bluuebunny/crossref_metadata_2025 huggingface dataset:
AI-Based Early Change Detection in Smart Living Environments
Authors: Diraco Giovanni , Leone Alessandro , Siciliano Pietro
Published: August 2019

In the smart environments we live today, a great variety of heterogeneous sensors are being increasingly deployed with the aim of providing more and more value-added services. This huge availability of sensor data, together with emerging Artificial Intelligence (AI) methods for Big Data analytics, can yield a wide array of actionable insights to help older adults continue to live independently with minimal support of caregivers. In this regard, there is a growing demand for technological solutions able to monitor human activities and vital signs in order to early detect abnormal conditions, avoiding the caregivers’ daily check of the care recipient. The aim of this study is to compare state-of-the-art machine and deep learning techniques suitable for detecting early changes in human behavior. At this purpose, specific synthetic data are generated, including activities of daily living, home locations in which such activities take place, and vital signs. The achieved results demonstrate the superiority of unsupervised deep-learning techniques over traditional supervised/semi-supervised ones in terms of detection accuracy and lead-time of prediction.
Collaborative Resource Management for Negotiable Multi-Operator Small Cell Networks
Authors: Shah Shashi , Kittipiyakul Somsak , Lim Yuto , Tan Yasuo
Published: August 2019

The ubiquitous coverage/connectivity requirement of wireless cellular networks has shifted mobile network operators’ (MNOs) interest toward dense deployment of small cells with coverage areas that are much smaller as compared to macrocell base stations (MBSs). Multi-operator small cells could provide virtualization of network resources (infrastructure and spectrum) and enable its efficient utilization, i.e., uninterrupted coverage and connectivity to subscribers, and an opportunity to avoid under-utilization of the network resources. However, a MNO with exclusive ownership to network resources would have little incentive to utilize its precious resources to serve users of other MNOs, since MNOs differentiate among others based on their ownership of the licensed spectrum. Thus, considering network resources scarcity and under-utilization, this paper proposes a mechanism for multi-operator small cells collaboration through negotiation that establishes a mutual agreement acceptable to all involved parties, i.e., a win–win situation for the collaborating MNOs. It enables subscribers of a MNO to utilize other MNOs’ network resources, and allows MNOs to offer small cells “as a service” to users with ubiquitous access to wireless coverage/connectivity, maximize the use of an existing network resources by serving additional users from a market share, and enhance per-user data rate. We validated and evaluated the proposed mechanism through simulations considering various performance metrics.
A Photoluminescent Colorimetric Probe of Bovine Serum Albumin-Stabilized Gold Nanoclusters for New Psychoactive Substances: Cathinone Drugs in Seized Street Samples
Authors: Yen Yao-Te , Chen Ting-Yueh , Chen Chun-Yu , Chang Chi-Lun , Chyueh San-Chong , Chang Huan-Tsung
Published: August 2019

Screening of illicit drugs for new psychoactive substances—namely cathinone—at crime scenes is in high demand. A dual-emission bovine serum albumin-stabilized gold nanoclusters probe was synthesized and used for quantitation and screening of 4-chloromethcathinone and cathinone analogues in an aqueous solution. The photoluminescent (PL) color of the bovine serum albumin-stabilized Au nanoclusters (BSA-Au NCs) probe solution changed from red to dark blue during the identification of cathinone drugs when excited using a portable ultraviolet light-emitting diodes lamp (365 nm). This probe solution allows the PL color-changing point and limit of detection down to 10.0 and 0.14 mM, respectively, for 4-chloromethcathinone. The phenomenon of PL color-changing of BSA-Au NCs was attributed to its PL band at 650 nm, quenching through an electron transfer mechanism. The probe solution was highly specific to cathinone drugs, over other popular illicit drugs, including heroin, cocaine, ketamine, and methamphetamine. The practicality of this BSA-Au NCs probe was assessed by using it to screen illicit drugs seized by law enforcement officers. All 20 actual cases from street and smuggling samples were validated using this BSA-Au NCs probe solution and then confirmed using gas chromatography–mass spectrometry. The results reveal this BSA-Au NCs probe solution is practical for screening cathinone drugs at crime scenes.
HoloLens-Based AR System with a Robust Point Set Registration Algorithm
Authors: Chien Jong-Chih , Tsai Yao-Ren , Wu Chieh-Tsai , Lee Jiann-Der
Published: August 2019

By the standard of today’s image-guided surgery (IGS) technology, in order to check and verify the progress of the surgery, the surgeons still require divert their attention from the patients occasionally to check against the display. In this paper, a mixed-reality system for medical use is proposed that combines an Intel RealSense sensor with Microsoft’s Hololens head-mounted display system, for superimposing medical data onto the physical surface of a patient, so the surgeons do not need to divert their attention from their patients. The main idea of our proposed system is to display the 3D medical images of the patients on the actual patients themselves by placing the medical images and the patients in the same coordinate space. However, the virtual medical data may contain noises and outliers, so the transformation mapping function must be able to handle these problems. The transform function in our system is performed by the use of our proposed Denoised-Resampled-Weighted-and-Perturbed-Iterative Closest Points (DRWP-ICP) algorithm, which performs denoising and removal of outliers before aligning the pre-operative medical image data points to the patient’s physical surface position before displaying the result using the Microsoft HoloLens display system. The experimental results shows that our proposed mixed-reality system using DRWP-ICP is capable of performing accurate and robust mapping despite the presence of noise and outliers.
Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
Authors: Perez Husein , Tah Joseph H. M. , Mosavi Amir
Published: August 2019

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. This paper aims at evaluating the application of convolutional neural networks (CNN) towards an automated detection and localisation of key building defects, e.g., mould, deterioration, and stain, from images. The proposed model is based on pre-trained CNN classifier of VGG-16 (later compaired with ResNet-50, and Inception models), with class activation mapping (CAM) for object localisation. The challenges and limitations of the model in real-life applications have been identified. The proposed model has proven to be robust and able to accurately detect and localise building defects. The approach is being developed with the potential to scale-up and further advance to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.
Exploiting Vehicular Social Networks and Dynamic Clustering to Enhance Urban Mobility Management
Authors: Akabane Ademar Takeo , Immich Roger , Pazzi Richard Wenner , Madeira Edmundo Roberto Mauro , Villas Leandro Aparecido
Published: August 2019

Transport authorities are employing advanced traffic management system (ATMS) to improve vehicular traffic management efficiency. ATMS currently uses intelligent traffic lights and sensors distributed along the roads to achieve its goals. Furthermore, there are other promising technologies that can be applied more efficiently in place of the abovementioned ones, such as vehicular networks and 5G. In ATMS, the centralized approach to detect congestion and calculate alternative routes is one of the most adopted because of the difficulty of selecting the most appropriate vehicles in highly dynamic networks. The advantage of this approach is that it takes into consideration the scenario to its full extent at every execution. On the other hand, the distributed solution needs to previously segment the entire scenario to select the vehicles. Additionally, such solutions suggest alternative routes in a selfish fashion, which can lead to secondary congestions. These open issues have inspired the proposal of a distributed system of urban mobility management based on a collaborative approach in vehicular social networks (VSNs), named SOPHIA. The VSN paradigm has emerged from the integration of mobile communication devices and their social relationships in the vehicular environment. Therefore, social network analysis (SNA) and social network concepts (SNC) are two approaches that can be explored in VSNs. Our proposed solution adopts both SNA and SNC approaches for alternative route-planning in a collaborative way. Additionally, we used dynamic clustering to select the most appropriate vehicles in a distributed manner. Simulation results confirmed that the combined use of SNA, SNC, and dynamic clustering, in the vehicular environment, have great potential in increasing system scalability as well as improving urban mobility management efficiency.
Catalyst State Diagnosis of Three-Way Catalytic Converters Using Different Resonance Parameters—A Microwave Cavity Perturbation Study
Authors: Steiner Carsten , Malashchuk Vladimir , Kubinski David , Hagen Gunter , Moos Ralf
Published: August 2019

Recently, radio frequency (RF) technology was introduced as a tool to determine the oxygen storage level of a three-way catalyst (TWC) for gasoline vehicles. Previous studies on the investigation of commercial catalysts mostly use only the resonant frequency to describe the correlation of oxygen storage level and RF signal. For the first time this study presents a comparison under defined laboratory conditions considering both, resonance frequency and also the quality factor as measurands. Furthermore, various advantages over the sole use of the resonant frequency in the technical application are discussed. Experiments with Ø4.66’’ catalysts and Ø1.66’’ catalyst cores with alternating (rich/lean) gas compositions showed that the relative change in signal amplitude due to a change in oxygen storage is about 100 times higher for the inverse quality factor compared to the resonant frequency. In addition, the quality factor reacts more sensitively to the onset of the oxygen-storage ability, and delivers precise information about the necessary temperature, which is not possible when evaluating the resonant frequency due to the low signal amplitude. As investigations on aged catalysts confirm, the quality factor also provides a new approach to determine operando the ageing state of a TWC.
Development of a High Stability Pd-Ni Alloy Thin-Film Coated SAW Device for Sensing Hydrogen
Authors: Wang Wen , Liu Xueli , Mei Shengchao , Liu Mengwei , Lu Chao , Lu Minghui
Published: August 2019

A Pd-Ni alloy thin-film coated surface acoustic wave (SAW) device is proposed for sensing hydrogen. The Pd-Ni thin-film was sputtered onto the SAW propagation path of a SAW device with a delay line pattern to build the chip-sized hydrogen sensor. The prepared sensor chip was characterized by employing a differential oscillation loop. The effect of the Pd-Ni film thickness on sensing performance was also evaluated, and optimal parameters were determined, allowing for fast response and high sensitivity. Excellent working stability (detection error of 3.7% in half a year), high sensitivity (21.3 kHz/%), and fast response (less than 10 s) were achieved from the 40 nm Pd-Ni alloy thin-film coated sensing device.
FEM-Analysis of 2D Micromachined Flow Transduers based on aGe-Thermistor Arrays and a Double Bridge Readout
Authors: Talic Almir , Cerimovic Samir , Beigelbeck Roman , Kohl Franz , Sauter Thilo , Keplinger Franz
Published: August 2019

This paper reports on a design and simulation study aiming at high-accuracy 2D micromachined thermal flow transducers. The scope is restricted to micromachined devices featuring a square-shaped membrane incorporating central symmetric thin-film devices. A microthermistor array probed spatial excess temperature variations while the main heat supply was alternatively established by optional heating resistors or by pronounced self-heating of the thermistor devices. Proper device designs enable leading edge transducer performance without sophisticated signal conditioning schemes. We found that a high azimuthal uniformity of flow magnitude transduction is tantamount to a precise azimuthal accuracy. The most advanced result gave a maximum azimuthal aberration of 0.17 and 1.7 degrees for 1 m/s and 10 m/s, respectively, while the corresponding magnitude uniformity amounted to 0.07% and 0.5%. Such excellent specifications exceed the need of ordinary meteorological applications by far. However, they are essential for, e.g., precise non-contact measurements of 2D relative movements of two quasi-planar surfaces via the related Couette flow in intermediate air gaps. The simulations predicted significantly better device characteristics than achieved by us in first experiments. However, this gap could be attributed to imperfect control of the flow velocity field by the measurement setup.
Implementation of a Depth from Light Field Algorithm on FPGA
Authors: Domínguez Conde Cristina , Lüke Jonas Philipp , Rosa González Fernando
Published: August 2019

A light field is a four-dimensional function that grabs the intensity of light rays traversing an empty space at each point. The light field can be captured using devices designed specifically for this purpose and it allows one to extract depth information about the scene. Most light-field algorithms require a huge amount of processing power. Fortunately, in recent years, parallel hardware has evolved and enables such volumes of data to be processed. Field programmable gate arrays are one such option. In this paper, we propose two hardware designs that share a common construction block to compute a disparity map from light-field data. The first design employs serial data input into the hardware, while the second employs view parallel input. These designs focus on performing calculations during data read-in and producing results only a few clock cycles after read-in. Several experiments were conducted. First, the influence of using fixed-point arithmetic on accuracy was tested using synthetic light-field data. Also tests on actual light field data were performed. The performance was compared to that of a CPU, as well as an embedded processor. Our designs showed similar performance to the former and outperformed the latter. For further comparison, we also discuss the performance difference between our designs and other designs described in the literature.
Modeling and Solution of Signal Oscillation Mechanism of the Multi-Coil Sensor
Authors: Huang Jiangbo , Wang Haowen , Fu Zhihong , Fu Wei
Published: August 2019

The multi-coil sensor consisting of a series of sub-coils provides a reliable way to avoid signal distortion from excitation field. Compared with conventional coil sensors, the multi-coil sensor exhibits more complex signal conversion performance, and the conventional equivalent circuit cannot reveal the possible attenuated oscillation, which seriously degrades the detection reliability. Based on a novel equivalent circuit model, this research investigates the causes of signal oscillation and proposes and validates an effective solution, which contributes to the signal transmission characteristics of multi-coil sensors for engineering applications.
Sensor Configuration and Algorithms for Power-Line Interference Suppression in Low Field Nuclear Magnetic Resonance
Authors: Huang Xiaolei , Dong Hui , Tao Quan , Yu Mengmeng , Li Yongqiang , Rong Liangliang , Krause Hans-Joachim , Offenhäusser Andreas , Xie Xiaoming
Published: August 2019

Low field (LF) nuclear magnetic resonance (NMR) shows potential advantages to study pure heteronuclear J-coupling and observe the fine structure of matter. Power-line harmonics interferences and fixed-frequency noise peaks might introduce discrete noise peaks into the LF-NMR spectrum in an open environment or in a conductively shielded room, which might disturb J-coupling spectra of matter recorded at LF. In this paper, we describe a multi-channel sensor configuration of superconducting quantum interference devices, and measure the multiple peaks of the 2,2,2-trifluoroethanol J-coupling spectrum. For the case of low signal to noise ratio (SNR) < 1, we suggest two noise suppression algorithms using discrete wavelet analysis (DWA), combined with either least squares method (LSM) or gradient descent (GD). The de-noising methods are based on spatial correlation of the interferences among the superconducting sensors, and are experimentally demonstrated. The DWA-LSM algorithm shows a significant effect in the noise reduction and recovers SNR > 1 for most of the signal peaks. The DWA-GD algorithm improves the SNR further, but takes more computational time. Depending on whether the accuracy or the speed of the de-noising process is more important in LF-NMR applications, the choice of algorithm should be made.
Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures
Authors: Xu Liang , Yuan Shenfang , Chen Jian , Ren Yuanqiang
Published: August 2019

Fatigue crack diagnosis (FCD) is of great significance for ensuring safe operation, prolonging service time and reducing maintenance cost in aircrafts and many other safety-critical systems. As a promising method, the guided wave (GW)-based structural health monitoring method has been widely investigated for FCD. However, reliable FCD still meets challenges, because uncertainties in real engineering applications usually cause serious change both to the crack propagation itself and GW monitoring signals. As one of deep learning methods, convolutional neural network (CNN) owns the ability of fusing a large amount of data, extracting high-level feature expressions related to classification, which provides a potential new technology to be applied in the GW-structural health monitoring method for crack evaluation. To address the influence of dispersion on reliable FCD, in this paper, a GW-CNN based FCD method is proposed. In this method, multiple damage indexes (DIs) from multiple GW exciting-acquisition channels are extracted. A CNN is designed and trained to further extract high-level features from the multiple DIs and implement feature fusion for crack evaluation. Fatigue tests on a typical kind of aircraft structure are performed to validate the proposed method. The results show that the proposed method can effectively reduce the influence of uncertainties on FCD, which is promising for real engineering applications.
Snow Albedo Seasonality and Trend from MODIS Sensor and Ground Data at Johnsons Glacier, Livingston Island, Maritime Antarctica
Authors: Calleja Javier F. , Corbea-Pérez Alejandro , Fernández Susana , Recondo Carmen , Peón Juanjo , de Pablo Miguel Ángel
Published: August 2019

The aim of this work is to investigate whether snow albedo seasonality and trend under all sky conditions at Johnsons Glacier (Livingston Island, Antarctica) can be tracked using the Moderate Resolution Imaging Spectroradiometer (MODIS) snow albedo daily product MOD10A1. The time span is from December 2006 to February 2015. As the MOD10A1 snow albedo product has never been used in Antarctica before, we also assess the performance for the MOD10A1 cloud mask. The motivation for this work is the need for a description of snow albedo under all sky conditions (including overcast days) using satellite data with mid-spatial resolution. In-situ albedo was filtered with a 5-day windowed moving average, while the MOD10A1 data were filtered using a maximum filter. Both in-situ and MOD10A1 data follow an exponential decay during the melting season, with a maximum decay of 0.049/0.094 day−1 (in-situ/MOD10A1) for the 2006–2007 season and a minimum of 0.016/0.016 day−1 for the 2009–2010 season. The duration of the decay varies from 85 days (2007–2008) to 167 days (2013–2014). Regarding the albedo trend, both data sets exhibit a slight increase of albedo, which may be explained by an increase of snowfall along with a decrease of snowmelt in the study area. Annual albedo increases of 0.2% and 0.7% are obtained for in-situ and MOD10A1 data, respectively, which amount to respective increases of 2% and 6% in the period 2006–2015. We conclude that MOD10A1 can be used to characterize snow albedo seasonality and trend on Livingston Island when filtered with a maximum filter.
Fabrics and Garments as Sensors: A Research Update
Authors: Wilson Sophie , Laing Raechel
Published: August 2019

Properties critical to the structure of apparel and apparel fabrics (thermal and moisture transfer, elasticity, and flexural rigidity), those related to performance (durability to abrasion, cleaning, and storage), and environmental effects have not been consistently addressed in the research on fabric sensors designed to interact with the human body. These fabric properties need to be acceptable for functionalized fabrics to be effectively used in apparel. Measures of performance such as electrical conductivity, impedance, and/or capacitance have been quantified. That the apparel/human body system involves continuous transient conditions needs to be taken into account when considering performance. This review highlights gaps concerning fabric-related aspects for functionalized apparel and includes information on increasing the inclusion of such aspects. A multidisciplinary approach including experts in chemistry, electronics, textiles, and standard test methods, and the intended end use is key to widespread development and adoption.
Mathematical Modeling of Heat Transfer in an Element of Combustible Plant Material When Exposed to Radiation from a Forest Fire
Authors: Baranovskiy Nikolay , Demikhova Alena
Published: August 2019

The last few decades have been characterized by an increase in the frequency and burned area of forest fires in many countries of the world. Needles, foliage, branches, and herbaceous plants are involved in burning during forest fires. Most forest fires are surface ones. The purpose of this study was to develop a mathematical model of heat transfer in an element of combustible plant material, namely, in the stem of a herbaceous plant, when exposed to radiation from a surface forest fire. Mathematically, the process of heat transfer in an element of combustible plant material was described by a system of non-stationary partial differential equations with corresponding initial and boundary conditions. The finite difference method was used to solve this system of equations in combination with a locally one-dimensional method for solving multidimensional tasks of mathematical physics. Temperature distributions were obtained as a result of modeling in a structurally inhomogeneous stem of a herbaceous plant for various scenarios of the impact of a forest fire. The results can be used to develop new systems for forest fire forecasting and their environmental impact prediction.
Evaluation of Dissolution Enhancement of Aprepitant Drug in Ternary Pharmaceutical Solid Dispersions with Soluplus® and Poloxamer 188 Prepared by Melt Mixing
Authors: Nanaki Stavroula , Eleftheriou Rodanthi Maria , Barmpalexis Panagiotis , Kostoglou Margaritis , Karavas Evangelos , Bikiaris Dimitrios
Published: August 2019

In the present study Aprepitant (APT) ternary solid dispersions (SDs) were developed and evaluated for the first time. Specifically, ternary SDs of APT with Poloxamer 188 and Soluplus® (SOL) were prepared via melt mixing and compared to binary APT/Poloxamer 188 and APT/SOL SDs. Initially, combined thermo-gravimetric and hot-stage polarized light microscopy studies indicated that all tested compounds were thermally stable up to 280 °C, while Poloxamer 188 acted as a plasticizer to SOL by significantly reducing the temperature required to fully solubilize the API during SD preparation. Differential scanning calorimetry combined with wide angle X-ray diffraction studies showed that crystalline API was dispersed in both binary and ternary SDs, while Fourier transformation-infrared spectroscopy studies revealed no molecular interactions among the components. Scanning electron microscopy combined with EDAX element analysis showed that the API was dispersed in nano-scale within the polymer matrices, while increasing APT content led to increasing API nano-crystals within the SDs. Finally, dissolution studies showed that the prepared formulations enhanced dissolution of Aprepitant and its mechanism analysis was further studied. A mathematical model was also investigated to evaluate the drug release mechanism.
Trend Analysis of Temperature Data for Narayani River Basin, Nepal
Authors: Chand Mohan Bahadur , Bhattarai Bikas Chandra , Pradhananga Niraj Shankar , Baral Prashant
Published: August 2019

Study of spatiotemporal dynamics of temperature is vital to assess changes in climate, especially in the Himalayan region where livelihoods of billions of people living downstream depends on water coming from the melting of snow and glacier ice. To this end, temperature trend analysis is carried out in Narayani river basin, a major river basin of Nepal characterized by three climatic regions: tropical, subtropical and alpine. Temperature data from six stations located within the basin were analyzed. The elevation of these stations ranges from 460 to 3800 m a.s.l. and the time period of available temperature data ranges from 1960–2015. Multiple regression and empirical mode decomposition (EMD) methods were applied to fill in missing data and to detect trends. Annual as well as seasonal trends were analyzed and a Mann-Kendall test was employed to test the statistical significance of detected trends. Results indicate significant cooling trends before 1970s, and warming trends after 1970s in the majority of the stations. The warming trends range from 0.028 ∘C year−1 to 0.035 ∘C year−1 with a mean increasing trend of 0.03 ∘C year−1 after 1971. Seasonal trends show highest warming trends in the monsoon season followed by winter, pre-monsoon, and the post-monsoon season. However, difference in warming rates between different seasons was not significant. An average temperature lapse rate of −0.006 ∘C m−1 with the steepest value (−0.0064 ∘C m−1) in pre-monsoon season and least negative (−0.0052 ∘C m−1) in winter season was observed for this basin. A comparative analysis of the gap-filled data with freely available global climate datasets show reasonable correlation thus confirming the suitability of the gap filling methods.
Identifying the Equilibrium Point between Sustainability Goals and Circular Economy Practices in an Industry 4.0 Manufacturing Context Using Eco-Design
Authors: Garcia-Muiña Fernando E. , González-Sánchez Rocío , Ferrari Anna Maria , Volpi Lucrezia , Pini Martina , Siligardi Cristina , Settembre-Blundo Davide
Published: August 2019

For manufacturing companies, the transition to circular business models (CBMs) can be hampered both by the lack of relevant data and by operational tools. Eco-design, associated with Industry 4.0 IoT (Internet of Things) technologies, can be an effective methodological approach in developing products that are consistent with the principles of the circular economy. The reason is that, in the design phase, decisions are made that can significantly influence the degree of sustainability of products during their lifecycle. Therefore, in the manufacturing environment, eco-design represents an innovative approach to include sustainability among the traditional industrial variables such as functionality, aesthetics, quality, and profit. This study aimed to test eco-design as a tool to define the equilibrium point between sustainability and circular economy in the manufacturing environment of ceramic tile production, and to demonstrate how new business opportunities can be created through evolution from a linear to a circular business model, thanks to IoT and Industry 4.0 technologies used as enabling factors. The main result of this paper was the empirical validation in a manufacturing environment of sustainability paradigms through eco-design tools and digital technologies, proposing the circular business model as an operational tool to promote the competitiveness of enterprises.
Physical Properties of Soils Altered by Invasive Pheretimoid Earthworms: Does Their Casting Layer Create Thermal Refuges?
Authors: Görres Josef H. , Martin Christina , Nouri-Aiin Maryam , Bellitürk Korkmaz
Published: August 2019

Pheretimoid earthworms are invasive in hardwood forests of formerly glaciated regions in the USA. They alter the forest floor structure by creating an extensive, several cm-deep casting layer comprising loose macro-aggregates. Little is known about the physical properties of the casting layer and how they relate to earthworm ecology. Here, thermal and macropore properties of three forest soil textures (clay, silt, and sandy soils, with and without pheretimoids) were measured and compared to explore the possible relationships to their ecology. Thermal properties were significantly different between the casting layer (CAST) and original soil (NOCAST). Results indicate that CAST soils dampen temperature fluctuations occurring at the surface more than NOCAST soil. The increased dampening may be of particular importance to pheretimoid survival in forest fires and during spring when surface fluctuations could expose the hatchlings to fatal temperatures. Macropore volume, an indicator of ease of movement of pheretimoids, was significantly greater in CAST than NOCAST soil. Together, the ease of movement and greater temperature dampening of CAST soils may provide thermal refuges to pheretimoids from temperature variations outside the optimal range. This may improve their chances of survival in newly colonized areas where the climate differs from the original range.
Effect of Biochar Application Rates on the Hydraulic Properties of an Agricultural-Use Boreal Podzol
Authors: Altdorff Daniel , Galagedara Lakshman , Abedin Joinal , Unc Adrian
Published: August 2019

Boreal agriculture struggles with soils of lower agronomic value, most of which are sandy with a low water holding capacity (WHC) and prone to nutrient leaching. Biochar amendments are associated with positive effects on soil hydraulic properties and enhanced nutrient retention. However, these effects are strongly related to feedstock type and pyrolysis parameters and depend on biochar application rates and soil types. While biochar could increase the productivity of boreal agriculture by improving water and nutrient use efficiency, little is known about its effects on hydraulic processes in podzol. In this study, we investigated the effects of biochar rates (10, 20, 40, 80 Mg carbon ha−1) and maturity on soil hydrology for an agriculturally used Podzol in Labrador, Canada. The in-situ soil water content (SWC) and weather data over an entire growing season were analysed. Hydrus 1D simulations were used to estimate changes in water fluxes. SWC showed clear differentiation among storage parameters (i.e., initial, peak and final SWC) and kinetic parameters (i.e., rate of SWC change). Storage parameters and soil wetting and drying rates were significantly affected by biochar rates and its maturity. The magnitude of the changes in SWC after either wetting or drying events was statistically not affected by the biochar rate. This confirms that biochar mostly affected the WHC. Nevertheless, reductions in cumulative lower boundary fluxes were directly related to biochar incorporation rates. Overall, biochar had positive effects on hydrological properties. The biochar rate of 40 Mg C ha−1 was the most beneficial to agriculturally relevant hydraulic conditions for the tested Podzol.
Negative Spatial Autocorrelation: One of the Most Neglected Concepts in Spatial Statistics
Authors: Griffith Daniel A.
Published: August 2019

Negative spatial autocorrelation is one of the most neglected concepts in quantitative geography, regional science, and spatial statistics/econometrics in general. This paper focuses on and contributes to the literature in terms of the following three reasons why this neglect exists: Existing spatial autocorrelation quantification, the popular form of georeferenced variables studied, and the presence of both hidden negative spatial autocorrelation, and mixtures of positive and negative spatial autocorrelation in georeferenced variables. This paper also presents details and insights by furnishing concrete empirical examples of negative spatial autocorrelation. These examples include: Multi-locational chain store market areas, the shrinking city of Detroit, Dallas-Fort Worth journey-to-work flows, and county crime data. This paper concludes by enumerating a number of future research topics that would help increase the literature profile of negative spatial autocorrelation.
A Unified Approach to Efficiency Decomposition for a Two-Stage Network DEA Model with Application of Performance Evaluation in Banks and Sustainable Product Design
Authors: Li Haitao , Xiong Jie , Xie Jianhui , Zhou Zhongbao , Zhang Jinlong
Published: August 2019

Data envelopment analysis (DEA) is a data-driven tool for performance evaluation, benchmarking and multiple-criteria decision-making. This article investigates efficiency decomposition in a two-stage network DEA model. Three major methods for efficiency decomposition have been proposed: uniform efficiency decomposition, Nash bargaining game decomposition, and priority decomposition. These models were developed on the basis of different assumptions that led to different efficiency decompositions and thus confusion among researchers. The current paper attempts to reconcile these differences by redefining the fairness of efficiency decomposition based on efficiency rank, and develops a rank-based model with two parameters. In our new rank-based model, these three efficiency decomposition methods can be treated as special cases where these parameters take special values. By showing the continuity of the Pareto front, we simplify the uniform efficiency decomposition, and indicate that the uniform efficiency decomposition and Nash bargaining game decomposition can converge to the same efficiency decomposition. To demonstrate the merits of our model, we use data from the literature to evaluate the performance of 10 Chinese banks, and compare the different efficiency decompositions created by different methods. Last, we apply the proposed model to the performance evaluation of sustainable product design in the automobile industry.
Closed-Loop Supply Chain Network Equilibrium Model with Subsidy on Green Supply Chain Technology Investment
Authors: Wu Haixiang , Xu Bing , Zhang Ding
Published: August 2019

The green supply chain (GSC) can effectively reduce the waste of resources and avoid environmental pollution. For a closed-loop supply chain network consisting of multiple manufacturers, multiple retailers, and multiple consumer and recycling markets, we assume that retailers are responsible for the recycling of used products, manufacturers use raw materials to produce new products and recycled products for remanufacturing, and government departments subsidize all manufacturers and retailers for GSC technology investment. Then, the equilibrium conditions of manufacturers, retailers, demand markets, and recycling markets are obtained by using the variational inequality method, complementarity theorem, and Nash equilibrium theory, and the variational inequality model of the closed-loop supply chain network multiphase equilibrium is established. Based on numerical simulation, the optimal technology investment decision of green supply chain under different government subsidy rates, and the influence of market structure and enterprise cost asymmetry on the equilibrium solution of supply chain network are analyzed. The results show that government subsidies can effectively promote enterprises to upgrade their level of GSC technology investment. The intensification of enterprise competition and the asymmetry of enterprise costs will affect the composition of enterprise profits and the allocation of profits between enterprises, and the former will weaken the effect of government subsidies.
Impact of the Built Environment and Bicycling Psychological Factors on the Acceptable Bicycling Distance of Rural Residents
Authors: Wang Yan , Ao Yibin , Zhang Yuting , Liu Yan , Zhao Lei , Chen Yunfeng
Published: August 2019

The ability to understand bicycling behavior in China’s rural areas is critical in constructing an improved, sustainable, countryside amid the rapid urbanization in the country. This study analyzes the influence of individual bicycling psychology, objective, and perceived built environment on the acceptable bicycling distance of rural residents. This research is conducted by controlling for the socio-demographic characteristics of the residents on the bases of a face-to-face questionnaire survey and an on-site measurement. Exploratory factor analysis shows three attitudinal common factors on bicycling infrastructure, namely, bicycling ancillary facilities, bicycle lane conditions, and safety, and two bicycling motivation factors, namely, convenience and other motivations. Multiple linear regression was estimated and results of the models were consistent. Individual bicycling psychology and built environment factors significantly influence the acceptable bicycling distance of rural residents. The socio-demographic variables insignificantly influence the acceptable bicycling distance, which is inconsistent with the existing literature. The research results provide a broad empirical base for the complex relationships among individual bicycling psychological factors, objective and perceived built environment, and bicycling behavior. This study presents the first research on bicycling in Chinese rural areas and provides guidance for the development of effective countermeasures in constructing ecovillages.
Relationship between Sustainable Disclosure and Performance—An Extension of Ullmann’s Model
Authors: Pajuelo Moreno María Luisa , Duarte-Atoche Teresa
Published: August 2019

Due to the lack of consensus on the Sustainable Disclosure (SD)–Sustainable Performance (SP) relationship and the absence of a robust theoretical framework base, this research tests this relationship. Based on Ullmann’s argument that the execution of corporate responsibility regarding SD, SP and EP (economic performance) is determined by the management’s (unobservable) overall strategy, we apply Partial Least Squares, introducing EP, size and membership in sensitive sectors and subjecting them to a multiplicity of external pressures (social, environmental and legislative) as determinants of the SD–SP link. There is a moderate SD–SP relationship, with a significant effect due to EP and conditioned by size. Specifically, (1) the companies that are concerned and which act sustainably have a higher SD, (2) the greater the EP, the greater its effect on this SD, but (3) when the sample is segmented by size, the moderating effect is only positive and significant for large companies. An awareness of the added value of the sustainable business model exists, more than simply reporting (actions beyond words), but the value that its profitability yields will not be determinant for SP, though it will affect SD, despite there being no direct relationship between performance and SD.
Technology-Enhanced Learning: An Optimal CPS Learning Application
Authors: Chien Yu-Hung
Published: August 2019

Collaborative problem-solving (CPS) is highly valued in the sustainability of learning to foster the key soft power of talent for the future. In this study, a CPS learning application was built to train and assess individuals with the aim of increasing CPS skills. For effective learning to take place, several issues need to be carefully considered, and these were investigated while testing the proposed application. This study examined the impact of collaborative interactions (CIs) (human–computer agent (HCA) and human–human (HH) interactions) on the CPS performance of students. Gender and learning styles, which may have interaction effects with CIs on CPS performance, were also explored. The results show that the students’ CPS performance in HCA was significantly greater than that in HH. The interaction effect between gender and CI was not significant. The impact of learning style on CPS performance in HH was not significant. In contrast, in HCA, students with verbal, global, and reflective learning styles performed significantly better on CPS tasks than did students with visual, sequential, and active learning styles. Finally, we discussed the optimal ways to teach CPS and the practical effects of a CPS learning application.
How Do Mobility Direction and Human Assets of Mobile Engineers Affect Joint Knowledge Creation after M&As?
Authors: Park Namgyoo K. , Chun Monica Youngshin , Lee Jeonghwan
Published: August 2019

We focused on mobile engineers, a distinctive employee group that may have unique reactions to mergers and acquisitions (M&As). Mobile engineers, employees that move from one firm to another, were previously recognized as an undesirable loss by most knowledge-intensive organizations. However, in this study, we show that they may return to their former organizations as effective knowledge creators when their previous and new organizations unite through M&As. We specifically investigated how their mobility direction, relational assets, and intellectual assets affect the amount of knowledge that is jointly created through inter-personal collaborations following the M&A. Using the data of 410 mobile engineers in high-technology M&As during 2000–2004 in the United States, we found that the mobility direction from acquiring firms to targets prior to M&A has a positive impact on joint knowledge creation. We also found that such mobility direction positively moderates the relationship between human assets of mobile engineers and their joint knowledge creation.
Agritourism Initiatives in the Context of Continuous Out-Migration: Comparative Perspectives for the Alps and Chinese Mountain Regions
Authors: Dax Thomas , Zhang Dachang , Chen Yanying
Published: August 2019

After World War II, the economic recovery of Western Europe implied a swift economic transition for all regions, including the area of the Alps, although affecting various parts at different paces and stages. The resulting out-migration led to population decline in some mountain valleys and regions since the 1950s. Such negative population development trends are widespread across mountain areas of the world, including China, where out-migration started after its rural reform in the 1970s. The effect was in some cases even more significant than in the Alps, with the first villages being deserted in the 1980s. Current estimations report about 380,000 rural villages in China being abandoned between 2000–2016, particularly in its mountain regions. While lower population densities might alleviate the pressures on ecology and contribute to environmental benefits, these movements aggravate a spiraling-down process of local economies and culture. In the Alps, many regions that were facing challenges of out-migration and economic weaknesses focused on local initiatives, including agritourism schemes that provided both economic incentives and stability to involved mountain farmers, and the continuation of local land management systems. However, China’s interest for promoting rural action and tourism-oriented farm diversification only started more recently, with a range of rural tourism and agricultural tourism initiatives emerging. This paper focuses on lessons from successful initiatives in the Alps that might induce and strengthen China’s search for elaborating agritourism activities in mountain areas. In consequence, agritourism might be assessed as a contribution to mitigate out-migration from mountain regions and a core element of the future sustainable development of the Alps and the Chinese countryside.
Social Sustainability Assessment in Livestock Production: A Social Life Cycle Assessment Approach
Authors: Rivera-Huerta Adriana , Rubio Lozano María de la Salud , Padilla-Rivera Alejandro , Güereca Leonor Patricia
Published: August 2019

This study evaluates the social performance of monoculture (MC), intensive silvopastoral (ISP), and native silvopastoral (NSP) livestock production systems in the tropical region of southeastern Mexico through a social life cycle assessment (SCLA) approach. The methodological framework proposed by the United Nations Environmental Program/Society of Environmental Toxicology and Chemistry (UNEP/SETAC) (2009) was employed based on a scoring approach with a performance scale ranging from 1 (very poor) to 4 (outstanding). Twelve livestock ranches for calf production were evaluated using 18 impact subcategories associated with the categories “human rights”, “working conditions”, “health and safety”, “socioeconomic repercussions”, and “governance”. The stakeholders evaluated were workers, the local community, society, and value chain actors. The ranches had performance scores between 1.78 (very poor) and 2.17 (poor). The overall average performance of the ranches by production system was 1.98, 1.96, and 1.97 for the MC, ISP, and NSP systems, respectively. The statistical analysis shows that there is no significant difference in the social performance of the livestock production systems. This assessment indicates that the cattle ranches analyzed in Mexico have poor or very poor social performance. The results show that socioeconomic and political contexts exert a greater influence on the social performance of livestock production systems than does their type of technology.
Responsible Tourism—Integrating Families with Disabled Children in Tourist Destinations
Authors: Tecău Alina Simona , Brătucu Gabriel , Tescașiu Bianca , Chițu Ioana Bianca , Constantin Cristinel Petrișor , Foris Diana
Published: August 2019

This article addresses issues regarding the intention of responsible tourism to increase the accessibility of tourist destinations for children with disabilities and their families. The main objective of this research is to identify the barriers that families with disabled children confront during their touristic experiences and to find ways to diminish these barriers. In this respect, qualitative marketing research based on the focus group method was conducted. The research results revealed that the most important barriers faced by such families are attitude barriers, followed by physical barriers and a lack of information. Starting from these results, we proposed the higher involvement of national authorities and other stakeholders in strategies meant to decrease or remove the barriers faced by people with disabilities and their attendants in order to develop responsible tourism. Such strategies could firstly involve the development of a primary school curriculum by including educational programs that are meant to avoid attitude barriers. Secondly, other stakeholders could use new emerging technologies, such as virtual reality, to offer tourists the opportunity to experience some tourism products and places before they travel.