Miracle-dz/UniFAR-FTUs huggingface dataset:
State-of-the-art methodologies used in preclinical studies to assess left ventricular diastolic and systolic function in mice, pitfalls and troubleshooting

Cardiovascular diseases (CVD) are still the leading cause of death worldwide. The improved survival of patients with comorbidities such as type 2 diabetes, hypertension, obesity together with the extension of life expectancy contributes to raise the prevalence of CVD in the increasingly aged society. Therefore, a translational research platform that enables precise evaluation of cardiovascular function in healthy and disease condition and assess the efficacy of novel pharmacological treatments, could implement basic science and contribute to reduce CVD burden. Heart failure is a deadly syndrome characterized by the inability of the heart to meet the oxygen demands of the body (unless there is a compensatory increased of filling pressure) and can manifest either with reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF). The development and progression of HFrEF is mostly attributable to impaired contractile performance (systole), while in HFpEF the main problem resides in decreased ability of left ventricle to relax and allow the blood filling (diastole). Murine preclinical models have been broadly used in research to understand pathophysiologic mechanisms of heart failure and test the efficacy of novel therapies. Several methods have been employed to characterise cardiac systolic and diastolic function including Pressure Volume (PV) loop hemodynamic analysis, echocardiography and Magnetic Resonance Imaging (MRI). The choice of one methodology or another depends on many aspects including budget available, skills of the operator and design of the study. The aim of this review is to discuss the importance of several methodologies that are commonly used to characterise the cardiovascular phenotype of preclinical models of heart failure highlighting advantages and limitation of each procedure. Although it requires highly skilled operators for execution, PV loop analysis represents the “gold standard” methodology that enables the assessment of left ventricular performance also independently of vascular loading conditions and heart rate, which conferee a really high physiologic importance to this procedure.
SIT-HANDS: A Novel RGB-D Dataset for Static Hand Gesture Recognition Using Kinect Sensor

Hand gesture recognition refers to the classification of various hand poses which conveys certain encoded signals used non-verbal communication. Hand gestures are also used in verbal communication to emphasize the meaning of spoken words. Hand gesture recognition finds various applications like human computer interaction and sign language recognition. It is an active area of research on solving the real time challenges like varying lighting conditions and uncluttered environment. Currently most of the datasets used in vision based hand gesture recognition are captured using RGB camera in lab environment which does not simulate the real time use case. Hence, we propose a novel dataset SIT-HANDS captured using Kinect V2 to obtain RGB frame and corresponding depth map data pair for static hand gesture recognition. Proposed dataset consists of 14 unique gestures captured from 10 different subjects with varying background and illumination.
Mitigation of 2w-Ripple in PMSG-based Wind Turbine Under Unbalanced Grid Voltages Using DOBC Along With Notch Filter

Typical PMSG based wind turbine consists of an electrical generator, machine-side converter (MSC), grid-side converter (GSC), dc-link capacitor, and a passive filter to connect the GSC to the grid. Under unbalanced grid voltage, the GSC can be used to control the active and reactive powers to implement fault ride through (FRT) algorithm. In such a case, the role of MSC reduces to regulating the dc-link voltage to a constant command value by controlling the stator currents of the PMSG. However, grid voltage unbalance can generate second-order harmonic ripples in the dc-link voltage, which can create a sinusoidal oscillation of frequency 2w in the torque of PMSG, where w is the grid frequency in rad/s. This paper proposes the use of notch filter to prevent the propagation of the dc-link voltage oscillation to the torque of PMSG. Moreover, disturbance observer-based control (DOBC) approach is used as a basis to design robust controllers for both GSC and MSC. The key feature of the proposed DOBC technique is its ability to achieve asymptotic regulation and accurate tracking of sinusoidal references of known frequencies to cope with voltage unbalance. Simulation tests have been conducted to verify the effectiveness of the proposed controllers.
Assessing ‘no evidence of disease activity’ status in patients with relapsing–remitting multiple sclerosis: a long-term follow-up

Introduction Multiple Sclerosis (MS) is a chronic inflammatory demyelinating disease of the CNS with an autoimmune pathogenesis. Over the years, numerous disease-modifying therapies (DMTs) have proven effective in disease control; to date, there is a need to identify a personalized treatment effective in ensuring disease-free status or no evidence of disease activity (NEDA). Objective identify clinical, demographic and treatment approach characteristics that affect the maintenance of NEDA-3 and the occurrence of clinical relapses during a 6-years follow-up. Materials and method a retrospective study was conducted on a cohort of MS patients followed up with six-year period. All participants were treated with first- or second-line MS drugs. Clinical relapse, NEDA-3 at 6 years and sustained EDSS were assessed as disease activity outcomes. Patients with follow-up of less than 6 years and insufficient clinical and radiological data were excluded from the study. Results Two-hundred-eighty naive patients (mean age was 49.8 years, SD ± 11.35 years, 23–76, F/M 182/98), with MS were followed up for 6 years. The mean age at diagnosis was 34.3 years (SD ±11.5, 14–62 years), the mean EDSS score at the onset was 1.9 (±1.3), 76.8% of patients had an EDSS below or equal to 2.5 at diagnosis. In the cohort 37 (13.2%) directly received second-line treatment, 243 (86.8%) received first-line drugs. The analysis showed that second-line treatment from beginning had a protective effect for the achievement of NEDA-3 (p = 0.029), on the prevention of clinical relapse (p = 0.018) and on number of relapses (p = 0.010); this finding was confirmed by logistic regression analysis (p = 0.04) and Kaplan–Meier analysis (p = 0.034). Conclusion The results of this study demonstrate the efficacy of targeted and early intervention so as to act in the right time window, ensuring a favorable outcome in both clinical and radiological terms; this could be decisive in reducing clinical relapse, disease progression and related disability. Therefore, prescribing highly effective drug in the early stages of the disease represents a leading strategy with the most favorable cost–benefit ratio.
Visual Tactile Sensor based on Feature Tracking of Patterns for Soft Human-Machine Interaction

Tactile sensors are used in various fields such as automated factories and human collaboration. Tactile sensors exist in a variety of technological ways. In particular, most of the contact determination methods are mainly performed through the detection of the physical surface. In contrast, recently, various Vision-based tactile sensors that can replace the existing method based only on visual data from an image viewed through a camera sensor have been proposed. The hardware proposed in this paper is also a Vision-based tactile sensor, and it is a method that determines contact based only on patterns. In addition, we propose a vision-based tactile sensor as hardware in the form of air bag based on an air cushion. As the biggest feature of the Vision-based tactile sensor is estimation through image reading, it is easy to update various functions through algorithm improvement. Based on these points, through continuous research, we will develop algorithms for position estimation stability improvement, force estimation, and multi-touch discrimination, among at the possibility of application to fields such as cooperative human interaction robots.
Functional and Neural Correlates Associated with Conditioned Pain Modulation in Patients with Chronic Knee Osteoarthritis Pain: A Cross-Sectional Study

Background: In this study, we aimed to assess the factors that predict a dysfunctional conditioned pain modulation (CPM) in chronic knee OA. Methods: This is a cross-sectional analysis of patients with chronic knee OA from a prospective cohort study in Brazil (n = 85). We performed linear and logistic multivariate regression models using the purposeful selection approach to test the relationship between the CPM in both knees (average) as a dependent variable and demographics, clinical, and neurophysiological as independent variables. Results: A significant negative association between WOMAC pain scores and CPM (β: −0.13) was found. This association was modified by the subjects’ race, being stronger in the non-white subjects. In our logistic regression models, pain intensity indexed with the WOMAC pain scale remained a significant association with dichotomized CPM. Furthermore, a significant CPM association with balance, indexed with the Berg Balance score, was evidenced (β: 0.04). Neurophysiological variables showed a significant negative relationship with CPM, such as the relative power of delta oscillations in the frontal area (β: −3.11) and central area (β: −3.23). There was no significant relationship between CPM and the following domains: cognitive, emotion, sleep, opioid receptor polymorphisms, and intrinsic variables of OA disease. There was no association of CPM with TMS-indexed inhibitory markers. Conclusions: These results may indicate that less function of the pain descending inhibitory system in patients with OA is correlated with higher activity-related pain (WOMAC), less balance, and cortical plasticity especially with increased low-frequency (delta) brain oscillations. These associations seem modified by race.
Strategies to Improve Smoking Cessation for Participants in Lung Cancer Screening Program: Analysis of Factors Associated with Smoking Cessation in Korean Lung Cancer Screening Project (K-LUCAS)

Purpose Smoking cessation intervention is one of the key components of successful lung cancer screening program. We investigated the effectiveness and related factors of smoking cessation services provided to the participants in a population-based lung cancer screening trial. Materials and Methods The Korean Lung Cancer Screening Project (K-LUCAS) is a nationwide, multi-center lung cancer screening trial that evaluates the feasibility of implementing population-based lung cancer screening. All 5,144 current smokers who participated in the K-LUCAS received a mandatory smoking cessation counseling. Changes in smoking status were followed up using a telephone survey in 6 months after lung cancer screening participation. The lung cancer screening’s impact on smoking cessation is analyzed by variations in the smoking cessation interventions provided in screening units. Results Among 4,136 survey responders, participant’s motivation to quit smoking increased by 9.4% on average after lung cancer screening. After 6 months from the initial screening, 24.3% of participants stopped smoking, and 10.6% of participants had not smoked continuously for at least 6 months after screening. Over 80% of quitters stated that participation in lung cancer screening motivated them to quit smoking. Low-cost public smoking cessation program combined with lung cancer screening increased the abstinence rates. The smokers were three times more likely to quit smoking when the smoking cessation counseling was provided simultaneously with low-dose computed tomography screening results than when provided separately. Conclusion A mandatory smoking cessation intervention integrated with screening result counselling by a physician after participation in lung cancer screening could be effective for increasing smoking cessation attempts.
Wave attenuation in Mangrove forests

Mangrove forests cover large parts of the tropical and subtropical shorelines and grow in the inter-tidal zone. Mangrove forests act as a natural coastal protection. The most important features of this protection are the prevention of erosion by the collecting of sediment and the reduction of the wave climate. There is a need to quantify and model hydraulic and morphologic processes, wave attenuation in particular, in order to predict and simulate the natural hydraulic behaviour and evaluate the consequences of any artificial measure in mangrove forests. In this study it is found, that the best numerical approximation to model wave attenuation in vegetation with SWAN (spectral wave model), was a formulation described by Dalrymple (1984). The formulation expresses the amount of dissipated wave energy in terms of vegetation characteristics and wave parameters. It has been implemented in SWAN, resulting in an adapted SWAN model in which wave attenuation can be modelled due to homogenous, horizontally variable or vertically variable vegetation. Subsequently the implementation for homogenous vegetation was validated on the results of physical model tests from Mendez and Losada (2004). This validation was successful and gave evidence the implementation was done correctly. The drag coefficient (Cd) is the only calibration parameter in the Dalrymple (1984) formulation and depends on the vegetation characteristics and wave parameters. For the vegetation characteristics it turned out that the drag coefficient depends mainly on the relative spacing. The dependency of the drag coefficient on the wave parameters is determined by the horizontal orbital velocity and the peak period, and could be best described by the relation between the drag coefficient and the Keulegan-Carpenter number. Next a sensitivity analysis was performed which resulted in the expected relations between the wave attenuation and the vegetation characteristics, respectively the hydraulic conditions. In a test simulation with patchy vegetation it was found to be possible to model wave induced setup causes by the energy dissipation in the vegetation, however the potential flow itself can not be modelled by SWAN, because the model cannot simulate flow. Two measurement campaigns in Vietnam on wave attenuation in mangrove forests were modelled as test cases in the adapted SWAN model. In these cases hydraulic conditions and vegetation characteristics were known from measurements and thus the drag coefficient could be calibrated. Finally it is concluded that the implementation in SWAN was successful for different types of vegetation. Nevertheless the dependency of the drag coefficient on both wave parameters and vegetation characteristics implies that the model can only be used when vegetation characteristics and wave parameters have been measured and related to the drag coefficient.
Solar Power Based Multipurpose Agriculture Robot with Leaf-Disease Detection

Real-time collision free route tracking presents increasingly difficult challenges for mobile robots operating in big, dynamic areas. Finding a practical collision-free path for the robot to go along while in a cultivated area is thus a crucial necessity. In India, agriculture is a major source of revenue. Bacterial and fungal illnesses are brought on by conditions including diseases, insect infestations, and abrupt climatic changes. Early illness detection and taking certain steps will produce better outcomes. The Agritech robot manoeuvres in a dynamic environment to look for tomato plant illnesses. While travelling around an agricultural area, the robot takes pictures of the plants and stores them in a database for comparison and analysis. These pictures are sorted as either sick or well. When a plant is infected, the pesticide sprayer unit will activate and spray the plant with pesticide. A tool is being created to detect three prevalent ailments that affect tomato plants, including Bacterial Blight, Leaf Spot, and Anthracnose. The identification of diseases is carried out using a trained neural network model. The Convolution Neural Network, K-means clustering, and Support Vector Machine utilised for image segmentation and illness classification, respectively, are trained into the model. The employment of autonomous robots in tomato plantations can lower the danger of human mistake associated with manual spraying, which makes them very beneficial in agriculture.
Implementation and Evaluation of Synchronized SS-CDMA Using Wireless Two-Way Interferometry (Wi-Wi)

Synchronized spread spectrum code division multiple access (SS-CDMA) is very effective for increasing the capacity and reducing the interference with a rapid spread of Internet of things (IoT) devices. Since the synchronized SS-CDMA requires receiving timing synchronization, it is essential to realize transmission timing control of each node using space-time synchronization. In this paper, we investigate precise time synchronization between nodes using Wireless Two-Way Interferometry (Wi-Wi). The measurement results show that the standard deviation of offset ($\sigma$) was less than 30ns. Furthermore, we implement a synchronized SS-CDMA communication function on Universal Software Radio Peripheral (USRP). The bit error rate (BER) characteristics are evaluated with different offsets of initial timing synchronization of the Wi-Wi module. It is found that BER characteristics when two signals are transmitted simultaneously become smaller as the Wi-Wi offset becomes smaller. For an offset of around 12 ns, the degradation of BER compared to the case with 1 transmission signal is negligibly small. As a result, we reveal that simultaneous communication between two terminals is possible without large degradation.
Bimodal Speech Emotion Recognition using Fused Intra and Cross Modality Features

The interactive speech between two or more inter locutors involves the text and acoustic modalities. These modalities consist of intra and cross-modality relationships at different time intervals which if modeled well, can avail emotionally rich cues for robust and accurate prediction of emotion states. This necessitates models that take into consideration long short-term dependency between the current, previous, and future time steps using multimodal approaches. Moreover, it is important to contextualize the interactive speech in order to accurately infer the emotional state. A combination of recurrent and/or convolutional neural networks with attention mechanisms is often used by researchers. In this paper, we propose a deep learning-based bimodal speech emotion recognition (DLBER) model that uses multi-level fusion to learn intra and cross-modality feature representations. The proposed DLBER model uses the transformer encoder to model the intra-modality features that are combined at the first level fusion in the local feature learning block (LFLB). We also use self-attentive bidirectional LSTM layers to further extract intramodality features before the second level fusion for further progressive learning of the cross-modality features. The resultant feature representation is fed into another self-attentive bidirectional LSTM layer in the global feature learning block (GFLB). The interactive emotional dyadic motion capture (IEMOCAP) dataset was used to evaluate the performance of the proposed DLBER model. The proposed DLBER model achieves 72.93% and 74.05% of F1 score and accuracy respectively.
USE OF THE BH CURVES APPROXIMATION FOR THE CALCULATION OF MAGNETIC CIRCUITS IN ELECTRICAL MACHINES

For the design of electrical machines, electrical steel BH curves are usually used in tabular or graphical form in magnetic circuit calculations. This is not convenient for the digitization of data and increases the requirement for computational accuracy. In this work, a partial approximation of the BH curves by the exponential function is proposed. The accuracy of the results obtained relative to the total approximation of these curves is demonstrated. The method for calculating the coefficients of the approximation functions is given. Application use of these curves is modeled in calculating the magnetic voltage of the trapezoidal-shaped stator tooth in AC machines. Finally, a numerical calculation example is carried out according to the proposed method.
Machine Learning Models and Technology for Classification of Forest on Satellite Data

The paper deals with the problem of semantic segmentation of satellite imagery to deliver forest type map with high resolution. To solve the problem, we propose 4 machine learning models. Two of them are based on Random Forest (RF) and other two - on Convolutional Neural Network (CNN) - U-Net. As an input we use 2 images of Sentinel-2 (one for summer and one for winter, 4 spectral bands from each). As an output (labels) we use the Copernicus Forest Type dataset for 2018 year. Our models showed promising results on validation data. Of all models the one based on U-Net ended up being the most efficient in forest classification with overall accuracy 91.7%. At the same time the best RF model scored with 86.5%. After comparing the results, in order to check our model transferability we created and compared forest map of northern part of Kyiv region of 2018 and 2022. The experiment confirmed the robustness of the model and it's scalability. The developed models have been implemented in the cloud platform specialized on satellite data - CREODIAS. The developed map can provide valuable data for foresters, biologists, or other researchers to make decisions about forest management and conservation, as well as to ensure that Europe's forests are managed in an ecologically sustainable way.
Integrated cassava research and development projects in Colombia, Ecuador and Brazil : an overview of CIAT`s experiences

This paper discusses CIAT's 12 year experience in developing an integrated cassava research and development project (ICRDP) approach. The origin, justification, methodology, results, and lessons learned from this approach are presented, using a comparative analysis of CIAT's experiences in Colombia, Ecuador and Brazil. Results have demonstrated to research and development institutions, donors, governments and policy makers that cassava is a crop than can play an important role in achieving development goals. Through the integrated approach, traditional cassava markets have diversified and overall demand for cassava has increased. This has reduced price variability while increasing yields and, as a result, created incentives for adopting improved technologies
Applied hydrology, McGraw-Hill series in water resources and environmental engineering; book review

Applied Hydrology is a valuable contribution to the literature. It is clearly written and meets all goals described by the authors. The book is intended as an upper level undergraduate or graduate level text but will also serve well as a reference handbook for applied hydrologists. The book is divided into three parts. The first, on hydrological processes, comprises six chapters which examine different elements of the hydrologic system. For the most part, these chapters are descriptive, providing background information and outlining basic principles pertinent to hydrology. Topics range from atmospheric processes to surface and subsurface flows. Finally, hydrologic measurement is discussed in chapter six.