bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantification Methods for Textile-Adhered Bacteria: Extraction, Colorimetric, and Microscopic Analysis
Authors: Hemmatian Tahmineh , Kim Jooyoun
Published: October 2019

Quantification of bacteria adhered on porous, multi-layered fibers is a challenging task. The goal of this study is to compare different assessment procedures on counting textile-adhered bacteria, and to guide relevant analytical techniques. Three different methods were compared in measuring the amount of Escherichia coli (E. coli) adhered to polymeric film and fibrous nonwovens. In the extraction method, the adhered bacteria were released with the assistance of surfactant/enzyme, where the measurement was rather reproducible. For colorimetric method, stained bacteria enabled direct visualization without needing to detach cells from the surface, yet the linearity of color absorbency to cell counts was limited. The microscopic analysis provided direct observation of bacterial distribution over the surface, but accurate quantification was not possible for porous, fibrous surfaces. This study intends to help choosing a suitable test method to accurately quantify the textile-adhered bacteria, as well as broadly impact the research on anti-bioadhesive surfaces.
Aminolysis of Various Aliphatic Polyesters in a Form of Nanofibers and Films
Authors: Jeznach Oliwia , Kolbuk Dorota , Sajkiewicz Paweł
Published: October 2019

Surface functionalization of polymer scaffolds is a method used to improve interactions of materials with cells. A frequently used method for polyesters is aminolysis reaction, which introduces free amine groups on the surface. In this study, nanofibrous scaffolds and films of three different polyesters–polycaprolactone (PCL), poly(lactide-co-caprolactone) (PLCL), and poly(l-lactide) (PLLA) were subjected to this type of surface modification under the same conditions. Efficiency of aminolysis was evaluated on the basis of ninhydrin tests and ATR–FTIR spectroscopy. Also, impact of this treatment on the mechanical properties, crystallinity, and wettability of polyesters was compared and discussed from the perspective of aminolysis efficiency. It was shown that aminolysis is less efficient in the case of nanofibers, particularly for PCL nanofibers. Our hypothesis based on the fundamentals of classical high speed spinning process is that the lower efficiency of aminolysis in the case of nanofibers is associated with the radial distribution of crystallinity of electrospun fiber with more crystalline skin, strongly inhibiting the reaction. Moreover, the water contact angle results demonstrate that the effect of free amino groups on wettability is very different depending on the type and the form of polymer. The results of this study can help to understand fundamentals of aminolysis-based surface modification.
Engineering Reactive Clay Systems by Ground Rubber Replacement and Polyacrylamide Treatment
Authors: Soltani Amin , Deng An , Taheri Abbas , O’Kelly Brendan C.
Published: October 2019

This study investigates the combined performance of ground rubber (GR), the additive, and polyacrylamide (PAM), the binder, as a sustainable solution towards ameliorating the inferior geotechnical attributes of an expansive clay. The first phase of the experimental program examined the effects of PAM concentration on the soil’s mechanical properties—consistency, sediment volume attributes, compactability, unconfined compressive strength (UCS), reactivity and microstructure features. The second phase investigated the effects of GR content, with and without the optimum PAM concentration. An increase in PAM beyond 0.2 g/L, the identified optimum concentration, caused the excess PAM to act as a lubricant rather than a flocculant. This feature facilitated reduced overall resistance to sliding of soil particles relative to each other, thereby adversely influencing the improvement in stress–strain–strength response achieved for ≤0.2 g/L PAM. This transitional mechanism was further verified by the consistency limits and sediment volume properties, both of which exhibited only minor variations beyond 0.2 g/L PAM. The greater the GR content, the higher the mobilized UCS up to 10% GR, beyond which the dominant GR-to-GR interaction (i.e., rubber-clustering) adversely influenced the stress–strain–strength response. Reduction in the soil’s swell–shrink capacity, however, was consistently in favor of higher GR contents. Addition of PAM to the GR-blended samples amended the soil aggregate–GR connection interface, thereby achieving further improvements in the soil’s UCS and volume change behaviors. A maximum GR content of 20%, paired with 0.2 g/L PAM, managed to satisfy a major decrease in the swell–shrink capacity while improving the strength-related features, and thus was deemed as the optimum choice.
Achieving Ultrahigh Cycling Stability and Extended Potential Window for Supercapacitors through Asymmetric Combination of Conductive Polymer Nanocomposite and Activated Carbon
Authors: Gul Hajera , Shah Anwar-ul-Haq Ali , Bilal Salma
Published: October 2019

Conducting polymers and carbon-based materials such as graphene oxide (GO) and activated carbon (AC) are the most promising capacitive materials, though both offer charge storage through different mechanisms. However, their combination can lead to some unusual results, offering improvement in certain properties in comparison with the individual materials. Cycling stability of supercapacitors devices is often a matter of concern, and extensive research is underway to improve this phenomena of supercapacitive devices. Herein, a high-performance asymmetric supercapacitor device was fabricated using graphene oxide–polyaniline (GO@PANI) nanocomposite as positive electrode and activated carbon (AC) as negative electrode. The device showed 142 F g−1 specific capacitance at 1 A g−1 current density with capacitance retention of 73.94% at higher current density (10 A g−1). Most importantly, the device exhibited very high electrochemical cycling stability. It retained 118.6% specific capacitance of the starting value after 10,000 cycles at 3 Ag−1 and with coulombic efficiency of 98.06 %, indicating great potential for practical applications. Very small solution resistance (Rs, 0.640 Ω) and charge transfer resistance (Rct, 0.200 Ω) were observed hinting efficient charge transfer and fast ion diffusion. Due to asymmetric combination, potential window was extended to 1.2 V in aqueous electrolyte, as a result higher energy density (28.5 Wh kg−1) and power density of 2503 W kg−1 were achieved at the current density 1 Ag−1. It also showed an aerial capacitance of 57 mF cm−2 at current 3.2 mA cm−2. At this current density, its energy density was maximum (0.92 mWh cm−2) with power density (10.47 W cm−2).
Nucleation and Crystallization of PA6 Composites Prepared by T-RTM: Effects of Carbon and Glass Fiber Loading
Authors: Zaldua Nerea , Maiz Jon , de la Calle Amaia , García-Arrieta Sonia , Elizetxea Cristina , Harismendy Isabel , Tercjak Agnieszka , Müller Alejandro J.
Published: October 2019

Thermoplastic resin transfer molding (T-RTM) is attracting much attention due to the need for recyclable alternatives to thermoset materials. In this work, we have prepared polyamide-6 (PA6) and PA6/fiber composites by T-RTM of caprolactam. Glass and carbon fibers were employed in a fixed amount of 60 and 47 wt.%, respectively. Neat PA6 and PA6 matrices (of PA6-GF and PA6-CF) of approximately 200 kg/mol were obtained with conversion ratios exceeding 95%. Both carbon fibers (CF) and glass fibers (GF) were able to nucleate PA6, with efficiencies of 44% and 26%, respectively. The α crystal polymorph of PA6 was present in all samples. The lamellar spacing, lamellar thickness and crystallinity degree did not show significant variations in the samples with or without fibers as result of the slow cooling process applied during T-RTM. The overall isothermal crystallization rate decreased in the order: PA6-CF > PA6-GF > neat PA6, as a consequence of the different nucleation efficiencies. The overall crystallization kinetics data were successfully described by the Avrami equation. The lamellar stack morphology observed by atomic force microscopy (AFM) is consistent with 2D superstructural aggregates (n = 2) for all samples. Finally, the reinforcement effect of fibers was larger than one order of magnitude in the values of elastic modulus and tensile strength.
Sea Surface Temperature in Global Analyses: Gains from the Copernicus Imaging Microwave Radiometer
Authors: Pearson Kevin , Good Simon , Merchant Christopher J. , Prigent Catherine , Embury Owen , Donlon Craig
Published: October 2019

Sea surface temperatures (SSTs) derived from passive microwave (PMW) observations benefit global ocean and SST analyses because of their near-all-weather availability. Present PMW SSTs have a real aperture-limited spatial resolution in excess of 50 km, limiting the spatial fidelity with which SST features, reflecting ocean dynamics, can be captured. This contrasts with the target resolution of global analyses of 5 to 10 km. The Copernicus Imaging Microwave Radiometer (CIMR) is a mission concept under consideration as a high-priority candidate mission for the expansion of the Copernicus space programme. This instrument would be capable of real aperture resolution < 15 km with low total uncertainties in the range 0.4–0.8 K for channels between 1.4 and 36.5 GHz, and a dual-view arrangement that further reduces noise. This paper provides a comparative study of SST uncertainty and feature resolution with and without the availability of CIMR in the future SST-observing satellite constellation based on a detailed simulation of CIMR plus infrared observations and the processing of global SST analyses with 0.05° final grid resolution. Simulations of CIMR data including structured errors were added to an observing system consisting of the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel-3A and the Advanced Very High Resolution Radiometer (AVHRR) on MetOp-A. This resulted in a large improvement in the global root-mean-square error (RMSE) for SST from 0.37 K to 0.21 K for January and 0.40 K to 0.25 K for July. There was a particularly noticeable improvement in the performance of the analysis, as measured by the reduction in RMSE, for dynamical and persistently cloudy areas. Of these, the Aghulas Current showed an improvement of 43% in January and 48% in July, the Gulf Stream showed 70% and 44% improvements, the Southern Ocean showed 57% and 74% improvements, and the Maritime Continent showed 50% and 40% improvements, respectively.
Evaluation of Approaches for Mapping Tidal Wetlands of the Chesapeake and Delaware Bays
Authors: Lamb Brian T. , Tzortziou Maria A. , McDonald Kyle C.
Published: October 2019

The spatial extent and vegetation characteristics of tidal wetlands and their change are among the biggest unknowns and largest sources of uncertainty in modeling ecosystem processes and services at the land-ocean interface. Using a combination of moderate-high spatial resolution (≤30 meters) optical and synthetic aperture radar (SAR) satellite imagery, we evaluated several approaches for mapping and characterization of wetlands of the Chesapeake and Delaware Bays. Sentinel-1A, Phased Array type L-band Synthetic Aperture Radar (PALSAR), PALSAR-2, Sentinel-2A, and Landsat 8 imagery were used to map wetlands, with an emphasis on mapping tidal marshes, inundation extents, and functional vegetation classes (persistent vs. non-persistent). We performed initial characterizations at three target wetlands study sites with distinct geomorphologies, hydrologic characteristics, and vegetation communities. We used findings from these target wetlands study sites to inform the selection of timeseries satellite imagery for a regional scale random forest-based classification of wetlands in the Chesapeake and Delaware Bays. Acquisition of satellite imagery, raster manipulations, and timeseries analyses were performed using Google Earth Engine. Random forest classifications were performed using the R programming language. In our regional scale classification, estuarine emergent wetlands were mapped with a producer’s accuracy greater than 88% and a user’s accuracy greater than 83%. Within target wetland sites, functional classes of vegetation were mapped with over 90% user’s and producer’s accuracy for all classes, and greater than 95% accuracy overall. The use of multitemporal SAR and multitemporal optical imagery discussed here provides a straightforward yet powerful approach for accurately mapping tidal freshwater wetlands through identification of non-persistent vegetation, as well as for mapping estuarine emergent wetlands, with direct applications to the improved management of coastal wetlands.
Railway Ballast Monitoring by GPR: A Test-Site Investigation
Authors: Bianchini Ciampoli Luca , Calvi Alessandro , D’Amico Fabrizio
Published: October 2019

Effective maintenance of railways requires a comprehensive assessment of the actual condition of the construction materials involved. In this regard, Ground-Penetrating Radar (GPR) stands as a viable alternative to the invasive and time-consuming traditional techniques for the inspection of these infrastructures. This work reports the experimental activities carried out on a test-site area within a railway depot in Rome, Italy. To this purpose, a 30 m-long railway section was divided into ten sub-sections reproducing different various physical and structural conditions of the track-bed. For more detail, combinations of varying scenarios of fragmentation and fouling of the ballast were reproduced. The set-up was then investigated using different multi-frequency GPR horn antenna systems. The effects of the different physical conditions of ballast on the electromagnetic response of the material were analysed for each scenario using time- and frequency-domain signal processing techniques. Parallel to this, modelling was provided to estimate fouling content. Interpretation of results has proven the viability of the GPR method in detecting signs of decay at the network level, thereby proving this technique to be worthy of implementation in asset management systems.
Estimation of Usable Area of Flat-Roof Residential Buildings Using Topographic Data with Machine Learning Methods
Authors: Dawid Leszek , Tomza Michał , Dawid Anna
Published: October 2019

The real estate appraisal largely consists of estimating the property’s value based on the transaction prices of similar buildings with the usable area being one of the main comparative units. A Polish appraiser finds data mentioned in the Price and Value Register (PVR). However, one of the authors’ previous studies indicated that the PVR contained highly incomplete information on usable area of residential buildings rendering it impractical for real estate appraisal purposes. Here, we propose a machine learning method to estimate the usable area of flat-roof residential buildings based on Light Detection and Ranging (LiDAR) data as well as the Database of Topographic Objects (BDOT10k). First, we train models with different architectures on the exact project data of residential buildings available online, obtained mostly from the design offices Lipińscy and Archon. Then, we apply trained algorithms on available residential building in Koszalin, Poland, using BDOT10k and LoD1 standard LiDAR data, and compare the results with usable area reported in PVR. Results show that the usable area of flat-roof houses without garages and extensions can be calculated with great accuracy up to 4%, while for more complex flat-roof buildings-up to 4–10%, depending on how detailed data are available. The model may be used by real estate appraisers to approximate the unknown usable area of residential buildings with known transaction prices, and as such increase the number of properties that can be compared to the evaluated real estate. To estimate the usable area of buildings with more complex roofs, a higher standard of LiDAR data is needed.
Surface Heterogeneity-Involved Estimation of Sample Size for Accuracy Assessment of Land Cover Product from Satellite Imagery
Authors: Ren Huiqun , Cai Guoyin , Du Mingyi
Published: October 2019

Sample size estimation is a key issue for validating land cover products derived from satellite images. Based on the fact that present sample size estimation methods account for the characteristics of the Earth’s subsurface, this study developed a model for estimating sample size by considering the scale effect and surface heterogeneity. First, we introduced a watershed with different areas to indicate the scale effect on the sample size. Then, by employing an all-subsets regression feature selection method, three landscape indicators describing the aggregation and diversity of the land cover patches were selected (from 14 indicators) as the main factors for indicating the surface heterogeneity. Finally, we developed a multi-level linear model for sample size estimation using explanatory variables, including the estimated sample size (n) calculated from the traditional statistical model, size of the test region, and three landscape indicators. As reference data for developing this model, we employed a case study in the Jiangxi Province using a 30 m spatial resolution global land cover product (Globeland30) from 2010 as a classified map, and national 30 m land use/cover change (LUCC) data from 2010 in China. The results showed that the adjusted square coefficient of R2 is 0.79, indicating that the joint explanatory ability of all predictive variables in the model to the sample size is 79%. This means that the predictability of this model is at a good level. By comparing the sample size NS obtained by the developed multi-level linear model and n as calculated from the statistics model, we find that NS is much smaller than n, which mainly contributes to the concerns regarding surface heterogeneity in this study. The validity of the established model is tested and is proven as effective in the Anhui Province. This indicates that the estimated sample size from considering the scale effect and spatial heterogeneity in this study achieved the same accuracy as that calculated from a probability statistical model, while simultaneously saving more time, labour, and money in the accuracy assessment of a land cover dataset.
From Eco-Civilization to City Branding: A Neo-Marxist Perspective of Sustainable Urbanization in China
Authors: de Jong Martin
Published: October 2019

While the national discourse on ‘eco-civilization’ drives conceptual thinking on sustainable urban development in China, in practice a systematic implementation gap appears to exist when it comes to local implementation. This paper examines how the leakage occurring in the trajectory from central government ideas to municipal and district level construction projects can be explained. More specifically, it aims to spot whether it is merely the result of mismatches resulting from ineffective interactions among players in the policy process, institutional misalignment between policy goals and policy instruments, or even if the mobilization of bias in the policy process is systematic enough to justify a neo-Marxist explanation of the abovementioned implementation gap. I found two main sources of structural bias: capital accumulation in the mechanisms for urban development and power accumulation in the mechanisms underlying the administrative process.
Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method
Authors: Do Myungsik , Byun Wanhee , Shin Doh Kyoum , Jin Hyeryun
Published: October 2019

It is common to call a taxi by taxi-apps in Korea and it was believed that an app-taxi service would provide customers with more convenience. However, customers’ requests can often be denied, as taxi drivers can decide whether to take calls from customers or not. Therefore, studies on factors that determine whether taxi drivers refuse or accept calls from customers are needed. This study investigated why taxi drivers might refuse calls from customers and factors that influence the success of matching within the service. This study used origin-destination data in Seoul and Daejeon obtained from T-map Taxis, which was analyzed via a decision tree using machine learning. Cross-validation was also performed. Results showed that distance, socio-economic features, and land uses affected matching success rate. Furthermore, distance was the most important factor in both Seoul and Daejeon. The matching success rate in Seoul was lowest for trips shorter than the average at midnight. In Daejeon, the rate was lowest when the calls were made for trips either shorter or longer than the average distance. This study showed that the matching success for ride-hailing services can be differentiated particularly by the distance of the requested trip depending on the size of the city.
Use of GIS Tools in Sustainable Heritage Management—The Importance of Data Generalization in Spatial Modeling
Authors: Ciski Mateusz , Rząsa Krzysztof , Ogryzek Marek
Published: October 2019

Cultural heritage is a very important element affecting the sustainable development. To analyze the various forms of spatial management inscribed into sustainable development, information on the location of objects and their concentration at specific areas is necessary. The main goal of the article was to show the possibility of using various GIS tools in modeling the distribution of historical objects. For spatial analysis, it is optimal to use the point location of objects. Often, however, it is extremely difficult, laborious, expensive, and sometimes impossible to obtain. Thus, various map content generalizations were analyzed in the article; the main goal was to find the level for which the data with an acceptable loss of accuracy can be generalized. Such analyses can be extremely useful in sustainable heritage management. Article also shows how cultural heritage fits into the sustainable heritage management. The research included non-movable monuments in Poland. The obtained results showed the universality of this type of research both in the thematic sense (can be used for various types of objects) and spatial sense (can be performed locally, at the country level, or even at the continental level).
A Novel Decision-Making Model with Pythagorean Fuzzy Linguistic Information Measures and Its Application to a Sustainable Blockchain Product Assessment Problem
Authors: Jin Feifei , Pei Lidan , Chen Huayou , Langari Reza , Liu Jinpei
Published: October 2019

This study presents a novel multi-attribute decision-making (MADM) model on the basis of Pythagorean fuzzy linguistic information measures. To do so, we first present a new concept of Pythagorean fuzzy linguistic sets to describe fuzziness and inconsistent information, in which the Pythagorean fuzzy linguistic values (PFLVs) are represented by the linguistic membership degree and linguistic non-membership degree. Then, we introduce two axiomatic definitions of information measures for PFLVs, including Pythagorean fuzzy linguistic entropy and the Pythagorean fuzzy linguistic similarity measure, to measure the uncertainty degree of PFLVs and the similarity degree between among PFLVs. In addition, based on the logarithmic function, we construct two new information measure formulas and verify that they satisfy the axiomatic conditions of the Pythagorean fuzzy linguistic entropy and similarity measure, respectively. We further explore the relationship between the Pythagorean fuzzy linguistic entropy and similarity measure. Finally, we present a novel Pythagorean fuzzy linguistic MADM model with the Pythagorean fuzzy linguistic entropy and similarity measure. A numerical example of selecting the most desirable sustainable blockchain product is given, and a comparison with the existing approach was performed to validate the reliability of the developed decision-making model.
Interrelations between the Media and Architecture: Contribution to Sustainable Development and the Conservation of Urban Spaces
Authors: Monserrat-Gauchi Juan , Novo-Domínguez Manuel , Torres-Valdés Rosa
Published: October 2019

The pace of architectural construction in the Western world today involves exploitation of environmental and natural resources that is unsustainable in the long term. The purpose of this article is to introduce a line of research on urban and industrial architecture, its use and its ultimate purpose in terms of social development and sustainability. We offer an overview of the main architectural movements that advocate the rational use of resources and of existing urban architectural constructions, and we explore the role of governments and the media in the use of architectural spaces. Various cases are considered, mostly in Spain, of derelict architectural spaces which, thanks to interventions by artists or major brands, have been given new uses with a view to contributing to a more sustainable world.
Using Big Data to Measure Tourist Sustainability: Myth or Reality?
Authors: Pérez Guilarte Yamilé , Barreiro Quintáns Daniel
Published: October 2019

The concern about the production of international standards to measure the sustainability of tourism is present today, especially the discourse on the introduction of new sources. This article aims to survey and describe the main approaches and methodologies to use big data to measure tourism sustainability. Successful cases are addressed by explaining the main opportunities and challenges for the creation of official tourist statistics. A comprehensive review of publications regarding this field was carried out by applying the systematic literature review technique. This contributes a knowledge base to destination management organisations to encourage the implementation of official tourism statistics systems using big data.
Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques
Authors: Mahmoud Ahmed Abdulhamid , Elkatatny Salaheldin , Ali Abdulwahab Z. , Abouelresh Mohamed , Abdulraheem Abdulazeez
Published: October 2019

Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation.
Geospatial Dashboards for Monitoring Smart City Performance
Authors: Jing Changfeng , Du Mingyi , Li Songnian , Liu Siyuan
Published: October 2019

Geospatial dashboards have attracted increasing attention from both user communities and academic researchers since the late 1990s. Dashboards can gather, visualize, analyze and advise on urban performance to support sustainable development of smart cities. We conducted a critical review of the research and development of geospatial dashboards, including the integration of maps, spatial data analytics, and geographic visualization for decision support and real-time monitoring of smart city performance. The research about this kind of system has mainly focused on indicators, information models including statistical models and geospatial models, and other related issues. This paper presents an overview of dashboard history and key technologies and applications in smart cities, and summarizes major research progress and representative developments by analyzing their key technical issues. Based on the review, we discuss the visualization model and validity of models for decision support and real-time monitoring that need to be further researched, and recommend some future research directions.
Nonuniform Woven Solar Shading Screens: Shading, Mechanical, and Daylighting Performance
Authors: Lu Yao , Lin Hankun , Liu Siwei , Xiao Yiqiang
Published: October 2019

This study investigated the potential of using a nonuniform woven panel with nonuniform strips—thick sticks and thin battens—as an external solar shading screen that addressed daylighting, shading, and mechanical performance factors. The sustainable material, namely, bamboo, was used as the demonstration material for the screen. An on-site experiment and ANSYS simulation were carried out to investigate the basic solar optical performance and structural strength of the proposed screen, respectively. Then, a series of daylighting simulations were conducted to optimize the configuration of the screen. The results showed that the nonuniform woven solar shading screen reduced up to 80.3% of the solar radiation gain in a room during summer months while ensuring a relatively even distribution of useful daylight during the year. Moreover, the screen effectively reduced the negative impact of glare to a level below “imperceptible” and enabled a relatively clear view through the window and shading. Regarding the structural strength, the screen with a size smaller than or equal to 1 × 1 m withstood a wind load of 12 m/s. Furthermore, this study proposed two optimal configurations: a screen woven of square sticks and battens with a distance of 10 mm between them, and a screen woven of round sticks and battens with a distance of 8 mm between them. This study illustrated the superiority of the nonuniform woven solar shading screens, which supports a wider application of solar shading screens made of other materials with similar structures and reflectance values.
Relationship between Corporate Sustainability and Compliance with State-Owned Enterprises in Central-Europe: A Case Study from Hungary
Authors: Boros Anita , Fogarassy Csaba
Published: October 2019

Our study deals with the control of Hungarian state-owned business associations in order to find out whether there is any correlation between corporate sustainability and compliance. According to our hypothesis, the state has a greater responsibility for the sustainable operation of state-owned enterprises—one of the tools of which can be the efficient construction of so-called compliance controls. A state-owned enterprise can be sustained in its operation and function by doing what it has been assigned to it as a task. The sustainable operation can be achieved through the use of circular feedback and continuous control. Corporate sustainability can be influenced by a number of factors that are crucial to the integrity and adequacy of companies. In our study, these are the Initial Hazard Factors (IHFs), Hazardous Increasing Factors (HIFs), and Control Enhancement Factors (CEFs), which were used in indexed form for testing. For the specific analysis, we used the Analytical Hierarchy Process (AHP) method to rank and evaluate risk avoidance options. We analyzed the practice of the State Audit Office of Hungary and its results and found that in the case of state-owned enterprises, the current Hungarian legal system does not contain uniform normative regulations that would regulate the control of conformity in a broader sense. As a result, corporate integrity and corporate compliance are also subject to a fragmented set of rules in different jurisdictions. This has a negative impact on the development of the company’s long-term, sustainable operating principles. Based on our research, a number of factors have been identified (including enterprise size and intensive use of EU funding resources) that may result in a loss of corporate sustainability but can be mitigated or even eliminated by creating an effective internal control environment. Based on literature analysis, most of the Hungarian phenomena are typical of the former socialist countries of Central Europe. The interpretation of sustainability—in transition management countries—for first-generation enterprises which were developed in a socialist market economy is quite different from the most developed countries of the European Union. The main reason for this is that generational rules do not yet exist.
Sustainable Performance of Buildings through Modular Prefabrication in the Construction Phase: A Comparative Study
Authors: Jiang Yongsheng , Zhao Dong , Wang Dedong , Xing Yudong
Published: October 2019

Prefabrication has been considered an effective alternative to conventional building. It has gained an increasing amount of attention over the last few decades as a way to advance sustainable construction. This study focused on the construction stage to compare sustainable performances of prefabrication with conventional building. Sixteen indicators were extracted from a literature review and specialist interviews to conduct a sustainability evaluation. A survey was delivered to developers, designers, superintendents, manufacturers, and contractors of a Chinese case project. A total of 51 valid responses were collected. Projection pursuit (PP), based on the real-coded accelerating genetic algorithm (RAGA), was employed to evaluate the sustainability of prefabrication. The results showed that there was a consensus among the participants that prefabrication has more obvious sustainable performances in the construction phase compared with conventional building. However, cognitive differences among the participants regarding the sustainability of prefabrication were also pronounced. The findings presented in this paper may assist the government to propose feasible policies and measures to promote the development of prefabrication in China.
How Fast Is Europe Getting Old? Analysis of Dynamics Applying the Spatial Shift–Share Approach
Authors: Antczak Elżbieta , Lewandowska-Gwarda Karolina
Published: October 2019

In this article, we analyzed the dynamics of the population aging process in Europe. The study was conducted on the basis of statistical data on the number of people aged 65 and above per 1000 of the population in 32 European countries in the years 1991–2018. The analyses also took into account the structure of the population by gender in five age groups: 65–69, 70–74, 75–79, 80–84, and 85 and above. An extensive analysis of the rate of changes in the magnitude of the phenomenon was carried out, which gave an answer to the question about how quickly Europe is aging. We applied the spatial dynamic shift–share method. The spatial variant of the method allowed, among others, indicating countries where the pace of population aging in a specific age group was faster/slower than in locations neighboring the examined country. Specific regions characterized by the fastest population aging were also indicated, and shares of structural and sectoral factors of the changes were estimated. Furthermore, based on the values of local competitiveness indicators, regions were identified where the aging of the population decelerated or accelerated the phenomenon in neighboring countries in the study period.
Three-Dimensional Urban Expansion Analysis of Valley-Type Cities: A Case Study of Chengguan District, Lanzhou, China
Authors: He Sijia , Wang Xiaoyun , Dong Jingru , Wei Baocheng , Duan Hanming , Jiao Jizong , Xie Yaowen
Published: October 2019

The development of cities in the vertical dimension is important in valley-type cities where physical growth is limited by terrain. However, little research has focused on three-dimensional urban expansion of valley-type cities. Lanzhou is a typical valley-type city in China and Chengguan District is the core area of Lanzhou City. This research is aimed at understanding the development of valley-type cities through the analysis of the three-dimensional urban expansion of Lanzhou Chengguan District and providing a reference for urban planning. We extracted five periods of architectural contours and height information between 1975 to 2018 with the support of multi-source remote sensing and network data. We used overlay analysis and mathematical statistical methods to analyze urban horizontal expansion and used the building density, floor area ratio, vertical expansion speed, fluctuation degree, and skyline to analyze urban vertical expansion. We found that the mode of horizontal expansion of Chengguan District shifted from adjacency to enclave through mountain area reclamation. The area with the fastest vertical expansion speed first appeared in the horizontal expansion completed area, and then in both the rapid horizontal expansion area and in the horizontal expansion completed area. Before 2007, the speed of horizontal expansion increased and reached its peak while the vertical expansion speed was relatively stable. After that, the former decreased, and the vertical expansion increased rapidly and dominated the urban development. The vertical expansion of the valley-type city gradually dominates urban development. Urban planning should consider the three-dimensional expansion, especially in the vertical dimension.
Collaborative Fleet Deployment and Routing for Sustainable Transport
Authors: Ypsilantis Panagiotis , Zuidwijk Rob
Published: October 2019

Efficient multi-modal transportation in the hinterland of seaport terminals depends on consolidation of container volumes in support of frequent services of high capacity means of transport, such that sustainable multi-modal transport can compete with uni-modal road transport in cost and time. The tactical design of barge scheduled transport services involves fleet selection and routing through the inland waterway network. The resulting network service design should meet expected demand and service time requirements set by the shippers. We develop a tight MILP formulation for the Fleet Size and Mix Vehicle Routing (FSMVRP) especially adapted for the Port-Hinterland multi-modal barge network design. Also, an analytical model is developed to help understand important design trade-offs made. We consider the case of horizontal cooperation of dry port container terminals that share capacity. Our results show that in case of cooperation, both cost savings and service levels are improved, and allow for sustainable multi-modal transport to be competitive with uni-modal truck transport.
Numerical Study for Magnetohydrodynamic Flow of Nanofluid Due to a Rotating Disk with Binary Chemical Reaction and Arrhenius Activation Energy
Authors: Asma Mir , Othman W.A.M. , Muhammad Taseer , Mallawi Fouad , Wong B.R.
Published: October 2019

This article examines magnetohydrodynamic 3D nanofluid flow due to a rotating disk subject to Arrhenius activation energy and heat generation/absorption. Flow is created due to a rotating disk. Velocity, temperature and concentration slips at the surface of the rotating disk are considered. Effects of thermophoresis and Brownian motion are also accounted. The nonlinear expressions have been deduced by transformation procedure. Shooting technique is used to construct the numerical solution of governing system. Plots are organized just to investigate how velocities, temperature and concentration are influenced by various emerging flow parameters. Skin-friction Local Nusselt and Sherwood numbers are also plotted and analyzed. In addition, a symmetry is noticed for both components of velocity when Hartman number enhances.
A Novel Orthogonal Extreme Learning Machine for Regression and Classification Problems
Authors: Cui Licheng , Zhai Huawei , Lin Hongfei
Published: October 2019

An extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression from the hidden layer to the output layer. With a focus on the output weight, we introduce the orthogonal constraint into the output weight matrix, and propose a novel orthogonal extreme learning machine (NOELM) based on the idea of optimization column by column whose main characteristic is that the optimization of complex output weight matrix is decomposed into optimizing the single column vector of the matrix. The complex orthogonal procrustes problem is transformed into simple least squares regression with an orthogonal constraint, which can preserve more information from ELM feature space to output subspace, these make NOELM more regression analysis and discrimination ability. Experiments show that NOELM has better performance in training time, testing time and accuracy than ELM and OELM.
Inclusion of a Furin Cleavage Site Enhances Antitumor Efficacy against Colorectal Cancer Cells of Ribotoxin α-Sarcin- or RNase T1-Based Immunotoxins
Authors: Ruiz-de-la-Herrán Javier , Tomé-Amat Jaime , Lázaro-Gorines Rodrigo , Gavilanes José , Lacadena Javier
Published: October 2019

Immunotoxins are chimeric molecules that combine the specificity of an antibody to recognize and bind tumor antigens with the potency of the enzymatic activity of a toxin, thus, promoting the death of target cells. Among them, RNases-based immunotoxins have arisen as promising antitumor therapeutic agents. In this work, we describe the production and purification of two new immunoconjugates, based on RNase T1 and the fungal ribotoxin α-sarcin, with optimized properties for tumor treatment due to the inclusion of a furin cleavage site. Circular dichroism spectroscopy, ribonucleolytic activity studies, flow cytometry, fluorescence microscopy, and cell viability assays were carried out for structural and in vitro functional characterization. Our results confirm the enhanced antitumor efficiency showed by these furin-immunotoxin variants as a result of an improved release of their toxic domain to the cytosol, favoring the accessibility of both ribonucleases to their substrates. Overall, these results represent a step forward in the design of immunotoxins with optimized properties for potential therapeutic application in vivo.
Chemical Composition, Phytotoxic, Antimicrobial and Insecticidal Activity of the Essential Oils of Dracocephalum integrifolium
Authors: Zhou Shixing , Wei Caixia , Zhang Chi , Han Caixia , Kuchkarova Nigora , Shao Hua
Published: October 2019

The present investigation studied the chemical composition of the essential oils extracted from Dracocephalum integrifolium Bunge growing in three different localities in northwest China and evaluated the phytotoxic, antimicrobial and insecticidal activities of the essential oils as well as their major constituents, i.e., sabinene and eucalyptol. GC/MS analysis revealed the presence of 21–24 compounds in the essential oils, representing 94.17–97.71% of the entire oils. Monoterpenes were the most abundant substances, accounting for 85.30–93.61% of the oils; among them, sabinene (7.35–14.0%) and eucalyptol (53.56–76.11%) were dominant in all three oils, which occupied 67.56–83.46% of the total oils. In general, phytotoxic bioassays indicated that the IC50 values of the oils and their major constituents were below 2 μL/mL (1.739–1.886 mg/mL) against Amaranthus retroflexus and Poa annua. Disc diffusion method demonstrated that the oils and their major constituents possessed antimicrobial activity against Bacillus subtilis, Pseudomonas aeruginosa, Escherichia coli, Saccharomyces cerevisiae, and Candida albicans, with MIC values ranging from 5–40 μL/mL (4.347–37.712 mg/mL). The oils, sabinene and eucalyptol also exhibited significant pesticidal activity, with the mortality rates of Aphis pomi reaching 100% after exposing to 10 μL oil/petri dish (8.694–9.428 mg/petri dish) for 24 h. To the best of our knowledge, this is the first report on the chemical composition, phytotoxic, antimicrobial and insecticidal activity of the essential oils extracted from D. integrifolium; it is noteworthy to mention that this is also the first report on the phytotoxicity of one of the major constituents, sabinene. Our results imply that D. integrifolium oils and sabinene have the potential value of being further exploited as natural pesticides.
Vector Competence of Italian Populations of Culicoides for Some Bluetongue Virus Strains Responsible for Recent Northern African and European Outbreaks
Authors: Federici Valentina , Goffredo Maria , Mancini Giuseppe , Quaglia Michela , Santilli Adriana , Di Nicola Francesca , De Ascentis Matteo , Cabras Pierangela , Volpicelli Carmela , De Liberato Claudio , Satta Giuseppe , Federico Giovanni , Leone Alessandra , Pisciella Maura , Portanti Ottavio , Pizzurro Federica , Teodori Liana , Savini Giovanni
Published: October 2019

The distribution of Bluetongue virus (BTV) in Europe can be represented by two distinct and interconnected epidemiological systems (episystems), each characterized by different ecological characteristics and vector species. This study investigated the vector competence of Italian populations of Culicoides imicola and Culicoides obsoletus/scoticus to some representative BTV strains after artificial oral infection. The BTV strains were selected according to their ability to spread to one or both episystems and included BTV-4 ITA, responsible of the recent Italian and French BTV-4 outbreaks; the BTV-2 strain which caused the first BTV incursion in Italy, Corsica, and Balearic Islands; BTV-4 MOR, responsible for the epidemic in Morocco; and BTV-8, the strain which spread through Europe between 2006 and 2008. Blood-soaked cotton pledgets and Hemotek membrane feeder using Parafilm® membrane were used to artificially feed midges. For each population/strain, recovery rates (positive/tested heads) were evaluated using serogroup- and serotype-specific RT-PCR. The trial demonstrated that, except for the Abruzzo population of C. obsoletus/C. scoticus, which was refractory to BTV-4 MOR infection, all the investigated Culicoides populations are susceptible to the selected BTV strains and that, if prompt vaccination programs and restriction measures had not been implemented, BTV-2 and BTV-4 MOR could have spread all over Europe.
Pre-Transplantation Assessment of BK Virus Serostatus: Significance, Current Methods, and Obstacles
Authors: Dakroub Fatima , Touzé Antoine , Akl Haidar , Brochot Etienne
Published: October 2019

The immunosuppression required for graft tolerance in kidney transplant patients can trigger latent BK polyomavirus (BKPyV) reactivation, and the infection can progress to nephropathy and graft rejection. It has been suggested that pre-transplantation BKPyV serostatus in donors and recipients is a predictive marker for post-transplantation BKPyV replication. The fact that research laboratories have used many different assay techniques to determine BKPyV serostatus complicates these data analysis. Even studies based on the same technique differed in their standard controls choice, the antigenic structure type used for detection, and the cut-off for seropositivity. Here, we review the different BKPyV VP1 antigens types used for detection and consider the various BKPyV serostatus assay techniques’ advantages and disadvantages. Lastly, we highlight the obstacles in the implementation of a consensual BKPyV serologic assay in clinics (e.g., the guidelines absence in this field).