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Precipitation images play an important role in meteorological forecasting and flood forecasting, but how to characterize precipitation images and conduct rainfall similarity analysis is challenging and meaningful work. This paper proposes a rainfall similarity research method based on deep learning by using precipitation images. The algorithm first extracts regional precipitation, precipitation distribution, and precipitation center of the precipitation images and defines the similarity measures, respectively. Additionally, an ensemble weighting method of Normalized Discounted Cumulative Gain-Improved Particle Swarm Optimization (NDCG-IPSO) is proposed to weigh and fuse the three extracted features as the similarity measure of the precipitation image. During the experiment on similarity search for daily precipitation images in the Jialing River basin, the NDCG@10 of the search results reached 0.964, surpassing other methods. This indicates that the method proposed in this paper can better characterize the spatiotemporal characteristics of the precipitation image, thereby discovering similar rainfall processes and providing new ideas for hydrological forecasting.

The Laeliinae Subtribe (Epidendroideae-Orchidaceae) is exclusively Neotropical, composed of 43 genera and 1466 species, presenting great floral and vegetative diversity. The genus Laelia has restricted geographic distribution, with species in Brazil and Mexico. However, in molecular studies, the Brazilian species have not been included despite remarkably similar floral structures in both Mexican and Brazilian groups of species. The main objective of the present study is to analyze vegetative structural characters of 12 species of Laelia in Mexico to find similarities to recognize them as a taxonomic group and their relationship with possible ecological adaptations. This work supports the proposal to recognize 12 species of Laelias from Mexico as a taxonomic group (except for the new species recognized as Laelia dawsonii J. Anderson), since a strong similarity was found, translated by 90% of structural characters shared by the Mexican Laelias, given the relationship between the structural characters and the altitudinal distribution in which the groups of Mexican Laelias species are found. We propose that Laelias of Mexico be recognized as a taxonomic group and their structural characteristics allow for better understanding of adaptation of species to their environment.

Recently, the e-learners are drastically increased from the last two decades. Everything is learnt through internet without help of the tutor as well. For this purpose, the e-learners are required more e-learning applications that are able to supply optimal and satisfied data based on their capability. No content recommendation system is available for recommending suitable contents to the learners. For this purpose, this paper proposes a new semantic and fuzzy aware content recommendation system for retrieving the suitable content for the users. In this content recommendation system, we propose two content pre-processing algorithms namely Target Keyword based Data Pre-processing Algorithm (TKDPA) and Intelligent Anova-T Residual Algorithm (IAATRA) for selecting the more relevant features from the document. Moreover, a new Fuzzy rule based Similarity Matching algorithm (FRSMA) is proposed and used in this system for finding the similarity between the two terms and also rank them by using the newly proposed Similarity and Temporal aware Weighted Document Ranking Algorithm (STWDRA). In addition, a content clustering process is also incorporated for gathering relevant content. Finally, a new Fuzzy, Target Keyword and Similarity Score based Content Recommendation Algorithm (FTKSCRA) is also proposed for recommending the more relevant content to the learners accurately. The experiments have been conducted for evaluating the proposed content recommendation system and proved as better than the existing recommendation systems in terms of precision, recall, f-measure and prediction accuracy.

Essa pesquisa teve como objetivo verificar a inter-relação entre: autismo, emoções e avaliação psicológica, termos presentes no cotidiano das psicoterapias comportamentais com foco no autismo, na intenção encontrar protocolos de avaliação neuropsicológica. Foi utilizado para análise de dados o software Iramuteq como ferramenta no processamento de dados qualitativos que utiliza como base o qui-quadrado (χ2). O corpus foi composto de oito textos, que se dividiu me 50 segmentos de textos, com 1,85 número de ocorrências. Dos 50 segmentos de textos, 40 foram considerados válidos para análise, corresponde 80% dos dados textos, dividido em 6 (seis) classes. Na análise fatorial de correspondência é apresentado a correlação dos artigos e na de similitude é apresentado como tema central palavra “criança”, palavras secundárias “transtorno” “grupo” e “resultado”. Existem dificuldades de definir protocolos de avaliação psicológica ou até mesmo neuropsicológicas para intervenção com crianças diagnosticadas com autismo, encontrado apenas 1 protocolo completo, onde esses termos estão presentes. A proposta de pesquisa abriu mais possibilidades de pesquisa, pois os resultados realizados pelo Iramuteq a partir de uma intervenção exploratória, apontou caminhos para construção de novos conhecimentos. This research aimed to verify the interrelationship between: autism, emotions and psychological assessment, terms present in the daily behavioral psychotherapies with a focus on autism, in the intention of finding neuropsychological assessment protocols. Iramuteq software was used for data analysis as a tool in the processing of qualitative data that uses the chi-square (χ2) as the basis. The corpus was composed of eight texts, which were divided into 50 text segments, with 1.85 number of occurrences. Of the 50 text segments, 40 were considered valid for analysis, corresponding to 80% of the text data, divided into 6 (six) classes. In the factor analysis of correspondence, the correlation of the articles is presented and in the similarity analysis, the word “child”, secondary words “disorder” “group” and “result” are presented as the central theme. There are difficulties in defining psychological or even neuropsychological assessment protocols for intervention with children diagnosed with autism, only one complete protocol was found, where these terms are present. The research proposal opened up more research possibilities, as the results made by Iramuteq from an exploratory intervention, pointed out ways to build new knowledge.

Similarity analysis of protein sequences can expose the evolutionary relationship among them. It is required to design effective computational algorithms that can compare the similarities among the...

Finding the density of specific nucleotides is an important task in DNA sequence analysis. The technique, which is based on graphical representations of DNA sequences, allows for comparison, testing, and storage of different sequences. We create three typical curves in this research based on the categorization of four base DNA sequences. The DNA sequence fluctuation and geometrical centers of three curves organized into 12 component vectors were represented. The Euclidean distance between generated vectors is used to study and compare coding sequences for ten different species.

Neste artigo averíguam-se as características do processo de formação inicial e atuação de 27 professores de matemática de educação básica quanto à educação financeira e o letramento. Os dados obtidos foram por meio de um questionário contendo 27 questões fechadas e 14 abertas, que foram analisados utilizando-se a metodologia de análise de similaridade com o suporte do software CHIC (Classificação Hierárquica, Implicativa e Coesitiva). No que tange especificamente à formação dos professores, em relação à educação financeira, há indícios que ocorreu de forma superficial. A análise também evidencia o fato de que a educação financeira ainda é muito confundida com a matemática financeira, sendo abordada em sala de aula de maneira muito superficial, apenas com conceitos básicos, sem a promoção da construção do letramento e consciência financeira dos alunos. Defende-se uma formação em serviço aos educadores matemáticos, com temáticas relacionadas à educação financeira escolar de acordo com as diretrizes da Estratégia Nacional de Educação Financeira (ENEF) e das orientações da Base Nacional Comum Curricular Brasileira (BNCC).

The cinnamon leaf is widely used in flavoring, cooking, essences and medicine. In this work, we systematically detected and identified the volatile compounds of the leaves from five cinnamon species (Cinnamomum cassia (Xijiang type), C. cassia (Fangcheng type), C. loureirii, C. verum and C. burmannii) collected in China by using the methods of GC–MS and FTIR analysis, and compared the correlation between the density of oil cells and the yields of essential oils among above detected samples. The results indicated that the variation of the yields of essential oils was significant positively correlated with the density oil cells of five cinnamon leaves. Based on the results of GC–MS, trans-cinnamaldehyde was the primary components in the four cinnamon species except for C. verum. Notably, the highest contents of trans-cinnamaldehyde in the essential oil are C. loureirii (72.49%), followed by C. cassia (Xijiang type) (60.65%). Eugenol (83.26%) was the main substance of C. verum leaves. In addition to trans-cinnamaldehyde, isoamyl benzoate (7.02%) and α-thujene (6.28%) were the main volatile compounds in C. burmannii leaves. Finally, through analyzing the FTIR data by using hierarchical cluster and similarity analysis were further revealed that the chemical compositions of essential oil were similar of C. loureirii, C. cassia (Xijiang type) and C. cassia (Fangcheng type), and they were significantly different from C. verum and C. burmannii. All these results confirm that species and cultivated environment can lead to the changes in essential oil composition.

Data stream mining techniques have recently received increasing research interest, especially in medical data classification. An unbalanced representation of the classification’s targets in these data is a common challenge because classification techniques are biased toward the major class. Many methods have attempted to address this problem but have been exaggeratedly biased toward the minor class. In this work, we propose a method for balancing the presence of the minor class within the current window of the data stream while preserving the data’s original majority as much as possible. The proposed method utilized similarity analysis for selecting specific instances from the previous window. This group of minor-class was then added to the current window’s instances. Implementing the proposed method using the Siena dataset showed promising results compared to the Skew ensemble method and some other research methods.

The fast expansion of ICT (information and communications technology) has provided rich sources of data for the analysis, modeling, and interpretation of human mobility patterns. Many researchers have already introduced behavior-aware protocols for a better understanding of architecture and realistic modeling of behavioral characteristics, similarities, and aggregation of mobile users. We are introducing the similarity analytical framework for the mobile encountering analysis to allow for more direct integration between the physical world and cyber-based systems. In this research, we propose a method for finding the similarity behavior of users’ mobility patterns based on location and time. This research was conducted to develop a technique for producing co-occurrence matrices of users based on their similar behaviors to determine their encounters. Our approach, named SAA (similarity analysis approach), makes use of the device info i.e., IP (internet protocol) and MAC (media access control) address, providing an in-depth analysis of similarity behaviors on a daily basis. We analyzed the similarity distributions of users on different days of the week for different locations based on their real movements. The results show similar characteristics of users with common mobility behaviors based on location and time to showcase the efficacy. The results show that the proposed SAA approach is 33% more accurate in terms of recognizing the user’s similarity as compared to the existing similarity approach.

This is the first study of the composition of leaf beetles attracted to light in Mexico. We analyze the richness, temporal abundance and similarity between four localities of tropical dry forest in Sierra de San Javier, Sonora. A total of 3217 individuals belonging to 45 morphospecies, 31 genera and 10 tribes within five subfamilies of Chrysomelidae were collected in light traps in the tropical dry forest of Sierra de San Javier, Sonora. Galerucinae was the subfamily with the greatest number of genera and species. The genera with the highest number of species were Pachybrachis Chevrolat and Alagoasa Bechyné. Eight genera were represented by two species and 21 genera by only one species. Metrioidea rugipennis (Blake) was the species with the highest number of individuals (80% of the abundance recorded). According to the taxonomic and species similarity analysis, the sites San Javier and La Barranca had the highest similarity, while the lowest values were recorded at Rancho Las Peñitas and Cañón de Lo de Campa. The low similarity between sites cannot be explained by the geographic distance since it was not a determining factor to explain the low similarity in Sierra de San Javier.

Headspace-gas chromatography-ion mobility spectroscopy (HS-GC-IMS) was used to detect the volatile organic compounds (VOCs) of yak milk powders (YMPs) under ultra-high-pressure sterilization (UHPS) and thermization (TH) methods. The analyses led to the identification of several characteristic of compounds, therefore, exploitation and comparison of the different flavors. A total of 46 peaks were detected, and 17 compounds were identified, including 7 aldehydes, 5 ketones, 3 acids, 1 terpene, and 1 ester. Furthermore, principal component analysis (PCA) and fingerprint similarity analysis based on Euclidean distance compared the YMPs and found that the YMPs had certain differences, which can distinguish the YMPs with different sterilization methods. In conclusion, different sterilization methods possibly affect the flavor of YMPs, and UHPS is bettedslfr than TH. Also, aldehydes were mainly be detected in UHPS groups, whereas the ketones and acids mostly appeared in TH groups. Most importantly, UHPS can retain the original flavor of yak milk to a greater extent.

Geological drilling process is operating under complex geological conditions, which may lead to a high risk of downhole incidents and thus compromise the drilling efficiency. To achieve prompt detection of downhole incidents and prevent them from developing to serious drilling accidents, this paper proposes a new data-driven detection method for downhole incidents based on the amplitude change detection and dynamic time warping. Two major phases are involved: the change monitoring phase detects whether there is any significant change in the drilling signals and extracts variational trend features by linear fitting and amplitude change detection; the incident detection phase determines if the cause of a change is a normal switching or a downhole incident by similarity analysis based on the dynamic time warping and the density-based spatial clustering. Industrial case studies show that the proposed method achieves good performance in downhole incidents detection for geological drilling processes.

In the nuclear medicine imaging, quality control (QC) process using quadrant bar phantom is fundamental aspect of evaluating the spatial resolution. In addition, QC process of gamma camera is performed by daily or weekly. Recently, Monte Carlo simulation using the Geant4 application for tomographic emission (GATE) is widely applied in the pre-clinical nuclear medicine field for modeling gamma cameras with pixelated cadmium telluride (CdTe) semiconductor detector. In this study, we modeled a pixelated CdTe semiconductor detector and quadrant bar phantom (0.5, 1.0, 1.5, and 2.0 mm bar thicknesses) using the GATE tool. Similarity analysis based on correlation coefficients and peak signal-to-noise ratios was performed to compare image qualities for various source to collimator distances (0, 2, 4, 6, and 8 cm) and collimator lengths (0.2, 0.4, 0.6, 0.8, and 1.0 cm). To this end, we selected reference images based on collimator length and source to collimator distance settings. The results demonstrate that as the collimator length increases and the source to collimator distance decreases, the similarity to reference images improves. Therefore, our simulation results represent valuable information for the modeling of CdTe-based semiconductor gamma imaging systems and QC phantoms in the field of nuclear medicine.

our manuscript at bioarxiv here: http://biorxiv.org/content/early/2017/04/15/125682 Face Stimuli + Code for analysis + Code for figure generation

Hazard identification and safety assessment of the huge variety of nanomaterials (NMs), calls for robust and validated toxicity screening tests in combination with cheminformatics approaches to identify factors that can drive toxicity. Cytotoxicity and genotoxicity of seventeen JRC repository NMs, derived from titanium dioxide, zinc oxide, silver and silica, were tested in vitro using human lung alveolar epithelial cells A549. Cytotoxicity was assessed with the AlamarBlue (AB) and colony forming efficiency (CFE) assays, and genotoxicity by the enzyme-linked version of the comet assay. Nanoparticle tracking analysis (NTA) was used to measure size of the NMs in stock and in cell culture medium at different time points. Categorization and ranking of cytotoxic and genotoxic potential were performed (EU-NanoREG2 project approach). Descriptors for prediction of NMs toxicity were identified by quantitative structure-activity relationship (QSAR) analysis. Our results showed that ZnO NMs (NM-110 and NM-111), and Ag NMs (NM-300K and NM-302) were cytotoxic, while the TiO2 and SiO2 NMs were non-cytotoxic. Regarding genotoxicity, TiO2 NM-100, ZnO NM-110, SiO2 NM-203 and Ag NM-300K were categorized as positive. Cheminformatics modeling identified electron properties and overall chemical reactivity as important descriptors for cytotoxic potential, HOMO-LUMO energy parameter, ionization potential, pristine size for the NMs´ genotoxic potential, and presence of surface coating as descriptor for induction of DNA oxidized base lesions. Hazard identification of nanomaterials: In silico unraveling of descriptors for cytotoxicity and genotoxicity

Face recognition is a well-researched domain however many issues for instance expression changes, illumination variations, and presence of occlusion in the face images seriously affect the performance of such systems. A recent survey shows that COVID-19 will also have a considerable and long-term impact on biometric face recognition systems. The work has presented two novel Savitzky-Golay differentiator (SGD) and gradient-based Savitzky-Golay differentiator (GSGD) feature extraction techniques to elevate issues related to face recognition systems. The SGD and GSGD feature descriptors are able to extract discriminative information present in different parts of the face image. In this paper, an efficient and robust person identification using symbolic data modeling approach and similarity analysis measure is devised and employed for feature representation and classification tasks to address the aforementioned issues of face recognition. Extensive experiments and comparisons of the proposed descriptors experimental results indicated that the proposed approaches can achieve optimal performance of 96-97, 92-96, 100, 84-93, and 87-96% on LFW, ORL, AR, IJB-A datasets, and newly devised VISA database, respectively.

A inserção de alunos com deficiência, transtornos globais de desenvolvimento e altas habilidades/superdotação na escola tem desafiado os espaços escolares a construírem novas alternativas de ensino. Com intuito de atender as necessidades educacionais do aluno com deficiência, é de extrema importância adaptar recursos e estratégias de ensino no meio educacional. Nesse sentido, este estudo teve por objetivo verificar se os professores de educação física aplicam estratégias de ensino e utilizam recursos pedagógicos para a inclusão de alunos com deficiência visual em suas aulas no Ensino Fundamental I da rede regular de ensino de Ubá e Tocantins, ambas de Minas Gerais. A presente pesquisa desenvolveu-se sob uma abordagem qualitativa, do tipo estudo de caso. Participaram da pesquisa dois alunos com deficiência visual e dois professores de educação física, os quais foram entrevistados a partir de um roteiro pré-estabelecido. Para os registros das aulas, foi utilizado o roteiro de observação. Os dados obtidos foram processados no software IRAMUTEQ mediante análise de similitude. Os resultados apontaram que ambos os alunos se sentem incluídos nas aulas de educação física, assim como participam das atividades desenvolvidas em aula. Entre as estratégias de ensino utilizadas pelos professores identificadas no estudo, destacam-se o falar, a demonstração da atividade, a alteração de regras, quando necessária, e o estilo de ensino por tarefa. Concluiu-se que, apesar de apresentarem uma atitude positiva frente ao processo de inclusão de alunos com deficiência na rede regular de ensino, os professores não utilizam de maneira planejada a elaboração de estratégias e recursos pedagógicos. Recebido em: 30/10/2020 Reformulado em: 15/01/2021 Aceito em: 15/01/2021

It is commonly acknowledged that visual imagery and perception rely on the same content-dependent brain areas in the high-level visual cortex (HVC). However, the way in which our brain processes and organizes previous acquired knowledge to allow the generation of mental images is still a matter of debate. Here, we performed a representation similarity analysis of three previous fMRI experiments conducted in our laboratory to characterize the neural representation underlying imagery and perception of objects, buildings and faces and to disclose possible dissimilarities in the neural structure of such representations. To this aim, we built representational dissimilarity matrices (RDMs) by computing multivariate distances between the activity patterns associated with each pair of stimuli in the content-dependent areas of the HVC and HC. We found that spatial information is widely coded in the HVC during perception (i.e. RSC, PPA and OPA) and imagery (OPA and PPA). Also, visual information seems to be coded in both preferred and non-preferred regions of the HVC, supporting a distributed view of encoding. Overall, the present results shed light upon the spatial coding of imagined and perceived exemplars in the HVC. Supplementary Information The online version contains supplementary material available at 10.1007/s00429-021-02266-z.

Photovoltaic (PV) power generation is the most widely adopted renewable energy source. However, its inherent unpredictability poses considerable challenges to the management of power grids. To address the arduous and time-consuming training process of PV prediction models, which has been a major focus of prior research, an improved approach for PV prediction based on neighboring days is proposed in this study. This approach is specifically designed to handle the preprocessing of training datasets by leveraging the results of a similarity analysis of PV power generation. Experimental results demonstrate that this method can significantly reduce the training time of models without sacrificing prediction accuracy, and can be effectively applied in both ensemble and deep learning approaches.

Opposed-flow flame spread over solid fuels is an important case of fire safety studies. Spread rate and flame structure depend on fuel nature and burning conditions, and they can be used to study m...

Objective: Turkish Journal of Sports Medicine (TJSM) has used a web-based software program to define the phrasal similarity of submitted articles since 2017. The aims of this study were; to determine the similarity scores obtained in the preliminary evaluation of the articles published in TJSM since 2017, and to evaluate the relationship between the similarity scores and the article type, article language, and publication year. Materials and Methods: A total of 125 articles published in TJSM from 2017 to 2020 were retrospectively reviewed from the digital archive. Research articles, review articles, and case reports were included in the analysis. Similarity scores, including total similarity score and highest match scores in All Sources mode and Match Overview mode, were obtained from Similarity Reports acquired using iThenticate plagiarism checker software. Data were recorded regarding the type and language of the article, the year of publication, whether the corresponding author was from Turkey or any other country, and similarity scores. Results: Of the 119 analyzed manuscripts, 76.5% (n=91) were research articles. The majority of the articles (95%, n=113) were submitted by authors from Turkey, and most of the articles (62.2%, n=74) were in Turkish. The median similarity score for all articles was 9.0% (Q1: 4.0 - Q3: 17.0), and the median highest matching scores were 2.0% (Q1: 1.0 - Q3: 3.0) and 3.0% (Q1: 2.0 - Q3: 6.0) for the Match Overview mode and All Sources mode, respectively. The median total similarity score and the median highest matching score from All Sources mode were significantly higher in research articles (p = 0.004 and p = 0.017, respectively) and in articles written in English (p < 0.001 for both). Conclusion: Total similarity and highest matching scores among articles published in TJSM between 2017 and 2020 were significantly higher in research articles and articles written in English.

ResumoCompreende-se que, para que o ensino da estatística e da probabilidade cumpra sua função de instrumento modificador, a formação continuada assume um papel essencial, sobretudo quando são propostos espaços formativos que superem a lógica da racionalidade técnica. Nesse direcionamento, o presente artigo é fruto de uma pesquisa qualitativa, na perspectiva (auto)biográfica, que tem por objetivo analisar narrativas (auto)biográficas de professores que ensinam probabilidade e estatística na educação básica, tomando como suporte o software IRAMUTEQ. As narrativas que compõem o corpus de análise foram produzidas durante o primeiro módulo de um curso de formação continuada no formato EaD, gratuito, que focaliza diferentes práticas pedagógicas para o ensino da estatística. Participaram dez docentes que ensinam estatística nos anos finais do ensino fundamental e no ensino médio. Como estratégia de análise tomaram-se como base estatísticas textuais clássicas, a nuvem de palavras e a análise de similitude. As análises lexicográficas do corpus textual das narrativas evidenciaram possíveis categorias que emergiram diretamente dos dados, das quais se elegeram “Formação”, “Atuação profissional” e “Anseios e expectativas” como eixos de análise. Os resultados deste estudo possibilitam enfatizar que as narrativas, quando utilizadas em processos de formação continuada de professores que ensinam probabilidade e estatística, podem auxiliar o docente na (re)construção da identidade do educador estatístico. Além disso, permitiram refletir sobre a importância da confrontação do conhecimento estatístico com os contextos sociais, para diferenciar as práticas educativas em matemática e estatística. Palavras-chave: IRAMUTEQ. Formação Professores. Atuação Profissional. Narrativas. Pesquisas (Auto)Biográficas. AbstractIt is believed that, for the teaching of statistics and probability to fulfill its role as a modifying instrument, continuing education plays an essential part, especially when formative spaces that surpass the logic of technical rationality are proposed. In this direction, the present article is the result of a qualitative research project, from an (auto)biographical perspective, which aims to analyze (auto)biographical narratives of teachers who teach probability and statistics in elementary education, using the IRAMUTEQ software as a support. The narratives that make up the corpus of analysis were produced during the first module of a free distance learning training course (EaD), focused on different pedagogical practices for teaching statistics. This continuing education course was attended by 10 teachers who teach statistics in the final years of elementary school and in high school. As an analysis strategy, classical textual statistics, word cloud and similarity analysis were used. The lexicographic analyses of the textual corpus of the narratives showed possible categories that emerged directly from the data, from which we chose “Training”, “Professional performance” and “Wishes and expectations” as axes of analysis. Research results enabled us to emphasize that, when used in continuous education processes for teachers who teach probability and statistics, narratives can assist the teacher in the process of (re)building their identity as statistical educators. In addition, they allowed us to reflect on the importance of confronting statistical knowledge with social contexts, making it possible to differentiate educational practices in mathematics and statistics. Keywords: IRAMUTEQ. Teacher Education. Professional Performance; Narratives, (Auto) Biographical Research.

Soils trafficability is measure of soil strength required to support the manoeuvring of military vehicles. Trafficability can be measured with the tests performed using cone penetrometer and moistu...

Mainstream music can be popularly categorized into specific genres: Rock, Country, Hip Hop, contemporary rhythm and blues (R&B) and Pop. Music of these genres are continually complied on the Billboard music charts based on popularity. This paper explores the uniqueness of these genres and the possible melding of acoustic characteristics over time. Principal Component Analysis (PCA) are applied to timbral and non-timbral characteristics and compared for each genre. Results show that Hip Hop and Pop maintained a unique distinction over time compared to the other genres while Rock, Country and R&B began to share similar acoustic characteristics within recent times. Further analysis make an attempt to predict the trend of the acoustic nature of genres.

The theory of cold–hot nature of Chinese herbal medicines (CHMs) is the core theory of CHM. It has been found that the volatile oil ingredients in CHMs are closely related to their cold–hot nature. Guided by the scientific hypothesis that “CHMs with similar component substances should have similar medicinal natures”, exploration of the intelligent identification of the cold–hot nature of CHMs based on the similarity of their volatile oil ingredients has become a research focus. Gas chromatography (GC) chemical fingerprints have been widely used in the separation of volatile oil ingredients to analyze the cold–hot nature of CHMs. To verify the above hypothesis, in this work, we study the quantification of the similarity of the volatile oil ingredients of CHMs to their fingerprint similarity and explore the relationship between the volatile oil ingredients of CHMs and their cold–hot nature. In this study, we utilize GC technology to analyze the chemical ingredients of 61 CHMs that have a clear cold–hot nature (including 30 ‘cold’ CHMs and 31 ‘hot’ CHMs). Using the constructed fingerprint dataset of CHMs, a distance metric learning algorithm is applied to measure the similarity of the GC fingerprints. Furthermore, an improved k-nearest neighbor (kNN) algorithm is proposed to build a predictive identification model to identify the cold–hot nature of CHMs. The experimental results prove our inference that CHMs with similar component substances should have similar medicinal natures. Compared with existing classical models, the proposed identification scheme has better predictive performance. The proposed prediction model is proved to be effective and feasible.

Increasing the utilization rate of wind energy is of great significance to the improvement of energy structure, which is inseparable from the support of wind power forecasting (WPF) technology. However, it is well known that there is no certain WPF model suitable for all conditions, such as different regions or seasons. Therefore, instead of focusing on the combination of machine learning models in a specific scenario, this article proposes a two-stage modeling strategy of “first classify and separately model, then perform pattern recognition” from the perspective of sample similarity analysis. That is, in offline mode, the historical database is divided into multiple categories with different characteristics, and prediction models are established for each category respectively; in online mode, pattern recognition is carried out on the prediction sample to select the corresponding prediction model. In this way, the WPF problem is decomposed into two strongly related tasks: wind power mode classification and wind power numerical prediction. Furthermore, the coupling and connection between mode classification task and numerical prediction task are strengthened through the transfer learning of sample features. Around the above ideas, specific methods of how to classify, identify, and predict are proposed, which are two-level clustering, Convolutional Neural Network (CNN) classification model and Long Short-term Memory (LSTM) prediction models. Simulation results based on real-world datasets prove the effectiveness and superiority of the proposed hybrid model.

No ano de 2014, foi mencionado na literatura o risco de disseminação do vírus Chikungunya e a necessidade do fortalecimento do combate ao Aedes aegypti no Brasil. Com isso, em 2017, o Ministério da Saúde lançou um programa com a finalidade de evitar a disseminação do Aedes aegypti. Entretanto, no mesmo ano, o país passou por uma epidemia de vírus Chikungunya. Trata-se de uma pesquisa de abordagem qualitativa, desenvolvida com os fisioterapeutas do Núcleo de Apoio à Saúde da Família (NASF), na cidade de Belém. Os dados foram coletados por meio de entrevista, processados no IRAMUTEQ e feita análise de similitude e nuvem de palavras. De acordo com a árvore de coocorrência sobre análise de similitude, foram percebidas indicações de conexidade entre os termos: “não”, “paciente”, “dor” e “Chikungunya”. A palavra “não” foi a de maior frequência no corpus. Dessa forma, teve o sentido de desconhecimento e/ou dispor de informações limitadas acerca da patologia Chikungunya. O estudo apontou que, na percepção dos profissionais, o trabalho depende de uma educação permanente realizada por meio de capacitações, que são fundamentais para atualização, manejo clínico e tratamento destes pacientes.ABSTRACTIn 2014, the risk of spreading the Chikungunya virus and the need to strength the fight against Aedes aegypti in Brazil were mentioned in the literature. Thereby, in 2017, the Ministry of Health launched a program to prevent the spread of Aedes aegypti. However, in the same year, the country experienced an epidemic of Chikungunya virus. This is a qualitative study, developed with the physiotherapists from NASF in the city of Belém. Data were collected through interview, processed in IRAMUTEQ and had the similarity analysis and word cloud made. According to the similarity analysis co-occurrence tree, connections were identified between the words: “no”, “patient”, “pain” and “Chikungunya”. The word “no” was the most frequent in the corpus. Accordingly, “no” indicated lack of knowledge and/or limited information about Chikungunya disease. The study pointed out that, in the view of the professionals, the work depends on a permanent process of education through training, essential for updating, clinical management and treatment of patients.

Semisupervised machine-learning methods are able to learn from fewer labeled patient data. We illustrate the potential use of a semisupervised automated machine-learning (AutoML) pipeline for phenotyping patients who underwent transcatheter aortic valve implantation and identifying patient groups with similar clinical outcome. Using the Transcatheter Valve Therapy registry data, we divided 344 patients into 2 sequential cohorts (cohort 1, n = 211, cohort 2, n = 143). We investigated patient similarity analysis to identify unique phenogroups of patients in the first cohort. We subsequently applied the semisupervised AutoML to the second cohort for developing automatic phenogroup labels. The patient similarity network identified 5 patient phenogroups with substantial variations in clinical comorbidities and in-hospital and 30-day outcomes. Cumulative assessment of patients from both cohorts revealed lowest rates of procedural complications in Group 1. In comparison, Group 5 was associated with higher rates of in-hospital cardiovascular mortality (odds ratio [OR] 35, 95% confidence interval [CI] 4 to 309, p = 0.001), in-hospital all-cause mortality (OR 9, 95% CI 2 to 33, p = 0.002), 30-day cardiovascular mortality (OR 18, 95% CI 3 to 94, p0.001), and 30-day all-cause mortality (OR 3, 95% CI 1.2 to 9, p = 0.02) . For 30-day cardiovascular mortality, using phenogroup data in conjunction with the Society of Thoracic Surgeon score improved the overall prediction of mortality versus using the Society of Thoracic Surgeon scores alone (AUC 0.96 vs AUC 0.8, p = 0.02). In conclusion, we illustrate that semisupervised AutoML platforms identifies unique patient phenogroups who have similar clinical characteristics and overall risk of adverse events post-transcatheter aortic valve implantation.

This paper discusses approaches to evaluating the quality of intelligent data analysis results in diagnostic tasks. The reliability (indisputability) of empirical dependencies established during training (interpolation–extrapolation) on precedents is evaluated using a special mathematical tool, that is, characteristic functions. Characteristic functions are generated on the available sample of empirical data based on similarity analysis of precedent descriptions, formalized as a binary algebraic operation. Some estimates of the computational complexity of applying the proposed mathematical technique of characteristic functions to predicting (diagnosing) the properties of newly studied precedents are presented.

We studied the leaf anatomy of 15 species of Byrsonima and a single species of Diacidia as an outgroup to test the relevance of leaf anatomical characters to the complicated taxonomy of this genus. We present complete anatomical descriptions for petioles and leaf blades, together with an anatomical matrix, dendrogram and an identification key for the studied taxa. The most informative characters were the presence of stomatal crest, trichome type, epidermis type, contour and conformation of the vascular system in the diaphragm and medium petiole and number of accessory bundles. The aforementioned characters revealed that the anatomy of the petioles in the genus differs from that of petioles in the outgroup taxon with regard to vascular system conformation and the absence of cortical sclereids. Furthermore, the anatomy of the leaf blade appears to be quite informative in Byrsonima, especially in synonymized species. The anatomical features evaluated here have great value for Byrsonima taxonomy. These features may be used in an evolutionary approach to the group, especially for proposing a new classification system based on morphoanatomical characters of the genus.

O objetivo do trabalho é analisar os resultados e reflexões geradas nos encontros do “Ciclo de Discussão de Segurança Alimentar e Nutricional”. Nestes Ciclos são compartilhadas as construções da disciplina “Determinantes da Insegurança Alimentar e Nutricional” difundindo e valorizando trabalhados dos pós-graduandos e docentes em temas relacionados à Segurança Alimentar e Nutricional (SAN). A análise dos temas discutidos nos Ciclos, por meio da análise de conteúdo e de similitude, considerou-se as oito diretrizes da Política Nacional de Segurança Alimentar e Nutricional (PNSAN). Foram realizados oito Ciclos, sendo estes anuais. Todas as discussões basearam-se em diferentes temáticas que permearam o contexto da SAN e contemplaram pelo menos um dos eixos das diretrizes da PNSAN. Conclui-se que ações como estas são importantes para estimular o pensamento crítico para além da sala de aula e propiciar a troca de saberes entre universidade e comunidade, consolidando a tríade ensino, pesquisa e extensão.

O estudo descreve a composicao e estrutura da comunidade de larvas e juvenis de peixes associada a bancos de macrofitas aquaticas e avalia a importância dessas vegetacoes para o desenvolvimento dos estagios iniciais de vida da ictiofauna em uma ilha aluvial no Baixo Amazonas, Para, Brasil. As amostragens foram realizadas durante os momentos de cheia/vazante de 2017 (ciclo I) e enchente de 2018 (ciclo II) em bancos de macrofitas por meio de um peneirao (malha 500 µm). Em laboratorio os individuos foram identificados e classificados quanto ao seu estagio de desenvolvimento e verificada a abundância, a composicao das especies e similaridade entre os ciclos de amostragens. Uma analise de variância e teste t foram utilizados para constatar diferencas das densidades entre os estagios de desenvolvimento e ciclos hidrologicos, respectivamente. Foram capturados 2.193 individuos, dos quais 87,91 % ainda em fase larval e 12,08 % juvenil, classificados em 84 taxons e distribuidos em nove ordens, 24 familias, 50 generos e 72 especies. Durante o ciclo I, houve maior predominância de individuos juvenis de especies de pequeno porte, que possuem importante funcao na transferencia de energia no ecossistema aquatico, enquanto no ciclo II os maiores valores de abundância foram registrados para as especies de interesse economico e de medio porte capturadas em estagios iniciais de desenvolvimento. Nao foi observada diferenca significativa na densidade de individuos entre os ciclos de amostragem, porem atraves da analise de similaridade foi registrada uma variacao na composicao de especies entre os ciclos. A maior riqueza de especies, dominância e densidade de individuos foram registradas durante a enchente. A cheia/vazante apresentou maiores valores de equitabilidade e diversidade de especies. Assim, a comunidade de larvas e juvenis de peixes em vegetacao aquatica em varzea do Baixo Amazonas demonstra ser influenciada pelo ciclo hidrologico, garantindo a manutencao e renovacao da ictiofauna. AQUATIC MACROPHYTE STANDS AS EARLY STAGES OF FISH DEVELOPMENT SITES IN THE FLOODPLAIN OF THE LOWER AMAZON: The study describes the composition and structure of the larval and juvenile fishes’ community associated with aquatic macrophyte stands and it evaluates the importance of this vegetation for the development of early life stages of the ichthyofauna in an alluvial island in the Lower Amazon River, state of Para, Brazil. The sampling was conducted during the phases of full flood/receding water of 2017 (cycle I) and rising water of 2018 (cycle II) in aquatic macrophyte stands using a fishing sieve (500 µm mesh). In the laboratory, specimens were identified and classified for their development stage and verified the species composition, abundance and similarity between the sampling cycles. Variance analysis and t-test were used to identify possible differences in the density between the development stages and the hydrological cycles, respectively. There were captured 2,193 individuals, from which 87.91 % were at the larval stage and 12.08 % at the juvenile, classified in 84 taxa and identified in nine orders, 24 families, 50 genera and 72 species. During the cycle I, there was a higher predominance of juvenile individuals of small species, which have an important role in energy transferring in the aquatic ecosystem, while in the cycle II the highest values of abundance were recorded from the medium-sized species with economic importance, captured in the early development stages. No significant difference was observed in the density of individuals among the sampling cycles however, the similarity analysis registered a variation on the composition of species between the cycles. The higher species richness, dominance and density of individuals were recorded during the rising water, while the full flood/receding water presented higher values of evenness and species diversity. Thus, larval and juvenile fishes’ community in aquatic vegetation in varzea in the Lower Amazon region is shown to be influenced by the hydrological cycle, ensuring the maintenance and renewal of the ichthyofauna.

Purpose of the study: The purpose of the present study is to find out the growth trend, Similarity in financial performance, and co-movement, sensitivity, or governance of firms' size determinants on the profitability of Indian pharmaceutical companies. Methodology: The analysis is based on ratio analysis, while Chain based index numbers are calculated to get the trend of financial performance and size determinants. Statistical tools, ANOVA, and coefficient of variations (CV) used to get the Similarity of profitability and Liquidity while Chain based index numbers are the base to calculate the movements of variables and Similarity. Main Findings: The trend of profit measures growth governs the growth trend of the size measurements of the Indian pharmaceutical companies. The pharmaceuticals companies are utilizing their funds equally and total resources in the pharmaceuticals companies govern the operational profitability (EBIT), short term, and long term liquidity while it does not affect the short-term movement symmetry of the profitability and size determinants. There is no significant difference in short term movement or sensitivity of profitability and firms' size determinants of Indian pharmaceutical companies. Applications of this study: The finding of the study implies that the funds invested in total resources enhance the profits while equal utilization of resources by selected pharmaceutical companies. So, this study recommends enhancing the investment in total resources to get increased amounts of profits. Novelty/Originality of this study: This study is one of the few studies that measure the trend of Similarity and sensitivity of financial performance and size determinants of Indian pharmaceutical companies. In this process, it performs the trend, movement sensitivity, and financial performance similarity analysis using the index numbers of financial variables of Indian pharmaceutical companies.

Adult caddisfly (Trichoptera) community assemblages were studied in nine streams located in the transitional zone of the Ozarks and tallgrass prairie ecoregions. Seventy-two species, 36 genera, and 15 families were collected from among these streams. Cluster analysis showed taxa similarity among sites was grouped by geographic proximity rather than stream type, but, within the primary clusters, there also was separation among the streams. Predominantly spring-fed streams were less closely related to the other streams in those clusters. Among streams within a park, there were distinct differences among their respective faunas, which is likely due to local physical and chemical differences in those streams. An NMDS analysis was consistent with the similarity analysis with streams ordinating primarily based on geographic proximity (stress = 0.07, R2 = 0.76) and not stream type (multiresponse permutation procedure: A = 0.289, p = 0.003). Results from free permutation testing showed half of the environmental variables included in the model were significant (p ≤ 0.05). The most important findings of this study are that highest taxa similarities were among streams in the same park (local scale) as opposed to similar stream types among parks (regional scale), but there also was substantial variation among stream types within parks due to local factors.

In this paper, a sequential gas-liquid chromatography and mass spectrometry route was proposed for characterization of polysaccharides in Panax ginseng (PG), P. notoginseng (PN), and P. quinquefolius (PQ). Due to the reflection of stepped structure parameters, the resulting integrative profiles were tentatively defined as structural-fingerprinting of polysaccharides (SFP) with monosaccharide compositional fingerprinting (MCF), Smith degradation and non-degradation fingerprinting (SDF and SNF), and oligosaccharide compositional fingerprinting (OCF). The MCF, OCF and SDF did not allow for visual discrimination of the three species due to the high interspecific similarity of PG and PQ, whereas SNF could intuitively distinguish PG, PN, and PQ by the presence or absence of Rha and the peak area ratio of Glc/Gal. Similarity analysis, heatmap analysis and principal component analysis were further performed to discern three Panax species based on SNF data sets. The linear →4)-Hexp-(1 → structures were clearly identified as the common structural backbones in side chains or smooth regions of the main chain in PPG, PPN, and PPQ using HILIC-UHPLC-ESI−-MS/MS for characterization of partial acid hydrolyzates. The experimental results displayed that the established SFP approach possesses high comprehensibility as well as satisfactory generalization capability for analysis of plant polysaccharides.

Designing a new drug has recently become a very important topic that many researches are concerned with. This work relates to a newly synthesized betulin and betulone derivatives which have anticancer activity. Thin-layer chromatography was applied to evaluate the lipophilicity of these triterpenes in order to find the correlation between theoretically and experimentally calculated values of lipophilicity and the structure of compounds investigated. Moreover, the relationships between lipophilicity and pharmacokinetic parameters or anticancer activity were carried out. The similarity analysis was also done for the purpose to divide the compounds investigated into groups pointing which of these can meet the criteria for medicine substances. The cluster analysis showed the differences in the compounds grouping in relation which the values of lipophilicity are considered, i.e., calculated by computer software or obtained experimentally by use of TLC. Analysis clearly shows that those theoretically calculated values of lipophilicity are strongly connected to the structure of the compounds.

The iso-scallop method has been long adopted to achieve a shorter overall machining length. The efficiency and machining performance of this method are largely dominated by the initial tool path. The preferred direction field supplies the local best feed directions. Usually, the offset paths generated by the iso-scallop method largely deviate from the preferred directions, even though the initial path is strictly along the preferred directions. The matching degree of the offset paths and preferred direction field should be taken into consideration when selecting an initial tool path. This paper presents a novel initial path selection method for the iso-scallop method to make whole iso-scallop tool paths and the preferred direction field consistent as much as possible. A surface is re-parameterized to keep the conformality between the surface and parametric domain, which leads to the more regular offset paths on the new parametric domain. By fitting the vector field, streamlines are generated for representing the preferred feed direction on the parametric domain. The offset similarity metric defined by the initial path and streamlines is constructed to measure the matching degree between offset paths and preferred feed directions. Then the feasible path with the best offset similarity for the streamlines will be selected as the initial tool path. In our case study, feed directions with the maximum strip width are chosen. The test results have shown that the tool paths generated by the proposed method achieved a better matching for the selected feed directions and a shorter overall length compared with some existing tool path generation methods.