bluuebunny/crossref_metadata_2025 huggingface dataset:
MPPIF-Net: Identification of Plasmodium Falciparum Parasite Mitochondrial Proteins Using Deep Features with Multilayer Bi-directional LSTM
Authors: Khan Samee Ullah , Baik Ran
Published: June 2020

Mitochondrial proteins of Plasmodium falciparum (MPPF) are an important target for anti-malarial drugs, but their identification through manual experimentation is costly, and in turn, their related drugs production by pharmaceutical institutions involves a prolonged time duration. Therefore, it is highly desirable for pharmaceutical companies to develop computationally automated and reliable approach to identify proteins precisely, resulting in appropriate drug production in a timely manner. In this direction, several computationally intelligent techniques are developed to extract local features from biological sequences using machine learning methods followed by various classifiers to discriminate the nature of proteins. Unfortunately, these techniques demonstrate poor performance while capturing contextual features from sequence patterns, yielding non-representative classifiers. In this paper, we proposed a sequence-based framework to extract deep and representative features that are trust-worthy for Plasmodium mitochondrial proteins identification. The backbone of the proposed framework is MPPF identification-net (MPPFI-Net), that is based on a convolutional neural network (CNN) with multilayer bi-directional long short-term memory (MBD-LSTM). MPPIF-Net inputs protein sequences, passes through various convolution and pooling layers to optimally extract learned features. We pass these features into our sequence learning mechanism, MBD-LSTM, that is particularly trained to classify them into their relevant classes. Our proposed model is experimentally evaluated on newly prepared dataset PF2095 and two existing benchmark datasets i.e., PF175 and MPD using the holdout method. The proposed method achieved 97.6%, 97.1%, and 99.5% testing accuracy on PF2095, PF175, and MPD datasets, respectively, which outperformed the state-of-the-art approaches.
Sustainable Water Responsive Mechanically Adaptive and Self-Healable Polymer Composites Derived from Biomass
Authors: Sahu Pranabesh , Bhowmick Anil K.
Published: June 2020

New synthetic biobased mechanically adaptive composites, responding to water and having self-healing property, were developed. These composites were prepared by introducing plant-based cellulose nanofibrils (CNFs) at 10, 20, and 25% (v/v) concentration into a biobased rubbery poly (myrcene-co-furfuryl methacrylate) (PMF) matrix by solution mixing and subsequent compression molding technique. The reinforcement of CNFs led to an increase in the tensile storage modulus (E’) of the dry composites. Upon exposure to water, water sensitivity and a drastic fall in storage moduli (E’) were observed for the 25% (v/v) CNF composite. A modulus reduction from 1.27 (dry state) to 0.15 MPa (wet state) was observed for this composite. The water-sensitive nature of the composites was also confirmed from the force modulation study in atomic force microscopy (AFM), revealing the average modulus as 82.7 and 32.3 MPa for dry and swollen composites, respectively. Interestingly, the composites also showed thermoreversibility and excellent healing property via Diels-Alder (DA) click chemistry using bismaleimide as a crosslinker, when the scratched samples were heated at 120 °C (rDA) for 10 h and then cooled down to 60 °C (DA) followed by room temperature. The healing efficiency was obtained as about 90% from the AFM 3D height images. Thus, the composites exhibited dual stimuli-responsive behavior as mechanically adaptive water sensitive polymers with water as the stimulus and self-healing polymer using bismaleimide as an external stimulus. Therefore, this study provides guidance and new frontiers to make use of composite materials based on biopolymers for various potential smart and biomedical applications.
Condensate-Banking Removal and Gas-Production Enhancement Using Thermochemical Injection: A Field-Scale Simulation
Authors: Hassan Amjed , Abdalla Mohamed , Mahmoud Mohamed , Glatz Guenther , Al-Majed Abdulaziz , Al-Nakhli Ayman
Published: June 2020

Condensate-liquid accumulation in the vicinity of a well is known to curtail gas production up to 80%. Numerous approaches are employed to mitigate condensate banking and improve gas productivity. In this work, a field-scale simulation is presented for condensate damage removal in tight reservoirs using a thermochemical treatment strategy where heat and pressure are generated in situ. The impact of thermochemical injection on the gas recovery is also elucidated. A compositional simulator was utilized to assess the effectiveness of the suggested treatment on reducing the condensate damage and, thereby, improve the gas recovery. Compared to the base case, represented by an industry-standard gas injection strategy, simulation studies suggest a significantly improved hydrocarbon recovery performance upon thermochemical treatment of the near-wellbore zone. For the scenarios investigated, the application of thermochemicals allowed for an extension of the production plateau from 104 days, as determined for the reference gas injection case, to 683 days. This represents a 6.5-fold increase in production plateau time, boosting gas recovery from 25 to 89%. The improved recovery is attributed to the reduction of both capillary pressure and condensate viscosity. The presented work is crucial for designing and implementing thermochemical treatments in tight-gas reservoirs.
Image-Based Model for Assessment of Wood Chip Quality and Mixture Ratios
Authors: Plankenbühler Thomas , Kolb Sebastian , Grümer Fabian , Müller Dominik , Karl Jürgen
Published: June 2020

This article focuses on fuel quality in biomass power plants and describes an online prediction method based on image analysis and regression modeling. The main goal is to determine the mixture fraction from blends of two wood chip species with different qualities and properties. Starting from images of both fuels and different mixtures, we used two different approaches to deduce feature vectors. The first one relied on integral brightness values while the latter used spatial texture information. The features were used as input data for linear and non-linear regression models in nine training classes. This permitted the subsequent prediction of mixture ratios from prior unknown similar images. We extensively discuss the influence of model and image selection, parametrization, the application of boosting algorithms and training strategies. We obtained models featuring predictive accuracies of R2 > 0.9 for the brightness-based model and R2 > 0.8 for the texture based one during the validation tests. Even when reducing the data used for model training down to two or three mixture classes—which could be necessary or beneficial for the industrial application of our approach—sampling rates of n < 5 were sufficient in order to obtain significant predictions.
Research on Combustion Characteristics of Air–Light Hydrocarbon Mixing Gas
Authors: Meng Zhiqun , Wang Jinggang , Xiong Chuchao , Qi Jiawen , Hou Liquan
Published: June 2020

Air–light hydrocarbon mixing gas is a new type of city gas which is composed of light hydrocarbon with the main component of n-pentane and air mixed in a certain proportion. To explore the dominant reactions for CO production of air–light hydrocarbon mixing gas with different mixing degrees at the critical equivalence ratios, a computational study was conducted on the combustion characteristics, including the ignition delay time, laminar flame speed, extinction residence time, and emission of air–light hydrocarbon mixing gas at atmospheric pressure and room temperature in the present study. The calculated results indicate that the ignition delay time of air–light hydrocarbon mixing gas at temperatures of 1000–1118 K is greater than that of n-pentane, while the opposite at temperatures of 1118–1600 K. From the study of the laminar flame speed and ignition delay time, it was found that the essence of air–light hydrocarbon mixing gas is that its attribute parameter is determined by the ratio of n-pentane to the total amount of air at the moment of combustion. The changes in the extinction residence time and the CO emission index of air–light hydrocarbon mixing gas are not synchronized, that is the CO emission index is not necessarily small for air–light hydrocarbon mixing gas with excellent extinction residence time. CO sensitivity analysis and CO rate of production identified key species and reactions that are primarily responsible for CO formation and annihilation. The mixing degree plays a key role in the CO emission index of air–light hydrocarbon mixing gas, which has a constructive opinion on the future experiment and application of air–light hydrocarbon mixing gas.
Development of Test Procedures Based on Chaotic Advection for Assessing Polymer Performance in High-Solids Tailings Applications
Authors: Costine Allan , Fawell Phillip , Chryss Andrew , Dahl Stuart , Bellwood John
Published: June 2020

Post-thickener polymer addition to initiate rapid tailings dewatering has gained considerable interest for tailings storage facility (TSF) management. However, the highly viscous and non-Newtonian rheology of dense suspensions presents unique challenges for mixing with polymer solutions. Such mixing is highly inefficient, often resulting in polymer overdosing and wide variations in deposited tailings characteristics, with the potential to significantly compromise TSF performance. In this study, a new type of mixer based on the principles of chaotic advection was used for treating kaolin suspensions with high molecular weight (MW) anionic copolymer solutions. Chaotic advection imparts efficient mixing by gently stretching and folding flows in a controlled manner, as opposed to random, high-shear flows associated with turbulent mixing, and this lower shear stress allows for the controlled formation of larger aggregate structures with vastly improved dewatering characteristics. A pre-conditioning pipe reactor prior to this mixer can also be advantageous in terms of providing a short burst of high shear for initial polymer distribution. Seven acrylamide/acrylate copolymers of a fixed anionic charge density (30%) spanning a distinct MW range, as characterized by intrinsic viscosity, were applied at elevated dosages to high-solids (20–30 wt %) kaolin suspensions in continuous flow through the chaotic mixer described above. Medium-to-high MW polymers were generally preferred, with further increases in MW resulting in significantly diminished dewatering outcomes. Direct analysis of polymer solution properties through oscillatory rheology gave a better indication of a polymer’s potential performance compared with intrinsic viscosity, offering a more robust basis for polymer selection. This represented the first systematic study into the effects of polymer properties on deposition behavior after dosing at high solids, which was only possible through the ability to apply controlled shear across the entire suspension during sample preparation.
Application of Combined Developments in Processes and Models to the Determination of Hot Metal Temperature in BOF Steelmaking
Authors: Díaz José , Fernández Francisco Javier
Published: June 2020

Nowadays, the steel industry is seeking to reduce its carbon footprint without affecting productivity or profitability. This challenge needs to be supported by continuous improvements in equipment, methods, sensors and models. The present work exposes how the combined development of processes and models (CDPM) has been applied to the improvement of hot metal temperature determination. The synergies that arise when both sides of this research are simultaneously approached are evidenced. A workflow that takes into account the CDPM approach is proposed. First, a thermal model of the process is developed, making it possible to identify that hot metal temperature is a key lever for carbon footprint reduction. Then, three main alternatives for hot metal temperature determination are compared: infrared thermometry, time-series forecasting and machine learning prediction. Despite considering only few process variables, machine learning techniques succeed in extracting relevant information from process databases. An accuracy close to infrared thermometry is obtained, with a much higher applicability. This research shows that process-model alternatives are complementary when judiciously nested in the process computer routines. Combining measurement and modelling techniques, 100% applicability is achieved with an error reduction of 7 °C.
Characterization of Licorice Root Waste for Prospective Use as Filler in more Eco-Friendly Composite Materials
Authors: Santulli Carlo , Rallini Marco , Puglia Debora , Gabrielli Serena , Torre Luigi , Marcantoni Enrico
Published: June 2020

The extraction of glycyrrhizin from licorice root and stolon with ethanol/water solutions leaves a lignocellulosic residue, which could be potentially applied in biocomposites. This process proved difficult in principle, given the considerable hardness of this material as received, which impedes its use in polymer resins in large amounts. After ball milling, up to 10% of this fibrous residue, which shows very variable aspect ratio, was introduced into an epoxy matrix, to investigate its possible future application in sustainable polymers. Of the three composites investigated, containing 1, 5 and 10 wt% of licorice waste, respectively, by performing flexural testing, it was found that the introduction of an intermediate amount of filler proved the most suitable for possible development. Thermal characterization by thermogravimetry (TGA) did not indicate large variation of degradation properties due to the introduction of the filler. Despite the preliminary characteristics of this study, an acceptable resin-filler interface has been obtained for all filler contents. Issues to be solved in future study would be the possibility to include a larger amount of filler by better compatibilization and a more uniform distribution of the filler, considering their orientation, since most of it maintains an elongated geometry after ball milling.
Screening of Different Ageing Technologies of Wine Spirit by Application of Near-Infrared (NIR) Spectroscopy and Volatile Quantification
Authors: Anjos Ofélia , Caldeira Ilda , Roque Rita , Pedro Soraia , Lourenço Sílvia , Canas Sara
Published: June 2020

The traditional ageing of wine spirits is done in wooden barrels, however, high costs have led to the search for alternative technologies, such as the use of stainless steel tanks with wooden staves and the application of micro-oxygenation. This work evaluates the changes in the major volatile compounds of wine spirits aged for 6, 12 and 18 months in wooden barrels and stainless steel tanks with micro-oxygenation. For both ageing technologies, two types of wood (Limousin oak and Portuguese chestnut wood) were used. The samples were analysed concerning their alcohol strength (electronic densimetry) and volatile composition, namely of methanol, acetaldehyde, ethyl acetate and other major volatile compounds ((GC-FID) and near-infrared spectroscopy (NIR)). The results show that the ageing technology was more influential than the wood species for the volatile composition of wine spirits, namely acetaldedehyde, methanol, 2-methylpropan-1-ol and 2+3-methylbutan-1-ol. However, the opposite behaviour was found for the spectral data. The ageing process was accelerated by using the alternative ageing technology, especially with chestnut wood staves. The most informative spectral regions to discriminate samples were around 6859 cm−1 and from 5200 cm−1 to 4200 cm−1. NIR is a promising technique to identify different technologies and different wood species used in the ageing process of wine spirits.
From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States
Authors: Mucia Anthony , Bonan Bertrand , Zheng Yongjun , Albergel Clément , Calvet Jean-Christophe
Published: June 2020

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde’s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017–2018 period over the contiguous United States (CONUS) at a 0.2° × 0.2° spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable.
Effects of Different Urbanization Levels on Land Surface Temperature Change: Taking Tokyo and Shanghai for Example
Authors: Chao Zhenhua , Wang Liangxu , Che Mingliang , Hou Shengfang
Published: June 2020

The influence of different urbanization levels on land surface temperature (LST) has attracted extensive attention. Though both are world megacities, Shanghai and Tokyo have gone through different urbanization processes that urban sprawl characterized by impervious surfaces was more notable in Shanghai than in Tokyo over the past years. Here, annual and seasonal mean LST in daytime (LSTday), in nighttime (LSTnight), and LSTdiff (annual and seasonal mean difference of LST in daytime and nighttime) were extracted from the MODIS LST product, MYD11A2 006, for 9 typical sites in Shanghai and Tokyo from 2003 to 2018, respectively. Then the effects of the urbanization levels were analyzed through Mann-Kendall statistics and Sen’s slope estimator. The trends of change in LSTday and LSTdiff for most sites in Shanghai, an urbanizing region, rose. In addition, there was no obvious regularity when considering seasonal factors, which could be due to the increasing fragmentized landscapes and scattered water bodies produced by urbanization. By comparison, the change in LST in Tokyo, a post-urbanizing region, was regular, especially in the spring. In other seasons, there was no obvious trend in temperature change regardless of whether the land cover was impervious surface or mountain forest. On the whole, vegetation cover and water bodies can mitigate the urban heat island (UHI) effect in urban regions. For more scientific urban planning, further analysis about the effect of urbanization on LST should focus on the compound stress from climate change and urbanization.
Remote Sensing Materials for a Preliminary Archaeological Evaluation of the Giove Countryside (Terni, Italy)
Authors: Barone Pier Matteo , Wueste Elizabeth , Hodges Richard
Published: June 2020

A collaboration between the American University of Rome, the Municipality of Giove, and Soprintendenza Archeologia, Belle Arti e Paesaggio dellʼUmbria has resulted in an academic project aimed at a preliminary evaluation of a particular area along the Tiber river that straddles the border between Umbria and Lazio. Archaeological prospection methods, such as Unmanned Aerial Vehicle (UAV)-based remote sensing, ground-penetrating radar (GPR), and photogrammetry, have made it possible to better study the landscape with respect to not only the changes the area has undergone recently, but also its evolution during the Roman and Medieval periods, while keeping the main communication route represented by the Tiber river as its fulcrum.
Morphological Band Registration of Multispectral Cameras for Water Quality Analysis with Unmanned Aerial Vehicle
Authors: Kim Wonkook , Jung Sunghun , Moon Yongseon , Mangum Stephen C.
Published: June 2020

Multispectral imagery contains abundant spectral information on terrestrial and oceanic targets, and retrieval of the geophysical variables of the targets is possible when the radiometric integrity of the data is secured. Multispectral cameras typically require the registration of individual band images because their lens locations for individual bands are often displaced from each other, thereby generating images of different viewing angles. Although this type of displacement can be corrected through a geometric transformation of the image coordinates, a mismatch or misregistration between the bands still remains, owing to the image acquisition timing that differs by bands. Even a short time difference is critical for the image quality of fast-moving targets, such as water surfaces, and this type of deformation cannot be compensated for with a geometric transformation between the bands. This study proposes a novel morphological band registration technique, based on the quantile matching method, for which the correspondence between the pixels of different bands is not sought by their geometric relationship, but by the radiometric distribution constructed in the vicinity of the pixel. In this study, a Micasense Rededge-M camera was operated on an unmanned aerial vehicle and multispectral images of coastal areas were acquired at various altitudes to examine the performance of the proposed method for different spatial scales. To assess the impact of the correction on a geophysical variable, the performance of the proposed method was evaluated for the chlorophyll-a concentration estimation. The results showed that the proposed method successfully removed the noisy spatial pattern caused by misregistration while maintaining the original spatial resolution for both homogeneous scenes and an episodic scene with a red tide outbreak.
Extracting Seasonal Signals in GNSS Coordinate Time Series via Weighted Nuclear Norm Minimization
Authors: Chen Baozhou , Bian Jiawen , Ding Kaihua , Wu Haochen , Li Hongwei
Published: June 2020

Global Navigation Satellite System (GNSS) coordinate time series contains obvious seasonal signals, which mainly manifest as a superposition of annual and semi-annual oscillations. Accurate extraction of seasonal signals is of great importance for understanding various geophysical phenomena. In this paper, a Weighted Nuclear Norm Minimization (WNNM) is proposed to extract the seasonal signals from the GNSS coordinate time series. WNNM assigns different weights to different singular values that enable us to estimate an approximate low rank matrix from its noisy matrix. To address this issue, the low rank characteristics of the Hankel matrix induced by GNSS coordinate time series was investigated first, and then the WNNM is applied to extract the seasonal signals in the GNSS coordinate time series. Meanwhile, the residuals have been analyzed, obtaining the estimation of the uncertainty of velocity. To demonstrate the effectiveness of the proposed algorithm, a number of tests have been carried out on both simulated and real GNSS dataset. Experimental results indicate that the proposed scheme offers preferable performances compared with many state-of-the-art methods.
Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning
Authors: Feng Luwei , Zhang Zhou , Ma Yuchi , Du Qingyun , Williams Parker , Drewry Jessica , Luck Brian
Published: June 2020

Alfalfa is a valuable and intensively produced forage crop in the United States, and the timely estimation of its yield can inform precision management decisions. However, traditional yield assessment approaches are laborious and time-consuming, and thus hinder the acquisition of timely information at the field scale. Recently, unmanned aerial vehicles (UAVs) have gained significant attention in precision agriculture due to their efficiency in data acquisition. In addition, compared with other imaging modalities, hyperspectral data can offer higher spectral fidelity for constructing narrow-band vegetation indices which are of great importance in yield modeling. In this study, we performed an in-season alfalfa yield prediction using UAV-based hyperspectral images. Specifically, we firstly extracted a large number of hyperspectral indices from the original data and performed a feature selection to reduce the data dimensionality. Then, an ensemble machine learning model was developed by combining three widely used base learners including random forest (RF), support vector regression (SVR) and K-nearest neighbors (KNN). The model performance was evaluated on experimental fields in Wisconsin. Our results showed that the ensemble model outperformed all the base learners and a coefficient of determination (R2) of 0.874 was achieved when using the selected features. In addition, we also evaluated the model adaptability on different machinery compaction treatments, and the results further demonstrate the efficacy of the proposed ensemble model.
R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images
Authors: Chen Shiqi , Zhang Jun , Zhan Ronghui
Published: June 2020

Recently, convolutional neural network (CNN)-based methods have been extensively explored for ship detection in synthetic aperture radar (SAR) images due to their powerful feature representation abilities. However, there are still several obstacles hindering the development. First, ships appear in various scenarios, which makes it difficult to exclude the disruption of the cluttered background. Second, it becomes more complicated to precisely locate the targets with large aspect ratios, arbitrary orientations and dense distributions. Third, the trade-off between accurate localization and improved detection efficiency needs to be considered. To address these issues, this paper presents a rotate refined feature alignment detector (R 2 FA-Det), which ingeniously balances the quality of bounding box prediction and the high speed of the single-stage framework. Specifically, first, we devise a lightweight non-local attention module and embed it into the stem network. The recalibration of features not only strengthens the object-related features yet adequately suppresses the background interference. In addition, both forms of anchors are integrated into our modified anchor mechanism and thus can enable better representation of densely arranged targets with less computation burden. Furthermore, considering the shortcoming of the feature misalignment existing in the cascaded refinement scheme, a feature-guided alignment module which encodes both the position and shape information of current refined anchors into the feature points is adopted. Extensive experimental validations on two SAR ship datasets are performed and the results demonstrate that our algorithm has higher accuracy with faster speed than some state-of-the-art methods.
Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification
Authors: Yang Xiaofei , Zhang Xiaofeng , Ye Yunming , Lau Raymond Y. K. , Lu Shijian , Li Xutao , Huang Xiaohui
Published: June 2020

Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to capture spectral or spatial information in hyperspectral images. On the other hand, few approaches make use of both spectral and spatial information simultaneously, which is critical to accurate hyperspectral image classification. This paper presents a novel Synergistic Convolutional Neural Network (SyCNN) for accurate hyperspectral image classification. The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. Additionally, it introduces a 3D attention mechanism before the fully-connected layer which helps filter out interfering features and information effectively. Extensive experiments over three public benchmarking datasets show that our proposed SyCNNs clearly outperform state-of-the-art techniques that use 2D/3D CNNs.
Surface Temperature of the Planet Earth from Satellite Data over the Period 2003–2019
Authors: Sobrino José , García-Monteiro Susana , Julien Yves
Published: June 2020

This is an update of Sobrino et al.’s paper, published in January 2020, which extends the calculation of the Earth’s surface temperature to the period 2003–2019 and uses the new version 2019.0 for the sea surface temperature product MODIS, which is available from 15 January 2020 and replaces version 2014.0. The land surface temperature was estimated from the MCD11C1 product for the same period. The results corroborate the temperature anomalies retrieved from climate models and improve the comparison with global annual air temperatures estimated by the NOAA’s National Climatic Data Center (NOAA-NCDC), with a correlation coefficient of 0.96. In addition, a trend of 0.021 ± 0.001 °C/year increase was found for the Earth’s surface temperature in this 17-year period.
Developing of Low-Cost Air Pollution Sensor—Measurements with the Unmanned Aerial Vehicles in Poland
Authors: Pochwała Sławomir , Gardecki Arkadiusz , Lewandowski Piotr , Somogyi Viola , Anweiler Stanisław
Published: June 2020

This article presents the capabilities and selected measurement results from the newly developed low-cost air pollution measurement system mounted on an unmanned aerial vehicle (UAV). The system is designed and manufactured by the authors and is intended to facilitate, accelerate, and ensure the safety of operators when measuring air pollutants. It allows the creation of three-dimensional models and measurement visualizations, thanks to which it is possible to observe the location of leakage of substances and the direction of air pollution spread by various types of substances. Based on these models, it is possible to create area audits and strategies for the elimination of pollution sources. Thanks to the usage of a multi-socket microprocessor system, the combination of nine different air quality sensors can be installed in a very small device. The possibility of simultaneously measuring several different substances has been achieved at a very low cost for building the sensor unit: 70 EUR. The very small size of this device makes it easy and safe to mount it on a small drone (UAV). Because of this device, many harmful chemical compounds such as ammonia, hexane, benzene, carbon monoxide, and carbon dioxide, as well as flammable substances such as hydrogen and methane, can be detected. Additionally, a very important function is the ability to perform measurements of PM2.5 and PM10 suspended particulates. Thanks to the use of UAV, the measurement is carried out remotely by the operator, which allows us to avoid the direct exposure of humans to harmful factors. A big advantage is the quick measurement of large spaces, at different heights above the ground, in different weather conditions. Because of the three-dimensional positioning from GPS receiver, users can plot points and use colors reflecting a concentration of measured features to better visualize the air pollution. A human-friendly data output can be used to determine the mostly hazardous regions of the sampled area.
Recent Advances in the Analysis of Vitamin D and Its Metabolites in Food Matrices
Authors: Socas-Rodríguez Bárbara , Sandahl Margareta , Holm Cecilia , Turner Charlotta
Published: June 2020

Vitamin D and its analogues are fat-soluble vitamins that carry out important functions in human and animal organisms. Many studies have pointed out the relationship between the deficiency of these substances and the development of both skeletal- and extra-skeletal diseases. Although vitamin D is fundamentally derived from the bio-transformation of its precursor, 7-dehydrocholesterol, through the action of UV-B radiation in the skin, dietary intake also plays an important role in the regulation of its status in an organism. For this reason, the application of reliable methodologies that enable monitoring the content of vitamin D and its analogues in food and supplements constitutes an aspect of special relevance to establish adequate habits, which avoid the deficiency of these substances in organisms and, consequently, the appearance of related diseases. The use of chromatographic techniques in combination with conventional and novel sample pre-treatments has become a suitable strategy to achieve this aim. This review compiles the most relevant methodologies reported in the last ten years for vitamin D analogues analysis in food matrices. Particular attention has been paid to provide a general overview of the most suitable approaches in terms of reliability, sensitivity and simplicity, used in the field of food analysis.
The Obama Effect on Perceived Mobility
Authors: Kesler Christel , Churchwell Amber
Published: June 2020

Using American General Social Survey data from 1994 to 2018, this paper examines how Americans of different racial backgrounds perceive their past intergenerational mobility and their, and their children’s, prospects for future mobility, before, during, and after Barack Obama’s presidency. We find that White Americans are generally less positive than Black and Latinx Americans about mobility, especially their children’s mobility prospects. However, racial gaps in optimism widened considerably during the Obama presidency, due to a significant decline in White respondents’ perceived mobility. A more detailed analysis of White respondents’ views by levels of racial resentment and political partisanship shows that the Obama-era dip among White respondents is concentrated among those who are racially resentful and among Republican voters, two groups that substantially overlap. For these two groups, perceived future prospects for their and their children’s mobility increased again during the Trump administration. Black and Latinx respondents’ perceptions of mobility are stable across all earlier presidential administrations, but decline somewhat with the Trump presidency.
‘The Feel of the Stones, Sounds of Cars, the Different Smells’: How Incorporating the Senses Can Help Support Equitable Health Promotion
Authors: Barnfield Andrew
Published: June 2020

There has been limited consideration to the role of the senses in health promotion regardless of the prominence placed on corporeality in intervention and prevention strategies. Touch as a form of sense-making challenges the representational approaches that characterize health promotion methods to increase participation in physical activity. This paper explores recreational running practices through the sense of touch and is drawn from an in-depth qualitative research project with recreational runners in the Bulgarian capital Sofia. The project examined how recreational running was established and maintained within the city. This paper concludes that there is potential for health promotion to adopt a more open stance towards the study of sensual experiences of the built environment. Insights from approaches attentive to the senses hold promise for agendas and interventions in health promotion practice and intervention.
The Influence of R&D Intensity on Financial Performance: The Mediating Role of Human Capital in the Semiconductor Industry in Taiwan
Authors: Chen Tsung-Chun , Wu Yenchun Jim
Published: June 2020

Knowledge transfer is a strategy used by high-tech companies to acquire new knowledge and skills. Knowledge can be internally generated or externally sourced. The access to external knowledge is a quick fix, but the risks associated with reliance on external sources are often overlooked. However, not acquiring such knowledge is even riskier. There have been a slew of litigations in the semiconductor industry in recent years. The acquisition and assurance of intangible assets is an important issue. This paper posits that internal R&D should take into consideration the knowledge intensity and capital investment in the industry. This study focuses on the relationship between intangible assets and financial performance. It sourced the 2004 to 2016 financial data of semiconductor companies in Taiwan for panel data modeling and examined case studies for empirical validation. This study found that the higher the R&D intensity (RDI) in the value-added component of human capital, the better the financial performance of the company. RDI has a positive influence on the accumulation of human capital and financial performance metrics, and such influence is deferred. Meanwhile, human capital is a mediating factor in the relationship between RDI and financial performance. RDI is integral to the semiconductor industry’s pursuit of business sustainability.
Neither Poor nor Cool: Practising Food Self-Provisioning in Allotment Gardens in the Netherlands and Czechia
Authors: Sovová Lucie , Veen Esther J.
Published: June 2020

While urban gardening and food provisioning have become well-established subjects of academic inquiry, these practices are given different meanings depending on where they are performed. In this paper, we scrutinise different framings used in the literature on food self-provisioning in Eastern and Western Europe. In the Western context, food self-provisioning is often mentioned alongside other alternative food networks and implicitly framed as an activist practice. In comparison, food self-provisioning in Central and Eastern Europe has until recently been portrayed as a coping strategy motivated by economic needs and underdeveloped markets. Our research used two case studies of allotment gardening from both Western and Eastern Europe to investigate the legitimacy of the diverse framings these practices have received in the literature. Drawing on social practice theory, we examined the meanings of food self-provisioning for allotment gardeners in Czechia and the Netherlands, as well as the material manifestations of this practice. We conclude that, despite minor differences, allotment gardeners in both countries are essentially ‘doing the same thing.’ We thus argue that assuming differences based on different contexts is too simplistic, as are the binary categories of ‘activist alternative’ versus ‘economic need.’
Burnout Syndrome in Reception Systems for Illegal Immigrants in the Mediterranean. A Quantitative and Qualitative Study of Italian Practitioners
Authors: Nonnis Marcello , Pirrone Monica Piera , Cuccu Stefania , Agus Mirian , Pedditzi Maria Luisa , Cortese Claudio Giovanni
Published: June 2020

Illegal immigration throughout the Mediterranean Sea is an intense and epoch-making phenomenon. This quantitative and qualitative study, based on the framework of the JD-R Model of burnout, described and assessed the risk of burnout among practitioners working in the reception system for illegal immigrants. A sample of Italian practitioners completed the Link Burnout Questionnaire (N = 193) and a semistructured interview (N = 108). The analysis of the questionnaires was carried out via ANOVA and χ2 test. The content of the interviews was examined using T-LAB. Quantitative results showed that the sample was generally at risk of burnout, and about a quarter were at severe risk. Qualitative results highlighted aspects of burnout that are specific to this working context: large workload, mental fatigue, and lack of social support; inability to understand the language and cultural differences of the immigrants; having to deal with organisational problems that come up repeatedly. This study offers coping strategies that can improve organisational health and performance of practitioners working in illegal immigration. Italy’s shape, geographical location, and geo-political role make it a case in point for the whole European continent regarding the sustainability of illegal immigration across the Mediterranean Sea.
In Vitro Study of Butyric Acid Deodorization Potential by Indigenously Constructed Bacterial Consortia and Pure Cultures from Pit Latrine Fecal Sludge
Authors: Njalam’mano John Bright Joseph , Chirwa Evans Martin Nkhalambayausi , Seabi Refilwe Lesego
Published: June 2020

The present study aims at developing an efficient bacterial consortium to biodegrade butyric acid, one of the odor-causing compounds that contribute significantly to pit latrine malodors. Six bacterial strains isolated from pit latrine fecal sludge were selected for the study. Nineteen bacterial consortia of different combinations were artificially constructed. The individual bacterial strains and bacterial consortia were compared by culturing in mineral salt medium supplemented with 1000 mg/L butyric acid as a sole carbon and energy source at pH 7, 30 °C, and 110 rpm under aerobic growth conditions. A co-culture of Serratia marcescens and Bacillus cereus was an effective bacterial consortium compared to individual component bacterial strains and other bacterial consortia, in which 1000 mg/L butyric acid was completely degraded within 16 h of incubation. A temperature of 30 °C and pH 7 were found to be optimum for the maximum degradation for both S. marcescens and B. cereus. The inoculation sizes of 2.0 and 2.5 were optimal for the maximum degradation for B. cereus and S. marcescens, respectively. The study provides insights that will be of substantial help in the development of effective biological treatment technologies for pit latrine odor to change the pit latrine user community’s and would be users’ perception of pit latrines.
Optimal Operation of Multi-Carrier Energy Networks Considering Uncertain Parameters and Thermal Energy Storage
Authors: Nazari-Heris Morteza , Mohammadi-Ivatloo Behnam , Asadi Somayeh
Published: June 2020

The coordination of energy carriers in energy systems has significant benefits in enhancing the flexibility, efficiency, and sustainability characteristics of energy networks. These benefits are of great importance for multi-carrier energy networks due to the complexity of obtaining optimal dispatch, considering the non-convex nature of their energy conversion. The current study proposes a robust operation model for the coordination of multi-carrier systems, including electricity, gas, heat, and water carriers concerning thermal energy storage technology. Thermal energy storage is for storing extra heat generated by combined heat and power (CHP) plants and boilers in time intervals with low heat demand on the system and discharging it when required. Energy network operators should have the capability to manage uncertain energy loads to study the impact of load variation on the decision-making process in network operation. Accordingly, this study employs an information gap decision theory (IGDT) method to model the uncertainty of the power demand in optimal system operation. By applying the IGDT approach, the operator of the energy system can use the appropriate methodology to obtain a robust optimal operation. Such a modeling approach helps the operator to make suitable decisions about probable variations in power load. The introduced model is applied in a test system for evaluating the performance and effectiveness of the introduced scheme.
Study on the Certification Policy of Zero-Energy Buildings in Korea
Authors: Kim Yeweon , Yu Ki-Hyung
Published: June 2020

This study presents a methodology and process to establish a mandatory policy of zero-energy buildings (ZEBs) in Korea. To determine the mandatory level to acquire the rating of a ZEB in Korea, this study was conducted under the assumption that the criteria of ZEB was a top 5% building considering the building’s energy-efficiency rating, which was certified through a quantitative building energy analysis. A self-sufficiency rate was also proposed to strengthen the passive standard of the buildings as well as to encourage new and renewable energy production. Accordingly, zero-energy buildings (ZEBs) in Korea are defined as having 60 kWh/(m2·yr) of non-renewable primary energy (NRPE) consumption in residential buildings and 80 kWh/(m2·yr) in non-residential buildings, and the self-reliance rate should be more than 20% of the renewable energy consumption as compared to the total energy consumption of the buildings. In addition, the mandatory installation of building energy management systems (BEMS) was promoted to investigate the energy behavior in buildings to be certified as zero-energy in the future. This study also investigated the number of ZEB certificates during the demonstration period from 2017 to 2019 to analyze the energy demand, non-renewable primary energy, renewable primary energy, and self-sufficiency rate as compared to those under the previous standards. For ZEB Grade 1 as compared to the existing building energy-efficiency rating, the sum of the NRPE decreased more than 50%, and renewable energy consumption increased more than four times.
Customer Involvement in Sustainable Tourism Planning at Lake Balaton, Hungary—Analysis of the Consumer Preferences of the Active Cycling Tourists
Authors: Lőrincz Katalin , Banász Zsuzsanna , Csapó János
Published: June 2020

This study uses an innovative tourism product development approach, based on co-creation or customer involvement, related to Lake Balaton, a mass tourism-based destination in Hungary, from the point of view of the market segment of active cycling tourists. The investigation of opportunities for the development of cycling tourism first of all relies on the new approach of attraction and product development around the destination, in which it is important to take into consideration the consumer preferences of the most important related group of tourists—active cycling tourists. The sustainable approach of tourism product development also provides an opportunity to decrease the spatial and temporal concentration of tourism, which is largely concentrated on the summertime season. The aim of this study is to explore aspects of the customers’ demand for tourism development in terms of cycling tourism with the help of primary data collection, in order to provide adequate directions for sustainable tourism development in the destination. Revealing the demand side of active cycling tourism related to Lake Balaton, the authors used both qualitative (focus group discussions and structured interviews) and quantitative questionnaire survey (computer-assisted data collection) research methods. The latter online surveys were carried out in November and December, 2019, and resulted with an appraisable sample of 809 questionnaires. As for the method, descriptive statistics and relationship analyses were applied. More than five thousand (5050) possible relationships were examined between the closed answers of the questionnaire by Kendall’s rank correlation coefficient (τ) and Cramer’s V, depending on whether they could be measured on a nominal or ordinal scale. The results show that the content analysis of the primary research provides well determined directions for the sustainable tourism development of cycling tourism at Lake Balaton, so customer involvement seems to be a win-win situation both for the customers (tourists) and the decision makers.