Miracle-dz/UniFAR-FTUs huggingface dataset:
Health Promotion for Students of Veterinary Medicine: A Preliminary Study on Active Microbreaks and Ergonomics Education

Simple Summary A relevant number of veterinarians leave the profession within the first years after graduation. In a preliminary study, we investigated the effects of very short, active interventions, called microbreaks, in 36 students of veterinary medicine. Students were encouraged to integrate these microbreaks into their days over 6 weeks, within the total observational period of 12 weeks. Additionally, information on the ergonomics of a variety of veterinary tasks was given in weekly interactive talks with practicing clinicians and a physiotherapist. At the start, many veterinary students reported musculoskeletal discomfort in the neck and the lower back. All students were able to incorporate the microbreaks well during their study activities and found them relieving. After 12 weeks, the participating students reported fewer painful body regions, and that their self-efficacy in potentially painful, risky, or dangerous interactions with animals in a veterinary setting had increased. The feeling of control over dangerous situations increased with dogs but decreased with horses. Most students wanted to continue the microbreaks in the future and judged the topic of the present study to be relevant to their profession. Abstract Occupational hazards, such as psychosocial stressors, physical injuries from human–animal interactions, and physically demanding work tasks, are common in the veterinary profession, and musculoskeletal discomfort and pain (MDP) may already be present in veterinary undergraduates. This preliminary study investigates the effects of very short, active interventions, called microbreaks, in 36 veterinary students. At the start, participants had a high prevalence of MDP, especially in the neck and lower back. Within a 12-week observational period, 6 weeks of active intervention comprised teaching microbreaks (nine strengthening, stretching, and relaxation exercises; 30–90 s each) and a weekly veterinary-specific ergonomics education and discussion. After the intervention, participants reported fewer painful body regions and an increase in their self-efficacy in potentially painful, risky, or dangerous human–animal interactions. After the 12-week observational period, participants had increased self-efficacy in the maintenance of physical health and self-protection but decreased self-efficacy in healing injuries after veterinary human–animal interactions. Participants felt to have increased and decreased control over dangerous situations with dogs and horses, respectively, although self-efficacy in handling horses increased. Participants integrated microbreaks well into their undergraduate activities and rated the topic relevant to their (later) profession. This should encourage the inclusion of similar programs in undergraduate curricula.
DNA Capture and Enrichment: A Culture-Independent Approach for Characterizing the Genomic Diversity of Pathogenic Leptospira Species

Because they are difficult to culture, obtaining genomic information from Leptospira spp. is challenging, hindering the overall understanding of leptospirosis. We designed and validated a culture-independent DNA capture and enrichment system for obtaining Leptospira genomic information from complex human and animal samples. It can be utilized with a variety of complex sample types and diverse species as it was designed using the pan-genome of all known pathogenic Leptospira spp. This system significantly increases the proportion of Leptospira DNA contained within DNA extracts obtained from complex samples, oftentimes reaching >95% even when some estimated starting proportions were <1%. Sequencing enriched extracts results in genomic coverage similar to sequenced isolates, thereby enabling enriched complex extracts to be analyzed together with whole genome sequences from isolates, which facilitates robust species identification and high-resolution genotyping. The system is flexible and can be readily updated when new genomic information becomes available. Implementation of this DNA capture and enrichment system will improve efforts to obtain genomic data from unculturable Leptospira-positive human and animal samples. This, in turn, will lead to a better understanding of the overall genomic diversity and gene content of Leptospira spp. that cause leptospirosis, aiding epidemiology and the development of improved diagnostics and vaccines.
Fully Automatic Unsupervised Approach based on Adaptive Encoder Decoder Process for Forest Fire Image Denoising and Segmentation

Forest fire is a major problem that affect humans and natural resources. Early detecting forest fires in images depends on the appearance of the flame and smoke in the images, and their recognizing can help firefighters assessing the risk degree and monitor fires. Wildfire images resulting either from a fixed camera or a drone in motion are susceptible to noise. For good segmentation results a proper image analysis equipped with noise removal steps is often required. In this paper, a novel technique for denoising and segmenting forest fires images is proposed. The presented methodology consists of denoising images with a new convolutional auto-encoder architecture then segmenting the output image with an adaptive active contour model. The method shows good results quantitatively and visually. The proposed denoising auto-encoder was trained and validated using a small dataset containing 450 images and succeed by removing the noise and also improving the brightness in the forest fire image, which leads to very promising segmentation results with a region-based active contour method.
Endothelial Dysfunction in Patients Undergoing Cardiac Surgery: A Narrative Review and Clinical Implications

Cardiac surgery is one of the highest-risk procedures, usually involving cardiopulmonary bypass and commonly inducing endothelial injury that contributes to the development of perioperative and postoperative organ dysfunction. Substantial scientific efforts are being made to unravel the complex interaction of biomolecules involved in endothelial dysfunction to find new therapeutic targets and biomarkers and to develop therapeutic strategies to protect and restore the endothelium. This review highlights the current state-of-the-art knowledge on the structure and function of the endothelial glycocalyx and mechanisms of endothelial glycocalyx shedding in cardiac surgery. Particular emphasis is placed on potential strategies to protect and restore the endothelial glycocalyx in cardiac surgery. In addition, we have summarized and elaborated the latest evidence on conventional and potential biomarkers of endothelial dysfunction to provide a comprehensive synthesis of crucial mechanisms of endothelial dysfunction in patients undergoing cardiac surgery, and to highlight their clinical implications.
An efficient GPU implementation for a faster simulation of unsteady bed-load transport

ABSTRACT Computational tools may help engineers in the assessment of sediment transport during the decision-making processes. The main requirements are that the numerical results have to be accurate and simulation models must be fast. The present work is based on the 2D shallow water equations in combination with the 2D Exner equation. The resulting numerical model accuracy was already discussed in previous work. Regarding the speed of the computation, the Exner equation slows down the already costly 2D shallow water model as the number of variables to solve is increased and the numerical stability is more restrictive. In order to reduce the computational effort required for simulating realistic scenarios, the authors have exploited the use of Graphics Processing Units in combination with non-trivial optimization procedures. The gain in computing cost obtained with the graphic hardware is compared against single-core (sequential) and multi-core (parallel) CPU implementations in two unsteady cases.
The Effect of Human Body Shadowing in ZigBee Radio Frequency Fingerprinting Identification

Security is a major concern for the Internet of Medical Things (IoMT), however, many of these devices are limited in their capabilities. Physical layer (PHY) security measures can be used to prevent unauthorized access by exploiting intrinsic emitter characteristics such as Specific Emitter Identification (SEI), known as Radio Frequency Fingerprinting Identification (RFFI). RFFI at the IoMT is a promising approach to secure wearable Body Sensor Network (WBSN) devices. In this paper, we evaluated the effect of human body shadowing on Radio Frequency Fingerprinting Identification (RFFI) systems. Results show that shadowing has a serious impact on RFFI models. We also show that it can be mitigated by applying log-normal shadowing augmentation. Results obtained from simulations and experimental trials show that the classification accuracy increases when the multipath channel model and shadowing block size of 640 are used. A new system model for classifying devices using RFFI is then proposed. The proposed model achieved better classification accuracy when evaluated using unseen shadowed data.
An Improved Convolutional Neural Network for the Image Enhancement

Four-dimensional cone-beam computed tomography, or 4D CBCT, is a technique used in image-guided radiation treatment to generate a series of phase-resolved reconstructions of the patient's anatomy. This is done to improve radiation therapy's capacity to target the tumour with more precision. However, both noise and streaking aberrations significantly reduce the quality of 4D CBCT pictures. This technique is known as "very sparse-view CT," and it gets its name from the fact that it only requires a limited number of under-sampled projections to reconstruct each phase of a phase-resolved picture. The strategy's name derives from the fact that it employs a limited number of predictions to achieve its objectives. Customers can select between two specialized Convolution Neural Network (CNN) models: N-Net and the proposed model. These models were developed to boost our confidence in the quality of the 4D CBCT images we provide. To execute this assignment, it was critical to have access to a large dataset. This is now achievable because of research that resulted in an advancement in 4D CBCT that made it practicable. Each phase-resolved picture produced by the N-Net network, which is presently under development, is expected to outperform the image produced before it in the production chain. This earlier picture was rebuilt using U-Net using the complete projection data. This was done to demonstrate the effectiveness of the N-Net technique. This is done to ensure that the reconstructed picture has the maximum possible level of accuracy. The proposed model network is based on the N-Net foundation and takes into consideration the temporal correlations visible in phase-resolved pictures. After assessing the validity of the suggested CNNs using XCAT simulation data, further testing was performed utilizing real patient 4D CBCT datasets to corroborate the findings. We went through all of this difficulty in order to assist CNN. Each of these networks exceeds the most powerful CNN models currently available as well as the two most complex iterative strategies in terms of their ability to minimize streaking artefacts and noise while also obtaining distinguishing features. The proposed approach's ability to tackle difficult tasks requiring a diverse mix of patient datasets and imaging technologies demonstrates its broad applicability. This was one of the first things we learned about the problem.
Dielectrophoretic mobility of a spherical particle in 2D hyperbolic quadrupole electrode geometry

The dielectric properties of material are of importance in various industrial and scientific applications of measurement technology. These properties are usually measured by microwave sensors, methods that provide an average result of the entire sample volume. However, the need to know the dielectric properties of single particles has increased in various segments. Dielectrophoresis (DEP) as a method provides a way to determine dielectric properties, such as permittivity and conductivity, of single particles with good precision by using a sufficiently simple system. In this thesis the DEP platform consists of four electrodes with hyperbolic edge geometry. Geometry produces a linear electric field gradient which leads to a constant DEP force in the active region and ensures fairly straightforward determination of particle properties by means of their DEP mobility. Particle mobility is dependent on particle size, the slope of the electric field gradient, the conductivity of the carrier fluid and the frequency of the electric field. For this thesis has been studied particle characterization on a 2D platform, particle 3D behavior, simultaneous multiphenomena observation and the applicability of transparent indiumtin-oxide (ITO) electrode material to DEP platforms. Experiments were made with polystyrene particles, using carrier fluids of varying conductivity. Electrode transparency enabled holographic 3D imaging and thus also the observation of particle behavior in the depth direction. This 3D imaging revealed the restricted working distance from the electrode plane, as well as the mobility of particles in the depth dimension under the DEP force. It was also observed that there was only a marginal difference between 3D mobility and 2D mobility inside the active region. The dielectric properties of the polystyrene particles were determined and found to differ from those of solid polystyrene, and thus the properties of the carrier fluid have a distinctive effect on particle properties. The measurement achieved good precision in a single particle measurement and thus the total particle consistency could be very low. The main restrictions of the DEP method are the limited working distance and restricted range of particle sizes: based on the results of this thesis, with a fixed size platform the particle size may not vary by more than tenfold.
Self-commutators of invertible weighted composition operators on

In this paper, we consider the self-commutator of an invertible weighted composition operator on the Hardy space where is continuous on . We show that both the self-commutator and the anti-self-commutator are expressed as compact perturbations of Toeplitz operators. Moreover, we give an alternative proof for the result in [2] that is unitary exactly when is an automorphism of and where , is the reproducing kernel at for , and is a constant with . We next show that when for all , the weighted composition operator is normal if and only if the composition operator is unitary and is constant on . We also provide some spectral properties of and .
A Deep Learning-Based Sentiment Analysis Approach for Online Product Ranking With Probabilistic Linguistic Term Sets

—The Probabilities linguistic term set (PLTS) is an efficient tool to represent sentimental intensities hidden in unstructured text reviews which are useful for multi-criteria online product ranking. Traditional machine learning-based sentiment analysis methods adopted in existing studies to obtain PLTSs often result in unsatisfying prediction accuracy and thus inevitably affect product ranking results. To overcome this limitation, in this study, we propose a deep learning-based sentiment analysis approach to produce PLTSs from online product reviews to rank online products. A natural language processing-based method is first applied to extract product features and corresponding feature texts from online reviews. Then, state-of-the-art deep learning-based models are implemented to conduct the sentiment classification for online product/feature review texts. To ensure classification accuracy, we propose an experimental matching mechanism to identify the level of sentiment tendency for all rating labels of a review dataset and then match each label with the most appropriate linguistic term. The experiment results reveal: 1) our matching mechanism can benefit the training of a text classification model to identify sentiment tendencies from review texts with high prediction accuracy; 2) with the help of the trained classification model, our approach can predict sentimental intensities of the extracted features' texts in the form of PLTSs with competitive accuracy. A case study of applying PLTSs output from our approach to an online product decision-making problem is also provided to validate the applicability of our approach.
Mutation-Driven S100A8 Overexpression Confers Aberrant Phenotypes in Type 1 CALR-Mutated MPN

Numerous pathogenic CALR exon 9 mutations have been identified in myeloproliferative neoplasms (MPN), with type 1 (52bp deletion; CALRDEL) and type 2 (5bp insertion; CALRINS) being the most prevalent. Despite the universal pathobiology of MPN driven by various CALR mutants, it is unclear why different CALR mutations result in diverse clinical phenotypes. Through RNA sequencing followed by validation at the protein and mRNA levels, we found that S100A8 was specifically enriched in CALRDEL but not in CALRINS MPN-model cells. The expression of S100a8 could be regulated by STAT3 based on luciferase reporter assay complemented with inhibitor treatment. Pyrosequencing demonstrated relative hypomethylation in two CpG sites within the potential pSTAT3-targeting S100a8 promoter region in CALRDEL cells as compared to CALRINS cells, suggesting that distinct epigenetic alteration could factor into the divergent S100A8 levels in these cells. The functional analysis confirmed that S100A8 non-redundantly contributed to accelerated cellular proliferation and reduced apoptosis in CALRDEL cells. Clinical validation showed significantly enhanced S100A8 expression in CALRDEL-mutated MPN patients compared to CALRINS-mutated cases, and thrombocytosis was less prominent in those with S100A8 upregulation. This study provides indispensable insights into how different CALR mutations discrepantly drive the expression of specific genes that contributes to unique phenotypes in MPN.
Monitoring the Stability of Satellite Altimeters with Tide Gauges

Abstract A method is described for using tide gauge sea levels to monitor time-dependent drift in satellite altimetric measurements of sea surface height. The method depends on a careful assessment of the quality of the tide gauge measurements available for this application and also takes into account the degree of independence between the altimeter minus tide gauge differences in order to construct an optimal drift estimate and an accurate error estimate for it. The method is applied to the TOPEX altimeter measurements, and a recently discovered algorithm error, which resulted in a slow drift in the TOPEX sea surface heights, is exploited to evaluate the success of the tide gauge drift estimation. It is important to note that the tide gauge analysis was done without any prior knowledge of this error. The result is that the tide gauge analysis reproduces the drift due to the algorithm error to within 6 mm rms, which is comparable to the 5–6-mm internal estimate of the uncertainty of the drift analysis. Th.
Modeling the drivers of eutrophication in Finland with a machine learning approach

Anthropogenic eutrophication is one of the most common threats to inland water quality, often causing toxic algal blooms and loss of aquatic biodiversity. Mitigating the harmful impacts of eutrophication requires managing nutrient inputs from the catchment focusing on the major local drivers of eutrophication. These drivers can be identified using models that predict lake trophic state based on characteristics of the lake and its catchment. In this study, we aimed to extend the spatial scope of these models by identifying drivers of eutrophication in a large sample of lakes (1547) distributed across Finland. Moreover, we used satellite-observed chlorophyll a (chl a ) concentration as trophic state indicator, instead of site-sampled data, which is commonly used in existing research. We identified major drivers of eutrophication on river basin district to country scale based on 11 catchment and lake characteristics, applying the random forest algorithm. On country scale, the catchment and lake characteristics explained 41% of the variation in lake chl a concentrations, and on river basin district scale, 20% – 44%. Catchment and lake hydromorphology were the most important explanatory characteristics. Especially, high natural eutrophication level, shallow mean depth of lake, and small share of lake area
Cachet: A High-Performance Joint-Subtree Integrity Verification for Secure Non-Volatile Memory

Data confidentiality, integrity, and persistence are essential in secure non-volatile memory (NVM) systems. However, the cost of persisting all affected security metadata is high and leads to non-negligible overheads, including performance degradation, memory lifetime reduction, and high energy consumption. This is because integrity trees, which are typically used for data authentication of NVMs, require additional cryptographic calculations and memory accesses to persist the metadata for the recovery. In this paper, we propose Cachet, a novel integrity verification scheme that leverages set hash functions to achieve high performance and crash consistency. Specifically, Cachet maintains two set hash values representing the metadata cache state to enable the lazy update of the integrity tree in a joint-subtree manner with minimal overheads. The observation that underlies Cachet is that regarding the metadata cache, the integrity of each cached node is never verified individually, and the recovery process requires just the digest of the cached metadata. Our evaluation results show that Cachet reduces the application execution time by 21%, NVM writes by 30%, and hash calculations by 36% compared to the state-of-art solutions.
HALE UAV: Output Stabilization with the Minimum Time Strategy

In this paper, we treat the problem of dynamic output stabilization in an unobservable situation. This subject is essential and fascinating from a control point of view because of its relevance to general realistic situations. When the system is strongly-observable, the problem has been solved in several different ways. However, when in the presence of inputs along which the observability fails, the problem becomes much more difficult. Yet, the existence of such bad inputs is a generic property. Here we are dealing with the basic kinematic model of a HALE (High Altitude Long Endurance) drone when the only observable quantity is the distance to the target. This drone model exemplifies the situation where it must encounter dynamic output stabilization in an observable situation. This model is extremely challenging from the theoretical point of view since, first, it is easy to track due to the low dimension. Second, it captures all the difficulties that may appear in the general situation. In long-distance missions, it is crucial to minimize the fuel consumption or, almost equivalently, the required time to reach the target. It turns out that the (highly non-trivial) minimum-time strategy is very bad from several points of view. In particular, there is a strong conflict between observability and optimality (time minimization.) Additionally, this strategy is not even continuous w.r. t. initial conditions. Here, we overcome these difficulties for the drone model and propose a general strategy applicable to a large class of nonlinear systems.