bluuebunny/crossref_metadata_2025 huggingface dataset:
Direct-Acting Antiviral Agents for HCV-Associated Glomerular Disease and the Current Evidence
Authors: Fabrizi Fabrizio , Cerutti Roberta , Porata Giulia , Messa Piergiorgio , Ridruejo Ezequiel
Published: October 2019

Glomerular disease is an extra-hepatic manifestation of hepatitis C virus infection (HCV) and membranoproliferative glomerulonephritis is the most frequent glomerular disease associated with HCV. It occurs commonly in patients with HCV-related mixed cryoglobulinemia syndrome. Patients with HCV-related glomerular disease have been historically a difficult-to-treat group. The therapeutic armamentarium for HCV-related glomerular disease now includes antiviral regimens, selective or non-specific immunosuppressive drugs, immunomodulators, and symptomatic agents. The treatment of HCV-associated glomerular disease is dependent on the clinical presentation of the patient. The recent introduction of all-oral, interferon (IFN)-free/ribavirin (RBV)-free regimens is dramatically changing the course of HCV in the general population, and some regimens have been approved for HCV even in patients with advanced chronic kidney disease. According to a systematic review of the medical literature, the evidence concerning the efficacy/safety of direct-acting antiviral agents (DAAs) of HCV-induced glomerular disease is limited. The frequency of sustained virological response was 92.5% (62/67). Full or partial clinical remission was demonstrated in many patients (n = 46, 68.5%) after DAAs. There were no reports of deterioration of kidney function in patients on DAAs. Many patients (n = 29, 43%) underwent immunosuppression while on DAAs. A few cases of new onset or relapsing glomerular disease in patients with HCV successfully treated with DAAs have been observed. In summary, DAA-based combinations are making easier the management of HCV. However, patients with HCV-induced glomerular disease are still a difficult-to-treat group even at the time of DAAs.
Swelling of Zein Matrix Tablets Benchmarked against HPMC and Ethylcellulose: Challenging the Matrix Performance by the Addition of Co-Excipients
Authors: Berardi Alberto , Abdel Rahim Safwan , Bisharat Lorina , Cespi Marco
Published: October 2019

Zein is an insoluble, yet swellable, biopolymer that has been extensively studied for its applications in drug delivery. Here, we screened the effect of co-excipients on the swelling and drug release of zein tablets. All throughout the study the behavior of zein was benchmarked against that of hydroxypropyl methylcellulose (HPMC) and ethylcellulose (EC). Tablets containing either zein, HPMC, or EC alone or in combination with co-excipients, namely lactose, dicalcium phosphate (DCP), microcrystalline cellulose (MCC), polyvinylpyrrolidone (PVP), or sodium lauryl sulfate (SLS) were prepared by direct compression. Matrix swelling was studied by taking continuous pictures of the tablets over 20 h, using a USB microscope connected to a PC. The overall size change and the axial and radial expansion of the tablets were automatically extrapolated from the pictures by image analysis. Moreover, drug release from tablets containing ternary mixtures of zein, co-excipients and 10% propranolol HCl was also studied. Results showed that zein matrices swelled rapidly at first, but then a plateau was reached, resulting in an initial rapid drug burst followed by slow drug release. HPMC tablets swelled to a greater extent and more gradually, providing a more constant drug release rate. EC did not practically swell, giving a nearly constant drug release pattern. Among the additives studied, only MCC increased the swelling of zein up to nearly three-fold, and thus suppressed drug burst from zein matrices and provided a nearly constant drug release over the test duration. Overall, the incorporation of co-excipients influenced the swelling behavior of zein to a greater extent compared to that of HPMC and EC, indicating that the molecular interactions of zein and additives are clearly more complex and distinct.
Human Adipose-Derived Stem Cell Secreted Extracellular Matrix Incorporated into Electrospun Poly(Lactic-co-Glycolic Acid) Nanofibrous Dressing for Enhancing Wound Healing
Authors: Tang Kao-Chun , Yang Kai-Chiang , Lin Che-Wei , Chen Yi-Kai , Lu Ting-Yu , Chen Hsien-Yeh , Cheng Nai-Chen , Yu Jiashing
Published: October 2019

Wound dressing, which prevents dehydration and provides a physical barrier against infection to wound beds, can improve wound healing. The interactions between extracellular matrix (ECM) and growth factors is critical to the healing process. Electrospun nanofibers are promising templates for wound dressings due to the structure similarity to ECM of skin. Otherwise, the ECM secreted by human adipose-derived stem cells (hASCs) is rich in growth factors known to enhance wound healing. Accordingly, we propose that the PLGA nanofibrous template incorporated with hASCs-secreted ECM may enhance wound healing. In this study, PLGA nanofibrous matrixes with an aligned or a random structure were prepared by electrospinning. Human ASCs cultured on the aligned matrix had a better viability and produced a larger amount of ECM relative to that of random one. After 7 days’ cultivation, the hASCs on aligned PLGA substrates underwent decellularization to fabricate cECM/PLGA dressings. By using immunohistochemical staining against F-actin and cell nucleus, the removal of cellular components was verified. However, the type I collagen and laminin were well preserved on the cECM/PLGA nanofibrous matrixes. In addition, this substrate was hydrophilic, with appropriate mechanical strength to act as a wound dressing. The L929 fibroblasts had good activity, survival and proliferation on the cECM/PLGA meshes. In addition, the cECM/PLGA nanofibrous dressings improved the wound healing of surgically created full-thickness skin excision in a mouse model. This hASCs-secreted ECM incorporated into electrospun PLGA nanofibrous could be a promising dressing for enhancing wound healing.
Additive Manufacturing of Nerve Decellularized Extracellular Matrix-Contained Polyurethane Conduits for Peripheral Nerve Regeneration
Authors: Chen Yi-Wen , Chen Chien-Chang , Ng Hooi Yee , Lou Ching-Wen , Chen Yueh-Sheng , Shie Ming-You
Published: October 2019

The nervous system is the part of our body that plays critical roles in the coordination of actions and sensory information as well as communication between different body parts through electrical signal transmissions. Current studies have shown that patients are likely to experience a functional loss if they have to go through a nerve repair for >15 mm lesion. The ideal treatment methodology is autologous nerve transplant, but numerous problems lie in this treatment method, such as lack of harvesting sites. Therefore, researchers are attempting to fabricate alternatives for nerve regeneration, and nerve conduit is one of the potential alternatives for nerve regeneration. In this study, we fabricated polyurethane/polydopamine/extracellular matrix (PU/PDA/ECM) nerve conduits using digital light processing (DLP) technology and assessed for its physical properties, biodegradability, cytocompatibility, neural related growth factor, and proteins secretion and expression and its potential in allowing cellular adhesion and proliferation. It was reported that PU/PDA/ECM nerve conduits were more hydrophilic and allowed enhanced cellular adhesion, proliferation, expression, and secretion of neural-related proteins (collagen I and laminin) and also enhanced expression of neurogenic proteins, such as nestin and microtubule-associated protein 2 (MAP2). In addition, PU/PDA/ECM nerve conduits were reported to be non-cytotoxic, had sustained biodegradability, and had similar physical characteristics as PU conduits. Therefore, we believed that PU/PDA/ECM nerve conduits could be a potential candidate for future nerve-related research or clinical applications.
Poplar Hot Water Extract Enhances Barrier and Antioxidant Properties of Chitosan/Bentonite Composite Film for Packaging Applications
Authors: Sun Mengya , Liu Na , Ni Shuzhen , Bian Huiyang , Fu Yingjuan , Chen Xiaoqian
Published: October 2019

Herein, the chitosan-based (CS) composite film was fabricated via a simple and efficient blending approach by adding poplar hot water extract (HWE), bentonite (BT) and chitosan. The addition of HWE largely improved the UV blocking ability and antioxidant properties of the resultant composite film, and simultaneously a tortuous path was constructed within the chitosan matrix to enhance the water vapor and oxygen barriers after the addition of BT. Specially, the content of HWE at 10 wt % gave a greatly decreased UV light transmittance at 280 nm to the CS-BT-HWE composite film that was 99.36% lower than that of CS-BT film, and the oxidation resistance was 9.65 times higher than that of CS-BT. The mechanical properties and surface morphological observation evaluated by scanning electron microscopy (SEM) and scanning probe microscope (SPM) confirmed the film had a denser structure. The internal chemical structure analyzed using solid state NMR, FTIR and X-ray spectra exhibited the resultant Maillard structure and strong hydrogen bonding that contributed to the improved mechanical properties. Overall, the as-prepared composite film has great potential as food packaging materials, and also provides a high-efficient utilization pathway for HWE.
Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery
Authors: Huryna Hanna , Cohen Yafit , Karnieli Arnon , Panov Natalya , Kustas William , Agam Nurit
Published: October 2019

A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms.
Trends in Remote Sensing Accuracy Assessment Approaches in the Context of Natural Resources
Authors: Morales-Barquero Lucia , Lyons Mitchell , Phinn Stuart , Roelfsema Chris
Published: October 2019

The utility of land cover maps for natural resources management relies on knowing the uncertainty associated with each map. The continuous advances typical of remote sensing, including the increasing availability of higher spatial and temporal resolution satellite data and data analysis capabilities, have created both opportunities and challenges for improving the application of accuracy assessment. There are well established accuracy assessment methods, but their underlying assumptions have not changed much in the last couple decades. Consequently, revisiting how map error and accuracy have been performed and reported over the last two decades is timely, to highlight areas where there is scope for better utilization of emerging opportunities. We conducted a quantitative literature review on accuracy assessment practices for mapping via remote sensing classification methods, in both terrestrial and marine environments. We performed a structured search for land and benthic cover mapping, limiting our search to journals within the remote sensing field, and papers published between 1998–2017. After an initial screening process, we assembled a database of 282 papers, and extracted and standardized information on various components of their reported accuracy assessments. We discovered that only 56% of the papers explicitly included an error matrix, and a very limited number (14%) reported overall accuracy with confidence intervals. The use of kappa continues to be standard practice, being reported in 50.4% of the literature published on or after 2012. Reference datasets used for validation were collected using a probability sampling design in 54% of the papers. For approximately 11% of the studies, the sampling design used could not be determined. No association was found between classification complexity (i.e. number of classes) and measured accuracy, independent from the size of the study area. Overall, only 32% of papers included an accuracy assessment that could be considered reproducible; that is, they included a probability-based sampling scheme to collect the reference dataset, a complete error matrix, and provided sufficient characterization of the reference datasets and sampling unit. Our findings indicate that considerable work remains to identify and adopt more statistically rigorous accuracy assessment practices to achieve transparent and comparable land and benthic cover maps.
The Effect of Mineral Sediments on Satellite Chlorophyll-a Retrievals from Line-Height Algorithms Using Red and Near-Infrared Bands
Authors: Zeng Chuiqing , Binding Caren
Published: October 2019

Red and near-infrared line-height algorithms such as the maximum chlorophyll index (MCI) are often considered optimal for remote sensing of chlorophyll-a (Chl-a) in turbid eutrophic waters, under the assumption of minimal influence from mineral sediments. This study investigated the impact of mineral turbidity on line-height algorithms using MCI as a primary example. Inherent optical properties from two turbid eutrophic lakes were used to simulate reflectance spectra. The simulated results: (1) confirmed a non-linear relationship between Chl-a and MCI; (2) suggested optimal use of the MCI at Chl-a < ~100 mg/m3 and saturation of the index at Chl-a ~300 mg/m3; (3) suggested significant variability in the MCI:Chl-a relationship due to mineral scattering, resulting in an RMSE in predicted Chl-a of ~23 mg/m3; and (4) revealed elevated Chl a retrievals and potential false positive algal bloom reports for sediment concentrations > 20 g/m3. A novel approach combining both MCI and its baseline slope, MCIslope reduced the RMSE to ~5 mg/m3. A quality flag based on MCIslope was proposed to mask erroneously high Chl-a retrievals and reduce the risk of false positive bloom reports in highly turbid waters. Observations suggest the approach may be valuable for all line-height-based Chl-a algorithms.
Editorial to Special Issue “Multispectral Image Acquisition, Processing, and Analysis”
Authors: Vozel Benoit , Lukin Vladimir , Bazi Yakoub
Published: October 2019

This Special Issue was announced in March 2018 [...]
Recent Developments of High-Resolution Chemical Imaging Systems Based on Light-Addressable Potentiometric Sensors (LAPSs)
Authors: Liang Tao , Qiu Yong , Gan Ying , Sun Jiadi , Zhou Shuqi , Wan Hao , Wang Ping
Published: October 2019

A light-addressable potentiometric sensor (LAPS) is a semiconductor electrochemical sensor based on the field-effect which detects the variation of the Nernst potential on the sensor surface, and the measurement area is defined by illumination. Thanks to its light-addressability feature, an LAPS-based chemical imaging sensor system can be developed, which can visualize the two-dimensional distribution of chemical species on the sensor surface. This sensor system has been used for the analysis of reactions and diffusions in various biochemical samples. In this review, the LAPS system set-up, including the sensor construction, sensing and substrate materials, modulated light and various measurement modes of the sensor systems are described. The recently developed technologies and the affecting factors, especially regarding the spatial resolution and temporal resolution are discussed and summarized, and the advantages and limitations of these technologies are illustrated. Finally, the further applications of LAPS-based chemical imaging sensors are discussed, where the combination with microfluidic devices is promising.
Wearables Meet IoT: Synergistic Personal Area Networks (SPANs)
Authors: Jovanov Emil
Published: October 2019

Wearable monitoring and mobile health (mHealth) revolutionized healthcare diagnostics and delivery, while the exponential increase of deployed “things” in the Internet of things (IoT) transforms our homes and industries. “Things” with embedded activity and vital sign sensors that we refer to as “smart stuff” can interact with wearable and ambient sensors. A dynamic, ad-hoc personal area network can span multiple domains and facilitate processing in synergistic personal area networks—SPANs. The synergy of information from multiple sensors can provide: (a) New information that cannot be generated from existing data alone, (b) user identification, (c) more robust assessment of physiological signals, and (d) automatic annotation of events/records. In this paper, we present possible new applications of SPANs and results of feasibility studies. Preliminary tests indicate that users interact with smart stuff—in our case, a smart water bottle—dozens of times a day and sufficiently long to collect vital signs of the users. Synergistic processing of sensors from the smartwatch and objects of everyday use may provide user identification and assessment of new parameters that individual sensors could not generate, such as pulse wave velocity (PWV) and blood pressure. As a result, SPANs facilitate seamless monitoring and annotation of vital signs dozens of times per day, every day, every time the smart object is used, without additional setup of sensors and initiation of measurements. SPANs creates a dynamic “opportunistic bubble” for ad-hoc integration with other sensors of interest around the user, wherever they go. Continuous long-term monitoring of user’s activity and vital signs can provide better diagnostic procedures and personalized feedback to motivate a proactive approach to health and wellbeing.
Fabrication, Measurement and Time Decay of the Electromagnetic Properties of Semi-Solid Water-Based Phantoms
Authors: Mendes Carlos , Peixeiro Custódio
Published: October 2019

This paper presents a complete and detailed description of the fabrication and measurement of the electromagnetic properties of water-based semi-solid phantoms with emphasis on the analysis of the time evolution of the complex permittivity of several samples stored in different conditions. A known recipe for a 2/3 muscle equivalent phantom is used as test material, and the several phantom sample properties are measured with an in-house developed coaxial probe technique. It is shown that the storing condition is of paramount importance to extend the lifetime of a given phantom. This behavior stems from the way the storing condition affects the water evaporation rate of the sample. In particular, while an unprotected sample can preserve its electromagnetic properties only for a few days, a very well-sealed one can last at least up to a year.
Measurement System for Lossy Capacitive Sensors: Application to Edible Oils Quality Assessment
Authors: Fendri Ahmed , Kallel Ahmed Yahia , Nouri Hanen , Ghariani Hamadi , Kanoun Olfa
Published: October 2019

This paper aimed to develop a portable, low-cost, and easy-to-use measurement system for oil quality degradation assessment. The main two chemical parameters affected by frying are the total polar compounds (TPC) and free fatty acids. The system should characterize the change of chemical parameters by measuring the changes in its dielectric parameters. The dielectric parameters, relative permittivity, and conductivity are measured by measuring the capacitance and resistance of a capacitive sensor dipped in oil. The main challenges are that the corresponding changes of the capacitance and resistance are very small and the presence of stray effects. For this reason, the measurement system should be able to detect changes in capacitance and resistance with high resolution and with good immunity to stray effects. The proposed measurement system is based on the conversion of impedance to voltage and time and combining, therefore, having two measurement methods in one circuit. In this way, it is possible to measure the dielectric and resistive parameters and not only the relative permittivity as was done in previous works. The results showed a strong correlation between the chemical and electrical parameters with a coefficient of determination in the range of 0.9.
A Power Spectral Density-Based Method to Detect Tremor and Tremor Intermittency in Movement Disorders
Authors: Luft Frauke , Sharifi Sarvi , Mugge Winfred , Schouten Alfred , Bour Lo , van Rootselaar Anne-Fleur , Veltink Peter , Heida Tijtske
Published: October 2019

There is no objective gold standard to detect tremors. This concerns not only the choice of the algorithm and sensors, but methods are often designed to detect tremors in one specific group of patients during the performance of a specific task. Therefore, the aim of this study is twofold. First, an objective quantitative method to detect tremor windows (TWs) in accelerometer and electromyography recordings is introduced. Second, the tremor stability index (TSI) is determined to indicate the advantage of detecting TWs prior to analysis. Ten Parkinson’s disease (PD) patients, ten essential tremor (ET) patients, and ten healthy controls (HC) performed a resting, postural and movement task. Data was split into 3-s windows, and the power spectral density was calculated for each window. The relative power around the peak frequency with respect to the power in the tremor band was used to classify the windows as either tremor or non-tremor. The method yielded a specificity of 96.45%, sensitivity of 84.84%, and accuracy of 90.80% of tremor detection. During tremors, significant differences were found between groups in all three parameters. The results suggest that the introduced method could be used to determine under which conditions and to which extent undiagnosed patients exhibit tremors.
Single-Baseline RTK Positioning Using Dual-Frequency GNSS Receivers Inside Smartphones
Authors: Dabove Paolo , Di Pietra Vincenzo
Published: October 2019

Global Navigation Satellite System (GNSS) positioning is currently a common practice thanks to the development of mobile devices such as smartphones and tablets. The possibility to obtain raw GNSS measurements, such as pseudoranges and carrier-phase, from these instruments has opened new windows towards precise positioning using smart devices. This work aims to demonstrate the positioning performances in the case of a typical single-base Real-Time Kinematic (RTK) positioning while considering two different kinds of multi-frequency and multi-constellation master stations: a typical geodetic receiver and a smartphone device. The results have shown impressive performances in terms of precision in both cases: with a geodetic receiver as the master station, the reachable precisions are several mm for all 3D components while if a smartphone is used as the master station, the best results can be obtained considering the GPS+Galileo constellations, with a precision of about 2 cm both for 2D and Up components in the case of L1+L5 frequencies, or 3 cm for 2D components and 2 cm for the Up, in the case of an L1 frequency. Moreover, it has been demonstrated that it is not feasible to reach the phase ambiguities fixing: despite this, the precisions are still good and also the obtained 3D accuracies of positioning solutions are less than 1 m. So, it is possible to affirm that these results are very promising in the direction of cooperative positioning using smartphone devices.
Damage Indexing Method for Shear Critical Tubular Reinforced Concrete Structures Based on Crack Image Analysis
Authors: Yang Yuan-Sen , Chang Chia-Hao , Wu Chiun-lin
Published: October 2019

Image analysis techniques have been applied to measure the displacements, strain field, and crack distribution of structures in the laboratory environment, and present strong potential for use in structural health monitoring applications. Compared with accelerometers, image analysis is good at monitoring area-based responses, such as crack patterns at critical regions of reinforced concrete (RC) structures. While the quantitative relationship between cracks and structural damage depends on many factors, cracks need to be detected and quantified in an automatic manner for further investigation into structural health monitoring. This work proposes a damage-indexing method by integrating an image-based crack measurement method and a crack quantification method. The image-based crack measurement method identifies cracks locations, opening widths, and orientations. Fractal dimension analysis gives the flexural cracks and shear cracks an overall damage index ranging between 0 and 1. According to the orientations of the cracks analyzed by image analysis, the cracks can be classified as either shear or flexural, and the overall damage index can be separated into shear and flexural damage indices. These damage indices not only quantify the damage of an RC structure, but also the contents of shear and flexural failures. While the engineering significance of the damage indices is structure dependent, when the damage indexing method is used for structural health monitoring, the damage indices safety thresholds can further be defined based on the structure type under consideration. Finally, this paper demonstrates this method by using the results of two experiments on RC tubular containment vessel structures.
Building a Compact Convolutional Neural Network for Embedded Intelligent Sensor Systems Using Group Sparsity and Knowledge Distillation
Authors: Cho Jungchan , Lee Minsik
Published: October 2019

As artificial intelligence (AI)- or deep-learning-based technologies become more popular, the main research interest in the field is not only on their accuracy, but also their efficiency, e.g., the ability to give immediate results on the users’ inputs. To achieve this, there have been many attempts to embed deep learning technology on intelligent sensors. However, there are still many obstacles in embedding a deep network in sensors with limited resources. Most importantly, there is an apparent trade-off between the complexity of a network and its processing time, and finding a structure with a better trade-off curve is vital for successful applications in intelligent sensors. In this paper, we propose two strategies for designing a compact deep network that maintains the required level of performance even after minimizing the computations. The first strategy is to automatically determine the number of parameters of a network by utilizing group sparsity and knowledge distillation (KD) in the training process. By doing so, KD can compensate for the possible losses in accuracy caused by enforcing sparsity. Nevertheless, a problem in applying the first strategy is the unclarity in determining the balance between the accuracy improvement due to KD and the parameter reduction by sparse regularization. To handle this balancing problem, we propose a second strategy: a feedback control mechanism based on the proportional control theory. The feedback control logic determines the amount of emphasis to be put on network sparsity during training and is controlled based on the comparative accuracy losses of the teacher and student models in the training. A surprising fact here is that this control scheme not only determines an appropriate trade-off point, but also improves the trade-off curve itself. The results of experiments on CIFAR-10, CIFAR-100, and ImageNet32 × 32 datasets show that the proposed method is effective in building a compact network while preventing performance degradation due to sparsity regularization much better than other baselines.
Analysis on the Live Coral Cover around Weizhou Island Using MODIS Data
Authors: Huang Rongyong , Zhang Huiya , Yu Kefu
Published: October 2019

Coral reefs are important as they can help to maintain ecological balance, biological resources, and species diversity on earth. However, they are globally threatened by human activities and climate change. As live coral cover (LCC) is regarded as an important measure of the health of coral reefs, analysis on LCC change associated with environmental parameters, such as chlorophyll-a concentration (Chl-a), sea surface temperature (SST), and photosynthetically active radiation (PAR), is of great value. Research on this front would help us comprehend the changes in coral reefs induced by human activities and global changes. Instead of using spasmodically in-field-measured environmental parameters, in this study, we chose to combine the successive Chl-a, SST, and PAR products of the Moderate Resolution Imaging Spectroradiometer (MODIS) with historic LCC records to establish an empirical relationship using nonlinear optimization. Thereafter, the established relationship was further used to discuss some possible developments of LCCs. According to the experiments, we concluded that the degradation of the LCC around Weizhou Island may be mainly caused by human-activity-caused eutrophication. Besides, we also showed that even if the Chl-a and the PAR can keep constant with current average levels, the corals around Weizhou Island may still be in a risk of disappearing between 2120–2140 as the SST continues to rise.
Can Complexity-Thinking Methods Contribute to Improving Occupational Safety in Industry 4.0? A Review of Safety Analysis Methods and Their Concepts
Authors: Adriaensen Arie , Decré Wilm , Pintelon Liliane
Published: October 2019

With the introduction of Industry 4.0, occupational health and safety finds itself confronted with new types of hazards. Many Industry 4.0 innovations involve increased machine intelligence. These properties make socio-technical work in Industry 4.0 applications inherently more complex. At the same time, system failure can become more opaque to its users. This paper reviews and assesses safety analysis methods as the breakdown of interaction coupling in socio-technical systems on the one hand, and the degree of failure tractability on the other hand; the latter being used as a proxy for complexity. Previous literature confirms that traditional health and safety risk assessment methods are unable or are ‘ill-equipped’ to deal with these system properties. This paper studies the need to introduce new paradigms and safety methods related to complexity thinking with theories borrowed from the study of complex adaptive systems, all to assess the arena of abruptly changing hazards introduced by Industry 4.0. At the same time, this review makes clear that there is no one-solution-fits-all method. Occupational health and safety (OHS) covers many different hazard types and will need a combination of old, new and yet-to-be-developed safety assessment methods.
Sustainability of Circular Economy Indicators and Their Impact on Economic Growth of the European Union
Authors: Busu Mihail , Trica Carmen Lenuta
Published: October 2019

In this paper, we develop a methodology for studying the sustainability of the circular economy model, based on environmental indicators, and its impact on European Union (EU) economic growth. In open-end systems, waste is converted back to materials and objects through recycling; hence, a linear economy is transformed into a circular economy (CE). Environmental factors support the argument for the sustainable implementation of a circular economy. The main objective of this paper is to analyze the sustainability of the CE indicators and to elaborate a multilinear regression model with panel data for determining the dependency of the main CE factors on EU economic growth. Starting with the model of economic growth based on circular material use rate, recycling rate of municipal waste (RRMW), trade in recycling materials, labor productivity, environmental taxes, and resource productivity as independent variables, six statistical hypotheses were validated through a multiple regression model with the use of the statistical software EViews 11. The research study was conducted for 27 EU countries, and the data was collected from the European Union Statistical Office (EUROSTAT), during the time frame 2010 to 2017. Based on econometric modeling, the paper highlights that circular economy generates sustainable economic growth across the EU.
Do Government R&D Subsidies Cultivate Enterprises’ Voluntary National/Industry Standard-Setting for Sustainable Development?
Authors: Zhang Xun , Shi Meng , Xu Biao
Published: October 2019

This study investigates the effects of government subsidies for R&D on resource and environmental enterprises’ voluntary national/industry standard- (N/IS-) setting, which is critical to sustainable development. Based on innovation motivation and capability theory, we propose a research framework and hypothesis, using a panel dataset on 11,556 Chinese resource and environmental enterprises from 2011 to 2013 to test our hypotheses. We find that government subsidies for R&D have a U-shaped relationship with N/IS-setting for sustainable development. Moreover, we also find that state ownership, position in industry, and patent properties are contingency factors suggesting that non-state-owned enterprises, industry-following enterprises, or enterprises with more patents come up with N/IS-settings of significantly greater value than state-owned enterprises, leading enterprises, or enterprises with fewer patents. Endogeneity issues are addressed by utilizing two-stage estimations with instrumental variables (IVs).
Self-Powered Bioelectrochemical Nutrient Recovery for Fertilizer Generation from Human Urine
Authors: Freguia Stefano , Logrieco Maddalena , Monetti Juliette , Ledezma Pablo , Virdis Bernardino , Tsujimura Seiya
Published: October 2019

Nutrient recovery from source-separated human urine has been identified by many as a viable avenue towards the circular economy of nutrients. Moreover, untreated (and partially treated) urine is the main anthropogenic route of environmental discharge of nutrients, most concerning for nitrogen, whose release has exceeded the planet’s own self-healing capacity. Urine contains all key macronutrients (N, P, and K) and micronutrients (S, Ca, Mg, and trace metals) needed for plant growth and is, therefore, an excellent fertilizer. However, direct reuse is not recommended in modern society due to the presence of active organic molecules and heavy metals in urine. Many systems have been proposed and tested for nutrient recovery from urine, but none so far has reached technological maturity due to usually high power or chemical requirements or the need for advanced process controls. This work is the proof of concept for the world’s first nutrient recovery system that powers itself and does not require any chemicals or process controls. This is a variation of the previously proposed microbial electrochemical Ugold process, where a novel air cathode catalyst active in urine conditions (pH 9, high ammonia) enables in situ generation of electricity in a microbial fuel cell setup, and the simultaneous harvesting of such electricity for the electrodialytic concentration of ionic nutrients into a product stream, which is free of heavy metals. The system was able to sustain electrical current densities around 3 A m–2 for over two months while simultaneously upconcentrating N and K by a factor of 1.5–1.7.
Risk of Low Productivity is Dependent on Farm Characteristics: How to Turn Poor Performance into an Advantage
Authors: Peltonen-Sainio Pirjo , Jauhiainen Lauri
Published: October 2019

The principle role of agriculture is to produce food for the increasing human population. However, the environmental footprint needs to be simultaneously reduced. Data from the Finnish Food Authority (181,108 parcels in the south-western crop production region of Finland) were used to identify the farming system, farm type, farm size, field parcel scale, physical parcel characteristics, cultivated crops, crop rotations and cultivars. Sentinel-2 derived Normalized Difference Vegetation Index (NDVI) values were used to identify fields with very low productivity. Thereby, the impacts of farm and field characteristics on risks of low NDVI values and their associated means of coping by the farmer were studied. High variations in field parcel characteristics and growth capacity were typical in the studied area. Although it is challenging for farmers, high variation can provide many opportunities for the development of multifunctional and resource-smart production systems, e.g., by optimizing land use: allocating high-quality fields for food production, and poorly performing fields for extensification, i.e., the production of environmental benefits. Many usable policy instruments are available to support such a transition, but more focus should be put onto the most efficient means to enable progress towards environmentally, economically and socially sustainable high-latitude agricultural systems.
Symmetry and Special Relativity
Authors: Friedman Yaakov , Scarr Tzvi
Published: October 2019

We explore the role of symmetry in the theory of Special Relativity. Using the symmetry of the principle of relativity and eliminating the Galilean transformations, we obtain a universally preserved speed and an invariant metric, without assuming the constancy of the speed of light. We also obtain the spacetime transformations between inertial frames depending on this speed. From experimental evidence, this universally preserved speed is c, the speed of light, and the transformations are the usual Lorentz transformations. The ball of relativistically admissible velocities is a bounded symmetric domain with respect to the group of affine automorphisms. The generators of velocity addition lead to a relativistic dynamics equation. To obtain explicit solutions for the important case of the motion of a charged particle in constant, uniform, and perpendicular electric and magnetic fields, one can take advantage of an additional symmetry—the symmetric velocities. The corresponding bounded domain is symmetric with respect to the conformal maps. This leads to explicit analytic solutions for the motion of the charged particle.
Reversible Steganographic Scheme for AMBTC-Compressed Image Based on (7,4) Hamming Code
Authors: Lin Juan , Lin Chia-Chen , Chang Chin-Chen
Published: October 2019

In recent years, compression steganography technology has attracted the attention of many scholars. Among all image compression method, absolute moment block truncation coding (AMBTC) is a simple and effective compression method. Most AMBTC-based reversible data hiding (RDH) schemes do not guarantee that the stego AMBTC compression codes can be translated by the conventional AMBTC decoder. In other words, they do not belong to Type I AMBTC-based RDH scheme and easily attract malicious users’ attention. To solve this problem and enhance the hiding capacity, we used (7,4) hamming code to design a Type I AMBTC-based RDH scheme in this paper. To provide the reversibility feature, we designed a prediction method and judgement mechanism to successfully select the embeddable blocks during the data embedding phase and data extraction and recovery phase. In comparing our approach with other BTC-based schemes, it is confirmed that our hiding capacity is increased while maintaining the limited size of the compression codes and acceptable image quality of the stego AMBTC-compressed images.
Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment with Multi-Criteria Decision Making Using Full Consistency Method
Authors: Zagradjanin Novak , Pamucar Dragan , Jovanovic Kosta
Published: October 2019

The progress in the research of various areas of robotics, artificial intelligence, and other similar scientific disciplines enabled the application of multi-robot systems in different complex environments and situations. It is necessary to elaborate the strategies regarding the path planning and paths coordination well in order to efficiently execute a global mission in common environment, prior to everything. This paper considers the multi-robot system based on the cloud technology with a high level of autonomy, which is intended for execution of tasks in a complex and crowded environment. Cloud approach shifts computation load from agents to the cloud and provides powerful processing capabilities to the multi-robot system. The proposed concept uses a multi-robot path planning algorithm that can operate in an environment that is unknown in advance. With the aim of improving the efficiency of path planning, the implementation of Multi-criteria decision making (MCDM) while using Full consistency method (FUCOM) is proposed. FUCOM guarantees the consistent determination of the weights of factors affecting the robots motion to be symmetric or asymmetric, with respect to the mission specificity that requires the management of multiple risks arising from different sources, optimizing the global cost map in that way.
Advanced Proteomics as a Powerful Tool for Studying Toxins of Human Bacterial Pathogens
Authors: Duport Catherine , Alpha-Bazin Béatrice , Armengaud Jean
Published: October 2019

Exotoxins contribute to the infectious processes of many bacterial pathogens, mainly by causing host tissue damages. The production of exotoxins varies according to the bacterial species. Recent advances in proteomics revealed that pathogenic bacteria are capable of simultaneously producing more than a dozen exotoxins. Interestingly, these toxins may be subject to post-transcriptional modifications in response to environmental conditions. In this review, we give an outline of different bacterial exotoxins and their mechanism of action. We also report how proteomics contributed to immense progress in the study of toxinogenic potential of pathogenic bacteria over the last two decades.
Water Governance in India: Evidence on Water Law, Policy, and Administration from Eight Indian States
Authors: Ahmed Masood , Araral Eduardo
Published: October 2019

Water governance is a widely acknowledged as an important factor for sustainable development. This study attempts to assess if the water governance in eight Indian states has improved after the announcement of the United Nations Sustainable Development Goals (SDGs). We utilize the Institutional Decomposition and Analysis (IDA) framework developed by Saleth and Dinar. Using survey data from two periods and 152 respondents, we calculate the scores on 17 indicators of water law, policy, and administration. We found that average scores on water law, policy, and administration have increased between the first and the second survey. We attributed this improvement to the fact that the Indian government is paying special attention to the provision of clean drinking water after the announcement of the SDGs. We also calculated nominal and weighted water governance indices (WGIs) for eight Indian states. This study made an important contribution to the existing water governance literature by capturing improvements in the water governance scores of eight Indian states after the announcement of the SDGs.
Quantifying the Uncertainty Related to Climate Change in the Assessment of Urban Flooding—A Case Study
Authors: Liuzzo Lorena , Freni Gabriele
Published: October 2019

Recent studies have pointed out that climate change is likely to have important implications on the extent and frequency of flooding events. Indeed, the intensification of the water cycle occurring in different areas of the world can dramatically affect the incidence of extreme events and, consequently, the flow in rivers or artificial channels, increasing the probability of disastrous floods. In this context, the criteria for the assessment of flood risk need to be improved to take into account the variability of rainfall due to climate change. In this study, a Bayesian procedure was used to update the parameters of the depth–duration–frequency (DDF) curves and quantify the uncertainty related to their assessment in some climate change scenarios. The critical storm obtained from these updated DDF curves was used as input for the FLO-2D hydraulic model, in order to investigate the effects of climate change on flood risk. The area of study was an urban catchment in Piazza Armerina, a small town located in Southern Italy. Results showed that rainfall variations remarkably affect not only the magnitude of flood events, but also the flood susceptibility of the study area.
Accounting for Economic Factors in Socio-Hydrology: Optimization under Uncertainty and Climate Change
Authors: Abadie Luis M. , Markandya Anil , Neumann Marc B.
Published: October 2019

This paper reframes the socio-hydrology analysis as an optimization problem. To achieve this, we first develop a valuation scheme to estimate net benefits of development in a flood plain, consisting of benefits obtained from land and housing, less the costs of flood management and flood damage. Then we look for an optimal safety factor for the levee heightening strategy within the ‘Technosociety’ scenario for a given time series of future flood events. This is further extended to finding an optimal strategy in the case of uncertainty concerning flood timings and intensities. We suggest an approach for both stationary and non-stationary evolution of flood dynamics and examine how the levee heightening strategy is affected by the magnitude of climate change. We find that the preferred management option depends strongly on the value of the land services the area provides.
Long-Term Study of Soluble Reactive Phosphorus Concentration in Fall Creek and Comparison to Northeastern Tributaries of Cayuga Lake, NY: Implications for Watershed Monitoring and Management
Authors: O’Leary Niamh , Johnston Roxanna , Gardner Erica L. , Penningroth Stephen M. , Bouldin David R.
Published: October 2019

This study focuses on soluble reactive phosphorus (SRP), a key driver of eutrophication worldwide and a potential contributor to the emerging global environmental problem of harmful algal blooms (HABs). Two studies of tributary SRP concentrations were undertaken in sub-watersheds of Cayuga Lake, NY, the subject of a total maximum daily load (TMDL) development process, due to phosphorus impairment of its southern shelf. The long-term study compared SRP concentration in Fall Creek in the 1970s with that in the first decade of the 2000s, thus spanning a period of change in phosphorus sources, as well as in regional climate. The spatial study used data collected between 2009 and 2018 and compared SRP concentrations in Fall Creek to levels in northeastern tributaries that flow into the lake close to areas where HABs have been problematic. SRP was measured using standard procedures. Flow-weighted mean SRP concentration ranged between 15.0 µg/L and 30.0 µg/L in all years studied in both the 1970s and 2000s, with the exception of 2010. Annual discharge in Fall Creek showed no trend between 1970 and 2018, but a higher proportion of high streamflow samples was captured in the 2000s compared to the 1970s, which resulted in proportionally increased SRP concentration in the latter time period. There was no significant difference in the SRP concentration—flow rate relationship between the two time periods. Adjusted for flow rate, SRP concentrations in Fall Creek have not changed over many decades. Increasing phosphorus contributions from growing population and urbanization since the 1970s may have been counterbalanced by improvements in wastewater treatment and agricultural practices. Mean SRP concentration in northeastern tributaries was significantly (p < 0.001) higher than in Fall Creek, likely reflecting more intense agricultural use and higher septic system density in the watersheds of the former. This finding justifies continued monitoring of minor northern tributaries. Future monitoring must emphasize the capture of high flow conditions. Historical stability and highly variable hydrology will slow the watershed response to management and confound the ability to detect changes attributable to decreased phosphorus inputs. Large scale monitoring on decadal timescales will be necessary to facilitate watershed management.