Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Cosserat Rod Theory to Configuration Estimation of Coionoscope

Biologically-inspired continuum robots have attracted much attention in variety of fields, especially in medical applications due to their compliance and high dexterity. This paper presents an application to colonoscopy surgery. Beam mechanics-based model is used to model the shape of the colonoscope. By measuring the force exerted on the distal end of the endoscope, we could reconstruct the shape of the endoscope in simulation. Furthermore, comparison of the simulation and experiment result shows that when the length of the endoscope becomes longer, the matching error becomes bigger. We found that this error can be reduced by using different material property of the distal end of the endoscope.
Anomaly Detection using System Identification Techniques

As cyber-physical systems are becoming more human independent any anomaly or system failure should be detected and solved in an autonomous way. In the last decade significant research was performed to find more intelligent and accurate anomaly detection methods. Most of these methods are analyzing only the output(s) of a system hoping to find some inconsistencies in the data stream. Our attempt is to consider the system’s model as well and develop an anomaly detection methodology that tries to identify slight changes in the behavior of the system, detectable through model changes. The key part of our detection method is the system identification step through which we compute the system’s model considered as a differential equation between input and output signals or as an autoregression formula. We demonstrate the feasibility of the proposed method through a simulated and a real-life example.
Opportunities and Challenges in the Industrial Internet of Things based on 5G Positioning

This paper discusses several features of 5G positioning in the context of applications for the Industrial Internet of Things (IIoT) which demand high accuracy of position information. The main opportunities to come with 5G networks, such as huge available spectrum, small cell networks, Multiple Input Multiple Output antennas and beamforming are summarized, and the challenges in the context of robot 5G positioning are pointed out. A case study for the localization of an indoor robot in a multi-wall multi-floor scenario is presented, based on various carrier frequencies and access node densities. We find out that sub-meter positioning accuracy required for most of the future industrial applications is theoretically achievable via a combination of small cell networks, mm Wave carriers and antenna arrays, but practical issues such as node synchronization, connectivity and ultra dense network deployment costs have to be tackled.
Robust Formation Control for Multiple Quadrotors Subject to Nonlinear Dynamics and Disturbances

In this paper, the robust formation control problem is dealt with for multiple unmnanned quadrotors, whose dynamics is subject to underactuation, nonlinearities, and external disturbances. A distributed robust controller is developed, which includes a position controller and an attitude controller. Simulation results of four quadrotors are given to show the effectiveness of the designed formation control protocol.
A Multi-Objective Adaptive Control Framework in Autonomous DC Microgrid

This paper presents a multi-objective adaptive power management scheme for an autonomous dc microgrid which consists of a novel adaptive gain-based proportionate charging/discharging strategy in order to provide energy balancing between battery energy storage systems (BESSs) of different capacities. As BESSs are a key component to maintain reliability and stability in an autonomous microgrid, control measures for longevity of these units should be properly investigated. Following the recommended way of charging for BESSs, i.e., constant current/constant voltage (CV) charging, the conventional methods undergo mode switching using a floating reference value to control CV charging which does not guarantee zero current when the battery is fully charged. To account for this issue, a novel progressive de-rating control scheme for PV in a seamless fashion to carry out CV charging of BESSs is done using the adaptive mechanism introduced by model reference adaptive control scheme for optimized power management between sources and loads. Furthermore, emergency events such as load shedding and load following operation of PV have also been discussed in detail. The proposed control strategy is simulated in MATLAB/SIMULINK environment and tested on a 1 kVA FPGA-based experimental prototype to validate the control approach under different scenarios.
A System to Generate Robot Emotional Reaction for Robot-Human Communication

Intelligent system emotion modeling process is dependent on various internal and external. This paper describes an emotional system for intelligent agents, robots specifically, that proposes the idea of emotional learning and generation system to realize human-like emotions in robots. Using basic emotions, a way to construct complex emotions is proposed. Results from experiments indicate that the system has a correlation with perceiving and generating emotions that are parallel with real human emotions.
Scenario Map Based Stochastic Unit Commitment

In the power system stochastic unit commitment (SUC) model, the uncertainty and variability of the renewable energy generation, such as wind power, are usually expressed by multiple scenarios. The performance of SUC is driven by how well the selected scenarios represent the stochastic nature, since too few scenarios may lose or distort the uncertainty or variability characteristics, while too many scenarios usually cause computational difficulty. This paper proposes a novel scenario representation method referred to as scenario mapping technology (SMT), which is able to compact large amount of scenarios while preserving the uncertainty and variability features of wind power as much as possible. Specifically, SMT proposes to combine similar wind power generation into a state at each period and contains the possible multiple transitions linking the states in adjacent periods. A novel two-stage SUC model is proposed based on SMT. The numerical results demonstrate a solid improvement of the performance for the SMT-based SUC model on both cost-efficiency and calculation efficiency, compared with traditional two-stage SUC model based on the scenario reduction technique.
Resilience of Atlantic Slippersnail Crepidula fornicata Larvae in the Face of Severe Coastal Acidification

Globally, average oceanic pH is dropping, and it will continue to decline into the foreseeable future. This ocean acidification (OA) will exacerbate the natural fluctuations in pH that nearshore ecosystems currently experience daily, potentially pushing marine organisms to their physiological limits. Adults of Crepidula fornicata (the Atlantic slippersnail) have proven remarkably resilient to many environmental changes, which is perhaps not surprising considering that they are common intertidally, have a geographically large native range, and have been extremely successful at invading coastal water in many other parts of the world. However, the larvae of C. fornicata have been shown to be somewhat more vulnerable than adults to the effects of reduced pH. Research to date has focused on the physiological impacts of OA on C. fornicata larvae; few studies have explored shifts in gene expression resulting from changes in pH. In the present study, we examined the response of young (4- day old) C. fornicata larvae to two extreme OA treatments (pH 7.5 and 7.6) relative to pH 8.0, documenting both phenotypic and genome-wide gene expression responses. We found that rearing larvae at reduced pH had subtle influences on gene expression, predominantly involving downregulation of genes related to growth and metabolism, accompanied by significantly reduced shell growth rates only for larvae reared at pH 7.5. Additionally, 10-day old larvae that had been reared at the two lower pH levels were far less likely to metamorphose within six hours when exposed to inducer. However, all larvae eventually reached similarly high levels of metamorphosis 24 hours after settlement induction. Finally, there were no observed impacts of OA on larval mortality. Taken together, our results indicate that far future OA levels have observable, but not severe, impacts on C. fornicata larvae, which is consistent with the resilience of this invasive snail across rapidly changing nearshore ecosystems. We propose that future work should delve further into the physiological and transcriptomic responses of all life history stages to gain a more comprehensive understanding of how OA impacts the intertidal gastropod C. fornicata.
A Neural Language Model for Multi-Dimensional Textual Data based on CNN-LSTM Network

Language Modeling (LM) is a subtask in Natural Language Processing (NLP), and the goal of LM is to build a statistical language model that can learn and estimate a probability distribution of natural language over sentences of terms. Recently, many recurrent neural network based LM, a type of deep neural network for dealing with sequential data, have been proposed and achieved remarkable results. However, they only rely upon the analysis on the words occurred in the sentences even though every sentence contains various useful morphological information, such as Part-of-Speech (POS) tag that is necessary for constituting a sentence and can be used for an analysis as a feature. Although morphological information can be useful for LM, using that information as the input data to neural network based LM is not straightforward because adding features between words as a one-dimensional array can cause the vanishing gradient problem by increasing the time steps of recurrent neural network. In order to solve this problem, in this paper, we propose a CNN-LSTM based language model that deals with textual data regarding a multi-dimensional data with respect to the input of the network. To train this multi-dimensional input to Long-Short Term Memory (LSTM), we use a convolutional neural network (CNN) with a 1×1 filter for dimensionality reduction of input data to avoid the vanishing gradient problem by decreasing the time step between input words. In addition, our approach that uses multi-dimension data reduced by CNN can be used as a plugin with many customized LSTM based LM. On the Penn Treebank corpus, our model has shown improvement of the perplexity with not only vanilla LSTM but customized LSTM models.
A Treebank for the Healthcare Domain

This paper presents a treebank for the healthcare domain developed at ezDI. The treebank is created from a wide array of clinical health record documents across hospitals. The data has been de-identified and annotated for constituent syntactic structure. The treebank contains a total of 52053 sentences that have been sampled for subdomains as well as linguistic variations. The paper outlines the sampling process followed to ensure a better domain representation in the corpus, the annotation process and challenges, and corpus statistics. The Penn Treebank tagset and guidelines were largely followed, but there were many syntactic contexts that warranted adaptation of the guidelines. The treebank created was used to re-train the Berkeley parser and the Stanford parser. These parsers were also trained with the GENIA treebank for comparative quality assessment. Our treebank yielded great-er accuracy on both parsers. Berkeley parser performed better on our treebank with an average F1 measure of 91 across 5-folds. This was a significant jump from the out-of-the-box F1 score of 70 on Berkeley parser’s default grammar.
A Multistage Deep Learning Algorithm for Detecting Arrhythmia

Deep Belief Networks (DBN) is a deep learning algorithm that has both greedy layer-wise unsupervised and supervised training. Arrhythmia is a cardiac irregularity caused by a problem of the heart. In this study, a multi-stage DBN classification is proposed for achieving the efficiency of the DBN on arrhythmia disorders. Heartbeats from the MIT-BIH Arrhythmia database are classified into five groups which are recommended by AAMI. The Wavelet packet decomposition, higher order statistics, morphology and Discrete Fourier transform techniques were utilized to extract features. The classification performances of the DBN are 94.15%, 92.64%, and 93.38%, for accuracy, sensitivity, and selectivity, respectively.
Embedded Iterative Learning Contouring Controller Based on Precise Estimation of Contour Error for CNC Machine Tools

In machine tool application, a machined shape of workpiece should be considered in machine tool control systems. The contour error relates to it directly. Although most existing contouring controllers are based on feedback control, this paper proposes an embedded iterative learning contouring controller (EILCC) by considering actual contour error (ACE) which is the minimum distance between actual position and desired trajectory. The proposed method modifies original trajectory by ACE compensation with a PID manner, iteratively. The proposed controller can be directly applied to existing commercial machines without any change of their original controllers. The proposed method has been verified by simulation and experiment in commercial CNC machine tool with a right-angled sharp-corner trajectory which normally produces a large contour error around the corner due to unsmoothness. Comparison with a previous EILCC without ACE was done to evaluate its performance. Experimental results have shown that the maximum contour error was reduced by 78.8 % and 9.7 % as compared to the typical feedback controller (FBC) and EILCC without ACE (with estimated contour error), respectively.
Crowd Anomaly Event Detection in Surveillance Video Based on the Evolution of the Spatial Position Relationship Feature

While cameras are widely used in surveillance for public security, the abnormal crowd behavior detection becomes an important research issue in computer vision. The traditional behavior methods mainly focus on the detection of individual behaviors and rely on a fixed detection threshold that is learned from the sample set which leads to greater computational complexity. The performance is susceptible by the quantity and quality of samples and thus limits their applications. In this paper, a method based on the evolution of spatial position relationship (ESPR) features is proposed to detect there types of abnormal crowd behaviors in videos: crowd spread behavior, crowd gather behavior and crowd collective movement behavior. First, the optical flow features of each frame are extracted though an improved optical flow method. And then, the features are analyzed through fuzzy clustering to acquire the tightness level of all moving object. Finally, the abnormal event is detected based on the ESPR feature converted by tightness level frame by frame. In the proposed method, the learning stage is unnecessary. Experiments are conducted on public available datasets, namely, UMN dataset and Web dataset. The performance of the proposed method is evaluated in various scenarios.
Nonsuppurative (Aseptic) Meningoencephalomyelitis Associated with Neurovirulent Astrovirus Infections in Humans and Animals

Astroviruses are thought to be enteric pathogens. Since 2010, a certain group of astroviruses has increasingly been recognized, using up-to-date random amplification and high-throughput next-generation sequencing (NGS) methods, as potential neurovirulent (Ni) pathogens of severe central nervous system (CNS) infections, causing encephalitis, meningoencephalitis, and meningoencephalomyelitis. SUMMARY Astroviruses are thought to be enteric pathogens. Since 2010, a certain group of astroviruses has increasingly been recognized, using up-to-date random amplification and high-throughput next-generation sequencing (NGS) methods, as potential neurovirulent (Ni) pathogens of severe central nervous system (CNS) infections, causing encephalitis, meningoencephalitis, and meningoencephalomyelitis. To date, neurovirulent astrovirus cases or epidemics have been reported for humans and domesticated mammals, including mink, bovines, ovines, and swine. This comprehensive review summarizes the virology, epidemiology, pathology, diagnosis, therapy, and future perspective related to neurovirulent astroviruses in humans and mammals, based on a total of 30 relevant articles available in PubMed (searched by use of the terms “astrovirus/encephalitis” and “astrovirus/meningitis” on 2 March 2018). A paradigm shift should be considered based on the increasing knowledge of the causality-effect association between neurotropic astroviruses and CNS infection, and attention should be drawn to the role of astroviruses in unknown CNS diseases.
Comparative Evaluation of Desensitizing Efficacy of Dentifrice Containing 5% Fluoro Calcium Phosphosilicate versus 5% Calcium Sodium Phosphosilicate: A Randomized Controlled Clinical Trial

Background: A considerable number of agents are effective in the treatment of dentin hypersensitivity (DH). The present 2 months' randomized clinical trial compared the desensitizing efficacy of dentifrice containing 5% fluoro calcium phosphosilicate versus 5% calcium sodium phosphosilicate in participants with sensitive teeth. Materials and Methods: A total of 60 participants above 18 years of age with a history of DH who displayed a visual analog scale (VAS) score of ≥ 4 to both subjective and thermal sensitivity in at least two teeth at the qualifying as well as baseline visit were considered eligible. Participants were randomly allocated to one of the following dentifrices: 5% fluoro calcium phosphosilicate; 5% calcium sodium phosphosilicate; and a standard dentifrice containing fluoride. Sensitivity scores (VAS) were measured at baseline, immediately after scaling and root planning, at 15, 30, and at 60 days. Results: A statistically significant reduction in symptoms for all treatment groups from baseline to 15, 30, and 60 days for both measures of sensitivity was observed. A significant reduction in DH with time for all the variables during the 8 weeks of the active phase of the study independent of treatment groups was noted. However, the fluoro calcium phosphosilicate group showed a higher degree of effectiveness in reducing DH, followed by calcium sodium phosphosilicate then standard fluoride dentifrices. Conclusion: Under the conditions of a clinical trial, the fluoro calcium phosphosilicate group showed a comparable reduction in the symptoms of DH.
A Skeleton-Free Fall Detection System From Depth Images Using Random Decision Forest

Interest in enhancing medical services and healthcare is emerging exploiting recent technological capabilities. An integrable fall detection sensor is an essential component toward achieving smart healthcare solutions. Traditional vision-based methods rely on tracking a skeleton and estimating the change in height of key body parts such as head, hips, and shoulders. These methods are often challenged by occluded body parts and abrupt posture changes. This paper presents a fall detection system consisting of a novel skeleton-free posture recognition method and an activity recognition stage. The posture recognition method analyzes local variations in depth pixels to identify the adopted posture. An input depth frame acquired using a Kinect-like sensor is densely represented using a depth comparison feature and fed to a random decision forest to discriminate among standing, sitting, and fallen postures. The proposed approach simplifies the posture recognition into a simple pixel labeling problem, after which determining the posture is as simple as counting votes from all labeled pixels. The falling event is recognized using a support vector machine. The proposed approach records a sensitivity rate of 99% on synthetic and live datasets as well as a specificity rate of 99% on synthetic datasets and 96% on popular live datasets without invasive accelerometer support.
Analytical and experimental study of effective parameters on process temperature during cortical bone drilling

Bone drilling process is a prominent step of internal fixation in orthopedic surgeries. Process forces, leading to chip production, produce heat in the vicinity of the drilled bore and increase the probability of necrosis phenomenon. In this article, an analytical model to predict process temperature is presented based on Sui and Sugita model. This heat transfer model is the combination of a heat equilibrium equation for tool–chip system and a heat distribution equation for the bone itself where heat generation in tool’s tip is due to cutting frictional forces. In an analytical model, it is possible to use material properties of the bone and geometry of the tool; therefore, the calibration test is not necessary. In order to validate analytical model, experiments were done using bovine bone. Using response surface method, a second-order linear regression mathematical model is derived using experimental results. The effect of each individual parameter as well as their interactions on the output of the process was investigated. Within the range of the parameters studied in this article, with an increase in rotational speed, process temperature boosts up. Effect of feed rate is complicated due to the tool–bone contact time issue. While higher temperature is achieved in lower feed rates because of higher tool–bone contact time but higher temperature is observed with high feed rates due to an increase in force and friction. Optimized combination of the parameters to minimize temperature of 35.6 °C is tool diameter of 2.5 mm, rotational speed of 500 r/min and feed rate of 30 mm/min. Good correlation was observed between analytical and experimental results.
A Novel Peptide Restricts Ethanol Modulation of the BK Channel In Vitro and In Vivo

Alcohol is a widely used and abused substance. A major unresolved issue in the alcohol research field is determining which of the many alcohol target proteins identified to date is responsible for shaping each specific alcohol-related behavior. The large-conductance, calcium- and voltage-activated potassium channel (BK channel) is a conserved target of ethanol. Genetic manipulation of the highly conserved BKα channel influences alcohol-related behaviors across phylogenetically diverse species that include worm, fly, mouse, and man. A pharmacological tool that prevents alcohol’s action at a single target, like the BK channel, would complement genetic approaches in the quest to define the behavioral consequences of alcohol at each target. To identify agents that specifically modulate the action of ethanol at the BK channel, we executed a high-throughput phagemid-display screen in combination with a Caenorhabditis elegans behavioral genetics assay. This screen selected a novel nonapeptide, LS10, which moderated acute ethanol intoxication in a BK channel–humanized C. elegans strain without altering basal behavior. LS10’s action in vivo was dependent upon BK channel functional activity. Single-channel electrophysiological recordings in vitro showed that preincubation with a submicromolar concentration of LS10 restricted ethanol-induced changes in human BKα channel gating. In contrast, no substantial changes in basal human BKα channel function were observed after LS10 application. The results obtained with the LS10 peptide provide proof-of-concept evidence that a combined phagemid-display/behavioral genetics screening approach can provide novel tools for understanding the action of alcohol at the BK channel and how this, in turn, exerts influence over central nervous system function.