Miracle-dz/UniFAR-FTUs huggingface dataset:
Yoda1’s energetic footprint on Piezo1 channels and its modulation by voltage and temperature

Significance The mechanosensitive ion channel Piezo1 plays many important physiological functions. This essential ion channel is selectively activated by a synthetic small molecule called Yoda1. Despite current efforts to develop Yoda1 analogs with potential clinical value, the nature and extent of Piezo1 functions modulated by Yoda1 remain unknown. Here we performed a comprehensive biophysical characterization of the effects of Yoda1 and of its antagonist analog Dooku1. We show that Yoda1 activates Piezo1 by energetically stabilizing and destabilizing conducting and nonconducting Piezo1 conformations, respectively. We further discovered that the electrophysiological effects of Yoda1 strongly depend on membrane potential and temperature, two known physiological Piezo1 modulators. Our study illuminates the complexity by which Piezo1 integrates physical and chemical cues.
[18F]Fluoride Positron-Emission Tomography (PET) and [18F]FDG PET for Assessment of Osteomyelitis of the Jaw in Comparison to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI): A Prospective PET/CT and PET/MRI Pilot Study

To investigate imaging features of osteomyelitis of the jaw (OMJ) using [18F]fluoride positron emission tomography (PET) and [18F]fluorodeoxyglucose (FDG)-PET compared with computed tomography (CT) and magnetic resonance imaging (MRI) to assess extent and disease activity. Six female patients (55.3 ± 10.0 years) were enrolled for assessment of symptomatic OMJ. 4/6 patients underwent [18F]FDG-PET/MRI and [18F]fluoride-PET/CT, one patient MRI and [18F]fluoride-PET/CT and another patient only [18F]FDG-PET/MRI. Image analysis was performed by two radiologists, an oral and maxillofacial surgeon, and a nuclear medicine specialist. The extent of affected jawbone was analyzed both qualitatively and quantitatively, including the PET tracer uptake, CT-Hounsfield-Units (HU) and MRI parameters in affected and healthy jawbone. All patients had trabecular sclerosis in the affected jawbone compared to healthy jawbone (560 ± 328 HU vs. 282 ± 211 HU; p > 0.05), while 3/6 patients had cortical erosions. Bone marrow edema and gadolinium enhancement were documented in 5/6 patients. In affected jawbone, [18F]fluoride-uptake was increased in all patients compared to healthy jawbone (SUVmean 15.4 ± 4.2 vs. 2.1 ± 0.6; p < 0.05), and [18F]FDG-uptake was moderately higher (SUVmean 1.9 ± 0.7 vs. 0.7 ± 0.2; p > 0.05). The extent of regions with increased metabolic activity was less than the extent of morphologic changes in all patients. Information on jawbone metabolism and inflammation is different from morphologic changes and therefore has the potential to provide a more accurate and objective assessment of the extent and activity of OMJ.
Probabilistic Sensitivity Amplification Control for Lower Extremity Exoskeleton

To achieve ideal force control of a functional autonomous exoskeleton, sensitivity amplification control is widely used in human strength augmentation applications. The original sensitivity amplification control aims to increase the closed-loop control system sensitivity based on positive feedback without any sensors between the pilot and the exoskeleton. Thus, the measurement system can be greatly simplified. Nevertheless, the controller lacks the ability to reject disturbance and has little robustness to the variation of the parameters. Consequently, a relatively precise dynamic model of the exoskeleton system is desired. Moreover, the human-robot interaction (HRI) cannot be interpreted merely as a particular part of the driven torque quantitatively. Therefore, a novel control methodology, so-called probabilistic sensitivity amplification control, is presented in this paper. The innovation of the proposed control algorithm is two-fold: distributed hidden-state identification based on sensor observations and evolving learning of sensitivity factors for the purpose of dealing with the variational HRI. Compared to the other state-of-the-art algorithms, we verify the feasibility of the probabilistic sensitivity amplification control with several experiments, i.e., distributed identification model learning and walking with a human subject. The experimental result shows potential application feasibility.
Karma Yoga: Scale development and studies of the impact on positive psychological outcomes at the workplace

Is achieving organizational performance and employee well-being a zero-sum game? Can cultural traditions provide an approach to realizing the spiritual aspects of ordinary vocations of life? We propose that the approach of Karm Yoga can answer these questions. This research presents the model of Karma Yoga derived from Vedantic tradition. We present three studies aimed at developing the construct and psychometric scale of Karma Yoga and examining its impact on workplace spirituality and positive psychological outcomes. Study 1 (n = 459) is about the development of the construct and the scale of Karma Yoga and testing the reliability and validity of the same. Study 2 (n = 337) is designed and carried out to examine the association of Karma Yoga on other widely studied indicators of workplace spirituality, i.e. mindfulness (β = 0.44, p < .05) and self-transcendence. Further, in Study 3 (n = 260), we investigate the hypothesis of a positive association of Karma Yoga with positive psychological outcomes like thriving, psychological capital, flourishing, and negative impact on job burnout. Empirical findings support these hypotheses. In this way, our research article presents the indigenous model of workplace spirituality by developing a theoretical framework and testing it empirically. Karma Yoga offers the ways and means to transcendence from an ego-centric approach of work to realizing the spirituality at work. This study also suggests that Karma Yoga reduces stress and leads to the path of well-being.
Potential Scattering on $R^3\otimes s^1

In this paper we consider non-relativistic quantum mechanics on a space with an additional internal compact dimension, i.e. $R^3\otimes S^1$ instead of $R^3$. More specifically we study potential scattering for this case and the analyticity properties of the forward scattering amplitude, $T_{nn}(K)$, where $K^2$ is the total energy and the integer n denotes the internal excitation of the incoming particle. The surprising result is that the analyticity properties which are true in $R^3$ do not hold in $R^3\otimes S^1$. For example, $T_{nn}(K)$, is \underline{not} analytic in K for $ImK>0$, for n such that $(|n|/R)>\mu$, where R is the radius of $S^1$, and $\mu^{-1}$ is the exponential range of the potential, $V(r,\phi)=O(e^{-\mu r})$ for large r. We show by explicit counterexample that $T_{nn}(K)$ for $n\neq0$, can have singularities on the physical energy sheet. We also discuss the motivation for our work, and briefly the lesson it teaches us.
Antenna with Hexa-Band Capabilities for Multiple Wireless Applications

In this paper, a novel multiband microstrip patch antenna with small frequency ratio is designed and analysed. One can design a multiband antenna at any desired frequencies through these proposed methods. The proposed antenna shows six operating frequencies with very small frequency ratio between two consecutive resonant frequencies 1.1248, 1.1123, 1.0792, 1.1469 and 1.3254 and can be used for various wireless applications, i.e., 2.5 GHz for UMTS and Wi-Fi, 2.812 GHz for CCTV with wireless video links, 3.128 GHz and 3.376 GHz for WiMAX, 3.872 GHz for C-band applications and 5.132 GHz for Lower WLAN. Design procedure and formation of all six bands are presented and discussed. Analysis is done by Ansoft HFSS v. 15 which is based on Finite Element Method (FEM), and simulated results are verified with experimental results of fabricated prototypes which are found in close agreement.
Molecular characterization and reclassification of a 1.18 Mbp DMD duplication following positive carrier screening for Duchenne/Becker muscular dystrophy

A 2‐month‐old male patient harboring a duplication of DMD exons 1–7 classified as pathogenic by an outside institution presented with mildly elevated creatine phosphokinase (CK); molecular breakpoint analysis by our laboratory reclassified the duplication as likely benign. To date, proband continues to develop normally with decreased CK, further supporting our reclassification.
Analyzing Human Muscle State with Flexible Sensors

Analyzing human muscle states has attracted extensive attention. EMG (electromyography) pattern recognition methods based on these works have been proposed for many years. However, uncomfortable wearing and high prices make it inconvenient for motion tracking and muscle analysis by using robotic arms and inertial sensors in daily life. In this study, we propose to use smart clothes integrated with flexible sensors to collect arm motion data, estimate the kinematic information of continuous arm motion, and predict the EMG signal of each arm muscle. Firstly, the neural network regression model integrated with the LSTM (long short-term memory) module is used to continuously estimate the sensor resistances collected by the smart clothes and the angles collected by Kinect. Then, six types of shoulder and elbow movements’ angles and the corresponding EMG signals of 5 subjects are preprocessed and aligned. The stacked regression model based on extremely randomized trees (extra trees) is used for regression. Our experimental results show that the average estimation absolute error from the sensor resistances to the joint angle is 3.45 degrees, and the absolute percentage error from the joint angle to the EMG signal is only 1.82%.
Magnitude of Depression and Associated Factors Among Patients on Tuberculosis Treatment at Public Health Facilities in Harari Regional State, Eastern Ethiopia: Multi-Center Cross-Sectional Study

Background Depression among tuberculosis patients remains a significant public health concern. Its burden and severity is increasing in low-income countries, especially in sub-Saharan Africa. Although it has negative impacts on quality of life, depression among tuberculosis patients is rarely studied in Ethiopia, particularly in the study area. Therefore, this study aimed to determine the magnitude of depression and associated factors among tuberculosis patients in Eastern Ethiopia. Methods A facility-based cross-sectional study was conducted from April 15 to May 15, 2021, among 213 tuberculosis patients in public health facilities of the Harari Regional State, Eastern Ethiopia. Study participants were selected using a systematic sampling technique. Data were collected using interviewer-administered questionnaire. Depression was assessed using Patients Health Questionnaire-9 (PHQ-9). Epi-data and SPSS were used for data processing and analysis. Bi-variable and multivariable logistic regressions were employed to determine the effects of predictors on depression. Statistical significance was considered at a p-value <0.05. Results The magnitude of depression among tuberculosis patients was 52.1% [95% CI (45.4, 58.8%)]. Patient’s age of 25–34 years [AOR = 0.31, 95% CI (0.128, 0.75)], female sex [AOR = 5.04; 95% CI (2.18, 11.62)], intensive phase of tuberculosis treatment [AOR = 2.56; 95% CI (1.118, 5.876)], HIV-positive status [AOR = 7.143; 95% CI (1.55, 32.93)], smoking history [AOR = 9.08; 95% CI (3.35, 24.61)] and having poor social support [AOR = 4.13; 95% CI (1.29, 13.22)] were factors statistically associated with depression. Conclusion In this study, the magnitude of depression was relatively high as more than half of the participants had depression. Advanced age, female sex, intensive phase of tuberculosis treatment, HIV-positive status, smoking history, and poor social support were identified as associated factors with depression. Therefore, this result calls all stakeholders to give duly emphasis on incorporating and integrating mental health support programs as routine practice in tuberculosis follow-up clinics, as screening, early detection and treatment, and training for all health care providers are very crucial in tackling depression among tuberculosis patients.
A New Capacitance Measuring System for Capacitive Sensor for Moving Target Detection

In this brief, a high sensitivity measuring system for capacitive sensor to detect moving target is presented. It mainly consists of a capacitance measuring circuit and a dc-blocking circuit. The capacitance measuring circuit is used to convert a capacitance to a dc voltage and the dc-blocking circuit is employed to extract the voltage variation. The experimental results show that when the measured capacitor is between 1 pF to 7.5 pF, the sensitivity is 3.53 V/pF, the linearity error is 6.13%, the SNR increases from 26.6 dB to 57.7 dB, the resolution is 3.1 fF and the time delay is about $4\,\mu \text{s}$ . Comparing to five other common capacitance measuring circuits, the proposed one offers plain design, high sensitivity, and fast speed. Simulation results show that the proposed dc-blocking circuit has short time delay and unattenuated output. Moreover, as demonstrated by system tests, the proposed measuring system is qualified for capacitive sensor for detection of moving target with different speeds.
Designing a Double LoRa Connectivity for the Arduino Portenta H7

Machine learning is moving to the smallest computing devices. Today machine learning is applied even in tiny IoT microcontroller boards. In the IoT, LoRa is a popular communication technology to connect remote devices with gateways. Still, the confluence of machine learning in microcontrollers and networked LoRa connectivity is not yet fully exploited. In this paper we design a new LoRa connectivity for the Arduino Portenta H7, a recent microcontroller board equipped with embedded sensors suitable for diverse machine learning tasks. With the solution that we found the Arduino Portenta H7 is able to become part of a LoRa mesh network. This capacity increases the Portenta’s range of applications. For the vision of distributed machine learning at the tiny edge, we can add with the Portenta an important board to become a smart compute node within a LoRa mesh network.
A proposal on voltage dip regulation for the Dutch MV distribution networks

Voltage quality regulation has been an ongoing discussion in many countries in the world to set up a regulatory framework where also voltage dips are included in the national grid codes. Regarding voltage dips, the regulatory framework is useful for establishing the coordination between the network operators, the equipment manufacturers, and the customers. The voltage dip regulation can provide benefits for the involved parties. By defining the responsibilities for the network operators and the customers, it can help distribution system operators to evaluate the quality of supply voltage and to take possible measures when the number of dips exceed the reference values defined in the regulation. The regulatory framework can also help customers to make an economic analysis for choosing appropriate mitigation methods to reduce the expected economic damages caused by voltage dips. This article presents two approaches for classifying different types of voltage dips into various clusters. Using datasets of voltage dips from the Dutch MV networks, the paper also describes the methodology for setting limits on the number of voltage dips for the regulatory purpose.
Secure Communications in Line-of-Sight Scenarios by Rotation-based Secret Key Generation

In this paper, we evaluate the achievable rates of the physical layer security (PLS)-based secret key generation (SKG) in a line-of-sight (LoS) channel, which occurs with a high probability in unmanned aerial vehicle (UAV) communications. SKG is a promising technique for lightweight scalable key distribution, and, despite the mature theory of PLS, this technique remains to be seen in practical applications. Thus, modelling realistic scenarios is of particular interest. One of the reasons why SKG has not been used in practice is due to the deterministic properties of LoS channels. To overcome this problem, in this paper we propose the rotation of the device antennas as an entropy source, reflected by the channel phases, from which the keys are derived. Simulation results show that by utilizing rotation in LoS scenarios, a number of secret bits can be generated, also given the presence of an eavesdropper. It is also shown that by controlling the transmit power we can maximize the conditional mutual information.
A Systematic Review of New Trends in Ionic Liquids Applied to Electrolytes on Polysaccharides

Polysaccharides are formed by a long chain of monosaccharides, with the main function of promoting energetic and structural reserves for plants and animals. They can be applied as a base of electrolytes, using ionic liquids (ILs) as a solvent base. The study of electrolytes is an emerging field, as they are applied as secondary batteries, fuel cells, solar cells, supercapacitors and chemical sensors. They operate stably under extreme conditions, maintaining their high thermal stability. Furthermore, their low cost and environmentally safe character, compared to conventional electrolytes, have attracted considerable attention in the scientific field. ILs are composed entirely of ions and could be potentially applied as solvents. As electrolytes, ILs are environmentally friendly, and their use in combination with polysaccharides leads to a synergic effect. In the present study, a systematic review was performed of all papers published from 2014 to 2022 regarding ILs and polysaccharides through a search of three databases. Due to the large number of results found, only papers about electrolytes were considered and the main findings described. This study allows for easy identification of the most relevant fields of study with respect to ILs and polysaccharides, as well as the main gaps to be explored in the literature.
Journal of Quantitative Analysis in Sports Structural Analysis of Action and Time in Sports : Judo

Judo is an intermittent sport. As such, it is important to characterize the actions involved in combat so that training may be structured in such a way as to simulate competitive demands. However, to do so, an objective notational analysis system is necessary. The aim of the present study was to design a computer program that would aid in the analysis of time structure of specific actions during judo combat and test its inter-evaluator and intra-evaluator reliability. Ten male judokas, divided by class and category, were evaluated during three combats each. The matches were filmed and the evaluators used the computer program SaatsTM (Structural Analysis of Action and Time in Sports) to analyze the following actions: break, grip, technique, fall and groundwork. The sequences were characterized by the sum of actions between each break. A total of 276 action sequences were evaluated, with a mean of 11 action sequences per combat, with four on the ground. Two evaluations were carried out by three evaluators for each judo match (inter-evaluator agreement), with only one being an expert in the software used. There was a lack of similarities in the results of only two of the variables (p<0.05). Evaluations by the same evaluator (intraevaluator agreement) demonstrated a high reliability on all six variables. It was concluded that the use of this computer software for notational analysis in judo greatly assists in the detailing of actions performed by the athletes. The use of this software by professionals unfamiliar with it likely requires a short learning period. Knowledge of judo actions will very likely allow practitioners of the sport to be trained more specifically.
Processing and Storage Rather of Mortadella from Nile Tilapia Filleting Residues

ABSTRACT The use of tilapia waste in the production of mortadella can add value to the Nile tilapia production chain. Aiming to develop and characterize mortadella from filleting residues, two experiments were conducted: (1) in a 2 × 3 factorial design, using two types of raw material (100% trimmings and 80% trimmings +20% minced fish) and three processing techniques (smoking, homemade and industrial cooking), and (2) in a 2 × 5 factorial scheme, using two processing techniques (homemade and industrial cooking) and five storage times under refrigeration (until 60 days). Mortadella elaborated with 100% trimmings, cooked by industrial immersion and in a homemade way, had physical-chemical and sensory characteristics that pleased the tasters more. This mortadella also remained within the microbiological and physical-chemical parameters recommended for consumption during 60 days of storage. It is recommended to cook mortadella by the industrial method, as it provides greater oxidative stability.
A Hybrid Hand-Crafted and Deep Neural Spatio-Temporal EEG Features Clustering Framework for Precise Emotional Status Recognition

Human emotions are variant with time, non-stationary, complex in nature, and are invoked as a result of human reactions during our daily lives. Continuously detecting human emotions from one-dimensional EEG signals is an arduous task. This paper proposes an advanced signal processing mechanism for emotion detection from EEG signals using continuous wavelet transform. The space and time components of the raw EEG signals are converted into 2D spectrograms followed by feature extraction. A hybrid spatio-temporal deep neural network is implemented to extract rich features. A differential-based entropy feature selection technique adaptively differentiates features based on entropy, based on low and high information regions. Bag of Deep Features (BoDF) is applied to create clusters of similar features and computes the features vocabularies for reduction of feature dimensionality. Extensive experiments are performed on the SEED dataset, which shows the significance of the proposed method compared to state-of-the-art methods. Specifically, the proposed model achieved 96.7%, 96.2%, 95.8%, and 95.3% accuracy with the SJTU SEED dataset, for SVM, ensemble, tree, and KNN classifiers, respectively.