bluuebunny/crossref_metadata_2025 huggingface dataset:
Insulin-Loaded Chitosan–Cellulose-Derivative Hydrogels: In Vitro Permeation of Hormone through Strat-M® Membrane and Rheological and Textural Analysis
Authors: Ostróżka-Cieślik Aneta , Strasser Claire , Dolińska Barbara
Published: September 2024

This work is part of the current research trend to develop a hydrogel carrier of insulin to promote wound healing. Topically applied insulin promotes keratinocyte proliferation and migration, increases collagen synthesis, reduces inflammation and oxidative stress, and exhibits antimicrobial activity. The aim of this study was to design an insulin hydrogel matrix based on selected cellulose derivatives (methylcellulose, hydroxyethylcellulose, and hydroxypropylmethylcellulose) and chitosan. Rheological parameters of the formulations were evaluated using rotational rheometry and an oscillation test. Textural tests were performed. In vitro pharmaceutical insulin availability studies were carried out using the innovative Strat-M® membrane to imitate the skin barrier. It was found that the pharmaceutical formulation of insulin based on chitosan and methylcellulose showed an acceptable balance between rheological and textural parameters and ease of application. The API was released from the carrier in a prolonged manner, eliminating the need to apply the formulation several times per day. The developed hydrogel shows potential for use in clinical practice.
Research on Hotspots and Evolutionary Trends in Coal Mine Gas Prevention
Authors: Zhao Chunguang , Gao Peng , Ruan Jinlin , Pu Yang , Sun Yuan , Cheng Xiaoyang , Sun Yu
Published: September 2024

Mine gas accidents have received widespread attention at home and abroad, because mine gas accidents often cause casualties and property losses once they occur. This study was conducted in order to have a more comprehensive understanding of the research status of and research hotspots in coal mine gas management at home and abroad, and to provide references for relevant researchers’ studies and literature searches. Using the Web of Science (WOS) core database as the data source, this paper analyzed the data from the period of 2000–2023 under the headings of “gas governance”, “Gas Control Technology”, “Pre-mining gas methods in coal mines”, “Methods of Gas Mining in Coal Mines”, “Post-mining gas”, and “Post-mining gas methods in coal mines”. “Methods in Coal Mines”, “Methods of Gas Mining in Coal Mines”, and “Post-mining Gas Methods in Coal Mines” were searched, and the CiteSpace tool was utilized to provide a multi-dimensional visual presentation of the literature, including authors, the number of journals issued, countries of issue, keywords, etc., in order to explore the research hotspots in this field. The analysis results show the following: the development process in the field of gas prevention and control is mainly divided into the three stages of initial exploration, steady development, and explosive surge; China, Australia, and the United States are the top three countries in terms of the number of articles published, and they have a greater influence in the field, but there is a closer connection between the regions in Europe; a certain group size has been formed by the researchers and research institutes in this field, the China University of Mining and Technology is more active in this field, ranking as the first in terms of the number of articles issued, and it has a cooperative relationship with many universities; Kai Wang and Enyuan Wang are the core authors in this field, while C. Ozgen Karacan also has a large body of research in the field, and his research results are widely recognized; the main journals issued in this field are Fuel and Energy Policy, while other journals have been frequently cited, and these journals have an important role in the field. In these journals, “Coal mine methane: A review of capture and utilization practices with benefits to mining safety and to greenhouse gas reduction” is one of the most important articles in the field. Thin coal seams, coal mining workings, and coal and gas protrusion are current research hotspots.
Rethinking the Complexities of the Body and Disability: Theological Account
Authors: Vuk Grgic Martina
Published: September 2024

The biological aspect of human embodiment frequently constitutes the primary basis for personal assessment, with an emphasis on rationality, free choice, material well-being, and happiness as fundamental attributes of worth. This perspective is also evident in cultural practices of body modification, which reflect societal standards and identity expression. The promotion of standards of bodily appearance that are often considered unrealistic within contemporary culture has the effect of creating a social environment in which those who do not conform to these standards are rejected and stigmatized. This can include individuals with disabilities, the elderly, or those with chronic illnesses and different bodily appearances. In the majority of cases, the so-called ‘body capital’ culture views the disabled body through the lens of a person’s physical appearance, which is, to a certain extent, associated with a biological dysfunction or reflects a kind of physical disability or vulnerable corporeality. This paper seeks to examine perspectives on the body through the lenses of major discourses surrounding disability, biblical anthropology, and disability theology. These perspectives advocate for the intrinsic dignity and value of the disabled body, challenging contemporary norms and projections upon the body, by underscoring the biographical, interdependent, and spiritual dimensions of human embodiment. This approach stands as an alternative to the reductionist view of the body, which prioritizes physical attributes over a comprehensive understanding of complete personhood.
Suicide Risks and Protections in Religious Communities: Two Exploratory Studies
Authors: Mason Karen , Rynbrandt Bellanira , Capehart Karen , Rando Anthony , Dokopoulos Stefanos , O’Shell Dylan
Published: September 2024

It is imperative to understand possible protections against suicide because of the steadily climbing U.S. suicide rate. Religion has been associated with fewer suicidal behaviors. This study sought to explore the perspective of congregants experiencing suicidal behaviors (CESBs) on the risks and protections that they experience in their religious communities (RCs) in two separate studies. Study One included a purposive sample of 15 Christian congregants yielding seven codes: routine practices and deliberate suicide prevention efforts were inconsistent both before and during the pandemic lockdown, though RCs continued to operate. Cultures provided participants both protections and risks, while the pandemic amplified deep cultural divides already present before the pandemic. Study Two included a purposive sample of 20 congregants yielding six codes, core protections of a routinely provided worldview and deliberately provided theological teachings, risks of absent protections, aspirational tasks for faith leaders and RCs, inspiring stories of recovery, and some consistency with human flourishing with caution. CESBs in some RCs that have a norm of well-being hide their norm violation and expect rejection. RCs need training in order to provide deliberate suicide prevention protections and CESBs may provide counter-narratives to stigma in RCs and cultures.
Who Will Be Saved: The Right or the Upright?
Authors: Abdelnour Mohammed Gamal
Published: September 2024

There is a growing body of literature on the Islamic theology and philosophy of salvation. This literature can be loosely grouped into three main groups: there are those that link the right path of salvation to a specific Muslim group, others that link it to believing in the Prophet Muhammad regardless of the theological group that a Muslim may follow, and there are those that link it to the belief in God and doing good. Despite this variety, what largely unites those various interpretations is that they all emphasize the “rightness” of one’s theological path, i.e., asking the question, what is the “right” track to God? However, what received scant attention so far is the question of “uprightness” as opposed to “rightness”, i.e., Is salvation primarily about being “right” (muḥiqq), or rather about being “upright” (ṣādiq/mukhliṣ)? Drawing on Q. 5:116-119, which presents a conflict between “rightness” and “uprightness” embodied in Jesus’ conversation with God regarding the fate of those who did not have the right theology, and taking its cues from Abū Ḥāmid al-Ghazālī (d. 1111) who attempted to rejuvenate Islamic theology through spirituality, this article takes “uprightness” as the primary requisite for one to attain salvation and argues that the Quran, despite the emphasis it places on pursuing the “right” path, gives primacy to the “uprightness” of one’s position instead. Uprightness in the article is used in reference to the quality of being honest, responsible, and moral, as opposed to being merely “right” or “correct” theologically.
Bidirectional Transmission Mapping of Architectural Styles of Tibetan Buddhist Temples in China from the 7th to the 18th Century
Authors: Min Tianyi , Zhang Tong
Published: September 2024

Architecture is the stone book of history, and the evolution of architectural styles showcases a non-verbal history constructed through images. As an important part of China’s historical and cultural heritage, the architectural forms and styles of Tibetan Buddhist temples were initially modeled on Tang dynasty temple architecture and gradually evolved into the most significant architectural types in regions such as Tibet and Qinghai in China. Religious architecture has also played a significant role in shaping regional cultural landscapes. Existing research on Tibetan Buddhist temples is primarily focused on qualitative studies of individual temple buildings. This research takes the spatiotemporal evolution of architectural styles of Tibetan Buddhist temples as an entry point and, for the first time, employs ArcGIS technology to visualize the spatial and geographical distribution of Tibetan Buddhist temples from the 7th to the 18th century, establishing a comprehensive academic vision that encompasses both historical stratification and cross-regional spatial correlations. By analyzing the cultural symbolic features embodied in the construction styles of Tibetan Buddhist temples and the visual characteristics reflected in their decorative arts, we propose two spatiotemporal dimensions for the formation and transmission of Tibetan Buddhist temple architectural styles: “Westward Transmission” and “Eastward Diffusion”. Firstly, from the 7th to the 9th centuries, the architectural style and construction techniques of Tang dynasty Buddhist temples were transmitted westward along the Tang–Tibet ancient road, integrating with local Tibetan elements to form the Tubo architectural style, which was further refined into the “Sino–Tibetan Combined Style” with strong visual characteristics around the 13th century. Subsequently, along with the spread of Tibetan Buddhism, this temple architectural style underwent an eastward diffusion from the 13th to the 18th century, reaching regions, such as Sichuan, Qinghai, Gansu, Inner Mongolia, Hebei, and Beijing, presenting a spatial gradient from west to east in the geographical dimension. On this basis, in this research, we construct a historical evolution mapping of Tibetan Buddhist temple architectural styles based on bidirectional transmission, attempting to elucidate that the intrinsic driving forces are religious and the cultural identity that guided the bidirectional transmission mechanism of these architectural styles under the historical context of the formation and dissemination of Tibetan Buddhism from the 7th to the 18th century.
Mitigating Disparate Elevation Differences between Adjacent Topobathymetric Data Models Using Binary Code
Authors: Cushing William M. , Tyler Dean J.
Published: September 2024

Integrating coastal topographic and bathymetric data for creating regional seamless topobathymetric digital elevation models of the land/water interface presents a complex challenge due to the spatial and temporal gaps in data acquisitions. The Coastal National Elevation Database (CoNED) Applications Project develops topographic (land elevation) and bathymetric (water depth) regional scale digital elevation models by integrating multiple sourced disparate topographic and bathymetric data models. These integrated regional models are broadly used in coastal and climate science applications, such as sediment transport, storm impact, and sea-level rise modeling. However, CoNED’s current integration method does not address the occurrence of measurable vertical discrepancies between adjacent near-shore topographic and bathymetric data sources, which often create artificial barriers and sinks along their intersections. To tackle this issue, the CoNED project has developed an additional step in its integration process that collectively assesses the input data to define how to transition between these disparate datasets. This new step defines two zones: a micro blending zone for near-shore transitions and a macro blending zone for the transition between high-resolution (3 m or less) to moderate-resolution (between 3 m and 10 m) bathymetric datasets. These zones and input data sources are reduced to a multidimensional array of zeros and ones. This array is compiled into a 16-bit integer representing a vertical assessment for each pixel. This assessed value provides the means for dynamic pixel-level blending between disparate datasets by leveraging the 16-bit binary notation. Sample site RMSE assessments demonstrate improved accuracy, with values decreasing from 0.203–0.241 using the previous method to 0.126–0.147 using the new method. This paper introduces CoNED’s unique approach of using binary code to improve the integration of coastal topobathymetric data.
Automatic Water Body Extraction from SAR Images Based on MADF-Net
Authors: Wang Jing , Jia Dongmei , Xue Jiaxing , Wu Zhongwu , Song Wanying
Published: September 2024

Water extraction from synthetic aperture radar (SAR) images has an important application value in wetland monitoring, flood monitoring, etc. However, it still faces the problems of low generalization, weak extraction ability of detailed information, and weak suppression of background noises. Therefore, a new framework, Multi-scale Attention Detailed Feature fusion Network (MADF-Net), is proposed in this paper. It comprises an encoder and a decoder. In the encoder, ResNet101 is used as a solid backbone network to capture four feature levels at different depths, and then the proposed Deep Pyramid Pool (DAPP) module is used to perform multi-scale pooling operations, which ensure that key water features can be captured even in complex backgrounds. In the decoder, a Channel Spatial Attention Module (CSAM) is proposed, which focuses on feature areas that are critical for the identification of water edges by fusing attention weights in channel and spatial dimensions. Finally, the high-level semantic information is effectively fused with the low-level edge features to achieve the final water detection results. In the experiment, Sentinel-1 SAR images of three scenes with different characteristics and scales of water body are used. The PA and IoU of water extraction by MADF-Net can reach 92.77% and 89.03%, respectively, which obviously outperform several other networks. MADF-Net carries out water extraction with high precision from SAR images with different backgrounds, which could also be used for the segmentation and classification of other tasks from SAR images.
Semi-Supervised Remote Sensing Building Change Detection with Joint Perturbation and Feature Complementation
Authors: Chen Zhanlong , Wang Rui , Xu Yongyang
Published: September 2024

The timely updating of the spatial distribution of buildings is essential to understanding a city’s development. Deep learning methods have remarkable benefits in quickly and accurately recognizing these changes. Current semi-supervised change detection (SSCD) methods have effectively reduced the reliance on labeled data. However, these methods primarily focus on utilizing unlabeled data through various training strategies, neglecting the impact of pseudo-changes and learning bias in models. When dealing with limited labeled data, abundant low-quality pseudo-labels generated by poorly performing models can hinder effective performance improvement, leading to the incomplete recognition results of changes to buildings. To address this issue, we propose a feature multi-scale information interaction and complementation semi-supervised method based on consistency regularization (MSFG-SemiCD), which includes a multi-scale feature fusion-guided change detection network (MSFGNet) and a semi-supervised update method. Among them, the network facilitates the generation of multi-scale change features, integrates features, and captures multi-scale change targets through the temporal difference guidance module, the full-scale feature fusion module, and the depth feature guidance fusion module. Moreover, this enables the fusion and complementation of information between features, resulting in more complete change features. The semi-supervised update method employs a weak-to-strong consistency framework to achieve model parameter updates while maintaining perturbation invariance of unlabeled data at both input and encoder output features. Experimental results on the WHU-CD and LEVIR-CD datasets confirm the efficacy of the proposed method. There is a notable improvement in performance at both the 1% and 5% levels. The IOU in the WHU-CD dataset increased by 5.72% and 6.84%, respectively, while in the LEVIR-CD dataset, it improved by 18.44% and 5.52%, respectively.
County-Level Cultivated Land Quality Evaluation Using Multi-Temporal Remote Sensing and Machine Learning Models: From the Perspective of National Standard
Authors: Duan Dingding , Li Xinru , Liu Yanghua , Meng Qingyan , Li Chengming , Lin Guotian , Guo Linlin , Guo Peng , Tang Tingting , Su Huan , Ma Weifeng , Ming Shikang , Yang Yadong
Published: September 2024

Scientific evaluation of cultivated land quality (CLQ) is necessary for promoting rational utilization of cultivated land and achieving one of the Sustainable Development Goals (SDGs): Zero Hunger. However, the CLQ evaluation system proposed in previous studies was diversified, and the methods were inefficient. In this study, based on China’s first national standard “Cultivated Land Quality Grade” (GB/T 33469-2016), we constructed a unified county-level CLQ evaluation system by selecting 15 indicators from five aspects—site condition, environmental condition, physicochemical property, nutrient status and field management—and used the Delphi method to calculate the membership degree of the indicators. Taking Jimo district of Shandong Province, China, as a case study, we compared the performance of three machine learning models, including random forest, AdaBoost, and support vector regression, to evaluate CLQ using multi-temporal remote sensing data. The comprehensive index method was used to reveal the spatial distribution of CLQ. The results showed that the CLQ evaluation based on multi-temporal remote sensing data and machine learning model was efficient and reliable, and the evaluation results had a significant positive correlation with crop yield (r was 0.44, p < 0.001). The proportions of cultivated land of high-, medium- and poor-quality were 27.43%, 59.37% and 13.20%, respectively. The CLQ in the western part of the study area was better, while it was worse in the eastern and central parts. The main limiting factors include irrigation capacity and texture configuration. Accordingly, a series of targeted measures and policies were suggested, such as strengthening the construction of farmland water conservancy facilities, deep tillage of soil and continuing to construct well-facilitated farmland. This study proposed a fast and reliable method for evaluating CLQ, and the results are helpful to promote the protection of cultivated land and ensure food security.
GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
Authors: Jiang Yao , Zhang Rui , Sun Bo , Wang Tianyu , Zhang Bo , Tu Jinsheng , Nie Shihai , Jiang Hang , Chen Kangyi
Published: September 2024

The accuracy and reliability of soil moisture retrieval based on Global Positioning System (GPS) single-star Signal-to-Noise Ratio (SNR) data is low due to the influence of spatial and temporal differences of different satellites. Therefore, this paper proposes a Random Forest (RF)-based multi-satellite data fusion Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) soil moisture retrieval method, which utilizes the RF Model’s Mean Decrease Impurity (MDI) algorithm to adaptively assign arc weights to fuse all available satellite data to obtain accurate retrieval results. Subsequently, the effectiveness of the proposed method was validated using GPS data from the Plate Boundary Observatory (PBO) network sites P041 and P037, as well as data collected in Lamasquere, France. A Support Vector Machine model (SVM), Radial Basis Function (RBF) neural network model, and Convolutional Neural Network model (CNN) are introduced for the comparison of accuracy. The results indicated that the proposed method had the best retrieval performance, with Root Mean Square Error (RMSE) values of 0.032, 0.028, and 0.003 cm3/cm3, Mean Absolute Error (MAE) values of 0.025, 0.022, and 0.002 cm3/cm3, and correlation coefficients (R) of 0.94, 0.95, and 0.98, respectively, at the three sites. Therefore, the proposed soil moisture retrieval model demonstrates strong robustness and generalization capabilities, providing a reference for achieving high-precision, real-time monitoring of soil moisture.
Sea–Land Segmentation of Remote-Sensing Images with Prompt Mask-Attention
Authors: Ji Yingjie , Wu Weiguo , Nie Shiqiang , Wang Jinyu , Liu Song
Published: September 2024

Remote-sensing technology has gradually become one of the most important ways to extract sea–land boundaries due to its large scale, high efficiency, and low cost. However, sea–land segmentation (SLS) is still a challenging problem because of data diversity and inconsistency, “different objects with the same spectrum” or “the same object with different spectra”, and noise and interference problems, etc. In this paper, a new sea–land segmentation method (PMFormer) for remote-sensing images is proposed. The contributions are mainly two points. First, based on Mask2Former architecture, we introduce the prompt mask by normalized difference water index (NDWI) of the target image and prompt encoder architecture. The prompt mask provides more reasonable constraints for attention so that the segmentation errors are alleviated in small region boundaries and small branches, which are caused by insufficiency of prior information by large data diversity or inconsistency. Second, for the large intra-class difference problem in the foreground–background segmentation in sea–land scenes, we use deep clustering to simplify the query vectors and make them more suitable for binary segmentation. Then, traditional NDWI and eight other deep-learning methods are thoroughly compared with the proposed PMFormer on three open sea–land datasets. The efficiency of the proposed method is confirmed, after the quantitative analysis, qualitative analysis, time consumption, error distribution, etc. are presented by detailed contrast experiments.
Semantic Segmentation-Driven Integration of Point Clouds from Mobile Scanning Platforms in Urban Environments
Authors: Koszyk Joanna , Jasińska Aleksandra , Pargieła Karolina , Malczewska Anna , Grzelka Kornelia , Bieda Agnieszka , Ambroziński Łukasz
Published: September 2024

Precise and complete 3D representations of architectural structures or industrial sites are essential for various applications, including structural monitoring or cadastre. However, acquiring these datasets can be time-consuming, particularly for large objects. Mobile scanning systems offer a solution for such cases. In the case of complex scenes, multiple scanning systems are required to obtain point clouds that can be merged into a comprehensive representation of the object. Merging individual point clouds obtained from different sensors or at different times can be difficult due to discrepancies caused by moving objects or changes in the scene over time, such as seasonal variations in vegetation. In this study, we present the integration of point clouds obtained from two mobile scanning platforms within a built-up area. We utilized a combination of a quadruped robot and an unmanned aerial vehicle (UAV). The PointNet++ network was employed to conduct a semantic segmentation task, enabling the detection of non-ground objects. The experimental tests used the Toronto 3D dataset and DALES for network training. Based on the performance, the model trained on DALES was chosen for further research. The proposed integration algorithm involved semantic segmentation of both point clouds, dividing them into square subregions, and performing subregion selection by checking the emptiness or when both subregions contained points. Parameters such as local density, centroids, coverage, and Euclidean distance were evaluated. Point cloud merging and augmentation enhanced with semantic segmentation and clustering resulted in the exclusion of points associated with these movable objects from the point clouds. The comparative analysis of the method and simple merging was performed based on file size, number of points, mean roughness, and noise estimation. The proposed method provided adequate results with the improvement of point cloud quality indicators.
Solar Cycle Dependence of Migrating Diurnal Tide in the Equatorial Mesosphere and Lower Thermosphere
Authors: Liu Shuai , Jiang Guoying , Luo Bingxian , Xu Jiyao , Lin Ruilin , Zhu Yajun , Liu Weijun
Published: September 2024

Atmospheric migrating diurnal tide (DW1) is one of the prominent variabilities in the mesosphere and lower thermosphere (MLT). The existence of the solar cycle dependence of DW1 is debated, and there exist different and even opposite findings at different latitudes. In this paper, the solar cycle dependence of temperature DW1 in the equatorial mesosphere and lower thermosphere (MLT) is investigated using temperature global observations from TIMED/SABER spanning 22 years (2002–2023). The results show that (a) the solar cycle dependence of temperature DW1 is seen very clearly at the equator. The maximum correlation coefficient between DW1 and the F10.7 index occurs at 87km, with 0.72; the second maximum coefficient occurs at 99 km, with 0.62. The coefficient could reach 0.87 at 87 km and 0.67 at 99 km after dropping the years influenced by the Stratosphere Quasi-biennial oscillation (SQBO) disruption event. (b) DW1 shows a lag response to the solar cycle at the equator. DW1 amplitudes show a 1-year lag to the F10.7 index at 87 km and a 2-year lag to the F10.7 index at 99 km.
The Latest Developments in Spaceborne High-Resolution Wide-Swath SAR Systems and Imaging Methods
Authors: Song Ruizhen , Wang Wei , Yu Weidong
Published: September 2024

Azimuth resolution and swath width are two crucial parameters in spaceborne synthetic aperture radar (SAR) systems. However, it is difficult for conventional spaceborne SAR to simultaneously achieve high-resolution wide-swath (HRWS) due to the minimum antenna area constraint. To mitigate this limitation, some representative HRWS SAR imaging techniques have been investigated, e.g., the azimuth multichannel technique, digital beamforming (DBF) technique, and pulse repetition interval (PRI) variation technique. This paper focus on a comprehensive review of the three techniques with respect to their latest developments. First, some key parameters of HRWS SAR are presented and analyzed to help the reader establish the general concept of SAR. Second, three techniques are introduced in detail, roughly following a simple-to-complex approach, i.e., start with the basic concept, then move to the core principles and classic technical details, and finally report the technical challenges and corresponding solutions. Third, some in-depth insights on the comparison among the three techniques are given. The purpose of this paper is to provide a review and brief perspective on the development of HRWS SAR.
Sensors and Fault Diagnostics in Power System
Authors: Kunicki Michał , Fulneček Jan , Rozga Pawel
Published: September 2024

The adequate technical condition assessment of key apparatuses is a crucial assumption in the delivery of reliable and continuous electric power to customers [...]
The Impact of an Automation System Built with Jenkins on the Efficiency of Container-Based System Deployment
Authors: Hyun Giwoo , Oak Jiwon , Kim Donghoon , Kim Kunwoo
Published: September 2024

This paper evaluated deployment efficiency by comparing manual deployment with automated deployment through a CI/CD pipeline using Jenkins. This study involved moving from a manual deployment process to an automated system using Jenkins and experimenting with both deployment methods in a real-world environment. The results showed that the automated deployment system significantly reduced the deployment time compared to manual deployment and significantly reduced the error rate. Manual deployment required human intervention at each step, making it time-consuming and prone to mistakes, while automated deployment using Jenkins automated each step to ensure consistency and maximized time efficiency through parallel processing. Automated testing verified the stability of the code before deployment, minimizing errors. This study demonstrates the effectiveness of adopting a CI/CD pipeline and shows that automated systems can provide high efficiency in real-world production environments. It also highlights the importance of security measures to prevent sensitive information leakage during CI/CD, suggesting the use of secrecy management tools and environment variables and limiting access rights. This research will contribute to exploring the applicability of CI/CD pipelines in different environments and, in doing so, validate the universality of automated systems.
Syndemic Connections: Overdose Death Crisis, Gender-Based Violence and COVID-19
Authors: Ning Ana M.
Published: September 2024

This article will use syndemic theory to identify and analyze overlapping health and social conditions, focusing specifically on how gender-based violence is systemically interconnected with contemporary public health issues. The overdose death crisis that continues to afflict Canadian populations is not an isolated health issue. Across Canada, it is intertwined with mental health, HIV/AIDS, COVID-19 and structural violence—the chronic and systemic disadvantages affecting those living in poverty and oppressive circumstances. Opioid use is an often-avoidant coping strategy for many experiencing the effects of trauma, relentless fear, pain, ill health and social exclusion. In particular, Indigenous and non-Indigenous women’s experiences with opioid addiction are entangled with encounters with gender based-violence, poverty and chronic ailments within structurally imposed processes and stressors shaped by a history of colonialism, ruptured lifeways and Western ways of knowing and doing, leading to disproportionate harms and occurrences of illness. While biomedical models of comorbidity and mortality approach substance misuse, gender-based violence and major infectious diseases such as HIV/AIDS and COVID-19 as distinct yet compounding realities, this article argues that these conditions are synergistically interrelated via the critical/reflexive lens of syndemic frameworks. Through secondary research using academic, media and policy sources from the past decade in Canada, complemented by prior ethnographic research, the synergistic connections among opioid addiction, gender-based violence and the effects of the COVID pandemic on diverse women will be shown to be driven by socio-structural determinants of health including poverty, intergenerational trauma, the legacy of colonialism and Western optics. Together, they embody a contemporary Canadian syndemic necessitating coordinated responses.
Deformation-Adapted Spatial Domain Filtering Algorithm for UAV Mining Subsidence Monitoring
Authors: Zha Jianfeng , Miao Penglong , Ling Hukai , Yu Minghui , Sun Bo , Zhong Chongwu , Hao Guowei
Published: September 2024

Underground coal mining induces surface subsidence, leading to disasters such as damage to buildings and infrastructure, landslides, and surface water accumulation. Preventing and controlling disasters in subsidence areas and reutilizing land depend on understanding subsidence regularity and obtaining surface subsidence monitoring data. These data are crucial for the reutilization of regional land resources and disaster prevention and control. Subsidence hazards are also a key constraint to mine development. Recently, with the rapid advancement of UAV technology, the use of UAV photogrammetry for surface subsidence monitoring has become a significant trend in this field. The periodic imagery data quickly acquired by UAV are used to construct DEM through point cloud filtering. Then, surface subsidence information is obtained by differencing DEM from different periods. However, due to the accuracy limitations inherent in UAV photogrammetry, the subsidence data obtained through this method are characterized by errors, making it challenging to achieve high-precision ground surface subsidence monitoring. Therefore, this paper proposes a spatial domain filtering algorithm for UAV photogrammetry combined with surface deformation caused by coal mining based on the surface subsidence induced by coal mining and combined with the characteristics of the surface change. This algorithm significantly reduces random error in the differential DEM, achieving high-precision ground subsidence monitoring using UAV. Simulation and field test results show that the surface subsidence elevation errors obtained in the simulation tests are reduced by more than 50% compared to conventional methods. In field tests, this method reduced surface subsidence elevation errors by 39%. The monitoring error for surface subsidence was as low as 8 mm compared to leveling survey data. This method offers a new technical pathway for high-precision surface subsidence monitoring in mining areas using UAV photogrammetry.
Accumulation of Potentially Toxic Metals in Ryegrass (Lolium perenne, L.) and Other Components of Lawn Vegetation in Variously Contaminated Sites of Urban Areas
Authors: Dradrach Agnieszka , Karczewska Anna , Bogacz Adam , Kawałko Dorota , Pruchniewicz Daniel
Published: September 2024

Green areas, in particular lawns, play important roles in cities. Unfortunately, they are often arranged in sites contaminated with heavy metals. This study analyzed soils and lawn swards in three districts of the city of Wrocław. Three different categories of lawns were examined: residential areas, street lawns and parks. Particular focus was placed on soil contamination with the metals Zn, Cu, Cd and Pb and their accumulation in the aboveground biomass of the perennial ryegrass Lolium perenne, the main grass species, and in the biomass of other components of the lawn sward. The research revealed local occurrence of elevated metal concentrations in soils, although in most of the studied sites, they did not exceed the safe values set byPolish law. The accumulation of metals in the aboveground parts of ryegrass and other plants forming the lawn sward depended primarily on the type of metal. The study confirmed the high phytoavailability of cadmium and zinc and the very low phytoavailability of lead. Perennial ryegrass accumulated considerably lower amounts of lead and copper compared with other components of the lawn sward, which indicates the potential suitability of this species for the phytostabilization and sustainable development of areas contaminated with these two metals.
Action Plans Study: Principles of Green Chemistry, Sustainable Development, and Smart Cities
Authors: Oliveira Jessica R. P. , Tusset Angelo M. , Andrade Dana I. , Balthazar Jose M. , Pagani Regina N. , Lenzi Giane G.
Published: September 2024

The sustainability of cities is a challenge due to the growth, functioning, and needs of the population. In this context, the concept of the smart city has gained prominence worldwide in the last decades. In Brazil, it has also drawn attention driven by technological advances and the search for innovative solutions to urban challenges. Several different reports were created aiming to assess and categorize the advancement of cities in this regard. Some of them established their own indicators for this evaluation, whereas others are based on standards ISO 37120 and ISO 37122. The focus of this study revolves around the examination of indicators presently utilized, which may be influenced by initiatives grounded in the principles of green chemistry (PGCs). Furthermore, it explores how these principles can aid in the realization of the targets outlined in the Sustainable Development Goals (SDGs) set forth by the United Nations. Two case studies are presented, the first pointing out how PGCs and SDGs can correlate with smart city reporting indicators. The second is a case study centered on the Brazilian city of Curitiba (PR). We aim to exemplify how the city’s action plans underscore the significance of synergy among the principles of green chemistry, the objectives of sustainable development, and indicators for smart cities. Assessing how chemically green a city can be is a relevant argument for future industrial installations and stakeholders and the influence of this index on the quality of life of its population. Such an approach not only fosters innovation and efficiency but also fosters environmental stewardship, thus contributing to overall sustainability.
Can Digital Transformation Drive Green Innovation in China’s Construction Industry under a Dual-Carbon Vision?
Authors: Li Hui , Liu Jiyu , Liu Yulong , Yang Ge , Zhang Lingyao , Yang Xin
Published: September 2024

Against the backdrop of increasing global environmental pollution and energy consumption, green innovation is necessary to achieve green transformation. As an industry with a huge demand for resources and energy consumption, the construction industry shoulders the mission of the times to promote green innovation to enhance the ability of sustainable development. Digital technology provides new opportunities for green innovation in the construction industry. However, the impacts and mechanisms of digital transformation driving green innovation have not been thoroughly studied. In this paper, 121 listed companies in China’s construction industry are selected as a sample from 2011 to 2021, and a total of 1331 annual observations are obtained, and the impact and mechanism of digital transformation on construction enterprises’ green innovation are empirically analyzed by establishing regression models. The study indicates that digital transformation can facilitate green innovation in construction companies by enhancing corporate risk-taking and improving corporate governance. Compared with non-state-owned enterprises, state-owned enterprises have more endogenous incentives for green transformation based on multiple pressures, which to some extent weakens the driving role of digital transformation. The driving effect of enterprises’ digital transformation is more significant when the intensity of regional environmental regulation is high. This paper examines how the digitization of construction enterprises can lead to new greening ideas from the perspective of green innovation. It provides an important theoretical basis and decision-making reference to support the construction industry in its digital transformation and realize the goal of “dual carbon”.
Challenges and Solutions for Sustainable ICT: The Role of File Storage
Authors: Mersico Luigi , Abroshan Hossein , Sanchez-Velazquez Erika , Saheer Lakshmi Babu , Simandjuntak Sarinova , Dhar-Bhattacharjee Sunrita , Al-Haddad Ronak , Saeed Nagham , Saxena Anisha
Published: September 2024

Digitalization has been increasingly recognized for its role in addressing numerous societal and environmental challenges. However, the rapid surge in data production and the widespread adoption of cloud computing has resulted in an explosion of redundant, obsolete, and trivial (ROT) data within organizations’ data estates. This issue adversely affects energy consumption and carbon footprint, leading to inefficiencies and a higher environmental impact. Thus, this opinion paper aims to discuss the challenges and potential solutions related to the environmental impact of file storage on the cloud, aiming to address the research gap in “digital sustainability” and the Green IT literature. The key findings reveal that technological issues dominate cloud computing and sustainability research. Key challenges in achieving sustainable practices include the widespread lack of awareness about the environmental impacts of digital activities, the complexity of implementing accurate carbon accounting systems compliant with existing regulatory frameworks, and the role of public–private partnerships in developing novel solutions in emerging areas such as 6G technology.
Manager’s Trust and Trustworthiness in Sustainable Practices: Impact on Turnover and Manager’s Performance in Restaurants in China
Authors: Ren Yufei , Xiu Lin , Lv Feng , Lange Thomas , Liang Xin
Published: September 2024

This study examines the effects of managers’ trusting and trustworthy behaviors toward employees on turnover rates and performance evaluations within 115 restaurants in China. Utilizing data from a large-scale hypothetical experiment involving over 2000 employees and managers, combined with performance and operational data from these restaurants, we applied both game theoretical and empirical methods to analyze the dataset. Our findings reveal that managers’ trusting and trustworthy actions are significantly associated with lower turnover rates, highlighting how trust-driven leadership can enhance organizational stability and sustainable business practices. For senior associate managers, their trusting actions toward employees positively impact their performance. Furthermore, senior associate managers working in restaurants where employees are more trusting and trustworthy toward their peers tend to receive higher performance ratings. This study uniquely connects behavioral trust and trustworthiness measures with objective performance outcomes, offering new insights into the critical role of trust in fostering sustainable business practices and improving economic performance.
Supply Chain Coordination of New Energy Vehicles under a Novel Shareholding Strategy
Authors: Liu Zijia , Liu Guoliang
Published: September 2024

As important methods of ecofriendly transportation, the supply chain coordination of new energy vehicles (NEVs) is an important issue in the field of sustainability. This study constructs a Stackelberg game composed of a power battery supplier and an NEV manufacturer. To better describe the coordination relationship in the NEV supply chain, we introduce the Nash bargaining framework into the fairness concern preference utility function. Through a comprehensive discussion of shareholding ratios and external environment factors, we discover that the traditional shareholding strategy fails to coordinate the NEV supply chain effectively, as enterprises seek to avoid losing management control, which occurs when excessive shares are held by others. In this context, this study proposes a novel industry–university–research (IUR) shareholding strategy, which can more easily achieve supply chain coordination and improve social welfare. In particular, this study reveals the superiority of the novel strategy in eliminating the double-marginal effect caused by high fairness concern preference among NEV enterprises. Based on these facts, we provide enterprises with optimal strategies under different conditions and offer a government-optimal subsidy to maximize the social welfare function.
Smarter and Cleaner? The Carbon Reduction Effect of Smart Cities: A Perspective on Green Technology Progress
Authors: An Xianjuan , Yang Yanjing , Zhang Xinyu , Zeng Xueting
Published: September 2024

In the context of the global climate change problem intensifying due to a dramatic increase in carbon emissions, smart cities, as a topical application of digitalization and intelligence, have become a new urban governance mode for countries, which helps to achieve sustainable development. This research studies the relationship between smart city construction (SCC) and carbon dioxide emissions based on the differences-in-differences model (DID) and propensity score matching (PSM) to promote China to achieve dual carbon goals and high-quality development. The findings are as follows: (a) SCC could promote carbon emission reduction by reducing urban carbon dioxide emissions by an average of 11.4%, which also has significant long-term dynamic effects. Specifically, SCC has more obvious emission reduction effects on activities, such as industrial production and waste treatment. (b) Mechanism verification shows that green technology progress is a significant booster for the carbon reduction effect in SCC. The pilot project can increase output of green patents, which helps transfer production mode and consumption patterns in an environmentally friendly manner. SCC could increase the total factor productivity (TFP) through the rational allocation and efficient use of resources, and thus reducing carbon emissions. (c) Research on city heterogeneity shows that a high level of human capital, material, and financial resources can provide support for smart cities to better achieve the carbon reduction effect. Among them, material resources have the best carbon reduction effect in the process of SCC, which could reduce carbon dioxide emissions by about 6.6–17.7%. This study is useful for policymakers to continuously and dynamically adjust urban development strategies in the future, to achieve a balance between socioeconomic prosperity and environmental sustainability.
Impacts of Land Use Changes on Landscape Patterns and Ecosystem Service Values in Counties (Villages) in Ethnic Regions of China: A Case Study of Jianghua Yao Autonomous County, Hunan Province
Authors: Shen Shiming , Zhu Liuyan , Xie Zhengying , Fang Ting , Zhao Haoxiang , Fang Zhengtao
Published: September 2024

This study, using Jianghua Yao Autonomous County in Hunan Province as a case, systematically analyzes the response of ecosystem service value (ESV) to land use and landscape pattern changes by employing landscape-level indices and landscape-type-level indices. The findings provide a reliable basis for scientifically formulating land use planning and ecological protection policies in ethnic regions, thereby promoting regional ecological security and sustainable development. This study reveals that (1) the land use structure in the county underwent significant changes between 2000 and 2020, with grassland and shrubland areas decreasing substantially by 71.66% and 78.41%, respectively, while urban and arable land areas increased significantly by 228.30% and 15.84%, respectively. Particularly under the scenario of prioritizing economic development, these changes led to increased landscape fragmentation and a decline in ecosystem service value (from CNY 296.571 billion in 2020 to CNY 287.959 billion in 2030). (2) In contrast, the scenarios of ecological protection and sustainable development significantly enhanced the region’s ecosystem service value by increasing forest and water area, effectively maintaining the stability of the landscape pattern. These findings provide important evidence for the formulation of scientifically sound land use planning and ecological protection policies, contributing to the dual goals of economic development, tourism growth and ecological protection in Jianghua Yao Autonomous County and similar regions.