Miracle-dz/UniFAR-FTUs huggingface dataset:
ECG signal classification based on adaptive multi-channel weighted neural network

The intelligent diagnosis of cardiovascular diseases is a topic of great interest. Many electrocardiogram (ECG) recognition technologies have emerged, but most of them have low recognition accuracy and poor clinical application. To improve the accuracy of ECG classification, this paper proposes a multi-channel neural network framework. Concretely, a multi-channel feature extractor is constructed by using four types of filters, which are weighted according to their importance, as measured by kurtosis. A bidirectional long short-term memory (BLSTM) network structure based on attention mechanism is constructed, and the extracted features are taken as the input of the network, and the algorithm is optimized by attention mechanism. An experiment conducted on the MIT-BIH arrhythmia database shows that the proposed algorithm obtains excellent results, with 99.20 % specificity, 99.87 % sensitivity, and 99.89 % accuracy. Therefore, the algorithm is practical and effective in the clinical diagnosis of cardiovascular diseases.
Challenges and opportunities in atomistic simulations of glasses: a review

Atomistic modeling and simulations have been pivotal in our understanding of the glassy state. Indeed, atomistic modeling o ff ers direct access to the structure and dynamics of atoms in glasses—which is typically hidden from conventional experiments. Simulations also o ff er a more economical, faster alternative to systematic experiments to decode composition-property relationships and accelerate the discovery of new glasses with desirable properties and functionalities. However, the atomistic modeling of glasses remains plagued by a series of challenges, e
Food Choice Values and Food Literacy in a Nationwide Sample of Japanese Adults: Associations with Sex, Age, and Body Mass Index

This cross-sectional study of 2231 Japanese adults described food choice values and food literacy in relation to sex, age, and body mass index. We assessed eight food choice values (accessibility, convenience, health/weight control, tradition, sensory appeal, organic, comfort, and safety, using a 25-item scale), as well as food literacy, which was characterized by nutrition knowledge (using a validated 143-item questionnaire), cooking and food skills (using 14- and 19-item scales, respectively), and eight eating behaviors (hunger, food responsiveness, emotional overeating, enjoyment of food, satiety responsiveness, emotional undereating, food fussiness, and slowness in eating, using the 35-item Adult Eating Behavior Questionnaire). Females had higher means of all the variables than males, except for food fussiness. Compared to participants aged 19–39 and/or 40–59 years, those aged 60–80 years had low means of some food choice values (accessibility, convenience, sensory appeal, and comfort), nutrition knowledge, and all the food approach behaviors (hunger, food responsiveness, emotional overeating, and enjoyment of food) and high means of other food choice values (tradition, organic, and safety) and slowness in eating. Age was inversely associated with cooking and food skills in males, whereas the opposite was observed in females. The associations with body mass index were generally weak. These findings serve as both a reference and an indication for future research.
Biotechnological Production of Carotenoid from Oleaginous Red yeast and Its Applications

The demand for carotenoids and their derivatives from natural sources is increasing rapidly due to public concern about food safety and health issues, and thus, carotenoid production from microbial fermentation is increasing significantly due to its ability to accumulate higher levels of carotene. Carotenoids, lipid-soluble pigments, are responsible for the vibrant colors in food and microorganisms. Carotenoids have the most important advantages in terms of antioxidant and anticancer activity. These possible applications are used for treating various diseases like xerophthalmia, keratomalacia, skin acne, breast cancer and tumor formation. They are widely used in the pharmaceutical, cosmetics and food industries. Due to the overall increase in the cost of carotenoids, carotenoids are produced in the pharmaceutical, food and cosmetics industries through chemical synthesis or extraction from plants. The oleaginous red yeast, Rhodotorula minuta, is well known for producing a high yield of carotenoids with a low production cost. Over the years, these carotenoids have been produced from oleaginous red yeast, using low-cost substrates or agricultural waste for cost-effective purposes. In this paper, we highlighted the production of carotenoids from oleaginous red yeast and its applications.
Truncated sum-of-squares estimation of fractional time series models with generalized power law trend

Abstract: We consider truncated (or conditional) sum-of-squares estimation of a parametric fractional time series model with an additive deterministic structure. The latter consists of both a drift term and a generalized power law trend. The memory parameter of the stochastic component and the power parameter of the deterministic trend component are both considered unknown real numbers to be estimated and belonging to arbitrarily large compact sets. Thus, our model captures different forms of nonstationarity and noninvertibility as well as a very flexible deterministic specification. As in related settings, the proof of consistency (which is a prerequisite for proving asymptotic normality) is challenging due to non-uniform convergence of the objective function over a large admissible parameter space and due to the competition between stochastic and deterministic components. As expected, parameter estimates related to the deterministic component are shown to be consistent and asymptotically normal only for parts of the parameter space depending on the relative strength of the stochastic and deterministic components. In contrast, we establish consistency and asymptotic normality of parameter estimates related to the stochastic component for the entire parameter space. Furthermore, the asymptotic distribution of the latter estimates is unaffected by the presence of the deterministic component, even when this is not consistently estimable. We also include Monte Carlo simulations to illustrate our results.
Analysis of Sideswipe Collision Precursors Considering the Spatial-Temporal Characteristics of Freeway Traffic

AbstractTo avoid bias caused by the high percentage of rear-end collisions in the generic models, only the observed sideswipe collisions on Highway I-5 in the central Puget Sound, Washington State, area are used to analyze the matched case-control logistic regression model. Considering the spatial-temporal characteristics of traffic flow and short-term variation of the sideswipe collision occurrence, a comprehensive analysis of sideswipe collision occurrence and its relationship with the freeway flow across lanes and detector locations are studied. The results imply that sideswipe collisions are more likely to occur at straight and level segments of multilane freeways in off-peak hours. High average occupancy and low average flow and speed variance upstream of collision location tend to increase the probability of sideswipe collision in congested scenarios. In contrast, high average speed and coefficient of variation in speed, low speed variance and coefficient of variation in occupancy, and high average .
Mechanical Behaviors of the Origami-Inspired Horseshoe-Shaped Solar Arrays

The importance of flexibility has been widely noticed and concerned in the design and application of space solar arrays. Inspired by origami structures, we introduce an approach to realizing stretchable and bendable solar arrays via horseshoe-shaped substrate design. The structure has the ability to combine rigid solar cells and soft substrates skillfully, which can prevent damage during deformations. The finite deformation theory is adapted to find the analytic model of the horseshoe-shaped structure via simplified beam theory. In order to solve the mechanical model, the shooting method, a numerical method to solve ordinary differential equation (ODE) is employed. Finite element analyses (FEA) are also performed to verify the developed theoretical model. The influences of the geometric parameters on deformations and forces are analyzed to achieve the optimal design of the structures. The stretching tests of horseshoe-shaped samples manufactured by three-dimensional (3D) printing are implemented, whose results shows a good agreement with those from theoretical predictions. The developed models can serve as the guidelines for the design of flexible solar arrays in spacecraft.
Machine Learning Strategies for Low-Cost Insole-Based Prediction of Center of Gravity during Gait in Healthy Males

Whole-body center of gravity (CG) movements in relation to the center of pressure (COP) offer insights into the balance control strategies of the human body. Existing CG measurement methods using expensive measurement equipment fixed in a laboratory environment are not intended for continuous monitoring. The development of wireless sensing technology makes it possible to expand the measurement in daily life. The insole system is a wearable device that can evaluate human balance ability by measuring pressure distribution on the ground. In this study, a novel protocol (data preparation and model training) for estimating the 3-axis CG trajectory from vertical plantar pressures was proposed and its performance was evaluated. Input and target data were obtained through gait experiments conducted on 15 adult and 15 elderly males using a self-made insole prototype and optical motion capture system. One gait cycle was divided into four semantic phases. Features specified for each phase were extracted and the CG trajectory was predicted using a bi-directional long short-term memory (Bi-LSTM) network. The performance of the proposed CG prediction model was evaluated by a comparative study with four prediction models having no gait phase segmentation. The CG trajectory calculated with the optoelectronic system was used as a golden standard. The relative root mean square error of the proposed model on the 3-axis of anterior/posterior, medial/lateral, and proximal/distal showed the best prediction performance, with 2.12%, 12.97%, and 12.47%. Biomechanical analysis of two healthy male groups was conducted. A statistically significant difference between CG trajectories of the two groups was shown in the proposed model. Large CG sway of the medial/lateral axis trajectory and CG fall of the proximal/distal axis trajectory is shown in the old group. The protocol proposed in this study is a basic step to have gait analysis in daily life. It is expected to be utilized as a key element for clinical applications.
Chemometric Investigation and Antimicrobial Activity of Salvia rosmarinus Spenn Essential Oils

To ensure the better production and sustainable management of natural resources, a chemometric investigation was conducted to examine the effect of cooperative and harvesting periods on the crop yields and chemical compositions of Salvia rosmarinus Spenn essential oils in the Oriental region of Morocco. The samples were collected from three cooperatives over nine time periods from January 2018 to April 2019. The chemical composition of Salvia rosmarinus Spenn essential oils was analyzed by gas chromatography coupled with mass spectrometry. The data from this study were processed by multivariate analyses, including principal component analysis (PCA) and hierarchical cluster analysis (HCA). The disc diffusion technique and a determination of the minimal inhibitory concentration were performed to study the antibacterial properties of the oils. Statistical analysis showed that the cooperative and harvest period have a significant effect on yields. The highest yield of essential oil was recorded in April 2019 at cooperative C1. The PCA and the HCA results were divided into two groups: Group A for the summer season and group B for the winter season. The samples collected during summer were characterized by a high amount of 1,8-cineole component and a high yield of essential oil, whereas the samples collected during winter were qualified by a high amount of α-pinene component and a low yield of essential oil. The antibacterial activity of Salvia rosmarinus Spenn essential oils showed that Mycobacterium smegmatis ATCC23857 and Bacillus subtilis ATCC 23857 are the most susceptible strains, stopping growth at 1/500 (v/v). The least susceptible strain is Escherichia coli ATCC25922, with an MIC value corresponding to 1/250 (v/v). The findings of this study could have a positive economic impact on the exploitation of rosemary in the Oriental region, especially during the best harvest periods, as they indicate how to obtain the best yields of oils richest in 1,8-cineole and α-pinene chemotypes.
Translation Techniques Used in Assassin Creed: Renaissance Novel

Several studies have been done on the technique of translating novel speech acts. However, impositive speech act translation research is still limited. Speakers perform impositive speech acts to burden their speech partners. Thus, the translator must comprehend the context. This study aimed to determine the translator's translation technique for adjusting target language meaning to the context. The data used in this study was gathered from the original English novel and compared to the same story in Indonesian. The researcher used the Forum Group Discussion with three translation professionals (FGD) to validate the data. The study found that most translators employ the conventional equivalency method. The translator determines the most suitable and acceptable meaning for the target language. Received: 7 January 2022 / Accepted: 8 March 2022 / Published: 5 May 2022
Concepção de alfabetização em documentos curriculares: comparação Brasil Argentina

A partir da abordagem da alfabetização na perspectiva do letramento, esta pesquisa objetivou analisar um documento curricular oficial argentino (“Diseño curricular para la escuela primaria”), com a finalidade de identificar as concepções de alfabetização subjacentes e comparar com as concepções presentes em documentos curriculares brasileiros e com a “Política Nacional de Alfabetização” (PNA), lançada pelo Ministério de Educação do Brasil em 2019. Foram realizadas análises documentais segundo pressupostos descritos por Bardin. Os dados brasileiros foram sistematizados a partir de estudo anterior realizado por Leal, Brandão, Almeida, Vieira (2013), no qual foram analisados 26 documentos curriculares de secretarias estaduais e de capitais brasileiras. As propostas foram classificadas em três categorias: 1 (alfabetização por imersão); 2 (alfabetização como aprendizagem do código alfabético); 3 (alfabetização na perspectiva do letramento). Predominavam, no Brasil, as tendências 1 e 3, que orientam um ensino em que a leitura e a produção de textos autênticos e integrais permeiam o processo de alfabetização. No documento argentino predominam termos que remetem à abordagem construtivista e defende-se a alfabetização em uma perspectiva de inserção nas práticas sociais de leitura e escrita e, ao mesmo tempo, o ensino das especificidades do SEA (categoria 3). Em contraste com as perspectivas dos documentos curriculares, a PNA está embasada em métodos sintéticos de alfabetização. Tal abordagem marca, portanto, uma retomada de concepções tradicionais de alfabetização focadas em modelos empiristas.
Research on traffic probabilistic forecasting with spatial-temporal graph neural networks

Existing Spatio-temporal graph models consider traffic flow prediction as a "node-level" task, and it is difficult to make decisions based on the results of point prediction alone. The modeling idea is to explore the hidden graph structure that better reflects the spatial dependency of the road network from the traffic flow "signal" to replace the less accurate graph structure that is calculated directly based on the geographical location. Here, we propose the Dilated SpatioTemporal Graph Convolutional Network (DSTGCN) model and the Adaptive Graph Recurrent Neural Network (AGRNN) model for traffic flow prediction. The main work is to propose a self-learning transfer matrix for directed graphs that can automatically discover potential graph structures from the historical traffic of each node, improve the gating mechanism in temporal convolution, and introduce probabilistic prediction into the traffic flow prediction problem to enhance the dimensionality of information used for decision making. DSTGCN and AGRNN are evaluated on two publicly available traffic datasets. METRLA and PEMS-BAY were evaluated and achieved the fastest computational speed (compared to other Spatio-temporal graph models) and the best prediction results, respectively.
Classification with the Neural Network Application of Basic Hearing Losses Determined by Audiometric Measuring

Pure tone audiometer is used to determine a person's hearing threshold level at different frequencies.The hearing test provides an evaluation of the sensitivity of a person's sense of hearing and is most often performed by an audiologist using an audiometer. There are different types of hearing loss, depending on which part of the hearing pathway is affected. In this study, air and bone path audiograms belong to conductive, sensorineural and mixed hearing losses and their sub groups were obtained. A specialist will always try to localize where in the hearing pathway the problem lays, so as to be able to classify the hearing loss as belonging to one of the groups. This is most important in determining the appropriate treatment. In our study, ANN that was an expert system was improved to classify of these hearing losses. With 98,75% correct classification satisfactory classification results were obtained, which were consistent with diagnostic results by clinical doctors.
Expert elicitation of Risk Control Options to reduce human error in winter navigation

Navigation in ice conditions possess a higher degree of complexity than open water navigation. This complexity has a significant influence in the human performance during the execution of operations. In this paper, a detailed expert elicitation to assess Human Error Probabilities (HEPs) and proposed Risk Control Options (RCOs) in the performance of winter navigation is presented. For this purpose, an evaluation of the Common Performance Conditions (CPCs) of winter navigation operations and the influence from proposed RCOs is elaborated. The process includes the estimation of cognitive failure probabilities extracted from the experts’ elicitation which are subsequently integrated into a Bayesian network model. The results of this analysis identified the operations with the highest need for new actions which can reduce the HEPs. Moreover, actions such as the improvement of winter navigational training, safety and risk management, and electronic navigation support, are identified as key measures to improve human performance. The rest of the paper is organized as follows. Section 2 describes the general background analyzed in this study. Section 3 presents the methods and data utilized in the presented analysis. Section 4 presents the results of the analyses performed for the assessment of human error and its influence in the development of winter navigation operations. Sections 5 discusses the research findings, and Section 6 presents the final conclusions and recommendations.
Is it Possible to Count the Earth’s Population from Outer Space? (Menschen Zählen Aus Dem All Möglichkeiten Und Grenzen Von Satellitendaten Zur Abschätzung Der Bevölkerungsentwicklung Und Des Gebäudebestandes In Deutschen Städten)

Is it possible to count the earth’s population from outer space? The answer is yes, in urban areas it is possible. However, this can only be done in an indirect manner: by identifying physical objects in the urban landscape in earth observation data and using these to estimate the number of inhabitants. Since the approach is indirect, data protection and the individual right to privacy are fully guaranteed. The data obtained using this method fill a gap, given that municipal population registers do not contain accurate population counts. However, remote sensing technology is not able to provide cadastral information. Nevertheless, as this paper shows, satellite imagery is capable of providing the basis for population estimates for small-scale areas. And, of course, remote sensing data also can be used to estimate the building stock. It would make sense to produce such estimates during the intervals between each building stock census, which is usually conducted every ten years with the population census. Remote sensing data cannot replace a population census, but can enrich the analytical power of population census data.Menschen zahlen aus dem All? Kann das denn moglich sein? Die Antwort lautet ja, allerdings ist diese Zahlung nur in urbanen Raumen und nur auf indirekte Weise moglich. Aufgrund der indirekten Messung wird freilich gleichzeitig der Datenschutz perfekt gewahrt. Die hier dargestellte Methode beruht darauf, dass die physischen Objekte der Stadtlandschaft, die direkt aus Erdbeobachtungsdaten abgeleitet werden konnen, dazu benutzt werden, indirekt auf die Anzahl der dort lebenden Personen zu schliesen. Diese Methode wird vor allem vor dem Hintergrund interessant, da Einwohnermelderegister insbesondere in Grosstadten keine zuverlassige Auskunft uber die Einwohnerzahlen geben. Sicherlich kann die Fernerkundung keine katastergenauen Informationen liefern, aber der Beitrag zeigt, dass aus dem All die Bevolkerung in ihrer kleinraumlichen Verteilung verbluffend genau indirekt geschatzt werden kann. Erdbeobachtungsdaten konnen direkt fur gute Schatzungen des Gebaudebestandes genutzt werden. Derartige Schatzungen sind im Zeitraum zwischen Gebaude- und Wohnungszahlungen, die typischerweise zusammen mit einem Zensus im Zehnjahresabstand stattfinden, sinnvoll. Erdbeobachtungsdaten konnen auch einen Bevolkerungszensus keineswegs ersetzen, aber die Erdbeobachtungsdaten vergrosern das Analysepotential von Zensusdaten.
Controlling Factors and Characteristics of Peak Runoff in an Alpine Headwater Under the Asian Monsoon Climate

Understanding the mountain hydrological cycle, including runoff processes, is important for water-related disaster prevention. Although the process of peak runoff generation is closely related to water hazards, this process has not been clarified in alpine headwaters with large amounts of precipitation. In this study, we conducted hydrological observations to clarify runoff characteristics and factors that determine peak runoff in an alpine headwater under the Asian monsoon climate. Total precipitation during the summer period (3 months) was 1581.4 mm, and the water runoff responded quickly and clearly to rainfall events. Focusing on baseflow, the runoff was terminated when the snow cover area decreased. This suggested that snowmelt water plays an important role in maintaining baseflow in alpine headwaters under the Asian monsoon climate, like other alpine areas worldwide. In addition, peak runoff was not significantly correlated with soil wetness (as indicated by the antecedent precipitation index), whereas it was correlated with the amount of rainfall just before the generation of peak runoff. Therefore, the amount of rainfall before peak runoff in a single event was important in determining peak runoff. Focusing on the snowmelt season, we confirmed that the runoff increased even during small rainfall events. This indicated that snowpack melting is another factor determining peak runoff when the snowpack remains in the catchment. Considering the immediate runoff generation after rainfall events, direct observation of hydrometeorological data in situ is crucial to predict water-related disasters and consider countermeasures in alpine regions.