bluuebunny/crossref_metadata_2025 huggingface dataset:
Confocal Microscopy Investigations of Biopolymeric PLGA Nanoparticle Uptake in Arabidopsis thaliana L. Cultured Cells and Plantlet Roots
Authors: De Angelis Giulia , Badiali Camilla , Chronopoulou Laura , Palocci Cleofe , Pasqua Gabriella
Published: June 2023

To date, most endocytosis studies in plant cells have focused on clathrin-dependent endocytosis, while limited evidence is available on clathrin-independent pathways. Since dynamin a is a key protein both in clathrin-mediated endocytosis and in clathrin-independent endocytic processes, this study investigated its role in the uptake of poly-(lactic-co-glycolic) acid (PLGA) nanoparticles (NPs). The experiments were performed on cultured cells and roots of Arabidopsis thaliana. Dynasore was used to inhibit the activity of dynamin-like proteins to investigate whether PLGA NPs enter plant cells through a dynamin-like-dependent or dynamin-like-independent endocytic pathway. Observations were performed by confocal microscopy using a fluorescent probe, coumarin 6, loaded in PLGA NPs. The results showed that both cells and roots of A. thaliana rapidly take up PLGA NPs. Dynasore was administered at different concentrations and exposure times in order to identify the effective ones for inhibitory activity. Treatments with dynasore did not prevent the NPs uptake, as revealed by the presence of fluorescence emission detected in the cytoplasm. At the highest concentration and the longest exposure time to dynasore, the fluorescence of NPs was not visible due to cell death. Thus, the results suggest that, because the NPs’ uptake is unaffected by dynasore exposure, NPs can enter cells and roots by following a dynamin-like-independent endocytic pathway.
Growth Increase in the Herbaceous Plant Centella asiatica by the Plant Growth-Promoting Rhizobacteria Priestia megaterium HyangYak-01
Authors: Jo HyungWoo , Lim Kyeongmo , Ibal Jerald Conrad , Kim Min-Chul , Kim Hye-Been , Baek Chaeyun , Heo Young Mok , Lee Haeun , Kang Seunghyun , Lee Dong-Geol , Shin Jae-Ho
Published: June 2023

Centella asiatica is a traditional herbaceous plant with numerous beneficial effects, widely known for its medicinal and cosmetic applications. Maximizing its growth can lead to beneficial effects, by focusing on the use of its active compounds. The use of plant growth-promoting rhizobacteria (PGPR) is known to be an alternative to chemical fertilizers. In this study, we used the PGPR Priestia megaterium HY-01 to increase the yield of C. asiatica. In vitro assays showed that HY-01 exhibited plant growth-promoting activities (IAA production, denitrification, phosphate solubilization, and urease activity). Genomic analyses also showed that the strain has plant growth-promoting-related genes that corroborate with the different PGP activities found in the assays. This strain was subsequently used in field experiments to test its effectiveness on the growth of C. asiatica. After four months of application, leaf and root samples were collected to measure the plant growth rate. Moreover, we checked the rhizosphere microbiome between the treated and non-treated plots. Our results suggest that treatment with Hyang-yak-01 not only improved the growth of C. asiatica (leaf length, leaf weight, leaf width, root length, root width, and chlorophyll content) but also influenced the rhizosphere microbiome. Biodiversity was higher in the treated group, and the bacterial composition was also different from the control group.
Root and Leaf Anatomy, Ion Accumulation, and Transcriptome Pattern under Salt Stress Conditions in Contrasting Genotypes of Sorghum bicolor
Authors: Karumanchi Appa Rao , Sivan Pramod , Kummari Divya , Rajasheker G. , Kumar S. Anil , Reddy Palakolanu Sudhakar , Suravajhala Prashanth , Podha Sudhakar , Kishor P. B. Kavi
Published: June 2023

Roots from salt-susceptible ICSR-56 (SS) sorghum plants display metaxylem elements with thin cell walls and large diameter. On the other hand, roots with thick, lignified cell walls in the hypodermis and endodermis were noticed in salt-tolerant CSV-15 (ST) sorghum plants. The secondary wall thickness and number of lignified cells in the hypodermis have increased with the treatment of sodium chloride stress to the plants (STN). Lignin distribution in the secondary cell wall of sclerenchymatous cells beneath the lower epidermis was higher in ST leaves compared to the SS genotype. Casparian thickenings with homogenous lignin distribution were observed in STN roots, but inhomogeneous distribution was evident in SS seedlings treated with sodium chloride (SSN). Higher accumulation of K+ and lower Na+ levels were noticed in ST compared to the SS genotype. To identify the differentially expressed genes among SS and ST genotypes, transcriptomic analysis was carried out. Both the genotypes were exposed to 200 mM sodium chloride stress for 24 h and used for analysis. We obtained 70 and 162 differentially expressed genes (DEGs) exclusive to SS and SSN and 112 and 26 DEGs exclusive to ST and STN, respectively. Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis unlocked the changes in metabolic pathways in response to salt stress. qRT-PCR was performed to validate 20 DEGs in each SSN and STN sample, which confirms the transcriptomic results. These results surmise that anatomical changes and higher K+/Na+ ratios are essential for mitigating salt stress in sorghum apart from the genes that are differentially up- and downregulated in contrasting genotypes.
Selection of High-Yielding and Stable Genotypes of Barley for the Cold Climate in Iran
Authors: Pour-Aboughadareh Alireza , Ghazvini Habibollah , Jasemi Seyed Shahriyar , Mohammadi Solaiman , Razavi Sayed Alireza , Chaichi Mehrdad , Ghasemi Kalkhoran Marefat , Monirifar Hassan , Tajali Hamid , Fathihafshjani Asadollah , Bocianowski Jan
Published: June 2023

The interaction between genotypes and environments plays an important role in selecting superior genotypes for target locations. The main objectives of the present study were to analyze the effect of the genotype-by-environment interaction (GEI) and identify superior, newly developed, and promising barley genotypes for cold regions in Iran. For these purposes, a set of genotypes obtained from breeding programs for cold climates in Iran, along with two reference genotypes, were investigated at eight research stations (Tabriz, Ardabil, Arak, Miandoab, Mashhad, Jolge Rokh, Karaj, and Hamadan) during two consecutive growing seasons (2019–2020 and 2020–2021). The results of the freezing test (LT50) showed that most of the tested genotypes had significant cold tolerance at the seedling stage. Based on the additive main effect and multiplicative interaction (AMMI) analysis, environment (E) and GEI effects explained 49.44% and 16.55% of the total variation in grain yield, respectively. Using AMMI1 and AMMI2 models, G2 and G20 were found to be superior genotypes in terms of grain yield and stability. Moreover, AMMI-based stability parameters considered the G20 genotype to be the ideal genotype. A two-plot analysis of the genotype-by-environment interaction (GGE) biplot showed that the 16 experimental environments were grouped into 2 mega-environments. Of the test environments, ARK1 and KAJ2 had the highest discriminating power and representativeness ability, and these were identified as ideal environments for testing advanced genotypes for yield and stability performance during early barley breeding practices in cold areas in Iran. In conclusion, both AMMI and GGE biplot models identified several superior genotypes, among which G20, with a high average yield relative to the overall average yield and the lowest IPC1 score, was found to have high yield stability and is recommended for inclusion in breeding programs for cold climates in Iran.
A Comparative Study on the Roll-to-Roll Processing of a Silicate–Polyvinyl Alcohol Composite Barrier Lacquer Using Slot-Die and Reverse Gravure Coating Techniques
Authors: Schiessl Stefan , Kucukpinar Esra , Rivollier Noémie , Langowski Horst-Christian , Eisner Peter
Published: June 2023

The integration of platelet-shaped montmorillonite particles to improve the oxygen barrier of polyvinyl-alcohol-based barrier layers is state-of-the-art, but research on roll-to-roll coatings of such composite barrier lacquers has not been widely published. In this study, two different coating techniques, slot-die and reverse gravure, were used on a roll-to-roll scale to apply barrier lacquers comprising polyvinyl alcohol and montmorillonite. The lacquers were analyzed regarding viscosity at certain shear rates and surface energy and the dried coating layers regarding oxygen barrier, surface morphology, and particle orientation. Low permeability coefficients delivering a high oxygen barrier of 0.14 and 0.12 cm3 (STP) 1 μmm2 d bar  were achieved for the coating layers with slot-die and reverse gravure coating, respectively. It turned out that the properties of the barrier lacquer need to be adjusted to the coating technique to achieve high oxygen barrier performance. By tailoring the barrier lacquer formulation, the orientation of the platelet-shaped montmorillonite particles can be achieved using both techniques. A low solid content of down to 3 wt% is preferable for the premetered slot-die coating, because it results in low agglomeration quantity in the coating layer. A high solid content of up to 9 wt% is preferable for the self-metered reverse gravure coating to assure a homogeneously coated layer.
A Regression Analysis on Steam Gasification of Polyvinyl Chloride Waste for an Efficient and Environmentally Sustainable Process
Authors: Hasanzadeh Rezgar , Abdalrahman Rzgar M.
Published: June 2023

Over the last few years, researchers have shown a growing interest in polyvinyl chloride (PVC) gasification and have conducted several studies to evaluate and enhance the process. These studies have recognized that processing parameters have a crucial impact on the assessment of PVC gasification. Despite this, there has been limited exploration of the use of machine learning techniques, particularly regression models, to optimize PVC waste gasification. This study aims to investigate the effectiveness of regression models as machine learning algorithms in predicting the performance of PVC waste gasification. The study uses data collected through a validated thermodynamic model, and three different regression models are tested and compared in detail. Cold gas efficiency and normalized carbon dioxide emission are predicted using linear, quadratic, and quadratic with interaction algorithms. The outcomes for emission algorithms reveal that the linear emission algorithm possesses a high R-square value of 97.49%, which indicates its strong predictive capability. Nevertheless, the quadratic algorithm outperforms it, exhibiting an R-square value of 99.81%. The quadratic algorithm with an interaction term, however, proves to be the best among them all, displaying a perfect R-square value of 99.90%. A similar observation is detected for the cold gas efficiency algorithms. These findings suggest that the quadratic algorithm with an interaction term is superior and has a greater predictive accuracy. This research is expected to provide valuable insight into how regression algorithms can be used to maximize the efficiency of PVC waste gasification and reduce its associated environmental concerns.
Improving YOLOv7-Tiny for Infrared and Visible Light Image Object Detection on Drones
Authors: Hu Shuming , Zhao Fei , Lu Huanzhang , Deng Yingjie , Du Jinming , Shen Xinglin
Published: June 2023

To address the phenomenon of many small and hard-to-detect objects in drone images, this study proposes an improved algorithm based on the YOLOv7-tiny model. The proposed algorithm assigns anchor boxes according to the aspect ratio of ground truth boxes to provide prior information on object shape for the network and uses a hard sample mining loss function (HSM Loss) to guide the network to enhance learning from hard samples. This study finds that the aspect ratio difference of vehicle objects under drone perspective is more obvious than the scale difference, so the anchor boxes assigned by aspect ratio can provide more effective prior information for the network than those assigned by size. This study evaluates the algorithm on a drone image dataset (DroneVehicle) and compares it with other state-of-the-art algorithms. The experimental results show that the proposed algorithm achieves superior average precision values on both infrared and visible light images, while maintaining a light weight.
Exploring the Real-Time WRF Forecast Skill for Four Tropical Storms, Isaias, Henri, Elsa and Irene, as They Impacted the Northeast United States
Authors: Khaira Ummul , Astitha Marina
Published: June 2023

Tropical storm Isaias (2020) moved quickly northeast after its landfall in North Carolina and caused extensive damage to the east coast of the United States, with electric power distribution disruptions, infrastructure losses and significant economic and societal impacts. Improving the real-time prediction of tropical storms like Isaias can enable accurate disaster preparedness and strategy. We have explored the configuration, initialization and physics options of the Weather Research and Forecasting (WRF) model to improve the deterministic forecast for Isaias. The model performance has been evaluated based on the forecast of the storm track, intensity, wind and precipitation, with the support from in situ measurements and stage IV remote sensing products. Our results indicate that the Global Forecasting System (GFS) provides overall better initial and boundary conditions compared to the North American Model (NAM) for wind, mean sea level pressure and precipitation. The combination of tropical suite physics options and GFS initialization provided the best forecast improvement, with error reduction of 36% and an increase of the correlation by 11%. The choices for model spin-up time and forecast cycle did not affect the forecast of the storm significantly. In order to check the consistency of the result found from the investigation related to TS Isaias, Irene (2011), Henri (2021) and Elsa (2021), three other tropical storms, were also investigated. Similar to Isaias, these storms are simulated with NAM and GFS initialization and different physics options. The overall results for Henri and Elsa indicate that the models with GFS initialization and tropical suite physics reduced error by 44% and 57%, respectively, which resonates with the findings from the TS Isaias investigation. For Irene, the initialization used an older GFS version and showed increases in error, but applying the tropical physics option decreased the error by 20%. Our recommendation is to consider GFS for the initialization of the WRF model and the tropical physics suite in a future tropical storm forecast for the NE US.
Assimilating FY-3D MWHS2 Radiance Data to Predict Typhoon Muifa Based on Different Initial Background Conditions and Fast Radiative Transfer Models
Authors: Huang Lizhen , Xu Dongmei , Li Hong , Jiang Lipeng , Shu Aiqing
Published: June 2023

In this study, the impact of assimilating MWHS2 radiance data under different background conditions on the analyses and deterministic prediction of the Super Typhoon Muifa case, which hit China in 2022, was explored. The fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data and the Global Forecast System (GFS) analysis data from the National Centers for Environmental Prediction (NCEP) were used as the background fields. To assimilate the Microwave Humidity Sounder II (MWHS2) radiance data into the numerical simulation experiments, the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system were employed. The results show that after the data assimilation, the standard deviation and root-mean-square error of the analysis significantly decrease relative to the observation, indicating the effectiveness of the assimilation process with both background fields. In the MWHS_GFS experiment, a subtropical high-pressure deviation to the east is observed around the typhoon, resulting in its northeast movement. In the differential field of the MWHS_ERA experiment, negative sea-level pressure differences around the typhoon are observed, which increases its intensity. In the deterministic predictions, assimilating the FY3D MWHS2 radiance data reduces the typhoon track error in the MWHS_GFS experiment and the typhoon intensity error in the MWHS_ERA experiment. In addition, it is found that the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for Tovs (RTTOV) model show similar performance in assimilating MWHS2 radiance data for this typhoon case. It seems that the data assimilation experiment with the CRTM significantly reduces the typhoon track error than the experiment with the RTTOV model does, while the intensity error of both experiments is rather comparable.
Target Detection in Hyperspectral Remote Sensing Image: Current Status and Challenges
Authors: Chen Bowen , Liu Liqin , Zou Zhengxia , Shi Zhenwei
Published: June 2023

Abundant spectral information endows unique advantages of hyperspectral remote sensing images in target location and recognition. Target detection techniques locate materials or objects of interest from hyperspectral images with given prior target spectra, and have been widely used in military, mineral exploration, ecological protection, etc. However, hyperspectral target detection is a challenging task due to high-dimension data, spectral changes, spectral mixing, and so on. To this end, many methods based on optimization and machine learning have been proposed in the past decades. In this paper, we review the representatives of hyperspectral image target detection methods and group them into seven categories: hypothesis testing-based methods, spectral angle-based methods, signal decomposition-based methods, constrained energy minimization (CEM)-based methods, kernel-based methods, sparse representation-based methods, and deep learning-based methods. We then comprehensively summarize their basic principles, classical algorithms, advantages, limitations, and connections. Meanwhile, we give critical comparisons of the methods on the summarized datasets and evaluation metrics. Furthermore, the future challenges and directions in the area are analyzed.
Gaussian Process Gaussian Mixture PHD Filter for 3D Multiple Extended Target Tracking
Authors: Yang Zhiyuan , Li Xiangqian , Yao Xianxun , Sun Jinping , Shan Tao
Published: June 2023

This paper addresses the problem of tracking multiple extended targets in three-dimensional space. We propose the Gaussian process Gaussian mixture probability hypothesis density (GP-PHD) filter, which is capable of tracking multiple extended targets with complex shapes in the presence of clutter. Our approach combines the Gaussian process regression measurement model with the probability hypothesis density filter to estimate both the kinematic state and the shape of the targets. The shape of the extended target is described by a 3D radial function and is estimated recursively using the Gaussian process regression model. Furthermore, we transform the recursive Gaussian process regression problem into a state estimation problem by deriving a state space model such that the estimation of the extent can be integrated into the kinematic part. We derive the predicted likelihood function of the PHD filter and provide a closed-form Gaussian mixture implementation. To evaluate the performance of the proposed filter, we simulate a typical extended target tracking scenario and compare the GP-PHD filter with the traditional Gamma Gaussian Inverse-Wishart PHD (GGIW-PHD) filter. Our results demonstrate that the proposed algorithm outperforms the GGIW-PHD filter in terms of estimating both kinematic states and shape. We also investigate the impact of the measurement rates on both filters; it is observed that the proposed filter exhibits robustness across various measurement rates, while the GGIW-PHD filter suffers under low-measurement-rate conditions.
Enhancing Human–Robot Collaboration through a Multi-Module Interaction Framework with Sensor Fusion: Object Recognition, Verbal Communication, User of Interest Detection, Gesture and Gaze Recognition
Authors: Paul Shuvo Kumar , Nicolescu Mircea , Nicolescu Monica
Published: June 2023

With the increasing presence of robots in our daily lives, it is crucial to design interaction interfaces that are natural, easy to use and meaningful for robotic tasks. This is important not only to enhance the user experience but also to increase the task reliability by providing supplementary information. Motivated by this, we propose a multi-modal framework consisting of multiple independent modules. These modules take advantage of multiple sensors (e.g., image, sound, depth) and can be used separately or in combination for effective human–robot collaborative interaction. We identified and implemented four key components of an effective human robot collaborative setting, which included determining object location and pose, extracting intricate information from verbal instructions, resolving user(s) of interest (UOI), and gesture recognition and gaze estimation to facilitate the natural and intuitive interactions. The system uses a feature–detector–descriptor approach for object recognition and a homography-based technique for planar pose estimation and a deep multi-task learning model to extract intricate task parameters from verbal communication. The user of interest (UOI) is detected by estimating the facing state and active speakers. The framework also includes gesture detection and gaze estimation modules, which are combined with a verbal instruction component to form structured commands for robotic entities. Experiments were conducted to assess the performance of these interaction interfaces, and the results demonstrated the effectiveness of the approach.
Motorway Traffic Emissions Estimation through Stochastic Fundamental Diagram
Authors: Gemma Andrea , Giannattasio Orlando , Mannini Livia
Published: June 2023

Travel time, or, more generally, level of service, has always been considered the main parameter with which to design roads, particularly in extra-urban areas where geometries and policies, such as speed limits, play a key role in the performance achieved. Unfortunately, this type of approach does not consider the impact on emissions that is obtained when only performance-based goals are pursued. The paper deals with the analysis of the impact on emissions and fuel consumption under different traffic conditions, and we present a new methodology for emission estimation based on the stochastic formulation of the fundamental diagram in a highway environment. The proposed methodology estimates the emissions using a stochastic adaptation of the CORINAIR methodology based on COPERT software on both specific vehicle types and the average Italian vehicle fleet. As expected, due to the convexity of the emission function, accounting for speed dispersion leads to an increase in energy consumption and emissions. Tests show that the stochastic component can lead to an increase in the emission estimation up to 5.5% and, therefore, it should be considered. The methodology has been applied by means of real trajectories, and the results of the application show that performance optimization strategies can contrast with sustainability and emission reduction policies. Results show that for some vehicular classes, emissions or fuel consumption are highly dependent on speed, with different proportionalities. In all cases, the minimum consumption is obtained at speeds ranging from 70 to 90 km/h. The analysis of the curves shows that an increase in speeds, even to reach low speeds, generally leads to an increase in energy consumption and emissions per kilometer traveled and, therefore, is independent of the decrease in travel time.
Measurement and Promotion Strategy of China’s Power System Regulation Capacity
Authors: Zhai Zhengyuan , Zhang Lei , Hou Xiaochao
Published: June 2023

Power system regulation capacity is the key factor affecting the development and consumption of renewable energy. Based on China’s policy to promote the consumption of renewable energy, this paper constructs an evaluation index system of power system regulation capability covering four dimensions: the supply side, grid side, load side, and support system. The entropy method is used to measure the power system regulation capability of 30 provinces during the 13th Five-Year Plan period. The results showed: (1) The national average power system regulation capacity index is 0.18, and only less than one-third of provinces scored higher than the average. (2) The contribution of each dimension is significantly different, and the supply side regulation capability was the highest (0.315). The regulation capability of the eastern region is stronger than that of the central region and the western region. From the perspective of subdivided fields, this study focuses on exploring five areas of power system regulation capacity construction, including electric vehicle energy storage, thermal power flexibility, regional power grid regulation, electric vehicle market, and grid construction, to tap greater development potential.
Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information
Authors: Alshehri Abdullah , Owais Mahmoud , Gyani Jayadev , Aljarbou Mishal H. , Alsulamy Saleh
Published: June 2023

Traffic management and control applications require comprehensive knowledge of traffic flow data. Typically, such information is gathered using traffic sensors, which have two basic challenges: First, it is impractical or impossible to install sensors on every arc in a network. Second, sensors do not provide direct information on origin-to-destination (O–D) demand flows. Consequently, it is essential to identify the optimal locations for deploying traffic sensors and then enhance the knowledge gained from this link flow sample to forecast the network’s traffic flow. This article presents residual neural networks—a very deep set of neural networks—to the problem for the first time. The suggested architecture reliably predicts the whole network’s O–D flows utilizing link flows, hence inverting the standard traffic assignment problem. It deduces a relevant correlation between traffic flow statistics and network topology from traffic flow characteristics. To train the proposed deep learning architecture, random synthetic flow data was generated from the historical demand data of the network. A large-scale network was used to test and confirm the model’s performance. Then, the Sioux Falls network was used to compare the results with the literature. The robustness of applying the proposed framework to this particular combined traffic flow problem was determined by maintaining superior prediction accuracy over the literature with a moderate number of traffic sensors.
Development of Scales for the Measurement of Executive Green Leadership and Exploration of Its Antecedents
Authors: Wang Yang , Tian Chenling , Jiang Xia , Tong Yang
Published: June 2023

Drawing on the existing research on green leadership, this paper first examines the concept and structure of executive green leadership and develops a preliminary scale to measure executive green leadership. The confirmatory factor analysis is adopted to verify and revise the scale. The results show that green leadership and green person are the two main structures of executive green leadership, and the scale developed in this paper is of good reliability and validity. After data analysis, this paper then explores the antecedents of executive green leadership. The results show that factors such as corporate executives’ internal moral identity, conscientiousness, pro-environmental intention, command-based environmental regulation, market-based environmental regulation, and corporate green image have a significant positive correlation with executive green leadership, while their short-term orientation has a significant negative correlation with the green leadership. This paper defines the concept and structure of executive green leadership and develops the corresponding scale for measuring it, to improve scholars’ and managers’ understanding of executive green leadership.
Association of the Built Environment with Residents’ Car Dependence: Evidence from Shenzhen, China
Authors: Jiang Jie , Wu Jiaorong , Zhang Xiaochun , Sun Maopeng , Chen Shu
Published: June 2023

Reducing car dependence is the key to achieving the goal of green and sustainable development. Compared with the existing studies, which mainly focus on administrative areas, this study takes residential areas as the research unit. Four spatial regression models were used to investigate the effect on car dependence of six factors of the built environment (land use mix, population density, jobs–housing balance, bus stop density, metro station density, and road network density). Various test results show that the geography-weighted regression (GWR) model has more substantial explanatory power and that the estimated coefficients of built environment characteristics vary positively or negatively in diverse residential communities. The findings demonstrate that the impact of built environment characteristics on car dependence is significantly spatially heterogeneous. These results are conducive to better comprehending how built environment factors affect car dependence and help establish policies and strategies to promote sustainable transportation.
Game Analysis on Energy Enterprises’ Digital Transformation—Strategic Simulation for Guiding Role, Leading Role and Following Role
Authors: Liu Pingkuo , Wu Jiahao
Published: June 2023

The aim of this study is to explore the dynamics and impediments in exploring the digital transformation process of energy enterprises, considering industry competition and government involvement. Compared with other industries, energy enterprises have both economic tasks and social responsibilities at the same time, while their business modes have certain “inertia”. Therefore, the process of their digital transformation cannot avoid the balance of interests between the different agents. From the perspective of competition and cooperation in the sector, this study constructs a tripartite evolutionary game model among the government and energy enterprises, analyzes the evolutionary stable strategies of the game system, and simulates different initial intentions and key parameters for all roles. The results show that in the process of digital transformation, the symbiotic relationship between energy enterprises and the cooperative relationship between enterprises and government can be embodied, and the effective game process has sufficient economic guidance. The government plays the guiding role in the digital transformation of energy enterprises, and its initial intention has a significantly stronger impact than the energy enterprise’s intentions. The effective strategy reflects the principle of “waiting for an opportunity to act, giving priority to efficiency, and giving consideration to justice”. Under the given policy environment, energy enterprises with comparative advantages in terms of transformation costs, direct benefits and synergy will become the leading role that is more sensitive to the opportunities of digital transformation, and the following energy enterprise will adjust its own strategies in time according to the effect of the leading role’s digital transformation so as to achieve the stability of the system. Accordingly, this study can provide reference support for energy enterprises to develop digital transformation strategies and for governments to formulate reasonable and effective policies.
On Gas Desorption-Diffusion Regularity of Bituminous Coal with Different Particle Sizes and Its Influence on Outburst-Coal Breaking
Authors: Zheng Jie , Liang Qinming , Zhang Xin , Huang Jinyong , Yan Wei , Huang Gun , Liu Honglin
Published: June 2023

Coal and gas outburst is an urgent and constantly perplexing problem with coal resource extraction, threatening coal mine safe and sustainable production severely. Its mechanism and the participation of gas in coal breaking are still unclear. To explore this problem, in this paper, gas desorption-diffusion regularity of bituminous coal with different particle sizes and its influence on outburst-coal breaking were investigated through mercury intrusion porosimetry (MIP) tests, isothermal adsorption tests, and desorption-diffusion tests for coal particles with different sizes. The results indicated that the cumulative diffusion amount (Qt) and rate (Qt/Q∞), the effective diffusion coefficient (D′), and the kinetic diffusion parameter (υ) decreased as particle size increased. That meant gas was easier to desorb and diffuse from the smaller coal blocks, consequently making coal break into more tiny particles and accelerating gas desorption. As a result, a positive feedback effect that coal breaks continuously and gas releases rapidly and abundantly was formed in a short time when outbursts started, which caused gas release in quantities and promoted the occurrence of outbursts. The findings of this study enhance our understanding of the mechanism of gas participating in coal fragmentation during outbursts, which are significantly conducive to the prevention and control of coal mine disasters and sustainable production of coal resources.
Development of Education Field Student Digital Competences—Student and Stakeholders’ Perspective
Authors: Sarva Edīte , Lāma Gatis , Oļesika Alise , Daniela Linda , Rubene Zanda
Published: June 2023

Alongside reading, writing, and numeracy digital literacy has become an increasingly crucial component of functional competences in the modern era. Digital competences are essential for educators who wish to stay updated with the changing needs of their students and the education sector. These competences can help them engage their students better, improve their teaching effectiveness, and advance their careers. Considering the ongoing changes in both the field of education and the field of technologies, it is important to explore the current needs for improving digital competences of educators including students in the field of education, to provide them with the necessary support. This study aims to develop recommendations for improving digital competence of educators by combining students’ self-assessment through surveys with stakeholder opinions concerning digital competence development of educators ascertained in a focus group discussion. Results of the research reveal that education students do not have a statistically different self-assessment of their digital competences than students from other study fields. And although students evaluate their digital competences as high, stakeholders point out that there is a lack of digital competences to carry out technology-enhanced learning and a negative attitude towards digitalization of education among all educators’ age groups. Improving the availability of digital resources and technological support as well as incorporating organisational level strategies that require application and improvement of digital competence could remedy the issue.
Year-Round Testing of Coastal Waters of the Gulf of Gdańsk in the Baltic Sea for Detecting Oil in a Seawater Column Using the Fluorescence Method
Authors: Baszanowska Emilia , Otremba Zbigniew
Published: June 2023

Progressive climate changes and the increase in the occurrence of extreme weather phenomena indicate the need to take action to mitigate the negative effects of climate change. One of the main factors affecting climate change is the state of waters that transport heat. Oil pollution present in the water contributes to the absorption of radiation and physico-chemical changes in the sea, which has an impact on the marine ecosystem. This indicates the need to develop methods for effective oil spill detection. This study aimed to improve the methods of early detection of threats related to oil spills in the marine environment, especially when the source of oil may be invisible in the depths of the sea. Therefore, the method based on the fluorometric index is proposed, and its effectiveness for oil detection in seawater is studied. The study has answered the question of how biological activity during a whole year influences the effectiveness of oil detection by the proposed fluorometric index method. Therefore, for the calculation of the fluorometric index, the changes in the seawater fluorescence spectrum in the ultraviolet range were determined, which occurred under the influence of diffusion of some oil components in the sea. The principle of detection of oil contaminants based on the excitation-emission fluorescence spectrum is described. For the measurements, natural seawater samples used in the laboratory were exposed to a mixture of crude oil and oils commonly found in navigation. The effectiveness of oil substance detection using the fluorometric index in the biologically productive and unproductive seasons was analyzed for seawater in the vicinity of Gdynia and Gdansk ports in Poland in northern Europe. The results of excitation-emission spectra and fluorometric index indicate that the changes in the biological activity during the year do not affect the detectability of oil present in seawater for the considered oil-to-water ratio. Summarize the sensitivity analysis of the method indicates the possibility of detection of oil contamination regardless of the season. The obtained results pave the way for the construction of an underwater device to detect oil in the vicinity of such a detector.
Resource-Oriented Sanitation: On-Farm Septage Treatment and Nutrient Recycling for Sustainable Agriculture in the Philippines
Authors: Pausta Carla Mae Jabar , Promentilla Michael Angelo Baliwag , Longos Alberto Leyes , Orbecido Aileen Huelgas , Beltran Arnel Bas , Damalerio Regina Gador , Suplido Maria Eda Apple Artesano , Saroj Devendra
Published: June 2023

Nutrient recovery technologies have been constantly developed and optimised to address challenges in water and wastewater management, sanitation, and agri-food systems, while promoting sustainable management of resources and circular phosphorous economy. However, these technologies have been rarely explored beyond the laboratory-scale in developing countries where it is mostly needed. In this study, a nutrient recovery batch reactor system was installed at a local farm in the Philippines to process raw septage from an onsite sanitation system, a septic tank, to recover a high-value fertiliser for local crop production. The batch reactor was used for two processes, namely acid hydrolysis for pre-treatment of septage and chemical precipitation for recovered phosphorous fertiliser (RPF). The recovered fertiliser was then applied to produce eggplants and tomatoes, which are the common crops grown in the farm. Results show that an average of 290 g of RPF was produced for every 100 L of raw septage processed. With hydrolysis, 77% of the phosphate concentration were released as phosphates from the solid component of the raw septage. About 98.5% of phosphates were recovered from the hydrolysed septage. The RPF when applied to the farm’s eggplants and tomatoes has yields comparable to that of the commercial fertilisers. This study was able to demonstrate the potential of a resource-oriented sanitation system that promotes nutrient recycling towards sustainable agriculture that further leads to meeting the United Nations sustainable development goals, particularly zero hunger (goal 2), clean water and sanitation (goal 6), sustainable cities and communities (goal 11), and responsible consumption and production (goal 12).
Influence of Low Air Pressure on the Partial Denitrification-Anammox (PD/A) Process
Authors: Dai Wen , Han Zhenpeng , Lu Yongze , Li Shuping , Yan Gangyin , Zhu Guangcan
Published: June 2023

Low air pressure is a feature of high-altitude regions. Domestic wastewater from such regions typically has a low carbon-to-nitrogen ratio (C/N ratio). These factors combine to make traditional biological nitrogen removal in high-altitude regions inefficient and more energy-intensive. The partial denitrification-anaerobic ammonium oxidation (PD/A) process was reported to remove ammonia nitrogen from municipal sewage, consuming fewer carbon sources and requiring no aeration supply. In this study, we set up laboratory-scale reactors in simulated high-altitude environmental conditions, and studied the effect of air pressure on the PD/A process. We found that low pressure promotes nitrogen removal efficiency (NRE), achieving 93.0 ± 0.3% at 65 kPa, and the contribution rate of anaerobic ammonium oxidation (anammox) to nitrogen removal increased to 77.7%. Lower dissolved oxygen (DO) concentrations caused by lower air pressure were the reason for higher nitrite accumulation efficiency (NAE) in a partial denitrification (PD) system, with measured values of 78.4 ± 2.8% at 65 kPa. The anammox process was promoted by low air pressure, mainly because the low air pressure resulted in higher anaerobic ammonia-oxidizing bacteria activity, with specific anammox activity (SAA) reaching 26.3 mg·N/(g·VSS·d). Although the relative abundance of partial-denitrifying bacteria declined slightly, at 65 kPa compared with 96 kPa, they were still the dominant genus of the PD/A sludge, and continued to generate nitrite nitrogen steadily, even at low air pressures. The anaerobic ammonia-oxidizing bacterial abundance remained relatively stable, but their activity was increased, which aided the PD/A process. This study demonstrates how low pressure promotes the PD/A process, indicating the possibility of sustainable improved nitrogen removal in high-altitude regions.
Investigating the Role of Perceived Risk, Perceived Security and Perceived Trust on Smart m-Banking Application Using SEM
Authors: Almaiah Mohammed Amin , Al-Otaibi Shaha , Shishakly Rima , Hassan Lamia , Lutfi Abdalwali , Alrawad Mahmoad , Qatawneh Mohammad , Alghanam Orieb Abu
Published: June 2023

Effective security support remains a challenge, even for mobile banking applications; this is leading to the loss of many customers due to limited protection of customer data and privacy. Cyber threats include everything from identity theft to malware threats and email and online fraud. Thus, businesses and individuals should use risk assessment methods and countermeasures to protect their m-banking apps. With this in mind, a new model using the Technology Acceptance Model (TAM) has been proposed. The model has been broken down into six main countermeasure categories, namely: perceived risk, perceived security, perceived trust, ease of use, usefulness and service quality. To test this model, structural equation modelling (SEM) was used. Our findings reveal that perceived security, perceived trust and service quality play key roles in improving the adoption of mobile banking apps. In addition, the findings indicate that perceived risk had a negative impact on both clients’ trust and their attitudes toward the use of mobile banking services. The proposed model could increase the adoption of m-banking apps by enhancing their defenses against security risk issues. The model enhances the risk reduction (63.0%), data protection (75.0%), trust (32.1%), quality of service (74.0%), ease of use (44.0%) and usefulness (45.3%) ratios.
Training Leaders to Facilitate an Energy Transition: Retrospective Evaluation of Course Design
Authors: Newman Jonathan , Mills Sarah , Soderstrom Sara
Published: June 2023

While there is a widely shared sense that policy action is required for the electricity system in the United States to decarbonize, most climate policy courses focus only on a handful of federal and state policies. In reality, however, there is a web of state- and local-government-level policy choices that can serve to facilitate or hinder an energy transition, which is less discussed and researched in higher education. A new graduate course, first taught in Winter 2019 at the University of Michigan in the Ford School of Public Policy, employs a range of unique course design elements to introduce the idea of a complex web of policies and actors in the energy transition, and to provide students with practical skills to prepare them to be leaders in the transition. By interviewing students 18 months after finishing the Winter 2019 iteration of the course, in addition to surveying students enrolled in the second interaction of the course in the Fall 2020 semester, this study finds that student experiential learning and applied projects, in tandem with the instructor’s focus on local, real-world implications, was found to be effective in preparing students to be climate leaders.
Green Corporate Governance, Green Finance, and Sustainable Performance Nexus in Chinese SMES: A Mediation Moderation Model
Authors: Wang Lei , Ur Rehman Amin , Xu Zhaocheng , Amjad Fiza , Ur Rehman Shams
Published: June 2023

This study explores the connection between corporate governance and sustainability performance through the mediating role of corporate governance and the moderating role of top management environmental concern, taking into account the perspectives of agency theory and stakeholder theory. Data were collected through a questionnaire survey of 314 employees working in SMEs operating in China, and the data analysis was carried out using Smart PLS 4 and SPSS. The results indicate that green corporate governance and green finance have a significant impact on corporate social responsibility, which in turn positively affects sustainable performance. Corporate social responsibility significantly mediates the link between green corporate governance and sustainable performance. Meanwhile, corporate social responsibility also mediates the relationship between green finance and sustainable performance. Additionally, top management environmental concern moderates the relationship between corporate governance and sustainable performance significantly, strengthening the impact of corporate social responsibility on sustainable performance. The study contributes to the literature by exploring the relationship between corporate governance, green finance, and sustainable performance in the context of Chinese SMEs. The study’s findings have significant implications for policymakers and managers interested in promoting sustainable development.
The Creative and Cultural Industries: Towards Sustainability and Recovery
Authors: Komorowski Marlen , Lewis Justin
Published: June 2023

We are delighted to introduce this Special Issue of Sustainability, entitled “The Creative and Cultural Industries: Towards Sustainability and Recovery” [...]