bluuebunny/crossref_metadata_2025 huggingface dataset:
Anomaly-Based Ship Detection Using SP Feature-Space Learning with False-Alarm Control in Sea-Surface SAR Images
Authors: Pan Xueli , Li Nana , Yang Lixia , Huang Zhixiang , Chen Jie , Wu Zhenhua , Zheng Guoqing
Published: June 2023

Synthetic aperture radar (SAR) can provide high-resolution and large-scale maritime monitoring, which is beneficial to ship detection. However, ship-detection performance is significantly affected by the complexity of environments, such as uneven scattering of ship targets, the existence of speckle noise, ship side lobes, etc. In this paper, we present a novel anomaly-based detection method for ships using feature learning for superpixel (SP) processing cells. First, the multi-feature extraction of the SP cell is carried out, and to improve the discriminating ability for ship targets and clutter, we use the boundary feature described by the Haar-like descriptor, the saliency texture feature described by the non-uniform local binary pattern (LBP), and the intensity attention contrast feature to construct a three-dimensional (3D) feature space. Besides the feature extraction, the target classifier or determination is another key step in ship-detection processing, and therefore, the improved clutter-only feature-learning (COFL) strategy with false-alarm control is designed. In detection performance analyses, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the method’s effectiveness. Many experimental results show that the proposed method can significantly improve the detection performance of ship targets, and has a high detection rate and low false-alarm rate in complex background and multi-target marine environments.
Long-Term Measurements of the Atmospheric Boundary Layer Height in Central Amazonia Using Remote Sensing Instruments
Authors: Souza Carla Maria Alves , Dias-Júnior Cléo Quaresma , D’Oliveira Flávio Augusto F. , Martins Hardiney Santos , Carneiro Rayonil Gomes , Portela Bruno Takeshi Tanaka , Fisch Gilberto
Published: June 2023

The height (zi) of the Atmospheric Boundary Layer (ABL) is a fundamental parameter for several areas of knowledge, especially for weather and climate forecasting, pollutant dispersion and air quality. In this work, we used data from a remote sensing instrument (ceilometer), located at the experimental site of the Amazon Tall Tower Observatory (ATTO) in the Central Amazonia rainforest, in order to obtain the height of the ABL. Data used were obtained from 2014 to 2020, with the exception of the year 2017. The results showed that the zi average varies from year to year (interannual variability) and the average of the maximum zi values (zi_max) was approximately 1400 ± 277 m, occurring at 15:00 local time. In addition, it was found that these maximum heights are higher in the dry season and during El Niño years (about 1741 ± 242 m) and they are lower during the wet period and in La Niña years (1263 ± 229 m). Taking into account all the years investigated, the month with the highest zi_max value is September (1710 ± 253 m), and the month with the lowest value is May (1108 ± 152 m). Finally, it was observed that the growth rate of the ABL during the early hours after sunrise varies from month to month (intraseasonal variability), reaching its maximum values in September and October (about 210 ± 53 m h−1 and 217 ± 59 m h−1, respectively) and minimum values in April and July (approximately 159 ± 48 m h−1 and 159 ± 50 m h−1, respectively). It is concluded that the values of zi in Central Amazonia are influenced by several seasonal factors (temperature, cloud cover, turbulent heat flux, etc.) which gives it a wide variability in terms of heights and growth rates. Additionally, a linear regression was proposed in order to model the maximum zi value as a function of its growth rate from 08:00 LT (Local Time) up to 10:00 LT. The results showed a good correlation compared with the experimental values.
Near Real-Time Flood Mapping with Weakly Supervised Machine Learning
Authors: Vongkusolkit Jirapa , Peng Bo , Wu Meiliu , Huang Qunying , Andresen Christian G.
Published: June 2023

Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have proved to be effective, they require intensive manual labeling of flooded pixels to train a multi-layer deep neural network that learns abstract semantic features of the input data. This research introduces a novel weakly supervised approach for pixel-wise flood mapping by leveraging multi-temporal remote sensing imagery and image processing techniques (e.g., Normalized Difference Water Index and edge detection) to create weakly labeled data. Using these weakly labeled data, a bi-temporal U-Net model is then proposed and trained for flood detection without the need for time-consuming and labor-intensive human annotations. Using floods from Hurricanes Florence and Harvey as case studies, we evaluated the performance of the proposed bi-temporal U-Net model and baseline models, such as decision tree, random forest, gradient boost, and adaptive boosting classifiers. To assess the effectiveness of our approach, we conducted a comprehensive assessment that (1) covered multiple test sites with varying degrees of urbanization, and (2) utilized both bi-temporal (i.e., pre- and post-flood) and uni-temporal (i.e., only post-flood) input. The experimental results showed that the proposed framework of weakly labeled data generation and the bi-temporal U-Net could produce near real-time urban flood maps with consistently high precision, recall, f1 score, IoU score, and overall accuracy compared with baseline machine learning algorithms.
Joint Detection and Reconstruction of Weak Spectral Lines under Non-Gaussian Impulsive Noise with Deep Learning
Authors: Li Zhen , Guo Junyuan , Wang Xiaohan
Published: June 2023

Non-Gaussian impulsive noise in marine environments strongly influences the detection of weak spectral lines. However, existing detection algorithms based on the Gaussian noise model are futile under non-Gaussian impulsive noise. Therefore, a deep-learning method called AINP+LR-DRNet is proposed for joint detection and the reconstruction of weak spectral lines. First, non-Gaussian impulsive noise suppression was performed by an impulsive noise preprocessor (AINP). Second, a special detection and reconstruction network (DRNet) was proposed. An end-to-end training application learns to detect and reconstruct weak spectral lines by adding into an adaptive weighted loss function based on dual classification. Finally, a spectral line-detection algorithm based on DRNet (LR-DRNet) was proposed to improve the detection performance. The simulation indicated that the proposed AINP+LR-DRNet can detect and reconstruct weak spectral line features under non-Gaussian impulsive noise, even for a mixed signal-to-noise ratio as low as −26 dB. The performance of the proposed method was validated using experimental data. The proposed AINP+LR-DRNet detects and reconstructs spectral lines under strong background noise and interference with better reliability than other algorithms.
Multilayer Densities of the Crust and Upper Mantle in the South China Sea Using Gravity Multiscale Analysis
Authors: Yu Hangtao , Xu Chuang , Chen Haopeng , Chai Yi , Qin Pengbo , Wang Gongxiang , Zhang Hui , Xu Ming , Xing Congcong , Wang Hao
Published: June 2023

The South China Sea (SCS) is the result of several major tectonic plates’ interaction, and a more detailed density structure is essential to study the tectonic evolution of the marginal seas. In the past decades, density studies in the SCS have focused more on profiles, and the lateral density structure has been less mentioned by scholars. Previously, gravity data have been used many times to study the SCS; this can satisfy the demands of large-scale lateral studies, but a point that cannot be ignored is that the gravity method has certain deficiencies in vertical resolution. Therefore, this paper applies a gravity multiscale analysis method to isolate the Bouguer gravity anomaly signal in the SCS, and then uses the power spectrum method to estimate the mean field source depth, which corresponds to the decomposed gravity anomaly signal. On this basis, we modeled stratified crustal and upper mantle structures in the SCS to determine the lateral density distribution at different depths. The results show the following: (1) There are two large high-density materials in the SCS. The first one is located near the Yinggehai Basin, where the depth ranges from 42.4 km to 71.2 km, with a diameter of nearly 220 km; the second one is located near the northwest sub-basin, where the depth ranges from 106.8 km to 128.8 km, which is probably part of the Paleo-Pacific remnant in the SCS. (2) The proto South China Sea subduction zone is located in the northwestern part of the Sulu Sea and has a northeast–southwest trend.
A Review of Piezoelectric Footwear Energy Harvesters: Principles, Methods, and Applications
Authors: Zhao Bingqi , Qian Feng , Hatfield Alexander , Zuo Lei , Xu Tian-Bing
Published: June 2023

Over the last couple of decades, numerous piezoelectric footwear energy harvesters (PFEHs) have been reported in the literature. This paper reviews the principles, methods, and applications of PFEH technologies. First, the popular piezoelectric materials used and their properties for PEEHs are summarized. Then, the force interaction with the ground and dynamic energy distribution on the footprint as well as accelerations are analyzed and summarized to provide the baseline, constraints, potential, and limitations for PFEH design. Furthermore, the energy flow from human walking to the usable energy by the PFEHs and the methods to improve the energy conversion efficiency are presented. The energy flow is divided into four processing steps: (i) how to capture mechanical energy into a deformed footwear, (ii) how to transfer the elastic energy from a deformed shoes into piezoelectric material, (iii) how to convert elastic deformation energy of piezoelectric materials to electrical energy in the piezoelectric structure, and (iv) how to deliver the generated electric energy in piezoelectric structure to external resistive loads or electrical circuits. Moreover, the major PFEH structures and working mechanisms on how the PFEHs capture mechanical energy and convert to electrical energy from human walking are summarized. Those piezoelectric structures for capturing mechanical energy from human walking are also reviewed and classified into four categories: flat plate, curved, cantilever, and flextensional structures. The fundamentals of piezoelectric energy harvesters, the configurations and mechanisms of the PFEHs, as well as the generated power, etc., are discussed and compared. The advantages and disadvantages of typical PFEHs are addressed. The power outputs of PFEHs vary in ranging from nanowatts to tens of milliwatts. Finally, applications and future perspectives are summarized and discussed.
Optimizing Time Resolution Electronics for DMAPs
Authors: López-Morillo Enrique , Luján-Martínez Clara , Hinojo-Montero José , Márquez-Lasso Fernando , Palomo Francisco Rogelio , Muñoz-Chavero Fernando
Published: June 2023

Depleted Monolithic Active Pixel Sensors (DMAPSs) are foreseen as an interesting choice for future high-energy physics experiments, mainly because of the reduced fabrication costs. However, they generally offer limited time resolution due to the stringent requirements of area and power consumption imposed by the targeted spatial resolution. This work describes a methodology to optimize the design of time-to-digital converter (TDC)-based timing electronics that takes advantage of the asymmetrical shape of the pulse at the output of the analog front-end (AFE). Following that methodology, a power and area efficient implementation fully compatible with the RD50-MPW3 solution is proposed. Simulation results show that the proposed solution offers a time resolution of 2.08 ns for a range of energies from 1000 e− to 20,000 e−, with minimum area and zero quiescent in-pixel power consumption.
Biomimetic Polarized Light Navigation Sensor: A Review
Authors: Li Shunzi , Kong Fang , Xu Han , Guo Xiaohan , Li Haozhe , Ruan Yaohuang , Cao Shouhu , Guo Yinjing
Published: June 2023

A polarized light sensor is applied to the front-end detection of a biomimetic polarized light navigation system, which is an important part of analyzing the atmospheric polarization mode and realizing biomimetic polarized light navigation, having received extensive attention in recent years. In this paper, biomimetic polarized light navigation in nature, the mechanism of polarized light navigation, point source sensor, imaging sensor, and a sensor based on micro nano machining technology are compared and analyzed, which provides a basis for the optimal selection of different polarized light sensors. The comparison results show that the point source sensor can be divided into basic point source sensor with simple structure and a point source sensor applied to integrated navigation. The imaging sensor can be divided into a simple time-sharing imaging sensor, a real-time amplitude splitting sensor that can detect images of multi-directional polarization angles, a real-time aperture splitting sensor that uses a light field camera, and a real-time focal plane light splitting sensor with high integration. In recent years, with the development of micro and nano machining technology, polarized light sensors are developing towards miniaturization and integration. In view of this, this paper also summarizes the latest progress of polarized light sensors based on micro and nano machining technology. Finally, this paper summarizes the possible future prospects and current challenges of polarized light sensor design, providing a reference for the feasibility selection of different polarized light sensors.
Gas-Phase Biosensors (Bio-Sniffers) for Measurement of 2-Nonenal, the Causative Volatile Molecule of Human Aging-Related Body Odor
Authors: Iitani Kenta , Mori Hidehisa , Ichikawa Kenta , Toma Koji , Arakawa Takahiro , Iwasaki Yasuhiko , Mitsubayashi Kohji
Published: June 2023

The molecule 2-nonenal is renowned as the origin of unpleasant human aging-related body odor that can potentially indicate age-related metabolic changes. Most 2-nonenal measurements rely on chromatographic analytical systems, which pose challenges in terms of daily usage and the ability to track changes in concentration over time. In this study, we have developed liquid- and gas-phase biosensors (bio-sniffers) with the aim of enabling facile and continuous measurement of trans-2-nonenal vapor. Initially, we compared two types of nicotinamide adenine dinucleotide (phosphate) [NAD(P)]-dependent enzymes that have the catalytic ability of trans-2-nonenal: aldehyde dehydrogenase (ALDH) and enone reductase 1 (ER1). The developed sensor quantified the trans-2-nonanal concentration by measuring fluorescence (excitation: 340 nm, emission: 490 nm) emitted from NAD(P)H that was generated or consumed by ALDH or ER1. The ALDH biosensor reacted to a variety of aldehydes including trans-2-nonenal, whereas the ER1 biosensor showed high selectivity. In contrast, the ALDH bio-sniffer showed quantitative characteristics for trans-2-nonenal vapor at a concentration range of 0.4–7.5 ppm (with a theoretical limit of detection (LOD) and limit of quantification (LOQ) of 0.23 and 0.26 ppm, respectively), including a reported concentration (0.85–4.35 ppm), whereas the ER1 bio-sniffer detected only 0.4 and 0.8 ppm. Based on these findings, headspace gas of skin-wiped alcohol-absorbed cotton collected from study participants in their 20s and 50s was measured by the ALDH bio-sniffer. Consequently, age-related differences in signals were observed, suggesting the potential for measuring trans-2-nonenal vapor.
Miniaturized Antenna Array-Based Novel Metamaterial Technology for Reconfigurable MIMO Systems
Authors: Hussein Humam , Atasoy Ferhat , Elwi Taha A.
Published: June 2023

In this work, a highly miniaturized microstrip antenna array based on two elements is proposed for multiple inputs multiple outputs (MIMO) application systems at sub-6 GHz frequency bands. The antenna is structured from a meander line in conjugate with an interdigital capacitor when excited through the monopole basic antenna. The proposed antenna elements are separated with a Minkowski factor-shaped metamaterial (MTM) column to achieve a separation distance (D) of 0.08λ at 3 GHz when printed on an FR-4 substrate. Later on, the antenna performance in terms of bandwidth and gain is controlled using a photonic process based on optical active switches based on light-dependent resistances (LDR). Therefore, the reconfiguration complexity with such a technique can be eliminated significantly without the need for a biasing circuit. The antenna design was conducted through several parametric studies to arrive at the optimal design that realizes the frequency bandwidth between 3 and 5.5 GHz with a maximum gain of about 4.5 dBi when all LDR terminals are off. For a wireless channel performance study-based massive MIMO environment, the proposed antenna is suitable to be configured in arrays of 64 × 64 elements. From this study, it was found the maximum bit error rate (BER) does not exceed 0.15 with a channel capacity (CC) of 2 Gbps. For validation, the antenna was fabricated based on two elements and tested experimentally. Finally, it was revealed that the measured results agree very well with simulations after comparing the theoretical calculations with the measured data.
Recent Progress in Self-Powered Sensors Based on Liquid–Solid Triboelectric Nanogenerators
Authors: Nguyen Quang Tan , Vu Duy Linh , Le Chau Duy , Ahn Kyoung Kwan
Published: June 2023

Recently, there has been a growing need for sensors that can operate autonomously without requiring an external power source. This is especially important in applications where conventional power sources, such as batteries, are impractical or difficult to replace. Self-powered sensors have emerged as a promising solution to this challenge, offering a range of benefits such as low cost, high stability, and environmental friendliness. One of the most promising self-powered sensor technologies is the L–S TENG, which stands for liquid–solid triboelectric nanogenerator. This technology works by harnessing the mechanical energy generated by external stimuli such as pressure, touch, or vibration, and converting it into electrical energy that can be used to power sensors and other electronic devices. Therefore, self-powered sensors based on L–S TENGs—which provide numerous benefits such as rapid responses, portability, cost-effectiveness, and miniaturization—are critical for increasing living standards and optimizing industrial processes. In this review paper, the working principle with three basic modes is first briefly introduced. After that, the parameters that affect L–S TENGs are reviewed based on the properties of the liquid and solid phases. With different working principles, L–S TENGs have been used to design many structures that function as self-powered sensors for pressure/force change, liquid flow motion, concentration, and chemical detection or biochemical sensing. Moreover, the continuous output signal of a TENG plays an important role in the functioning of real-time sensors that is vital for the growth of the Internet of Things.
Diversifying Emotional Dialogue Generation via Selective Adversarial Training
Authors: Li Bo , Zhao Huan , Zhang Zixing
Published: June 2023

Emotional perception and expression are very important for building intelligent conversational systems that are human-like and attractive. Although deep neural approaches have made great progress in the field of conversation generation, there is still a lot of room for research on how to guide systems in generating responses with appropriate emotions. Meanwhile, the problem of systems’ tendency to generate high-frequency universal responses remains largely unsolved. To solve this problem, we propose a method to generate diverse emotional responses through selective perturbation. Our model includes a selective word perturbation module and a global emotion control module. The former is used to introduce disturbance factors into the generated responses and enhance their expression diversity. The latter maintains the coherence of the response by limiting the emotional distribution of the response and preventing excessive deviation of emotion and meaning. Experiments are designed on two datasets, and corresponding results show that our model outperforms existing baselines in terms of emotional expression and response diversity.
TMNet: A Two-Branch Multi-Scale Semantic Segmentation Network for Remote Sensing Images
Authors: Gao Yupeng , Zhang Shengwei , Zuo Dongshi , Yan Weihong , Pan Xin
Published: June 2023

Pixel-level information of remote sensing images is of great value in many fields. CNN has a strong ability to extract image backbone features, but due to the localization of convolution operation, it is challenging to directly obtain global feature information and contextual semantic interaction, which makes it difficult for a pure CNN model to obtain higher precision results in semantic segmentation of remote sensing images. Inspired by the Swin Transformer with global feature coding capability, we design a two-branch multi-scale semantic segmentation network (TMNet) for remote sensing images. The network adopts the structure of a double encoder and a decoder. The Swin Transformer is used to increase the ability to extract global feature information. A multi-scale feature fusion module (MFM) is designed to merge shallow spatial features from images of different scales into deep features. In addition, the feature enhancement module (FEM) and channel enhancement module (CEM) are proposed and added to the dual encoder to enhance the feature extraction. Experiments were conducted on the WHDLD and Potsdam datasets to verify the excellent performance of TMNet.
Welding Fume: A Comparison Study of Industry Used Control Methods
Authors: Knott Peter , Csorba Georgia , Bennett Dustin , Kift Ryan
Published: June 2023

Welding fume is generated during welding activities and is a known cancer-causing hazard for those working in the welding industry. Worker exposure has been shown to regularly exceed the applicable workplace exposure standard, and control measures are required to reduce worker exposure. The aim of this study is to compare the effectiveness of control measures to prevent welding fume exposure to workers. To achieve this aim, three common welding fume control measures (local exhaust ventilation (LEV), powered air purifying respirators (PAPRs) and on-gun extraction) were used during four different welding tasks. Compared to using no controls, LEV hood capture is likely to reduce welding fume concentrations in the breathing zone of a welder by up to a factor of 9. The use of on-gun LEV is likely to reduce welding fume concentrations in the breathing zone of a welder by up to a factor of 12. The 5th percentile effective protection factors of the PAPR for all sampled welding activities were considerably greater than the required minimum protection factor of 50 specified in AS/NZS 1715:2009 for powered air purifying respirators (PAPRs) with class PAPR-P3 particulate filters with any head covering.
Screening the Lipid Production Potential of Oleaginous Yeast Yarrowia lipolytica under Wood Hydrolysates
Authors: Ünver Hülya , Polat Ece , Altınbaş Mahmut
Published: June 2023

Since Yarrowia lipolytica, an oily yeast, contains many valuable products as biomass, to increase its widespread use, it is very important to develop a sustainable production model. In this study, the production of high acid hydrolysate from sawdust biomass, which is produced in very high amounts in our environment, and the effect of these products on the growth efficiency of Yarrowia lipolytica were investigated. A preliminary statistical analysis was carried out to assess sugar production from sawdust by hydrolysis, which estimated 85% of total sugar recovery (TSR) using 96 g of sawdust, while TSR at the highest sawdust was as high as 79%. There was a higher loss in the recovery of C5 sugars with acid and time. This study revealed that Y. lipolytica could grow with high biomass yields in varying hydrolysate concentrations, approaching yields achieved in synthetic glucose media. The maximum biomass obtained was 13 and 26 g in batch and airlift operations, respectively, using organic nutrients. Biomass and lipid yields from kinetic modeling provided a close approximation to the experimental yields performed on an airlift reactor. It was found that Y. lipolytica biomass cultivated on sawdust hydrolysates had significant lipid and protein compositions, as high as 29 and 38%, respectively.
Identification of Bioactive Compounds in Plant Extracts of Greek Flora and Their Antimicrobial and Antioxidant Activity
Authors: Tsakni Aliki , Chatzilazarou Archontoula , Tsakali Efstathia , Tsantes Andreas G. , Van Impe Jan , Houhoula Dimitra
Published: June 2023

This study examined five species of the Lamiaceae family, Origanum vulgare L., Origanum dictamnus L., Origanum majorana L., Mentha spicata L. and Lavandula angustifolia L., and one species of the Hypericaceae family, Hypericum perforatum L., for their potential to enhance health and wellbeing or to prevent diseases. An analysis of secondary metabolites was performed by mass spectrometry and HPLC coupled with diode array detection to determine the phenolic compounds included in the plant extracts. The samples were evaluated for their total phenolic content, antioxidant potential, ability to prevent DNA scission caused by peroxyl radicals and antimicrobial activity against pathogens (MIC). All the samples, except Lavandula angustifolia L., had a high DPPH radical scavenging activity and showed significant antibacterial activity. Origanum majorana L. demonstrated the lowest IC50 value (10.31 μg·mL−1). Extracts of medicinal herbs had a remarkably high phenolic content varying between 428 and 1512.74 μg GAE/mg of dry extract, with Origanum vulgare L. having the largest amount of polyphenols. Moreover, the plant extracts exhibited a high level of resistance against DNA damage, with Origanum majorana L. showing the greatest level of protection with 98.05% inhibition. According to the results of this study, these plants from the Greek flora provide beneficial effects for health as natural antioxidants. A baseline can be established through the analysis of the findings of this survey for future research on the same plant species.
Revisiting Tourism Development and Economic Growth: A Framework for Configurational Analysis in Chinese Cities
Authors: Zhang Yan , Zhang Jiekuan
Published: June 2023

This paper comparatively analyzes the sufficiency and necessity of tourism’s influence on economic growth in different cities from a systematic configurational perspective. Two important time points in China’s tourism development, 2010 and 2019, are also considered in this paper to explore whether the impact of tourism on urban economic growth is temporally heterogeneous. The results demonstrate that tourism is not necessary for urban economic growth. However, the dependence on the tourism economy plays an important role in several urban economic growth patterns. Only one tourism-driven economic growth pattern exists, where tourism drives economic growth led by investment, and this pattern did not change significantly from 2010 to 2019. A tourism-driven low economic growth model also suggests that a high dependence on tourism leads to low economic growth. Two tourism-constrained low economic growth patterns exist: investment–industrial structure tourism-constrained and investment–innovation tourism-constrained. These two patterns indicate that economic growth rates are difficult to increase if the tourism economy is underdeveloped. In addition, tourism-driven or -constrained economic growth patterns have specific spatial clustering characteristics. This paper argues that tourism should actively seek foreign capital utilization and fixed asset investment, and also constantly reduce its independence and blur its industrial boundaries to better integrate or link with other industries to play its economic growth role. Furthermore, city policymakers should be fully aware of their own (tourism) resource endowment and the internal and external environment changes to choose a suitable economic growth model.
Pastoral Differentiations’ Effects on Willingness to Accept Valuation for Grassland Eco-Subsidy—Empirical Study of 410 Herder Households in Grass–Livestock Balance Sub-Policy Zones in Inner Mongolia, China
Authors: Dong Jiayu , Ren Zimeng , Zhang Xinling , Liu Xiaoling
Published: June 2023

China’s Grassland Eco-Subsidy Program has been implemented since 2011 to protect and sustain grassland resources and improve the economic situation of herders. In this case study, we aimed to evaluate the policy from the perspective of pastoral differentiations and their willingness to accept (WTA) the eco-subsidy. Using the contingent valuation method (CVM), we interviewed 410 herder households in the grass–livestock balance sub-policy zones across Inner Mongolia, and examined how their horizontal and vertical differentiation affected their WTA and compensation expectations in a sorted Logistic model and a Tobit model, respectively. Our findings suggest that horizontal differentiation of herders (variations of income sources and employment) promoted the WTA for the eco-subsidy and inhibited the increase in compensation expectations. A large vertical differentiation extent (income disparity) lowered the WTA but raised expectations for payment levels. Additionally, factors such as operating pasture area, precipitation, and supervision intensity had significant impacts. Grazing income remained the primary income source for most herder households, reflecting the characteristics of “the rational economic man”. Our results suggest a need for policy improvements to guide herders towards becoming “rational ecological men”. By considering this differentiation, policy makers can better tailor the program to meet the needs and expectations of diverse herder households.
Hydropower Planning in Combination with Batteries and Solar Energy
Authors: Coban Hasan Huseyin
Published: June 2023

Battery storage is an important factor for power systems made up of renewable energy sources. Technologies for battery storage are crucial to accelerating the transition from fossil fuels to renewable energy. Between responding to electricity demand and using renewable energy sources, battery storage devices will become increasingly important. The aim of this study is to examine how battery storage affects a power system consisting of solar and hydroelectric energy and to draw conclusions about whether energy storage recommends a power system. The method involves designing a model of eight real cascade hydropower power plants and solving an optimization problem. This power system model is based on existing hydroelectric power plants powered by solar energy and batteries in the Turkish cities of Yozgat and Tokat. A case study with four different battery capacities in the system was carried out to assess the implications of energy storage in the power system. The stochastic nonlinear optimization problem was modeled for 72 h and solved with the MATLAB programming tool. The stochastic Quasi-Newton method performs very well in hybrid renewable problems arising from large-scale machine learning. When solar energy and batteries were added to the system, the maximum installed wind power was found to be 2 MW and 3.6 MW, respectively. In terms of profit and hydropower planning, a medium-proportion battery was found to be the most suitable. Increased variability in hydropower generation results from the installation of an energy storage system.
Methods for Assessing the Psychological Tension of Social Network Users during the Coronavirus Pandemic and Its Uses for Predictive Analysis
Authors: Khakimova Aida , Zolotarev Oleg , Sharma Bhisham , Agrawal Shweta , Jain Sanjiv Kumar
Published: June 2023

This article address approaches to the development of methods for assessing the psychological state of social network members during the coronavirus pandemic through sentiment analysis of messages. The purpose of the work is to determine the psychological tension index by using a previously developed thematically ranked dictionary. Researchers have investigated methods to evaluate psychological tension among social network users and to forecast the psychological distress. The approach is novel in the sense that it ranks emojis by mood, considering both the emotional tone of tweets and the emoji’s dictionary meanings. A novel method is proposed to assess the dynamics of the psychological state of social network users as an indicator of their subjective well-being, and develop targeted interventions for help. Based on the ranking of the Emotional Vocabulary Index (EVI) and Subjective Well-being Index (SWI), a scheme is developed to predict the development of psychological tension. The significance lies in the efficient assessment of the fluctuations in the mental wellness of network users as an indication of their emotions and a prerequisite for further predictive analysis. The findings gave a computed value of EVI of 306.15 for April 2022. The prediction accuracy of 88.75% was achieved.
Water and Food Sustainability in the Riparian Countries of Lake Chad in Africa
Authors: Olowoyeye Oluwatuyi S. , Kanwar Rameshwar S.
Published: June 2023

Lake Chad is a strategic water resource shared by more than 40 million people in Sub-Saharan Africa. In the 1960s, it served as a primary source of water for irrigation and fishing in the region, but the capacity of Lake Chad to supply water for irrigation plummeted by 90% at the beginning of the twenty-first century. With some initiatives taken by the neighboring countries, Lake Chad has recovered about 5% of its water volume in recent years. This research conducted an extensive literature review on Lake Chad and its riparian countries. The four major riparian countries were given particular attention due to their significant stake in the sustainability of lake Chad. This review identified and analyzed the water usage trends in this region, both before and after the lake’s decline in water levels. Our research findings revealed that riparian countries around Lake Chad have experienced an 80% increase in population growth and that the lake has now been reduced to 10% of its original size in the 1960s. Animal production in the region has increased significantly, too, particularly in Chad, and this increase of over 75% has contributed to the conflicts between farmers and herders in the region. The possible solutions proposed for the restoration of Lake Chad include increased water harvesting activities in the basin, developing a legal framework for sustainable water use, incentive-based policies for stakeholders to mitigate climate extremes events, establishing a joint water administration for the basin, and introducing regenerative agricultural practices with a highly efficient micro irrigation system.
Analysis of Wind Field Characteristics of Ideal V-Shaped and U-Shaped Canyons
Authors: Zhou Zelin , Xin Xu , Yu Jiaxin , Liu Maoyi
Published: June 2023

As an important part of road transportation, bridge engineering plays a pivotal role in infrastructure construction. The wind field characteristics of the bridge site area have an essential influence on both the construction and operation period of the bridge, especially in mountainous canyon terrain. In this paper, a numerical simulation using computational fluid dynamics software was conducted to examine the intricate wind field characteristics in mountainous regions. The study focused on ideal V-shaped and U-shaped canyons, aiming to investigate the influence of various parameters. These parameters included three distinct heights, seven angles, and seven widths of the canyon. The findings indicate that in both ideal V-shaped and U-shaped canyons, the canyon acceleration effect weakens as the angles or widths of the canyon increase. The wind speed amplification effect gradually disappears when the V-shaped canyon angle exceeds 160° or when the U-shaped canyon has a width-to-height ratio of approximately 5:1. The wind speed amplification effect strengthens as the canyon height increases. The wind speed acceleration effect exhibits a linear relationship with the angle of the V-shaped canyon, while it demonstrates a logarithmic relationship with the width of the U-shaped canyon. Additionally, the wind speed amplification factor follows a logarithmic distribution along the canyon height. The wind field characteristics observed in this study offer valuable insights for future bridge designs in mountainous regions featuring V-shaped and U-shaped canyons.
How Do Spatial Forms Influence Psychophysical Drivers in a Campus City Community Life Circle?
Authors: Li Shi-Ying , Chen Zhu , Guo Lian-Huan , Hu Fangbing , Huang Yi-Jun , Wu Dan-Cheng , Wu Zhigang , Hong Xin-Chen
Published: June 2023

The physical environment of urban public facilities is an important driver for public health and work efficiency. Unfortunately, citizens are exposed to negative physical environments because of inappropriate spatial forms in urban growth boundaries. This study aims to explore psychophysical drivers and their spatial distribution in campus city community life circles during the COVID-19 pandemic. Questionnaires and measuring equipment were used to gather psychophysical information in a 15 min campus city community life circle in Fuzhou, China. To this end, acoustic, light and thermal environments were used to map spatial distributions. We then explored relationships between spatial form and psychophysical parameters. The study results show that the distance to road (DTR), green area ratio (GR) and street width (SW) are all potential spatial drivers for psychophysical information. Furthermore, the acoustical, light and thermal environments provide interactions for the public understanding of the environment. These findings contribute to the understanding and evaluation of psychophysical drivers, spurring regional industry in community life circles and contributing to developing suitable plans and industrial distribution in urban areas.
Cold Chain Logistics Network Design for Fresh Agricultural Products with Government Subsidy
Authors: Zhu Hongli , Liu Congcong , Wu Guanghua , Gao Yanjun
Published: June 2023

This paper investigates the cold chain logistics network design in the first mile for fresh agricultural products with government subsidy, involving the capacity, location of cold storage facilities, and transportation from production areas to cold storage facilities after harvest. A bi-level programming model is formulated considering the quality degradation of fresh agricultural products. Based on the proposition and KKT conditions, a solution method is designed for reformulating the bi-level model into a single-level programming model. Numerical experiments are conducted to verify the proposed model. Experimental results show that the solution method efficiently solves the problem of the cold chain logistics network design for fresh agricultural products with subsidy, and sensitivity analysis provides managerial insights for decision-makers.
An Estimation of Daily PM2.5 Concentration in Thailand Using Satellite Data at 1-Kilometer Resolution
Authors: Buya Suhaimee , Usanavasin Sasiporn , Gokon Hideomi , Karnjana Jessada
Published: June 2023

This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-km resolution. The study employs multiple linear regression and three machine learning models and finds that the random forest model performs the best for PM2.5 estimation over the period of 2011–2020. The model incorporates several factors such as Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Elevation (EV), Week of the year (WOY), and year and applies them to the entire region of Thailand without relying on monitoring station data. Model performance is evaluated using the coefficient of determination (R2) and root mean square error (RMSE), and the results indicate high accuracy for training (R2: 0.95, RMSE: 5.58 μg/m3), validation (R2: 0.78, RMSE: 11.18 μg/m3), and testing (R2: 0.71, RMSE: 8.79 μg/m3) data. These PM2.5 data can be used to analyze the short- and long-term effects of PM2.5 on population health and inform government policy decisions and effective mitigation strategies.
Factors Influencing Consumers’ Continuous Purchase Intentions on TikTok: An Examination from the Uses and Gratifications (U&G) Theory Perspective
Authors: Wang Jing , Oh Jay In
Published: June 2023

After the COVID-19 pandemic, traditional online shopping with pictures and videos has been transformed into livestreaming shopping. Various apps for livestreaming shopping have gained popularity, and TikTok livestreaming (TTL) accounts for more than half of livestreaming shopping in China. Therefore, consumers’ ability to continue shopping is the core factor for the sustainable development of TTL. The purpose of this study was to explore what kinds of gratifications affect the continuous purchase intentions of TTL consumers and to examine the moderating effect of education level. We collected data from 234 TTL consumers in China and then used a structural equation model to analyze, while SPSS23.0 and AMOS24.0 were applied to evaluate and empirically test the research hypotheses. This study confirmed the significant impact of four kinds of satisfaction on consumers’ continuous shopping intentions and also confirmed the differences among consumers with different levels of education. This provides theoretical support for the sustainable development of e-commerce in the future and the maximization of income from live shopping.
Study on the Coupling Development of Industry, City and Population in the Yellow River Basin from the Perspective of Green Economy
Authors: Cao Xiangdong , Ci Fuyi
Published: June 2023

Based on the perspective of green economy, we established an index system to evaluate industry, city and population, and obtained data from 78 cities in the Yellow River Basin from 2011 to 2020. The entropy weight method, coupling model, kernel density analysis and exploratory data analysis methods were used to analyze the coupling coordination degree and spatio-temporal differentiation characteristics of industry, city and population in the Yellow River Basin. We constructed a Tobit model to analyze the influencing factors. The results show that from the perspective of green economy, the coupling coordination degree of industry, city and population in the Yellow River Basin has improved continuously. Among them, the coupling coordination degree of Shandong Peninsula and provincial capital cities is high, but the coupling coordination degree of some areas in the middle and upper reaches of the Yellow River is low due to human capital factors. The Yellow River Basin should strengthen environmental control, optimize government regulation, guide the inflow of foreign capital and develop green finance to promote the coupling development of industry, city and population.
Pollution Reduction, Informatization and Sustainable Urban Development—Evidence from the Smart City Projects in China
Authors: Hu Xiaoya , Huang Huimin , Ruan Jun , Wang Weijia
Published: June 2023

China’s modernization endeavors to advance both material and spiritual civilization concomitantly; nevertheless, the nation presently faces a pressing issue of urban pollution. This research examines how the introduction of information technology has affected environmental pollution in Chinese cities. By utilizing a panel dataset consisting of 214 prefecture-level cities in China spanning from 2005 to 2017, we employed a time-varying DID model with smart city pilot projects serving as a quasi-natural experiment to assess the impact of informatization on environmental pollution. The establishment of smart cities has remarkably elevated the information quotient of urban areas. Additionally, the results indicate a significant reduction in pollution levels in urban areas as a result of informatization, which remained robust even after conducting a series of rigorous tests to ensure the reliability of the findings. Mechanism analysis shows that informatization of smart cities affects the environmental governance behavior of enterprises and governments. Consequently, the implementation of informatization in smart cities can effectively alleviate environmental pollution by means of both the technique effect and structure effect. This finding calls for the Chinese government to improve informatization by promoting smart city pilot projects to build sustainable cities.
Multicriteria Decision-Making Tools for the Selection of Biomasses as Supplementary Cementitious Materials
Authors: Álvarez-López Germán , Múnera Alejandra María , Villegas Juan G.
Published: June 2023

Using biomass ash to partially replace cement reduces the cement industry’s environmental impact and prevents these agro-industrial wastes from ending up in landfills, eroding soils, or being openly burned. This research aims to select three biomasses to produce supplementary cementitious materials (SCM) through the analytic hierarchy process, considering expert judgments from different domains. Complementary to up-to-date research, we evaluated biomasses taking into account biomass production, ash obtained from combustion, and logistics processes for supplying concrete plants with SCM. We also dealt with an industrial context instead of a laboratory one and validated our approach on a real case study using Colombian data. The results indicate experts count the technical viability of biomass (concrete properties) as the most crucial criteria, followed by the availability and transport characteristics of the waste (production criteria) and the combustion process as the least important criteria. In the baseline scenario (all experts’ judgments having the same weights), we found that cane bagasse is the best alternative, thanks to its large and highly concentrated production, even if it is not the biomass with the best pozzolanic properties. We also analyzed other scenarios in which we changed the weights of the experts’ judgments and the importance of the criteria. We found that cane bagasse, rice husk, and palm rachis remain the three biomasses selected as SCM, showing the robustness of the proposed multicriteria decision-making (MCDM) methodology. The results provide a methodological reference to appraise biomasses for SCM nationally, using a MCDM framework in a group decision-making context.