bluuebunny/crossref_metadata_2025 huggingface dataset:
R-Unet: A Deep Learning Model for Rice Extraction in Rio Grande do Sul, Brazil
Authors: Fu Tingyan , Tian Shufang , Ge Jia
Published: August 2023

Rice is one of the world’s three major food crops, second only to sugarcane and corn in output. Timely and accurate rice extraction plays a vital role in ensuring food security. In this study, R-Unet for rice extraction was proposed based on Sentinel-2 and time-series Sentinel-1, including an attention-residual module and a multi-scale feature fusion (MFF) module. The attention-residual module deepened the network depth of the encoder and prevented information loss. The MFF module fused the high-level and low-level rice features at channel and spatial scales. After training, validation, and testing on seven datasets, R-Unet performed best on the test samples of Dataset 07, which contained optical and synthetic aperture radar (SAR) features. Precision, intersection, and union (IOU), F1-score, and Matthews correlation coefficient (MCC) were 0.948, 0.853, 0.921, and 0.888, respectively, outperforming the baseline models. Finally, the comparative analysis between R-Unet and classic models was completed in Dataset 07. The results showed that R-Unet had the best rice extraction effect, and the highest scores of precision, IOU, MCC, and F1-score were increased by 5.2%, 14.6%, 11.8%, and 9.3%, respectively. Therefore, the R-Unet proposed in this study can combine open-source sentinel images to extract rice timely and accurately, providing important information for governments to implement decisions on agricultural management.
Water Surface Acoustic Wave Detection by a Millimeter Wave Radar
Authors: Zeng Yuming , Shen Siyi , Xu Zhiwei
Published: August 2023

Feature extraction and recognition of underwater targets are important in military and civilian areas. This paper studied water surface acoustic wave (WSAW) detection by a millimeter wave (mmWave) radar. The mmWave-based endpoint detection method of the WSAW was introduced. Simulated results show that the continuous wavelet transform (CWT) method has a better detection performance. A 77 GHz large aperture antenna mmWave radar sensor and an underwater acoustic transmitter have been applied to conduct laboratory experiments. Still water surface experimental results verify that the CWT method has better detection capability, and the mmWave radar can accurately detect even 155 nm WSAW. Wavy water surface experimental results demonstrate the ability of the mmWave radar to analyze the time-frequency feature of the weak WSAW signal. These works indicate the potential of mmWave radar for the cross-medium detection and recognition of underwater targets.
Improved On-Orbit MTF Measurement Method Based on Point Source Arrays
Authors: Li Litao , Cao Jiayang , Wei Shaodong , Jiang Yonghua , Shen Xin
Published: August 2023

The modulation transfer function (MTF) is a key characteristic used to assess the performance of optical remote sensing satellite sensors. MTF detection can directly measure a sensor’s two-dimensional (2D) point spread function (PSF); therefore, it has been applied to various high-resolution remote sensing satellites (e.g., Pleiades) using point sources. However, current point source methods mainly use 2D Gaussian functions to fit the discrete digital number (DN) of the point source on the image to extract the center of the point source and fit the PSF after encrypting multiple point sources; thus, noise robustness is poor and measurement accuracy varies widely. In this study, we developed a noise-resistant on-orbit MTF detection method based on the object space constraint among point source arrays. Utilizing object space constraint relationships among points in a point source array, a homography transformation model was established, enabling accurate extraction of sub-pixel coordinates for each point source response. Subsequently, aligning the luminosity distribution of all point sources concerning a reference point source, the encrypted PSF was obtained and then fitted to obtain the MTF. To validate the method, Gaofen-2 (GF-2) satellite images were used to conduct an in-orbit imaging experiment on the point source array of the Chinese Zhongwei remote sensing satellite calibration site. Compared with the Gaussian model methods, the proposed method yielded more accurate peak positions for each point source. Standard deviations of peak position constant ratios in along- and cross-track directions improved by 2.8 and 4.8 times, respectively. The root-mean-square error (RMSE) of the collinearity test results increased by 92%, and the noise resistance of the MTF curve improved by two times. Dynamic MTF values at the Nyquist frequency for the GF-2 panchromatic band in along- and cross-track directions were 0.0476 and 0.0705, respectively, and MTF values in different directions were well distinguished.
Distinct Impacts of Two Types of Developing El Niño–Southern Oscillations on Tibetan Plateau Summer Precipitation
Authors: Liu Minghong , Ren Hong-Li , Wang Run , Ma Jieru , Mao Xin
Published: August 2023

El Niño–Southern Oscillation (ENSO) has remarkable impacts on Tibetan Plateau (TP) summer precipitation. However, recently identified ENSO spatial diversity brings complexity to these impacts. This study investigates the distinct impacts of the Eastern Pacific (EP) and Central Pacific (CP) ENSOs on TP summer precipitation based on numerous precipitation data and satellite-observed and reanalyzed circulation data. The results show that the EP El Niño and the CP La Niña have opposite effects on summer precipitation in the southwestern TP, with significant decreases and increases, respectively, indicating a trans-type inversion. In contrast, the CP El Niño causes significant decreases in summer precipitation in the central-eastern TP. No significant anomaly occurs during the EP La Niña. Moisture budget and circulation analyses suggest that these distinct precipitation characteristics can be attributed to different atmospheric teleconnections, which provide varying vertical motion and moisture conditions. The EP El Niño triggers an atmospheric response similar to the Indian Summer Monsoon–East Asian Summer Monsoon teleconnection, and the CP La Niña generates a teleconnection in the opposite phase. In contrast, the CP El Niño mainly causes a teleconnection resembling the East Asian–Pacific pattern. This study may deepen our understanding of ENSO impacts on TP summer precipitation and have implications for regional climate predictions.
A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science
Authors: Velichko Andrei , Boriskov Petr , Belyaev Maksim , Putrolaynen Vadim
Published: August 2023

The study presents a bio-inspired chaos sensor model based on the perceptron neural network for the estimation of entropy of spike train in neurodynamic systems. After training, the sensor on perceptron, having 50 neurons in the hidden layer and 1 neuron at the output, approximates the fuzzy entropy of a short time series with high accuracy, with a determination coefficient of R2~0.9. The Hindmarsh–Rose spike model was used to generate time series of spike intervals, and datasets for training and testing the perceptron. The selection of the hyperparameters of the perceptron model and the estimation of the sensor accuracy were performed using the K-block cross-validation method. Even for a hidden layer with one neuron, the model approximates the fuzzy entropy with good results and the metric R2~0.5 ÷ 0.8. In a simplified model with one neuron and equal weights in the first layer, the principle of approximation is based on the linear transformation of the average value of the time series into the entropy value. An example of using the chaos sensor on spike train of action potential recordings from the L5 dorsal rootlet of rat is provided. The bio-inspired chaos sensor model based on an ensemble of neurons is able to dynamically track the chaotic behavior of a spike signal and transmit this information to other parts of the neurodynamic model for further processing. The study will be useful for specialists in the field of computational neuroscience, and also to create humanoid and animal robots, and bio-robots with limited resources.
A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft
Authors: Wisal Muhammad , Oh Ki-Yong
Published: August 2023

Rotor unbalance is the most common cause of vibration in industrial machines. The unbalance can result in efficiency losses and decreased lifetime of bearings and other components, leading to system failure and significant safety risk. Many complex analytical techniques and specific classifiers algorithms have been developed to study rotor imbalance. The classifier algorithms, though simple to use, lack the flexibility to be used efficiently for both low and high numbers of classes. Therefore, a robust multiclass prediction algorithm is needed to efficiently classify the rotor imbalance problem during runtime and avoid the problem’s escalation to failure. In this work, a new deep learning (DL) algorithm was developed for detecting the unbalance of a rotating shaft for both binary and multiclass identification. The model was developed by utilizing the depth and efficacy of ResNet and the feature extraction property of Convolutional Neural Network (CNN). The new algorithm outperforms both ResNet and CNN. Accelerometer data collected by a vibration sensor were used to train the algorithm. This time series data were preprocessed to extract important vibration signatures such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). STFT, being a feature-rich characteristic, performs better on our model. Two types of analyses were carried out: (i) balanced vs. unbalanced case detection (two output classes) and (ii) the level of unbalance detection (five output classes). The developed model gave a testing accuracy of 99.23% for the two-class classification and 95.15% for the multilevel unbalance classification. The results suggest that the proposed deep learning framework is robust for both binary and multiclass classification problems. This study provides a robust framework for detecting shaft unbalance of rotating machinery and can serve as a real-time fault detection mechanism in industrial applications.
Microservice Application Scheduling in Multi-Tiered Fog-Computing-Enabled IoT
Authors: Ashraf Maria , Shiraz Muhammad , Abbasi Almas , Alqahtani Omar , Badshah Gran , Lasisi Ayodele
Published: August 2023

Fog computing extends mobile cloud computing facilities at the network edge, yielding low-latency application execution. To supplement cloud services, computationally intensive applications can be distributed on resource-constrained mobile devices by leveraging underutilized nearby resources to meet the latency and bandwidth requirements of application execution. Building upon this premise, it is necessary to investigate idle or underutilized resources that are present at the edge of the network. The utilization of a microservice architecture in IoT application development, with its increased granularity in service breakdown, provides opportunities for improved scalability, maintainability, and extensibility. In this research, the proposed schedule tackles the latency requirements of applications by identifying suitable upward migration of microservices within a multi-tiered fog computing infrastructure. This approach enables optimal utilization of network edge resources. Experimental validation is performed using the iFogSim2 simulator and the results are compared with existing baselines. The results demonstrate that compared to the edgewards approach, our proposed technique significantly improves the latency requirements of application execution, network usage, and energy consumption by 66.92%, 69.83%, and 4.16%, respectively.
Application-Aware Scheduling for IEEE 802.15.4e Time-Slotted Channel Hopping Using Software-Defined Wireless Sensor Network Slicing
Authors: Sayjari Tarek , Melo Silveira Regina , Borges Margi Cintia
Published: August 2023

Given the improvements to network flexibility and programmability, software-defined wireless sensor networks (SDWSNs) have been paired with IEEE 802.15.4e time-slotted channel hopping (TSCH) to increase network efficiency through slicing. Nonetheless, ensuring the quality of service (QoS) level in a scalable SDWSN remains a significant difficulty. To solve this issue, we introduce the application-aware (AA) scheduling approach, which isolates different traffic types and adapts to QoS requirements dynamically. To the best of our knowledge, this approach is the first to support network scalability using shared timeslots without the use of additional hardware while maintaining the application’s QoS level. The AA approach is deeply evaluated compared with both the application traffic isolation (ATI) approach and the application’s QoS requirements using the IT-SDN framework and by varying the number of nodes up to 225. The evaluation process took into account up to four applications with varying QoS requirements in terms of delivery rate and delay. In comparison with the ATI approach, the proposed approach enhanced the delivery rate by up to 28% and decreased the delay by up to 57%. Furthermore, even with four applications running concurrently, the AA approach proved capable of meeting a 92% delivery rate requirement for up to 225 nodes and a 900 ms delay requirement for up to 144 nodes.
Enhancing the Functionality of a Grid-Connected Photovoltaic System in a Distant Egyptian Region Using an Optimized Dynamic Voltage Restorer: Application of Artificial Rabbits Optimization
Authors: Ibrahim Nagwa F. , Alkuhayli Abdulaziz , Beroual Abderrahmane , Khaled Usama , Mahmoud Mohamed Metwally
Published: August 2023

Photovoltaic (PV) systems are crucial to the production of electricity for a newly established community in Egypt, especially in grid-tied systems. Power quality (PQ) issues appear as a result of PV connection with the power grid (PG). PQ problems cause the PG to experience faults and harmonics, which affect consumers. A series compensator dynamic voltage restorer (DVR) is the most affordable option for resolving the abovementioned PQ problems. To address PQ difficulties, this paper describes a grid-tied PV combined with a DVR that uses a rotating dq reference frame (dqRF) controller. The main goal of this study is to apply and construct an effective PI controller for a DVR to mitigate PQ problems. The artificial rabbits optimization (ARO) is used to obtain the best tune of the PI controller. The obtained results are compared with five optimization techniques (L-SHADE, CMAES, WOA, PSO, and GWO) to show its impact and effectiveness. Additionally, Lyapunov’s function is used to analyze and evaluate the proposed controller stability. Also, a mathematical analysis of the investigated PV, boost converter, and rotating dqRF control is performed. Two fault test scenarios are examined to confirm the efficacy of the suggested control approach. The parameters’ (voltage, current, and power) waveforms for the suggested system are improved, and the system is kept running continuously under fault periods, which improves the performance of the system. Moreover, the findings demonstrate that the presented design successfully keeps the voltage at the required level with low THD% values at the load side according to the IEEE standards and displays a clear enhancement in voltage waveforms. The MATLAB/SIMULINK software is used to confirm the proposed system’s performance.
Dynamic 3D Measurement without Motion Artifacts Based on Feature Compensation
Authors: Hu Guoce , Wang Jun , Deng Huaxia , Ma Mengchao , Zhong Xiang
Published: August 2023

Phase-shift profilometry (PSP) holds great promise for high-precision 3D shape measurements. However, in the case of measuring moving objects, as PSP requires multiple images to calculate the phase, the movement of the object causes artifacts in the measurement, which in turn has a significant impact on the accuracy of the 3D surface measurement. Therefore, we propose a method to reduce motion artifacts using feature information in the image and simulate it using the six-step term shift method as a case study. The simulation results show that the phase of the object is greatly affected when the object is in motion and that the phase shift due to motion can be effectively reduced using this method. Finally, artifact optimization was carried out by way of specific copper tube vibration experiments at a measurement frequency of 320 Hz. The experimental results prove that the method is well implemented.
Roll Eccentricity Detection and Application Based on SFT and Regional DFT
Authors: Yang Kexin , Zheng Gang , Yang Zhe
Published: August 2023

Roll eccentricity disturbance is a high-frequency periodic change signal caused by the irregular shape of the roll and roll bearing, which is difficult to identify and affects the periodic deviation of the exit thickness of the strip. To achieve rapid identification of the source and a mathematical model of roll eccentricity signals, a sparse Fourier transform (SFT) and regional DFT method for roll eccentricity signal recognition and detection was proposed. This method utilizes SFT to calculate the signal FFT more quickly based on the sparsity of the signal frequency domain. Under the premise of knowing the roll diameter, the signal frequency spectrum is identified online, the amplitude and phase are identified through regional DFT, and the eccentricity disturbance is compensated on site. The simulation results show that this method can accurately identify the source of roll disturbance, quickly update and replace the problematic rolls, and improve the online recognition efficiency by more than 3000 times. This method has good results in online detection and recognition of roll eccentricity signals, greatly improving engineering application efficiency, and ultimately achieving the goal of improving the accuracy of strip outlet thickness.
Assessment of Land Cover Status and Change in the World and “the Belt and Road” Region from 2016 to 2020
Authors: Yang Aixia , Zhong Bo , Hu Longfei , Kai Ao , Li Li , Zhao Fei , Wu Junjun
Published: August 2023

The assessment of land cover and changes will help to understand the temporal and spatial pattern of land cover in the world and the Belt and Road (B&R) region, and provide reference information for global sustainable development and the Belt and Road construction. In this paper, the 1 km global land cover classification maps of 2016 and 2020 with a high accuracy of 88% are mapped using the Moderate Resolution Imaging Spectroradiometer (MODIS) time series surface reflectance products. Based on the maps, the land cover status of the world and the Belt and Road region, the land cover change from 2016 to 2020, and the mutual transformation characteristics between various types, are analyzed. The research results indicate that from 2016 to 2020, the global change rates of cropland, forest, grassland, and impervious surface are 0.25%, 0.22%, 0.08% and 3.41%, respectively. In the Belt and Road region, the change rates of cropland, forest, grassland, and impervious surface are 0.42%, 0.60%, −0.55% and 2.98% respectively. The assessment results will help to clarify the spatial pattern of land cover change in the five years from 2016 to 2020, so as to provide valuable scientific information for the global realization of sustainable development goals and the construction of the B&R.
Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation
Authors: Chen Siyuan , Zeng Xiangding , Laefer Debra F. , Truong-Hong Linh , Mangina Eleni
Published: August 2023

Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection.
Transmission Line Voltage Measurement Utilizing a Calibrated Suspension Grounding Voltage Sensor
Authors: Huang Rujin , Zhang Wenbin , Zhu Junyu , Zou Xiangqi , Wu Hetao , Suo Chunguang
Published: August 2023

The accurate voltage measurement of distribution networks is of great significance in power dispatching and fault diagnosis. Voltage sensors based on the spatial electric field effect do not require grounding, which provides the possibility for the distributed measurement of transmission line voltages. However, the divider ratio of suspension grounding voltage sensors is affected by the height between the sensor and the ground, as well as the distance between the sensor and the telegraph pole. In this paper, a self-calibration method based on internal capacitance transformation is proposed to realize the on-line calibration of suspension grounding voltage sensors. The calibration is accomplished by switching different parameters in the conditioning circuit, and the calibration process does not require power failure or known input excitation. In addition, the impact of electric fields in the other two phases of three-phase transmission lines on measurement through simulation research is quantified in this paper. In order to reduce the impact of interference electric fields, an equipotential shielding structure is designed. The circuit topology and probe prototype have been developed and testing has been conducted in laboratory conditions; the experimental results show that the maximum relative error of voltage amplitude is 1.65%, and the phase relative error is 0.94%. The measurement accuracy is not limited by the height to ground or the distance to the telegraph pole. In addition, in the application of an equipotential shielding probe, the maximum deviation of measured voltage is 0.7% with and without interference electric fields.
Expression, Purification and Characterization of a Novel Rusticyanin from the Psychrotolerant Acidithiobacillus ferrivorans
Authors: Liu Yuandong , Chen Lixiang , Shangguan Xiangdong , Ouyang Jingying , He Jiayu , Wang Kan , Tong Yan , Yu Runlan , Zeng Weimin , Wu Xueling , Shen Li , Qiu Guanzhou
Published: August 2023

Rusticyanin plays a crucial role in ferrous oxidation of sulfide minerals during bioleaching for industrial metal extraction. Diverse isoforms of rusticyanin have been found, but until now, except for type-A rusticyanin, other isoforms or sources of rusticyanin have been scarcely investigated. Here, a rusticyanin (gene locus 0470) from the psychrophilic Acidithiobacillus ferrivorans was gene-cloned, expressed, purified, and assembled in vitro. All forms of the protein exhibit extreme acid stability, even at pH 0.3. The stability of the protein is obviously enhanced after binding of the copper cofactor; the oxidation state is more stable than the reduced state. The protein has characteristic UV-vis peaks and EPR signals similar to type-A or type-B rusticyanin but is different with a small position shift and an obvious intensity change. The vibrational spectrum of the apoprotein was more different than these between the oxidation and reduced states of the protein. The ferrous oxidation kinetic rate constant of the protein is obviously faster than that of both the type-A and type-B rusticyanins previously reported. Further bioinformatics analysis reveal their changes in sequence and molecule: the mutations related to the peculiar shield belt mostly account for the variation in the properties of the protein, and the classification of the protein as a new isoform, type-C rusticyanin, is proposed.
Resilient Communities in Disasters and Emergencies: Exploring their Characteristics
Authors: Milofsky Carl
Published: August 2023

This paper discusses the capacity of communities to be resilient in the face of disasters. This is the question of what allows communities to rebuild after a major destructive event and preferably to “build back better.” The paper lists six qualities of resilient communities drawn from the literature researching these events: organizations are flexible; they have strong leadership; there is strong community learning; they are effective at collective problem solving and cooperation; social capital and civil society are strong; and communities effectively engage with helping institutions beyond their boundaries. The paper relates each quality to social capital, to the ways the three types of social capital—bonding, bridging, and linking—are interconnected, and to preparatory methods that might be used to strengthen social capital so that communities may be more resilient.
An Investigation of the Effect of Propylene Gas Flame on Emissions and Temperature Distribution of a Preheated Metal Plate
Authors: Quach Y. Nhu , Lim Ocktaeck
Published: August 2023

This study investigates the effect of the propylene gas flame on the emissions and temperature distribution of the metal plate during the preheating process. Experimental tests were carried out using a preheating system with a cylindrical chamber for emissions measurement and a metal plate placed near the torch head. Emissions were measured using a gas analyzer, while the temperature distribution of the metal plate was measured using an infrared thermal camera and thermocouples. The findings reveal that the emissions decrease as the equivalence ratio is increased as it approaches a ratio of 1. However, when the appropriate equivalence ratio is reached, NOx emissions will rise and then gradually fall. The peak temperature of propane fuel is higher than that of other fuels because of the concentrated flame. Propane fuel can achieve a peak temperature of 347.65 °C, surpassing both propylene fuel (275.45 °C) and acetylene fuel (335.45 °C). Using a propylene gas flame results in a reduction in emissions of carbon monoxide and nitrogen oxides compared to a propane flame. But, acetylene fuel produces the most NOx emissions, reaching 450.79 ppm for the experimental conditions. Additionally, the temperature distribution of the preheated metal plate was more uniform with the propylene gas flame, indicating improved heat transfer. However, the peak temperature of the metal plate was slightly lower when using the propylene gas flame.
Entrepreneurship and Export Product Quality Improvement
Authors: Liu Lei , He Kai , Qiao Yi , Fu Peilin , Meng Qinggang
Published: August 2023

Entrepreneurship is the sustainable driving force and stable source of economic development, and it is an important guarantee for the continuous improvement of export product quality and high-quality economic development. This paper selects entrepreneurship and export product quality as research objects. It measures the export product quality at the enterprise level using data from the China Industrial Enterprise Database and the China Customs Import and Export Trade Database. Furthermore, based on matching entrepreneurship at the macro-level and the export product quality at the micro-level, the impact and mechanism of entrepreneurship on the export product quality are empirically examined. It was found that entrepreneurial innovation and entrepreneurship further significantly contribute to the improvement of export product quality by promoting the total factor productivity of enterprises. Entrepreneurial innovation and entrepreneurship do not significantly contribute to the export product quality of state-owned and collective enterprises, while they significantly contribute to privately-owned, independent legal entities, Hong Kong–Macao–Taiwan, and foreign-funded enterprises. Furthermore, the promoting effect on privately-owned and independent enterprises is greater than that on Hong Kong–Macao–Taiwan and foreign-funded enterprises. Further analysis shows that improving the rule of law, reducing excessive government intervention, and improving the market mechanism for products and production factors, as well as vigorously developing the non-state-owned economy, will all be conducive to the cultivation and development of entrepreneurs and better promotion of the improvement of export product quality.
Does Extended Producer Responsibility System Promote Green Technological Innovation in China’s Power Battery Enterprises?
Authors: Jiang Cailou , Zhang Yue
Published: August 2023

In an effort to accelerate the advancement of green and low-carbon development, China introduced the extended producer responsibility (EPR) system in 2016, mandating producers to assume responsibility for waste recycling. Notably, power battery enterprises emerged as a primary focal point within the EPR system. Consequently, the interplay between this system and the green technological innovation of enterprises has a substantial impact on the sustainable development of power battery companies. To comprehensively explore this relationship, we conducted an empirical investigation utilizing a sample comprising listed power battery enterprises in China from 2010 to 2020. Employing the difference-in-difference (DID) model, this study aims to analyze the implications of the EPR system on green technological innovation within these enterprises. The results indicate that: (1) The EPR system significantly promotes green technological innovation in power battery enterprises, leading to an increase in the quantity of green invention patents and improvement in the quality of green patents. (2) The mechanism test reveals that the EPR system can stimulate green innovation in power battery enterprises by increasing government subsidies and raising executives’ environmental awareness. The future endeavors aimed at promoting green innovation in power battery enterprises should be concentrated on four key aspects: (1) Expanding and optimizing the implementation of the EPR system to encompass a broader spectrum of enterprises. (2) Developing precise subsidy mechanisms in conjunction with the EPR system to effectively offset recycling costs and provide incentives for fostering green innovation within power battery enterprises. (3) Encouraging proactive engagement of power battery enterprises in strategic self-reform to align with the EPR system requirements and formulate comprehensive green technology innovation strategies. (4) Enhancing the awareness of innovation among executives to accelerate the development and advancement of green products. By focusing on these four crucial dimensions, the promotion of green innovation in power battery enterprises can be strategically and efficiently pursued, thus contributing to the sustainable development of the sector.
Eco-Geography of Dioscorea composita (Hemsl.) in México and Central America under the Influence of Climate Change
Authors: Velázquez-Hernández Jocelyn M. , Ruíz-Corral José Ariel , Durán-Puga Noé , González-Eguiarte Diego R. , Santacruz-Ruvalcaba Fernando , García-Romero Giovanni Emmanuel , de la Mora-Castañeda Jesús Germán , Barrera-Sánchez Carlos Félix , Gallegos-Rodríguez Agustín
Published: August 2023

Dioscorea composita is a plant with historical recognition for the production of secondary metabolites of pharmaceutical importance, including diosgenin, and with great nutritional and ethnobotanical value in its center of origin (México and Central America). Furthermore, it is considered a promising therapeutic agent against cancer. Currently, México is one of the two most important countries producing this yam; however, climate change is altering the environmental conditions of its natural habits, threatening its preservation and productivity. This is why this research was focused on characterizing the eco-geography of D. composita and predicting its potential geographic distribution under climate change scenarios in México-Central America. A collection of 408 geo-referenced accessions was used to determine its climatic adaptation, ecological descriptors, and the current and future potential geographic distribution, which was modeled with the MaxEnt model through the Kuenm R-package. For future climate scenarios, an ensemble of the GCMs HadGEM-ES and CCSM4 was used. Results showed that D. composita adapts to warm and humid and very humid agro-climates and, the most contributing variables for its presence are annual and seasonal moisture availability indices, the seasonal photoperiod, annual thermal range, and Bio14 and Bio11. The current potential distribution (692,123 km2) of D. composita might decrease by the year 2050 RCP4.5 (365,680 km2) and might increase by 2050 under the scenario RCP8.5 (763,589 km2), showing this plant could be a good crop option for this climate change scenario. The findings obtained provide valuable information that will allow for the effective utilization of this plant, both in terms of developing new pharmaceutical products and implementing appropriate conservation strategies.
Barriers and Levers in the Development of the Value Chain of Organic Vegetables in Romania
Authors: Dan Iulia Sorina , Jitea Ionel Mugurel
Published: August 2023

As people become more and more aware of health issues, environmental protection, and climate change, there is a major shift to organic food consumption in our society. Among the main organic products consumed, we highlight vegetables because they are a major source of vitamins and minerals. The paper aims to carry out an analysis of the value chain of organic vegetables in Romania. This study presents the results of the analysis of the value chain of organic vegetables regarding aspects such as chain mapping, governance structure, and chain modernization. Data were collected through semi-structured interviews with key chain actors. The research shows that the value chain of organic vegetables in Romania is simple and short; in 45.5% of cases, there is only a distributor between the producer and the final customer. The processing function being poorly developed, 90% of the vegetables get to be marketed fresh. The study identified the main barriers to the development of this system, namely low demand for organic products and high legislative volatility. Key actors also suggested some levers to overcome barriers, such as campaigns to inform and educate consumers about organic products and their benefits and better targeted public policies.
Impact of Financial Development and Remittances on Educational Attainment within the Context of Sustainable Development: A Panel Evidence from Emerging Markets
Authors: Sezgin Funda H. , Tekin Turhan Gökçe , Sart Gamze , Danilina Marina
Published: August 2023

Education is one of the Sustainable Development Goals (SDGs) of the United Nations and is also a vital factor for nearly all the other SDGs. Therefore, factors underlying educational attainment are crucial for achieving the SDGs by 2030. In this context, the financial system has become critical in building various schools and covering educational expenditures such as teachers’ salaries, teaching materials, and training. This paper uses static and dynamic regression methods to study the impact of financial sector development, remittances, real GDP per capita, information and communications technologies (ICT) development, and globalization on educational attainment in 18 emerging economies over the 2000–2020 period. The results indicate that financial development, remittances, real GDP per capita, ICT development, and globalization positively impact educational attainment. Real GDP per capita, ICT development, globalization, and financial development have the highest impact of these factors. In contrast, remittances have a limited positive influence on educational attainment compared with other variables.
Sustainability in the Airports Ecosystem: A Literature Review
Authors: Raimundo Ricardo Jorge , Baltazar Maria Emilia , Cruz Sandra P.
Published: August 2023

The global challenges of sustainability are transforming the economy of the 21st Century. Economic, social, and environmental sustainability impacts regulators and businesses, particularly in the aviation industry, and facilitates access to productive services and market linkages. Airport stakeholders are increasingly pressuring companies to consider their socioeconomic impacts and manage them sustainably and resiliently. Business is paramount to airport companies in this competitive and economically sensitive climate. Many studies have addressed the issue of airport sustainability from specific perspectives. These perspectives include economic sustainability, benefits related to economic and environmental sustainability, and the impact of operations on the airport environment. This report summarises the current state of airport environmental sustainability practices, including greenhouse gas emissions from airport pavements, energy management strategies for airport buildings and operations, water conservation, and waste management. We aim to provide a clear and integrative picture of these impacts on airport sustainability through a systematic literature review while advancing knowledge A systematic literature review was performed using the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. This systematic literature review sought to consolidate knowledge on the subject. In order to illustrate the link between major categories and their corresponding trends, authors used VOSviewer scientific software. The compilation of existing knowledge on the three components of sustainability likely underscores the importance of overall airport sustainability. It is suggested that further studies be conducted.
Study on Trade Effects of Green Maritime Transport Efficiency: An Empirical Test for China Based on Trade Decision Model
Authors: Guo Xiaoming , Li Jinyu , Huang Sen
Published: August 2023

As the natural environment continues to deteriorate, countries have begun to shift their emphasis to sustainable development, and the study of green shipping—as the main realization of international trade—is an important prerequisite for global sustainable trade. This paper measures the green maritime transport efficiency considering greenhouse gas emissions using the Super-slacks-based measurement (Super-SBM) method, then extrapolates the theoretical model of trade decision covering maritime transport efficiency and maritime transport distance based on the transcendental logarithmic utility function. A panel econometric model based on this theoretical model was constructed, and then the trade effects of green maritime transport efficiency and its transmission mechanism were studied empirically based on the data of 60 sample countries (regions) in five continents from the years 2010 to 2020. The study shows that green maritime transport efficiency significantly promotes China’s foreign trade through three channels: promoting technological progress, reducing trade costs and curbing environmental deterioration. Additionally, this effect tends to be stronger for countries that are IMO members and have higher incomes. This article’s research helps to provide new empirical evidence to explain the growth of international trade.
Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Gazipur City Corporation, Bangladesh
Authors: Uddin Md Shihab , Mahalder Badal , Mahalder Debabrata
Published: August 2023

Anthropogenic activities have a significant influence on land use and land cover (LULC) changes, especially in rapidly growing areas. Among several models, the combination of a cellular automata–artificial neural network (CA-ANN) model is being widely used for assessing future LULC changes using satellite images. This study aimed to investigate LULC changes in Gazipur City Corporation (GCC), Bangladesh, and the changes in LULC patterns over the last two decades (2002 to 2022). In this study, the maximum likelihood supervised classification technique was used for processing the available satellite images. The results show that the urban area and vegetation coverage increased by 150% and 22.78%, whereas the bare land and waterbody decreased by 7.02% and 78.9%, respectively, from 2002 to 2022 inside the GCC area. For future LULC predictions, the CA-ANN model was developed, the accuracy percentage of which was 86.49%, and the kappa value was 0.83. The future LULC prediction model results show that the urban area will increase by 47.61%, whereas the bare land and waterbody are supposed to decrease by 24.17% and 67.23%, respectively, by 2042. The findings of this study could be useful for future sustainable urban planning and management, as well as enabling decision making by authorities for improvements in environmental and ecological conditions in the study area.
Suitability Evaluation and Engineering Matching for Agricultural Development of Barren Grassland in Mountainous Area: A Case Study of County Scale
Authors: Chen Zhaoya , Guo Niandong , Chen Yaheng
Published: August 2023

Barren grasslands are an important reserve resource of cultivated land in mountainous areas. The exploitation of barren grassland for agricultural use has played a role in ensuring food security and people’s livelihood in many countries and regions. The suitability evaluation of agricultural-use development, based on the conditions of barren grassland itself and the engineering matching, can not only ensure the sustainable development of agriculture but also avoid the ecological negative effects caused by excessive engineering. According to the research, the agricultural development of barren grassland needs to be evaluated from the two angles of natural suitability and engineering suitability, and an innovative “index level serial number summation method” is proposed. The suitability of barren grassland for agricultural use development was divided into three categories: barren grassland suitable for cultivation, barren grassland suitable for forestry, and barren grassland suitable for prataculture. The barren grassland suitable for cultivation was selected for type division and engineering-accurate matching. Taking Tang County as a research area, an example was provided through a combination of theoretical research. According to the characteristics of barren grassland in Tang County, the evaluation indexes of natural suitability and engineering suitability were selected, and the suitability of barren grassland for agricultural development was evaluated and graded. A classification system for the utilization of barren grassland suitable for cultivation is constructed, the required engineering types are explored to eliminate the main limiting factors, and the utilization types are matched with the engineering combination types. The barren grassland suitable for cultivation in the study area can be divided into 37 types, which can match eight engineering combinations. This study proposes a systematic method for the identification, classification, rating, and engineering matching for arable land reserve resources. The study can provide the basis for the effective utilization and accurate development of land resources.
AI and Big Data-Empowered Low-Carbon Buildings: Challenges and Prospects
Authors: Huang Huakun , Dai Dingrong , Guo Longtao , Xue Sihui , Wu Huijun
Published: August 2023

Reducing carbon emissions from buildings is crucial to achieving global carbon neutrality targets. However, the building sector faces various challenges, such as low accuracy in forecasting, lacking effective methods of measurements and accounting in terms of energy consumption and emission reduction. Fortunately, relevant studies demonstrate that artificial intelligence (AI) and big data technologies could significantly increase the accuracy of building energy consumption prediction. The results can be used for building operation management to achieve emission reduction goals. For this, in this article, we overview the existing state-of-the-art methods on AI and big data for building energy conservation and low carbon. The capacity of machine learning technologies in the fields of energy conservation and environmental protection is also highlighted. In addition, we summarize the existing challenges and prospects for reference, e.g., in the future, accurate prediction of building energy consumption and reasonable planning of human behavior in buildings will become promising research directions.
Belt and Road Initiative in Developing Countries: Lessons from Five Selected Countries in Africa
Authors: Komakech Robert Agwot , Ombati Thomas Ogoro
Published: August 2023

The Belt and Road Initiative (BRI) has become a household name for developing countries, especially in Africa. The BRI proposal by Chinese President Xi Jinping was positively received by many countries, including policymakers in China. In response, the Chinese Government committed to investing USD 1 trillion over ten years from 2013 to 2023. As a result, 152 countries signed a cooperation agreement with China to work under the BRI framework. The BRI has played a vital role in addressing the global infrastructures gap through the construction of modern highways, airports, high-speed railways, bridges, power generation (hydropower), and industrial parks. As a result, this has enhanced connectivity and economic growth between Asia, Europe, and Africa. Despite the BRI’s significant role in strengthening trade, infrastructure and investment links between China and other countries, there is limited literature on specific countries’ experience with the initiative. This study, therefore, will advance our understanding of the BRI, especially on the conceptualization of the term; comparative analysis of Africa–China relationships before and after the BRI; the benefits in relation to the “Five Connectivities” and the challenges the BRI is facing in Africa. The article is based on a literature review and case study as research methodologies mainly used the Policies, Projects, Initiatives, and Strategies (PPIS) as a data source. The study focuses on five African countries; Uganda, Kenya, Egypt, Djibouti, and Mozambique. These countries were selected purposefully for analysis because of their experience, long-term relationships with China, and strategic locations. The findings revealed that the BRI lacked a clear description and that it was difficult to distinguish between BRI projects and other regular economic or diplomatic relations. The study also identified four differences between Africa–China relationships before and after the BRI. Furthermore, the findings revealed that the BRI has positively contributed to all five connectivity pillars. However, the major challenges reported concerning the initiative from the various countries were: procurement corruption, low/lack of involvement of stakeholders, high compensation prices, labor violations, increasing debts, and environmental hazards. In conclusion, while the BRI has brought about significant infrastructure development and economic benefits, the project has also experienced some challenges. This study, therefore, contributes to the body of knowledge on China’s Belt and Road Initiative and its impact on African countries, specifically in Uganda, Kenya, Djibouti, Mozambique, and Egypt. The paper then provides conclusions and policy implications as well as future research opportunities in the current body of the literature.