bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data
Authors: Beaudoin André , Hall Ronald J. , Castilla Guillermo , Filiatrault Michelle , Villemaire Philippe , Skakun Rob , Guindon Luc
Published: February 2022

Satellite forest inventories are the only feasible way to map Canada’s vast, remote forest regions, such as those in the Northwest Territories (NWT). A method used to create such inventories is the k-nearest neighbour (k-NN) algorithm, which spatially extends information from forest inventory (FI) plots to the entire forest land base using wall-to-wall features typically derived from Landsat data. However, the benefits of integrating L-band synthetic aperture radar (SAR) data, strongly correlated to forest biomass, have not been assessed for Canadian northern boreal forests. Here we describe an optimized multivariate k-NN implementation of a 151,700 km2 area in southern NWT that included ca. 2007 Landsat and dual-polarized Phased Array type L-band SAR (PALSAR) data on board the Advanced Land Observing Satellite (ALOS). Five forest attributes were mapped at 30 m cells: stand height, crown closure, stand/total volume and aboveground biomass (AGB). We assessed accuracy gains compared to Landsat-based maps. To circumvent the scarcity of FI plots, we used 3600 footprints from the Geoscience Laser Altimeter System (GLAS) as surrogate FI plots, where forest attributes were estimated using Light Detection and Ranging (LiDAR) metrics as predictors. After optimization, k-NN predicted forest attribute values for each pixel as the average of the 4 nearest (k = 4) surrogate FI plots within the Euclidian space of 9 best features (selected among 6 PALSAR, 10 Landsat, and 6 environmental features). Accuracy comparisons were based on 31 National Forest Inventory ground plots and over 1 million airborne LiDAR plots. Maps that included PALSAR HV backscatter resulted in forest attribute predictions with higher goodness of fit (adj. R2), lower percent mean error (ME%), and percent root mean square error (RMSE%), and lower underestimation for larger attribute values. Predictions were most accurate for conifer stand height (RMSE% = 32.1%, adj. R2 = 0.58) and AGB (RMSE% = 47.8%, adj. R2 = 0.74), which is much more abundant in the area than mixedwood or broadleaf. Our study demonstrates that optimizing k-NN parameters and feature space, including PALSAR, Landsat, and environmental variables, is a viable approach for inventory mapping of the northern boreal forest regions of Canada.
pH Measurement at Elevated Temperature with Vessel Gate and Oxygen-Terminated Diamond Solution Gate Field Effect Transistors
Authors: Kawaguchi Shuto , Nomoto Reona , Sato Hirotaka , Takarada Teruaki , Chang Yu Hao , Kawarada Hiroshi
Published: February 2022

Diamond has many appealing properties, including biocompatibility, ease of surface modification, and chemical–physical stability. In this study, the temperature dependence of the pH-sensitivity of a oxygen-terminated boron-doped diamond solution gate FET (C-O BDD SGFET) is reported. The C-O BDD SGFET operated in an electrolyte solution at 95 °C. At 80 °C, the pH sensitivity of C-O BDD SGFET dropped to 4.27 mV/pH. As a result, we succeeded in developing a highly sensitive pH sensing system at −54.6 mV/pH at 80 °C by combining it with a highly pH sensitive stainless-steel vessel.
Dielectric Dispersion Modulated Sensing of Yeast Suspension Electroporation
Authors: Pintarelli Guilherme B. , da Silva Jessica R. , Yang Wuqiang , Suzuki Daniela O. H.
Published: February 2022

A specific pulsed electric field protocol can be used to induce electroporation. This is used in the food industry for yeast pasteurization, in laboratories for generic transfer and the medical field for cancer treatment. The sensing of electroporation can be done with simple ‘instantaneous’ voltage-current analysis. However, there are some intrinsic low-frequency phenomena superposing the electroporation current, such as electrode polarization. The biological media are non-homogeneous, giving them specific characterization in the broad frequency spectrum. For example, the cell barrier, i.e., cell membrane, causes so called β-dispersion in the frequency range of tens to thousands of kHz. Electroporation is a dynamic phenomenon characterized by altering the cell membrane permeability. In this work, we show that the impedance measurement at certain frequencies could be used to detect the occurrence of electroporation, i.e., dielectric dispersion modulated sensing. This approach may be used for the design and implementation of electroporation systems. Yeast suspension electroporation is simulated to show changes in the frequency spectrum. Moreover, the alteration depends on characteristics of the system. Three types of external buffers and their characteristics are evaluated.
CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
Authors: Deng Xiaoping , Ma Lujuan , Liu Xiaoping
Published: February 2022

Wireless information collecting and processing terminals, such as cell phones, sensors and smart wearable devices, are expected to be deployed on a large scale in the future to promote the continuous advancement of the global information revolution. Since most of these terminals connect to each other using long-distance and high-speed networks by multiple routers and eventual access the internet, the application of mobile internet is gradually increasing and data traffic on the mobile internet is growing exponentially, from which arises congestion in wireless networks on multiple routers. This research solves the congestion problem for wireless networks with multiple bottleneck routers. First, the wireless network model is expanded to multi-router networks, which considers the interrelationships between connecting routers. Afterwards, a new Active Queue Management (AQM) method called Congestion Control Based on Adaptive Integral Backstepping (CCAIB) is designed to handle congestion in wireless networks. In CCAIB, an adaptive control method is used to estimate the packet loss ratios of wireless links and a controller is designed based on the estimation results through a backstepping procedure. It can be shown from the simulation results that the performance of CCAIB is better than the H∞ algorithm in queue length stability. Besides, the window size of CCAIB is 100 times that of the H∞ algorithm, and the proportion of packets marked as discarded when using CCAIB is about 0.1% of the H∞ algorithm. Moreover, CCAIB has satisfactory adaptability to network parameters such as wireless link capacity, propagation delay, wireless packet loss ratios, desired queue length and router location.
Use of CIED Generated Heart Failure Risk Score (HFRS) Alerts in an Integrated, Multi-Disciplinary Approach to HF Management—A Prospective Cohort Study
Authors: Garner Daniel , Lunt Lindsay , Leung Wing , Llewellyn Jennifer , Kahn Matthew , Wright David Jay , Rao Archana
Published: February 2022

Aim: To evaluate use of CIED-generated Heart Failure Risk Score (HFRS) alerts in an integrated, multi-disciplinary approach to HF management. Methods: We undertook a prospective, single centre outcome study of patients implanted with an HFRS-enabled Medtronic CIED, generating a “high risk” alert between November 2018 and November 2020. All patients generating a “high risk” HFRS alert were managed within an integrated HF pathway. Alerts were shared with local HF teams, prompting patient contact and appropriate intervention. Outcome data on health care utilisation (HCU) and mortality were collected. A validated questionnaire was completed by the HF teams to obtain feedback. Results: 367 “High risk” alerts were noted in 188 patients. The mean patient age was 70 and 49% had a Charlson Comorbidity Score of >6. Mean number of alerts per patients was 1.95 and 44 (23%) of patients had >3 “high risk” alerts in the follow up period. Overall, 75 (39%) patients were hospitalised in the 4–6-week period of the alert; 53 (28%) were unplanned of which 24 (13%) were for decompensated HF. A total of 33 (18%) patients died in the study period. Having three or more alerts significantly increased the risk of hospitalisation for heart failure (HR 2.5, CI 1.1–5.6 p = 0.03). The feedback on the pathway was positive. Conclusions: Patients with “high risk” alerts are co-morbid and have significant HCU. An integrated approach can facilitate timely risk stratification and intervention. Intervention in these patients is not limited to HF alone and provides the opportunity for holistic management of this complex cohort.
Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles
Authors: Jarray Raja , Al-Dhaifallah Mujahed , Rezk Hegazy , Bouallègue Soufiene
Published: February 2022

The path planning of Unmanned Aerial Vehicles (UAVs) is a complex and hard task that can be formulated as a Large-Scale Global Optimization (LSGO) problem. A higher partition of the flight environment leads to an increase in route’s accuracy but at the expense of greater planning complexity. In this paper, a new Parallel Cooperative Coevolutionary Grey Wolf Optimizer (PCCGWO) is proposed to solve such a planning problem. The proposed PCCGWO metaheuristic applies cooperative coevolutionary concepts to ensure an efficient partition of the original search space into multiple sub-spaces with reduced dimensions. The decomposition of the decision variables vector into several sub-components is achieved and multi-swarms are created from the initial population. Each sub-swarm is then assigned to optimize a part of the LSGO problem. To form the complete solution, the representatives from each sub-swarm are combined. To reduce the computation time, an efficient parallel master-slave model is introduced in the proposed parameters-free PCCGWO. The master will be responsible for decomposing the original problem and constructing the context vector which contains the complete solution. Each slave is designed to evolve a sub-component and will send the best individual as its representative to the master after each evolutionary cycle. Demonstrative results show the effectiveness and superiority of the proposed PCCGWO-based planning technique in terms of several metrics of performance and nonparametric statistical analyses. These results show that the increase in the number of slaves leads to a more efficient result as well as a further improved computational time.
Hand Pronation–Supination Movement as a Proxy for Remotely Monitoring Gait and Posture Stability in Parkinson’s Disease
Authors: Cakmak Yusuf Ozgur , Olcek Can , Ozsoy Burak , Khwaounjoo Prashanna , Kiziltan Gunes , Apaydin Hulya , Günduz Aysegul , Oztop Cakmak Ozgur , Ertan Sibel , Gursoy-Ozdemir Yasemin , Gokcay Didem
Published: February 2022

The Unified Parkinson’s Disease Rating Scale (UPDRS) is a subjective Parkinson’s Disease (PD) physician scoring/monitoring system. To date, there is no single upper limb wearable/non-contact system that can be used objectively to assess all UPDRS-III motor system subgroups (i.e., tremor (T), rigidity (R), bradykinesia (B), gait and posture (GP), and bulbar anomalies (BA)). We evaluated the use of a non-contact hand motion tracking system for potential extraction of GP information using forearm pronation–supination (P/S) motion parameters (speed, acceleration, and frequency). Twenty-four patients with idiopathic PD participated, and their UPDRS data were recorded bilaterally by physicians. Pearson’s correlation, regression analyses, and Monte Carlo validation was conducted for all combinations of UPDRS subgroups versus motion parameters. In the 262,125 regression models that were trained and tested, the models within 1% of the lowest error showed that the frequency of P/S contributes to approximately one third of all models; while speed and acceleration also contribute significantly to the prediction of GP from the left-hand motion of right handed patients. In short, the P/S better indicated GP when performed with the non-dominant hand. There was also a significant negative correlation (with medium to large effect size, range: 0.3–0.58) between the P/S speed and the single BA score for both forearms and combined UPDRS score for the dominant hand. This study highlights the potential use of wearable or non-contact systems for forearm P/S to remotely monitor and predict the GP information in PD.
Damage-Detection Approach for Bridges with Multi-Vehicle Loads Using Convolutional Autoencoder
Authors: Lee Kanghyeok , Jeong Seunghoo , Sim Sung-Han , Shin Do Hyoung
Published: February 2022

Deep learning has been widely employed in recent studies on bridge-damage detection to improve the performance of damage-detection methods. Unsupervised deep learning can be effectively utilized to increase the applicability of damage-detection approaches. Hence, the authors propose a convolutional-autoencoder (CAE)-based damage-detection approach, which is an unsupervised deep-learning network. However, the CAE-based damage-detection approach demonstrates only satisfactory accuracy for prestressed concrete bridges with a single-vehicle load. Therefore, this study was performed to verify whether the CAE-based damage-detection approach can be applied to bridges with multi-vehicle loads, which is a typical scenario. In this study, rigid-frame and reinforced-concrete-slab bridges were modeled and simulated to obtain the behavior data of bridges. A CAE-based damage-detection approach was tested on both bridges. For both bridges, the results demonstrated satisfactory damage-detection accuracy of over 90% and a false-negative rate of less than 1%. These results prove that the CAE-based approach can be successfully applied to various types of bridges with multi-vehicle loads.
Analysis of Methods for Determining Shallow Waterbody Depths Based on Images Taken by Unmanned Aerial Vehicles
Authors: Specht Mariusz , Wiśniewska Marta , Stateczny Andrzej , Specht Cezary , Szostak Bartosz , Lewicka Oktawia , Stateczny Marcin , Widźgowski Szymon , Halicki Armin
Published: February 2022

Hydrographic surveys enable the acquisition and processing of bathymetric data, which after being plotted onto nautical charts, can help to ensure safety of navigation, monitor changes in the coastal zone, and assess hydro-engineering structure conditions. This study involves the measurement of waterbody depth, identification of the seabed shape and geomorphology, the coastline course, and the location of underwater obstacles. Hydroacoustic systems mounted on vessels are commonly used in bathymetric measurements. However, there is also an increasing use of Unmanned Aerial Vehicles (UAV) that can employ sensors such as LiDAR (Light Detection And Ranging) or cameras previously not applied in hydrography. Current systems based on photogrammetric and remote sensing methods enable the determination of shallow waterbody depth with no human intervention and, thus, significantly reduce the duration of measurements, especially when surveying large waterbodies. The aim of this publication is to present and compare methods for determining shallow waterbody depths based on an analysis of images taken by UAVs. The perspective demonstrates that photogrammetric techniques based on the SfM (Structure-from-Motion) and MVS (Multi-View Stereo) method allow high accuracies of depth measurements to be obtained. Errors due to the phenomenon of water-wave refraction remain the main limitation of these techniques. It was also proven that image processing based on the SfM-MVS method can be effectively combined with other measurement methods that enable the experimental determination of the parameters of signal propagation in water. The publication also points out that the Lyzenga, Satellite-Derived Bathymetry (SDB), and Stumpf methods allow satisfactory depth measurement results to be obtained. However, they require further testing, as do methods using the optical wave propagation properties.
A Deconvolutional Deblurring Algorithm Based on Short- and Long-Exposure Images
Authors: Bai Yang , Tan Zheng , Lv Qunbo , Huang Min
Published: February 2022

An iterative image restoration algorithm, directed at the image deblurring problem and based on the concept of long- and short-exposure deblurring, was proposed under the image deconvolution framework by investigating the imaging principle and existing algorithms, thus realizing the restoration of degraded images. The effective priori side information provided by the short-exposure image was utilized to improve the accuracy of kernel estimation, and then increased the effect of image restoration. For the kernel estimation, a priori filtering non-dimensional Gaussianity measure (BID-PFNGM) regularization term was raised, and the fidelity term was corrected using short-exposure image information, thus improving the kernel estimation accuracy. For the image restoration, a P norm-constrained relative gradient regularization term constraint model was put forward, and the restoration result realizing both image edge preservation and texture restoration effects was acquired through the further processing of the model results. The experimental results prove that, in comparison with other algorithms, the proposed algorithm has a better restoration effect.
A Novel Framework for Generating Personalized Network Datasets for NIDS Based on Traffic Aggregation
Authors: Velarde-Alvarado Pablo , Gonzalez Hugo , Martínez-Peláez Rafael , Mena Luis J. , Ochoa-Brust Alberto , Moreno-García Efraín , Félix Vanessa G. , Ostos Rodolfo
Published: February 2022

In this paper, we addressed the problem of dataset scarcity for the task of network intrusion detection. Our main contribution was to develop a framework that provides a complete process for generating network traffic datasets based on the aggregation of real network traces. In addition, we proposed a set of tools for attribute extraction and labeling of traffic sessions. A new dataset with botnet network traffic was generated by the framework to assess our proposed method with machine learning algorithms suitable for unbalanced data. The performance of the classifiers was evaluated in terms of macro-averages of F1-score (0.97) and the Matthews Correlation Coefficient (0.94), showing a good overall performance average.
Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting
Authors: Villia Maria Myrto , Tsagkatakis Grigorios , Moghaddam Mahta , Tsakalides Panagiotis
Published: February 2022

Forecasting the values of essential climate variables like land surface temperature and soil moisture can play a paramount role in understanding and predicting the impact of climate change. This work concerns the development of a deep learning model for analyzing and predicting spatial time series, considering both satellite derived and model-based data assimilation processes. To that end, we propose the Embedded Temporal Convolutional Network (E-TCN) architecture, which integrates three different networks, namely an encoder network, a temporal convolutional network, and a decoder network. The model accepts as input satellite or assimilation model derived values, such as land surface temperature and soil moisture, with monthly periodicity, going back more than fifteen years. We use our model and compare its results with the state-of-the-art model for spatiotemporal data, the ConvLSTM model. To quantify performance, we explore different cases of spatial resolution, spatial region extension, number of training examples and prediction windows, among others. The proposed approach achieves better performance in terms of prediction accuracy, while using a smaller number of parameters compared to the ConvLSTM model. Although we focus on two specific environmental variables, the method can be readily applied to other variables of interest.
A Distributed Algorithm for Real-Time Multi-Drone Collision-Free Trajectory Replanning
Authors: Sabetghadam Bahareh , Cunha Rita , Pascoal António
Published: February 2022

In this paper, we present a distributed algorithm to generate collision-free trajectories for a group of quadrotors flying through a common workspace. In the setup adopted, each vehicle replans its trajectory, in a receding horizon manner, by solving a small-scale optimization problem that only involves its own individual variables. We adopt the Voronoi partitioning of space to derive local constraints that guarantee collision avoidance with all neighbors for a certain time horizon. The obtained set of collision avoidance constraints explicitly takes into account the vehicle’s orientation to avoid infeasiblity issues caused by ignoring the quadrotor’s rotational motion. Moreover, the resulting constraints can be expressed as Bézier curves, and thus can be evaluated efficiently, without discretization, to ensure that collision avoidance requirements are satisfied at any time instant, even for an extended planning horizon. The proposed approach is validated through extensive simulations with up to 100 drones. The results show that the proposed method has a higher success rate at finding collision-free trajectories for large groups of drones compared to other Voronoi diagram-based methods.
Cyberattack Models for Ship Equipment Based on the MITRE ATT&CK Framework
Authors: Jo Yonghyun , Choi Oongjae , You Jiwoon , Cha Youngkyun , Lee Dong Hoon
Published: February 2022

Cybersecurity is important on ships that use information and communication technology. On such ships, the work, control, and sensor systems are connected for steering, navigation, and cargo management inside the hull, and a cyberattack can have physical consequences such as sinking and crashing. Research on ship cybersecurity is a new challenge, and related studies are lacking. Cyberattack models can provide better insight. With this study, we aim to introduce a cyberattack analysis method based on the MITRE ATT&CK framework so that a cyberattack model for ships can be established. In addition, we identify the characteristics of the attack phase by analyzing cases of hacking and vulnerability research for ship systems using tactics, techniques, and procedures, and suggest the minimum measures essential for defense. Using the ship cyberattack model, we aim to identify the characteristics of the systems used for ship navigation, communication, and control; provide an understanding of the threats and vulnerabilities; and suggest mitigation measures through the proposed model. We believe the results of this study could guide future research.
Bernstein Polynomial-Based Method for Solving Optimal Trajectory Generation Problems
Authors: Kielas-Jensen Calvin , Cichella Venanzio , Berry Thomas , Kaminer Isaac , Walton Claire , Pascoal Antonio
Published: February 2022

This paper presents a method for the generation of trajectories for autonomous system operations. The proposed method is based on the use of Bernstein polynomial approximations to transcribe infinite dimensional optimization problems into nonlinear programming problems. These, in turn, can be solved using off-the-shelf optimization solvers. The main motivation for this approach is that Bernstein polynomials possess favorable geometric properties and yield computationally efficient algorithms that enable a trajectory planner to efficiently evaluate and enforce constraints along the vehicles’ trajectories, including maximum speed and angular rates as well as minimum distance between trajectories and between the vehicles and obstacles. By virtue of these properties and algorithms, feasibility and safety constraints typically imposed on autonomous vehicle operations can be enforced and guaranteed independently of the order of the polynomials. To support the use of the proposed method we introduce BeBOT (Bernstein/Bézier Optimal Trajectories), an open-source toolbox that implements the operations and algorithms for Bernstein polynomials. We show that BeBOT can be used to efficiently generate feasible and collision-free trajectories for single and multiple vehicles, and can be deployed for real-time safety critical applications in complex environments.
Energy Autonomous Wireless Sensor Nodes for Freight Train Braking Systems Monitoring
Authors: Zanelli Federico , Mauri Marco , Castelli-Dezza Francesco , Sabbioni Edoardo , Tarsitano Davide , Debattisti Nicola
Published: February 2022

Nowadays, railway freight transportation is becoming more and more crucial since it represents the best alternative to road transport in terms of sustainability, pollution, and impact on the environment and on public health. Upgrading the potentiality of this kind of transportation, it would be possible to avoid delays in goods deliveries due to road accidents, traffic jams, and other situation occurring on roads. A key factor in this framework is therefore represented by monitoring and maintenance of the train components. Implementing a real time monitoring of the main components and a predictive maintenance approach, it would be possible to avoid unexpected breakdowns and consequently unavailability of wagons for unscheduled repair activities. As highlighted in recent statistical analysis, one of the elements more critical in case of failure is represented by the brake system. In this view, a real time monitoring of pressure values in some specific points of the system would provide significant information on its health status. In addition, since the braking actions are related to the load present on the convoy, thanks to this kind of monitoring, it would be possible to appreciate the different behavior of the system in case of loaded and unloaded trains. This paper presented an innovative wireless monitoring system to perform brake system diagnostics. A low-power system architecture, in terms of energy harvesting and wireless communication, was developed due to the difficulty in applying a wired monitoring system to a freight convoy. The developed system allows acquiring brake pressure data in critical points in order to verify the correct behavior of the brake system. Experimental results collected during a five-month field test were provided to validate the approach.
AI Enabled Bridge Bidding Supporting Interactive Visualization
Authors: Zhang Xiaoyu , Liu Wei , Lou Linhui , Yang Fangchun
Published: February 2022

With the fast progress in perfect information game problems such as AI chess and AI Go, researchers have turned to imperfect information game problems, including Texas Hold’em and Bridge. Bridge is one of the most challenging card games that have significant research value. Bridge playing is divided into two phases: bidding and playing. This paper focuses on bridge bidding and proposes a bridge bidding service framework using deep neural networks, and supports bidding visualization for the first time. The framework consists of two parts: the bidding model (BM) with a multilayer neural network, and a visualization system. The framework predicts not only reasonable bids from the existing bidding system of humans, but also provides intuitive explanations for decisions to enable human–computer information interaction. Experimental results show that this bidding AI outperforms majority of existing systems.
Logic Solver Diagnostics in Safety Instrumented Systems for Oil and Gas Applications
Authors: Catelani Marcantonio , Ciani Lorenzo , Patrizi Gabriele
Published: February 2022

A safety instrumented system (SIS) is a complex unit composed of a set of hardware and software controls which are expressly used in critical process systems. A SIS should be specifically designed to obtain the failsafe state of the monitored plant or maintain safety of the procedure or a process when unacceptable or dangerous conditions occur. This paper focuses on condition monitoring and different diagnostic solutions used in safety instrumented systems, such as limit alarm trips, on-board diagnostics, and logic solver diagnostics. A case study consisting of the design of a safety loop using standard IEC 61508 for a complex safety instrumented system in the oil and gas field is presented in the paper using a diagnostics-oriented approach. The presented methodology aims at reaching the optimal tradeoff between IEC 61508 and the market requirements focusing on the best technological solutions to optimize diagnostics and safety and minimize the system’s response time in case of failure. The results of the application emphasize the importance of an accurate diagnostic strategy on safety instrumented systems for oil and gas plants.
Dangerous Overtaking of Cyclists in Montréal
Authors: Henao Andres , Apparicio Philippe
Published: February 2022

It is largely consented that the bicycle is a sustainable mobility alternative in the city. Despite its many benefits, cycling comprises risks of injury or death. Among others, these risks are a result of unsafe overtaking manoeuvres performed by motorized vehicles against cyclists. This study aims to identify the characteristics of the road network and traffic influencing the lateral distance and duration of overtaking. Using bicycles equipped with distance sensors, GPS, and cameras, four cyclists covered 1689 km in Montréal. Hence, 3591 overtakings were identified with an average distance of 176 cm; 111 overtaking manoeuvres took place at distances less than 1 m, resulting in an unsafe event for every 32 overtakings. On average, the duration of an overtaking was 1.082 s and dangerous overtakings (less than one metre) lasted 0.57 s more than safe overtakings (one metre and over). A generalized additive logit model (GAM) is built to predict the likelihood of a dangerous lateral passing (less than 1 m). The results show that in taking a major route, the presence of parked vehicles and the time required for overtaking significantly increase the probability of experiencing a dangerous overtaking. However, the participant, type of vehicle, or presence of a bike lane have no significant effect. Therefore, the results demonstrate the importance of keeping cyclists isolated from traffic. Furthermore, providing a bike path along parking spaces seems to be a solution that does not enhance cyclist safety.
Use of Augmented Reality for Students with Educational Needs: A Systematic Review (2016–2021)
Authors: Fernández-Batanero José María , Montenegro-Rueda Marta , Fernández-Cerero José
Published: February 2022

In recent years, interest in applying Augmented Reality technology as a teaching/learning resource in education has increased. However, few studies focus on the possibilities and challenges of these tools to support learners with educational needs. In this review, we aggregate the current knowledge of how Augmented Reality technologies are applicable and their impact on the learning of students with educational needs considering the above-mentioned factors. In total, 18 studies indexed in the Scopus and Web of Science databases were analysed. The main findings of this review provide the current state of Augmented Reality research in special education and show positive results in the learning of students with educational needs.
Methodology of Cyclical Research of the Tourist Image of Podkarpackie Province (Poland)—A Proposal of a Solution and Selected Research Results
Authors: Gierczak-Korzeniowska Beata Jolanta , Stopa Mateusz , Szpara Krzysztof
Published: February 2022

The attractiveness to tourists of Podkarpackie Province is related to its natural diversity and rich cultural heritage. The communes located in the southern, Carpathian part of the province are the most attractive tourist destinations. However, the volume of tourist traffic in Podkarpackie Province differs from many other regions in Poland. It is visited by far fewer tourists than might be expected. In order to conduct a more effective tourist policy, it was necessary to acquire knowledge on the structure of tourism in the region and its image. The aim of this article is to present the proprietary methodology of cyclical research of the tourist image of Podkarpackie Province and the key results of the research conducted in 2021. The proposed set of methods and techniques includes: (1) an analysis of strategic documents in terms of the assumed tourist image (Atlas.ti), (2) a verification of search trends for keywords related to the identified associations (Google Trends), and (3) a search of widely available internet resources for spontaneously generated content (Google Search). The obtained results indicate that potential tourists find online information about tourist assets deviating from the planned tourist image presented in the strategic document. First of all, they look for opinions and tips on specific tourist attractions. In the perceptions of average, potential tourists, some sub-regions such as the Low Beskids and the Southern Roztocze are practically unnoticeable. They clearly ‘lose’ with the Bieszczady, which are most associated with the region. Ultimately, the comprehensive image research consists of qualitative and quantitative modules. Only the combination of both will allow for a holistic view of the image of the region.
Status Quo of Households’ Backyard Food Gardens in South Africa: The “Drivers”
Authors: David Oladipo Olalekan , Grobler Wynand
Published: February 2022

South Africa is one of the most food-secured countries at the national level but is food insecure at the household level. The disconnect in the food security at the national and at household level in the economy is a result of high food prices that most households cannot afford. One of the strategies of ameliorating food insecurity at the household level is the practice of backyard food gardens. This study identifies farmland size, land tenure system, agriculture-related assistance to households, location of residence of the household, agricultural training offered to households, and monetary grants for households for agriculture purpose as the determinants of households’ backyard food gardens in South Africa. The study used descriptive (horizontal bar chart) and inferential (Pearson’s chi-square) analyses to evaluate the household-level impacts of farmland size, land tenure system, agriculture-related assistance, location of residence, agricultural training, and monetary grants for agriculture purposes of the backyard food gardens in South Africa. The data for the study were sourced from the Statistics South Africa’s General Household Survey for 2019. The findings revealed that farmland size, land tenure system, agriculture-related assistance to households, location of residence of the household, agricultural training offered to households, and monetary grants for households for agriculture purposes are significant to households’ backyard food gardens in South Africa. It is clear that agriculture-related assistance is welcomed by the households but the spread across all dwelling locations is limited; therefore, there is need to spread agriculture-related assistance to all dwelling areas in South Africa. This will increase the drive towards food production in South Africa.
Site Selection of Co-Working Spaces under the Influence of Multiple Factors: A Case Study in Hangzhou, China
Authors: Wang Weiwu , Liang Jingyi , Niu Jie
Published: February 2022

Co-working spaces (CWSs) have gradually become a new form of spatial economic activity in large cities in China. This not only innovates the traditional office model, but also helps to realize the efficient utilization of office buildings and the sustainable development of office spaces. Taking Hangzhou as a case study, this paper uses big data analysis technologies including Python and ArcGIS to reveal the distribution characteristics of CWSs. From the perspectives of traffic accessibility, business atmosphere, innovation environment, living convenience, and rental cost, we innovatively constructed an indicator system of factors affecting site selection of CWSs. We then conducted an empirical study to reveal the influence mechanism behind different factors. Our conclusions are as follows: (1) CWSs in Hangzhou generally present a multi-center distribution pattern; (2) based on the different degrees of dependence of the target customer groups on resources such as commerce, capital, and information, the factor that has the most significant impact on the site of CWSs is the regional innovation environment, and its weight is 0.3941. The order of importance of other influencing factors is the convenience of life (0.3147), business atmosphere (0.1352), and traffic conditions (0.1171). The cost of rent has the most negligible impact on site selection, and its weight is only 0.0195. We hope that the research can be used to provide a scientific basis for the rational planning and development guidance of CWSs.
Predictors of Hotel Clients’ Satisfaction in the Cape Verde Islands
Authors: Furtado Ariana , Ramos Ricardo F. , Maia Bruno , Costa Joana Martinho
Published: February 2022

Tourism has been fundamental for countries’ economic development, and Africa is the destination with the biggest tourism growth potential. Using 1414 travelers’ online reviews collected from TripAdvisor, the present work aims to understand which variables predict the satisfaction of Cape Verde’s hotel clients. Satisfaction was analyzed using sentiment analysis and ANOVA to predict the effect of the gathered variables on clients’ satisfaction. Results indicate that 90% of the clients revealed positive satisfaction and that nationality, date of stay, and previous traveler experiences affect satisfaction. Contrarily to our predictions, there is no statistically significant evidence that gender influences satisfaction. The findings of this study will help hotel marketing managers to align their strategies accordingly and meet their clients’ expectations.
How Hybrid Organizations Adopt Circular Economy Models to Foster Sustainable Development
Authors: Zaccone Maria Cristina , Santhià Cristina , Bosone Martina
Published: February 2022

Relying on the hybrid organization literature and the circular economy literature, our study aims at theorizing how hybrid organizations adopt circular economy business models to contribute to sustainable development. Through six in-depth interviews with social entrepreneurs located in different regions of Italy and operating in different industries, we theorize that four aspects are unique to hybrid organizing that adopt a circular economy business model. We find that hybrid organizations foster sustainable development thanks to their ability to develop and maintain relationships with the surrounding organizational realities. Additionally, we find that hybrid organizations contribute to sustainable development thanks to their motivation to guard and watch over the natural environment and human beings. In addition, we find that hybrid organizations foster sustainability by educating, spreading knowledge, and ennobling the human spirit. We therefore present a conceptual model that shows how hybrid organizations that adopt circular economy models contribute to sustainable development. In sum, our findings expand on previous literature, add new knowledge, and integrate fields of research that usually develop in silos.
Does Intellectual Capital Affect Financial Leverage of Chinese Agricultural Companies? Exploring the Role of Firm Profitability
Authors: Jin Guangchun , Xu Jian
Published: February 2022

The objective of this paper is to examine the relationship between intellectual capital (IC) and firms’ financial leverage by exploring whether firm profitability mediates this relationship, using a dataset of Chinese agricultural listed companies during the period of 2014–2020. Financial leverage is measured by the debt-to-asset ratio, and IC is measured via the modified value-added intellectual coefficient (MVAIC) model. The results reveal that financial leverage is lower in firms with higher levels of IC, and IC positively affects firm profitability. In addition, firm profitability partially mediates the relationship between IC and financial leverage. When MVAIC is disaggregated into its four components, firm profitability has a partially mediating effect on the relationship between physical and human capitals and financial leverage. This paper might provide corporate managers with a clear understanding of IC’s impact on firm indebtedness.
Creating a Nationwide Composite Hazard Index Using Empirically Based Threat Assessment Approaches Applied to Open Geospatial Data
Authors: Emrich Christopher T. , Zhou Yao , Aksha Sanam K. , Longenecker Herbert E.
Published: February 2022

The US is exposed to myriad natural hazards causing USD billions in damages and thousands of fatalities each year. Significant population and economic growth during the last several decades have resulted in more people residing in hazardous places. However, consistent national-scale hazard threat assessment techniques reflecting the state of hazard knowledge are not readily available for application in risk and vulnerability assessments. Mapping natural hazard threats is the crucial first step in identifying and implementing threat reduction or mitigation strategies. In this study, we demonstrate applied GIS approaches for creating and synthesizing US hazard threat extents using publicly available data for 15 natural hazards. Individually mapping each threat enables empirically supported intervention development and the building of a Composite Hazard Index (CHI). Summarizing the hazard frequencies provides a novel representation of US hazardousness. Implementing cluster analysis to regionalize US counties based on their underlying hazard characteristics offers insight into hazard threats’ spatial (non-political) natures. The results indicate that the southeast, central plains, and coastal regions of the northeast had high hazard occurrence scores, whereas more moderate hazard scores were observed west of the continental divide. Furthermore, while no place is safe from hazard occurrence, identifying each region’s distinct “hazardousness” can support individualized risk assessments and mitigation intervention development.
Advocating Urban Transition: A Qualitative Review of Institutional and Grassroots Initiatives in Shaping Climate-Aware Cities
Authors: Boulanger Saveria Olga Murielle , Massari Martina
Published: February 2022

Climate change and its challenges have long been incorporated into the policy-making process. Advocacy actions urge to strengthen the socio-ecological resilience through engagement with stakeholders, feedback recollection, and testing of solutions. Several initiatives have been born to boost cities’ actions toward climate change mitigation and adaptation. Institutional coordinated actions such as transnational municipal networks (TMNs) and non-institutional, grassroots movements for climate action, are among them. The study focuses on four TMNs and two grassroots movements, which have an impact on the European and/or worldwide contexts. They are investigated qualitatively, reflecting on the roles and contributions to climate change that they provide both alone and together. The research questions focus on the instruments/elements/factors that they put in place to support the transition, the key messages, and how these are conferred to their key targets. The initiatives have been investigated in both the grey and scientific literature. The main results show that grassroots movements for climate action and TMNs have the potential to better support cities in their climate transition. However, local governments are urged to take advantage of both initiatives’ ability to develop networks of support, innovation and a sense of belonging. In conclusion, the research states that the two initiatives should be effectively connected and integrated with a complementary role concerning planning actions.
A Comparison of Biosolids Production and System Efficiency between Activated Sludge, Moving Bed Biofilm Reactor, and Sequencing Batch Moving Bed Biofilm Reactor in the Dairy Wastewater Treatment
Authors: Boavida-Dias Rui , Silva João R. , Santos Andreia D. , Martins Rui C. , Castro Luis M. , Quinta-Ferreira Rosa M.
Published: February 2022

Dairy industry wastewater is rich in organic content, presenting a high biodegradability, and therefore biological treatments are widely employed. This study aimed to evaluate biosolids production in three systems: activated sludge (AS), movingbed biofilm reactor (MBBR), and sequencingbatch movingbed biofilm reactor (SBMBBR). Simulated dairy wastewater was used at different organic load rates (OLRs): 1.22, 2.87, and 5.44 gCOD L−1d−1. Besides biosolids production, COD, total carbon (TC), and total nitrogen (TN) removal efficiency was evaluated. Biosolids production was measured in the mixed liquor, carrier-adhered biomass, treated wastewater, and surplus sludge. The operational conditions were kept similar for the three systems, with a carrier filling ratio of 50% for MBBR and SBMBBR. The SBMBBR proved to have better performance in the removal efficiencies of COD, TC, and TN for all OLRs studied. The MBBR presented a similar COD and TC removal efficiency as the SBBR for the two highest OLRs (2.87 and 5.44 gCOD L−1d−1). Concerning biosolids production, the MBBR system produced less biomass and delivered the lowest amount of adhered biomass inside the carriers. The AS treatment generated the highest amount of sludge and offered the worst treatment capability for all OLRs evaluated.
The Innovative Human Resource Management Framework: Impact of Green Competencies on Organisational Performance
Authors: Mirčetić Vuk , Ivanović Tatjana , Knežević Snežana , Arsić Vesna Bogojević , Obradović Tijana , Karabašević Darjan , Vukotić Svetlana , Brzaković Tomislav , Adamović Miljan , Milojević Stefan , Milašinović Marko , Mitrović Aleksandra , Špiler Marko
Published: February 2022

Scholars have emphasised the importance of green settings in today’s business paradigms. Studies on green behaviour have produced a plethora of noteworthy discoveries, whether focused on financial success, individual capabilities, or development. However, despite significant growth in interest in green business practices, the relationship between individuals’ willingness and green competencies has received little attention. This article used the customised green competencies conceptual model to investigate how green skills influence organisational performance and their relationship with the willingness moment. This article developed an innovative human resource management approach to address these difficulties. A questionnaire was used to perform empirical statistical research with 516 respondents from Serbian universities. Different mathematical and statistical methodologies were used to analyse the results. The findings corroborate the suggested theoretical model, and they suggest that green competencies will influence people’s willingness to participate in green activities. This article gives new information on human behaviour and organisational effectiveness in a green atmosphere. It includes managerial and practical consequences and recommendations for businesses looking to improve their social responsibility and environmental sustainability.
Is Ecotourism an Opportunity for Large Wild Animals to Thrive?
Authors: Ivanova Svetlana , Prosekov Alexander , Kaledin Anatoly
Published: February 2022

The development of ecotourism involving wild animals in Russia is overlooked despite the fact that the country’s territory is significant not only in terms of area but also in terms of the diversity of its flora and fauna. A significant part of Russia’s territory has a low population density, especially beyond the Ural ridge. It retains its natural primeval nature, which can contribute to the development of ecotourism. Initial attempts have been made to develop this, mainly in the European part (Tatarstan, Murmansk Region, the Baltic Sea, Baikal, Altai), but the commercial use of wild animals within ecotourism programs, including the ones in Siberia and the Far East, has not been discussed. This work focuses on the basics of launching ecotourism in the industrial region of Siberia (Kuzbass, Russia) as part of the Alcesalces conservation program.