bluuebunny/crossref_metadata_2025 huggingface dataset:
Lonicera caerulea Extract Attenuates Non-Alcoholic Fatty Liver Disease in Free Fatty Acid-Induced HepG2 Hepatocytes and in High Fat Diet-Fed Mice
Authors: Park Miey , Yoo Jeong-Hyun , Lee You-Suk , Lee Hae-Jeung
Published: February 2019

Honeyberry (Lonicera caerulea) has been used for medicinal purposes for thousands of years. Its predominant anthocyanin, cyanidin-3-O-glucoside (C3G), possesses antioxidant and many other potent biological activities. We aimed to investigate the effects of honeyberry extract (HBE) supplementation on HepG2 cellular steatosis induced by free fatty acids (FFA) and in diet-induced obese mice. HepG2 cells were incubated with 1 mM FFA to induce lipid accumulation with or without HBE. Obesity in mice was induced by a 45% high fat diet (HFD) for 6 weeks and subsequent supplementation of 0.5% HBE (LH) and 1% HBE (MH) for 6 weeks. HBE suppressed fatty acid synthesis and ameliorated lipid accumulation in HepG2 cells induced by FFA. Moreover, HBE also decreased lipid accumulation in the liver in the supplemented HBE group (LH, 0.5% or MH, 1%) compared with the control group. The expressions of adipogenic genes involved in hepatic lipid metabolism of sterol regulatory element-binding protein-1 (SREBP-1c), CCAAT/enhancer-binding protein alpha (C/EBPα), peroxisome proliferator-activated receptor gamma (PPARγ), and fatty acid synthase (FAS) were decreased both in the HepG2 cells and in the livers of HBE-supplemented mice. In addition, HBE increased mRNA and protein levels of carnitine palmitoyltransferase (CPT-1) and peroxisome proliferator-activated receptor α (PPARα), which are involved in fatty acid oxidation. Furthermore, HBE treatment increased the phosphorylation of AMP-activated protein kinase (AMPK) and Acetyl-CoA Carboxylase (ACC). Honeyberry effectively reduced triglyceride accumulation through down-regulation of hepatic lipid metabolic gene expression and up-regulation of the activation of AMPK and ACC signaling in both the HepG2 cells as well as in livers of diet-induced obese mice. These results suggest that HBE may actively ameliorate non-alcoholic fatty liver disease.
The Development of Oral Amphotericin B to Treat Systemic Fungal and Parasitic Infections: Has the Myth Been Finally Realized?
Authors: Cuddihy Grace , Wasan Ellen K. , Di Yunyun , Wasan Kishor M.
Published: February 2019

Parenteral amphotericin B has been considered as first-line therapy in the treatment of systemic fungal and parasitic infections, however its use has been associated with a number of limitations including affordability, accessibility, and an array of systemic toxicities. Until very recently, it has been very challenging to develop a bioavailable formulation of amphotericin B due to its physical chemical properties, limited water and lipid solubility, and poor absorption. This perspective reviews several novel oral Amphotericin B formulations under development that are attempting to overcome these limitations.
Neuronanomedicine: An Up-to-Date Overview
Authors: Teleanu Daniel Mihai , Chircov Cristina , Grumezescu Alexandru Mihai , Teleanu Raluca Ioana
Published: February 2019

The field of neuronanomedicine has recently emerged as the bridge between neurological sciences and nanotechnology. The possibilities of this novel perspective are promising for the diagnosis and treatment strategies of severe central nervous system disorders. Therefore, the development of nano-vehicles capable of permeating the blood–brain barrier (BBB) and reaching the brain parenchyma may lead to breakthrough therapies that could improve life expectancy and quality of the patients diagnosed with brain disorders. The aim of this review is to summarize the recently developed organic, inorganic, and biological nanocarriers that could be used for the delivery of imaging and therapeutic agents to the brain, as well as the latest studies on the use of nanomaterials in brain cancer, neurodegenerative diseases, and stroke. Additionally, the main challenges and limitations associated with the use of these nanocarriers are briefly presented.
A Coupled Thermodynamic Model for Transport Properties of Thin Films during Physical Aging
Authors: Hu Hongjiu , Fan Xiaoming , He Yaolong
Published: February 2019

A coupled diffusion model based on continuum thermodynamics is developed to quantitatively describe the transport properties of glassy thin films during physical aging. The coupled field equations are then embodied and applied to simulate the transport behaviors of O2 and CO2 within aging polymeric membranes to validate the model and demonstrate the coupling phenomenon, respectively. It is found that due to the introduction of the concentration gradient, the proposed direct calculating method on permeability can produce relatively better consistency with the experimental results for various film thicknesses. In addition, by assuming that the free volume induced by lattice contraction is renewed upon CO2 exposure, the experimental permeability of O2 within Matrimid® thin film after short-time exposure to CO2 is well reproduced in this work. Remarkably, with the help of the validated straightforward permeability calculation method and free volume recovery mechanism, the permeability behavior of CO2 is also well elucidated, with the results implying that the transport process of CO2 and the variation of free volume are strongly coupled.
Physical Processes in Polymeric Filters Used for Dialysis
Authors: Voinova Marina , Repin Nikolay , Sokol Evgen , Tkachuk Bogdan , Gorelik Leonid
Published: February 2019

The key physical processes in polymeric filters used for the blood purification include transport across the capillary wall and the interaction of blood cells with the polymer membrane surface. Theoretical modeling of membrane transport is an important tool which provides researchers with a quantification of the complex phenomena involved in dialysis. In the paper, we present a dense review of the most successful theoretical approaches to the description of transport across the polymeric membrane wall as well as the cell–polymer surface interaction, and refer to the corresponding experimental methods while studying these phenomena in dialyzing filters.
Postscript of Special Issue “Religion, Welfare and Social Service Provision: Common Ground”
Authors: Poole Jay
Published: February 2019

The roots of social work and other helping professions run deep in community-based connections, and joining with local faith-based entities to explore strengths and challenges is essential to good organization and planning [...]
A Theological Phenomenology of Listening: God’s ‘Voice’ and ‘Silence’ after Auschwitz
Authors: Welz Claudia
Published: February 2019

This paper develops a theological phenomenology of listening by exploring the following questions: First, what is the relation, in prayer, between speech and silence? Second, may we legitimately determine prayer as a ‘dialogue’ with God? Third, what does it mean to speak of God’s ‘silence’ after Auschwitz—is God completely ‘absent’ or just ‘hidden’? Fourth, how can we identify what God wants us to say and do, and how can we know whether a prayer has been answered? Texts by Jewish, Christian, and Muslim authors (from Rûmî via Luther, Kierkegaard, and Chrétien to Buber, Fackenheim, Levinas, and Derrida) provide the basis for the discussion.
Deleuze’s Interpretation of Job as a Heroic Figure in the History of Rationality
Authors: McLean Bradley H.
Published: February 2019

Traditional rationality takes the form of thinking-as-representation. Motivated by the conviction that it is possible to articulate one true account of the real, the three theologians in the Book of Job employed concepts to reduce objects to categories of sameness. In his exposition of such thinking-as-representation, Deleuze demonstrates how the four elements of representation thinking subordinate difference to conceptual categories of identity, opposition, analogy, and resemblance. Deleuze considers Job to be a heroic figure in the history of thinking, for Job demonstrates that the subject has nothing to say in his own name, as long as the subject adheres to norms of representational thinking. Job’s disavowal of blame amounts to a transgression against traditional theology of his time. The figure of Job exemplifies the heroic potential that lies within this crisis of theological representation.
Application of Machine Learning to Mortality Modeling and Forecasting
Authors: Levantesi Susanna , Pizzorusso Virginia
Published: February 2019

Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models. Out-of sample forecasts are provided to verify the model accuracy.
Object-Based Classification of Forest Disturbance Types in the Conterminous United States
Authors: Huo Lian-Zhi , Boschetti Luigi , Sparks Aaron M.
Published: February 2019

Forest ecosystems provide critical ecosystem goods and services, and any disturbance-induced changes can have cascading impacts on natural processes and human socioeconomic systems. Forest disturbance frequency, intensity, and spatial and temporal scale can be altered by changes in climate and human activity, but without baseline forest disturbance data, it is impossible to quantify the magnitude and extent of these changes. Methodologies for quantifying forest cover change have been developed at the regional-to-global scale via several approaches that utilize data from high (e.g., IKONOS, Quickbird), moderate (e.g., Landsat) and coarse (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) spatial resolution satellite imagery. While detection and quantification of forest cover change is an important first step, attribution of disturbance type is critical missing information for establishing baseline data and effective land management policy. The objective here was to prototype and test a semi-automated methodology for characterizing high-magnitude (>50% forest cover loss) forest disturbance agents (stress, fire, stem removal) across the conterminous United States (CONUS) from 2003–2011 using the existing University of Maryland Landsat-based Global Forest Change Product and Web-Enabled Landsat Data (WELD). The Forest Cover Change maps were segmented into objects based on temporal and spatial adjacency, and object-level spectral metrics were calculated based on WELD reflectance time series. A training set of objects with known disturbance type was developed via high-resolution imagery and expert interpretation, ingested into a Random Forest classifier, which was then used to attribute disturbance type to all 15,179,430 forest loss objects across CONUS. Accuracy assessments of the resulting classification was conducted with an independent dataset consisting of 4156 forest loss objects. Overall accuracy was 88.1%, with the highest omission and commission errors observed for fire (32.8%) and stress (31.9%) disturbances, respectively. Of the total 172,686 km2 of forest loss, 83.75% was attributed to stem removal, 10.92% to fire and 5.33% to stress. The semi-automated approach described in this paper provides a promising framework for the systematic characterization and monitoring of forest disturbance regimes.
An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US
Authors: Blyverket Jostein , Hamer Paul D. , Bertino Laurent , Albergel Clément , Fairbairn David , Lahoz William A.
Published: February 2019

A number of studies have shown that assimilation of satellite derived soil moisture using the ensemble Kalman Filter (EnKF) can improve soil moisture estimates, particularly for the surface zone. However, the EnKF is computationally expensive since an ensemble of model integrations have to be propagated forward in time. Here, assimilating satellite soil moisture data from the Soil Moisture Active Passive (SMAP) mission, we compare the EnKF with the computationally cheaper ensemble Optimal Interpolation (EnOI) method over the contiguous United States (CONUS). The background error–covariance in the EnOI is sampled in two ways: (i) by using the stochastic spread from an ensemble open-loop run, and (ii) sampling from the model spinup climatology. Our results indicate that the EnKF is only marginally superior to one version of the EnOI. Furthermore, the assimilation of SMAP data using the EnKF and EnOI is found to improve the surface zone correlation with in situ observations at a 95 % significance level. The EnKF assimilation of SMAP data is also found to improve root-zone correlation with independent in situ data at the same significance level; however this improvement is dependent on which in situ network we are validating against. We evaluate how the quality of the atmospheric forcing affects the analysis results by prescribing the land surface data assimilation system with either observation corrected or model derived precipitation. Surface zone correlation skill increases for the analysis using both the corrected and model derived precipitation, but only the latter shows an improvement at the 95 % significance level. The study also suggests that assimilation of satellite derived surface soil moisture using the EnOI can correct random errors in the atmospheric forcing and give an analysed surface soil moisture close to that of an open-loop run using observation derived precipitation. Importantly, this shows that estimates of soil moisture could be improved using a combination of assimilating SMAP using the computationally cheap EnOI while using model derived precipitation as forcing. Finally, we assimilate three different Level-2 satellite derived soil moisture products from the European Space Agency Climate Change Initiative (ESA CCI), SMAP and SMOS (Soil Moisture and Ocean Salinity) using the EnOI, and then compare the relative performance of the three resulting analyses against in situ soil moisture observations. In this comparison, we find that all three analyses offer improvements over an open-loop run when comparing to in situ observations. The assimilation of SMAP data is found to perform marginally better than the assimilation of SMOS data, while assimilation of the ESA CCI data shows the smallest improvement of the three analysis products.
Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years
Authors: Martin Maria Anna , Ghent Darren , Pires Ana Cordeiro , Göttsche Frank-Michael , Cermak Jan , Remedios John J.
Published: February 2019

Global land surface temperature (LST) data derived from satellite-based infrared radiance measurements are highly valuable for various applications in climate research. While in situ validation of satellite LST data sets is a challenging task, it is needed to obtain quantitative information on their accuracy. In the standardised approach to multi-sensor validation presented here for the first time, LST data sets obtained with state-of-the-art retrieval algorithms from several sensors (AATSR, GOES, MODIS, and SEVIRI) are matched spatially and temporally with multiple years of in situ data from globally distributed stations representing various land cover types in a consistent manner. Commonality of treatment is essential for the approach: all satellite data sets are projected to the same spatial grid, and transformed into a common harmonized format, thereby allowing comparison with in situ data to be undertaken with the same methodology and data processing. The large data base of standardised satellite LST provided by the European Space Agency’s GlobTemperature project makes previously difficult to perform LST studies and applications more feasible and easier to implement. The satellite data sets are validated over either three or ten years, depending on data availability. Average accuracies over the whole time span are generally within ±2.0 K during night, and within ± 4.0 K during day. Time series analyses over individual stations reveal seasonal cycles. They stem, depending on the station, from surface anisotropy, topography, or heterogeneous land cover. The results demonstrate the maturity of the LST products, but also highlight the need to carefully consider their temporal and spatial properties when using them for scientific purposes.
A Comparison of Hybrid Machine Learning Algorithms for the Retrieval of Wheat Biophysical Variables from Sentinel-2
Authors: Upreti Deepak , Huang Wenjiang , Kong Weiping , Pascucci Simone , Pignatti Stefano , Zhou Xianfeng , Ye Huichun , Casa Raffaele
Published: February 2019

This study focuses on the comparison of hybrid methods of estimation of biophysical variables such as leaf area index (LAI), leaf chlorophyll content (LCC), fraction of absorbed photosynthetically active radiation (FAPAR), fraction of vegetation cover (FVC), and canopy chlorophyll content (CCC) from Sentinel-2 satellite data. Different machine learning algorithms were trained with simulated spectra generated by the physically-based radiative transfer model PROSAIL and subsequently applied to Sentinel-2 reflectance spectra. The algorithms were assessed against a standard operational approach, i.e., the European Space Agency (ESA) Sentinel Application Platform (SNAP) toolbox, based on neural networks. Since kernel-based algorithms have a heavy computational cost when trained with large datasets, an active learning (AL) strategy was explored to try to alleviate this issue. Validation was carried out using ground data from two study sites: one in Shunyi (China) and the other in Maccarese (Italy). In general, the performance of the algorithms was consistent for the two study sites, though a different level of accuracy was found between the two sites, possibly due to slightly different ground sampling protocols and the range and variability of the values of the biophysical variables in the two ground datasets. For LAI estimation, the best ground validation results were obtained for both sites using least squares linear regression (LSLR) and partial least squares regression, with the best performances values of R2 of 0.78, rott mean squared error (RMSE) of 0.68 m2 m−2 and a relative RMSE (RRMSE) of 19.48% obtained in the Maccarese site with LSLR. The best results for LCC were obtained using Random Forest Tree Bagger (RFTB) and Bagging Trees (BagT) with the best performances obtained in Maccarese using RFTB (R2 = 0.26, RMSE = 8.88 μg cm−2, RRMSE = 17.43%). Gaussian Process Regression (GPR) was the best algorithm for all variables only in the cross-validation phase, but not in the ground validation, where it ranked as the best only for FVC in Maccarese (R2 = 0.90, RMSE = 0.08, RRMSE = 9.86%). It was found that the AL strategy was more efficient than the random selection of samples for training the GPR algorithm.
Estimating Crop Nutritional Status Using Smart Apps to Support Nitrogen Fertilization. A Case Study on Paddy Rice
Authors: Paleari Livia , Movedi Ermes , Vesely Fosco M. , Thoelke William , Tartarini Sofia , Foi Marco , Boschetti Mirco , Nutini Francesco , Confalonieri Roberto
Published: February 2019

Accurate nitrogen (N) management is crucial for the economic and environmental sustainability of cropping systems. Different methods have been developed to increase the efficiency of N fertilizations. However, their costs and/or low usability have often prevented their adoption in operational contexts. We developed a diagnostic system to support topdressing N fertilization based on the use of smart apps to derive a N nutritional index (NNI; actual/critical plant N content). The system was tested on paddy rice via dedicated field experiments, where the smart apps PocketLAI and PocketN were used to estimate, respectively, critical (from leaf area index) and actual plant N content. Results highlighted the system’s capability to correctly detect the conditions of N stress (NNI < 1) and N surplus (NNI > 1), thereby effectively supporting topdressing fertilizations. A resource-efficient methodology to derive PocketN calibration curves for different varieties—needed to extend the system to new contexts—was also developed and successfully evaluated on 43 widely grown European varieties. The widespread availability of smartphones and the possibility to integrate NNI and remote sensing technologies to derive variable rate fertilization maps generate new opportunities for supporting N management under real farming conditions.
Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture
Authors: Lee Hyo Jong , Ullah Ihsan , Wan Weiguo , Gao Yongbin , Fang Zhijun
Published: February 2019

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.
The Design of an Energy Harvesting Wireless Sensor Node for Tracking Pink Iguanas
Authors: Loreti Pierpaolo , Catini Alexandro , De Luca Massimiliano , Bracciale Lorenzo , Gentile Gabriele , Di Natale Corrado
Published: February 2019

The design of wireless sensor nodes for animal tracking is a multidisciplinary activity that presents several research challenges both from a technical and a biological point of view. A monitoring device has to be designed accounting for all system requirements including the specific characteristics of animals and environment. In this work we present some aspects of the design of a wireless sensor node to track and monitor the pink iguana of the Galápagos: a recently discovered species living in remote locations at the Galápagos Islands. The few individuals of this species live in a relatively small area that lacks of any available communication infrastructure. We present and discuss the energy harvesting architecture and the related energy management logic. We also discuss the impact of packaging on the sensor performance and the consequences of the limited available energy on the GPS tracking.
Double-Layer Cubature Kalman Filter for Nonlinear Estimation
Authors: Yang Feng , Luo Yujuan , Zheng Litao
Published: February 2019

The cubature Kalman filter (CKF) has poor performance in strongly nonlinear systems while the cubature particle filter has high computational complexity induced by stochastic sampling. To address these problems, a novel CKF named double-Layer cubature Kalman filter (DLCKF) is proposed. In the proposed DLCKF, the prior distribution is represented by a set of weighted deterministic sampling points, and each deterministic sampling point is updated by the inner CKF. Finally, the update mechanism of the outer CKF is used to obtain the state estimations. Simulation results show that the proposed algorithm has not only high estimation accuracy but also low computational complexity, compared with the state-of-the-art filtering algorithms.
Removal of Artifacts from EEG Signals: A Review
Authors: Jiang Xiao , Bian Gui-Bin , Tian Zean
Published: February 2019

Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.
A Review of Advanced Localization Techniques for Crowdsensing Wireless Sensor Networks
Authors: Coluccia Angelo , Fascista Alessio
Published: February 2019

The wide availability of sensing modules and computing capabilities in modern mobile devices (smartphones, smart watches, in-vehicle sensors, etc.) is driving the shift from mote-class wireless sensor networks (WSNs) to the new era of crowdsensing WSNs. In this emerging paradigm sensors are no longer static and homogeneous, but are rather worn/carried by people or cars. This results in a new type of wide-area WSN—crowd-based and overlaid on top of heterogeneous communication technologies—that paves the way for very innovative applications. To this aim, the positioning of mobile devices operating in the network becomes crucial. Indeed, the pervasive, almost ubiquitous availability of smart devices brings unprecedented opportunities but also poses new research challenges in their precise location under mobility and dense-multipath environments typical of urban and indoor scenarios. In this paper, we review recent advances in the field of wireless positioning with focus on cooperation, mobility, and advanced array processing, which are key enablers for the design of novel localization solutions for crowdsensing WSNs.
Psychophysiological Indicators for Modeling User Experience in Interactive Digital Entertainment
Authors: Čertický Martin , Čertický Michal , Sinčák Peter , Magyar Gergely , Vaščák Ján , Cavallo Filippo
Published: February 2019

Analyses of user experience in the electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems—mainly the misinterpretation and temporal delay during longer experiments—and therefore, should not be used as a sole metric. To tackle this problem, we propose the possibility of modeling consumer experience using psychophysiological measures and demonstrate how such models can be trained using machine learning methods. We use a machine learning approach to model user experience using real-time data produced by the autonomic nervous system and involuntary psychophysiological responses. Multiple psychophysiological measures, such as heart rate, electrodermal activity, and respiratory activity, have been used in combination with self-reporting to prepare training sets for machine learning algorithms. The training data was collected from 31 participants during hour-long experiment sessions, where they played multiple video-games. Afterwards, we trained and compared the results of four different machine learning models, out of which the best one produced ∼96% accuracy. The results suggest that psychophysiological measures can indeed be used to assess the enjoyment of digital entertainment consumers.
Two-layer Electrospun System Enabling Wound Exudate Management and Visual Infection Response
Authors: Bazbouz Mohamed Basel , Tronci Giuseppe
Published: February 2019

The spread of antimicrobial resistance calls for chronic wound management devices that can engage with the wound exudate and signal infection by prompt visual effects. Here, the manufacture of a two-layer fibrous device with independently-controlled exudate management capability and visual infection responsivity was investigated by sequential free surface electrospinning of poly(methyl methacrylate-co-methacrylic acid) (PMMA-co-MAA) and poly(acrylic acid) (PAA). By selecting wound pH as infection indicator, PMMA-co-MAA fibres were encapsulated with halochromic bromothymol blue (BTB) to trigger colour changes at infection-induced alkaline pH. Likewise, the exudate management capability was integrated via the synthesis of a thermally-crosslinked network in electrospun PAA layer. PMMA-co-MAA fibres revealed high BTB loading efficiency (>80 wt.%) and demonstrated prompt colour change and selective dye release at infected-like media (pH > 7). The synthesis of the thermally-crosslinked PAA network successfully enabled high water uptake (WU = 1291 ± 48 − 2369 ± 34 wt.%) and swelling index (SI = 272 ± 4 − 285 ± 3 a.%), in contrast to electrospun PAA controls. This dual device functionality was lost when the same building blocks were configured in a single-layer mesh of core-shell fibres, whereby significant BTB release (~70 wt.%) was measured even at acidic pH. This study therefore demonstrates how the fibrous configuration can be conveniently manipulated to trigger structure-induced functionalities critical to chronic wound management and monitoring.
Odor Recognition with a Spiking Neural Network for Bioelectronic Nose
Authors: Li Ming , Ruan Haibo , Qi Yu , Guo Tiantian , Wang Ping , Pan Gang
Published: February 2019

Electronic noses recognize odors using sensor arrays, and usually face difficulties for odor complicacy, while animals have their own biological sensory capabilities for various types of odors. By implanting electrodes into the olfactory bulb of mammalian animals, odors may be recognized by decoding the recorded neural signals, in order to construct a bioelectronic nose. This paper proposes a spiking neural network (SNN)-based odor recognition method from spike trains recorded by the implanted electrode array. The proposed SNN-based approach exploits rich timing information well in precise time points of spikes. To alleviate the overfitting problem, we design a new SNN learning method with a voltage-based regulation strategy. Experiments are carried out using spike train signals recorded from the main olfactory bulb in rats. Results show that our SNN-based approach achieves the state-of-the-art performance, compared with other methods. With the proposed voltage regulation strategy, it achieves about 15% improvement compared with a classical SNN model.
Automatic and Robust Infrared-Visible Image Sequence Registration via Spatio-Temporal Association
Authors: Zhao Bingqing , Xu Tingfa , Chen Yiwen , Li Tianhao , Sun Xueyuan
Published: February 2019

To solve the problems of the large differences in gray value and inaccurate positioning of feature information during infrared-visible image registration, we propose an automatic and robust algorithm for registering planar infrared-visible image sequences through spatio-temporal association. In particular, we first create motion vector distribution descriptors which represent the temporal motion information of foreground contours in adjacent frames to complete coarse registration without feature extraction. Then, for precise registration, we extracted FAST corners of the foreground, which are described by the spatial location distribution of contour points based on connected blob detection, and match these corners using bidirectional optimal maximum strategy. Finally, a reservoir updated by Better-In, Worse-Out (BIWO) strategy is established to save matched point pairs and obtain the optimal global transformation matrix. Extensive evaluations on the LITIV dataset well demonstrate the effectiveness of the proposed algorithm. Particularly, our algorithm achieves lower registration overlapping errors than the other two state-of-the-arts.
An Energy-Efficient Two-Stage Cooperative Routing Scheme in Wireless Multi-Hop Networks
Authors: Cheng Jianming , Gao Yating , Zhang Ningbo , Yang Hongwen
Published: February 2019

Cooperative routing is one of the most widely used technologies for improving the energy efficiency and energy balance of wireless multi-hop networks. However, the end-to-end energy cost and network lifetime are greatly restricted if the cooperative transmission model is not designed properly. The main aim of this paper is to explore a two-stage cooperative routing scheme to further improve the energy efficiency and prolong the network lifetime. A two-stage cooperative (TSC) transmission model is firstly designed in which the core helper is introduced to determine the helper set for cooperation. Then, the two-stage link cost is formulated where x, the weight of residual energy, is introduced to be adjusted for different design goals. By selecting the optimal helper set, the two-stage link cost of each link can be optimized. Finally, based on the designed TSC transmission model and the optimized two-stage link cost, a distributed two-stage cooperative routing (TSCR) scheme is further proposed to minimize the end-to-end cooperative routing cost. Simulation results evaluate the effect of x on the different performance metrics. When x equals 0, TSCR can achieve the shortest end-to-end transmission delay and highest energy efficiency, while a larger x can achieve a longer network lifetime. Furthermore, simulation results also show that the proposed TSCR scheme can effectively improve both the energy efficiency and network lifetime compared with the existing schemes.
Learning Sustainability Entrepreneurship by Doing: Providing a Lecturer-Oriented Service Learning Framework
Authors: Halberstadt Jantje , Schank Christoph , Euler Mark , Harms Rainer
Published: February 2019

Due to its growing practical relevance, sustainability entrepreneurship receives a high degree of academic attention. However, literature on how to educate sustainability entrepreneurs remains scarce. A promising didactical approach in this context is service learning. We ask if service learning is an effective way to educate sustainability entrepreneurs, and which framework conditions impact those educators. First, we draw on an established sustainable entrepreneurship capability framework and provide direct evidence from entrepreneurship educators about the effectiveness of service learning. Second, based on grounded theory, qualitative interviews with those educators reveal a framework composed of personal and institutional factors that they have to navigate when provide service learning. Our findings contribute to the interface of service learning and sustainability entrepreneurship by highlighting its effectiveness and the framework conditions for educators.
Effects of Combined Training on Physical Fitness and Anthropometric Measures among Boys Aged 8 to 12 Years in the Physical Education Setting
Authors: Qi Fengxue , Kong Zhaowei , Xiao Tao , Leong Kinwa , Zschorlich Volker R. , Zou Liye
Published: February 2019

Some studies have shown that resistance training combined with plyometric training is more time-efficient and effective for fitness development. The degree and frequency of combined training might influence the benefit of training to maintain time. To better understand this phenomenon of the degree and frequency of combined training in pre- and early pubertal boys, we have provided a more specific recommendation for physical education teachers thus that they are able to prescribe an exercise plan for their students in the physical education setting. We aimed to investigate the effects of combined training followed by 12 weeks of a reduced/detraining period on physical fitness and anthropometric features in 8 to 12-year old boys. Forty-six healthy boys were randomly assigned to either an experimental group (EG, n = 31) or a control group (CG, n = 15). Besides regular physical education classes (PE), participants in the EG performed a combined training program (resistance and plyometric training) twice weekly for 12 weeks. In the CG, participants only underwent PE classes twice per week. Then, the trained boys were randomly assigned to either a reduced training group (RTG, n = 14) or a training cessation group (TCG, n = 17). The RTG continued a reduced combined training program once a week with PE for additional 12 weeks. In the TCG, participants only kept their PE classes twice a week for 12 weeks. We assessed physical fitness performances (biceps curl, leg power, and running speed) and anthropometric features at baseline, after 12 weeks of combined training, and after 12 weeks of reduced training/detraining. We observed a significantly greater decrease in biceps girth in the EG (p = 0.049, d = 0.636), as compared to the CG. With regard to the physical fitness measures after the 12-week combined training, the EG showed significantly greater increase on biceps curl (p = 0.005, d = 0.92) and standing long jump (p = 0.015, d = 0.8) in comparison with the CG, whereas a significantly greater improvement on 30 m sprint speed (p = 0.031, d = 0.707) was observed in the EG, as compared to the CG. With regard to detraining and reduced training, results indicated a significantly greater increase on this outcome in the RTG (p = 0.038, d = 0.938), as compared to the TCG. Resistance training combined with plyometric training can be a safe and effective way to enhance the selected aspects of physical fitness, as well as reduce muscular girth in boys aged between 8 and 12 years. The frequency of one session per week in the combined training seems to provide an additional benefit to prevent leg power of lower limb from rapid deterioration.
Flipping the Tortilla: Social-Ecological Innovations and Traditional Ecological Knowledge for More Sustainable Agri-Food Systems in Spain
Authors: Guerrero Lara Leonie , Pereira Laura M. , Ravera Federica , Jiménez-Aceituno Amanda
Published: February 2019

The conventional dominant global agri-food system is a main driver in the Anthropocene: food production entails profound global environmental changes from greenhouse gas emissions to biodiversity loss, and shifting diets further impact planetary and human health. Innovative approaches are needed to shift towards more sustainable, equitable and healthy agri-food systems. Building on the increasing recognition of the relevance of traditional agroecological knowledge (TAeK) in sustainable food systems, this paper aims to describe innovative agri-food initiatives and explore how the use and valorization of TAeK may transform conventional agri-food systems. It employs a case-study approach in Spain, where we conducted semi-structured interviews with 12 representatives of alternative agri-food initiatives. We found that, to promote sustainable agri-food systems, TAeK has to span from farm-to-fork. Innovative agroecological practices and knowledge help to safeguard biocultural diversity, while gastronomic knowledge among consumers on how to process and prepare local varieties and species is crucial for the implementation of shorter value chains. We discuss how TAeK enhances the success of conventional systems of innovation, challenging dominant epistemological frameworks. By scaling deep (changing values), scaling out (dissemination, reproduction) and scaling up (changing institutions), the agri-food initiatives may act on leverage points to enable broader transformation of the Spanish agri-food system.
Mapping and Characterizing Spatiotemporal Dynamics of Impervious Surfaces Using Landsat Images: A Case Study of Xuzhou, East China from 1995 to 2018
Authors: Li Han , Li Long , Chen Longqian , Zhou Xisheng , Cui Yifan , Liu Yunqiang , Liu Weiqiang
Published: February 2019

As an effective indicator of urbanization, impervious surfaces play a significant role in urban planning and ecological protection. It is, therefore, important to characterize impervious surfaces in urban geographical studies. As a key city in East China, Xuzhou has experienced rapid urbanization in recent decades and is now becoming an environmentally friendly city. To better understand the spatiotemporal heterogeneity of Xuzhou’s urban development, we extracted its impervious surfaces from Landsat images of 1995, 2003, 2010, and 2018 by a linear spectral mixture analysis. Then, a range of complementary methods including landscape indices, profile lines, median centers, standard deviational ellipses, and spatial autocorrelation were adopted to analyze the landscape pattern and expansion of impervious surfaces on both city and district scales. Results show that (1) there was a constant impervious surface expansion, originating in downtown Xuzhou; (2) promoting ecological protection in urban areas fragmented impervious surfaces with increasing heterogeneity and diversity overall; and (3) expansion directions and rates of impervious surfaces varied with district and town, and the central urban area expanded towards east and southeast, which could be related to their own resources and governmental policies. Findings from this study provide useful insights into urban planning of this economically prospective region.
The Process of Creating Yongsan Park from the Urban Resilience Perspective
Authors: Choi Hyeyoung , Seo Young-Ai
Published: February 2019

The concept of resilience implies uncertainty and the ability to adapt to unexpected changes. Projects in cities that require long periods of time and extensive budgets, which include large parks, need resilience to flexibly cope with political, economic, social, and physical changes. The concept of resilience emerged from the design of large parks in the early 2000s and has continued in more recent urban design competitions, but there is still a lack of research on specific planning strategies in the literature. This paper aims to interpret the process of creating a large park and to explore the strategies needed for a resilient process by examining the first national urban park being planned in Seoul, Korea as an example. After discussions began in 1990 and the General Basic Plan was established in 2011, the winning design was announced through an international design competition in 2012. Although the park master plan was considered complete as of 2018, its progress seems unclear because of various changes in the surrounding urban planning and political and economic conditions. This study intends to examine the processes that have already been executed and to assist in setting the future direction of the project. Through the framework derived from prior research on the concept of resilience, this project’s process is examined and analyzed in six main categories: park infrastructure, social dynamics, economic dynamics, health and well-being, governance networks, and planning and institutions. The results show that the categories park infrastructure, health and well-being, and planning and institutions are consistent with the resilience planning strategy to some degree, but social dynamics, economic dynamics, and governance networks are unsatisfactory. From a resilience perspective, a holistic approach to designing the process is most important in planning a large park, beginning from the conceptual stage through to the long-term implementation phase. An integrative process should aim to incorporate specialized knowledge and experiences from a variety of fields, not to resolve single aspects in a piecemeal fashion. This study aims to provide a practical link between large-scale park projects and the concept of resilience for the future.
Assessment of Public–Private Partnership in Municipal Solid Waste Management in Phnom Penh, Cambodia
Authors: Spoann Vin , Fujiwara Takeshi , Seng Bandith , Lay Chanthy , Yim Mongtoeun
Published: February 2019

The overall responsibility for waste management in Phnom Penh Capital (PPC) has rested with the municipal authorities and contracted waste collection companies. Providing waste collection services is a major challenge for Phnom Penh due to the increasing waste volume and the deficiency of the system under public–private partnership. In response to continuing population growth and urbanization, sustainable management is necessary. This study reviewed the details of the processes and examined the performance of the private sector and local government authorities (LGAs). The study used sustainability assessment, according to a success and efficiency factor method. This assessment method was developed to support solid waste management in developing countries. Multiple sustainability domains were evaluated: institutional, legislative, technical, environmental and health aspects as well as social, economic, financial and critical aspects. The results indicate that the long-term contract design attempts for partnership may actually result in a worsening of the situation by facilitating new ways of concentration, inefficiency and political interest. The limited institutional capacity of the public sectors is a consequence of the inefficient decentralization of municipal solid waste management (MSWM) policies. Public–private partnerships can be neither effective nor sustainable if LGAs and CINTRI co. Ltd. waste collection company do not build proper incentives into their management of the two sectors. Revisiting the legal framework, establishing a facilitating agency that will assist in the design and the nurturing of partnerships, competitive tendering, and transparency and financial accountability are essential elements for PPP on the provision of waste services in PPC.
The Association between Urban Public Transport Infrastructure and Social Equity and Spatial Accessibility within the Urban Environment: An Investigation of Tramlink in London
Authors: Cuthill Neil , Cao Mengqiu , Liu Yuqi , Gao Xing , Zhang Yuerong
Published: February 2019

The pursuit of sustainability has been at the forefront of contemporary planning initiatives. However, most recent research has focused on the environmental and economic aspects of developing sustainable urban environment, whilst largely neglecting the social aspects. Contemporary political thinking in the UK often disregards the potential of the urban infrastructure to improve social equity. The aim of this study was to analyse the impact of transport infrastructure on a variety of social measures, in an empirical and ideologically unbiased fashion, using both quantitative and qualitative methods. We selected “Tramlink” as a case study: a light-rail system in the London Borough of Croydon which began operation in 2000. We used quantitative methods, including advanced spatial statistics, to produce a more detailed analysis of social equity than has been previously published. This acknowledges that determining localised issues can produce more informed and effective policy interventions. Our results demonstrate that the physical properties of transport infrastructure and the non-physical attributes of society, in combination, help to create opportunities for individuals to succeed. We also find that in order to reduce the negative effects of austerity, public money could be more effectively spent if diverted to areas that are most in need which can be highlighted through localised investigations.