bluuebunny/crossref_metadata_2025 huggingface dataset:
Significant Disparity in Spatiotemporal Changes of Terrestrial Evapotranspiration across Reanalysis Datasets in China from 1982 to 2020
Authors: Bai Jiaxin , Wu Guocan , Mao Yuna
Published: September 2023

Due to limited observational data, there remains considerable uncertainty in the estimation and spatiotemporal variations of land surface evapotranspiration (ET). Reanalysis products, with their advantages of high spatiotemporal resolution, global coverage, and long-term data availability, have emerged as powerful tools for studying ET. Nevertheless, the accuracy of reanalysis ET products varies among different products and the reasons for these accuracy differences have not been thoroughly investigated. This study evaluates the ability of different reanalysis ET products to reproduce the spatiotemporal patterns and long-term trends of ET in China, using remote sensing and water-balance-derived ET as reference. We investigate the possible reasons for their disparity by analyzing the three major climatic factors influencing ET (precipitation, solar radiation, and temperature). The findings reveal that compared to the water balance ET, the Global Land Evaporation Amsterdam Model (GLEAM) product is capable of reproducing the mean, interannual variability, and trends of ET, making it suitable for validating reanalysis ET products. In comparison to GLEAM ET, all reanalysis ET products exhibit consistent climatology and spatial distribution but show a clear overestimation, with multi-year averages being overestimated by 16–40%. There are significant differences among the reanalysis products in terms of interannual variability, long-term trends, and attribution. Within the common period of 2003–2015, GLEAM and water balance ET products demonstrate consistent increasing trends. The second-generation Modern-Era Retrospective analysis for Research and Applications (MERRA2) and the offline (land-only) replay of MERRA (MERRA-Land) could produce similar increasing trends because of the consistent precipitation trends with observed precipitation. The European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) and ERA5-Land cannot capture the consistent increasing trends as they obtain decreasing precipitation. These findings have significant implications for the development of reanalysis products.
Increasing the Observability of Near Inertial Oscillations by a Future ODYSEA Satellite Mission
Authors: Wang Jinbo , Torres Hector , Klein Patrice , Wineteer Alexander , Zhang Hong , Menemenlis Dimitris , Ubelmann Clement , Rodriguez Ernesto
Published: September 2023

Near Inertial Oscillations (NIOs) are ocean oscillations forced by intermittent winds. They are most energetic at mid-latitudes, particularly in regions with atmospheric storm tracks. Wind-driven, large-scale NIOs are quickly scattered by ocean mesoscale eddies (with sizes ranging from 100 to 400 km), causing a significant portion of the NIO energy to propagate into the subsurface ocean interior. This kinetic energy pathway illustrates that the wind energy input to NIO is critical for maintaining deep ocean stratification and thus closing the total energy budget, as emphasised by numerous modelling studies. However, this wind energy input to NIO remains poorly observed on a global scale. A remote sensing approach that observes winds and ocean currents co-located in time and space with high resolution is necessary to capture the intermittent air-sea coupling. The current satellite observations do not meet these requirements. This study assesses the potential of a new satellite mission concept, Ocean DYnamics and Surface Exchange with the Atmosphere (OSYSEA), to recover wind-forced NIOs from co-located winds and currents. To do this, we use an Observation System Simulation Experiment (OSSE) based on hourly observations of ocean surface currents and surface winds from five surface moorings covering latitudes from 15° to 50°. ODYSEA wind and current observations are expected to have a spatial resolution of 10 km with about a 12 h sampling frequency in mid-latitudes. Results show that NIOs can be recovered with high accuracy using the ODYSEA spatial and temporal resolution, but only if observations are made over a wide area of 1800 km. A narrower swath (1000 km) may lead to significant aliasing.
Robust GNSS Positioning Using Unbiased Finite Impulse Response Filter
Authors: Dou Jie , Xu Bing , Dou Lei
Published: September 2023

In a typical GNSS receiver, pseudorange and pseudorange rate measurements are generated through the code and carrier tracking loops, respectively. These measurements are then employed to calculate the user’s position and velocity (PV) solutions, which is typically achieved using a Kalman filter (KF) or the least squares (LS) algorithm. However, the LS method only uses the current observation without error analysis. The positioning result is greatly affected by the errors in the observed data. In KF, by using an iterative approach that combines predictions and measurements of PV information, more accurate estimates can be obtained because the PV information is time-correlated. Meanwhile, its optimal estimate requires that both the model and noise statistics are exactly known. Otherwise, achieving optimality cannot be guaranteed. To address this issue, this paper proposes and implements a novel GNSS solution method based on an unbiased finite impulse response (UFIR) filter. Two different field tests were conducted. The position results of UFIR are compared with those from the LS and KF methods, and the horizon positioning mean error is improved by 44% and 29%, respectively, which highlights its efficacy. The method offers two primary benefits: it is robust to noise uncertainty, and it leverages historical data within the UFIR framework to provide a more accurate estimate of the current state.
C2S-RoadNet: Road Extraction Model with Depth-Wise Separable Convolution and Self-Attention
Authors: Yin Anchao , Ren Chao , Yan Zhiheng , Xue Xiaoqin , Zhou Ying , Liu Yuanyuan , Lu Jiakai , Ding Cong
Published: September 2023

In order to effectively utilize acquired remote sensing imagery and improve the completeness of information extraction, we propose a new road extraction model called C2S-RoadNet. C2S-RoadNet was designed to enhance the feature extraction capability by combining depth-wise separable convolution with lightweight asymmetric self-attention based on encoder and decoder structures. C2S-RoadNet is able to establish long-distance dependencies and fully utilize global information, and it better extracts road information. Based on the lightweight asymmetric self-attention network, a multi-scale adaptive weight module was designed to aggregate information at different scales. The use of adaptive weights can fully harness features at different scales to improve the model’s extraction performance. The strengthening of backbone information plays an important role in the extraction of road main branch information, which can effectively improve the integrity of road information. Compared with existing deep learning algorithms based on encoder–decoder, experimental results on various public road datasets show that the C2S-RoadNet model can produce more complete road extraction, especially when faced with scenarios involving occluded roads or complex lighting conditions. On the Massachusetts road dataset, the PA, F1 score, and IoU reached 98%, 77%, and 72%, respectively. Furthermore, on the DeepGlobe dataset, the PA, F1 score, and IoU reached 98%, 78%, and 64%, respectively. The objective performance evaluation indicators also significantly improved on the LSRV dataset, and the PA, F1 score, and IoU reached 96%, 82%, and 71%, respectively.
MCSPF-Net: A Precipitation Forecasting Method Using Multi-Channel Cloud Observations of FY-4A Satellite by 3D Convolution Neural Network
Authors: Jiang Yuhang , Gao Feng , Zhang Shaoqing , Cheng Wei , Liu Chang , Wang Shudong
Published: September 2023

Accurate precipitation forecasting plays an important role in disaster prevention and mitigation. Currently, precipitation forecasting mainly depends on numerical weather prediction and radar observation. However, ground-based radar observation has limited coverage and is easily influenced by the environment, resulting in the limited coverage of precipitation forecasts. The infrared observations of geosynchronous earth orbit (GEO) satellites have been widely used in precipitation estimation due to their extensive coverage, continuous monitoring, and independence from environmental influences. In this study, we propose a multi-channel satellite precipitation forecasting network (MCSPF-Net) based on 3D convolutional neural networks. The network uses real-time multi-channel satellite observations as input to forecast precipitation for the future 4 h (30-min intervals), utilizing the observation characteristics of GEO satellites for wide coverage precipitation forecasting. The experimental results showed that the precipitation forecasting results of MCSPF-Net have a high correlation with the Global Precipitation Measurement product. When evaluated using rain gauges, the forecasting results of MCSPF-Net exhibited higher critical success index (0.25 vs. 0.21) and correlation coefficients (0.33 vs. 0.23) and a lower mean square error (0.36 vs. 0.93) compared to the numerical weather prediction model. Therefore, the multi-channel satellite observation-driven MCSPF-Net proves to be an effective approach for predicting near future precipitation.
A Film Electrode upon Nanoarchitectonics of Bacterial Cellulose and Conductive Fabric for Forehead Electroencephalogram Measurement
Authors: Gao Kunpeng , Wu Nailong , Ji Bowen , Liu Jingquan
Published: September 2023

In this paper, we present a soft and moisturizing film electrode based on bacterial cellulose and Ag/AgCl conductive cloth as a potential replacement for gel electrode patches in electroencephalogram (EEG) recording. The electrode materials are entirely flexible, and the bacterial cellulose membrane facilitates convenient adherence to the skin. EEG signals are transmitted from the skin to the bacterial cellulose first and then transferred to the Ag/AgCl conductive cloth connected to the amplifier. The water in the bacterial cellulose moisturizes the skin continuously, reducing the contact impedance to less than 10 kΩ, which is lower than commercial gel electrode patches. The contact impedance and equivalent circuits indicate that the bacterial cellulose electrode effectively reduces skin impedance. Moreover, the bacterial cellulose electrode exhibits lower noise than the gel electrode patch. The bacterial cellulose electrode has demonstrated success in collecting α rhythms. When recording EEG signals, the bacterial cellulose electrode and gel electrode have an average coherence of 0.86, indicating that they have similar performance across different EEG bands. Compared with current mainstream conductive rubber dry electrodes, gel electrodes, and conductive cloth electrodes, the bacterial cellulose electrode has obvious advantages in terms of contact impedance. The bacterial cellulose electrode does not cause skin discomfort after long-term recording, making it more suitable for applications with strict requirements for skin affinity than gel electrode patches.
FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning
Authors: Min Yongzhi , Wang Ziwei , Liu Yang , Wang Zheng
Published: September 2023

As an important component of the railway system, the surface damage that occurs on the rails due to daily operations can pose significant safety hazards. This paper proposes a simple yet effective rail surface defect detection model, FS-RSDD, for rail surface condition monitoring, which also aims to address the issue of insufficient defect samples faced by previous detection models. The model utilizes a pre-trained model to extract deep features of both normal rail samples and defect samples. Subsequently, an unsupervised learning method is employed to learn feature distributions and obtain a feature prototype memory bank. Using prototype learning techniques, FS-RSDD estimates the probability of a test sample belonging to a defect at each pixel based on the prototype memory bank. This approach overcomes the limitations of deep learning algorithms based on supervised learning techniques, which often suffer from insufficient training samples and low credibility in validation. FS-RSDD achieves high accuracy in defect detection and localization with only a small number of defect samples used for training. Surpassing benchmarked few-shot industrial defect detection algorithms, FS-RSDD achieves an ROC of 95.2% and 99.1% on RSDDS Type-I and Type-II rail defect data, respectively, and is on par with state-of-the-art unsupervised anomaly detection algorithms.
Strategy for Implementation of Seaworthiness of Large Pelagic Purse Seine at Nizam Zachman Ocean Fishing Port
Authors: Alfianto Nur , Suherman Agus , Suryanti Suryanti , Hernuryadin Yayan
Published: September 2023

As an ocean fishing port in practice, the management of Nizam Zachman has taken action to regulate fishing vessels and large pelagic purse seine fishing gear (a form of control on the application of fishing vessel seaworthiness) in an effort to ensure compliance with provisions on safety practices and the seaworthiness of fishing activities and to realize sustainable capture fishery management. This study aimed to examine and determine the priority strategy of PPS Nizam Zachman in implementing the seaworthiness of large pelagic purse seine vessels. The analytical method used is descriptive statistics using SWOT and AHP analysis. The results showed that the SWOT analysis based on the internal and external factors in the IFAS and EFAS matrices obtained factor values of 0.36 and −0.1, which were located in quadrant IV or the S-T strategy. By strengthening the role of PPS Nizam Zachman in complying with the rules for large pelagic purse seine fishing owners, with good supervision, the sustainability of fishery resources can be supported. The results of the AHP analysis of prioritizing the four implementation aspects of the fishing vessel seaworthiness policy are as follows: communication (0.346) by holding regular outreach; disposition (0.242) by systematically assessing the seaworthiness and need for evaluation; resource (0.234) by increasing the number and capacity of fishing vessel seaworthiness inspectors; and bureaucracy (0.177) by applying the rules of supervision with prevention via assistance to large pelagic purse seine owners.
Antecedents of Real Estate Investment Intention among Filipino Millennials and Gen Z: An Extended Theory of Planned Behavior
Authors: Gumasing Ma. Janice J. , Niro Renée Hannah A.
Published: September 2023

The Philippines’ real estate (RE) market vis à vis its government’s collective efforts to accelerate its digital transformation faces prevalent illegal RE practices online on top of limited publicly accessible data for decision-making and decentralized and highly regional RE markets. As the middle-income millennials’ and Gen Zs’ purchasing power rises, these increasingly important consumer groups might fall prey to online RE colorums or make bad RE investment decisions. In anticipation of big data, ML, and AI becoming integral to the Philippine RE industry, this study extends the theory of the planned behavior model to account for RE investment and illegal practice and to build a theoretical basis for foundational models. A total of 400 Filipinos aged 15 to 42 from different regions in the country responded to a self-administered online survey questionnaire. The model was assessed using partial least squares structural equation modeling (PLS-SEM) and was proven to be sufficient to explain the proposed model. Results from the partial least squares structural equation modeling (PLS-SEM) revealed that during inflation, risk tolerance (RT), perceived property value (PPV), and aversion from illegal practice (IP) significantly influence millennials’ and Gen Zs’ intention to invest in the RE market, implying that they would consider prioritizing profitability at the expense of sustainability. Thus, a collective commitment to provide transparent and real-time data on RE KPIs and projects is necessary for safer and optimized operations while ensuring the sustainability of current and future projects.
Hands-on Learning: Assessing the Impact of a Mobile Robot Platform in Engineering Learning Environments
Authors: Suarez Adrian , García-Costa Daniel , Perez Joaquin , López-Iñesta Emilia , Grimaldo Francisco , Torres Jose
Published: September 2023

As the world continues to change and evolve, students must acquire a diverse set of competencies and skills that focus on sustainability. This term extends beyond environmental matters, encompassing educational aspects, such as critical thinking, communication, creativity, collaboration, and problem solving, all of which are crucial components. In order to promote these aspects in an engineering learning environment, using educational tools that emulate real-life tasks related to students’ future careers can significantly boost their motivation. It could be worth considering the integration of teaching techniques that align more closely with the professional work of engineering. By embracing this pedagogical approach, educators can empower students, contributing to the advancement of science and technology. The field of programming embedded or integrated systems presents numerous professional opportunities for students of Telecommunications and Electronics Engineering degrees. An embedded systems engineer is a specialized professional responsible for co-designing electronic devices based on a processor. This contribution analyzes the impact of introducing a mobile robot platform as a cutting-edge teaching approach that merges problem-based learning (PBL) with hands-on learning. The platform’s main features include robustness in reducing interconnection problems and the possibility of co-designing projects with multiple integrated sensors and actuators. This learning tool makes it possible for students to work with a professional embedded system that they can find in their future careers. Hence, assessing the impact of this learning strategy using the robot and how students perceive it to enhance their professional skills is fundamental. This evaluation compares students’ experiences in previous subjects with the learning approach proposed in this research that intends to support students to prepare them more effectively for transitioning to professional life. The evaluation involves a previous and post-questionnaire that examines three dimensions: energy, absorption, and dedication. Based on the findings, it can be concluded that the general satisfaction item showed the highest growth rate (1.05 out of 5.00) and the best score in the post-questionnaire. This indicates that, overall, the students evaluated the impact of using the learning strategy described positively. After completing the learning experience, the dimension of dedication showed the highest increase (0.73 out of 5.00) among all three dimensions.
Hyperspectral Estimation Model of Organic Matter Content in Farmland Soil in the Arid Zone
Authors: Subi Xayida , Eziz Mamattursun , Zhong Qing
Published: September 2023

Soil organic matter (SOM) is one of the most important indicators of soil quality. Hyperspectral remote sensing technology has been recognized as an effective method to rapidly estimate SOM content. In this study, 173 samples (0–20 cm) were collected from farmland soils in the northwestern arid zones of China. Partial least squares regression (PLSR), support vector machine regression (SVMR), and random forests regression (RFR), based on 15 types of mathematical transformations of the original spectral data of soil, were applied for identifying the optimal estimation method. Distribution of SOM content was mapped using both ground-measured values and predicted values estimated based on the optimum models. Obtained results indicated that the important spectral wavebands with the highest correlation were identified as 421 nm, 441 nm, 1014 nm, 1045 nm, and 2351 nm for SOM in the soil. Spectral transformations had obvious effects on the spectral characteristics of SOM. The optimal estimation was obtained when RFR was combined with the reciprocal logarithmic first-order differential (RLFD) (R2 = 0.884, RMSE = 2.817%, MAE = 2.222) for SOM contents. Finally, the RFR-RLFD method had much better performance compared with the PLSR and SVMR models. Results of this study can provide an alternative to the application of the hyperspectral estimation of SOM in farmland soils in arid zones.
Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis
Authors: Alsaeedi Ali , Al-juboori Ali , Al-Mahmood Haider , Hadi Suha , Mohammed Husam , Aziz Mohammad , Aljibawi Mayas , Nuiaa Riyadh
Published: September 2023

Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially in the early stages of development. When working with tree leaves to identify the type of disease, diseases often show up through changes in leaf color. Therefore, it is crucial to improve the color brightness before using them in intelligent agricultural systems. Color improvement should achieve a balance where no new colors appear, as this could interfere with accurate identification and diagnosis of the disease. This is considered one of the challenges in this field. This work proposes an effective model for olive disease diagnosis, consisting of five modules: image enhancement, feature extraction, clustering, and deep neural network. In image enhancement, noise reduction, balanced colors, and CLAHE are applied to LAB color space channels to improve image quality and visual stimulus. In feature extraction, raw images of olive leaves are processed through triple convolutional layers, max pooling operations, and flattening in the CNN convolutional phase. The classification process starts by dividing the data into clusters based on density, followed by the use of a deep neural network. The proposed model was tested on over 3200 olive leaf images and compared with two deep learning algorithms (VGG16 and Alexnet). The results of accuracy and loss rate show that the proposed model achieves (98%, 0.193), while VGG16 and Alexnet reach (96%, 0.432) and (95%, 1.74), respectively. The proposed model demonstrates a robust and effective approach for olive disease diagnosis that combines image enhancement techniques and deep learning-based classification to achieve accurate and reliable results.
Supply Chain Green Manufacturing and Green Marketing Strategies under Network Externality
Authors: He Binbin , Cai Haiya , Ji Yingchen , Zhu Siyu
Published: September 2023

This paper discusses the impact of network externalities on the green strategies of enterprises at each node of the supply chain. The existing related research mainly addresses the influence of factors such as government regulation, consumer behavior characteristics, and node enterprise behavior on green supply chain decision making. While these studies provide excellent ideas, the impact of network externalities on both green manufacturing and green marketing strategies is often disregarded. This paper uses evolutionary game theory to construct a utility function based on network externalities and consumer green preferences. The Stackelberg game is used to analyze the revenue function of supply chain members under different strategies, showing that under different strategies, network externalities and consumer green preferences have different effects on revenue in the supply chain. To understand the influence of network externalities on green supply chain strategy choices, the evolutionary game model is used. This model allows analyzing the evolutionary stable strategies of manufacturers’ green manufacturing and retailers’ green marketing. The mechanism underlying the influence of network externalities and consumer green preferences on green supply chain decision making is demonstrated. This helps to explain the green strategy decisions of upstream and downstream enterprises in the supply chain.
How Does the Living Street Environment in the Old Urban Districts Affect Walking Behavior? A General Multi-Factor Framework
Authors: Dong Jingyi , Zhang Jun , Yang Xudong
Published: September 2023

To strike a trade-off between walking behavior and street resource constraint, extensive research tends to focus on how the urban environment affects walking behavior. However, most of the existing impact measurements focus on the cities in low-latitude temperate environments, which may not truly reflect the situation when assessing high-latitude cities. To address this drawback, in this paper, a general multi-factor framework is introduced to quantify the influence of street-level environmental factors on walking behavior. Specifically, a framework is constructed by comprehensively considering the subjective data and the objective data of Harbin, China, which is mainly composed of multivariate measurement indicators, a multi-source data analysis library, and four-dimensional evaluation paradigm. The results indicate that two main measures can promote the current situation of human-oriented living street environment planning, namely, increasing the distribution of green facilities and life service facilities in the old urban districts living street, and paying attention to the diversity of street greening and street landscape. The proposed framework is conducive to improve the planning status of human-centered street environments and guide the construction of pedestrian-friendly life and healthy streets.
Numerical Study on the Influence of Fault Structure on the Geostress Field
Authors: Zhu Lei , Huang Tao , Li Haifeng , Zhou Zelin , Liao Xin
Published: September 2023

A geostress field continuously evolves with long-term tectonic activity. A fault, as a general product of tectonic movements, has a great influence on the geostress field in the vicinity. To analyze the geostress field characteristics and influencing factors near the fault fracture zone in high-altitude areas, this study takes the Dianda-Piru fault on the Qinghai–Tibet Plateau as its research object. Based on the geological conditions and in situ stress measurement data in the study area, a refined numerical model was established using numerical simulation to invert the geostress field in the vicinity of the fault fracture zone, and a quantitative analysis of the factors influencing the geostress distribution was carried out. The results show that the overall relationship between large horizontal principal stress σH, vertical stress σv, and small horizontal principal stress σh is σH > σv > σh, and the surrounding rock stress is dominated by horizontal stress. Geostress is released within the fault fracture zone to a certain extent, and there is a certain degree of stress concentration within the intact rock mass on the upper plate of the fault. The elastic modulus has a greater influence on the geostress field near the fracture structure area than Poisson’s ratio, and the range of the stress-weakening zone increases with the decrease in the elastic modulus. The maximum principal stress inside the fault increases with the increase in the angle between the fault strike and regional principal stress, while the deflection angle of the surrounding principal stress direction decreases with the increase in this angle. The study of the distribution law of geostress fields with developed fracture structures can provide theoretical guidance for the sustainable development of engineering construction in tectonically active areas.
Spatial Morphological Characteristics and Evolution of Policy-Oriented Urban Agglomerations—Take the Yangtze River Middle Reaches Urban Agglomeration as an Example
Authors: Fan Zaiyu , Zhong Zhen
Published: September 2023

The Yangtze River Middle Reaches urban agglomeration (YRMRUA) is a typical representation of policy-oriented urban agglomeration in China. In this study, we analyze the morphological characteristics of the built-up area of the YRMRUA, which is the research object, and we try to clarify changes in its development and the supporting role of policies. First, we used satellite image data provided by Google Earth Engine for supervised learning to obtain images of the built-up area land needed by the research. Then, we adopted radius dimension, spatial expansion intensity, and standard deviation ellipse successively to explore the spatial morphological characteristics of the YRMRUA. The following conclusions are drawn: (1) The built-up area of the YRMRUA with significant fractal characteristics has formed two parts, with diffusion-type fractals forming within its boundary and cohesion-type fractals forming outside its boundary. In addition, the fractal dimension has exhibited a gradual decline as time has passed. (2) The YRMRUA has gradually stabilized at a low rate of expansion of 0.2% per year. From the perspective of the grid, the spatial expansion intensity presented breakthrough and expansion in the second and third stages, respectively. The hot spots of space expansion with 95% confidence gradually changed from a point-like shape to a ribbon-like shape. (3) The expansion pattern of the YRMRUA showed a tendency of “northwest–southeast”, and gradually moved towards equilibrium. (4) A policy impetus has limitations, and the stages of space shaping has resulted in spatial solidification and differentiation. In response, we put forward suggestions for the objects and ways of policy functions, in order to provide references for the development of policy-oriented urban agglomerations.
A Transient Seepage–Thermal Stability Model for Cohesionless Soil Slopes in the Semi-Arid Continental Climate of the Canadian Prairies
Authors: Akram Ilyas , Azam Shahid
Published: September 2023

Seasonal weather variations have a profound effect on the integrity of cohesionless soil slopes in the Canadian Prairies owing to the prevalent semi-arid continental climate. The primary contribution of this research is the development of a transient and two-dimensional stability model that is fully coupled with hydraulic and thermal flows. The model was used to predict the factor of safety (FS) for two slope geometries (18 m height and 26 m height) subjected to three weather scenarios (mean, extreme wet, and extreme dry) and four ponding combinations on the upstream and downstream sides of the slopes under no-load and train-load conditions. Results indicated that for mean climate conditions, FS trends fluctuate till April, followed by an increase that remains constant up to November and decreases thereafter. Generally, the FS shows subdued fluctuations and higher values for the high slope compared with the low slope. For wet climate conditions, the FS patterns are similar to mean conditions, albeit with reduced durations of stability during summer. For dry climate conditions, FS values are higher than mean conditions and nearly constant for most of the year. For no ponding and downstream ponding, FS > 1 throughout the year. In contrast, FS > 1 only during the summer (with reduced time in wet climate and extended time in dry climate conditions) for upstream ponding and upstream–downstream ponding. For train loading, FS shows subdued fluctuations and lower values than the corresponding no-loading scenarios for both slopes. The effects of climate conditions and ponding scenarios are further reduced for the high slope. These findings are useful for decision making with regard to the initial design and ongoing performance of natural slopes and embankments in the cohesionless soils of the region.
Is Regulation Protection? Forest Logging Quota Impact on Forest Carbon Sinks in China
Authors: Zhang Ziqiang , He Jie , Huang Ming , Zhou Wei
Published: September 2023

As the central part of terrestrial ecosystems, forests have an irreplaceable role in regulating climate, prompting various efforts to protect them. Logging regulation is the most commonly used forest conservation strategy. Although the logging permit scheme was written into the Forest Law in China, its effect on forest carbon sequestration has rarely been subject to careful empirical scrutiny. In this paper, we develop and estimate a spatial econometric model to disentangle its potential effects on forest carbon sinks based on a panel dataset of 29 provinces from 1989 to 2018 in China. Our calculations show that China’s forest carbon sinks are still growing and are connected geographically, with a tendency towards “high-high” and “low-low” aggregation. Increasing the logging quota produced a spatial spillover effect that might encourage the formation of forest carbon sinks in nearby areas. It considerably encouraged the expansion of forest carbon sinks. Additional mechanism testing is consistent with the claim that rising logging quotas have significantly boosted the proportion of timber forests in afforestation but had no effect on the movement of rural labor to urban areas. The development of forest carbon sinks is impacted in different ways by various logging quota types, with an increasing tendency for logging quotas to have a more significant contribution. Additionally, the non-collective forest region has a more significant spatial spillover effect of the logging quota on forest carbon sinks. The logging quota scheme should be improved by policymakers, beginning with eliminating tending quotas in the southern collective forest region. After that, the logging quota would gradually be eliminated nationwide, notably for commercial forests.
Too Defeated to Work? The Effects of Dispatched Employees’ Upward Social Comparison on Time Theft
Authors: Li Yi , Lin Yunhan
Published: September 2023

Organizations are under pressure to seek out new and sustainable practices to address the deepening inequity and waste of limited resources. This study aims to explore the relationship between upward social comparison (USC) and time theft of dispatched employees by proposing a mediated moderation model and examining (1) whether emotional exhaustion mediates this relationship and (2) the roles of relational energy and psychological availability as different kinds of resources on the basis of conservation of resource (COR) theory. Data were collected from a questionnaire survey completed by 978 dispatched employees from a Chinese labor dispatch enterprise. SPSS Macro Process and MPLUS were used to test the hypotheses of the proposed mediated moderation model. Results showed that (1) emotional exhaustion partially mediates the positive relationship between USC and time theft; (2) both relational energy and psychological availability negatively moderate the positive relationship between USC and emotional exhaustion and further influence time theft; (3) relational energy is positively related to psychological availability; (4) psychological availability fully mediates the moderating effect of relational energy. This study not only contributes to the existing literature on social comparison and COR theory but also provides insights into how to mitigate employee time theft, thereby fostering the sustainable development of both employees and organizations. The implications, limitations, and future directions were discussed.
The Influence of Firms’ Pragmatic Legitimacy on Investors’ Perceptions of Their Environmental Protection Activities
Authors: Fujikura Keigo , Oe Akitsu
Published: September 2023

This study demonstrates the mechanisms by which the pragmatic characteristics of legitimacy influence the promotion of corporate social responsibility activities, focusing on firms and investors. We hypothesize that the more aggressive a firm is in its environmental protection efforts, the higher the reduction in investment risk from its environmental performance. Multiple regression analysis was performed for Japanese chemical-related industries from 2017 to 2019. The results revealed that firms that balance environmental performance with business profits should invest in environmental protection activities and improve their environmental performance without touting profitability. The findings reveal the need to incorporate a profitability perspective when considering the relationship between environmental protection activities and investor perceptions.
Multifaceted Pathways of Carbon Emission Reduction Policies: A Study Based on the Decoupling Effect and fsQCA Method in the Yangtze River Delta Urban Agglomeration
Authors: Xu Qianqi , Xu Peng , Bao Cunkuan
Published: September 2023

The urban carbon administration discussion is becoming dominant in the carbon emission reduction landscape. China, the world’s paramount carbon emitter, has instituted a series of policies geared towards carbon emissions reduction. The co-dependent nature of these policies becomes paramount in outlining accurate and comprehensive evaluation results. This research scrutinizes the effect of various policy mixtures on curbing urban carbon emissions, utilizing the Tapio decoupling elasticity coefficient and fuzzy set qualitative comparative analysis (fsQCA) techniques. The consistencies surpass the widely accepted theoretical threshold of 0.8, and each configuration can explain 14.3% to 19.8% of the sample. The findings reveal that industrial and energy policies drive the decoupling of urban carbon emissions in three of four configurations. Management-based policies merely play an auxiliary role, while social and economic policies have yet to exhibit significant implications. The outcomes of this research offer a framework for augmenting the carbon emission reduction policy system and fostering the advancement of carbon reduction initiatives.
Study on Urban Spatial Expansion and Its Scale Benefit in the Yellow River Basin
Authors: Zhang Zhongwu , Liu Liping , Zhang Jinyuan
Published: September 2023

Based on nighttime light data from 2000, 2005, 2010, 2015 and 2020, the urban built-up area of 90 prefecture-level cities in the Yellow River Basin was extracted, and the urban spatial expansion characteristics of 90 prefecture-level cities were explored from the two elements of expansion speed and expansion intensity. The entropy method was used to calculate the comprehensive level of economic-, social- and ecological-scale benefits of 90 prefecture-level cities. The gray correlation degree was used to measure the correlation between urban spatial expansion and scale benefits, and the geographical detector was used to identify the driving factors of urban spatial expansion. The results show the following: (1) The urban spatial expansion characteristics of 90 prefecture-level cities in the Yellow River Basin are clearly different. From 2000 to 2020, the urban spatial area increased to 2.94 times the original, and the expansion speed and intensity increased but fluctuated significantly. (2) The comprehensive level of economic-, social- and ecological-scale benefits of 90 prefecture-level cities in the Yellow River Basin has gradually improved. (3) There is a high correlation between urban spatial expansion and economic-, social- and ecological-scale benefits in 90 prefecture-level cities in the Yellow River Basin, but there are obvious regional differences in the eastern, central and western prefecture-level cities. (4) The urban spatial expansion of the Yellow River Basin is the result of the joint action of natural factors, the economic development level, industrial structure, government regulation ability, population size and opening level. Among them, per capita GDP, population density and the proportion of secondary and tertiary industry output value to the total output value are the most important driving factors.
Sustainable Energy Progress via Integration of Thermal Energy Storage and Other Performance Enhancement Strategies in FPCs: A Synergistic Review
Authors: Pathak Sudhir Kumar , Tazmeen Tagamud , Chopra K. , Tyagi V. V. , Anand Sanjeev , Abdulateef Ammar M. , Pandey A. K.
Published: September 2023

Flat plate collectors (FPCs) are the leading solar thermal technology for low-medium range temperature applications. However, their expansion in developing countries is still lacking because of their poor thermal performance. Improving the thermal performance of flat plate collectors (FPCs) is a crucial concern addressed in this review This study comprehensively discussed the performance improvement methods of FPCs, such as design modification, reflectors, working fluid, and energy storage materials, by covering current issues and future recommendations. Design factors such as coating and glass cover thickness, thickness of absorber plate and material, air gap between the glass cover and absorber plate, and riser spacing, along with insulation materials, are examined for their impact on FPC performance. Absorber design changes with selective coatings for improving the heat transmission rate between the working fluid and absorber are critical for enhancing collectors’ thermal output. The nanofluids utilization improved FPC’s thermal performance in terms of energetic and exergetic outcomes in the 20–30% range. Moreover, adding a heat storage unit extends the operating hours and thermal output fluctuations of FPCs. Research suggests that employing turbulators and nanofluids as heat transfer fluids are particularly effective for enhancing heat transfer in FPCs. This comprehensive review serves as a critical tool for evaluating and comparing various heat transfer augmentation techniques, aiding in the selection of the most suitable option.
Earthquake Consideration in Architectural Design: Guidelines for Architects
Authors: Shareef Sardar S.
Published: September 2023

Architectural planners must give due consideration to seismic events as they present substantial hazards to both critical infrastructure and human well-being. This research investigates the fundamental concepts and methodologies employed by architects to enhance seismic resilience in buildings and ensure the safety of occupants. It emphasizes the importance of seismic hazard assessment, design standards, structural systems, and cutting-edge technology in reducing earthquake-related dangers. A mixed method has been adopted: surveying the literature, applying inductive reasoning, and conducting a case study. This research highlights the value of interdisciplinary cooperation between structural engineers, geotechnical experts, and architects to design resilient built environments that can survive the pressures unleashed by seismic occurrences. The findings demonstrated that architectural design solutions and approaches might significantly impact earthquake risk reduction techniques in seismic and non-seismic locations. In the cases taken, different techniques—in some cases multiple techniques—had been applied based on the buildings’ geographical locations, sizes, and shapes. Finally, we prepared a checklist for these strategies, including mass distribution, openings, rooftop structures, and other considerations to be applied by architects to make the solutions easier.
Environmental Connection, Awareness, and Behaviors in University Students: An Exploratory Portuguese Study
Authors: Torres Ana , Carvalho Paula , Costa Jorge , Silva Claudia , Afonso Rosa Marina , Nascimento Carla , Loureiro Manuel
Published: September 2023

Addressing the Sustainable Development Goals (SDGs) within universities is an urgent necessity. Understanding and measuring the environmental behaviors of university students is an essential initial step in this process. This study had two primary objectives: to translate and adapt the General Ecological Behavior (GEB) Scale for European Portuguese and to provide an exploratory analysis of environmental connection, awareness, and behaviors within a Portuguese university student sample. A convenience sample of 112 university students, aged 18–65, was surveyed using a paper-based, self-report questionnaire. This instrument included sections on sociodemographics, environmental concerns, preferences, and self-report measures (GEB, GAD-7, and PHQ-9). We present the translation and adaptation procedure for the GEB, alongside preliminary findings that suggest acceptable reliability, factorial validity, and discriminant validity. Moreover, exploratory data on environmental connection, awareness, and behaviors are discussed. This study provides a foundational measure for future research into pro-environmental behaviors in the Portuguese context and contributes preliminary insights into the environmental attitudes and behaviors of Portuguese university students. We urge future research to focus on the SDGs, examining factors, measures, and strategies to enhance pro-environmental behaviors.
Extracting Evaluation Factors of Social Resilience in Water Resource Protection Areas Using the Fuzzy Delphi Method
Authors: Huang Ming-Feng , Peng Li-Pei
Published: September 2023

Development in water resource protection areas frequently grapples with balancing environmental sustainability and local economic growth. Consequently, a nuanced assessment of social resilience becomes imperative. This research presented a case study of the Water Resource Protection Area in Taipei, gauging its social resilience across five dimensions: “social support function”, “inclusive governance”, “economic allocation”, “built environment”, and “resources for sustainability”. From these, 49 influential factors that could impact the social resilience of the water resource protection area were discerned. Through the engagement of 21 experts via questionnaires and subsequent analysis using the Fuzzy Delphi method, this study identified 23 core factors. Notably, influential factors pertaining to the “social support function” significantly impacted the water resource protection area. The findings aim to streamline the evaluation of social resilience in water resource protection areas, potentially guiding future research utilizing the multi-criteria decision-making (MCDM) method and steering industrial developments in these areas.
On Classes of Meromorphic Functions Defined by Subordination and Convolution
Authors: Cotîrlă Luminiţa-Ioana , Totoi Elisabeta-Alina
Published: September 2023

For p∈N*, let Σp denote the class of meromorphic p-valent functions. We consider an operator for meromorphic functions denoted by Tbn, which generalizes some previously studied operators. We introduce some new subclasses of the class Σp, associated with subordination using the above operator, and we prove that these classes are preserved regarding the operator Jp,γ, so we have symmetry when we look at the form of the class in which we consider the function g and at the form of the class of the image Jp,γ(g), where Jp,γ(g)(z)=γ−pzγ∫0zg(t)tγ−1dt, γ∈C with Reγ>p.
Genomic Epidemiology and Lineage Dynamics of SARS-CoV-2 in Bulgaria: Insights from a Three-Year Pandemic Analysis
Authors: Giovanetti Marta , Cella Eleonora , Ivanov Ivan , Grigorova Lyubomira , Stoikov Ivan , Donchev Deyan , Dimitrova Reneta , Slavov Svetoslav Nanev , Mavian Carla , Fonseca Vagner , Scarpa Fabio , Borsetti Alessandra , Korsun Neli , Trifonova Ivelina , Dobrinov Veselin , Kantardjiev Todor , Christova Iva , Ciccozzi Massimo , Alexiev Ivailo
Published: September 2023

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has brought about significant challenges worldwide. In this study, we present a comprehensive analysis of the genomic epidemiology and lineage dynamics of SARS-CoV-2 in Bulgaria over a three-year period. Through extensive genomic sequencing and data analysis, we investigated the evolution of the virus, the emergence of variants of concern (VOCs), and their impact on the country’s pandemic trajectory. We also assessed the relationship between viral diversity and COVID-19 morbidity and mortality in Bulgaria. Our findings shed light on the temporal and spatial distribution of SARS-CoV-2 lineages and provide crucial insights into the dynamics of the pandemic in the country. The interplay between international travel and viral transmission plays a significant role in the emergence and dissemination of different SARS-CoV-2 variants. The observed proportions of exportation to various continents provide insights into the potential pathways through which these lineages spread globally. Understanding the genomic epidemiology of SARS-CoV-2 in Bulgaria is essential for formulating targeted public health strategies, enhancing vaccination efforts, and effectively managing future outbreaks.