bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods
Authors: Song Chao , Chen Xiaohong
Published: March 2021

It has become increasingly difficult in recent years to predict precipitation scientifically and accurately due to the dual effects of human activities and climatic conditions. This paper focuses on four aspects to improve precipitation prediction accuracy. Five decomposition methods (time-varying filter-based empirical mode decomposition (TVF-EMD), robust empirical mode decomposition (REMD), complementary ensemble empirical mode decomposition (CEEMD), wavelet transform (WT), and extreme-point symmetric mode decomposition (ESMD) combined with the Elman neural network (ENN)) are used to construct five prediction models, i.e., TVF-EMD-ENN, REMD-ENN, CEEMD-ENN, WT-ENN, and ESMD-ENN. The variance contribution rate (VCR) and Pearson correlation coefficient (PCC) are utilized to compare the performances of the five decomposition methods. The wavelet transform coherence (WTC) is used to determine the reason for the poor prediction performance of machine learning algorithms in individual years and the relationship with climate indicators. A secondary decomposition of the TVF-EMD is used to improve the prediction accuracy of the models. The proposed methods are used to predict the annual precipitation in Guangzhou. The subcomponents obtained from the TVF-EMD are the most stable among the four decomposition methods, and the North Atlantic Oscillation (NAO) index, the Nino 3.4 index, and sunspots have a smaller influence on the first subcomponent (Sc-1) than the other subcomponents. The TVF-EMD-ENN model has the best prediction performance and outperforms traditional machine learning models. The secondary decomposition of the Sc-1 of the TVF-EMD model significantly improves the prediction accuracy.
Hydrocarbon Pollution Detection and Mapping Based on the Combination of Various Hyperspectral Imaging Processing Tools
Authors: Achard Véronique , Foucher Pierre-Yves , Dubucq Dominique
Published: March 2021

Oil extraction and transportation may lead to small or large scale accidental spills, whether at sea or on land. Detecting these spills is a major problem that can be addressed by means of hyperspectral images and specific processing methods. In this work, several cases of onshore oil spills are studied. First, a controlled experiment was carried out: four boxes containing soil or sand mixed with crude oil or gasoil were deployed on the ONERA site near Fauga, France, and were overflown by HySpex hyperspectral cameras. Owing to this controlled experiment, different detection strategies were developed and tested, with a particular focus on the most automated methods requiring the least supervision. The methods developed were then applied to two very different cases: mapping of the shoreline contaminated due to the explosion of the Deepwater Horizon (DWH) platform based on AVIRIS images (AVIRIS: Airborne Visible/InfraRed Imaging Spectrometer), and detection of a tar pit on a former oil exploration site. The detection strategy depends on the type of oil, light or heavy, recently or formerly spilled, and on the substrate. In the first case (controlled experiment), the proposed methods included spectral index calculations, anomaly detection and spectral unmixing. In the case of DWH, spectral indices were computed and the unmixing method was tested. Finally, to detect the tar pit, a strategy based on anomaly detection and spectral indices was applied. In all the cases studied, the proposed methods were successful in detecting and mapping the oil pollution.
Resource Management Techniques for Cloud/Fog and Edge Computing: An Evaluation Framework and Classification
Authors: Mijuskovic Adriana , Chiumento Alessandro , Bemthuis Rob , Aldea Adina , Havinga Paul
Published: March 2021

Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management techniques in that domain. Consequently, we classify current research contributions to support in conducting an evaluation framework. One of the main contributions is an overview and analysis of research papers addressing resource management techniques. Concluding, this review highlights opportunities of using resource management techniques within the cloud/fog/edge paradigm. This practice is still at early development and barriers need to be overcome.
Exhaustive Description of the System Architecture and Prototype Implementation of an IoT-Based eHealth Biometric Monitoring System for Elders in Independent Living
Authors: Vizitiu Cristian , Bîră Călin , Dinculescu Adrian , Nistorescu Alexandru , Marin Mihaela
Published: March 2021

In this paper, we present an exhaustive description of an extensible e-Health Internet-connected embedded system, which allows the measurement of three biometric parameters: pulse rate, oxygen saturation and temperature, via several wired and wireless sensors residing to the realm of Noncommunicable Diseases (NCDs) and cognitive assessment through Choice Reaction Time (CRT) analysis. The hardware used is based on ATMEGA AVR + MySignals Hardware printed circuit board (Hardware PCB), but with multiple upgrades (including porting from ATMEGA328P to ATMEGA2560). Multiple software improvements were made (by writing high-level device drivers, text-mode and graphic-mode display driver) for increasing functionality, portability, speed, and latency. A top-level embedded application was developed and benchmarked. A custom wireless AT command firmware was developed, based on ESP8266 firmware to allow AP-mode configuration and single-command JavaScript Object Notation (JSON) data-packet pushing towards the cloud platform. All software is available in a git repository, including the measurement results. The proposed eHealth system provides with specific NCDs and cognitive views fostering the potential to exploit correlations between physiological and cognitive data and to generate predictive analysis in the field of eldercare.
Radio-Frequency Biosensors for Real-Time and Continuous Glucose Detection
Authors: Jang Chorom , Lee Hee-Jo , Yook Jong-Gwan
Published: March 2021

This review paper focuses on radio-frequency (RF) biosensors for real-time and continuous glucose sensing reported in the literature, including our recent research. Diverse versions of glucose biosensors based on RF devices and circuits are briefly introduced, and their performances are compared. In addition, the limitations of the developed RF glucose biosensors are discussed. Finally, we present perspectives on state-of-art RF biosensing chips for point-of-care diagnosis and describe their future challenges.
Hydrogel-Core Microstructured Polymer Optical Fibers for Selective Fiber Enhanced Raman Spectroscopy
Authors: Azkune Mikel , Ayesta Igor , Ruiz-Rubio Leire , Arrospide Eneko , Vilas-Vilela Jose Luis , Zubia Joseba
Published: March 2021

A new approach of Fiber Enhanced Raman Spectroscopy (FERS) is described within this article based on the use of Hydrogel-Core microstructured Polymer Optical Fibers (HyC-mPOF). The incorporation of the hydrogel only on the core of the Hollow-Core microstructured Polymer Optical Fiber (HC-mPOF) enables to perform FERS measurements in a functionalized matrix, enabling high selectivity Raman measurements. The hydrogel formation was continuously monitored and quantified using a Principal Component Analysis verifying the coherence between the components and the Raman spectrum of the hydrogel. The performed measurements with high and low affinity target molecules prove the feasibility of the presented HyC-mPOF platform.
Smart Patch for Skin Temperature: Preliminary Study to Evaluate Psychometrics and Feasibility
Authors: Kim Heejung , Kim Sunkook , Lee Mingoo , Rhee Yumie , Lee Sungho , Jeong Yi-Rang , Kang Sunju , Naqi Muhammad , Hong Soyun
Published: March 2021

There is a need for continuous, non-invasive monitoring of biological data to assess health and wellbeing. Currently, many types of smart patches have been developed to continuously monitor body temperature, but few trials have been completed to evaluate psychometrics and feasibility for human subjects in real-life scenarios. The aim of this feasibility study was to evaluate the reliability, validity and usability of a smart patch measuring body temperature in healthy adults. The smart patch consisted of a fully integrated wearable wireless sensor with a multichannel temperature sensor, signal processing integrated circuit, wireless communication feature and a flexible battery. Thirty-five healthy adults were recruited for this test, carried out by wearing the patches on their upper chests for 24 h and checking their body temperature six times a day using infrared forehead thermometers as a gold standard for testing validity. Descriptive statistics, one-sampled and independent t-tests, Pearson’s correlation coefficients and Bland-Altman plot were examined for body temperatures between two measures. In addition, multiple linear regression, receiver operating characteristic (ROC) and qualitative content analysis were conducted. Among the 35 participants, 29 of them wore the patch for over 19 h (dropout rate: 17.14%). Mean body temperature measured by infrared forehead thermometers and smart patch ranged between 32.53 and 38.2 °C per person and were moderately correlated (r = 0.23–0.43) overall. Based on a Bland-Altman plot, approximately 94% of the measurements were located within one standard deviation (upper limit = 4.52, lower limit = −5.82). Most outliers were identified on the first measurement and were located below the lower limit. It is appropriate to use 37.5 °C in infrared forehead temperature as a cutoff to define febrile conditions. Users’ position while checking and ambient temperature and humidity are not affected to the smart patch body temperature. Overall, the participants showed high usability and satisfaction on the survey. Few participants reported discomfort due to limited daily activity, itchy skin or detaching concerns. In conclusion, epidermal electronic sensor technologies provide a promising method for continuously monitoring individuals’ body temperatures, even in real-life situations. Our study findings show the potential for smart patches to monitoring non-febrile condition in the community.
Driving Environment Perception Based on the Fusion of Vehicular Wireless Communications and Automotive Remote Sensors
Authors: Baek Minjin , Mun Jungwi , Kim Woojoong , Choi Dongho , Yim Janghyuk , Lee Sangsun
Published: March 2021

Driving environment perception for automated vehicles is typically achieved by the use of automotive remote sensors such as radars and cameras. A vehicular wireless communication system can be viewed as a new type of remote sensor that plays a central role in connected and automated vehicles (CAVs), which are capable of sharing information with each other and also with the surrounding infrastructure. In this paper, we present the design and implementation of driving environment perception based on the fusion of vehicular wireless communications and automotive remote sensors. A track-to-track fusion of high-level sensor data and vehicular wireless communication data was performed to accurately and reliably locate the remote target in the vehicle surroundings and predict the future trajectory. The proposed approach was implemented and evaluated in vehicle tests conducted at a proving ground. The experimental results demonstrate that using vehicular wireless communications in conjunction with the on-board sensors enables improved perception of the surrounding vehicle located at varying longitudinal and lateral distances. The results also indicate that vehicle future trajectory and potential crash involvement can be reliably predicted with the proposed system in different cut-in driving scenarios.
Optical Sensing Using Fiber-Optic Multimode Interference Devices: A Review of Nonconventional Sensing Schemes
Authors: Guzmán-Sepúlveda José Rafael , Guzmán-Cabrera Rafael , Castillo-Guzmán Arturo Alberto
Published: March 2021

We review fiber-based multimode interference (MMI) devices with a particular focus on optical fiber-based sensing applications. The present review complements a recently published, extensive review where the sensing of conventional physical variables such as refractive index, temperature, displacement, and strain was covered. This review focuses on MMI fiber sensors for nonconventional physical variables, including mechanical, electromagnetic, chemical, and optical, covering around fifteen years of work in the field. Finally, by the end of this paper, we also review some new trends of MMI-based schemes based on polymer fibers, for wavelength-locking applications, for retrieving the thermo-optic coefficient of liquid samples, and for measuring the dynamics of complex fluids.
Developing the Next Generation of Augmented Reality Games for Pediatric Healthcare: An Open-Source Collaborative Framework Based on ARCore for Implementing Teaching, Training and Monitoring Applications
Authors: Vidal-Balea Aida , Blanco-Novoa Óscar , Fraga-Lamas Paula , Fernández-Caramés Tiago M.
Published: March 2021

Augmented Reality (AR) provides an alternative to the traditional forms of interaction between humans and machines, and facilitates the access to certain technologies to groups of people with special needs like children. For instance, in pediatric healthcare, it is important to help children to feel comfortable during medical procedures and tests that may be performed on them. To tackle such an issue with the help of AR-based solutions, this article presents the design, implementation and evaluation of a novel open-source collaborative framework that enables to develop teaching, training, and monitoring pediatric healthcare applications. Specifically, such a framework allows for building collaborative applications and shared experiences for AR devices, providing functionalities for connecting with other AR devices and enabling real-time visualization and simultaneous interaction with virtual objects. Since all the communications involved in AR interactions are handled by AR devices, the proposed collaborative framework is able to operate autonomously through a Local Area Network (LAN), thus requiring no cloud or external servers. In order to demonstrate the potential of the proposed framework, a practical use case application is presented. Such an application has been designed to motivate pediatric patients and to encourage them to increase their physical activity through AR games. The presented games do not require any previous configuration, as they use ARCore automatic surface detection technology. Moreover, the AR mobile gaming framework allows multiple players to engage in the same AR experience, so children can interact and collaborate among them sharing the same AR content. In addition, the proposed AR system provides a remote web application that is able to collect and to visualize data on patient use, aiming to provide healthcare professionals with qualified data about the mobility and mood of their patients through an intuitive and user-friendly web tool. Finally, to determine the performance of the proposed AR system, this article presents its evaluation in terms of latency and processing time. The results show that both times are low enough to provide a good user experience.
Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition
Authors: Huang Yuanyuan , Tong Shuiguang , Tong Zheming , Cong Feiyun
Published: March 2021

As an essential part of the transmission system, gearboxes are considered as a major source of vibration. Signal identification of gear vibration is necessary for online monitoring of the mechanical systems. However, in engine-gearbox systems, the ignition impact of the engine is strong, so that the gear vibration is generally submerged. To overcome this issue, the resonance-based signal sparse decomposition (RSSD) method is used in this paper based on different oscillatory behaviors of the gear meshing impact and the engine ignition impact. To improve the accuracy of RSSD under interferences, the meshing frequency energy ratio (MF–ER) index is introduced into RSSD to adaptively choose the decomposition parameters. Before applying the RSSD method, the auto-regression (AR) model is used as a pre-whitening step to eliminate the normal gear meshing vibration, which improves the decomposition performance of RSSD. The effectiveness of the proposed AR-ORSSD (AR-based optimized RSSD) algorithm is tested using both simulated signals and measured vibration signals from an engine-gearbox system in a forklift. Comparisons were made with the RSSD algorithm based on a genetic algorithm. Experimental results indicate that the AR-ORSSD algorithm is superior at identifying gear vibration signals especially when under strong interferences.
EEG-Based Emotion Recognition Using an Improved Weighted Horizontal Visibility Graph
Authors: Kong Tianjiao , Shao Jie , Hu Jiuyuan , Yang Xin , Yang Shiyiling , Malekian Reza
Published: March 2021

Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.
Small-Sized Interferometer with Fabry–Perot Resonators for Gravitational Wave Detection
Authors: Petrov Nikolai , Pustovoit Vladislav
Published: March 2021

It is highly desirable to have a compact laser interferometer for detecting gravitational waves. Here, a small-sized tabletop laser interferometer with Fabry–Perot resonators consisting of two spatially distributed “mirrors” for detecting gravitational waves is proposed. It is shown that the spectral resolution of 10−23 cm−1 can be achieved at a distance between mirrors of only 1–3 m. The influence of light absorption in crystals on the limiting resolution of such resonators is also studied. A higher sensitivity of the interferometer to shorter-wave laser radiation is shown. A method for detecting gravitational waves is proposed based on the measurement of the correlation function of the radiation intensities of non-zero-order resonant modes from the two arms of the Mach–Zehnder interferometer.
Ozone Sensing by In2O3 Films Modified with Rh: Dimension Effect
Authors: Korotcenkov Ghenadii , Nehasil Vaclav
Published: March 2021

We considered the effect of coverage of the surface of In2O3 films with rhodium on the sensitivity of their electrophysical properties to ozone (1 ppm). The surface coverage with rhodium varied in the range of 0–0.1 ML. The In2O3 films deposited by spray pyrolysis had a thickness of 40–50 nm. The sensor response to ozone depends on the degree of rhodium coverage. This dependence has a pronounced maximum at a coverage of ~0.01 ML of Rh. An explanation is given for this effect. It is concluded that the observed changes are associated with the transition from the atomically dispersed state of rhodium to a 3D cluster state.
AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks
Authors: Luna-Perejón Francisco , Muñoz-Saavedra Luis , Civit-Masot Javier , Civit Anton , Domínguez-Morales Manuel
Published: March 2021

Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system’s feasibility.
Kernel Probabilistic K-Means Clustering
Authors: Liu Bowen , Zhang Ting , Li Yujian , Liu Zhaoying , Zhang Zhilin
Published: March 2021

Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with constraints of linear equalities and linear inequalities. To accelerate the AGP method, a fast AGP (FAGP) algorithm was designed. The proposed FAGP uses a maximum-step strategy to estimate the step length, and uses an iterative method to update the projection matrix. Experiments demonstrated the effectiveness of the proposed method through a performance comparison of KPKM with KFCM, KKM, FCM and k-means. Experiments showed that the proposed KPKM is able to find nonlinearly separable structures in synthetic datasets. Ten real UCI datasets were used in this study, and KPKM had better clustering performance on at least six datsets. The proposed fast AGP requires less running time than the original AGP, and it reduced running time by 76–95% on real datasets.
Unsupervised Trademark Retrieval Method Based on Attention Mechanism
Authors: Cao Jiangzhong , Huang Yunfei , Dai Qingyun , Ling Wing-Kuen
Published: March 2021

Aiming at the high cost of data labeling and ignoring the internal relevance of features in existing trademark retrieval methods, this paper proposes an unsupervised trademark retrieval method based on attention mechanism. In the proposed method, the instance discrimination framework is adopted and a lightweight attention mechanism is introduced to allocate a more reasonable learning weight to key features. With an unsupervised way, this proposed method can obtain good feature representation of trademarks and improve the performance of trademark retrieval. Extensive comparative experiments on the METU trademark dataset are conducted. The experimental results show that the proposed method is significantly better than traditional trademark retrieval methods and most existing supervised learning methods. The proposed method obtained a smaller value of NAR (Normalized Average Rank) at 0.051, which verifies the effectiveness of the proposed method in trademark retrieval.
Willingness-to-Pay for Environmental Measures in Non-Profit Sport Clubs
Authors: Thormann Tim F. , Wicker Pamela
Published: March 2021

To reduce global warming and climate change, the German government plans to implement a carbon tax, which will also affect sport organizations. This study investigates how much sport club members are willing to pay for environmental measures and how sport-specific, club-specific, environmental, and socio-demographic factors are associated with their willingness-to-pay. In 2019 and 2020, active adult sport club members in five team/racket sports were sampled using an online survey in Germany (n = 3036). The contingent valuation method was applied to estimate sport club members’ willingness-to-pay for environmental measures. Regression analyses were employed to investigate a set of factors that are associated with club members’ decision to pay at all and the amount of willingness-to-pay. The results show that 64.3% of respondents reported a positive willingness-to-pay. The average willingness-to-pay for environmental measures amounts to EUR 14.53 per year and to EUR 22.59 for those reporting a positive willingness-to-pay. The results of t-tests show that club members stating a positive WTP differ significantly from members who are not willing to pay anything in terms of sport-specific, club-specific, environmental, and socio-demographic factors. The logistic regression results indicate that the likelihood of reporting a positive willingness-to-pay increases with increasing environmental consciousness, educational level, weekly practice hours, identification and satisfaction with the club, and subjective well-being, while age has a U-shaped effect. The results of a Tobit model show that the amount of willingness-to-pay is positively determined by environmental consciousness, educational level, and satisfaction with the club. The findings suggest that the majority of club members are willing to pay higher membership fees for the implementation of environmental measures in sport clubs. Increasing members’ level of environmental consciousness through educational initiatives represents a way for sport policy and sport managers to help increase financial support for environmental measures among club members.
Ensuring Sustainable Evaluation: How to Improve Quality of Evaluating Grant Proposals?
Authors: Wieczorkowska Grażyna , Kowalczyk Katarzyna
Published: March 2021

The principle of sustainable development is an obligation placed on all entities involved in the implementation and delivery of the structural funds made available not only by the European Commission but also by grant donors from all over the world. For this reason, when applying for a grant, proposals need to demonstrate the positive or neutral impact of the project on sustainable development. To be able to select projects that will ensure sustainability, we need to ensure the effective evaluation of the proposals. The process of their evaluation should be objective, unbiased and transparent. However, current processes have several limitations. The process by which grants are awarded and proposals evaluated has come under increasing scrutiny, with a particular focus on the selection of reviewers, fallibility of their assessments, the randomness of assessments and the low level of common agreement. In our studies, we demonstrated how some of those limitations may be overcome. Our topic of interest is the work of reviewers/experts who evaluate scientific grant proposals. We analyse data coming from two prominent scientific national grant foundations, which differ in terms of expert’s selection procedure. We discuss the problems associated with both procedures (rating style of the reviewers, lack of calibration and serial position effect) and present potential solutions to prevent them. We conclude that, to increase the unbiasedness and fairness of the evaluation process, reviewers’ work should be analysed. We also suggest that, within a certain panel, all grant proposals should be evaluated by the same set of reviewers, which would help to eliminate the distorting influence of the selection of a very severe or very lenient expert. Such effective assessment and moderation of the process would help ensure the quality and sustainability of evaluations.
Managing Traffic Data through Clustering and Radial Basis Functions
Authors: Hernández Heber , Alberdi Elisabete , Pérez-Acebo Heriberto , Álvarez Irantzu , García María José , Eguia Isabel , Fernández Kevin
Published: March 2021

Due to the importance of road transport an adequate identification of the various road network levels is necessary for an efficient and sustainable management of the road infrastructure. Additionally, traffic values are key data for any pavement management system. In this work traffic volume data of 2019 in the Basque Autonomous Community (Spain) were analyzed and modeled. Having a multidimensional sample, the average annual daily traffic (AADT) was considered as the main variable of interest, which is used in many areas of the road network management. First, an exploratory analysis was performed, from which descriptive statistical information was obtained continuing with the clustering by various variables in order to standardize its behavior by translation. In a second stage, the variable of interest was estimated in the entire road network of the studied country using linear-based radial basis functions (RBFs). The estimated model was compared with the sample statistically, evaluating the estimation using cross-validation and highest-traffic sectors are defined. From the analysis, it was observed that the clustering analysis is useful for identifying the real importance of each road segment, as a function of the real traffic volume and not based on other criteria. It was also observed that interpolation methods based on linear-type radial basis functions (RBF) can be used as a preliminary method to estimate the AADT.
Functional Diversity Can Predict Ecosystem Functions Better Than Dominant Species: The Case of Desert Plants in the Ebinur Lake Basin
Authors: Hou Zhufeng , Lv Guanghui , Jiang Lamei
Published: March 2021

Studying the impact of biodiversity on ecosystem multifunctionality is helpful for clarifying the ecological mechanisms (such as niche complementary effects and selection) of ecosystems providing multiple services. Biodiversity has a significant impact on ecosystem versatility, but the relative importance of functional diversity and dominant species to ecosystem functions needs further evaluation. We studied the desert plant community in Ebinur Lake Basin. Based on field survey data and experimental analysis, the relationship between the richness and functional diversity of dominant species and the single function of ecosystem was analyzed. The relative importance of niche complementary effect and selective effect in explaining the function of plant diversity in arid areas is discussed. There was no significant correlation between desert ecosystem functions (soil available phosphorus, organic matter, nitrate nitrogen, and ammonium nitrogen) and the richness of the dominant species Nitraria tangutorum (p < 0.05). Soil organic matter and available phosphorus had significant effects on specific leaf area and plant height (p < 0.05). Functional dispersion (FDis) had a significant effect on soil available phosphorus, while dominant species dominant species richness (SR) had no obvious effect on single ecosystem function. A structural equation model showed that dominant species had no direct effect on plant functional diversity and ecosystem function, but functional diversity had a strong direct effect on ecosystem function, and its direct coefficients of action were 0.226 and 0.422. The results can help to explain the response mechanism of multifunctionality to biodiversity in arid areas, which may provide referential significance for vegetation protection and restoration for other similar areas.
A Spatial Improved-kNN-Based Flood Inundation Risk Framework for Urban Tourism under Two Rainfall Scenarios
Authors: Liu Shuang , Liu Rui , Tan Nengzhi
Published: March 2021

Urban tourism has been suffering socio-economic challenges from flood inundation risk (FIR) triggered by extraordinary rainfall under climate extremes. The evaluation of FIR is essential for mitigating economic losses, and even casualties. This study proposes an innovative spatial framework integrating improved k-nearest neighbor (kNN), remote sensing (RS), and geographic information system (GIS) to analyze FIR for tourism sites. Shanghai, China, was selected as a case study. Tempo-spatial factors, including climate, topography, drainage, vegetation, and soil, were selected to generate several flood-related gridded indicators as inputs into the evaluation framework. A likelihood of FIR was mapped to represent possible inundation for tourist sites under a moderate-heavy rainfall scenario and extreme rainfall scenario. The resultant map was verified by the maximum inundation extent merged by RS images and water bodies. The evaluation outcomes deliver the baseline and scientific information for urban planners and policymakers to take cost-effective measures for decreasing and evading the pressure of FIR on the sustainable development of urban tourism. The spatial improved-kNN-based framework provides an innovative, effective, and easy-to-use approach to evaluate the risk for the tourism industry under climate change.
Load Resistance Optimization of a Broadband Bistable Piezoelectric Energy Harvester for Primary Harmonic and Subharmonic Behaviors
Authors: Bae Sungryong , Kim Pilkee
Published: March 2021

In this study, optimization of the external load resistance of a piezoelectric bistable energy harvester was performed for primary harmonic (period-1T) and subharmonic (period-3T) interwell motions. The analytical expression of the optimal load resistance was derived, based on the spectral analyses of the interwell motions, and evaluated. The analytical results are in excellent agreement with the numerical ones. A parametric study shows that the optimal load resistance depended on the forcing frequency, but not the intensity of the ambient vibration. Additionally, it was found that the optimal resistance for the period-3T interwell motion tended to be approximately three times larger than that for the period-1T interwell motion, which means that the optimal resistance was directly affected by the oscillation frequency (or oscillation period) of the motion rather than the forcing frequency. For broadband energy harvesting applications, the subharmonic interwell motion is also useful, in addition to the primary harmonic interwell motion. In designing such piezoelectric bistable energy harvesters, the frequency dependency of the optimal load resistance should be considered properly depending on ambient vibrations.
Evaluation of Community Tourism Empowerment of Ancient Town Based on Analytic Hierarchy Process: A Case Study of Zhujiajiao, Shanghai
Authors: Zhou Xinyue , Wang Jing , Zhang Song
Published: March 2021

The research on community empowerment in the protection and utilization of ancient towns has practical significance for the sustainable development of ancient towns. Combined with empirical research, questionnaire survey, this paper analyzes the residents’ awareness of empowerment in the community participation of tourism, taking Zhujiajiao ancient town as a case study. Firstly, the evaluation system of community empowerment is established from four dimensions, including economy, society, politics, and psychology. Secondly, the weights of various factors are calculated by using the analytic hierarchy process, and the status quo of community tourism empowerment is comprehensively evaluated by the fuzzy analysis method. The results show that the degree of community empowerment in Zhujiajiao ancient town is between “average” and “good”. Psychological empowerment has the best score, while political empowerment is relatively weak. So, the dilemma of the top-down participation system needs to be changed. Self-government tourism organizations and the rules of income distribution should be established along with the ability training of tourism management to optimize the community participation mechanism. Application of the analytic hierarchy process to ancient town tourism empowerment is a new attempt and the evaluation scale can be used to other similar sites. Strategies in this paper have referential value for tourism sustainability in practice.
Bio-Flocculation Property Analyses of Oleaginous Microalgae Auxenochlorella protothecoides UTEX 2341
Authors: Li Jinyu , Li Baozhen , Yang Jinshui
Published: March 2021

The bio-flocculation ability of UTEX 2341 was studied for the purpose of improving microalgae harvesting efficiency to cut the high cost of biofuel production. The algae cells of UTEX 2341 cultured under heterotrophic and municipal wastewater conditions were found to have better self-flocculation ability, with flocculation rates of 92% and 85% at 2 h, respectively. Moreover, the flocculation rates of 16 freeze-dried microalgae powder samples cultured under different stress conditions were 0~72% with an algae powder dosage of 35 mg L−1. The flocculation efficiency of DIM, DCd1, DT28, and L6S was stable under different pH of 3~9 and temperatures of 15~50 °C. For samples of IM, LCd0.6, LMn2, and LZn2, the flocculation efficiency decreased or increased respectively with increased pH or temperatures. Though the flocculation properties of the eight samples showed wide differences, their flocculant compositions were almost the same with unknown components occupying large proportions. More studies needed to be further carried out to reveal the flocculation mechanisms and analyze the flocculation abilities in practical application, which would be conducive to future large-scale application of the bio-flocculation method and also cost reduction.
Investigation on the Emission and Diffusion of Hydrogen Sulfide during Landfill Operations: A Case Study in Shenzhen
Authors: He Haijie , Wu Tao , Xu Hui , Lu Yuhua , Qiu Zhanhong , Wang Xiaogang , Zhang Pan
Published: March 2021

This study investigated the emission and diffusion of hydrogen sulfide (H2S), as one of the odorous gases generated from landfills, in a municipal solid waste landfill of a south Chinese city. To this end, the flux of the H2S emissions in the working area of the landfill and its diffusion in the surrounding area were measured. The diffusion of the H2S was simulated at different wind speeds, wind directions, bare working areas of the landfill, heights of the landfill, and angles between the wind direction and the long side of the working area. The results indicated that the concentration of the H2S around the monitoring point ranged from 0 to 60 µg/m3, and the simulated data were consistent with the measured results. At a uniform wind direction, the pollution range of the H2S was narrow. Furthermore, with an increase in the height of the waste dump, the concentration of the H2S decreased in the working area but rose in the surrounding area. Notably, when the angle between the long side of the working area and the wind direction was 0°, the H2S largely spread along the extension cord of the long side of the working area. When the angle increased to 90°, the influence range of the H2S extended significantly. The working area in the landfill site should be regulated based on the monitored data to reduce the effect of this harmful gas on the living environment, and the health of the landfill staff and nearby residents.
Fulfilling External Stakeholders’ Demands—Enhancement Workplace Safety Using Fuzzy MCDM
Authors: Komatina Nikola , Djapan Marko , Ristić Igor , Aleksić Aleksandar
Published: March 2021

Sustainable development and project stakeholder management indicate a business practice where an organization strives to fulfil the demands of the important stakeholders for the project’s success. If one company relies on subassembly parts from its supplier, then it might be considered that it has high interest for enhancing the business continuity of the supplier. This issue has become more complex during 2020 due to turbulent business conditions where the problem of the safety and health of workers during daily work has become one of main reasons for business vulnerability. Besides the above-mentioned, project stakeholders may have different demands. The implementation of the management actions that lead to the fulfilment of stakeholder demands (SDs), such as addressing ongoing issues, are almost always limited by the available budget. The contribution of this research is providing the input for determining the actions which should address the most important SDs. Those activities may be seen as part of the strategy for external stakeholder management and successful long-term relationship. The determination of the priorities of SDs is based on a fuzzy multicriteria optimization model with type-2 fuzzy sets.
Parameter Optimization and Effect Analysis of Low-Pressure Abrasive Water Jet (LPAWJ) for Paint Removal of Remanufacturing Cleaning
Authors: Xiong Sheng , Jia Xiujie , Wu Shuangshuang , Li Fangyi , Ma Mingliang , Wang Xing
Published: March 2021

As an environmentally friendly method, water jet (WJ) technology plays a significant role in the field of remanufacturing cleaning. The cleaning capacity of a WJ is severely restricted by the water pressure, while the impact force will be too large and may damage the cleaned substrate as well as cause energy waste if the pressure is too high. However, by adding abrasives, the cleaning capacity of a low-pressure water jet (LPWJ) will be considerably improved. Although abrasive water jet (AWJ) technology has been used in mechanical machining for decades, very limited research work can be found in the literature for remanufacturing cleaning. In this paper, the role of abrasives in low-pressure abrasive water jet (LPAWJ) cleaning was described. Cleaning performance with different parameters (abrasive feed rate condition, water pressure and standoff distance) in paint removal was experimentally investigated by using the Taguchi design of experiment. The experimental results indicated that the water pressure was the most dominant factor and the optimal parameter combination was the second feed rate condition, 9 MPa water pressure and 300 mm standoff distance. The influence law between the cleaning performance and various factors was explored, which can provide remanufacturers with directions in selection of the optimal parameters in the LPAWJ cleaning process. By designing contrast experiments, the results showed that the cleaning capacity of an LPAWJ is better than that of a pure LPWJ and the residual effect in terms of changes in surface roughness, residual stress and morphology is a little larger.
Does Air Pollution Decrease Labor Supply of the Rural Middle-Aged and Elderly?
Authors: Huang Qiaolong , Zhang Yu Yvette , Chen Qin , Ning Manxiu
Published: March 2021

This paper investigates the impact of air pollution on the labor supply of the middle-aged and elderly in rural China. The results suggest that exposure to high concentrations of PM2.5 has detrimental effects on working hours of the middle-aged and elderly. We further demonstrate the heterogeneous effects of PM2.5 by income and geographic regions. In particular, people with vulnerable economic conditions are more likely than others to reduce their labor hours due to PM2.5 exposure. Moreover, higher PM2.5 levels affected people in Central and Northeastern China more significantly than those in other regions.
How Promotion Incentives and Environmental Regulations Affect China’s Environmental Pollution?
Authors: Liu Shiwen , Zhang Zhong , Xu Guangyao , Zhang Zhen , Li Hongyuan
Published: March 2021

As for the academics and policymakers, more attention has been given to the issue on how to reduce environmental pollution through the cooperation of environmental regulation and local officials’ promotion incentives. With the use of a city-level panel data of 266 Chinese cities from 2005 to 2016, this study preliminary explores the impacts of environmental regulations, local officials’ promotion incentives, and their interaction terms on urban environmental pollution at national and regional levels by using the spatial Durbin model. The results indicate that the impacts of environmental regulations and local officials’ promotion incentives on urban environmental pollution have achieved the desired goal with the other’s cooperation, and their interaction term’s coefficients on urban environmental pollution are significantly negative. Moreover, spatial heterogeneity is established, and the uneven development of urban environmental pollution among different regions deserves more attention. In order to effectively reduce the level of urban environmental pollution in China, the government should focus on such solutions as enhancing the implementation and supervision efficiency of environmental regulation, optimizing the performance appraisal system of local officials, improving the synergistic effects of environmental regulations and local officials’ promotion incentives, and establishing a multi-scale spatial cooperation mechanism based on both geographical and economic correlations.
Influence of Cairo Metro Tunnel Excavation on Pile Deep Foundation of the Adjacent Underground Structures: Numerical Study
Authors: Ayasrah Mo’men , Qiu Hongsheng , Zhang Xiedong
Published: March 2021

Day by day the call to solve traffic congestion problems is increasing. Subway tunnels and high-speed railway are commonly used for transportation. Therefore, tunnel construction induces soil movement, which in turn affects the stability and integrity of adjacent existing buildings. A series of numerical simulations have been established to investigate the effects of tunnel construction of the Greater Cairo Metro–Line 3-Phase-1 on adjacent pile cap foundations of Garage El-Attaba building. Many parameters have been investigated such as tunnel diameter and the distance between pile and tunnel at different tunnel axis and deep and shallow tunnel. After thorough analysis of the results’ simulation, it was found that the tunneling induces additional axial forces and bending moment as well as increasing axial settlement and lateral deflection. Moreover, the results obtained from the parametric study for the shallow and deep tunnel show that the tunnel depth has a much significant effect on piles responses. Finally, the tunnel diameter has an impact on pile responses as well as the pile cap foundation influenced by the tunnel when the tunnel is in very close vicinity of the pile, and its effect is modest to negligible if located far away from the buildings.