bluuebunny/crossref_metadata_2025 huggingface dataset:
Characteristics of the Total Suspended Matter Concentration in the Hongze Lake during 1984–2019 Based on Landsat Data
Authors: Du Chenggong , Li Yunmei , Lyu Heng , Shi Kun , Liu Naisen , Yan Chen , Pan Jinheng , Guo Yulong , Li Yuan
Published: June 2022

The Hongze Lake is the fourth largest freshwater lake in China and an important lake for the East Route of the South-to-North Water Diversion Project. The water quality of the lake affects social development and the lives of residents. To assess the impacts of environmental changes and human activities on the distribution of the total suspended matter (TSM) in the Hongze Lake, we developed an algorithm that utilizes the near-infrared (NIR) band to estimate TSM based on in situ measurements. The algorithm was applied to Landsat images to derive TSM distribution maps from 1984 to 2019, revealing significant inter-annual, seasonal, and spatial variability. The relationship between TSM, precipitation, and wind speed was analyzed, and we found that: (1) The estimation model of TSM concentration in the Hongze Lake constructed for TM and OLI has a high accuracy, and it can be used to jointly monitor TSM concentration in the Hongze Lake for long-term series; (2) From 1984 to 2019, the concentration of TSM in the Hongze Lake showed a trend of first rising and then falling, with the maximum value in 2010 at 100.18 mg/L mainly caused by sand mining activities. Precipitation and wind speed weakly influence the inter-annual variation of TSM concentration; (3) The concentration of TSM in the Hongze Lake in summer is easily affected by flooding in the Huai River, and the concentration of TSM in other seasons is significantly negatively correlated with precipitation; (4) TSM is highest in the Huaihe Bay, followed by the Lihe Bay and Chengzi Bay. The main reason for this is that the input of the Huaihe Bay flows directly into this lake area and is also the main navigation channel. The results of this study are of great significance for the protection and management of the water environment of the Hongze Lake.
A Newly Developed Algorithm for Cloud Shadow Detection—TIP Method
Authors: Zekoll Viktoria , de los Reyes Raquel , Richter Rudolf
Published: June 2022

The masking of cloud shadows in optical satellite imagery is an important step in automated processing chains. A new method (the TIP method) for cloud shadow detection in multi-spectral satellite images is presented and compared to current methods. The TIP method is based on the evaluation of thresholds, indices and projections. Most state-of-the-art methods solemnly rely on one of these evaluation steps or on a complex working mechanism. Instead, the new method incorporates three basic evaluation steps into one algorithm for easy and accurate cloud shadow detection. Furthermore the performance of the masking algorithms provided by the software packages ATCOR (“Atmospheric Correction”) and PACO (“Python-based Atmospheric Correction”) is compared with that of the newly implemented TIP method on a set of 20 Sentinel-2 scenes distributed over the globe, covering a wide variety of environments and climates. The algorithms incorporated in each piece of masking software use the class of cloud shadows, but they employ different rules and class-specific thresholds. Classification results are compared to the assessment of an expert human interpreter. The class assignment of the human interpreter is considered as reference or “truth”. The overall accuracies for the class cloud shadows of ATCOR and PACO (including TIP) for difference areas of the selected scenes are 70.4% and 76.6% respectively. The difference area encompasses the parts of the classification image where the classification maps disagree. User and producer accuracies for the class cloud shadow are strongly scene-dependent, typically varying between 45% and 95%. The experimental results show that the proposed TIP method based on thresholds, indices and projections can obtain improved cloud shadow detection performance.
The Different Impacts of Climate Variability and Human Activities on NPP in the Guangdong–Hong Kong–Macao Greater Bay Area
Authors: Wu Yanyan , Luo Zhaohui , Wu Zhifeng
Published: June 2022

As two main drivers of vegetation dynamics, climate variability and human activities greatly influence net primary productivity (NPP) variability by altering the hydrothermal conditions and biogeochemical cycles. Therefore, studying NPP variability and its drivers is crucial to understanding the patterns and mechanisms that sustain regional ecosystem structures and functions under ongoing climate variability and human activities. In this study, three indexes, namely the potential NPP (NPPp), actual NPP (NPPa), and human-induced NPP (NPPh), and their variability from 2000 to 2020 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) were estimated and analyzed. Six main scenarios were generated based on change trends in the three indexes over the past 21 years, and the different relative impacts of climate variability and human activities on NPPa variability were quantitatively analyzed and identified. The results showed that the NPPp, NPPa, and NPPh had heterogeneous spatial distributions, and the average NPPp and NPPa values over the whole study area increased at rates of 3.63 and 6.94 gC·m−2·yr−1 from 2000 to 2020, respectively, while the NPPh decreased at a rate of −4.43 gC·m−2·yr−1. Climate variability and the combined effects of climate variability and human activities were the major driving factors of the NPPa increases, accounting for more than 72% of the total pixels, while the combined effects of the two factors caused the NPPa values to increase by 32–54% of the area in all cities expect Macao and across all vegetation ecosystems. Human activities often led to decreases in NPPa over more than 16% of the total pixels, and were mainly concentrated in the central cities of the GBA. The results can provide a reference for understanding NPP changes and can offer a theoretical basis for implementing ecosystem restoration, ecological construction, and conservation practices in the GBA.
Land Subsidence Monitoring and Dynamic Prediction of Reclaimed Islands with Multi-Temporal InSAR Techniques in Xiamen and Zhangzhou Cities, China
Authors: Li Guangrong , Zhao Chaoying , Wang Baohang , Liu Xiaojie , Chen Hengyi
Published: June 2022

Artificial islands and land reclamation are one of the most important ways to expand urban space in coastal cities. Long-term consolidation of reclaimed material and compaction of marine sediments can cause ground subsidence, which may threaten the buildings and infrastructure on the reclaimed lands. Therefore, it is crucial to monitor the land subsidence and predict the future deformation trend to mitigate the damage and take measures for the land reclamation and any infrastructure. In this paper, a total of 125 SAR images acquired by the C-band Sentinel-1A satellite between June 2017 and September 2021 are collected. The small baseline subsets (SBAS) SAR interferometry (InSAR) method is first conducted to detect the land deformation in Xiamen and Zhangzhou cities of Fujian Province, China, and the distributed scatterers (DS)-InSAR method is used to recover the complete deformation history of some typical areas including Xiamen Airport in Dadeng Island and Shuangyu Island. Then, the sequential estimation and the geotechnical model are jointly applied to demonstrate the current and future evolution of land subsidence of the constructed roads on Shuangyu Island. The results show that the maximum cumulative deformation reaches 425 mm of Xiamen Xiang’an Airport and 626 mm of Shuangyu Island, and the maximum deformation is predicted to be as large as 1.1 m by 2026 of Shuangyu Island. This research will provide important guidelines for the design and construction of Xiamen Xiang’an Airport and Shuangyu Island to prevent and control land subsidence.
Variability in the Spatiotemporal Distribution Patterns of Greater Amberjack in Response to Environmental Factors in the Taiwan Strait Using Remote Sensing Data
Authors: Mammel Mubarak , Naimullah Muhamad , Vayghan Ali Haghi , Hsu Jhen , Lee Ming-An , Wu Jun-Hong , Wang Yi-Chen , Lan Kuo-Wei
Published: June 2022

The environmental characteristics of the Taiwan Strait (TS) have been linked to variations in the abundance and distribution of greater amberjack (Seriola dumerili) populations. Greater amberjack is a commercially and ecologically valuable species in ecosystems, and its spatial distribution patterns are pivotal to fisheries management and conservation. However, the relationship between the catch rates of S. dumerili and the environmental changes and their impact on fish communities remains undetermined in the TS. The goal of this study was to determine the spatiotemporal distribution pattern of S. dumerili with environmental characteristics in the TS from south to north (20°N–29°N and 115°E–127°E), applying generalized additive models (GAMs) and spatiotemporal fisheries data from logbooks and voyage data recorders from Taiwanese fishing vessels (2014–2017) as well as satellite-derived remote sensing environmental data. We used the generalized linear model (GLM) and GAM to analyze the effect of environmental factors and catch rates. The predictive performance of the two statistical models was quantitatively assessed by using the root mean square difference. Results reveal that the GAM outperforms the GLM model in terms of the functional relationship of the GAM for generating a reliable predictive tool. The model selection process was based on the significance of model terms, increase in deviance explained, decrease in residual factor, and reduction in Akaike’s information criterion. We then developed a species distribution model based on the best GAMs. The deviance explained indicated that sea surface temperature, linked to high catch rates, was the key factor influencing S. dumerili distributions, whereas mixed layer depth was the least relevant factor. The model predicted a relatively high S. dumerili catch rate in the northwestern region of the TS in summer, with the area extending to the East China Sea. The target species is strongly influenced by biophysical environmental conditions, and potential fishing areas are located throughout the waters of the TS. The findings of this study showed how S. dumerili populations respond to environmental variables and predict species distributions. Data on the habitat preferences and distribution patterns of S. dumerili are essential for understanding the environmental conditions of the TS, which can inform future priorities for conservation planning and management.
Using Deep Neural Networks for Human Fall Detection Based on Pose Estimation
Authors: Salimi Mohammadamin , Machado José J. M. , Tavares João Manuel R. S.
Published: June 2022

Requests for caring for and monitoring the health and safety of older adults are increasing nowadays and form a topic of great social interest. One of the issues that lead to serious concerns is human falls, especially among aged people. Computer vision techniques can be used to identify fall events, and Deep Learning methods can detect them with optimum accuracy. Such imaging-based solutions are a good alternative to body-worn solutions. This article proposes a novel human fall detection solution based on the Fast Pose Estimation method. The solution uses Time-Distributed Convolutional Long Short-Term Memory (TD-CNN-LSTM) and 1Dimentional Convolutional Neural Network (1D-CNN) models, to classify the data extracted from image frames, and achieved high accuracies: 98 and 97% for the 1D-CNN and TD-CNN-LSTM models, respectively. Therefore, by applying the Fast Pose Estimation method, which has not been used before for this purpose, the proposed solution is an effective contribution to accurate human fall detection, which can be deployed in edge devices due to its low computational and memory demands.
Adaptive Aggregate Stereo Matching Network with Depth Map Super-Resolution
Authors: Liu Botao , Chen Kai , Peng Sheng-Lung , Zhao Ming
Published: June 2022

In order to avoid the direct depth reconstruction of the original image pair and improve the accuracy of the results, we proposed a coarse-to-fine stereo matching network combining multi-level residual optimization and depth map super-resolution (ASR-Net). First, we used the u-net feature extractor to obtain the multi-scale feature pair. Second, we reconstructed global disparity in the lowest resolution. Then, we regressed the residual disparity using the higher-resolution feature pair. Finally, the lowest-resolution depth map was refined by using the disparity residual. In addition, we introduced deformable convolution and group-wise cost volume into the network to achieve adaptive cost aggregation. Further, the network uses ABPN instead of the traditional interpolation method. The network was evaluated on three datasets: scene flow, kitti2015, and kitti2012 and the experimental results showed that the speed and accuracy of our method were excellent. On the kitti2015 dataset, the three-pixel error converged to 2.86%, and the speed was about six times and two times that of GC-net and GWC-net.
Vehicle Detection and Tracking Using Thermal Cameras in Adverse Visibility Conditions
Authors: Bhadoriya Abhay Singh , Vegamoor Vamsi , Rathinam Sivakumar
Published: June 2022

Level 5 autonomy, as defined by the Society of Automotive Engineers, requires the vehicle to function under all weather and visibility conditions. This sensing problem becomes significantly challenging in weather conditions that include events such as sudden changes in lighting, smoke, fog, snow, and rain. No standalone sensor currently in the market can reliably perceive the environment in all conditions. While regular cameras, lidars, and radars will suffice for typical driving conditions, they may fail in some edge cases. The goal of this paper is to demonstrate that the addition of Long Wave Infrared (LWIR)/thermal cameras to the sensor stack on a self-driving vehicle can help fill this sensory gap during adverse visibility conditions. In this paper, we trained a machine learning-based image detector on thermal image data and used it for vehicle detection. For vehicle tracking, Joint Probabilistic Data association and Multiple Hypothesis Tracking approaches were explored where the thermal camera information was fused with a front-facing radar. The algorithms were implemented using FLIR thermal cameras on a 2017 Lincoln MKZ operating in College Station, TX, USA. The performance of the tracking algorithm has also been validated in simulations using Unreal Engine.
Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note
Authors: Tiwari Abhishek , Cassani Raymundo , Kshirsagar Shruti , Tobon Diana P. , Zhu Yi , Falk Tiago H.
Published: June 2022

Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance. In this technical note, we overview a signal processing representation called the modulation spectrum. The representation quantifies the rate-of-change of different spectral magnitude components and is shown to separate signal from noise, thus allowing for improved quality measurement, quality enhancement, and noise-robust feature extraction, as well as for disease characterization. We provide an overview of numerous applications developed by the authors over the last decade spanning different wearable modalities and list the results obtained from experimental results alongside comparisons with various state-of-the-art benchmark methods. Open-source software is showcased with the hope that new applications can be developed. We conclude with a discussion on possible future research directions, such as context awareness, signal compression, and improved input representations for deep learning algorithms.
Methods of Generating Emotional Movements and Methods of Transmitting Behavioral Intentions: A Perspective on Human-Coexistence Robots
Authors: Matsumaru Takafumi
Published: June 2022

The purpose of this paper is to introduce and discuss the following two functions that are considered to be important in human-coexistence robots and human-symbiotic robots: the method of generating emotional movements, and the method of transmitting behavioral intentions. The generation of emotional movements is to design the bodily movements of robots so that humans can feel specific emotions. Specifically, the application of Laban movement analysis, the development from the circumplex model of affect, and the imitation of human movements are discussed. However, a general technique has not yet been established to modify any robot movement so that it contains a specific emotion. The transmission of behavioral intentions is about allowing the surrounding humans to understand the behavioral intentions of robots. Specifically, informative motions in arm manipulation and the transmission of the movement intentions of robots are discussed. In the former, the target position in the reaching motion, the physical characteristics in the handover motion, and the landing distance in the throwing motion are examined, but there are still few research cases. In the latter, no groundbreaking method has been proposed that is fundamentally different from earlier studies. Further research and development are expected in the near future.
Study on the Pressure Regulation Method of New Automatic Pressure Regulating Valve in the Electronically Controlled Pneumatic Brake Systems in Commercial Vehicles
Authors: Li Gangyan , Wei Xiaoxu , Wang Zaiyu , Bao Hanwei
Published: June 2022

In order to adapt the development of vehicle driving automation technology for driving conditions under different levels of automation and based on the independently invented LF automatic pressure regulating valve (LF-APRV) for electronically controlled pneumatic brake systems (ECPBS), the dynamic PWM coupling pressure regulation method is proposed. This method realizes pressure regulation by adjusting the duty cycle of the control signal of the LF-APRV at different stages in the pressure regulation cycle. A co-simulation model was established to verify the feasibility of the method, and a test system was built to verify the correctness of the co-simulation model. Through the test, the pressure regulation performance of dynamic PWM coupling pressure regulation method and conventional on/off pressure regulation method was compared. The results show that the new method can improve the stability of pressure regulation, although the response time increases; under the new method, the overshoot of the pressure rising from 0 to 0.5 MPa was reduced by 69%, and the overshoot of the pressure decreasing from 0.5 MPa to 0.2 MPa was basically 0. Finally, tests and simulations showed that the dynamic PWM coupling pressure regulation method can meet the continuous graded braking requirements of vehicles, and the pressure response has good tracking performance on the target pressure.
YOLO-SASE: An Improved YOLO Algorithm for the Small Targets Detection in Complex Backgrounds
Authors: Zhou Xiao , Jiang Lang , Hu Caixia , Lei Shuai , Zhang Tingting , Mou Xingang
Published: June 2022

To improve the detection ability of infrared small targets in complex backgrounds, an improved detection algorithm YOLO-SASE is proposed in this paper. The algorithm is based on the YOLO detection framework and SRGAN network, taking super-resolution reconstructed images as input, combined with the SASE module, SPP module, and multi-level receptive field structure while adjusting the number of detection output layers through exploring feature weight to improve feature utilization efficiency. Compared with the original model, the accuracy and recall rate of the algorithm proposed in this paper were improved by 2% and 3%, respectively, in the experiment, and the stability of the results was significantly improved in the training process.
A New Approach for Including Social Conventions into Social Robots Navigation by Using Polygonal Triangulation and Group Asymmetric Gaussian Functions
Authors: Sousa Raphaell Maciel de , Barrios-Aranibar Dennis , Diaz-Amado Jose , Patiño-Escarcina Raquel E. , Trindade Roque Mendes Prado
Published: June 2022

Many authors have been working on approaches that can be applied to social robots to allow a more realistic/comfortable relationship between humans and robots in the same space. This paper proposes a new navigation strategy for social environments by recognizing and considering the social conventions of people and groups. To achieve that, we proposed the application of Delaunay triangulation for connecting people as vertices of a triangle network. Then, we defined a complete asymmetric Gaussian function (for individuals and groups) to decide zones where the robot must avoid passing. Furthermore, a feature generalization scheme called socialization feature was proposed to incorporate perception information that can be used to change the variance of the Gaussian function. Simulation results have been presented to demonstrate that the proposed approach can modify the path according to the perception of the robot compared to a standard A* algorithm.
An Adaptive Fusion Algorithm for Depth Completion
Authors: Chen Long , Li Qing
Published: June 2022

Dense depth perception is critical for many applications. However, LiDAR sensors can only provide sparse depth measurements. Therefore, completing the sparse LiDAR data becomes an important task. Due to the rich textural information of RGB images, researchers commonly use synchronized RGB images to guide this depth completion. However, most existing depth completion methods simply fuse LiDAR information with RGB image information through feature concatenation or element-wise addition. In view of this, this paper proposes a method to adaptively fuse the information from these two sensors by generating different convolutional kernels according to the content and positions of the feature vectors. Specifically, we divided the features into different blocks and utilized an attention network to generate a different kernel weight for each block. These kernels were then applied to fuse the multi-modal features. Using the KITTI depth completion dataset, our method outperformed the state-of-the-art FCFR-Net method by 0.01 for the inverse mean absolute error (iMAE) metric. Furthermore, our method achieved a good balance of runtime and accuracy, which would make our method more suitable for some real-time applications.
Generalized Radar Range Equation Applied to the Whole Field Region
Authors: Xiao Luyin , Xie Yongjun , Gao Shida , Li Junbao , Wu Peiyu
Published: June 2022

Most terahertz (THz) radar systems can only work in the near-field region, because the THz source power is limited and the size of the target scattered near field is up to tens of kilometers. Such conditions will result in the conventional radar range equation being unsuitable. Therefore, the near-field radar cross section (RCS) formula is given according to the numerical simulation on different targets. By modifying the parameters in the near field, including the gain of radar antennas and the RCS of targets, the generalized radar range equation is proposed. The THz radar working efficiency in the whole range and the simulation of the near-field RCS simulation model were employed to validate its effectiveness. Through comparison with the radar range equation, it can be concluded that the calculation results of the proposed equation are smaller in the near field, and the outcomes in the far field are identical. The proposed generalized radar range equation can be applied to the whole radiation area including the near field and the far field. Furthermore, more complicated real targets are calculated according to the generalized radar range equation and it can be extended from the submillimeter wave band to a much wider band range. Finally, the near-field radar theory is established, which shows its potential application to the radar cross section estimation in the extremely high frequency and fine design of THz radar systems.
Development of a Non-Contacting Muscular Activity Measurement System for Evaluating Knee Extensors Training in Real-Time
Authors: Gu Zixi , Liu Shengxu , Cosentino Sarah , Takanishi Atsuo
Published: June 2022

To give people more specific information on the quality of their daily motion, it is necessary to continuously measure muscular activity during everyday occupations in an easy way. The traditional methods to measure muscle activity using a combination of surface electromyography (sEMG) sensors and optical motion capture system are expensive and not suitable for non-technical users and unstructured environment. For this reason, in our group we are researching methods to estimate leg muscle activity using non-contact wearable sensors, improving ease of movement and system usability. In a previous study, we developed a method to estimate muscle activity via only a single inertial measurement unit (IMU) on the shank. In this study, we describe a method to estimate muscle activity during walking via two IMU sensors, using an original sensing system and specifically developed estimation algorithms based on ANN techniques. The muscle activity estimation results, estimated by the proposed algorithm after optimization, showed a relatively high estimation accuracy with a correlation efficient of R2 = 0.48 and a standard deviation STD = 0.10, with a total system average delay of 192 ms. As the average interval between different gait phases in human gait is 250–1000 ms, a 192 ms delay is still acceptable for daily walking requirements. For this reason, compared with the previous study, the newly proposed system presents a higher accuracy and is better suitable for real-time leg muscle activity estimation during walking.
Emotion Recognition for Partial Faces Using a Feature Vector Technique
Authors: Khoeun Ratanak , Chophuk Ponlawat , Chinnasarn Krisana
Published: June 2022

Wearing a facial mask is indispensable in the COVID-19 pandemic; however, it has tremendous effects on the performance of existing facial emotion recognition approaches. In this paper, we propose a feature vector technique comprising three main steps to recognize emotions from facial mask images. First, a synthetic mask is used to cover the facial input image. With only the upper part of the image showing, and including only the eyes, eyebrows, a portion of the bridge of the nose, and the forehead, the boundary and regional representation technique is applied. Second, a feature extraction technique based on our proposed rapid landmark detection method employing the infinity shape is utilized to flexibly extract a set of feature vectors that can effectively indicate the characteristics of the partially occluded masked face. Finally, those features, including the location of the detected landmarks and the Histograms of the Oriented Gradients, are brought into the classification process by adopting CNN and LSTM; the experimental results are then evaluated using images from the CK+ and RAF-DB data sets. As the result, our proposed method outperforms existing cutting-edge approaches and demonstrates better performance, achieving 99.30% and 95.58% accuracy on CK+ and RAF-DB, respectively.
Manual 3D Control of an Assistive Robotic Manipulator Using Alpha Rhythms and an Auditory Menu: A Proof-of-Concept
Authors: Santos Cardoso Ana S. , Kæseler Rasmus L. , Jochumsen Mads , Andreasen Struijk Lotte N. S.
Published: June 2022

Brain–Computer Interfaces (BCIs) have been regarded as potential tools for individuals with severe motor disabilities, such as those with amyotrophic lateral sclerosis, that render interfaces that rely on movement unusable. This study aims to develop a dependent BCI system for manual end-point control of a robotic arm. A proof-of-concept system was devised using parieto-occipital alpha wave modulation and a cyclic menu with auditory cues. Users choose a movement to be executed and asynchronously stop said action when necessary. Tolerance intervals allowed users to cancel or confirm actions. Eight able-bodied subjects used the system to perform a pick-and-place task. To investigate the potential learning effects, the experiment was conducted twice over the course of two consecutive days. Subjects obtained satisfactory completion rates (84.0 ± 15.0% and 74.4 ± 34.5% for the first and second day, respectively) and high path efficiency (88.9 ± 11.7% and 92.2 ± 9.6%). Subjects took on average 439.7 ± 203.3 s to complete each task, but the robot was only in motion 10% of the time. There was no significant difference in performance between both days. The developed control scheme provided users with intuitive control, but a considerable amount of time is spent waiting for the right target (auditory cue). Implementing other brain signals may increase its speed.
Regional Differences, Distribution Dynamics, and Convergence of Air Quality in Urban Agglomerations in China
Authors: Xue Yuting , Liu Kai
Published: June 2022

The urban agglomeration (UA), with a high concentration of population and economy, represents an area with grievous air pollution. It is vital to examine the regional differences, distribution dynamics, and air quality convergence in UAs for sustainable development. In this study, we measured the air quality of ten UAs in China through the Air Quality Index (AQI). We analyzed regional differences, distribution dynamics, and convergence using Dagum’s decomposition of the Gini coefficient, kernel density estimation, and the convergence model. We found that: the AQI of China’s UAs shows a downward trend, and the index is higher in northern UAs than in southern UAs; the differences in air quality within UAs are not significant, but there is a gap between them; the overall difference in air quality tends to decrease, and regional differences in air quality are the primary contributor to the overall difference; the overall distribution and the distribution of each UA move rightward; the distribution pattern, ductility, and polarization characteristics are different, indicating that the air quality has improved and is differentiated between UAs; except for the Guanzhong Plain, the overall UA and each UA have obvious σ convergence characteristics, and each UA presents prominent absolute β convergence, conditional β convergence, and club convergence.
Thermal Regulation of Coastal Urban Forest Based on ENVI-Met Model—A Case Study in Qinhuangdao, China
Authors: Lai Yue , Ning Qiwen , Ge Xiaoyu , Fan Shuxin
Published: June 2022

Urban forests can improve human comfort by improving the near-surface microclimate. Exploring the microclimate characteristics of urban forests and their impact on human comfort is particularly important to improve the urban thermal environment through scientific urban forest design. Since most previous studies were conducted under typical inland urban climates and little is known about the thermal regulation services of coastal urban forests, this study field measured the thermal surface radiation temperature of 16 open spaces in the urban forest site in Qinhuangdao. Then the ENVI-met software was used to simulate and analyze the microclimate characteristics before and after the park’s building-up, and compare the differences in the human comfort index in different scenarios. Results indicated that: (1) Vegetation have a significant cooling effect on surrounding environment. The farther away from vegetation area, the higher the average surface temperature. (2) Water bodies have the best cooling and humidifying effect on the 9–18 m surrounding environment with the best human comfort index. (3) In summer, air temperature in park decreased by 0.4~1.4 °C, and the relative humidity increased by 0.07~2.81%. (4) After the park’s built-up, the average human comfort index was optimized by 0.02~1.87. Arranging the leisure open space around the water bodies and forest edge space of the deciduous broad-leaved forest could improve human comfort. This research is expected to provide a scientific basis and planning inspiration for the construction of coastal urban forests in the same latitude area.
Opportunities for Integrating Social Science into Research on Dry Forest Restoration: A Mini-Review
Authors: Powers Jennifer S.
Published: June 2022

Seasonally dry tropical forest ecosystems have been greatly reduced in areas through conversions to alternate land uses such as grazing and crop production. The U.N. Decade on Restoration has focused attention on both restoration globally, and also regional attention on tropical dry forests, as they are excellent candidates for regeneration and reforestation. As such, the science of how we restore dry forests is advancing; however, few studies of dry forest restoration include collaborations with social scientists. This is unfortunate, because restoration projects that embrace a people-centered approach have the highest chances of success. Here, I review recent studies that have incorporated aspects of social science and human dimensions into the study and design of dry forest restoration practices. I focus on three key topics that merit a closer integration of restoration research and practice and social science: (1) recognizing that local people are central to project success, (2) cost benefit or effectiveness analyses that evaluate the relative costs of alternative management strategies, and (3) identification of land-use tradeoffs, synergisms and priority mapping. I conclude that closer collaborations among dry forest restoration researchers and a wider group of partners including social scientists, local communities, environmental educators, and geographers will increase the value of restoration research and the likelihood that such projects achieve multiple ecological and societal benefits.
Hydrogen Production by the Thermophilic Dry Anaerobic Co-Fermentation of Food Waste Utilizing Garden Waste or Kitchen Waste as Co-Substrate
Authors: Wang Na , Chui Chunmeng , Zhang Siying , Liu Qianjing , Li Baoguo , Shi Jiping , Liu Li
Published: June 2022

Multicomponent collaborative anaerobic fermentation has been considered a promising technology for treating perishable organic solid wastes and producing clean energy. This study evaluated the potential of hydrogen production by thermophilic dry anaerobic co-fermentation of food waste (FW) with garden waste (GW) or kitchen waste (KW) as co-substrate. The results showed that when the ratio of FW to GW was 60:40, the maximum cumulative hydrogen production and organic matter removal rate reached 85.28 NmL g−1 VS and 63.29%, respectively. When the ratio of FW to KW was 80:20, the maximum cumulative hydrogen production and organic matter removal rate reached 81.31 NmL g−1 VS and 61.91%, respectively. These findings suggest that thermophilic dry anaerobic co-fermentation of FW using GW or KW as co-substrate has a greater potential than single-substrate fermentation to improve hydrogen production and the organic matter removal rate.
Changes of SOC Content in China’s Shendong Coal Mining Area during 1990–2020 Investigated Using Remote Sensing Techniques
Authors: Yang Xuting , Yao Wanqiang , Li Pengfei , Hu Jinfei , Latifi Hooman , Kang Li , Wang Ningjing , Zhang Dingming
Published: June 2022

Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990–2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R2 > 0.69, p < 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg−1 in 1990 to 9.23 gC·kg−1 in 2010 and then declined to 5.31 gC·Kg−1 in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.
Exploring the Use of Mobile Apps for Fostering Sustainability-Oriented Corporate Culture: A Qualitative Analysis
Authors: Isensee Carmen , Teuteberg Frank , Griese Kai Michael
Published: June 2022

Information systems (IS) play a central role in promoting corporate sustainability and pro-environmental behavior. This study explores the use of mobile apps for fostering sustainability-oriented corporate culture. It accentuates issues relating to sustainability-oriented corporate culture (RQ1), app meta-requirements as a strategic approach to addressing these issues (RQ2), as well as design and implementation principles (RQ3). Referring to the literature on sustainability apps, gamification, and nudging, our qualitative research design combines an analysis of four corporate apps intending to promote sustainable behavior and expert interviews (Grounded Theory). The proposed framework supports the planning, realization, and monitoring of this targeted app use. Single cultural dimensions inform seven issues in culture development. Five meta-requirements address this multi-dimensionality, challenges provided by new digital working environments, and principles of Green IT. Four design principles support, extend, and integrate current knowledge on app features, nudging, and gamification. Five variables determine the app intervention and maturity level. We conclude that this targeted app use should intend to foster the sustainability orientation within all dimensions of corporate culture instead of being limited to promoting sustainable behavior.
Ethical Marketing Model for Luxury Hotel Chains: Development and Validation of a Performance Evaluation Tool
Authors: Santos Vasco , Almeida Nuno
Published: June 2022

The aim of this study is to develop a new performance evaluation tool that is stringently applied to the hotel chains, considering the associated ethical marketing dimensions to measure its proper strategic, tactical, and operational role and impact within hospitality marketing management plans, campaigns, and strategies implemented. A Delphi technique was conducted as the selected research qualitative method, comprising three rounds. A total of 23 panel participants, such as directors and managers of marketing, e-commerce, sales, and branding, completed all three rounds. Two major areas of ethical marketing, internal and external, both divided into five dimensions, each with underlying items, were found to comprise the ethical marketing model for luxury hotel chains: (1) internal area of marketing (with five dimensions: integration, training, equal opportunities, performance evaluation, and smart policies) and (2) external area of marketing (with five dimensions: stakeholders, booking platforms and CRM, marketing plan, digital marketing campaigns, and social media platforms). The study concludes with useful insights and remarks. The generalisability of the results may be limited owning to the partial or not full applicability across all luxury hotel chains. The model still needs to be empirically applied in luxury hotel chains to enrich its robustness, covering a wider spread of four- and five-star luxury hotels. There is a growing potential for researchers, hotel decision makers, and marketing and sales managers and directors to achieve many advantages and benefits from the proposed model, supporting the efforts for ethical marketing theory and practice, such as hotel brand positioning strategies and formulation of more targeted and finely tuned ethical marketing strategies, tactics, and plans. This is the first study to develop and validate a performance evaluation tool of ethical marketing for luxury hotel chains. This pioneering approach extends the scope into ethical marketing because this model has never been used in this area.
Climate Policy Reform in Nepal through the Lenses of the Institutional Analysis and Development Framework
Authors: Havukainen Minna , Mikkilä Mirja , Kahiluoto Helena
Published: June 2022

Successful climate change mitigation requires the commitment of rapidly developing low-income countries. Although most of them have strategies to tackle a fair share of the burden, implementation is low despite large amounts of international aid. We aimed to identify the dynamics underpinning their low implementation, using Nepal as a case study. Aid-dependent Nepal is vulnerable to climate change and committed to its mitigation while pursuing democracy and development. We applied an institutional analysis and development framework as well as an institutional grammar tool to analyze national climate policy. We found that the current national institutions did not enable effective climate change mitigation. Despite relevant political decisions being made, the arrangements were enacted slowly. Contrary to development issues, climate issues were not tackled across all of the relevant sectors, such as waste management, traffic, and agriculture, nor across governance levels, while there was little coherence between development and climate policies. Instead, community forestry was set in the main charge of climate actions, as explained by the history of development collaboration. Additionally, climate education was mainly addressed to local communities rather than to decision-makers. We conclude that building local institutions and funding addressed effectively, even to local actors, are key options to improve the implementation of the national climate strategies of Nepal and low-income countries.
Estimation of Terrestrial Net Primary Productivity in the Yellow River Basin of China Using Light Use Efficiency Model
Authors: Xiao Fengjin , Liu Qiufeng , Xu Yuqing
Published: June 2022

The net primary productivity (NPP) of vegetation is an essential factor of ecosystem functions, including the biological geochemical carbon cycle, which is often impacted by climate change and human activities. It plays a significant role in comprehending the nature of carbon balance in an ecosystem and demonstrates the global and regional carbon cycle dynamics. The present study used an upgraded CASA model to calculate the NPP in the Yellow River Basin (YRB), China. The model’s simulation ability was improved by changing the model parameters. Further, the CASA model was validated by comparing with MODIS-NPP and in situ observed NPP, wherein the accuracy of the CASA model estimation was found satisfactory to estimate NPP changes in the study area. The simulated results of the improved CASA model showed that the mean annual NPP value of vegetation in the YRB was 283.4 gC m–2 a–1 from 2001 to 2020, with a declining trend in spatial distribution from south to north. In contrast, the NPP appeared as an increasing trend in the YRB temporally from 212 gC m–2 a–1 in 2001 to 342 gC m–2 a–1 in 2020, with a mean annual growth rate of 4.6 gC m–2 a–1. The total NPP in the YRB increased by 40,088.3 GgC between 2001 and 2020, from 226.06 TgC to 266.15 TgC. This rise can be attributed to the increase in forests. The average grassland area has reduced by 4651 km2 during the last two decades, significantly impacting the total NPP of grasslands. Although the increase in NPP in wetlands was minimal, accounting for 815.53 GgC, the highest change percentage of 79.78%, could be observed among the six vegetation types due to the anthropogenic influences and climate change. The conditions favorable for vegetation growth and a sustained environment were enhanced by the increased precipitation and temperature and the reinforced ecological protection by the government.
Disaster Risks Management through Adaptive Actions from Human-Based Perspective: Case Study of 2014 Flood Disaster
Authors: Yusoff Sarina , Yusoff Nur Hafizah
Published: June 2022

In Malaysia, floods are often considered a normal phenomenon in the lives of some communities, which can sometimes cause disasters to occur beyond expectations, as shown during the flood of 2014. The issue of flood disasters, which particularly impacts SDG 13 of the integrated Sustainable Development Goals (SDGs), still lacks widespread attention from sociology researchers in Malaysia. Similarly, questions related to the welfare of victims, especially in regards to aspects of disaster management from a humanitarian perspective, are still neglected. This study aims to identify the adaptive actions through a solution from a humanitarian perspective in managing flood disaster risks. For the purpose of obtaining data, this study used a qualitative approach with a case study design. Data were collected using in-depth interviews and non-participant observation methods. A total of ten experts, consisting of the flood management teams involved in managing the 2014 flood disaster in Hulu Dungun, Terengganu, Malaysia, were selected through a purposive random sampling method. The results showed that adaptive actions in managing flood disaster risks from a humanitarian point of view include the provision of social support, collective cooperation from the flood management teams, and adaptation efforts after the floods.