bluuebunny/crossref_metadata_2025 huggingface dataset:
HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land
Authors: Li Fangting , Zhou Fangdong , Zhang Guo , Xiao Jianfeng , Zeng Peng
Published: April 2024

Cultivated land plays a fundamental role in the sustainable development of the world. Monitoring the non-agricultural changes is important for the development of land-use policies. A bitemporal image transformer (BIT) can achieve high accuracy for change detection (CD) tasks and also become a key scientific tool to support decision-making. Because of the diversity of high-resolution RSIs in series, the complexity of agricultural types, and the irregularity of hierarchical semantics in different types of changes, the accuracy of non-agricultural CD is far below the need for the management of the land and for resource planning. In this paper, we proposed a novel non-agricultural CD method to improve the accuracy of machine processing. First, multi-resource surveying data are collected to produce a well-tagged dataset with cultivated land and non-agricultural changes. Secondly, a hierarchical semantic aggregation mechanism and attention module (HSAA) bitemporal image transformer method named HSAA-CD is performed for non-agricultural CD in cultivated land. The proposed HSAA-CD added a hierarchical semantic aggregation mechanism for clustering the input data for U-Net as the backbone network and an attention module to improve the feature edge. Experiments were performed on the open-source LEVIR-CD and WHU Building-CD datasets as well as on the self-built RSI dataset. The F1-score, intersection over union (IoU), and overall accuracy (OA) of these three datasets were 88.56%, 84.29%, and 68.50%; 79.84%, 73.41%, and 59.29%; and 98.83%, 98.39%, and 93.56%, respectively. The results indicated that the proposed HSAA-CD method outperformed the BIT and some other state-of-the-art methods and proved to be suitable accuracy for non-agricultural CD in cultivated land.
Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features
Authors: Prodromou Maria , Theocharidis Christos , Gitas Ioannis Z. , Eliades Filippos , Themistocleous Kyriacos , Papasavvas Konstantinos , Dimitrakopoulos Constantinos , Danezis Chris , Hadjimitsis Diofantos
Published: April 2024

Accurate mapping of forest habitats, especially in NATURA sites, is essential information for forest monitoring and sustainable management but also for habitat characterisation and ecosystem functioning. Remote sensing data and spatial modelling allow accurate mapping of the presence and distribution of tree species and habitats and are valuable tools for the long-term assessment of habitat status required by the European Commission. In order to serve the above, the present study aims to propose a methodology to accurately map the spatial distribution of forest habitats in three NATURA2000 sites of Cyprus by employing Sentinel-1 and Sentinel-2 data as well as topographic features using the Google Earth Engine (GEE). A pivotal aspect of the methodology identified was that the best band combination of the Random Forest (RF) classifier achieves the highest performance for mapping the dominant habitats in the three case studies. Specifically, in the Akamas region, eight habitat types have been mapped, in Paphos nine and six in Troodos. These habitat types are included in three of the nine habitat groups based on the EU’s Habitat Directive: the sclerophyllous scrub, rocky habitats and caves and forests. The results show that using the RF algorithm achieves the highest performance, especially using Dataset 6, which is based on S2 bands, spectral indices and topographical features, and Dataset 13, which includes S2, S1, spectral indices and topographical features. These datasets achieve an overall accuracy (OA) of approximately 91–94%. In contrast, Dataset 7, which includes only S1 bands and Dataset 9, which combines S1 bands and spectral indices, achieve the lowest performance with an OA of approximately 25–43%.
Early-Season Crop Classification Based on Local Window Attention Transformer with Time-Series RCM and Sentinel-1
Authors: Zhou Xin , Wang Jinfei , Shan Bo , He Yongjun
Published: April 2024

Crop classification is indispensable for agricultural monitoring and food security, but early-season mapping has remained challenging. Synthetic aperture radar (SAR), such as RADARSAT Constellation Mission (RCM) and Sentinel-1, can meet higher requirements on the reliability of satellite data acquisition with all-weather and all-day imaging capability to supply dense observations in the early crop season. This study applied the local window attention transformer (LWAT) to time-series SAR data, including RCM and Sentinel-1, for early-season crop classification. The performance of this integration was evaluated over crop-dominated regions (corn, soybean and wheat) in southwest Ontario, Canada. Comparative analyses against several machine learning and deep learning methods revealed the superiority of the LWAT, achieving an impressive F1-score of 97.96% and a Kappa coefficient of 97.08% for the northern crop region and F1-scores of 98.07% and 97.02% for the southern crop region when leveraging time-series data from RCM and Sentinel-1, respectively. Additionally, by the incremental procedure, the evolution of accuracy determined by RCM and Sentinel-1 was analyzed, which demonstrated that RCM performed better at the beginning of the season and could achieve comparable accuracy to that achieved by utilizing both datasets. Moreover, the beginning of stem elongation of corn was identified as a crucial phenological stage to acquire acceptable crop maps in the early season. This study explores the potential of RCM to provide reliable prior information early enough to assist with in-season production forecasting and decision making.
A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification
Authors: Zhang Xiangrong , Liu Zitong , Zhang Xianhao , Liu Tianzhu
Published: April 2024

Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy.
Detection of Typical Forest Degradation Patterns: Characteristics and Drivers of Forest Degradation in Northeast China
Authors: Hai Yue , Liang Mei , Yang Yuze , Sun Hailian , Li Ruonan , Yang Yanzheng , Zheng Hua
Published: April 2024

The accurate identification of forest degradation and its driving factors is a prerequisite for implementing high-quality forest management. However, distinguishing degradation patterns is often neglected in large-scale forest quality assessments. The indicators were constructed to identify typical forest degradation patterns using remote sensing indexes, followed by an analysis of the spatiotemporal dynamics of forest degradation and quantification of the contributions from various driving factors. The results indicated that the constructed indicators could effectively distinguish typical forest degradation patterns, with a fire degradation identification accuracy of 90.0% and a fitting accuracy of drought and pest degradation higher than 0.7. The cold temperate conifer forest zone had the largest proportion of fire degradation, accounting for 67.7% of the area, and totals of 99.0% of the subtropical evergreen broadleaf forest zone and 92.8% of the temperate conifer and broadleaf mixed forest zone were moderately to severely affected by drought, with long-term stability. Additionally, 0.1% of the temperate grassland region and 0.1% of the cold temperate conifer forest zone underwent severe pest infestations, with a long-term stable trend. Meteorological factors were the primary contributors to all typical degradation patterns, accounting for 81.35%, 58.70%, and 82.29%, respectively. The research developed an index for assessing forest degradation and explained the importance of natural and anthropogenic factors in forest degradation. The results are beneficial for the scientific management of forest degradation and for improving forest management efficiency.
Assessing Seasonal and Inter-Annual Changes in the Total Cover of Submerged Aquatic Vegetation Using Sentinel-2 Imagery
Authors: Vahtmäe Ele , Argus Laura , Toming Kaire , Möller-Raid Tiia , Kutser Tiit
Published: April 2024

Remote sensing is a valuable tool for surveying submerged aquatic vegetation (SAV) distribution patterns at extensive spatial and temporal scales. Only regular mapping over successive time periods (e.g., months, years) allows for a quantitative assessment of SAV loss or recolonization extent. Still, there are only a limited number of studies assessing temporal changes in SAV patterns. ESA Sentinel-2 (S2) has a high revisiting frequency permitting the multi-temporal assessment of SAV dynamics both seasonally and inter-annually. In the current study, a physics-based IDA (Image Data Analysis) model was used for the reconstruction of past SAV percent cover (%cover) patterns in the Baltic Sea coastal waters based on S2 archived images. First, we aimed at capturing and quantifying intra-annual spatiotemporal SAV dynamics happening during a growing season. Modeling results showed that significant changes took place in SAV %cover: the extent of low-cover (0–30% coverage) and intermediate-cover (30–70% coverage) areas decreased, while high-cover (70–100% coverage) areas increased during the growing period. Secondly, we also aimed at detecting SAV %cover spatiotemporal variations inter-annually (over the years 2016–2022). Inter-annual variability in %cover patterns was greater in the beginning of the vegetation period (May). The peak of the growing period (July/August) showed greater stability in the areal extent of the %cover classes.
Long-Tailed Effect Study in Remote Sensing Semantic Segmentation Based on Graph Kernel Principles
Authors: Cui Wei , Feng Zhanyun , Chen Jiale , Xu Xing , Tian Yueling , Zhao Huilin , Wang Chenglei
Published: April 2024

The performance of semantic segmentation in remote sensing, based on deep learning models, depends on the training data. A commonly encountered issue is the imbalanced long-tailed distribution of data, where the head classes contain the majority of samples while the tail classes have fewer samples. When training with long-tailed data, the head classes dominate the training process, resulting in poorer performance in the tail classes. To address this issue, various strategies have been proposed, such as resampling, reweighting, and transfer learning. However, common resampling methods suffer from overfitting to the tail classes while underfitting the head classes, and reweighting methods are limited in the extreme imbalanced case. Additionally, transfer learning tends to transfer patterns learned from the head classes to the tail classes without rigorously validating its generalizability. These methods often lack additional information to assist in the recognition of tail class objects, thus limiting performance improvements and constraining generalization ability. To tackle the abovementioned issues, a graph neural network based on the graph kernel principle is proposed for the first time. By leveraging the graph kernel, structural information for tail class objects is obtained, serving as additional contextual information beyond basic visual features. This method partially compensates for the imbalance between tail and head class object information without compromising the recognition accuracy of head classes objects. The experimental results demonstrate that this study effectively addresses the poor recognition performance of small and rare targets, partially alleviates the issue of spectral confusion, and enhances the model’s generalization ability.
The Health Monitoring of Bonded Composite Joints Using Both Thickness and Radial Impedance Resonance Responses
Authors: Caldwell Steven P. , Radford Donald W.
Published: April 2024

With the advent of bonded composites in today’s aircraft, there is a need to verify the structural integrity of the bonded joints that comprise their structure. To produce adequate joint integrity, strict process control is required during bonding operations. The latest non-destructive joint inspection techniques cannot quantify the strength of the bond and only indicate the presence of disbonds or delaminations. Expensive and timely proof-load testing of the joints is required to demonstrate structural performance. This work focuses on experimentally evaluating joint-health monitoring with piezoelectric sensors exposed to repeated loadings until failure. Single-lap-shear composite joints are structurally tested while using sensor electromechanical impedance response as a health indicator. Based on these experiments, validation of this novel method is achieved through detailed evaluation of the sensor impedance response characteristics during loading, which enable initial and prognostic joint health assessments. The experimental results indicate that the embedded piezoelectric sensors are able to measure the sensor impedance radial and thickness resonance response changes prior to joint failure, without sacrificing the joints’ structural performance.
A Phosphenotron Device for Sensoric Spatial Resolution of Phosphenes within the Visual Field Using Non-Invasive Transcranial Alternating Current Stimulation
Authors: Sadrzadeh-Afsharazar Faraz , Douplik Alexandre
Published: April 2024

This study presents phosphenotron, a device for enhancing the sensory spatial resolution of phosphenes in the visual field (VF). The phosphenotron employs a non-invasive transcranial alternating current stimulation (NITACS) to modulate brain activity by applying weak electrical currents to the scalp or face. NITACS’s unique application induces phosphenes, a phenomenon where light is perceived without external stimuli. Unlike previous invasive methods, NITACS offers a non-invasive approach to create these effects. The study focused on assessing the spatial resolution of NITACS-induced phosphenes, crucial for advancements in visual aid technology and neuroscience. Eight participants were subjected to NITACS using a novel electrode arrangement around the eye orbits. Results showed that NITACS could generate spatially defined phosphene patterns in the VF, varying among individuals but consistently appearing within their VF and remaining stable through multiple stimulations. The study established optimal parameters for vibrant phosphene induction without discomfort and identified electrode positions that altered phosphene locations within different VF regions. Receiver Operating characteristics analysis indicated a specificity of 70.7%, sensitivity of 73.9%, and a control trial accuracy of 98.4%. These findings suggest that NITACS is a promising, reliable method for non-invasive visual perception modulation through phosphene generation.
Validity Verification of Human Pose-Tracking Algorithms for Gait Analysis Capability
Authors: Matsuda Tadamitsu , Fujino Yuji , Makabe Hitoshi , Morisawa Tomoyuki , Takahashi Tetsuya , Kakegawa Kei , Matsumoto Takanari , Kiyohara Takehiko , Torimoto Yasuo , Miwa Masaki , Fujiwara Toshiyuki , Daida Hiroyuki
Published: April 2024

Two-dimensional (2D) clinical gait analysis systems are more affordable and portable than contemporary three-dimensional (3D) clinical models. Using the Vicon 3D motion capture system as the standard, we evaluated the internal statistics of the Imasen and open-source OpenPose gait measurement systems, both designed for 2D input, to validate their output based on the similarity of results and the legitimacy of their inner statistical processes. We measured time factors, distance factors, and joint angles of the hip and knee joints in the sagittal plane while varying speeds and gaits during level walking in three in-person walking experiments under normal, maximum-speed, and tandem scenarios. The intraclass correlation coefficients of the 2D models were greater than 0.769 for all gait parameters compared with those of Vicon, except for some knee joint angles. The relative agreement was excellent for the time–distance gait parameter and moderate-to-excellent for each gait motion contraction range, except for hip joint angles. The time–distance gait parameter was high for Cronbach’s alpha coefficients of 0.899–0.993 but low for 0.298–0.971. Correlation coefficients were greater than 0.571 for time–distance gait parameters but lower for joint angle parameters, particularly hip joint angles. Our study elucidates areas in which to improve 2D models for their widespread clinical application.
Adaptive UAV Navigation Method Based on AHRS
Authors: Lu Yin , Li Zhipeng , Xiong Jun , Lv Ke
Published: April 2024

To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by Attitude and Heading Reference System (AHRS). The proposed method utilizes the AHRS output attitude parameters as the benchmark for dead reckoning and derives a relative navigation state equation with attitude error as process noise. By integrating the extended Kalman filter output for relative state estimation and employing an adaptive decision rule designed using the innovation of the filter update phase, the proposed method recalculates motion states deviating from the actual motion using the Tasmanian Devil Optimization (TDO) algorithm. The simulation results show that, compared with the CA/CV model, the proposed method reduces the relative position errors by 12%, 23%, and 32% in the X, Y, and Z directions, respectively, and that it reduces the relative velocity errors by 350%, 330%, and 300%, respectively. There is a significant improvement in the relative navigation accuracy.
Energy Expenditure Prediction from Accelerometry Data Using Long Short-Term Memory Recurrent Neural Networks
Authors: Vibæk Martin , Peimankar Abdolrahman , Wiil Uffe Kock , Arvidsson Daniel , Brønd Jan Christian
Published: April 2024

The accurate estimation of energy expenditure from simple objective accelerometry measurements provides a valuable method for investigating the effect of physical activity (PA) interventions or population surveillance. Methods have been evaluated previously, but none utilize the temporal aspects of the accelerometry data. In this study, we investigated the energy expenditure prediction from acceleration measured at the subjects’ hip, wrist, thigh, and back using recurrent neural networks utilizing temporal elements of the data. The acceleration was measured in children (N = 33) performing a standardized activity protocol in their natural environment. The energy expenditure was modelled using Multiple Linear Regression (MLR), stacked long short-term memory (LSTM) networks, and combined convolutional neural networks (CNN) and LSTM. The correlation and mean absolute percentage error (MAPE) were 0.76 and 19.9% for the MLR, 0.882 and 0.879 and 14.22% for the LSTM, and, with the combined LSTM-CNN, the best performance of 0.883 and 13.9% was achieved. The prediction error for vigorous intensities was significantly different (p < 0.01) from those of the other intensity domains: sedentary, light, and moderate. Utilizing the temporal elements of movement significantly improves energy expenditure prediction accuracy compared to other conventional approaches, but the prediction error for vigorous intensities requires further investigation.
Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression
Authors: Wen Xiaomin , Fang Shengliang , Fan Youchen
Published: April 2024

The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the training dataset is equally crucial to its performance. This paper is based on graph neural networks to predict and complete the target radio environment map (REM) through multiple complete REMs and sparse spectrum monitoring data in the target domain. Due to the complexity of radio wave propagation in space, it is difficult to accurately and explicitly construct the spatial graph structure of the spectral data. In response to the two above issues, we propose a multi-source domain adaptive of GNN for regression (GNN-MDAR) model, which includes two key modules: (1) graph structure alignment modules are used to capture and learn graph structure information shared by cross-domain radio propagation and extract reliable graph structure information for downstream reference signal receiving power (RSRP) prediction task; and (2) a spatial distribution matching module is used to reduce the feature distribution mismatch across spatial grids and improve the model’s ability to remain domain invariant. Based on the measured REMs dataset, the comparative results of simulation experiments show that the GNN-MDAR outperforms the other four benchmark methods in accuracy when there is less RSRP label data in the target domain.
Shape Sensing of Cantilever Column Using Hybrid Frenet–Serret Homogeneous Transformation Matrix Method
Authors: Zhang Peng , Li Duanshu , An Ran , Devendra Patil
Published: April 2024

The Frenet–Serret (FS) framework stands as a pivotal tool in shape sensing for various infrastructures. However, this tool suffers from accumulative errors, particularly at inflection points where the normal vector undergoes sign changes. To minimize the error, the traditional FS framework is modified by incorporating the homogeneous matrix transformation (HMT) method for segments containing inflection points. Additionally, inclination information is also used to calculate the unit tangent vector and the unit norm vector at the start point of each segment. This novel approach, termed the FS-HMT method, aims to enhance accuracy. To validate the effectiveness of the proposed method, a simulation of a cantilever column was conducted using finite element software ANSYS 19.2. The numerical results demonstrate the capability of the proposed method to accurately predict curves with inflection points, yielding a maximum error of 1.1%. Subsequently, experimental verification was performed using a 1 m long spring steel sheet, showcasing an error of 4.9%, which is notably lower than that of the traditional FS framework. Our proposed modified FS framework exhibits improved accuracy, especially in scenarios involving inflection points. These findings underscore its potential as a valuable tool for enhanced shape sensing in practical applications.
Burnout Syndrome and Emotional Labor in Leaders and Subordinates: A Dyad Analysis
Authors: Taube Michelle Engers , Carlotto Mary Sandra , Gondim Sonia Maria Guedes , Carvalho Carla
Published: April 2024

Burnout Syndrome is considered a chronic response to occupational stressors in the work environment. Social interactions constitute one of the stressors at work that can generate negative feelings that trigger a process of contagion of the syndrome among workers in interdependent relationships. This study aimed to analyze whether emotional labor (emotional demands, emotional dissonance) at the level of the leader and subordinate dyad contributes to the manifestation of Burnout Syndrome. The participants included 244 leader–subordinate dyads who answered a questionnaire with sociodemographic and labor data, the Spanish Burnout Inventory, a subscale of the Questionnaire on the Experience and Assessment of Work, and a subscale of the Frankfurt Emotion Work Scale. Analyses were performed using the actor–partner interdependence model (APIM) through path analysis. The results indicate that the emotional demands of the leaders and the emotional dissonance of the subordinates predict the leader’s Burnout Syndrome. The Burnout Syndrome of subordinates was predicted only by the emotional demands of subordinates. Organizational actions are necessary for the better functioning of this dyad, aiming to mitigate the negative consequences of emotional labor on workers’ mental health.
Analyzing the Spatiotemporal Dynamics and Driving Forces of Ecological Environment Quality in the Qinling Mountains, China
Authors: Liu Jiachen , Xie Tao , Lyu Du , Cui Lu , Liu Qiuman
Published: April 2024

The Qinling Mountains serve as a vital ecological barrier for China, needing regular monitoring for sustainable progress. The present study analyzed the Qinling Mountains (sections in Shaanxi province) via the Google Earth Engine (GEE) and Landsat images, constructing a Remote Sensing Ecological Index (RSEI) for quantitatively analyzing the spatiotemporal evolution of the ecological environment quality from 1990 to 2020. Additionally, this study integrated geodetector and multiscale geographically weighted regression (MGWR) to discern the driving factors influencing ecological quality. The results indicated the following: (1) over the 30 years, the RSEI in the study area initially declined (1990–2005) and then began to improve (2005–2020), demonstrating an overall upward trend; (2) RSEI varied with topography: lower values corresponded to lower elevations (<1200 m) and gentle slopes (<10°), whereas higher values were linked to higher elevations (>1600 m) and steeper slopes (>20°); and (3) NDVI, DEM, land use, and the presence of protected areas emerged as the main driving forces of ecological quality. The interaction between ecological conservation policies and socioeconomic factors enhanced the explanatory strength for the differentiation in ecological environment quality. In conclusion, factors like increased vegetation cover and the establishment of protected areas have significantly promoted the improvement of ecological environment quality in the Qinling region, making this study a scientific reference for providing ecological environment protection and promoting high-quality development.
Assessing the Impact of Regional Integration Strategies on the Development of Urban Green Economies: A Study Based on an Empirical Analysis of Ten Major Urban Agglomerations
Authors: Gong Zeyu , Huo Xuexi
Published: April 2024

Green development represents a critical pathway to achieving high-quality growth. From a regional perspective, examining the impact of regional integration on urban green economic development holds significant importance. This study leverages a quasi-natural experiment of regional integration strategy implementation, utilizing unbalanced panel data from China’s ten major urban agglomerations and non-agglomeration cities from 2003 to 2022. Employing a multi-period difference-in-difference approach, we empirically assess the policy effects of regional integration strategies on urban green economic development. We further analyze the heterogeneity of policy effects in terms of the geographic areas, urban agglomeration scales, and development driving models of the cities under study. Lastly, we use a moderating effect model to explore the mechanisms through which regional integration strategies impact urban green economic development. The research results are helpful for promoting urban green economy development through a regional integration strategy.
Analysing the Suitability of Using Different Biodegradable Fluids for Power Transformers with Thermally Upgraded Paper
Authors: Méndez Cristina , Olmo Cristian , Antolín Ismael , Ortiz Alfredo , Renedo Carlos J.
Published: April 2024

Mineral oil has been used for many years in various electrical equipment, including transformers, as a cooling and insulation medium. However, its low biodegradability and poor performance in terms of fire protection have prompted the search for fluids to replace it, with vegetable oils being prominently considered. In this study, the dielectric, chemical, and physical properties of four vegetable oils obtained from different seeds (sunflower, rapeseed, soybean, and palm) and a biodegradable synthetic fluid are analysed throughout their lifespan in transformers. Their performances are compared with a traditional mineral oil to assess which one is more suitable for use in transformers employing this type of paper. To achieve this, the fluids were subjected to thermal ageing in combination with copper and a thermally upgraded kraft (TUK) paper, with its degradation controlled by measuring the degree of polymerisation. The results demonstrate that the origin of the vegetable oils affects their properties and degradation rates. It was found that most of the alternative fluids are suitable for use in transformers with the TUK paper and that they can increase their lifespan.
Can an Energy Transition Strategy Induce Urban Green Innovation? Evidence from a Quasi-Natural Experiment in China
Authors: Feng Yuan , Li Ying , Nie Changfei , Chen Zhi
Published: April 2024

Green innovation is a new driving force to promote green and low-carbon development. Whether an energy transition strategy can induce green innovation is an important question that has not been clearly answered. With the help of panel data from 281 cities in China during 2007–2021, this study considers China’s new energy exemplary city policy as a quasi-natural experiment and conducts a difference-in-differences model to explore the effects and mechanisms of energy transition strategy on urban green innovation. The results show that a new energy exemplary city effectively induces urban green innovation, with mediating mechanisms of increasing government financial support, promoting human capital agglomeration, and improving energy efficiency. The moderating mechanisms test reveals that environmental regulation and intellectual property protection play a positive moderating role in the promotion of green innovation through the new energy exemplary city policy. Heterogeneity analysis suggests that the new energy exemplary city policy has a stronger effect on promoting green innovation in eastern regions and non-resource-based cities. Our findings not only enrich our understanding of the relationship between energy transition strategy and green innovation but also provide a reference for policymakers to promote energy transition and green innovation.
Assessment of the Greenery Content in Suburban Multi-Family Housing Models in Poland: A Case Study of the Poznań Metropolitan Area
Authors: Gyurkovich Magdalena , Kołata Joanna , Pieczara Marta , Zierke Piotr
Published: April 2024

(1) Contemporary approaches to sustainable housing design tend to prioritize technological solutions supporting energy efficiency and climate change mitigation. On the contrary, spatial planning of housing estates does not always address all pro-ecological aspects, such as the role of greenery. This research aimed to assess the greenery content and its environmental importance in typical housing districts in the selected study area. (2) The research methodology was based on indicators reflecting the biologically active area ratio, the length of communication routes lined with trees, the tree number per area unit, the tree canopy, and the environmental benefits delivered by the trees. The above indicators allowed us to compare selected models of suburban residential districts typical of specific timeframes. (3) The results indicated that the greenery content and its environmental benefits in suburban districts are decreasing in the study area. Another finding concerned the importance of selecting tree species for their ability to develop a canopy and provide ecosystem services. (4) The proposed methodology, based on inter-related indicators, validly compared the greenery content in the analyzed districts, giving it application value. The problems observed contributed to the proposal of a revision of Poland’s planning practices. Local zoning plans could include streetscape standards, indicating paving solutions and plantings to improve the situation.
Biotechnological Approach of Technological Advancements for Sustainable Probiotic Bread Production
Authors: Pejcz Ewa
Published: April 2024

The pursuit of probiotic-enriched bread, driven by the dual objectives of enhancing nutritional value and promoting health while ensuring sustainability, has spurred significant research and technological advancements. However, a persistent challenge lies in preserving the viability of microorganisms throughout the rigorous processes of production, storage, and exposure to the stomach’s acidic environment. This study investigates biotechnological innovations for sustainable probiotic bread production, conducting a thorough review of probiotic encapsulation methods and analyzing prior research on the viability of encapsulated probiotics in bread across different baking conditions and storage periods. Encapsulation emerges as a promising strategy, involving the protection of microorganisms with specialized layers, notably multilayered alginate-chitosan coatings, to shield them from degradation. Studies suggest that encapsulated probiotics, particularly the L. casei 431 strain within smaller-sized products subjected to shorter baking times, exhibit minimal viability reduction. Moreover, incorporating microcapsules into the dough, rather than post-baking surface application, further mitigates bacterial losses during storage. Despite these advancements, further investigations are necessary to identify strains resilient to processing, storage, and consumption while prioritizing sensory attributes to meet consumer preferences. Ultimately, research in probiotic bread production aims for a sustainable approach, placing significant emphasis on health considerations and disease prevention. Implementing encapsulation technology aligns with consumer demands for healthy, environmentally friendly products, highlighting the urgent need for innovation in this field with a focus on sustainability.
Identifying the Optimal Valuation Model for Maritime Data Assets with the Analytic Hierarchy Process (AHP)
Authors: Lim Sangseop , Lee Chang-Hee , Bae Jae-Hwan , Jeon Young-Hun
Published: April 2024

Data are becoming the most important factor in the development of the socio-economy, and data can be reevaluated as the owner’s valuable asset, which can increase the owner’s value. Therefore, each company is fiercely competing to secure data. Even in the marine field, maritime data are being produced exponentially, but it is difficult to expect more value creation because data are only stored rather than being used. This study used the analytic hierarchy process (AHP) methodology to select a suitable valuation model necessary to discover new values for maritime data. As a result of AHP analysis of 33 experts based on the stratified factors extracted from previous studies and expert opinions, the market approach (A2) was found to be the most suitable model. In addition, the most important factors to consider when selecting a valuation model were in the order of the characteristics of the maritime data (M1), the features of the maritime data market (M2), and the features of the maritime data valuation model (M3). The potential impact of this implementation could contribute to the establishment of an intelligent technology market by estimating the value of data and developing a platform for maritime data trading, allowing for more efficient data sharing and utilization by maritime autonomous surface ships (MASSs).
Effects of Land-Use Type and Salinity on Soil Carbon Mineralization in Coastal Areas of Northern Jiangsu Province
Authors: Yang Xu , Chu Dongsheng , Hu Haibo , Deng Wenbin , Chen Jianyu , Guo Shaojun
Published: April 2024

Sea level rise due to glacier melting caused by climate warming is a major global challenge, but the mechanism of the effect of salinity on soil carbon (C) mineralization in different land types is not clear. The pathways by which salinity indirectly affects soil carbon mineralization rates need to be investigated. Whether or not the response mode is consistent among different land-use types, as well as the intrinsic links and interactions between soil microbial resource limitation, environmental stress, microbial extracellular enzyme activity, and soil carbon mineralization, remain to be demonstrated. In this paper, three typical land-use types (wetland, forest, and agroforestry) were selected, and different salinity levels (0‰, 3‰, 6‰, and 32‰) were designed to conduct a 125-day laboratory incubation experiment to determine the soil CO2 release rate, soil physicochemical properties, and soil enzyme activities, and to correlate C mineralization with biotic and abiotic factors. A correlation analysis of soil physical and chemical properties, extracellular enzyme activities, and carbon mineralization rates was conducted to investigate their intrinsic linkages, and a multiple linear regression of C mineralization at different sites was performed to explore the variability of mineralization among different site types. Structural equation models were established in the pre- and post-incubation stages to study the pathways of soil C mineralization at different incubation times, and the mechanism of mineralization was further verified by enzyme stoichiometry. The results showed that, at the end of 125 days of incubation, the 32‰ salinity addition reduced the cumulative mineralization of forest and agroforestry types by 28.41% and 34.35%, respectively, compared to the 0‰ salinity addition. Soil C mineralization in the three different land-use types was highly correlated with the active C fractions of readily oxidizable C (ROC), dissolved organic C, and microbial biomass C (MBC) in the soil, with the standardized coefficients of multivariate linear regression reaching 0.67 for MBC in the wetland and −0.843 for ROC in the forest. Under long-term salinity additions, increased salinity would reduce the microbial respiratory quotient value by inhibiting β-glucosidase activity, thus indirectly affecting the rate of CO2 release. With added salinity, the mineralization of non-saline soil was more susceptible to the inhibitory effect of salinity, whereas the mineralization of salinized soil was more controlled by soil C pools.
Geological Hazards and Risk Management
Authors: Chen Jian , Xu Chong
Published: April 2024

The occurrence of geological hazards is widespread, particularly in mountainous regions [...]
Model of Family and the Propensity to Build Sustainable Savings Attitudes in the Post-COVID World: A Case Study of Poland
Authors: Merło Paweł , Michalak Jacek , Andruszkiewicz Katarzyna
Published: April 2024

This article investigates the role of the model of family and their savings as a support to growth and source of economic sustainability. The central objective of the present article was to determine the impact of the model of a family on the propensity to save money in the population of Poland. As indicated by independence tests, in most studied cases, the model of a family does not have a key role in sustainable consumption and saving behavior. The only exception are the forms of allocation of the saved capital. The study results suggest that households in Poland hold traditional views on the family model and the allocation of their savings. Decisions in everyday life are often reached together with the partner, which may attest to the growing egalitarian tendencies in Polish families. The research shows that the funds saved monthly by households are not planned in advance but constitute a financial surplus after all the expenses have been paid, which is then set aside as a reserve for the future. Partners usually prefer to have separate bank accounts with funds for day-to-day spending. However, the awareness concerning the forms of allocating capital is still poor, which is confirmed by the fact that most of the financial surplus is kept in the current account. The funds saved this way are spent to satisfy current needs, such as holidays or durable goods, and cover expenses in emergencies, which may be particularly important in the context of minimizing the risk of poverty and social exclusion, which should be prevented in line with the implementation of the UN Sustainable Development Goals 2030.
Construction of the Ecological Security Pattern in Xishuangbanna Tropical Rainforest Based on Circuit Theory
Authors: Yan Mengmeng , Duan Jilin , Li Yubin , Yu Yang , Wang Yu , Zhang Jiawei , Qiu Yu
Published: April 2024

Urban modernization and economic globalization have led to significant changes in traditional natural landscapes. The unregulated and large-scale expansion of rubber plantations in Xishuangbanna has resulted in water and scenic forests being replaced by rubber forests and the complex rainforest ecosystem being replaced by simple artificial forests. This has resulted in increasingly prominent ecological problems such as soil erosion, regional microclimate changes, and sharp declines in biodiversity. The ecological security pattern is an important way to protect regional ecological sustainability. Taking the tropical rainforest in Xishuangbanna as an example, this study identified ecological sources through the evaluation of the importance of ecosystem services, constructed resistance surfaces through ecological sensitivity evaluation, and used circuit theory to simulate ecological processes in heterogeneous landscapes by calculating “electricity” or “resistance”, thereby identifying ecological corridors and key ecological nodes. The results identified 31 ecological source areas, 65 key ecological corridors, 7 potential ecological corridors, 37 ecological pinch points, and 99 ecological barriers. The overall distribution of ecological sources was scattered, with higher density in the northwest and southeast regions. Ecological corridors were distributed along high mountains, and both ecological sources and corridors were mainly composed of forest land. Based on circuit theory, this study filled the gap in the MCR model’s inability to identify the true width of corridors due to ignoring the randomness of biological migration. It determined the spatial range of ecological corridors and the specific locations of ecological nodes and barriers, providing a reference for solving ecological problems in Xishuangbanna, such as “rainforest fragmentation”.
Assessment on the Environmental Concern Level and Driving Mechanism of Beach Recreationists Based on the New Ecological Paradigm Scale
Authors: Wu Xiaowei , Zhong Chongqing , Chen Han , Zou Xinqing
Published: April 2024

This study uses the new ecological paradigm (NEP) scale to measure the level of environmental concern among beach recreationists, innovatively constructs a model based on the measurement results to depict a group portrait, and analyzes the driving mechanism of beach recreation pro-environmental behavior. The level of environmental concern of beach tourists is 66.2%, which is slightly higher than the average level of Chinese urban residents, and is significantly correlated with age, occupation, education level, and income level. The newly constructed environmental behavior group portrait index indicates that the group that actively participates in recreational environmental behavior at Haichow Bay Beach includes college students, primary and secondary school students, retirees, and surrounding residents. Key factors in the generation of environmentally friendly behavior for beach enthusiasts include individual self-control constraints, regional ecological environment management, the improvement in environmental literacy, overcoming individualism, external environmental impacts, and environmental education.
Evaluating the Impact of Heat Mitigation Strategies Using Added Urban Green Spaces during a Heatwave in a Medium-Sized City
Authors: Skarbit Nóra , Unger János , Gál Tamás
Published: April 2024

Recognizing the growing trend of the urban population and the undeniable fact of global and regional climate change, it becomes increasingly important to explore how we can improve the livability of our cities not only in the distant future but also in the next few years. A critical aspect of this endeavor involves studying how we can effectively mitigate human heat load in urban areas. In our research, in the case of a medium-sized city (Szeged, Hungary), we examined the effect of surface modifications caused by vegetation on human thermal perception during the day and night of two heatwave days. To achieve this, we used the MUKLIMO_3 micro-scale climate model to simulate the thermal climate of Szeged, while the thermal load was assessed with the perceived temperature calculated by the Klima-Michel model. Our analysis also relied on the local climate zone (LCZ) system to describe the original land cover and the additional urban green spaces in the study area. We scrutinized the effects of added vegetation of different types and densities, as well as the presence of protective forests surrounding the city. Our findings revealed that the effect of the added vegetation can only be detected on the modified surfaces and in their immediate vicinity. Notably, dense urban greenery resulted in up to a 2–3 °C reduction in perceived temperature in certain areas during the daytime, highlighting the profound impact of targeted green space development. In addition, it is crucial to consider the airflow-blocking effect of woody vegetation, which can increase thermal load by 1–3 °C in the areas located in a downwind direction. Therefore, the changing regional climatic conditions (e.g., wind direction) and the development of the right type and location of urban green areas deserve special attention during modern urban planning processes.