between greenhouses on the Loess Plateau in northwest China. A total of 269 Pepino metabolites in the fruits were identified using a UPLC-QTOF-MS approach from plants grown in three major Pepino growing regions. Their differential distribution characteristics were analyzed. 99 metabolites differed among the Pepino fruits in the three regions. The main classes of the differentially accumulated metabolites were ranked as Amino acids and derivatives, Nucleotides and derivatives, Organic acids, Alkaloids, Vitamins, Saccharides and Alcohols, Phenolic acids, Lipids, and others. Environmental factor analysis indicated that soil nutrients were the primary differentiating factor. Five soil nutrient indicators: TN（total nitrogen）, TP（total phosphorus）, AP（available phosphorus）, AK（available potassium）, and OM（organic matter）, exhibited significant differences in three growing sites. Metabolite and soil nutrient association analysis using redundancy analysis (RDA) and Mantel test indicated that TNand OM contributed to the accumulation of amino acids and derivatives, nucleotides and derivatives, and alkaloids while inhibiting organic acids, vitamins coagulation biosynthesis. Moreover, AP and TP were associated with the highest accumulation of saccharides and, alcohols, phenolic acids. Consequently, differences in soil nutrients were reflected in Pepino metabolites variability. This study clarified the metabolite variability and the relationship between Pepino and soil nutrients in the main planting areas of northwest China. It provides a theoretical basis for the subsequent development of Pepino's nutritional value and cultivation management.

Viral variant analysis is a bedrock of the disease surveillance. When combined with temporospatial analysis variant analysis can further the knowledge of disease spread in a study area. This paper suggests a method to perform the analysis in an operational setting which will allow for real-time surveillance of viral variants and allow local public health professionals to rapidly respond to changes in the evolution of the disease. This method includes three main subprocesses: preprocessing, analysis, and rendering. This method can be performed across multiple software platforms. A use case is given in which it was found that this method helped a hospital system understand the spread of SARS-CoV-2 in Northeast, Ohio.

Background: Pharmacologic and non-pharmacologic treatments for stroke are essential but could be costly or harmful whereas probiotic has been a promising alternative. This scoping review aimed to synthesize the in vitro and in vivo evidence of probiotics on stroke-related neurological, biochemical, and histochemical outcomes. Method: We searched in PubMed, Embase, and Cochrane Central Register of Clinical up to May 7, 2021, and screened by two independent reviewers. We included the use of probiotics, prebiotics, and symbiotics both in vitro and in vivo for the prevention or treatment of the stroke-related model. Result: Of 6,293 articles, 4,990 passed the initial screen, of which 36 theme-related full-texts were assessed and 13 were included in this review. Probiotics could ameliorate the neurological deficit and show their property as an anti-inflammation and anti-oxidative stress. Histopathologically decreased loss of cerebral volume and inhibition of neuronal apoptosis were found. Conclusion: There are potential cognitive benefits of probiotic supplementation, especially among animal models, on decreasing cerebral volume, increasing neurological score, and decreasing the inflammatory response. However, further investigation is needed to validate these conclusions in various populations.

This study examined the last four decades of the existing academic literature related to the environmental impacts of using cover crops in agricultural production systems. Data were collected from the Web of Science database, resulting in a sample of 3,246 peer-reviewed articles published between 1980 and 2021. We combined two advanced scientometrics analysis software (i.e., CiteSpace 6.0.R1 and Gephi 0.9.2) to identify the trajectory of the literature, hotspots, and frontiers. We developed authorship-, institution-and country-levels networks to examine academic cooperation over the last forty years. Our findings revealed that the number of peer-reviewed outputs documenting the environmental effects of cover crops has consistently increased, with a notable rise in publications between 2015 and 2021. Eighteen salient research topics were identified in the literature, including winter cover crops' effects on soil health, cover crops' effects on nitrous oxide emissions, and the relationship between cover crops and nitrate leaching. Based on the citation-clustering analysis, the trajectory of the literature may be divided into three stages. Studies in Stage 1_A (1980-2000) mainly assessed the role of cover crops in nitrogen management. In Stage 1_B (2001-2010), the research evaluated the impact of using different cover crop mixtures on farming systems. In Stage 2 (2011-2021), studies primarily addressed the environmental impacts of cover crops, particularly their effects on physical and chemical soil properties. Finally, the countries with the most outputs were the United States, Brazil, and Spain. The U.S. Department of Agriculture-Agricultural Research Service was the main contributor to the literature on the environmental impacts of cover crops.

The Covid-19 also known as the Coronavirus is a viral disease from the SARS-CoV-2 family of virus, as at December 2019 the first case of this virus infection was identified at Wuhan, China, this seemingly isolated case soon became a global pandemic, whose effect was felt globally which also had colossal effects on both health, economic and politics . As at the time of this research about 4.5 million people have died of the Coronavirus and over 215 million people already infected by it. This pandemic stood out not just for its scale but for how social media was a major contribution to its spread as well as to curbing it. The power of social media was used to spread misinformation as well as to spread awareness on the subject, with both having massive impact on the people. In this paper we will be running a sentimental analysis on twitter under the keyword “Covid-19 and Coronavirus”, twitter is a powerful social media tool that is known for its ability to keep trends in the form of tweets, we will be drawing correlations between the peaks of tweet with the peak of infection. We will also be analyzing to know what the impact of these tweets are having on the rate of the infection and vice versa. We will also be analyzing what people are tweeting most about, what are the talking points, comparing both real time and past tweets with real time infection and death rates using deep different learning methods to access what information can be derived from it.

Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsApp, etc are playing a very vital role in developing virtual communities. A pandemic situation like COVID-19 accelerated people's involvement in social sites to express their concerns or views regarding crucial issues. Mining public sentiment from these social sites especially from Twitter will help various organizations to understand the people's thoughts about the COVID-19 pandemic and to take necessary steps as well. To analyze the public sentiment from COVID-19 tweets is the main objective of our study. We proposed a deep learning architecture based on Bidirectional Gated Recurrent Unit (BiGRU) to accomplish our objective. We developed two different corpora from unlabelled and labeled COVID-19 tweets and use the unlabelled corpus to build an improved labeled corpus. Our proposed architecture draws a better accuracy of 87% on the improved labeled corpus for mining public sentiment from COVID-19 tweets.

Therapeutic patient education (TPE) aims to empower the patients and their caregivers to effectively care for and manage their conditions. Such educational programs have been shown to improve health behaviors, disease outcomes and quality of life among different patient populations. The field of TPE has evolved extensively over decades, owing to interdisciplinary research. No study so far has been done to map this field, to identify the stakeholders and gaps requiring future research. By leveraging the theory of co-citation, Citespace was used to visualize the bibliographic data pertaining to TPE research. A total of 54,533 articles published in English language were analyzed to identify influential funders, regions and institutes contributing to this field. Besides these, important theoretical and empirical work that has shaped this field has also been mapped. Our analysis revealed several important insights. Most of the important theories which helped shape TPE were inspired from the social sciences. Five important themes of research were identified including disorders, study designs utilized in TPE research, scope of the TPE literature, and outcomes, and populations. The research focused on improving perceptions, behaviors, and attitudes for health promotion, reducing stigma as well as self-management and medication adherence. Most of the research was developed in the context of high income countries. The future research should more involve patients and use digital technology. Meta-analytical studies need to be done to identify the specificities of TPE interventions across different disorders. More research should involve low and middle income countries (LMIC) to ensure knowledge and technology transfer.

The present article analyzes the connection between, on the one hand, gender equality and, on the other hand, loneliness and social isolation. It hypothesizes that modern relational institutions, that support gender equality, such as no-fault divorce laws, reduce loneliness in close relationships. This hypothesis is put to the test through a multilevel analysis of the ISSP 2017. The analysis reveals that the data agrees, to a large extent, with the theoretical arguments. The prevalence of loneliness is higher in countries with higher levels of gender inequality (as measured by the Gender Inequality Index [GII]). This can be attributed to a moderation effect: at lower levels of gender inequality, partnerships provide better protection from loneliness. These results are robust to controls for demographic composition, level of health, educational attainment, income poverty, and interview mode. Last, the analyses show that the threat of emotional isolation is more widespread in countries with low gender inequality. This finding, however, is only significant before controlling for demographic composition, level of health, educational attainment, income poverty, and interview mode, and it requires further analysis. The concluding section relates these findings to the popular tendency to argue that modern society has created a “loneliness epidemic.”

As commonly used chemical plasticizers in plastic products, phthalate esters had become a serious ubiquitous environmental pollutant, such as in soil of plastic film mulch culture. Microbial degradation or transformation was regarded as a suitable strategy to solve the phthalate esters pollution. Thus, a new phthalate esters degrading strain Gordonia sp. GZ-YC7 was isolated in this study, which exhibited the highest di-(2-ethylhexyl) phthalate degradation efficiency under 1000 mg/L and the strongest tolerance to 4000 mg/L. The comparative genomic analysis showed that there exist diverse degradation pathways for various phthalate esters such as di-(2-ethylhexyl) phthalate and dibutyl phthalate in Gordonia sp. GZ-YC7, which possibly contributes to its broad substrate spectrum, high degrading efficiency and high tolerance to phthalate esters. Gordonia sp. GZ-YC7 is potential for bioremediation of phthalate esters in polluted soil environments.

We have used Mass Multivariate Method on structural, resting state and task related fMRI data from two groups of patients with schizophrenia and depression, respectively, in order to define several regions of significant relevance to the differential diagnosis between those conditions. The regions included the left Planum polare, Left opercular part of the inferior frontal gyrus (OpIFG), Medial orbital gyrus (MOrG), Posterior Insula (PIns), and Parahippocampal gyrus (PHG). This study delivers evidence that multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnosis. Either structural, or resting state or task related functional MRI modality cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity to combine clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry.

The effects of ELICE16INDURES, a well-known plant conditioner developed by the Research Institute for Medicinal Plants and Herbs Ltd. Budakalasz, Hungary, were studied in a soybean population. The active ingredients of the compound have been selected to help elicit general immunity in plants without pathogenic damage, thereby roborizing the healthy plant population and preparing it for possible future biotic stressors. Here we have analyzed changes in the expression levels of genes encoding enzymes involved in the catalysis of metabolic pathways that induce and regulate PAMP-triggered immunity (PTI) at two different time points and treatments. Twenty-three different enzymes were analyzed that catalyze different metabolic pathways, such as the biosyntheses of jasmonic acid, salicylic acid, ethylene, phenylpropanoid, flavonoid, and phytoalexin biosynthesis and cellular detoxification processes. Bioinformatical softwares werw used to analyze the results. It has been found that some of the primary defense mechanisms (e.g., Mitogen-Activated-Protein Kinase (MAPK) cascade, jasmonic acid biosynthesis, flavonoid and phytoalexin biosynthesis, etc.) that intensify following the attack of pathogens can be activated without the intrusion of the actual pathogen by an immunochemical. Thus, we proved that plant resistance can be artificially conditioned.

The larvae of some species of the subgenus Orthocladius s. str. (Diptera, Chironomidae) are here described for the first time with corrections and additions to the descriptions of adult males and pupal exuviae. The identification of larvae is generally not possible without association with their pupal exuviae and/or adult males, so the descriptions here are based only on reared material or on pupae with the associated larval exuviae. Usually, Chironomidae larvae can be separated on the basis of morphometric characters, and the most discriminant characters ones are: 1- the ratio between the width of median tooth of mentum (Dm) and the width of the first lateral tooth (Dl) = mental ratio (DmDl), 2- the ratio between the length of the first antennal segment (A1) and the combined length of segments 2-5 (A(2-5)2-5) = antennal ratio (AR). The shape of mandible, maxilla, and other body parts are almost identical in all the species considered in this study. The larva of Orthocladius (Symposiocladius) lignicola is very characteristic and can be separated by the shape of mentum and the larvae of all the known species of Symposiocladius are characterized by the presence of large Lauterborn organs on antennae and of tufts of setae on abdominal segments. The larvae of Orthocladius (Orthocladius) oblidens and Orthocladius (Orthocladius) rhyacobius can be distinguished from other species basing on their large Dm and to each other by AR. A principal component analysis was carried out using 5 characters: 1- Dm, 2- Dl, 3- length of A1, 4- width of A1 (A1W), 5- combined length of segments 2-5 (A2-5). The most discriminant characters were Dm and A1, confirming that DmDl and AR can be used to separate species at larval stage, but the large superposition of morphometric characters in different species confirms that association with pupal exuviae is in any case needed to identify larvae. In future perspective, the development of reference DNA barcodes from specimens identified by specialists is recommended since possibly the best tool for larvae identification, but association of barcodes with morphotypes is in any case fundamental.

In this short note we present a technique using which one attributes frequency and wavevector to (almost) arbitrary scalar fields. Our proposed definition is then applied to the classical wave equation to yield a novel nonlinear PDE.

Formation in soccer is among the most important tactical choices for a successful match.Herein, the simulations of 420000 match-plays have been performed varying the formation, the number of opponents that are actively pressing the team, the speed of the opponents in attempting a pass interception. Dribbling has been neglected. The match-play ends either with a successful series of passes from a central back to the line of the strikers or with the opponents that steal the ball. In this work, I demonstrate that 3-4-3 formation, which is among the most employed formations, relates to the highest probability of success.

Daily thousands of people and goods move along Brazilian Federal highways. Traffic accidents are numerous on these highways and have a significant impact, whether on the economy or the health system. Identifying predictor variables, the probability of an event occurring and how to mitigate them are of paramount importance for the actions of the transit authorities that manage these roads. The main contribution of this study is the development of a predictive machine learning model which uses open data to shows graphically the critical points in the highways. This model is fully reproducible and can be applied to any region worldwide helping to minimize the number of accidents and to prevent deaths by automotive collisions. For this study, 43 variables were analyzed supporting the identification of the causes of accidents with fatal victims on the main highways in the south of Brazil. RoadLytics is proposed as a supervised machine learning model, using the Random Forest algorithm to analyze about 33 thousand occurrences between 2017 and 2020. An exploratory analysis of the data was carried out to support the modeling and to facilitate data visualization. In this sense, heat maps were developed to support the analysis and identification of potential risk areas. The results show that BR386 highway registers the highest number of fatal occurrences, regardless of the season. Additionally, concerning the weather conditions, the analysis shows that 52% of accidents occurred in favorable conditions, such as clear skies, victimizing 501 people. The driver’s lack of attention is the main reason for the accidents’ occurrences. Applying the developed model, an accuracy of 77% was achieved for the classification of fatal accidents.

Glioma accounts for 80% of all malignant brain tumors and is the most common adult primary brain tumor. Age is an important factor affecting the development of cancer as somatic mutations accumulate with age. In this study, we aimed to analyze the significance of age-related non-silent somatic mutations in glioma prognosis. Histological tumor grade depends on age at diagnosis in patients with IDH1, TP53, ATRX, and EGFR mutations. The hierarchical clustering of patients was dominantly separated by IDH1 and EGFR mutations. Furthermore, patients with IDH1 mutation were dominantly separated by TP53 and ATRX double mutation and its double wildtype counterpart. Patients with the double mutation showed poorer prognosis than those with the double wild type genotype. In conclusion, among the many somatic mutations, those in IDH1, TP53, ATRX, and EGFR are important for glioma classification based on histological grade. Patients with EGFR mutation had the poorest prognosis, whereas those with only IDH1 mutation showed the best prognosis.

During composting process, soils undergo many changes in their physical, chemical and biological properties. Composting has been widely known as an aerobic process during which organic matter is decomposed to humus like substances broken into many organic materials or compounds. The project aims to compare and analyze different combination of composting that yield different properties and nature of soils, and testing their soil physical properties. Dug three compost pits each 1.5 m in depth, labeled compost pit A consisting of potato peels – the only kitchen waste most common around the college hostels, compost pit B consisting of leaf litter and other garden trimmings and compost pit C consisting of cow dung and the other as a controlled experiment with no composting practices.

The development of unmanned satellite space technology is increasingly willing, the emergence of medium resolution satellites with sensitivity and spectral variants such as Landsat is very effective in observing environmental changes, while the purpose of this study is to monitor the development of built-in land using image transformation techniques, estimating built-in land changes. The research method uses the NDVI image transformation technique, NDBI and Built Up Index, with Landsat satellite image data obtained from USGS. Accuracy sampling is done by purposive sampling with confusion matrix accuracy test technique. The research results were found. developed land for the period 2004 - 2010 with a percentage of 19.25%, for stages 2010 - 2018 with a percentage of 30.25%. The land development was built based on the area of ​​the highest sub-district in the Kubung area in the early period with a percentage of 7.20% then in the second period with a percentage of 32.23%. The quality of the accuracy of the results of image analysis using confusion matrix technique with an image accuracy level in a field sample of 185 with an image accuracy of 86.04%.

Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1 - considering the flexibility, modularity and portability - and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e. the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. 32 years' continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatterplot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modelling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales.

This paper provides a framework for the assessment of household-level risk, incorporating both a individual social risk perspective and a location-based perspective. We use this framework as a heuristic to explore the effect of social reintegration choices individuals face, which are not be addressed by current policies. For example, we explore how integrating extended family households during COVID-19 without social distancing may affect household and community risk. The goal is to aid individual decision makers, who are seeking to maintain quality-of-life while navigating local policy, with nuance relating to location-specific behavior and disease prevalence.

Sentiment analysis to characterize properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy transform to capture stylized facts and mutual connections between time series having different nature. Six years of daily Bitcoin Prices and Google Trends are analyzed in order to establish new perspectives in the management of their dynamics.

To evaluate the efficacy of fish oil for protection against coronary heart disease (CHD), we conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) evaluating the use of fish oil for protection against CHD. We retrieved relevant articles published from January 1966 to January 2020 by searching the PubMed, EMBASE, Cochrane CENTRAL, and Web of Science databases. RCTs of fish oil in preventing CHD were selected. The study quality was evaluated using the Cochrane Risk of Bias tool with RevMan 5.3 software. The first selection involved 350 citations. After screening and evaluation of suitability, 19 RCTs adjusted for clustering were included in the meta-analysis. All selected manuscripts considered that fish oil was effective in preventing CHD, secondary outcome measures included angina, sepsis and death. Compared with the control group, fish oil may confer significant protection against CHD (odds ratio = 0.84; 95% confidence interval: 0.72–0.98). There was no significant difference in the incidence of secondary outcomes between the observation group and the control group (P > 0.05). The above results show that fish oil plays an important role in reducing CHD and cardiovascular events. However, because of the suboptimal quality of the studies included into the meta-analysis, these results do not justify adding fish oils systematically to the heavy pharmaceutical assortment already recommended in CHD patients.

Randomized clinical trials represent the gold standard in therapeutic research. Nevertheless, observational cohorts of patients treated for multidrug-resistant (MDR) or rifampin-resistant (RR) tuberculosis (TB) also play an important role in generating evidence to guide MDR/RR TB Generally, summary exposure classifications (e.g., ‘ever versus never’, ‘exposed at baseline’) have been used to characterize drug exposure, in the absence of detailed longitudinal data on MDR-TB regimen These summary classifications, along with an absence of data on covariates that change throughout the course of treatment, constrain researchers’ ability to answer the most relevant questions while accounting for known This paper highlights the importance of regimen changes in improving inference from observational studies of longer MDR-TB treatment regimens and offers an overview of the data and analytic strategies required to do

This preprint is focused in the presence of plastics and microplastics in food. We will discuss how many we eat, and how they arrive to the food, and why. We will treat many other things, such as the waste treatment in Europe and in Spain, with updated data; how much plastic waste is generated; what are microplastics and how they are analyzed, I will tell about the experience we have at the University of Alicante (UA); how they can be removed and we will estimate how many we eat over the course of a year.

More than 1.1 billion people lack access to electricity in the world, among them 600 million are in sub-Saharan African countries. The socio-economic life of the rural communities is highly affected by electricity supply. The demanding Interest for growth and development in developing countries led to a renewed interest in solar lights. Liter of the night light is a solar-powered light that is constructed from waste plastic bottles which light up the rural community. This paper is about experimental analysis of liter of night light for potential use in rural electrification. The technology uses environmentally friendly and locally available materials. The system is provided with battery storage, which allows it to operate for 13.5 hours continuously at full charge. Solar radiation is converted by PV solar panel to electric current. Circuit board is developed and fabricated to regulate and deliver a consumable amount of voltage by the rechargeable lead-acid battery. After testing five different solutions, the study found out that vinegar solution as the optimal solutions for the technology that can deliver 27lux of light to the surrounding. Simultaneously the study found out, that the light that is emitted from the bottle bulb is a function of volume, depth and solution type. And for this relationship curves are developed. And, the technology can be used for rural communities as it is.

Recently the increased cost-effectiveness of high-throughput technologies has made available a large number of RNA sequencing datasets to identify circular RNAs (circRNAs). However, despite many computational tools were developed to predict circRNAs, a limited number of workflows exists to predict and to characterize circRNAs. Moreover, to the best of our knowledge, these available workflows do not ensure computational reproducibility and require advanced bash scripting skills to be correctly installed and used. To cope with these critical aspects we present Docker4Circ, a new computational framework designed for a comprehensive analysis of circRNAs composed of: circRNAs prediction, classification and annotation using public databases, the back-splicing sequence reconstruction; the internal alternative splicing of circularizing exons; the alignment-free circRNAs quantification from RNA-Seq reads, and, finally, their differential expression analysis. Docker4Circ was specifically designed for making easier and more accessible circRNAs analysis thanks to the following features: (i) its R interface; (ii) the encapsulation of its computational tasks into a docker image; (iii) an available user-friendly Java GUI Interface. Furthermore, Docker4Circ ensures a reproducible analysis because all its tasks were embedded into a docker image following the guidelines provided by Reproducible Bioinformatics Project (RBP, http://reproducible-bioinformatics.org/). The effectiveness of Docker4Circ was demonstrated on a real case study whose goal is to characterize the circRNAs predicted in colorectal cancer cell lines and quantified in public RNA-Seq experiments performed on primary tumor tissues. In conclusion, we propose Docker4Circ as a framework for reproducible and comprehensive analyses of circRNAs to efficiently exploit their biological role.

Systematic traders employ algorithmic strategies to manage their investments. As a result of the deterministic nature of such strategies, it is possible to determine their exact responses to any conceivable set of market conditions. Consequently, sensitivity analysis can be conducted to systematically uncover undesirable strategy behavior and enhance strategy robustness by adding controls to reduce exposure during periods of poor performance / unfavorable market conditions or increase exposure during periods of strong performance / favorable market conditions. In this study, we formulate both a simple systematic trend-following strategy (i.e., trading model) to simulate investment decisions, and a market model to simulate the evolution of instrument prices. We then map the relationship between market model parameters under various conditions and strategy performance. We focus, in particular, on identifying the performance impact of changes in both serial dependence in price variability and changes in the trend. The long-range serial dependence of the true range worsens performance of the simple classic trend-following strategy. During periods of strong performance, the dispersion of trading outcomes increases significantly as long-range serial dependence increases.

Aims of the paper are the results of a research on a wooden box that holds an important historical document, which is a hand Bible handwritten in the thirteenth century. The tradition connect this Bible to the name of Marco Polo (Venice, 1254 - Venice, 1324), who would be the owner and that it would accompany him on his travels (1262 and 1271) in China. The Bible, of fine workmanship, written on thin parchment, and its container, along with a yellow silk cloth, is preserved in the ancient and prestigious Laurentian Library in Florence. The manuscript was in very poor condition and in the course of the study (2011) was being restored. Aims of survey were to determine the place and period of realization of the box, or rather if it be contemporary or later than the manuscript it contains and whether it was made in the East or in Europe.

The Bay of Espirito Santo is located in the Espírito Santo State, in the eastern part of Brazil. It is surrounded by the city of Vitoria on one side and by the Atlantic Ocean on the other. Superficial sediments of Espirito Santo Bay were analyzed at 12 (western shallow silt sediments) + 8 (eastern sandy sediments and relatively deep sampling stations) = 20 uniformly distributed sampling points where geochemical analysis was performed. Nineteen elements were analyzed: Mo, Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, U, Th, Sr, Cd, Sb, Bi, V, Cr, Ba, and Al. This selection was made based on the most representative heavy metals present in this area and according to the results obtained from the geochemical analysis. Their concentrations were compared with metal contamination benchmarks like Screening Quick Reference Tables (SQuiRTs), Effects Range-Low (ERLs) and Effects Range-Median (ERMs), TELs (Threshold Effects Levels), PELs (Probable Effects Levels), ERM (Effects range median), and AETs (Apparent Effects Thresholds). Results indicate that there is no particular pollution condition able to alter the condition of any part of this water body. The Authors conclude that the Espirito Santo Bay is only moderately polluted and some elements are virtually absent.

The Bay of Espirito Santo is located in the Espírito Santo State, in the eastern part of Brazil. It is surrounded by the city of Vitoria on one side and by the Atlantic Ocean on the other. Superficial sediments of Espirito Santo Bay were analyzed at 12 (western shallow silt sediments) + 8 (eastern sandy sediments and relatively deep sampling stations) = 20 uniformly distributed sampling points where geochemical analysis was performed. Nineteen elements were analyzed: Mo, Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, U, Th, Sr, Cd, Sb, Bi, V, Cr, Ba, and Al. This selection was made based on the most representative heavy metals present in this area and according to the results obtained from the geochemical analysis. Their concentrations were compared with metal contamination benchmarks like Screening Quick Reference Tables (SQuiRTs), Effects Range-Low (ERLs) and Effects Range-Median (ERMs), TELs (Threshold Effects Levels), PELs (Probable Effects Levels), ERM (Effects range median), and AETs (Apparent Effects Thresholds). Results indicate that there is no particular pollution condition able to alter the condition of any part of this water body. The Authors conclude that the Espirito Santo Bay is only moderately polluted and some elements are virtually absent.

The optimum conditions for the fermentation of African locust bean (Parkia biglobosa) into a vegetable protein based condiment (Iru) were developed using Levenberg-Marquardt (or Powell) method (using PSI software) with three (3) variables namely; inoculum concentration (bacillus subtilis), temperature and the fermentation duration. African locust bean seeds were fermented at various temperature of 40 - 70 °С for five days (120 hours) with different concentrations of Inoculum. The proximate analysis shows that fermentation increased the percentage protein. Protein had the highest composition with about 51 % after 72 hours at the lowest fermentation temperature of 40°С.

Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and

CO2 injection into geological formations is considered one way of mitigating the increasing levels of carbon dioxide concentrations in the atmosphere and its effect on and global warming. In regard to sequestering carbon underground, different countries have conducted projects at commercial scale or pilot scale and some have plans to develop potential formations for carbon dioxide storage. In this study, pure CO2 injection is examined on a model with the properties of bunter sandstone and then sensitivity analyses were conducted for some of the important parameters. The results of this study show that the extent to which CO2 has been convected in the porous media in the reservoir plays a vital role in improving the CO2 dissolution in brine and safety of its long term storage. We conclude that heterogeneity plays a crucial role on the saturation distribution and can increase or decrease the amount of dissolved CO2 in water. Furthermore, the value of absolute permeability controls the effect of the Kv/Kh ratio on the CO2 dissolution in brine. In other words, as the value of vertical and horizontal permeability decreases (i.e. tight reservoirs) the impact of Kv/Kh ratio on the dissolved CO2 in brine becomes more prominent. Additionally, reservoir engineering parameters, such as well location, injection rate and scenarios, also have a high impact on the amount of dissolved CO2.

The effects of buckwheat intake on cardiovascular diseases (CVD) have not been systematically investigated. The aim of the present study was to comprehensively summarise studies in humans and animals evaluating the impact of buckwheat consumption on CVD risk markers and to conduct a meta-analysis of relevant data. Thirteen randomised, controlled human studies, two cross-sectional human studies and twenty-one animal studies were identified. Using random effects models, the weighted mean difference of post-intervention concentrations of blood glucose, total cholesterol and triglycerides were significantly decreased following buckwheat intervention compared with controls [differences in blood glucose: -0.85 mmol/L (95% CI: -1.31, -0.39), total cholesterol: 0.50 mmol/L (95% CI: -0.80, -0.20) and triglycerides: 0.25 mmol/L (95% CI: -0.49, -0.02)]. Responses of a similar magnitude were seen in two cross-sectional studies. For animal studies, nineteen of twenty-one studies showed a significant reduction in total cholesterol of between 12 and 54%, and fourteen of twenty studies showed a significant reduction in triglycerides of between 2 and 74%. All exhibited high unexplained heterogeneity. There was inconsistency in HDL cholesterol outcomes in both human and animal studies. It remains unclear whether increased buckwheat intake significantly benefits other markers of CVD risk, such as weight, blood pressure, insulin, and LDL-cholesterol, and underlying mechanisms responsible for any effects are unclear.