Miracle-dz/UniFAR-FTUs huggingface dataset:
A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems

Multimodal transportation has become a main focus of logistics systems due to environmental concerns, road safety issues, and traffic congestion. Consequently, research and policy interests in multimodal freight transportation problems are increasing. However, there are major challenges in the development of multimodal transportation associated with inherent risks and numerous uncertainties. Since risks are potential threats that directly impact logistics and transportation systems, comprehensive risk analysis should be carried out. Risk analysis is a critical process of identifying and analyzing significant issues to help industry mitigate those risks. However, identifying and prioritizing risks is more complex because of the ambiguity of the relevant data. This study proposes the integration of the fuzzy analytic hierarchy process (FAHP) and data envelopment analysis (DEA) for identifying and assessing quantitative risks. The proposed FAHP-DEA methodology uses the FAHP method to determine the weights of each risk criterion. The DEA method is employed to evaluate the linguistic variables and generate the risk scores. The simple additive weighting (SAW) method is used to aggregate risk scores under different risk criteria into an overall risk score. A case study of the coal industry demonstrates that the proposed risk analysis model is practical and allows users to more accurately prioritize risks while selecting an optimal multimodal transportation route. The process raises user’s attention to the high-priority risks and is useful for industries in optimizing a multimodal transportation route under risk decision criteria.
Multi-scale U-net with Edge Guidance for Multimodal Retinal Image Deformable Registration

Registration of multimodal retinal images is of great importance in facilitating the diagnosis and treatment of many eye diseases, such as the registration between color fundus images and optical coherence tomography (OCT) images. However, it is difficult to obtain ground truth, and most existing algorithms are for rigid registration without considering the optical distortion. In this paper, we present an unsupervised learning method for deformable registration between the two images. To solve the registration problem, the structure achieves a multi-level receptive field and takes contour and local detail into account. To measure the edge difference caused by different distortions in the optics center and edge, an edge similarity (ES) loss term is proposed, so loss function is composed by local cross-correlation, edge similarity and diffusion regularizer on the spatial gradients of the deformation matrix. Thus, we propose a multi-scale input layer, U-net with dilated convolution structure, squeeze excitation (SE) block and spatial transformer layers. Quantitative experiments prove the proposed framework is best compared with several conventional and deep learningbased methods, and our ES loss and structure combined with Unet and multi-scale layers achieve competitive results for normal and abnormal images.
Subject Independent Dementia Risk Prediction Models Using Paralinguistic and Memory Test Features with Feature Warping

Clinical and biological changes during the prodromal stages of dementia are both complicated and expensive. A biomarker for cognitive reserve exposure would be highly useful as a dementia risk predictor, but has eluded researchers. Speech, which exhibits disfluencies due to dementia, is a good candidate as it is easy to collect and non-invasive. However, previous studies have only looked at the impact of dementia on speech after diagnosis. Here we extend our previous work that showed paralinguistic features extracted from audio recordings of older participants completing the LOGOS episodic memory test can be used to discriminate between high vs low cognitive reserve, hence low vs high risk of dementia. Specifically, we use the clinically validated Lifetime of Experiences Questionnaire (LEQ) to refine our ground truth estimate of cognitive reserve instead of an abridged version. Also, we improve the generalizability of our system by using feature warping to normalize across speakers. Our k-nearest neighbours (KNN) based classifier achieved an accuracy of 84% when trained with paralinguistic features alone and 91% with paralinguistic and episodic memory features.Clinical Relevance— This establishes efficacy of using speech from older participants completing the LOGOS episodic memory test to estimate risk of dementia.
Machine Learning Approaches For Improved Continuous, Non-occlusive Arterial Pressure Monitoring Using Photoplethysmography

Arterial pressure (AP) is a crucial biomarker for cardiovascular disease prevention and management. Photoplethysmography (PPG) could provide a novel, paradigm-shifting approach for continuous, non-obtrusive AP monitoring, comfortably integrated in wearable and mobile devices; yet, it still faces challenges in accuracy and robustness. In this work, we sought to integrate machine learning (ML) techniques into a previously established, clinically-validated classical approach (oBPM®) to develop new accurate AP estimation tools based on PPG, and at the same time improve our understanding of the underlying physiological parameters. In this novel approach, oBPM® was used to pre-process PPG signals and robustly extract physiological features, and ML models were trained on these features to estimate systolic AP (SAP). A feature relevance analysis showed that reference (calibration) information, followed by various morphological parameters of the PPG pulse wave, comprised the most important features for SAP estimation. A performance analysis then revealed that LASSO-regularized linear regression, Gaussian process regression and support vector regression are effective for SAP estimation, particularly when operating on reduced feature sets previously obtained with e.g. LASSO. These approaches yielded substantial reductions in error standard deviation of 9–15% relative to conventional oBPM®. Altogether, these results indicate that ML approaches are well-suited, and promising tools to help overcoming the challenges of ubiquitous AP monitoring.
Hardware and Power-Efficient Compression Technique Based on Discrete Tchebichef Transform for Neural Recording Microsystems

In this paper a new compression technique based on the discrete Tchebichef transform is presented. To comply with strict on-implant hardware implementation requirements, such as low power dissipation and small silicon area consumption, the discrete Tchebichef transform is modified and truncated. An algorithm is proposed to generate approximate transform matrices capable of truncation without suffering from destructive energy leakage among the coefficients. This is achieved by preserving orthogonality of the basis functions that convey majority portion of the signal energy. Based on the presented algorithm, a new truncated transformation matrix is proposed, which reduces the hardware complexity by up to 74% compared to that of the original transform. Hardware implementation of the proposed neural signal compression technique is prototyped using standard digital hardware. With pre-recorded neural signals as the input, compression rate of 26.15 is achieved while the root-mean-square of error is kept as low as 1.1%. Clinical Relevance— This paper proposes a technique for data compression in high-density neural recording brain implants, along with a power- and area-efficient hardware implementation. From among clinical applications of such implants one can point to neuro-prostheses, and brain-machine interfaces for therapeutic purposes.
Responsiveness of Retinal Ganglion Cells Through Frequency Modulation of Electrical Stimulation: A Computational Modeling Study*

Electrical stimulation of surviving retinal neurons has proven effective in restoring sight to totally blind patients affected by retinal degenerative diseases. Morphological and biophysical differences among retinal ganglion cells (RGCs) are important factors affecting their response to epiretinal electrical stimulation. Although detailed models of ON and OFF RGCs have already been investigated, here we developed morphologically and biophysically realistic computational models of two classified RGCs, D1-bistratified and A2-monostratified, and analyzed their response to alternations in stimulation frequency (up to 200 Hz). Results show that the D1-bistratified cell is more responsive to high frequency stimulation compared to the A2-monostratified cell. This differential RGCs response suggests a potential avenue for selective activation, and in turn different encoded percept of RGCs.
Discrete Optimal Reconstruction Distributions for Itakura-Saito Distortion Measure

The optimal reconstruction distribution achieving the rate-distortion function is elusive except for limited examples of sources and distortion measures if the rate-distortion function is strictly greater than the Shannon lower bound. In this paper, focusing on the Itakura-Saito distortion measure, we prove that if the Shannon lower bound is not tight, the optimal reconstruction distribution is purely discrete. Combined with the fact that the Shannon lower bound is tight for the gamma source, this result shows that it is the only source that has continuous optimal reconstruction distributions for the range of entire positive rate.
Design and Experimental Research of Implantable Lower Esophageal Sphincter Stimulator Based on Android Bluetooth*

The aim of this study is to design an implantable Lower Esophageal Sphincter (LES) stimulator connected and controlled by an Android Bluetooth for the treatment of the gastroesophageal reflux disease (GERD). Then the animal experiments are carried out to evaluate the function of the system. The LES stimulator is composed of an external controller, an Android application (APP) via a smart phone and an implantable electronic device (IED). The external controller is designed to receive the settings parameters information sent by the Android APP via a Bluetooth module, and then is programmed to generate specific electrical stimulation pulses to the LES. The Android APP controls the start and stop of stimulation and the settings of stimulation parameters. The in vivo IED consists of a bipolar stimulating lead, a bipolar head connector and a receiving module. The bipolar stimulating lead is constructed of biocompatible materials: platinum-iridium electrodes which are coated with parylene and an outer silicone rubber sheathing. The size of the receiving module has been significantly decreased to 20×20×2 mm3, which is packaged by polydimethylsiloxane (PDMS) and proposed to deliver stimulation pulses from the external controller to the implantable lead. The one-month implantation experiment on rabbits has been performed to evaluate the LES stimulator. The results indicate that the proposed LES stimulator meets the requirements of the functions, effectiveness and safety.
Effect of stimulation duration to the existence of initial dip

In this paper, we investigate the effect of stimulation durations on the hemodynamic responses (HRs) in the somatosensory cortex. In doing so, the relationship between stimulation duration and the initial dip is also investigated. The HRs are measured using functional near-infrared spectroscopy (fNIRS). The HR signals related to finger poking are acquired from the left somatosensory cortex. Two different stimulation durations (i.e., 1 and 5 sec) were tested in this study. From the results of the study, it is concluded that the stimulation duration of 1 sec (short stimulus) evokes initial dip in the somatosensory cortex, but it disappears as the stimulation duration gets longer. Therefore, the 1-sec stimulation duration can serve the purpose of the fNIRS-based brain-computer interface.
Impact of Graded Back-Barrier on Linearity of Recessed Gate InAlN/GaN HEMT

Work presented in this paper reports the impact of graded AlGaN back-barrier on the linearity performance of lattice-matched InAlN/AlN/GaN recess gate device. Specifically, a comparison is made on the linearity of the proposed device with and without a graded back barrier layer. Various device performance matrices namely transconductance (gm) and its higher-order derivatives, second and third-order voltage intercept point (VIP2, VIP3), 3rd order input intercept point (IIP3), 3rd inter-modulation distortion (IMD3) and 1-dB compression point have been investigated and discussed for the proposed device architecture. Results obtained from TCAD simulation of the proposed device confirms that by using graded back-barrier the linearity performance matrices of the device are improved, which further shows that the proposed device is also suitable for wireless applications.
Sensory Feedback in Upper Limb Amputees Impacts Cortical Activity as Revealed by Multiscale Connectivity Analysis

Sensory feedback in upper limb amputees is crucial for improving movement decoding and also to enhance embodiment of the prosthetic limb. Recently, an increasing number of invasive and noninvasive solutions for sensory stimulation have demonstrated the capability of providing a range of sensations to upper limb amputees. However, the cortical impact of restored sensation is not clearly understood. Particularly, understanding the cortical connectivity changes at multiple scales (nodal and modular) in response to sensory stimulation, can reveal crucial information on how amputees brain process the sensory stimuli. Using Electroencephalography (EEG) signals, we compared the cortical connectivity network in response to sensory feedback provided by targeted transcutaneous electrical nerve stimulation (tTENS) in an upper limb amputee during phantom upper limb movements. We focused our cortical connectivity analysis on four functional modules comprising of 20 brain regions that are primarily associated with a visually guided motor task (visual, motor, somatosensory and multisensory integration (MI)) used in this study. At the modular level, we observed that the hubness (a graph theoretic measure quantifying the importance of brain regions in integrating brain function) of the motor module decreases whereas that of the somatosensory module increases in presence of tTENS feedback. At the nodal level, similar observations were made for the visual and MI regions. This is the first work to reveal the impact of sensory feedback at multiple scales in the cortex of amputees in response to sensory stimulation.
Message-Passing Based Channel Estimation for Reconfigurable Intelligent Surface Assisted MIMO

In this paper, we study the channel acquisition problem in a reconfigurable intelligent surface (RIS) assisted multiuser multiple-input multiple-output (MIMO) system, where an RIS with fully passive phase-shift elements is deployed to assist the MIMO communication. The state-of-the-art channel acquisition approach in such a system estimates the cascaded transmitter-to-RIS and RIS-to-receiver channels by adopting excessively long training sequences. To estimate the cascaded channels with an affordable training overhead, we formulate the channel estimation problem as a matrix-calibration based matrix factorization task. By exploiting the information on the slow-varying channel components and the hidden channel sparsity, we propose a novel message-passing based algorithm to factorize the cascaded channels.
Application of Neural Network Based on Flexible Neural Tree in Personal Credit Evaluation

With the development of society and economy, the business scope of banks is constantly expanding, especially the credit business is getting stronger and bigger, and then the credit evaluation problem appears. We use the Flexible Neural Tree-Neural Network (FNT-NN) method to classify bank customer credit card default data and find that a neural network constructed based on a flexible neural tree can achieve better classification results. FNT-NN is based on the FNT solution to construct a neural network and use Back Propagation (BP) neural network algorithm to optimize the connection weights. The flexible neural tree uses a tree structure coding. The parameters are usually optimized using the Particle Swarm optimization (PSO) algorithm. It can find a globally optimal network suitable for the data set. By comparing logistic regression, support vector machine and decision tree, the experimental results show that FNT-NN can get better results.
DC Electric Arc Furnace Modelling for Power Quality Indices Assessment

The operation of an Electric Arc Furnace (EAF) determines power quality disturbances in terms of voltage fluctuations and waveform distortions, due to the particular nature of the heating process and of the electric arc phenomenon. The analysis of DC EAFs and their induced power quality disturbances may be conveniently posed within a chaotic modeling framework. This paper presents a procedure for tuning the parameters of a chaotic model for the DC electrical arc. The procedure is based on a Simulink representation of the DC EAF installation and on the Chua’s model to represent the arc chaotic behaviour. The parameters of the Chua’s model are iteratively tested and optimized to minimize the errors in the evaluation of the spectral content of current waveforms at the point of common coupling. Data measured at the point of common coupling of an actual DC EAF are considered to carry out numerical simulations aimed at verifying the effectiveness of the proposed tuning procedure.
Identifying the Predominant Site of Upper Airway Collapse in Obstructive Sleep Apnoea Patients Using Snore Signals

Knowledge regarding the site of airway collapse could help in choosing an appropriate structure-specific or individualized treatment for obstructive sleep apnoea (OSA). We investigated if the audio signal recorded during hypopnoea (partial obstruction) events can predict the site-of-collapse of the upper airway. In this study, we designed an automatic classifier that predicts the predominant site of upper airway collapse for a patient as "lateral wall", "palate", "tongue-based" related collapse or "multi-level" site-of-collapse by processing of the audio signal. The probable site-of-collapse was determined by manual analysis of the shape of the airflow signal during hypopnoea, which has been reported to correlate with the site of collapse. Audio signal was recorded simultaneously with full-night polysomnography during sleep with a ceiling microphone. Various time and frequency features of the audio signal were extracted to classify the audio signal into lateral wall, palate and tongue-base related collapse. We introduced an unbiased process using nested leave-one patient-out cross-validation to choose the optimal features. The classification was carried out with a multi-class linear discriminant analysis classifier. Performance of the proposed model showed that our automatic system can achieve an overall accuracy of 65% for determining the predominant site-of-collapse for all site-of-collapse classes and an accuracy of 80% for classifying tongue/non-tongue related collapse. Our results indicate that the audio signal recorded during sleep can be helpful in identifying the site-of-collapse and therefore could potentially be used as a new tool for deciding appropriate treatment for OSA.
Pollinators and visitors of Aroid inflorescences.

Data on aroid pollinators was first summarized by Grayum (1984) who documented 35 genera and about 90 species. A second summary was published in 1997 in The Genera of Araceae (Mayo et al., 1997) with 38 genera and less than 100 species listed including data from Grayum 0986, 1990). This paper brings the reference list up to date since 1997, documenting the pollinators of 49 genera and about 125 species. These numbers are still very low in comparison with the diversity of the Araceae family which contains 105 genera and about 3,300 species. Some questions on aroid pollination are developed in the discussion.
A Dynamic Bayesian Network Approach to Behavioral Modelling of Elderly People during a Home-based Augmented Reality Balance Physiotherapy Programme

In this study, we propose a dynamic Bayesian network (DBN)-based approach to behavioral modelling of community dwelling older adults at risk for falls during the daily sessions of a hologram-enabled vestibular rehabilitation therapy programme. The component of human behavior being modelled is the level of frustration experienced by the user at each exercise, as it is assessed by the NASA Task Load Index. Herein, we present the topology of the DBN and test its inference performance on real-patient data.Clinical Relevance— Precise behavioral modelling will provide an indicator for tailoring the rehabilitation programme to each individual’s personal psychological needs.
Large-Scale Analysis of the Head Proximity Effects on Antenna Performance Using Machine Learning Based Models

Owing to the variations in subject-specific body morphology and anatomy, the radiation performance of a wireless device in the presence of human body is different across subjects. To quantify the inter-subject variations, a large number of highly realistic human models are required. One recent approach is the fast development of body models directly from medical images with machine learning. In this study, a total of eighteen anatomical head models were developed using a fast machine learning approach and were then adopted for large-scale evaluation of the inter-subject variations in antenna performance. The antenna impedance, return loss (RL), total radiated power (TRP), directivity, radiation patterns, and specific absorption rate (SAR) were investigated. The results show rather large variations in impedance, RL, and SAR across subjects, while TRP, directivity, and radiation pattern are less likely to be affected by internal tissue distributions when compared with homogeneous models.
Wall shear stress alterations at left atrium and left atrial appendage employing abnormal blood velocity profiles

In this study, we developed and analyzed different patient-specific 3D anatomical models of the left atrium including left atrial Appendage, in order to investigate the local hemodynamics. Particularly, we focused on the left atrial appendage and its impact on thrombus formation due to wall shear stress alterations. A 3D semi-automated reconstruction approach was carried out to segment and reconstruct the left atrium from CT scans. Six different patients were studied applying their patient-specific clinical data. Three different velocity profiles simulated for each patient case, representing one normal and two abnormal conditions. Simulations varied significantly according to different appendage morphologies. Our scope is to describe the hemodynamic behavior at the left atrium and the left atrial appendage according to different blood velocities based on their anatomic variety (chicken wing 0.14 m/s, windsock 0.10, cactus 0.08, and cauliflower 0.04). Wall shear stress results were demonstrated and correlated with the velocities and the thrombus formation inside the appendage cavity.
Differential Evolution Algorithm Based Energy Management of Residential Microgrid Under Appliance Scheduling DSM

Depending on the appliance scheduling strategy of the demand side management (DSM), this article elucidates a new mixed-integer linear programming-based method to optimize the operation of a residential microgrid furnished with several deferrable but non-interruptible devices. Unlike previous literatures here flexible loads are not curtailed but delayed and distributed over the entire operation time window, specified by the users. The solution process is designed based on differential evolution (DE) algorithm. Numerical simulation on a practical residential area data proves the efficacy of the proposed strategy in both grid tied and islanded for certain hours scenarios. Further, the simulations results are compared with other heuristic search techniques to prove better performance of DE. Again, convergence of the DE algorithm is validated by linearizing the problem and solved using mixed integer linear programming commercial solver present in MATLAB.