Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying of Recurrent Neural Networks for Industrial Processes Anomaly Detection

The paper considers the issue of recurrent neural networks applicability for detecting industrial process anomalies to detect intrusion in Industrial Control Systems. Cyberattack on Industrial Control Systems often leads to appearing of anomalies in industrial process. Thus, it is proposed to detect such anomalies by forecasting the state of an industrial process using a recurrent neural network and comparing the predicted state with actual process' state. In the course of experimental research, a recurrent neural network with one-dimensional convolutional layer was implemented. The Secure Water Treatment dataset was used to train model and assess its quality. The obtained results indicate the possibility of using the proposed method in practice. The proposed method is characterized by the absence of the need to use anomaly data for training. Also, the method has significant interpretability and allows to localize an anomaly by pointing to a sensor or actuator whose signal does not match the model's prediction.
Adaptive PID control for time-varying fermentation processes

PID controller is a common control regulator for industrial processes and is widely used in chemical processes. However, conventional proportional-integral-derivative controller has been found insufficient for nonlinear and time-varying fermentation processes. In this work, an auto-tuning neuron PID (ANPID) method based on particle swarm optimization is proposed to handle the time-varying fermentation process. Specifically, the PSO method is first adopted to initialize the parameters of PID. Then, an ANPID controller is utilized to adaptively manipulate the process. The design advantage of ANPID is that its control parameters can be updated using useful historical information. Consequently, the time-varying behavior can be captured and good control performance can be achieved. Simulation results illustrate that the proposed controller can obtain better performance than traditional PID.
Recognition of Dangerous Rhythm Disturbances from Short ECG Fragments

Recognition methods of dangerous rhythm disturbances using the spectral description of short electrocardiosignals are the subject of this paper. Each object is described by 15 spectral features in the range from 0 to 15 Hz, all data are normalized to total signal strength. The sample size is 270 objects of each class. The method of Fisher linear discriminant (FLD) is used, as well as the method of convex hulls classification. The results of the binary classification of arrhythmias into non-dangerous and dangerous using two methods are presented. The performances of the classification efficiency (sensitivity, specificity, and overall accuracy) are calculated. Leave-one-out cross-validation (LOO CV) is used during the classification experiment to empirically evaluated the generalization ability of the algorithm. Also, mapping of two classes' objects onto the plane of vectors, obtained using the FLD method is presented. The convex hull classification based on linear programming has an overall accuracy of 91.5%. The results of this work can be used to solve practical problems in medicine.
Acoustic Sensing as a Novel Wearable Approach for Heart Rate Variability Monitoring at the Wrist

Heart rate variability (HRV) is an important physiological parameter to assess the status of the autonomic nervous system (ANS) and diagnose cardiovascular diseases (CVDs). HRV is generally extracted from the cardiac activity monitored at the chest using electrocardiography (ECG). This article presents a novel algorithm to accurately extract HRV, from signals obtained using an innovative wearable approach based on acoustic sensing of cardiac activity at the wrist. The proposed methodology is based on the relative energy method, which utilizes the relative information between short and long-term energies of the acoustic signal to enhance the S1’s characteristics in comparison to other signal transitions. The algorithm was tested on a dataset consisting of acoustic recordings of 5-min duration obtained from 12 subjects. For the instantaneous heart rate (IHR) determination, 98.50% and 98.96% of the total samples achieved a relative error of less than ±5% when compared to ECG and photoplethysmography (PPG) signals, respectively. The comparison of the HRV parameters in the time-domain and the frequency-domain, with the reference signals, also demonstrated a strong statistical agreement and correlation of close to one, hence proving the feasibility of extracting the HRV from acoustic signals recorded at the wrist.
Block-Level Storage Caching for Hypervisor-Based Cloud Nodes

Virtual block devices are heavily used to fulfill the block storage needs of hypervisor-based virtual machine (VM) instances through either local or remote storage spaces. However, a high degree of VM co-location makes it increasingly difficult to physically provision all the necessary block devices using only local storage space. Also, the local storage performance degrades rapidly as workloads interleave. On the other hand, when block devices are acquired through remote storage services, the aggregated network traffic may consume too much cluster-wide network bandwidth in a cloud data center. In order to solve these challenges, we propose a caching scheme for virtual block devices within the hypervisor. The scheme utilizes the physical node’s finite local storage space as a block-level cache for the remote storage blocks to reduce the network traffic bound to the storage servers. This allows hypervisor-based compute nodes to serve the hosted VMs’ I/O (Input/Output) requests from its local storage as much as possible while enabling VMs to exercise large storage space beyond the capacity of local disks for new virtual disks. Caching virtual disks at block-level in a cloud data center poses several challenges in maintaining high performance while adhering to the virtual disk semantics. We have realized the proposed scheme, called vStore, on Xen hypervisor nodes with factual assessment on its design effectiveness and implementation efficiency. Our comprehensive experimental evaluations show that the proposed scheme substantially reduces the network traffic (49% on average), and incurs less than 12% overheads on the storage I/O performance.
Analysis of Mouse Motor Activity in Free Behavior by Bioradiolocation

Currently, there are no inexpensive methods for long-term non-contact assessment of the motor activity of small laboratory animals in free behavior, so the development of new reliable methods is an urgent task of modern medicine. The paper proposes a method for registering motor activity based on the bioradiolocation principles, which allows remotely registering the vital signs of an animal determined by the movements of the surface of the animal's body during movement, breathing and heartbeat. In particular, this paper proposes an algorithm for classifying episodes of mouse motor activity, the effectiveness of which was evaluated on the base of the Institute of Higher Nervous Activity and Neurophysiology of RAS (IHNA and NPh RAS, Russia, Moscow). The algorithm is based on the k-nearest neighbors method, which uses as a feature vector a set of 8 characteristics of the bioradiolocation signal, the choice of which is justified in the work. This method classifies experimental data with a sensitivity of 97 % and a specificity of 99 %.
Complex Kalman Filter Algorithm For Smartphone-based Indoor UWB/INS Navigation Systems

Precision indoor positioning is an urgent engineering task, since in the modern world it is increasingly difficult for a person to do without navigation tools that are applicable in indoor or in underground facilities where the signals of global navigation satellite systems are not available. Among the many existing systems, it is possible to distinguish systems where the consumer's smartphone is used as a tag. Such systems allow using smartphone sensor measurements - from narrow-band radio sensors and inertial sensors. And in the near future, some smartphones will include ultra-wideband sensors with signals of a navigation-communication standard, which means that they can be used as part of navigation systems. This paper presents a complex Kalman filter algorithm for indoor navigation system based on smartphones using UWB and INS measurements. The efficiency of the algorithm has been tested both by simulation and by experiments. In the simulation, the gain of the complex UWB / INS filter relative to the UWB filter under the same conditions is more than 5, and in real measurements-more than 3.
Mining atmospheric data

This paper overviews two interdependent issues important for mining remote sensing data (e.g. images) obtained from atmospheric monitoring missions. The first issue relates to building new public datasets and benchmarks, which are hot priority of the remote sensing community. The second issue is to investigate deep learning methodologies for atmospheric data classification based on vast amount of data and without ground truth or with very limited ground truth. The targeted application is air quality assessment and prediction. Air quality is defined as the pollution level linked with several atmospheric constituent such as gases and aerosols. Low levels of air quality and thus high levels of air pollution led to increase in public health issues. The target application is the development of a fast prediction model for local and regional air quality assessment and tracking. The results of mining data will have significant implication for citizen and decision makers by providing a fast prediction and reliable air quality monitoring system able to cover the local and regional scale through intelligent extrapolation of sparse ground-based in situ measurement networks.
Robust Model Predictive Control for Autonomous Lane-Changing

Autonomous vehicles need to plan trajectories to a specific goal while avoiding collisions with surrounding vehicles. To this aim, it is essential to take into account the inherited uncertainties due to unmodeled dynamics, uncertain localization, and disturbances. This paper deals with the problem of robust trajectory planning for autonomous lane-changing in the presence of uncertainties. Considering trajectory planning as an online decision-making problem, we propose a robust model predictive control (rMPC), which minimizes deviations from a reference speed and a lateral target position while keeping a subject vehicle within road limits and avoiding collisions with an in-lane vehicle. Uncertainties are explicitly modeled as an additive disturbance in the formulation, wherein the optimal control decisions are obtained by solving a quadratic program (QP). The resulting rMPC guarantees robust state-input satisfaction under the additive disturbance even when the QP solver iterations are stopped prematurely. A set of simulation experiments is studied under different initial scenarios to validate the design, demonstrating the potential utility of the proposed control algorithm for reliable lane-changing.
iPRIns: A Tool with the Improved Precision and Recall for Insertion Detection in the Human Genome

An insertion is a specific type of the structural variations. The identification of insertions in a human genome is essential for the study of diseases or their functional effects. There are many tools available for identifying the insertion type with different methods and strategies. However, most of them could not deliver both good recall and precision, especially for the real datasets sequenced with the paired-end short reads. In this paper, we propose iPRIns, a new computational method for detecting insertions aiming to improve both precision and recall. The proposed method with discovering and filtering processes outperformed all other three tools for 5 out of 10 real datasets, the variations of NA12878, for both precision and recall. iPRIns is released under the open-source GPLv3 license. The source code and documentation are available at https://github.com/cucpbioinfo/iPRIns.
A New Approach for the 10.7-cm Solar Radio Flux Forecasting: Based on Empirical Mode Decomposition and LSTM

The daily 10.7-cm Solar Radio Flux (F10.7) data is a time series with highly volatile. The accurate prediction of F10.7 has a great significance in the fields of aerospace and meteorology. At present, the prediction of F10.7 is mainly carried out by linear models, nonlinearmodels, or a combination of the two. The combinationmodel is a promising strategy, which attempts to benefit from the strength of both. This paper proposes an Empirical Mode Decomposition (EMD) -Long Short-Term Memory Neural Network (LSTMNN) hybrid method, which is assembled by a particular frame, namely EMD–LSTM. The original dataset of F10.7 is firstly processed by EMD and decomposed into a series of components with different frequency characteristics. Then the output values of EMD are respectively fed to a developed LSTM model to acquire the predicted values of each component. The final forecasting values are obtained after a procedure of information reconstruction. The evaluation is undertaken by some statistical evaluation indexes in the cases of 1-27 days ahead and different years. Experimental results show that the proposed method gives superior accuracy as compared with benchmarkmodels, including other isolated algorithms and hybrid methods.
First report of

Areca triandra Roxb. ex Buch. -Ham. , a wild relative of cultivated arecanut (Areca catechu L.) is known as a resistant source for fruit rot caused by Phytophthora meadii Mc Rae. However, during monsoon 2019–20, a heavy shedding of immature nuts with whitish mycelial growth was observed from A. triandra plantations in Dakshina Kannada district, Karnataka, India. Pure culture of the associated oomycete was isolated from the symptomatic nuts and pathogenicity was confirmed. Based on morphological characteristics and internal transcribed spacer (ITS) sequences the pathogen was identified as P. palmivora E. J. Butler. To the best of our knowledge, this is the first report of Phytophthora palmivora infecting Areca triandra in the world.
Designing and Comparing Performances of Image Processing Pipeline for Enhancement of I-131-metaiodobenzylguanidine Images

Objective: An image processing pipeline can have more than one image processing technique in sequence, and the output of the first technique becomes input for the next technique and so on. In this study, we have designed and compared the performances of image processing pipelines for enhancement of I-131-metaiodobenzylguanidine (mIBG) images. Materials and Methods: Five different image processing pipelines (A [Gaussian filter, normalization], B [histogram specification (image 1), Gaussian filter, normalization], C [histogram specification (image 2), Gaussian filter, normalization], D [{histogram specification (image 3), Gaussian filter, and normalization], and E [histogram specification (image 4), Gaussian filter, normalization]) were designed and their performances were evaluated on I-131-mIBG images (n = 122). The image quality was assessed objectively using Perception-based Image Quality Evaluator (PIQE) score and subjectively (on scale 1–4) by two nuclear medicine physician. Sign test was applied to find the statistically significant difference between the image quality obtained using image processing pipelines. We applied test of proportion to compute difference in proportion of image quality score assigned to images obtained using image processing pipelines. Results: Based on PIQE score, the quality of images obtained using all the five image processing pipelines were significantly better than that of input images (P < 0.001). The highest image quality score (=4) was assigned maximum number of times (n = 90) to the images obtained using image processing pipeline D and was significantly different from that of the second best image processing pipeline E (P = 0.015). Conclusions: The image processing pipeline D was found to be better for enhancement of I-131-mIBG images.
Boosting of Contextual Information in ASR for Air-Traffic Call-Sign Recognition

Contextual adaptation of ASR can be very beneﬁcial for multi-accent and often noisy Air-Trafﬁc Control (ATC) speech. Our focus is call-sign recognition, which can be used to track conversations of ATC operators with individual airplanes. We developed a two-stage boosting strategy, consisting of HCLG boosting and Lattice boosting. Both are implemented as WFST compositions and the contextual information is speciﬁc to each utterance. In HCLG boosting we give score discounts to individual words, while in Lattice boosting the score discounts are given to word sequences. The context data have origin in surveillance database of OpenSky Network. From this, we obtain lists of call-signs that are made more likely to appear in the best hypothesis of ASR. This also improves the accuracy of the NLU module that recognizes the call-signs from the best hypothesis of ASR. As part of ATCO 2 project, we collected liveatc test set2. The boosting of call-signs leads to 4.7% absolute WER improvement and 27.1% absolute increase of Call-Sign recognition Accuracy (CSA). Our best result of 82.9% CSA is quite good, given that the data is noisy, and WER 28.4% is relatively high. We believe there is still room for improvement.
A nanoemulsion-based delivery system for imatinib and in vitro anticancer efficacy

A self-nanoemulsifying drug delivery system (SNEDDS) composed of ethyl oleate, Tween 80 and polyethylene glycol 600 was prepared as a new route to improve the efficacy of imatinib. The drug-loaded SNEDDS formed nanodroplets of ethyl oleate stabilized by Tween 80 and polyethylene glycol 600 with a diameter of 81.0±9.5 nm. The nanoemulsion-based delivery system was stable for at least two months, with entrapment efficiency and loading capacity of 16.4±0.1 and 48.3±0.2%, respectively. Imatinib-loaded SNEDDS was evaluated for the drug release profiles, and its effectiveness against MCF-7 cell line was investigated. IC50 values for the imatinib-loaded SNEDDS and an imatinib aqueous solution were 3.1 and 6.5 µg mL-1, respectively.
Copper Efflux Is Induced during Anaerobic Amino Acid Limitation in <named-content content-type='genus-species' xlink:type='simple'>Escherichia coli</named-content> To Protect Iron-Sulfur Cluster Enzymes and Biogenesis

Adaptation to changing environments is essential to bacterial physiology. Here we report a unique role of the copper homeostasis system in adapting Escherichia coli to its host-relevant environment of anaerobiosis coupled with amino acid limitation. We found that expression of the copper/silver efflux pump CusCFBA was significantly upregulated during anaerobic amino acid limitation in E. coli without the supplement of exogenous copper. Inductively coupled plasma mass spectrometry analysis of the total intracellular copper content combined with transcriptional assay of the PcusC-lacZ reporter in the presence of specific Cu(I) chelators indicated that anaerobic amino acid limitation led to the accumulation of free Cu(I) in the periplasmic space of E. coli, resulting in Cu(I) toxicity. Cells lacking cusCFBA and another copper transporter, copA, under this condition displayed growth defects and reduced ATP production during fumarate respiration. Ectopic expression of the Fe-S cluster enzyme fumarate reductase (Frd), or supplementation with amino acids whose biosynthesis involves Fe-S cluster enzymes, rescued the poor growth of cusC cells. Yet, Cu(I) treatment did not impair the Frd activity in vitro. Further studies revealed that the alternative Fe-S cluster biogenesis system Suf was induced during the anaerobic amino acid limitation, and cusC enhanced this upregulation, indicating the impairment of the Fe-S cluster assembly machinery and the increased Fe-S cluster demands under this condition. Taken together, we conclude that the copper efflux system CusCFBA is induced during anaerobic amino acid limitation to protect Fe-S cluster enzymes and biogenesis from the endogenously originated Cu(I) toxicity, thus facilitating the physiological adaptation of E. coli.
Characterization and Application of Mangosteen Peel Activated Carbon for Ammonia Gas Removal

Received: 9 Jan 2021 Received in revised: 3 May 2021 Accepted: 21 May 2021 Published online: 10 Jun 2021 DOI:10.32526/ennrj/19/2020298 Mangosteen peel can be used as an activated carbon precursor because of its high lignin content and hardness. In this study, mangosteen peel activated carbon (MP-AC) was prepared by a physical activation method using CO2 at 850°C. The Brunauer-Emmett-Teller (BET) analysis was used to assess the optimal activation time to identify the largest surface area. The properties of MP-AC were characterized by the SEM-EDS and FTIR analyses. The results showed that MP-AC obtained from the 120-minute activation time had the largest BET specific surface area of 588.41 m/g and was selected as an adsorbent in the dynamic adsorption of ammonia gas. The values of moisture content, ash content, and iodine number of MP-AC were 6.07%, 9.8%, and 1153.69 mg/g, respectively. Breakthrough curve indicated that with lower inlet concentration and higher adsorbent mass, longer breakthrough time is reached. Equilibrium data was best fitted to the Langmuir isotherm, while the pseudofirst order kinetic model favorably described the adsorption kinetics. The results revealed a potential to utilize MP-AC as an adsorbent for ammonia gas removal with average NH3 adsorption capacity of 0.41 mg/g.
Not all goals are equally important - a study for the NHL

The evaluation of player performance is an important topic in sport analytics and is used by coaches for team management, in scouting and in sports broadcasts. When evaluating the performance of ice hockey players many metrics are used, including traditional metrics such as goals, assists, points and modern metrics such as Corsi. One weakness of such metrics is that they do not take into consideration the context in which the value for the metric was assigned. For instance, when a player scores a goal, then the value of the goals metric for that player is raised by one, regardless of the importance of the goal. In this paper, we introduce new variants of classical metrics based on the importance of the goals regarding their contribution to team wins and ties. Further, we investigate using play-by-play data from the 2013-2014 NHL season how these new metrics relate to the classical metrics and which players stand out with respect to important goals.
A Markerless-based Gait Analysis and Visualization Approach for ASD Children

This study proposed a new method in gait acquisition and analysis for autistic children based on the markerless technique versus the gold standard marker-based technique. Here, the gait acquisition stage is conducted using a depth camera with a customizable skeleton tracking function that is the Microsoft Kinect sensor for recording the walking gait trials of the 23 children with autism spectrum disorder (ASD) and 30 typically healthy developing (TD) children. Next, the Kinect depth sensor outputs information is translated into kinematic gait features. Further, analysis and evaluation are done specifically the kinematic angles of the hip, knee, and ankle in analyzing and visualizing the pattern of the plots versus the kinematic plots acquired from the marker-based that is the Vicon motion system gait technique. In addition, these kinematic angles are also validated using the statistical method namely the Analysis of Variance (ANOVA). Results showed that the ρ-values are insignificant for all angles upon computing both the intragroup and inter-group normalization. Hence, these findings have proven that the proposed markerless-based gait technique is indeed apt to be used as a new alternative markerless method for gait analysis of ASD children. Keywords—Autism spectrum disorder (ASD); kinematic; marker-based; markerless-based; gait analysis