Miracle-dz/UniFAR-FTUs huggingface dataset:
Talent Identification in Elite Adolescent Ice Hockey Players: The Discriminant Capacity of Fitness Tests, Skating Performance and Psychological Characteristics

Background: The process of talent identification in ice hockey occurs during middle adolescence when players are selected to participate in “off-season” evaluation camps, where coaches observe their fitness levels and status of development. Recently, the Quebec ice hockey federation opted for a holistic approach by evaluating players based on three criteria: (1) fitness, (2) skating abilities and (3) personality traits and psychological assets. This study aimed to analyze the discriminant validity of a multi-dimensional talent identification testing protocol in competitive ice hockey. Method: Data were collected from 160 adolescent hockey players who took part in Team Quebec summer evaluation camps. Off-ice fitness, skating abilities and psychological variables were measured on two consecutive days. Descriptive statistics, group comparisons (gender, positions) and discriminant analyses (selected versus non-selected) were performed. Results: No differences were observed among males in which selected players were similar to non-selected. Results from discriminant analyses also showed no discriminant function for male players. For females, selected players displayed higher fitness, on-ice agility and psychological characteristics. Nine performance markers were significantly discriminant. Conclusions: A holistic evaluation protocol allows for the discrimination of selected and non-selected players in elite ice hockey. Developing more discriminant tests is a promising avenue of research in male ice hockey. Knowing the factors that are associated with team selection in competitive ice hockey allow to focus on the specific attributes to work with young promising players.
Sleep Deprivation: Effects on Weight Loss and Weight Loss Maintenance

This narrative review presents the findings from intervention studies on the effects of sleep deprivation on eating habits, metabolic rate, and the hormones regulating metabolism, and discusses their relevance to weight loss efforts. Disturbed sleeping patterns lead to increased energy intake, partly from excessive snacking, mainly on foods high in fat and carbohydrates. The studies focused mainly on the effects of sleep duration, but also of sleep quality, on dietary intake during weight loss trials, and on weight loss maintenance. It is important to explore sleep routines that could enhance the efforts of obese and overweight people to lose weight, maintain their weight loss, and improve their overall health.
Study of Optimal MBA Lattice Structures for the SOLEIL Upgrade

Within the context of a future upgrade of SOLEIL, the present paper reports on the low emittance lattice design studies made using 7BA and 6BA cells in the SOLEIL ring. With this combination of the MBA lattices, it is found that the horizontal emittance in the range below 200 pm·rad can be achieved by basically keeping the optical functions in the straight sections unchanged as in the previous 5BA-4BA solutions. In particular, a solution giving the horizontal emittance of 145 pm·rad with the use of longitudinal gradient bends (LGBs) is shown. On the other hand, the required transverse gradients in quadrupoles and combined dipoles as well as the chromaticities increase significantly as anticipated. Future directions including the ways to improve the encountered difficulties are considered.
An investigation on environmental pollution due to essential heavy metals: a prediction model through multilayer perceptrons

Abstract This research is to predict heavy metal levels in plants, particularly in Robinia pseudoacacia L. , and soils using an effective artificial intelligence approach with some ecological parameters, thereby significantly eliminating common defects such as high cost and seriously tedious and time-consuming laboratory procedures. In this respect, the artificial neural network (ANN) is employed to estimate the concentrations of essential heavy metals such as Fe, Mn and Ni, depending on the Cu and Zn concentrations of plant and soil samples collected from five different locations. The derived relative errors for the constructed ANN model have been computed within the ranges 0.041–0.051, 0.017–0.025, and 0.026–0.029 for the training, testing and holdout data regarding Fe, Mn, and Ni, respectively. In addition, it has been realized that the relative errors could be diminished up to 0.007 for Fe, 0.014 for Mn and 0.022 for Ni by considering the Cu, Zn, location and plant parts as independent variables during the analysis. The results produced seem instructive and pioneering for environmentalists and scientists to design optimal study programs to leave a livable ecosystem. Novelty statement The levels of essential heavy metals, Fe, Mn, Ni, based on Zn and Cu in plant and soil samples have been predicted through an AI-based prediction model, a class of feedforward artificial neural networks (ANNs) with a multilayer perceptron (MLP). Thereby common drawbacks such as high cost and severely time-consuming laboratory procedures have been significantly eradicated. In the evaluation of different pollution levels at locations, it has been shown that the ANN method can overcome several disadvantages of analytical element analyzers to monitor the amounts of heavy metals such as Fe, Mn, and Ni in soil and plants.
Use of Instabilities in Electrostatic Micro-Electro-Mechanical Systems for Actuation and Sensing

This thesis develops methods to exploit static and dynamic instabilities in electrostatic MEMS to develop new MEMS devices, namely dynamically actuated micro switches and binary micro gas sensors. Models are developed for the devices under consideration where the structures are treated as elastic continua. The electrostatic force is treated as a nonlinear function of displacement derived under the assumption of parallel-plate theorem. The Galerkin method is used to discretize the distributed-parameter models, thus reducing the governing partial differential equations into sets of nonlinear ordinary-differential equations. The shooting method is used to numerically solve those equations to obtain the frequency-response curves of those devices and the Floquet theory is used to investigate their stability. To develop the dynamically actuated micro switches, we investigate the response of microswitches to a combination of DC and AC excitations. We find that dynamically actuated micro switches can realize significant energy savings, up to 60%, over comparable switches traditionally actuated by pure DC voltage. We devise two dynamic actuation methods: a fixed-frequency method and a shiftedfrequency method. While the fixed-frequency method is simpler to implement, the shifted-frequency method can minimize the switching time to the same order as that realized using traditional DC actuation. We also introduce a parameter identification technique to estimate the switch geometrical and material properties, namely thickness, modulus of elasticity, and residual stress. We also develop a new detection technique for micro mass sensors that does not require any readout electronics. We use this method to develop static and dynamic binary mass sensors. The sensors are composed of a cantilever beam connected to a rigid plate at its free end and electrostatically coupled to an electrode underneath it. Two versions of micro mass sensors are presented: static binary mass sensor and dynamic binary mass sensor. Sensitivity analysis shows that the sensitivity of our static mass sensor represents an upper bound for the sensitivity of comparable statically detected inertial mass sensors. It also shows that the dynamic binary mass sensors is three orders of magnitude more sensitive than the static binary mass sensor. We equip our mass sensor with a polymer detector, doped Polyaniline, to realize a formaldehyde vapor sensor and demonstrate its functionality experimentally. We find that while the static binary gas sensor is simpler to realize than the dynamic binary gas sensor, it is more susceptible to external disturbances.
Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA’s Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky–Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.
COMPARATIVE THE ANTIOXIDANTS CHARACTERISTICS OF ORANGE
AND POTATO PEELS EXTRACTS UNDER DIFFERENCES IN PRESSURE
AND CONVENTIONAL EXTRACTIONS

1Food Science Department, Faculty of Agriculture, Zagazig University, Zagazig, Egypt,44511 el.hadary. a.r. e@gmail.com https://doi.org/10.34302/crpjfst/2022.14. 1.13 Article history: Received, 22 December 2021 Accepted, 25 February 2022 ABSTRACT This investigation aimed to decrease the extraction time of natural antioxidants and add commercial dimension to plant extracts. Impact the difference in pressure (DE) on antioxidant properties was studied by estimating total phenolic and total flavonoid contents (TPC, and TFC), DPPH ̇ scavenging radical activity (IC50 ), inhibition lipids peroxidation by both TBARs and β-Carotene/Linoleic acid bleaching (βCB) assays, antimicrobial activities, and yield of extracts, comparison with the resulting by conventional extractions (SE). The results showed positive effects of OPE, and PPE on antioxidants and antimicrobial activities, and the extracts of DE were the highest value to both orange and potato peel extracts. However, increase the yield of extracts and TFC by the decrease of ethanolic concentration of both orange and potato peel extracts, TPC, DPPH ̇ scavenging radical activity, TBARs, βCB, and antimicrobial activity was increased by the increase of ethanolic concentration, and the extraction by DE was the highest value. The absolute ethanolic potato peel extract by soak extraction method (SE) was the lowest value of yield of extract and TFC (21.38±1.08, and 29.73±1.03; respectively), while absolute ethanolic orange peel extract by extraction method by DE was the highest value of TPC, DPPH ̇ scavenging radical activity (IC50 ), TBARs, and βCB (262.19±1.19, 21.18±1.18, 78.82±0.85, and 83.15±1.15; respectively). Also, the effect of absolute ethanolic orange peel extract by the difference in pressure on antimicrobial activity was the highest.
Analysis on Risk Causation of Traffic Accidents in Expressway Tunnel Group

The correlation of rescue time and traffic accident risk caused by special geographical environments of tunnel group on freeway is explored. The risk causation model for traffic accidents in tunnel group is put forward,which is divided into three risk phases,including initial accident risk phase,developing accident risk phase and final accident risk phase. And the accident risk values in different phases will vary with persons,vehicles,tunnel group environments and disaster prevention and rescue ability. According to the analysis on risk factors of tunnel group,it was considered that there possibly exists smog-producing risk between two adjacent tunnels,illumination risk of driver's fast vision transferring,and disaster preventing and controlling risk of traffic accident. In addition,statistics on the probability distribution of disaster rescue time shows that rescue team could fast arrive at accident site within certain time. Finally,for the late rescue situation,some countermeasures are presented on the basis of risk analysis.
Applying a Portable Backpack Lidar to Measure and Locate Trees in a Nature Forest Plot: Accuracy and Error Analyses

Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>101 hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually.
Teachers’ strategies for managing shy students’ anxiety at school

Abstract The aim of this quantitative study was to analyze teachers’ most common and perceived effective strategies for reducing anxiety in shy elementary-school students. Participants were 275 elementary-school teachers, representative of the teacher population of Norway. Participants nominated a shy student they had taught and completed a questionnaire including strategies for reducing anxiety, reporting their use and effectiveness of each strategy. Latent class and profile models identified groups of teachers that differed in terms of how often they applied strategies and in how useful they found their attempts to intervene. Strategy use and usefulness ratings were consistent across participants although there was evidence of an association with student grade, student gender and school size on a number of strategies. A consistent theme across the strategies is the reliance on protective strategies, which may help a child cope with anxiety in the short term but can be less productive in the longer term. Results are discussed in terms of best practices for teachers in helping shy students cope with anxiety at school.
Median floor acceleration spectra of linear structures with uncertain properties

Response parameters used to estimate nonstructural damage differ depending on whether deformation‐sensitive or acceleration‐sensitive components are considered. In the latter case, seismic demand is usually represented through floor spectra, that is response spectra in terms of pseudo‐acceleration, which are calculated at the floor levels of the structure where the nonstructural components are attached to. Objective of this paper is to present a new spectrum‐to‐spectrum method for calculating floor acceleration spectra, which is able to explicitly account for epistemic uncertainties in the modal properties of the supporting structure. By using this method, effects on the spectra of possible variations from nominal values of the periods of vibration of the structure can be estimated. The method derives from the extension of closed‐form equations recently proposed by the authors to predict uniform hazard floor acceleration spectra. These equations are built to rigorously account for the input ground motion uncertainty, that is the record‐to‐record variability of the nonstructural response. In order to evaluate the proposed method, comparisons with exact spectra obtained from a standard probabilistic seismic demand analysis, as well as spectra calculated using the Eurocode 8 equation, are finally shown. Copyright © 2017 John Wiley & Sons, Ltd.
Managing Security Risks in Wireless Web Services

Over the last twenty years, the Internet has become highly interactive and is not merely a static repository of websites, which allows its users to interact proactively. The functions and tasks of the Internet have evolved during this twenty-year period, such that it is no longer a rudimentary collection of websites. The Internet empowers individual users to explore the Internet in an exponentially greater capacity than in years past. In this study, we will consider the issues like security and trust issues in IoT and local cloud interfaces. Additionally, the security, requirements of M2M services and security solutions for the internet are analyzed, and their ineffectiveness is discussed.
Product label identification with OCR for model‐specific reuse, repair, and recycling: A case study for washing machines

In this study, a procedure to automatically identify the product model by evaluating an image of the label is presented. First, object character recognition (OCR) extracts text from the product label. Second, to identify the product model, the extracted text is compared with unique model references in a database, giving access to other model‐specific information. For this comparison, a novel variation of the partial ratio matching algorithm was developed. The product‐model identification procedure is integrated into an interactive web application, which allows for model identification to be performed during preparation for reuse, repair, and recycling: The SmartRe application. Three datasets consisting of 466, 422, and 771 images of washing machine product labels were gathered in collaboration with a (1) professional repair company for consumer devices, (2) a nonprofit repair and reuse center that resells devices in second‐hand stores, and (3) a large recycling company. Results demonstrate that 96%, 91%, and 40% of the product models were correctly read from the product label with OCR, respectively. Of these recognized models, 51%, 88%, and 76% were successfully identified with the SmartRe application by comparing the extracted text with the model database. Further analysis also demonstrated that 72% of the washing machine models identified at the nonprofit repair and reuse center were also found at the recycling facility and that 12% of these models are predicted to be less than 10‐years‐old. This highlights the potential of the SmartRe application to assist in product triage for reuse at recycling centers.
Mechanistic Insight into Biotransformation of Inorganic Selenium to Selenomethionine and Selenocysteine by Saccharomyces boulardii: In-silico Study

In recent years, the biosynthesis of seleno amino acids from inorganic selenium has been a subject of investigation. Saccharomyces cerevisiae has been reported to bioaccumulate selenium through the metabolism of selenate. Different authors have postulated a metabolic pathway in which selenate is converted into selenomethionine or selenocysteine based on the research carried out so far. However, little has been known how other types of yeast achieve this bioconversion. For that reason, and due to the importance of Saccharomyces boulardii as a probiotic yeast, the present study proposes a biosynthetic route used by this yeast to incorporate inorganic selenium into organoselenium compounds. A comparative in-silico study was carried out using Saccharomyces boulardii ASM141397V1 and a metabolic model at the genomic scale of Saccharomyces cerevisiae S288C. Basic local alignment database BLASTp-NCBI was used to identify orthologous genes in both strains, and the generated data were visualized in a circular layout using CIRCOS software. The metabolic route of selenium assimilation was proposed based on the obtained results.
Machine learning-based prediction of the post-thrombotic syndrome: Model development and validation study

Background Prevention is highly involved in reducing the incidence of post-thrombotic syndrome (PTS). We aimed to develop accurate models with machine learning (ML) algorithms to predict whether PTS would occur within 24 months. Materials and methods The clinical data used for model building were obtained from the Acute Venous Thrombosis: Thrombus Removal with Adjunctive Catheter-Directed Thrombolysis study and the external validation cohort was acquired from the Sun Yat-sen Memorial Hospital in China. The main outcome was defined as the occurrence of PTS events (Villalta score ≥5). Twenty-three clinical variables were included, and four ML algorithms were applied to build the models. For discrimination and calibration, F scores were used to evaluate the prediction ability of the models. The external validation cohort was divided into ten groups based on the risk estimate deciles to identify the hazard threshold. Results In total, 555 patients with deep vein thrombosis (DVT) were included to build models using ML algorithms, and the models were further validated in a Chinese cohort comprising 117 patients. When predicting PTS within 2 years after acute DVT, logistic regression based on gradient descent and L1 regularization got the highest area under the curve (AUC) of 0.83 (95% CI:0.76–0.89) in external validation. When considering model performance in both the derivation and external validation cohorts, the eXtreme gradient boosting and gradient boosting decision tree models had similar results and presented better stability and generalization. The external validation cohort was divided into low, intermediate, and high-risk groups with the prediction probability of 0.3 and 0.4 as critical points. Conclusion Machine learning models built for PTS had accurate prediction ability and stable generalization, which can further facilitate clinical decision-making, with potentially important implications for selecting patients who will benefit from endovascular surgery.