bluuebunny/crossref_metadata_2025 huggingface dataset:
Routing Protocols for UAV-Aided Wireless Sensor Networks
Authors: Arafat Muhammad Yeasir , Habib Md Arafat , Moh Sangman
Published: June 2020

Recently, unmanned aerial vehicles (UAVs) attracted significant popularity in both military and civilian domains for various applications and services. Moreover, UAV-aided wireless sensor networks (UAWSNs) became one of the interesting hot research topics. This is mainly because UAWSNs can significantly increase the network coverage and energy utilization compared to traditional wireless sensor networks (WSNs). However, the high mobility, dynamic path, and variable altitude of UAVs can cause not only unforeseen changes in the network topology but also connectivity and coverage problems, which can affect the routing performance of the network. Therefore, the design of a routing protocol for UAWSNs is a critical task. In this paper, the routing protocols for UAWSNs are extensively investigated and discussed. Firstly, we classify the existing routing protocols based on different network criteria. They are extensively reviewed and compared with each other in terms of advantages and limitation, routing metrics and policies, characteristics, difference performance factors, and different performance optimization factors. Furthermore, open research issues and challenges are summarized and discussed.
Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery
Authors: Manni Francesca , van der Sommen Fons , Zinger Svitlana , Shan Caifeng , Holthuizen Ronald , Lai Marco , Buström Gustav , Hoveling Richelle J. M. , Edström Erik , Elmi-Terander Adrian , de With Peter H. N.
Published: June 2020

In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers.
Weak Signal Detection Method Based on the Coupled Lorenz System and Its Application in Rolling Bearing Fault Diagnosis
Authors: Li Guozheng , Tan Nanlin , Li Xiang
Published: June 2020

Rolling bearings are widely used in rotating machinery. Their fault feature signals are often submerged in strong noise and are difficult to identify. This paper presents a new method of bearing fault diagnosis that combines the coupled Lorenz system and power spectrum technology. The process is achieved in the following three steps. First, a synchronization system based on the Lorenz system is constructed using the driving-response method. Second, when the tested signal is connected to the driving end, the synchronization error between the two sub-chaotic systems is obtained. Finally, the power spectrum density of the synchronization error is calculated and compared with the corresponding fault characteristic frequency. The coupled Lorenz system makes full use of the noise immunity and nonlinear amplification of the chaotic system. The detection characteristics and feasibility of the new method are verified by simulation and actual measured vibration data. The result shows that the noise reduction effect of the coupled Lorenz system is obvious. This method can improve the signal-to-noise ratio of the tested signal and provide a new way to perform fault diagnosis of rolling bearings.
The Influence of Wave Nonlinearity on Cross-Shore Sediment Transport in Coastal Zone: Experimental Investigations
Authors: Saprykina Yana
Published: June 2020

On the basis of field experiment data, the main features of influence of non-linear wave transformation scenarios on cross-shore sediment transport in coastal zones were investigated. The bottom deformations due to the non-linear wave transformation follow the specific scenario. The increase in the second non-linear harmonic amplitude leads to the erosion of the underwater slope at the distances corresponding to this process, with the subsequent accumulation of sandy material closer to the shore at distances where the amplitude decreases during the backward energy transfer to the first harmonic. This can be explained by the change in the phase shift between harmonics during non-linear wave transformation. The second harmonic maximum provides the point near which the bottom deformations occur in different directions. Scenarios of non-linear wave transformation in which backward energy transfer from the second non-linear harmonic to the first is close to the shoreline will contribute to the transport and accumulation of the sediment on the coast. These scenarios are more characteristic of “small waves”. The scenario without a periodical exchange of wave energy between non-linear harmonics (with an increase in the second harmonic only) that is characteristic of large storm waves and plunging breaking waves will lead to the erosion of the underwater bottom profile.
Simpler Learning of Robotic Manipulation of Clothing by Utilizing DIY Smart Textile Technology
Authors: Verleysen Andreas , Holvoet Thomas , Proesmans Remko , Den Haese Cedric , wyffels Francis
Published: June 2020

Deformable objects such as ropes, wires, and clothing are omnipresent in society and industry but are little researched in robotics research. This is due to the infinite amount of possible state configurations caused by the deformations of the deformable object. Engineered approaches try to cope with this by implementing highly complex operations in order to estimate the state of the deformable object. This complexity can be circumvented by utilizing learning-based approaches, such as reinforcement learning, which can deal with the intrinsic high-dimensional state space of deformable objects. However, the reward function in reinforcement learning needs to measure the state configuration of the highly deformable object. Vision-based reward functions are difficult to implement, given the high dimensionality of the state and complex dynamic behavior. In this work, we propose the consideration of concepts beyond vision and incorporate other modalities which can be extracted from deformable objects. By integrating tactile sensor cells into a textile piece, proprioceptive capabilities are gained that are valuable as they provide a reward function to a reinforcement learning agent. We demonstrate on a low-cost dual robotic arm setup that a physical agent can learn on a single CPU core to fold a rectangular patch of textile in the real world based on a learned reward function from tactile information.
Identification of Epileptic EEG Signals Using Convolutional Neural Networks
Authors: Abiyev Rahib , Arslan Murat , Bush Idoko John , Sekeroglu Boran , Ilhan Ahmet
Published: June 2020

Epilepsy is one of the chronic neurological disorders that is characterized by a sudden burst of excess electricity in the brain. This abnormality appears as a seizure, the detection of which is an important research topic. An important tool used to study brain activity features, neurological disorders and particularly epileptic seizures, is known as electroencephalography (EEG). The visual inspection of epileptic abnormalities in EEG signals by neurologists is time-consuming. Different scientific approaches have been used to accurately detect epileptic seizures from EEG signals, and most of those approaches have obtained good performance. In this study, deep learning based on convolutional neural networks (CNN) was considered to increase the performance of the identification system of epileptic seizures. We applied a cross-validation technique in the design phase of the system. For efficiency, comparative results between other machine-learning approaches and deep CNNs have been obtained. The experiments were performed using standard datasets. The results obtained indicate the efficiency of using CNN in the detection of epilepsy.
Online Speech Recognition Using Multichannel Parallel Acoustic Score Computation and Deep Neural Network (DNN)- Based Voice-Activity Detector
Authors: Oh Yoo Rhee , Park Kiyoung , Park Jeon Gyu
Published: June 2020

This paper aims to design an online, low-latency, and high-performance speech recognition system using a bidirectional long short-term memory (BLSTM) acoustic model. To achieve this, we adopt a server-client model and a context-sensitive-chunk-based approach. The speech recognition server manages a main thread and a decoder thread for each client and one worker thread. The main thread communicates with the connected client, extracts speech features, and buffers the features. The decoder thread performs speech recognition, including the proposed multichannel parallel acoustic score computation of a BLSTM acoustic model, the proposed deep neural network-based voice activity detector, and Viterbi decoding. The proposed acoustic score computation method estimates the acoustic scores of a context-sensitive-chunk BLSTM acoustic model for the batched speech features from concurrent clients, using the worker thread. The proposed deep neural network-based voice activity detector detects short pauses in the utterance to reduce response latency, while the user utters long sentences. From the experiments of Korean speech recognition, the number of concurrent clients is increased from 22 to 44 using the proposed acoustic score computation. When combined with the frame skipping method, the number is further increased up to 59 clients with a small accuracy degradation. Moreover, the average user-perceived latency is reduced from 11.71 s to 3.09–5.41 s by using the proposed deep neural network-based voice activity detector.
A Simple Distortion-Free Method to Handle Variable Length Sequences for Recurrent Neural Networks in Text Dependent Speaker Verification
Authors: Yoon Sung-Hyun , Yu Ha-Jin
Published: June 2020

Recurrent neural networks (RNNs) can model the time-dependency of time-series data. It has also been widely used in text-dependent speaker verification to extract speaker-and-phrase-discriminant embeddings. As with other neural networks, RNNs are trained in mini-batch units. In order to feed input sequences into an RNN in mini-batch units, all the sequences in each mini-batch must have the same length. However, the sequences have variable lengths and we have no knowledge of these lengths in advance. Truncation/padding are most commonly used to make all sequences the same length. However, the truncation/padding causes information distortion because some information is lost and/or unnecessary information is added, which can degrade the performance of text-dependent speaker verification. In this paper, we propose a method to handle variable length sequences for RNNs without adding information distortion by truncating the output sequence so that it has the same length as corresponding original input sequence. The experimental results for the text-dependent speaker verification task in part 2 of RSR 2015 show that our method reduces the relative equal error rate by approximately 1.3% to 27.1%, depending on the task, compared to the baselines but with an associated, small overhead in execution time.
From Erosion to Remineralization: The Possible Role of Two Topic Home Devices Used as Combined Treatment
Authors: Fanfoni Lidia , Costantinides Fulvia , Berton Federico , Marchesi Giulio , Polo Leila , Di Lenarda Roberto , Nicolin Vanessa
Published: June 2020

Severe dental erosion could be one of the complications of gastroesophageal reflux disease and food disorders such as bulimia nervosa. The aim of the present in vitro study was to evaluate the remineralization efficiency and the erosion prevention capability obtained by combining the use of Elmex Erosion Protection (Elm-EP) and GC Tooth Mousse (GC-TM) in cases of strongly eroded enamel surfaces. Twenty-four specimens of bovine tooth were superficially treated with hydrochloric acid to mimic severe erosion conditions. The specimens were divided into 3 groups accordingly to a different remineralizing treatment for 15 days: immersion in artificial remineralizing saliva, brushing with Elm-EP and brushing with Elm-EP in association with a daily application of GC-TM paste. After the remineralization procedures, the specimens were exposed to a treatment with acid. The effectiveness of the treatments were assessed by surface profilometric analysis and scanning electron microscopy at four different steps. Furthermore, the quantity of the Ca2+ lost during erosion was determined. The combined action of Elm-EP and GC-TM led to a 50% roughness decrease of critically eroded dental surfaces. The tandem use of Elmex Erosion Protection and GC Tooth Mousse resulted a promising protecting strategy for the prevention of the dental mineral loss.
Natural Epigenetic Modulators of Vitamin D Receptor
Authors: Apprato Giulia , Fiz Camilla , Fusano Isabella , Bergandi Loredana , Silvagno Francesca
Published: June 2020

Vitamin D plays an important role in every tissue due to its differentiating properties and the control of calcium homeostasis. The reversion of the epigenetic repression of the vitamin D receptor (VDR) could lead to an increased sensitivity of the cells to the beneficial activity of the hormone and could be exploited in many vitamin D-resistant diseases. In this study we analyzed the effects of three natural epigenetic modulators: sulforaphane, curcumin, and the products of the fermentative activity of probiotics. Sulforaphane and curcumin are inhibitors of the DNA methyltransferases (DNMT) and of the histone deacetylases (HDAC); it has been demonstrated that sulforaphane and curcumin increase VDR expression in intestinal epithelial cells and in a human liver cancer cell line, respectively. The anti-inflammatory properties associated with the probiotic administration in vivo can be linked to the increased activity of intestinal VDR. Butyrate, an inhibitor of HDAC and a known modulator of VDR expression, is the candidate byproduct of fermentation by gut microbiome that could mediate the enhanced expression of VDR triggered by probiotics in vivo. Many other natural compounds wait to be investigated and recognized as epigenetic modulators of VDR, thus opening promising therapeutic avenues for many diseases by natural means.
Effects of Impactor Size on Biomechanical Characteristics of Spinal Cord in Hemicontusion Injury Model Using Finite Element Analysis
Authors: Khuyagbaatar Batbayar , Kim Kyungsoo , Batbayar Temuujin , Kim Yoon Hyuk
Published: June 2020

A cervical hemicontusion spinal cord injury (SCI) produces forelimb deficits on the ipsilateral side of the injury while sparing the function of the limbs on the contralateral side of the injury, allowing for the evaluation of experimental therapeutics for functional recovery. Although the effects of contusion force on the functional and behavioral outcomes were adequately described in previous experimental studies, the size of the impactor tip also had significant effects on the extent of the lesion on the contralateral side of the injury in the hemicontusion rat model. However, studies regarding the effects of impactor size on the spinal cord for the hemicontusion model are limited. In this study, a finite element (FE) model of the rat cervical spinal cord was developed to investigate the effects of impactor size in the hemicontusion SCI model on the stress, strain, and displacement of the spinal cord for the New York University (NYU) and Infinite Horizon (IH) impactors. The impactor tip diameters of 1.2 mm and 1.6 mm with high impact loading resulted in the highest stresses and strains in the right (ipsilateral) side of the spinal cord. Thus, impactor tip diameters between 1.2 mm and 1.6 mm would be convenient to use in the rat hemicontusion SCI models for the cervical region without damaging the left (contralateral) side of the spinal cord. Our findings provide an insight into SCI mechanisms in the rat cervical hemicontusion model.
Patient-Specific 3-Dimensional Printing Titanium Implant Biomechanical Evaluation for Complex Distal Femoral Open Fracture Reconstruction with Segmental Large Bone Defect: A Nonlinear Finite Element Analysis
Authors: Wong Kin Weng , Wu Chung Da , Chien Chi-Sheng , Lee Cheng-Wei , Yang Tai-Hua , Lin Chun-Li
Published: June 2020

This study proposes a novel titanium 3D printing patient-specific implant: a lightweight structure with enough biomechanical strength for a distal femur fracture with segmental large defect using nonlinear finite element (FE) analysis. CT scanning images were processed to identify the size and shape of a large bone defect in the right distal femur of a young patient. A novel titanium implant was designed with a proximal cylinder tube for increasing mechanical stability, proximal/distal shells for increasing bone ingrowth contact areas, and lattice mesh at the outer surface to provide space for morselized cancellous bone grafting. The implant was fixed by transverse screws at the proximal/distal host bone. A pre-contoured locking plate was applied at the lateral site to secure the whole construct. A FE model with nonlinear contact element implant-bone interfaces was constructed to perform simulations for three clinical stages under single leg standing load conditions. The three stages were the initial postoperative period, fracture healing, and post fracture healing and locking plate removal. The results showed that the maximum implant von Mises stress reached 1318 MPa at the sharp angles of the outer mesh structure, exceeding the titanium destruction value (1000 MPa) and requiring round mesh angles to decrease the stress in the initial postoperative period. Bone stress values were found decreasing all the way from the postoperative period to fracture healing and locking plate removal. The overall construct deformation value reached 4.8 mm in the postoperative period, 2.5 mm with fracture healing assisted by the locking plate, and 2.1 mm after locking plate removal. The strain value at the proximal/distal implant-bone interfaces were valuable in inducing bone grafting in the initial postoperative period. The proposed patient-specific 3D printed implant is biomechanically stable for treating distal femoral fractures with large defect. It provides excellent lightweight structure, proximal/distal bone ingrowth contact areas, and implant rounded outer lattice mesh for morselized cancellous bone grafting.
Could a Multitask Balance Training Program Complement the Balance Training in Healthy Preschool Children: A Quasi-Experimental Study
Authors: Abuín-Porras Vanesa , Jiménez Antona Carmen , Blanco-Morales María , Palacios Antonio , Romero-Morales Carlos , López-López Daniel , Villafañe Jorge Hugo , Rodríguez-Costa Isabel
Published: June 2020

This prospective quasi-experimental study investigated the efficacy of a multitask balance training program to improve the balancing ability of healthy preschool children. A total of 85 (51.8% boys) healthy preschool children aged 4–6 years were randomly divided into an intervention group and a control group. The intervention group received a multitask balance training program, while the control group received a placebo training program. Outcomes were collected at baseline and immediately following the intervention. Independent members of the research team recorded balance skill measurements in the cohort at pre- and post-training using the Pediatric Balance Scale (PBS). Balance-related outcomes were recorded using the Battelle Development Inventory in Spanish using the second edition (BDI-II). A significant effect of time interaction (F = 7.27, p = 0.015; F = 6.16, p = 0.02 and F = 7.41, p = 0.014) existed for the PBS and BDI-II over balance immediately after the intervention (p < 0.001 for each). A significant group-by-time interaction was detected for the PBS and BDI-II (p < 0.001 and p = 0.006, respectively). This study supports the efficacy of multitask-based balance training programs to improve balance in children aged 4–6 years.
Effects of Different Roller Profiles on the Microstructure and Peel Strength of the Ultrasonic Welding Joints of Nonwoven Fabrics
Authors: Nguyen Thanh-hai , Thanh Le Quang , Loc Nguyen Huu , Huu Manh Ngo , Nguyen Van Anh
Published: June 2020

Nonwoven fabrics are widely used in the textile manufacturing industry due to their advanced characteristics, such as their soft, water-repellent, recycle, ecological, and resilient functions. Nowadays, one of the innovated technologies applied to bond nonwoven fabrics is the ultrasonic welding method, due to the advantages afforded by its clean, fast, and reliable approach. In this work, isotactic polypropylene (PP) nonwoven fabrics were bonded by a continuous ultrasonic welding process. In order to consider the influence of the roller on the formation of welding joints and their mechanical properties, different roller profiles were designed, fabricated, and tested. Eight types of roller profiles corresponding to No. 1–No. 8 in the experiments were divided into four groups. After bonding, the microstructure in a typical case (i.e., No. 1) was captured by scanning electron microscopy (SEM) to examine the formation of the welding joints. Additionally, the load and the peel strength of the welding joints of all eight roller profiles were analyzed. The results showed that no welding defects, such as cracks or blowholes, were visible in the melted zone. The load depended on the area ratio(s) of the welding area (S0) to the cycling area (S1). Furthermore, it was found out that the peel strength of the welding joints with brick structures were higher than the peel strength in the case of solid line structures.
Improving the Signal to Noise Ratio of QTF Preamplifiers Dedicated for QEPAS Applications
Authors: Wieczorek Piotr Z. , Starecki Tomasz , Tittel Frank K.
Published: June 2020

The signal-to-noise ratio (SNR) is a major factor that limits the detection sensitivity of quartz-enhanced photoacoustic spectroscopy (QEPAS) sensors. The higher the electrical signal level compared to the noise amplitude is the lower the concentration of gases that can be detected. For this reason the preamplifier circuits used in QEPAS should be optimized for low-frequency narrow-band applications. Moreover, special care should be taken when choosing a particular operational amplifier in either a transimpedance or voltage (differential) configuration. It turns out that depending on the preamp topology different operational amplifier parameters should be carefully considered when a high SNR of the whole QEPAS system is required. In this article we analyzed the influence of the crucial parameters of low-noise operational preamplifiers used in QEPAS applications and show the resulting limitations of transimpedance and voltage configurations.
Growth and Photosynthetic Response of Capsicum annuum L. in Biochar Amended Soil
Authors: Pokovai Klára , Tóth Eszter , Horel Ágota
Published: June 2020

The present study investigated the growth of Capsicum annuum L. (pepper) in an outdoor pot experiment. Changes in the plants’ aboveground and root biomass, leaf area, plant height, stem thickness, and yield, as a response to different doses of biochar amendments were observed. During the 12.5-week-long study, four treatments with biochar amounts of 0, 0.5%, 2.5%, and 5.0% (by weight) were added to silt loam soil. Photochemical responses of plants, the plants photochemical reflectance index (PRI) modified by the different doses of biochar were continuously monitored. Plant height and fruit yield were initially the highest for BC5.0; however, by the end of the experiment, both parameters showed higher values for BC2.5, e.g., 15.9 and 9.1% higher plant height and 32.5 and 22.6% higher fruit yield for BC2.5 and BC5.0 compared to control, respectively. By the end of the experiment the BC2.5 treatments had significantly higher stem thickness (p < 0.001) compared to all other amendments. Root dry matter in biochar treatments increased relative to controls with the highest values (54.9% increase) observed in the BC2.5 treatment. Biochar treatment increased leaf area index (LAI) values for the higher doses (1.58, 1.59, 2.03, and 1.89 for C, BC0.5, BC2.5, and BC5.0, respectively). Significant differences between control and biochar amended soils’ PRI measurements were observed (p < 0.001), showing less plant sensitivity to environmental changes when biochar was applied to the soil. While biochar amendment could greatly enhance plant growth and development, there is an optimal amount of biochar after which additional amount might not result in substantial differences, or even can result in lower fruit yield as found in the present study.
Cycling Biomechanics and Its Relationship to Performance
Authors: Turpin Nicolas A. , Watier Bruno
Published: June 2020

State-of-the-art biomechanical laboratories provide a range of tools that allow precise measurements of kinematic, kinetic, motor and physiologic characteristics. Force sensors, motion capture devices and electromyographic recording measure the forces exerted at the pedal, saddle, and handlebar and the joint torques created by muscle activity. These techniques make it possible to obtain a detailed biomechanical analysis of cycling movements. However, despite the reasonable accuracy of such measures, cycling performance remains difficult to fully explain. There is an increasing demand by professionals and amateurs for various biomechanical assessment services. Most of the difficulties in understanding the link between biomechanics and performance arise because of the constraints imposed by the bicycle, human physiology and musculo-skeletal system. Recent studies have also pointed out the importance of evaluating not only output parameters, such as power output, but also intrinsic factors, such as the cyclist coordination. In this narrative review, we present various techniques allowing the assessment of a cyclist at a biomechanical level, together with elements of interpretation, and we show that it is not easy to determine whether a certain technique is optimal or not.
Quantitative Analysis of Movements in Children with Attention-Deficit Hyperactivity Disorder Using a Smart Watch at School
Authors: Lin Lung-Chang , Ouyang Chen-Sen , Chiang Ching-Tai , Wu Rong-Ching , Yang Rei-Cheng
Published: June 2020

Attention-deficit hyperactivity disorder (ADHD) is primarily diagnosed using set criteria and checklists. However, authors have indicated that such criteria and checklists are subjective. In this study, data from the gyroscope and accelerometer in a smart watch were used to analyze the movements of children with ADHD. This study cohort comprised 15 children with ADHD and 15 age- and sex-matched control participants. The children with ADHD and controls wore the watches on their non-writing hands simultaneously in class. The recordings of one patient and one control were tracked for 2 h daily for three consecutive days with desk and seated class activities. We compared the measurements of variance and the zero-crossing rate (ZCR) of the gyroscope and accelerometer between the children with ADHD and controls. All average variance and ZCR values of the three axes (x, y, and z) in the gyroscope and accelerometer were higher in children with ADHD than in the controls. Significant differences in average variance values on the y-axis (p < 0.001) and ZCR values on all three axes (x, p = 0.005; y, p = 0.003; and z, p = 0.004) of the gyroscope were observed. Similarly, significant differences in the average variance values on the three axes (x, p = 0.001; y, p < 0.001; and z, p < 0.001) and ZCR values on the z-axis (p = 0.006) of the accelerometer were observed. The proposed method is a promising tool to objectively analyze the movements of children with ADHD at school.
Integrating In-Situ Data and RS-GIS Techniques to Identify Groundwater Potential Sites in Mountainous Regions of Taiwan
Authors: Lin Jung-Jun , Liou Yuei-An
Published: June 2020

Due to rapid urbanization, the development of megacities and metropolises worldwide is creating water scarcity, social-environmental risk, and challenges to the regions where water supply from rivers and alluvial aquifers is insufficient and unstable. Groundwater exploration in fractured bedrock of mountainous regions is thus a crucial issue in the search for substitute water resources. To achieve cost effectiveness on groundwater exploration, the use of comprehensive remote sensing (RS)- and geographic information system (GIS)-based models appears feasible. The required parameters selected and analyzed from the literature depend on the hydrogeological characteristics. This study intends to investigate and improve the proposed parameters and data sources upon those presented in the literature. A total of 17 hydrogeological units of concern was delineated from 105 complex geological formations of the geological sections and main rock types. The other parameters related to groundwater potential were derived from the digital elevation model and Landsat imagery. In addition, 118 drilling cores were inspected and in-situ well yield data from 72 wells were employed to assess the normalized groundwater potential index in the raster-based empirical GIS model with a higher spatial resolution. The results show that the accuracy of the interpretation of groundwater potential sites improved from 48.6% to 84.7%. The three-dimensional (3D) visualization of a thematic map integrated with satellite imagery is useful as a cost-effective approach for assessing groundwater potential.
Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems
Authors: Calabrese Francesca , Regattieri Alberto , Botti Lucia , Mora Cristina , Galizia Francesco Gabriele
Published: June 2020

Predictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety level. Studies in this field have focused on a novel approach, prognostic health management (PHM), which relies on condition monitoring (CM) for predicting the remaining useful life (RUL) of a system. However, several issues remain in its application to real industrial contexts, e.g., the difficulties in conducting tests simulating each fault condition, the dynamic nature of industrial environments, and the need to handle large amounts of data collected from machinery. In this paper, a data-driven methodology for PHM implementation is proposed, which has the following characteristics: it is unsupervised, i.e., it does not require any prior knowledge regarding fault behaviors and it does not rely on pre-trained classification models, i.e., it can be applied “from scratch”; it can be applied online due to its low computational effort, which makes it suitable for edge computing; and, it includes all of the steps that are involved in a prognostic program, i.e., feature extraction, health indicator (HI) construction, health stage (HS) division, degradation modelling, and RUL prediction. Finally, the proposed methodology is applied in this study to a rotating component. The study results, in terms of the ability of the proposed approach to make a timely prediction of component fault conditions, are promising.
Strategies for Dielectric Contrast Enhancement in 1D Planar Polymeric Photonic Crystals
Authors: Lova Paola , Megahd Heba , Stagnaro Paola , Alloisio Marina , Patrini Maddalena , Comoretto Davide
Published: June 2020

Historically, photonic crystals have been made of inorganic high refractive index materials coupled to air voids to maximize the dielectric contrast and in turn the light confinement. However, these systems are complex, costly, and time-demanding, and the fabrication processes are difficult to scale. Polymer structures promise to tackle this issue thanks to their easy solution and melt processing. Unfortunately, their low dielectric contrast limits their performance. In this work, we propose a concise but exhaustive review of the common polymers employed in the fabrication of planar 1D photonic crystals and new approaches to the enhancement of their dielectric contrast. Transfer matrix method modeling will be employed to quantify the effect of this parameter in standardized structures and to propose a new polymer structure for applications dealing with light management.
Time-Series Classification Based on Fusion Features of Sequence and Visualization
Authors: Wang Baoquan , Jiang Tonghai , Zhou Xi , Ma Bo , Zhao Fan , Wang Yi
Published: June 2020

For the task of time-series data classification (TSC), some methods directly classify raw time-series (TS) data. However, certain sequence features are not evident in the time domain and the human brain can extract visual features based on visualization to classify data. Therefore, some researchers have converted TS data to image data and used image processing methods for TSC. While human perceptionconsists of a combination of human senses from different aspects, existing methods only use sequence features or visualization features. Therefore, this paper proposes a framework for TSC based on fusion features (TSC-FF) of sequence features extracted from raw TS and visualization features extracted from Area Graphs converted from TS. Deep learning methods have been proven to be useful tools for automatically learning features from data; therefore, we use long short-term memory with an attention mechanism (LSTM-A) to learn sequence features and a convolutional neural network with an attention mechanism (CNN-A) for visualization features, in order to imitate the human brain. In addition, we use the simplest visualization method of Area Graph for visualization features extraction, avoiding loss of information and additional computational cost. This article aims to prove that using deep neural networks to learn features from different aspects and fusing them can replace complex, artificially constructed features, as well as remove the bias due to manually designed features, in order to avoid the limitations of domain knowledge. Experiments on several open data sets show that the framework achieves promising results, compared with other methods.
Fingerprint Classification through Standard and Weighted Extreme Learning Machines
Authors: Zabala-Blanco David , Mora Marco , Barrientos Ricardo J. , Hernández-García Ruber , Naranjo-Torres José
Published: June 2020

Fingerprint classification is a stage of biometric identification systems that aims to group fingerprints and reduce search times and computational complexity in the databases of fingerprints. The most recent works on this problem propose methods based on deep convolutional neural networks (CNNs) by adopting fingerprint images as inputs. These networks have achieved high classification performances, but with a high computational cost in the network training process, even by using high-performance computing techniques. In this paper, we introduce a novel fingerprint classification approach based on feature extractor models, and basic and modified extreme learning machines (ELMs), being the first time that this approach is adopted. The weighted ELMs naturally address the problem of unbalanced data, such as fingerprint databases. Some of the best and most recent extractors (Capelli02, Hong08, and Liu10), which are based on the most relevant visual characteristics of the fingerprint image, are considered. Considering the unbalanced classes for fingerprint identification schemes, we optimize the ELMs (standard, original weighted, and decay weighted) in terms of the geometric mean by estimating their hyper-parameters (regularization parameter, number of hidden neurons, and decay parameter). At the same time, the classic accuracy and penetration-rate metrics are computed for comparison purposes with the superior CNN-based methods reported in the literature. The experimental results show that weighted ELM with the presence of the golden-ratio in the weighted matrix (W-ELM2) overall outperforms the rest of the ELMs. The combination of the Hong08 extractor and W-ELM2 competes with CNNs in terms of the fingerprint classification efficacy, but the ELMs-based methods have been demonstrated their extremely fast training speeds in any context.
Disrupting Heteronormative Temporality through Queer Dramaturgies: Fun Home, Hadestown and A Strange Loop
Authors: Whitfield Sarah K.
Published: June 2020

This article considers how André De Shields performance in Hadestown (2019), and the musicals Fun Home (2015) and A Strange Loop (2019) can be seen to respond to the present moment and argues that they disrupt heteronormative temporality through queer dramaturgy. It explores musicals that present queer performativity and/or queer dramaturgies, and addresses how they enact queer strategies of resistance through historical materialist critiques of personal biographies. It suggests that to do this, they disrupt the heteronormative dramaturgical time of the musical, and considers how they may enact structural change to the form of the musical. The article carries out a close reading of De Shields’ performance practice, and analyses the dramaturgy of Fun Home and A Strange Loop through drawing on the methodologies of José Muñoz and Elizabeth Freeman. It considers how they make queer labour visible by drawing on post-dramatic strategies, ultimately suggesting that to varying extents, these musicals offer resistance to the heteronormative musical form.
Role of Wind Shear in the Decay of Convective Boundary Layers
Authors: Park Seung-Bu , Baik Jong-Jin , Han Beom-Soon
Published: June 2020

The role of wind shear in the decay of the convective boundary layer (CBL) is systematically investigated using a series of large-eddy simulations. Nine CBLs with weak, intermediate, and strong wind shear are simulated, and their decays after stopping surface heat flux are investigated. After the surface heat flux is stopped, the boundary-layer-averaged turbulent kinetic energy (TKE) stays constant for almost one convective time scale and then decreases following a power law. While the decrease persists until the end of the simulation in the buoyancy-dominated (weak-shear) cases, the TKE in the other cases decreases slowly or even increases to a level which can be maintained by wind shear. In the buoyancy-dominated cases, convective cells occur, and they decay and oscillate over time. The oscillation of vertical velocity is not distinct in the other cases, possibly because wind shear disturbs the reversal of vertical circulations. The oscillations are detected again in the profiles of vertical turbulent heat flux in the buoyancy-dominated cases. In the strong-shear cases, mechanical turbulent eddies are generated, which transport heat downward in the lower boundary layers when convective turbulence decays significantly. The time series of vertical velocity skewness demonstrates the shear-dependent flow characteristics of decaying CBLs.
High Resolution Air Quality Forecasting over Prague within the URBI PRAGENSI Project: Model Performance during the Winter Period and the Effect of Urban Parameterization on PM
Authors: Ďoubalová Jana , Huszár Peter , Eben Kryštof , Benešová Nina , Belda Michal , Vlček Ondřej , Karlický Jan , Geletič Jan , Halenka Tomáš
Published: June 2020

The overall impact of urban environments on the atmosphere is the result of many different nonlinear processes, and their reproduction requires complex modeling approaches. The parameterization of these processes in the models can have large impacts on the model outputs. In this study, the evaluation of a WRF/Comprehensive Air Quality Model with Extensions (CAMx) forecast modeling system set up for Prague, the Czech Republic, within the project URBI PRAGENSI is presented. To assess the impacts of urban parameterization in WRF, in this case with the BEP+BEM (Building Environment Parameterization linked to Building Energy Model) urban canopy scheme, on Particulate Matter (PM) simulations, a simulation was performed for a winter pollution episode and compared to a non-urbanized run with BULK treatment. The urbanized scheme led to an average increase in temperature at 2 m by 2 ∘ C, a decrease in wind speed by 0.5 m s − 1 , a decrease in relative humidity by 5%, and an increase in planetary boundary layer height by 100 m. Based on the evaluation against observations, the overall model error was reduced. These impacts were propagated to the modeled PM concentrations, reducing them on average by 15–30 μ g m − 3 and 10–15 μ g m − 3 for PM 10 and PM 2.5 , respectively. In general, the urban parameterization led to a larger underestimation of the PM values, but yielded a better representation of the diurnal variations.
Observations of Turbulent Heat Fluxes Variability in a Semiarid Coastal Lagoon (Gulf of California)
Authors: Benítez-Valenzuela Lidia Irene , Sanchez-Mejia Zulia Mayari
Published: June 2020

Despite the critical role latent (LE) and sensible (H) heat play in turbulent processes and heat exchange in the water–air interface, there is a lack of studies of turbulent fluxes over the surface in semiarid regions. We collected continuous measurements of net radiation (Rn), LE, H, and micrometeorological data at a coastal lagoon in the Gulf of California during 2019 with an eddy covariance (EC) system. We analyzed the time series, considering the North American Monsoon System, the pre-monsoon, and post-monsoon season. Results show that Rn (276 ± 118 W m−2) and turbulent fluxes were higher during the monsoon season (July–September) LE (129 ± 18 W m−2), and H (29 ± 9 W m−2). The monthly average of Rn, LE, and H was highest in June (493.9 W m−2), August (142 W m−2), and May (50 W m−2), respectively. Furthermore, during the monsoon season, the (H + LE)/Rn ratio (0.74) suggests that more than half of the Rn reaching the coastal lagoon is used for the turbulent exchange of LE and H. During the pre-monsoon, LE (r2 = 0.36) increases with a higher vapor pressure deficit (VPD), while H (r2 = 0.66) increases with a higher friction velocity (u*) during the monsoon season. Quantitative observations are essential for further research.
Face Temperature as an Indicator of Thermal Stress in Outdoor Work Environments
Authors: Hastings Scott , Kim Se Woong , Brown Robert D.
Published: June 2020

The frequency and duration of heatwaves are steadily increasing as climate change becomes more serious. These changes particularly endanger the health of those who must work outdoors in hot environments. This study introduces a novel approach to monitor the heat-health of airport outdoor workers using infrared thermography. The faces of airport workers who were refueling airplanes in extreme heat conditions were monitored using a thermal infrared thermometer during their work cycle throughout the day. Changes in temperature on their exposed faces (e.g., the ear, cheek, chin) were monitored throughout the day over a two-month period. In every test, the subject’s face temperature increased, then suddenly dropped for a short time, and then continued increasing. Subjects were also asked to assess their thermal perception of the work each time they were tested throughout the study. They reported that they felt discomfort in terms of thermal comfort when the facial skin temperature went down temporarily before the temperature rose. These results show that the physical measurement criteria when outdoor workers’ thermal health is in jeopardy can be based on the results of facial skin temperature measurements.
A Comparison of Northern Hemisphere Atmospheric Rivers Detected by a New Image-Processing Based Method and Magnitude-Thresholding Based Methods
Authors: Xu Guangzhi , Ma Xiaohui , Chang Ping , Wang Lin
Published: June 2020

A majority of the existing atmospheric rivers (ARs) detection methods is based on magnitude thresholding on either the integrated water vapor (IWV) or integrated vapor transport (IVT). One disadvantage of such an approach is that the predetermined threshold does not have the flexibility to adjust to the fast changing conditions where ARs are embedded. To address this issue, a new AR detection method is derived from an image-processing algorithm that makes the detection independent of AR magnitude. In this study, we compare the North Pacific and Atlantic ARs tracked by the new detection method and two widely used magnitude thresholding methods in the present day climate. The results show considerable sensitivities of the detected AR number, shape, intensities and their accounted IVT accumulations to different methods. In many aspects, ARs detected by the new method lie between those from the two magnitude thresholding methods, but stand out with a greater number of AR tracks, longer track durations, and stronger AR-related moisture transport in the AR tracks. North Pacific and North Atlantic ARs identified by the new method account for around 100–120 ×   10 3 kg/m/s IVT within the AR track regions, about 50 % more than the other two methods. This is primarily due to the fact that the new method captures the strong IVT signals more effectively.