bluuebunny/crossref_metadata_2025 huggingface dataset:
Data Fusion Approach to Simultaneously Evaluate the Degradation Process Caused by Ozone and Humidity on Modern Paint Materials
Authors: Pagnin Laura , Calvini Rosalba , Sterflinger Katja , Izzo Francesca Caterina
Published: April 2022

The knowledge of the atmospheric degradation reactions affecting the stability of modern materials is still of current interest. In fact, environmental parameters, such as relative humidity (RH), temperature, and pollutant agents, often fluctuate due to natural or anthropogenic climatic changes. This study focuses on evaluating analytical and statistical strategies to investigate the degradation processes of acrylic and styrene-acrylic paints after exposure to ozone (O3) and RH. A first comparison of FTIR and Py-GC/MS results allowed to obtain qualitative information on the degradation products and the influence of the pigments on the paints’ stability. The combination of these results represents a significant potential for the use of data fusion methods. Specifically, the datasets obtained by FTIR and Py-GC/MS were combined using a low-level data fusion approach and subsequently processed by principal component analysis (PCA). It allowed to evaluate the different chemical impact of the variables for the characterization of unaged and aged samples, understanding which paint is more prone to ozone degradation, and which aging variables most compromise their stability. The advantage of this method consists in simultaneously evaluating all the FTIR and Py-GC/MS variables and describing common degradation patterns. From these combined results, specific information was obtained for further suitable conservation practices for modern and contemporary painted films.
Comparative X-ray Shielding Properties of Single-Layered and Multi-Layered Bi2O3/NR Composites: Simulation and Numerical Studies
Authors: Thumwong Arkarapol , Darachai Jitsuna , Saenboonruang Kiadtisak
Published: April 2022

This work theoretically compared the X-ray attenuation capabilities in natural rubber (NR) composites containing bismuth oxide (Bi2O3) by determining the effects of multi-layered structures on the shielding properties of the composites using two different software packages (XCOM and PHITS). The shielding properties of the single-layered and multi-layered Bi2O3/NR composites investigated consisted of the transmission factor (I/I0), effective linear attenuation coefficient (µeff), effective mass attenuation coefficient (µm,eff), and effective half-value layer (HVLeff). The results, with good agreement between those obtained from XCOM and PHITS (with less than 5% differences), indicated that the three-layered NR composites (sample#4), with the layer arrangement of pristine NR (layer#1)-Bi2O3/NR (layer#2)-pristine NR (layer#3), had relatively higher X-ray shielding properties than either a single-layer or the other multi-layered structures for all X-ray energies investigated (50, 100, 150, and 200 keV) due to its relatively larger effective percentage by weight of Bi2O3 in the composites. Furthermore, by varying the Bi2O3 contents in the middle layer (layer#2) of sample#4 from 10 to 90 wt.%, the results revealed that the overall X-ray shielding properties of the NR composites were further enhanced with additional filler, as evidenced by the highest values of µeff and µm,eff and the lowest values of I/I0 and HVLeff observed in the 90 wt.% Bi2O3/NR composites. In addition, the recommended Bi2O3 contents for the actual production of three-layered Bi2O3/NR composites (the same layer structure as sample#4) were determined by finding the least Bi2O3 content that enabled the sample to attenuate incident X-rays with equal efficiency to that of a 0.5-mm lead sheet (with an effective lead equivalence of 0.5 mmPb). The results suggested that the recommended Bi2O3 contents in layer#2 were 82, 72, and 64 wt.% for the combined 6 mm, 9 mm, and 12 mm samples, respectively.
Advanced Machine Learning Modeling Approach for Prediction of Compressive Strength of FRP Confined Concrete Using Multiphysics Genetic Expression Programming
Authors: Ilyas Israr , Zafar Adeel , Afzal Muhammad , Javed Muhammad , Alrowais Raid , Althoey Fadi , Mohamed Abdeliazim , Mohamed Abdullah , Vatin Nikolai
Published: April 2022

The purpose of this article is to demonstrate the potential of gene expression programming (GEP) in anticipating the compressive strength of circular CFRP confined concrete columns. A new GEP model has been developed based on a credible and extensive database of 828 data points to date. Numerous analyses were carried out to evaluate and validate the presented model by comparing them with those presented previously by different researchers along with external validation comparison. In comparison to other artificial intelligence (AI) techniques, such as Artificial Neural Networks (ANN) and the adaptive neuro-fuzzy interface system (ANFIS), only GEP has the capability and robustness to provide output in the form of a simple mathematical relationship that is easy to use. The developed GEP model is also compared with linear and nonlinear regression models to evaluate the performance. Afterwards, a detailed parametric and sensitivity analysis confirms the generalized nature of the newly established model. Sensitivity analysis results indicate the performance of the model by evaluating the relative contribution of explanatory variables involved in development. Moreover, the Taylor diagram is also established to visualize how the proposed model outperformed other existing models in terms of accuracy, efficiency, and being closer to the target. Lastly, the criteria of external validation were also fulfilled by the GEP model much better than other conventional models. These findings show that the presented model effectively forecasts the confined strength of circular concrete columns significantly better than the previously established conventional regression-based models.
Monomer Release from Dental Resins: The Current Status on Study Setup, Detection and Quantification for In Vitro Testing
Authors: Hampe Tristan , Wiessner Andreas , Frauendorf Holm , Alhussein Mohammad , Karlovsky Petr , Bürgers Ralf , Krohn Sebastian
Published: April 2022

Improvements in mechanical properties and a shift of focus towards esthetic dentistry led to the application of dental resins in various areas of dentistry. However, dental resins are not inert in the oral environment and may release monomers and other substances such as Bisphenol-A (BPA) due to incomplete polymerization and intraoral degradation. Current research shows that various monomers present cytotoxic, genotoxic, proinflammatory, and even mutagenic effects. Of these eluting substances, the elution of BPA in the oral environment is of particular interest due to its role as an endocrine disruptor. For this reason, the release of residual monomers and especially BPA from dental resins has been a cause for public concern. The assessment of patient exposure and potential health risks of dental monomers require a reliable experimental and analytical setup. However, the heterogeneous study design applied in current research hinders biocompatibility testing by impeding comparative analysis of different studies and transfer to the clinical situation. Therefore, this review aims to provide information on each step of a robust experimental and analytical in vitro setup that allows the collection of clinically relevant data and future meta-analytical evaluations.
Blends Based on Poly(ε-Caprolactone) with Addition of Poly(Lactic Acid) and Coconut Fibers: Thermal Analysis, Ageing Behavior and Application for Embossing Process
Authors: Priselac Dino , Mahović Poljaček Sanja , Tomašegović Tamara , Leskovac Mirela
Published: April 2022

In this research a biodegradable blend of poly(ɛ-caprolactone) (PCL) and poly(lactic acid) (PLA) is proposed as a new material for the production of a relief printing plate used for special applications on packaging materials, i.e., the embossing process. Coconut fibers (CFs) were added as a natural filler to PCL/PLA blends to improve the functional properties of the prepared blends. Thermal, mechanical and surface analyses were performed on the unaged and artificially aged blends. The results showed that CF has been proven to optimize the hardness of the blend, which is crucial for the production of relief plate for embossing applications. The lowest hardness was measured on neat PCL (53.30° Sh D) and the highest value on PCL/PLA/CF 70/30/3.0 blend (60.13° Sh D). Stronger interfacial interactions were present at the PLA/CF interface because the interfacial free energy is closer to zero and the work of adhesion and spreading coefficient are higher than for the PCL/CF interface. The results of thermal analysis of unaged and aged blends showed that ageing for 3 weeks resulted in significantly lower thermal stability, especially for neat PCL and PCL/PLA 80/20 blends. Blends with a higher content of PLA and CF showed a slightly increased ageing resistance, which is attributed to the increased crystallinity of PLA after ageing due to the addition of CF showed in the DSC diagrams.
What Is the Most Suitable Height Range of ALS Point Cloud and LiDAR Metric for Understorey Analysis? A Study Case in a Mixed Deciduous Forest, Pokupsko Basin, Croatia
Authors: Martín-García Saray , Balenović Ivan , Jurjević Luka , Lizarralde Iñigo , Buján Sandra , Alonso Ponce Rafael
Published: April 2022

Understorey evaluation is essential in wildlife habitat management, biomass storage and wildfire suppression, among other areas. The lack of a standardised methodology in the field measurements, and in their subsequent analysis, forces researchers to look for procedures that effectively extract understorey data to make management decisions corresponding to actual stand conditions. In this sense, when analysing the understorey characteristics from LiDAR data, it is very usual to ask: “what value should we set the understorey height range to?” It is also usual to answer by setting a numeric value on the basis of previous research. Against that background, this research aims to identify the optimal height to canopy base (HCB) filter–LiDAR metric relationship for estimating understorey height (UH) and understorey cover (UC) using LiDAR data in the Pokupsko Basin lowland forest complex (Croatia). First, several HCB values per plot were obtained from field data (measured HCBi—HCBM-i, where i ϵ (minimum, maximum, mean, percentiles)), and then they were modelled based on LiDAR metrics (estimated HCBi—HCBE-i). These thresholds, measured and estimated HCBi per plot, were used as point cloud filters to estimate understorey parameters directly on the point cloud located under the canopy layer. In this way, it was possible to predict the UH with errors (RMSE) between 0.90 and 2.50 m and the UC with errors (RMSE) between 8.8 and 18.6 in cover percentage. Finally, the sensitivity analysis showed the HCB filter (the upper threshold to select the understorey LiDAR points) is the most important factor affecting the UH estimates, while this factor and the LiDAR metric are the most important factors affecting the UC estimates.
Saltation Activity on Non-Dust Days in the Taklimakan Desert, China
Authors: Yang Xinghua , Zhou Chenglong , Yang Fan , Meng Lu , Huo Wen , Mamtimin Ali , He Qing
Published: April 2022

Dust aerosols persistently affect nearly all landscapes worldwide, and the saltation activity caused by dusty weather (e.g., dust days) releases considerable amounts of aerosol into the atmosphere. Nevertheless, dust-induced saltation activity may also occur on non-dust days. To date, few studies have investigated the saltation activity characteristics on non-dust days. Moreover, the contribution of non-dust days to the total saltation activity remains ambiguous. This study comprehensively investigates the characteristics of saltation activity on non-dust days. Specifically, we analyze the influence of the saltation activity of non-dust days on dust aerosols by utilizing saltation, atmospheric, soil, dust aerosol (i.e., PM10 and aerosol optical depth), and weather record data obtained from the Taklimakan Desert, China, between 2008 and 2010. Our results show that lower temperature, vapor pressure, and soil moisture on non-dust days reduces the saltation threshold velocity (5.9 m/s) more compared to on dust days (6.5 m/s). Furthermore, regarding wind speed, relatively strong monthly saltation activity occurs from March to August, and daily saltation activity occurs from 9:00 to 16:00. Although non-dust days only contribute 18.5% and 7.7% to saltation time and saltation count, respectively, both significantly influence the dust aerosols. Therefore, the effect of saltation activity on non-dust days cannot be undervalued, particularly while performing dust aerosol studies.
Human Disturbance on the Land Surface Environment in Tropical Islands: A Remote Sensing Perspective
Authors: Fu Tianmeng , Zhang Li , Chen Bowei , Yan Min
Published: April 2022

Geographically isolated islands are under great stress due to global climate change, as well as the social and environmental issues relating to human activities. It is necessary to monitor and analyze the spatial–temporal changes of the land surface environment in species-rich tropical islands in order to realize the sustainable development and protection of island areas. In this study, we extracted the land cover and coastline information of three tropical islands from 1990 to 2020 based on the Google Earth Engine platform and the Random Forest algorithm. The results showed that: (1) different tropical islands have similar characteristics in terms of land surface environment changes, with the amount of artificial surface and cultivated land increasing, the forest and mangrove areas decreasing, and the amount of artificial coastline increasing; (2) human disturbance plays an important role in changes in the land surface environment. Population growth, immigration policies, food security, and human activities related to achieving economic profits are likely responsible for these land cover changes; and (3) the main factors driving coastline changes include natural processes (topography, ecological ecosystems, sea-level rise, sea waves, and storms) and human activities (sand mining, tourism, port construction, aquaculture expansion, and mangrove deforestation). Understanding these changes will help tropical islands and coastal zones to make suitable policies for land management and respond to climate change and sustainable development challenges.
Geospatial Modelling for Delineation of Crop Management Zones Using Local Terrain Attributes and Soil Properties
Authors: Jena Roomesh Kumar , Bandyopadhyay Siladitya , Pradhan Upendra Kumar , Moharana Pravash Chandra , Kumar Nirmal , Sharma Gulshan Kumar , Roy Partha Deb , Ghosh Dibakar , Ray Prasenjit , Padua Shelton , Ramachandran Sundaram , Das Bachaspati , Singh Surendra Kumar , Ray Sanjay Kumar , Alsuhaibani Amnah Mohammed , Gaber Ahmed , Hossain Akbar
Published: April 2022

Defining nutrient management zones (MZs) is crucial for the implementation of site-specific management. The determination of MZs is based on several factors, including crop, soil, climate, and terrain characteristics. This study aims to delineate MZs by means of geostatistical and fuzzy clustering algorithms considering remotely sensed and laboratory data and, subsequently, to compare the zone maps in the north-eastern Himalayan region of India. For this study, 896 grid-wise representative soil samples (0–25 cm depth) were collected from the study area (1615 km2). The soils were analysed for soil reaction (pH), soil organic carbon and available macro (N, P and K) and micronutrients (Fe, Mn, Zn and Cu). The predicted soil maps were developed using regression kriging, where 28 digital elevation model-derived terrain attributes and two vegetation derivatives were used as environmental covariates. The coefficient of determination (R2) and root mean square error were used to evaluate the model’s performance. The predicted soil parameters were accurate, and regression kriging identified the highest variability for the majority of the soil variables. Further, to define the management zones, the geographically weighted principal component analysis and possibilistic fuzzy c-means clustering method were employed, based on which the optimum clusters were identified by employing fuzzy performance index and normalized classification entropy. The management zones were constructed considering the total pixel points of 30 m spatial resolution (17, 86,985 data points). The area was divided into four distinct zones, which could be differently managed. MZ 1 covers the maximum (43.3%), followed by MZ 2 (29.4%), MZ 3 (27.0%) and MZ 4 (0.3%). The MZs map thus would not only serve as a guide for judicious location-specific nutrient management, but would also help the policymakers to bring sustainable changes in the north-eastern Himalayan region of India.
Investigation on Geometry Computation of Spaceborne GNSS-R Altimetry over Topography: Modeling and Validation
Authors: Song Minfeng , He Xiufeng , Asgarimehr Milad , Li Weiqiang , Xiao Ruya , Jia Dongzhen , Wang Xiaolei , Wickert Jens
Published: April 2022

The spaceborne Global Navigation Satellite Systems Reflectometry (GNSS-R) offers versatile Earth surface observation. While the accuracy of the computed geometry, required for the implementation of the technique, degrades when Earth’s surface topography is complicated, previous studies ignored the effects of the local terrain surrounding the ideal specular point at a suppositional Earth reference surface. The surface slope and its aspect have been confirmed that it can lead to geolocation-related errors in the traditional radar altimetry, which will be even more intensified in tilt observations. In this study, the effect of large-scale slope on the spaceborne GNSS-R technique is investigated. We propose a new geometry computation strategy based on the property of ellipsoid to carry out forward and inverse calculations of path geometries. Moreover, it can be extended to calculate unusual reflected paths over versatile Earth’s topography by taking the surface slope and aspects into account. A simulation considering the slope effects demonstrates potential errors as large as meters to tens kilometers in geolocation and height estimations in the grazing observation condition over slopes. For validation, a single track over the Greenland surface received by the TechDemoSat 1 (TDS-1) satellite with a slope range from 0% to 1% was processed and analyzed. The results show that using the TanDEM-X 90 m Digital Elevation Model (DEM) as a reference, a slope of 0.6% at an elevation angle of 54 degrees can result in a geolocation inaccuracy of 10 km and a height error of 50 m. The proposed method in this study greatly reduces the standard deviation of geolocations of specular points from 4758 m to 367 m, and height retrievals from 28 m to 5.8 m. Applications associated with topography slopes, e.g., cryosphere could benefit from this method.
Accounting for Almond Crop Water Use under Different Irrigation Regimes with a Two-Source Energy Balance Model and Copernicus-Based Inputs
Authors: Jofre-Čekalović Christian , Nieto Héctor , Girona Joan , Pamies-Sans Magi , Bellvert Joaquim
Published: April 2022

Accounting for water use in agricultural fields is of vital importance for the future prospects for enhancing water use efficiency. Remote sensing techniques, based on modelling surface energy fluxes, such as the two-source energy balance (TSEB), were used to estimate actual evapotranspiration (ETa) on the basis of shortwave and thermal data. The lack of high temporal and spatial resolution of satellite thermal infrared (TIR) missions has led to new approaches to obtain higher spatial resolution images with a high revisit time. These new approaches take advantage of the high spatial resolution of Sentinel-2 (10–20 m), and the high revisit time of Sentinel-3 (daily). The use of the TSEB model with sharpened temperature (TSEBS2+S3) has recently been applied and validated in several study sites. However, none of these studies has applied it in heterogeneous row crops under different water status conditions within the same orchard. This study assessed the TSEBS2+S3 modelling approach to account for almond crop water use under four different irrigation regimes and over four consecutive growing seasons (2017–2020). The energy fluxes were validated with an eddy covariance system and also compared with a soil water balance model. The former reported errors of 90 W/m2 and 87 W/m2 for the sensible (H) and latent heat flux (LE), respectively. The comparison of ETa with the soil water balance model showed a root-mean-square deviation (RMSD) ranging from 0.6 to 2.5 mm/day. Differences in cumulative ETa between the irrigation treatments were estimated, with maximum differences obtained in 2019 of 20% to 13% less in the most water-limited treatment compared to the most well-watered one. Therefore, this study demonstrates the feasibility of using the TSEBS2+S3 for monitoring ETa in almond trees under different water regimes.
An Intelligent Traffic Surveillance System Using Integrated Wireless Sensor Network and Improved Phase Timing Optimization
Authors: Naveed Quadri Noorulhasan , Alqahtani Hamed , Khan Riaz Ullah , Almakdi Sultan , Alshehri Mohammed , Abdul Rasheed Mohammed Aref
Published: April 2022

The transportation industry is crucial to the realization of a smart city. However, the current growth in vehicle numbers is not being matched by an increase in road capacity. Congestion may boost the number of accidents, harm economic growth, and result in higher gas emissions. Currently, traffic congestion is seen as a severe threat to urban life. Suffering as a result of increased car traffic, insufficient infrastructure, and inefficient traffic management has exceeded the tolerance limit. Since route decisions are typically made in a short amount of time, the visualization of the data must be presented in a highly conceivable way. Also, the data generated by the transportation system face difficulties in processing and sometimes lack effective usage in certain fields. Hence, to overcome the challenges in computer vision, a novel computer vision-based traffic management system is proposed by integrating a wireless sensor network (WSN) and visual analytics framework. This research aimed to analyze average message delivery, average latency, average access, average energy consumption, and network performance. Wireless sensors are used in the study to collect road metrics, quantify them, and then rank them for entry. For optimization of the traffic data, improved phase timing optimization (IPTO) was used. The whole experimentation was carried out in a virtual environment. It was observed from the experimental results that the proposed approach outperformed other existing approaches.
External Human–Machine Interfaces for Autonomous Vehicles from Pedestrians’ Perspective: A Survey Study
Authors: Guo Jiawen , Yuan Quan , Yu Jingrui , Chen Xizheng , Yu Wenlin , Cheng Qian , Wang Wuhong , Luo Wenhui , Jiang Xiaobei
Published: April 2022

With the increasing number of automated vehicles (AVs) being tested and operating on roads, external Human–Machine Interfaces (eHMIs) are proposed to facilitate interactions between AVs and other road users. Considering the need to protect vulnerable road users, this paper addresses the issue by providing research evidence on various designs of eHMIs. Ninety participants took part in this experiment. Six sets of eHMI prototypes—Text, Arrowed (Dynamic), Text and Symbol, Symbol only, Tick and Cross and Traffic Lights, including two sub-designs (Cross and Do Not Cross)—were designed. The results showed that 65.1% of participants agreed that external communication would have a positive effect on pedestrians’ crossing decisions. Among all the prototypes, Text, and Text and Symbol, eHMIs were the most widely accepted. In particular, for elderly people and those unfamiliar with traffic rules, Text, and Text and Symbol, eHMIs would lead to faster comprehension. The results confirmed that 68.5% of participants would feel safer crossing if the eHMI had the following features: ‘Green’, ‘Text’, ‘Symbol’, or ‘Dynamic’. These features are suggested in the design of future systems. This research concluded that eHMIs have a positive effect on V2X communication and that textual eHMIs were clear to pedestrians.
A Performance Improvement Strategy for Concrete Damage Detection Using Stacking Ensemble Learning of Multiple Semantic Segmentation Networks
Authors: Li Shengyuan , Zhao Xuefeng
Published: April 2022

Semantic segmentation network-based methods can detect concrete damage at the pixel level. However, the performance of a single semantic segmentation network is often limited. To improve the concrete damage detection performance of a semantic segmentation network, a stacking ensemble learning-based concrete crack detection method using multiple semantic segmentation networks is proposed. To realize this method, a database including 500 images and their labels with concrete crack and spalling is built and divided into training and testing sets. At first, the training and prediction of five semantic segmentation networks (FCN-8s, SegNet, U-Net, PSPNet and DeepLabv3+) are respectively implemented on the built training set according to a five-fold cross-validation principle, where 80% of the training images are used in the training process, and 20% training images are reserved. Then, in predicting the results of reserved training images from trained semantic segmentation networks, the class labels of all pixels are collected, and then four softmax regression-based ensemble learning models are trained using the collected class labels and their true classification labels. The trained ensemble learning models are applied to regressed testing results of semantic segmentation network models. Compared with the best single semantic segmentation network, the best ensemble learning model provides performance improvement of 0.21% PA, 0.54% MPA, 3.66% MIoU, and 0.12% FWIoU, respectively. The study results show that the stacking ensemble learning strategy can indeed improve concrete damage detection performance through ensemble learning of multiple semantic segmentation networks.
Low-Cost Electromagnetic Split-Ring Resonator Sensor System for the Petroleum Industry
Authors: Rivera-Lavado Alejandro , García-Lampérez Alejandro , Jara-Galán María-Estrella , Gallo-Valverde Emilio , Sanz Paula , Segovia-Vargas Daniel
Published: April 2022

The use of a low-cost split-ring resonator (SRR) passive sensor for the real-time permittivity characterization of hydrocarbon fluids is proposed in this paper. The characterization of the sensor is performed through both full-wave simulation and measurements. Thanks to the analysis of several crude samples, the possibility of discrimination between different types of crude and the estimation of several of their properties are demonstrated. Between them, the estimation of sulfur, aromatic hydrocarbons, and salt-water concentrations either in normal ambient conditions or in a high-pressure and high-temperature environment can be mentioned. Experiments were run both at normal ambient conditions and pressures up to 970 bar and temperatures up to 200 °C.
Differentiation of Livestock Internal Organs Using Visible and Short-Wave Infrared Hyperspectral Imaging Sensors
Authors: Coombs Cassius E. O. , Allman Brendan E. , Morton Edward J. , Gimeno Marina , Horadagoda Neil , Tarr Garth , González Luciano A.
Published: April 2022

Automatic identification and sorting of livestock organs in the meat processing industry could reduce costs and improve efficiency. Two hyperspectral sensors encompassing the visible (400–900 nm) and short-wave infrared (900–1700 nm) spectra were used to identify the organs by type. A total of 104 parenchymatous organs of cattle and sheep (heart, kidney, liver, and lung) were scanned in a multi-sensory system that encompassed both sensors along a conveyor belt. Spectral data were obtained and averaged following manual markup of three to eight regions of interest of each organ. Two methods were evaluated to classify organs: partial least squares discriminant analysis (PLS-DA) and random forest (RF). In addition, classification models were obtained with the smoothed reflectance and absorbance and the first and second derivatives of the spectra to assess if one was superior to the rest. The in-sample accuracy for the visible, short-wave infrared, and combination of both sensors was higher for PLS-DA compared to RF. The accuracy of the classification models was not significantly different between data pre-processing methods or between visible and short-wave infrared sensors. Hyperspectral sensors, particularly those in the visible spectrum, seem promising to identify organs from slaughtered animals which could be useful for the automation of quality and process control in the food supply chain, such as in abattoirs.
MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios
Authors: Zhou Yan , Wen Sijie , Wang Dongli , Meng Jiangnan , Mu Jinzhen , Irampaye Richard
Published: April 2022

Object detection is one of the key tasks in an automatic driving system. Aiming to solve the problem of object detection, which cannot meet the detection speed and detection accuracy at the same time, a real-time object detection algorithm (MobileYOLO) is proposed based on YOLOv4. Firstly, the feature extraction network is replaced by introducing the MobileNetv2 network to reduce the number of model parameters; then, part of the standard convolution is replaced by depthwise separable convolution in PAnet and the head network to further reduce the number of model parameters. Finally, by introducing an improved lightweight channel attention modul—Efficient Channel Attention (ECA)—to improve the feature expression ability during feature fusion. The Single-Stage Headless (SSH) context module is introduced to the small object detection branch to increase the receptive field. The experimental results show that the improved algorithm has an accuracy rate of 90.7% on the KITTI data set. Compared with YOLOv4, the parameters of the proposed MobileYOLO model are reduced by 52.11 M, the model size is reduced to one-fifth, and the detection speed is increased by 70%.
New Results for the Error Rate Performance of LoRa Systems over Fading Channels
Authors: Peppas Kostas , Chronopoulos Spyridon K. , Loukatos Dimitrios , Arvanitis Konstantinos
Published: April 2022

Long Range (LoRa) systems have recently attracted significant attention within the research community as well as for commercial use due to their ability to transmit data over long distances at a relatively low energy cost. In this study, new results for the bit error rate performance of Long Range (LoRa) systems operating in the presence of Rayleigh, Rice, Nakagami-m, Hoyt, η-μ and generalized fading channels are presented. Specifically, we propose novel exact single integral expressions as well as simple, accurate expressions that yield tight results in the entire signal-to-noise ratio (SNR) region. The validity of our newly derived formulas is substantiated by comparing numerically evaluated results with equivalent ones, obtained using Monte-Carlo simulations and exact analytical expressions.
Metal Oxide Chemiresistors: A Structural and Functional Comparison between Nanowires and Nanoparticles
Authors: Ponzoni Andrea
Published: April 2022

Metal oxide nanowires have become popular materials in gas sensing, and more generally in the field of electronic and optoelectronic devices. This is thanks to their unique structural and morphological features, namely their single-crystalline structure, their nano-sized diameter and their highly anisotropic shape, i.e., a large length-to-diameter aspect ratio. About twenty years have passed since the first publication proposing their suitability for gas sensors, and a rapidly increasing number of papers addressing the understanding and the exploitation of these materials in chemosensing have been published. Considering the remarkable progress achieved so far, the present paper aims at reviewing these results, emphasizing the comparison with state-of-the-art nanoparticle-based materials. The goal is to highlight, wherever possible, how results may be related to the particular features of one or the other morphology, what is effectively unique to nanowires and what can be obtained by both. Transduction, receptor and utility-factor functions, doping, and the addition of inorganic and organic coatings will be discussed on the basis of the structural and morphological features that have stimulated this field of research since its early stage.
SmartSwim, a Novel IMU-Based Coaching Assistance
Authors: Hamidi Rad Mahdi , Gremeaux Vincent , Massé Fabien , Dadashi Farzin , Aminian Kamiar
Published: April 2022

Swimming coaches provide regular timed and technical feedback to swimmers and guide them efficiently in training sessions. Due to the complexity of swimmers’ performance, which is not visible in qualitative observation, quantitative and objective performance evaluation can better assist the coach in this regard. Inertial measurement units (IMUs) are used in swimming for objective performance evaluation. In this study, we propose a new performance evaluation feedback (SmartSwim) using IMU and investigate its effects on the swimmer’s weekly progress. Measurements were conducted each week with 15 competitive swimmers for 10 weeks using a Sacrum IMU. The SmartSwim report included a comprehensive representation of performance based on goal metrics of each phase extracted from the IMU signals. The swimmers were divided into two groups: the experimental and control groups. The SmartSwim report for each swimmer in the experimental group was given to the coach, who used it to adjust the training accordingly. The results showed that the experimental group outperformed the control group when comparing each swimmer, each session and the whole sessions. At the level of each individual, more members of the experimental group showed significant downward trend of average lap time (Mann-Kendall trend test, 95% confidence level). While comparing the sessions, the experimental group showed significantly lower lap time than the control group from the sixth session onwards (p-value < 0.05 from t-test). Considering all sessions, the experimental group showed significantly higher progress, lower average lap time, and more consistent records (Mann-Whitney U test at 95% confidence level) than the control group. This study demonstrated that SmartSwim can assist coaching by quantitatively assessing swimmers’ performance, leading to more efficient training.
Design of a Robust System Architecture for Tracking Vehicle on Highway Based on Monocular Camera
Authors: Wu Zhihong , Li Fuxiang , Zhu Yuan , Lu Ke , Wu Mingzhi
Published: April 2022

Multi-Target tracking is a central aspect of modeling the environment of autonomous vehicles. A mono camera is a necessary component in the autonomous driving system. One of the biggest advantages of the mono camera is it can give out the type of vehicle and cameras are the only sensors able to interpret 2D information such as road signs or lane markings. Besides this, it has the advantage of estimating the lateral velocity of the moving object. The mono camera is now being used by companies all over the world to build autonomous vehicles. In the expressway scenario, the forward-looking camera can generate a raw picture to extract information from and finally achieve tracking multiple vehicles at the same time. A multi-object tracking system, which is composed of a convolution neural network module, depth estimation module, kinematic state estimation module, data association module, and track management module, is needed. This paper applies the YOLO detection algorithm combined with the depth estimation algorithm, Extend Kalman Filter, and Nearest Neighbor algorithm with a gating trick to build the tracking system. Finally, the tracking system is tested on the vehicle equipped with a forward mono camera, and the results show that the lateral and longitudinal position and velocity can satisfy the need for Adaptive Cruise Control (ACC), Navigation On Pilot (NOP), Auto Emergency Braking (AEB), and other applications.
Predicting Determinants of Lifelong Learning Intention Using Gradient Boosting Machine (GBM) with Grid Search
Authors: Kim Chayoung , Park Taejung
Published: April 2022

The purpose of this study is to explore the factors that have the most decisive influence on actual learning intention that leads to participation in adult education. For developing the predictive model, we used tree-based machine learning, with the longitudinal big data (2017~2020) of Korean adults. Based on the gradient boosting machine (GBM) results, among the eleven variables used, the most influential variables in predicting the possibility of lifelong education participation were self-pay education expenses and then highest level of education completed. After the grid search, not only the importance of the two variables but also the overall figures including the false positive rate improved. In future studies, it will be possible to improve the performance of the machine learning model by adjusting the hyper-parameters that can be directly set by less computational methods.
From Claiming to Creating Value: The Psychology of Negotiations on Common Resource Dilemmas
Authors: Trötschel Roman , van Treek Marie , Heydenbluth Caroline , Zhang Kai , Majer Johann M.
Published: April 2022

Current sustainability challenges often reflect common resource dilemmas where peoples’ short-term self-interests are at odds with collective interests in the present and future. In this article, we highlight the key role of joint decision-making processes in negotiations to facilitate the management of common resource dilemmas and to promote the transition toward sustainability. By reflecting on psychological drivers and barriers, we argue that the limited availability, the restricted accessibility, and the dynamic alterability of resources in negotiations on common resource dilemmas may cause a myopic mindset that fosters value claiming strategies and, ultimately, results in distributive-consumptive negotiation outcomes. To promote value creation in negotiations on common resource dilemmas, we argue that agents must perform a mindset shift with an inclusive social identity on a superordinate group level, an embracive prosocial motivation for other parties’ interests at and beyond the table, and a forward-looking cognitive orientation towards long-term consequences of their joint decisions. By shifting their mindset from a myopic towards a holistic cognitive orientation, agents may explore negotiation strategies to create value through increasing the availability, improving the accessibility, and using the alterability of resources. Applying these value creation strategies may help achieve integrative-transformative negotiation outcomes and promote sustainable agreements aimed at intersectional, interlocal, and intergenerational justice. We conclude by discussing additional psychological factors that play a pivotal role in negotiations on common resource dilemmas as well as further developments for future research.
Efficiency Evaluation of Chinese Provincial Industrial System Based on Network DEA Method
Authors: He Kai , Zhu Nan , Jiang Wu , Zhu Chuanjin
Published: April 2022

The operation process of the Chinese provincial industrial system consists of four stages, namely the production (P) stage, wastewater treatment (WWT) stage, solid waste treatment (SWT) stage, and sulfur dioxide treatment (SDT) stage. Based on this structure, a four-stage data envelopment analysis (DEA) model is developed to evaluate the eco-efficiency, production efficiency, wastewater treatment efficiency, solid waste treatment efficiency, and sulfur dioxide treatment efficiency of provincial industrial systems in China, considering the undesirable output and variable returns to scale (VRS). Based on the objective data from 2011 to 2015, the following conclusions are drawn: Firstly, the eco-efficiency of the Chinese provincial industrial system has not been significantly improved during the study period, and the average eco-efficiency score is low, only 0.3805. Secondly, the reasons for the low eco-efficiency of the industrial system are different in the Eastern, Central, Western, and Northeastern regions. Thirdly, compared with the P stage, industrial WWT stage, and SWT stage, the efficiency of SDT stage is still relatively weak.
Interactions between Geomorphology and Production Chain of High-Quality Coffee in Costa Rica
Authors: Quesada-Román Adolfo , Quirós-Arias Lilliam , Zamora-Pereira Juan Carlos
Published: April 2022

High-altitude coffee has an international reputation due to its high quality, especially in countries with a long production history, such as Costa Rica. Specific geographical characteristics determine the regions where high-altitude coffee can be cultivated. Over the last two decades, new production conditions have promoted the growth of smallholder coffee farms in the Upper Buenavista Catchment (UBC) in the South of Costa Rica. To understand this phenomenon’s process, we initially performed a detailed geomorphological mapping of the high-elevation production sites in the UBC. Then, we used remote sensing to determine the coffee land cover (2005, 2012, and 2018) to compare their landforms. Furthermore, we analyzed the production–processing–market chain that has promoted coffee plantations since 2005. Our results show that coffee farmers chose more unstable and erosive areas with short-term production prospects to cultivate premium-priced coffee. Moreover, farmers have changed their role in the coffee sector, evolving from small producers to entrepreneurs with specialized knowledge. These actions may reduce economic risks and improve the household incomes of smallholder coffee producers. However, limited research has been conducted along the tropics about the relationships between landforms, socioeconomic drivers, and high-altitude coffee yield. Therefore, our results are essential to present geomorphology and applied geography as baselines in land-use planning for agricultural landscapes.
Sustainability Analysis, Implications, and Effects of the Teleworking System in Romania
Authors: Ionescu Constantin Aurelian , Fülöp Melinda Timea , Topor Dan Ioan , Duică Mircea Constantin , Stanescu Sorina Geanina , Florea Nicoleta Valentina , Zamfir Mariana , Coman Mihaela Denisa
Published: April 2022

The COVID-19 pandemic is a mobilizing circumstance for rethinking the economic activities of companies, as well as reorganizing the way employees work. To comply with the regulations imposed worldwide, most economic activities were transferred to the online environment. The purpose of the paper is to carry out an investigation of the Romanian telework system implemented during the pandemic based on the perception of employees and employers. Our research was conducted based on an online opinion poll in which 438 respondents participated. The questionnaire included the socio-demographic characteristics of the respondents, methods and techniques for implementing telework among Romanian companies, and a correlation of the advantages and opportunities with the limits experienced both among companies and employees in carrying out this process. The results showed that 7.80% of companies wanted to maintain the conditions implemented for telework, 12.30% wanted to expand flexible practices to promote telework, and 27.60% would not make any changes in terms of telework methods. At the same time, 81.10% of employees preferred office work rather than teleworking. Successful implementation of the telework system involves efficient management that coordinates and motivates the performance of remote employees.
Social Sustainability in Construction Projects—A Systematic Review of Assessment Indicators and Taxonomy
Authors: Rostamnezhad Mozhdeh , Thaheem Muhammad Jamaluddin
Published: April 2022

Despite its importance and appeal, the social dimension of sustainability in construction projects is less explored and lacks a comprehensive and standardized framework. This diminishes the holistic view of sustainability. The existing customized frameworks make the selection of factors challenging across different contexts. Eventually, the practitioners have to pick and choose the factors. This reduces the reliability of social sustainability assessments and makes them a procedural challenge. To fill this gap, the current review synthesizes a framework of social sustainability for construction projects. For this purpose, a systematic review of the literature published until 2021 is performed. The indicators are extracted from the selected 22 papers and their content is analyzed to check for similarities. The final set of 76 factors is synthesized into an assessment framework through a thematic analysis based on a bottom-up approach. The framework is organized into 7 enablers or themes, 27 indicators, and 76 sub-indicators. The enablers of social sustainability are stakeholder, safety and health, human resource development, project, industry, community, and government. The framework provides a comprehensive and precise view of social sustainability which can be leveraged to ensure better planning and sustainable development of construction projects.
Searching for Sustainability in Health Systems: Toward a Multidisciplinary Evaluation of Mobile Health Innovations
Authors: Degavre Florence , Kieffer Suzanne , Bol David , Dekimpe Rémi , Desterbecq Charlotte , Pirson Thibault , Sandu Georgiana , Tubeuf Sandy
Published: April 2022

Mobile health (mHealth) innovations are considered by governments as game changers toward more sustainable health systems. The existing literature focuses on the clinical aspects of mHealth but lacks an integrated framework on its sustainability. The foundational idea for this paper is to include disciplinary complementarities into a multi-dimensional vision to evaluate the non-clinical aspects of mHealth innovations. We performed a targeted literature review to find how the sustainability of mHealth innovations was appraised in each discipline. We found that each discipline considers a different outcome of interest and adopts different time horizons and perspectives for the evaluation. This article reflects on how the sustainability of mHealth innovation can be assessed at both the level of the device itself as well as the level of the health system. We identify some of the challenges ahead of researchers working on mobile health innovations in contributing to shaping a more sustainable health system.
The Rural and Nature Tourism Development Potential in Islands
Authors: Batista Maria da Graça , Couto Gualter , Castanho Rui Alexandre , Sousa Áurea , Pimentel Pedro , Carvalho Célia
Published: April 2022

It has become evident that rural tourism grew significantly during the pandemic compared to mass tourism. However, at this time, all the forecasts were wrapped in considerable uncertainty regarding travel, which is not without the elevated associated risk. Therefore, we assessed the perceptions of the entrepreneurs in relation to rural tourism activities regarding the advantages of their rural tourism activity in the Azores for customers. Furthermore, the potential for developing this typology of tourism is pivotal for obtaining very reliable data and information on this topic and enabling us to provide guidelines for the leading regional players. In this regard, an exploratory method was used to assess the perceptions of the entrepreneurs with rural tourism activities in the Azores Islands regarding the customers’ experiences and some other important aspects concerning the promotion of this type of tourism in the region. Throughout this study, it was possible to verify that the most relevant obstacles to the development of rural tourism in the Azores region are the preservation and conservation of nature and endogenous resources and delivering proper infrastructures and regional accessibilities. Contrarily, the increase in community and regional funding and support for rural tourism could be key to the prosperity of this typology of tourism in this insular territory.