Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of a Combined Conductive Fabric-Based Suspender System and Machine Learning Approach for Human Activity Recognition

Accelerometer-based human activity recognition (HAR) wearable systems are location-centric and noisy, needing multiple sensors with complex signal processing and filtering mechanisms. A recently reported alternative approach using a wearable suspender integrated with strain sensors and machine learning presented a viable option for nonlocalized measurement with less noise and better recognition capabilities. The washability and wearability of the strain sensor instrumented suspenders due to the physical wires are limited, and the power consumption is higher, which needs to be minimized to extend the battery life of the wearable device. This article proposes an improved body-worn suspender-based HAR system built using a conductive knit jersey fabric material that overcomes the existing strain sensor-based wearable device’s limitations and at the same time provides improved sensitivity. The proposed suspender system recognizes 14 human activities using machine learning and deep learning algorithms with the best accuracy of 98.11%. A performance comparison of machine learning models based on two dimensionality reduction techniques using kernel and linear discriminatory analysis was conducted. The kernel-based method outperformed the linear one in recognizing human activities across all classifiers. The durability of the wearable is tested by washing the sensor, and the recognition capabilities were consistent before and after the wash.
Comparaison de deux Modèles de Turbulence dans la Simulation d un Jet Turbulent Axisymetrique

La simulation du phenomene de refroidissement d`une surface plane par un jet d`air. La methode des volumes finis est utilisee pour resoudre les equations de transport regissant les transferts de chaleur et de quantite de mouvement prenant place lors de l'impact. La turbulence est decrite grâce a l'application du modele de Chien et les resultats us sont compares a ceux du modele present etude decrite simulation du phenomene de refroidissement d'une surface plane par un jet d'air. La methode des classique k-e. (.)
Morphological phenotyping after mouse whole embryo culture

Morphological phenotyping of the mouse embryo is described at neurulation stages, primarily as a guide to evaluating the outcome of whole embryo cultures between embryonic days 8.5 and 9.5. During this period, neural tube closure is initiated and progresses to completion in the cranial region. Spinal closure is still underway at the end of the culture period. The focus of this article is particularly on phenotyping that can be performed at the bench, using a stereomicroscope. This involves assessment of embryonic health, through observation and scoring of yolk sac blood circulation, measurement of developmental stage by somite counting, and determination of crown-rump length as a measure of growth. Axial rotation (“turning”) can also be assessed using a simple scoring system. Neural tube closure assessment includes: 1) determining whether closure has been initiated at the Closure 1 site; 2) evaluating the complex steps of cranial neurulation including initiation at Closure sites 2 and 3, and completion of closure at the anterior and hindbrain neuropores; 3) assessment of spinal closure by measurement of posterior neuropore length. Interpretation of defects in neural tube closure requires an appreciation of, first, the stages that particular events are expected to be completed and, second, the correspondence between embryonic landmarks, for example, somite position, and the resulting adult axial levels. Detailed embryonic phenotyping, as described in this article, when combined with the versatile method of whole embryo culture, can form the basis for a wide range of experimental studies in early mouse neural development.
A mathematical model to serve as a clinical tool for assessing obstructive sleep apnea severity

Obstructive sleep apnea (OSA) is a sleep disorder caused by periodic airway obstructions and has been associated with numerous health consequences, which are thought to result from tissue hypoxia. However, challenges in the direct measurement of tissue-level oxygenation make it difficult to analyze the hypoxia exposure pattern in patients. Furthermore, current clinical practice relies on the apnea-hypopnea index (AHI) and pulse oximetry to assess OSA severity, both of which have limitations. To overcome this, we developed a clinically deployable mathematical model, which outputs tissue-level oxygenation. The model incorporates spatial pulmonary oxygen uptake, considers dissolved oxygen, and can use time-dependent patient inputs. It was applied to explore a series of breathing patterns that are clinically differentiated. Supporting previous studies, the result of this analysis indicated that the AHI is an unreliable indicator of hypoxia burden. As a proof of principle, polysomnography data from two patients was analyzed with this model. The model showed greater sensitivity to breathing in comparison with pulse oximetry and provided systemic venous oxygenation, which is absent from clinical measurements. In addition, the dissolved oxygen output was used to calculate hypoxia burden scores for each patient and compared to the clinical assessment, highlighting the importance of event length and cumulative impact of obstructions. Furthermore, an intra-patient statistical analysis was used to underscore the significance of closely occurring obstructive events and to highlight the utility of the model for quantitative data processing. Looking ahead, our model can be used with polysomnography data to predict hypoxic burden on the tissues and help guide patient treatment decisions.
Prediction of Alzheimer ’ s Disease and Mild Cognitive Impairment Using Cortical Morphological Patterns

This article describes a novel approach to extract cortical morphological abnormality patterns from structural magnetic resonance imaging (MRI) data to improve the prediction accuracy of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Conventional approaches extract cortical morphological information, such as regional mean cortical thickness and regional cortical volumes, independently at different regions of interest (ROIs) without considering the relationship between these regions. Our approach involves constructing a similarity map where every element in the map represents the correlation of regional mean cortical thickness between a pair of ROIs. We will demonstrate in this article that this correlative morphological information gives significant improvement in classification performance when compared with ROI-based morphological information. Classification performance is further improved by integrating the correlative information with ROI-based information via multi-kernel support vector machines. This integrated framework achieves an accuracy of 92.35% for AD classification with an area of 0.9744 under the receiver operating characteristic (ROC) curve, and an accuracy of 83.75% for MCI classification with an area of 0.9233. In differentiating MCI subjects who converted to AD within 36 months from non-converters, an accuracy of 75.05% with an area of 0.8426 under ROC curve was achieved, indicating excellent diagnostic power and generalizability. The current work provides an alternative approach to extraction of high-order cortical information from structural MRI data for prediction of neurodegenerative diseases such as AD. Hum Brain Mapp 00:000–000, 2012. VC 2012 Wiley Periodicals, Inc.
Eyelid’s Intrinsic Motion-Aware Feature Learning for Real-Time Eyeblink Detection in the Wild

Real-time eyeblink detection in the wild is a recently emerged challenging task that suffers from dramatic variations in face attribute, pose, illumination, camera view and distance, etc. One key issue is to well characterize eyelid’s intrinsic motion (i.e., approaching and departure between upper and lower eyelid) robustly, under unconstrained conditions. Towards this, a novel eyelid’s intrinsic motion-aware feature learning approach is proposed. Our proposition lies in 3 folds. First, the feature extractor is led to focus on informative eye region adaptively via introducing visual attention in a coarse-to-fine way, to guarantee robustness and fine-grained descriptive ability jointly. Then, 2 constraints are proposed to make feature learning be aware of eyelid’s intrinsic motion. Particularly, one concerns the fact that the inter-frame feature divergence within eyeblink processes should be greater than non-eyeblink ones to better reveal eyelid’s intrinsic motion. The other constraint minimizes the inter-frame feature divergence of non-eyeblink samples, to suppress motion clues due to head or camera movement, illumination change, etc. Meanwhile, concerning the high ambiguity between eyeblink and non-eyeblink samples, soft sample labels are acquired via self-knowledge distillation to conduct feature learning with finer supervision than the hard ones. The experiments verify that, our proposition is significantly superior to the state-of-the-art ones (i.e., advantage on F1-score over 7%) and with real-time running efficiency. It is also of strong generalization capacity towards constrained conditions. The source code is available at https://github.com/wenzhengzeng/blink_eyelid.
Wetland vegetation cover changes and its response to climate changes across Heilongjiang-Amur River Basin

The Heilongjiang-Amur River Basin is one of the largest and most complex aquatic systems in Asia, comprising diverse wetland resources. The wetland vegetation in mid-high latitude areas has high natural value and is sensitive to climate changes. In this study, we investigated the wetland vegetation cover changes and associated responses to climate change in the Heilongjiang-Amur River Basin from 2000 to 2018 based on the growing season (May to September) climate and LAI data. Our results indicated that the wetland LAI increased at 0.014 m2·m-2/yr across Heilongjiang-Amur River Basin with the regional climate showed wetting and warming trends. On a regional scale, wetland vegetation in China and Russia had positive partial correlation with solar radiation and minimum air temperature, with precipitation showing a slight lag effect. In contrast, wetland vegetation in Mongolia had positive partial correlation with precipitation. These correlations were further investigated at different climate intervals. We found the precipitation is positively correlated with LAI in the warm regions while is negatively correlated with LAI in the wet regions, indicating an increase in precipitation is beneficial for the growth of wetland vegetation in heat sufficient areas, and when precipitation exceeds a certain threshold, it will hinder the growth of wetland vegetation. In the cold regions, we found solar radiation and minimum air temperature are positively correlated with LAI, suggesting SR and minimum air temperature instead of mean air temperature and maximum air temperature play more important roles in affecting the wetland vegetation growth in the heat limited areas. The LAI was found to be negatively correlated with maximum air temperature in the arid areas, indicating excessive temperature would inhibit the wetland vegetation growth when the water is limited. Our investigation can provide a scientific foundation for the trilateral region in wetland ecosystem protection and is beneficial for a more comprehensive understanding of the responses of wetlands in the middle and high latitudes to climate change.
Analysis and Evolution of Environmental Law in Ecuador with the Constitution of 2008 and its Relation to Political Marketing in the Good Way of Living

This article is a review and reflection of the new elements of rights and laws, applied to the principle of justice and sovereignty, but above all in the demonstration that law as a science once again allows us to conceive that as a science it evolves and must be modified according to the new conducts that the State and society require, such is the case of the constitutional recognition that this type of rights have. In the last decades, human beings have evidenced that the way in which they interact with nature is causing serious consequences to the ecosystems that compose it. In the past, these consequences were not yet evident and, therefore, the environmental awareness that exists today did not exist. Therefore, it is necessary to analyze and reflect on certain conditions of this right in Ecuador.
Power-law statistics of neurophysiological processes analyzed using short signals

We discuss the problem of quantifying power-law statistics of complex processes from short signals. Based on the analysis of electroencephalograms (EEG) we compare three interrelated approaches which enable characterization of the power spectral density (PSD) and show that an application of the detrended fluctuation analysis (DFA) or the wavelet-transform modulus maxima (WTMM) method represents a useful way of indirect characterization of the PSD features from short data sets. We conclude that despite DFA- and WTMM-based measures can be obtained from the estimated PSD, these tools outperform the standard spectral analysis when characterization of the analyzed regime should be provided based on a very limited amount of data.
Immunogenicity Characterization of the Recombinant gI Protein Fragment from Pseudorabies Virus and an Evaluation of Its Diagnostic Use in Pigs

Simple Summary Pseudorabies virus (PRV) is a double-stranded linear DNA virus with an envelope, and it can cause acute neurological symptoms and death in piglets, which has caused large economic loss in pig farms. The eradication of PRV in pig farms has always been a goal, and the diagnosis of wild virus infection is particularly important in this process. In this study, we designed specific primers to amplify the fragment of gI with the region of antigen epitopes and the transmembrane regions removed. The recombinant truncated fragment of gI was expressed, and the native gI protein was obtained. After a small-scale ELISA test for clinical serum samples, it was found that purified gI protein may potentially be applied to clinically diagnose PRV infection. Abstract Serological testing is an important method for the diagnosis of pseudorabies virus (PRV) infection. We aimed to investigate the envelope glycoprotein I (gI) of PRV, a strong immunogen, and its potential as an efficient and low-cost diagnostic reagent. In this study, the DNA of the PRV SC strain was used as the template, and the recombinant fragment of gI (633 bp) was amplified via PCR using synthetic primers, and was then ligated into the pET-30a expression vector. The constructs were transferred into Escherichia coli (E. coli) for prokaryotic expression, and the antigenicity of the expression products was identified by Western blot analysis with pig positive serum against PRV. The recombinant protein was purified by a Ni column, and BALB/c mice were immunized with purified gI protein to obtain anti-gI-positive serum. After PK-15 cells had been infected by PRV for 48 h, the immunogenicity of purified gI protein was identified with a fluorescence immunoassay using anti-gI mouse serum. The recombinant plasmid (pET-30a-gI) was expressed, and the native gI protein was obtained after denaturation by urea and renaturation by dialysis. A small-scale ELISA test containing 1.0 µg/mL of purified gI protein was designed to evaluate pig serum (80 samples), and the results of the ELISA test were compared to those of competitive ELISA (cELISA) tests using IDEXX Kits, which resulted in 97.5% consistency. The results suggested that the truncated gI protein may be a potential diagnostic reagent.
Studies on the Quality and Heavy Metals in Common Salt Production Using Sea Brine and Sub-Soil Brine in South Tamil Nadu, India

The manufacture of common salt using sea brine and sub-soil brine contain several chemical and trace metal elements.This study estimated the salt quality parameters like moisture content, insoluble matter, calcium sulphate, magnesium sulphate, magnesium chloride and sodium chloride content of the salt and also estimated the different trace metals like copper, iron, zinc, manganese and chromium in salts harvested from the Salt samples of two selected salt works, Kovalam SW1 (sea brine) and Puthalam SW2 (sub-soil brine) in Kanyakumari District and the two selected salt works, Vepalodai SW3 (sea brine) and Sahupuram SW4 (sub-soil brine) in Tuticorin District. The salt samples have maximum moisture content in Kanyakumari salt works than Tuticorin District because of the climatic conditions. The salt works using sea brine, harvested salt of the maximum mean sodium chloride content than sub-soil brine using salt works. In both the Districts, sea brine using salt works is having almost similar concentration of heavy metals, likewise sub soil brine using salt works.
Use of Danish National Somatic Cell Count Data to Assess the Need for Dry-Off Treatment in Holstein Dairy Cattle

Simple Summary This scientific study aimed to compare somatic cell count (SCC) curves throughout lactation in dairy cows according to PCR test results for four major intramammary infection (IMI) pathogens. Data from 133,877 Holstein cows in Danish conventional dairy herds were analysed using a nonlinear mixed-effects model with a modified four-parameter Wilmink function. The PCR tests were conducted before dry-off to determine animals eligible for selective dry cow therapy. The findings revealed that cows testing positive for IMI pathogens had higher mean SCC across all parity groups and lactations. Using SCC data fitted to the entire lactation allowed for quantifying overall differences in SCC curves. Abstract In Denmark, PCR testing of dairy cattle is commonly used to select animals for the antibacterial treatment of intramammary infection (IMI) during the dry-off period. IMI is associated with a high somatic cell count (SCC), routinely recorded for milk quality control for most commercial dairy herds. This study aimed to compare SCC curves over the lactation among dairy cows with positive vs. negative PCR test results for four major IMI pathogens. Data from 133,877 PCR-tested Holstein cows from 1364 Danish conventional dairy herds were used to fit a nonlinear mixed-effects model using a modified four-parameter Wilmink function. We stratified the data into first, second, third or fourth and later parity and fitted Wilmink curves to all SCC observations between 6 and 305 days in milk. The PCR tests were taken before dry-off at the end of the lactation to investigate which animals qualified for selective dry cow therapy. A PCR Ct-value of 37 and below was used to determine if an animal was PCR positive for any of the following IMI pathogens: Staphylococcus aureus, Streptococcus agalactiae, Str. dysgalactiae and Str. uberis. Our findings showed that mean SCC curve fits were higher for PCR-positive animals in all four parity groups and across lactations. The use of SCC data fitted to the entire lactation for multiple lactations enabled quantification of overall differences in SCC curves between cattle with and without detected IMI, adjusted for parity group and stage of lactation. These findings are relevant to the use of SCC to support treatment decisions.
ICT Framework for Supporting Applied Behavior Analysis in the Social Inclusion of Children with Neurodevelopmental Disorders

The applied behavior analysis (ABA) model emphasizes observable and measurable behaviors by carrying out decision making using experimental data (behavioral observation assessment strategies). In this framework, information and communication technology (ICT) becomes highly suitable for enhancing the efficiency and effectiveness of the methodology. This paper aims to delve into the potential of ICT in providing innovative solutions to support ABA applications. It focuses on how ICT can contribute to fostering social inclusion with respect to children with neurodevelopmental disorders. ICT offers advanced solutions for continuous and context-aware monitoring, as well as automatic real-time behavior assessments. Wireless sensor systems (wearable perceptual, biomedical, motion, location, and environmental sensors) facilitate real-time behavioral monitoring in various contexts, enabling the collection of behavior-related data that may not be readily evident in traditional observational studies. Moreover, the incorporation of artificial intelligence algorithms that are appropriately trained can further assist therapists throughout the different phases of ABA therapy. These algorithms can provide intervention guidelines and deliver an automatic behavioral analysis that is personalized to the child’s unique profile. By leveraging the power of ICT, ABA practitioners can benefit from cutting-edge technological advancements to optimize their therapeutic interventions and outcomes for children with neurodevelopmental disorders, ultimately contributing to their social inclusion and overall wellbeing.
A single‐stage bridgeless isolated positive output Cuk configuration‐based unidirectional onboard battery charger

As power factor correction rectifiers play a key component of electric vehicle (EV) battery charger, the advancements in designing of improved power quality‐based EV charger become more significant in modern era. This article exposes a single‐stage bridgeless isolated positive output (BIPO) Cuk PFC converter with a noninverted output signal. The input diode rectifier present at the input is redeemed by the bridgeless topology that eliminates the serious power quality (PQ) issues and proffers the eminent charging solution for the light electric vehicles (LEV). Unlike the conventional Cuk converter, the noninverted outcome of the BIPO converter does not require a separate amplifier for converting negative to positive output voltage. The preferred charger utilizes a single voltage and current sensor to synchronize the battery charging control in the constant current (CC) and constant voltage (CV) phases of charging. Therefore, the converter encompasses intrinsic zero current switching (ZCS) and offers low‐cost solution to the charger with reduced control complexities. The extensive state space approach is executed to analyze the marginal stability of the presented system. Moreover, the detailed loss modeling is addressed to show the charger's efficacy curve at the standard conditions. A hardware prototype of 350 W, 60 V/4 A charger is implemented to validate its steady‐state and dynamic performance.
El ácido ascórbico en el tratamiento de vaginosis recurrente

PROBLEMA: La vaginosis Bacteriana es una enfermedad que afecta de manera recurrente a la mayoría de mujeres en edad fértil, principalmente durante el embarazo llevando a ser factor de riegos para muchas complicaciones prenatales tanto corioamnioitis, ruptura prematura de membrana. Esto ha dado lugar al mejoramiento del tratamiento de esta patología, y utilización de otros medicamentos coadyuvantes como el ácido ascórbico que tiene una gran eficacia a la regulación del pH vaginal y por tanto mantener la flora vaginal estable. OBJETIVO: Describir el ácido ascórbico en el tratamiento de vaginosis bacteriana recurrente. METODO: Se realizó una investigación de carácter documental con la utilización de literaturas y publicaciones científicas preexistentes en motores de búsqueda de libre acceso como Elservier, PubMed o The New England para describir el ácido ascórbico en el tratamiento de vaginosis recurrente. RESULTADOS: El ácido ascórbico en el tratamiento de vaginosis intestinal que, por su mecanismo de acción antioxidante, vuelve a niveles normales el pH manteniendo la acidez y así mismo con mejor rapidez ocasionando que la recurrencia disminuya y que la flora vaginal se mantenga siempre en condiciones óptimas. CONCLUSION: El ácido ascórbico en el tratamiento de vaginosis bacteriana recurrente es una buena opción ya que mejora el nivel de pH a nivel vaginal por su mecanismo de acción antioxidante y antiescorbuto, eso mejora la flora vaginal teniendo más defensas y evita la recurrencia de esta patología principalmente en las mujeres con vida sexual activa y mujeres embarazas.