bluuebunny/crossref_metadata_2025 huggingface dataset:
Mesoscale Simulations of pH-Responsive Amphiphilic Polymeric Micelles for Oral Drug Delivery
Authors: Wu Zhimin , Duan Manzhen , Xiong Di , Zhang Can Yang
Published: November 2019

It is of great significance to study the structure property and self-assembly of amphiphilic block copolymer in order to effectively and efficiently design and prepare drug delivery systems. In this work, dissipative particle dynamics (DPD) simulation method was used to investigate the structure property and self-assembly ability of pH-responsive amphiphilic block copolymer poly(methyl methacrylate-co-methacrylic acid)-b-poly(aminoethyl methacrylate) (poly(MMA-co-MAA)-b-PAEMA). The effects of different block ratios (hydrophilic PAEMA segment and pH-sensitive PMAA segment) in copolymer on self-assembly and drug loading capacity including drug distribution were extensively investigated. The increase of hydrophilic PAEMA facilitated the formation of a typical core-shell structure as well as a hydrophobic PMAA segment. Furthermore, the optimal drug-carrier ratio was confirmed by an analysis of the drug distribution during the self-assembly process of block copolymer and model drug Ibuprofen (IBU). In addition, the drug distribution and nanostructure of IBU-loaded polymeric micelles (PMs) self-assembled from precise block copolymer (PMMA-b-PMAA-b-PAEMA) and block copolymer (poly(MMA-co-MAA)-b-PAEMA) with random pH-responsive/hydrophobic structure were evaluated, showing that almost all drug molecules were encapsulated into a core for a random copolymer compared to the analogue. The nanostructures of IBU-loaded PMs at different pH values were evaluated. The results displayed that the nanostructure was stable at pH < pKa and anomalous at pH > pKa which indicated drug release, suggesting that the PMs could be used in oral drug delivery. These findings proved that the amphiphilic block copolymer P(MMA30-co-MAA33)-b-PAEMA38 with random structure and pH-sensitivity might be a potential drug carrier. Moreover, DPD simulation shows potential to study the structure property of PMs self-assembled from amphiphilic block copolymer.
Study of the Influence of the Reprocessing Cycles on the Final Properties of Polylactide Pieces Obtained by Injection Molding
Authors: Agüero Angel , Morcillo Maria del Carmen , Quiles-Carrillo Luis , Balart Rafael , Boronat Teodomiro , Lascano Diego , Torres-Giner Sergio , Fenollar Octavio
Published: November 2019

This research work aims to study the influence of the reprocessing cycles on the mechanical, thermal, and thermomechanical properties of polylactide (PLA). To this end, PLA was subjected to as many as six extrusion cycles and the resultant pellets were shaped into pieces by injection molding. Mechanical characterization revealed that the PLA pieces presented relatively similar properties up to the third reprocessing cycle, whereas further cycles induced an intense reduction in ductility and toughness. The effect of the reprocessing cycles was also studied by the changes in the melt fluidity, which showed a significant increase after four reprocessing cycles. An increase in the bio-polyester chain mobility was also attained with the number of the reprocessing cycles that subsequently favored an increase in crystallinity of PLA. A visual inspection indicated that PLA developed certain yellowing and the pieces also became less transparent with the increasing number of reprocessing cycles. Therefore, the obtained results showed that PLA suffers a slight degradation after one or two reprocessing cycles whereas performance impairment becomes more evident above the fourth reprocessing cycle. This finding suggests that the mechanical recycling of PLA for up to three cycles of extrusion and subsequent injection molding is technically feasible.
Shortening Stabilization Time Using Pressurized Air Flow in Manufacturing Mesophase Pitch-Based Carbon Fiber
Authors: Shimanoe Hiroki , Ko Seunghyun , Jeon Young-Pyo , Nakabayashi Koji , Miyawaki Jin , Yoon Seong-Ho
Published: November 2019

Oxidation–stabilization using pressurized air flows of 0.5 and 1.0 MPa could successfully shorten the total stabilization time to less than 60 min for manufacturing mesophase pitch-based carbon fibers without deteriorating mechanical performance. Notably, the carbonized fiber heat-treated at 1000 °C for 30 min, which was oxidative–stabilized at 260 °C without soaking time with a heating rate of 2.0 °C/min using 100 mL/min of pressurized air flow of 0.5 MPa (total stabilization time: 55 min), showed excellent tensile strength and Young′s modulus of 3.4 and 177 GPa, respectively, which were higher than those of carbonized fiber oxidation–stabilized at 270 °C without soaking time with a heating rate of 0.5 °C/min using 100 mL/min of atmospheric air flow (total stabilization time: 300 min). Activation energies for oxidation reactions in stabilization using pressurized air flows were much lower than those of oxidation reactions using atmospheric air flow because of the higher oxidation diffusion from the outer surface into the center part of pitch fibers for the use of the pressurized air flows of 0.5 and 1.0 MPa than the atmospheric one. The higher oxygen diffusivities resulted in a more homogeneous distribution of oxygen weight uptake across the transverse section of mesophase pitch fibers, and allowed the improvement of the mechanical properties.
Melt-Spun Nanocomposite Fibers Reinforced with Aligned Tunicate Nanocrystals
Authors: Redondo Alexandre , Chatterjee Sourav , Brodard Pierre , Korley LaShanda T. J. , Weder Christoph , Gunkel Ilja , Steiner Ullrich
Published: November 2019

The fabrication of nanocomposite films and fibers based on cellulose nanocrystals (P-tCNCs) and a thermoplastic polyurethane (PU) elastomer is reported. High-aspect-ratio P-tCNCs were isolated from tunicates using phosphoric acid hydrolysis, which is a process that affords nanocrystals displaying high thermal stability. Nanocomposites were produced by solvent casting (films) or melt-mixing in a twin-screw extruder and subsequent melt-spinning (fibers). The processing protocols were found to affect the orientation of both PU hard segments and the P-tCNCs within the PU matrix and therefore the mechanical properties. While the films were isotropic, both the polymer matrix and the P-tCNCs proved to be aligned along the fiber direction in the fibers, as shown using SAXS/WAXS, angle-dependent Raman spectroscopy, and birefringence analysis. Tensile tests reveal that fibers and films, at similar P-tCNC contents, display Young’s moduli and strain-at-break that are within the same order of magnitude, but the stress-at-break was found to be ten-times higher for fibers, conferring them a superior toughness over films.
Experimental and Numerical Study of Polymer-Retrofitted Masonry Walls under Gas Explosions
Authors: Gu Meng , Ling Xiaodong , Wang Hanxiang , Yu Anfeng , Chen Guoxin
Published: November 2019

Unreinforced masonry walls are extensively used in the petrochemical industry and they are one of the most vulnerable components to blast loads. To investigate the failure modes and improve the blast resistances of masonry walls, four full-scale field tests were conducted using unreinforced and spray-on polyurea-reinforced masonry walls subjected to gas explosions. The results suggested that the primary damage of the unreinforced masonry wall was flexural deformation and the wall collapsed at the latter stage of gas explosion. The presence of polyurea coatings could effectively improve the anti-explosion abilities of masonry walls, prevent wall collapses, and retain the flying fragments, which would reduce the casualties and economic losses caused by petrochemical explosion accidents. The bond between the polymer and masonry wall was critical, and premature debonding resulted in a failure of the coating to exert the maximum energy absorption effect. A numerical model for masonry walls was developed in ANSYS/LS-Dyna and validated with the test data. Parametric studies were conducted to explore the influences of the polyurea-coating thickness and spray pattern on the performances of masonry walls. The polyurea-coating thickness and spray pattern affected the resistance capacities of masonry walls significantly.
Anekāntavāda and Dialogic Identity Construction
Authors: Barbato Melanie
Published: November 2019

While strong religious identity is often associated with violence, Jainism, one of the world’s oldest practiced religions, is often regarded as one of the most peaceful religions and has nevertheless persisted through history. In this article, I am arguing that one of the reasons for this persistence is the community’s strategy of dialogic identity construction. The teaching of anekāntavāda allows Jainas to both engage with other views constructively and to maintain a coherent sense of self. The article presents an overview of this mechanism in different contexts from the debates of classical Indian philosophy to contemporary associations of anekāntavāda with science. Central to the argument is the observation that anekāntavāda is in all these contexts used to stabilize Jaina identity, and that anekāntavāda should therefore not be interpreted as a form of relativism.
High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013
Authors: Wang Hong , Li Jiawen , Gao Zhiqiu , Yim Steve H.L. , Shen Huanfeng , Ho Hung Chak , Li Zhiyuan , Zeng Zhaoliang , Liu Chao , Li Yubin , Ning Guicai , Yang Yuanjian
Published: November 2019

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban–suburban–rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5.
Improved Performance of Fundamental Mode Orthogonal Fluxgate Using a Micro-Patterned Meander-Shaped Ribbon Core
Authors: Zhi Shaotao , Feng Zhu , Lei Chong
Published: November 2019

In this paper, the performance of orthogonal fluxgate sensors with meander-shaped cores is studied in fundamental mode. The meander-shaped cores are made by micro-patterning technology based on a Co-based amorphous ribbon. The main advantage of this structure is that the linear operating range of the sensor can be adjusted simply by changing the number of strips, without affecting the excitation mechanism. Experiments show that a linear range of 560 μT is obtained by a meander-shaped core sensor with 12 strips. The changes in the number of strips can also increase sensitivity and reduce noise of the sensor. We can achieve a sensitivity of 600 V/T and a noise level of 0.64 nT/√Hz at 1 Hz for a meander-shaped core sensor with eight strips. Compared with the performance of the sensors built using a single strip core having the same equivalent cross-sectional area, the use of meander-shaped core can provide a higher sensitivity and linearity, and a lower noise level. We also compare the performance of an eight-strip meander-shaped core orthogonal fluxgate operated in the fundamental and second-harmonic modes. Similar sensitivity for the two modes can be obtained by adjusting the excitation current. In this case, we find that the noise of sensor operating in fundamental mode is about five times lower than that of the sensor operating in second-harmonic mode. This can be interpreted as the suppression of Barkhausen noise by unipolar bias in the fundamental mode.
Video Smoke Detection Method Based on Change-Cumulative Image and Fusion Deep Network
Authors: Liu Tong , Cheng Jianghua , Du Xiangyu , Luo Xiaobing , Zhang Liang , Cheng Bang , Wang Yang
Published: November 2019

Smoke detection technology based on computer vision is a popular research direction in fire detection. This technology is widely used in outdoor fire detection fields (e.g., forest fire detection). Smoke detection is often based on features such as color, shape, texture, and motion to distinguish between smoke and non-smoke objects. However, the salience and robustness of these features are insufficiently strong, resulting in low smoke detection performance under complex environment. Deep learning technology has improved smoke detection performance to a certain degree, but extracting smoke detail features is difficult when the number of network layers is small. With no effective use of smoke motion characteristics, indicators such as false alarm rate are high in video smoke detection. To enhance the detection performance of smoke objects in videos, this paper proposes a concept of change-cumulative image by converting the YUV color space of multi-frame video images into a change-cumulative image, which can represent the motion and color-change characteristics of smoke. Then, a fusion deep network is designed, which increases the depth of the VGG16 network by arranging two convolutional layers after each of its convolutional layer. The VGG16 and Resnet50 (Deep residual network) network models are also arranged using the fusion deep network to improve feature expression ability while increasing the depth of the whole network. Doing so can help extract additional discriminating characteristics of smoke. Experimental results show that by using the change-cumulative image as the input image of the deep network model, smoke detection performance is superior to the classic RGB input image; the smoke detection performance of the fusion deep network model is better than that of the single VGG16 and Resnet50 network models; the smoke detection accuracy, false positive rate, and false alarm rate of this method are better than those of the current popular methods of video smoke detection.
The Response of UV/Blue Light and Ozone Sensing Using Ag-TiO2 Planar Nanocomposite Thin Film
Authors: Lo Tzu-Hsuan , Shih Pen-Yuan , Wu Chiu-Hsien
Published: November 2019

We successfully fabricated a planar nanocomposite film that uses a composite of silver nanoparticles and titanium dioxide film (Ag-TiO2) for ultraviolet (UV) and blue light detection and application in ozone gas sensor. Ultraviolet-visible spectra revealed that silver nanoparticles have a strong surface plasmon resonance (SPR) effect. A strong redshift of the plasmonic peak when the silver nanoparticles covered the TiO2 thin film was observed. The value of conductivity change for the Ag-TiO2 composite is 4–8 times greater than that of TiO2 film under UV and blue light irradiation. The Ag-TiO2 nanocomposite film successfully sensed 100 ppb ozone. The gas response of the composite film increased by roughly six and four times under UV and blue light irradiation, respectively. We demonstrated that a Ag-TiO2 composite gas sensor can be used with visible light (blue). The planar composite significantly enhances photo catalysis. The composite films have practical application potential for wearable devices.
Simplifying Sample Preparation for Soil Fertility Analysis by X-ray Fluorescence Spectrometry
Authors: Tavares Tiago Rodrigues , Nunes Lidiane Cristina , Alves Elton Eduardo Novais , Almeida Eduardo de , Maldaner Leonardo Felipe , Krug Francisco José , Carvalho Hudson Wallace Pereira de , Molin José Paulo
Published: November 2019

Portable X-ray fluorescence (pXRF) sensors allow one to collect digital data in a practical and environmentally friendly way, as a complementary method to traditional laboratory analyses. This work aimed to assess the performance of a pXRF sensor to predict exchangeable nutrients in soil samples by using two contrasting strategies of sample preparation: pressed pellets and loose powder (<2 mm). Pellets were prepared using soil and a cellulose binder at 10% w w−1 followed by grinding for 20 min. Sample homogeneity was probed by X-ray fluorescence microanalysis. Exchangeable nutrients were assessed by pXRF furnished with a Rh X-ray tube and silicon drift detector. The calibration models were obtained using 58 soil samples and leave-one-out cross-validation. The predictive capabilities of the models were appropriate for both exchangeable K (ex-K) and Ca (ex-Ca) determinations with R2 ≥ 0.76 and RPIQ > 2.5. Although XRF analysis of pressed pellets allowed a slight gain in performance over loose powder samples for the prediction of ex-K and ex-Ca, satisfactory performances were also obtained with loose powders, which require minimal sample preparation. The prediction models with local samples showed promising results and encourage more detailed investigations for the application of pXRF in tropical soils.
A Method of FPGA-Based Extraction of High-Precision Time-Difference Information and Implementation of Its Hardware Circuit
Authors: Li Jian , Yan Xinlei , Li Maojin , Meng Ming , Yan Xin
Published: November 2019

The positioning technology to find shallow underground vibration sources based on a wireless sensor network is receiving great interest in the field of underground position measurements. The slow peaking and strong multi-waveform aliasing typical of the underground vibration signal result in a low extraction accuracy of the time difference and a poor source-positioning accuracy. At the same time, the transmission of large amounts of sensor data and the host computer’s slow data processing speed make locating a source a slow process. To address the above problems, this paper proposes a method for high-precision time-difference measurements in near-field blasting and a method for its hardware implementation. First, based on the broadband that is typical of blast waves, the peak frequency of the P-wave was obtained in the time–frequency domain, taking advantage of the difference in the propagation speed of the P-wave, S-wave, and the surface wave. Second, the phase difference between two sensor nodes was found by means of a spectral decomposition and a correlation measurement. Third, the phase ambiguity was eliminated using the time interval of the first break and the dynamic characteristics of the sensors. Finally, following a top-down design idea, the hardware system was designed using Field Programmable Gate Array(FPGA). Verification, using both numerical simulations and experiments, suggested that compared with generalized cross-correlation-based time-difference measurement methods, the proposed method produced a higher time-difference resolution and accuracy. Compared with the traditional host computer post-position positioning method, the proposed method was significantly quicker. It can be seen that the proposed method provides a new solution for solving high-precision and quick source-location problems, and affords a technical means for developing high-speed, real-time source-location instruments.
A Wearable Textile 3D Gesture Recognition Sensor Based on Screen-Printing Technology
Authors: Ferri Josue , Llinares Llopis Raúl , Moreno Jorge , Ibañez Civera Javier , Garcia-Breijo Eduardo
Published: November 2019

Research has developed various solutions in order for computers to recognize hand gestures in the context of human machine interface (HMI). The design of a successful hand gesture recognition system must address functionality and usability. The gesture recognition market has evolved from touchpads to touchless sensors, which do not need direct contact. Their application in textiles ranges from the field of medical environments to smart home applications and the automotive industry. In this paper, a textile capacitive touchless sensor has been developed by using screen-printing technology. Two different designs were developed to obtain the best configuration, obtaining good results in both cases. Finally, as a real application, a complete solution of the sensor with wireless communications is presented to be used as an interface for a mobile phone.
Zero-Error Coding via Classical and Quantum Channels in Sensor Networks
Authors: Yu Wenbin , Xiong Zijia , Dong Zanqiang , Wang Siyao , Li Jingya , Liu Gaoping , Liu Alex X.
Published: November 2019

Today’s sensor networks need robustness, security and efficiency with a high level of assurance. Error correction is an effective communicational technique that plays a critical role in maintaining robustness in informational transmission. The general way to tackle this problem is by using forward error correction (FEC) between two communication parties. However, by applying zero-error coding one can assure information fidelity while signals are transmitted in sensor networks. In this study, we investigate zero-error coding via both classical and quantum channels, which consist of n obfuscated symbols such as Shannon’s zero-error communication. As a contrast to the standard classical zero-error coding, which has a computational complexity of O ( 2 n ) , a general approach is proposed herein to find zero-error codewords in the case of quantum channel. This method is based on a n-symbol obfuscation model and the matrix’s linear transformation, whose complexity dramatically decreases to O ( n 2 ) . According to a comparison with classical zero-error coding, the quantum zero-error capacity of the proposed method has obvious advantages over its classical counterpart, as the zero-error capacity equals the rank of the quantum coefficient matrix. In particular, the channel capacity can reach n when the rank of coefficient matrix is full in the n-symbol multilateral obfuscation quantum channel, which cannot be reached in the classical case. Considering previous methods such as low density parity check code (LDPC), our work can provide a means of error-free communication through some typical channels. Especially in the quantum case, zero-error coding can reach both a high coding efficiency and large channel capacity, which can improve the robustness of communication in sensor networks.
Vision-Based Sensor for Three-Dimensional Vibrational Motion Detection in Biological Cell Injection
Authors: Sadak Ferhat , Saadat Mozafar , Hajiyavand Amir
Published: November 2019

Intracytoplasmic sperm injection (ICSI) is an infertility treatment where a single sperm is immobilised and injected into the egg using a glass injection pipette. Minimising vibration in three orthogonal axes is essential to have precise injector motion and full control during the egg injection procedure. Vibration displacement sensing using physical sensors in ICSI operation is challenging since the sensor interfacing is not practically feasible. This study proposes a non-invasive technique to measure the three-dimensional vibrational motion of the injection pipette by a single microscope camera during egg injection. The contrast-limited adaptive histogram equalization (CHALE) method and blob analyses technique were employed to measure the vibration displacement in axial and lateral axes, while the actual dimension of the focal axis was directly measured using the Brenner gradient algorithm as a focus measurement algorithm. The proposed algorithm operates between the magnifications range of 4× to 40× with a resolution of half a pixel. Experiments using the proposed vision-based algorithm were conducted to measure and verify the vibration displacement in axial and lateral axes at various magnifications. The results were compared against manual procedures and the differences in measurements were up to 2% among all magnifications. Additionally, the effect of injection speed on lateral vibration displacement was measured experimentally and was used to determine the values for egg deformation, force fluctuation, and penetration force. It was shown that increases in injection speed significantly increases the lateral vibration displacement of the injection pipette by as much as 54%. It has been demonstrated successfully that visual sensing has played a key role in identifying the limitation of the egg injection speed created by lateral vibration displacement of the injection pipette tip.
A Strong Machine Learning Classifier and Decision Stumps Based Hybrid AdaBoost Classification Algorithm for Cognitive Radios
Authors: Chen Siji , Shen Bin , Wang Xin , Yoo Sang-Jo
Published: November 2019

Machine learning (ML) based classification methods have been viewed as one kind of alternative solution for cooperative spectrum sensing (CSS) in recent years. In this paper, ML techniques based CSS algorithms are investigated for cognitive radio networks (CRN). Specifically, a strong machine learning classifier (MLC) and decision stumps (DS) based adaptive boosting (AdaBoost) classification mechanism is proposed for pattern classification of the primary user’s behavior in the network. The conventional AdaBoost algorithm only combines multiple sub-classifiers and produces a strong weight based on their weights in classification. Taking into account the fact that the strong MLC and the weak DS serve as different sub-classifiers in classification, we propose employing a strong MLC as the first-stage classifier and DS as the second-stage classifiers, to eventually determine the class that the spectrum energy vector belongs to. We verify in simulations that the proposed hybrid AdaBoost algorithms are capable of achieving a higher detection probability than the conventional ML based spectrum sensing algorithms and the conventional hard fusion based CSS schemes.
Environmental Upgrading and Suppliers’ Agency in the Leather Global Value Chain
Authors: De Marchi Valentina , Di Maria Eleonora
Published: November 2019

The paper explores the role of suppliers in the process of environmental upgrading (EnvU) within global value chains (GVCs). Theoretical contributions to EnvU have highlighted, in particular, the role of global buyers in supporting the EnvU of products and processes, while limited attention has been given to suppliers as proactive actors within GVCs. This paper approaches EnvU through the lens of innovation and focuses on the agency of suppliers. Through the analysis of innovations developed within the leather GVC, with a special focus on the Arzignano cluster (Italy), the analysis shows how suppliers can autonomously develop sustainability strategies to maintain their competitiveness and achieve higher value in the GVC. However, results stress the limits of green strategies as buyers and suppliers do not share the same vision of how to foster sustainability.
Optimization of a Bikeway Network with Selective Nodes
Authors: Shui C. S. , Chan W. L.
Published: November 2019

Setting up a bikeway network has been recognized as one of the most effective measures to motivate cycling. In fact, a highly connected, exclusive bikeway network that covers all demand sources can be an attractive and time-saving measure, but it requires very high setup costs. The planner often needs to have a trade-off between demand coverage and travel time under a given construction cost. This paper introduces a novel bikeway design problem which determines an optimal bikeway network that covers all potential cycling demand sources with minimal total travel time and under budget constraints. In the context of designing a bike sharing system, the resultant node set of the bikeway network can be interpreted as the locations of the shared bike stations which can cover all cycling demands. A two-stage solution method, by combining the genetic algorithm and a novel elimination heuristic, is proposed to solve the problem by firstly determining the subset of nodes (selected nodes) that can cover all the demand sources and then designing the bikeway network that connects all selected nodes within a given budget. Numerical studies illustrate the advantages of elimination heuristics in solving the proposed problem and the effect of the budget towards the solution fitness with or without a solution. Case studies of two proposed new towns in Hong Kong are provided to illustrate the applicability and effectiveness of the method in bikeway design. This optimization model can be applied to bike-sharing system design problems which aims to cover all demand sources by providing bike stations that are also well connected with exclusive bikeways subject to budget constraints.
Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model
Authors: Kim Jee-Heon , Seong Nam-Chul , Choi Wonchang
Published: November 2019

Accurate calculations and predictions of heating and cooling loads in buildings play an important role in the development and implementation of building energy management plans. This study aims to improve the forecasting accuracy of cooling load predictions using an optimized nonlinear autoregressive exogenous (NARX) neural network model. The preprocessing of training data and optimization of parameters were investigated for model optimization. In predictive models of cooling loads, the removal of missing values and the adjustment of structural parameters have been shown to help improve the predictive performance of a neural network model. In this study, preprocessing the training data eliminated missing values for times when the heating, ventilation, and air-conditioning system is not running. Also, the structural and learning parameters were adjusted to optimize the model parameters.
Sustained Participation in Virtual Communities from a Self-Determination Perspective
Authors: Zhang Zhe
Published: November 2019

Customers’ participation and contribution are vital to the sustainability of virtual communities (VCs) platform while people have many options to freely surf on the Internet. Sustained participation, instead of initial participation, is more meaningful to virtual communities’ sustained development. From the perspective of self-determination theory, this paper explores the effect of community artifacts on sustained participations through users’ satisfaction of psychological need and virtual community identification. With empirical studies in two types of virtual community platforms (interest-based and relational-based), our results reveal several important findings. Firstly, this study finds that virtual co-presence and deep profiling can increase users’ satisfaction of inner psychological needs. But the use of persistent labeling does not affect the user’s satisfaction of psychological needs. In addition, self-presentation is positively related to relational-based community, and rather has no impact on interest-based community. Secondly, this study finds that there exists a positive relationship between users’ satisfied psychological needs and virtual community identification. Finally, virtual community identification significantly impacts sustained participation. This paper offers a new perspective on the psychological mechanism of sustained participation and yields important implications for the managerial practice.
Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm
Authors: Mohamed Al-Attar Ali , Ali Shimaa , Alkhalaf Salem , Senjyu Tomonobu , Hemeida Ashraf M.
Published: November 2019

This article offers a multi-objective framework for an optimal mix of different types of distributed energy resources (DERs) under different load models. Many renewable and non-renewable energy resources like photovoltaic system (PV), micro-turbine (MT), fuel cell (FC), and wind turbine system (WT) are incorporated in a grid-connected hybrid power system to supply energy demand. The main aim of this article is to maximize environmental, technical, and economic benefits by minimizing various objective functions such as the annual cost, power loss and greenhouse gas emission subject to different power system constraints and uncertainty of renewable energy sources. For each load model, optimum DER size and its corresponding location are calculated. To test the feasibility and validation of the multi-objective water cycle algorithm (MOWCA) is conducted on the IEEE-33 bus and IEEE-69 bus network. The concept of Pareto-optimality is applied to generate trilateral surface of non-dominant Pareto-optimal set followed by a fuzzy decision-making mechanism to obtain the final compromise solution. Multi-objective non-dominated sorting genetic (NSGA-III) algorithm is also implemented and the simulation results between two algorithms are compared with each other. The achieved simulation results evidence the better performance of MOWCA comparing with the NSGA-III algorithm and at different load models, the determined DER locations and size are always righteous for enhancement of the distribution power system performance parameters.
How Do Passengers with Different Using Frequencies Choose between Traditional Taxi Service and Online Car-Hailing Service? A Case Study of Nanjing, China
Authors: Wang Ting , Zhang Yong , Li Meiye , Liu Lei
Published: November 2019

The rapid development of online car-hailing services (OCSs) has a huge impact on traditional taxi service (TTS) and is triggering a revolution in the taxi industry. Due to the differences in age, monthly income level, etc., travelers’ using frequencies of taxi service are different. It is necessary for online car-hailing platforms and traditional taxi companies to know the choice behavior of different types of passengers to enhance competitiveness. Based on the survey data of taxi passengers in Nanjing, China, the passengers are characterized by using frequency per week as infrequent passengers, moderately frequent passengers, and frequent passengers. The group characteristics and the differences among groups are analyzed. Further, three binary logit models are applied to analyze the taxi service choice behavior of different groups. The model results show the significant factors vary among three types of passengers. The result indicates that the impacts of safety level improvement, comfort level improvement and travel cost reduction for OCS on passengers’ choice behavior are higher than that of safety level decreasing, comfort level decreasing, and travel cost increasing. Moderately frequent passengers are more sensitive to comfort level than travel cost. The conclusions contribute to both the OCS and TTS business strategies. The results also provide insights into taxi industry management for governments.
Local External/Internal Symmetry of Smooth Manifolds and Lack of Tovariance in Physics
Authors: Asselmeyer-Maluga Torsten , Król Jerzy
Published: November 2019

Category theory allows one to treat logic and set theory as internal to certain categories. What is internal to SET is 2-valued logic with classical Zermelo–Fraenkel set theory, while for general toposes it is typically intuitionistic logic and set theory. We extend symmetries of smooth manifolds with atlases defined in Set towards atlases with some of their local maps in a topos T . In the case of the Basel topos and R 4 , the local invariance with respect to the corresponding atlases implies exotic smoothness on R 4 . The smoothness structures do not refer directly to Casson handless or handle decompositions, which may be potentially useful for describing the so far merely putative exotic R 4 underlying an exotic S 4 (should it exist). The tovariance principle claims that (physical) theories should be invariant with respect to the choice of topos with natural numbers object and geometric morphisms changing the toposes. We show that the local T -invariance breaks tovariance even in the weaker sense.
Repeated Oral Administration of a KDEL-Tagged Recombinant Cholera Toxin B Subunit Effectively Mitigates DSS Colitis despite a Robust Immunogenic Response
Authors: Royal Joshua M. , Reeves Micaela A. , Matoba Nobuyuki
Published: November 2019

Cholera toxin B subunit (CTB), a non-toxic homopentameric component of Vibrio cholerae holotoxin, is an oral cholera vaccine antigen that induces an anti-toxin antibody response. Recently, we demonstrated that a recombinant CTB variant with a Lys-Asp-Glu-Leu (KDEL) endoplasmic reticulum retention motif (CTB-KDEL) exhibits colon mucosal healing effects that have therapeutic implications for inflammatory bowel disease (IBD). Herein, we investigated the feasibility of CTB-KDEL for the treatment of chronic colitis. We found that weekly oral administration of CTB-KDEL, dosed before or after the onset of chronic colitis, induced by repeated dextran sodium sulfate (DSS) exposure, could significantly reduce disease activity index scores, intestinal permeability, inflammation, and histological signs of chronicity. To address the consequences of immunogenicity, mice (C57BL/6 or C3H/HeJ strains) were pre-exposed to CTB-KDEL then subjected to DSS colitis and CTB-KDEL treatment. While the pre-dosing of CTB-KDEL elicited high-titer anti-drug antibodies (ADAs) of the immunoglobin A (IgA) isotype in the intestine of C57BL/6 mice, the therapeutic effects of CTB-KDEL were similar to those observed in C3H/HeJ mice, which showed minimal ADAs under the same experimental conditions. Thus, the immunogenicity of CTB-KDEL does not seem to impede the protein’s mucosal healing efficacy. These results support the development of CTB-KDEL for IBD therapy.
Qualitative Assay to Detect Dopamine Release by Ligand Action on Nicotinic Acetylcholine Receptors
Authors: Marquart Leanna A. , Turner Matthew W. , McDougal Owen M.
Published: November 2019

A pheochromocytoma of the rat adrenal medulla derived (a.k.a. PC12) cell-based assay for dopamine measurement by luminescence detection was customized for the qualitative evaluation of agonists and antagonists of nicotinic acetylcholine receptors (nAChRs). The assay mechanism begins with ligand binding to transmembrane nAChRs, altering ion flow into the cell and inducing dopamine release from the cell. Following release, dopamine is oxidized by monoamine oxidase generating hydrogen peroxide that catalyzes a chemiluminescence reaction involving luminol and horseradish peroxidase, thus producing a detectable response. Results are presented for the action of nAChR agonists (acetylcholine, nicotine, and cytisine), and antagonists (α-conotoxins (α-CTxs) MII, ImI, LvIA, and PeIA) that demonstrate a luminescence response correlating to the increase or decrease of dopamine release. A survey of cell growth and treatment conditions, including nerve growth factor, nicotine, ethanol, and temperature, led to optimal assay requirements to achieve maximal signal intensity and consistent response to ligand treatment. It was determined that PC12 cells treated with a combination of nerve growth factor and nicotine, and incubated at 37 °C, provided favorable results for a reduction in luminescence signal upon treatment of cells with α-CTxs. The PC12 assay is intended for use as a fast, efficient, and economic qualitative method to assess the bioactivity of molecules that act on nAChRs, in which testing of ligand–nAChR binding hypotheses and computational predictions can be validated. As a screening method for nAChR bioactivity, lead compounds can be assessed for their likelihood of exhibiting desired bioactivity prior to being subjected to more complex quantitative methods, such as electrophysiology or live animal studies.
Real-Time, Smart Rainwater Storage Systems: Potential Solution to Mitigate Urban Flooding
Authors: Liang Ruijie , Di Matteo Michael , Maier Holger , Thyer Mark
Published: November 2019

Urban water systems are being stressed due to the effects of urbanization and climate change. Although household rainwater tanks are primarily used for water supply purposes, they also have the potential to provide flood benefits. However, this potential is limited for critical storms, as they become ineffective once their capacity is exceeded. This limitation can be overcome by controlling tanks as systems during rainfall events, as this can offset the timing of outflow peaks from different tanks. In this paper, the effectiveness of such systems is tested for two tank sizes under a wide range of design rainfall conditions for three Australian cities with different climates. Results show that a generic relationship exists between the ratio of tank:runoff volume and percentage peak flow reduction, irrespective of location and storm characteristics. Smart tank systems are able to reduce peak system outflows by between 35% and 85% for corresponding ranges in tank:runoff volumes of 0.15–0.8. This corresponds to a relative performance improvement on the order of 35% to 50% compared with smart tanks that are not operated in real-time. These results highlight the potential for using household rainwater tanks for mitigating urban flooding, even for extreme events.
Characterization of Diffuse Groundwater Inflows into Stream Water (Part II: Quantifying Groundwater Inflows by Coupling FO-DTS and Vertical Flow Velocities)
Authors: Le Lay Hugo , Thomas Zahra , Rouault François , Pichelin Pascal , Moatar Florentina
Published: November 2019

Temperature has been used to characterize groundwater and stream water exchanges for years. One of the many methods used analyzes propagation of the atmosphere-influenced diurnal signal in sediment to infer vertical velocities. However, despite having good accuracy, the method is usually limited by its small spatial coverage. The appearance of fiber optic distributed temperature sensing (FO-DTS) provided new possibilities due to its high spatial and temporal resolution. Methods based on the heat-balance equation, however, cannot quantify diffuse groundwater inflows that do not modify stream temperature. Our research approach consists of coupling groundwater inflow mapping from a previous article (Part I) and deconvolution of thermal profiles in the sediment to obtain vertical velocities along the entire reach. Vertical flows were calculated along a 400 m long reach, and a period of 9 months (October 2016 to June 2017), by coupling a fiber optic cable buried in thalweg sediment and a few thermal lances at the water–sediment interface. When compared to predictions of hyporheic discharge by traditional methods (differential discharge between upstream and downstream of the monitored reach and the mass-balance method), those of our method agreed only for the low-flow period and the end of the high-flow period. Our method underestimated hyporheic discharge during high flow. We hypothesized that the differential discharge and mass-balance methods included lateral inflows that were not detected by the fiber optic cable buried in thalweg sediment. Increasing spatial coverage of the cable as well as automatic and continuous calculation over the reach may improve predictions during the high-flow period. Coupling groundwater inflow mapping and vertical hyporheic flow allows flow to be quantified continuously, which is of great interest for characterizing and modeling fine hyporheic processes over long periods.
Development and Validation of a Low-Cost Gas Density Method for Measuring Biochemical Methane Potential (BMP)
Authors: Justesen Camilla G. , Astals Sergi , Mortensen Jacob R. , Thorsen Rasmus , Koch Konrad , Weinrich Sören , Triolo Jin Mi , Hafner Sasha D.
Published: November 2019

Accurate determination of biochemical methane potential (BMP) is important for both biogas research and practice. However, access to laboratory equipment limits the capacity of small laboratories or biogas plants to conduct reliable BMP assays, especially in low- and middle-income countries. This paper describes the development and validation of a new gas density-based method for measuring BMP (GD-BMP). In the GD-BMP method, biogas composition is determined from biogas density. Biogas density is based on bottle mass loss and biogas volume, and these can be accurately measured using only a standard laboratory scale, inexpensive syringes, and a simple manometer. Results from four experiments carried out in three different laboratories showed that the GD-BMP method is both accurate (no significant bias compared to gravimetric or volumetric methods with biogas analysis by gas chromatography) and precise (<3% relative standard deviation is possible). BMP values from the GD-BMP method were also comparable to those measured for the same substrates with an industry standard automated system (AMPTS II) in two independent laboratories (maximum difference 10%). Additionally, the GD-BMP method was shown to be accurate even in the presence of leakage by excluding leakage from mass loss measurements. The proposed GD-BMP method represents a significant breakthrough for both biogas research and the industry. With it, accurate BMP measurement is possible with only a minimal investment in supplies and equipment.
New Methods for the Assessment of Flow Regime Alteration under Climate Change and Human Disturbance
Authors: Shi Pengfei , Liu Jiahong , Yang Tao , Xu Chong-Yu , Feng Jie , Yong Bin , Cui Tong , Li Zhenya , Li Shu
Published: November 2019

Climate change and anthropogenic activities do collectively lead to an alteration of the flow regime, posing a great influence upon the structure and persistence of native biotic communities within river ecosystems. The range of variability approach (RVA) method is commonly used to evaluate the flow regime alteration. However, it was reported to underestimate the degree of flow regime potentially. In this study, two new assessment methods/metrics for evaluating the process behaviors of the flow regime are developed based on Euclidean distance and dynamic time warping (DTW) distance. They are then integrated with the metric of RVA, generating two composite metrics that represent both frequency and process changes of the flow regime. The new methods/metrics were applied to identify the flow regime alteration in a typical basin in the middle reaches of the Yellow River, China. The results show that the composite metrics consistently reveal a high alteration degree of flow regime in the basin. The decreased biological integrity of fish demonstrates the reasonability of the high-level overall alteration to some degree. The updated methods enable more scientific evaluation for the complex hydrologic alteration under a changing environment.