bluuebunny/crossref_metadata_2025 huggingface dataset:
Validating the Financial Literacy Index of Hungarian SMEs during the COVID-19 Pandemic and the Russian–Ukrainian War
Authors: Toth Robert , Kasa Richard , Lentner Csaba
Published: March 2023

The effects of the COVID-19 pandemic and the Russian–Ukrainian war have had a significant impact on economies around the world, with pivotal implications for the activities of companies. The issue of corporate financial literacy has been within our scope of interest for a matter of years now, and this study aims at re-enforcing our previous overall theoretical and literacy-based analysis from a methodological approach. We use our own previous databases to explore and analyze the importance of corporate financial literacy, taking into account the economic factors inside and outside the organization that affect the businesses. For this, a confirmative factor analysis (CFA) model has been created. The article aims at two things with this. On the one hand, we intend to introduce the wider scope of the fit tests applicable in the CFA, thus giving a direction to other authors. It also allows for adequate verification for their models, while at the same time conducting the fit test for our corporate financial literacy model as well as a valid model framework suitable for making measurements and deductions. With the resulting model, this paper aims to examine the corporate financial literacy, the current economic challenges, and the issues faced by managers during crises. In addition to all this, with our article, we also want to make some contribution to the methodology of empirical data analysis: in the article we collect the fit tests that can be used to validate confirmatory factor models, the way they are determined, and most importantly, we try to sort out the literature approaches to the acceptable values of these tests, giving the reader a kind of guide and a reference base. The results of the research identify response measures that can contribute to increasing companies’ resilience based on the principles of financial awareness.
A New Method for Hour-by-Hour Bias Adjustment of Satellite Precipitation Estimates over Mainland China
Authors: Li Ji , Yong Bin , Shen Zhehui , Wu Hao , Yang Yi
Published: March 2023

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (>1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data.
Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation
Authors: Ghali Rafik , Akhloufi Moulay A.
Published: March 2023

The world has seen an increase in the number of wildland fires in recent years due to various factors. Experts warn that the number of wildland fires will continue to increase in the coming years, mainly because of climate change. Numerous safety mechanisms such as remote fire detection systems based on deep learning models and vision transformers have been developed recently, showing promising solutions for these tasks. To the best of our knowledge, there are a limited number of published studies in the literature, which address the implementation of deep learning models for wildland fire classification, detection, and segmentation tasks. As such, in this paper, we present an up-to-date and comprehensive review and analysis of these vision methods and their performances. First, previous works related to wildland fire classification, detection, and segmentation based on deep learning including vision transformers are reviewed. Then, the most popular and public datasets used for these tasks are presented. Finally, this review discusses the challenges present in existing works. Our analysis shows how deep learning approaches outperform traditional machine learning methods and can significantly improve the performance in detecting, segmenting, and classifying wildfires. In addition, we present the main research gaps and future directions for researchers to develop more accurate models in these fields.
On Doppler Shifts of Breaking Waves
Authors: Yurovsky Yury Yu. , Kudryavtsev Vladimir N. , Grodsky Semyon A. , Chapron Bertrand
Published: March 2023

Field-tower-based observations were used to estimate the Doppler velocity of deep water plunging breaking waves. About 1000 breaking wave events observed by a synchronized video camera and dual-polarization Doppler continuous-wave Ka-band radar at incidence angles varying from 25 to 55 degrees and various azimuths were analyzed using computer vision methods. Doppler velocities (DVs) associated with breaking waves were, for the first time, directly compared to whitecap optical velocities measured as the line-of-sight projection of the whitecap velocity vector (LOV). The DV and LOV were found correlated; however, the DV was systematically less than the LOV with the ratio dependent on the incidence angle and azimuth. The largest DVs observed at up-wave and down-wave directions were accompanied by an increase of the cross-section polarization ratio, HH/VV, up to 1, indicating a non-polarized backscattering mechanism. The observed DV was qualitatively reproduced in terms of a combination of fast specular (coherent) and slow non-specular (incoherent) returns from two planar sides of an asymmetric wedge-shaped breaker. The difference in roughness and tilt between breaker sides (the front face was rougher than the rear face) explained the observed DV asymmetry and was consistent with previously reported mean sea surface Doppler centroid data and normalized radar cross-section measurements.
Raking over the Ashes—The Analysis of the LBA Ashmounds from NE Romania
Authors: Brașoveanu Casandra , Asăndulesei Andrei , Brunchi Radu-Alexandru , Tencariu Felix-Adrian
Published: March 2023

During the end of the Bronze Age, the territory of present-day eastern Romania was occupied by Noua communities, belonging to the Noua-Sabatinovka-Coslogeni (NSC) cultural complex. Although these communities have left us a large number of archaeological sites, this period is rather poorly known and understood, mostly because the investigation of Late Bronze Age (LBA) sites is very rare, usually consisting of small test trenches or fieldwalks. The main characteristic of these communities and the subject of our study is represented by the so-called ashmounds (grey, quasi-circular spots, visible on the soil surface, with small elevations and diameters of 25–30 m), present inside most settlements. Our paper aims at highlighting the spatial characteristics of these sites, using GIS (Geographic Information System) tools, as well as aerial photographs, LiDAR (Light Detection and Ranging) measurements, magnetometry and geo-electrical methods, in order to identify the relationship existing between Noua communities and the inhabited environment, in the area known as the Jijia River catchment. Thus, our approach was able to outline the way in which the geographical peculiarities determined the establishment of new settlements, revealing that the human groups from the end of the Bronze Age preferred low terrains with smooth slopes, located in the immediate vicinity of the most important watercourse of the inhabited micro-area. Additionally, our geophysical studies allowed us to confirm the lack of ash located within the ashmound, as well as to signal the possibility that these features have become visible on the soil surface only due to the irreversible damage caused by intensive agricultural processes. Despite the small number of excavations, to this day an important number of studies have been dedicated to the communities and features in question; however, no analysis has yet been performed that unites the tools specific to GIS software with the usage of non-invasive methods (such as aerial photographs, LiDAR measurements and geophysical techniques).
A Comprehensive Correction Method for Radiation Distortion of Multi-Strip Airborne Hyperspectral Images
Authors: Zhao Yibo , Tian Yu , Lei Shaogang , Li Yuanyuan , Hua Xia , Guo Dong , Ji Chuning
Published: March 2023

Airborne hyperspectral imaging plays an increasingly important role in environmental monitoring. However, due to the limitations of the acquisition conditions, there are uneven radiation and chromatic aberrations in the mosaic data. Accurate preprocessing of the original data is the premise of qualitative and quantitative remote sensing. In this study, we proposed a comprehensive radiation distortion correction method that integrates radiation attenuation difference correction, topographic correction, and multi-strip images consistency adjustment (RA-TOC-CA). First, the radiation attenuation equation was constructed by combining the viewing geometry, terrain, and the elevation difference between the UAV and the ground to eliminate the radiation attenuation difference of pixels acquired at the different instantaneous field of view (IFOV). Second, an improved kernel-driven BRDF model was built combining terrain information and illumination-viewing (flight attitude and sensor IFOV) geometry to eliminate the radiation unevenness and BRDF distortion caused by topography. Third, adjusting the reflectance of multi-strip images according to the homonymous points’ reflectance of adjacent strips should be equal, eliminating the radiation differences between multiple strips. Based on multi-strip airborne hyperspectral images collected in the Shaanxi province of China, the correction results of the RA-TOC-CA method were compared with those of the SCS+C and Minnaert+SCS methods regarding various evaluation criteria. The results showed that SCS+C and Minnaert+SCS can reduce the topographic effect but cannot eliminate the reflectance difference at the edges of adjacent images, and SCS+C overcorrects the reflectance. RA-TOC-CA weakened the topographic effects and brightness gradient, which was physically stable and generalizable. Compared with previous studies, RA-TOC-CA provided a complete radiation distortion correction method for airborne hyperspectral images and had a solid theoretical basis. This study introduces an effective method for radiation distortion correction of airborne hyperspectral images and provides technical support for large-scale applications of hyperspectral images.
Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany
Authors: Dhillon Maninder Singh , Kübert-Flock Carina , Dahms Thorsten , Rummler Thomas , Arnault Joel , Steffan-Dewenter Ingolf , Ullmann Tobias
Published: March 2023

The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km2) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R2 = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R2 = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world.
The Vanishing and Renewal Landscape of Urban Villages Using High-Resolution Remote Sensing: The Case of Haidian District in Beijing
Authors: Wei Hubin , Cao Yue , Qi Wei
Published: March 2023

How to recognize the land use change in urban villages during dynamic transformation in Haidian District, Beijing, has become a hot topic with the promotion of urban renewal. The GF-1 high-resolution remote sensing images of 2013, 2015, and 2020 were used in this study to reflect the land use change in urban villages before and after urban renewal by using a hierarchical machine learning recognition method based on scene-based and random forest classification. The overall scale of urban village blocks in Haidian was 10.46 km2, showing the distribution pattern along the traffic arteries in 2013. In 2015, it dropped to 10.11 km2. The scale of urban village blocks in 2020 decreased to 1.02 km2, 9.75% of that in 2013. Three kinds of urban village renewal logic are revealed by further taking Chuanying Village as an example: “urban village–blue–green space”, “urban village–real estate”, and “urban village–municipal facilities”.
Global Multi-Attention UResNeXt for Semantic Segmentation of High-Resolution Remote Sensing Images
Authors: Chen Zhong , Zhao Jun , Deng He
Published: March 2023

Semantic segmentation has played an essential role in remote sensing image interpretation for decades. Although there has been tremendous success in such segmentation with the development of deep learning in the field, several limitations still exist in the current encoder–decoder models. First, the potential interdependencies of the context contained in each layer of the encoder–decoder architecture are not well utilized. Second, multi-scale features are insufficiently used, because the upper-layer and lower-layer features are not directly connected in the decoder part. In order to solve those limitations, a global attention gate (GAG) module is proposed to fully utilize the interdependencies of the context and multi-scale features, and then a global multi-attention UResNeXt (GMAUResNeXt) module is presented for the semantic segmentation of remote sensing images. GMAUResNeXt uses GAG in each layer of the decoder part to generate the global attention gate (for utilizing the context features) and connects each global attention gate with the uppermost layer in the decoder part by using the Hadamard product (for utilizing the multi-scale features). Both qualitative and quantitative experimental results demonstrate that use of GAG in each layer lets the model focus on a certain pattern, which can help improve the effectiveness of semantic segmentation of remote sensing images. Compared with state-of-the-art methods, GMAUResNeXt not only outperforms MDCNN by 0.68% on the Potsdam dataset with respect to the overall accuracy but is also the MANet by 3.19% on the GaoFen image dataset. GMAUResNeXt achieves better performance and more accurate segmentation results than the state-of-the-art models.
Estimation of Azimuth Angle Using an Ultrasonic Sensor for Automobile
Authors: Chandrasegar Vasantha , Koh Jinhwan
Published: March 2023

A typical ultrasonic sensor has a major lobe that extends beyond 45 degrees. Because the wide beam of the ultrasonic sensor’s main lobe, which is used for straightforward distance measurement, has a low angular resolution, conventional methods such as incidence angle and linear angle measurements cannot accurately determine the azimuthal angle. Determining whether one or more objects are present in a single beam is also challenging. In this study, the azimuthal angles of two or more objects placed beneath a single beam are determined by the Doppler frequency shift. An ultrasonic sensor is mounted on an automobile to transmit and receive an ultrasound when the car moves towards stationary objects. The sensor picks up the object’s reflected Doppler shift signal. The azimuth angle of the objects is determined by estimating the received Doppler shift signal using a standard signal processing method. Near-field motion detection systems and autonomous driving heavily rely on the ability to evaluate the azimuthal angle of objects in a vehicle’s surroundings using the Doppler Effect. These are examples of low-cost technology and active safety, which the experimental results support. Based on the results and error estimation, there is an average error of less than 3% between measured and computed values.
A Multi-Objective Semantic Segmentation Algorithm Based on Improved U-Net Networks
Authors: Hao Xuejie , Yin Lizeyan , Li Xiuhong , Zhang Le , Yang Rongjin
Published: March 2023

The construction of transport facilities plays a pivotal role in enhancing people’s living standards, stimulating economic growth, maintaining social stability and bolstering national security. During the construction of transport facilities, it is essential to identify the distinctive features of a construction area to anticipate the construction process and evaluate the potential risks associated with the project. This paper presents a multi-objective semantic segmentation algorithm based on an improved U-Net network, which can improve the recognition efficiency of various types of features in the construction zone of transportation facilities. The main contributions of this paper are as follows: A multi-class target sample dataset based on UAV remote sensing and construction areas is established. A new virtual data augmentation method based on semantic segmentation of transport facility construction areas is proposed. A semantic segmentation model for the construction regions based on data augmentation and transfer learning is developed and future research directions are given. The results of the study show that the validity of the virtual data augmentation approach has been verified; the semantic segmentation of the transport facility model can semantically segment a wide range of target features. The highest semantic segmentation accuracy of the feature type was 97.56%.
Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
Authors: Machumilane Achilles , Gotta Alberto , Cassará Pietro , Amato Giuseppe , Gennaro Claudio
Published: March 2023

In non-terrestrial networks, where low Earth orbit satellites and user equipment move relative to each other, line-of-sight tracking and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous line-of-sight estimation and channel impairment compensation are crucial for user equipment to access a satellite and maintain connectivity. In this paper, we propose a framework based on actor-critic reinforcement learning for traffic scheduling in non-terrestrial networks scenario where the channel state is non-stationary due to the variability of the line of sight, which depends on the current satellite elevation. We deploy the framework as an agent in a multipath routing scheme where the user equipment can access more than one satellite simultaneously to improve link reliability and throughput. We investigate how the agent schedules traffic in multiple satellite links by adopting policies that are evaluated by an actor-critic reinforcement learning approach. The agent continuously trains its model based on variations in satellite elevation angles, handovers, and relative line-of-sight probabilities. We compare the agent’s retraining time with the satellite visibility intervals to investigate the effectiveness of the agent’s learning rate. We carry out performance analysis while considering the dense urban area of Paris, where high-rise buildings significantly affect the line of sight. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1times the satellite visibility time at given elevations, which guarantees efficient use of satellite visibility.
Offshore Hydrocarbon Exploitation Target Extraction Based on Time-Series Night Light Remote Sensing Images and Machine Learning Models: A Comparison of Six Machine Learning Algorithms and Their Multi-Feature Importance
Authors: Ma Rui , Wu Wenzhou , Wang Qi , Liu Na , Chang Yutong
Published: March 2023

The continuous acquisition of spatial distribution information for offshore hydrocarbon exploitation (OHE) targets is crucial for the research of marine carbon emission activities. The methodological framework based on time-series night light remote sensing images with a feature increment strategy coupled with machine learning models has become one of the most novel techniques for OHE target extraction in recent years. Its performance is mainly influenced by machine learning models, target features, and regional differences. However, there is still a lack of internal comparative studies on the different influencing factors in this framework. Therefore, based on this framework, we selected four different typical experimental regions within the hydrocarbon basins in the South China Sea to validate the extraction performance of six machine learning models (the classification and regression tree (CART), random forest (RF), artificial neural networks (ANN), support vector machine (SVM), Mahalanobis distance (MaD), and maximum likelihood classification (MLC)) using time-series VIIRS night light remote sensing images. On this basis, the influence of the regional differences and the importance of the multi-features were evaluated and analyzed. The results showed that (1) the RF model performed the best, with an average accuracy of 90.74%, which was much higher than the ANN, CART, SVM, MLC, and MaD. (2) The OHE targets with a lower light radiant intensity as well as a closer spatial location were the main subjects of the omission extraction, while the incorrect extractions were mostly caused by the intensive ship activities. (3) The coefficient of variation was the most important feature that affected the accuracy of the OHE target extraction, with a contribution rate of 26%. This was different from the commonly believed frequency feature in the existing research. In the context of global warming, this study can provide a valuable information reference for studies on OHE target extraction, carbon emission activity monitoring, and carbon emission dynamic assessment.
Xenophobia and Hate Speech towards Refugees on Social Media: Reinforcing Causes, Negative Effects, Defense and Response Mechanisms against That Speech
Authors: Aldamen Yasmin
Published: March 2023

This study aims to recognize the possible role of social media in forming xenophobia and hate speech against Syrian refugees and to understand the reinforcing causes and negative effects of that speech on the refugees, as well as the refugees’ response mechanisms against that speech. A mixed approach was used in this study. In addition to a questionnaire tool that was used to collect data, focus group discussions were conducted to support the obtained data. The study used a statistically representative sample of Syrian refugees who live in Turkey and Jordan. The participants see the negative representation of their image via social media as leading to hatred towards them. The findings showed that there are some causes that reinforce xenophobic speech, such as otherization and demonization of refugees, by the negative representation and spreading of fake news on social media, which are published continuously. Moreover, gatekeepers have a role in promoting negative representation by approving xenophobic speech transmission without filtering or ethical control. The results also showed that the negative representation containing hate speech contributes to more negative effects on the refugees, such as psychological effects, as a sense of hatred towards refugees is created in the host country, and the effect of changing the feelings of the public from empathy to compassion fatigue concluding to hatred towards those refugees. Furthermore, the results showed that the Syrian refugees follow different defense mechanisms against xenophobia and hate speech against them published and circulated through social media platforms. The participants were divided into two main categories; the first one is those who prefer replying, not being silent, and defending the refugees, and the second category is those who prefer keeping silent. The first category of refugees stated that they do not accept keeping silent and they prefer to reply using logical, emotional, or defensive and offensive ways to bad comments against Syrian refugees in general. While the second category claimed that either they do not have time to reply or they sometimes agree that it was only a reaction to the behavior of some individual refugees. Some of them explained that they are afraid of being subjected to more bad comments and negative reactions against them. When they encounter aggressive comments and see that such views represent the majority, they prefer to keep silent to avoid being attacked. This is understood within the framework of the spiral of silence theory, where refugees hide their opinions, views, and preferences when they think that they fall within a minority group. They fear social isolation through social media.
Digital Competence of Special Education Teachers: An Analysis from the Voices of Members of School Management Teams
Authors: Montenegro-Rueda Marta , Fernández-Cerero José
Published: March 2023

The use of digital technologies is one of the fundamental resources to favour the inclusion of students with special educational needs. However, recent studies continue to show a lack of use by teachers during the development of their teaching practices, especially in the field of special education. This article aims to analyse the level of digital competence of special education teachers through the perceptions of the school management team in Andalusia (Spain). The information is obtained through interviews with 62 members of school management teams. The results suggest that the low level of training and digital competence of special education teachers is the reason why they do not make use of digital tools in their teaching practice, due to a lack of teacher awareness, as well as the non-existent or insufficient development of training activities that hinder such training. In conclusion, there is a need to improve the professional development of special education teachers in digital competencies, as well as greater institutional involvement through strategic plans that promote such training.
A Geospatial Modelling Approach to Understand the Spatio-Temporal Impacts of Grazing on Soil Susceptibility to Erosion
Authors: Pereira Fabiellen C. , Donovan Mitchell , Smith Carol M. S. , Charters Stuart , Maxwell Thomas M. R. , Gregorini Pablo
Published: March 2023

Grazing management to reduce soil erosion is paramount for preserving and enhancing grassland health under pastoral livestock production systems. However, as the focus of these production systems is to increase productivity, the inclusion of the soil and its complexity in grazing management has been usually neglected. Detailed consideration of the soil spatio-temporal susceptibility to erosion may be best approached with simulation modelling. To understand and explore the spatio-temporal impact of grazing strategies on soil surface erosion, this work used a geospatial model approach in a high-country pastoral livestock production system in New Zealand as a case study. We modelled 45 scenarios characterized by different stock densities and occupation periods applied for each season of the year and for different livestock types: sheep, beef, and deer, producing a total of 540 scenarios. In addition, we included scenarios to represent ungrazed pastures for each season and the current grazing management of the case study station as the baseline for comparisons (resulting in a total of 545 scenarios). Spatio-temporal variation of natural soil superficial erosion from ungrazed pastures appears to be more relevant than the impact of manipulating grazing intensity and indicates that paddocks in our study area have different capacities to support grazing which also changes during seasons. Increases in occupation period seem more detrimental to soil erosion compared to increases in stock density, and cattle are the most detrimental stock type compared to sheep and deer. Our results suggest that grassland health can be enhanced in LUMGS by applying context-adjusted grazing management strategies according to the station spatio-temporal heterogeneity and susceptibility to erosion.
Topology Optimization with Matlab: Geometrically Non-Linear Optimum Solid Structures at Random Force Strengths
Authors: Werner Marek , Bieler Sören , Weinberg Kerstin
Published: March 2023

This paper aims to investigate multiple large-strain topology-optimized structures, by interpreting their overlay as a probability density function. Such a strategy is suited to finding an optimum design of silicon electrodes subject to a random contact. Using this method, and prescribing a zero net-force constraint on the global system, the optimum structure is identified with a Schwarz P minimum-surface structure. Then, the optimum structure is subject to chemo-mechanically coupled cycling, in terms of an irreversible thermodynamic process, which shows the interplay between the mechanical and chemical fields. The Matlab-based optimization code is attached.
Design of a Nuclear Monitoring System Based on a Multi-Sensor Network and Artificial Intelligence Algorithm
Authors: Baek Min Kyu , Chung Yoon Soo , Lee Seongyeon , Kang Insoo , Ahn Jae Joon , Chung Yong Hyun
Published: March 2023

Nuclear power is a sustainable energy source, but radiation management is required for its safe use. Radiation-detection technology has been developed for the safe management of radioactive materials in nuclear facilities but its performance may vary depending on the size and complexity of the structure of nuclear facilities. In this study, a nuclear monitoring system using a multi-sensor network was designed to monitor radioactive materials in a large nuclear facility. Additionally, an artificial-intelligence-based localization algorithm was developed to accurately locate radioactive materials. The system parameters were optimized using the Geant4 Application for Tomographic emission (GATE) toolkit, and the localization algorithm was developed based on the performance evaluation of the Artificial Neural Network (ANN) and Decision Tree (D-Tree) models. In this article, we present the feasibility of the proposed monitoring system by converging the radiation detection system and artificial intelligence technology.
Dynamic Scenario Predictions of Peak Carbon Emissions in China’s Construction Industry
Authors: Wang Xilian , Qu Lihang , Wang Yueying , Xie Helin
Published: March 2023

As the largest carbon emitter in the world, China aims to reach its peak carbon emissions goal by the year 2030, while the construction industry makes a significant contribution to carbon emissions, directly affecting the country’s commitment to meet its target. The present paper investigates the dynamic characteristics of carbon emissions released by China’s construction industry under single- and multiple-scenario settings with altering economic growth rates, optimizing energy structures, adjusting industrial structures, and modifying carbon emission policy factors. The research results show that the total carbon emissions generally present a steady increase from the year 2000 and will reach 12,880.40 million tons (MT) by 2030 under a scenario without any intervention. Indirect carbon emissions released from associated industries account for over 96% of the total carbon emissions, while direct carbon emissions make a minor contribution to the total. Single and comprehensive scenarios have positive effects on reducing emissions; it was also observed that only under energy structure scenario III and comprehensive scenario III could carbon emissions released from the construction sector reach a peak value by 2030. The effects of emissions reductions as a result of single policies can be presented in the following order: energy structure, economic growth, carbon emissions policy factor, and industrial structure. All of the emissions reduction effects of multiple scenarios are superior to the single scenarios. The research results provide a basis and guidance for policymakers to adopt the correct steps to fulfill China’s aim of achieving peak carbon emissions by the projected date.
Effect of Ultra-Fine Cement on the Strength and Microstructure of Humic Acid Containing Cemented Soil
Authors: Cao Jing , Liu Fangyi , Song Zhigang , Ding Wenyun , Guo Yongfa , Li Jianyun , Liu Guoshou
Published: March 2023

The peat soil in the Dianchi Lake area of Yunnan, China, is widely distributed, bringing many problems to engineering. The peat soil foundation is usually treated by the cement mixing method, and the reinforcement effect of cemented soil is mainly affected by humic acid (HA). Ultra-fine cement (UFC) can improve cement performance and reduce cement consumption, decreasing CO2 emissions and the impact of human activities on the environment. Simulated peat soils in different environments are prepared with HA reagent and cohesive soil, reinforced by composite cement curing agent mixed with ultrafine cement (UFC). The relationship among the UFC proportion, HA reagent content, soaking time, and sample strength was studied. The unconfined compressive strength test (UCS), scanning electron microscope (SEM), and PCAS microscopic quantitative test techniques were used to explore the mechanism of the effect of UFC on the strength of HA-containing cemented soil. The increasing UFC proportion in the composite cement curing agent gradually increased HA-containing cemented soil’s strength. UFC significantly reduced the percentage of macropores in HA-containing cemented soil and made the microstructure denser. The HA-containing cemented soil’s qu increased the most when the UFC proportion increased from 0% to 10%. The solidification effect of the composite cement curing agent mixed with UFC was always stronger than that of OPC. The composite cement curing agent with a UFC proportion of 10% is practical. Cement is still an important building material in the current construction industry, and UFC provides a new method for reducing environmental impact in engineering construction.
Economic Analysis of Sustainable Transportation Transitions: Case Study of the University of Saskatchewan Ground Services Fleet
Authors: Aniegbunem George , Kraj Andrea
Published: March 2023

The global transport sector of the world economy contributes about 15% of Greenhouse Gas (GHG) emissions in the world today, and this must be drastically curbed. To reduce GHG emissions and achieve carbon neutrality, the University of Saskatchewan’s Office of Sustainability has directed a green energy transition for the institution in recent years, leading diverse sustainability projects and agendas due to the importance of curbing climate change and advancing sustainability. However, there is a strong need to pursue deep decarbonization within the campus, and the university transport system is a critical operational area that the Sustainability Office has considered for decarbonization to achieve the net-zero agenda of the university. The motivation for this work stems from the directive of the Sustainability Office to transform the campus vehicle fleet as an identified area for curbing GHG emissions and meet the University agenda. This study was organized in partnership with the Sustainability Office and involved an economic benefit analysis of the campus fleet (consisting of 91 ICE vehicles) to determine if it was economically or financially feasible to transition from Internal Combustion Engines (ICEs) or PVs (Petrol Vehicles) to Electric Vehicles (EVs). The analysis used RETScreen Expert (a software for analyzing renewable energy technology projects) to model diverse transition scenarios. The variables of Payback Period (PBP), cash flow projections, savings made from transitioning (fuel cost savings and energy cost savings), benefit–cost ratio, and GHG emission reduction potential were analyzed. The findings revealed that the GHG emissions from the campus fleet could be reduced by 100%, resulting in the removal of 298.1 tCO₂ from the environment. The fleet manager could save $CAD 129,049 (88.9%) in fuel costs, and the return on investment could be achieved in year 5 but could be reduced to year 2 if the vehicles were put into constant and active use, eliminating idle times. Lastly, the Sustainability Office would achieve a GHG reduction revenue of CAD 14,906. These findings show that pursuing sustainable transport transitions in the transportation transition for a university campus is financially and economically viable and should be pursued vigorously. The contribution of this work provides examples and evidence to advance policy recommendations to aid the effective and efficient transitioning of the transportation sector, specifically for communities at the scale of university campuses.
Adoption of Circular Economy by Construction Industry SMEs: Organisational Growth Transition Study
Authors: John Iniobong Beauty , Adekunle Samuel A. , Aigbavboa Clinton O.
Published: March 2023

A megatrend in the business environment poised at practically entrenching sustainability while ensuring global business competitiveness is viewed as a circular economy. At the firm level, circular economy practices distinguish firms for excellent product and service delivery, thus ensuring competitive advantage. The construction firm of the future will practice circular economy while adopting sustainable technology. This study is focused on assessing organisational growth transition among small and medium construction enterprises. A quantitative approach was adopted and structured questionnaires were administered among SME construction firms. The findings reveal the critical factors affecting organisational growth and transition in achieving a circular economy include availability of logistics infrastructure and firm’s market share, among others. Factor analysis indicated PCA extractions showing the component’s rotation indicating four structure components in the variables. The study named the four clusters required for achieving organisational growth transition as: firm positioning for competitive advantage, personnel management, service delivery and government policy and support. The study provides a foundation for other organisational growth transition studies stemming from circular economy adoption among construction industry SMEs.
A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope
Authors: Salehi Ahmad Waleed , Khan Shakir , Gupta Gaurav , Alabduallah Bayan Ibrahimm , Almjally Abrar , Alsolai Hadeel , Siddiqui Tamanna , Mellit Adel
Published: March 2023

This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demonstrated significant improvements in image analysis and classification tasks. Transfer learning, which involves reusing pre-trained CNN models, has also shown promise in addressing challenges related to small datasets and limited computational resources. This paper reviews the advantages of CNN and transfer learning in medical imaging, including improved accuracy, reduced time and resource requirements, and the ability to address class imbalances. It also discusses challenges, such as the need for large and diverse datasets, and the limited interpretability of deep learning models. What factors contribute to the success of these networks? How are they fashioned, exactly? What motivated them to build the structures that they did? Finally, the paper presents current and future research directions and opportunities, including the development of specialized architectures and the exploration of new modalities and applications for medical imaging using CNN and transfer learning techniques. Overall, the paper highlights the significant potential of CNN and transfer learning in the field of medical imaging, while also acknowledging the need for continued research and development to overcome existing challenges and limitations.
Climate Change and Building Renovation: Effects on Energy Consumption and Internal Comfort in a Social Housing Building in Northern Italy
Authors: Marco Manzan , Ramezani Atlas , Buoite Stella Alex , Pezzi Amedeo
Published: March 2023

Climate change is becoming a crucial factor to consider within human activities and the building sector is particularly influenced by aspects of internal comfort and energy. In Italy, great attention has been paid to the energy refurbishment of buildings. However, such interventions are mostly focused on reducing heating energy consumption, thus neglecting summer season performance. Moreover, climate change is barely considered during the design phase. This issue is addressed in this work which analyzes some of the most common refurbishment interventions applied to a social housing building in Trieste, while also considering internal comfort during the summer season. A dynamic analysis of the building-plant system is carried out using EnergyPlus. Fanger, UTCI and the adaptive comfort models were used to represent internal health, while three TRY data sets were generated using two GCM–RCM projections to evaluate the influence of climate change. The results show that both building insulation and climatic change affect heating consumption reaching a 70% reduction. However, building insulation does not greatly affect internal comfort, although different models show different behavior to protect against external temperatures. On the contrary, climatic change influences the percentage of hours of discomfort, with a 20% increase for all of the models. The final consideration is that people’s internal health should always be considered when carrying out refurbishment activities.
Virtual Educational Intervention of Craftswomen Working with Native Peruvian Cotton during COVID-19 for Reactivating the Artisian Tourism
Authors: Esparza-Huamanchumo Rosse Marie , Diaz-Manchay Rosa Jeuna , Díaz-Vásquez Maribel Albertina
Published: March 2023

The pandemic has significantly affected the tourism sector worldwide; however, craftswomen are a vulnerable group that has been affected economically by this crisis. This research evaluated the level of compliance with preventive measures before and after carrying out the virtual educational intervention for craftswomen working with native cotton in the Lambayeque Region, Peru. The methodology applied was a pilot study, quasi-experimental, without a control group. The population consisted of 30 craftswomen from the populated areas of La Raya–Túcume, Pómac III-Pitipo and Jotoro-Jayanca. SPSS Statistics v25 was used for data processing. The χ2 test was used in order to evaluate the variation before and after the intervention. The correlational findings demonstrate that after applying the virtual educational intervention, it is sufficient to apply specific measures in the first (before) and second stage (during) to obtain a higher result in compliance with the general level of the regulations against COVID-19. It is concluded that the virtual educational intervention for the craftswomen has generated awareness, impacting the care of their personal health, their family and their community, as well as being prepared for the reactivation of tourism.
Correction: Al-Ashmori et al. “Values, Challenges, and Critical Success Factors” of Building Information Modelling (BIM) in Malaysia: Experts Perspective. Sustainability 2022, 14, 3192
Authors: Al-Ashmori Yasser Yahya , Othman Idris , Al-Aidrous Al-Hussein M. H.
Published: March 2023

The authors would like to make the following corrections to the published paper [...]