bluuebunny/crossref_metadata_2025 huggingface dataset:
Distribution and Characteristics of Hypouricemia within the Japanese General Population: A Cross-Sectional Study
Authors: Kawasoe Shin , Ide Kazuki , Usui Tomoko , Kubozono Takuro , Yoshifuku Shiro , Miyahara Hironori , Maenohara Shigeho , Ohishi Mitsuru , Kawakami Koji
Published: March 2019

Background and objectives: There is insufficient epidemiological knowledge of hypouricemia. In this study, we aimed to describe the distribution and characteristics of Japanese subjects with hypouricemia. Materials and Methods: Data from subjects who underwent routine health checkups from January 2001 to December 2015 were analyzed in this cross-sectional study. A total of 246,923 individuals, which included 111,117 men and 135,806 women, met the study criteria. The participants were divided into quartiles according to their serum uric acid (SUA) levels. We subdivided the subjects with hypouricemia, which was defined as SUA level ≤ 2.0 mg/dL, into two groups and compared their characteristics, including their cardiovascular risks. Results: The hypouricemia rates were 0.46% overall, 0.21% for the men and 0.66% for the women (P < 0.001). The number of the subjects with hypouricemia showed two distributions at SUA levels of 0.4–1.1 mg/dL (lower hypouricemia group), which included a peak at 0.7–0.8 mg/dL, and at SUA levels of 1.4–2.0 mg/dL (higher hypouricemia group). The men in the higher hypouricemia group had lower body mass indexes (BMI) and triglyceride (TG) levels and had higher fasting blood glucose levels than those in the lower hypouricemia group. The women in the higher hypouricemia group were younger; had lower BMI, total protein, TG, total cholesterol and low-density lipoprotein cholesterol levels; and had higher estimated glomerular filtration rates levels compared to those in the lower hypouricemia group. Conclusions: The characteristics of the individuals in the lower and higher hypouricemia groups differed significantly, indicating different pathophysiologies within each group.
Bacillus subtilis DSM 32315 Supplementation Attenuates the Effects of Clostridium perfringens Challenge on the Growth Performance and Intestinal Microbiota of Broiler Chickens
Authors: Bortoluzzi Cristiano , Serpa Vieira Bruno , de Paula Dorigam Juliano Cesar , Menconi Anita , Sokale Adebayo , Doranalli Kiran , Applegate Todd Jay
Published: March 2019

The objective of this study was to evaluate the effects of the dietary supplementation of Bacillus subtilis DSM 32315 (probiotic) on the performance and intestinal microbiota of broiler chickens infected with Clostridium perfringens (CP). One-day-old broiler chickens were assigned to 3 treatments with 8 replicate pens (50 birds/pen). The treatments were: non-infected control; infected control; and infected supplemented with probiotic (1 × 106 CFU/g of feed). On day of hatch, all birds were sprayed with a coccidia vaccine based on the manufacturer recommended dosage. On d 18–20 the infected birds were inoculated with CP via feed. Necrotic enteritis (NE) lesion score was performed on d 21. Digestive tract of 2 birds/pen was collected on d 21 to analyze the ileal and cecal microbiota by 16S rRNA sequencing. Performance was evaluated on d 28 and 42. On d 21, probiotic supplementation reduced (p < 0.001) the severity of NE related lesion versus infected control birds. On d 28, feed efficiency was improved (p < 0.001) in birds supplemented with probiotic versus infected control birds. On d 42, body weight gain (BW gain) and feed conversion ratio (FCR) were improved (p < 0.001) in probiotic supplemented birds versus infected control birds. The diversity, composition and predictive function of the intestinal microbial digesta changed with the infection but the supplementation of probiotic reduced these variations. Therefore, dietary supplementation of Bacillus subtilis DSM 32315 was beneficial in attenuating the negative effects of CP challenge on the performance and intestinal microbiota of broilers chickens.
Engineering Peptide-Based Polyelectrolyte Complexes with Increased Hydrophobicity
Authors: Tabandeh Sara , Leon Lorraine
Published: March 2019

Polyelectrolyte complexation is a versatile platform for the design of self-assembled materials. Here we use rational design to create ionic hydrophobically-patterned peptides that allow us to precisely explore the role of hydrophobicity on electrostatic self-assembly. Polycations and polyanions were designed and synthesized with an alternating sequence of d- and l-chiral patterns of lysine or glutamic acid with either glycine, alanine or leucine due to their increasing hydrophobicity index, respectively. Two motifs were considered for the oppositely charged patterned peptides; one with equal residues of charged and uncharged amino acids and the other with increased charge density. Mass spectroscopy, circular dichroism, H- and F-NMR spectroscopy were used to characterize the polypeptides. Polyelectrolyte complexes (PECs) formed using the sequences were characterized using turbidity measurements, optical microscopy and infrared spectroscopy. Our results show that the critical salt concentration, a key measure of PEC stability, increased with both increasing charge density as well as hydrophobicity. Furthermore, by increasing the hydrophobicity, the amount of PEC formed increased with temperature, contrary to purely ionic PECs. Lastly, we assessed the encapsulation behavior of these materials using a hydrophobic dye. Concluding that encapsulation efficiency increased with hydrophobic content of the complexes providing insight for future work on the application of these materials for drug delivery.
Alternative Ultrasound-Assisted Method for the Extraction of the Bioactive Compounds Present in Myrtle (Myrtus communis L.)
Authors: V. González de Peredo Ana , Vázquez-Espinosa Mercedes , Espada-Bellido Estrella , Ferreiro-González Marta , Amores-Arrocha Antonio , Palma Miguel , F. Barbero Gerardo , Jiménez-Cantizano Ana
Published: March 2019

The bioactive compounds in myrtle berries, such as phenolic compounds and anthocyanins, have shown a potentially positive effect on human health. Efficient extraction methods are to be used to obtain maximum amounts of such beneficial compounds from myrtle. For that reason, this study evaluates the effectiveness of a rapid ultrasound-assisted method (UAE) to extract anthocyanins and phenolic compounds from myrtle berries. The influence of solvent composition, as well as pH, temperature, ultrasound amplitude, cycle and solvent-sample ratio on the total phenolic compounds and anthocyanins content in the extracts obtained were evaluated. The response variables were optimized by means of a Box-Behnken design. It was found that the double interaction of the methanol composition and the cycle, the interaction between methanol composition and temperature, and the interaction between the cycle and solvent-sample ratio were the most influential variables on the extraction of total phenolic compounds (92.8% methanol in water, 0.2 s of cycle, 60 °C and 10:0.5 mL:g). The methanol composition and the interaction between methanol composition and pH were the most influential variables on the extraction of anthocyanins (74.1% methanol in water at pH 7). The methods that have been developed presented high repeatability and intermediate precision (RSD < 5%) and the bioactive compounds show a high recovery with short extraction times. Both methods were used to analyze the composition of the bioactive compounds in myrtle berries collected from different locations in the province of Cadiz (Spain). The results obtained by UAE were compared to those achieved in a previous study where microwave-assisted extraction (MAE) methods were employed. Similar extraction yields were obtained for phenolic compounds and anthocyanins by MAE and UAE under optimal conditions. However, UAE presents the advantage of using milder conditions for the extraction of anthocyanins from myrtle, which makes of this a more suitable method for the extraction of these degradable compounds.
Design and Synthesis of Novel Anti-Proliferative Emodin Derivatives and Studies on their Cell Cycle Arrest, Apoptosis Pathway and Migration
Authors: Yang Kun , Jin Ming-Ji , Quan Zhe-Shan , Piao Hu-Ri
Published: March 2019

Emodin is a cell arrest and apoptosis-inducing compound that is widely distributed in different plants (rhubarb, aloe), lichens and terrestrial fungi, and also isolated from marine-derived fungi and marine sponge-associated fungi. In this study, we designed and synthesized a novel series of emodin derivatives by binding emodin to an amino acid using linkers of varying lengths and composition, and evaluated their anti-proliferative activities using HepG2 cells (human hepatic carcinoma), MCF-7 cells (human breast cancer) and human normal liver L02 cells. Most of these derivatives showed moderate to potent anti-proliferative activities. Notably, compound 7a exhibited potent anti-proliferative activity against HepG2 cells with the half maximal inhibitory concentration (IC50) value of 4.95 µM, which was enhanced 8.8-fold compared to the parent compound emodin (IC50 = 43.87 µM), and it also exhibited better selective anti-proliferative activity and specificity than emodin. Moreover, further experiments demonstrated that compound 7a displayed a significant efficacy of inducing apoptosis through mitochondrial pathway via release of cytochrome c from mitochondria and subsequent activation of caspase-9 and caspase-3, inducing cell arrest at G0/G1 phase, as well as suppression of cell migration of tumor cells. The preliminary results suggested that compound 7a could be a promising lead compound for the discovery of novel anti-tumor drugs and has the potential for further investigations as an anti-cancer drug.
The Concept of an Ideal Antibiotic: Implications for Drug Design
Authors: Gajdács Márió
Published: March 2019

The emergence and spread of antibiotic-resistant pathogens is a major public health issue, which requires global action of an intersectoral nature. Multidrug-resistant (MDR) pathogens—especially “ESKAPE” bacteria—can withstand lethal doses of antibiotics with various chemical structures and mechanisms of action. Pharmaceutical companies are increasingly turning away from participating in the development of new antibiotics, due to the regulatory environment and the financial risks. There is an urgent need for innovation in antibiotic research, as classical discovery platforms (e.g., mining soil Streptomycetes) are no longer viable options. In addition to discovery platforms, a concept of an ideal antibiotic should be postulated, to act as a blueprint for future drugs, and to aid researchers, pharmaceutical companies, and relevant stakeholders in selecting lead compounds. Based on 150 references, the aim of this review is to summarize current advances regarding the challenges of antibiotic drug discovery and the specific attributes of an ideal antibacterial drug (a prodrug or generally reactive compound with no specific target, broad-spectrum antibacterial activity, adequate penetration through the Gram-negative cell wall, activity in biofilms and in hard-to-treat infections, accumulation in macrophages, availability for oral administration, and for use in sensitive patient groups).
Design and Fabrication by Thermal Imprint Lithography and Mechanical Characterization of a Ring-Based PDMS Soft Probe for Sensing and Actuating Forces in Biological Systems
Authors: Dattoma Tommaso , Qualtieri Antonio , Epifani Gianmichele , De Vittorio Massimo , Rizzi Francesco
Published: March 2019

In this paper, the design, fabrication and mechanical characterization of a novel polydimethylsiloxane (PDMS) soft probe for delivering and sensing forces in biological systems is proposed. On the basis of preliminary finite element (FEM) analysis, the design takes advantage of a suitable core geometry, characterized by a variable spring-like ring. The compliance of probes can be finely set in a wide range to measure forces in the micronewton to nanonewton range. In particular, this is accomplished by properly resizing the ring geometry and/or exploiting the mixing ratio-based elastic properties of PDMS. Fabrication by the thermal imprint lithography method allows fast and accurate tuning of ring sizes and tailoring of the contact section to their targets. By only varying geometrical parameters, the stiffness ranges from 1080 mNm−1 to 50 mNm−1, but by changing the base-curing agent proportion of the elastomer from 10:1 to 30:1, the stiffness drops to 37 mNm−1. With these compliances, the proposed device will provide a new experimental tool for investigating force-dependent biological functions in sensory systems.
Editorial for Special Issue: Metaproteomics
Authors: Seifert Jana , Muth Thilo
Published: March 2019

As the proteome-level counterpart of metagenomics, metaproteomics extends conventional single-organism proteomics and allows researchers to characterize the entire protein complement of complex microbiomes on a large scale [...]
CEO Overconfidence and Shadow-Banking Life Insurer Performance Under Government Purchases of Distressed Assets
Authors: Chen Shi , Lin Jyh-Horng , Yao Wenyu , Huang Fu-Wei
Published: March 2019

In this paper, we develop a contingent claim model to evaluate the equity, default risk, and efficiency gain/loss from managerial overconfidence of a shadow-banking life insurer under the purchases of distressed assets by the government. Our paper focuses on managerial overconfidence where the chief executive officer (CEO) overestimates the returns on investment. The investment market faced by the life insurer is imperfectly competitive, and investment is core to the provision of profit-sharing life insurance policies. We show that CEO overconfidence raises the default risk in the life insurer’s equity returns, thereby adversely affecting the financial stability. Either shadow-banking involvement or government bailout attenuates the unfavorable effect. There is an efficiency gain from CEO overconfidence to investment. Government bailout helps to reduce the life insurer’s default risk, but simultaneously reduce the efficiency gain from CEO overconfidence. Our results contribute to the managerial overconfidence literature linking insurer shadow-banking involvement and government bailout in particular during a financial crisis.
Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
Authors: Albergel Clément , Dutra Emanuel , Bonan Bertrand , Zheng Yongjun , Munier Simon , Balsamo Gianpaolo , de Rosnay Patricia , Muñoz-Sabater Joaquin , Calvet Jean-Christophe
Published: March 2019

This study aims to assess the potential of the LDAS-Monde platform, a land data assimilation system developed by Météo-France, to monitor the impact on vegetation state of the 2018 summer heatwave over Western Europe. The LDAS-Monde is driven by ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite-derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). The study of long time series of satellite derived CGLS LAI (2000–2018) and SSM (2008–2018) highlights marked negative anomalies for July 2018 affecting large areas of northwestern Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the domain of interest have never been observed in the LAI product over this 19-year period. LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25° × 0.25° (January 2008 to October 2018) and 0.10° × 0.10° (April 2016 to December 2018). Both configurations of LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5- and IFS HRES-driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g., associated to a better representation of the land cover, topography) and using HRES forcing still enhances the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedback in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model-only initial conditions.
Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway
Authors: Chen Jianfei , Dai Zhaohua , Chen ZhiQiang
Published: March 2019

The advent of autonomous navigation, positioning, and general robotics technologies has enabled the improvement of small to miniature-sized unmanned aerial vehicles (UAVs, or ‘drones’) and their wide uses in engineering practice. Recent research endeavors further envision a systematic integration of aerial drones and traditional contact-based or ground-based sensors, leading to an aerial–ground wireless sensor network (AG-WSN), in which the UAV serves as both a gateway besides and a remote sensing platform. This paper serves two goals. First, we will review the recent development in architecture, design, and algorithms related to UAVs as a gateway and particularly illustrate its nature in realizing an opportunistic sensing network. Second, recognizing the opportunistic sensing need, we further aim to focus on achieving energy efficiency through developing an active radio frequency (RF)-based wake-up mechanism for aerial–ground data transmission. To prove the effectiveness of energy efficiency, several sensor wake-up solutions are physically implemented and evaluated. The results show that the RF-based wake-up mechanism can potentially save more than 98.4% of the energy that the traditional duty-cycle method would otherwise consume, and 96.8% if an infrared-receiver method is used.
GaN-Based Ultraviolet Passive Pixel Sensor on Silicon (111) Substrate
Authors: Lee Chang-Ju , Won Chul-Ho , Lee Jung-Hee , Hahm Sung-Ho , Park Hongsik
Published: March 2019

The fabrication of a single pixel sensor, which is a fundamental element device for the fabrication of an array-type pixel sensor, requires an integration technique of a photodetector and transistor on a wafer. In conventional GaN-based ultraviolet (UV) imaging devices, a hybrid-type integration process is typically utilized, which involves a backside substrate etching and a wafer-to-wafer bonding process. In this work, we developed a GaN-based UV passive pixel sensor (PPS) by integrating a GaN metal-semiconductor-metal (MSM) UV photodetector and a Schottky-barrier (SB) metal-oxide-semiconductor field-effect transistor (MOSFET) on an epitaxially grown GaN layer on silicon substrate. An MSM-type UV sensor had a low dark current density of 3.3 × 10−7 A/cm2 and a high UV/visible rejection ratio of 103. The GaN SB-MOSFET showed a normally-off operation and exhibited a maximum drain current of 0.5 mA/mm and a maximum transconductance of 30 μS/mm with a threshold voltage of 4.5 V. The UV PPS showed good UV response and a high dark-to-photo contrast ratio of 103 under irradiation of 365-nm UV. This integration technique will provide one possible way for a monolithic integration of the GaN-based optoelectronic devices.
PDMS Microlenses for Focusing Light in Narrow Band Imaging Diagnostics
Authors: Costa Adriana C. , Pimenta Sara , Ribeiro João F. , Silva Manuel F. , Wolffenbuttel Reinoud F. , Dong Tao , Yang Zhaochu , Correia José H.
Published: March 2019

Minimally invasive medical devices can greatly benefit from Narrow Band Imaging (NBI) diagnostic capabilities, as different wavelengths allow penetration of distinct layers of the gastrointestinal tract mucosa, improving diagnostic accuracy and targeting different pathologies. An important performance parameter is the light intensity at a given power consumption of the medical device. A method to increase the illumination intensity in the NBI diagnostic technique was developed and applied to minimally invasive medical devices (e.g., endoscopic capsules), without increasing the size and power consumption of such instruments. Endoscopic capsules are generally equipped with light-emitting diodes (LEDs) operating in the RGB (red, green, and blue) visible light spectrum. A polydimethylsiloxane (PDMS) µ-lens was designed for a maximum light intensity at the target area of interest when placed on top of the LEDs. The PDMS µ-lens was fabricated using a low-cost hanging droplet method. Experiments reveal an increased illumination intensity by a factor of 1.21 for both the blue and green LEDs and 1.18 for the red LED. These promising results can increase the resolution of NBI in endoscopic capsules, which can contribute to early gastric lesions diagnosis.
CropDeep: The Crop Vision Dataset for Deep-Learning-Based Classification and Detection in Precision Agriculture
Authors: Zheng Yang-Yang , Kong Jian-Lei , Jin Xue-Bo , Wang Xiao-Yi , Su Ting-Li , Zuo Min
Published: March 2019

Intelligence has been considered as the major challenge in promoting economic potential and production efficiency of precision agriculture. In order to apply advanced deep-learning technology to complete various agricultural tasks in online and offline ways, a large number of crop vision datasets with domain-specific annotation are urgently needed. To encourage further progress in challenging realistic agricultural conditions, we present the CropDeep species classification and detection dataset, consisting of 31,147 images with over 49,000 annotated instances from 31 different classes. In contrast to existing vision datasets, images were collected with different cameras and equipment in greenhouses, captured in a wide variety of situations. It features visually similar species and periodic changes with more representative annotations, which have supported a stronger benchmark for deep-learning-based classification and detection. To further verify the application prospect, we provide extensive baseline experiments using state-of-the-art deep-learning classification and detection models. Results show that current deep-learning-based methods achieve well performance in classification accuracy over 99%. While current deep-learning methods achieve only 92% detection accuracy, illustrating the difficulty of the dataset and improvement room of state-of-the-art deep-learning models when applied to crops production and management. Specifically, we suggest that the YOLOv3 network has good potential application in agricultural detection tasks.
Cabin as a Home: A Novel Comfort Optimization Framework for IoT Equipped Smart Environments and Applications on Cruise Ships
Authors: Nolich Massimiliano , Spoladore Daniele , Carciotti Sara , Buqi Raol , Sacco Marco
Published: March 2019

The international tourism competition poses new challenges to the cruise sector, such as the achievement of the tourists’ satisfaction and the increase in on board comfort. Moreover, the growing sophistication of tourists’ needs leads to a more user-centric touristic offer. Consequently, a personalized cabin environment, which fits the users’ activities and their characteristics, could be a plus value during the cruise vacation. These topics, however, are strictly connected with the diffusion of digital technologies and dynamics, which represent the tools to achieve the goal of a customized on-cruise experience. This paper presents E-Cabin, a novel Internet of Things (IoT) framework architecture that has at its core a reasoning system tuned on data gathered from the environment and from each specific passenger and the activities he/she performs. The framework leverages on knowledge representation with ontologies and consists of a publisher–subscriber communication framework that allows all of the IoT applications to use the reasoner and the provided ontologies. The paper demonstrates the proposed system in a demo cruise cabin where, by using the E-Cabin application, it is possible to set various atmospheres based on the users and activities occurring in the cabin.
Ion Chromatographic Fingerprinting of STC-1 Cellular Response for Taste Sensing
Authors: Zabadaj Marcin , Szuplewska Aleksandra , Balcerzak Maria , Chudy Michał , Ciosek-Skibińska Patrycja
Published: March 2019

Taste sensing is of great importance in both the pharmaceutical and foodstuff industries, and is currently mainly based on human sensory evaluation. Many approaches based on chemical sensors have been proposed, leading to the development of various electronic tongue systems. However, this approach is limited by the applied recognition methods, which do not consider natural receptors. Biorecognition elements such as taste receptor proteins or whole cells can be involved in the development of taste sensing biosensors usually equipped with various electrochemical transducers. Here, we propose a new approach: intestinal secretin tumor cell line (STC-1) chemosensory cells were applied for taste recognition, and their taste-specific cellular response was decoded from ion chromatographic fingerprints with the use of multivariate data processing by partial least squares discriminant analysis (PLS-DA). This approach could be useful for the development of various non-invasive taste sensing assays, as well as for studying taste transduction mechanisms in vitro.
A Deep Learning Approach to Position Estimation from Channel Impulse Responses
Authors: Niitsoo Arne , Edelhäußer Thorsten , Eberlein Ernst , Hadaschik Niels , Mutschler Christopher
Published: March 2019

Radio-based locating systems allow for a robust and continuous tracking in industrial environments and are a key enabler for the digitalization of processes in many areas such as production, manufacturing, and warehouse management. Time difference of arrival (TDoA) systems estimate the time-of-flight (ToF) of radio burst signals with a set of synchronized antennas from which they trilaterate accurate position estimates of mobile tags. However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. This article shows how deep learning (DL) can be used to estimate the position of mobile objects directly from the raw channel impulse responses (CIR) extracted at the receivers. Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations.
Lamb Wave-Minimum Sampling Variance Particle Filter-Based Fatigue Crack Prognosis
Authors: Yang Weibo , Gao Peiwei
Published: March 2019

Fatigue cracks are one of the common failure types of key aircraft components, and they are the focus of prognostics and health management (PHM) systems. Monitoring and prediction of fatigue cracks show great application potential and economic benefit in shortening aircraft downtime, prolonging service life, and enhancing maintenance. However, the fatigue crack growth process is a non-linear non-Gaussian dynamic stochastic process, which involves a variety of uncertainties. Actual crack initiation and growth sometimes deviate from the results of fracture mechanics analysis. The Lamb wave-particle filter (LW-PF) fatigue-crack-life prediction based on piezoelectric transducer (PZT) sensors has the advantages of simple modeling and on-line prediction, making it suitable for engineering applications. Although the resampling algorithm of the standard particle filter (PF) can solve the degradation problem, the discretization error still exists. To alleviate the accuracy decrease caused by the discretization error, a Lamb wave-minimum sampling variance particle filter (LW-MSVPF)-based fatigue crack life prediction method is proposed and validated by fatigue test of the attachment lug in this paper. Sampling variance (SV) is used as a quantitative index to analyze the difference of particle distribution before and after resampling. Compared with the LW-PF method, LW-MSVPF can increase the prediction accuracy with the same computational cost. By using the minimum sampling variance (MSV) resampling method, the original particle distribution is retained to a maximum degree, and the discretization error is significantly reduced. Furthermore, LW-MSVPF maintains the characteristic of dimensional freedom, which means a broader application in on-line prognosis for more complex structures.
Collision Detection and Identification on Robot Manipulators Based on Vibration Analysis
Authors: Min Feiyan , Wang Gao , Liu Ning
Published: March 2019

Robot manipulators should be able to quickly detect collisions to limit damage due to physical contact. Traditional model-based detection methods in robotics are mainly concentrated on the difference between the estimated and actual applied torque. In this paper, a model independent collision detection method is presented, based on the vibration features generated by collisions. Firstly, the natural frequencies and vibration modal features of the manipulator under collisions are extracted with illustrative examples. Then, a peak frequency based method is developed for the estimation of the vibration modal along the manipulator structure. The vibration modal features are utilized for the construction and training of the artificial neural network for the collision detection task. Furthermore, the proposed networks also generate the location and direction information about contact. The experimental results show the validity of the collision detection and identification scheme, and that it can achieve considerable accuracy.
Acoustic Detection Module Design of a Quartz-Enhanced Photoacoustic Sensor
Authors: Wei Tingting , Wu Hongpeng , Dong Lei , Tittel Frank K.
Published: March 2019

This review aims to discuss the latest advancements of an acoustic detection module (ADM) based on quartz-enhanced photoacoustic spectroscopy (QEPAS). Starting from guidelines for the design of an ADM, the ADM design philosophy is described. This is followed by a review of the earliest standard quartz tuning fork (QTF)-based ADM for laboratory applications. Subsequently, the design of industrial fiber-coupled and free-space ADMs based on a standard QTF for near-infrared and mid-infrared laser sources respectively are described. Furthermore, an overview of the latest development of a QEPAS ADM employing a custom QTF is reported. Numerous application examples of four QEPAS ADMs are described in order to demonstrate their reliability and robustness.
Total and Free Fatty Acids Analysis in Milk and Dairy Fat
Authors: Amores Gustavo , Virto Mailo
Published: March 2019

Dairy fat is one of the most complex natural fats because of its fatty acid (FA) composition. Ruminant dairy fat contains more than 400 different FA varying in carbon chain length, and degree, position and configuration of unsaturation. The following article reviews the different methods available to analyze FA (both total and free) in milk and dairy products. The most widely used methodology for separating and analyzing dairy FA is gas chromatography, coupled to a flame ionization detector (CG-FID). Alternatively, gas chromatography coupled to a mass spectrometer (GC-MS) is also used. After lipid extraction, total FA (TFA) are commonly converted into their methyl esters (fatty acid methyl esters, FAME) prior to chromatographic analysis. In contrast, free FA (FFA) can be analyzed after conversion to FAME or directly as FFA after extraction from the product. One of the key questions when analyzing FAME from TFA is the selection of a proper column for separating them, which depends mainly on the objective of the analysis. Quantification is best achieved by the internal standard method. Recently, near-infrared spectroscopy (NIRS), Raman spectroscopy (RS) and nuclear magnetic resonance (NMR) have been reported as promising techniques to analyze FA in milk and dairy products.
Social Responsibility Initiatives for Public-Private Partnership Projects: A Comparative Study between China and Ghana
Authors: Osei-Kyei Robert , Chan Albert P. C. , Yu Yao , Chen Chuan , Ke Yongjian , Tijani Bashir
Published: March 2019

Sustainability is a global issue and its enhancement through modern forms of procurements, such as public-private partnership (PPP), has become topical considering the huge impact of PPP activities on society, the economy, and the environment. However, one way of promoting sustainability thorough PPPs is the adoption of social responsibility (SR) initiatives/factors. This paper aims to empirically investigate the SR factors in PPPs through a comparative study between China and Ghana. An empirical questionnaire survey was conducted in both China and Ghana. Further, the mean score ranking, Kendall’s coefficient of concordance, and Mann-Whitney U test were used for data analysis. Results show that SR factors related to the economic efficiency of PPP projects and climate change adaptation are critical in China, whereas in Ghana, SR factors directly related to job creation and environmental protection are critical. The outputs of this study inform investors of the critical SR initiatives to consider when engaging in PPPs in Asia and Africa. In addition, they provide a solid knowledge base for the continuous international debate on how sustainability could be enhanced through PPP policy.
Development of an Issue-Situation-Based Board Game: A Systemic Learning Environment for Water Resource Adaptation Education
Authors: Cheng Ping-Han , Yeh Ting-Kuang , Tsai Jen-Che , Lin Ching-Rong , Chang Chun-Yen
Published: March 2019

Climate change has severely affected water resources (WRs) today, highlighting the importance of promoting education on WR adaptation (WRA). The goal of WRA education is to cultivate learners’ cross-oriented system knowledge (COSK), responsibility for WR environment (RWRE), value of public benefits (VPB), and empathy and negotiating thinking (EaNT). This study developed an issue-situation-based board game, namely Water Ark, to enhance participants’ WR knowledge. In this study, participants were divided into four groups with each playing the role of a WR-related organization: the government, agricultural sector, industrial sector and public guild. Scales and interviews were used to assess participants’ performance. The findings revealed that during the game, participants gradually changed from profit-oriented self-interest strategies to altruistic strategies based on social public benefit. Furthermore, after playing Water Ark, participants’ COSK, RWRE, VPB, and EaNT were found to have improved substantially. Thus, Water Ark is believed to be a useful tool for assisting WRA education.
Prioritizing Spatially Aggregated Cost-Effective Sites in Natural Reserves to Mitigate Human-Induced Threats: A Case Study of the Qinghai Plateau, China
Authors: Yang Jianxin , Gong Jian , Tang Wenwu
Published: March 2019

Anthropogenic activities often lead to the degradation of valuable natural habitats. Many efforts have been taken to counteract this degradation process, including the mitigation of human-induced stressors. However, knowing-doing gaps exist in stakeholder’s decision-making of prioritizing sites to allocate limited resources in these mitigation activities in both spatially aggregated and cost-effective manner. In this study, we present a spatially explicit prioritization framework that integrates basic cost effectiveness analysis (CEA) and spatial clustering statistics. The advantages of the proposed framework lie in its straightforward logic and ease of implementation to assist stakeholders in the identification of threat mitigation actions that are both spatially clumped and cost-effective using innovative prioritization indicators. We compared the utility of three local autocorrelation-based clustering statistics, including local Moran’s I, Getis-Ord Gi*, and AMOEBA, in quantifying the spatial aggregation of identified sites under given budgets. It is our finding that the CEA method produced threat mitigation sites that are more cost-effective but are dispersed in space. Spatial clustering statistics could help identify spatially aggregated management sites with only minor loss in cost effectiveness. We concluded that integrating basic CEA with spatial clustering statistics provides stakeholders with straightforward and reliable information in prioritizing spatially clustered cost-effective actions for habitat threat mitigation.
Relationship among HIV/AIDS Prevalence, Human Capital, Good Governance, and Sustainable Development: Empirical Evidence from Sub-Saharan Africa
Authors: Odugbesan Jamiu Adetola , Rjoub Husam
Published: March 2019

Sub-Saharan Africa is regarded as the region that accommodates about 75% of the world HIV/AIDS prevalence as of 2016. Research on the relationship between the epidemic and sustainable development is scant in this part of the world, as available literature is dominated by studies that focus on HIV and economic growth. Therefore, this study examines the relationship between sustainable development and HIV/AIDS prevalence, along with other determinants of sustainable development, such as good governance and human capital in 26 sub-Saharan Africa countries over a 27-year period from 1990—2016. The pooled mean group (PMG) estimator was employed for analysis after it was confirmed by the Hausman test for the estimation of the relationship among the variables. The results revealed a unidirectional long-run and significant relationship between HIV/AIDS prevalence and sustainable development, human capital and good governance, and human capital and sustainable development. Also, a bidirectional long-run relationship was found between good governance and HIV/AIDS prevalence. Estimation of subgroups provides a robustness check for our findings. Therefore, the paper gives new insight to the government of sub-Saharan Africa countries and major stakeholders about how to attain sustainable development in the region, while intensifying efforts on reducing HIV/AIDS prevalence, and at the same time ensuring effective good governance and human capital development.
Labour Migration in the Middle Hills of Nepal: Consequences on Land Management Strategies
Authors: Jaquet Stéphanie , Kohler Thomas , Schwilch Gudrun
Published: March 2019

Labour migration in Nepal is having profound effects on land management. We take two examples from the hills of Nepal where the increasing trend in outmigration continues unabated and explore its consequences. The purpose of this study is to understand the impacts of the subsequent labour shortage on land management and how it affects households. We used data from two surveys and assessed land use change and degradation with a qualitative mapping method. The findings show that the local context leads to very different strategies in terms of land management. In one study area, land was left to lie fallow without any use, leading to overgrowth and forest recovery due to favourable climate conditions. In the other, land was no longer used as cropland but turned into grazing land with consequences such as land degradation. This study provides strong empirical data and also contributes to the mountain research community by shedding light on the effects of outmigration on land management in the hills of Nepal. We suggest that these effects, including the labour shortage and the increasingly important role of remittances, should be addressed in an integrative but differentiated way that takes into account the regional context.
Experimental Verification of Effectiveness of English Language Teaching Using MyEnglishLab
Authors: Vymetalkova Danuse , Milkova Eva
Published: March 2019

The paper deals with a research focused on the blended learning model represented by an online component MyEnglishLab complementing English paper-based materials. At first, we viewed the blended learning model and we presented it in the context of sustainability in education. This is followed by the description of MyEnglisLab component representing the distant part of blended learning. Then, we presented a complete research carried out within undergraduate students at the Faculty of Science, University of Hradec Kralove attending English courses, where we applied the mentioned teaching design and verifying the effectiveness of MyEnglishLab tool. The study compared two groups of students; the group using MyEnglishLab reached better results.
Heterogeneous Causal Relationships between Spot and Futures Oil Prices: Evidence from Quantile Causality Analysis
Authors: Su Xianfang , Zhu Huiming , Yang Xinxia
Published: March 2019

The causal relationships between spot and futures crude oil prices have attracted the attention of many researchers in the past several decades. Most of the studies, however, do not distinguish among the various oil market situations in analyses of linear and nonlinear causalities. In light of the fact that a booming or depressing oil market produces heterogeneous investment behaviors, this study applied a quantile causality framework to capture different causalities across various quantile levels and found that the causal relationships between crude oil spot and futures prices significantly derive from tail quantile intervals and appear as heterogeneous effects. Before the Iraq War, crude oil spot and futures prices were mutually Granger-caused at lower quantile levels, and only futures prices led spot prices at upper quantile levels. Since the war, a clear bidirectional causality has existed at the upper quantile levels, but only in lower quantile levels have futures prices led spot prices. These results provide useful information to investors using crude spot or futures prices to hedge or manage downside or upside risks in their portfolios.
Strategies to Hierarchical Porosity in Carbon Nanofiber Webs for Electrochemical Applications
Authors: Yarova Svitlana , Jones Deborah , Jaouen Frédéric , Cavaliere Sara
Published: March 2019

Morphology and porosity are crucial aspects for designing electrodes with facile transport of electrons, ions and matter, which is a key parameter for electrochemical energy storage and conversion. Carbon nanofibers (CNFs) prepared by electrospinning are attractive for their high aspect ratio, inter-fiber macroporosity and their use as self-standing electrodes. The present work compares several strategies to induce intra-fiber micro-mesoporosity in self-standing CNF webs prepared by electrospinning polyacrylonitrile (PAN). Two main strategies were investigated, namely i) a templating method based on the addition of a porogen (polymethyl methacrylate, polyvinylpyrrolidone, Nafion® or ZnCl2) in the electrospinning solution of PAN, or ii) the activation in ammonia of previously formed CNF webs. The key result of this study is that open intra-fiber porosity could be achieved only when the strategies i) and ii) were combined. When each approach was applied separately, only closed intra-fiber porosity or no intra-fiber porosity was observed. In contrast, when both strategies were used in combination all CNF webs showed high mass-specific areas in the range of 325 to 1083 m2·g−1. Selected webs were also characterized for their carbon structure and electrical conductivity. The best compromise between high porosity and high electrical conductivity was identified as the fibrous web electrospun from PAN and polyvinylpyrrolidone.
A Feature Extraction Method for P2P Botnet Detection Using Graphic Symmetry Concept
Authors: Yang Zhixian , Wang Buhong
Published: March 2019

A DDoS (Distributed Denial of Service) attack makes use of a botnet to launch attacks and cause node congestion of wireless sensor networks, which is a common and serious threat. Due to the various kinds of features required in a Peer-to-Peer (P2P) botnet for DDoS attack detection via current machine learning methods and the failure to effectively detect encrypted botnets, this paper extracts the data packet size and the symmetric intervals in flow according to the concept of graphic symmetry. Combined with flow information entropy and session features, the frequency domain features can be sorted so as to obtain features with better correlations, which solves the problem of multiple types of features required for detection. Information entropy corresponding to the flow size can distinguish an encrypted botnet. This method is implemented through machine learning techniques. Experimental results show that the proposed method can detect the P2P botnet for DDoS attack and the detection accuracy is higher than that of traditional feature detection.
Generalized Neutrosophic Extended Triplet Group
Authors: Ma Yingcang , Zhang Xiaohong , Yang Xiaofei , Zhou Xin
Published: March 2019

Neutrosophic extended triplet group is a new algebra structure and is different from the classical group. In this paper, the notion of generalized neutrosophic extended triplet group is proposed and some properties are discussed. In particular, the following conclusions are strictly proved: (1) an algebraic system is a generalized neutrosophic extended triplet group if and only if it is a quasi-completely regular semigroup; (2) an algebraic system is a weak commutative generalized neutrosophic extended triplet group if and only if it is a quasi-Clifford semigroup; (3) for each n ∈ Z + , n ≥ 2 , ( Z n , ⊗ ) is a commutative generalized neutrosophic extended triplet group; (4) for each n ∈ Z + , n ≥ 2 , ( Z n , ⊗ ) is a commutative neutrosophic extended triplet group if and only if n = p 1 p 2 ⋯ p m , i.e., the factorization of n has only single factor.
Terminase Large Subunit Provides a New Drug Target for Herpesvirus Treatment
Authors: Yang Linlin , Yang Qiao , Wang Mingshu , Jia Renyong , Chen Shun , Zhu Dekang , Liu Mafeng , Wu Ying , Zhao Xinxin , Zhang Shaqiu , Liu Yunya , Yu Yanling , Zhang Ling , Chen Xiaoyue , Cheng Anchun
Published: March 2019

Herpesvirus infection is an orderly, regulated process. Among these viruses, the encapsidation of viral DNA is a noteworthy link; the entire process requires a powered motor that binds to viral DNA and carries it into the preformed capsid. Studies have shown that this power motor is a complex composed of a large subunit, a small subunit, and a third subunit, which are collectively known as terminase. The terminase large subunit is highly conserved in herpesvirus. It mainly includes two domains: the C-terminal nuclease domain, which cuts the viral concatemeric DNA into a monomeric genome, and the N-terminal ATPase domain, which hydrolyzes ATP to provide energy for the genome cutting and transfer activities. Because this process is not present in eukaryotic cells, it provides a reliable theoretical basis for the development of safe and effective anti-herpesvirus drugs. This article reviews the genetic characteristics, protein structure, and function of the herpesvirus terminase large subunit, as well as the antiviral drugs that target the terminase large subunit. We hope to provide a theoretical basis for the prevention and treatment of herpesvirus.