bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Climate Extremes on Spring Phenology of Temperate Vegetation in China
Authors: Mo Yunhua , Zhang Xuan , Liu Zunchi , Zhang Jing , Hao Fanghua , Fu Yongshuo
Published: January 2023

The response of vegetation spring phenology to climate warming has received extensive attention. However, there are few studies on the response of vegetation spring phenology to extreme climate events. In this study, we determined the start of the growing season (SOS) for three vegetation types in temperate China from 1982 to 2015 using the Global Inventory Modeling and Mapping Study’s third-generation normalized difference vegetation index and estimated 25 extreme climate events. We analyzed the temporal trends of the SOS and extreme climate events and quantified the relationships between the SOS and extreme climate events using all-subsets regression methods. We found that the SOS was significantly advanced, with an average rate of 0.97 days per decade in China over the study period. Interestingly, we found that the SOS was mainly associated with temperature extremes rather than extreme precipitation events. The SOS was mainly influenced by the frost days (FD, r = 0.83) and mean daily minimum temperature (TMINMEAN, r = 0.34) for all three vegetation types. However, the dominant influencing factors were vegetation-type-specific. For mixed forests, the SOS was most influenced by TMINMEAN (r = 0.32), while for grasslands and barren or sparsely vegetated land, the SOS was most influenced by FD (r > 0.8). Our results show that spring phenology was substantially affected by extreme climate events but mainly by extreme temperature events rather than precipitation events, and that low temperature extremes likely drive spring phenology.
Merged Multi-Sensor Ocean Colour Chlorophyll Product Evaluation for the British Columbia Coast
Authors: Pramlall Sejal , Jackson Jennifer M. , Konik Marta , Costa Maycira
Published: January 2023

Phytoplankton phenology studies require a dataset that is continuous in time and space since missing data have been shown to affect the accuracy of seasonality metrics. The interpolated GlobColour product provided by the Copernicus Marine Environment Monitoring Service (CMEMS) meets these requirements by being ‘gap filled’, thus yielding the highest spatial coverage. Despite being validated on a global scale, a regional comparison to in situ Chl-a concentrations should be conducted to enable product application in optically complex waters. This study aims to evaluate the performance of the GlobColour interpolated product in British Columbia coastal waters via a statistical match-up analysis and a qualitative analysis to determine whether the data reflect the region’s large-scale seasonal trends and latitudinal dynamics. Additionally, the statistical performance of the GlobColour interpolated product was compared to the original GlobColour and Ocean Colour Climate Change Initiative (OC-CCI) merged chlorophyll-a products based on in situ observations. The GlobColour interpolated product performed relatively well and was comparable to the best-performing product for each water type (RMSE = 0.28, r2 = 0.77, MdAD = 1.5, BIAS = 0.90). The statistics for all the products degraded in Case 2 waters, thus highlighting the dilemma of applying algorithms designed for Case 1 waters in Case 2 waters. Our results indicate how the quality of products can vary in different environmental conditions.
China’s Wealth Capital Stock Mapping via Machine Learning Methods
Authors: Ren Lulu , Li Feixiang , Chen Bairu , Chen Qian , Ye Guanqiong , Yang Xuchao
Published: January 2023

The frequent occurrence of extreme weather and the development of urbanization have led to the continuously worsening climate-related disaster losses. Socioeconomic exposure is crucial in disaster risk assessment. Social assets at risk mainly include the buildings, the machinery and the equipment, and the infrastructure. In this study, the wealth capital stock (WKS) was selected as an indicator for measuring social wealth. However, the existing WKS estimates have not been gridded accurately, thereby limiting further disaster assessment. Hence, the multisource remote sensing and the POI data were used to disaggregate the 2012 prefecture-level WKS data into 1000 m × 1000 m grids. Subsequently, ensemble models were built via the stacking method. The performance of the ensemble models was verified by evaluating and comparing the three base models with the stacking model. The stacking model attained more robust prediction results (RMSE = 0.34, R2 = 0.9025), and its prediction spatially presented a realistic asset distribution. The 1000 m × 1000 m WKS gridded data produced by this research offer a more reasonable and accurate socioeconomic exposure map compared with existing ones, thereby providing an important bibliography for disaster assessment. This study may also be adopted by the ensemble learning models in refining the spatialization of the socioeconomic data.
Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data
Authors: Barzycka Barbara , Grabiec Mariusz , Jania Jacek , Błaszczyk Małgorzata , Pálsson Finnur , Laska Michał , Ignatiuk Dariusz , Aðalgeirsdóttir Guðfinna
Published: January 2023

Changes in glacier zones (e.g., firn, superimposed ice, ice) are good indicators of glacier response to climate change. There are few studies of glacier zone detection by SAR that are focused on more than one ice body and validated by terrestrial data. This study is unique in terms of the dataset collected—four C- and L-band quad-pol satellite SAR images, Ground Penetrating Radar data, shallow glacier cores—and the number of land ice bodies analyzed, namely, three tidewater glaciers in Svalbard and one ice cap in Iceland. The main aim is to assess how well popular methods of SAR analysis perform in distinguishing glacier zones, regardless of factors such as the morphologic differences of the ice bodies, or differences in SAR data. We test and validate three methods of glacier zone detection: (1) Gaussian Mixture Model–Expectation Maximization (GMM-EM) clustering of dual-pol backscattering coefficient (sigma0); (2) GMM-EM of quad-pol Pauli decomposition; and (3) quad-pol H/α Wishart segmentation. The main findings are that the unsupervised classification of both sigma0 and Pauli decomposition are promising methods for distinguishing glacier zones. The former performs better at detecting the firn zone on SAR images, and the latter in the superimposed ice zone. Additionally, C-band SAR data perform better than L-band at detecting firn, but the latter can potentially separate crevasses via the classification of sigma0 or Pauli decomposition. H/α Wishart segmentation resulted in inconsistent results across the tested cases and did not detect crevasses on L-band SAR data.
Cooperated Moving Target Detection Approach for PA-FDA Dual-Mode Radar in Range-Ambiguous Clutter
Authors: Liu Zhixin , Zhu Shengqi , Xu Jingwei , Lan Lan , He Xiongpeng , Li Ximin
Published: January 2023

This paper proposes a cooperated range ambiguous clutter suppression method for moving target detection in the background of range-ambiguous clutter with a phased array (PA)–frequency diverse array (FDA) dual-mode radar. With the FDA mode, the range-ambiguous clutters are discriminated in the transmit spatial frequency domain, and thus the clutter covariance matrixes (CCMs) corresponding to unambiguous and ambiguous regions can be independently estimated. Therefore, the enhanced CCM can be reconstructed by using a linear combination of these distinguished CCMs from different range regions. With the PA mode, the enhanced CCM is applied, thus taking advantages of its high beampattern gain as well as alleviating the range ambiguous clutter suppression problem. Simulation results are presented to verify the effectiveness of the proposed method in range-ambiguous clutter scenarios.
A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images
Authors: Wu Junzheng , Fu Ruigang , Liu Qiang , Ni Weiping , Cheng Kenan , Li Biao , Sun Yuli
Published: January 2023

The very high spatial resolution (VHR) remote sensing images have been an extremely valuable source for monitoring changes occurring on the Earth’s surface. However, precisely detecting relevant changes in VHR images still remains a challenge, due to the complexity of the relationships among ground objects. To address this limitation, a dual neighborhood hypergraph neural network is proposed in this article, which combines multiscale superpixel segmentation and hypergraph convolution to model and exploit the complex relationships. First, the bi-temporal image pairs are segmented under two scales and fed to a pre-trained U-net to obtain node features by treating each object under the fine scale as a node. The dual neighborhood is then defined using the father-child and adjacent relationships of the segmented objects to construct the hypergraph, which permits models to represent higher-order structured information far more complex than the conventional pairwise relationships. The hypergraph convolutions are conducted on the constructed hypergraph to propagate the label information from a small amount of labeled nodes to the other unlabeled ones by the node-edge-node transformation. Moreover, to alleviate the problem of imbalanced sampling, the focal loss function is adopted to train the hypergraph neural network. The experimental results on optical, SAR and heterogeneous optical/SAR data sets demonstrate that the proposed method offersbetter effectiveness and robustness compared to many state-of-the-art methods.
Global Leaf Chlorophyll Content Dataset (GLCC) from 2003–2012 to 2018–2020 Derived from MERIS and OLCI Satellite Data: Algorithm and Validation
Authors: Qian Xiaojin , Liu Liangyun , Chen Xidong , Zhang Xiao , Chen Siyuan , Sun Qi
Published: January 2023

Leaf chlorophyll content (LCC) is a prominent plant physiological trait and a proxy for leaf photosynthetic capacity. The acquisition of LCC data over large spatial and temporal scales facilitates vegetation growth monitoring and terrestrial carbon cycle modeling. In this study, a global 500 m LCC weekly dataset (GLCC) was produced from ENVISAT MERIS and Sentinel-3 OLCI satellite data using a physical radiative transfer modeling approach that considers the influence of canopy structure and soil background. Firstly, five look-up-tables (LUTs) were generated using PROSPECT-D+4-Scale and PROSAIL-D models for woody and non-woody plants. For the four LUTs applicable to woody plants, each LUT contains three sub-LUTs corresponding to three types of crown height. The one LUT applicable to non-woody vegetation type includes 25 sub-LUTs corresponding to five kinds of canopy structures and five kinds of soil backgrounds. The final retrieval was considered the aggregation of the LCC inversion results of all sub-LUTs for each plant function type (PFT). Then, the GLCC dataset was generated and validated using field measurements, yielding an overall accuracy of R2 = 0.41 and RMSE = 8.94 μg cm−2. Finally, the GLCC dataset presented acceptable consistency with the existing MERIS LCC dataset. OLCI, as the successor to MERIS data, was used for the first time to co-produce LCC data from 2003–2012 to 2018–2020 in conjunction with MERIS data. This new GLCC dataset spanning nearly 20 years will provide a valuable opportunity to analyze variations in vegetation dynamics.
Deep Clouds on Jupiter
Authors: Wong Michael H. , Bjoraker Gordon L. , Goullaud Charles , Stephens Andrew W. , Luszcz-Cook Statia H. , Atreya Sushil K. , de Pater Imke , Brown Shannon T.
Published: January 2023

Jupiter’s atmospheric water abundance is a highly important cosmochemical parameter that is linked to processes of planetary formation, weather, and circulation. Remote sensing and in situ measurement attempts still leave room for substantial improvements to our knowledge of Jupiter’s atmospheric water abundance. With the motivation to advance our understanding of water in Jupiter’s atmosphere, we investigate observations and models of deep clouds. We discuss deep clouds in isolated convective storms (including a unique storm site in the North Equatorial Belt that episodically erupted in 2021–2022), cyclonic vortices, and northern high-latitude regions, as seen in Hubble Space Telescope visible/near-infrared imaging data. We evaluate the imaging data in continuum and weak methane band (727 nm) filters by comparison with radiative transfer simulations, 5 micron imaging (Gemini), and 5 micron spectroscopy (Keck), and conclude that the weak methane band imaging approach mostly detects variation in the upper cloud and haze opacity, although sensitivity to deeper cloud layers can be exploited if upper cloud/haze opacity can be separately constrained. The cloud-base water abundance is a function of cloud-base temperature, which must be estimated by extrapolating 0.5-bar observed temperatures downward to the condensation region near 5 bar. For a given cloud base pressure, the largest source of uncertainty on the local water abundance comes from the temperature gradient used for the extrapolation. We conclude that spatially resolved spectra to determine cloud heights—collected simultaneously with spatially-resolved mid-infrared spectra to determine 500-mbar temperatures and with improved lapse rate estimates—would be needed to answer the following very challenging question: Can observations of deep water clouds on Jupiter be used to constrain the atmospheric water abundance?
Haulage Cycles Identification for Wheeled Transport in Underground Mine Using Neural Networks
Authors: Skoczylas Artur , Rot Artur , Stefaniak Paweł , Śliwiński Paweł
Published: January 2023

The task of ore transportation is performed in all mines, regardless of their type (open pit/underground) or mining process. A substantial number of enterprises utilize wheeled machines to perform ore haulage, especially haul trucks and loaders. These machines’ work consists of repeating cycles, and each cycle can be divided into 4 operations: loading, driving with full box/bucket, unloading and driving with empty box/bucket. Monitoring this process is essential to create analytical tools that support foremen and other management crew in achieving effective and optimal production and planning activities. Unfortunately, information gathered regarding the process is frequently based on operators’ oral testimony. This process not only allows for abuse but is also a repetitive and tedious task that must be performed by foremen. The time and attention of foremen is valuable as they are responsible for managing practically everything in their current mine section (machines, operators, works, repairs, emergencies, safety, etc.). Therefore, the automatization of the described process of information gathering should be performed. In this article, we present two neural network models (one for haul trucks and one for loaders) build for detecting work cycles of the ore haulage process. Both models were built utilizing a 2-stage approach. In the first stage, the models’ structures were optimized, while the second was focused on optimizing hyperparameters for the structure with best performance. Both of the proposed models were trained using data collected from on-board monitoring systems over hundreds of the machines’ work hours and utilized the same input features: vehicle speed, fuel consumption, selected gear and engine rotational speed. Models have been subjected to comprehensive testing during which the efficiency and stability of the model responsible for haul trucks was proven. Results for loaders were not as high quality for haul trucks; however, some interesting facts were discovered that indicate possible directions for future development.
Flood Detection in Steel Tubes Using Guided Wave Energy Leakage
Authors: Mijarez Rito
Published: January 2023

A study that evaluated the use of ultrasonic-guided waves to detect water in hollow pipes is presented. In this work, a guided wave system employed a 40 kHz piezoelectric (PZT) transmitter and a PZT ultrasound transducer. The transmitter was based on a battery-operated microcontroller, and the receiver was composed of a digital signal processor (DSP) module connected to a PC via a USB for monitoring purposes. The transmitter and receiver were attached, non-intrusively without perfect alignment, to the external wall of a steel tube 1 m × 270 mm × 2 mm in size. Flood detection was performed based on guided wave attenuation due to energy leakage from the internal steel wall of the tube to water. Two approaches were carried out. The former was an off-line signal response based on the wavelet energy entropy analysis of a received pulse; the latter was a real-time hit-and-miss analysis centered on measuring the time–space in-between two transmitted pulses. Experiments performed in the laboratory successfully identified flooded tubes.
Environmental Protection Fee-to-Tax and Corporate Environmental Social Responsibility: A Test Based on Corporate Life Cycle Theory
Authors: Jiang Xin , Li Guanglong , Fan Xianxian
Published: January 2023

Corporate environmental social responsibility enhancement is an important way to promote green and low-carbon economic transformation and achieve high-quality development. As an important reform of China’s environmental taxation system, environmental protection fees and taxes play a unique role in environmental governance. However, faced with the increase in the intensity of environmental regulations after the implementation of the environmental protection fee-to-tax, will firms take the initiative to assume environmental social responsibility (ESR)? This paper uses the implementation of China’s Environmental Protection Tax Law in 2018 as a quasi-natural experiment to study the impact of environmental protection fee-to-tax policy on corporate ESR from a life-cycle perspective. We find that, overall, the environmental protection fee-to-tax reform policy significantly increases the likelihood of corporate ESR by 66%. By the life cycle stage, the effect of environmental protection fee-to-tax policy is mainly reflected in maturity and decline stage firms, and the impact on growth stage firms is not obvious. The design of the current environmental protection tax system and the differences in financial status, business strategies, and environmental management levels of firms in different life cycle stages are important factors in this phenomenon. Further sub-sample regressions show that the environmental protection fee-to-tax policy significantly increases the ESR among state-owned, key regulated and higher financing-constrained firms, especially those in the maturity and decline periods. The findings of this paper enrich corporate life cycle theory and provide a reference for decision making to further improve environmental tax policies to stimulate a firm’s green and low-carbon transformation.
Life Cycle Assessment (LCA) of Alternative Pavement Rehabilitation Solutions: A Case Study
Authors: Plati Christina , Tsakoumaki Maria
Published: January 2023

In the context of sustainability, the pavement construction industry is taking a more holistic approach to environmental, economic, and social issues. In particular, pavements are believed to be responsible for up to 24% of total greenhouse gas emissions over the last century. Therefore, it is critical to include a tool to assess the environmental and health impacts of pavement construction or rehabilitation in the related design studies. This tool is called Life Cycle Assessment (LCA). With this in mind, this case study examines two alternative solutions for the rehabilitation of an existing highway pavement: one using conventional materials and processes and one using conventional recycled materials and recycling processes. These two alternatives are ecologically evaluated using an LCA method to increase the importance of quantifying environmental and health impacts. By comparing the corresponding results, this study aims to quantitatively demonstrate how recycled materials and recycling processes contribute to the sustainability of pavements. As a final result, it is shown that the environmental and human health benefits are significant, even though the use of recycled materials and processes for pavements may be limited.
Growth Conditions and Growth Kinetics of Chlorella Vulgaris Cultured in Domestic Sewage
Authors: Ma Xingguan , Jian Wenhao
Published: January 2023

To assess the feasibility of achieving the dual objectives of domestic wastewater treatment and biomass accumulation, growth kinetic models were used to analyze the growth pattern of Chlorella in domestic wastewater. The logistic model simulated the growth trend of Chlorella in domestic wastewater better than the other two models. However, the currently developed model still cannot fully predict the growth of Chlorella. Factors such as nutrient removal and aging and the death of algae need to be taken into account to develop a more accurate model.
Gamma Attenuation and Mechanical Characteristics of a Lead/NBR/SBR Rubber Composite with Black Nanocarbon Reinforcement
Authors: Gamal Romisaa , Salama Elsayed , Elshimy Hassan , El-Nashar Doaa E. , Bakry Assem , Ehab Mohamed
Published: January 2023

In this work, black nanocarbon-loaded 0–100 parts-per-hundred (phr) PbO-filled acrylonitrile butadiene rubber (NBR)/styrene-butadiene rubber (SBR) blend composites were prepared by using an ordinary standard rubber mixer. Both mechanical and gamma attenuation properties of the prepared samples were investigated. Maximum tensile strength and elongation at break were obtained at 40 phr PbO concentration. The obtained values for the mass attenuation coefficient with the increased PbO concentration from 0–100 phr ranged from 0.12–0.22 cm2/g at 0.239 MeV. Scanning electron microscope (SEM) with the elemental mapping analysis results showed high homogeneity at 40 phr of the prepared rubber composites, with some areas of elemental agglomeration at a high concentration of lead oxide. The obtained results highly recommend the use of the prepared nanocarbon-reinforced PbO/NBR/SBR blend compared to those previously used as personal protective equipment in radiation-shielding applications.
Impact of Bioaugmentation on the Bioremediation of Saline-Produced Waters Supplemented with Anaerobic Digestate
Authors: Parsy Aurélien , Sambusiti Cecilia , Gassie Claire , Baldoni-Andrey Patrick , Périé Frédéric , Guyoneaud Rémy
Published: January 2023

Bioremediation of produced waters has been widely investigated in the last decades. More recently, microalgae-based treatments have been developed to produce biomass. The objective of this study was to determine, at lab scale, the remediation efficiency of three origin of microorganisms: a consortium of three halotolerant and halophilic microalgae and their associated bacteria, bacteria from liquid digestate, and aromatic-degrading bacteria selected to perform bioaugmentation. The medium was composed of artificial oil-produced water and seawater, and contained nutrients from liquid digestate. In order to identify what plays a role in nitrogen, chemical oxygen demand, and aromatics compounds elimination, and to determine the effectiveness of bioaugmentation to treat this mix of waters, 16S rRNA analyses were performed. Combination of microorganisms from different origins with the selected aromatic-degrading bacteria were also realized, to determine the effectiveness of bioaugmentation to treat these waters. Each population of microorganisms achieved similar percentage of removal during the biological treatment, with 43–76%, 59–77%, and 86–93% of elimination for ammonium, chemical oxygen demand, and aromatic compounds (with 50% of volatilization), respectively, after 7 days, and up to with 100%, 77%, and 99% after 23 days, demonstrating that in the case of this produced water, bioaugmentation with the specialized aromatic-degrading bacteria had no significant impact on the treatment. Regarding in detail the populations present and active during the tests, those from genus Marinobacter always appeared among the most active microorganisms, with some strains of this genus being known to degrade aromatic compounds.
The Impact of Rewetting Peatland on Fire Hazard in Riau, Indonesia
Authors: Taufik Muh , Haikal Mudrik , Widyastuti Marliana Tri , Arif Chusnul , Santikayasa I. Putu
Published: January 2023

Rewetting peatland is an ongoing effort in Indonesia to restore the hydrological cycle and carbon balance of the ecosystem. However, quantifying the impact of rewetting on mitigating fire remains a challenge. Here, we assess the impact of large-scale rewetting on fire risks and occurrences (duration, coverage area, and the number of events) in 2015–2021. The weather research and forecasting (WRF) model was integrated with a drought–fire model to spatially quantify fire hazards in Riau, Sumatra. The results show that rewetting has resulted in decreasing the frequency of extreme events in the study area (pre- and post-rewetting, respectively, were seven and four events). Although the area influenced by extreme events reduced following rewetting by 5%, the mean duration of extreme events increased. Our findings reveal that widespread prolonged extreme fire hazards only occurred during drying El Niño events in 2015 and 2019. The findings obtained in this case study provide quantitative evidence of the reduced fire hazard resulting from peat restoration in Indonesia. Further, the findings assist in assessing the success of peatland restoration programs and improve our knowledge of the ability to monitor and forecast fire risks in tropical peatlands.
Online Education and Subjective Well-Being in China: Multiple Mediating Roles of Social Class Mobility and Social Tolerance
Authors: Liu Shuang , Cao Yan , Zhang Hao
Published: January 2023

(1) Background: Online education has developed into a new form of education. However, the relationship between online education and subjective well-being has seldom been extensively studied in the literature. Thus, this study provides quantitative evidence regarding the effect of online education on subjective well-being. (2) Objective: The objective of this study was to reveal the net effect of online education on subjective well-being and explore the mediating roles of social class mobility and social tolerance between online education and subjective well-being. (3) Methods: Based on the 2019 China Comprehensive Social Survey data, the “counterfactual framework” was constructed using the propensity score matching method, and 1029 matched samples were analyzed. (4) Results: Online education is significantly positively correlated with subjective well-being (average treatment effect on the treated, ATT = 0.189, p < 0.01). Social class mobility and social tolerance serially mediate the relationship of online education and subjective well-being (the intermediary role of social class mobility is 0.0163; the mediating role of social tolerance is 0.0064). (5) Conclusion: This study confirms the positive predictive effect of online education on subjective well-being and affirms the multiple mediating roles of social class mobility and social tolerance between online education and subjective well-being.
Phytoremediation Potential of Sorghum as a Bioenergy Crop in Pb-Amendment Soil
Authors: Osman Hanan E. , Fadhlallah Ruwaydah S. , Alamoudi Wael M. , Eid Ebrahem M. , Abdelhafez Ahmed A.
Published: January 2023

Lead contamination is among the most significant threats to the environment. The phytoextraction approach uses plants that can tolerate and accumulate metals in their tissues. Lately, biofuel plants have been recommended to be suitable for remediation and implementation of potentially toxic elements (PTEs)-polluted soil. This research assessed the Pb phytoremediation potential of three Sorghum bicolor [red cultivar (S1), white cultivar (S2) and shahla cultivar (S3)]. A pot experiment with five treatments (0, 100, 200, 400 and 800 mg Pb/kg soil) was carried out to assess the potential possibility of using these cultivars to remediate the soil of Pb. The potential possibility of using these plants to phytoremediate the soil of Pb was also assessed. The results emphasized that all the examined cultivars could attain growth to maturity in high Pb spiked soil. However, Pb influenced morphological and chlorophyll contents, especially in plants grown in soil amended with 800 mg/kg. The S1 cultivar had the most significant reduction in total chlorophyll with an average of 72%, followed by the S2 and S3 cultivars (65% and 58% reduction, respectively). The highest Pb content in root (110.0, 177.6 and 198.9 mg/kg, respectively) and in-plant shoot (83.9, 103.6 and 99.0 mg/kg, respectively) were detected by sorghum (S1, S2 and S3, respectively) grown in soil enriched by 800 mg/kg of Pb. From the calculated results of the contamination indices, contamination factor (CF), translocation factor (TF), plant uptake (UT) and tolerance index (TI), none of the investigated cultivars were considered Pb hyperaccumulators, but all were identified as particularly ideal for phytostabilization.
Examining the Promotional Effect and Mechanism of Innovative City Pilot Policy on City Brand Development
Authors: Yuan Shengjun , Li Jie , Wu Jun
Published: January 2023

This study used Staggered DID on the panel data of 254 prefecture-level cities in China from 2005 to 2016 to explore the promotion effect and mechanism of innovative city pilot policy on urban brand development. The research shows that compared with cities without the pilot policy, the construction of innovative cities can increase the city brand value by 10% and has significantly promoted eastern, non-central cities with large populations and higher levels of science and education. Innovative city pilot policy can promote city brand development through cultural inheritance and innovation and entrepreneurship mechanisms. The results indicate that innovative city pilot policy has a significant spatial spillover effect on city brand development. Our findings add to the field of city brand evaluation by providing policy suggestions and experiential references for evaluating city brand development.
A National and Regional Greenhouse Gas Breakeven Assessment of EVs across North America
Authors: Rasbash Daniel , Dillman Kevin Joseph , Heinonen Jukka , Ásgeirsson Eyjólfur Ingi
Published: January 2023

Electrification is considered key to decarbonizing the transport sector. While electric vehicles (EVs) lack tailpipe emissions, battery and electricity production can lead to significant emissions. This study analysed whether EVs can effectively mitigate GHG emissions in North America, by calculating two GHG breakeven indicators for EVs and comparing them to internal combustion engine vehicles (ICEVs). EV life cycle emissions were compared to those of ICEVs in Canada, Mexico, and the USA. In addition, this study considered potential national electricity grids evolutions and improvements in battery production and vehicle efficiency. The study estimated that EVs in Canada, the USA, and Mexico would see environmental benefits after 18.0, 25.1, and 25.6 thousand driven kilometres, respectively, as compared to petrol vehicles. Regionally, Québec had the lowest emissions (12.9 tCO2eq) for EVs while Iowa (62.0) had the highest. In several states, EVs did not outperform ICEVs. Emissions from EVs are expected to decrease in coming years as the carbon intensity of electrical grids decreases. Policies should consider prioritising grid decarbonization over EV uptake where regional grid GHG intensity is high. This work provides one of the first regional and international case studies determining the environmental breakeven points of EVs when considering trade.
Operation Optimization Method of Distribution Network with Wind Turbine and Photovoltaic Considering Clustering and Energy Storage
Authors: Zheng Fangfang , Meng Xiaofang , Wang Lidi , Zhang Nannan
Published: January 2023

The problem of distribution network operation optimization is diversified and uncertain. In order to solve this problem, this paper proposes a method of distribution network operation optimization considering wind-solar clustering, which includes source load and storage. Taking the total operating cost as the objective function, it includes network loss cost, unit operating cost, and considers a variety of constraints such as energy storage device constraints and demand response constraints. This paper aims to optimize the operation according to different wind-solar clustering scenes to improve the economy of distribution network. Taking the 365-day wind-solar output curves as the research object, K-means clustering is carried out, and the best k value is obtained by elbow rule. The second-order cone programming method and solver are used to solve the optimization model of each typical scenario, and the operation optimization analysis of each typical scenario obtained by clustering is carried out. Taking IEEE33 system and local 365-day wind-solar units output scenes as examples, the period is 24 h, which verifies the effectiveness of the proposed method. The proposed method has guiding significance for the operation optimization of distribution network.
The Effect of Green Human Resources Management Practices on Employees’ Affective Commitment and Work Engagement: The Moderating Role of Employees’ Biospheric Value
Authors: Gomes Jorge F. S. , Sabino Ana , Antunes Vanessa
Published: January 2023

Organizational Sustainability is an increasingly important movement in the business world because of its social impact and also for the obligations imposed by state agendas and programs by global entities, such as the United Nations. At the forefront of such a movement is the Human Resources function, given its boundary activities with several critical internal and external stakeholders. The term Green Human Resource Management (GHRM) has been used to describe people management with a focus on green issues. The main objective of this research was to explore the relationship between personal values associated with sustainable behaviors (altruistic, biospheric, egoistic, and hedonic values), organizational outcomes such as engagement and commitment, and GHRM. A questionnaire was prepared and used to collect 532 responses from employees from various organizations. Results show that of the four personal values only the biospheric one moderates the relationship between GHRM practices and affective commitment so that the relationship between HRM and affective commitment is stronger when biospheric values score higher. This is an important finding, as it shows that when people value the biosphere, the effect of GHRM practices on affective commitment and work engagement is stronger than when people value other matters.
Evaluation and Design of Reusable Takeaway Containers Based on the AHP–FCE Model
Authors: Sun Hu , Yang Qihang , Wu Yueqin
Published: January 2023

As a strong and effective alternative to disposable food boxes that cause serious pollution consequences, reusable takeaway containers are promising in terms of environmental protection. However, at present, in the service process of leasing, using, distribution, and recycling, reusable takeaway containers have many problems, such as incomplete cleaning, resulting in unhygienic conditions, repeated use of materials that are not safe enough, food spillage, leakage or theft of customer phone and address information due to exposed takeaway order labels, and wrong containers taken by customers. In addition, there is a lack of objective and comprehensive evaluation methods and systems to guide their design and improvement. In order to solve the current problems of reusable takeaway containers and explore a quantitative approach for evaluation of design solutions from the perspective of users, this paper analyzed user behaviors and needs through user interviews, questionnaires, user journey maps (UJM), and other methods. Analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) were used to construct an evaluation model for the design of reusable takeaway containers, and it was calculated as a function of the survey dataset. The comprehensive index weight model was established, and the design indicators were sorted to obtain the priority of design elements. Three design schemes were proposed and calculated by combining questionnaire data and the FCE method to obtain the optimal Scheme B. The results showed that the combination of UJM and AHP–FCE method framework was suitable for scheme evaluation and design development, which could provide effective and detailed user evaluation for designers and guide the direction of product improvement.
Linguistic Repertoires Embodied and Digitalized: A Computer-Vision-Aided Analysis of the Language Portraits by Multilingual Youth
Authors: Mu Siqing , Li Aoxuan (Douglas) , Shen Lu , Han Lili , Wen Zhisheng (Edward)
Published: January 2023

The present study sets out to investigate how multilingual youth perceive and represent their linguistic repertoires. To achieve this goal, we introduced a computer-vision-aided analytical method to deal with the obtained visual data, which comprised digital images of language portraits created by a group of young multilingual speakers. An OpenCV module is used to build and complete the graphic data processing, enabling quantitative evaluations of participants’ colored clusters and linguistic codes that express their language repertoires. In combination with oral narratives provided in their language portraits, the findings demonstrate that Macanese heritage speakers show a higher degree of “scope” than the Chinese mainland sojourners in Macao but a lower degree of “access”. Follow-up interviews further corroborated the self-perceptions of their linguistic resources across different registers. Overall, the computer-vision-aided analysis of language portraits enhances the current understanding of the “scope” and “access” of multilingual repertoires in lived experience.
Techno-Economic Assessment of APS-Based Poultry Feed Production with a Circular Biorefinery Process
Authors: Buccaro Marta , Toscano Armando , Balzarotti Melissa , Re Ilaria , Bosco Diego , Bettiga Maurizio
Published: January 2023

Poultry livestock profitability significantly depends on feed, accounting for 60–70% of the total production cost, of which protein sources are among of the most expensive ingredients. The maintenance of profitability while meeting feed demand and reducing the environmental impact represents a considerable challenge driving research of alternative protein sources (APS), such as insects and algae meals. This study employs, for the first time, techno-economic assessment (TEA) methodology to evaluate the technological performance and the industrial feasibility of an APS-based poultry feed production method based on the valorization of the pre-treated organic fraction of municipal solid waste (OFMSW) as a substrate for Hermetia illucens larval growth and microalgae cultivation. The Excel-based analysis, which evaluated the mass and energy balance as well as the income statement, was integrated with a thematic analysis focused on exploring how the overall value attributed to the sustainability concept is reflected in the willingness to adopt sustainable business models by entrepreneurs in the poultry sector. Despite the ability to generate revenues, the model cannot be said to be profitable for animal feed production due to the strong dependence of its profitability on scale economy logics. Enabling solutions could be derived from the recovery of abandoned infrastructures, government financial incentives, and integrated systems associating OFMSW treatment with poultry farming, thus resulting in marked economic sustainability and profitability: key elements from the poultry entrepreneurs’ point of view.
Dynamic Fit Optimization and Effect Evaluation of a Female Wetsuit Based on Virtual Technology
Authors: Wu Xinzhou , Cheng Zhe , Kuzmichev Victor E.
Published: January 2023

At present, the traditional mode of research and development for mass-produced wetsuits usually requires repeated sample making and try-on evaluation, and performance cannot be predicted, monitored and evaluated in real time; this can lead to problems including low material utilization and production efficiency. In this study, real human body static and dynamic measurements, material properties and structure data are applied through 3D software to build an accurate virtual model, and new wetsuits are designed through simulation, optimization and evaluation. The static and dynamic fitting performance above and underwater is comprehensively evaluated in virtual and real environments, and it is proved that the virtual development mode can accurately and effectively guide the development and evaluation of wetsuits, and can meet personalized comfort and functional requirements today. This simulation evaluation method avoids repeated sample preparation, some unnecessary waste of materials and environmental pollution, and improves manufacturing efficiency.
Spirituality and Sustainable Development: A Systematic Word Frequency Analysis and an Agenda for Research in Pacific Island Countries
Authors: Luetz Johannes M. , Nichols Elizabeth , du Plessis Karen , Nunn Patrick D.
Published: January 2023

While different in emphasis, spirituality and sustainable development are intertwined concepts that cannot be meaningfully discussed in isolation from each other. This is especially pertinent in Pacific Island countries that are characterised by both high degrees of vulnerability to climate change and high degrees of religious engagement. There is a paucity of research that examines the relationship between spirituality and sustainable development in contemporary human development discourse. To address this gap in the literature, this research employs an inductive and exploratory methodological approach to the study of major development organisations in Australia. It investigates what significance contemporary NGOs ascribe to matters of spirituality in the design and implementation of their community aid and development programming in the Pacific and beyond. To achieve its goal, the study conducts a systematic term frequency analysis in the annual reports of government-funded and independently funded NGOs, both faith-based and secular. It extends previous research by focusing expressly on the intersectionality of sustainable development and spirituality as a fertile space for interdisciplinary inquiry. The findings link development policy and practice more closely to the needs and worldviews of Pacific peoples. A better understanding of the spirituality–sustainability nexus will enable more effective, sustainable, equitable, ethical, and culturally acceptable development programming. Crucially, integrated approaches promise to make ongoing community development programmes and adaptation responses to climate-driven environmental change more effective and sustainable. Finally, it is an important aim of this study to conceptualise various opportunities for future research, thus laying the foundation for an important emergent research agenda.
Motivations of Households towards Conserving Water and Using Purified Water in Czechia
Authors: Lyach Roman , Remr Jiří
Published: January 2023

The need to assess reasons why households do not want to conserve water or use purified water is critical when facing water scarcity during the climate change crisis. This study aimed to provide an analysis of perceptions of the public in Czechia towards water conservation. A representative questionnaire survey (n = 1824) was conducted in the whole Czech Republic to see why households hesitate to conserve water and use purified water. We discovered that most household owners are interested in conserving water and some of them are interested in using purified water. The household owners are willing to conserve water if it does not cost them too much time or comfort. They are mostly willing to think about using purified water for purposes that are hygienically safe. They mostly trust people and public figures that are closest to them, and they consume media like television and the Internet. We recommend that any communication campaign aiming to persuade households to conserve water and use purified water should explain how to effectively conserve water in households. It should also explain that using purified water in households is not unhygienic and is almost completely safe.
Design of Metaheuristic Optimization Algorithms for Deep Learning Model for Secure IoT Environment
Authors: Sagu Amit , Gill Nasib Singh , Gulia Preeti , Singh Pradeep Kumar , Hong Wei-Chiang
Published: January 2023

Because of the rise in the number of cyberattacks, the devices that make up the Internet of Things (IoT) environment are experiencing increased levels of security risks. In recent years, a significant number of centralized systems have been developed to identify intrusions into the IoT environment. However, due to diverse requirements of IoT devices such as dispersion, scalability, resource restrictions, and decreased latency, these strategies were unable to achieve notable outcomes. The present paper introduces two novel metaheuristic optimization algorithms for optimizing the weights of deep learning (DL) models, use of DL may help in the detection and prevention of cyberattacks of this nature. Furthermore, two hybrid DL classifiers, i.e., convolutional neural network (CNN) + deep belief network (DBN) and bidirectional long short-term memory (Bi-LSTM) + gated recurrent network (GRU), were designed and tuned using the already proposed optimization algorithms, which results in ads to improved model accuracy. The results are evaluated against the recent approaches in the relevant field along with the hybrid DL classifier. Model performance metrics such as accuracy, rand index, f-measure, and MCC are used to draw conclusions about the model’s validity by employing two distinct datasets. Regarding all performance metrics, the proposed approach outperforms both conventional and cutting-edge methods.
Convolutional Neural Network-Based Personalized Program Recommendation System for Smart Television Users
Authors: Dudekula Khasim Vali , Syed Hussain , Basha Mohamed Iqbal Mahaboob , Swamykan Sudhakar Ilango , Kasaraneni Purna Prakash , Kumar Yellapragada Venkata Pavan , Flah Aymen , Azar Ahmad Taher
Published: January 2023

The smart home culture is rapidly increasing across the globe and driving smart home users toward utilizing smart appliances. Smart television (TV) is one such appliance that is embedded with smart technology. The users of smart TV have their interests in the programs. However, automatic recommendation of programs for user-to-user is still under-researched. Several papers discussed recommendation systems, but those are related to different applications. Even though there are some works on recommending programs to smart TV users (single-user and multi-user), they did not discuss the smart TV camera module to capture and validate the user image for recommending personalized programs. Hence, this paper proposes a convolutional neural network (CNN)-based personalized program recommendation system for smart TV users. To implement this proposed approach, the CNN algorithm is trained on the datasets ‘CelebFaces Attribute Dataset’ and ‘Labeled Faces in the Wild-People’ for feature extraction and to detect a human face. The trained CNN model is applied to the user image captured by using the smart TV camera module. Further, the captured image is matched with the user image in the ‘synthetic dataset’. Based on this matching, the hybrid filtering technique is proposed and applied; thereby the recommendation of the respective program is done. The proposed CNN algorithm has achieved approximately 95% training performance. Besides, the performance of hybrid filtering is approximately 85% from the single-user perspective and approximately 81% from the multi-user perspective. From this, it is observed that hybrid filtering outperformed conventional content-based filtering and collaborative filtering techniques.