bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Different Deep Eutectic Solvents and Plant Extracts on Antioxidant, Mechanical, and Color Properties of Alginate Film
Authors: Kowalonek Jolanta , Hamieau Malo , Szydłowska-Czerniak Aleksandra
Published: July 2024

Eco-friendly functional alginate films with plant extracts (chokeberry pomace (ChP) or lemon balm (LB) herb) were obtained. Moreover, deep eutectic solvents (DESs) based on choline chloride, glucose, and betaine were used to acquire the active substances from plant materials. The films were tested regarding the antioxidant, mechanical, and color properties. The results revealed that the films’ antioxidant capacities (AC) depended on the extract type and DES used, namely AC values for alginate films with LB were higher than those with ChP. Moreover, the results of the films’ mechanical properties depended only on the DES, which acted as a plasticizer in most cases. Furthermore, the color analysis of the studied films showed a dependence on the type of extract and DES. The lightness (L*) was influenced only by the DES type, while the solvent and extract type affected the a* and b* values. Our results show that the films can be applied as active packaging for food products.
Stability of Conducting Polymer-Coated Carbon Microfibers for Long-Term Electrical Stimulation of Injured Neural Tissue
Authors: Vara Hugo , Hernández-Labrado Gabriel Raúl , Alves-Sampaio Alexandra , Collazos-Castro Jorge E.
Published: July 2024

Electroactive microfiber-based scaffolds aid neural tissue repair. Carbon microfibers (CMFs) coated with the conducting polymer poly(3,4-ethylenedioxythiophene) doped with poly[(4-styrenesulfonic acid)-co-(maleic acid)] (PEDOT:PSS-co-MA) provide efficient support and guidance to regrowing axons across spinal cord lesions in rodents and pigs. We investigated the electrical and structural performance of PEDOT:PSS-co-MA-coated carbon MFs (PCMFs) for long-term, biphasic electrical stimulation (ES). Chronopotentiometry and electrochemical impedance spectroscopy (EIS) allowed the characterization of charge transfer in PCMFs during ES in vitro, and morphological changes were assessed by scanning electron microscopy (SEM). PCMFs that were 4 mm long withstood two-million-biphasic pulses without reaching cytotoxic voltages, with a 6 mm length producing optimal results. Although EIS and SEM unveiled some polymer deterioration in the 6 mm PCMFs, no significant changes in voltage excursions appeared. For the preliminary testing of the electrical performance of PCMFs in vivo, we used 12 mm long, 20-microfiber assemblies interconnected by metallic microwires. PCMFs-assemblies were implanted in two spinal cord-injured pigs and submitted to ES for 10 days. A cobalt–alloy interconnected assembly showed safe voltages for about 1.5 million-pulses and was electrically functional at 1-month post-implantation, suggesting its suitability for sub-chronic ES, as likely required for spinal cord repair. However, improving polymer adhesion to the carbon substrate is still needed to use PCMFs for prolonged ES.
Mercury Discharge Inventory Based on Sewage Treatment Process in China
Authors: Wei Chenglong , Guo Jiaxu , Fan Rongyang , Zhang Tingting , Wang Xianbin , Chen Hao , Huang Song , Hu Yufei , Zhang Gang
Published: July 2024

Mercury pollution is a serious public health problem. China’s extensive use and reliance on mercury has led to water pollution, particularly the presence of methylmercury in water. Estimating total mercury emissions from wastewater in China is challenging due to the large amount and wide range of emissions. An estimation model for total mercury content in sewage in China was established by establishing a relationship between sewage treatment volume, mercury content in effluent after sewage treatment, and the data of sludge production and mercury content in the sewage treatment plant. It was determined that only 3% of mercury entered the air during sewage treatment, 27.5% of mercury entered the effluent, and about 69.5% of mercury entered the sludge, based on the treatment of existing wastewater treatment plants in China. From 2002 to 2021, the average annual sewage mercury emission in China was 32.07 Mg, and the emissions were higher in densely populated and economically developed provinces such as Beijing, Shandong, Hebei, and Guangdong. By 2025, China’s mercury emissions are projected to reach 55.41 Mg. By 2035, China’s mercury emissions are projected to reach 49.3 Mg.
Detecting Steam Leakage in Nuclear Power Systems Based on the Improved Background Subtraction Method
Authors: Liu Jie , Huang Yanping , Zhang Minglu , Zhou Suting , Nie Changhua , Li Minggang , Zhang Lin
Published: July 2024

As a key system in nuclear power plants, nuclear power systems contain high-temperature, high-pressure water media. A steam leak, if it occurs, can at minimum cause system functional loss and at worst lead to casualties. Therefore, it is urgent to carry out steam leakage detection work for high-temperature, high-pressure loop systems. Currently, steam leaks are primarily detected through visual monitoring and pressure gauges. However, if there is a minor leak under high system pressure, the slight decrease in pressure may not be enough to alert the operators, leading to a delay in detecting the steam leak. Thus, this detection method has certain drawbacks. In view of these issues, this paper introduces computer vision technology to monitor the high-temperature, high-pressure loop system and proposes the use of an improved background subtraction method to detect steam leaks in the loop system. The results show the following advantages of this method: (1) It can effectively identify steam leaks at an early stage; (2) it overcomes the difficulty of determining the threshold value for the binarization of grayscale images in traditional background subtraction methods; (3) it eliminates the noise impact brought by the binarization of grayscale images in existing improved background subtraction methods. The introduction of this method provides a new approach for detecting steam leaks in high-temperature, high-pressure loop systems and can be effectively applied in engineering fields. It also offers reference value for the detection of high-temperature, high-pressure media leaks in other fields.
Effect of Target Properties on Regolith Production
Authors: Xie Minggang , Li Yan
Published: July 2024

Based on the measurements of regolith thicknesses on the lunar maria (basalts), the lunar regolith was determined to have accumulated at a rate of about 1 m/Gyr since the era of the late heavy bombardment. However, regolith production on porous targets (e.g., crater ejecta deposits) is less studied, especially for Copernican units, and how target properties affect regolith production is not well understood. Here, we measured regolith thicknesses on the ejecta blanket of the Copernicus crater, showing that the regolith production rate sensitively depends on the initial target properties. The regolith production rate of the Copernicus ejecta blanket (3.0 ± 0.1 m/Gyr) is significantly larger than that of the Copernicus impact melt, which was previously estimated to be 1.2 ± 0.2 m/Gyr. Although crater production varies with different targets, our observed crater density of the Copernicus impact melt is indistinguishable from that of the Copernicus ejecta because impacts fracture the melt, causing it to resemble the ejecta. However, due to the fact that the formation of crater ejecta had already caused them to undergo fragmentation, ejecta require fewer fragmentation times to become regolith compared to impact melt; thus, the growth of regolith on the ejecta is faster than the melt. This indicates that similar observed size–frequency distributions do not indicate similar regolith production, especially for the targets with significant differences in initial physical properties.
An Effective Scheme for Modeling and Compensating Differential Age Errors in Real-Time Kinematic Positioning
Authors: Huang Wei , Zhao Zhiqin , Zhu Xiaozhang
Published: July 2024

In many real-time kinematic (RTK) positioning applications, reference observations are transmitted over wireless links that can experience frequent interruptions or substantial delays. This results in large differential ages between base and rover observations, which, in turn, leads to a deterioration in positioning performance. To bridge the significant age difference, in this work, we propose a simple and effective scheme for modeling and compensating for such errors. Firstly, the overall differential age error was modeled using truncated Taylor expansion. Then, a time-differenced carrier phase (TDCP)-based observation model was established to estimate the errors with the Kalman framework. Since estimating the receiver’s clock error is unnecessary, equivalent transformation and sequential filtering technology were adopted to significantly reduce the computational complexity. Furthermore, a predictor performance monitor was introduced to mitigate the integrity risks that may occur due to model mismatches. The effectiveness of this scheme was validated by static and dynamic field experiments. The static experiment results showed that when the differential age was 60 s, the GPS and BDS satellites’ overall root mean square error (RMSE) with the asynchronous RTK (ARTK) prediction method was 2.8 and 5.5 times that of the proposed method, respectively. Moreover, when the differential age was 120 s, these values were 3.3 and 5.4 times that of the proposed method, respectively. The field experiment results showed that when the differential age was 60 s, the integer ambiguity fixed rate and false fixed rate of the ARTK method were 0.90 and 1.63 times that of the proposed method, respectively. Finally, at a 120 s differential age, these values were 0.78 and 4.78 times that of the proposed, respectively.
Impacts of the Middle Route of the South-to-North Water Diversion Project on Land Surface Temperature and Fractional Vegetation Coverage in the Danjiang River Basin
Authors: Wang Shidong , Liu Yuanyuan , Guo Jianhua , Liu Jinping , Chai Huabin
Published: July 2024

The Middle Route of the South-to-North Water Diversion Project is a critical infrastructure that ensures optimal water resource distribution across river basins and safeguards the livelihood of people in China. This study investigated its effects on the land surface temperature (LST) and fractional vegetation coverage (FVC) in the Danjiang River Basin. Moreover, it examined the spatial and temporal patterns of this project, providing a scientific basis for the safe supply of water and ecological preservation. We used the improved interpolation of mean anomaly (IMA) method based on the digital elevation model (DEM) to reconstruct LST while FVC was estimated using the image element dichotomous model. Our findings indicated a general increase in the average LST in the Danjiang River Basin post-project implementation. During both wet and dry seasons, the cooling effect was primarily observed in the south-central region during the daytime, with extreme values of 6.1 °C and 5.9 °C. Conversely, during the nighttime, the cooling effect was more prevalent in the northern region, with extreme values of 3.0 °C and 2.3 °C. In contrast, the warming effect during both seasons was predominantly located in the northern region during the daytime, with extreme values of 5.3 °C and 5.5 °C. At night, the warming effect was chiefly observed in the south-central region, with extreme values of 5.8 °C and 5.9 °C. FVC displayed a seasonal trend, with higher values in the wet season and overall improvement over time. Statistical analysis revealed a negative correlation between vegetation change and daytime temperature variations in both periods (r = −0.184, r = −0.195). Furthermore, a significant positive correlation existed between vegetation change and nighttime temperature changes (r = 0.315, r = 0.328). Overall, the project contributed to regulating LST, fostering FVC development, and enhancing ecological stability in the Danjiang River Basin.
RIRNet: A Direction-Guided Post-Processing Network for Road Information Reasoning
Authors: Zhou Guoyuan , He Changxian , Wang Hao , Xie Qiuchang , Chen Qiong , Hong Liang , Chen Jie
Published: July 2024

Road extraction from high-resolution remote sensing images (HRSIs) is one of the tasks in image analysis. Deep convolutional neural networks have become the primary method for road extraction due to their powerful feature representation capability. However, roads are often obscured by vegetation, buildings, and shadows in HRSIs, resulting in incomplete and discontinuous road extraction results. To address this issue, we propose a lightweight post-processing network called RIRNet in this study, which include an information inference module and a road direction inference task branch. The information inference module can infer spatial information relationships between different rows or columns of feature images from different directions, effectively inferring and repairing road fractures. The road direction inference task branch performs the road direction prediction task, which can constrain and promote the road extraction task, thereby indirectly enhancing the inference ability of the post-processing model and realizing the optimization of the initial road extraction results. Experimental results demonstrate that the RIRNet model can achieve an excellent post-processing effect, which is manifested in the effective repair of broken road segments, as well as the handling of errors such as omission, misclassification, and noise, proving the effectiveness and generalization of the model in post-processing optimization.
Has IMERG_V07 Improved the Precision of Precipitation Retrieval in Mainland China Compared to IMERG_V06?
Authors: Guo Hao , Tian Yunfei , Li Junli , Guo Chunrui , Meng Xiangchen , Wang Wei , De Maeyer Philippe
Published: July 2024

Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) is the primary high spatiotemporal resolution precipitation product of the GPM era. To assess the applicability of the latest released IMERG_V07 in mainland China, this study systematically evaluates the error characteristics of IMERG_V07 from the perspective of different seasons, precipitation intensity, topography, and climate regions on an hourly scale. Ground-based meteorological observations are used as the reference, and the performance improvement of IMERG_V07 relative to IMERG_V06 is verified. Error evaluation is conducted in terms of precipitation amount and precipitation frequency, and an improved error component procedure is utilized to trace the error sources. The results indicate that IMERG_V07 exhibits a smaller RMSE in mainland China, especially with significant improvements in the southeastern region. IMERG_V07 shows better consistency with ground station data. IMERG_V07 shows an overall improvement of approximately 4% in capturing regional average precipitation events compared to IMERG_V06, with the northwest region showing particularly notable enhancement. The error components of IMERG_V06 and IMERG_V07 exhibit similar spatial distributions. IMERG_V07 outperforms V06 in terms of lower Missed bias but slightly underperforms in Hit bias and False bias compared to IMERG_V06. IMERG_V07 shows improved ability in capturing precipitation frequency for different intensities, but challenges remain in capturing heavy precipitation events, missing light precipitation, and winter precipitation events. Both IMERG_V06 and IMERG_V07 exhibit notable topography dependency in terms of Total bias and error components. False bias is the primary error source for both versions, except in winter, where high-altitude regions (DEM > 1200 m) primarily contribute to Missed bias. IMERG_V07 has enhanced the accuracy of precipitation retrieval in high-altitude areas, but there are still limitations in capturing precipitation events. Compared to IMERG_V06, IMERG_V07 demonstrates more concentrated error component values in the four climatic regions, with reduced data dispersion and significant improvement in Missed bias. The algorithm improvements in IMERG_V07 have the most significant impact in arid regions. False bias serves as the primary error source for both satellite-based precipitation estimations in the four climatic regions, with a secondary contribution from Hit bias. The evaluation results of this study offer scientific references for enhancing the algorithm of IMERG products and enhancing users’ understanding of error characteristics and sources in IMERG.
A CNN- and Self-Attention-Based Maize Growth Stage Recognition Method and Platform from UAV Orthophoto Images
Authors: Ni Xindong , Wang Faming , Huang Hao , Wang Ling , Wen Changkai , Chen Du
Published: July 2024

The accurate recognition of maize growth stages is crucial for effective farmland management strategies. In order to overcome the difficulty of quickly obtaining precise information about maize growth stage in complex farmland scenarios, this study proposes a Maize Hybrid Vision Transformer (MaizeHT) that combines a convolutional algorithmic structure with self-attention for maize growth stage recognition. The MaizeHT model utilizes a ResNet34 convolutional neural network to extract image features to self-attention, which are then transformed into sequence vectors (tokens) using Patch Embedding. It simultaneously inserts category information and location information as a token. A Transformer architecture with multi-head self-attention is employed to extract token features and predict maize growth stage categories using a linear layer. In addition, the MaizeHT model is standardized and encapsulated, and a prototype platform for intelligent maize growth stage recognition is developed for deployment on a website. Finally, the performance validation test of MaizeHT was carried out. To be specific, MaizeHT has an accuracy of 97.71% when the input image resolution is 224 × 224 and 98.71% when the input image resolution is 512 × 512 on the self-built dataset, the number of parameters is 15.446 M, and the floating-point operations are 4.148 G. The proposed maize growth stage recognition method could provide computational support for maize farm intelligence.
Digital Mapping and Scenario Prediction of Soil Salinity in Coastal Lands Based on Multi-Source Data Combined with Machine Learning Algorithms
Authors: Zhou Mengge , Li Yonghua
Published: July 2024

Salinization is a major soil degradation process threatening ecosystems and posing a great challenge to sustainable agriculture and food security worldwide. This study aimed to evaluate the potential of state-of-the-art machine learning algorithms in soil salinity (EC1:5) mapping. Further, we predicted the distribution patterns of soil salinity under different future scenarios in the Yellow River Delta. A geodatabase comprising 201 soil samples and 19 conditioning factors (containing data based on remote sensing images such as Landsat, SPOT/VEGETATION PROBA-V, SRTMDEMUTM, Sentinel-1, and Sentinel-2) was used to compare the predictive performance of empirical bayesian kriging regression, random forest, and CatBoost models. The CatBoost model exhibited the highest performance with both training and testing datasets, with an average MAE of 1.86, an average RMSE of 3.11, and an average R2 of 0.59 in the testing datasets. Among explanatory factors, soil Na was the most important for predicting EC1:5, followed by the normalized difference vegetation index and soil organic carbon. Soil EC1:5 predictions suggested that the Yellow River Delta region faces severe salinization, particularly in coastal zones. Among three scenarios with increases in soil organic carbon content (1, 2, and 3 g/kg), the 2 g/kg scenario resulted in the best improvement effect on saline–alkali soils with EC1:5 > 2 ds/m. Our results provide valuable insights for policymakers to improve saline–alkali land quality and plan regional agricultural development.
A Study of Electronic Product Supply Chain Decisions Considering Extended Warranty Services and Manufacturer Misreporting Behavior
Authors: Chen Rui , Luo Zhen , Ren Haiping , Huang Xiaoqing , Xiao Shixiao
Published: July 2024

In the supply chain management of electronic products, asymmetric cost information is a prevalent issue that can lead manufacturer to misreport costs, thereby exacerbating supply chain imbalances. This study focuses on the electronic product supply chain with an extended warranty service, where the manufacturer bears the after-sales responsibility during the extended warranty period. It explores the decision-making (DM) issues within the supply chain under different information environments and power structures. The Stackelberg game theory is employed to solve and analyze these models, and the main findings are as follows: (1) When supply chain information is symmetrical, centralized DM is the best choice. However, in cases where the supply chain adopts decentralized DM, it is more beneficial for the retailer and the supply chain if the retailer assumes the role of DM leader. Additionally, when the retail price sensitivity coefficient is low, the manufacturer will compete with the retailer for DM priority. Conversely, when the retail price sensitivity coefficient is higher, the manufacturer is better off as a follower in DM; (2) When the supply chain information is asymmetric, the manufacturer may engage in misreporting, which benefits the manufacturer but is detrimental to both the supply chain and the retailer. Moreover, if the price sensitivity coefficient is low, the manufacturer should lead the supply chain DM. Otherwise, the retailer should take the lead in supply chain DM. Adopting such a flexible strategy will prove advantageous for all parties involved in the supply chain. (3) The strategy of “reducing the retail price and increasing the extended warranty price” is a favorable strategy for the supply chain.
Can the Digital Economy Achieve Low-Carbon Development? An Analysis Based on the Dual Perspectives of Reducing Emissions and Increasing Efficiency
Authors: Liu Haiyun , Lei Haowen , Xiao Wenjin , Zhao Shijia
Published: July 2024

The digital economy is an important driver for promoting the green transformation of the manufacturing industry, as well as a key engine for achieving China’s goals of reaching peak carbon, carbon neutrality, and high-quality economic development. This study uses provincial panel data from 2011 to 2021 as research samples and empirically analyzes the impact of the digital economy on carbon emissions by constructing a two-way fixed-effects model. The research findings show that the development of the digital economy can restrain the increasing scale of carbon emissions and improve carbon efficiency. A mediation effect analysis reveals that the digital economy can achieve emission reduction and efficiency improvement through the upgrading of consumption structures, while the upgrading of energy structures has the opposite effect, increasing carbon emissions. Further, a heterogeneity analysis indicates that the carbon reduction effect of the digital economy shows a trend of being higher in the west and lower in the east, higher in the north and lower in the south, higher in provinces with high openness compared to those with low openness, and higher in provinces with a good business environment compared to those with an average business environment. Based on these conclusions, recommendations are proposed at three levels: promoting the integration of the digital economy with manufacturing; fully leveraging the effects of the digital economy in relation to upgrading energy structures and consumption structures; and exploring collaborative mechanisms for regional digital development, in order to provide a theoretical basis for the development of the digital economy and realization of the “dual carbon” goals.
The Role of Demographic Changes and Digitalization in Eco-Innovations and Their Effects on the Growth of Companies
Authors: Antončič Jasna Auer , Hojnik Jana , Ruzzier Mitja , Ruzzier Maja Konečnik , Soltwisch Brandon
Published: July 2024

This research explores the role of demographic changes and digitalization in fostering eco-innovations and their subsequent effects on company growth. Employing a quantitative approach, the study investigates how shifts in demographics and the adoption of digital technologies influence eco-innovative practices across companies. The findings reveal that demographic changes are driving the adoption of various eco-innovations, which indirectly contributes to company growth. Additionally, digitalization shows a significant positive relationship with organizational eco-innovations, but its impact on product and process innovations is less pronounced. These results underscore the importance of both demographic factors and strategic digitalization in promoting eco-innovations that yield environmental and economic benefits. This research addresses a crucial gap in the existing literature by uncovering how demographic and digital changes influence eco-innovation and growth, specifically within the context of Slovenian enterprises.
The Importance of the Vinted Application in Popularizing Sustainable Behavior among Representatives of Generation Z
Authors: Balińska Agata , Jaska Ewa , Werenowska Agnieszka
Published: July 2024

Changes in the fashion market (mainly fast fashion) mean that the negative impact of this sector on the natural and social environment is increasing. An important measure to limit this negative impact is to create more responsible consumer behavior in this market. This applies especially to young consumers, who are the recipients of most fast fashion campaigns, and thanks to new media (including mobile applications), their sustainable behavior can be shaped. The main aim of this study was to identify the features of the useful Vinted mobile application in terms of promoting socially and environmentally sustainable behaviors of Generation Z on the clothing market. At the stage of collecting research material, the desk research and CAWI (computer-assisted web interview) methods were used, and the analysis included, among others, measures of descriptive statistics, a customer satisfaction index (CSI) and a Mann–Whitney Z test. The research was carried out at the turn of 2022/23. The responses of 370 respondents using the Vinted application were qualified for analysis. A convenient selection of the research sample was used. The link to the survey was shared through online channels, including social media. The snowball method was used to obtain respondents. This study shows that less frequent use of the application corresponds to lower ratings of the analyzed parameters. The most important features (attributes) of the application are free application, ease of creating an account and secure payment. The main reason for using the Vinted application is the economic factor (looking for bargains). There is a difference between the CSI value in the group of women and men. To the best of the authors’ knowledge, this is the first survey conducted among representatives of Gen Z, which simultaneously took into account satisfaction with using the Vinted application and its impact on popularizing sustainable attitudes.
Does Adaptation to Saltwater Intrusion Improve the Livelihoods of Farmers? Evidence for the Central Coastal Region of Vietnam
Authors: Nguyen Thi Dieu Linh , Defloor Bart , Speelman Stijn , Bleys Brent
Published: July 2024

Saltwater intrusion poses severe threats to rice farming in Vietnam. Farmers can adapt by producing other crops or switching to other production models. This study evaluates the impact of implementing different saltwater-intrusion-adaptation strategies on farmers’ livelihoods by applying propensity score matching to cross-sectional survey data for 414 farmers in the Central Coastal region of Vietnam. We consider both economic and social indicators and find that there is considerable heterogeneity in the outcomes. With the exception of switching to new rice varieties, all adaptation strategies considered in the study significantly increase saline-land productivity, mainly as a result of higher revenues. Moreover, for these strategies, food security is found to be significantly higher, whereas life satisfaction is only higher for those farm households that cultivate vegetables, shrimp, or lotus-fish. Adopting new rice varieties is found to significantly decrease saline-land productivity, whereas the social impacts are not significant.
Sustainable IoT Security in Entrepreneurship: Leveraging Univariate Feature Selection and Deep CNN Model for Innovation and Knowledge
Authors: Gupta Brij B. , Gaurav Akshat , Attar Razaz Waheeb , Arya Varsha , Alhomoud Ahmed , Chui Kwok Tai
Published: July 2024

Due to the rapid increase in Internet of Things (IoT) devices in entrepreneurial environments, innovative cybersecurity advancements are needed to defend against escalating cyber threats. The present paper proposes an approach involving univariate feature selection leading to Sustainable IoT security. This method aims at increasing the efficiency and accuracy of the deep Convolutional Neural Network (CNN) model concerning botnet attack detection and mitigation. The approach to obtaining Sustainable IoT Security goes beyond the focus on technical aspects by proving that increased cybersecurity in IoT environments also fosters entrepreneurship in terms of stimulation, knowledge increase, and innovation. This approach is a major step towards providing entrepreneurs with the necessary tools to protect them in this digital era, which will enable and support the defense against cyber threats. A secure, innovative, and knowledgeable entrepreneurial environment is the result of Sustainable IoT security.
Urban Renewal and Transformation of Residents’ Pro-Environmental Behaviors: Evidence from the Renovation of Old Residential Areas in Chengdu, China
Authors: He Xingbang , Ran Xiaoxia , Mao Jie
Published: July 2024

Based on research data from the renovation of old residential areas in Chengdu, China, this study provides empirical evidence regarding the causal relationship between urban renewal and the transformation of residents’ pro-environmental behaviors. The research results show a significant enhancement in residents’ engagement in pro-environmental behaviors due to the renovation of old residential areas. Robustness tests based on replacement samples and two-stage difference–difference model also confirm the reliability of the conclusions. The preliminary mechanism test indicates that the mechanism of the impact of renovation of old residential areas on the transformation of residents’ pro-environmental behaviors can be attributed to two main factors. Firstly, improving environmental infrastructure, such as upgrading waste segregation facilities and installing intelligent resource recycling devices, provides residents with better conditions for engaging in pro-environmental behaviors. Secondly, an increase in residents’ environmental awareness has led to more voluntary participation in pro-environmental behaviors. This study proposes that decision makers acknowledge that urban renewal serves as both a means of improving urban infrastructure and as a hands-on environmental education for the entire population. Taking urban renewal as an opportunity to enhance citizens’ awareness of environmental protection will effectively promote the transformation of pro-environmental behaviors and support sustainable social development.
Physico-Mechanical Characterization of Masonry Mortars for Sustainable Construction: Experimental Study with Four Different Aggregates
Authors: Ruano Gutiérrez Enrique , Ferrández Daniel , Atanes-Sánchez Evangelina , Ruano de Pablo Miguel
Published: July 2024

The construction sector generates a strong environmental impact every year as a result of the high consumption of raw materials and the large waste volumes associated with this productive activity. In this sense, the search for alternative and sustainable solutions that allow progress towards responsible economic growth has become a priority activity. This work presents an exhaustive characterisation of masonry mortars made with four different types of aggregates: standard sand, natural sand, concrete waste recycled sand and ceramic components recovered sand. Differently from other studies, this research addresses the previous characterisation of the aggregates as raw material for the manufacture of masonry mortars, and, afterwards, a study of the most relevant properties for these cement composites in the fresh and hardened state is carried out. The most relevant properties of the mortars made with these raw materials are presented, and the repercussion of aggregate washing on their physical-mechanical characteristics is analysed. The results show how mortars made with 100% recycled aggregate can be competitive in the industry, presenting excellent properties in the fresh state and achieving an optimal mechanical strength. In addition, it has been observed that the introduction of a previous washing step of the aggregates improves their physical-mechanical properties and results in a higher quality of the cement mortars finally produced. In this way, the most representative properties of this type of materials have been collected in a well-structured and complete way, thus showing their possibilities of application in the construction industry.
Energy and Environmental Analyses of a Solar–Gas Turbine Combined Cycle with Inlet Air Cooling
Authors: Abubaker Ahmad , Ahmad Adnan , Singh Binit , Manaserh Yaman , Al-Ghussain Loiy , Akafuah Nelson , Saito Kozo
Published: July 2024

Sensitivity to ambient air temperatures, consuming a large amount of fuel, and wasting a significant amount of heat dumped into the ambient atmosphere are three major challenges facing gas turbine power plants. This study was conducted to simultaneously solve all three aforementioned GT problems using solar energy and introducing a new configuration that consists of solar preheating and inlet-air-cooling systems. In this study, air was preheated at a combustion chamber inlet using parabolic trough collectors. Then, inlet air to the compressor was cooled by these collectors by operating an absorption cooling cycle. At the design point conditions, this novel proposed integration resulted in a 6.87% relative increase in generated power and a 10.53% relative decrement in fuel consumption, achieving a 19.45% relative increment in the plant’s thermal efficiency. This was accompanied by a reduction of 0.026 kg/s, 4.2 kg/s, and 0.278 kg/s in CO2, CO, and NOx emissions, respectively. Finally, spider diagrams were employed to assess the impact of the operating parameters on the overall system’s performance and its associated environmental implications.
Exploring Thermal Discomfort during Mediterranean Heatwaves through Softscape and Hardscape ENVI-Met Simulation Scenarios
Authors: Tousi Evgenia , Tseliou Areti , Mela Athina , Sinou Maria , Kanetaki Zoe , Jacques Sébastien
Published: July 2024

The study examines the effectiveness of various design strategies in alleviating the impacts of heatwaves in the Mediterranean region, focusing on a densely populated post-refugee urban area in Greece. By analyzing five different design scenarios, the study aims to identify the most efficient approach to mitigate thermal stress outdoors. The five design scenarios include changes in albedo values and coatings and alterations in the number and type of trees. The methodology includes a literature review, field work and microclimate simulations with the use of ENVI-met 5.6.1. The study evaluates ENVI-met data through potential air temperature, PET and UTCI analysis. The experimental results indicate that the most effective strategy is associated with urban greening. In particular, increasing tree cover considerably reduces air temperature, PET and UTCI values by 4 to 10 degrees Celsius. This finding highlights the potential of urban greening to enhance thermal comfort and combat heatwave effects. The research findings may be useful to landscape architects and urban designers, in light of a more climate-responsive urban design in the Mediterranean region. Future research may also assess the combined impact of multiple mitigation strategies on a larger scale, informing evidence-based policies for heatwave resilience.
Olfactory and Gustatory Perception among Plant-Based vs. Omnivorous Dieters: A Systematic Review and Meta-Analysis
Authors: Mo Yunfan , McLeod Stephanie C. , McCormack Jessica C. , Leong Sze Ying , Conner Tamlin S. , Oey Indrawati , Peng Mei
Published: July 2024

Driven by heightened awareness of environmental sustainability, personal health, and animal welfare, there has been a rapid surge in adoption of plant-based diets (PBDs) by global consumers. Despite the important implications of potential links between PBDs and chemosensory (e.g., olfactory and gustatory) perception, the empirical evidence has yet to be comprehensively reviewed. This systematic review aims to synthesise existing data comparing olfactory and gustatory perceptions among individuals following plant-based (including vegan, vegetarian, and flexitarian) and omnivorous diets. Seven databases produced 1038 publications for screening against the pre-registered criteria. Eleven studies were included in this review, with three focusing on olfaction and eight on gustation. Of these, three gustatory studies provided sufficient data for meta-analysis, revealing little difference between plant-based and omnivore dieters on an overall level. The PBD group exhibited a significantly lower threshold (i.e., higher sensitivity) to sweetness compared to the omnivore group (ES = −0.336, 95% CI between −0.630 and −0.042, p < 0.05). Additionally, olfactory studies indicated that PBD followers perceived meat-related odours to be less pleasant compared to omnivores. Overall, this review points to a sensory–diet relationship, highlighting the potential role of sensory perception in sustainable plant-based dietary behaviours. The findings also encourage food manufacturers to consider potential chemosensory alterations among plant-based consumers.
Intervention Options for Enhancing Smallholder Compliance with Regulatory and Market Standards for High-Value Fruits and Vegetables in Rwanda and Zambia
Authors: Akiri Morris , Mbugua Fredrick , Njunge Rahab , Agwanda Charles , Gurmessa Negussie E. , Phiri Noah A. , Musebe Richard , Kalisa Jean Pierre , Uzayisenga Bellancile , Kansiime Monica K. , Karanja Daniel
Published: July 2024

Agriculture is vital for economic growth in sub-Saharan Africa. In Rwanda and Zambia, the sector drives economic output, particularly the horticulture sector, which has emerged as a significant contributor to export revenue and employment for resource-poor rural communities. As agri-food value chains become globally connected, there are increasing concerns about food safety and both the social and environmental sustainability benefits of production, which has underscored the need for producers to comply with certain regulatory standards. This paper uses a value chain analysis approach to assess the factors influencing compliance among smallholder fresh fruit and vegetable (FFV) producers in Zambia and Rwanda, and identify practices essential for achieving widespread compliance, to enhance the competitiveness of the sector. The data were gathered from 340 FFV farmers and key informants using structured questionnaires. The results reveal that the value chains for target FFVs are still traditional, predominantly targeting local markets. These markets pose low barriers to entry for smallholders, and often with few safety and quality requirements. As such, there remains a paucity of understanding of market needs and regarding quality and safety standards amongst smallholders, which, coupled with low-input, low-technology farming methods, and inadequate pre- and post-harvest handling, limit the market potential. To bridge these gaps, this study recommends organising farmers into legally recognised entities, to enable access to compliance information, reduce transaction costs, and provide access to high-quality niche markets through public–private partnerships. Countries also need to develop industry codes of practice and quality management systems and support farmers to adhere to them.
Innovative Professional Learning Communities and Sustainable Education Practices through Digital Transformation
Authors: Vičič Krabonja Maja , Kustec Simona , Skrbinjek Vesna , Aberšek Boris , Flogie Andrej
Published: July 2024

Education is the most important part of sustainable society development. Traditional approaches to permanent lifelong educator training often fail to produce lasting changes in teaching practices (UN, Goal 4). This paper examines how educator involvement in a professional learning community (PLC) ensures equitable quality education and promotes lifelong learning opportunities for all and a sustainable digital transformation of the teaching and learning process. Participation in PLC and the use of contemporary learning environments positively impact the quality of educators’ work, thereby enhancing the overall educational process. This study utilised a quantitative methodology, employing both descriptive and inferential statistics to analyse the data. Results indicate that a balanced integration of formal PLC structures and guidance, combined with elements of a non-formal, broader PLC and external incentives and support (such as projects), can surpass mere exchanges of best practices, foster innovation, and support the sustainable digital transformation of educators. This approach leads to a sustainable shift in pedagogical paradigms. The PLC_IP model (focused on including digital technologies and effective teaching strategies) enhances collective educator efficacy, contributing to improved teaching quality. For PLCs to achieve sustainability, they must be dynamic entities that adapt to the evolving educational landscape, ensuring their continued relevance and impact with rising completion rates over time. Our findings underscore the importance of innovative PLCs in driving sustainable educational development and improvement and provide a framework for future research and practical applications in educational settings. In addition, PLCs can strengthen the means of implementation and revitalise the global partnership for sustainable development (UN, Goal 17).
Study on the Reinforced Properties of Geopolymer Fibers with a Sustainable Development Role
Authors: Qu Yongping , Li Wentao , Wang Caiping , Cao Honghong , Zhou Rui
Published: July 2024

Geopolymers are of great significance in reducing the consumption of mineral resources, saving energy, protecting the environment, and realizing sustainable economic and social development. This experiment investigated geopolymer mortar with fly ash and metakaolin as the primary binders, assessing the impact of different fiber types and volume fractions on the mortar’s flexural and compressive strength. The results indicated that optimal mechanical properties could be achieved with a fly ash-to-metakaolin ratio of 35:65. The mechanical performance is the best, with a compressive strength of 54 MPa, a flexural strength of 3.4 MPa, and a split tensile strength of 1.9 MPa at 28 days. Different fibers influenced the splitting tensile strength to varying degrees; with a 1.5% volume fraction of steel fibers, geopolymer mortar exhibited the best reinforcement effect, showing a 70% increase in flexural strength and a 142% increase in tensile strength. Mechanistic analysis revealed that the reinforcement from refined various fibers could refine the structure and further enhance the strength. Of steel geopolymer fibers’ The reinforcing effect of steel fibers is the best among them, and the internal structure is the most compact. The geopolymer mortar hydration products of geopolymer mortar reinforced with PP fibers, PVA fibers, steel fibers, and carbon fibers were amorphous network-structured zeolites (Na2[Al2Si3O10]·2H2O). The limitations of geopolymers can be effectively addressed through the aforementioned research, which can effectively reduce the use of cement and achieve the goal of sustainable development.
The Optimal Selection of Renewable Energy Systems Based on MILP for Two Zones in Mexico
Authors: Contreras Alan Ortiz , Badaoui Mohamed , Baltazar David Sebastián
Published: July 2024

This paper presents a series of enhancements to a previously proposed mixed-integer linear programming (MILP) model for investment decisions and operational planning in distributed generation (DG) systems. The main contribution of this study consists of integrating a wind generation system and multiple loads at different buses in a network. The model considers dynamic weather data, energy prices, costs related to photovoltaic and wind systems, storage systems, operational and maintenance costs, and other pertinent factors, such as efficiencies, geographical locations, resource availability, and different load profiles. The simulation results obtained through implementation in Julia’s programming language illustrate that the MILP formulation maximizes the net present value, and four configurations for hybrid power generation systems in Mexico are analyzed. The objective is to enable profitability assessment for investments in large-capacity DG systems in two strategic zones of Mexico. The results show that the configurations in the NE zone, especially in Tamaulipas, are the most cost-effective. Case 1 stands out for its highest net present value and shortest payback time, while Case 2 offers the highest energy savings. In addition, Cases 3 and 4, which incorporate storage systems, exhibit the longest payback periods and the lowest savings, indicating less favorable economic performance compared with Cases 1 and 2. Moreover, the sales of two case studies, one without a storage system and the other with a storage system, are shown. The model also incorporates instruments for buying or selling energy in the wholesale electricity market, including variables that depict the injected energy into the electrical grid. This comprehensive approach provides a detailed overview of optimal energy management.