bluuebunny/crossref_metadata_2025 huggingface dataset:
Engineering the 2-Oxoglutarate Dehydrogenase Complex to Understand Catalysis and Alter Substrate Recognition
Authors: Chakraborty Joydeep , Nemeria Natalia , Shim Yujeong , Zhang Xu , Guevara Elena L. , Patel Hetal , Farinas Edgardo T. , Jordan Frank
Published: February 2022

The E. coli 2-oxoglutarate dehydrogenase complex (OGDHc) is a multienzyme complex in the tricarboxylic acid cycle, consisting of multiple copies of three components, 2-oxoglutarate dehydrogenase (E1o), dihydrolipoamide succinyltransferase (E2o) and dihydrolipoamide dehydrogenase (E3), which catalyze the formation of succinyl-CoA and NADH (+H+) from 2-oxoglutarate. This review summarizes applications of the site saturation mutagenesis (SSM) to engineer E. coli OGDHc with mechanistic and chemoenzymatic synthetic goals. First, E1o was engineered by creating SSM libraries at positions His260 and His298.Variants were identified that: (a) lead to acceptance of substrate analogues lacking the 5-carboxyl group and (b) performed carboligation reactions producing acetoin-like compounds with good enantioselectivity. Engineering the E2o catalytic (core) domain enabled (a) assignment of roles for pivotal residues involved in catalysis, (b) re-construction of the substrate-binding pocket to accept substrates other than succinyllysyldihydrolipoamide and (c) elucidation of the mechanism of trans-thioesterification to involve stabilization of a tetrahedral oxyanionic intermediate with hydrogen bonds by His375 and Asp374, rather than general acid–base catalysis which has been misunderstood for decades. The E. coli OGDHc is the first example of a 2-oxo acid dehydrogenase complex which was evolved to a 2-oxo aliphatic acid dehydrogenase complex by engineering two consecutive E1o and E2o components.
Risk Management Committee and Textual Risk Disclosure
Authors: Ayuningtyas Eka Sari , Harymawan Iman
Published: February 2022

This research examines the relationship between the risk management committee and textual risk disclosure. Textual risk disclosure is measured using the use of a risk-contained tone in the annual report. We employed empirical analysis for the Indonesian listed firms for the period 2010 to 2018. The findings of this research suggest that the existence of the risk management committee gives more risk disclosure. This finding implicates that firms with a risk management committee will give more risk disclosure, because they have a specific committee which have abilities concerning firm risk. The first additional analysis suggests that the results are more pronounced for firms within the period after the regulation to have a risk management committee was applied in Indonesia. We also make second additional analysis for different level of technology within industry. The existence of risk management committee for managing risk disclosure is more pronounced for company within high level of technology industry. We provide several contributions to the users of the financial statements such as shareholders and other stakeholders, especially regulatory bodies in Indonesia.
On the Diversification of Fixed Income Assets
Authors: Le Courtois Olivier
Published: February 2022

This article introduces a new approach for dealing with the diversification/concentration risk of fixed income assets. Because Government bonds, corporate bonds, and mortgage backed securities constitute a large proportion of the assets of institutional investors in most countries, it is important to be able to determine the number of lines/issuers of such assets, not only for portfolio management but also for risk management purposes. The approach that I introduce shows the dependence of the critical number of lines of fixed income assets on the main interest rate risk and credit risk drivers. Specifically, I examine the importance of volatility risk, force of mean reversion, default risk, recovery risk, and default dependence risk on the critical number of assets in a fixed income portfolio. The methodology in this paper relies on the use of the coefficient of variation for the computation of the critical number of credit-sensitive securities in a fixed income portfolio. To the best of my knowledge, this paper is the first to develop such an approach.
Artificial Neural Network-Based Ionospheric Delay Correction Method for Satellite-Based Augmentation Systems
Authors: Wang Shan , Wang Ding , Sun Junren
Published: February 2022

Ionospheric delay is a critical error source in Global Navigation Satellite Systems (GNSSs) and a principal aspect of Satellite Based Augmentation System (SBAS) corrections. Grid Ionospheric Vertical Delays (GIVDs) are derived from the delays on Ionosphere Pierce Points (IPPs), which are observed by SBAS reference stations. SBAS master stations calculate ionospheric delay corrections by several methods, such as planar fit or Kriging. However, when there are not enough IPPs around an Ionosphere Grid Point (IGP) or the IPPs are unevenly distributed, the fitting accuracy of planar fit or Kriging is unsatisfactory. Moreover, the integrity bounds of Grid Ionospheric Vertical Errors (GIVEs) are overly conservative. Since Artificial Neural Networks (ANNs) are widely used in ionospheric research due to their self-adaptation, parallelism, non-linearity, robustness, and learnability, the ANN method for GIVD and GIVE derivation is proposed in this article. Networks are separately trained for IGPs, and five years of historical data are applied on network training. Principal Component Analysis (PCA) is applied for dimensionality reduction of geomagnetic and solar indices, which is employed as a network input feature. Furthermore, the GIVE algorithm of the ANN method is derived based on the distribution of the residual random variable. Finally, experiments are conducted on 12 IGPs over the East China region. Under normal ionospheric conditions, compared with the planar fit and Kriging methods, the residual reduction of the ANN method is approximately 15%. The ANN method fits the ionospheric delay residual error better. The percentage of GIVE availability under 2.7 m increases at least 25 points in comparison to Kriging. Under disturbed conditions, due to a lack of training samples, the ANN method is incompetent compared with planar fit or Kriging.
Modification of Temperature Lapse Rates and Cloud Properties during a Spatiotemporally Extended Dust Aerosol Episode (16–18 June 2016) over the Mediterranean Basin Based on Satellite and Reanalysis Data
Authors: Gavrouzou Maria , Hatzianastassiou Nikolaos , Lolis Christos J. , Korras-Carraca Marios-Bruno , Mihalopoulos Nikolaos
Published: February 2022

A spatiotemporally extended dust aerosol episode that occurred over the Mediterranean Basin (MB) from 16 to 18 June 2016 is investigated using observational satellite and reanalysis data, focusing on the effects of high dust loads on cloud formation and temperature fields, including the creation of temperature inversions. The atmospheric conditions before and during the 3-day dust aerosol episode case (DAEC) are also analyzed. The dust episode, which is identified using a contemporary satellite algorithm, consists of long-range transport of African dust to the western and central MB. The day to day, before and during the DAEC, atmospheric circulation, dust-cloud interactions, and dust effect on temperature are examined using a variety of Moderate Resolution Imaging Spectroradiometer (MODIS) Level-3 Collection 6.1 satellite and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data. According to the obtained results, the dust export from N. Africa, which occurs under the prevalence of a trough over the western MB, and a ridge over the central MB, extends from southwest to northeast along two axes, one in the western and another in the central Mediterranean, covering remote areas up to the coasts of southern Europe, including the Balearic and Tyrrhenian Seas, the Italian peninsula, the Ionian and Adriatic Seas, and the Balkan peninsula. The analysis provides evidence of the formation of mixed-phase clouds, with high cloud-top heights (CTH higher than 10 km) and low cloud-top temperatures (CTT as low as 230 K), which spatiotemporally coincide with the high dust loadings that provide the necessary CCN and IN. Dust aerosols are transported either in the boundary layer (within the first 1–2 km) of areas close to the North African dust source areas or in the free troposphere over the Mediterranean Sea and the Italian and Balkan peninsulas (between 2 and 8 km). Distinct and extended layers of remarkable temperature inversions (up to 20 K/km) are created below the exported dust layers in the boundary layer of Mediterranean Sea areas, while weak/reduced lapse rates are formed over continental areas of MB undergoing the dust transport. Such modifications of temperature fields are important for the dynamics of the atmosphere of MB.
Mapping of Dwellings in IDP/Refugee Settlements from Very High-Resolution Satellite Imagery Using a Mask Region-Based Convolutional Neural Network
Authors: Gella Getachew Workineh , Wendt Lorenz , Lang Stefan , Tiede Dirk , Hofer Barbara , Gao Yunya , Braun Andreas
Published: February 2022

Earth-observation-based mapping plays a critical role in humanitarian responses by providing timely and accurate information in inaccessible areas, or in situations where frequent updates and monitoring are required, such as in internally displaced population (IDP)/refugee settlements. Manual information extraction pipelines are slow and resource inefficient. Advances in deep learning, especially convolutional neural networks (CNNs), are providing state-of-the-art possibilities for automation in information extraction. This study investigates a deep convolutional neural network-based Mask R-CNN model for dwelling extractions in IDP/refugee settlements. The study uses a time series of very high-resolution satellite images from WorldView-2 and WorldView-3. The model was trained with transfer learning through domain adaptation from nonremote sensing tasks. The capability of a model trained on historical images to detect dwelling features on completely unseen newly obtained images through temporal transfer was investigated. The results show that transfer learning provides better performance than training the model from scratch, with an MIoU range of 4.5 to 15.3%, and a range of 18.6 to 25.6% for the overall quality of the extracted dwellings, which varied on the bases of the source of the pretrained weight and the input image. Once it was trained on historical images, the model achieved 62.9, 89.3, and 77% for the object-based mean intersection over union (MIoU), completeness, and quality metrics, respectively, on completely unseen images.
Noise Floor and Signal-to-Noise Ratio of Radio Occultation Observations: A Cross-Mission Statistical Comparison
Authors: Gorbunov Michael , Irisov Vladimir , Rocken Christian
Published: February 2022

Multiple radio occultation (RO) missions are currently providing observations that are assimilated by the world’s leading numerical weather prediction centers. These RO missions use the same signals originating from the Global Navigation Satellite Systems (GNSS), but they have different satellite designs and sizes with different antennas and receivers. This results in different noise levels for different missions. Although the amplitude data are characterized by the Signal-to-Noise Ratio (SNR), the noise, to which they are normalized, is not the real Noise Floor (NF) of the RO observations. We study the statistical distributions of the SNR and NF for RO missions including COSMIC, COSMIC2, METOP-A, METOP-B, METOP-C, and Spire. We demonstrate that different missions have different NF values and different NF and SNR distributions, sometimes multimodal. We propose to use the most probable NF value as an SNR normalization constant in order to compare the SNR values from different RO missions.
Comparison of GHRSST SST Analysis in the Arctic Ocean and Alaskan Coastal Waters Using Saildrones
Authors: Vazquez-Cuervo Jorge , Castro Sandra L. , Steele Michael , Gentemann Chelle , Gomez-Valdes Jose , Tang Wenqing
Published: February 2022

There is high demand for complete satellite SST maps (or L4 SST analyses) of the Arctic regions to monitor the rapid environmental changes occurring at high latitudes. Although there are a plethora of L4 SST products to choose from, satellite-based products evolve constantly with the advent of new satellites and frequent changes in SST algorithms, with the intent of improving absolute accuracies. The constant change of these products, as reflected by the version product, make it necessary to do periodic validations against in situ data. Eight of these L4 products are compared here against saildrone data from two 2019 campaigns in the western Arctic, as part of the MISST project. The accuracy of the different products is estimated using different statistical methods, from standard and robust statistics to Taylor diagrams. Results are also examined in terms of spatial scales of variability using auto- and cross-spectral analysis. The three products with the best performance, at this point and time, are used in a case study of the thermal features of the Yukon–Kuskokwim delta. The statistical analyses show that two L4 SST products had consistently better relative accuracy when compared to the saildrone subsurface temperatures. Those are the NOAA/NCEI DOISST and the RSS MWOI SSTs. In terms of the spectral variance and feature resolution, the UK Met Office OSTIA product appears to outperform all others at reproducing the fine scale features, especially in areas of high spatial variability, such as the Alaska coast. It is known that L4 analyses generate small-scale features that get smoothed out as the SSTs are interpolated onto spatially complete grids. However, when the high-resolution satellite coverage is sparse, which is the case in the Arctic regions, the analyses tend to produce more spurious small-scale features. The analyses here indicate that the high-resolution coverage, attainable with current satellite infrared technology, is too sparse, due to cloud cover to support very high resolution L4 SST products in high latitudinal regions. Only for grid resolutions of ~9–10 km or greater does the smoothing of the gridding process balance out the small-scale noise resulting from the lack of high-resolution infrared data. This scale, incidentally, agrees with the Rossby deformation radius in the Arctic Ocean (~10 km).
Mapping Phragmites australis Aboveground Biomass in the Momoge Wetland Ramsar Site Based on Sentinel-1/2 Images
Authors: Zhao Yuxin , Mao Dehua , Zhang Dongyou , Wang Zongming , Du Baojia , Yan Hengqi , Qiu Zhiqiang , Feng Kaidong , Wang Jingfa , Jia Mingming
Published: February 2022

Phragmites australis (P. australis) is one of the most important plant species found in wetland ecosystems, and its aboveground biomass (AGB) is a key indicator for assessing the quality or health of a wetland site. In this study, we combined Sentinel-1/2 images and field observation data collected in 2020, to delineate the distribution of P. australis in the Momoge Ramsar Wetland site by using a random forest method, and further, to estimate AGB by comparing multiple linear regression models. The results showed that the overall classification accuracy of P. australis using the random forest method was 89.13% and the P. australis area in the site was 135.74 km2 in 2020. Among various remote sensing variables, the largest correlation coefficient was observed between dry weight of AGB of P. australis and Sentinel-2 red edge B7, and between fresh weight of P. australis AGB and red edge B5. The optimal models for estimating dry and fresh weight of P. australis AGB were multiple linear regression models, with an accuracy of 75.4% and 69.2%, respectively. In 2020, it was estimated that the total fresh weight of P. australis AGB in this Ramsar site was 21.2 × 107 kg and the total dry weight was 7.2 × 107 kg. The larger weight of P. australis AGB was identified mainly at central and western sites. The application of Sentinel-2 red-edge band for AGB estimation can significantly improve the model estimation accuracy. The findings of this study will provide a scientific basis for the management and protection of wetland ecosystems and sustainable utilization of P. australis resources.
PolSAR Cell Information Representation by a Pair of Elementary Scatterers
Authors: Karachristos Konstantinos , Koukiou Georgia , Anastassopoulos Vassilis
Published: February 2022

This study exploits Cameron’s decomposition for polarimetric data analysis and presents an information extraction process so that each PolSAR cell (pixel) is interpreted by two dominating elementary scattering mechanisms each one contributing to the scattering behavior of the SAR pixel with its own weight. The co-distance matrix is introduced to depict the metric distances between these two nearest scattering mechanisms. For most of the scattering mechanisms in each resolution cell, the strength between the first and the second nearest elementary scatterer usually differs slightly. This indicates that the interpretation of the available information in a PolSAR pixel by a single dominant scatterer, as most methods employ, is not adequate. The proposed method presents an alternative to Cameron’s spherical topology by taking advantage of the elementary scattering mechanisms complementary nature and inspired by the philosophy of principal component analysis. According to the introduced topology four elementary scatterers, which are in pairs complementary to each other, are adequate to characterize each PolSAR pixel. The aim of this research is to present a new feature-toοl with a more stochastic nature that could fit a variety of techniques that utilize fully polarimetric data. To prove the strength of the proposed method, the double-scatterer model is applied for interpreting each pixel on a variety of land cover types presenting a richer feature extraction capability, effective in detection and classification procedures.
Automatic Identification of Forest Disturbance Drivers Based on Their Geometric Pattern in Atlantic Forests
Authors: Alonso Laura , Picos Juan , Armesto Julia
Published: February 2022

Monitoring forest disturbances has become essential towards the design and tracking of sustainable forest management. Multiple methodologies have been developed to detect these disturbances. However, few studies have focused on the automatic detection of disturbance drivers, an essential task as each disturbance has different implications for the functioning of the ecosystem and associated management actions. Wildfires and harvesting are two of the major drivers of forest disturbances across different ecosystems. In this study, an automated methodology is presented to automatically distinguish between the two once the disturbance is detected, using the properties of its geometry and shape. A cluster analysis was performed to automatically individualize each disturbance and afterwards calculate its geometric properties. Using these properties, a decision tree was built that allowed for the distinction between wildfires and harvesting with an overall accuracy of 91%. This methodology and further research relating to it could pose an essential aid to national and international agencies for incorporating forest-disturbance-driver-related information into forest-focused reports.
Research on Minimization of Data Set for State of Charge Prediction
Authors: Liu Tun , Zhao Jundong , Xiang Chaoqun , Cheng Shu
Published: February 2022

The quick estimation and prediction of lithium-ion batteries’ (LIBs) state of charge (SoC) are attracting growing attention, since the LIB has become one of the most essential power sources for daily consumer electronics. Most deep learning methods require plenty of data and more than two LIB parameters to train the model for predicting SoC. In this paper, a single-parameter SoC prediction based on deep learning is realized by cleaning the data for lithium-ion battery parameters and constructing the feature matrix based on the cleaned data. Then, by analyzing the feature matrix’s periodicity and principal component to obtain two kinds of the original eigenmatrix’s substitution matrices, the two substitutions are fused to obtain an excellent prediction effect. In the end, the minimization method is verified with newly measured lithium battery data, and the results show that the MAPE of the SoC prediction reaches 0.96%, the input data are reduced by 93.33%, and the training time is reduced by 96.68%. Fast and accurate prediction of the SoC is achieved by using only a minimum amount of voltage data.
A Machine-Learning Model for Lung Age Forecasting by Analyzing Exhalations
Authors: Pifarré Marc , Tena Alberto , Clarià Francisco , Solsona Francesc , Vilaplana Jordi , Benavides Arnau , Mas Lluis , Abella Francesc
Published: February 2022

Spirometers are important devices for following up patients with respiratory diseases. These are mainly located only at hospitals, with all the disadvantages that this can entail. This limits their use and consequently, the supervision of patients. Research efforts focus on providing digital alternatives to spirometers. Although less accurate, the authors claim they are cheaper and usable by many more people worldwide at any given time and place. In order to further popularize the use of spirometers even more, we are interested in also providing user-friendly lung-capacity metrics instead of the traditional-spirometry ones. The main objective, which is also the main contribution of this research, is to obtain a person’s lung age by analyzing the properties of their exhalation by means of a machine-learning method. To perform this study, 188 samples of blowing sounds were used. These were taken from 91 males (48.4%) and 97 females (51.6%) aged between 17 and 67. A total of 42 spirometer and frequency-like features, including gender, were used. Traditional machine-learning algorithms used in voice recognition applied to the most significant features were used. We found that the best classification algorithm was the Quadratic Linear Discriminant algorithm when no distinction was made between gender. By splitting the corpus into age groups of 5 consecutive years, accuracy, sensitivity and specificity of, respectively, 94.69%, 94.45% and 99.45% were found. Features in the audio of users’ expiration that allowed them to be classified by their corresponding lung age group of 5 years were successfully detected. Our methodology can become a reliable tool for use with mobile devices to detect lung abnormalities or diseases.
Measurement of Secondary Structure Changes in Poly-L-lysine and Lysozyme during Acoustically Levitated Single Droplet Drying Experiments by In Situ Raman Spectroscopy
Authors: Perlitz Julian F. A. , Gentner Lukas , Braeuer Phillipp A. B. , Will Stefan
Published: February 2022

Drying processes such as spray drying, as commonly used in the pharmaceutical industry to convert protein-based drugs into their particulate form, can lead to an irreversible loss of protein activity caused by protein secondary structure changes. Due to the nature of these processes (high droplet number, short drying time), an in situ investigation of the structural changes occurring during a real drying process is hardly possible. Therefore, an approach for the in situ investigation of the expected secondary structural changes during single droplet protein drying in an acoustic levitator by time-resolved Raman spectroscopy was developed and is demonstrated in this paper. For that purpose, a self-developed NIR–Raman sensor generates and detects the Raman signal from the levitated solution droplet. A mathematical spectral reconstruction by multiple Voigt functions is used to quantify the relative secondary structure changes occurring during the drying process. With the developed setup, it was possible to detect and quantify the relative secondary structure changes occurring during single droplet drying experiments for the two chosen model substances: poly-L-lysine, a homopolypeptide widely used as a protein mimic, and lysozyme. Throughout drying, an increase in the β-sheet structure and a decrease in the other two structural elements, α-helix, and random coil, could be identified. In addition, it was observed that the degree of structural changes increased with increasing temperature.
Semi-Empirical Time-Dependent Parameter of Shear Strength for Traction Force between Deep-Sea Sediment and Tracked Miner
Authors: Yi Wei , Xu Feng
Published: February 2022

Based on our direct shear creep experiment and the direct shear rheological constitutive model, a semi-empirical time-dependent parameter of the shear strength is obtained by Mohr–Coulomb shear strength theory, and different time-dependent traction force calculations between deep-sea sediment and a tracked miner are conducted by the work-energy principle. The time-dependent traction force calculation under its influencing factors, including the time, track shoe number, and grounding pressure, are analyzed and proved to be valid by the traction force experiment of a single-track shoe. The results show that the time-dependent cohesion force obtained by a semi-empirical way can be easily used to deduce the time-dependent traction force models under the different grounding pressure distributions and applied into deep-sea engineering application conveniently; the verified traction force models by the traction force experiment of a single-track shoe illustrate that traction force under the decrement grounding pressure distribution is the worst among the four kinds of grounding pressure distributions and suggested for evaluating the most unfavorable traction force and calculating the trafficability and stability of the deep-sea tracked miner.
Measuring Setup for Experimental Research of Two-Coordinate Fiber-Optic Acceleration Sensors with Cylindrical Lenses
Authors: Badeeva Elena , Murashkina Tatiana , Motin Andrey , Uvaysov Saygid , Kozbakova Ainur , Sawicki Daniel
Published: February 2022

The article presents the possibilities of using fiber-optic acceleration (FOC) sensors on products of rocket-space and aviation technology as part of information-measuring systems. A special measuring device has been developed for experimental confirmation of the main characteristics of the technical characteristics of the developed, two-coordinate fiber-optic acceleration sensors. The developed measuring setup for the experimental research of a two-coordinate fiber-optic acceleration sensor with two, cylindrical lenses fixed on two H-shaped elastic elements deflected under the influence of acceleration in two mutually perpendicular directions X and Y, intended for operation in harsh conditions of rocket and space technology. The experimental equipment consists of the developed setup for setting micromovements and an information conversion unit, including modules for signal conversion, transmission, power supply, signal amplification, and indication. Experimental dependences of the output voltage from the information conversion unit’s output on the micro-displacement in the range corresponding to the micro-displacements of the inertial mass with a cylindrical lens under acceleration in the range of ±100 m/s2 were obtained on the micro-displacement setting unit. The maximum value of the linearity error of the prototype acceleration sensor together with the information conversion unit was 0.07%. The conversion sensitivity of a two-coordinate fiber-optic acceleration sensor per the experimental dependences obtained on the Data Physics LE-612 MST/DSA 10–40 k vibration stand when exposed to sinusoidal vibration with an acceleration amplitude from 2 to 10 g in the frequency range from 5 to 2560 Hz was, on average, 3 mV/m/s2. The conducted experimental research confirms the performance of experimental samples of fiber-optic acceleration sensors together with an information conversion unit, as well as the achievement of high metrological characteristics.
Technological Convergence Assessment of the Smart Factory Using Patent Data and Network Analysis
Authors: Hussain Adnan , Jeon Jeonghwan , Rehman Mudassar
Published: February 2022

The smart factory has evolved as a key and distinctive idea for Industry 4.0. These industries impart a significant influence on sustainable production because of their consistent industrial evolution/development. Recently, their technological advancements are deemed inevitable to survive in this competitive industry due to increasing market needs. Therefore, technological convergence analysis can provide deep insight into industrial progress and convergence. Consequently, contemporary research trends are centered on evaluating technological convergence. Although various studies are already available on the technological development of the smart factory concerning Industry 4.0, however far less significant work is available on the technological convergence assessment of the smart factory by employing data networks and patents. Therefore, this work is focused on the investigation of reliable data analysis of the smart factory’s technologies and its technological convergence. This said methodology assisted in examining the network’s hidden linkages using network analysis. A relevant case study of a smart factory is also discussed to evaluate its technological convergence. Thus, data-driven technologies have diverted focus from International Patent Classification (IPC) visual networks using convergence assessment tools. The findings of this study are intended to aid companies and government officials in forecasting future sustainable technological developments and decision making.
Effects of Climate Change and Human Activities on Aeolian Desertification Reversal in Mu Us Sandy Land, China
Authors: Xie Jiali , Lu Zhixiang , Feng Kun
Published: February 2022

The aeolian desertification in Mu Us Sandy Land (MUSL) in northern China have been paid much attention, but the relative contributions of climate change and human activities to desertification dynamics are still not clear. Based on the Landsat MSS, TM, ETM+ and OLI images in 1975, 1990, 1995, 2000, 2005, 2010 and 2015, we developed a database of aeolian desertification land distribution, discussed the spatial and temporal variation of aeolian desertification, and discovered the relative contributions of climate change and human activities to desertification reversal, using the trends of the potential net primary productivity (NPP) and the human-influenced NPP with meteorological data and MODIS NPP products. The results indicated that aeolian desertification developed firstly from 1975 to 2000, with serious and severe aeolian desertification land continually increasing, and then changed into a reversal state from 2000 to 2015, as the serious aeolian desertification land decreased, although the severe, moderate and light aeolian desertification land lightly increased. Human activities were the dominant factor in desertification dynamics in MUSL and had different contributions to aeolian desertification reversal in different periods. This study will improve our understanding of the processes of aeolian desertification.
Reshaping the Module: The Path to Comprehensive Photovoltaic Panel Recycling
Authors: Isherwood Patrick J. M.
Published: February 2022

The market for photovoltaic modules is expanding rapidly, with more than 500 GW installed capacity. Consequently, there is an urgent need to prepare for the comprehensive recycling of end-of-life solar modules. Crystalline silicon remains the primary photovoltaic technology, with CdTe and CIGS taking up much of the remaining market. Modules can be separated by crushing or cutting, or by thermal or solvent-based delamination. Separation and extraction of semiconductor materials can be achieved through manual, mechanical, wet or dry chemical means, or a combination. Crystalline silicon modules are currently recycled through crushing and mechanical separation, but procedures do exist for extraction and processing of intact wafers or wafer pieces. Use of these processes could lead to the recovery of higher grades of silicon. CdTe panels are mostly recycled using a chemical leaching process, with the metals recovered from the leachate. CIGS can be recycled through oxidative removal of selenium and thermochemical recovery of the metals, or by electrochemical or hydrometallurgical means. A remaining area of concern is recycling of the polymeric encapsulant and backsheet materials. There is a move away from the use of fluorinated backsheet polymers which may allow for improved recycling, but further research is required to identify materials which can be recycled readily whilst also being able to withstand outdoor environments for multi-decadal timespans.
Environmental Externalities of Secondhand Markets—Based on a Dutch Auctioning Company
Authors: Mak Martijn , Heijungs Reinout
Published: February 2022

Buying reused products rather than new ones could reduce the emissions of greenhouse gases (GHGs). There are many aspects that can influence whether used products actually cause emissions to increase or decrease. This paper assesses the effects of secondhand markets on GHG emissions by using data on twelve product categories from Troostwijk Auctions, which is a Dutch auctioning company. Data came from a carbon footprint database, survey data and many other sources. The net impact of secondhand trade is calculated by combining existing formulas in the literature and by performing regressions to estimate the values of unknown data. A methodology is proposed to find appropriate assumptions to handle uncertainty of carbon footprints. The main result is that the emission savings due to reuse of almost all products in the analysis are offset because many buyers purchase goods that they would not have bought new. Trade in the vehicles included in this study even adds emissions.
Will COVID-19 Boost Sustainable Tourism: Wishful Thinking or Reality?
Authors: Tauber Verena , Bausch Thomas
Published: February 2022

Since the outbreak of the COVID-19 pandemic, scholars have presented publications discussing a shift of tourism towards a higher level of sustainability. Many argue that in 2020, people were not able to travel as usual and therefore could discover the added value of a sustainable vacation through new experiences in new, often domestic destinations. Using a quantitative online panel-based study in five European countries and the USA, we looked for evidence supporting such arguments. We analyzed demographics, the observed change in destination choice, and important criteria when selecting a different destination, including potential effects of the pandemic on traveling. We uncovered possible impacts of the 2020 vacation experience on future traveling and looked at both travel push factors and social values of non-travelers and travelers for explanation. Overall, we could not find any evident signals for the pandemic to be a trigger for more sustainable traveling, nor a long-term change in future demand.
Green Densities: Accessible Green Spaces in Highly Dense Urban Regions—A Comparison of Berlin and Qingdao
Authors: Mumm Olaf , Zeringue Ryan , Dong Nannan , Carlow Vanessa Miriam
Published: February 2022

Recreational green spaces are associated with human thriving and well-being. During the ongoing COVID-19 pandemic a spotlight has been shed on the interconnection between access to these spaces, human well-being and social equity. Containment measures enacted in many cities effectively precluded people from reaching distant recreational areas during the pandemic and consequently, recreational areas close to home became particularly important. Urban density is often associated with building or population density with the assumption that if either parameter has a high value, the availability of open (green) space is low. Certain densities are associated with specific spatial qualities. Addressing challenges of sustainable development, a detailed evaluation of density is necessary to allow evidence-based arguments, planning and decision-making. In this study we develop a multi-scale analysis method for quantifying and assessing green infrastructures from settlement unit to building level to reach a differentiated view on density, arguing that density can be organized in different ways achieving very different qualities. For this purpose, we use geospatial-data analysis and in-depth neighborhood studies to compare two cities in Asia and Europe, revealing different ways of organizing density in the built environment and identifying a derivation of approaches for sustainable development in dense urban regions.
Analysis of Crash Severity of Texas Two Lane Rural Roads Using Solar Altitude Angle Based Lighting Condition
Authors: Abbasi Mohammadhossein , Piccioni Cristiana , Sierpiński Grzegorz , Farzin Iman
Published: February 2022

Many studies have examined the impact of factors affecting accident severity in rural areas; however, little attention has been paid to different lighting conditions (LCs), and less to the detailed categories and precise determining of twilight. In this paper, solar altitude angle (SAA), as a basis for differentiating and categorizing LCs, is proposed to investigate explanatory variables in much greater detail. For each LC, namely, dark, twilight, dark lit (dark with street lights) and daylight, separate random parameter models are developed to investigate the impacts of some factors on crash injury severity data of 2017 and 2018 in two lane rural roads of Texas. The model estimation results indicated that different LCs have various contributing factors, indeed, to each injury severity, further stressing the significance of investigating crashes based on SAA. The key differences include crash location, marked lane, grade direction, no passing zone, shoulder width, weekday and collision type. The important findings were that developing artificial lighting at intersections and LED raised pavement markers on two lane rural roads could lead to enhanced road safety under dark LCs. Furthermore, increasing shoulder width in straight segments of two lane rural roads is important for decreasing severe injury in daylight conditions.
The Financial Performance of Family versus Non-Family Firms Operating in Nautical Tourism
Authors: Santos Eleonora , Lisboa Inês , Eugénio Teresa
Published: February 2022

This article analyses the financial performance of family versus non-family firms operating in nautical tourism, in 2015–2019. The sample of 39 Portuguese companies was collected from the SABI database. We use a regression of financial performance, measured by three alternative proxies: return on assets, return on equity and operating profit margin, on liquidity, leverage, turnover of assets, asset structure, company size and age. The regressions are performed across Nuts II regions on mainland and across types of firms (family and non-family). The results uncover several patterns. First, family firms are larger and older, make higher investments and therefore are less liquid. Second, liquidity, leverage and investment in tangible assets impact negatively and significantly the corporate financial performance, while the turnover of assets, size and age impacts positively and significantly. Third, the sign of the impacts depends on the measure of performance. Finally, firms in the Northern region show superior performance, which can be explained by the higher share of family firms. These findings can serve as a roadmap for managers when selecting strategies to improve performance. Additionally, they will contribute to the understanding of tourism destination dynamics and competitiveness.
Corporate Social Responsibility and Marketing: A Bibliometric and Visualization Analysis of the Literature between the Years 1994 and 2020
Authors: Quezado Tarcia Camily Cavalcante , Cavalcante William Quezado Figueiredo , Fortes Nuno , Ramos Ricardo Filipe
Published: February 2022

Several studies explored the effect of Corporate Social Responsibility (CSR) on marketing. However, bibliometric research that organizes this production is scarce. Thus, this study aims to provide a bibliometric view of marketing-related CSR research, identifying this field’s state-of-the-art literature. Two thousand and forty-two articles were collected through the Web of Science (WoS) platform. Data were analyzed using VOSviewer software to map the data graphically. The results show that: (a) the literature on CSR in the marketing area is growing; (b) five articles alone accounted for 9940 citations, and there are several prolific authors; (c) the prominent journals identified in this research published 42.16% of the total; (d) The “Journal of Business Ethics” is the leader in the number of publications, followed by “Sustainability,” which has shown strong growth in recent years, and; (e) The US is the leading country, according to the number of articles and citations. The keyword trending network analysis revealed that CSR is becoming a strategic marketing approach for companies. This study offers an insight into the state-of-the-art and trends identification in CSR and marketing.
Performance Comparison between Fixed and Dual-Axis Sun-Tracking Photovoltaic Panels with an IoT Monitoring System in the Coastal Region of Ecuador
Authors: Ponce-Jara Marcos A. , Velásquez-Figueroa Carlos , Reyes-Mero María , Rus-Casas Catalina
Published: February 2022

Solar photovoltaic (PV) energy systems are one of the most widely deployed renewable technologies in the world. The efficiency of solar panels has been studied during the last few decades, and, to date, it has not been possible to displace the production of energy using crystalline silicon wafer-based technology whose efficiency has reached values around 26.1%. Moreover, using solar tracking PV systems has become a feasible alternative to increase the electric output of PV silicon technologies instead of using the conventional fixed PV installation on a flat or sloping surface. The following study has compared fixed and dual-axis sun-tracking PV panels in order to quantify the enhancement associated with the amount of energy harvested when using dual-axis tracking PV systems in the city of Manta, located in a coastal region of Ecuador. In order to carry out this study, an IoT monitoring system based on Raspberry Pi3 and Arduino platforms was used. Measurements of solar radiation (W/m2), light intensity (Lux), temperature (°C), short-circuit current (A), and open-circuit voltage (V) were taken every minute for both systems. The results prove that the dual-axis tracking PV system produces, on average, 19.62% more energy than the static PV system. These results present an 8.62% energy increase with respect to a previous study carried out in an equatorial region with similar characteristics to those of the city of Manta, where a one-axis tracking PV system was used.
Application of Modern Digital Systems and Approaches to Business Process Management
Authors: Zahar Djordjevic Marija , Djordjevic Aleksandar , Klochkova Elena , Misic Milan
Published: February 2022

In order to adapt to ever-changing customer needs and satisfy them, good Business Process Management (BPM) in Small and Medium-sized Enterprises (SMEs) is crucial. The target group of this research is production SMEs whose BPM can be monitored respecting the values of key performance indicators (KPIs). This paper shows how improving the performance of the observed business processes can improve the level of customer satisfaction. This improvement should lead to the sustainability of SMEs in the market. In this paper, evaluation of business processes performance is defined as a multi-criteria decision problem. The relative importance of considered KPIs and their imprecise values are described by linguistic expressions, which are then modeled by triangular intuitionistic fuzzy numbers (TIFNs). Calculation of KPI weights is done by using the fuzzy analytic hierarchy process (FAHP). Evaluation of BPM success is conducted respecting the obtained KPI weights and KPI values. An optimal solution for BPM success improvement, respecting customer satisfaction indicators, is calculated using the Artificial Neural Network (ANN) and Genetic Algorithm (GA) approaches. By applying the proposed model, managers of production SMEs can determine the management initiatives that will improve their business and the sustainability of their companies.
Basic Psychological-Need Satisfaction and Thwarting: A Study with Brazilian Professional Players of League of Legends
Authors: Lopes Angelo Daniela , Villas Boas Junior Marcelo , Freitas Corrêa Mariana de , Hernandez Souza Vânia , Paula Moura Larissa de , Oliveira Rosemeire de , Reyes Bossio Mario , Ferreira Brandão Maria Regina
Published: February 2022

Recently, the skill to play games has led to the professionalization of the activity in the form of “eSports” (electronic sports). Despite the popularity of eSports, little is known about its professional players from a psychological perspective. Given the importance of the coach-created environment in the athletes’ motivational processes, this study aimed to investigate the key psychological dimensions of the coach-created climate in 75 Brazilian professional players of League of Legends (LoL) considering the Self-Determination Theory (SDT) and Achievement Goal Theory (AGT). Fourteen hypotheses were tested, of which seven were confirmed. The empowering climate was a predictor of basic psychological-needs satisfaction and indirectly influenced autonomous motivation. The need satisfaction had a significant impact on both autonomous motivation and on lack of motivation, which, in turn, explained 56% of the variance in well-being and the intention to keep playing eSports. The disempowering climate was a predictor of psychological-needs thwarting but had no significant impact on autonomous motivation or lack of motivation. The results obtained support SDT and AGT in the context of eSports and were similar to those conducted with athletes from traditional sports, indicating that the empowering-and-disempowering-coaching-climates conceptualization applies not only to traditional sports athletes but also to professional eSports players.
Assessing Education for Sustainable Development in Engineering Study Programs: A Case of AI Ecosystem Creation
Authors: Paulauskaite-Taraseviciene Agne , Lagzdinyte-Budnike Ingrida , Gaiziuniene Lina , Sukacke Vilma , Daniuseviciute-Brazaite Laura
Published: February 2022

The issue of sustainability in education has never been more important for the future of our environment, and strategies to develop the skills needed by younger generations to meet this significant global challenge should be developed across all curricula. There is much focus on the topic of sustainability in business, finance, climate, health, water and education; however, there are some challenges when sustainability needs to be integrated into engineering or fundamental study programs (SPs). In the latter, sustainability is more often emphasized and implemented through its general principles or separate modules in social sciences and project activities. There are a number of questions and challenges in how to highlight sustainability aspects and evaluation metrics due to the specifics of the engineering study field. For evaluating the sustainability level in engineering studies, a hierarchical methodology employing the SAMR (Substitution, Augmentation, Modification, Redefinition) model is proposed, taking a technological university in Lithuania as the case study. As a more concrete example, the first and second cycle SPs titled ‘Artificial Intelligence’ are described and analyzed in all relevant perspectives of sustainability. The study proposes five tangible criteria that must be emphasized in the learning process in order to ensure the development of sustainability goals in IT/AI study programs.
The Future of Facilities Management: Managing Facilities for Sustainable Development
Authors: Opoku Alex , Lee Jeoung Yul
Published: February 2022

Facilities management (FM) is currently shifting emphasis towards a long-term focus through the adoption of maintenance and operation practices that considers the social, environmental, and economic benefits of all business decisions [...]