bluuebunny/crossref_metadata_2025 huggingface dataset:
coastTrain: A Global Reference Library for Coastal Ecosystems
Authors: Murray Nicholas J. , Bunting Pete , Canto Robert F. , Hilarides Lammert , Kennedy Emma V. , Lucas Richard M. , Lyons Mitchell B. , Navarro Alejandro , Roelfsema Chris M. , Rosenqvist Ake , Spalding Mark D. , Toor Maren , Worthington Thomas A.
Published: November 2022

Estimating the distribution, extent and change of coastal ecosystems is essential for monitoring global change. However, spatial models developed to estimate the distribution of land cover types require accurate and up-to-date reference data to support model development, model training and data validations. Owing to the labor-intensive tasks required to develop reference datasets, often requiring intensive campaigns of image interpretation and/or field work, the availability of sufficiently large quality and well distributed reference datasets has emerged as a major bottleneck hindering advances in the field of continental to global-scale ecosystem mapping. To enhance our ability to model coastal ecosystem distributions globally, we developed a global reference dataset of 193,105 occurrence records of seven coastal ecosystem types—muddy shorelines, mangroves, coral reefs, coastal saltmarshes, seagrass meadows, rocky shoreline, and kelp forests—suitable for supporting current and next-generation remote sensing classification models. coastTrain version 1.0 contains curated occurrence records collected by several global mapping initiatives, including the Allen Coral Atlas, Global Tidal Flats, Global Mangrove Watch and Global Tidal Wetlands Change. To facilitate use and support consistency across studies, coastTrain has been harmonized to the International Union for the Conservation of Nature’s (IUCN) Global Ecosystem Typology. coastTrain is an ongoing collaborative initiative designed to support sharing of reference data for coastal ecosystems, and is expected to support novel global mapping initiatives, promote validations of independently developed data products and to enable improved monitoring of rapidly changing coastal environments worldwide.
Analysis and Correction of the Rolling Shutter Effect for a Star Tracker Based on Particle Swarm Optimization
Authors: Fu Zongqiang , Yang Xiubin , Wu Mo , Yan Andong , Du Jiamin , Gao Suining , Tang Xingyu
Published: November 2022

The rolling shutter effect decreases the accuracy of the attitude measurement of star trackers when they work in rolling shutter exposure mode, especially under dynamic conditions. To solve this problem, a rolling shutter effect correction method based on particle swarm optimization is proposed. Firstly, a collinear reverse installation method between the star tracker and the satellite is proposed, which simplifies the relationship between the velocity of the star centroid and the star tracker angular velocity. Next, the centroid error model is obtained by the star centroid velocity. Based on the centroid error model and angular distance invariance, the loss function of the centroid error is proposed. Then, the particle swarm optimization algorithm is used to determine the star tracker angular velocity by minimizing the loss function. Finally, the simulation and experiments are carried out to verify the proposed method. The experimental results show that the convergence times of the algorithm are less than 50 and the root mean square error (RMSE) of the angular velocity is better than 0.02°/s when the angular velocity of the star tracker is no more than 5°/s.
Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers
Authors: Shaik Riyaaz Uddien , Unni Aiswarya , Zeng Weiping
Published: November 2022

A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.
Real-Time Detection and 3D Localization of Coronary Atherosclerosis Using a Microwave Imaging Technique: A Simulation Study
Authors: Islam Md Asiful , Volakis John L.
Published: November 2022

Obtaining the exact position of accumulated calcium on the inner walls of coronary arteries is critical for successful angioplasty procedures. For the first time to our knowledge, in this work, we present a high accuracy imaging of the inner coronary artery using microwaves for precise calcium identification. Specifically, a cylindrical catheter radiating microwave signals is designed. The catheter has multiple dipole-like antennas placed around it to enable a 360° field-of-view around the catheter. In addition, to resolve image ambiguity, a metallic rod is inserted along the axis of the plastic catheter. The reconstructed images using data obtained from simulations show successful detection and 3D localization of the accumulated calcium on the inner walls of the coronary artery in the presence of blood flow. Considering the space and shape limitations, and the highly lossy biological tissue environment, the presented imaging approach is promising and offers a potential solution for accurate localization of coronary atherosclerosis during angioplasty or other related procedures.
Using a Clustering Method to Detect Spatial Events in a Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment
Authors: Boumchich Ayoub , Picaut Judicaël , Bocher Erwan
Published: November 2022

Noise has become a very notable source of pollution with major impacts on health, especially in urban areas. To reduce these impacts, proper evaluation of noise is very important, for example by using noise mapping tools. The Noise-Planet project seeks to develop such tools in an open science platform, with a key open-source smartphone tool “NoiseCapture” that allows users to measure and share the noise environment as an alternative to classical methods, such as simulation tools and noise observatories, which have limitations. As an alternative solution, smartphones can be used to create a low-cost network of sensors to collect the necessary data to generate a noise map. Nevertheless, this data may suffer from problems, such as a lack of calibration or a bad location, which lowers its quality. Therefore, quality control is very crucial to enhance the data analysis and the relevance of the noise maps. Most quality control methods require a reference database to train the models. In the context of NC, this reference data can be produced during specifically organized events (NC party), during which contributors are specifically trained to collect measurements. Nevertheless, these data are not sufficient in number to create a big enough reference database, and it is still necessary to complete them. Other communities around the world use NC, and one may want to integrate the data they collected into the learning database. In order to achieve this, one must detect these data within the mass of available data. As these events are generally characterized by a higher density of measurements in space and time, in this paper we propose to apply a classical clustering method, called DBSCAN, to identify them in the NC database. We first tested this method on the existing NC party, then applied it on a global scale. Depending on the DBSCAN parameters, many clusters are thus detected, with different typologies.
Robust Synchronization of Ambient Vibration Time Histories Based on Phase Angle Compensations and Kernel Density Function
Authors: Saeed Salman , Chouinard Luc , Sajid Sikandar
Published: November 2022

The output-only modal analysis is ubiquitously used for structural health monitoring of civil engineering systems. The measurements for such applications require the use of multiple data acquisition systems (DAS) to avoid complicated meshes of cables in high-rise buildings, avoid traffic constriction on a bridge during measurements, or to avoid having limited channels in a single DAS. Nevertheless, such requirements introduce time synchronization problems which potentially lead to erroneous structural dynamic characterization and hence misleading or inconclusive structural health monitoring results. This research aims at proposing a system-identification-based time synchronization algorithm for output-only modal analysis using multiple DAS. A new procedure based on the compensation of the phase angle shifts is proposed to identify and address the time synchronization issue in ambient vibration data measured through multiple DAS. To increase the robustness of the proposed algorithm to the inherent inconsistencies in these datasets, the kernel density function is applied to rank multiple time-shift estimates that are sometimes detected by the algorithm when inaccuracies exist in the data arising from low signal-to-noise ratio and/or presence of colored noise in the ambient excitations. First, the synchronized ambient vibration dataset of a full-scale bridge is artificially de-synchronized and used to present a proof of concept for the proposed algorithm. Next, the algorithm is applied to ambient vibration data of a 30-story, reinforced concrete building, where the synchronization of the data could not be achieved using two DAS despite best efforts. The application of the proposed time synchronization algorithm is shown to both detect and correct the time synchronization discrepancies in the output-only modal analysis.
Developing Low-Cost Mobile Device and Apps for Accurate Skin Spectrum Measurement via Low-Cost Spectrum Sensors and Deep Neural Network Technology
Authors: Hsu Ling-Cheng , Hsu Shiang , Tan Tan-Hsu , Cheng Chia-Hsing , Chang Cheng-Chun
Published: November 2022

In recent years, skin spectral information has been gradually applied in various fields, such as the cosmetics industry and clinical medicine. However, the high price and the huge size of the skin spectrum measurement device make the related applications of the skin spectrum unable to be widely used in practical applications. We used convolutional neural network (CNN) to achieve a satisfying accuracy of the Fitzpatrick skin-type classification by using a simple self-developed device in 2018. Leveraging on the hardware, firmware, and software app-developing experience, a low-cost miniature skin spectrum measurement system (LMSSMS) using deep neural network (DNN) technology was further studied, and the feasibility of the system is verified in this paper. The developed LMSSMS is divided into three parts: (1) miniature skin spectrum measurement device (MSSMD), (2) DNN model, and (3) mobile app. The MSSMD was developed with innovative low-cost MSSC, 3D printing, and a simple LED light source. The DNN model is designed to enhance measurement accuracy. Finally, the mobile app is used to control and show the measurement results. The developed app also includes a variety of skin-spectrum-related applications, such as erythema index and melanin index (EI/MI) measurement, Fitzpatrick skin-type classification, Pantone SkinTone classification, sun-exposure estimation, and body-fat measurement. In order to verify the feasibility of LMSSMS, we used the standard instrumentation device as a reference. The results show that the accuracy of the LMSSMS can reach 94.7%, which also confirms that this development idea has much potential for further development.
Implementation of Kalman Filtering with Spiking Neural Networks
Authors: Juárez-Lora Alejandro , García-Sebastián Luis M. , Ponce-Ponce Victor H. , Rubio-Espino Elsa , Molina-Lozano Herón , Sossa Humberto
Published: November 2022

A Kalman filter can be used to fill space–state reconstruction dynamics based on knowledge of a system and partial measurements. However, its performance relies on accurate modeling of the system dynamics and a proper characterization of the uncertainties, which can be hard to obtain in real-life scenarios. In this work, we explore how the values of a Kalman gain matrix can be estimated by using spiking neural networks through a combination of biologically plausible neuron models with spike-time-dependent plasticity learning algorithms. The performance of proposed neural architecture is verified with simulations of some representative nonlinear systems, which show promising results. This approach traces a path for its implementation in neuromorphic analog hardware that can learn and reconstruct partial and changing dynamics of a system without the massive power consumption that is typically needed in a Von Neumann-based computer architecture.
Design and Validation of a Multimodal Wearable Device for Simultaneous Collection of Electrocardiogram, Electromyogram, and Electrodermal Activity
Authors: McNaboe Riley , Beardslee Luke , Kong Youngsun , Smith Brittany N. , Chen I-Ping , Posada-Quintero Hugo F. , Chon Ki H.
Published: November 2022

Bio-signals are being increasingly used for the assessment of pathophysiological conditions including pain, stress, fatigue, and anxiety. For some approaches, a single signal is not sufficient to provide a comprehensive diagnosis; however, there is a growing consensus that multimodal approaches allow higher sensitivity and specificity. For instance, in visceral pain subjects, the autonomic activation can be inferred using electrodermal activity (EDA) and heart rate variability derived from the electrocardiogram (ECG), but including the muscle activation detected from the surface electromyogram (sEMG) can better differentiate the disease that causes the pain. There is no wearable device commercially capable of collecting these three signals simultaneously. This paper presents the validation of a novel multimodal low profile wearable data acquisition device for the simultaneous collection of EDA, ECG, and sEMG signals. The device was validated by comparing its performance to laboratory-scale reference devices. N = 20 healthy subjects were recruited to participate in a four-stage study that exposed them to an array of cognitive, orthostatic, and muscular stimuli, ensuring the device is sensitive to a range of stressors. Time and frequency domain analyses for all three signals showed significant similarities between our device and the reference devices. Correlation of sEMG metrics ranged from 0.81 to 0.95 and EDA/ECG metrics showed few instances of significant difference in trends between our device and the references. With only minor observed differences, we demonstrated the ability of our device to collect EDA, sEMG, and ECG signals. This device will enable future practical and impactful advances in the field of chronic pain and stress measurement and can confidently be implemented in related studies.
Embedding Weather Simulation in Auto-Labelling Pipelines Improves Vehicle Detection in Adverse Conditions
Authors: Broughton George , Janota Jiří , Blaha Jan , Rouček Tomáš , Simon Maxim , Vintr Tomáš , Yang Tao , Yan Zhi , Krajník Tomáš
Published: November 2022

The performance of deep learning-based detection methods has made them an attractive option for robotic perception. However, their training typically requires large volumes of data containing all the various situations the robots may potentially encounter during their routine operation. Thus, the workforce required for data collection and annotation is a significant bottleneck when deploying robots in the real world. This applies especially to outdoor deployments, where robots have to face various adverse weather conditions. We present a method that allows an independent car tansporter to train its neural networks for vehicle detection without human supervision or annotation. We provide the robot with a hand-coded algorithm for detecting cars in LiDAR scans in favourable weather conditions and complement this algorithm with a tracking method and a weather simulator. As the robot traverses its environment, it can collect data samples, which can be subsequently processed into training samples for the neural networks. As the tracking method is applied offline, it can exploit the detections made both before the currently processed scan and any subsequent future detections of the current scene, meaning the quality of annotations is in excess of those of the raw detections. Along with the acquisition of the labels, the weather simulator is able to alter the raw sensory data, which are then fed into the neural network together with the labels. We show how this pipeline, being run in an offline fashion, can exploit off-the-shelf weather simulation for the auto-labelling training scheme in a simulator-in-the-loop manner. We show how such a framework produces an effective detector and how the weather simulator-in-the-loop is beneficial for the robustness of the detector. Thus, our automatic data annotation pipeline significantly reduces not only the data annotation but also the data collection effort. This allows the integration of deep learning algorithms into existing robotic systems without the need for tedious data annotation and collection in all possible situations. Moreover, the method provides annotated datasets that can be used to develop other methods. To promote the reproducibility of our research, we provide our datasets, codes and models online.
Chromium Poisoning in Buffaloes in the Vicinity of Contaminated Pastureland, Punjab, Pakistan
Authors: Ghazzal Maria , Hussain Muhammad Iftikhar , Khan Zafar Iqbal , Habib ur Rahman M. , El-Habeeb Abeer A. , Yang Hsi-Hsien
Published: November 2022

This article focuses on the toxic element chromium (Cr) in wastewater, its incorporation into soil plant systems, and its relevant toxicity in the food chain as assessed by a health risk assessment from dietary intake. The Nili Ravi buffalo is an important cattle inhabiting Sargodha, Punjab, Pakistan, and forage crops grown on soils contaminated with Cr might cause toxicity in the food chain by local inhabitants eating meat. The soil, forage and animal blood samples were collected from five different locations in Tehsil Sahiwal (Chak Dhool, Bagabalocha, Chandia, Dhool Bala and Kakrani) twice at six-month intervals. A total of 30 samples from each ecological zone were collected from the soil and forage crops (Zea mays, Sorghum bicolor, Trifolium alexandrinum). The samples from zone-V and zone-IV showed the maximum concentration of Cr because these areas receive highly contaminated water for irrigation. The Cr was greater than the permissible limits. Environmental indices for all samples ranged below 1. The bioaccumulation and pollution load of Cr in soil and forage crops due to wastewater irrigation can contaminate the whole food chain via the soil, forages and animals. The health risk index (HRI) and a high value of enrichment factor were found for Cr in some sites. The Cr concentration was higher during the summer season than winter. Fodder crops with different concentrations and an elevated level of Cr were observed in maize. Attention should be paid when wastewater is used for fodder crop irrigation and its potential risks to human health following dairy product (milk, meat) entry into the food chain.
Does Viewing Green Advertising Promote Sustainable Environmental Behavior? An Experimental Study of the Licensing Effect of Green Advertising
Authors: Gu Chenyu , Liu Shiyu , Chen Subai
Published: November 2022

Current research on consumer behavior of green advertising mostly focuses on advertising attitude or consumer behavior, while few studies have extended the topic to explore the consumers’ behavior after green consumption. The “Licensing effect”, which is a paradoxical side effect of green advertising, has been verified to exist in the consumption context in many countries. This paradoxical effect between cognition and behavior refers to the circumstance that consumers show non-green behavior after green consumption, which is contrary to the original intention of green advertising. However, at present, few scholars have verified and deeply explored that effect in the context of China. This study explores the “licensing effect” of green advertising through two factors: environmental protection cognition and advertising appeal. Through a 2 × 3 experiment, we find that: 1. The licensing effect is applicable in the Chinese consumption context; 2. The licensing effect only exists in individuals with low environmental protection cognition; 3. The appeal mode of green advertising turns out to be an effective moderator, and rational appeal can effectively prevent the licensing effect. This research expands the research scope of green advertising and provides a new vision for the study of consumer behavior in green advertising. In addition, the moderation role of advertising appeal verified by our study has guiding significance for green advertising practice.
Drought-Induced Nitrogen and Phosphorus Carryover Nutrients in Corn/Soybean Rotations in the Upper Mississippi River Basin
Authors: Meki Manyowa N. , Osorio Javier M. , Steglich Evelyn M. , Kiniry James R.
Published: November 2022

Droughts reduce crop yields, which translates to reduced nutrient uptake or removal from the soil. Under such conditions, residual plant nutrients such as nitrogen (N) and phosphorus (P) can be carried over for subsequent crops. We applied the Agricultural Policy Environmental eXtender (APEX) model to simulate continuous corn (Zea mays L.)/soybean (Glycine max [L.] Merr.) rotations on 3703 farm fields within the Upper Mississippi River Basin (UMRB) over a 47-year timescale: 1960 to 2006. We used the Standardized Precipitation Index (PSI) to identify the drought years between 1960 to 2006, following which we evaluated potential drought-induced carryover N and P nutrients in corn/soybean rotations relative to near normal and very to extremely wet years. Overall, drought reduced N uptake, total N losses, N mineralization and N fixation, the main driver of the soybean carryover N. Given the high cost of fertilizers and concerns over nutrient loss impacts on offsite water quality, farmers are compelled to account for every plant nutrient that is already in the soil. Information from this study could be applied to develop optimal N and P recommendations after droughts, while identification of region-wide potential reductions in N and P applications has implications for conservation efforts aimed at minimizing environmental loading and associated water quality concerns.
Coal Discards and Sewage Sludge Derived-Hydrochar for HIV Antiretroviral Pollutant Removal from Wastewater and Spent Adsorption Residue Evaluation for Sustainable Carbon Management
Authors: Kahilu Gentil Mwengula , Bada Samson , Mulopo Jean
Published: November 2022

The effects of various parameter interactions on the textural structure of hydrochars produced via hydrothermal (HTC) and co-hydrothermal (Co-HTC) treatments of coal discards and sewage sludge (wastes), as well as the subsequent use of the hydrochars (HCs) synthesized for HIV drug (nevirapine and lamivudine) removal from wastewater, were investigated in this study. The HTC and Co-HTC process improved the carbon content of the raw material by 13.47%, 7.08%, and 30.65% for hydrochar coal tailing (HCT), hydrochar coal slurry (HCS), and hydrochar from coal–sewage blend (HCB), respectively. The Co-HTC-derived HCB had a high SBET of 20.35 m2/g and pore volume of 0.38 cm3/g, leading to significant adsorptive reductions of nevirapine (NEV) and lamivudine (LAM) (97.19% and 93.32%, respectively). HCT and HCS displayed high NEV and LAM adsorption capacities (50 mg g−1, 42 mg g−1 and 52 mg g−1, 41 mg g−1), respectively, despite being less effective than HCB (53.8 mg g−1, 42.8 mg g−1). In addition, the use of spent adsorption residues for energy storage applications was investigated further. The findings showed that spent adsorption residues are an effective carbonaceous material precursor to produce electrical double-layer capacitors (EDLCs).
Study on Coal Seepage Characteristics and Secondary Enhanced Gas Extraction Technology under Dual Stress Disturbance
Authors: Ding Xiong , Zhai Cheng , Xu Jizhao , Yu Xu , Sun Yong
Published: November 2022

During the mining of coal seams with outburst hazard, abnormal gas emissions in front of the coal mining working face (CMWF) may induce gas overrun. To address this technical problem, this study analyzed the permeability variation of coal in front of the CMWF at different stress paths through physical experiments, numerical simulation and on-site tests. The spatial-temporal evolution law of the unloading area of the working face under dual stress disturbance caused by hydraulic punching (HP) and coal seam mining was explored; next, a secondary enhanced extraction technology was proposed and applied in the Shoushan No. 1 Coal Mine, Henan Province, China. The results reveal the following: (1) the coal permeability decreases linearly with increasing confining pressure (CP) and axial pressure (AP) under Stress Paths 1 and 2 (that is, fixed AP and CP). (2) The coal permeability is negatively related to the distance from the stress peak point under Stress Paths 3 and 4 (that is, AP and CP are, respectively, the vertical stress and horizontal stress before the stress peak). (3) As the distance from the peak stress declines, the reduction amplitude of coal permeability in the test area first decreases, and then increases, under Stress Paths 5 and 6 (that is, the vertical stress as CP and the horizontal stress as AP). The plastic damage range of coal around the HP cavities expands due to the dual impact of HP and coal seam mining, which can realize both regional unloading and provide channels for gas extraction within 60 m in front of the CMWF. According to the gas extraction concentration of boreholes, the coal body in front of the CMWF is divided into three zones: efficient, effective and original extraction zones. The efficient extraction zone is within 20 m in front of the CMWF, with an average gas extraction concentration of over 30%. In the effective extraction zone, the gas extraction concentration falls with the increase in the distance from the CMWF. The original extraction zone is beyond 50–60 m, and the borehole gas concentration stabilizes below 10%. The number of extraction boreholes in the stress disturbance area of the middle-floor gas extraction roadway accounts for 5–10% of the total number of boreholes, but its maximum monthly extraction volume can reach 38.5% of the total volume.
Towards Low Carbon: A Lightweight Design of Automotive Brake Hub
Authors: Li Qiang , Tong Mo , Jia Mian , Yang Jie
Published: November 2022

Carbon peaking and carbon neutrality have become important considerations in today’s manufacturing industry. Vehicle lightweight design can reduce carbon emissions and it is an important means to achieve carbon peak and carbon neutrality. In this study, the lightweight design method of automotive brake hub towards low carbon and the calculation method of low-carbon benefit are presented. A brake hub is the core of a drum brake, working together with a friction plate and brake shoe to complete the braking process. The requirements for the safety performance of brake hub are becoming increasingly more stringent in order to improve the stability and safety of the braking process. The brake hub ZD02-151122A manufactured by Anhui Axle Co., Ltd.(Suzhou, China), was used as the research object. The lightweight optimization of the brake hub was designed under the lightweight drive to reduce the shape variables and stress values of the brake hub and to reduce the mass. The proposed optimization scheme changed the chamfering to 45 × 45 and increased the number of bolt holes to eight. Compared with the original brake hub, the maximum strain, maximum stress value, stress concentration coefficient, and mass were reduced by 15.38%, 17.66%, 1.50%, and 17.40%, respectively, which achieved the specified optimization goal of improving mechanical properties and reducing mass. Towards low carbon, the reduction in carbon emissions from the optimized brake hub manufacturer and the vehicle during operation was calculated. For Anhui Axle Co., Ltd., the carbon emission can be reduced by 4.21 × 106 kg per year. Moreover, vehicle exhaust emissions can be reduced by 8.76 × 108 kg if all trucks produced by a medium-sized vehicle assembly company are driven on the road until being scrapped. This study serves as a reference for design optimization and low-carbon benefit analysis of other major automotive components.
Research on Tourism Carrying Capacity and the Coupling Coordination Relationships between Its Influencing Factors: A Case Study of China
Authors: Dong Xianlei , Gao Shan , Xu Airong , Luo Zhikun , Hu Beibei
Published: November 2022

The large increase in the number of tourists brings challenges to the tourist carrying capacity of tourist destinations. By constructing a tourism carrying capacity indicator system and a coupling coordination model, we calculate and compare the development of tourism carrying capacity and the coupling coordination between all first-class indicators of tourism carrying capacity for provinces and cities in China. We find that the tourism carrying capacity and the coupling coordination between all first-class indicators of tourism carrying capacity for provinces and cities in China both showed an upward trend from 2008 to 2017, but the development was not balanced. In coastal provinces, their development level was high although lately showing a downward trend. In the provinces of the northwest China, their development level is low, and their development speed is relatively slow. The provinces and cities in the middle reaches of the Yangtze River and the southwest China recorded the fastest growth rate. In addition, we found that the development of tourism carrying capacity is closely related to coupling coordination between all first-class indicators of tourism carrying capacity.
Effects of Sound Source Landscape in Urban Forest Park on Alleviating Mental Stress of Visitors: Evidence from Huolu Mountain Forest Park, Guangzhou
Authors: Guo Yanlong , Jiang Xuemei , Zhang Linfu , Zhang Han , Jiang Zuoqing
Published: November 2022

Urban Forest parks are indispensable areas in cities and have ecological and social advantages, and the quality of their acoustic landscapes is an important factor influencing visitors’ visiting experience. The purpose of this study was to investigate the factors influencing the acoustic landscape on psychological stress relief in selected urban forest parks in China. This study used a questionnaire to construct an index system for psychological stress reduction and designed a questionnaire for the perceived impact of stress-reducing acoustic sources with user perception and stress-reducing restoration evaluation as the entry point. The research team conducted field research and online questionnaire survey on the questionnaire.com platform from 1–5 July 2022, in Huolu Mountain Forest Park, an urban forest park in Guangzhou, and collected 266 valid questionnaires. SPSS26.0 was used to analyze the results of the impact of the urban forest park on users’ psychological stress relief. Spearman’s rho correlation analysis and a Kruskal–Wallis nonparametric test were used to reveal the relationship between social, demographic and behavioral factors of tourism and sound source perception, and multiple regression analysis was used to identify the key factors of urban forest park. The results showed that, first, there were significant individual differences and correlations between respondents’ age, education level, visit time and frequency on soundscape perceptions; the older the respondent or the higher the education level, the higher the psychological stress relief evaluation of natural sounds such as birdsong in urban forest parks; the more frequent the visit, the more sensitive the users were to sound source perceptions; and the different visit time periods showed that, when the frequency of the visit was higher, the more sensitive the users were to the sound source. Secondly, natural sounds are important factors for psychological stress relief in soundscape, and natural sounds such as birdsong, cicadas, flowing water, and the sound of breeze blowing leaves have positive benefits and significant contributions to psychological stress relief. Third, the overall restorative evaluation of soundscape perception and the separate evaluation of natural, mechanical, and human-made sounds had significant effects, and the overall restorative evaluation showed that the cumulative contribution value of attractiveness was greater than that of compatibility and consistency. Fourth, soundscape plays a role in psychological stress relief, as evidenced by significant differences in pleasure and arousal levels but not in control.
Estimation of Indoor Temperature Increments in Summers Using Heat-Flow Sensors to Assess the Impact of Roof Slab Insulation Methods
Authors: Li Yutong , Teramoto Atsushi , Ohkubo Takaaki , Sugiyama Akihiro
Published: November 2022

Improving the thermal insulation performance of buildings is crucial for saving energy. Currently, the insulation performance can be quantified based on the thermal resistance and thermal transmittance (U-value). However, for owners, these data are not readily available for the verification of different insulation methods. To address this, a solution could involve establishing a connection between specialized evaluation indicators and temperature, a common physical quantity. In this study, static and dynamic heat-transfer experiments were performed using an environmental simulation chamber and heat-flow sensors. Based on the tests, a simple predictive formula for the heat-flow density over time was established. After analyzing a full-scale building model, six cases of the heat-flow density versus temperature rise in indoor environments were obtained. This approach may aid owners in visually assessing the insulation performance of buildings by establishing a conversion relationship between the heat-flow density and temperature. In addition, the performance of 14 experimental specimens, including self-developed and code-documented thermal insulation materials and construction methods, was evaluated. In the simulations, after turning off indoor cooling equipment for 6 h during hot summers, the average indoor temperature increase for a roof with insulation was only 52% of that without insulation.
User Strategies for Prolonging Product Lifetimes: A New Starting Point for Circular Conceptual Design
Authors: Haase Louise Møller , Lythje Line Sand
Published: November 2022

Product life extension is recognized as an important tool for creating more sustainable production and consumption patterns; yet, there is a lack of studies with comprehensive insights into how consumers interact with products throughout the product’s life span, or knowledge about the strategies consumers apply to change their behaviors and avoid premature disposal. The purpose of this study is to explore the motives and strategies users apply that positively affect product lifetimes, which provides much-needed input on the consumer’s perspective in the circular economy. The study applies a qualitative ethnographic research methodology on 26 households, in which we investigated how differently motivated users engaged with products in their homes; how they approached, developed, and modified products to fit personal needs, and, eventually, end up prolonging product lifetimes. The study contributes a new perspective on user research in the circular economy and suggests a new paradigm for circular conceptual design. Specifically, the study shows how existing user strategies for product longevity can be identified and used as a starting point for designing products and services that prolong product lifetimes.
An Overview of Carbon Footprint of Coal Mining to Curtail Greenhouse Gas Emissions
Authors: Ivanova Svetlana , Vesnina Anna , Fotina Nataly , Prosekov Alexander
Published: November 2022

Despite the trend of a transition to “clean” energy, the coal industry still plays a significant role in the global economy. The constant need for raw materials and energy for production leads to an environmental crisis—an increase in the content of greenhouse gases in the atmosphere, especially in the mining regions. The purpose of this study was to analyze the impact of the carbon footprint on the environment and to study ways to reduce the negative impact of coal mining enterprises on the ecology. To analyze the chosen topic, the available reviews and research articles on the impact of the carbon footprint of coal mining enterprises, and the ways to reduce it and restore the biodiversity of wastelands, were used. It was found out that a complete ban on the extraction and use of coal in the industry will not lead to the desired result. The main ways to reduce the negative impact of coal mining enterprises on the environment were considered. The most promising direction for reducing the carbon footprint is the restoration of the vegetation cover by phytoremediation methods and the creation of carbon landfills in reclaimed territories in technogenically polluted coal mining regions.
Assessing Spatial-Temporal Changes in Monetary Values of Urban Ecosystem Services through Remotely Sensed Data
Authors: Zaman-ul-Haq Muhammad , Kanwal Ambrina , Gardezi Akber Abid , Fatima Hina , Saqib Zafeer , Bokhari Syed Atif , Nasr Emad Abouel , Ahmad Shafiq , Shafiq Muhammad
Published: November 2022

Reckless urbanization in developing regions is leading to the deterioration of the urban environment. The ensuing impacts can place a burden on urban ecology, urban infrastructure, and residents. This scenario requires a combination of avoidance measures and a detailed assessment of the ecological sustainability of the city. While monetary assessments are certainly conceivable, in this study, the contributions of urban environmental infrastructure are weighed financially. Semi-planned (Jhang) and planned (Faisalabad) urban settlements provided the context for this survey. The study uses the Benefit Transfer Method (BTM) to assess changes in the monetary value of urban ecosystem services (UES) from remote sensing data. This finding suggests that urbanization in Pakistan is devouring productive ecological land in urban areas. The assessment shows that between 1989 and 2019, the agricultural area in Faisalabad shrank (−17.38%), and the built-up area increased (16.05%). Likewise, in Jhang City, the built-up area (4.44%) and wasteland (3.10%) swelled. However, during this period (1989–2019), the proportion of agricultural land in Jhang City decreased (−8.93%). As a result, prime areas of UES are falling back into low-return areas. It also found that provisioning ecosystem services (PES) accounted for a significant portion (68.12%) of the UES produced in Faisalabad and Jhang (69.72%), respectively. In contrast, Cultural Ecosystem Services (CES) contributed the smallest share of UES in Faisalabad (1.63%) and Jhang (1.65%). However, the remaining two services, regulatory and support services, made significant contributions. The assessment shows the role of incoherence, inconsistency, resource constraints, and neglect in compromising the urban environmental integrity of these cities. This situation requires a comprehensive assessment and coordinated effort. For this, it is feasible and useful to combine socioeconomic information with land cover data through computerized equipment.
Students’ Willingness to Plant Trees and Pay for Their Maintenance on Campuses in the Democratic Republic of Congo
Authors: Balasha Arsene Mushagalusa , Balasha Benjamin Murhula , Masheka Lebon Hwali , Mulume Dominique Aganze , Mwisha Sage Weremubi , Ngabo Vianney Mulema , Amatcho Alexander-Armand , Ndele Aloise Bitagirwa , Adrien Rodrigue Lugendo , Bienvenu Mulunda Selenge , Bahati Kapoya , Patrick Kibazi , Kazadi Laurent Kabala , Kalumbu John Tshomba , Bogaert Jan , Sikuzani Yannick Useni
Published: November 2022

There is a growing interest in greening schools, campuses, and workplaces due to the perceived ecosystem services provided by trees. However, students’ willingness to participate in and financially support the greening process is less examined. Using a questionnaire survey based on the contingent valuation method (CVM) and Likert scale, 1278 students from 13 universities were interviewed on their willingness to participate in tree planting and pay for their maintenance to promote green and clean campuses in the Democratic Republic of Congo (DRC). Most of the students interviewed were male (61%) and enrolled as undergraduates (60%). While 65% of the respondents agreed with the idea of planting trees, this agreement was significantly associated with students’ awareness of climate change, the university attended, and the sources of information on the roles of trees in the community. The binary logit results showed that university courses (environment-related) and television broadcasts significantly affected students’ willingness to participate in tree planting. Overall, students (70%) agreed to pay for tree maintenance, and their willingness to pay (WTP) was estimated mainly at less than USD 5 per year. The price to pay was a significant factor in determining students’ willingness to pay for tree maintenance. Findings suggest that students’ willingness to support tree planting and maintenance is a crucial factor for academic authorities and planners to consider in order to successfully implement green infrastructures to improve the campus environment and make educational and work spaces sustainably attractive.
Using Isotopic Labeling to Investigate Artemisia ordosica Root Water Uptake Depth in the Eastern Margin of Mu Us Sandy Land
Authors: Yang Yingming , Wang Xikai , He Yunlan , Zhang Kaiming , Mo Fan , Zhang Weilong , Liu Gang
Published: November 2022

The annual precipitation in the eastern Mu Us sandy land is about 400 mm, but the precipitation varies greatly between years and seasons and severe meteorological and seasonal droughts often occur, which makes the ecological environment very fragile. Artemisia ordosica is the most dominant species in the area. We used depth-controlled deuterium labeling technology to study the root water uptake depth of adult Artemisia ordosica to explore how Artemisia ordosica can survive in extreme droughts. In addition, the soil moisture content was analyzed after the rainy season in October 2020 and the dry season in June 2021. We found that under the influence of an extreme seasonal drought in the study area, the soil layer below 180 cm in depth still maintained high water content of more than 2%; the dry sandy soil in the surface layer inhibited the loss of soil water below 180 cm. The maximum water uptake depth of the roots of adult Artemisia ordosica can reach 240–260 cm. In periods of drought, Artemisia ordosica can still maintain life by absorbing deep soil water. In drought-prone environments, Artemisia ordosica evolved a deeper vertical root system to survive dry periods by absorbing soil water from deeper layers, showing a broad water intake capacity and strong adaptability to arid environments. This study can provide a reference for afforestation projects and ecological restoration in Mu Us sandy land and also provide a reference for the ecological restoration of coal mining areas in this area.
Extreme Positive Indian Ocean Dipole in 2019 and Its Impact on Indonesia
Authors: Iskandar Iskhaq , Lestari Deni Okta , Saputra Agus Dwi , Setiawan Riza Yuliratno , Wirasatriya Anindya , Susanto Raden Dwi , Mardiansyah Wijaya , Irfan Muhammad , Rozirwan  , Setiawan Joga Dharma , Kunarso
Published: November 2022

The evolution of an extreme positive Indian Ocean Dipole (pIOD) that took place in the tropical Indian Ocean during the late boreal summer to early winter of 2019 is examined in terms of coupled ocean–atmosphere dynamics. The patterns of anomalous sea-surface temperature (SST) revealed a typical pIOD characteristic: cooling (warming) in the southeastern (western) tropical Indian Ocean. Based on the Dipole Mode Index (DMI), the evolution of the event started in mid-July and gradually strengthened with an abrupt weakening in early September before coming to its peak in late October/early November. It quickly weakened in November, and then it terminated in mid-December. During the peak phase of the event, the SST anomaly in the southeastern (western) tropical Indian Ocean reached about −2 °C (+1 °C). The pattern of anomalous SST was followed by an anomalous pattern in precipitation, in which deficit precipitation was observed over the eastern Indian Ocean, particularly over the Indonesia region. Earlier study has shown that dry conditions associated with the pIOD event created a favorable condition for a forest-peat fire in southern Sumatra. The number of fire hotspots has increased significantly during the peak phase of the 2019 pIOD event. In addition, anomalously strong upwelling forced by strong southeasterly wind anomalies along the southern coast of Java and Sumatra had induced a surface chlorophyll-a (Chl-a) bloom in this region. High surface Chl-a concentration was collocated with the negative SST anomalies observed off the southwest Sumatra coast and south Java.
Spatial Distribution Characteristics of Japan’s Forest Therapy Bases and Their Influencing Factors
Authors: Li Hui , Xu Mingrui , Li Jianzhe , Li Zhenyu , Wang Ziyao , Zhuang Weijie , Li Chunyi
Published: November 2022

Forest therapy bases are essential ecological recreational locations to promote mental and physical health while at the same time allowing people to enjoy and appreciate the forest. The article took Japan, where the development of forest therapy is in a mature stage, as the research object. Using multi-data and the methodologies of Kernel Density Analysis in ArcGIS and GeoDetector, the spatial distribution characteristics of Japan’s forest therapy bases were investigated, as well as the influencing factors. The results reveal that the spatial distribution of forest therapy bases in Japan is unbalanced, with an aggregated distribution in economically developed and densely populated areas. The spatial density of natural landscape resources, Gross Domestic Product (GDP), the spatial density of population, distance from three major metropolitan areas, rail transit ridership, and spatial density of tourism resources are factors influencing the distribution of forest therapy bases in Japan. The factors interact with each other, forming the spatial distribution pattern. Among these factors, GDP has the greatest explanatory power for the spatial distribution of forest therapy bases in Japan, followed by the distance from Japan’s three major metropolitan areas and spatial density of tourism resources, while the spatial density of population, spatial density of natural landscape resources, and rail transit ridership have a relatively weaker influence on forest therapy bases in Japan. The findings provide some insight into the macroscopic layout of forest therapy bases in various regions of different countries, demonstrating that excellent transportation facilities and good natural resources are the fundamental considerations for the location of forest therapy bases and that densely populated urban areas with a strong economic foundation are key areas for the development of forest therapy bases. Additionally, to take advantage of industrial agglomeration and synergize regional development, considerations for merging with existing resources, such as national parks, forest parks, and recreation forests, should be made.