bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring Thermokarst Lake Drainage Dynamics in Northeast Siberian Coastal Tundra
Authors: Liu Aobo , Chen Yating , Cheng Xiao
Published: September 2023

Thermokarst lakes in permafrost regions are highly dynamic due to drainage events triggered by climate warming. This study focused on mapping lake drainage events across the Northeast Siberian coastal tundra from 2000 to 2020 and identifying influential factors. An object-based lake analysis method was developed to detect 238 drained lakes using a well-established surface water dynamics product. The LandTrendr change detection algorithm, combined with continuous Landsat satellite imagery, precisely dated lake drainage years with 83.2% accuracy validated against manual interpretation. Spatial analysis revealed the clustering of drained lakes along rivers and in subsidence-prone Yedoma regions. The statistical analysis showed significant warming aligned with broader trends but no evident temporal pattern in lake drainage events. Our machine learning model identified lake area, soil temperature, summer evaporation, and summer precipitation as the top predictors of lake drainage. As these climatic parameters increase or surpass specific thresholds, the likelihood of lake drainage notably increases. Overall, this study enhanced the understanding of thermokarst lake drainage patterns and environmental controls in vulnerable permafrost regions. Spatial and temporal dynamics of lake drainage events were governed by complex climatic, topographic, and permafrost interactions. Integrating remote sensing with field studies and modeling will help project lake stability and greenhouse gas emissions under climate change.
Validation of Surface Waves Investigation and Monitoring Data against Simulation by Simulating Waves Nearshore and Wave Retrieval from Gaofen-3 Synthetic Aperture Radar Image
Authors: Hao Mengyu , Shao Weizeng , Shi Shaohua , Liu Xing , Hu Yuyi , Zuo Juncheng
Published: September 2023

The Chinese-French Oceanography SATellite (CFOSAT) jointly developed by the Chinese National Space Agency (CNSA) and the Centre National d’Etudes Spatiales (CNES) of France carries a wave spectrometer (Surface Waves Investigation and Monitoring, SWIM). SWIM has one nadir and five off-nadir beams to measure ocean surface waves. These near-nadir beams range from 0° to 10° at an interval of 2°. In this work, we investigated the performance of wave parameters derived from wave spectra measured by SWIM at off-nadir beams during the period 2020 to December 2022, e.g., incidence angles of 6°, 8° and 10°, which were collocated with the wave simulated by Simulating Waves Nearshore (SWAN). The validation of SWAN-simulated significant wave heights (SWHs) against National Data Buoy Center (NDBC) buoys of National Oceanic and Atmospheric Administration (NOAA) exhibited a 0.42 m root mean square error (RMSE) in the SWH. Our results revealed a RMSE of 1.02 m for the SWIM-measured SWH in the East Pacific Ocean compared with the SWH simulated by SWAN, as well as a 0.79 correlation coefficient (Cor) and a 1.17 squared error (Err) for the wave spectrum at an incidence angle of 10°, which are better than those (i.e., the RMSEs were > 1.1 m with Cors < 0.76 and Errs > 1.2) achieved at other incidence angles of SWH up to 14 m. This analysis indicates that the SWIM product is a relevant resource for wave monitoring over global seas. The collocated wave retrievals for more than 300 cases from Gaofen-3 (GF-3) synthetic aperture radar (SAR) images in China Seas were also used to verify the accuracy of SWIM-measured wave spectra. The energy of the SWIM-measured wave spectra represented by SWH was found to decrease with an increasing incidence angle in a case study. Moreover, the SWIM-measured wave spectra were most consistent with the SAR-derived wave spectra at an incidence angle of 10°, yielding a 0.77 Cor and 1.98 Err between SAR-derived and SWIM wave spectra under regular sea state conditions (SWH < 2 m). The error analysis indicates that the difference in SWH between SWIM at an incidence angle of 10° and SWAN has an increasing tendency with the growth in sea surface wind and sea state and it stabilizes to be 0.6 m at SWH > 4 m; however, the current and sea level have less influence on the uncertainties of the SWIM product.
Spatio-Temporal Knowledge Graph-Based Research on Agro-Meteorological Disaster Monitoring
Authors: Zhang Wenyue , Peng Ling , Ge Xingtong , Yang Lina , Chen Luanjie , Li Weichao
Published: September 2023

Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative manner. This paper proposes a method for monitoring agro-meteorological disasters based on a spatio-temporal knowledge graph. It employs a semantic ontology framework to achieve the organic fusion of multi-source heterogeneous data, including remote sensing data, meteorological data, farmland data, crop information, etc. And it formalizes expert knowledge and computational models into knowledge inference rules, thereby enabling monitoring, early warning, and disaster analysis of agricultural crops within the observed area. The experimental area for this research is the wheat planting region in three counties in Henan Province. The method is tested using simulation monitoring, early warning, and impact calculation of the past two occurrences of dry hot wind disasters. The experimental results demonstrate that the proposed method can provide more specific and accurate warning information and post-disaster analysis results compared to raw records. The statistical results of NDVI decline also validate the correlation between the severity of wheat damage caused by dry hot winds and the intensity and duration of their occurrences. Regarding remote sensing data, this paper proposes a method that directly incorporates remote sensing data into spatio-temporal knowledge inference calculations. By integrating remote sensing data into the regular monitoring process, the advantages of remote sensing data granted by continuous observation are utilized. This approach represents a beneficial attempt to organically integrate remote sensing and meteorological data for monitoring, early warning, and evaluation analysis of agro-meteorological disasters.
Performance of Wide Dynamic Photomultiplier Applied in a Low Blind Zone Lidar
Authors: Wang Longlong , Yin Zhenping , Zhao Bing , Mao Song , Zhang Qinlang , Yi Yang , Wang Xuan
Published: September 2023

Aerosol lidars play a vital role in the investigations of atmospheric pollution formation and meteorological processes. The intensity of lidar return signals in the near range changes much faster compared with the one in the far range, so extremely wide dynamic outputs from the photomultiplier tube (PMT) are needed to avoid saturation in the near range. Usually, to obtain the wide dynamic range, simultaneously, a transient digitizer (Licel) is applied to provide an analog detection chain for strong signals and a photon counting (PC) detection chain for weak signals. However, the near-range signals are still often saturated due to the very high aerosol loading. In this paper, we proposed to use a new PMT module with eight orders of magnitude for a low blind zone lidar, which can achieve both analog and PC separately. A comprehensive evaluation of this potential PMT, which could perform better in near-range detection, compared with the ordinary PMT was tested, but similar features are maintained in the far-range. The photon count rate and signal-to-noise ratio were tested for both the new PMT module and the ordinary PMT module. The results showed that the new PMT module is useful to extend the dynamic range of lidar detection.
Long-Term Characteristics of Surface Soil Moisture over the Tibetan Plateau and Its Response to Climate Change
Authors: Zhu Chenxia , Li Shijie , Hagan Daniel Fiifi Tawia , Wei Xikun , Feng Donghan , Lu Jiao , Ullah Waheed , Wang Guojie
Published: September 2023

Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change.
Mutual Coupling Reduction in MIMO DRA through Metamaterials
Authors: Khan Muhammad Sabir , Khan Shahid , Khan Owais , Aqeel Sajid , Gohar Neelam , Dalarsson Mariana
Published: September 2023

A single negative metamaterial structure with hexagonal split-ring resonators (H-SRRs) is inserted within a two-port multiple-input multiple-output (MIMO) dielectric resonator antenna (DRA) in order to achieve a reduction of mutual coupling between closed multiple antenna elements. Between closed, tightly coupled, high-profile antenna elements, the single negative magnetic inclusions (H-SRRs) are embedded. By incorporating magnetic structures within antenna elements, the mutual coupling is significantly diminished. Mutual coupling reduction is attained by inserting an array of hexagonal split-ring resonators between the inter-spacing elements. An operative approach for the reduction of the mutual coupling between two × two MIMO DRAs initially operating at 5.2-GHz band is provided. To make the simulated design replica of the fabricated prototype, an air gap is introduced between the substrate, DRs, and H-SSRs. The addition of the air gap shifts the simulated results to 5.9 GHz, which closely resembles the measured values. The mutual coupling reduction is realized by integrating a meta-surface amid the two × two MIMO DRAs, which are settled in the H-plane. The meta-surface embraces an array of hexagonal split-ring resonator (H-SRR) cells that are unified along the E-plane. The H-SRR structure is designed to offer band-stop functionality within the antenna bandwidth. The proposed design has an overall dimension of 40 × 58.3 × 4.75 mm3 (1.5λ × 1.02λ × 0.079λ). By stacking the DRA with a one × three array of H-SRR unit cells, a 30 dB reduction in the mutual coupling level is attained without compromising on the antenna performance. The corresponding mutual impedance of the MIMO DRA is better than 30 dB over 5.9–6.1 GHz operating bandwidth. The proposed design has a DG of 10 db, ECC < 0.02, CCL < 0.02 bits/s/Hz, and an MEG of 0 dB. The overall design has a promising performance, which shows its suitability for the target wireless application.
Utilizing Low-Cost Sensors to Monitor Indoor Air Quality in Mongolian Gers
Authors: Flowerday Callum E. , Lundrigan Philip , Kitras Christopher , Nguyen Tu , Hansen Jaron C.
Published: September 2023

Air quality has important climate and health effects. There is a need, therefore, to monitor air quality both indoors and outdoors. Methods of measuring air quality should be cost-effective if they are to be used widely, and one such method is low-cost sensors (LCS). This study reports on the use of LCSs in Ulaanbataar, Mongolia to measure PM2.5 concentrations inside yurts or “gers”. Some of these gers were part of a non-government agency (NGO) initiative to improve insulating properties of these housing structures. The goal of the NGO was to decrease particulate emissions inside the gers; a secondary result was to lower the use of coal and other biomass material. LCSs were installed in gers heated primarily by coal, and interior air quality was measured. Gers that were modified by increasing their insulating capacities showed a 17.5% reduction in PM2.5 concentrations, but this is still higher than recommended by health organizations. Gers that were insulated and used a combination of both coal and electricity showed a 19.1% reduction in PM2.5 concentrations. Insulated gers that used electricity for both heating and cooking showed a 48% reduction in PM2.5 but still had higher concentrations of PM2.5 that were 6.4 times higher than recommended by the World Health Organization (WHO). Nighttime and daytime trends followed similar patterns and trends in PM2.5 concentrations with slight variations. It was found that at nighttime the outside PM2.5 concentrations were generally higher than the inside concentrations of the gers in this study, meaning that PM2.5 would flow into the ger whenever the doors were opened, causing spikes in PM2.5 concentrations.
Deep Neural Network Quantization Framework for Effective Defense against Membership Inference Attacks
Authors: Famili Azadeh , Lao Yingjie
Published: September 2023

Machine learning deployment on edge devices has faced challenges such as computational costs and privacy issues. Membership inference attack (MIA) refers to the attack where the adversary aims to infer whether a data sample belongs to the training set. In other words, user data privacy might be compromised by MIA from a well-trained model. Therefore, it is vital to have defense mechanisms in place to protect training data, especially in privacy-sensitive applications such as healthcare. This paper exploits the implications of quantization on privacy leakage and proposes a novel quantization method that enhances the resistance of a neural network against MIA. Recent studies have shown that model quantization leads to resistance against membership inference attacks. Existing quantization approaches primarily prioritize performance and energy efficiency; we propose a quantization framework with the main objective of boosting the resistance against membership inference attacks. Unlike conventional quantization methods whose primary objectives are compression or increased speed, our proposed quantization aims to provide defense against MIA. We evaluate the effectiveness of our methods on various popular benchmark datasets and model architectures. All popular evaluation metrics, including precision, recall, and F1-score, show improvement when compared to the full bitwidth model. For example, for ResNet on Cifar10, our experimental results show that our algorithm can reduce the attack accuracy of MIA by 14%, the true positive rate by 37%, and F1-score of members by 39% compared to the full bitwidth network. Here, reduction in true positive rate means the attacker will not be able to identify the training dataset members, which is the main goal of the MIA.
Body Weight Estimation for Pigs Based on 3D Hybrid Filter and Convolutional Neural Network
Authors: Liu Zihao , Hua Jingyi , Xue Hongxiang , Tian Haonan , Chen Yang , Liu Haowei
Published: September 2023

The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig’s back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding.
Electrochemical Sensing Platform Based on Carbon Dots for the Simultaneous Determination of Theophylline and Caffeine in Tea
Authors: Di Matteo Paola , Trani Alessandro , Bortolami Martina , Feroci Marta , Petrucci Rita , Curulli Antonella
Published: September 2023

A simple and selective method for the determination of caffeine (CAF) and theophylline (THEO) has been developed for a glassy carbon electrode (GCE) modified with a composite including carbon dots (CDs) and chitosan (CS). To our knowledge, there are no previous studies that analyze a CDs-modified GCE for the presence of CAF and THEO. The electrochemical behavior of a GCE modified with a CDs-CS composite was studied in acidic medium by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). Considering the sensor analytical parameters, the same linear concentrations range was found for CAF and THEO ranging from 1 × 10−5 to 5 × 10−3 mol L−1 with the same detection limit (LOD) of 1 × 10−6 mol L−1. The reproducibility and repeatability data were satisfactory in terms of RSD%. Moreover, the storage stability was evaluated, evidencing good results whatever the experimental conditions used. The developed sensor was applied for the simultaneous determination of CAF and THEO in tea and drug, and results were compared with those obtained with HPLC-ESI-MS in SIR mode as an independent method optimized on purpose. The electrochemical sensor presents the undoubled advantages in terms of cheapness, portability, and ease of use, since it does not require skilled personnel.
Development of a Cloud-Based Image Processing Health Checkup System for Multi-Item Urine Analysis
Authors: Wu Yu-Lin , Wang Chien-Shun , Weng Wei-Chien , Lin Yu-Cheng
Published: September 2023

With the busy pace of modern life, an increasing number of people are afflicted by lifestyle diseases. Going directly to the hospital for medical checks is not only time-consuming but also costly. Fortunately, the emergence of rapid tests has alleviated this burden. Accurately interpreting test results is extremely important; misinterpreting the results of rapid tests could lead to delayed medical treatment. Given that URS-10 serve as a rapid test capable of detecting 10 distinct parameters in urine samples, the results of assessing these parameters can offer insights into the subject’s physiological condition. These parameters encompass aspects such as metabolism, renal function, diabetes, urinary tract disorders, hemolytic diseases, and acid–base balance, among others. Although the operational procedure is straightforward, the variegated color changes exhibited in the outcomes of individual parameters render it challenging for lay users to deduce causal factors solely from color variations. Moreover, potential misinterpretations could arise due to visual discrepancies. In this study, we successfully developed a cloud-based health checkup system that can be used in an indoor environment. The system is used by placing a URS-10 test strip on a colorimetric board developed for this study, then using a smartphone application to take images which are uploaded to a server for cloud computing. Finally, the interpretation results are stored in the cloud and sent back to the smartphone to be checked by the user. Furthermore, to confirm whether the color calibration technology can eliminate color differences between different cameras, and also whether the colorimetric board and the urine test strips can perform color comparisons correctly in different light intensity environments, indoor environments that could simulate a specific light intensity were established for testing purposes. When comparing the experimental results to real test strips, only two groups failed to reach an identification success rate of 100%, and in both of these cases the success rate reached 95%. The experimental results confirmed that the system developed in this study was able to eliminate color differences between camera devices and could be used without special technical requirements or training.
CNN-ViT Supported Weakly-Supervised Video Segment Level Anomaly Detection
Authors: Sharif Md. Haidar , Jiao Lei , Omlin Christian W.
Published: September 2023

Video anomaly event detection (VAED) is one of the key technologies in computer vision for smart surveillance systems. With the advent of deep learning, contemporary advances in VAED have achieved substantial success. Recently, weakly supervised VAED (WVAED) has become a popular VAED technical route of research. WVAED methods do not depend on a supplementary self-supervised substitute task, yet they can assess anomaly scores straightway. However, the performance of WVAED methods depends on pretrained feature extractors. In this paper, we first address taking advantage of two pretrained feature extractors for CNN (e.g., C3D and I3D) and ViT (e.g., CLIP), for effectively extracting discerning representations. We then consider long-range and short-range temporal dependencies and put forward video snippets of interest by leveraging our proposed temporal self-attention network (TSAN). We design a multiple instance learning (MIL)-based generalized architecture named CNN-ViT-TSAN, by using CNN- and/or ViT-extracted features and TSAN to specify a series of models for the WVAED problem. Experimental results on publicly available popular crowd datasets demonstrated the effectiveness of our CNN-ViT-TSAN.
Crime Light Imaging (CLI): A Novel Sensor for Stand-Off Detection and Localization of Forensic Traces
Authors: Chiuri Andrea , Chirico Roberto , Angelini Federico , Andreoli Fabrizio , Menicucci Ivano , Nuvoli Marcello , Cano-Trujillo Cristina , Montalvo Gemma , Lazic Violeta
Published: September 2023

Stand-off detection of latent traces avoids the scene alteration that might occur during close inspection by handheld forensic lights. Here, we describe a novel sensor, named Crime Light Imaging (CLI), designed to perform high-resolution photography of targets at a distance of 2–10 m and to visualize some common latent traces. CLI is based on four high-power illumination LEDs and one color CMOS camera with a motorized objective plus frontal filters; the LEDs and camera could be synchronized to obtain short-exposure images weakly dependent on the ambient light. The sensor is integrated into a motorized platform, providing the target scanning and necessary information for 3D scene reconstruction. The whole system is portable and equipped with a user-friendly interface. The preliminary tests of CLI on fingerprints at distance of 7 m showed an excellent image resolution and drastic contrast enhancement under green LED light. At the same distance, a small (1 µL) blood droplet on black tissue was captured by CLI under NIR LED, while a trace from 15 µL semen on white cotton became visible under UV LED illumination. These results represent the first demonstration of true stand-off photography of latent traces, thus opening the way for a completely new approach in crime scene forensic examination.
Advancements in Phase Change Materials in Asphalt Pavements for Mitigation of Urban Heat Island Effect: Bibliometric Analysis and Systematic Review
Authors: Pinheiro Claver , Landi Salmon , Lima Orlando , Ribas Larissa , Hammes Nathalia , Segundo Iran Rocha , Homem Natália Cândido , Castelo Branco Verônica , Freitas Elisabete , Costa Manuel Filipe , Carneiro Joaquim
Published: September 2023

This research presents a dual-pronged bibliometric and systematic review of the integration of phase change materials (PCM) in asphalt pavements to counteract the urban heat island (UHI) effect. The bibliometric approach discerns the evolution of PCM-inclusion asphalt research, highlighting a marked rise in the number of publications between 2019 and 2022. Notably, Chang’an University in China has emerged as a leading contributor. The systematic review addresses key questions like optimal PCM types for UHI effect mitigation, strategies for PCM leakage prevention in asphalt, and effects on mechanical properties. The findings identify polyethylene glycols (PEGs), especially PEG2000 and PEG4000, as prevailing PCM due to their wide phase-change temperature range and significant enthalpy during phase transitions. While including PCM can modify asphalt’s mechanical attributes, such mixtures typically stay within performance norms. This review emphasises the potential of PCM in urban heat management and the need for further research to achieve optimal thermal and mechanical balance.
Algorithmic Discriminations and New Forms of Protections: An Analysis of the Italian Case
Authors: De Angelis Marina , Donà Silvia , Bergamante Francesca
Published: September 2023

This research aims to investigate how to protect workers from discrimination dictated by an algorithm in the contractual conditions. Article 15 of the Italian Workers’ Statute declares invalid any agreement or act aimed at: dismissing a worker, discriminating him in the assignment of qualifications or tasks, transfers, disciplinary measures, or otherwise prejudicing him because of his affiliation or union activity, or his participation in a strike. These provisions shall also apply to pacts or acts for the purposes of political, religious, racial, language, sex, disability, age, sexual orientation, or belief. Our work intends to explore the risk of gender or age discrimination in the contractual terms for platform workers in Italy. How can workers’ protections be preserved when decisions are made by an algorithm? The research is conducted with a multidisciplinary methodology. We first analyze both national and international literature and jurisprudence. Then, by means of probit models on INAPP PLUS 2021 survey data, we analyze contract characteristics, in particular the written form of the contract and the hourly minimum wage. Controlling for individual and job characteristics, we find evidence of discrimination according to gender and age of workers. We conclude with policy recommendations.
Towards a New Design Methodology for Vertical Traffic Calming Devices
Authors: D’Apuzzo Mauro , Evangelisti Azzurra , Santilli Daniela , Nardoianni Sofia , Cappelli Giuseppe , Nicolosi Vittorio
Published: September 2023

With increasing emphasis on new soft mobility in urban areas, it becomes more and more important to provide effective speed control measures for vehicular traffic. Among those, the ones based on vehicle vertical deflection, namely vertical traffic calming measures, are historically the most widespread. However, since the basic operating principle of these devices is related to the vertical dynamic response due to the interaction between the moving vehicle and the road profile, different vehicles may exhibit different speed behaviors when traveling on a specific road profile shape. As a matter of fact, recent research provided evidence of this connection, and therefore it has been worth investigating the dynamics underlying the phenomenon in order to develop a new approach to the design of vertical traffic calming devices. In this paper, following an initial state-of-the-art review, an in-depth study on the dynamic interaction between vehicle and road profile has been presented by means of an ad hoc-developed mathematical model. The proposed simulation model has been used to evaluate the root mean square acceleration value associated with each vehicle/traffic calming device/crossing speed. Following the outcomes provided by numerical simulations, an experimental investigation has been designed and carried out on a vertical traffic calming device. Speed profiles of different vehicles have been acquired, and preliminary results seem to provide evidence of a different dynamic response for each vehicle type, yielding the basis to reconsider the design approach of such devices in order to control urban traffic speed.
The Moderating Role of Ownership Concentration on Financing Decisions and Firm’s Sustainability: Evidence from China
Authors: Wen Kankan , Agyemang Andrew , Alessa Noha , Sulemana Inusah , Osei Abednego
Published: September 2023

We examined the impact of financing decisions on a firm’s sustainability in China as it aspires to achieve carbon neutrality. To proxy firms’ sustainability performance, we proposed an index for environmental, social, and governance (ESG) performance. The financing decision was proxied by debt funding and equity funding. Using secondary data from China Stock Market Accounting Data from 2016 to 2022, we utilize the fixed effect and fully modified ordinary least squares estimators for the empirical analysis. The analysis indicated a favorable link between debt funding and ESG performance. We uncovered an inconsistent association between equity funding and ESG performance. Moreover, ownership concentration revealed a significant role in moderating the impact of debt financing and ESG performance in China. The findings affirm that firms should rely on debt funding rather than equity funding to enhance their ESG performance. Hence, policymakers should enact laws allowing easy access to debt funding for companies to ensure higher ESG performance. This, in the long term, will contribute to the Chinese dream of carbon neutrality.
Drama/Theatre Performance in Education through the Use of Digital Technologies for Enhancing Students’ Sustainability Awareness: A Literature Review
Authors: Zakopoulos Vassilis , Makri Agoritsa , Ntanos Stamatios , Tampakis Stilianos
Published: September 2023

The use of digital technologies in drama performances in education that address the sustainability issues that plague today’s world is highly acknowledged globally by the scientific community since digital technologies offer a variety of ways to provide students with an interactive and engaging learning experience. This article aims to present a literature review on drama/theatre performance in educational environments by applying digital technologies to increase students’ sustainability awareness. More specifically, this article presents a state-of-the-art study on the recent developments concerning digital drama performance and explores how the cultivation of sustainability consciousness by students in all educational contexts can be achieved. Additionally, it provides valuable insights into how combining digital drama performances and sustainability issues could improve crucial 21st-century skills for students at all levels of education. The dataset was collected using the Web of Science, Scopus, and Google Scholar databases. This review is mainly interested in identifying the research gaps since the current literature focuses primarily on digital technologies in drama/theatre education. However, developing the sustainability awareness of students by performing digital drama seems to be under researched. The results of this study directly impact a wide range of educational experts, such as drama teachers, drama faculty members, drama education researchers, scholars, and theatre practitioners, including art critics, culture specialists, stage designers, drama directors, managers, consultants, and policymakers, to influence them to reflect and reconsider the potential benefits of introducing drama/theatre performance lessons in the official curriculum programs to motivate students to become aware of critical sustainable issues.
Housing Satisfaction: A Comparison between Post-Second World War Large Housing Estates and Post-Socialist Multifamily Residential Neighbourhoods in Slovenia
Authors: Sendi Richard , Šeme Ajda , Kerbler Boštjan
Published: September 2023

As a reaction to the critical writings of urban sociologists regarding the quality of post-Second World War large housing estates (monotonous architectural design, cheap/poor quality building materials, high building density, minimum dwelling sizes, overcrowding, etc.), fewer problematic types of mass housing neighbourhoods started to emerge, first in Western European countries in the 1970s, and later in Eastern European countries after they abandoned the planned economy system of the communist era and adopted the market economy system. One of the major concerns often raised in this regard is that, due to a variety of negative aspects associated with post-WWII housing estates, these neighbourhoods may eventually end up as concentrations of low-income households, after the more affluent residents relocate to the more attractive, less problematic residential neighbourhoods. Such developments are, understandably, undesirable. This concern inherently assumes that the inhabitants of post-WWII housing estates are not satisfied with their housing and are, as a result, expected to move away, as soon as they can. This hypothesis presents the focus of discussion in this paper. We examine its validity with the help of the findings of a comparative study between post-WII large housing estates and post-socialist multifamily residential neighbourhoods that was conducted in Slovenia in 2021. Contrary to the hypothetical assumptions, our analyses of the research results reveal that the inhabitants of post-WWII large housing estates are (a) generally quite satisfied with their dwellings and residential neighbourhood and that (b) a large majority of them have no intention to move. Understanding people’s satisfaction with their housing circumstances requires a complex analysis of their attitudes that goes beyond surface-level observations and encompasses a broader range of psychological and social influences on housing choices, suggesting the need to incorporate these aspects into future research. The consideration of these aspects is essential in order to ensure the longer-term sustainability of post-WWII large housing estates.
Exploring the Impact of Group Tourists’ Citizenship Behavior on Engagement: The Intimacy as a Mediating Variable
Authors: Wang Dan , Shen Ching-Cheng , Liu Hsi-Lin
Published: September 2023

Tourist engagement not only yields advantages for the destination but also holds significance within the realm of group travel. In this context, if tour guides are able to leverage tourist citizenship behaviors (TCB) to augment interactivity and participation, tourists can essentially transform into “tour guide assistants”. To this end, the study introduces intimacy as a mediating variable, aiming to investigate the influence of TCB on engagement levels among group tourists. The subjects were Taiwanese tourists who had engaged in outbound group tourism for more than five days within three years. A total of 436 online questionnaires were collected by convenience sampling and analyzed in LISREL and SPSS 21.0. The research results: 1. TCB significantly positively affects intimacy and engagement; 2. Intimacy is a mediating variable between TCB and engagement. 3. Different levels of TCB have significant differences in intimacy and engagement. The research results can help tour guides and travel agency operators know the characteristics of group tourists and improve the experience of group tour tourists.
Metamodelling of Naturalised Groundwater Levels at a Regional Level in New Zealand
Authors: Yang Jing , Rajanayaka Channa , Daughney Christopher J. , Booker Doug , Morris Rebecca , Thompson Mike
Published: September 2023

Groundwater is under pressure from increasing demands for agriculture, industry, domestic uses and support of ecosystems. Understanding the natural state of a groundwater system helps policy makers manage groundwater sustainably. Here we developed a metamodelling approach based on stepwise linear regression that emulates the functionality of physically-based models in the three primary aquifers of the Greater Wellington region of New Zealand. The inputs for the metamodels included local weather data, and nearby river flow data. The metamodels were calibrated and validated against the available simulations of naturalised groundwater level time series from physically-based models for 47 selected wells. For 36 of these wells, the metamodels had Nash-Sutcliffe Efficiency and coefficient of determination over 0.5, showing that they could adequately mimic naturalised groundwater level dynamics as simulated by the physically-based groundwater models. The remaining 11 wells had unsatisfactory performance and were typically located far away from rivers or along the coast. The results also showed that modelled groundwater levels in the aquifer’s recharge zone were more sensitive to short-term (less than 2 weeks lag) than long-term river flow (above 4 weeks to 1 year lag), whereas the converse pattern was observed for the aquifer’s discharge zone. Although some special considerations are needed, this metamodelling framework can be generally applied to other aquifers to support groundwater resource management at a lower cost than updating physically-based models.
Impacts of Land Use Changes on Soil Functions and Water Security: Insights from a Three-Year-Long Study in the Cantareira System, Southeast of Brazil
Authors: Santana Monna Lysa Teixeira , Faria Vanêssa Lopes de , Barbosa Samara Martins , Serafim Milson Evaldo , Uezu Alexandre , Silva Bruno Montoani , Avanzi Junior Cesar
Published: September 2023

Maintaining soil functions is crucial for human well-being, but there is a lack of integration between soil, water security, ecosystem services, and climate change. To bridge this knowledge gap and address erosion-induced soil and water losses and considering intrinsic impacts of soil structure, a three-year-long study was conducted focused on three dominant soil types (Typic Hapludult, Typic Dystrudept, and Typic Usthortent) combined with different land uses (native forest, eucalyptus plantation, rotational grazing, and extensive grazing) in a critical water supply region for the São Paulo metropolitan area in Southeastern Brazil. Surface runoff, evaluated for erosion resistance, was measured using the Cornell infiltrometer, and soil electrical resistivity tomography estimated soil water content to a depth of 1.5 m for groundwater recharge analysis. Soil hydraulic properties were also measured. The results revealed that native forest soils had higher hydraulic conductivity, particularly in the surface layer, compared to eucalyptus and pastures. Native forests in Typic Hapludult showed a higher runoff rate (200 to 250 mm h−1) due to a naturally dense subsoil layer that negatively impacted water infiltration and recharge with a high erosion potential, therefore reducing the amount of water stored. Typic Usthortent maintained a higher soil water content in pastures than in other land uses and also showed a low rate of water infiltration, resulting in perched water in the surface layer. In Typic Dystrudept, the native forest presented higher hydraulic conductivity (0–5 cm: 115.9 cm h−1) than eucalyptus (0–5 cm: 36.4 cm h−1), rotational grazing (0–5 cm: 19.4 cm h−1), and extensive grazing (0–5 cm: 2.6 cm h−1), but there were no significant differences in soil water content among land uses. This work illustrates the crucial role of native forests in affecting deep water recharge, reducing the soil surface erosion, mainly in soils without naturally subsoil layer, maintaining recharge potential. For Ultisols, pastures preserved soil structure and are therefore less impacted by soil management. With these results, a contribution is made to soil and water conservation, providing support for sustainable management practices in erosion-prone areas.
Bibliometric Analysis of Spatial Accessibility from 1999–2022
Authors: Zhu Hong , Li Jin , Yuan Zhenjie , Li Jie
Published: September 2023

Spatial accessibility is an essential manifestation of social equity and spatial planning. However, when examining spatial accessibility, systematic review studies dominate the literature, with fewer studies employing evaluative and relational bibliometric approaches. Thus, to provide a comprehensive overview of spatial accessibility research from 1999 to 2022, the works of literature referenced and analyzed by the Web of Science during 1999–2022 were analyzed: bibliometrics and scientific mapping methods using R software, the biblioshiny web program, tidyverse (ggplot2), and VOSviewer. The analysis results indicate a significant increase in the number of research papers addressing spatial accessibility throughout the research period accompanied by a continuous expansion of the research area. Collaboration and research endeavors among countries in this domain have deepened, leading to a continual improvement in the quality of papers. In addition, influential journals, authors, and articles in the field of spatial accessibility were also counted. New data, methods, and technology will become substantially more critical in accessibility research.
The Environmental Sustainability Assessment of an Orthopedics Emergency Hospital Supported by a New Innovative Framework
Authors: Moldovan Flaviu , Moldovan Liviu , Bataga Tiberiu
Published: September 2023

Due to their high consumption of resources and enormous amount of generated waste, healthcare systems are not considered to be sustainable. Given the constant changes in internal needs, improvement of this state cannot be achieved only through policies, strategies, and interventions administered from the outside. Thus, it is necessary to design tools that, through their constant application, facilitate the sustainable development of health institutions. The objective of this research is to develop and validate in practice an innovative framework for assessing the sustainability of healthcare facilities that is compatible with hospital accreditation legislation and other community frameworks. The research is limited to the study of environmental sustainability and its results are validated in a healthcare facility with an orthopedic profile. The research method consists of defining the domains of the new innovative framework, collecting the latest medical practices related to environmental sustainability, designing indicators related to environmental responsibility and a matrix of indicators, followed by its validation in practice at an emergency hospital. The new innovative framework is organized in three areas, including environmental, economic, and social sustainability, to which is added organizational capacity along with management and provision of sustainable healthcare services. It contains 57 indicators, of which 8 are dedicated to the environmental area. The indicators are described in detail together with evaluation grids structured by the coupled degrees of importance and achievement. The practical implementation of the innovative framework at the County Emergency Clinical Hospital of Targu Mures highlights its added value by promoting sustainability strategies together with continuous quantification of the organization’s sustainability level.
Cyber-Resilience Evaluation Methods Focusing on Response Time to Cyber Infringement
Authors: Choi Se-Ho , Youn Jaepil , Kim Kookjin , Lee Seongkee , Kwon Oh-Jin , Shin Dongkyoo
Published: September 2023

Though multilevel, in-depth information protection systems are employed to defend against unknown cyber threats, vulnerabilities in these systems are frequently exploited by cyberattacks. As a result, it becomes challenging to comprehensively counter these attacks within a constrained time frame. When a cyberattack is detected, immediate measures are necessary to prevent widespread damage and maintain the system’s regular functioning. Possessing sustainable cyber-resilience capabilities, which can promptly restore the system to its pre-attack state, is crucial. In this paper, a cyber-defense activity optimization procedure is introduced, drawing on the failure recovery time of the information system, aiming to enhance both the response and recovery phases of cyber resilience. Through training, the response time for various types of cyberattack was determined. Notably, a decrease in response time by 17.8% compared to the baseline was observed. By optimizing response times and integrating them with sustainable cyber-resilience assessment activities, a robust framework is presented for evaluating an organization’s overall cyber-defense stance. Research on the cyber combat capability index, dissecting the response time for each distinct cyber-defense activity, is planned for future endeavors.
Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption
Authors: Almuhaini Salma Hamad , Sultana Nahid
Published: September 2023

This study utilized different methods, namely classical multiple linear regression (MLR), statistical approach exponential smoothing (EXPS), and deep learning algorithm long short-term memory (LSTM) to forecast long-term electricity consumption in the Kingdom of Saudi Arabia. The originality of this research lies in (1) specifying exogenous variables that significantly affect electrical consumption; (2) utilizing the Bayesian optimization algorithm (BOA) to develop individual super learner BOA-LSTM models for forecasting the residential and total long-term electric energy consumption; (3) measuring forecasting performances of the proposed super learner models with classical and statistical models, viz. MLR and EXPS, by employing the broadly used evaluation measures regarding the computational efficiency, model accuracy, and generalizability; and finally (4) estimating forthcoming yearly electric energy consumption and validation. Population, gross domestic products, imports, and refined oil products significantly impact residential and total annual electricity consumption. The coefficient of determination (R2) for all the proposed models is greater than 0.93, representing an outstanding fitting of the models with historical data. Moreover, the developed BOA-LSTM models have the best performance with R2>0.99, enhancing the predicting accuracy (Mean Absolute Percentage Error (MAPE)) by 59.6% and 54.8% compared to the MLR and EXPS models, respectively, of total annual electricity consumption. This forecasting accuracy in residential electricity consumption for the BOA-LSTM model is improved by 62.7% and 68.9% compared to the MLR and EXPS models. This study achieved a higher accuracy and consistency of the proposed super learner model in long-term electricity forecasting, which can be utilized in energy strategy management to secure the sustainability of electric energy.