Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques

This study attempts to optimize the prediction accuracy of the compressive strength of high-performance concrete (HPC) by comparing data-mining methods. Modeling the dynamics of HPC, which is a highly complex composite material, is extremely challenging. Concrete compressive strength is also a highly nonlinear function of ingredients. Several studies have independently shown that concrete strength is determined not only by the water-to-cement ratio but also by additive materials. The compressive strength of HPC is a function of all concrete content, including cement, fly ash, blast-furnace slag, water, superplasticizer, age, and coarse and fine aggregate. The quantitative analyses in this study were performed by using five different data-mining methods: two machine learning models (artificial neural networks and support vector machines), one statistical model (multiple regression), and two metaclassifier models (multiple additive regression trees and bagging regression trees). The methods were developed and tested against a data set derived from 17 concrete strength test laboratories. The cross-validation of unbiased estimates of the prediction models for performance comparison purposes indicated that multiple additive regression tree (MART) was superior in prediction accuracy, training time, and aversion to overfitting. Analytical results suggested that MART-based modeling is effective for predicting the compressive strength of varying HPC age. DOI: 10.1061/(ASCE)CP.1943-5487 . 0000088. © 2011 American Society of Civil Engineers. CE Database subject headings: Concrete; Compressive strength; Data collection; Artificial intelligence; Predictions. Author keywords: High-performance concrete; Compressive strength; Data mining; Computing intelligence; Predictive techniques.
VALIDITY OF RPE SESSION IN YOUNG MALE KARATE ATHLETES

Purpose. This study aimed to assess the validity of the rating of perceived exertion (RPE) session method vs. HR-based methods (Edwards and Banister) for quantifying karate's training load (TL). Methods. Eleven young karatekas (age 11.34±1.76 years, height 143±6.786 cm, body mass 48.13±7.33 kg, HRmax 205±0.98 bpm) participated in this study. The training sessions was performed on 5 consecutive days with two training sessions for day. During the training Heart Rate was recorded with Cardio "Suunto Memory-Belt" so to calculate the Trimp (Banister) and Training Load (Edwards), while the session-RPE scale is based on the Borg category ratio RPE scale and then modified by Foster et al (CR-10). The validity of RPE method in young Karate athletes, was studied by correlation analysis between RPE session's training load and both Edwards and Banister's training impulse score' method. Results. The HR and RPE were collected from 10 training sessions with inter-day (two sessions for days) correlations were determined (n=11 × 5 days). The mean session-RPE values and correlations in each exercise mode between session-RPE and HR-based TLs (ie, Edwards's TL and Banister's TRIMP) showed TL, r = 0.79; 95% CI: 0.67 to 0.87 and Banister's TRIMP, r = 0.63; 95% CI: 0.44 to 0.77. Conclusion. The results of the present study provide evidence that the session-RPE method is a simple and inexpensive tool that accurately provides a similar quantification of internal TLs as assessed by the HR-based methods during the various training modes with young karate practitioners. The complex physiological interactions during karate sessions that were dedicated for developing a wide variety of physical capacities makes it difficult for coaches to accurately quantify training load using HR or time measures. Thus, the session-RPE method provides a practical, low- cost, and non invasive tool of quantifying karate training loads, thus making it a valuable tool for athletes, coaches, and sports scientists.
Measurement of plethysmogram and diagnosis of stress by using photosensor

The plethysmograph, which can measure plethysmogram, velocity plethysmogram and acceleration plethysmogram, is designed. The plethysmogram of fingertip is measured by photocensor. Some artificial stress is imposed on subjects and the plethysmogram is measured before and after the stress is put on. The plethysmogram is analyzed by plotting the return map in chaos theory, which enables us to discuss the characteristics of stress clearly in a visual way. The fractal dimension of the plethysmogram as a time series is also calculated to discuss complexity of the plethysmogram quantitatively. It is shown to be useful to the diagnosis of heart disease.
Genetic variation is predominantly structured by geography rather than host in feather mites (Acariformes: Sarcoptiformes) associated with tanagers (Aves: Thraupidae) in Brazil

Feather mites are the most common ectosymbionts on birds. These obligatory symbionts are mainly transmitted during their host’s parental care, which creates high host specificity. Due to this intimate relationship, it is thought that their geographic distribution is restricted by their host distribution, or that a host species harbors the same mite composition across its whole range. However, our knowledge regarding the geographic distribution of feather mites remains scarce, with only a few studies indicating disconnections between mite and host distributions, especially in widespread hosts. Here, we investigate the feather mites distribution on four tanager species, three widespread – Thraupis sayaca (L.), T. palmarum (Wied), and Stilpnia cayana (L.) from Northern and Southern Brazil; and the Amazonian T. episcopus (L.). Feather mites were identified using the molecular barcode marker COX-1 using K2P genetic distances. We found a strong genetic structure between Northern and Southern populations of tanagers of more than 10%, even among conspecific hosts. Therefore, the mite distribution on Brazilian tanagers is predominantly shaped by geography rather than by host species. These features in turn reflect historical horizontal transmissions among the hosts, suggesting a high potential for frequent host switches in these symbionts.
Discrimination between two classes of radar emitters based on the radio frequency distributions

The subject of this thesis is to investigate methods for discrimination between two classes of radar emitters on the basis of the distribution of the radio frequencies. For the first class, the radio frequency is uniformly distributed within a certain frequency band. For the second class, the radio frequency belongs to a set of discrete frequencies where each frequency is equally probable. Two methods has been investigated. The first method is a recursive algorithm that applies multiple Kalman filters. The second method is a clustering algorithm. It is a non-recursive algorithm that for each acquired measurement processes all measurements in a batch. To compare the different methods, the posterior probability of each class was calculated given a sequence of measurements. The posterior probability represents all knowledge of the emitters, and constitutes the base for further decisions. The results show that both methods are capable of discriminating between the two classes. For the method that applies multiple Kalman filters, the probability of each class quickly converges towards the desired results. The number of discrete frequencies is chosen as 10 in the simulations, and the number of required measurements is then approximately 30. For the clustering algorithm, the number of required measurements is substantially larger than for the method with the multiple Kalman filters. Date Rev No. Prepared (also subject responsible if other) Approved Checked Reference 2005-03-21 A EMW/FN/P-05:014 Uen 4 (60) MO/EMW/FN/PZ Henrik Ranthin, Erika Svanstrom FN/PZC (Lars Sundell) KEAN Open REPORT Content Page
Nutrient Intake and Dietary Inflammatory Potential in Current and Recovered Anorexia Nervosa

Anorexia nervosa (AN) is characterised by disrupted and restrictive eating patterns. Recent investigations and meta-analyses have found altered concentrations of inflammatory markers in people with current AN. We aimed to assess nutrient intake in participants with current or recovered AN, as compared to healthy individuals, and explore group differences in dietary inflammatory potential as a possible explanation for the observed alterations in inflammatory markers. We recruited participants with current AN (n = 51), those recovered from AN (n = 23), and healthy controls (n = 49). We used the Food Frequency Questionnaire (FFQ), to calculate a Dietary Inflammatory Index (DII®) score and collected blood samples to measure serum concentrations of inflammatory markers. In current AN participants, we found lower intake of cholesterol, compared to HCs, and lower consumption of zinc and protein, compared to HC and recovered AN participants. A one-way ANOVA revealed no significant group differences in DII score. Multivariable regression analyses showed that DII scores were significantly associated with tumour necrosis factor (TNF)-α concentrations in our current AN sample. Our findings on nutrient intake are partially consistent with previous research. The lack of group differences in DII score, perhaps suggests that diet is not a key contributor to altered inflammatory marker concentrations in current and recovered AN. Future research would benefit from including larger samples and using multiple 24-h dietary recalls to assess dietary intake.
Human Behavior in a Matrix of Hazards: Risk, Rules, and Ratio in Biomedical Laboratory Safety

Laboratory safety presents special challenges in occupational safety and health management around the world. In scientific laboratories, all kinds of hazardous materials (biological, chemical, and radiological) are present—either individually or in some combination. Additionally, physical hazards in laboratories are ubiquitous and add to the overall risk faced by scientists, students, and the environment in all but the most benign settings. Managing the risks found in laboratories encompasses many aspects including safety rules, attitudes, opinions, and hazard and risk perception. A comparative look at the safety and health concerns of biomedical research scientists in The Netherlands and the United States lead the authors to take a broad, international view of laboratory safety management. One empirical finding, a regulatory context, and one assumption underly this viewpoint. First, the safety and health concerns of biomedical research scientists in a Dutch research institution and an American research institution turn out to be similar in many respects. Second, the relevant laws and regulations in the European Union (EU) and the United States (US) form the basis of “rules” intended to address biomedical laboratory safety concerns. Rules, however, tend to cover only a portion of the concerns expressed by biomedical research scientists. Our assumption is that attitudes and opinions are also important determinants in the establishment of a balance—a ratio—between the hazards and the rules intended to manage those risks. Based on this international view, the authors propose the development of a consensus-based international training curriculum and introductory program for biomedical research scientists covering relevant issues in health, safety, and environmental protection.
Seasonal Variations of Solar‐Induced Fluorescence, Precipitation, and Carbon Dioxide Over the Amazon

Previous studies suggested that the Amazon, the largest rainforest on Earth, changes from a CO2 sink to a CO2 source during the dry/fire season. However, the biospheric contributions to atmospheric CO2 are not well understood during the two main seasons, the dry/fire season and the wet season. In this article, we utilize Orbiting Carbon Observatory 2 (OCO‐2) Solar‐Induced Fluorescence (SIF) to explore photosynthetic activity during the different seasons. The spatiotemporal variability of OCO‐2 SIF, OCO‐2 CO2, precipitation, and burned area are investigated over the Amazon from September 2014 to December 2019. Averaging over the entire Amazon region, we found a positive temporal correlation (0.94) between OCO‐2 SIF and Global Precipitation Climatology Project precipitation and a negative temporal correlation (−0.64) between OCO‐2 SIF and OCO‐2 CO2, consistent with the fact that precipitation enhances photosynthesis, which results in higher values for SIF and rate of removal of CO2 from the atmosphere above the Amazon region. We also observed seasonality in the spatial variability of these variables within the Amazon region. During the dry/fire (August–October) season, low SIF values, low precipitation, high vapor pressure deficit (VPD), large burned areas, and high atmospheric CO2 are mainly found over the southern Amazon region. In contrast, during the wet season (January–March), high SIF values, high precipitation, low VPD, smaller burned areas, and low CO2 are found over both the central and southern Amazon regions. The seasonal difference in SIF suggests that photosynthetic activity is reduced during the dry/fire season relative to the wet season as a result of low precipitation and high VPD, especially over the southern Amazon region, which will contribute to more CO2 in the atmosphere during the dry/fire season.
Learning leaders, new professional identities and theimplications for children’s centres and early years

The purpose of this paper is to explore the findings from a research project conducted in England in 2010 on the impact of the programme National Professional Qualification for Integrated Centre Leaders (NPQICL) on two cohorts of participants (2006‐8). The research focused on the outcomes for the participants as leaders and their professional identities; their organisation; learning within the Children’s Centre and the wider community. The UK Government announced (2010) that Sure Start (which includes Children’s Centres) has been one of the great success stories of the last decade delivering 3,500 Children’s Centres ahead of time. This has been evidenced by various research projects and evaluation reports (Blesky et al, 2007; Canavan et al, 2009). The Centres have enabled over 2.7 million children under 5 and their families to access a range of integrated services. However, in the announcements there was no mention of the impact of how the NPQICL programme had transformed the leadership in the Centres, particularly as it was heralded as an innovative programme grounded in research, both in its construction and delivery. Therefore, this research also seeks to relate the story of those most influential in achieving this success, the participants of the programme as Centre leaders.
State of the Art of Non-Invasive Electrode Materials for Brain–Computer Interface

The brain–computer interface (BCI) has emerged in recent years and has attracted great attention. As an indispensable part of the BCI signal acquisition system, brain electrodes have a great influence on the quality of the signal, which determines the final effect. Due to the special usage scenario of brain electrodes, some specific properties are required for them. In this study, we review the development of three major types of EEG electrodes from the perspective of material selection and structural design, including dry electrodes, wet electrodes, and semi-dry electrodes. Additionally, we provide a reference for the current chaotic performance evaluation of EEG electrodes in some aspects such as electrochemical performance, stability, and so on. Moreover, the challenges and future expectations for EEG electrodes are analyzed.
MMSE Decision-Feedback Equalizers and Coding-Part 11: Coding Results

Abstrucf- The minimum-mean-squared-error decision-feedback equalizer (MMSE-DFE) has properties that suggest that it is a canonical equalization structure in systems that combine equalization with coded modulation. With a given symbol rate UT and transmit spectrum, the output signal-to-noise ratio SNRMMSE-DFE,~ of a MMSE-DFE with an unbiased decision rule is a single parameter that characterizes the channel for coding purposes. Indeed, the transmit spectrum that maximizes SNRMMSE-DFE,~ is the capacity-achieving (water-pouring) spectrum, and the capacity C(T) (in bits per two dimensions) is given by C(T) lO&[l + SNRMMSE-DFE,U] (1)
Plant Community Response and Implications for Wildlife Following Control of a Nonnative Perennial Grass

Restoration of early successional plant communities dominated by nonnative plant species is a central focus of many state and federal agencies to improve habitat for wildlife associated with these communities. Restoration e ﬀ orts largely have concentrated on controlling nonnative species followed by planting native grasses and forbs. However, there are numerous establishment problems associated with planting that warrant evaluation of alternative approaches for restoration. We conducted a ﬁ eld experiment to compare vegetation composition and structure as related to habitat for focal wildlife among plant communities established by planting (Planted) native grasses and forbs and revegetation from the seedbank (Seedbank) without planting following control of tall fescue ( Schedonorus arundinaceus ) at 15 replicated sites in Tennessee and Alabama, USA. Planted and Seedbank treatments produced similar plant communities. Vegetation structure providing cover for nesting and brooding northern bobwhite ( Colinus virginianus ) and wild turkey ( Meleagris gallopavo ) was similar between Seedbank and Planted treatments except native grass cover was greatest in Planted, and we recorded greater openness at ground level in Seedbank than Planted or tall fescue control (Control). Abundance of northern bobwhite food plants and selected white ‐ tailed deer ( Odocoileus virginianus ) forage were similar between Planted and Seedbank treatments, but nutritional carrying capacity for deer was greatest in Seedbank. Despite similarities in food abundance, and even though all forbs included in the planting mixtures were food plants, the majority of food plants in Planted were from the seedbank. The compositional and structural characteristics deemed most in ﬂ uential in previous studies to selection of breeding sites by dickcissel ( Spiza americana ), ﬁ eld sparrow ( Spizella pusilla ), grasshopper sparrow ( Ammodramus savannarum ), Henslow ’ s sparrow ( Ammodramus henslowii ), and northern bobwhite were similar in Planted and Seedbank. Tall fescue Control was most similar to char - acteristics of eastern meadowlark ( Sturnella magna ) breeding sites. Revegetation following Seedbank pro - duced a plant community that provided habitat for many wildlife species equal to or better than Planted and was 3.7 times less expensive than Planted. © 2021 The Wildlife Society.
Regression based peak load forecasting using a transformation technique

This paper presents a regression based daily peak load forecasting method with a transformation technique. In order to forecast the load precisely through a year, one should consider seasonal load change, annual load growth and the latest daily load change. To deal with these characteristics in the load forecasting, a transformation technique is presented. This technique consists of a transformation function with translation and reflection methods. The transformation function is estimated with the previous year's data points, in order that the function converts the data points into a set of new data points with preservation of the shape of temperature-load relationships in the previous year. Then, the function is slightly translated so that the transformed data points will fit the shape of temperature-load relationships in the year. Finally, multivariate regression analysis, with the latest daily loads and weather observations, estimates the forecasting model. Large forecasting errors caused by the weather-load nonlinear characteristic in the transitional seasons such as spring and fall are reduced. Performance of the technique which is verified with simulations on actual load data of Tokyo Electric Power Company is also described. >
Comparison of Machine Learning Techniques in Cotton Yield Prediction Using Satellite Remote Sensing

The use of machine learning techniques to predict yield based on remote sensing is a no-return path and studies conducted on farm aim to help rural producers in decision-making. Thus, commercial fields equipped with technologies in Mato Grosso, Brazil, were monitored by satellite images to predict cotton yield using supervised learning techniques. The objective of this research was to identify how early in the growing season, which vegetation indices and which machine learning algorithms are best to predict cotton yield at the farm level. For that, we went through the following steps: 1) We observed the yield in 398 ha (3 fields) and eight vegetation indices (VI) were calculated on five dates during the growing season. 2) Scenarios were created to facilitate the analysis and interpretation of results: Scenario 1: All Data (8 indices on 5 dates = 40 inputs) and Scenario 2: best variable selected by Stepwise regression (1 input). 3) In the search for the best algorithm, hyperparameter adjustments, calibrations and tests using machine learning were performed to predict yield and performances were evaluated. Scenario 1 had the best metrics in all fields of study, and the Multilayer Perceptron (MLP) and Random Forest (RF) algorithms showed the best performances with adjusted R2 of 47% and RMSE of only 0.24 t ha-1, however, in this scenario all predictive inputs that were generated throughout the growing season (approx. 180 days) are needed, so we optimized the prediction and tested only the best VI in each field, and found that among the eight VIs, the Simple Ratio (SR), driven by the K-Nearest Neighbor (KNN) algorithm predicts with 0.26 and 0.28 t ha-1 of RMSE and 5.20% MAPE, anticipating the cotton yield with low error by ±143 days, and with important aspect of requiring less computational demand in the generation of the prediction when compared to MLP and RF, for example, enabling its use as a technique that helps predict cotton yield, resulting in time savings for planning, whether in marketing or in crop management strategies.
Fungal Endophytes of Vitis vinifera—Plant Growth Promotion Factors

Endophytes are microorganisms that live asymptomatically inside plant tissues. They are beneficial to their host in many aspects, especially as a defense against foreign phytopathogens through the production of a variety of metabolites. These substances can serve as sources of new natural products for medicinal, agricultural, and industrial purposes. This article is focused on endophytic fungi from Vitis vinifera. The purpose of the research was their isolation and identification during the Vitis vinifera growing season. Subsequently, the isolates were tested for the production of biotechnologically interesting metabolites (siderophores, antioxidants, and antifungal compounds). In total, 24 endophytic fungi were isolated, the most represented genus was Cladosporium sp. The results of the test for antioxidant and antifungal properties, as well as siderophore production, have shown that the population of Vitis vinifera endophytic microscopic fungi could serve as a promising source of metabolites with a wide range of applications.