bluuebunny/crossref_metadata_2025 huggingface dataset:
The Thought Experimenting Qualities of Kierkegaard’s Fear and Trembling
Authors: Malm-Lindberg Ingrid
Published: June 2019

In this article, I examine the possible thought experimenting qualities of Søren Kierkegaard’s novel Fear and Trembling and in which way (if any) it can be explanatory. Kierkegaard’s preference for pseudonyms, indirect communication, Socratic interrogation, and performativity are identified as features that provide the narrative with its thought experimenting quality. It is also proposed that this literary fiction functions as a Socratic–theological thought experiment due to its influences from both philosophy and theology. In addition, I suggest three functional levels of the fictional narrative that, in different ways, influence its possible explanatory force. As a theoretical background for the investigation, two accounts of literary cognitivism are explored: Noël Carroll’s Argument Account and Catherine Elgin’s Exemplification Account. In relation to Carroll’s proposal, I conclude that Fear and Trembling develops a philosophical argumentation that is dependent on the reader’s own existential contribution. In relation to Elgin’s thought, the relation between truth and explanatory force is acknowledged. At the end of the article, I argue that it is more accurate to see the explanatory force of Fear and Trembling in relation to its exploratory function.
Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR
Authors: Tian Jinyan , Wang Le , Li Xiaojuan , Yin Dameng , Gong Huili , Nie Sheng , Shi Chen , Zhong Ruofei , Liu Xiaomeng , Xu Ronglong
Published: June 2019

Forest biomass is an important descriptor for studying carbon storage, carbon cycles, and global change science. The full-waveform spaceborne Light Detection And Ranging (LiDAR) Geoscience Laser Altimeter System (GLAS) provides great possibilities for large-scale and long-term biomass estimation. To the best of our knowledge, most of the existing research has utilized average tree height (or height metrics) within a GLAS footprint as the key parameter for biomass estimation. However, the vertical distribution of tree height is usually not as homogeneous as we would expect within such a large footprint of more than 2000 m2, which would limit the biomass estimation accuracy vastly. Therefore, we aim to develop a novel canopy height layering biomass estimation model (CHL-BEM) with GLAS data in this study. First, all the trees with similar height were regarded as one canopy layer within each GLAS footprint. Second, the canopy height and canopy cover of each layer were derived from GLAS waveform parameters. These parameters were extracted using a waveform decomposition algorithm (refined Levenberg–Marquardt—RLM), which assumed that each decomposed vegetation signal corresponded to a particular canopy height layer. Third, the biomass estimation model (CHL-BEM) was established by using the canopy height and canopy cover of each height layer. Finally, the CHL-BEM was compared with two typical biomass estimation models of GLAS in the study site located in Ejina, China, where the dominant species was Populus euphratica. The results showed that the CHL-BEM presented good agreement with the field measurement biomass (R2 = 0.741, RMSE = 0.487, %RMSE = 24.192) and achieved a significantly higher accuracy than the other two models. As a whole, we expect our method to advance all the full-waveform LiDAR development and applications, e.g., the newly launched Global Ecosystem Dynamics Investigation (GEDI).
Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds
Authors: Brieger Frederic , Herzschuh Ulrike , Pestryakova Luidmila A. , Bookhagen Bodo , Zakharov Evgenii S. , Kruse Stefan
Published: June 2019

Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra–taiga ecotone should be adapted to the forest structure and have a radius of >15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure.
Inland Waters Suspended Solids Concentration Retrieval Based on PSO-LSSVM for UAV-Borne Hyperspectral Remote Sensing Imagery
Authors: Wei Lifei , Huang Can , Zhong Yanfei , Wang Zhou , Hu Xin , Lin Liqun
Published: June 2019

Suspended solids concentration (SSC) is an important indicator of the degree of water pollution. However, when using an empirical or semi-empirical model adapted to some of the inland waters to estimate SSC on unmanned aerial vehicle (UAV)-borne hyperspectral images, the accuracy is often not sufficient. Thus, in this study, we attempted to use the particle swarm optimization (PSO) algorithm to find the optimal parameters of the least-squares support vector machine (LSSVM) model for the quantitative inversion of SSC. A reservoir and a polluted riverway were selected as the study areas. The spectral data of the 36-point and 29-point 400–900 nm wavelength range on the UAV-borne images were extracted. Compared with the semi-empirical model, the random forest (RF) algorithm and the competitive adaptive reweighted sampling (CARS) algorithm combined with partial least squares (PLS), the accuracy of the PSO-LSSVM algorithm in predicting the SSC was significantly improved. The training samples had a coefficient of determination ( R 2 ) of 0.98, a root mean square error (RMSE) of 0.68 mg/L, and a mean absolute percentage error (MAPE) of 12.66% at the reservoir. For the polluted riverway, PSO-LSSVM also performed well. Finally, the established SSC inversion model was applied to UAV-borne hyperspectral remote sensing (HRS) images. The results confirmed that the distribution of the predicted SSC was consistent with the observed results in the field, which proves that PSO-LSSVM is a feasible approach for the SSC inversion of UAV-borne HRS images.
Application of a Novel Measurement Setup for Characterization of Graphene Microelectrodes and a Comparative Study of Variables Influencing Charge Injection Limits of Implantable Microelectrodes
Authors: Cisnal Ana , R. Ihmig Frank R. , Fraile Juan-Carlos , Pérez-Turiel Javier , Muñoz-Martinez Víctor
Published: June 2019

Depending on their use, electrodes must have a certain size and design so as not to compromise their electrical characteristics. It is fundamental to be aware of all dependences on external factors that vary the electrochemical characteristics of the electrodes. When using implantable electrodes, the maximum charge injection capacity (CIC) is the total amount of charge that can be injected into the tissue in a reversible way. It is fundamental to know the relations between the characteristics of the microelectrode itself and its maximum CIC in order to develop microelectrodes that will be used in biomedical applications. CIC is a very complex measure that depends on many factors: material, size (geometric and effectiveness area), and shape of the implantable microelectrode and long-term behavior, composition, and temperature of the electrolyte. In this paper, our previously proposed measurement setup and automated calculation method are used to characterize a graphene microelectrode and to measure the behavior of a set of microelectrodes that have been developed in the Fraunhofer Institute for Biomedical Engineering (IBMT) labs. We provide an electrochemical evaluation of CIC for these microelectrodes by examining the role of the following variables: pulse width of the stimulation signal, electrode geometry and size, roughness factor, solution, and long-term behavior. We hope the results presented in this paper will be useful for future studies and for the manufacture of advanced implantable microelectrodes.
First Principles Simulations of Phenol and Methanol Detector Based on Pristine Graphene Nanosheet and Armchair Graphene Nanoribbons
Authors: Haroon Rashid Muhammad , Koel Ants , Rang Toomas
Published: June 2019

Over the last decade graphene based electronic devices have attracted the interest of researchers due to their exceptional chemical, electrical and optical properties. Graphene is very sensitive to any physical changes in its surrounding environment and, inherently, has very low electronic noise. This property of graphene makes it a suitable candidate for sensor applications. The purpose of the work presented in this article is to demonstrate the ability of graphene derivatives to detect toxic organic compounds like phenol and methanol. A novel method for the detection of organic compounds (phenol and methanol) has been introduced in this article. In this method, a change in the photocurrent, as well as electric current, have been used as detection signals to improve the sensor accuracy and selectivity for specific target molecules. A nanoscale electronic device simulator, Quantumwise Atomistix Toolkit (ATK), has been used to simulate graphene nanosheet and armchair graphene nanoribbon based sensors. Devices density of states (DOS), current–voltage curves and photocurrent curves have been calculated with the ATK simulator. In the proximity of target molecules, a significant change in DOS, electric current and photocurrent have been observed. The simulated graphene based structures can be converted into physical sensors to obtain a low cost, small sized, integrated sensing device.
Data-Driven Automated Cardiac Health Management with Robust Edge Analytics and De-Risking
Authors: Ukil Arijit , Jara Antonio J. , Marin Leandro
Published: June 2019

Remote and automated healthcare management has shown the prospective to significantly impact the future of human prognosis rate. Internet of Things (IoT) enables the development and implementation ecosystem to cater the need of large number of relevant stakeholders. In this paper, we consider the cardiac health management system to demonstrate that data-driven techniques produce substantial performance merits in terms of clinical efficacy by employing robust machine learning methods with relevant and selected signal processing features. We consider phonocardiogram (PCG) or heart sound as the exemplary physiological signal. PCG carries substantial cardiac health signature to establish our claim of data-centric superior clinical utility. Our method demonstrates close to 85% accuracy on publicly available MIT-Physionet PCG datasets and outperform relevant state-of-the-art algorithm. Due to its simpler computational architecture of shallow classifier with just three features, the proposed analytics method is performed at edge gateway. However, it is to be noted that healthcare analytics deal with number of sensitive data and subsequent inferences, which need privacy protection. Additionally, the problem of healthcare data privacy prevention is addressed by de-risking of sensitive data management using differential privacy, such that controlled privacy protection on sensitive healthcare data can be enabled. When a user sets for privacy protection, appropriate privacy preservation is guaranteed for defense against privacy-breaching knowledge mining attacks. In this era of IoT and machine intelligence, this work is of practical importance, which enables on-demand automated screening of cardiac health under minimizing the privacy breaching risk.
Fault Identification Ability of a Robust Deeply Integrated GNSS/INS System Assisted by Convolutional Neural Networks
Authors: Zou Xiaojun , Lian Baowang , Wu Peng
Published: June 2019

The problem of fault propagation which exists in the deeply integrated GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) system makes it difficult to identify faults. Once a fault occurs, system performance will be degraded due to the inability to identify and isolate the fault accurately. After analyzing the causes of fault propagation and the difficulty of fault identification, maintaining correct navigation solution is found to be the key to prevent fault propagation from occurring. In order to solve the problem, a novel robust algorithm based on convolutional neural network (CNN) is proposed. The optimal expansion factor of the robust algorithm is obtained adaptively by utilizing CNN, thus the adverse effect of fault on navigation solution can be reduced as much as possible. At last, the fault identification ability is verified by two types of experiments: artificial fault injection and outdoor occlusion. Experiment results show that the proposed robust algorithm which can successfully suppress the fault propagation is an effective solution. The accuracy of fault identification is increased by more than 20% compared with that before improvement, and the robustness of deep GNSS/INS integration is also improved.
A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization
Authors: Wang Shipeng , Yang Xiaoping , Wang Xingqiao , Qian Zhihong
Published: June 2019

The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embedded Grey Wolf Optimizer, Cuckoo Search algorithm, and Chaotic Particle Swarm Optimization. The VFLGWO algorithm has good adaptability with respect to changes of the number of sensor nodes and the size of the monitoring area.
Exploration of Chlorophyll a Fluorescence and Plant Gas Exchange Parameters as Indicators of Drought Tolerance in Perennial Ryegrass
Authors: Dąbrowski Piotr , Baczewska-Dąbrowska Aneta H. , Kalaji Hazem M. , Goltsev Vasilij , Paunov Momchil , Rapacz Marcin , Wójcik-Jagła Magdalena , Pawluśkiewicz Bogumiła , Bąba Wojciech , Brestic Marian
Published: June 2019

Perennial ryegrass (Lolium perenne L.) belongs to the common cultivated grass species in Central and Western Europe. Despite being considered to be susceptible to drought, it is frequently used for forming the turf in urban green areas. In such areas, the water deficit in soil is recognized as one of the most important environmental factors, which can limit plant growth. The basic aim of this work was to explore the mechanisms standing behind the changes in the photosynthetic apparatus performance of two perennial ryegrass turf varieties grown under drought stress using comprehensive in vivo chlorophyll fluorescence signal analyses and plant gas exchange measurements. Drought was applied after eight weeks of sowing by controlling the humidity of the roots ground medium at the levels of 30, 50, and 70% of the field water capacity. Measurements were carried out at four times: 0, 120, and 240 h after drought application and after recovery (refilling water to 70%). We found that the difference between the two tested varieties’ response resulted from a particular re-reduction of P700+ (reaction certer of PSI) that was caused by slower electron donation from P680. The difference in the rate of electron flow from Photosystem II (PSII) to PSI was also detected. The application of the combined tools (plants’ photosynthetic efficiency analysis and plant gas exchange measurements) allowed exploring and explaining the specific variety response to drought stress.
Classification for Human Balance Capacity Based on Visual Stimulation under a Virtual Reality Environment
Authors: Jin Haiyan , Xie Le , Xiao Zhaolin , Zhou Ting
Published: June 2019

The normal and disordered people balance ability classification is a key premise for rehabilitation training. This paper proposes a multi-barycentric area model (MBAM), which can be applied for accurate video analysis based classification. First, we have invited fifty-three subjects to wear an HTC (High Tech Computer Corporation) VIVE (Very Immersive Virtual Experience) helmet and to walk ten meters while seeing a virtual environment. The subjects’ motion behaviors are collected as our balance ability classification dataset. Secondly, we use background differential algorithm and bilateral filtering as the preprocessing to alleviate the video noise and motion blur. Inspired by the balance principle of a tumbler, we introduce a MBAM model to describe the body balancing condition by computing the gravity center of a triangle area, which is surrounded by the upper, middle and lower parts of the human body. Finally, we can obtain the projection coordinates according to the center of gravity of the triangle, and get the roadmap of the subjects by connecting those projection coordinates. In the experiments, we adopt four kinds of metrics (the MBAM, the area variance, the roadmap and the walking speed) innumerical analysis to verify the effect of the proposed method. Experimental results show that the proposed method can obtain a more accurate classification for human balance ability. The proposed research may provide potential theoretical support for the clinical diagnosis and treatment for balance dysfunction patients.
Integration of Ground- Penetrating Radar and Gamma-Ray Detectors for Nonintrusive Characterisation of Buried Radioactive Objects
Authors: Ukaegbu Ikechukwu K. , Gamage Kelum A. A. , Aspinall Michael D.
Published: June 2019

The characterisation of buried radioactive wastes is challenging because they are not readily accessible. Therefore, this study reports on the development of a method for integrating ground-penetrating radar (GPR) and gamma-ray detector measurements for nonintrusive characterisation of buried radioactive objects. The method makes use of the density relationship between soil permittivity models and the flux measured by gamma ray detectors to estimate the soil density, depth and radius of a disk-shaped buried radioactive object simultaneously. The method was validated using numerical simulations with experimentally-validated gamma-ray detector and GPR antenna models. The results showed that the method can simultaneously retrieve the soil density, depth and radius of disk-shaped radioactive objects buried in soil of varying conditions with a relative error of less than 10%. This result will enable the development of an integrated GPR and gamma ray detector tool for rapid characterisation of buried radioactive objects encountered during monitoring and decontamination of nuclear sites and facilities.
Slice Management for Quality of Service Differentiation in Wireless Network Slicing
Authors: An Namwon , Kim Yonggang , Park Juman , Kwon Dae-Hoon , Lim Hyuk
Published: June 2019

Network slicing is a technology that virtualizes a single infrastructure into multiple logical networks (called slices) where resources or virtualized functions can be flexibly configured by demands of applications to satisfy their quality of service (QoS) requirements. Generally, to provide the guaranteed QoS in applications, resources of slices are isolated. In wired networks, this resource isolation is enabled by allocating dedicated data bandwidths to slices. However, in wireless networks, resource isolation may be challenging because the interference between links affects the actual bandwidths of slices and degrades their QoS. In this paper, we propose a slice management scheme that mitigates the interference imposed on each slice according to their priorities by determining routes of flows with a different routing policy. Traffic flows in the slice with the highest priority are routed into shortest paths. In each lower-priority slice, the routing of traffic flows is conducted while minimizing a weighted summation of interference to other slices. Since higher-priority slices have higher interference weights, they receive lower interference from other slices. As a result, the QoS of slices is differentiated according to their priorities while the interference imposed on slices is reduced. We compared the proposed slice management scheme with a naïve slice management (NSM) method that differentiates QoS among slices by priority queuing. We conducted some simulations and the simulation results show that our proposed management scheme not only differentiates the QoS of slices according to their priorities but also enhances the average throughput and delay performance of slices remarkably compared to that of the NSM method. The simulations were conducted in grid network topologies with 16 and 100 nodes and a random network topology with 200 nodes. Simulation results indicate that the proposed slice management increased the average throughput of slices up to 6%, 13%, and 7% and reduced the average delay of slices up to 14%, 15%, and 11% in comparison with the NSM method.
A Novel Relative Position Estimation Method for Capsule Robot Moving in Gastrointestinal Tract
Authors: Wang Min , Shi Qinyuan , Song Shuang , Hu Chao , Meng Max Q.-H.
Published: June 2019

Recently, a variety of positioning and tracking methods have been proposed for capsule robots moving in the gastrointestinal (GI) tract to provide real-time unobstructed spatial pose results. However, the current absolute position-based result cannot match the GI structure due to its unstructured environment. To overcome this disadvantage and provide a proper position description method to match the GI tract, we here present a relative position estimation method for tracking the capsule robot, which uses the moving distance of the robot along the GI tract to indicate the position result. The procedure of the proposed method is as follows: firstly, the absolute position results of the capsule robot are obtained with the magnetic tracking method; then, the moving status of the robot along the GI tract is determined according to the moving direction; and finally, the movement trajectory of the capsule robot is fitted with the Bézier curve, where the moving distance can then be evaluated using the integral method. Compared to state-of-the-art capsule tracking methods, the proposed method can directly help to guide medical instruments by providing physicians the insertion distance in patients’ bodies, which cannot be done based on absolute position results. Moreover, as relative distance information was used, no reference tracking objects needed to be mounted onto the human body. The experimental results prove that the proposed method achieves a good distance estimation of the capsule robot moving in the simulation platform.
SmarTEG: An Autonomous Wireless Sensor Node for High Accuracy Accelerometer-Based Monitoring
Authors: Magno Michele , Sigrist Lukas , Gomez Andres , Cavigelli Lukas , Libri Antonio , Popovici Emanuel , Benini Luca
Published: June 2019

We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the low complexity algorithm implemented, our solution works perpetually while also achieving high accuracy. The onboard algorithm processes sensor data, extracts features, performs the classification needed for the blade’s wear detection, and sends the report wirelessly. Experimental results in a real-world deployment scenario demonstrate that its accuracy is comparable to state-of-the-art algorithms executed on a PC and show the energy-neutrality of the solution using a small thermoelectric generator to harvest energy. The impact of various low-power techniques implemented on the node is analyzed, highlighting the benefits of onboard processing, the nano-power wake-up radio, and the combination of harvesting and low power design. Finally, accurate in-field energy intake measurements, coupled with simulations, demonstrate that the proposed approach is energy autonomous and can work perpetually.
Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform
Authors: Li Guoqiang , Deng Chao , Wu Jun , Xu Xuebing , Shao Xinyu , Wang Yuanhang
Published: June 2019

Accurate and timely bearing fault diagnosis is crucial to decrease the probability of unexpected failures of rotating machinery and improve the efficiency of its scheduled maintenance. Since convolutional neural networks (CNN) have poor feature extraction capability for sensor data with 1D format, CNN combined with signal processing algorithm is often adopted for fault diagnosis. This increases manual conversion work and expertise dependence while reducing the feasibility and robustness of the corresponding fault diagnosis method. In this paper, a novel sensor data-driven fault diagnosis method is proposed by fusing S-transform (ST) algorithm and CNN, namely ST-CNN. First of all, a ST layer is designed based on S-transform algorithm. In the ST layer, sensor data is automatically converted into 2D time-frequency matrix without manual conversion work. Then, a new ST-CNN model is constructed, and the time-frequency coefficient matrixes are inputted into the constructed ST-CNN model. After the training process of the ST-CNN model is completed, the classification layer such as softmax performs the fault diagnosis. Finally, the diagnosis performance of the proposed method is evaluated by using two public available datasets of bearings. The experimental results show that the proposed method performs the higher and more robust diagnosis performance than other existing methods.
Review on V2X, I2X, and P2X Communications and Their Applications: A Comprehensive Analysis over Time
Authors: Lozano Domínguez José Manuel , Mateo Sanguino Tomás Jesús
Published: June 2019

Smart cities are ecosystems where novel ideas and emerging technologies meet to improve economy, environment, governance, living, and mobility. One of the pillars of smart cities is transport, with the improvement of mobility and the reduction of traffic accidents being some of the current key challenges. With this purpose, this manuscript reviews the state-of-the-art of communications and applications in which different actors of the road are involved. Thus, the objectives of this survey are intended to determine who, when, and about what is being researched around smart cities. Particularly, the goal is to situate the focus of scientific and industrial progress on V2X, I2X, and P2X communication to establish a taxonomy that reduces ambiguous acronyms around the communication between vehicles, infrastructure, and pedestrians, as well as to determine what the trends and future technologies are that will lead to more powerful applications. To this end, this literature review article presents a comprehensive study including a representative collection of the 100 most cited papers and patents published in the literature together with a statistical bibliometric analysis of 14,364 keywords over 3422 contributions between 1997 and 2018. As a result, this work provides a technological profile considering different dimensions along the paper, such as the type of communication, use case, country, organization, terminology, and year.
Self-Calibration of Nonlinear Signal Model for Angular Position Sensors by Model-Based Automatic Search Algorithm
Authors: Gao Zhenyi , Zhou Bin , Hou Bo , Li Chao , Wei Qi , Zhang Rong
Published: June 2019

This study proposes a novel model-based automatic search algorithm to realize the self-calibration of nonlinear signal model for angular position sensors. In some high-precision angular position sensors, nonlinearity of the signal model is the main source of errors and cannot be handled effectively. By constructing a signal flow network framework and by embedding a modeling search network, the parameters of the nonlinear signal model can be searched, and the calibration signal can be obtained. The convergence of the network search process was analyzed. The relationship between the optimization threshold and the convergence accuracy was also studied in simulations. Compared with the maximum angular error reduction to 47.42% after the calibration with simplified model that ignores signal nonlinearities, the proposed scheme was able to reduce this error to 0.0025% in simulations. By implementing the technique in a capacitive angular position sensor, the experimental results showed that the maximum angular error was reduced to 1.63% compared to a reduction of 86.02% achieved with the simplified model calibration. The effects of the search network order and layer number on the calibration accuracy were also analyzed, and the optimal parameters under experimental conditions were obtained. Correspondingly, the proposed scheme is able to handle calibration of nonlinear signal model and further improve sensor accuracy.
Effects of Self-Talk Training on Competitive Anxiety, Self-Efficacy, Volitional Skills, and Performance: An Intervention Study with Junior Sub-Elite Athletes
Authors: Walter Nadja , Nikoleizig Lucie , Alfermann Dorothee
Published: June 2019

(1) Background: Self-talk (ST) is used to influence athletes’ thoughts, feelings, and behaviors. Samples of squad and competitive athletes are underrepresented, although research has proven the positive effects of ST in the context of sports. Thus, the present study focused on the impact of ST on psychological and performance outcomes of junior sub-elite athletes. (2) Methods: N = 117 athletes (55 females, 62 males; M = 16.0 years) were randomly assigned to either one of two experimental groups or to a control group (n = 30). The experimental groups received an ST intervention for either one week (n = 36) or eight weeks (n = 38), and the control group received no ST training. The dependent variables (competitive anxiety, volitional skills, self-efficacy, and coaches’ performance ratings) were assessed three times before and after the intervention. It was expected that (a) an ST intervention would reduce the competitive anxiety and increase volitional skills, self-efficacy, and performance; and, (b) long-term training would lead to higher effects than short-term training. (3) Results: As expected, ST training led to (less) somatic state anxiety and (higher) state self-confidence, self-optimization, self-efficacy, and performance. Additionally, long-term training was more effective than short-term training. (4) Conclusions: Targeted ST interventions may help to improve junior athletes’ psychological states and performance.
Exploring Potential Soil Bacteria for Sustainable Wheat (Triticum aestivum L.) Production
Authors: Sheirdil Rizwan Ali , Hayat Rifat , Zhang Xiao-Xia , Abbasi Nadeem Akhtar , Ali Safdar , Ahmed Mukhtar , Khattak Jabar Zaman Khan , Ahmad Shakeel
Published: June 2019

The application of plant growth-promoting rhizobacteria (PGPR) could allow growers to reduce the use of synthetic fertilizers and increase the sustainability of crop production. Wheat is the main staple food crop of Pakistan, and few studies have reported on the impact of PGPR on wheat crops. To determine if PGPR can maintain wheat productivity with reduced fertilizer applications, we isolated bacteria from the rhizosphere of wheat grown in sandy loam. We selected 10 strains based on in vitro assays for traits associated with PGPR: ACC deaminase activity, siderophore productivity, P-solubilization, and productivity of indole acetic acid (IAA). Furthermore, the strains were tested in three experiments (using a growth-chamber, pots with an experimental area of 0.05 m2, and a field). Strains that possessed the four traits associated with PGPR increased the shoot length, root length, and fresh and dry weight of plants in the growth chamber study. Similarly, under the pot trial, maximum crop traits were observed under the consortium + half dose, while under field conditions maximum crop parameters were detected in the case of consortium 1 and consortium 2 along with half the recommended dose of fertilizer. This confirms that this consortium could provide growers with a sustainable approach to reduce synthetic fertilizer usage in wheat production.
How Does Destination Social Responsibility Impact Residents’ Pro-Tourism Behaviors? The Mediating Role of Place Attachment
Authors: Hu Bing , Tuou Yangying , Liu Jing
Published: June 2019

There has been little research on how destination social responsibility impacts the pro-tourism behaviors of residents. Drawing from place attachment theory and sustainable tourism literature, this study proposes that place attachment mediates the link between destination social responsibility and pro-tourism behaviors. The results from a field survey of local residents around three scenic spots in the Xuefeng mountain area in China reveal that destination social responsibility exerts a significantly positive impact on pro-tourism behaviors of residents, and confirm a mediating effect of place attachment (i.e., place identity and place dependence) in the linkage of destination social responsibility and pro-tourism behaviors. This research provides theoretical and managerial contributions to sustainable tourism literature and advances the understanding of the antecedents of residents’ pro-tourism behaviors.
Physical Properties of Straw Bales as a Construction Material: A Review
Authors: Cascone Stefano , Rapisarda Renata , Cascone Dario
Published: June 2019

Straw bale buildings provide significant benefits in terms of costs, human health, and environmental sustainability. Several studies in different regions have underlined the remarkable properties of straw bales as insulating and construction material; however, to the authors’ knowledge, there are no reviews published on this topic. The main objective of this paper is to provide a better understanding of straw bale systems, focusing on durability and thermal and acoustic insulation properties. To this end, previous tests and studies on straw bale buildings around the world were reviewed, comparing their results, assessing where research currently stands, and identifying the aspects that need to be further investigated. Results from previous tests have highlighted their ability to achieve excellent living comfort and encouraged their use. Guidelines for the characteristics to be achieved during the baling process are now required. Combining straw bale walls with a render or any type of high-density layer can improve both the thermal and acoustic properties of straw bale constructions. Finally, a quantitative assessment of the most significant properties, such as thermal resistance and acoustic insulation, is necessary to reduce the gap between straw bales and traditional building materials.
Fire Spread of Thermal Insulation Materials in the Ceiling of Piloti-Type Structure: Comparison of Numerical Simulation and Experimental Fire Tests Using Small- and Real-Scale Models
Authors: Suh Heong-Won , Im Su-Min , Park Tae-Hoon , Kim Hyung-Jun , Kim Hong-Sik , Choi Hyun-Ki , Chung Joo-Hong , Bae Sung-Chul
Published: June 2019

Large-scale fires mainly due to the ignition of thermal insulation materials in the ceiling of piloti-type structures are becoming frequent. However, the fire spread in these cases is not well understood. Herein we performed small-scale and real-scale model tests, and numerical simulations using a fire dynamics simulator (FDS). The experimental and FDS results were compared to elucidate fire spread and effects of thermal insulation materials on it. Comparison of real-scale fire test and FDS results revealed that extruded polystyrene (XPS) thermal insulation material generated additional ignition sources above the ceiling materials upon melting and propagated and sustained the fire. Deformation of these materials during fire test generated gaps, and combustible gases leaked out to cause fire spread. When the ceiling materials collapsed, air flew in through the gaps, leading to flashover that rapidly increased fire intensity and degree of spread. Although the variations of temperatures in real-scale fire test and FDS analysis were approximately similar, melting of XPS and generation of ignition sources could not be reproduced using FDS. Thus, artificial settings that increase the size and intensity of ignition sources at the appropriate moment in FDS were needed to achieve results comparable to those recorded by heat detectors in real-scale fire tests.
Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network
Authors: Li Yiting , Xie Qingsheng , Huang Haisong , Chen Qipeng
Published: June 2019

To accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using acceleration sensors); these signals are processed to denoise and segmented to provide a larger number of uniform samples. This processing helps to improve the robustness of the model. Secondly, a new deep convolutional neural network is proposed to extract features adaptively, by combining the idea of a recursive residual network and a dense network. Notably, this method is specifically tailored to the tool wear value detection problem. In this way, the limitations of traditional manual feature extraction steps can be avoided. The experimental results demonstrate that the proposed method is promising in terms of detection accuracy and speed; it provides a new way to detect tool wear values in practical industrial scenarios.
Magnetohydrodynamic and Nanoparticle Effects in Vertical Annular Subcooled Flow Boiling
Authors: Abdollahzadeh Jamalabadi Mohammad
Published: June 2019

The control of heated fluid is of interest in many fields of engineering, such as boiler and heat exchanger design. The broken symmetry of a thermo-physical system within a multi-sized media could be used to control its physical characteristics. In the current study, the effects of magnetohydrodynamic (MHD) forces and nanoparticles on boiling in a subcooled region inside an upright annular pipe have been investigated. The effect of magneto hydrodynamics on the base fluid (liquid water) was measured, and different nanoparticle concentrations were employed as the working fluids. The magnetic field perpendicular to fluid flow is used to control the liquid water and vapor water phase motion. The governing equation of motion and conservation of energy in both phases is solved with the aid of correlation for vaporization and condensation of nucleate boiling on the wall. The results of the mathematical simulation are in suitable agreement with the results of previous experiments. As associated with pure water, the results with dilute Nanofluids presented that the application of nanoparticles homogenized the temperature difference through the fluid and vapor phase. The results show that the MHD controller is a powerful method to decrease the amplitude of the vaporization and resulted in oscillations.
Iterative Algorithms for a System of Variational Inclusions in Banach Spaces
Authors: Ceng Lu-Chuan , Postolache Mihai , Yao Yonghong
Published: June 2019

A system of variational inclusions (GSVI) is considered in Banach spaces. An implicit iterative procedure is proposed for solving the GSVI. Strong convergence of the proposed algorithm is given.
Determining Fuzzy Distance between Sets by Application of Fixed Point Technique Using Weak Contractions and Fuzzy Geometric Notions
Authors: Saha Parbati , Guria Shantau , Choudhury Binayak S. , De la Sen Manuel
Published: June 2019

In the present paper, we solve the problem of determining the fuzzy distance between two subsets of a fuzzy metric space. We address the problem by reducing it to the problem of finding an optimal approximate solution of a fixed point equation. This approach is well studied for the corresponding problem in metric spaces and is known as proximity point problem. We employ fuzzy weak contractions for that purpose. Fuzzy weak contraction is a recently introduced concept intermediate to a fuzzy contraction and a fuzzy non-expansive mapping. Fuzzy versions of some geometric properties essentially belonging to Hilbert spaces are considered in the main theorem. We include an illustrative example and two corollaries, one of which comes from a well-known fixed point theorem. The illustrative example shows that the main theorem properly includes its corollaries. The work is in the domain of fuzzy global optimization by use of fixed point methods.
Tissue Accumulations of Toxic Aconitum Alkaloids after Short-Term and Long-Term Oral Administrations of Clinically Used Radix Aconiti Lateralis Preparations in Rats
Authors: Ji Xiaoyu , Yang Mengbi , Or Ka Hang , Yim Wan Sze , Zuo Zhong
Published: June 2019

Although Radix Aconiti Lateralis (Fuzi) is an extensively used traditional Chinese medicine with promising therapeutic effects and relatively well-reported toxicities, the related toxic aconitum alkaloid concentrations in major organs after its short-term and long-term intake during clinical practice are still not known. To give a comprehensive understanding of Fuzi-induced toxicities, current study is proposed aiming to investigate the biodistribution of the six toxic alkaloids in Fuzi, namely Aconitine (AC), Hypaconitine (HA), Mesaconitine (MA), Benzoylaconine (BAC), Benzoylhypaconine (BHA) and Benzoylmesaconine (BMA), after its oral administrations at clinically relevant dosing regimen. A ultra-performance liquid chromatography-tandem mass spectrometry (UPLC–MS/MS) method was developed and validated for simultaneous quantification of six toxic alkaloids in plasma, urine and major organs of Sprague Dawley rats after oral administrations of two commonly used Fuzi preparations, namely Heishunpian and Paofupian, at their clinically relevant dose for single and 15-days. Among the studied toxic alkaloids and organs, BMA demonstrated the highest concentrations in all studied organs with liver containing the highest amount of the studied alkaloids, indicating their potential hepatotoxicity. Moreover, tissue accumulation of toxic alkaloids after multiple dose was observed, suggesting the needs for dose adjustment and more attention to the toxicities induced by chronic use of Fuzi in patients.
Membrane Permeabilization by Pore-Forming RTX Toxins: What Kind of Lesions Do These Toxins Form?
Authors: Ostolaza Helena , González-Bullón David , Uribe Kepa B. , Martín Cesar , Amuategi Jone , Fernandez-Martínez Xabier
Published: June 2019

Pore-forming toxins (PFTs) form nanoscale pores across target membranes causing cell death. The pore-forming cytolysins of the RTX (repeats in toxin) family belong to a steadily increasing family of proteins characterized by having in their primary sequences a number of glycine- and aspartate-rich nonapeptide repeats. They are secreted by a variety of Gram-negative bacteria and form ion-permeable pores in several cell types, such as immune cells, epithelial cells, or erythrocytes. Pore-formation by RTX-toxins leads to the dissipation of ionic gradients and membrane potential across the cytoplasmic membrane of target cells, which results in cell death. The pores formed in lipid bilayers by the RTX-toxins share some common properties such as cation selectivity and voltage-dependence. Hemolytic and cytolytic RTX-toxins are important virulence factors in the pathogenesis of the producing bacteria. And hence, understanding the function of these proteins at the molecular level is critical to elucidating their role in disease processes. In this review we summarize the current state of knowledge on pore-formation by RTX toxins, and include recent results from our own laboratory regarding the pore-forming activity of adenylate cyclase toxin (ACT or CyaA), a large protein toxin secreted by Bordetella pertussis, the bacterium causative of whooping cough.
Mammalian orthoreovirus Infection is Enhanced in Cells Pre-Treated with Sodium Arsenite
Authors: Lutz Michael M. , Worth Megan P. , Hinchman Meleana M. , Parker John S.L. , Ledgerwood Emily D.
Published: June 2019

Following reovirus infection, cells activate stress responses that repress canonical translation as a mechanism to limit progeny virion production. Work by others suggests that these stress responses, which are part of the integrated stress response (ISR), may benefit rather than repress reovirus replication. Here, we report that compared to untreated cells, treating cells with sodium arsenite (SA) to activate the ISR prior to infection enhanced viral protein expression, percent infectivity, and viral titer. SA-mediated enhancement was not strain-specific, but was cell-type specific. While SA pre-treatment of cells offered the greatest enhancement, treatment within the first 4 h of infection increased the percent of cells infected. SA activates the heme-regulated eIF2α (HRI) kinase, which phosphorylates eukaryotic translation initiation factor 2 alpha (eIF2α) to induce stress granule (SG) formation. Heat shock (HS), another activator of HRI, also induced eIF2α phosphorylation and SGs in cells. However, HS had no effect on percent infectivity or viral yield but did enhance viral protein expression. These data suggest that SA pre-treatment perturbs the cell in a way that is beneficial for reovirus and that this enhancement is independent of SG induction. Understanding how to manipulate the cellular stress responses during infection to enhance replication could help to maximize the oncolytic potential of reovirus.