bluuebunny/crossref_metadata_2025 huggingface dataset:
Glass Transition Behaviors of Poly (Vinyl Pyridine)/Poly (Vinyl Phenol) Revisited
Authors: Urakawa Osamu , Yasue Ayaka
Published: July 2019

We examined the composition and molecular weight dependence of the glass transition temperature in detail for two types of hydrogen bonding miscible blends: poly (2-vinyl pyridine)/poly (vinyl phenol) (2VPy/VPh) and poly (4-vinyl pyridine)/poly (vinyl phenol) (4VPy/VPh). Regarding the functional form of the glass transition temperature, Tg, as a function of the weight fraction, we found a weak deviation from the Kwei equation for 2VPy/VPh blends. In contrast, such a deviation was not observed for the 4VPy/VPh blend. By relating the difference in the functional forms of Tg between the two blend systems to the difference in hydrogen bonding ability, we proposed a modified version of the Kwei equation. As for the interaction parameter, q in the Kwei equation, clear molecular weight dependence was observed for 2VPy/VPh blends: the lower the VPh molecular weight in the oligomer level, the higher the q values, suggesting the higher hydrogen bonding formability near the polymer chain ends than the middle part of a polymer chain.
Some Applications of Vibrational Spectroscopy for the Analysis of Polymers and Polymer Composites
Authors: Bokobza Liliane
Published: July 2019

Vibrational spectroscopies, including infrared and Raman techniques, are important tools for the characterization of chemical composition, molecular structures, and chain orientation under mechanical deformation of polymeric materials. The development of fiber-optic-based spectrometers has broadened the use of vibrational spectroscopy for process monitoring in various fields including polymerization, curing, and manufacturing processes. Combined with chemometrics, near-infrared (NIR) spectroscopy is now recognized as one of the most important techniques for polymer analyses. Infrared and Raman studies also offer invaluable means for the analysis of inorganic particles used as reinforcing fillers for polymers. The characterization of surface species and the nature of interfacial bonding between the organic and inorganic phases are important issues for the understanding of composite properties. Infrared spectroscopy is particularly convenient for the detection and analysis of hydroxyl groups on filler surfaces, and Raman spectroscopy is particularly well suited for the study of carbon-based materials. In both techniques, polymer-filler interactions can be evidenced through frequency shifts or width changes of bands associated with vibrational modes of functional groups of either macromolecular chains or filler particles. Selected examples of application of infrared and Raman spectroscopies illustrate their potential for monitoring polymer processes, measuring polymer orientation, and characterizing polymer composites.
Effects of Operating Parameters on the Cut Size of Turbo Air Classifier for Particle Size Classification of SAC305 Lead-Free Solder Powder
Authors: Denmud Nipon , Baite Kradsanai , Plookphol Thawatchai , Janudom Somjai
Published: July 2019

In the present study, the effects of operating parameters, namely, rotor speed, feed rate, and inlet air velocity, on the cut diameter of a cage-type separator were studied. The design of experiments (DOE) method was used to investigate the relationship between the operating parameters and the cut size. The experimental results were statistically analyzed using MINITAB 16 software. Both the rotor speed and air inlet velocity had significant main effects on the cut size. The feed rate was also significant but had a weak effect with respect to the rotor speed and inlet air velocity effects. The cut size decreased with an increase in rotor speed and increased with an increase in air inlet velocity. However, the cut size slightly decreased with an increase in feed rate. An empirical multiple-variable linear model for predicting the cut size of the classification was created and presented. The results derived from the statistical analysis were in good agreement with those from the experiments, additionally extended from the DOE. The optimal conditions for classification of SAC305 powder with size range 25–40 μm were obtained when the turbo air classifier was operated at rotor speed 406 RPM, the feed rate 4 kg/h, and the air velocity 5 m/s. The smallest cut size of the classifier was about 27.8 μm.
Modelling Hydration Swelling and Weakening of Montmorillonite Particles in Mudstone
Authors: Sun Changlun , Li Guichen , Sun Yuantian , He Jintao , Rong Haoyu
Published: July 2019

It is of paramount importance to understand the hydration swelling and weakening properties of clay minerals, such as montmorillonite, to determine their mechanical responses during deep underground argillaceous engineering. In this study, the mineral components and microscopic structure of mudstone were characterised using X-ray powder diffraction and field-emission scanning electron microscopy. Experimental schemes were devised to determine the properties of mudstone under the influence of underground water and stress; these involved compacting montmorillonite particles with various water contents and conducting uniaxial compression tests. Experimental results demonstrated that compaction stress changes the microscopic structure of the montmorillonite matrix and affects its properties, and stress independency was found at particular water and stress conditions. Two equations were then obtained to describe the swelling and weakening properties of the montmorillonite matrix based on the discrete element method; further, the hydration swelling equation represents the linear decrease in the density of the montmorillonite matrix with an increase in the water content. It was also determined that the water dependency of uniaxial compressive strength can be described by negative quartic equations, and the uniaxial compressive strength of the montmorillonite matrix is just 0.04 MPa with a water content of 0.6. The experimental results are in good agreement with the calculated solutions and provide an important experimental basis to the understanding of the mechanical properties of montmorillonite-rich mudstones under the influence of underground water and stress.
Sacramentality, Chaos Theory and Decoloniality
Authors: Foley Edward
Published: July 2019

This essay considers how an expanded understanding of sacramentality is enhanced by engagement with chaos theory and decolonial theory. These unique lenses enlarge traditional Roman Catholic frameworks for considering God’s self-communication through sacramental action as well as the agency of ordinary believers and even non-believers in the sacramental enterprise.
Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery
Authors: He Li , Li Ainong , Yin Gaofei , Nan Xi , Bian Jinhu
Published: July 2019

The estimation of aboveground biomass (AGB), an important indicator of grassland production, is crucial for evaluating livestock carrying capacity, understanding the response and feedback to climate change, and achieving sustainable development. Most existing grassland AGB estimation studies were based on empirical methods, in which field measurements are indispensable, hindering their operational use. This study proposed a novel physically-based grassland AGB retrieval method through the inversion of PROSAIL model against MCD43A4 imagery. This method relies on the basic understanding that grassland is herbaceous, and therefore AGB can be represented as the product of leaf dry matter content (Cm) and leaf area index (LAI), i.e., AGB = Cm × LAI. First, the PROSAIL model was parameterized according to the literature regarding grassland parameters retrieval, then Cm and LAI were retrieved using a lookup table (LUT) algorithm, finally, the retrieved Cm and LAI were multiplied to obtain the AGB. The method was assessed in Zoige Plateau, China. Results show that it could reproduce the reference AGB map, which is generated by upscaling the field measurements, in terms of magnitude (with RMSE and R-RMSE of 60.06 g·m−2 and 18.1%, respectively) and spatial distribution. The estimated AGB time series also agreed reasonably well with the expected temporal dynamic trends of the grassland in our study area. The greatest advantage of our method is its fully physical nature, i.e., no field measurement is needed. Our method has the potential for operational monitoring of grassland AGB at regional and even larger scales.
A Hybrid Model Integrating Spatial Pattern, Spatial Correlation, and Edge Information for Image Classification
Authors: Tang Yunwei , Jing Linhai , Shi Fan , Li Xiao , Qiu Fang
Published: July 2019

This paper develops a novel hybrid model that integrates three spatial contexts into probabilistic classifiers for remote sensing classification. First, spatial pattern is introduced using multiple-point geostatistics (MPGs) to characterize the general distribution and arrangement of land covers. Second, spatial correlation is incorporated using spatial covariance to quantify the dependence between pixels. Third, an edge-preserving filter based on the Sobel mask is introduced to avoid the over-smoothing problem. These three types of contexts are combined with the spectral information from the original image within a higher-order Markov random field (MRF) framework for classification. The developed model is capable of classifying complex and diverse land cover types by allowing effective anisotropic filtering of the image while retaining details near edges. Experiments with three remote sensing images from different sources based on three probabilistic classifiers obtained results that significantly improved classification accuracies when compared with other popular contextual classifiers and most state-of-the-art methods.
Adjustment of Transceiver Lever Arm Offset and Sound Speed Bias for GNSS-Acoustic Positioning
Authors: Chen Guanxu , Liu Yang , Liu Yanxiong , Tian Ziwen , Li Menghao
Published: July 2019

Global Navigation Satellite System—Acoustic (GNSS-A) positioning is the main technique for seafloor geodetic positioning. A transceiver lever arm offset and sound velocity bias in seawater are the main systematic errors of the GNSS-A positioning technique. Based on data from a sea trial in shallow water, this paper studies the functional model of GNSS-A positioning. The impact of the two systematic errors on seafloor positioning is analysed and corresponding processing methods are proposed. The results show that the offset in the lever arm measurement should be parameterised in the observation equation. Given the high correlation between the vertical lever arm offset and the vertical coordinate of the seafloor station, a sample search method was introduced to fix the vertical offset correction. If the calibration of the sound velocity profiler cannot be ensured, the correction parameter of the sound velocity bias should be solved. According to the refined functional model and corrections, the position of a seafloor station in shallow water can be determined with a precision of better than 1 cm.
Joint Congestion Control and Resource Allocation in Cache-Enabled Sensor Networks
Authors: Ren Yuan , Lu Guangyue , Sun Changyin
Published: July 2019

In this paper, we investigate the optimal beamforming design to achieve joint congestion control and energy-efficient resource allocation in cache-enabled sensor networks. The network of interest works in the time-slotted mode. The dynamic buffering queue for each node is introduced to reflect the degree of network congestion and service delay. Then, a time-averaged sum rate maximization problem is proposed under the constraints of queue stability, instantaneous power consumption, average power consumption, and the minimum quality of service requirements. By introducing the method of Lyapunov optimization, the importance of buffering queue backlogs and sum rate maximization can be traded off, then the original queue-aware and time-averaged optimization problem is transformed into a weighted sum rate maximization problem at each time slot. It can be further converted into a second-order cone-programming problem by successive convex approximation, which is convex and can be efficiently solved by off-the-shelf solvers. Numerical results validate that wireless caching can greatly relieve the network congestion by reducing the buffering backlogs, and show that the proposed scheme can trade off the average queue length and time-averaged sum rate by selecting different control parameters.
Vision-Based Novelty Detection Using Deep Features and Evolved Novelty Filters for Specific Robotic Exploration and Inspection Tasks
Authors: Contreras-Cruz Marco Antonio , Ramirez-Paredes Juan Pablo , Hernandez-Belmonte Uriel Haile , Ayala-Ramirez Victor
Published: July 2019

One of the essential abilities in animals is to detect novelties within their environment. From the computational point of view, novelty detection consists of finding data that are different in some aspect to the known data. In robotics, researchers have incorporated novelty modules in robots to develop automatic exploration and inspection tasks. The visual sensor is one of the preferred sensors to perform this task. However, there exist problems as illumination changes, occlusion, and scale, among others. Besides, novelty detectors vary their performance depending on the specific application scenario. In this work, we propose a visual novelty detection framework for specific exploration and inspection tasks based on evolved novelty detectors. The system uses deep features to represent the visual information captured by the robots and applies a global optimization technique to design novelty detectors for specific robotics applications. We verified the performance of the proposed system against well-established state-of-the-art methods in a challenging scenario. This scenario was an outdoor environment covering typical problems in computer vision such as illumination changes, occlusion, and geometric transformations. The proposed framework presented high-novelty detection accuracy with competitive or even better results than the baseline methods.
Chimerical Dataset Creation Protocol Based on Doddington Zoo: A Biometric Application with Face, Eye, and ECG
Authors: Lopes Silva Pedro , Luz Eduardo , Moreira Gladston , Moraes Lauro , Menotti David
Published: July 2019

Multimodal systems are a workaround to enhance the robustness and effectiveness of biometric systems. A proper multimodal dataset is of the utmost importance to build such systems. The literature presents some multimodal datasets, although, to the best of our knowledge, there are no previous studies combining face, iris/eye, and vital signals such as the Electrocardiogram (ECG). Moreover, there is no methodology to guide the construction and evaluation of a chimeric dataset. Taking that fact into account, we propose to create a chimeric dataset from three modalities in this work: ECG, eye, and face. Based on the Doddington Zoo criteria, we also propose a generic and systematic protocol imposing constraints for the creation of homogeneous chimeric individuals, which allow us to perform a fair and reproducible benchmark. Moreover, we have proposed a multimodal approach for these modalities based on state-of-the-art deep representations built by convolutional neural networks. We conduct the experiments in the open-world verification mode and on two different scenarios (intra-session and inter-session), using three modalities from two datasets: CYBHi (ECG) and FRGC (eye and face). Our multimodal approach achieves impressive decidability of 7.20 ± 0.18, yielding an almost perfect verification system (i.e., Equal Error Rate (EER) of 0.20% ± 0.06) on the intra-session scenario with unknown data. On the inter-session scenario, we achieve a decidability of 7.78 ± 0.78 and an EER of 0.06% ± 0.06. In summary, these figures represent a gain of over 28% in decidability and a reduction over 11% of the EER on the intra-session scenario for unknown data compared to the best-known unimodal approach. Besides, we achieve an improvement greater than 22% in decidability and an EER reduction over 6% in the inter-session scenario.
Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases
Authors: Palatnik de Sousa Iam , Maria Bernardes Rebuzzi Vellasco Marley , Costa da Silva Eduardo
Published: July 2019

An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on how a Convolutional Neural Network (CNN) detects tumor tissue in patches extracted from histology whole slide images. This is achieved using the “locally-interpretable model-agnostic explanations” methodology. Two publicly-available convolutional neural networks trained on the Patch Camelyon Benchmark are analyzed. Three common segmentation algorithms are compared for superpixel generation, and a fourth simpler parameter-free segmentation algorithm is proposed. The main characteristics of the explanations are discussed, as well as the key patterns identified in true positive predictions. The results are compared to medical annotations and literature and suggest that the CNN predictions follow at least some aspects of human expert knowledge.
Preamble-Based Adaptive Channel Estimation for IEEE 802.11p
Authors: Choi Joo-Young , Jo Han-Shin , Mun Cheol , Yook Jong-Gwan
Published: July 2019

Recently, research into autonomous driving and traffic safety has been drawing a great deal of attention. To realize autonomous driving and solve traffic safety problems, wireless access in vehicular environments (WAVE) technology has been developed, and IEEE 802.11p defines the physical (PHY) layer and medium access control (MAC) layer in the WAVE standard. However, the IEEE 802.11p frame structure, which has low pilot density, makes it difficult to predict the properties of wireless channels in a vehicular environment with high vehicle speeds; thus, the performance of the system is degraded in realistic vehicular environments. The motivation for this paper is to improve the channel estimation and tracking performance without changing the IEEE 802.11p frame structure. Therefore, we propose a channel estimation technique that can perform well over the entire SNR range of values by changing the method of channel estimation accordingly. The proposed scheme selectively uses two channel estimation schemes, each with outstanding performance for either high-SNR or low-SNR signals. To implement this, an adaptation algorithm based on a preamble is proposed. The preamble is a signal known to the transmitter–receiver, so that the receiver can obtain channel estimates without demapping errors, evaluating performance of the channel estimation schemes. Simulation results comparing the proposed method to other schemes demonstrate that the proposed scheme can selectively switch between the two schemes to improve overall performance.
A Strain-Based Method to Estimate Tire Parameters for Intelligent Tires under Complex Maneuvering Operations
Authors: Mendoza-Petit Mª Fernanda , Garcia-Pozuelo Daniel , Diaz Vicente , Olatunbosun Oluremi
Published: July 2019

The possibility of using tires as active sensors opens the door to a huge number of different ways to accomplish this goal. In this case, based on a tire equipped with strain sensors, also known as an Intelligent Tire, relevant vehicle dynamics information can be provided. The purpose of this research is to improve the strain-based methodology for Intelligent Tires to estimate all tire forces, based only on deformations measured in the contact patch. Firstly, through an indoor test rig data, an algorithm has been developed to pick out the relevant features of strain data and correlate them with tire parameters. This information of the tire contact patch is then transmitted to a fuzzy logic system to estimate the tire parameters. To evaluate the reliability of the proposed estimator, the well-known simulation software CarSim has been used to back up the estimation results. The software CarSim has been used to provide the vehicle parameters in complex maneuvers. Finally, the estimations have been checked with the simulation results. This approach has enabled the behaviour of the intelligent tire to be tested for different maneuvers and velocities, providing key information about the tire parameters directly from the only contact that exists between the vehicle and the road.
Single Inertial Sensor-Based Neural Networks to Estimate COM-COP Inclination Angle During Walking
Authors: Choi Ahnryul , Jung Hyunwoo , Mun Joung Hwan
Published: July 2019

A biomechanical understanding of gait stability is needed to reduce falling risk. As a typical parameter, the COM-COP (center of mass–center of pressure) inclination angle (IA) could provide valuable insight into postural control and balance recovery ability. In this study, an artificial neural network (ANN) model was developed to estimate COM-COP IA based on signals using an inertial sensor. Also, we evaluated how different types of ANN and the cutoff frequency of the low-pass filter applied to input signals could affect the accuracy of the model. An inertial measurement unit (IMU) including an accelerometer, gyroscope, and magnetometer sensors was fabricated as a prototype. The COM-COP IA was calculated using a 3D motion analysis system including force plates. In order to predict the COM-COP IA, a feed-forward ANN and long-short term memory (LSTM) network was developed. As a result, the feed-forward ANN showed a relative root-mean-square error (rRMSE) of 15% while the LSTM showed an improved accuracy of 9% rRMSE. Additionally, the LSTM displayed a stable accuracy regardless of the cutoff frequency of the filter applied to the input signals. This study showed that estimating the COM-COP IA was possible with a cheap inertial sensor system. Furthermore, the neural network models in this study can be implemented in systems to monitor the balancing ability of the elderly or patients with impaired balancing ability.
An Efficient Framework for Estimating the Direction of Multiple Sound Sources Using Higher-Order Generalized Singular Value Decomposition
Authors: Suksiri Bandhit , Fukumoto Masahiro
Published: July 2019

This paper presents an efficient framework for estimating the direction-of-arrival (DOA) of wideband sound sources. The proposed framework provides an efficient way to construct a wideband cross-correlation matrix from multiple narrowband cross-correlation matrices for all frequency bins. In addition, the proposed framework is inspired by the coherent signal subspace technique with further improvement of linear transformation procedure, and the new procedure no longer requires any process of DOA preliminary estimation by exploiting unique cross-correlation matrices between the received signal and itself on distinct frequencies, along with the higher-order generalized singular value decomposition of the array of this unique matrix. Wideband DOAs are estimated by employing any subspace-based technique for estimating narrowband DOAs, but using the proposed wideband correlation instead of the narrowband correlation matrix. It implies that the proposed framework enables cutting-edge studies in the recent narrowband subspace methods to estimate DOAs of the wideband sources directly, which result in reducing computational complexity and facilitating the estimation algorithm. Practical examples are presented to showcase its applicability and effectiveness, and the results show that the performance of fusion methods perform better than others over a range of signal-to-noise ratios with just a few sensors, which make it suitable for practical use.
Single-Step Formation of Ni Nanoparticle-Modified Graphene–Diamond Hybrid Electrodes for Electrochemical Glucose Detection
Authors: Cui Naiyuan , Guo Pei , Yuan Qilong , Ye Chen , Yang Mingyang , Yang Minghui , Chee Kuan W. A. , Wang Fei , Fu Li , Wei Qiuping , Lin Cheng-Te , Gao Jingyao
Published: July 2019

The development of accurate, reliable devices for glucose detection has drawn much attention from the scientific community over the past few years. Here, we report a single-step method to fabricate Ni nanoparticle-modified graphene–diamond hybrid electrodes via a catalytic thermal treatment, by which the graphene layers are directly grown on the diamond surface using Ni thin film as a catalyst, meanwhile, Ni nanoparticles are formed in situ on the graphene surface due to dewetting behavior. The good interface between the Ni nanoparticles and the graphene guarantees efficient charge transfer during electrochemical detection. The fabricated electrodes exhibit good glucose sensing performance with a low detection limit of 2 μM and a linear detection range between 2 μM–1 mM. In addition, this sensor shows great selectivity, suggesting potential applications for sensitive and accurate monitoring of glucose in human blood.
Enabling the Orchestration of IoT Slices through Edge and Cloud Microservice Platforms
Authors: Fernandez Juan-Manuel , Vidal Ivan , Valera Francisco
Published: July 2019

This article addresses one of the main challenges related to the practical deployment of Internet of Things (IoT) solutions: the coordinated operation of entities at different infrastructures to support the automated orchestration of end-to-end Internet of Things services. This idea is referred to as “Internet of Things slicing” and is based on the network slicing concept already defined for the Fifth Generation (5G) of mobile networks. In this context, we present the architectural design of a slice orchestrator addressing the aforementioned challenge, based on well-known standard technologies and protocols. The proposed solution is able to integrate existing technologies, like cloud computing, with other more recent technologies like edge computing and network slicing. In addition, a functional prototype of the proposed orchestrator has been implemented, using open-source software and microservice platforms. As a first step to prove the practical feasibility of our solution, the implementation of the orchestrator considers cloud and edge domains. The validation results obtained from the prototype prove the feasibility of the solution from a functional perspective, verifying its capacity to deploy Internet of Things related functions even on resource constrained platforms. This approach enables new application models where these Internet of Things related functions can be onboarded on small unmanned aerial vehicles, offering a flexible and cost-effective solution to deploy these functions at the network edge. In addition, this proposal can also be used on commercial cloud platforms, like the Google Compute Engine, showing that it can take advantage of the benefits of edge and cloud computing respectively.
Mapping Neural Networks to FPGA-Based IoT Devices for Ultra-Low Latency Processing
Authors: Wielgosz Maciej , Karwatowski Michał
Published: July 2019

Internet of things (IoT) infrastructure, fast access to knowledge becomes critical. In some application domains, such as robotics, autonomous driving, predictive maintenance, and anomaly detection, the response time of the system is more critical to ensure Quality of Service than the quality of the answer. In this paper, we propose a methodology, a set of predefined steps to be taken in order to map the models to hardware, especially field programmable gate arrays (FPGAs), with the main focus on latency reduction. Multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) was employed along with custom scores for sparsity, bit-width of the representation and quality of the model. Furthermore, we created a framework which enables mapping of neural models to FPGAs. The proposed solution is validated using three case studies and Xilinx Zynq UltraScale+ MPSoC 285 XCZU15EG as a platform. The results show a compression ratio for quantization and pruning in different scenarios with and without retraining procedures. Using our publicly available framework, we achieved 210 ns of latency for a single processing step for a model composed of two long short-term memory (LSTM) and a single dense layer.
Changes in Muscle Power and Muscle Morphology with Different Volumes of Fast Eccentric Half-Squats
Authors: Zacharia Evangelia , Spiliopoulou Polyxeni , Methenitis Spyridon , Stasinaki Angeliki-Nikoletta , Zaras Nikolaos , Papadopoulos Constantinos , Papadimas Giorgos , Karampatsos Giorgos , Bogdanis Gregory C. , Terzis Gerasimos
Published: July 2019

The aim of the study was to evaluate power performance and muscle morphology adaptations in response to 5 weeks of fast-eccentric squat training (FEST) performed twice per week, with three different training volumes. Twenty-five moderately trained females were assigned into three groups performing eight repetitions of FEST of either four sets (4 × 8 group; N = 9), 6 sets (6 × 8 group; N = 8) or eight sets (8 × 8 group, N = 8). Before and after the intervention, countermovement jumping height (CMJh) and power (CMJp), half squat maximal strength (1-RM), quadriceps cross-sectional area (QCSA) and vastus lateralis (VL) architecture and fiber type composition were evaluated. Significant increases (p < 0.05) were found for all groups, with no differences among them in 1-RM (4 × 8: 14.8 ± 8.2%, 6 × 8: 13.1 ± 9.2% and 8 × 8: 21.6 ± 7.0%), CMJh (4 × 8: 12.5 ± 8.5%, 6 × 8: 11.3 ± 9.3% and 8 × 8: 7.0 ± 6.2%), CMJp (4 × 8: 9.1 ± 6.0%, 6 × 8: 7.1 ± 5.2% and 8 × 8: 5.0 ± 3.9%) and QCSA (4 × 8: 7.7 ± 4.7%, 6 × 8: 9.0 ± 6.8% and 8 × 8: 8.2 ± 6.5%). Muscle fiber type distribution remained unaltered after training in all groups. VL fascicle length increased and fascicle angle decreased only in 6 × 8 and 8 × 8 groups. In conclusion, four sets of eight fast-eccentric squats/week increase lower body power and strength performance and maintain type IIX muscle fibers after 5 weeks, at least in moderately trained females.
Relationship Between the Built Environment and the Location Choice of High-Tech Firms: Evidence from the Pearl River Delta
Authors: Wu Kangmin , Wang Yang , Ye Yuyao , Zhang Hongou , Huang Guangqing
Published: July 2019

With the transition in the regional development discourse to knowledge- and innovation-based economics, the cultivation of innovation capacity has gained importance as an initiative to enhance regional sustainability and has emerged as a policy goal. An understanding of the formation of innovation clusters is critical to the cultivation of regional innovation capabilities. Except for the location and regional development conditions’ factors, researchers emphasize a built environment’s role in the formation of innovation clusters. Based on the spatial database of 12,516 high-tech firms in 2017 in the Pearl River Delta (PRD), this study developed a conceptual framework for the built environment. The framework comprises living, service, perceptual, industrial, and physical aspects. The direction and intensity of the correlation between built environment factors and high-tech firms are discussed using the spatial regression model and geographical detector (GD) technique. The results show a highly concentrated spatial distribution pattern of high-tech firms in the PRD. A significant county-level spatial autocorrelation is revealed through Moran’s I. According to the model, we determine the positive impacts of technology support, transport infrastructure, and living service levels on the agglomeration of high-tech firms as well as the negative impact of the public service level. The GD’s result demonstrates different levels of impact intensity of built environmental factors. We argue that a comprehensive understanding of the influence of built environment factors on innovation agglomeration will help policymakers develop targeted policies to foster local innovation capabilities and promote sustainable regional development.
A Demountable Connection for Low-Rise Precast Concrete Structures with DfD for Construction Sustainability-A Preliminary Test under Cyclic Loads
Authors: Cai Gaochuang , Xiong Feng , Xu Yong , Si Larbi Amir , Lu Yang , Yoshizawa Mikio
Published: July 2019

As a demountable structure, the structure with design for deconstruction (DfD) is considered as a key contribution on the promotion of current construction sustainability by directly reusing valuable components from old structures. As a preliminary study, this paper investigated the cyclic behavior of bolted joints consisting of three reinforced concrete blocks bolted by steel bolts under axial compressive, focusing on the damage and failure modes, resistance mechanism and stiffness development of the joints. Results showed that the number of steel bolts, the tightening process of the bolts and concrete compressive strength all had a significant effect on the overall performance and capacity of the joints. The failure mode of most of tested joints was considered as fracture of stirrups and steel bolts in the tested joints. According to the investigation of this study, several recommendations on the design of the joints were provided.
Intrapreneurial Self-Capital and Connectedness to Nature within Organizations
Authors: Duradoni Mirko , Di Fabio Annamaria
Published: July 2019

Research exists regarding personality traits in relation to connectedness with nature. However, it is not possible to increase personality through training. As an alternative, intrapreneurial self-capital (ISC) could be a promising core of resources for enhancing the sustainable development of behaviors and practices. Using a sample of 198 workers, this study exploratively analyzed the relationship between the extraversion personality trait, ISC, and connectedness to nature. A mediation model was employed to assess the effects of extraversion on connectedness to nature (outcome variable) through ISC (conceived as an intervening mediator variable). The mediation analysis highlighted that ISC potentially promotes workers’ connectedness to nature within organizations. Thus, implementing dedicated interventions to increase ISC could encourage sustainable development by enhancing workers’ levels of connectedness to nature.
Does Fixed Asset Revaluation Build Trust between Management and Investors?
Authors: Bae Jincheol , Lee Jaehong , Kim Eunsoo
Published: July 2019

In this paper, we examine whether fixed asset revaluation has an impact on the timeliness and relevance of information disclosed in financial reporting. Using firms listed in the Korea Stock Exchange market during 2007–2017, this study investigates the change in transparency of the information disclosure environment as proxied by stock price crash risk. We find that, on average, fixed asset revaluation has a positive effect on sustainability by improving timeliness and relevance of disclosed information, thereby decreasing stock price crash risk. In contrast, firms with unhealthy financial conditions and a high degree of information asymmetry show an increase in crash likelihood after fixed asset revaluation. These findings suggest that the relationship between fixed asset revaluation and stock price cash risk is dependent upon management’s motivation for honesty during the revaluation process.
Loyalty Model Proposal of Travel Agency Customers
Authors: Dudek Andrzej , Jaremen Daria Elżbieta , Michalska-Dudek Izabela , Walesiak Marek
Published: July 2019

Customer loyalty to a destination and accommodation services constitutes a frequent object of research; however, customer loyalty to travel agencies is rarely analyzed. The presented article is an attempt to fill in this research gap. Its purpose is to construct and verify the model covering the impact of the selected factors on the loyalty level of customers of travel agencies operating in Poland. The conceptualization of the loyalty model of travel agency customers (based on the European path-based EPSI model) proposed in the article was first used to illustrate the existing correlations in customer behavior to analyze and explain the development of the loyalty phenomenon vis-a-vis travel agency customers. The aforementioned assumptions—having applied the structural equation modelling (SEM)—were reflected in the development of the theoretical model of travel agencies customer loyalty, the empirical verification of which (N = 1151) allowed us to determine the impacts of selected factors (i.e., the perceived quality of the travel agency’s offers, its image and the satisfaction with its service buyers) on the loyalty level of travel agency customers. It has been shown that two major factors have positive impacts on the loyalty of travel agency customers: (i) the perceived quality of travel agency offers, and (ii) its image. Furthermore, the conducted analysis highlights the positive influence of the perceived value of travel agency offers on the loyalty of customers.
Symmetries and Reductions of Integrable Nonlocal Partial Differential Equations
Authors: Peng Linyu
Published: July 2019

In this paper, symmetry analysis is extended to study nonlocal differential equations. In particular, two integrable nonlocal equations are investigated, the nonlocal nonlinear Schrödinger equation and the nonlocal modified Korteweg–de Vries equation. Based on general theory, Lie point symmetries are obtained and used to reduce these equations to nonlocal and local ordinary differential equations, separately; namely, one symmetry may allow reductions to both nonlocal and local equations, depending on how the invariant variables are chosen. For the nonlocal modified Korteweg–de Vries equation, analogously to the local situation, all reduced local equations are integrable. We also define complex transformations to connect nonlocal differential equations and differential-difference equations.
Analysis, Optimization, and Characterization of Magnetic Photonic Crystal Structures and Thin-Film Material Layers
Authors: Vasiliev Mikhail , Alameh Kamal , Nur-E-Alam Mohammad
Published: July 2019

The development of magnetic photonic crystals (MPC) has been a rapidly evolving research area since the late 1990s. Magneto-optic (MO) materials and the techniques for their characterization have also continually undergone functional and property-related improvements. MPC optimization is a feature-rich Windows software application designed to enable researchers to analyze the optical and magneto-optical spectral properties of multilayers containing gyrotropic constituents. We report on a set of computational algorithms which aim to optimize the design and the optical or magneto-optical spectral analysis of 1D MPC, together with a Windows software implementation. Relevant material property datasets (e.g., the spectral dispersion data for the refractive index, absorption, and gyration) of several important optical and MO materials are included, enabling easy reproduction of the previously published results from the field of MPC-based Faraday rotator development, and an effective demonstration-quality introduction of future users to the multiple features of this package. We also report on the methods and algorithms used to obtain the absorption coefficient spectral dispersion datasets for new materials, where the film thickness, transmission spectrum, and refractive index dispersion function are known.
Starvation-Induced Differential Virotherapy Using an Oncolytic Measles Vaccine Virus
Authors: Scheubeck Gabriel , Berchtold Susanne , Smirnow Irina , Schenk Andrea , Beil Julia , Lauer Ulrich M.
Published: July 2019

Starvation sensitizes tumor cells to chemotherapy while protecting normal cells at the same time, a phenomenon defined as differential stress resistance. In this study, we analyzed if starvation would also increase the oncolytic potential of an oncolytic measles vaccine virus (MeV-GFP) while protecting normal cells against off-target lysis. Human colorectal carcinoma (CRC) cell lines as well as human normal colon cell lines were subjected to various starvation regimes and infected with MeV-GFP. The applied fasting regimes were either short-term (24 h pre-infection) or long-term (24 h pre- plus 96 h post-infection). Cell-killing features of (i) virotherapy, (ii) starvation, as well as (iii) the combination of both were analyzed by cell viability assays and virus growth curves. Remarkably, while long-term low-serum, standard glucose starvation potentiated the efficacy of MeV-mediated cell killing in CRC cells, it was found to be decreased in normal colon cells. Interestingly, viral replication of MeV-GFP in CRC cells was decreased in long-term-starved cells and increased after short-term low-glucose, low-serum starvation. In conclusion, starvation-based virotherapy has the potential to differentially enhance MeV-mediated oncolysis in the context of CRC cancer patients while protecting normal colon cells from unwanted off-target effects.
The Clinical Perspective on Hepatitis E
Authors: Horvatits Thomas , Schulze zur Wiesch Julian , Lütgehetmann Marc , Lohse Ansgar W. , Pischke Sven
Published: July 2019

Every year, there are an estimated 20 million hepatitis E virus (HEV) infections worldwide, leading to an estimated 3.3 million symptomatic cases of hepatitis E. HEV is largely circulating in the west and is associated with several hepatic and extrahepatic diseases. HEV Genotype 1 and 2 infections are waterborne and causative for epidemics in the tropics, while genotype 3 and 4 infections are zoonotic diseases and are mainly transmitted by ingestion of undercooked pork in industrialized nations. The clinical course of these infections differs: genotype 1 and 2 infection can cause acute illness and can lead to acute liver failure (ALF) or acute on chronic liver failure (ACLF) with a high mortality rate of 20% in pregnant women. In contrast, the majority of HEV GT-3 and -4 infections have a clinically asymptomatic course and only rarely lead to acute on chronic liver failure in elderly or patients with underlying liver disease. Immunosuppressed individuals infected with genotype 3 or 4 may develop chronic hepatitis E, which then can lead to life-threatening cirrhosis. Furthermore, several extra-hepatic manifestations affecting various organs have been associated with ongoing or previous HEV infections but the causal link for many of them still needs to be proven. There is no approved specific therapy for the treatment of acute or chronic HEV GT-3 or -4 infections but off-label use of ribavirin has been demonstrated to be safe and effective in the majority of patients. However, in approximately 15% of chronically HEV infected patients, cure is not possible.
Importance of the Host Phenotype on the Preservation of the Genetic Diversity in Codling Moth Granulovirus
Authors: Graillot Benoit , Blachere-López Christine , Besse Samantha , Siegwart Myriam , López-Ferber Miguel
Published: July 2019

To test the importance of the host genotype in maintaining virus genetic diversity, five experimental populations were constructed by mixing two Cydia pomonella granulovirus isolates, the Mexican isolate CpGV-M and the CpGV-R5, in ratios of 99% M + 1% R, 95% M + 5% R, 90% M + 10% R, 50% M + 50% R, and 10% M + 90% R. CpGV-M and CpGV-R5 differ in their ability to replicate in codling moth larvae carrying the type I resistance. This ability is associated with a genetic marker located in the virus pe38 gene. Six successive cycles of replication were carried out with each virus population on a fully-permissive codling moth colony (CpNPP), as well as on a host colony (RGV) that carries the type I resistance, and thus blocks CpGV-M replication. The infectivity of offspring viruses was tested on both hosts. Replication on the CpNPP leads to virus lineages preserving the pe38 markers characteristic of both isolates, while replication on the RGV colony drastically reduces the frequency of the CpGV-M pe38 marker. Virus progeny obtained after replication on CpNPP show consistently higher pathogenicity than that of progeny viruses obtained by replication on RGV, independently of the host used for testing.
Impacts of Sea Level Rise and River Discharge on the Hydrodynamics Characteristics of Jakarta Bay (Indonesia)
Authors: Yahya Surya Martin , He Zhiguo , Xia Yuezhang , Li Li
Published: July 2019

Jakarta city has been vulnerable to sea level rise and flooding for many years. A Giant Seawall (GSW) was proposed in Jakarta Bay to protect the city. The impacts of sea level rise and river discharge on the tidal dynamics in Jakarta Bay and flooding areas in Jakarta city were investigated using the finite-volume coastal ocean model (FVCOM). Model results showed that the bay is diurnally dominated by the K1 tidal component. The diurnal tides propagate westward, while the semidiurnal tides propagate eastward in the bay. The rise of sea level increases the diurnal tidal component and the inundation areas due to the increased tidal forcing: when considering a sea level rise of 0.6 m, the K1 amplitude increases by ~1% (0.25 cm) near the coastline and the current magnitude increases by 16.6% (0.05 m/s). The inundation area increases with the sea level rise in the low land elevation areas occurring near the coastlines: the inundation area increased by 29.68 km2 (7.1%) with a sea level rise of 0.6 m. The increase of river discharge amplified the diurnal tidal component as well as the inundation areas at the river mouth due to increased fluvial forcing: if 10 times the mean river discharge occurs, the K1 amplitude increases by ~1% (0.25 cm) and the current magnitude increases by 100% (0.4 m/s), and the inundation areas increase by 26.61 km2 (6.2%). The K1 tidal phase remains almost unchanged under both the sea level rise and river discharge conditions. The combined increase of sea level rise and the river discharge amplifies the inundation areas and the tidal currents due to increased tidal and fluvial forcing. The construction of GSW would decrease the tidal prism and dissipation effects of the bay, thus slightly increasing the K1 amplitude of the tidal level: by less than 1% (0.2 cm). There would be no significant change of phase lag for the K1 component. Although this study is site specific, the findings could be applied more widely to any open-type bays.
Identification of Sulfate Sources and Biogeochemical Processes in an Aquifer Affected by Peatland: Insights from Monitoring the Isotopic Composition of Groundwater Sulfate in Kampinos National Park, Poland
Authors: Porowski Adam , Porowska Dorota , Halas Stanislaw
Published: July 2019

Temporal and spatial variations of the concentration and the isotopic composition of groundwater sulfate in an unconfined sandy aquifer covered by peatland have been studied to better understand the sources and biogeochemical processes that affect sulfate distribution in shallow groundwater systems influenced by organic rich sediments. The groundwater monitoring was carried out for one year at hydrogeological station Pożary located within the protected zone of the Kampinos National Park. Sulfur (δ34SSO4) and oxygen (δ18OSO4) isotopic composition of dissolved sulfates were analyzed together with oxygen (δ18OH2O) and hydrogen (δ2HH2O) isotopic composition of water and major ions concentration at monthly intervals. The research revealed three main sources of sulfates dissolved in groundwater, namely, (a) atmospheric sulfates—supplied to the aquifer by atmospheric deposition (rain and snow melt), (b) sulfates formed by dissolution of evaporite sulfate minerals, mainly gypsum—considerably enriched in 34S and 18O, and (c) sulfate formed during oxidation of reduced inorganic sulfur compounds (RIS), mainly pyrite—depleted in 34S and 18O. The final isotopic composition and concentration of dissolved SO42− in groundwater are the result of overlapping processes of dissimilatory sulfate reduction, oxidation of sulfide minerals, and mixing of water in aquifer profile.