bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of the Moist Material Relative Permittivity Readouts Using the Non-Invasive Reflectometric Sensors and Microwave Antenna
Authors: Suchorab Zbigniew , Tabiś Krzysztof , Brzyski Przemysław , Szczepaniak Zenon , Rogala Tomasz , Susek Waldemar , Łagód Grzegorz
Published: May 2022

The article concerns the issue of non-invasive moisture sensing in building materials. Two techniques that enable evaluating the value of the relative permittivity of the material, being the measure of porous material moisture, have been utilized for the research. The first is the microwave technique that utilizes the non-contact measurement of velocity of microwave radiation across the tested material and the second is the time domain reflectometry (TDR) technique based on the measurement of electromagnetic pulse propagation time along the waveguides, being the elements of sensor design. The tested building material involved samples of red ceramic brick that differed in moisture, ranging between 0% and 14% moisture by weight. The main goal of the research was to present the measuring potential of both techniques for moisture evaluation as well as emphasize the advantages and disadvantages of each method. Within the research, it was stated that both methods provide similar measuring potential, with a slight advantage in favor of a microwave non-contact sensor over surface TDR sensor designs.
Measurement and Region Identification in Deep Displacement of Slopes Based on Rod-Fiber Coupling Structure
Authors: Liu Pengzhen , Liu Zhen , Zhou Cuiying
Published: May 2022

For measuring and region-identifying the deep displacement of slopes, a rod-fiber coupling structure based on optical time-domain reflection technology was designed. Accuracy of measurement and region identification in the deep displacement of slopes were studied by calibration experiment and model experiment. A rod-fiber coupling structure was able to calculate the variation and accurately identify the region of deep displacement of a slope compared with the measured downslide displacement of the slope model. The maximum measurement error of the deep displacement of the slope was 10.1%, the identification error of the displacement region was less than 4.4%, and the accuracy of the displacement-region identification of the rod-fiber coupling structure was 3.1 cm. Thus, the rod-fiber coupling structure based on optical time-domain reflection technology can be used for measuring and for region identification in the deep displacement of the slopes, and can provide a new method for the identification of the sliding surfaces of slopes.
Error Reduction in Vision-Based Multirotor Landing System
Authors: Llerena Caña Juan Pedro , García Herrero Jesús , Molina López José Manuel
Published: May 2022

New applications are continuously appearing with drones as protagonists, but all of them share an essential critical maneuver—landing. New application requirements have led the study of novel landing strategies, in which vision systems have played and continue to play a key role. Generally, the new applications use the control and navigation systems embedded in the aircraft. However, the internal dynamics of these systems, initially focused on other tasks such as the smoothing trajectories between different waypoints, can trigger undesired behaviors. In this paper, we propose a landing system based on monocular vision and navigation information to estimate the helipad global position. In addition, the global estimation system includes a position error correction module by cylinder space transformation and a filtering system with a sliding window. To conclude, the landing system is evaluated with three quality metrics, showing how the proposed correction system together with stationary filtering improves the raw landing system.
Distributed Bragg Reflectors Employed in Sensors and Filters Based on Cavity-Mode Spectral-Domain Resonances
Authors: Gryga Michal , Ciprian Dalibor , Hlubina Petr
Published: May 2022

Spectral-domain resonances for cavities formed by two distributed Bragg reflectors (DBRs) were analyzed theoretically and experimentally. We model the reflectance and transmittance spectra of the cavity at the normal incidence of light when DBRs are represented by a one-dimensional photonic crystal (1DPhC) comprising six bilayers of TiO2/SiO2 with a termination layer of TiO2. Using a new approach based on the reference reflectance, we model the reflectance ratio as a function of both the cavity thickness and its refractive index (RI) and show that narrow dips within the 1DPhC band gap can easily be resolved. We revealed that the sensitivity and figure of merit (FOM) are as high as 610 nm/RIU and 938 RIU−1, respectively. The transmittance spectra include narrow peaks within the 1DPhC band gap and their amplitude and spacing depend on the cavity’s thickness. We experimentally demonstrated the sensitivity to variations of relative humidity (RH) of moist air and FOM as high as 0.156 nm/%RH and 0.047 %RH−1, respectively. In addition, we show that, due to the transmittance spectra, the DBRs with air cavity can be employed as spectral filters, and this is demonstrated for two LED sources for which their spectra are filtered at wavelengths 680 nm and 780 nm, respectively, to widths as narrow as 2.3 nm. The DBR-based resonators, thus, represent an effective alternative to both sensors and optical filters, with advantages including the normal incidence of light and narrow-spectral-width resonances.
Visual Sentiment Analysis from Disaster Images in Social Media
Authors: Hassan Syed Zohaib , Ahmad Kashif , Hicks Steven , Halvorsen Pål , Al-Fuqaha Ala , Conci Nicola , Riegler Michael
Published: May 2022

The increasing popularity of social networks and users’ tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in the literature, sentiment analysis from images and videos is relatively new. This article focuses on visual sentiment analysis in a societally important domain, namely disaster analysis in social media. To this aim, we propose a deep visual sentiment analyzer for disaster-related images, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation, and evaluations. For data annotation and analyzing people’s sentiments towards natural disasters and associated images in social media, a crowd-sourcing study has been conducted with a large number of participants worldwide. The crowd-sourcing study resulted in a large-scale benchmark dataset with four different sets of annotations, each aiming at a separate task. The presented analysis and the associated dataset, which is made public, will provide a baseline/benchmark for future research in the domain. We believe the proposed system can contribute toward more livable communities by helping different stakeholders, such as news broadcasters, humanitarian organizations, as well as the general public.
A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars
Authors: Hozhabr Pour Hawzhin , Li Frédéric , Wegmeth Lukas , Trense Christian , Doniec Rafał , Grzegorzek Marcin , Wismüller Roland
Published: May 2022

Identifying accident patterns is one of the most vital research foci of driving analysis. Environmental or safety applications and the growing area of fleet management all benefit from accident detection contributions by minimizing the risk vehicles and drivers are subject to, improving their service and reducing overhead costs. Some solutions have been proposed in the past literature for automated accident detection that are mainly based on traffic data or external sensors. However, traffic data can be difficult to access, while external sensors can end up being difficult to set up and unreliable, depending on how they are used. Additionally, the scarcity of accident detection data has limited the type of approaches used in the past, leaving in particular, machine learning (ML) relatively unexplored. Thus, in this paper, we propose a ML framework for automated car accident detection based on mutimodal in-car sensors. Our work is a unique and innovative study on detecting real-world driving accidents by applying state-of-the-art feature extraction methods using basic sensors in cars. In total, five different feature extraction approaches, including techniques based on feature engineering and feature learning with deep learning are evaluated on the strategic highway research program (SHRP2) naturalistic driving study (NDS) crash data set. The main observations of this study are as follows: (1) CNN features with a SVM classifier obtain very promising results, outperforming all other tested approaches. (2) Feature engineering and feature learning approaches were finding different best performing features. Therefore, our fusion experiment indicates that these two feature sets can be efficiently combined. (3) Unsupervised feature extraction remarkably achieves a notable performance score.
Commercial Off-the-Shelf Components (COTS) in Realizing Miniature Implantable Wireless Medical Devices: A Review
Authors: Khan Sadeque Reza , Mugisha Andrew J. , Tsiamis Andreas , Mitra Srinjoy
Published: May 2022

Over the past decade, there has been exponential growth in the per capita rate of medical patients around the world, and this is significantly straining the resources of healthcare institutes. Therefore, the reliance on smart commercial off-the-shelf (COTS) implantable wireless medical devices (IWMDs) is increasing among healthcare institutions to provide routine medical services, such as monitoring patients’ physiological signals and the remote delivery of therapeutic drugs. These smart COTS IWMDs reduce the necessity of recurring visits of patients to healthcare institutions and also mitigate physical contact, which can minimize the possibility of any potential spread of contagious diseases. Furthermore, the devices provide patients with the benefit of recuperating in familiar surroundings. As such, low-cost, ubiquitous COTS IWMDs have engendered the proliferation of telemedicine in healthcare to provide routine medical services. In this paper, a review work on COTS IWMDs is presented at a macro level to discuss the history of IWMDs, different networked COTS IWMDs, health and safety regulations of COTS IWMDs and the importance of organized procurement. Furthermore, we discuss the basic building blocks of IWMDs and how COTS components can contribute to build these blocks over widely researched custom-built application-specific integrated circuits.
Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation
Authors: Song Jiayin , Zhao Yue , Song Wenlong , Zhou Hongwei , Zhu Di , Huang Qiqi , Fan Yiming , Lu Chao
Published: May 2022

Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F1 score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies.
Shaking Table Attached to Magnetorheological Damper: Simulation and Experiments for Structural Engineering
Authors: Vargas Alessandro N. , Raminelli João G. , Montezuma Marcio A. F. , Cavalini Junior Aldemir Aparecido , Breganon Ricardo , Caruntu Constantin F.
Published: May 2022

This paper details how to construct a small-scale shaking table attached to a magnetorheological (MR) damper. The motivation for this construction relies on the increasing interest in modeling the dynamics of MR dampers—MR dampers have been used in structures for safety reasons. To model the MR damper, we use the so-called ‘Dahl model’, which is useful to represent systems with a hysteresis. The Dahl model, validated through experimental data collected in a laboratory, was combined with a linear model to represent a two-story building. This two-story building model allows us to simulate the dynamics of that building when its floors are attached to MR dampers. By doing so, we can assess—through simulation—to what extent MR dampers can protect structures from vibrations. Using data from the ‘El Centro’ earthquake (1940), we can conclude that MR dampers have the potential to reduce the impact of earthquakes upon structures. This finding emphasizes the potential benefits of MR dampers for the safety of structures, which is a conclusion taken from the apparatus detailed in this paper.
Relationship of Work-Related Stress and Offline Social Leisure on Political Participation of Voters in the United States
Authors: Šubrt Oldřich
Published: May 2022

In the United States (US), citizens’ political participation is 15%. Contemporary psychological models explaining political participation are based on education and socioeconomic status, which are unable to explain the overall low political participation figures. The study suggests a holistic approach, with two societal tendencies: increasing work-related stress and diminishing offline social leisure, together with a mediating effect of participatory efficacy to assess associations with the political participation of US voters. The quantitative correlational study uses structural equation modelling (SEM) analysis on the General Social Survey representative sample of US voters (N = 295, Mage = 44.49, SD = 13.43), controlled for education and socioeconomic status. Work-related stress was not significantly associated with political participation (β = 0.08, p = 0.09). Offline social leisure was positively associated with political participation (β = 0.28, p < 0.001). The mediating effect of participatory efficacy on the relationship between offline social leisure and political participation was positive and significant (β = 0.05, p < 0.001). Additional analyses, regression and SEM on the European Social Survey sample (N = 27,604) boosted internal and external validity. Results indicate that offline social leisure is more predictive than education and socioeconomic status, showing that examining societal trends leads to a better understanding of political participation.
Numerical Simulation of Particle-Laden Flow and Soot Layer Formation in Porous Filter
Authors: Yamamoto Kazuhiro , Yagasaki Shota
Published: May 2022

So far, diesel particulate filters (DPFs) have been widely used to collect diesel particulates including soot in the exhaust after-treatment. However, as the soot is continuously collected in the porous filter, the exhaust pressure (pressure drop) increases. To optimize the filter design for reducing its pressure drop, we need a numerical simulation. In this study, we simulated the particle-laden flow across the DPF. Structure of SiC-DPF was obtained by an X-ray CT technique. We conducted the numerical simulation by changing the soot aggregation diameter (simply called soot size), and evaluated the time-variation of the pressure drop. For discussing the soot deposition process, the contributions of the Brownian diffusion and the interception effect were separately estimated. Especially, we focused on the soot deposition region which could affect the pressure drop, together with the soot cake permeability and the soot packing density. Results show that, as the soot size is smaller, more soot is trapped. As a result, the shift from the depth filtration to the surface filtration is observed earlier. Therefore, for discussing the pressure drop, it is important to consider where the soot deposition occurs as well as the deposited soot mass in the filter.
Concurrent Training Programming: The Acute Effects of Sprint Interval Exercise on the Subsequent Strength Training
Authors: Mathieu Bertrand , Robineau Julien , Piscione Julien , Babault Nicolas
Published: May 2022

Exercise modality has been proposed to reduce the interferences between aerobic and resistance sessions during concurrent training. The aim of the study was to examine the acute effects of cycling or running sprint interval exercise on subsequent resistance training sessions. Twenty-five competitive male rugby union players were recruited. Players were tested during three conditions: CONTROL (resistance training session only), CYCLE and RUN (corresponding to a concurrent training scheme with cycling or running sprint interval exercise conducted on the morning, followed by a resistance training session). Four hours rest was proposed between the aerobic and resistance training session. Muscle performance (bar velocity during bench press and box squat, counter movement jump height) and subjective ratings (rate of perceived exertion, wellbeing) were assessed during and after aerobic or resistance training sessions. No significant difference was observed for muscle performance (vertical jump height and bar velocity). However, significant higher perceived exertion and low-value scaled subjective wellbeing were observed in RUN (7.7 ± 1.1 and 17.9 ± 4.1, respectively) as compared with the two other conditions (6.7 ± 1.5 and 21.1 ± 3.6 for CONTROL and 7.4 ± 1.1 and 20.1 ± 3.9 for CYCLE). It was concluded that the exercise modality (running or cycling) during the aerobic exercise using a sprint interval exercise did not impact the quality of the resistance session. However, subjective ratings were affected the following days. Cycling exercises might be more adequate when performing a sprint interval training session during concurrent training programs.
Analysing Key Steps of the Photogrammetric Pipeline for Museum Artefacts 3D Digitisation
Authors: Farella Elisa Mariarosaria , Morelli Luca , Rigon Simone , Grilli Eleonora , Remondino Fabio
Published: May 2022

In recent years, massive digitisation of cultural heritage (CH) assets has become a focus of European programmes and initiatives. Among CH settings, attention is reserved to the immense and precious museum collections, whose digital 3D reproduction can support broader non-invasive analyses and stimulate the realisation of more attractive and interactive exhibitions. The reconstruction pipeline typically includes numerous processing steps when passive techniques are selected to deal with object digitisation. This article presents some insights on critical operations, which, based on our experience, can rule the quality of the final models and the reconstruction times for delivering 3D heritage results, while boosting the sustainability of digital cultural contents. The depth of field (DoF) problem is explored in the acquisition phase when surveying medium and small-sized objects. Techniques for deblurring images and masking object backgrounds are examined relative to the pre-processing stage. Some point cloud denoising and mesh simplification procedures are analysed in data post-processing. Hints on physically-based rendering (PBR) materials are also presented as closing operations of the reconstruction pipeline. This paper explores these processes mainly through experiments, providing a practical guide, tricks, and suggestions when tackling museum digitisation projects.
Integrating the Strength of Multi-Date Sentinel-1 and -2 Datasets for Detecting Mango (Mangifera indica L.) Orchards in a Semi-Arid Environment in Zimbabwe
Authors: Mudereri Bester Tawona , Abdel-Rahman Elfatih M. , Ndlela Shepard , Makumbe Louisa Delfin Mutsa , Nyanga Christabel Chiedza , Tonnang Henri E. Z. , Mohamed Samira A.
Published: May 2022

Generating tree-specific crop maps within heterogeneous landscapes requires imagery of fine spatial and temporal resolutions to discriminate among the rapid transitions in tree phenological and spectral features. The availability of freely accessible satellite data of relatively high spatial and temporal resolutions offers an unprecedented opportunity for wide-area land use and land cover (LULC) mapping, including tree crop (e.g., mango; Mangifera indica L.) detection. We evaluated the utility of combining Sentinel-1 (S1) and Sentinel-2 (S2) derived variables (n = 81) for mapping mango orchard occurrence in Zimbabwe using machine learning classifiers, i.e., support vector machine and random forest. Field data were collected on mango orchards and other LULC classes. Fewer variables were selected from ‘All’ combined S1 and S2 variables using three commonly utilized variable selection methods, i.e., relief filter, guided regularized random forest, and variance inflation factor. Several classification experiments (n = 8) were conducted using 60% of field datasets and combinations of ‘All’ and fewer selected variables and were compared using the remaining 40% of the field dataset and the area underclass approach. The results showed that a combination of random forest and relief filter selected variables outperformed (F1 score > 70%) all other variable combination experiments. Notwithstanding, the differences among the mapping results were not significant (p ≤ 0.05). Specifically, the mapping accuracy of the mango orchards was more than 80% for each of the eight classification experiments. Results revealed that mango orchards occupied approximately 18% of the spatial extent of the study area. The S1 variables were constantly selected compared with the S2-derived variables across the three variable selection approaches used in this study. It is concluded that the use of multi-modal satellite imagery and robust machine learning classifiers can accurately detect mango orchards and other LULC classes in semi-arid environments. The results can be used for guiding and upscaling biological control options for managing mango insect pests such as the devastating invasive fruit fly Bactrocera dorsalis (Hendel) (Diptera: Tephritidae).
A Robust Adaptive Traffic Signal Control Algorithm Using Q-Learning under Mixed Traffic Flow
Authors: Wei Zibin , Peng Tao , Wei Sijia
Published: May 2022

The operational and safety performance of intersections is the key to ensuring the efficient operation of urban traffic. With the development of automated driving technologies, the ability of adaptive traffic signal control has been improved according to data detected by connected and automated vehicles (CAVs). In this paper, an adaptive traffic signal control was proposed to optimize the operational and safety performance of the intersection. The proposed algorithm based on Q-learning considers the data detected by loop detectors and CAVs. Furthermore, a comprehensive analysis was conducted to verify the performance of the proposed algorithm. The results show that the average delay and conflict rate have been significantly optimized compared with fixed timing and traffic actuated control. In addition, the performance of the proposed algorithm is good in the test of the irregular intersection. The algorithm provides a new idea for the intelligent management of isolated intersections under the condition of mixed traffic flow. It provides a research basis for the collaborative control of multiple intersections.
A Fuzzy Analytic Hierarchy Process Model to Evaluate Logistics Service Expectations and Delivery Methods in Last-Mile Delivery in Brazil
Authors: Alves de Araújo Fernanda , Mendes dos Reis João Gilberto , Terra da Silva Marcia , Aktas Emel
Published: May 2022

Nowadays, postal services and third-party logistics services (3PL) have been pressured by the increasing demand for delivery services. Therefore, they need to improve their last-mile delivery strategies to meet customers’ expectations. This paper aims to investigate how logistics service expectations affect the delivery process in urban areas using a multiple-criteria decision support system based on the Fuzzy Analytic Hierarchy Process (FAHP). We developed a decision-making model employing six criteria and five delivery methods indicated in the literature and collected information from 27 experts working in academia and local and multinational third-party logistics providers in Brazil to validate this model. The results indicate that cost (21.4%) and tracking and tracing (19.3%) are the most important two criteria in the decision model, and the best delivery methods are smart lockers (21.8%) followed by small trucks (21.3%). Our results suggest that service expectations regarding last-mile delivery are aligned with extensive use of road transport and the increase in e-commerce sales can raise greenhouse gas emissions and compromise the environment in urban areas.
Influencing Factors of Enterprise R&D Investment: Post-Subsidy, Sustainability, and Heterogeneity
Authors: Yu Yabin , Xu Qian
Published: May 2022

Sustainable innovation is an important factor for enterprises to obtain core competitiveness in modern society, and only continuous R&D investment can ensure the smooth progress of enterprise innovation. Therefore, this study uses the post-subsidy data of enterprise R&D investment released by Hangzhou Science and Technology Bureau to explore the influencing factors of R&D investment and its sustainability by Chinese high-tech enterprises. In contrast to previous research which mainly focused on the pre-subsidy, this study focuses on the post-subsidy policy of R&D investment sustainability. Empirical analysis methods, such as cross-sectional linear regression and the propensity-score-matching method, were used to draw the following conclusions: (1) The quantity and sustainability of R&D investment of enterprises are obviously unbalanced among regions. Regions where high-tech enterprises are concentrated have higher levels of R&D investment and sustainability than other regions. (2) Under different scales, there are significant differences in the amount and sustainability of R&D investment. Large enterprises have stronger R&D investment strength, while small enterprises have stronger R&D investment willingness. However, the effect of scale on R&D investment will be reduced by regional factors. (3) The evaluation of high-tech enterprises and the enterprises’ R&D investments affect each other. (4) The sustainability of enterprise R&D investment will be affected by enterprise heterogeneity factors, and in turn, the sustainability of enterprise R&D investment will also affect the amount of enterprise R&D investment. Based on the results, the study provides suggestions for the government to make more targeted policies.
What Determines the Shadow Economy? An Extreme Bounds Analysis
Authors: Abu Alfoul Mohammed Nayel , Khatatbeh Ibrahim Naser , Jamaani Fouad
Published: May 2022

The purpose of this paper was to identify the leading causes determining the shadow economy at the global level. The empirical analysis used was the Sala-i-Martin version of extreme bounds analysis (EBA) applied to a cross-sectional sample of 132 countries. The results suggested that the quality of institutions is the primary determinant of the shadow economy. The results showed that only four out of six factors of the quality of institutions proved to be robust determining factors of the shadow economy; they are bureaucracy quality, law and order, corruption, and internal conflict. Moreover, monetary freedom and secured property rights are also robust and negatively related to the shadow economy. An interesting result is that information and communication technology (ICT) development is vital to the shadow economy. Mainly, internet usage is robust and negatively associated with the shadow economy. Furthermore, inflation and poverty emerge as key determining factors of the shadow economy. Our findings will aid in the development of recommendations for potential strategies to minimize the international extent of the shadow economy.
Ergonomics Risk Assessment for Manual Material Handling of Warehouse Activities Involving High Shelf and Low Shelf Binning Processes: Application of Marker-Based Motion Capture
Authors: Zhao Yong Sze , Jaafar Mohd Hafiidz , Mohamed Ahmad Sufril Azlan , Azraai Nur Zaidi , Amil Norhaniza
Published: May 2022

Lower back pain is a musculoskeletal disorder that is commonly reported among warehouse workers due to the nature of the work environment and manual handling activities. The objective of this study was to assess the ergonomic risks among warehouse workers carrying out high shelf (HS) and low shelf (LS) binning processes. A questionnaire was used to determine the prevalence of musculoskeletal symptoms, while a marker-based motion capture (MoCap) system worksheet was used to record the participants’ motion and determine the action risk level. A total of 33% of the participants reported lower back pain in the past seven days, based on the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) results. Analysis of the body velocities showed that the HS binning process had four major velocity peaks, defined as the initial, lowering, lifting, and final phases. In comparison, the LS binning process had two major peaks defined, the crouching and rising phases. There were significant differences between the mean velocities of the workers for the HS binning process, indicating that the workers have different movement patterns with varying velocities.
Recent Advancements in MOF/Biomass and Bio-MOF Multifunctional Materials: A Review
Authors: Liu Jie , Li Yanjun , Lou Zhichao
Published: May 2022

Metal–organic frameworks (MOFs) and their derivatives have delivered perfect answers in detection, separation, solving water and electromagnetic pollution and improving catalysis and energy storage efficiency due to their advantages including their highly tunable porosity, structure and versatility. Recently, MOF/biomass, bio-MOFs and their derivatives have gradually become a shining star in the MOF family due to the improvement in the application performance of MOFs using biomass and biomolecules. However, current studies lack a systematic summary of the synthesis and advancements of MOF/biomass, bio-MOFs and their derivatives. In this review, we describe their research progress in detail from the following two aspects: (1) synthesis of MOF/biomass using biomass as a template to achieve good dispersion and connectivity at the same time; (2) preparing bio-MOFs by replacing traditional organic linkers with biomolecules to enhance the connection stability between metal ions/clusters and ligands and avoid the formation of toxic by-products. This enables MOFs to possess additional unique advantages, such as improved biocompatibility and mechanical strength, ideal reusability and stability and lower production costs. Most importantly, this is a further step towards green and sustainable development. Additionally, we showcase some typical application examples to show their great potential, including in the fields of environmental remediation, energy storage and electromagnetic wave absorption.
Developing a Building Stock Model to Enable Clustered Renovation—The City of Leuven as Case Study
Authors: Verellen Evelien , Allacker Karen
Published: May 2022

The existing building patrimony is responsible for 36% of the global energy use and 37% of the greenhouse gas emissions. It is hence a major challenge to improve its energy performance. According to the Renovation Wave, the average annual renovation rate should be doubled by 2030 up to 3% and deep energy renovations should be encouraged. The Belgian city of Leuven works towards this target and is even more ambitious, setting their goal on becoming climate neutral by 2050. The strategy investigated in this study is to increase the renovation rate by clustering renovations, which is challenging since the Belgian building stock is highly privatised. Based on a thorough literature study, this paper examines various methodologies for building stock modelling. The main focus is comparing the required input data with the data availability, handling the data gaps, and defining their influence on the model’s accuracy. The findings are applied to Leuven by analysing the main drivers to cluster renovation measures. However, many data gaps appeared, leading to the selection of a GIS-enhanced archetype model enriched by energy data as the most suitable approach. To avoid misinterpretation due to differences in data quality, transparent reporting in stock modelling is recommended.
Small-Scale Farmers’ Preference Heterogeneity for Green Agriculture Policy Incentives Identified by Choice Experiment
Authors: Zhu Yaying , Chen Juan
Published: May 2022

This study addresses differentiation among small-scale farmers’ preferences for green agriculture policy incentive mixes. Transforming modern agriculture to ecological fertilization and pest extermination practices is paramount in developing green agriculture, but policy incentives aimed at stimulating small-scale farmers’ adoption of ecological fertilization and deinsectization techniques are often challenged by those farmers’ heterogeneous characteristics and their consequent mixed incentive preferences. We establish a model examining the interplay between small-scale farmers’ characteristics (e.g., age, education level, family size, participation in agricultural organization) and combinations of incentive policies (i.e., green subsidy, technical support, environmental propaganda, agricultural insurance) in farmers’ willingness to participate in ecological fertilization/deinsectization, using a sample of 1032 Chinese farmers. By applying a mixed logit model and latent class model regressions, we find that farmers’ age, education level, family size, and farming organization participation are the most important characteristics influencing farmers’ preferences. Specifically, senior farmers tend to accept an incentive policy combination of green subsidy and technical support; farmers with higher education levels prefer an incentive policy combination of technical support and environmental propaganda; and larger families prefer an incentive policy combination of technical support and agricultural insurance. Additionally, participation in any agricultural organization reduces the household’s preference for incentive policy combinations of technical support, agricultural insurance, and green subsidy. Based on these findings, a typology of small farmers’ green agriculture incentive preferences (including security, monetary, and autonomy orientations) is proposed, offering suggestions for future green agriculture policy optimization.
A Nonparametric Weighted Cognitive Diagnosis Model and Its Application on Remedial Instruction in a Small-Class Situation
Authors: Li Cheng-Hsuan , Ju Yi-Jin , Hsieh Pei-Jyun
Published: May 2022

CDMs can provide a discrete classification of mastery skills to diagnose relevant conceptions immediately for Education Sustainable Development. Due to the problem of parametric CDMs with only a few training sample sizes in small classroom teaching situations and the lack of a nonparametric model for classifying error patterns, two nonparametric weighted cognitive diagnosis models, NWSD and NWBD, for classifying mastery skills and knowledge bugs were proposed, respectively. In both, the variances of items with respect to the ideal responses were considered for computing the weighted Hamming distance, and the inverse distances between the observed and ideal responses were used as weights to obtain the probabilities of the mastering attributes of a student. Conversely, NWBD can classify students’ “bugs”, so teachers can provide suitable examples for precision assistance before teaching non-mastery skills. According to the experimental results on simulated and real datasets, the proposed methods outperform some standard methods in a small-class situation. The results also demonstrate that a remedial course with NWSD and NWBD is better than one with traditional group remedial teaching.
«Let’s Go Deep into the Game to Save Our Planet!» How an Immersive and Educational Video Game Reduces Psychological Distance and Raises Awareness
Authors: Bekoum Essokolo Vicky-Lauren , Robinot Elisabeth
Published: May 2022

Climate change appears to be the ecological issue which benefits from the most attention in the literature, compared to equally alarming situations such as plastic pollution. In fact, waste management issues took a new step with the recent discovery of microplastics in human blood for the first time, as it used to be a hypothesis. Instead of separating those questions, some researchers tend to consider that a link exists between the effects of global warming and plastic degradation in the ocean. Research focusing on the construal-level theory and the psychological distance explain the lack of public interest in the environmental crisis. However, recent studies highlight the empirical support of the psychological distance instead of the CLT, especially regarding climate change, but a few studies explore the psychological distance related to plastic pollution. With that in mind, any means to reduce the perceived psychological distance regarding environmental issues such as plastic pollution might increase their sensitivity and motivation to act. Moreover, the change of habit could be induced by a new event that would disrupt someone’s daily life according to the habit discontinuity hypothesis, and the use of immersive media such as video games might be the solution. Given numerous possibilities of creation with the scenarios, gameplay, public of interest and gaming contexts, video games also influence motivation, engagement and learning ability. We can also find specific components and mechanisms from game design in media that do not focus on entertainment first but on pedagogical purpose: serious games. Thus, this study investigates how immersive media might reduce specific psychological distance dimensions and trigger emotions using an educational video game on plastic pollution, which might play a major role in changing ones’ daily habits. The research uses a qualitative method centered on semi-structured individual interviews and the experimentation of a video game named Plasticity. Results support all the propositions and show that different types of immersion might reduce each dimension of the psychological distance, which is a first, reinforcing environmental awareness and new intentions of pro-environmental behavior. Other areas of discussion are furthered explored.
Will the Effects of COVID-19 on Commuting and Daily Activities of the University Students Be Maintained? Evidence from a Small Town in Sicily
Authors: Campisi Tiziana , Nahiduzzaman Kh Md , Nikiforiadis Andreas , Stamatiadis Nikiforos , Basbas Socrates
Published: May 2022

As many studies have already shown, the COVID-19 pandemic has had a great impact on the daily routines of people all over the world. University students form one of the most affected groups of people, since they have had to interrupt many of the activities that they usually perform, and have also had to get used to a new way of learning (e-learning). An important question that now arises is whether the changes that were identified within the pandemic period are to be maintained when the risk of being infected is eliminated. To this end, 537 university students of the Kore University of Enna, Italy, were surveyed. Their responses are analyzed descriptively, and an ordinal regression model is being developed to shed more light on the likelihood of retaining changes related with to transport mode choice. The results show that the likelihood of retaining all the changes when commuting and during daily activities is very high, demonstrating such willingness from the participants. Moreover, it has been shown that public transport has increased the probability of people being negatively affected by the pandemic in the long-term, and opportunities appear for increasing the modal share of active modes.
Experimental Assessment of the Reflection of Solar Radiation from Façades of Tall Buildings to the Pedestrian Level
Authors: Speroni Alberto , Mainini Andrea Giovanni , Zani Andrea , Paolini Riccardo , Pagnacco Tommaso , Poli Tiziana
Published: May 2022

Urban climates are highly influenced by the ability of built surfaces to reflect solar radiation, and the use of high-albedo materials has been widely investigated as an effective option to mitigate urban overheating. While diffusely solar reflective walls have attracted concerns in the architectural and thermal comfort community, the potential of concave and polished surfaces, such as glass and metal panels, to cause extreme glare and localized thermal stress has been underinvestigated. Furthermore, there is the need for a systematic comparison of the solar concentration at the pedestrian level in front of tall buildings. Herein, we show the findings of an experimental campaign measuring the magnitude of the sunlight reflected by scale models reproducing archetypical tall buildings. Three 1:100 scaled prototypes with different shapes (classic vertical façade, 10% tilted façade, curved concave façade) and different finishing materials (representative of extremes in reflectance properties of building materials) were assessed. A specular surface was assumed as representative of a glazed façade under high-incidence solar angles, while selected light-diffusing materials were considered sufficient proxies for plaster finishing. With a diffusely reflective façade, the incident radiation at the pedestrian level in front of the building did not increase by more than 30% for any geometry. However, with a specular reflective (i.e., mirror-like) flat façade, the incident radiation at the pedestrian level increased by more than 100% and even by more than 300% with curved solar-concentrating geometries. In addition, a tool for the preliminary evaluation of the solar reflectance risk potential of a generic complex building shape is developed and presented. Our findings demonstrate that the solar concentration risk due to mirror-like surfaces in the built environment should be a primary concern in design and urban microclimatology.
Estimation of Greenhouse Gas Emissions of Petrol, Biodiesel and Battery Electric Vehicles in Malaysia Based on Life Cycle Approach
Authors: Kosai Shoki , Zakaria Sazalina , Che Hang Seng , Hasanuzzaman Md , Rahim Nasrudin Abd , Tan Chiakwang , Ahmad Radin Diana R. , Abbas Ahmad Rosly , Nakano Katsuyuki , Yamasue Eiji , Woon Wei Kian , Amer Ammar Harith Ahmad
Published: May 2022

A steady rise in the ownership of vehicles in Malaysia has drawn attention to the need for more effective strategies to reduce the emissions of the road transport sector. Although the electrification of vehicles and replacing petrol with biofuel are the strategies being considered in Malaysia, these strategies have yet to be fully evaluated from an environmental perspective. In this study, a life cycle assessment was conducted to compare the greenhouse gas emissions of different types of transportation means (passenger cars, two-wheelers (motorbikes), and buses) with several types of powertrains (petrol, biodiesel, electricity) based on multiple lifecycle stages in Malaysia. The impact of considering land use change for the biodiesel production in the LCA was also considered in this study. It was found that the transition from internal combustion engine vehicles fueled by petrol to electric vehicles would reduce the greenhouse gas emission for passenger cars, two-wheelers, and buses. However, because the greenhouse gas emissions of biodiesel-fueled vehicles are higher than those of petrol-fueled vehicles, even without considering land use change, the results indicate that the transition from a 10% to 20% biofuel blend, which is a current strategy in Malaysia, will not result in a reduction in greenhouse gas emissions for the transport sector in Malaysia.
Significant Increase in Population Exposure to Extreme Precipitation in South China and Indochina in the Future
Authors: Tang Bin , Hu Wenting
Published: May 2022

Extreme precipitation events cause severe economic losses and can seriously impact human health. Therefore, it is essential to project possible future changes in the population’s exposure to precipitation extremes against the background of global warming. On the basis of model outputs from phase 6 of the Coupled Model Intercomparison Project, our study shows that both the frequency and intensity of extreme precipitation are likely to increase in the South China and Indochina region in the coming century, especially under the business-as-usual Shared Socioeconomic Pathway (SSP) scenario, SSP5-8.5. The largest population exposure can be expected under the SSP2-4.5 scenario, both in South China and Indochina. If early adoption of mitigation measures via the SSP1-2.6 scenario can be achieved, it may be possible to limit the average population exposure in South China to a relatively low level, while Indochina’s may even be smaller than it is currently. In terms of spatial distribution, the maximum population exposure is most likely to be centered in southern South China. This study also reveals that the contribution of the population–climate interaction to population exposure is likely to increase in the future, and different contributions from the factors of climate and population correspond to different emission policies. Under SSP2-4.5, the importance of climate change and the population–climate interaction is more likely to increase.
Studies on Hydrological Processes on Karst Slopes for Control of Soil and Water Loss
Authors: Wang Zehui , Luo Ding , Xiong Kangning , Gu Xing , Zhu Zhenzhen
Published: May 2022

Soil and water loss in karst areas seriously restricts the sustainable development of karst ecosystems, the economy and society in southwest China, which has been a concern of, and studied by, many scholars in China and abroad. Soil and water loss has a great influence on the evolution of rocky desertification, groundwater quality, drought and flood disasters in karst regions. This paper aimed to provide a review of studies of slope hydrological processes in soil and water loss. In this paper, 322 related articles retrieved from the Web of Science database and CNKI database were systematically reviewed. Firstly, a quantitative study was conducted to analyze the annual number, countries and research progress of the published literature. Secondly, the main progress and achievements of slope hydrology and soil erosion control technology were classified and summarized according to theoretical research, mechanism research, technology research and technical demonstration. Finally, the key problems and future research prospects are put forward, starting with the existing technology, in order to find more suitable soil and water loss control measures in karst regions and achieve economic and ecological benefits.