bluuebunny/crossref_metadata_2025 huggingface dataset:
Financial Risk Management of the Russian Economy during the COVID-19 Pandemic
Authors: Kolchin Sergey , Glubokova Nadezda , Gordienko Mikhail , Semenova Galina , Khalilova Milyausha
Published: April 2023

The research objects are the tax and budgetary policies of the Russian Federation. In this research, financial (budgetary) risks are understood as a decrease in the balance of the state (national) budget resulting from a reduction in revenues or an increase in expenditures. This research considers production in the main sectors of the economy as a key factor of financial risk during the COVID-19 pandemic. The research aims to analyze the main directions of the budgetary and tax policy of the Russian Federation that aimed at supporting the economy and the population during the spread of COVID-19, which is especially relevant in connection with the expected recession in a number of sectors of the economy and a decrease in the level of employment and, accordingly, the well-being of citizens. In these conditions, it is necessary to adjust the budgetary and tax policy to preserve the state’s social obligations and expand social and economic support for businesses and citizens to smooth out the negative consequences of the impact of restrictive measures. The authors applied systemic and institutional approaches and statistical methods. The main results of the research reflect the need to (1) implement support measures (tax and budgetary incentives) for small and medium-sized enterprises, on which the crisis provoked by the COVID-19 pandemic has had the most destructive impact, and (2) to expand the volume of budgetary financing of social programs for financial risk management of the Russian economy during the COVID-19 pandemic. Compositionally, the article consists of the following sections: the introduction, which provides an overview of the publication activity in the field of financing measures to overcome the spread of COVID-19 and substantiates the relevance and purpose of the study; the literature review, which lists modern authors whose works were aimed at studying similar issues as well as the methodological apparatus used by them, which are suitable for adaptation; the section ‘materials and methods’, which provides more adaptive methods of other people’s research and the authors selected in accordance with them are listed; the results section, in which the authors present the main array of statistical data, which is then discussed. At the end of the article, the authors draw conclusions about the applied fiscal policy tools that can be used effectively in the new economic reality.
Underground Water Level Prediction in Remote Sensing Images Using Improved Hydro Index Value with Ensemble Classifier
Authors: Stateczny Andrzej , Narahari Sujatha Canavoy , Vurubindi Padmavathi , Guptha Nirmala S. , Srinivas Kalyanapu
Published: April 2023

The economic sustainability of aquifers across the world relies on accurate and rapid estimates of groundwater storage changes, but this becomes difficult due to the absence of in-situ groundwater surveys in most areas. By closing the water balance, hydrologic remote sensing measures offer a possible method for quantifying changes in groundwater storage. However, it is uncertain to what extent remote sensing data can provide an accurate assessment of these changes. Therefore, a new framework is implemented in this work for predicting the underground water level using remote sensing images. Generally, the water level is defined into five levels: Critical, Overexploited, Safe, Saline, and Semi-critical, based on water quantity. In this manuscript, the remote sensing images were acquired from remote sensing images. At first, Wiener filtering was employed for preprocessing. Secondly, the Vegetation Indexes (VI) (Normalized Difference Vegetation Index (NDVI), Normalized Difference Snow Index (NDSI), Infrared index (IRI), Radar Vegetation Index (RVI)), and statistical features (entropy, Root Mean Square (RMS), Skewness, and Kurtosis) were extracted from the preprocessed remote sensing images. Then, the extracted features were combined as a novel hydro index, which was fed to the Ensemble Classifier (EC): Neural Networks (NN), Support Vector Machine (SVM), and improved Deep Convolutional Neural Network (DCNN) models for underground water level prediction in the remote sensing images. The obtained results prove the efficacy of the proposed framework by using different performance measures. The results shows that the False Positive Rate (FPR) of the proposed EC model is 0.0083, which is better than that of existing methods. On the other hand, the proposed EC model has a high accuracy of 0.90, which is superior to the existing traditional models: Long Short-Term Memory (LSTM) network, Naïve Bayes (NB), Random Forest (RF), Recurrent Neural Network (RNN), and Bidirectional Gated Recurrent Unit (Bi-GRU).
Quantitative Inversion Ability Analysis of Oil Film Thickness Using Bright Temperature Difference Based on Thermal Infrared Remote Sensing: A Ground-Based Simulation Experiment of Marine Oil Spill
Authors: Wang Meiqi , Yang Junfang , Liu Shanwei , Zhang Jie , Ma Yi , Wan Jianhua
Published: April 2023

Oil spills on the sea surface have caused serious harm to the marine ecological environment and coastal environment. Oil film thickness (OFT) is an important parameter for estimating oil spills amount, and accurate quantification of OFT is of great significance for rapid response and risk assessment of oil spills. In recent years, thermal infrared remote sensing has been gradually applied to quantify the OFT. In this paper, the outdoor oil spill simulation experiments were designed, and the bright temperature (BT) data of different OFTs were obtained for 24 consecutive hours in summer and autumn. On the basis of the correlation analysis of OFT and bright temperature difference (BTD) between oil and water, the traditional regression fitting model, classical machine learning model, ensemble learning model, and deep learning model were applied to the inversion of OFT. At the same time, inversion results of the four models were compared and analyzed. In addition, the best OFT inversion time using thermal infrared was studied based on 24-h thermal infrared data. Additionally, the inversion results were compared with the measured results; the optimal OFT range detectable using thermal infrared was explored. The experimental results show that: (1) Compared with ensemble learning model, traditional regression fitting model, and classical machine learning model, Convolutional Neural Network (CNN) has the advantages of high stability while maintaining high-precision inversion, and can be used as the preferred model for oil film thickness inversion; (2) The optimal time for OFT detection is around 10:00 to 13:00 of the day, and is not affected by seasonal changes; (3) During the day, thermal infrared has good detection ability for OFT greater than 0.4 mm, and weak detection ability for thinner oil films; (4) At night, thermal infrared has certain detection ability for relatively thick oil film, but the accuracy is lower than that in the daytime.
Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment
Authors: Schmidt Toni , Kuester Theres , Smith Taylor , Bochow Mathias
Published: April 2023

Plastics are part of our everyday life, as they are versatile materials and can be produced inexpensively. Approximately 10 Gt of plastics have been produced to date, of which the majority have been accumulated in landfills or have been spread into the terrestrial and aquatic environment in an uncontrolled way. Once in the environment, plastic litter—in its large form or degraded into microplastics—causes several harms to a variety of species. Thus, the detection of plastic waste is a pressing research question in remote sensing. The majority of studies have used Sentinel-2 or WorldView-3 data and empirically explore the usefulness of the given spectral channels for the detection of plastic litter in the environment. On the other hand, laboratory infrared spectroscopy is an established technique for the differentiation of plastic types based on their type-specific absorption bands; the potential of hyperspectral remote sensing for mapping plastics in the environment has not yet been fully explored. In this study, reflectance spectra of the five most commonly used plastic types were used for spectral sensor simulations of ten selected multispectral and hyperspectral sensors. Their signals were classified in order to differentiate between the plastic types as would be measured in nature and to investigate sensor-specific spectral configurations neglecting spatial resolution limitations. Here, we show that most multispectral sensors are not able to differentiate between plastic types, while hyperspectral sensors are. To resolve absorption bands of plastics with multispectral sensors, the number, position, and width of the SWIR channels are decisive for a good classification of plastics. As ASTER and WorldView-3 had/have narrow SWIR channels that match with diagnostic absorption bands of plastics, they yielded outstanding results. Central wavelengths at 1141, 1217, 1697, and 1716 nm, in combination with narrow bandwidths of 10–20 nm, have the highest capability for plastic differentiation.
Seasonal Transitions in the Thermosphere Inferred from Ionospheric Observations
Authors: Perrone Loredana , Mikhailov Andrey V.
Published: April 2023

Ionospheric observations along with CHAMP/STAR neutral gas density measurements were used to retrieve thermospheric parameters and to check whether the equinox transition season exists separately from the December solstice and June solstice seasons. Juliusruh and Boulder ionosonde stations located in “far-from-pole” and “near-pole” longitudinal sectors were analyzed during deep solar minimum in 2008–2009. The results were compared to GOLD column O/N2 ratio observations. The retrieved thermospheric parameters have shown that equinoctial transition period exists separately from the winter one at Juliusruh, while column O/N2 ratios, exospheric temperatures Tex, and vertical plasma drifts related to thermospheric winds retrieved at Boulder for the winter season do not significantly differ from vernal values. This means that the December solstice season just does not exist as it merges with the vernal season in the “near-pole” longitudinal sector. The obtained results indicate that two longitudinal sectors manifest different seasonal variations both in thermospheric circulation and neutral composition.
Case Study of a Mesospheric Temperature Inversion over Maïdo Observatory through a Multi-Instrumental Observation
Authors: Chane Ming Fabrice , Hauchecorne Alain , Bellisario Christophe , Simoneau Pierre , Keckhut Philippe , Trémoulu Samuel , Listowski Constantino , Berthet Gwenaël , Jégou Fabrice , Khaykin Sergey , Tidiga Mariam , Le Pichon Alexis
Published: April 2023

The dynamic vertical coupling in the middle and lower thermosphere (MLT) is documented over the Maïdo observatory at La Réunion island (21°S, 55°E). The investigation uses data obtained in the framework of the Atmospheric dynamics Research InfraStructure in Europe (ARISE) project. In particular, Rayleigh lidar and nightglow measurements combined with other observations and modeling provide information on a mesospheric inversion layer (MIL) and the related gravity waves (GWs) on 9 and 10 October 2017. A Rossby wave breaking (RWB) produced instabilities in the sheared background wind and a strong tropospheric activity of GWs on 9–11 October above La Réunion. The MIL was observed on the night of 9 October when a large amount of tropospheric GWs propagated upward into the middle atmosphere and disappeared on 11 October when the stratospheric zonal wind filtering became a significant blocking. Among other results, dominant mesospheric GW modes with vertical wavelengths of about 4–6 km and 10–13 km can be traced down to the troposphere and up to the mesopause. Dominant GWs with a wavelength of ~2–3 km and 6 km also propagated upward and eastward from the tropospheric source into the stratosphere on 9–11 October. Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperature and OH profiles indicate that GW activity in the middle atmosphere affects the upper atmosphere with waves breaking at heights below the MIL and in the mesopause. Several techniques are illustrated on nightglow images to access GW activity and spectral characteristics at the mesopause for high and low frequency GWs on the nights of 9–10 October. In conclusion, intense tropospheric activity of GWs induced by RWB events can be linked with MILs at the subtropical barrier in the South-West Indian Ocean during austral winter.
Improved Understanding of Flash Drought from a Comparative Analysis of Drought with Different Intensification Rates
Authors: Han Jiaqi , Zhang Jiahua , Yang Shanshan , Seka Ayalkibet M.
Published: April 2023

The rapid intensification of drought, commonly known as flash drought, has recently drawn widespread attention from researchers. However, how the characteristics and drivers, as well as the ecological impacts of rapidly intensified droughts, differ from those of slowly intensified ones still remains unclear over the globe. To this end, we defined three types of droughts based on the root zone soil moisture (RZSM) decline rates, flash droughts, general droughts, and creep droughts, and then implemented a comparative analysis between them across the globe and the 26 Intergovernmental Panel on Climate Change Special Report on Extremes (IPCC-SREX) regions. The ensemble of RZSM from multiple reanalysis datasets was used to reduce the uncertainties. According to the frequency analysis, our findings suggest that flash droughts contributed to the majority of drought events during 1980–2019, indicating the prevalence of rapid transition from an energy-limited to a water-limited condition in most of the regions. The comparative results of vegetation responses show that flash droughts are more likely to happen in the growing season, leading to faster but relatively minor vegetation deterioration compared to the slowly intensified ones. By analyzing the precipitation and temperature anomalies in the month of drought onset, we found the role of temperature (precipitation) on drought intensification can be generalized as the warmer (drier) the climate is or the faster the drought intensifies, but the main driving forces vary by region. Unlike temperature dominating in midwestern Eurasia and northern high latitudes, precipitation plays a prominent role in the monsoon regions. However, the temperature is expected to be the decisive driver in the warming future, given its monotonically increased contribution over the past four decades.
A Robust Algorithm for Photon Denoising and Bathymetric Estimation Based on ICESat-2 Data
Authors: Zhong Junsheng , Liu Xiuguo , Shen Xiang , Jiang Liming
Published: April 2023

The Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) is equipped with an Advanced Terrain Laser Altimeter System (ATLAS) with the capability of penetrating water bodies, making it a widely utilized tool for the bathymetry of various aquatic environments. However, the laser sensor often encounters a significant number of noise photons due to various factors such as sunlight, water quality, and after-pulse effect. These noise photons significantly compromise the accuracy of bathymetry measurements. In an effort to address this issue, this study proposes a two-step method for photon denoising by utilizing a method combining the DBSCAN algorithm and a two-dimensional window filter, achieving an F1 score of 0.94. A robust M-estimation method was employed to estimate the water depth of the denoised and refraction-corrected bathymetric photons, achieving an RMSE of 0.30 m. The method proposed in this paper preserves as much information as possible about signal photons, increases the number of bathymetric points, enhances the resistance to gross error, and guarantees the accuracy of bathymetry measurements while outlining the underwater topography. While the method is not fully automated and requires setting parameters, the fixed parameter values allow for efficient batch denoising of underwater photon points in different environments.
Prediction of Soil Properties in a Field in Typical Black Soil Areas Using in situ MIR Spectra and Its Comparison with vis-NIR Spectra
Authors: Yin Jianxin , Shi Zhan , Li Baoguo , Sun Fujun , Miao Tianyu , Shi Zhou , Chen Songchao , Yang Meihua , Ji Wenjun
Published: April 2023

As a precious soil resource, black soils in Northeast China are currently facing severe land degradation. Visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm) and mid-infrared spectroscopy (MIR, 2500–25,000 nm) have shown great potential to predict soil properties. However, there is still limited research on using MIR in situ. The aim of this study was to explore the feasibility of in situ MIR for the prediction of soil total nitrogen (TN) and total phosphorus (TP) and to compare its performance with the use of laboratory MIR, as well as the use of in situ and laboratory vis-NIR. A total of 450 samples from 90 soil profiles, along with their in situ and laboratory spectra of MIR and vis-NIR, were collected in a field with ten different tillage and management practices in a typical black soil area of Northeast China. Partial least square regression (PLSR), random forest (RF) and multivariate adaptive regression splines (MARS) were used to generate the calibrations between the spectra and the two properties. The results showed that both MIR and vis-NIR were able to predict the TN whether in laboratory or in situ conditions, but neither of them could predict the TP quantitatively since there was no sensitive band on both spectra regarding the TP. The prediction accuracy of the TN with laboratory spectra was higher than that with in situ spectra, for both vis-NIR and MIR. The optimal prediction accuracy of the TN with laboratory MIR (RMSE = 0.11 g/kg, RPD = 3.12) was higher than that of laboratory vis-NIR (RMSE = 0.14 g/kg, RPD = 2.45). The optimal prediction accuracy of in situ MIR (RMSE = 0.20 g/kg, RPD = 1.80) was lower than that of in situ vis-NIR (RMSE = 0.16 g/kg, RPD = 2.14). The prediction performance of the spectra followed laboratory MIR > laboratory vis-NIR > in situ vis-NIR > in situ MIR. The performance of in situ MIR was relatively poor, mainly due to the fact that MIR was more influenced by soil moisture. This study verified the feasibility of in situ MIR for soil property prediction and provided an approach for obtaining rapid soil information and a reference for soil research and management in black soil areas.
Volcanic Clouds Characterization of the 2020–2022 Sequence of Mt. Etna Lava Fountains Using MSG-SEVIRI and Products’ Cross-Comparison
Authors: Guerrieri Lorenzo , Corradini Stefano , Theys Nicolas , Stelitano Dario , Merucci Luca
Published: April 2023

From December 2020 to February 2022, 66 lava fountains (LF) occurred at Etna volcano (Italy). Despite their short duration (an average of about two hours), they produced a strong impact on human life, environment, and air traffic. In this work, the measurements collected from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) instrument, on board Meteosat Second Generation (MSG) geostationary satellite, are processed every 15 min to characterize the volcanic clouds produced during the activities. In particular, a quantitative estimation of volcanic cloud top height (VCTH) and ash/ice/SO2 masses’ time series are obtained. VCTHs are computed by integrating three different retrieval approaches based on coldest pixel detection, plume tracking, and HYSPLIT models, while particles and gas retrievals are realized simultaneously by exploiting the Volcanic Plume Retrieval (VPR) real-time procedure. The discrimination between ashy and icy pixels is carried out by applying the Brightness Temperature Difference (BTD) method with thresholds obtained by making specific Radiative Transfer Model simulations. Results indicate a VCTH variation during the entire period between 4 and 13 km, while the SO2, ash, and ice total masses reach maximum values of about 50, 100, and 300 Gg, respectively. The cumulative ash, ice, and SO2 emitted from all the 2020–2022 LFs in the atmosphere are about 750, 2300, and 670 Gg, respectively. All the retrievals indicate that the overall activity can be grouped into 3 main periods in which it passes from high (December 2020 to March 2021), low (March to June 2021), and medium/high (June 2021 to February 2022). The different products have been validated by using TROPOspheric Monitoring Instrument (TROPOMI) polar satellite sensor, Volcano Observatory Notices for Aviation (VONA) bulletins, and by processing the SEVIRI data considering a different and more accurate retrieval approach. The products’ cross-comparison shows a generally good agreement, except for the SO2 total mass in case of high ash/ice content in the volcanic cloud.
Joint Clustering and Resource Allocation Optimization in Ultra-Dense Networks with Multiple Drones as Small Cells Using Game Theory
Authors: Bui Tinh T. , Nguyen Long D. , Kha Ha Hoang , Vo Nguyen-Son , Duong Trung Q.
Published: April 2023

In this study, we consider the combination of clustering and resource allocation based on game theory in ultra-dense networks that consist of multiple macrocells using massive multiple-input multiple-output and a vast number of randomly distributed drones serving as small-cell base stations. In particular, to mitigate the intercell interference, we propose a coalition game for clustering small cells, with the utility function being the ratio of signal to interference. Then, the optimization problem of resource allocation is divided into two subproblems: subchannel allocation and power allocation. We use the Hungarian method, which is efficient for solving binary optimization problems, to assign the subchannels to users in each cluster of small cells. Additionally, a centralized algorithm with low computational complexity and a distributed algorithm based on the Stackelberg game are provided to maximize the network energy efficiency (EE). The numerical results demonstrate that the game-based method outperforms the centralized method in terms of execution time in small cells and is better than traditional clustering in terms of EE.
A Novel IoT-Enabled Wireless Sensor Grid for Spatial and Temporal Evaluation of Tracer Gas Dispersion
Authors: Tsang Tsz-Wun , Mui Kwok-Wai , Wong Ling-Tim , Law Kwok-Yung , Shek Ka-Wing
Published: April 2023

Current IoT applications in indoor air focus mainly on general monitoring. This study proposed a novel IoT application to evaluate airflow patterns and ventilation performance using tracer gas. The tracer gas is a surrogate for small-size particles and bioaerosols and is used in dispersion and ventilation studies. Prevalent commercial tracer-gas-measuring instruments, although highly accurate, are relatively expensive, have a long sampling cycle, and are limited in the number of sampling points. To enhance the spatial and temporal understanding of tracer gas dispersion under the influence of ventilation, a novel application of an IoT-enabled, wireless R134a sensing network using commercially available small sensors was proposed. The system has a detection range of 5–100 ppm and a sampling cycle of 10 s. Using Wi-Fi communication, the measurement data are transmitted to and stored in a cloud database for remote, real-time analysis. The novel system provides a quick response, detailed spatial and temporal profiles of the tracer gas level, and a comparable air change rate analysis. With multiple units deployed as a wireless sensing network, the system can be applied as an affordable alternative to traditional tracer gas systems to identify the dispersion pathway of the tracer gas and the general airflow direction.
Supporting Tremor Rehabilitation Using Optical See-Through Augmented Reality Technology
Authors: Wang Kai , Tan Dong , Li Zhe , Sun Zhi
Published: April 2023

Tremor is a movement disorder that significantly impacts an individual’s physical stability and quality of life, and conventional medication or surgery often falls short in providing a cure. Rehabilitation training is, therefore, used as an auxiliary method to mitigate the exacerbation of individual tremors. Video-based rehabilitation training is a form of therapy that allows patients to exercise at home, reducing pressure on rehabilitation institutions’ resources. However, it has limitations in directly guiding and monitoring patients’ rehabilitation, leading to an ineffective training effect. This study proposes a low-cost rehabilitation training system that utilizes optical see-through augmented reality (AR) technology to enable tremor patients to conduct rehabilitation training at home. The system provides one-on-one demonstration, posture guidance, and training progress monitoring to achieve an optimal training effect. To assess the system’s effectiveness, we conducted experiments comparing the movement magnitudes of individuals with tremors in the proposed AR environment and video environment, while also comparing them with standard demonstrators. Participants wore a tremor simulation device during uncontrollable limb tremors, with tremor frequency and amplitude calibrated to typical tremor standards. The results showed that participants’ limb movement magnitudes in the AR environment were significantly higher than those in the video environment, approaching the movement magnitudes of the standard demonstrators. Hence, it can be inferred that individuals receiving tremor rehabilitation in the AR environment experience better movement quality than those in the video environment. Furthermore, participant experience surveys revealed that the AR environment not only provided a sense of comfort, relaxation, and enjoyment but also effectively guided them throughout the rehabilitation process.
Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies
Authors: Asad Usman , Khan Madeeha , Khalid Azfar , Lughmani Waqas Akbar
Published: April 2023

The last decade saw the emergence of highly autonomous, flexible, re-configurable Cyber-Physical Systems. Research in this domain has been enhanced by the use of high-fidelity simulations, including Digital Twins, which are virtual representations connected to real assets. Digital Twins have been used for process supervision, prediction, or interaction with physical assets. Interaction with Digital Twins is enhanced by Virtual Reality and Augmented Reality, and Industry 5.0-focused research is evolving with the involvement of the human aspect in Digital Twins. This paper aims to review recent research on Human-Centric Digital Twins (HCDTs) and their enabling technologies. A systematic literature review is performed using the VOSviewer keyword mapping technique. Current technologies such as motion sensors, biological sensors, computational intelligence, simulation, and visualization tools are studied for the development of HCDTs in promising application areas. Domain-specific frameworks and guidelines are formed for different HCDT applications that highlight the workflow and desired outcomes, such as the training of AI models, the optimization of ergonomics, the security policy, task allocation, etc. A guideline and comparative analysis for the effective development of HCDTs are created based on the criteria of Machine Learning requirements, sensors, interfaces, and Human Digital Twin inputs.
Apply Graph Signal Processing on NILM: An Unsupervised Approach Featuring Power Sequences
Authors: Zhao Bochao , Li Xuhao , Luan Wenpeng , Liu Bo
Published: April 2023

As a low-cost demand-side management application, non-intrusive load monitoring (NILM) offers feedback on appliance-level electricity usage without extra sensors. NILM is defined as disaggregating loads only from aggregate power measurements through analytical tools. Although low-rate NILM tasks have been conducted by unsupervised approaches based on graph signal processing (GSP) concepts, enhancing feature selection can still contribute to performance improvement. Therefore, a novel unsupervised GSP-based NILM approach with power sequence feature (STS-UGSP) is proposed in this paper. First, state transition sequences (STS) are extracted from power readings and featured in clustering and matching, instead of power changes and steady-state power sequences featured in other GSP-based NILM works. When generating graph in clustering, dynamic time warping distances between STSs are calculated for similarity quantification. After clustering, a forward-backward power STS matching algorithm is proposed for searching each STS pair of an operational cycle, utilizing both power and time information. Finally, load disaggregation results are obtained based on STS clustering and matching results. STS-UGSP is validated on three publicly accessible datasets from various regions, generally outperforming four benchmarks in two evaluation metrics. Besides, STS-UGSP estimates closer energy consumption of appliances to the ground truth than benchmarks.
Comparative Analysis of Selected Geostatistical Methods for Bottom Surface Modeling
Authors: Biernacik Patryk , Kazimierski Witold , Włodarczyk-Sielicka Marta
Published: April 2023

Digital bottom models are commonly used in many fields of human activity, such as navigation, harbor and offshore technologies, or environmental studies. In many cases, they are the basis for further analysis. They are prepared based on bathymetric measurements, which in many cases have the form of large datasets. Therefore, various interpolation methods are used for calculating these models. In this paper, we present the analysis in which we compared selected methods for bottom surface modeling with a particular focus on geostatistical methods. The aim was to compare five variants of Kriging and three deterministic methods. The research was performed with real data acquired with the use of an autonomous surface vehicle. The collected bathymetric data were reduced (from about 5 million points to about 500 points) and analyzed. A ranking approach was proposed to perform a complex and comprehensive analysis integrating typically used error statistics—mean absolute error, standard deviation and root mean square error. This approach allowed the inclusion of various views on methods of assessment while integrating various metrics and factors. The results show that geostatistical methods perform very well. The best results were achieved with the modifications of classical Kriging methods, which are disjunctive Kriging and empirical Bayesian Kriging. For these two methods, good statistics were calculated compared to other methods (for example, the mean absolute error for disjunctive Kriging was 0.23 m, while for universal Kriging and simple Kriging, it was 0.26 m and 0.25 m, respectively). However, it is worth mentioning that interpolation based on radial basis function in some cases is comparable to Kriging in its performance. The proposed ranking approach was proven to be useful and can be utilized in the future for choosing and comparing DBMs, mostly in mapping and analyzing seabed changes, for example in dredging operations. The research will be used during the implementation of the new multidimensional and multitemporal coastal zone monitoring system using autonomous, unmanned floating platforms. The prototype of this system is at the design stage and is expected to be implemented.
Correction: Chang et al. Weighting of Firefighting Turnout Gear Risk Factors According to Expert Opinion. Sustainability 2022, 14, 7040
Authors: Chang Tsui-Yuan , Lu Hsi-Peng , Luor Tain-Yi , Chang Ping-Wu
Published: April 2023

The authors would like to make the following corrections about the published paper [...]
Development of New Alkylated Carrageenan Derivatives: Physicochemical, Rheological, and Emulsification Properties Assessment
Authors: Toumi Selma , Yahoum Madiha Melha , Lefnaoui Sonia , Hadjsadok Abdelkader , Sid Asma Nour El Houda , Hassein-Bey Amel Hind , Amrane Abdeltif , Zhang Jie , Assadi Amin Aymen , Mouni Lotfi
Published: April 2023

In this research, amphiphilic derivatives of kappa carrageenan (KC) were synthesized by hydrophobic modification with an alkyl halide (1-Octyl chloride). Three hydrophobic polymers with different degrees of substitution (DS) were obtained by the Williamson etherification reaction in an alkaline medium. The effect of the molar ratio (R = reagent/polymer) on the DS was investigated at different ratios (1, 2, and 3). The KC derivatives (KCRs) were characterized by different techniques such as FT-IR, 1H-NMR, X-ray Diffraction, Scanning electron microscopy, and a rheological assessment. The FT-IR and 1HNMR analyses confirmed the binding of the hydrophobic groups onto the KC molecule. The degrees of substitution calculated by 1H-NMR demonstrated that the derivative KCR3 (0.68) presented a higher degree of substitution compared to KCR1(0.45) and KCR2 (0.53). The XRD and SEM analyses revealed that the alkaline etherification conditions did not alter the morphological and crystallographic properties, as well as the rheological behavior of the obtained derivatives. The amphiphilic character of the KCRs was investigated using a conductivity method which revealed that the molecular aggregation occurred above the critical aggregation concentration (CAC). Decreasing CAC values of 0.15% (KCR1), 0.11% (KCR2) and 0.08% (KCR3)with the degree of substitution (DS) were found. Furthermore, KCR’s derivatives greatly improved the stability of oil/water emulsions as the droplet size decreased with increasing DS. The derivative (KCR3) with higher DS, showed a greater amphiphilic character, and improved emulsifying power.
Transnational Students’ Epistemic Participation in English-Medium Instruction Programs
Authors: Qiu Yixi , Zheng Yongyan
Published: April 2023

Past research on English-medium instruction (EMI) has primarily focused on language-related challenges with scant attention paid to how language is entangled with epistemic access and epistemic injustice. Informed by the perspective of “epistemic (in)justice”, this study focused on how a cohort of students from diverse linguistic and cultural backgrounds negotiate a more epistemologically effective and equal access to knowledge negotiation in an EMI international relations master’s program in a Chinese university. Data were drawn from classroom observation, semi-structured interviews, and students’ reflexive journals. Qualitative thematic analysis of the data revealed unequal power relations in students’ epistemic participation and their resulting epistemic silence in classroom discussions. By illustrating how students cope with the epistemic challenges by drawing on individual-cognitive and social-cognitive resources, the findings suggest potential strategies for transnational students to counter the hegemony of English in EMI learning contexts. Implications for decoloniality in EMI education are discussed.
Beneficial Microorganisms in the Anaerobic Digestion of Cattle and Swine Excreta
Authors: Vidal-Espinosa Paulina-Soledad , Alvarez-Vera Manuel , Cárdenas Andrés , Cobos-Torres Juan-Carlos
Published: April 2023

The accumulation of solid organic waste is reaching critical levels in almost all regions of the world. It must be managed sustainably to avoid the depletion of natural resources, minimize risks to human health, reduce environmental burdens, and maintain an overall balance in the ecosystem. This research focuses on the anaerobic digestion of bovine and swine excreta with and without applying beneficial microorganisms as a viable option for recycling agricultural solid wastes. Three greenhouse gases (GHGs)—methane, carbon dioxide, and ammonia—produced by cattle and swine excreta that were treated with and without beneficial microorganisms in bioreactors were quantified. A monitoring and gas concentration measurement system was implemented inside the bioreactors. The behavior of the GHGs and the efficiency of the beneficial microorganisms in treating the farm animal waste were analyzed according to the phases of anaerobic digestion. Average reductions in the concentration in units of ppm of CH4 during the composting process of 46.95% and 34.48% were observed for the cattle and swine excreta treatments, respectively. It was concluded that the studied GHGs had different behaviors in the anaerobic digestion of the treatments in cattle and swine excreta with and without beneficial microorganisms due to the different types of feeding. However, it must be emphasized that beneficial microorganisms are an essential tool for reducing GHGs in anaerobic digestion.
Impact of Underground Coal Seam Mining on Stability and Slippage of the Loess Slope
Authors: Zhao Bingchao , Guo Yaxin , Wang Wei , He Shenglin
Published: April 2023

How to quantitatively characterise the impact of underground coal mining on the stability and slippage of loess slopes is a key problem in the evaluation of mining damage under loess slopes, but it is more difficult to study this problem under the impact of the particular mechanical properties and topographical features of loess slopes. In order to clarify the impact of underground coal seam mining on the stability and slippage of the loess slope, theoretical analysis, numerical simulation and physical similarity simulation experiments are used to address the problem based on the theory of slope stability and strata movement. The results show that the stability coefficient of a mining slope (Kms) is introduced to quantitatively characterise the stability of a mining loess slope, and to measure the degree of landslide risk. Due to the superposition of slope movement caused by mining subsidence and slope sliding tendency, the slope is more unstable when mining along the slope than when mining against the slope. The slope angle and slope height are the most important factors influencing the Kms. The ratio of rock stratum thickness to mining height and the ratio of rock stratum thickness to soil stratum thickness are positively correlated with Kms, and the correlation is relatively strong. The range of variation of the volume weight, internal friction angle and cohesion of the loess is small, and the influence on Kms is relatively weak. Probability integral theory is used to construct the relationship between stability and slippage of mining loess slopes. Taking the mining of a working face under the loess slope of Ningtiaota Coal Mine (China) as an example, the predicted results of the slope movement and deformation theory are in good agreement with the similar simulation test results, reaching 93.57~97.97%.
Participatory Action Research Challenges Amidst the COVID-19 Pandemic: A Review and Comparison across Two European Projects
Authors: Matos Fábio André , Alves Filipe Moreira , Roebeling Peter , Mendonça Rita , Mendes Rúben , López-Maciel Max , Vizinho André
Published: April 2023

The Coronavirus Disease 2019 (COVID-19) pandemic that spread through the world in 2020 had a major effect on academia. Research projects relying on participatory methods and action research approaches were especially harmed by the restrictions and changes the situation imposed. This study performs a rapid literature review to identify common themes in the narratives of published studies concerning the difficulties of carrying-out participatory research during the COVID-19 pandemic. Perceptions and experiences of the authors of these studies are compiled and summarized. Additionally, insights and lessons learned from two projects in which the authors of this study participated are discussed and used to establish a comparison with the common challenges found in the literature. Over 90% of authors experienced challenges related to digitalization and methodological changes, 70% encountered difficulties with organizational and operational aspects of research, and over 30% felt that personal challenges, as well as issues with participatory aspects of research, were significant. On a positive note, almost 40% of authors from our sample recognize that the pandemic also brought unexpected benefits, such as a reduction in logistical expenses, and the creation of new opportunities for participation. This study adds to the growing body of literature regarding the effects of the COVID-19 pandemic on the research community. Additionally, it comments on the adaptation of participatory action research methods for added sustainability and social inclusion in the face of global crises.
Wetland Contracts as Sustainable Governance Tools: A Review of the Output of the Interreg Project CREW “Coordinated Wetland Management in Italy-Croatia Cross Border Region”
Authors: Cantaluppi Maria Giulia , De Marchi Marta , Pace Michela , Tosi Maria Chiara
Published: April 2023

Wetlands are complex ecosystems, considered among the most productive environments in the world. They contain important biodiversity hotspots, provide the resources on which countless plant and animal species depend and perform important environmental and economic functions. Wetlands reduce flooding events, improve water quality and represent a valuable cultural and natural heritage. The European Directive (2000/60/EC) requires fostering an integrated approach to wetland management through collaborative governance processes. The Wetland Contract is a tool that has been developed and implemented in order to promote the sustainable governance of water systems. Among the Mediterranean countries, Italy and Croatia count on a rich variety of coastal wetlands that, together with the plants and animals that inhabit and pass through them, constitute an extremely valuable natural heritage. This paper presents and discusses the results of the Interreg Project CREW that, between 2018–2021, contributed to the drafting of seven new Wetland Contracts. Through a comparative reading, the aim is to understand, on the one hand, how the different Contracts have responded individually to a shared programme based on European requirements; on the other hand, the aim is also to bring similar experiences closer together to assess their overall potential on an international scale. The article shows how the constant exchange between CREW partners and the continuous updating on ongoing activities, methodologies and proposed solutions in the seven target areas facilitated the construction of a shared cross-border strategy for wetland governance. The comparison also highlights some principles considered fundamental by all partners (protection and enhancement; integrated governance; awareness-raising and learning) that have served the construction of a transnational Observatory, aimed at monitoring, sharing practices and able to guide the formation of future Wetland Contracts.
Information Technology Governance and Corporate Boards’ Relationship with Companies’ Performance and Earnings Management: A Longitudinal Approach
Authors: Singh Harman Preet , Alhulail Hilal Nafil
Published: April 2023

In accordance with the segregation of oversight from management decision making, the board-level information technology governance is accountable for supervising managerial IT activities. This research empirically analyzes the impact of board-level IT governance on the performance and earnings management practices of Saudi Arabian stock exchange (Tadawul)-listed companies between 2008 and 2020. The study sample includes cross-sectional time-series data from 154 firms with 18,018 firm-year observations. This study used regression analysis and other econometric models to examine probable endogeneities. The findings show that only the return on assets’ operational performance is positively and significantly related to board-level IT governance among the three performance metrics (return on assets, return on equity, and Tobin’s Q). This indicates that a higher proportion of members with IT experience and the presence of a board-level IT professional as chief information officer/chief technology officer and an IT committee positively impact operational performance. Finally, board-level IT governance competence and other governance attributes do not deter earnings management practices. Therefore, countries like Saudi Arabia should enhance their corporate governance environment considering the increasing significance of IT governance (control, service, and monitoring). There is also a need to review provisions of the Saudi Arabia Corporate Governance Regulations, especially for board composition, the appointment of independent and IT-literate directors, and penalties for non-compliance with regulations.
Key Drivers and Performances of Smart Manufacturing Adoption: A Meta-Analysis
Authors: Kim Juil , Jeong Hye-ryun , Park Hyesu
Published: April 2023

This study focused on the smart factory, one of the critical paradigms in the digital transformation in manufacturing, and attempted a meta-analysis to systematically integrate statistical results from existing empirical analysis studies. An integration model, key factors—smart manufacturing adoption—performances, was established from collecting 42 Korean examples of literature. To compare effect sizes between domestic and foreign empirical study results, 11 foreign articles were added, and the moderating effect verification was conducted. As a result of the analysis, (1) the key factors of the adoption and continuous use of smart manufacturing were the network effect, social influences, finances, performance expectancy, facilitating condition, technological capabilities, and entrepreneurship. (2) The adoption and continuous use of smart manufacturing had a significant impact on business performances, especially the financial performance. (3) The impacts of entrepreneurship and the network effect as factors influencing the decision making of smart manufacturing adoption in Korea can be seen to be significantly higher than those of foreign countries. (4) The impact of smart manufacturing adoption on performances in Korea was higher than other countries. The findings of this study will provide practical implications for practitioners optimizing digital transformation manufacturing policies and supporting the adoption of smart manufacturing systems.
Enhancing Sustainability with the Triple-Layered Business Model Canvas: Insights from the Fruit and Vegetable Industry in Spain
Authors: Mili Samir , Loukil Tasnim
Published: April 2023

Developing more sustainable business models (BMs) has become increasingly crucial for the survival of agri-food industries as environmental and social pressures are escalating, and the fruit and vegetable (F&V) industry is no exception to this pattern. However, sustainable business modelling is still poorly understood despite its importance to managers, scientists, and policymakers confronting the complexities of business environment changes. The purpose of this research was to explore new approaches for the transition toward more sustainable BMs in the Spanish F&V industry. We used the Triple Layered Business Model Canvas (TLBMC) methodological framework and data gathered from different sources to elucidate how sustainability concerns are integrated in BMs for F&V in a way that can deliver value on a triple bottom line of economic profit, environmental protection, and social fairness. The study focuses first on the F&V sector as a whole and then on a selected Spanish F&V company taken as functional units of analysis, respectively. Results indicate that, overall, sustainability goals can be represented appropriately within the TLBMC framework both at sector and company level, allowing guidance of the creative process of communicating and implementing business model (BM) improvements. This is despite the challenges encountered in terms of complexity of the F&V industry and the limited data for several areas, especially in the environmental and social layers. The sectoral perspective helps to portray the whole picture of the impacts associated with sustainable F&V production and marketing, which is a prior step that supports companies to embed sustainability concerns in their business system. New BMs can be generated by innovating strategically in a series of domains to improve F&V value propositions in terms of sustainability targets.