bluuebunny/crossref_metadata_2025 huggingface dataset:
Food and Beverage Consumption and Melanoma Risk: A Population-Based Case-Control Study in Northern Italy
Authors: Malagoli Carlotta , Malavolti Marcella , Farnetani Francesca , Longo Caterina , Filippini Tommaso , Pellacani Giovanni , Vinceti Marco
Published: September 2019

It has been suggested that diet may influence the risk of melanoma, but few studies are available on this topic. We assessed the relation between food consumption and the risk of cutaneous melanoma in a Northern Italy population. We carried out a population-based case-control study involving 380 cases of melanoma and 719 age- and sex-matched controls. Dietary habits were established through a self-administered semi-quantitative food frequency questionnaire. We computed the odds ratios (ORs) of melanoma and the corresponding 95% confidence intervals (CIs) according to tertiles of daily intake of each food item, using multiple logistic regression models adjusted for major confounding factors. We observed an indication of a positive association between melanoma risk and consumption of cereals and cereal products (OR = 1.32; 95% CI 0.89–1.96, higher vs. lowest tertile), sweets (OR = 1.22; 95% CI 0.84–1.76), chocolate, candy bars. etc., (OR = 1.51; 95% CI 1.09–2.09) and cabbages (OR = 1.51; 95% CI 1.09–2.09). Conversely, an inverse association with disease risk was found for the intake of legumes (OR = 0.77; 95% CI 0.52–1.13), olive oil (OR = 0.77; 95% CI 0.51–1.16), eggs (OR = 0.58; 95% CI 0.41–0.82), and onion and garlic (OR = 0.80; 95% CI 0.52–1.14). No relationship was observed with beverage consumption. Our results suggest potentially adverse effects on melanoma risk of foods characterized by high contents of refined flours and sugars, while suggesting a protective role for eggs and two key components of the Mediterranean diet, legumes and olive oil. These associations warrant further investigation and, if confirmed, they might have important public health implications for the reduction of melanoma incidence through dietary modification.
Comparative Study of CDST & Multiplex PCR to Detect MBL Producing Gram-Negative Bacilli among VAP Patients Admitted in a Public Medical College Hospital of Bangladesh
Authors: Nusrat Tanzina , Akter Nasima , Haque Mainul , Rahman Nor Azlina A. , Dewanjee Arup Kanti , Ahmed Shakeel , Rozario Diana Thecla D.
Published: September 2019

Background: Ventilator-associated pneumonia (VAP) is the most common nosocomial infection in intensive care units (ICU), which accounts for 25% of all ICU infection. Documenting carbapenem-resistant gram-negative bacilli is very important as these strains may often cause outbreaks in the ICU setting and are responsible for the increased mortality and morbidity or limiting therapeutic options. The classical phenotypic method cannot provide an efficient means of diagnosis of the metallo-β-lactamases (MBLs) producer. Polymerase chain reaction (PCR) assays have lessened the importance of the phenotypic approach by detecting metallo-β-lactamase resistance genes such as New Delhi metallo-β-lactamase (NDM), Imipenemase (IMP), Verona integron-encoded metallo-β-lactamase (VIM), Sao Paulo metallo-β-lactamase (SPM), Germany Imipenemase (GIM). Objective: To compare the results of the Combined Disc Synergy Test (CDST) with that of the multiplex PCR to detect MBL-producing gram-negative bacilli. Materials and Method: A total of 105 endotracheal aspirates (ETA) samples were collected from the ICU of a public school in Bangladesh. This cross-sectional study was carried out in the Department of Microbiology, Chittagong for quantitative culture, CDST test, and multiplex PCR for blaIMP, blaVIM, blaNDM genes of MBL producers. Results: Among the 105 clinically suspected VAP cases, the quantitative culture was positive in 95 (90%) and among 95 g-negative bacilli isolated from VAP patients, 46 (48.42%) were imipenem resistant, 30 (65.22%) were MBL producers by CDST, 21 (45.65%) were identified as MBL producers by multiplex PCR. Conclusion: PCR was highly sensitive and specific for the detection of MBL producers.
Development and Evaluation of Multifunctional Poly(Lactic-co-glycolic acid) Nanoparticles Embedded in Carboxymethyl β-Glucan Porous Microcapsules as a Novel Drug Delivery System for Gefitinib
Authors: Li Xiaonan , Wang Jinglei , Li Shang , Liu Zhaorong , Zheng Zhiru , Zhang Yanzhuo
Published: September 2019

In this study, a new kind of folic acid (FA)-conjugated and chitosan (CS)-coated poloxamer 407 (P407)/poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs), FCPP NPs, were prepared, and further micro-encapsulated by carboxymethyl β-glucan microcapsules (MCs) to produce a multifunctional system of NPs embedded in MCs (NEMs) for potential lung tumor-targeted delivery of gefitinib. The prepared gefitinib-loaded FCPP (GFB/FCPP) NPs showed a hydrodynamic diameter of 255.4 ± 14.5 nm and existed in an amorphous state. After encapsulation in carboxymethyl β-glucan MCs, the GFB/FCPP-based NEMs (GFB/FCPP-NEMs) also exhibited a spherical morphology with a median diameter (d50) of around 2.2 μm. After hydration, GFB/FCPP- NEMs can quickly dissociate into its primary particles, GFB/FCPP NPs. Our in vitro drug release study revealed that these GFB/FCPP-NEMs exhibited a pH-responsive prolonged release property. In addition, the cellular uptake study demonstrated that FCPP-NEMs serve as an efficient carrier to enhance the delivery of the entrapped drug into the target lung tumor cells. Moreover, the GFB/FCPP-NEMs induced a superior cytotoxic effect compared with free gefitinib. The inhibitory concentration to achieve 50% cell death (IC50) of GFB/FCPP-NEMs in A549 cells was 3.82-fold lower than that of free gefitinib. According to these results, FCPP-NEMs hold a great potential as a multifunctional and high-performance biomaterial for lung tumor targeting delivery, pH-responsive sustained release, facilitated cellular uptake, and enhanced antitumor effect of antitumor drugs, like gefitinib.
Cellulose Degradation by Calcium Thiocyanate
Authors: Jeong Myung-Joon , Lee Sinah , Yang Bong Suk , Potthast Antje , Kang Kyu-Young
Published: September 2019

The dissolution process of cellulose aerogels is an important part of their production. However, if the cellulose is severely degraded during the dissolution process, the quality may be low. To evaluate the degradation of cellulose during the dissolution process using calcium thiocyanate, the hydrolysis and oxidation of cellulose were evaluated by the change in absolute molecular weight and by the changes in the content of carboxyl and carbonyl groups introduced into the cellulose hydroxyl group, respectively. A noteworthy hydrolysis phenomenon was found in the cellulose dissolution process. The rate of hydrolysis increased as the number of hydrates in calcium thiocyanate decreased and as the reaction temperature increased. In the case of the reaction with calcium thiocyanate containing six hydrates, the time to reach a 50% loss of the degree of polymerization of cellulose reduced from 196 to 47 min as the reaction temperature was increased from 100 to 120 °C; however, the effect on oxidation was not significant. The Brunauer–Emmett–Teller (BET) surface area reduced as the degree of cellulose polymerization decreased. Therefore, it is necessary to consider how the cellulose degradation occurring during the cellulosic dissolution process can affect the quality of the final cellulose aerogels.
Water-In-Oil Emulsions through Porous Media and the Effect of Surfactants: Theoretical Approaches
Authors: Perez-Sanchez Josue , Diaz-Zavala Nancy , Gonzalez-Santana Susana , Izquierdo-Kulich Elena , Suarez-Dominguez Edgardo
Published: September 2019

The most complex components in heavy crude oils tend to form aggregates that constitute the dispersed phase in these fluids, showing the high viscosity values that characterize them. Water-in-oil (W/O) emulsions are affected by the presence and concentration of this phase in crude oil. In this paper, a theoretical study based on computational chemistry was carried out to determine the molecular interaction energies between paraffin–asphaltenes–water and four surfactant molecules to predict their effect in W/O emulsions and the theoretical influence on the pressure drop behavior for fluids that move through porous media. The mathematical model determined a typical behavior of the fluid when the parameters of the system are changed (pore size, particle size, dispersed phase fraction in the fluid, and stratified fluid) and the viscosity model determined that two of the surfactant molecules are suitable for applications in the destabilization of W/O emulsions. Therefore, an experimental study must be set to determine the feasibility of the methodology and mathematical model displayed in this work.
Climate Prediction of Satellite-Based Spring Eurasian Vegetation Index (NDVI) using Coupled Singular Value Decomposition (SVD) Patterns
Authors: Ji Liuqing , Fan Ke
Published: September 2019

Satellite-based normalized difference vegetation index (NDVI) data are widely used for estimating vegetation greenness. Seasonal climate predictions of spring (April–May–June) NDVI over Eurasia are explored by applying the year-to-year increment approach. The prediction models were developed based on the coupled modes of singular value decomposition (SVD) analyses between Eurasian NDVI and climate factors. One synchronous predictor, the spring surface air temperature from the NCEP’s Climate Forecast System (SAT-CFS), and three previous-season predictors (winter (December–January–February) sea-ice cover over the Barents Sea (SICBS), winter sea surface temperature over the equatorial Pacific (SSTP), and winter North Atlantic Oscillation (NAO) were chosen to develop four single-predictor schemes: the SAT-CFS scheme, SICBS scheme, SSTP scheme, and NAO scheme. Meanwhile, a statistical scheme that involves the three previous-season predictors (i.e., SICBS, SSTP, and NAO) and a hybrid scheme that includes all four predictors are also proposed. To evaluate the prediction skills of the schemes, one-year-out cross-validation and independent hindcast results are analyzed, revealing the hybrid scheme as having the best prediction skill. The results indicate that the temporal correlation coefficients at 92% of grid points over Eurasia are significant at the 5% significance level in the hybrid scheme, which is the best among all the schemes. Furthermore, spatial correlation coefficients (SCCs) of the six schemes are significant at the 1% significance level in most years during 1983–2015, with the averaged SCC of the hybrid scheme being the highest (0.60). The grid-averaged root-mean-square-error of the hybrid scheme is 0.04. By comparing the satellite-based NDVI value with the independent hindcast results during 2010–2015, it can be concluded that the hybrid scheme shows high prediction skill in terms of both the spatial pattern and the temporal variability of spring Eurasian NDVI.
Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds
Authors: Kim Jongwon , Cho Jeongho
Published: September 2019

In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it difficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve the nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in complex spatial data clustering, as well as to improve robustness against various types of noise in spatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that employs Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of applications with noise). In the performance evaluation using simulated synthetic data as well as real 3D point clouds, the proposed method maintained better clustering and separability of neighboring clusters compared to other clustering techniques, and is expected to be of practical use in the field of spatial data mining.
Efficient Resource Scheduling for Multipath Retransmission over Industrial WSAN Systems
Authors: Wang Hongchao , Ma Jian , Yang Dong , Gidlund Mikael
Published: September 2019

With recent adoption of Wireless Sensor-Actuator Networks (WSANs) in industrial automation, wireless control systems have emerged as a frontier of industrial networks. Hence, it has been shown that existing standards and researches concentrate on the reliability and real-time performance of WSANs. The multipath retransmission scheme with multiple channels is a key approach to guarantee the deterministic wireless communication. However, the efficiency of resource scheduling is seldom considered in applications with diverse data sampling rates. In this paper, we propose an efficient resources scheduling algorithm for multipath retransmission in WSANs. The objective of our algorithm is to improve efficiency and schedulability for the use of slot and channel resources. In detail, the proposed algorithm uses the approaches of CCA (clear channel assessment)-Embedded slot and Multiple sinks with Rate Monotonic scheme (CEM-RM) to decrease the number of collisions. We have simulated and implemented our algorithm in hardware and verified its performance in a real industrial environment. The achieved results show that the proposed algorithm significantly improves the schedulability without trading off reliability and real-time performance.
Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement
Authors: Tsagkatakis Grigorios , Aidini Anastasia , Fotiadou Konstantina , Giannopoulos Michalis , Pentari Anastasia , Tsakalides Panagiotis
Published: September 2019

Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.
Monitoring Severe Slugging in Pipeline-Riser System Using Accelerometers for Application in Early Recognition
Authors: Jung Sunah , Yang Haesang , Park Kiheum , Seo Yutaek , Seong Woojae
Published: September 2019

The use of accelerometer signals for early recognition of severe slugging is investigated in a pipeline-riser system conveying an air–water two-phase flow, where six accelerometers are installed from the bottom to the top of the riser. Twelve different environmental conditions are produced by changing water and gas superficial velocities, of which three conditions are stable states and the other conditions are related to severe slugging. For online recognition, simple parameters using statistics and linear prediction coefficients are employed to extract useful features. Binary classification to recognize stable flow and severe slugging is performed using a support vector machine and a neural network. In multiclass classification, the neural network is adopted to identify four flow patterns of stable state, two types of severe slugging, and an irregular transition state between severe slugging and dual-frequency severe slugging. The performance is compared and analyzed according to the signal length for three cases of sensor location: six accelerometers, one accelerometer at the riser base, and one accelerometer at the top of the riser.
Experimental Evaluation of an RSSI-Based Localization Algorithm on IoT End-Devices
Authors: Pita Rosa , Utrilla Ramiro , Rodriguez-Zurrunero Roberto , Araujo Alvaro
Published: September 2019

In recent years, wireless sensor networks (WSNs) have experienced a significant growth as a fundamental part of the Internet of Things (IoT). WSNs nodes constitute part of the end-devices present in the IoT, and in many cases location data of these devices is expected by IoT applications. For this reason, many localization algorithms for WSNs have been developed in the last years, although in most cases the results provided are obtained from simulations that do not consider the resource constraints of the end-devices. Therefore, in this work we present an experimental evaluation of a received signal strength indicator (RSSI)-based localization algorithm implemented on IoT end-devices, comparing its results with those obtained from simulations. We have implemented the fuzzy ring-overlapping range-free (FRORF) algorithm with some modifications to make its operation feasible on resource-constrained devices. Multiple tests have been carried out to obtain the localization accuracy data in three different scenarios, showing the difference between simulation and real results. While the overall behaviour is similar in simulations and in real tests, important differences can be observed attending to quantitative accuracy results. In addition, the execution time of the algorithm running in the nodes has been evaluated. It ranges from less than 10 ms to more than 300 ms depending on the fuzzification level, which demonstrates the importance of evaluating localization algorithms in real nodes to prevent the introduction of large overheads that may not be affordable by resource-constrained nodes.
Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software
Authors: Khongnawang Tibet , Zare Ehsan , Zhao Dongxue , Srihabun Pranee , Triantafilis John
Published: September 2019

Most cultivated upland areas of northeast Thailand are characterized by sandy and infertile soils, which are difficult to improve agriculturally. Information about the clay (%) and cation exchange capacity (CEC—cmol(+)/kg) are required. Because it is expensive to analyse these soil properties, electromagnetic (EM) induction instruments are increasingly being used. This is because the measured apparent soil electrical conductivity (ECa—mS/m), can often be correlated directly with measured topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m) clay and CEC. In this study, we explore the potential to use this approach and considering a linear regression (LR) between EM38 acquired ECa in horizontal (ECah) and vertical (ECav) modes of operation and the soil properties at each of these depths. We compare this approach with a universal LR relationship developed between calculated true electrical conductivity (σ—mS/m) and laboratory measured clay and CEC at various depths. We estimate σ by inverting ECah and ECav data, using a quasi-3D inversion algorithm (EM4Soil). The best LR between ECa and soil properties was between ECah and subsoil clay (R2 = 0.43) and subsoil CEC (R2 = 0.56). We concluded these LR were unsatisfactory to predict clay or CEC at any of the three depths, however. In comparison, we found that a universal LR could be established between σ with clay (R2 = 0.65) and CEC (R2 = 0.68). The LR model validation was tested using a leave-one-out-cross-validation. The results indicated that the universal LR between σ and clay at any depth was precise (RMSE = 2.17), unbiased (ME = 0.27) with good concordance (Lin’s = 0.78). Similarly, satisfactory results were obtained by the LR between σ and CEC (Lin’s = 0.80). We conclude that in a field where a direct LR relationship between clay or CEC and ECa cannot be established, can still potentially be mapped by developing a LR between estimates of σ with clay or CEC if they all vary with depth.
Reinforcement Learning-Based Energy Management of Smart Home with Rooftop Solar Photovoltaic System, Energy Storage System, and Home Appliances
Authors: Lee Sangyoon , Choi Dae-Hyun
Published: September 2019

This paper presents a data-driven approach that leverages reinforcement learning to manage the optimal energy consumption of a smart home with a rooftop solar photovoltaic system, energy storage system, and smart home appliances. Compared to existing model-based optimization methods for home energy management systems, the novelty of the proposed approach is as follows: (1) a model-free Q-learning method is applied to energy consumption scheduling for an individual controllable home appliance (air conditioner or washing machine), as well as the energy storage system charging and discharging, and (2) the prediction of the indoor temperature using an artificial neural network assists the proposed Q-learning algorithm in learning the relationship between the indoor temperature and energy consumption of the air conditioner accurately. The proposed Q-learning home energy management algorithm, integrated with the artificial neural network model, reduces the consumer electricity bill within the preferred comfort level (such as the indoor temperature) and the appliance operation characteristics. The simulations illustrate a single home with a solar photovoltaic system, an air conditioner, a washing machine, and an energy storage system with the time-of-use pricing. The results show that the relative electricity bill reduction of the proposed algorithm over the existing optimization approach is 14%.
The Role of Millimeter-Waves in the Distance Measurement Accuracy of an FMCW Radar Sensor
Authors: Bhutani Akanksha , Marahrens Sören , Gehringer Michael , Göttel Benjamin , Pauli Mario , Zwick Thomas
Published: September 2019

High-accuracy, short-range distance measurement is required in a variety of industrial applications e.g., positioning of robots in a fully automated production process, level measurement of liquids in small containers. An FMCW radar sensor is suitable for this purpose, since many of these applications involve harsh environments. Due to the progress in the field of semiconductor technology, FMCW radar sensors operating in different millimeter-wave frequency bands are available today. An important question in this context, which has not been investigated so far is how does a millimeter-wave frequency band influence the sensor accuracy, when thousands of distance measurements are performed with a sensor. This topic has been dealt with for the first time in this paper. The method used for analyzing the FMCW radar signal combines a frequency- and phase-estimation algorithm. The frequency-estimation algorithm based on the fast Fourier transform and the chirp-z transform provides a coarse estimate of the target distance. Subsequently, the phase-estimation algorithm based on a cross-correlation function provides a fine estimate of the target distance. The novel aspects of this paper are as follows. First, the estimation theory concept of Cramér-Rao lower bound (CRLB) has been used to compare the accuracy of two millimeter-wave FMCW radars operating at 60 GHz and 122 GHz. In this comparison, the measurement parameters (e.g., bandwidth, signal-to-noise ratio) as well as the signal-processing algorithm used for both the radars are the same, thus ensuring an unbiased comparison of the FMCW radars, solely based on the choice of millimeter-wave frequency band. Second, the improvement in distance measurement accuracy obtained after each step of the combined frequency- and phase-estimation algorithm has been experimentally demonstrated for both the radars. A total of 5100 short-range distance measurements are made using the 60 GHz and 122 GHz FMCW radar. The measurement results are analyzed at various stages of the frequency- and phase-estimation algorithm and the measurement error is calculated using a nanometer-precision linear motor. At every stage, the mean error values measured with the 60 GHz and 122 GHz FMCW radars are compared. The final accuracy achieved using both radars is of the order of a few micrometers. The measured standard deviation values of the 60 GHz and 122 GHz FMCW radar have been compared against the CRLB. As predicted by the CRLB, this paper experimentally validates for the first time that the 122 GHz FMCW radar provides a higher repeatability of micrometer-accuracy distance measurements than the 60 GHz FMCW radar.
Assessment of the Steering Precision of a Hydrographic Unmanned Surface Vessel (USV) along Sounding Profiles Using a Low-Cost Multi-Global Navigation Satellite System (GNSS) Receiver Supported Autopilot
Authors: Specht Mariusz , Specht Cezary , Lasota Henryk , Cywiński Piotr
Published: September 2019

The performance of bathymetric measurements by traditional methods (using manned vessels) in ultra-shallow waters, i.e., lakes, rivers, and sea beaches with a depth of less than 1 m, is often difficult or, in many cases, impossible due to problems related to safe vessel maneuvering. For this reason, the use of shallow draft hydrographic Unmanned Surface Vessels (USV) appears to provide a promising alternative method for performing such bathymetric measurements. This article describes the modernisation of a USV to switch from manual to automatic mode, and presents a preliminary study aimed at assessing the suitability of a popular autopilot commonly used in Unmanned Aerial Vehicles (UAV), and a low-cost multi-Global Navigation Satellite System (GNSS) receiver cooperating with it, for performing bathymetric measurements in automated mode, which involves independent movement along a specified route (hydrographic sounding profiles). The cross track error (XTE) variable, i.e., the distance determined between a USV’s position and the sounding profile, measured transversely to the course, was adopted as the measure of automatic control precision. Moreover, the XTE value was statistically assessed in the publication.
Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging
Authors: Li Ziheng , Cai Ailong , Wang Linyuan , Zhang Wenkun , Tang Chao , Li Lei , Liang Ningning , Yan Bin
Published: September 2019

Limited-angle computed tomography (CT) image reconstruction is a challenging problem in the field of CT imaging. In some special applications, limited by the geometric space and mechanical structure of the imaging system, projections can only be collected with a scanning range of less than 90°. We call this kind of serious limited-angle problem the ultra-limited-angle problem, which is difficult to effectively alleviate by traditional iterative reconstruction algorithms. With the development of deep learning, the generative adversarial network (GAN) performs well in image inpainting tasks and can add effective image information to restore missing parts of an image. In this study, given the characteristic of GAN to generate missing information, the sinogram-inpainting-GAN (SI-GAN) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction. We propose the U-Net generator and patch-design discriminator in SI-GAN to make the network suitable for standard medical CT images. Furthermore, we propose a joint projection domain and image domain loss function, in which the weighted image domain loss can be added by the back-projection operation. Then, by inputting a paired limited-angle/180° sinogram into the network for training, we can obtain the trained model, which has extracted the continuity feature of sinogram data. Finally, the classic CT reconstruction method is used to reconstruct the images after obtaining the estimated sinograms. The simulation studies and actual data experiments indicate that the proposed method performed well to reduce the serious artifacts caused by ultra-limited-angle scanning.
Real-Time Object Tracking with Template Tracking and Foreground Detection Network
Authors: Dai Kaiheng , Wang Yuehuan , Song Qiong
Published: September 2019

In this paper, we propose a fast and accurate deep network-based object tracking method, which combines feature representation, template tracking and foreground detection into a single framework for robust tracking. The proposed framework consists of a backbone network, which feeds into two parallel networks, TmpNet for template tracking and FgNet for foreground detection. The backbone network is a pre-trained modified VGG network, in which a few parameters need to be fine-tuned for adapting to the tracked object. FgNet is a fully convolutional network to distinguish the foreground from background in a pixel-to-pixel manner. The parameter in TmpNet is the learned channel-wise target template, which initializes in the first frame and performs fast template tracking in the test frames. To enable each component to work closely with each other, we use a multi-task loss to end-to-end train the proposed framework. In online tracking, we combine the score maps from TmpNet and FgNet to find the optimal tracking results. Experimental results on object tracking benchmarks demonstrate that our approach achieves favorable tracking accuracy against the state-of-the-art trackers while running at a real-time speed of 38 fps.
Near-Field Communication Sensors
Authors: Cao Zhonglin , Chen Ping , Ma Zhong , Li Sheng , Gao Xingxun , Wu Rui-xin , Pan Lijia , Shi Yi
Published: September 2019

Near-field communication is a new kind of low-cost wireless communication technology developed in recent years, which brings great convenience to daily life activities such as medical care, food quality detection, and commerce. The integration of near-field communication devices and sensors exhibits great potential for these real-world applications by endowing sensors with new features of powerless and wireless signal transferring and conferring near field communication device with sensing function. In this review, we summarize recent progress in near field communication sensors, including the development of materials and device design and their applications in wearable personal healthcare devices. The opportunities and challenges in near-field communication sensors are discussed in the end.
A Sensor Platform for Athletes’ Training Supervision: A Proof of Concept Study
Authors: Zompanti Alessandro , Sabatini Anna , Santonico Marco , Grasso Simone , Gianfelici Antonio , Donatucci Bruno , Di Castro Andrea , Pennazza Giorgio
Published: September 2019

One of the basic needs of professional athletes is the real-time and non-invasive monitoring of their activities. The use of these kind of data is necessary to develop strategies for specific tailored training in order to improve performances. The sensor system presented in this work has the aim to adopt a novel approach for the monitoring of physiological parameters, and athletes’ performances, during their training. The anaerobic threshold is herein identified with the monitoring of the lactate concentration and the respiratory parameters. The data collected by the sensor are used to build a model using a supervised method (based on the partial least squares method, PLS) to predict the values of the parameters of interest. The sensor is able to measure the lactate concentration from a sample of saliva and it can estimate a respiratory parameter, such as maximal oxygen consumption, maximal carbon dioxide production and respiratory rate from a sample of exhaled breath. The main advantages of the device are the low power; the wireless communication; and the non-invasive sampling method, which allow its use in a real context of sport practice.
Preliminary Clinical Application of Textile Insole Sensor for Hemiparetic Gait Pattern Analysis
Authors: Wang Changwon , Kim Young , Shin Hangsik , Min Se Dong
Published: September 2019

Post-stroke gait dysfunction occurs at a very high prevalence. A practical method to quantitatively analyze the characteristics of hemiparetic gait is needed in both clinical and community settings. This study developed a 10-channeled textile capacitive pressure sensing insole (TCPSI) with a real-time monitoring system and tested its performance through hemiparetic gait pattern analysis. Thirty-five subjects (18 hemiparetic, 17 healthy) walked down a 40-m long corridor at a comfortable speed while wearing TCPSI inside the shoe. For gait analysis, the percentage of the plantar pressure difference (PPD), the step count, the stride time, the coefficient of variation, and the phase coordination index (PCI) were used. The results of the stroke patients showed a threefold higher PPD, a higher step count (41.61 ± 10.7), a longer average stride time on the affected side, a lower mean plantar pressure on the affected side, higher plantar pressure in the toe area and the lateral side of the foot, and a threefold higher PCI (hemi: 19.50 ± 13.86%, healthy: 5.62 ± 5.05%) compared to healthy subjects. This study confirmed that TCPSI is a promising tool for distinguishing hemiparetic gait patterns and thus may be used as a wearable gait function evaluation tool, the external feedback gait training device, and a simple gait pattern analyzer for both hemiparetic patients and healthy individuals.
Running Propensities of Athletes with Hamstring Injuries
Authors: Sugimoto Dai , Kelly Brian D. , Mandel David L. , d’Hemecourt Duncan A. , Carpenito Sara C. , d’Hemecourt Charles A. , d’Hemecourt Pierre A.
Published: September 2019

The current study aims to compare the mechanical propensities between healthy runners and runners with hamstring injuries. Retrospective case-control video analysis was used. A total of 35 (12 male and 23 female) videos of runners with hamstring injuries were compared with videos of sex-, age-, mass-, and height-matched healthy control runners. The main outcome variables were trunk posture angles, overstride angles, and foot strike patterns. An independent t-test and chi-squared tests were employed to analyze the main outcome variables between the runners with hamstring injuries and the healthy control runners. The statistical significance of less than 0.05 (p < 0.05) was used. The runners with hamstring injuries had a 1.6° less forward-trunk posture angles compared with the healthy control runners (p = 0.043). Also, the runners with hamstring injuries demonstrated a 4.9° greater overstride angles compared with the healthy control runners (p = 0.001). Finally, the runners with hamstring injuries had a tendency of rearfoot strike, while the healthy control runners showed a forefoot strike pattern (p = 0.004). In conclusion, the runners with hamstring injuries demonstrated different running mechanical propensities compared with the healthy runners.
Sustainable Transformative Economy: Community-Based Ecotourism
Authors: Kim Marin , Xie Yi , Cirella Giuseppe T.
Published: September 2019

Ecotourism has a high potential impact on remote communities, by improving economic opportunity and natural resources conservation, and is increasingly accepted as an alternative livelihood for rural people. This study examines ecotourism development from the perspective of participation and economic impact for the Bousra people in Cambodia. A total of 237 households were selected as the sample size. Data collection was carried out with face-to-face interviews and analyzed using logistic regression and ordinary least square methods. Results revealed that local households depend mostly on agriculture (i.e., crop plantation and farming) and utilize ecotourism as a secondary source of income. Most households acknowledged ecotourism had a positive impact on environmental, social, and economic perspectives, while some signaled negative backlash due to depleted natural resources and impact on local culture. Household participation in ecotourism was not significantly affected from assistance issued by government or non-governmental organizations. However, causal relationships were found based on household demographic factors, attitude to environmental conservation, and village life. It was shown that the percentage of people involvement in ecotourism is high, but their income percentage is low due to education, skill, and capacity to expand. As a low-impact alternative to standard commercial tourism, community-based ecotourism has potential in becoming a transformative form of economics for local communities.
Does Participation in Agricultural Cooperatives Affect Farm Sustainability? Empirical Evidence from Taiwan
Authors: Wang Bo , Cheng Po-Yuan , Lee Brian , Sun Lih-Chyun , Chang Hung-Hao
Published: September 2019

Recent research has highlighted the importance of agricultural cooperatives on farm production. Although the consensus from the literature suggests that participating in these organizations significantly affects farm production, there is inconclusive evidence on whether this effect is positive or negative. Moreover, previous studies solely focus on the magnitude of this effect and fail to explain the mechanism behind it. This study contributes to this knowledge gap by estimating the impact of agricultural cooperatives on farm profits. To do this, we apply the causal mediation analysis to explain the potential mechanism behind this relationship. Using a nationally representative survey of farm households from Taiwan in 2013, we find that participating in cooperatives increases farm profits. Furthermore, this effect is more pronounced for producers with higher profits. Concerning the mechanism, we find that the use of food labels accounts for approximately 15 to 28% of the total effect of cooperative participation on farm profits.
Bottlenecks, Shockwave, and Off-Ramp Blockage on Freeways
Authors: Guo Jingqiu , Chen Xinyao , Pang Yuqi , Wang Yibing , Zheng Pengjun
Published: September 2019

Freeway congestion may spill back for several kilometers, blocking a number of on/off-ramps upstream. As a consequence, flows at the off-ramps may be substantially reduced, and vehicles bound for the off-ramps are trapped in the mainstream congestion, causing intensified spillback of congestion that blocks even more off-ramps further upstream. Such off-ramp blockage is readily understood and its impact is empirically recognized, but there is a lack of analysis to provide more insights. In this paper, some flow conditions for the activation of bottlenecks and congestion propagation are first established, and the mechanism of the off-ramp blockage is theoretically explored. Macroscopic and microscopic simulations are conducted to demonstrate the analytical results, and some general relations between the total demand, total inflow, total off-ramp outflow, and the number of vehicles within a freeway system are examined.
The Impact of Job Retention on Continuous Growth of Engineering and Informational Technology SMEs in South Korea
Authors: Park Chongryol , McQuaid Ronald , Lee Jiwoon , Kim Seungjin , Lee Insuk
Published: September 2019

This study aims to explore what factors are critically associated with job retention in Engineering and Information Technology small- and medium-sized enterprises (SMEs) in South Korea, and how employers think about staff retention policy in relation to business growth. This contrasts with previous studies that mainly focus on employee motivation, job retention, and turnover. Qualitative semi-structured interviews were conducted face-to-face with founder Chief Executive Officers (CEOs). The results suggest that an important factor influencing job retention policies of these SMEs was to motivate employees to make greater voluntary effort, including through developing a collaborative organizational culture, rather than solely offering them additional financial rewards or using other Human Resource Management (HRM) practices to improve individual performances. Interviewees believed that job retention and business growth were closely related, and they discussed various ways of eliciting emotional commitment from employees. Unlike research on larger firms, these suggestions did not involve immediate financial rewards. How employers thought that the roles played by employees strongly influenced their firm’s productivity and competitiveness. This study suggests SME employers adjust their retention policy specifically to improve their firm’s survival and long-term growth.
Structural Models in Corporate Social Responsibility: Attraction of Investment in Tunisia
Authors: Bousselmi Hosn el Woujoud , Caridad Lorena , Villamandos Nuria Ceular
Published: September 2019

The attraction of foreign direct investment is a common objective in developing countries, and this broad aim is carried out with different approaches in public policies. Corporate social responsibility is very common in international corporations, and it tends to produce a positive image for investors and in the surrounding society. This study aims to clarify the influence of the enhancement of corporate social responsibility by companies established in Tunisia as a consequence of the host country government general policies on the attraction of direct foreign investment. We propose the testing of a conceptual framework that describes this influence and explains the benefits of the social commitment, especially when it will be encouraged by public policies which can favor the attraction of foreign investments. The paper opted for an exploratory analysis on a sample of foreign companies with subsidiaries in the country. It contains a descriptive analysis, a study of the reliability of the scales of measurement and a principal components analysis. This approach is completed by an analysis of moment structures (AMOS) through a structural equation model linking the interactions of public policies with the development of strategies in social responsibility in companies, and their induced effects on the investment decisions in their subsidiaries. This approach tends to be associated with the sustainability and the commitment in the country, which is especially important in the present moment, with the political changes in the Maghreb region. With the models proposed, it has been shown that public policies, in addition to having a direct impact on investment decisions, can produce positive effects when they are carried out with the aim of promoting sustainable growth, and using indirect tools like the promotion of corporate social strategies in the companies that are already established in the country.
New Insights into the Roles of Monocytes/Macrophages in Cardiovascular Calcification Associated with Chronic Kidney Disease
Authors: Hénaut Lucie , Candellier Alexandre , Boudot Cédric , Grissi Maria , Mentaverri Romuald , Choukroun Gabriel , Brazier Michel , Kamel Saïd , Massy Ziad A.
Published: September 2019

Cardiovascular disease (CVD) is an important cause of death in patients with chronic kidney disease (CKD), and cardiovascular calcification (CVC) is one of the strongest predictors of CVD in this population. Cardiovascular calcification results from complex cellular interactions involving the endothelium, vascular/valvular cells (i.e., vascular smooth muscle cells, valvular interstitial cells and resident fibroblasts), and monocyte-derived macrophages. Indeed, the production of pro-inflammatory cytokines and oxidative stress by monocyte-derived macrophages is responsible for the osteogenic transformation and mineralization of vascular/valvular cells. However, monocytes/macrophages show the ability to modify their phenotype, and consequently their functions, when facing environmental modifications. This plasticity complicates efforts to understand the pathogenesis of CVC—particularly in a CKD setting, where both uraemic toxins and CKD treatment may affect monocyte/macrophage functions and thereby influence CVC. Here, we review (i) the mechanisms by which each monocyte/macrophage subset either promotes or prevents CVC, and (ii) how both uraemic toxins and CKD therapies might affect these monocyte/macrophage functions.
Evaluation of High-Resolution Mass Spectrometry for the Quantitative Analysis of Mycotoxins in Complex Feed Matrices
Authors: Jensen Tolke , de Boevre Marthe , Preußke Nils , de Saeger Sarah , Birr Tim , Verreet Joseph-Alexander , Sönnichsen Frank
Published: September 2019

The selective and sensitive analysis of mycotoxins in highly complex feed matrices is a great challenge. In this study, the suitability of OrbitrapTM-based high-resolution mass spectrometry (HRMS) for routine mycotoxin analysis in complex feeds was demonstrated by the successful validation of a full MS/data-dependent MS/MS acquisition method for the quantitative determination of eight Fusarium mycotoxins in forage maize and maize silage according to the Commission Decision 2002/657/EC. The required resolving power for accurate mass assignments (<5 ppm) was determined as 35,000 full width at half maximum (FWHM) and 70,000 FWHM for forage maize and maize silage, respectively. The recovery (RA), intra-day precision (RSDr), and inter-day precision (RSDR) of measurements were in the range of 94 to 108%, 2 to 16%, and 2 to 12%, whereas the decision limit (CCα) and the detection capability (CCβ) varied from 11 to 88 µg/kg and 20 to 141 µg/kg, respectively. A set of naturally contaminated forage maize and maize silage samples collected in northern Germany in 2017 was analyzed to confirm the applicability of the HRMS method to real samples. At least four Fusarium mycotoxins were quantified in each sample, highlighting the frequent co-occurrence of mycotoxins in feed.
Correction: Gisele Cristina Rubert et al., Evapotranspiration of the Brazilian Pampa Biome: Seasonality and Influential Factors. Water 2018, 10, 1864
Authors: Rubert Gisele , Roberti Débora , Santos Pereira Luis , Quadros Fernando , Campos Velho Haroldo , de Moraes Osvaldo
Published: September 2019

The authors wish to make the following corrections to this paper [...]
Application of MODFLOW with Boundary Conditions Analyses Based on Limited Available Observations: A Case Study of Birjand Plain in East Iran
Authors: Aghlmand Reza , Abbasi Ali
Published: September 2019

Increasing water demands, especially in arid and semi-arid regions, continuously exacerbate groundwater resources as the only reliable water resources in these regions. Groundwater numerical modeling can be considered as an effective tool for sustainable management of limited available groundwater. This study aims to model the Birjand aquifer using GMS: MODFLOW groundwater flow modeling software to monitor the groundwater status in the Birjand region. Due to the lack of the reliable required data to run the model, the obtained data from the Regional Water Company of South Khorasan (RWCSK) are controlled using some published reports. To get practical results, the aquifer boundary conditions are improved in the established conceptual method by applying real/field conditions. To calibrate the model parameters, including the hydraulic conductivity, a semi-transient approach is applied by using the observed data of seven years. For model performance evaluation, mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) are calculated. The results of the model are in good agreement with the observed data and therefore, the model can be used for studying the water level changes in the aquifer. In addition, the results can assist water authorities for more accurate and sustainable planning and management of groundwater resources in the Birjand region.