Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tale of Two Perspectives: Harvesting System Views and User Views to Understand Patient Portal Engagement

Patient engagement is recognized as a key factor in promoting care quality and experience. Although patient portals as a prevalent information infrastructure provide a viable means to achieve engaging patients, there is still a limited understanding of how to objectively and systematically evaluate engagement levels in the context of patient portals. We develop the Patient Portal Engagement Framework (PPEF) to objectively and systematically evaluate patient portal engagement and demonstrated its utilization and effectiveness in two scenarios: portal utilization and user feedback. Four engagement levels included in the PPEF are - Inform Patients that allows patients to access health information; Involve Patients that encourages patients to take initiatives; Partner with Patients that supports long-term collaboration between patients and providers; and Support Ecology of Care that extends the scope beyond hospitals into personal and social factors. We find more portal utilization and user feedback focus in lower levels of patient portal engagement (i.e., patients receiving information and taking active actions in managing care). Our thematic analysis of online user reviews reveals four core themes: conflicts between system and user views, evolving benefits and needs towards patient portals, debates about balancing emotional and informational needs, and reconsideration of power, accessibility, and privacy. We discuss how PPEF can help harvest and synthesize data from the system and user levels, as well as the design implications for patient portals. These results show that patient portals can be designed with practical guidance for engaging patients, complementing current efforts that focus on conceptualizing engagement or rely on psychometrics.
Reliability Assessment for Dual-Active-Bridge Converter with Fault Tolerant Capability

Currently, dual-active-bridge (DAB) converters are widely adopted as the power interface to achieve a bidirectional power flow and galvanic isolation in high-frequency dc power transmission systems. However, the power devices are statistically reported as the main factor of possible failures in practical operation, which is harmful to the stability of power electronic dominated systems. Thus, the fault-tolerant capability is essential for the DAB converter to maintain a reliable system operation, which guarantees the avoidance of faults-induced instability. However, the impact of fault fault-tolerant capability on the system reliability is not thoroughly investigated and quantified. In this paper, the DAB converter-based system is modeled by a simplified reliability model and further developed to the Markov process. The reliability of the DAB converter with and without fault-tolerant capability is analyzed with the proposed mathematical model. Meanwhile, the long-term steady-state availability of the system is investigated in this paper.
KNN Based GA for Performance Improvement in Neck Movement Classification of EMG signal

Providing significant features to the classifier is essential for the enhancement of classification accuracy. To meet this demand, feature selection plays a vital role in the field of bio signals. This work engaged in the classification of movements based on electromyography (EMG) signals acquired from the selected neck muscles namely the trapezius and sternocleidomastoid. For the relevant feature selection, an optimization technique named genetic algorithm (GA) is implemented. This GA is an iterative process that is involved in performance improvement by reducing the classification error. Here, the k-nearest neighbor (KNN) algorithm is used iteratively from which the error function is returned as a fitness function that is used to fit the selected individual to an optimal solution. The selected features are classified by the application of KNN, support vector machine (SVM), and Naive Bayes (NB) classifier, of which KNN classifier defeats others with a better performance of about 99.93±0.14%.
Generation of Synthetic Data for the Comparison of Different 3D-3D Registration Approaches in Laparoscopic Surgery

In laparoscopic surgery image-guided navigation systems could support the surgeon by providing subsurface information such as the positions of tumors and vessels. For this purpose, one option is to perform a reliable registration of preoperative 3D data and a surface patch from laparo-scopic video data. A robust and automatic 3D-3D registration pipeline for the application during laparoscopic surgery has not yet been found due to application-specific challenges. To gain a better insight, we propose a framework enabling a qualitative and quantitative comparison of different registration approaches. The introduced framework is able to evaluate 3D feature descriptors and registration algorithms by generating and modifying synthetic data from clinical examples. Different confounding factors are considered and thus the reality can be reflected in any simplified or more complex way. Two exemplary experiments with a liver model, using the RANSAC algorithm, showed an increasing registration error for a decreasing size of the surface patch size and after introducing modifications. Moreover, the registration accuracy was dependent on the position and structure of the surface patch. The framework helps to quantitatively assess and optimize the registration pipeline, and hereby suggests future software improvements even with only few clinical examples. Clinical relevance— The introduced framework permits a quantitative and comprehensive comparison of different registration approaches which forms the basis for a supportive navigation tool in laparoscopic surgery.
SOIL DEGRADATION

The rise and fall of ancient civilizations were in direct proportion to the wise use or misuse respectively of the natural resources, in particular, land and water resources. Land is the finite resource which is devoted to the largest primary “Private Industry” namely, “agriculture”. But unfortunately land is the least cared; most neglected and misused resource by almost everyone, either knowingly or unknowingly. The results are obvious and for everyone to see in the form of degradation and declining productivity of our resource base. The situation demands everyone's attention and immediate correction. The pressure on our finite land resources is tremendous at present due to increasing population and competing demands of various land uses. The decreasing land-man ratio and continued dependence of a high proportion of the population on agriculture in developing nations is a matter of grave concern. That is why we are witnessing high rate of unemployment and under employment in the rural areas. Under such circumstances, the extent of unemployment can rise alarmingly unless measures are taken to either increase the intensity of land use or shift a significant proportion of the human resource out of agriculture to non-agricultural activities, or both, and both are not likely to happen that easily in the developing countries. It is obvious that the population pressure, both human and cattle, and competing demands and needs of the society exerts tremendous pressure on the limited and shrinking land resources like soil, water, forests, vegetation, bio-diversity etc. Due to this pressure, there is severe degradation of the resources and large-scale change in the land use and land cover. Apart from this, particularly of late, there is a significant diversion of farm lands and water resources to nonagricultural purposes, exerting further strain on the already shrinking land and fast-depleting water resources. Due to the population pressure in most developing nations, the existing forest areas are facing deforestation, cutting beyond the silviculturally permissible limit, unsustainable fuel wood and fodder extraction, shifting cultivation, encroachment into forest lands, forest fires and over grazing. These changes affect drastically the vegetation, rainfall and sedimentation levels in the lakes and reservoirs, hydrological cycle, bio-diversity and ultimately the land use. All such issues are now being widely discussed under the broad heading “global climate change”. In spite of concerted efforts by the governments to check deforestation, large forest areas are already degraded and the remaining areas are in various stages of
Situation Awareness Method and Simulation Test of UAVs and Observer Wards Air-Ground Cooperation

Target localization and situational awareness of air-ground cooperation have become an important trend and inevitable choice in the field of unmanned equipment and swarm intelligence. The mode framework and related algorithms of air-ground cooperation between UAV and ground vehicles, ground personnel, unmanned vehicles, etc. have also become a research hotspot in the field of artificial intelligence. The purpose of this study is to explore a cheap, novel and efficient UAV air-ground cooperation mode. Specifically, this study proposes a “UAV-Observer Ward” air-ground cooperation mode, and creatively designs a new type of equipment called “observer ward” (small volume long-distance wireless human body detector), and also designs a flexible cooperation framework to let the observer ward discover and guide the UAV s to intercept the detected target. “Target Interception Rate” is creatively introduced as the evaluation factor of the equipment efficiency of the perception system. Based on this, our research proposes an air-ground cooperation situational awareness algorithm that considering information transmission. The algorithm senses and predicts the moving trajectory of the intrusion target through the communication between several observer wards, and guides the UAV cluster to intercept the intrusion target more intelligently. This study uses simulation tests based on AnyLogic software to verify the superiority of the “UAV-Observer Ward” air-ground cooperation mode. The experimental results show that the “target interception rate” of the new air-ground cooperation mode is 2.87, 2.81, 3.04, 4.37 and 4.31 times higher than that of the traditional mode, respectively, at the target intrusion speeds of 10rlh, 20r/h, 30r/h, 60r/h and 90r/h.
XIDER: First Prototypes and Results with the Digital Integration Readout Scheme

The ESRF Extremely Brilliant Source (EBS) is the first fourth-generation high-energy synchrotron facility worldwide. Next-generation sources of synchrotron radiation pose significant challenges for 2D pixelated X-ray detectors. In particular, scattering and diffraction experiments require fast detectors with a high dynamic range, from single-photon sensitivity to pile-up conditions under high photon fluxes of up to 109 Mphpixel−1 s−1. The XIDER project aims to fulfil the needs of these high-energy applications by implementing a novel on-chip incremental digital integration readout scheme. XIDER detectors seek to operate efficiently under the high-flux EBS beam of up to 100keV photons, with a time resolution that can cope with near-continuous and pulsed beams of up to 5.7MHz. The first XIDER prototypes have been recently assembled by hybridising 1mm cadmium-telluride sensors with a custom-designed ASIC readout chip in TSMC 65nm, realising the first implementation of an on-chip digital integration readout scheme. These prototypes are functional and characterisation measurements have begun, including irradiation using X-ray sources.
Game-based Evaluation of Whole-body Movement Functions with CoM Stability and Motion Smoothness

In the field of rehabilitation, there is a great demand for an automatic and quantitative evaluation system. The balance ability is an essential factor for motor function evaluation related to posture control. Although balance ability is assessed using various indices in current clinical situations, most of previous studies developing an automatic evaluation system have used only a single particular index for balance evaluation. In this study, we developed a system that evaluates whole-body motor function using multiple indices based on the trajectory of the center of mass (CoM) and the motion smoothness. The system is inexpensive and little physical burden because the evaluation indices are calculated from the skeleton tracked by Kinect in a game environment. We attempt to capture the differences in individual motor functions which are difficult to be detected by qualitative visual observation.
Automatic Archiving and Classification of Positron Emission Tomography Images using Deep Learning Models at Different Scan Times

Positron Emission Tomography provides physicians with important metabolic and semi-quantitative information. With the growing interest in applications of deep learning methods in medical imaging, a tool for archiving and categorizing images based on the body region is needed for the data preparation phase of the deep learning applications. We developed deep learning models for classifying images based on the body region (Quadratic Classification) and radiopharmaceuticals used in imaging (Binary Classification) for different scan times. Sensitivity and Specificity for Binary Classification (68Ga-PSMA and 18F-FDG) are in the range of 0.9933 – 0.9990. For the Quadratic Classification of 18F-FDG, Sensitivity and Specificity are 0.9873 ± 0.0114 and 0.9958± 0.0038, respectively. Likewise, the sensitivity and specificity of 68Ga-PSMA are 0.9788± 0.0123 and 0.9929 ± 0.0041, respectively. Results are promising and are indicative of the models' capability as a complementary tool for archiving and classifying images for further investigations.
Calcium Activation of Parvalbumin Neurons Induced by Electrical Motor Cortex Stimulation

Electrical motor cortex stimulation (EMCS) has been used for Parkinson's Disease (PD) treatment. Some studies found that distinct cell types might lead to selective effects. As the largest subgroup of interneurons, Parvalbumin (PV) neurons have been reported to be involved in the mechanisms of therapeutic efficacy for PD treatment. However, little is known about their responses to the EMCS. In this study, we used in-vivo two-photon imaging to record calcium activities of PV neurons (specific type) and all neurons (non-specific type) in layer 2/3 primary motor cortex (MI) during EMCS with various stimulus parameters. We found PV neurons displayed different profiles of activation property compared to all neurons. The cathodal polarity preference of PV neurons decreased at a high-frequency stimulus. The calcium transients of PV neurons generated by EMCS trended to be with large amplitude and short active duration. The optimal activation frequency of PV neurons is higher than that of all neurons. These results improved our understanding of the selective effects of EMCS on specific cell types, which could bring more effective stimulation protocols for PD treatment.
Methods Used to Estimate EEG Source-Space Networks: A Comparative Simulation-Based Study

Along with the study of the brain activity evoked by external stimuli, an important advance in current neuroscience involves understanding the spontaneous brain activity that occurs during resting conditions. Interestingly, the identification of the connectivity patterns in “resting-state” has been the subject of a great number of electrophysiology-based studies. In this context, the Electroencephalography (EEG) source connectivity method enables estimating resting-state cortical networks from scalp-EEG recordings. However, there is still no consensus over a unified pipeline adapted in all cases (e.g., type of task, a priori on studied networks) and numerous methodological questions remain unanswered. In order to address this problem, we simulated, using neural mass models, EEG data corresponding to the default mode network (DMN), the most widely studied resting-state network, and tested the effect of different channel densities, two inverse solutions and two functional connectivity measures on the correspondence between the reconstructed networks and the reference networks. Results showed that increasing the number of electrodes enhances the accuracy of the network reconstruction, and that eLORETA/PLV led to better accuracy than other inverse solution/connectivity measure combinations in terms of the correlation between reconstructed and reference connectivity matrices. This work has a wide range of implications in the field of electrophysiology connectomics, and is a step towards a convergence and standardization of approaches in this emerging field.
Computer-aided Detection of Lesions from Coronary Angiograms Based on Contrast Learning Technique

Coronary artery disease is one of the prevalent cardiovascular diseases in the world. Clinically, coronary artery angiography (CAG) is the most efficient diagnostic tool for detecting the stenosis caused by the presence of coronary lesions. Here, we proposed a simple but efficient methodology for predicting the coronary arterial block. The technique of classifying the angiograms collected from 369 patients is implemented using the contrast learning approach. ResNet 152 V2 is used as the deep network. Region of interest (ROI) is found for the diseased arteries for deciding the type of treatment procedure. Four different losses were implemented in this two-level classification technique. This framework achieved an accuracy of 0.81 recall of 0.76, precision of 0.86, specificity of 0.87, and F-score of 0.80. A comparative study with the state-of-the-art is carried out to establish the advantage of the proposed method. This computer-aided approach could be implemented by clinicians quite easily. Clinical relevance—An easy, simple, and fast technique of detecting and deciding treatment of coronary artery stenosis was studied using contrast learning and ROI. A computerised model was designed which can draw conclusion from CAGs even in the absence of clinical annotations.
Deep Generative Modelling for Enhanced Monte Carlo Simulation of Radionuclide Imaging Data

Monte Carlo simulations are widely used in radionuclide imaging, including for modelling radionuclide imaging systems, as well as for the development of new image reconstruction algorithms. However, discrepancies in data quality (e.g. spatial resolution) can be observed between simulated and experimental data due to an inability to fully model many bespoke effects, including charge-to-signal conversion processes and detector imperfections. In this study, a deep generative modelling framework is proposed for the enhancement of GATE simulations through the use of cycle-consistent generative adversarial networks (CycleGAN). The networks can be trained in an unsupervised manner to learn the mapping between simulated and experimentally obtained data, obviating the need for paired training data which is difficult to obtain in radionuclide imaging studies. The feasibility of the method was assessed for sensor array outputs from a CMOS intraoperative probe intended for single photon imaging/detection for cancer surgery. Overall, the proposed network was able to learn the distribution of images in both the simulated and the experimental domains. Through the analysis of the Frechet inception distance (FID) metric, the network was able to achieve a reduction of 95% in the FID score compared to purely GATE simulated data, indicating far greater consistency with measured data. This framework presents a method to generate training data which is unavailable to obtain experimentally.
Individuals with moderate to severe hand impairments may struggle to use EMG control for assistive devices

Neurological trauma, such as stroke, traumatic brain injury (TBI), spinal cord injury, and cerebral palsy can cause mild to severe upper limb impairments. Hand impairment makes it difficult for individuals to complete activities of daily living, especially bimanual tasks. A robotic hand orthosis or hand exoskeleton can be used to restore partial function of an intact but impaired hand. It is common for upper extremity prostheses and orthoses to use electromyography (EMG) sensing as a method for the user to control their device. However some individuals with an intact but impaired hand may struggle to use a myoelectrically controlled device due to potentially confounding muscle activity. This study was conducted to evaluate the application of conventional EMG control techniques as a robotic orthosis/exoskeleton user input method for individuals with mild to severe hand impairments. Nine impaired subjects and ten healthy subjects were asked to perform repeated contractions of muscles in their forearm and then onset analysis and feature classification were used to determine the accuracy of the employed EMG techniques. The average accuracy for contraction identification across employed EMG techniques was 95.4% ± 4.9 for the healthy subjects and 73.9% ± 13.1 for the impaired subjects with a range of 47.0% ± 19.1 – 91.6% ± 8.5. These preliminary results suggest that the conventional EMG control technologies employed in this paper may be difficult for some impaired individuals to use due to their unreliable muscle control.
Characterization and Simulation of Interfacial Adhesion in Embedded Device Package

In order to improve the performance and long-term reliability of the embedded package, this study carried out a failure risk assessment for the hazardous area of the package. Firstly, experiments are taken to measure the adhesive strength of the key material interfaces (Polyimide/Passivation) in the packaging structure. The critical energy release rate (Gc) of the interface is derived from the measured adhesive strength. Then, finite element simulation is conducted to obtain the energy release rate of the interface cracks at the easily failed parts. The two critical energy release rates gained by the experiment and simulation are compared, and the failure risk of each interface in the temperature cycle test can be evaluated.
A Meta-Transfer Learning Approach to ECG Arrhythmia Detection

Automatic classification of cardiac abnormalities is becoming increasingly popular with the prevalence of ECG recordings. Many signal processing and machine learning algorithms have shown the potential to identify cardiac ab-normalities accurately. However, most of these methods heavily rely on a large amount of relatively homogeneous datasets. In real life, chances are that there is not enough data for a specific category, and regular deep learning may fail in this scenario. A straightforward intuition is to use the knowledge learned from previous data to solve the problem. This idea leads to learning-to-learn: extrapolating the knowledge accumulated from the old dataset and using it in a different but somewhat related dataset. In this way, we do not need to have considerable data to learn the new task because the underlying features of the old and new datasets resemble one another. In this paper, a novel machine learning method is introduced to solve the ECG arrhythmia detection problem with a limited amount of data. The proposed method combines the popular techniques of meta-learning and transfer learning. It is shown that our method achieves much higher accuracy in ECG arrhythmia classification with a few data and learns the new task faster than regular deep learning.