bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating Resilience-Centered Development Interventions with Remote Sensing
Authors: Kerle Norman , Ghaffarian Saman , Nawrotzki Raphael , Leppert Gerald , Lech Malte
Published: October 2019

Natural disasters are projected to increase in number and severity, in part due to climate change. At the same time a growing number of disaster risk reduction (DRR) and climate change adaptation measures are being implemented by governmental and non-governmental organizations, and substantial post-disaster donations are frequently pledged. At the same time there has been increasing demand for transparency and accountability, and thus evidence of those measures having a positive effect. We hypothesized that resilience-enhancing interventions should result in less damage during a hazard event, or at least quicker recovery. In this study we assessed recovery over a 3 year period of seven municipalities in the central Philippines devastated by Typhoon Haiyan in 2013. We used very high resolution optical images (<1 m), and created detailed land cover and land use maps for four epochs before and after the event, using a machine learning approach with extreme gradient boosting. The spatially and temporally highly variable recovery maps were then statistically related to detailed questionnaire data acquired by DEval in 2012 and 2016, whose principal aim was to assess the impact of a 10 year land-planning intervention program by the German agency for technical cooperation (GIZ). The survey data allowed very detailed insights into DRR-related perspectives, motivations and drivers of the affected population. To some extent they also helped to overcome the principal limitation of remote sensing, which can effectively describe but not explain the reasons for differential recovery. However, while a number of causal links between intervention parameters and reconstruction was found, the common notion that a resilient community should recover better and more quickly could not be confirmed. The study also revealed a number of methodological limitations, such as the high cost for commercial image data not matching the spatially extensive but also detailed scale of field evaluations, the remote sensing analysis likely overestimating damage and thus providing incorrect recovery metrics, and image data catalogues especially for more remote communities often being incomplete. Nevertheless, the study provides a valuable proof of concept for the synergies resulting from an integration of socio-economic survey data and remote sensing imagery for recovery assessment.
Integrated Precise Orbit Determination of Multi-GNSS and Large LEO Constellations
Authors: Li Xingxing , Zhang Keke , Ma Fujian , Zhang Wei , Zhang Qian , Qin Yujie , Zhang Hongmin , Meng Yansong , Bian Lang
Published: October 2019

Global navigation satellite system (GNSS) orbits are traditionally determined by observation data of ground stations, which usually need even global distribution to ensure adequate observation geometry strength. However, good tracking geometry cannot be achieved for all GNSS satellites due to many factors, such as limited ground stations and special stationary characteristics for the geostationary Earth orbit (GEO) satellites in the BeiDou constellation. Fortunately, the onboard observations from low earth orbiters (LEO) can be an important supplement to overcome the weakness in tracking geometry. In this contribution, we perform large LEO constellation-augmented multi-GNSS precise orbit determination (POD) based on simulated GNSS observations. Six LEO constellations with different satellites numbers, orbit types, and altitudes, as well as global and regional ground networks, are designed to assess the influence of different tracking configurations on the integrated POD. Then, onboard and ground-based GNSS observations are simulated, without regard to the observations between LEO satellites and ground stations. The results show that compared with ground-based POD, a remarkable accuracy improvement of over 70% can be observed for all GNSS satellites when the entire LEO constellation is introduced. Particularly, BDS GEO satellites can obtain centimeter-level orbits, with the largest accuracy improvement being 98%. Compared with the 60-LEO and 66-LEO schemes, the 96-LEO scheme yields an improvement in orbit accuracy of about 1 cm for GEO satellites and 1 mm for other satellites because of the increase of LEO satellites, but leading to a steep rise in the computational time. In terms of the orbital types, the sun-synchronous-orbiting constellation can yield a better tracking geometry for GNSS satellites and a stronger augmentation than the polar-orbiting constellation. As for the LEO altitude, there are almost no large-orbit accuracy differences among the 600, 1000, and 1400 km schemes except for BDS GEO satellites. Furthermore, the GNSS orbit is found to have less dependence on ground stations when incorporating a large number of LEO. The orbit accuracy of the integrated POD with 8 global stations is almost comparable to the result of integrated POD with a denser global network of 65 stations. In addition, we also present an analysis concerning the integrated POD with a partial LEO constellation. The result demonstrates that introducing part of a LEO constellation can be an effective way to balance the conflict between the orbit accuracy and computational efficiency.
Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data
Authors: Jin Huaan , Xu Weixing , Li Ainong , Xie Xinyao , Zhang Zhengjian , Xia Haoming
Published: October 2019

As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas.
Enhanced Room Temperature NO2 Sensing Performance of RGO Nanosheets by Building RGO/SnO2 Nanocomposite System
Authors: Du Hongfei , Xie Guangzhong , Zhang Qiuping
Published: October 2019

RGO/SnO 2 nanocomposites were prepared by a simple blending method and then airbrushed on interdigitated electrodes to obtain the corresponding gas sensors. The characterizations of SEM, TEM, Raman, XRD and FTIR were used to characterize the microstructures, morphologies and surface chemical compositions of the nanocomposites, indicating that the two materials coexist in the composite films and the concentration of surface defects is affected by the amount of SnO 2 nanoparticles. It is also found that the room temperature sensing performance of RGO to NO 2 can be improved by introducing appropriate amount of SnO 2 nanoparticles. The enhanced NO 2 sensing properties are attributed to the rough surface structure and increased surface area and surface defects of the nanocomposite films. Since further reduction of RGO, heat treating the sensing films resulted in a decrease in the response and recovery times of the sensors. Furthermore, the sensor annealed at 200 ∘ C exhibited a small baseline drift, wide detection range, good linearity, high stability and better selectivity.
Dark Field Microscopy-Based Biosensors for the Detection of E. coli in Environmental Water Samples
Authors: La Spina Rita , António Diana C. , Desmet Cloe , Valsesia Andrea , Bombera Radoslaw , Norlén Hedvig , Lettieri Teresa , Colpo Pascal
Published: October 2019

Development of sensitive methods for the determination of E. coli bacteria contamination in water distribution systems is of paramount importance to ensure the microbial safety of drinking water. This work presents a new sensing platform enabling the fast detection of bacteria in field samples by using specific antibodies as the biorecognition element and dark field microscopy as the detection technique. The development of the sensing platform was performed using non-pathogenic bacteria, with the E. coli DH5α strain as the target, and Bacillus sp. 9727 as the negative control. The identification of the captured bacteria was made by analyzing the dark field microscopy images and screening the detected objects by using object circularity and size parameters. Specificity tests revealed the low unspecific attachment of either E. coli over human serum albumin antibodies (negative control for antibody specificity) and of Bacillus sp. over E. coli antibodies. The system performance was tested using field samples, collected from a wastewater treatment plant, and compared with two quantification techniques (i.e., Colilert-18 test and quantitative polymerase chain reaction (qPCR)). The results showed comparable quantification capability. Nevertheless, the present method has the advantage of being faster, is easily adaptable to in-field analysis, and can potentially be extended to the detection of other bacterial strains.
No-Reference Objective Video Quality Measure for Frame Freezing Degradation
Authors: Dumic Emil , Bjelopera Anamaria
Published: October 2019

In this paper we present a novel no-reference video quality measure, NR-FFM (no-reference frame–freezing measure), designed to estimate quality degradations caused by frame freezing of streamed video. The performance of the measure was evaluated using 40 degraded video sequences from the laboratory for image and video engineering (LIVE) mobile database. Proposed quality measure can be used in different scenarios such as mobile video transmission by itself or in combination with other quality measures. These two types of applications were presented and studied together with considerations on relevant normalization issues. The results showed promising correlation values between the user assigned quality and the estimated quality scores.
Wearable Cardiopulmonary Function Evaluation System for Six-Minute Walking Test
Authors: Lin Bor-Shing , Jhang Ruei-Jie , Lin Bor-Shyh
Published: October 2019

As a submaximal exercise test, a 6-min walking test (6MWT) can be considered a suitable index for the exercise capacity of patients with a respiratory problem. Traditionally, medical staff manually collect cardiopulmonary information using different devices. However, no integrated monitoring system is currently available to simultaneously record the real-time breathing sound, heart rhythm, and precise walking information (i.e., walking distance, speed, and acceleration) during the 6MWT. In this study, a wearable and wireless multiparameter monitoring system is proposed to simultaneously monitor the breathing sound, oxygen saturation (SpO2), electrocardiograph (ECG) signals, and precise walking information during the 6MWT. Here, a wearable mechanical design was successfully used to reduce the effect of motion artifacts on the breathing sound and ECG signal. A multiparameter detection algorithm was designed to effectively estimate heart and breathing rates. Finally, the cardiopulmonary function of smokers was evaluated using the proposed system. The evaluation indicated that this system could reveal dynamic changes and differences in the breathing rate, heart rate, SpO2, walking speed, and acceleration during the 6MWT. The proposed system can serve as a more integrated approach to monitor cardiopulmonary parameters and obtain precise walking information simultaneously during the 6MWT.
Tongue Rehabilitation Device for Dysphagic Patients
Authors: Milazzo Mario , Panepinto Andrea , Sabatini Angelo Maria , Danti Serena
Published: October 2019

Dysphagia refers to difficulty in swallowing often associated with syndromic disorders. In dysphagic patients’ rehabilitation, tongue motility is usually treated and monitored via simple exercises, in which the tongue is pushed against a depressor held by the speech therapist in different directions. In this study, we developed and tested a simple pressure/force sensor device, named “Tonic Tongue (ToTo)”, intended to support training and monitoring tasks for the rehabilitation of tongue musculature. It consists of a metallic frame holding a ball bearing support equipped with a sterile disposable depressor, whose angular displacements are counterbalanced by extensional springs. The conversion from angular displacement to force is managed using a simple mechanical model of ToTo operation. Since the force exerted by the tongue in various directions can be estimated, quantitative assessment of the outcome of a given training program is possible. A first prototype of ToTo was tested on 26 healthy adults, who were trained for one month. After the treatment, we observed a statistically significant improvement with a force up to 2.2 N (median value) in all tested directions of pushing, except in the downward direction, in which the improvement was slightly higher than 5 N (median value). ToTo promises to be an innovative and reliable device that can be used for the rehabilitation of dysphagic patients. Moreover, since it is a self-standing device, it could be used as a point-of-care solution for in-home rehabilitation management of dysphasia.
Dual-Probe Near-Field Phaseless Antenna Measurement System on Board a UAV
Authors: García Fernández María , Álvarez López Yuri , Las-Heras Fernando
Published: October 2019

On-site antenna measurement has been recently attracting an increasing interest in order to assess the antenna performance in real operational environments. The complexity and cost of these kind of measurements have been significantly cut down due to recent developments in unmanned aerial vehicles’ (UAVs) hardware and antenna measurement post-processing techniques. In particular, the introduction of positioning and geo-referring subsystems capable of providing centimeter-level accuracy together with the use of phase retrieval techniques and near-field to far-field transformation algorithms, have enabled near field measurements using UAVs. This contribution presents an improved UAV-based on-site antenna measurement system. On the one hand, the simultaneous acquisition on two measurement surfaces has been introduced and calibrated properly, thus reducing geo-referring uncertainties and flight time. On the other hand, the positioning and geo-referring subsystem has been enhanced by means of a dual-band real time kinematics (RTK) unit. The system capabilities were validated by measuring an offset reflector antenna, and the results were compared with the measurements at the spherical range in the anechoic chamber and with the measurements collected with a previous version of the implemented system.
Augmenting Deep Learning Performance in an Evidential Multiple Classifier System
Authors: Vandoni Jennifer , Hégarat-Mascle Sylvie Le , Aldea Emanuel
Published: October 2019

The main objective of this work is to study the applicability of ensemble methods in the context of deep learning with limited amounts of labeled data. We exploit an ensemble of neural networks derived using Monte Carlo dropout, along with an ensemble of SVM classifiers which owes its effectiveness to the hand-crafted features used as inputs and to an active learning procedure. In order to leverage each classifier’s respective strengths, we combine them in an evidential framework, which models specifically their imprecision and uncertainty. The application we consider in order to illustrate the interest of our Multiple Classifier System is pedestrian detection in high-density crowds, which is ideally suited for its difficulty, cost of labeling and intrinsic imprecision of annotation data. We show that the fusion resulting from the effective modeling of uncertainty allows for performance improvement, and at the same time, for a deeper interpretation of the result in terms of commitment of the decision.
Development of an In Vivo Sensor to Monitor the Effects of Vapour Pressure Deficit (VPD) Changes to Improve Water Productivity in Agriculture
Authors: Vurro Filippo , Janni Michela , Coppedè Nicola , Gentile Francesco , Manfredi Riccardo , Bettelli Manuele , Zappettini Andrea
Published: October 2019

Environment, biodiversity and ecosystem services are essential to ensure food security and nutrition. Managing natural resources and mainstreaming biodiversity across agriculture sectors are keys towards a sustainable agriculture focused on resource efficiency. Vapour Pressure Deficit (VPD) is considered the main driving force of water movements in the plant vascular system, however the tools available to monitor this parameter are usually based on environmental monitoring. The driving motif of this paper is the development of an in-vivo sensor to monitor the effects of VPD changes in the plant. We have used an in vivo sensor, termed “bioristor”, to continuously monitor the changes occurring in the sap ion’s status when plants experience different VPD conditions and we observed a specific R (sensor response) trend in response to VPD. The possibility to directly monitor the physiological changes occurring in the plant in different VPD conditions, can be used to increase efficiency of the water management in controlled conditions thus achieving a more sustainable use of natural resources.
Underwater Acoustic Impulsive Noise Monitoring in Port Facilities: Case Study of the Port of Cartagena
Authors: Enguix Ivan Felis , Sánchez Egea Marta , Guerrero González Antonio , Arenas Serrano David
Published: October 2019

Recording underwater impulsive noise data is an important aspect of mitigating its environmental impact and improving maritime environmental management systems. This paper describes the method used and results of the spatial monitoring of both the baseline noise level and the impulsive noise sources in the Port of Cartagena. An autonomous vessel was equipped with a smart digital hydrophone with a working frequency range between 10 and 200 kHz and a received voltage response (RVR) of, approximately, −170 dB re 1V/µPa. A GIS map was drawn up with the spatiotemporal distribution of the basal sound pressure levels by coupling the acoustic data with the vessel’s GPS positions to identify the sources of the impulsive noise of interest and their temporal characteristics. The loading of cargo containers was identified as the main source of impulse noise. This study is the first of a series designed to obtain accurate information on underwater noise pollution and its potential impact on biodiversity in the Port of Cartagena.
Estimation of Biological Parameters of Cutaneous Ulcers Caused by Leishmaniasis in an Animal Model Using Diffuse Reflectance Spectroscopy
Authors: Botina Deivid , Franco Ricardo , Murillo Javier , Galeano July , Zarzycki Artur , Torres-Madronero Maria C. , Bermúdez Camilo , Montaño Jaime , Garzón Johnson , Marzani Franck , Robledo Sara M.
Published: October 2019

Cutaneous leishmaniasis (CL) is a neglected tropical disease that requires novel tools for its understanding, diagnosis, and treatment follow-up. In the cases of other cutaneous pathologies, such as cancer or cutaneous ulcers due to diabetes, optical diffuse reflectance-based tools and methods are widely used for the investigation of those illnesses. These types of tools and methods offer the possibility to develop portable diagnosis and treatment follow-up systems. In this article, we propose the use of a three-layer diffuse reflectance model for the study of the formation of cutaneous ulcers caused by CL. The proposed model together with an inverse-modeling procedure were used in the evaluation of diffuse-reflectance spectral signatures acquired from cutaneous ulcers formed in the dorsal area of 21 golden hamsters inoculated with Leishmanisis braziliensis. As result, the quantification of the model’s variables related to the main biological parameters of skin were obtained, such as: diameter and volumetric fraction of keratinocytes, collagen; volumetric fraction of hemoglobin, and oxygen saturation. Those parameters show statistically significant differences among the different stages of the CL ulcer formation. We found that these differences are coherent with histopathological manifestations reported in the literature for the main phases of CL formation.
A Model-Based System for Real-Time Articulated Hand Tracking Using a Simple Data Glove and a Depth Camera
Authors: Jiang Linjun , Xia Hailun , Guo Caili
Published: October 2019

Tracking detailed hand motion is a fundamental research topic in the area of human-computer interaction (HCI) and has been widely studied for decades. Existing solutions with single-model inputs either require tedious calibration, are expensive or lack sufficient robustness and accuracy due to occlusions. In this study, we present a real-time system to reconstruct the exact hand motion by iteratively fitting a triangular mesh model to the absolute measurement of hand from a depth camera under the robust restriction of a simple data glove. We redefine and simplify the function of the data glove to lighten its limitations, i.e., tedious calibration, cumbersome equipment, and hampering movement and keep our system lightweight. For accurate hand tracking, we introduce a new set of degrees of freedom (DoFs), a shape adjustment term for personalizing the triangular mesh model, and an adaptive collision term to prevent self-intersection. For efficiency, we extract a strong pose-space prior to the data glove to narrow the pose searching space. We also present a simplified approach for computing tracking correspondences without the loss of accuracy to reduce computation cost. Quantitative experiments show the comparable or increased accuracy of our system over the state-of-the-art with about 40% improvement in robustness. Besides, our system runs independent of Graphic Processing Unit (GPU) and reaches 40 frames per second (FPS) at about 25% Central Processing Unit (CPU) usage.
Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots
Authors: Yan Hao , Wang Hongbo , Vladareanu Luige , Lin Musong , Vladareanu Victor , Li Yungui
Published: October 2019

In the process of rehabilitation training for stroke patients, the rehabilitation effect is positively affected by how much physical activity the patients take part in. Most of the signals used to measure the patients’ participation are EMG signals or oxygen consumption, which increase the cost and the complexity of the robotic device. In this work, we design a multi-sensor system robot with torque and six-dimensional force sensors to gauge the patients’ participation in training. By establishing the static equation of the mechanical leg, the man–machine interaction force of the patient can be accurately extracted. Using the impedance model, the auxiliary force training mode is established, and the difficulty of the target task is changed by adjusting the K value of auxiliary force. Participation models with three intensities were developed offline using support vector machines, for which the C and σ parameters are optimized by the hybrid quantum particle swarm optimization and support vector machines (Hybrid QPSO-SVM) algorithm. An experimental statistical analysis was conducted on ten volunteers’ motion representation in different training tasks, which are divided into three stages: over-challenge, challenge, less challenge, by choosing characteristic quantities with significant differences among the various difficulty task stages, as a training set for the support vector machines (SVM). Experimental results from 12 volunteers, with tasks conducted on the lower limb rehabilitation robot LLR-II show that the rehabilitation robot can accurately predict patient participation and training task difficulty. The prediction accuracy reflects the superiority of the Hybrid QPSO-SVM algorithm.
Absolute Depth Measurement Using Multiphase Normalized Cross-Correlation for Precise Optical Profilometry
Authors: Duong Duc-Hieu , Chen Chin-Sheng , Chen Liang-Chia
Published: October 2019

In a multifrequency phase-shifting (MFPS) algorithm, the temporal phase unwrapping algorithm can extend the unambiguous phase range by transforming the measurement range from a short fringe pitch into an extended synthetic pitch of two different frequencies. However, this undesirably amplifies the uncertainty of measurement, with each single-frequency phase map retaining its measurement uncertainty, which is carried over to the final unwrapped phase maps in fringe-order calculations. This article analyzes possible causes and proposes a new absolute depth measurement algorithm to minimize the propagation of measurement uncertainty. Developed from normalized cross-correlation (NCC), the proposed algorithm can minimize wrong fringe-order calculations in the MFPS algorithm. The experimental results demonstrated that the proposed measurement method could effectively calibrate the wrong fringe order. Moreover, some extremely low signal-to-noise ratio (SNR) regions of a captured image could be correctly reconstructed (for surface profiles). The present findings confirmed measurement precision at one standard deviation below 5.4 µm, with an absolute distance measurement of 16 mm. The measurement accuracy of the absolute depth could be significantly improved from an unacceptable level of measured errors down to 0.5% of the overall measuring range. Additionally, the proposed algorithm was capable of extracting the absolute phase map in other optical measurement applications, such as distance measurements using interferometry.
Potassium and Metal Release Related to Glaucony Dissolution in Soils
Authors: Oze Christopher , Smaill Joshua B. , Reid Catherine M. , Palin Michael
Published: October 2019

Plant nutrients such as potassium (K) may be limited in soil systems and additions (i.e., fertilizer) are commonly required. Glaucony is a widely distributed and abundant marine-derived clay mineral present in soils worldwide which may serve as a source of potassium. The South Island of New Zealand contains numerous deposits of glaucony-rich rocks and related soils providing an opportunity to explore how glaucony might be a beneficial source of potassium. Here, the geochemistry of glaucony and its suitability as a mineral source of soil K from four deposits in New Zealand was examined using spatially resolved chemical analyses and dissolution experiments. Geochemical and morphological analyses revealed that glaucony from all deposits were K-enriched and were of the evolved (6%–8% K2O) to highly evolved type (>8% K2O). Glaucony derived from growth inside pellets contain elevated K and Fe concentrations compared to bioclast-hosted glaucony. Solubility analysis showed that K was released from glaucony at rates higher than any other metal present in the mineral. Additionally, decreasing the pH and introducing an oxidizing agent (i.e., birnessite which is ubiquitous in soil environments) appeared to accelerate K release. Trace metals including Cr, Zn, Cu, and Ni were present in the solid phase analysis; however, further investigation with a focus on Cr revealed that these elements were released into solution at low concentrations and may present a source of soil micronutrients. These results suggest that glaucony may offer a source of slow releasing K into soils, and so could be used as a locally sourced environmentally sustainable K resource for agriculture, whether in New Zealand or worldwide.
Prediction of Soil Carbon Fractions Using a Field Spectroradiometer Equipped with an Illuminating Contact Probe
Authors: Fortuna Ann-Marie , Starks Patrick J. , Nelson Amanda M. , Steiner Jean L.
Published: October 2019

This research compared the accuracy of laboratory reference measurements of soil C and N fractions with soil reflectance spectra acquired using a portable field spectroradiometer with an illuminating contact probe. Soil samples were taken from eight, 1.6 ha watersheds, located in El Reno, Oklahoma on native warm season grasslands and agronomic managements with landform complexes serving as replicates within and among treatments. Soil samples were taken from 0–30-cm. Measurements included total soil organic carbon (TSOC), total soil nitrogen (TSN), residual C of acid hydrolysis (RCAH), and particulate organic matter C (POMC) and N (POMN). Soil reflectance in the 350 to 2500 nm region was correlated with individual laboratory measurements. Each reference dataset was divided into model development data (70%) and model validation data (30%). Calibrated models were applied to validation datasets. Statistical analysis revealed that prediction efficiencies of soil reflectance models were highly quantitative. Coefficients of determination (R2) were near 1 (≥0.90) and ratios of predicted values to the measured standard deviation (RPD) were >2, indicative of good predictive models. The field spectroradiometer enabled us to parameterize soil spatial variability and soil reflectance measurements, reducing the resources required to acquire edaphic measurements.
GIS-Based and Statistical Approaches in Archaeological Predictive Modelling (NE Romania)
Authors: Nicu Ionut , Mihu-Pintilie Alin , Williamson James
Published: October 2019

Archaeological predictive modelling (APM) is an important method for archaeological research and cultural heritage management. This study tests the viability of a new statistical method for APM. Frequency ratio (FR) is widely used in the field of geosciences but has not been applied in APM. This study tests FR in a catchment from the north-eastern part of Romania to predict the possible location(s) of Eneolithic sites. In order to do that, three factors were used: soils, heat load index and slope position classification. Eighty percent of the sites were used to build the model, while the remaining 20% were used to externally test the model’s performance. The final APM was made with the help of GIS software and classified into four susceptibility classes: very high, high, medium and low. The success rate curve and the prediction rate curve reported values of the area under curve (AUC) of 0.72, and 0.75 respectively. The Kvamme’s Gain value for the model has a value of 0.56. Therefore, the final APM is reliable, so FR is a viable technique for APM. The final map can be successfully used in archaeological research, cultural heritage management and protection, preventive archaeology and sustainable development.
Simulation-Based Analysis of the Effect of Significant Traffic Parameters on Lane Changing for Driving Logic “Cautious” on a Freeway
Authors: Farooq Danish , Juhasz Janos
Published: October 2019

Lane changing of traffic flow is a complicated and significant behavior for traffic safety on the road. Frequent lane changing can cause serious traffic safety issues, particularly on a two-lane road section of a freeway. This study aimed to analyze the effect of significant traffic parameters for traffic safety on lane change frequency using the studied calibrated values for driving logic “conscious” in VISSIM. Video-recorded traffic data were utilized to calibrate the model under specified traffic conditions, and the relationship between observed variables were estimated using simulation plots. The results revealed that changes in average desired speed and traffic volume had a positive relationship with lane change frequency. In addition, lane change frequency was observed to be higher when the speed distribution was set large. 3D surface plots were also developed to show the integrated effect of specified traffic parameters on lane change frequency. Results showed that high average desired speed and large desired speed distribution coupled with high traffic volume increased the lane change frequency tremendously. The study also attempted to develop a regression model to quantify the effect of the observed parameters on lane change frequency. The regression model results showed that desired speed distribution had the highest effect on lane change frequency compared to other traffic parameters. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency.
Sustainable Cooperation in the Green Supply Chain under Financial Constraints
Authors: Xing Guangyuan , Xia Bing , Guo Jue
Published: October 2019

Investment on product greenness in green supply chain is always restricted by the emerging supplier’ financial constraints, so manufacturers always share the suppliers’ investment to encourage the suppliers’ green innovation. Based on the two-stage cooperation model between one manufacturer and one emerging supplier, and the assumption that emerging suppliers need to reach a certain survival threshold at the end of each period, this paper studies investment on product greenness and sustainability of cooperation in the supply chain. The impacts of consumers’ preference for greenness (CPG), market volatility, financial constraints, and investment-sharing proportion are also examined. It was found that when market volatility and CPG exist at the same time, compared with the deterministic environment, emerging suppliers will improve (or reduce) the wholesale price and greenness at the same time to balance the short-term bankruptcy risk and the long-term profit, and suppliers’ green investment would be stimulated by the increasing demand uncertainty. Besides, when suppliers’ financial constraints increase, manufacturers will also increase its sharing proportion of green investment. Lastly, there always exists an investment-sharing proportion that optimizes the sustainability of cooperation and profits jointly.
Inclusion of Workers with Disabilities in Production 4.0: Legal Foundations in Europe and Potentials Through Worker Assistance Systems
Authors: Mark Benedikt G. , Hofmayer Sarah , Rauch Erwin , Matt Dominik T.
Published: October 2019

The inclusion of employees with disabilities in production is an issue that has rarely been addressed by scientists from the manufacturing sector. In this article, we examine to what extent the trend towards Industry 4.0 offers potential for the inclusion of people with disabilities in Production 4.0. First, we examine relevant legal foundations and restrictions in Europe and in more detail in Austria, Italy, and Norway. Next, based on a literature review, we examine which technological aids in the form of worker assistance systems derived from Industry 4.0 can make jobs in the manufacturing sector accessible for people with disabilities. Three types of assistance systems have been examined: sensorial aid systems, physical aid systems, and cognitive aid systems. In a concluding discussion of the results, we finally summarize the implications on management and policies as well as the potential and limitations of identified worker assistance technologies. On the one hand, the study is intended to draw the attention of researchers and industrial companies to new technological possibilities for the inclusion of people with disabilities in production. On the other hand, difficulties and grievances due to the legal foundations are pointed out to stimulate a critical discussion here as well.
The Influence of Multi-Scale Atmospheric Circulation on Severe Haze Events in Autumn and Winter in Shanghai, China
Authors: Zhao Zezheng , Xi Hailing , Russo Ana , Du Huadong , Gong Youguo , Xiang Jie , Zhou Zeming , Zhang Jiping , Li Chengcai , Zhou Chengjun
Published: October 2019

Severe haze events have many adverse effects on agricultural production and human activity. Haze events are often associated with specific patterns of atmospheric circulation. Therefore, studying the relationship between atmospheric circulation and haze is particularly important for early warning and forecasting of urban haze events. In order to study the relationship between multi-scale atmospheric circulation and severe haze events in autumn and winter in Shanghai, China, we used a T-mode objective classification method to classify autumn and winter atmospheric circulation patterns into six types based on sea level pressure data from 2007 to 2016 in the Shanghai area. For the period between September 2016 and February 2017, we used the Allwine–Whiteman method to classify the local wind in Shanghai into three categories: stagnation, recirculation, and ventilation. By further studying the PM2.5 concentration distribution, visibility distribution, and other meteorological characteristics of each circulation type (CT) and local wind field type, we found that the Shanghai area is most prone to severe haze when exposed to certain circulation patterns (CT1, CT2, and CT4), mainly associated to the cold air activity and the displacement of the high pressure system relative to Shanghai. We also found that the local wind fields in the Shanghai area are dominated by recirculation and stagnation events. These conclusions were further verified by studying a typical pollution process in Shanghai in November 2016 and the pollutant diffusion path using the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory model) simulation model.
City Branding and Industrial Transformation from Manufacturing to Services: Which Pathways do Cities in Central China Follow?
Authors: Han Meiling , de Jong Martin , Jiang Minghui
Published: October 2019

A potentially attractive way for cities to maintain economic growth while reducing environmental harm is to let their production structures undergo industrial transformation, a process otherwise known as ecological modernization. This attraction lies mainly in the fact that residents, visitors and corporations prefer clean air, water and soil as a milieu to invest their resources in. Municipal governments can use city branding as an important instrument to force off such a transformation, if it is taken as a point of departure for the adoption of a strategy to which they are deeply committed and for the benefit of which they are willing to deploy their various policy instruments. In the literature on ecological modernization, five different pathways for industrial transformation in cities have been identified and these have been matched with city branding practices. In this contribution, the abovementioned conceptual framework is further detailed and specified to account for a variety in types of secondary and tertiary sector industries. In the empirical sections, all cities in the Chinese provinces Hubei and Hunan, where the transition from manufacturing to services is typically most pressing, are examined in terms of their industrial structures, pathways to industrial transformation and city branding choices. The results indicate, inter alia, that further subdivision of the secondary and tertiary economic sectors is useful in understanding key features of the transformation, and that different sub-pathways affect tradeoffs between economic expansion and ecological preservation differently. Branding practices among Hubei and Hunan cities also indicate that some industries are more easily embraced and utilized than others in establishing brand identities and adopting popular city labels.
Diffusion Correntropy Subband Adaptive Filtering (SAF) Algorithm over Distributed Smart Dust Networks
Authors: Guo Ying , Li Jingjing , Li Yingsong
Published: October 2019

The diffusion subband adaptive filtering (DSAF) algorithm has attracted much attention in recent years due to its decorrelation ability for colored input signals. In this paper, a modified DSAF algorithm using the symmetry maximum correntropy criterion (MCC) with individual weighting factors is proposed and discussed to combat impulsive noise, which is denoted as the MCC-DSAF algorithm. During the iterations, the negative exponent in the Gaussian kernel of the MCC-DSAF eliminates the interference of outliers to provide a robust performance in non-Gaussian noise environments. Moreover, in order to enhance the convergence for sparse system identifications, a variant of MCC-DSAF named as improved proportionate MCC-DSAF (MCC-IPDSAF) is presented and investigated, which provides a dynamic gain assignment matrix in the MCC-DSAF to adjust the weighted values of each coefficient. Simulation results verify that the newly presented MCC-DSAF and MCC-IPDSAF algorithms are superior to the popular DSAF algorithms.
Synthetic Peptides Elicit Strong Cellular Immunity in Visceral Leishmaniasis Natural Reservoir and Contribute to Long-Lasting Polyfunctional T-Cells in BALB/c Mice
Authors: De Brito Rory Cristiane Fortes , Cardoso Jamille Mirelle de Oliveira , Reis Levi Eduardo Soares , Mathias Fernando Augusto Siqueira , Aguiar-Soares Rodrigo Dian de Oliveira , Teixeira-Carvalho Andréa , Roatt Bruno Mendes , Corrêa-Oliveira Rodrigo , Ruiz Jeronimo Conceição , Resende Daniela de Melo , Reis Alexandre Barbosa
Published: October 2019

Reverse vaccinology or immunoinformatics is a computational methodology which integrates data from in silico epitope prediction, associated to other important information as, for example, the predicted subcellular location of the proteins used in the design of the context of vaccine development. This approach has the potential to search for new targets for vaccine development in the predicted proteome of pathogenic organisms. To date, there is no effective vaccine employed in vaccination campaigns against visceral leishmaniasis (VL). For the first time, herein, an in silico, in vitro, and in vivo peptide screening was performed, and immunogenic peptides were selected to constitute VL peptide-based vaccines. Firstly, the screening of in silico potential peptides using dogs naturally infected by L. infantum was conducted and the peptides with the best performance were selected. The mentioned peptides were used to compose Cockt-1 (cocktail 1) and Cockt-2 (cocktail 2) in combination with saponin as the adjuvant. Therefore, tests for immunogenicity, polyfunctional T-cells, and the ability to induce central and effector memory in T-lymphocytes capacity in reducing the parasite load on the spleen for Cockt-1 and Cockt-2 were performed. Among the vaccines under study, Cockt-1 showed the best results, eliciting CD4+ and CD8+ polyfunctional T-cells, with a reduction in spleen parasitism that correlates to the generation of T CD4+ central memory and T CD8+ effector memory cells. In this way, our findings corroborate the use of immunoinformatics as a tool for the development of future vaccines against VL.
The Louder, the Longer: Object Length Perception Is Influenced by Loudness, but Not by Pitch
Authors: Hauck Pia , Hecht Heiko
Published: October 2019

Sound by itself can be a reliable source of information about an object’s size. For instance, we are able to estimate the size of objects merely on the basis of the sound they make when falling on the floor. Moreover, loudness and pitch are crossmodally linked to size. We investigated if sound has an effect on size estimation even in the presence of visual information, that is if the manipulation of the sound produced by a falling object influences visual length estimation. Participants watched videos of wooden dowels hitting a hard floor and estimated their lengths. Sound was manipulated by (A) increasing (decreasing) overall sound pressure level, (B) swapping sounds among the different dowel lengths, and (C) increasing (decreasing) pitch. Results showed that dowels were perceived to be longer with increased sound pressure level (SPL), but there was no effect of swapped sounds or pitch manipulation. However, in a sound-only-condition, main effects of length and pitch manipulation were found. We conclude that we are able to perceive subtle differences in the acoustic properties of impact sounds and use them to deduce object size when visual cues are eliminated. In contrast, when visual cues are available, only loudness is potent enough to exercise a crossmodal influence on length perception.
Inconsistent Relationships of Primary Consumer N Stable Isotope Values to Gradients of Sheep/Beef Farming Intensity and Flow Reduction in Streams
Authors: Lange Katharina , Townsend Colin R. , Matthaei Christoph D.
Published: October 2019

Stable isotope values of primary consumers have been proposed as indicators of human impacts on nitrogen dynamics. Until now, these values have been related only to single-stressor gradients of land-use intensity in stream ecology, whereas potential interactive effects of multiple stressors are unknown. It also remains unknown whether stable isotope values of different primary consumers show similar relationships along gradients of stressor intensities. We sampled three common invertebrate grazers along gradients of sheep/beef farming intensity (0–95% intensively managed exotic pasture) and flow reduction (0–92% streamflow abstracted for irrigation). The δ15N values of the three primary consumers differed substantially along stressor gradients. Deleatidium δ15N values were positively related to farming intensity, showing a saturation curve, whereas Physella snail δ15N values were negatively related to farming intensity and Potamopyrgus snail δ15N values showed no relationship. In addition, Deleatidium stable isotope values responded positively to flow reduction intensity, a previously unstudied variable. An antagonistic multiple-stressor interaction was detected only for the mayfly Deleatidium, which occurred in streams experiencing up to 53% farming intensity. The lack of consistency in the relationships of the most important primary consumer grazers along the studied gradients may reduce their suitability as an indicator of anthropogenic N inputs.