bluuebunny/crossref_metadata_2025 huggingface dataset:
Bacillus cabrialesii subsp. cabrialesii Strain TE5: A Promising Biological Control Bacterium Against the Causal Agent of Spot Blotch in Wheat
Authors: Campos-Avelar Ixchel , García Jaime Michelle Fabiela , Morales Sandoval Pamela Helué , Parra-Cota Fannie Isela , de los Santos Villalobos Sergio
Published: January 2025

Strain TE5 was isolated from a wheat (Triticum turgidum L. subsp. durum) rhizosphere grown in a commercial field of wheat in the Yaqui Valley in Mexico. In this work, we present strain TE5 as a promising biological control agent against Bipolaris sorokiniana. First, after its genome sequencing through Illumina NovaSeq, this strain showed a genome size of 4,262,927 bp, with a 43.74% G + C content, an N50 value of 397,059 bp, an L50 value of 4 bp, and 41 contigs (>500 bp). Taxonomical affiliation was carried out by using overall genome relatedness indexes (OGRIs) and the construction of a phylogenomic tree based on the whole genome. The results indicated that strain TE5 identifies with Bacillus cabrialesii subsp. cabrialesii. Genomic annotation using Rapid Annotation Using Subsystems Technology (RAST) and Rapid Prokaryotic Genome Annotation (Prokka) indicated the presence of 4615 coding DNA sequences (CDSs) distributed across 330 subsystems, which included gene families associated with biocontrol, stress response, and iron competition. Furthermore, when the antiSMASH 7.1 platform was used for genome mining, the results indicated the presence of seven putative biosynthetic gene clusters related to the production of biocontrol metabolites, namely subtilosin A, bacillibactin, fengycin, bacillaene, bacilysin, surfactin, and rhizocticin A. Moreover, the antifungal activity of strain TE5 and its cell-free extract (CFE) was evaluated against Bipolaris sorokiniana, an emergent wheat pathogen. The results of in vitro dual confrontation showed fungal growth inhibition of 67% by strain TE5. Additionally, its CFE almost completely inhibited (93%) the growth of the studied phytopathogenic fungus on liquid media. Further observations of the impact of these bacterial metabolites on fungal spore germination exhibited inhibition of fungal spores through degrading the germinative hypha, avoiding mycelium development. Finally, the protective effect of strain TE5 against Bipolaris sorokiniana was evaluated for wheat seedlings. The results showed a significant decrease (83%) in disease severity in comparison with the plant infection without inoculation of the biological control agent. Thus, this work proposes Bacillus cabrialesii subsp. cabrialesii strain TE5 as a promising biological control agent against the wheat pathogen Bipolaris sorokiniana while suggesting lipopeptides as the potential mode of action, together with plant growth and defense stimulation.
Numerical Modelling of Hybrid Polymer Composite Frame for Selected Construction Parts and Experimental Validation of Mechanical Properties
Authors: Akshat Tegginamath , Petru Michal , Mishra Rajesh Kumar
Published: January 2025

This article is a numerical and experimental study of the mechanical properties of different glass, flax and hybrid composites. By utilizing hybrid composites consisting of natural fibers, the aim is to eventually reduce the percentage usage of synthetic or man-made fibers in composites and obtain similar levels of mechanical properties that are offered by composites using synthetic fibers. This in turn would lead to greener composites being utilized. The advantage of which would be the presence of similar mechanical properties as those of composites made from synthetic fibers along with a reduction in the overall weight of components, leading to much more eco-friendly vehicles. Finite element simulations (FEM) of mechanical properties were performed using ANSYS. The FEM simulations and analysis were performed using standards as required. Subsequently, actual beams/frames with a defined geometry were fabricated for applications in automotive body construction. The tensile performance of such frames was also simulated using ANSYS-based models and was experimentally verified. A correlation with the results of the FEM simulations of mechanical properties was established. The maximum tensile strength of 415 MPa was found for sample 1: G-E (glass–epoxy composite) and the minimum strength of 146 MPa was found for sample 2: F-G-E (G-4) (flax–glass–epoxy composite). The trends were similar, as obtained by simulation using ANSYS. A comparison of the results showed the accuracy of the numerical simulation and experimental specimens with a maximum error of about 8.05%. The experimental study of the tensile properties of polymer matrix composites was supplemented with interlaminar shear strength, and a high accuracy was found. Further, the maximum interlaminar shear strength (ILSS) of 18.5 MPa was observed for sample 1: G-E and the minimum ILSS of 17.0 MPa was observed for sample 2: F-G-E (G-4). The internal fractures were analyzed using a computer tomography analyzer (CTAn). Sample 2: F-G-E (G-4) showed significant interlaminar cracking, while sample 1: G-E showed fiber failure through the cross section rather than interlaminar failure. The results indicate a practical solution of a polymer composite frame as a replacement for existing heavier components in a car, thus helping towards weight reduction and fuel efficiency.
Development of Poultry Sausages Utilizing Manually and Mechanically Deboned Meat from Spent Laying Quails
Authors: Bueno Lethícia Olimpio , Dinali Dalila Lima , Ramos Alcinéia de Lemos Souza , Ramos Eduardo Mendes
Published: January 2025

The growth in quail egg production presents opportunities to utilize spent laying quail meat in value-added products, thereby enhancing the poultry industry. This study aimed to develop and evaluate sausages made with quail breast meat (QBM), chicken breast meat (CBM), mechanically deboned quail meat (MDQM), and mechanically deboned chicken meat (MDCM). The poultry breast and deboned meat samples were characterized and used to prepare five sausages (S1 = 100% CBM, S2 = 100% QBM, S3 = 60% CBM + 40% MDQM, S4 = 60% QBM + 40% MDCM, and S5 = 50% MDCM + 50% MDQM). QBM exhibited a higher protein content (25.26%) than CBM (22.33%), while MDQM contained higher collagen (1.23%) and ash (3.78%) contents than that of MDCM. MDCM displayed a darker (lower L*), redder (lower h), and more intense (higher C*) color than MDQM. Sausages containing mechanically deboned poultry meat (S3, S4, and S5) exhibited a more yellowish (h = 62.9°) tone compared to those containing only breast meat (h = 56.2°). S4 sausages exhibited the highest sensory acceptability (score 7.2), whereas S5 sausages, described as having a dark, non-uniform appearance and sandy texture, were less preferred (score 5.1). These findings highlight the potential of spent quail meat in sausage production, offering a promising avenue for product innovation and market expansion.
Numerical Simulation and Experimental Analysis of Mare’s Milk Sublimation Drying
Authors: Rakhmatulina Ayaulym , Altybay Arshyn , Imanbayeva Nurbibi , Bagitova Saltanat , Baikonys Anuar
Published: January 2025

This study presents a combined numerical and experimental analysis of heat and mass transfer in mare’s milk during vacuum sublimation drying, which is a process essential for producing high-quality powdered products. The numerical model was developed using two-dimensional simulations and validated against experimental data obtained for varying sample thicknesses (3 mm, 5 mm, and 7 mm). Results demonstrated a strong agreement between the model and experimental temperature data, with a coefficient of determination (R2) of 95% during the sublimation process. The findings revealed that thinner samples (3 mm) exhibited a 20% faster drying rate than thicker samples (7 mm), highlighting the critical role of sample thickness in sublimation dynamics. Additionally, the effects of heat flux distribution and mass loss due to sublimation were analyzed to understand the drying dynamics. This study highlights the importance of optimizing process parameters such as chamber pressure, shelf temperature, and sample thickness to enhance drying efficiency and reduce processing time. The findings provide valuable insights for scaling vacuum sublimation drying of mare’s milk for industrial applications.
Application of Nighttime Light Data Simulation Based on Multi-Indicator System and Machine Learning Model in Predicting Potentially Suitable Economic Development Areas: A Case Study of the Turpan–Hami Region
Authors: Zhang Guangpeng , Zhang Li , Chen Yiyang , Chen Meng , Tian Jingjing , Wu Yin
Published: January 2025

In recent years, the accelerated urbanization process in China has led to increased land resource constraints and unregulated expansion, imposing significant pressure on ecosystems and the environment. As a critical node along the Silk Road Economic Belt, the Turpan–Hami region has experienced rapid urban development under policy support but faces challenges in resource utilization efficiency and sustainable development. To address these challenges, this study innovatively combines nighttime light remote sensing data to quantify urban economic development intensity and integrates socioeconomic and natural environment indicators based on previous research. Four tree-based ensemble learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)—were employed to predict potential urban economic development suitability zones and their suitability intensity. The results show that the CatBoost model performed the best in suitability prediction, revealing significant spatial disparities: high-suitability areas are concentrated in regions with superior resource conditions and well-developed infrastructure, whereas areas with terrain constraints and inadequate infrastructure exhibit lower suitability. An analysis of changes over historical periods (2010, 2015, and 2020) demonstrates a gradual expansion of high-suitability regions over time.
Investigating LiDAR Metrics for Old-Growth Beech- and Spruce-Dominated Forest Identification in Central Europe
Authors: Adiningrat Devara P. , Skidmore Andrew , Schlund Michael , Wang Tiejun , Abdullah Haidi , Heurich Marco
Published: January 2025

Old-growth forests are essential for maintaining biodiversity, as they are formed by the complexity of diverse forest structures, such as broad variations in tree height and diameter (DBH) and conditions of living and dead trees, leading to various ecological niches. However, many efforts of old-growth forest mapping from LiDAR have targeted only one specific forest structure (e.g., stand height, basal area, or stand density) by deriving information through a large number of LiDAR metrics. This study introduces a novel approach for identifying old-growth forests by optimizing a set of selected LiDAR standards and structural metrics. These metrics effectively capture the arrangement of multiple forest structures, such as canopy heterogeneity, multilayer canopy profile, and canopy openness. To determine the important LiDAR standard and structural metrics in identifying old-growth forests, multicollinearity analysis using the variance inflation factor (VIF) approach was applied to identify and remove metrics with high collinearity, followed by the random forest algorithm to rank which LiDAR standard and structural metrics are important in old-growth forest classification. The results demonstrate that the LiDAR structural metrics (i.e., advanced LiDAR metrics related to multiple canopy structures) are more important and effective in distinguishing old- and second-growth forests than LiDAR standard metrics (i.e., height- and density-based LiDAR metrics) using the European definition of a 150-year stand age threshold for old-growth forests. These structural metrics were then used as predictors for the final classification of old-growth forests, yielding an overall accuracy of 78%, with a true skill statistic (TSS) of 0.58 for the test dataset. This study demonstrates that using a few structural LiDAR metrics provides more information than a high number of standard LiDAR metrics, particularly for identifying old-growth forests in mixed temperate forests. The findings can aid forest and national park managers in developing a practical and efficient old-growth forest identification and monitoring method using LiDAR.
Spatiotemporal Changes in Evapotranspiration and Its Influencing Factors in the Jiziwan Region of the Yellow River from 1982 to 2018
Authors: Liu Wenting , Tang Rong , Zhang Ge , Xue Jiacong , Xue Baolin , Wang Yuntao
Published: January 2025

Evapotranspiration (ET) is a critical process in the interaction between the terrestrial climate system and vegetation. In recent years, ET has undergone significant changes in the Jiziwan region of the Yellow River Basin, primarily due to the implementation of ecological restoration programs and the dual impacts of climate change. As a result, hydrological cycle processes have been profoundly affected, making it crucial to accurately capture trends in ET and its components, as well as to identify the key drivers of these changes. In this study, we first systematically analyzed the dynamic evolution of ET and its components in the Jiziwan of the Yellow River area between 1982 and 2018 from the perspective of land use change. To achieve accurate ET simulations, we introduced a multiple linear regression algorithm and quantitatively evaluated the specific contributions of five climate factors, including precipitation, temperature, wind speed, specific humidity, and radiation, as well as the normalized difference vegetation index (NDVI), a vegetation factor, to ET and its components. On this basis, we explored the combined influence mechanism of climate change and vegetation change on ET in detail. The results revealed that the structure of ET in the Jiziwan of the Yellow River area has changed significantly and that vegetation evapotranspiration has gradually replaced soil evaporation, occupies a dominant position, and has become the main component of ET in this area. Among the many factors affecting ET, the contribution of climate change is the most significant, with an average contribution rate of approximately 59%. Moreover, the influence of human activities on total ET and its components is also high. The factors that had the greatest impact on total ET, soil evaporation, and vegetation transpiration were precipitation, radiation, and the NDVI, respectively. In terms of spatial distribution, the eastern part of Jiziwan was more significantly affected by environmental changes, and the trends of the ET changes were more dramatic. This study not only enhances our scientific understanding of the changes in ET and their driving mechanisms in the Jiziwan area of the Yellow River but also provides a solid scientific foundation for the development of water resource management and ecological restoration strategies in the region.
LCMorph: Exploiting Frequency Cues and Morphological Perception for Low-Contrast Road Extraction in Remote Sensing Images
Authors: Li Xin , Yang Shumin , Meng Fan , Li Wenlong , Yang Zongchi , Wei Ruoyu
Published: January 2025

Road extraction in remote sensing images is crucial for urban planning, traffic navigation, and mapping. However, certain lighting conditions and compositional materials often cause roads to exhibit colors and textures similar to the background, leading to incomplete extraction. Additionally, the elongated and curved road morphology conflicts with the rectangular receptive field of traditional convolution. These challenges significantly affect the accuracy of road extraction in remote sensing images. To address these issues, we propose an end-to-end low-contrast road extraction network called LCMorph, which leverages both frequency cues and morphological perception. First, Frequency-Aware Modules (FAMs) are introduced in the encoder to extract frequency cues, effectively distinguishing low-contrast roads from the background. Subsequently, Morphological Perception Blocks (MPBlocks) are employed in the decoder to adaptively adjust the receptive field to the elongated and curved nature of roads. MPBlock relies on snake convolution, which mimics snakes’ twisting behavior for accurate road extraction. Experiments demonstrate that our method achieves state-of-the-art performance in terms of F1 score and IoU on the self-constructed low-contrast road dataset (LC-Roads), as well as the public DeepGlobe and Massachusetts Roads datasets.
Deep-TEMNet: A Hybrid U-Net–2D LSTM Network for Efficient and Accurate 2.5D Transient Electromagnetic Forward Modeling
Authors: Qu Zhijie , Gao Yuan , Xing Kang , Zhang Xiaojuan
Published: January 2025

The transient electromagnetic (TEM) method is a crucial tool for subsurface exploration, providing essential insights into the electrical resistivity structures beneath the Earth’s surface. Traditional forward modeling approaches, such as the finite-difference time-domain (FDTD) method and the finite-element method (FEM), are computationally intensive, limiting their practicality for real-time, high-resolution, or large-scale investigations. To address these challenges, we present Deep-TEMNet, an advanced deep learning framework specifically designed for two-dimensional TEM forward modeling. Deep-TEMNet integrates the U-Net architecture with a tailored two-dimensional long short-term memory (2D LSTM) module, allowing it to effectively capture complex spatial-temporal relationships in TEM data. The U-Net component enables high-resolution spatial feature extraction, while the 2D LSTM module enhances temporal modeling by processing spatial sequences in two dimensions, thereby optimizing the representation of electromagnetic field dynamics over time. Trained on high-fidelity FEM-generated datasets, Deep-TEMNet achieves exceptional accuracy in reproducing electromagnetic field distributions across diverse geological scenarios, with a mean squared error of 0.00000134 and a root mean square percentage error of 0.002373019. The framework offers over 150 times the computational speed of traditional FEMs, with an average inference time of just 3.26 s. Extensive validation across varied geological conditions highlights Deep-TEMNet’s robustness and adaptability, establishing its potential for efficient, large-scale subsurface mapping and real-time data processing. By combining U-Net’s spatial resolution capabilities with the sequential processing strength of the 2D LSTM module, Deep-TEMNet significantly advances computational efficiency and accuracy, positioning it as a valuable tool for geophysical exploration, environmental monitoring, and other applications requiring scalable, real-time TEM analyses that are easily integrated into remote sensing workflows.
Multi-Temporal Image Fusion-Based Shallow-Water Bathymetry Inversion Method Using Active and Passive Satellite Remote Sensing Data
Authors: Li Jie , Dong Zhipeng , Chen Lubin , Tang Qiuhua , Hao Jiaoyu , Zhang Yujie
Published: January 2025

In the active–passive fusion-based bathymetry inversion method using single-temporal images, image data often suffer from errors due to inadequate atmospheric correction and interference from neighboring land and water pixels. This results in the generation of noise, making high-quality data difficult to obtain. To address this problem, this paper introduces a multi-temporal image fusion method. First, a median filter is applied to separate land and water pixels, eliminating the influence of adjacent land and water pixels. Next, multiple images captured at different times are fused to remove noise caused by water surface fluctuations and surface vessels. Finally, ICESat-2 laser altimeter data are fused with multi-temporal Sentinel-2 satellite data to construct a machine learning framework for coastal bathymetry. The bathymetric control points are extracted from ICESat-2 ATL03 products rather than from field measurements. A backpropagation (BP) neural network model is then used to incorporate the initial multispectral information of Sentinel-2 data at each bathymetric point and its surrounding area during the training process. Bathymetric maps of the study areas are generated based on the trained model. In the three study areas selected in the South China Sea (SCS), the validation is performed by comparing with the measurement data obtained using shipborne single-beam or multi-beam and airborne laser bathymetry systems. The root mean square errors (RMSEs) of the model using the band information after image fusion and median filter processing are better than 1.82 m, and the mean absolute errors (MAEs) are better than 1.63 m. The results show that the proposed method achieves good performance and can be applied for shallow-water terrain inversion.
Novel Visualization of Building Earthquake Response Recorded by a Dense Network of Sensors
Authors: Cruz Lichiel , Todorovska Maria I. , Trifunac Mihailo D. , Aihemaiti Alimu , Lin Guoliang , Cui Jianwen
Published: January 2025

The strong motion records collected in full-scale structures provide the ultimate evidence of how real structures, in situ, respond to earthquakes. This paper presents a novel method for visualization, in three dimensions (3D), of the collective motion recorded by a dense array of sensors in a building. The method is based on one- and two-dimensional biharmonic spline interpolation of the motion recorded by multiple sensors on the same or multiple floors. It is demonstrated on novel data that have been recorded recently in a 50-story skyscraper, uniquely instrumented with multiple triaxial accelerometers per floor, approximately at every five floors above ground and at two basement levels, and with rotational seismometers and two borehole arrays measuring the motion of the soil very near the building foundation. The method is computationally efficient and suitable for real-time application and rapid assessment of structural health. The animations provide invaluable insight into the 3D structural response of the building as a whole, including wave propagation through the structure and the interplay between translations and rotations, which will be useful for testing existing and developing new methods for structural health monitoring of buildings and for the further development of building design codes. Animations of selected earthquakes can be found on YouTube at @TPYC-seismic.
The Impact of Exercise Play on the Biomechanical Characteristics of Single-Leg Jumping in 5- to 6-Year-Old Preschool Children
Authors: Song Zhanbing , Hou Bojie , Ji Zhongqiu , Jiang Guiping
Published: January 2025

Objective: This study aimed to investigate the effects of a 12-week self-designed exercise game intervention on the kinematic and kinetic data of the supporting leg in preschool children during the single-leg jump. Methods: Thirty 5- to 6-year-old preschool children were randomly divided into an experimental group (EG) and a control group (CG). The BTS SMART DX motion capture analysis system was used to collect single-leg jump data before the intervention. The experimental group underwent a 12-week intervention, with self-designed exercise games conducted three times a week for 30 min each session, while the control group only participated in regular kindergarten recess activities and physical education classes. After the intervention, the same equipment was used to collect single-leg jump data again, and the kinematic and kinetic data were analyzed using Anybody 7.4 simulation software. Results: After the intervention, the experimental group showed significant changes in joint angles and joint torques, with a notable increase in the force exerted by dominant muscles such as the vastus medialis, vastus lateralis, and gastrocnemius and a significant increase in the ground reaction force. Although the control group also showed some changes in the dominant muscles, the changes were not as significant as those in the experimental group. Conclusions: A 12-week exercise game intervention significantly improved the technique and force characteristics of 5- to 6-year-old preschool children during the single-leg jump, making muscle exertion more focused and efficient and effectively enhancing explosive power and performance during the single-leg jump.
LDDP-Net: A Lightweight Neural Network with Dual Decoding Paths for Defect Segmentation of LED Chips
Authors: Zhang Jie , Chen Ning , Li Mengyuan , Zhang Yifan , Suo Xinyu , Li Rong , Liu Jian
Published: January 2025

Chip defect detection is a crucial aspect of the semiconductor production industry, given its significant impact on chip performance. This paper proposes a lightweight neural network with dual decoding paths for LED chip segmentation, named LDDP-Net. Within the LDDP-Net framework, the receptive field of the MobileNetv3 backbone is modified to mitigate information loss. In addition, dual decoding paths consisting of a coarse decoding path and a fine-grained decoding path in parallel are developed. Specifically, the former employs a straightforward upsampling approach, emphasizing macro information. The latter is more detail-oriented, using multiple pooling and convolution techniques to focus on fine-grained information after deconvolution. Moreover, the integration of intermediate-layer features into the upsampling operation enhances boundary segmentation. Experimental results demonstrate that LDDP-Net achieves an mIoU (mean Intersection over Union) of 90.29% on the chip dataset, with parameter numbers and FLOPs (Floating Point Operations) of 2.98 M and 2.24 G, respectively. Comparative analyses with advanced methods reveal varying degrees of improvement, affirming the effectiveness of the proposed method.
Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS
Authors: Khan M. N. Afzal , Zahour Nada , Tariq Usman , Masri Ghinwa , Almadani Ismat F. , Al-Nashah Hasan
Published: January 2025

Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge. In this study, we employ a classification strategy to explore stress and its impact on spatial activation patterns and brain connectivity caused by the Stroop color–word task (SCWT). To improve our results and increase our dataset, we use data augmentation with a deep convolutional generative adversarial network (DCGAN). The study is carried out at two separate times of day (morning and evening) and involves 21 healthy participants. Additionally, we introduce binaural beats (BBs) stimulation to investigate its potential for stress reduction. The morning session includes a control phase with 10 SCWT trials, whereas the afternoon session is divided into three phases: stress, mitigation (with 16 Hz BB stimulation), and post-mitigation, each with 10 SCWT trials. For a comprehensive evaluation, the acquired fNIRS data are classified using a variety of machine-learning approaches. Linear discriminant analysis (LDA) showed a maximum accuracy of 60%, whereas non-augmented data classified by a convolutional neural network (CNN) provided the highest classification accuracy of 73%. Notably, after augmenting the data with DCGAN, the classification accuracy increases dramatically to 96%. In the time series data, statistically significant differences were noticed in the data before and after BB stimulation, which showed an improvement in the brain state, in line with the classification results. These findings illustrate the ability to detect changes in brain states with high accuracy using fNIRS, underline the need for larger datasets, and demonstrate that data augmentation can significantly help when data are scarce in the case of brain signals.
Social Media, Conspiracy Beliefs, and COVID-19 Vaccines: A Survey Study of Emerging and Middle-Aged Adults in the United States
Authors: Nugent Gillianne R. , Anselmi Dina , Chin Brian N.
Published: January 2025

This study examined the connections between social media use and behaviors, COVID-19 vaccine conspiracy beliefs, and COVID-19 vaccine uptake in 809 emerging and middle-aged adults. Emerging adults reported more overall social media use, active and passive social media behaviors, and use of most platforms (i.e., Instagram, Snapchat, TikTok, Twitter/X, Reddit, and YouTube), whereas middle-aged adults reported more Facebook use and higher vaccine uptake. COVID-19 vaccine conspiracy beliefs were linked to lower vaccine uptake, with this association unexpectedly stronger among individuals who reported less social media use and fewer active and passive social media behaviors. Active social media behaviors were associated with stronger vaccine conspiracy beliefs, whereas passive social media behaviors and overall use did not show a similar association. Exploratory analyses of platform-specific effects revealed nuanced patterns: TikTok use was associated with stronger vaccine conspiracy beliefs, Instagram use was associated with higher vaccine uptake, and Snapchat use was associated with lower vaccine uptake. Our findings highlight the complex, platform-specific influences of social media use and behaviors on COVID-19 vaccine conspiracy beliefs and vaccine uptake. Future studies are needed to investigate the role of specific social media platforms in spreading, perpetuating, or countering misinformation about the COVID-19 vaccine.
Improvement of the Reliability of Urban Park Location Results Through the Use of Fuzzy Logic Theory
Authors: Calka Beata , Siok Katarzyna , Szostak Marta , Bielecka Elzbieta , Kogut Tomasz , Zhran Mohamed
Published: January 2025

Green areas, thanks to their relatively unified natural systems, play several key roles. They contribute to the proper functioning and sustainable development of cities and also determine the quality of life for their inhabitants. As a result, urban planners and policy-makers frequently aim to maximize the benefits of green spaces by creating various programs and strategies focused on green infrastructure development, such as the Green City initiative. One of the objectives of this program is to create new urban parks. This research focuses on developing a new method for selecting sites for urban parks, taking into account factors related to the environment, accessibility, and human activity. The research was carried out for the area of Ciechanów city. To make the city areas more attractive to residents, the authorities aim to increase green spaces and also revitalize the existing greenery. The combination of the Fuzzy AHP method and fuzzy set theory (selecting appropriate fuzzy membership for each factor), along with the use of large and diverse geospatial datasets, minimized subjectivity in prioritizing criteria and allowed for a fully automated analysis process. Among the factors analyzed, land use emerged as the most significant, followed by the normalized difference vegetation index (NDVI) and proximity to surface water. The results indicated that 16% of the area was deemed highly suitable for urban park development, while 15% was considered unsuitable. One-at-a-time (OAT) sensitivity analysis, based on changes in the weight of the land-use factor, revealed that a 75% reduction in weight resulted in a nearly 57.2% decrease in unsuitable areas, while a 75% increase in weight led to a 40% expansion of the most suitable locations. The potential park locations were compared with a heat map of urban activity in the city. The developed method contributes to the discourse on the transparency of location decisions and the validity of the criteria used, to promote sustainable urban development that provides residents with access to active recreation.
Emergency Preparedness in China’s Urban Rail Transit System: A Systematic Review
Authors: Ge Shiying , Shan Ming , Zhai Zhao
Published: January 2025

Urban rail transit is one of the vital lifeline projects of megacities worldwide. While it brings convenience and economic growth to the city, the construction of urban rail transit projects is often associated with safety accidents. Emergency preparedness plays a significant role in the prevention of safety accidents and emergency rescue in urban rail transit construction projects. However, the extant literature rarely looks into this topic. The aims of this study are to review the emergency preparedness of current urban rail transit construction projects in China, to summarize their key elements, to identify their advantages and limitations, and to make recommendations accordingly. To achieve these goals, this study systematically investigates the emergency preparedness documents implemented by 52 cities in mainland China; from these, five key elements of emergency preparedness are systematically reviewed: organization; monitoring and early warning; emergency response; post-disaster recovery and reconstruction; emergency support. The advantages and limitations of existing emergency preparedness are examined, and recommendations for updates to emergency preparedness are made based on the experience and knowledge of advanced economies. The findings of the study can enhance understanding among authorities and industry practitioners of emergency preparedness as it is implemented in current urban rail construction projects. It can also provide a practical reference for the improvement of emergency preparedness of urban rail transit construction projects in the future, thereby contributing to the resilience and long-term sustainability of urban rail transit systems.
Bibliometric Analysis of Research on Child-Friendly Cities from the Web of Science, 2004–2024
Authors: Shu Huangna , Zheng Guoquan
Published: January 2025

The construction of child-friendly cities has become a global issue, and the diverse needs of children during their growth has attracted widespread attention. This study uses CiteSpace 6.4.1 to perform a visual analysis of 966 papers published from 2004 to 2024 in the SSCI, SCI, ESCI, and A&HCI citation indexes of the Web of Science database. First, the study analyzes the publication trends over the past two decades in the context of different citation indexes and research fields. Then, we identify influential journals, institutions, countries, author institutions, and funding agencies within this domain. Additionally, this study uses the visualized results to outline the core keywords and research themes in child-friendly city research, clarifying the development trajectory of the field and identifying future research priorities and hotspots. The results derived from CiteSpace and bibliometric analysis will assist future researchers and practitioners in a better understanding of the comprehensive framework of child-friendly city research, thereby advancing the progress of child-friendly city construction under the broader context of sustainable urban development.
Integrated Risk Framework (IRF)—Interconnection of the Ishikawa Diagram with the Enhanced HACCP System in Risk Assessment for the Sustainable Food Industry
Authors: Glevitzky Mirel , Glevitzky Ioana , Mucea-Ștef Paul , Popa Maria , Dumitrel Gabriela-Alina , Vică Mihaela Laura
Published: January 2025

This paper presents a new risk assessment methodology called the Integrated Risk Framework (IRF) through the application of Ishikawa diagrams combined with the enhanced Hazard Analysis and Critical Control Point (HACCP) system. This risk investigation technique aims to ensure a significantly higher level of quality, safety, and sustainability in food products by using improved classical methods with strong intercorrelation capabilities. The methodology proposes expanding the typology of basic physical, chemical, and biological risks outlined by the ISO 22000 Food Safety Management System standard, adding other auxiliary risks such as allergens, fraud/sabotage, Kosher/Halal compliance, Rapid Alert System for Food and Feed notification, or additional specific risks such as irradiation, radioactivity, genetically modified organisms, polycyclic aromatic hydrocarbons, African swine fever, peste of small ruminants, etc. depending on the specific technological process or ingredients. Simultaneously, it identifies causes for each operation in the technological flow based on the 5M diagram: Man, Method, Material, Machine, and Environment. For each identified risk and cause, its impact was determined according to its severity and likelihood of occurrence. The final effect is defined as the risk class, calculated as the arithmetic mean of the impact derived at each process stage based on the identified risks and causes. Within the study, the methodology was applied to the spring water bottling process. This provided a new perspective on analyzing the risk factors during the bottling operations by concurrently using Ishikawa diagrams and HACCP principles throughout the product’s technological flow. The results of the study can form new methodologies aimed at enhancing sustainable food safety management strategy. In risk assessment using these two tools, the possibility of cumulative or synergistic effects is considered, resulting in better control of all factors that may affect the manufacturing process. This new perspective on studying the dynamics of risk factor analysis through the simultaneous use of the fishbone diagram and the classical HACCP system can be extrapolated and applied to any manufacturing process in the food industry and beyond.
Having Faith in the Sustainable Livelihood Approach: A Review
Authors: Morse Stephen
Published: January 2025

This review paper focuses on the development and application of the Sustainable Livelihood Approach (SLA), especially regarding its limitations, both in terms of its formulation and link to theory as well as practice. The SLA has proved to be a popular approach, not least because it is holistic and ‘people-centered’, and forces a requirement that livelihoods, along with their vulnerability and institutional contexts, are well understood before interventions are designed and implemented to help the community. However, its theoretical underpinning has been questioned, and some have pointed to the weak representation of important dimensions such as power, including its link to globalization, and culture, with the latter including faith. This paper explores the various ways that these issues have been addressed by using faith as a lens, and makes a case for a ‘Sustainable Living Approach’ (SLivA) to provide a stronger dovetailing with the capabilities/functionings approach of Amartya Sen. However, there is a trade-off between the complexity of frameworks and their practicability, and more work is needed in this area, especially in terms of the potential contribution of technologies such as very-high-resolution Earth Observation, machine learning, and artificial intelligence.
Evaluation and Analysis of Passive Energy Saving Renovation Measures for Rural Residential Buildings in Cold Regions: A Case Study in Tongchuan, China
Authors: Cao Ping , Wang Jiawei , Huang Dinglei , Cao Zhi , Li Danyang
Published: January 2025

Energy-saving renovation of rural residences is an effective means of promoting sustainable rural development. This study focuses on a single-story rural residential building located in Tongchuan City, Shaanxi Province, China (a cold region), as a case study. Retrofits were conducted on the exterior windows, roof, and exterior walls, with the addition of a sunroom. Using life cycle assessments (LCAs) and orthogonal experimental methods combined with value engineering principles, we calculated various indicators including the energy efficiency improvement rate, implied carbon emissions, proportion of implied carbon emissions, carbon footprint, carbon reduction rate, carbon payback period, and investment payback period. The impact of traditional retrofitting measures on these indicators was analyzed. The results indicate that carbon emissions from the production of building materials are a key concern among the embodied carbon emissions from the retrofits, while transportation, construction, and demolition contribute minimally. Changes in the depth of the sunroom had the most significant impact on comprehensive indicators, followed by changes to the roof. After retrofitting, the carbon reduction rate was underestimated by 9.35% to 12.02% due to embodied carbon emissions. The carbon payback period for all schemes is estimated to be between 3.27 and 4.21 years. Based on current market conditions, developing corresponding carbon economics can enhance the economic viability of the project. This approach extends the investment payback period by more than 7% while also helping to narrow the income gap between urban and rural residents to some extent. Overall, the environmental impact assessment of the alternative schemes promotes sustainable rural development and provides scientific and effective guidance for the construction of project decision-making evaluation systems and architectural designers.
Impact of Venture Capital on Urban Carbon Emissions: Evidence from the Yangtze River Delta Urban Agglomeration in China
Authors: Huang Lijiali , Wang Xueqiong , Sheng Yanwen , Zhao Jinli
Published: January 2025

Venture capital is vital for developing capital markets and the low-carbon transformation of the economy. We used panel data from 27 cities in the Yangtze River Delta urban agglomeration from 2011 to 2022 to investigate how the scale and structure of venture capital influence the intensity of urban carbon emissions using spatial econometric models. We show that an increase in the scale of venture capital can inhibit the increase in the intensity of urban carbon emissions, and the effect is more pronounced in cities with higher pollution and better geographical location. Heterogeneity exists in the carbon-reduction effects of venture capital across industries. The direct carbon reduction effect of venture capital flowing to mid- and low-end industries is stronger and more prominent in cities with higher pollution and less favourable geographical locations. The long-term carbon reduction effect of venture capital flowing to high-end industries is stronger. The mediating effect of technological innovation is prominent, while the effect of industrial structure upgrade is not prominent. The enterprises’ willingness and ability to engage in green transformation acts as a positive moderator, whereas the positive moderating effect of the government in that respect is insufficient. This study clarifies the mechanism of venture capital on urban carbon emissions and offers valuable insights for optimising the structure and system of venture capital.
Assessment of Anthropogenic Disturbances of Landscapes: West Kazakhstan Region
Authors: Tokbergenova Aigul , Skorintseva Irina , Ryskeldiyeva Aizhan , Kaliyeva Damira , Salmurzauly Ruslan , Mussagaliyeva Aizhan
Published: January 2025

The study analyzes anthropogenic disturbances of landscapes in Western Kazakhstan, which occupies 27% of the country’s territory. The main focus is on the impact of industry and agriculture, especially pasture use and the development of oil and gas fields. The application of remote sensing data and field surveys allowed us to identify the degree of landscape disturbance and to propose their classification into five levels of disturbance, from virtually undisturbed to severely disturbed. Cartographic analysis revealed that pastures occupy 53.83% of the territory, while industrial-technogenic impact accounts for 23.12%. This indicates a significant level of landscape transformation. The findings of this study can serve as a foundation for environmental monitoring and the formulation of recommendations aimed at reducing anthropogenic impacts. The study underscores the necessity for the sustainable management of natural resources in the context of industrial development in the region and provides crucial insights for maintaining ecological balance.
Nitrogen Transformation Mechanisms and Compost Quality Assessment in Sustainable Mesophilic Aerobic Composting of Agricultural Waste
Authors: Zhao Lin , Huang Yuhan , Ran Xue , Xu Yuwei , Chen Yuanyuan , Wu Chuansheng , Tang Jun
Published: January 2025

This study examines nitrogen transformation mechanisms and compost quality in mesophilic aerobic composting of wheat straw, utilizing cow manure as a co-substrate to promote sustainable agricultural waste management. Two composting systems were established: group A (control) and group B (10% cow manure addition by wet weight). The addition of cow manure accelerated early organic matter decomposition and increased total nitrogen retention in group B. Nitrogen losses occurred primarily via ammonia volatilization during the initial and final composting stages, while functional gene analysis revealed enhanced ammonification and nitrification in both systems. Microbial community analysis showed that cow manure addition promoted nitrogen-fixing bacteria in the early phase and fungi associated with complex organic degradation in later stages. These findings underscore the potential of cow manure to enhance compost maturity, improve nitrogen efficiency, and support the development of sustainable composting practices that contribute to resource conservation.
Research on Fault Section Location in an Active Distribution Network Based on Improved Subtraction-Average-Based Optimizer
Authors: Dai Jinjin , Zhang Ziyu , Li Shaoyong , Li Lingling
Published: January 2025

The high penetration of distributed generation (DG) in the distribution system poses a challenge to the protection techniques and strategies of active distribution networks, making it difficult to adapt traditional methods to the fault diagnosis of the new power system. A method based on the improved subtractive optimiser algorithm for fault diagnosis is proposed to address this situation. Firstly, a fault localization model applicable to DG grid connection is constructed, which can effectively deal with the impact of the dynamic switching of DGs on the system and make up for the shortcomings of the traditional single-power network model; secondly, to solve the model, the original algorithm is improved using multi-strategy fusion, and the improved subtraction-average-based optimizer (ISABO) is obtained. Through the test of classical functions, its excellent solving performance and decoupling ability are verified; finally, the ISABO algorithm is applied to the 33-node test system to make it operate in various complex fault conditions. The results show that the ISABO algorithm is feasible in solving the fault location problem and can adapt to the connect/disconnect state of the interconnection switch and the dynamic casting and cutting of multiple DGs. Compared with the original SABO algorithm, its positioning accuracy can always be maintained at 100%, and the positioning speed is increased by 46.68%, symmetrically improving positioning speed, positioning accuracy, and fault tolerance.