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Enteromonas hominis, a human intestinal protozoan parasite of the diplomonad group, has been overlooked because of its commensal features; therefore, molecular studies on this parasite are limited. To address this gap, we designed a molecular screening protocol using polymerase chain reaction (PCR) and DNA sequencing targeting the 18S small subunit ribosomal RNA gene and applied this screening method to the molecular epidemiological analysis of Enteromonas spp. in humans and various livestock. We validated our methodology using stool samples collected from 215 humans and 270 animal hosts (buffaloes, pigs, dogs, goats, horses, rodents, chickens, and ducks) during an annual epidemiological investigation conducted from 2013 to 2016 on Sumba Island, Indonesia. The overall prevalences of Enteromonas spp. were 33.9 % (n = 73/215) in humans and 25.2 % (n = 68/270) in mammals and avians. The positive predictive value of this PCR method for Enteromonas spp., as evaluated through sequencing, was 90.1 % in human samples and 58.1 % in non-human samples (particularly low, 11.4 % in rodents). Although the specificity of the PCR approach may not be perfect, in combination with DNA sequencing, it was effective in detecting and identifying a partial sequence (1458 bp) of the target gene region in Enteromonas species.

The research aims to evaluate the effectiveness of a computerized cognitive training program in improving executive functions and attention in elementary school children, compared to a traditional paper-and-pencil intervention. The study has been formulated as a randomized controlled trial with pre- and post-intervention measures. For the study, third-grade children with typical development aged between 7 and 9 years will be recruited. Participants will be randomly assigned to the two study arms (control and experimental). The experimental group will participate in a computerized intervention using the NeuronUp cognitive stimulation platform for 8 weeks, twice a week. Sessions will be conducted using computers in the computer lab of the participating educational institution. The active control group will engage in paper-and-pencil cognitive training for the same duration and intensity as the experimental group. Evaluators will be blinded to the assignment, and participants will be blinded to the target intervention. Hypothesis testing will be conducted through ANOVA-MR, and logistic regressions will be implemented to assess the effect of socioeconomic variables on children's performance. These results are expected to contribute to the discussion on the opportunities and benefits offered by cognitive training programs on the cognitive development of typically developing children.

Sustainable intensification (SI) of agriculture can produce more food to meet the demand of a growing population while considering ecosystem health. The current SI estimation framework ignores the complex coupling between input and output intensity of arable land. A method for coupled analysis of arable land input intensity and output intensity based on sliding windows is proposed. By calculating the correlation coefficient and partial correlation coefficient between input intensity and output intensity in different value ranges as the order parameter, the phase transition and the influence process of input intensity on output intensity can be explained. Meanwhile, a python-based framework is developed.

An application of the method was made to reveal the interaction process between annual provincial input intensity and output intensity in mainland China. Researchers in many fields may benefit from the method by obtaining a fast way to analysis the coupling relationship between driving and dependent variables in complex systems.

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  New method for SI estimation is presented.

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  The order parameter of the coupling relationship between input and output intensity is calculated based on sliding windows.

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  Analysis of coupling relationships between driving and dependent variables in complex systems.

Soil erodibility (K-factor) is an important parameter in erosion modeling, is one of five factors of the Revised Universal Soil Loss Equation (RUSLE), and generally represents the soil's response to rainfall and run-off erosivity. The erodibility could be determined based on direct measurements of soil properties and mathematical calculations. In this study, the K-factor was calculated based on a formula from RUSLE, proposed by Renard et al. (1997). All input parameters: soil organic carbon (SOC), soil structure, and permeability classes were measured by one method, but particle size distribution – in two ways by sedimentation and laser diffraction methods to assess the impact the K-factor variability and the values of soil erosion rates. The 107 soil samples of Chernozems from Kursk Oblast (Russia) were studied. The texture for the most of samples was classified as silty loam in both analyses. However, the laser diffraction underestimates the clay content by an average of 13.2 % compared to the pipette method. The average K-factor estimated based on laser diffraction data was 0.050, and 0.034 t ha h ha⁻¹ MJ⁻¹ mm⁻¹ – sedimentation method. Thus, depending on the method of soil texture analysis, the RUSLE calculated soil loss could underestimated/overstated by 32 % (or 4 t ha^-1 yr^-1 on average in the study site). Therefore, we propose a regression equation-based conversion method of laser diffraction data to sedimentation method data for Chernozems.

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  The Laska-TM laser analyzer measured on ∼ 13 % less clay fraction (more on ∼ 8 % silt and ∼ 5 % fine sand) compared with sedimentation method data.

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  For erosional researchers/modelers it is suggested to state the method of soil texture analysis (based on sedimentation law or laser diffraction) was used for RUSLE K-factor calculations.

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  To convert K-factor values (for Chernozems) calculated and based on data of the sedimentation method to laser sedimentation – it suggested utilize the coefficient 1.47 (0.68 – vice versa).

Environmental Flow Assessment is crucial, when assessing the impact of water withdrawal on riverine habitat. Still, standard methods to tackle such exercise are not available hitherto, while several techniques were developed generally to be tailored ad hoc for the considered case study. Here, we propose a multi-criteria approach, usable to quantify Environmental Flow, and uncertainty thereby, basing upon a hydraulic model of the river, and habitat viability for a given target fish species. Six criteria are considered that gather 3 classes of constraints for fish development.

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  Geometric, and biological constraints Fish show to cherish specific areas of the river, changing with discharge level (criterion I), they need a least water level to move comfortably (criterion II), with some degree of interconnection along the whole river bed (criterion III).

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  Riverbed quality Fine sediments (criterion IV) and gravel (criterion V) would be washed away to avoid clogging of spawning areas, and to clean and reset the gap system.

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  Macro-benthos requirement (criterion VI) Benthic invertebrates, i.e. preys of target fish, need a minimum flow velocity to form a community. Finally, a table is generated to compare optimal values of each criterion, supporting the choice of a “globally viable” EF value.

This article presents a direct method for temperature control in solid-state lasers, where temperature stability is crucial for optimizing the performance and reliability of such lasers. The proposed method utilizes Peltier chips for both cooling and heating the laser crystal to achieve precise temperature regulation.

The system design is based on the step response of the open-loop thermal system and employs a proportional-integral (PI) controller for closed-loop temperature control. Comprehensive testing on a femtosecond Titanium-Sapphire Laser (Ti:Sapphire laser) demonstrated that the system is capable of maintaining the desired operating temperature with remarkable stability and efficiency, highlighting its practicality for real-world applications.

Method Outline:

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  Utilization of Peltier chips for precise temperature control.

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  Estimation of first-order transfer function based on step response.

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  Implementation of a proportional-integral (PI) controller for closed-loop temperature regulation.

Color-blind is a generic disability whereby the affected individuals are not given the opportunity to benefit from the various functions provided by color that would impact humans physically and psychologically. Although this disability is not fatal, it brought plenty of turbulence in the affected individuals’ daily activities. This paper aims to develop a system for recognizing and detecting colors of clothes in images, improve accuracy by using advanced algorithms to handle lighting variations, and provide color matching recommendations to assist color-blind individuals in making informed choices when purchasing shirts. The proposed methodology for color recognition involves:

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  retrieving the RGB values of a given point from the input image and converting them into HSV values.

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  creating web application integrated with a machine learning model to classify and predict the corresponding color based on the HSV values.

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  predicting the color name with suggestions of matching colors will be displayed on the interface.

OCT imaging is an important technique to study fouling in spacer-filled channels of reverse osmosis systems for seawater desalination. However, OCT imaging of membrane filtration channels with feed spacers is challenging because the spacer material can be (partly) transparent, making it difficult to detect and possibly mistaken for fouling, and the longer optical pathway through the spacer material distorts the image below the spacer. This study presents an automated 3D OCT image processing method in MATLAB for visualization and quantification of biofouling in spacer-filled channels. First, a spacer template of arbitrary size and rotation was generated from a CT scan of the feed spacer. Second, background noise and file size were reduced by representing the OCT image with a list of discrete reflectors. Finally, the spacer template was overlayed with the feed spacer in the 3D OCT image, enabling automated visualization of the feed spacer and correction of the distortions. Moreover, the method allows the selection of datasets with the same location relative to the position of the spacer, enabling systematic comparison between datasets and quantitative analysis.

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  A spacer template of arbitrary size and rotation was generated from a CT scan.

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  The background noise was removed, and the file size was reduced by representing the OCT dataset with a list of discrete reflectors.

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  The spacer template was overlayed with the feed spacer in the 3D OCT image.

A smart grid is a power network from generation to consumers and provides beneficial, steady, and safe electricity. It utilizes smart meters for billing, phasor measurement units to check the system's health., etc. As a result, it contains enormous volumes of real-time data that may be shared and stored by users, control centers, and services in a smart grid. It weakens the smart grid's communication networks. The size of the data in a smart grid will grow extremely in the future. As a result, it must reduce distortion in data compression and denoise while minimizing the demand on storage and communication networks. The goal of data compression and denoising should be to maximally conserve the useful data while accurately reflecting the state of the system and providing sufficient data regeneration at the receiving end. This paper has used lower-order different wavelets to represent a design to compress and reconstruct data at level three using wavelet Packet Transform. It works on the phasor measurement unit's current magnitude and voltage sag signals.

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  The proposed design has a better compression ratio.

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  Low reconstruction error.

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  This design is easy to access, systematic, profitable, and not time-consuming.