bluuebunny/crossref_metadata_2025 huggingface dataset:
Cooperative Reception of Multiple Satellite Downlinks
Authors: Al-Anbagi Haidar N. , Vertat Ivo
Published: April 2022

Popular small satellites host individual sensors or sensor networks in space but require ground stations with directional antennas on rotators to download sensors’ data. Such ground stations can establish a single downlink communication with only one satellite at a time with high vulnerability to system outages when experiencing severe channel impairments or steering engine failures. To contribute to the area of improving the reception quality of small satellites signals, this paper presents a simple receive diversity scheme with proposed processing algorithms to virtually combine satellite downlink streams collected from multiple omnidirectional receivers. These algorithms process multiple received versions of the same signal from multiple geographically separated receiving sites to be combined in one virtual ground station. This virtual ground station helps detect the intended signal more reliably based only on a network of simple and cooperating software-defined radio receivers with omnidirectional antennas. The suggested receive diversity combining techniques can provide significant system performance improvement if compared to the performance of each individual receiving site. In addition, the probability of system outages is decreased even if one or more sites experience severe impairment consequences. Simulation results showed that the bit error rate (BER) of the combined stream is lower than the BER of the best quality receiving site if considered alone. Moreover, virtual ground stations with cooperative omnidirectional reception at geographically separated receivers also allow data to be received from multiple satellites in the same frequency band simultaneously, as software-defined receivers can digitize a wider portion of the frequency band. This can be a significant conceptual advantage as the number of small satellites transmitting data grows, and it is reasonable to avoid the corresponding necessary increase in the number of fully equipped ground stations with rotators.
Evaluation of the Effectiveness of Active and Passive Safety Measures in Preventing Ship–Bridge Collision
Authors: Ma Wenqing , Zhu Yini , Grifoll Manel , Liu Guiyun , Zheng Pengjun
Published: April 2022

The risk of ship–bridge collisions should be evaluated using advanced models to consider different anti-collision and bridge-protection measures. This study aimed to propose a method to evaluate the effectiveness of active and passive safety measures in preventing ship–bridge collision. A novel ship–bridge collision probability formulation taking into consideration different safety measures was proposed. The model was applied at Jintang Bridge in China where the surrounding vessel traffic is ultra-crowded. We calculated the collision probability between the bridge and passing traffic using automatic identification system (AIS) data, Monte Carlo simulation, and Bayesian networks. Results under four different safety measures (i.e., active measures, passive measures, both measures and none) were analyzed and compared. The analysis concluded that both active and passive safety measures are effective in reducing the ship–bridge collision probability. Active measures, if deployed properly, can provide protection at an equivalent level than passive measures against collision risks. However, passive measures, such as setting arresting cables, are necessary in cases where the response time of the active measures is long. The proposed method and the results obtained from the case study may be useful for robust and systematic effectiveness evaluation of safety measures in other cases worldwide.
Multipath-Assisted Radio Sensing and State Detection for the Connected Aircraft Cabin
Authors: Ninnemann Jonas , Schwarzbach Paul , Schultz Michael , Michler Oliver
Published: April 2022

Efficiency and reliable turnaround time are core features of modern aircraft transportation and key to its future sustainability. Given the connected aircraft cabin, the deployment of digitized and interconnected sensors, devices and passengers provides comprehensive state detection within the cabin. More specifically, passenger localization and occupancy detection can be monitored using location-aware communication systems, also known as wireless sensor networks. These multi-purpose communication systems serve a variety of capabilities, ranging from passenger convenience communication services, over crew member devices, to maintenance planning. In addition, radio-based sensing enables an efficient sensory basis for state monitoring; e.g., passive seat occupancy detection. Within the scope of the connected aircraft cabin, this article presents a multipath-assisted radio sensing (MARS) approach using the propagation information of transmitted signals, which are provided by the channel impulse response (CIR) of the wireless communication channel. By performing a geometrical mapping of the CIR, reflection sources are revealed, and the occupancy state can be derived. For this task, both probabilistic filtering and k-nearest neighbor classification are discussed. In order to evaluate the proposed methods, passenger occupancy detection and state detection for the future automation of passenger safety announcements and checks are addressed. Therefore, experimental measurements are performed using commercially available wideband communication devices, both in close to ideal conditions in an RF anechoic chamber and a cabin seat mockup. In both environments, a reliable radio sensing state detection was achieved. In conclusion, this paper provides a basis for the future integration of energy and spectrally efficient joint communication and sensing radio systems within the connected aircraft cabin.
Impact of Gender and Feature Set on Machine-Learning-Based Prediction of Lower-Limb Overuse Injuries Using a Single Trunk-Mounted Accelerometer
Authors: Bogaert Sieglinde , Davis Jesse , Van Rossom Sam , Vanwanseele Benedicte
Published: April 2022

Even though practicing sports has great health benefits, it also entails a risk of developing overuse injuries, which can elicit a negative impact on physical, mental, and financial health. Being able to predict the risk of an overuse injury arising is of widespread interest because this may play a vital role in preventing its occurrence. In this paper, we present a machine learning model trained to predict the occurrence of a lower-limb overuse injury (LLOI). This model was trained and evaluated using data from a three-dimensional accelerometer on the lower back, collected during a Cooper test performed by 161 first-year undergraduate students of a movement science program. In this study, gender-specific models performed better than mixed-gender models. The estimated area under the receiving operating characteristic curve of the best-performing male- and female-specific models, trained according to the presented approach, was, respectively, 0.615 and 0.645. In addition, the best-performing models were achieved by combining statistical and sports-specific features. Overall, the results demonstrated that a machine learning injury prediction model is a promising, yet challenging approach.
Image Processing Algorithm for In Situ Monitoring Fiber Laser Remote Cutting by a High-Speed Camera
Authors: Schleier Max , Adelmann Benedikt , Esen Cemal , Hellmann Ralf
Published: April 2022

We present an in situ process monitoring approach for remote fiber laser cutting, which is based on evaluating images from a high-speed camera. A specifically designed image processing algorithm allows the distinction between complete and incomplete cuts by analyzing spectral and geometric information of the melt pool from the captured images of the high-speed camera. The camera-based monitoring system itself is fit to a conventional laser deflection unit for use with high-power fiber lasers, with the optical detection path being coaxially aligned to the incident laser. Without external illumination, the radiation of the melt from the process zone is recorded in the visible spectral range from the top view and spatially and temporally resolved. The melt pool size and emitted sparks are evaluated in dependence of machining parameters such as feed rate, cycles, and focus position during cutting electrical sheets.
Gridless Underdetermined Direction of Arrival Estimation in Sparse Circular Array Using Inverse Beamspace Transformation
Authors: Tian Ye , Huang Yonghui , Zhang Xiaoxu , Tang Xiaogang
Published: April 2022

Underdetermined DOA estimation, which means estimating more sources than sensors, is a challenging problem in the array signal processing community. This paper proposes a novel algorithm that extends the underdetermined DOA estimation in a Sparse Circular Array (SCA). We formulate this problem as a matrix completion problem. Meanwhile, we propose an inverse beamspace transformation combined with the Gridless SPICE (GLS) algorithm to complete the covariance matrix sampled by SCA. The DOAs are then obtained by solving a polynomial equation with using the Root-MUSIC algorithm. The proposed algorithm is named GSCA. Monte-Carlo simulations are performed to evaluate the GSCA algorithm, the spatial spectrum plots and RMSE curves demonstrated that the GSCA algorithm can give reasonable results of underdetermined DOA estimation in SCA. Meanwhile, the performance of the algorithm under various configurations of SCA is also evaluated. Numerical results indicated that the GSCA algorithm can provide access to solve the DOA estimation problem in Uniform Circular Array (UCA) when random sensor failures occur.
Method for Film Thickness Mapping with an Astigmatic Optical Profilometer
Authors: Liao Hsien-Shun , Cheng Shih-Han , Hwu En-Te
Published: April 2022

An astigmatic optical profilometer is a precision instrument with advantages such as high resolution, high bandwidth, a compact size, and low cost. However, current astigmatic optical profilometers measure only surface morphology, and their potential for capturing subsurface information remains underutilized. In this study, we developed a method for measuring the thickness of transparent thin films with an astigmatic optical profilometer. Experimental results demonstrate that the thickness of transparent films tens of micrometers thick can be accurately measured. The maximum thickness measurable through our system is approximately 100 μm, which may be increased to 1.2 mm through the use of a scanner with a greater travel range. A coupling problem occurs for films <25 μm in thickness. However, to solve this problem, we devised a decoupling method, which was experimentally implemented to successfully measure a 18-μm-thick film. Moreover, the ability to obtain 3D images, including of both the upper and lower surfaces, was demonstrated.
Cascade Brillouin Lasing in a Tellurite-Glass Microsphere Resonator with Whispering Gallery Modes
Authors: Anashkina Elena A. , Marisova Maria P. , Dorofeev Vitaly V. , Andrianov Alexey V.
Published: April 2022

Brillouin microlasers based on microresonators with whispering gallery modes (WGMs) are in high demand for different applications including sensing and biosensing. We fabricated a microsphere resonator with WGMs from a synthesized high-quality tellurite glass with record high Q-factors for tellurite microresonators (Q ≥ 2.5 × 107), a high Brillouin gain coefficient (compared to standard materials, e.g., silica glasses), and a Brillouin frequency shift of 9 ± 0.5 GHz. The high density of excited resonance modes and high loaded Q-factors allowed us to achieve experimentally cascade Stokes-Brillouin lasing up to the 4th order inclusive. The experimental results are supported by the results of the theoretical analysis. We also theoretically obtained the dependences of the output Brillouin powers on the pump power and found the pump-power thresholds for the first five Brillouin orders at different values of pump frequency detuning and Q-factors, and showed a significant effect of these parameters on the processes under consideration.
Chaotic Search-and-Rescue-Optimization-Based Multi-Hop Data Transmission Protocol for Underwater Wireless Sensor Networks
Authors: Anuradha Durairaj , Subramani Neelakandan , Khalaf Osamah Ibrahim , Alotaibi Youseef , Alghamdi Saleh , Rajagopal Manjula
Published: April 2022

Underwater wireless sensor networks (UWSNs) have applications in several fields, such as disaster management, underwater navigation, and environment monitoring. Since the nodes in UWSNs are restricted to inbuilt batteries, the effective utilization of available energy becomes essential. Clustering and routing approaches can be employed as energy-efficient solutions for UWSNs. However, the cluster-based routing techniques developed for conventional wireless networks cannot be employed for a UWSN because of the low bandwidth, spread stay, underwater current, and error probability. To resolve these issues, this article introduces a novel chaotic search-and-rescue-optimization-based multi-hop data transmission (CSRO-MHDT) protocol for UWSNs. When using the CSRO-MHDT technique, cluster headers (CHs) are selected and clusters are prearranged, rendering a range of features, including remaining energy, intracluster distance, and intercluster detachment. Additionally, the chaotic search and rescue optimization (CSRO) algorithm is discussed, which is created by incorporating chaotic notions into the classic search and rescue optimization (SRO) algorithm. In addition, the CSRO-MHDT approach calculates a fitness function that takes residual energy, distance, and node degree into account, among other factors. A distinctive aspect of the paper is demonstrated by the development of the CSRO algorithm for route optimization, which was developed in-house. To validate the success of the CSRO-MHDT method, a sequence of tests were carried out, and the results showed the CSRO-MHDT method to have a packet delivery ratio (PDR) of 88%, whereas the energy-efficient clustering routing protocol (EECRP), the fuzzy C-means and moth–flame optimization (FCMMFO), the fuzzy scheme and particle swarm optimization (FBCPSO), the energy-efficient grid routing based on 3D cubes (EGRC), and the low-energy adaptive clustering hierarchy based on expected residual energy (LEACH-ERE) methods have reached lesser PDRs of 83%, 81%, 78%, 77%, and 75%, respectively, for 1000 rounds. The CSRO-MHDT technique resulted in higher values of number of packets received (NPR) under all rounds. For instance, with 50 rounds, the CSRO-MHDT technique attained a higher NPR of 3792%.
A User-Centered Approach to the Application of BIM in Smart Working Environments
Authors: Park Da Yeon , Choi Jungsik , Ryu Soyeong , Kim Mi Jeong
Published: April 2022

Considerable research has been performed on smart working environments in the architecture, engineering, and construction industry, with building information modeling considered as a critical element for implementing intelligent working systems. Although much software has been developed, a lack of understanding inhibits a user-centered approach to the application of building information modeling in architectural design offices. This study focuses on usability factors for the development of software and proposes a direction for the adoption of building information modeling in architectural design offices. This study adopts a persona method that focuses on user experience, starting with a survey conducted in two large domestic architectural offices. For developing the persona scenarios, this study provides a conceptual framework of usability, identifies user demands, and characterizes user experience. Four representative personas were developed for the representative types of users in smart working environments. The persona scenarios provide a basis that directly reflects user needs and experiences regarding the use of building information modeling software in architectural design offices. Two implications of the application of building information modeling are proposed based on the scenarios: a user-friendly working environment for smart workflows and a customized training program focusing on user experience for the use of building information modeling software.
3D Global Localization in the Underground Mine Environment Using Mobile LiDAR Mapping and Point Cloud Registration
Authors: Baek Jieun , Park Junhyeok , Cho Seongjun , Lee Changwon
Published: April 2022

This study proposes a 3D global localization method that implements mobile LiDAR mapping and point cloud registration to recognize the locations of objects in an underground mine. An initial global point cloud map was built for an entire underground mine area using mobile LiDAR; a local LiDAR scan (local point cloud) was generated at the point where underground positioning was required. We calculated fast point feature histogram (FPFH) descriptors for the global and local point clouds to extract point features. The match areas between the global and the local point clouds were searched and aligned using random sample consensus (RANSAC) and iterative closest point (ICP) registration. The object’s location on the global coordinate system was measured using the LiDAR sensor trajectory. Field experiments were performed at the Gwan-in underground mine using three mobile LiDAR systems. The local point cloud dataset formed for the six areas of the underground mine precisely matched the global point cloud, with a low average error of approximately 0.13 m, regardless of the type of mobile LiDAR system used. In addition, the LiDAR senor trajectory was aligned on the global coordinate system to confirm the change in the dynamic object’s position over time.
Multi-Step Attack Detection Based on Pre-Trained Hidden Markov Models
Authors: Zhang Xu , Wu Ting , Zheng Qiuhua , Zhai Liang , Hu Haizhong , Yin Weihao , Zeng Yingpei , Cheng Chuanhui
Published: April 2022

Currently, hidden Markov-based multi-step attack detection models are mainly trained using the unsupervised Baum–Welch algorithm. The Baum–Welch algorithm is sensitive to the initial values of model parameters. However, its training uses random or average parameter initialization methods, which frequently results in the model training into a local optimum, thus, making the model unable to fit the alert logs well and thereby reducing the detection effectiveness of the model. To solve this issue, we propose a pre-training method for multi-step attack detection models based on the high semantic similarity of alerts in the same attack phase. The method first clusters the alerts based on their semantic information and pre-classifies the attack phase to which each alert belongs. Then, the distance of the alert vector to each attack stage is converted into the probability of generating alerts in each attack stage, replacing the initial value of Baum–Welch. The effectiveness of the proposed method is evaluated using the DARPA 2000 dataset, DEFCON21 CTF dataset, and ISCXIDS 2012 dataset. The experimental results show that the hidden Markov multi-step attack detection method based on pre-training of the proposed model parameters had higher detection accuracy than the Baum–Welch-based, K-means-based, and transfer learning differential evolution-based hidden Markov multi-step attack detection methods.
The Usage of ANN for Regression Analysis in Visible Light Positioning Systems
Authors: Chaudhary Neha , Younus Othman Isam , Alves Luis Nero , Ghassemlooy Zabih , Zvanovec Stanislav
Published: April 2022

In this paper, we study the design aspects of an indoor visible light positioning (VLP) system that uses an artificial neural network (ANN) for positioning estimation by considering a multipath channel. Previous results usually rely on the simplistic line of sight model with limited validity. The study considers the influence of noise as a performance indicator for the comparison between different design approaches. Three different ANN algorithms are considered, including Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, to minimize the positioning error (εp) in the VLP system. The ANN design is optimized based on the number of neurons in the hidden layers, the number of training epochs, and the size of the training set. It is shown that, the ANN with Bayesian regularization outperforms the traditional received signal strength (RSS) technique using the non-linear least square estimation for all values of signal to noise ratio (SNR). Furthermore, in the inner region, which includes the area of the receiving plane within the transmitters, the positioning accuracy is improved by 43, 55, and 50% for the SNR of 10, 20, and 30 dB, respectively. In the outer region, which is the remaining area within the room, the positioning accuracy is improved by 57, 32, and 6% for the SNR of 10, 20, and 30 dB, respectively. Moreover, we also analyze the impact of different training dataset sizes in ANN, and we show that it is possible to achieve a minimum εp of 2 cm for 30 dB of SNR using a random selection scheme. Finally, it is observed that εp is low even for lower values of SNR, i.e., εp values are 2, 11, and 44 cm for the SNR of 30, 20, and 10 dB, respectively.
Visualization of Customized Convolutional Neural Network for Natural Language Recognition
Authors: Singh Tajinder , Gupta Sheifali , Garg Meenu , Gupta Deepali , Alharbi Abdullah , Alyami Hashem , Anand Divya , Ortega-Mansilla Arturo , Goyal Nitin
Published: April 2022

For analytical approach-based word recognition techniques, the task of segmenting the word into individual characters is a big challenge, specifically for cursive handwriting. For this, a holistic approach can be a better option, wherein the entire word is passed to an appropriate recognizer. Gurumukhi script is a complex script for which a holistic approach can be proposed for offline handwritten word recognition. In this paper, the authors propose a Convolutional Neural Network-based architecture for recognition of the Gurumukhi month names. The architecture is designed with five convolutional layers and three pooling layers. The authors also prepared a dataset of 24,000 images, each with a size of 50 × 50. The dataset was collected from 500 distinct writers of different age groups and professions. The proposed method achieved training and validation accuracies of about 97.03% and 99.50%, respectively for the proposed dataset.
Accuracy and Precision of Agents Orientation in an Indoor Positioning System Using Multiple Infrastructure Lighting Spotlights and a PSD Sensor
Authors: De-La-Llana-Calvo Álvaro , Lázaro-Galilea José Luis , Alcázar-Fernández Aitor , Gardel-Vicente Alfredo , Bravo-Muñoz Ignacio , Iamnitchi Andreea
Published: April 2022

In indoor localization there are applications in which the orientation of the agent to be located is as important as knowing the position. In this paper we present the results of the orientation estimation from a local positioning system based on position-sensitive device (PSD) sensors and the visible light emitted from the illumination of the room in which it is located. The orientation estimation will require that the PSD sensor receives signal from either 2 or 4 light sources simultaneously. As will be shown in the article, the error determining the rotation angle of the agent with the on-board sensor is less than 0.2 degrees for two emitters. On the other hand, by using 4 light sources the three Euler rotation angles are determined, with mean errors in the measurements smaller than 0.35° for the x- and y-axis and 0.16° for the z-axis. The accuracy of the measurement has been evaluated experimentally in a 2.5 m-high ceiling room over an area of 2.2 m2 using geodetic measurement tools to establish the reference ground truth values.
Low-Dose CT Image Post-Processing Based on Learn-Type Sparse Transform
Authors: Zheng Wenfeng , Yang Bo , Xiao Ye , Tian Jiawei , Liu Shan , Yin Lirong
Published: April 2022

As a detection method, X-ray Computed Tomography (CT) technology has the advantages of clear imaging, short detection time, and low detection cost. This makes it more widely used in clinical disease screening, detection, and disease tracking. This study exploits the ability of sparse representation to learn sparse transformations of information and combines it with image decomposition theory. The structural information of low-dose CT images is separated from noise and artifact information, and the sparse expression of sparse transformation is used to improve the imaging effect. In this paper, two different learned sparse transformations are used. The first covers more organizational information about the scanned object. The other can cover more noise artifacts. Both methods can improve the ability to learn sparse transformations to express various image information. Experimental results show that the algorithm is effective.
Using Connected Vehicle Trajectory Data to Evaluate the Impact of Automated Work Zone Speed Enforcement
Authors: Mathew Jijo K. , Li Howell , Landvater Hannah , Bullock Darcy M.
Published: April 2022

Work zone safety is a high priority for transportation agencies across the United States. Enforcing speed compliance in work zones is an important factor for reducing the frequency and severity of crashes. This paper uses connected vehicle trajectory data to evaluate the impact of automated work zone speed enforcement on three work zones in Pennsylvania and two work zones in Indiana. Analysis was conducted on more than 300 million datapoints from over 71 billion records between April and August 2021. Speed distribution and speed compliance studies with and without automated enforcement were conducted along every tenth of a mile, and the results found that overall speed compliance inside the work zones increased with the presence of enforcement. In the three Pennsylvania work zones analyzed, the proportions of vehicles travelling within the allowable 11 mph tolerance were 63%, 75% and 84%. In contrast, in Indiana, a state with no automated enforcement, the proportions of vehicles travelling within the same 11 mph tolerance were found to be 25% and 50%. Shorter work zones (less than 3 miles) were associated with better compliance than longer work zones. Spatial analysis also found that speeds rebounded within 1–2 miles after leaving the enforcement location.
Evaluation of Creep Behavior of Soft Soils by Utilizing Multisensor Data Combined with Machine Learning
Authors: Kovačević Meho Saša , Bačić Mario , Librić Lovorka , Gavin Kenneth
Published: April 2022

To identify the unknown values of the parameters of Burger’s constitutive law, commonly used for the evaluation of the creep behavior of the soft soils, this paper demonstrates a procedure relying on the data obtained from multiple sensors, where each sensor is used to its best advantage. The geophysical, geotechnical, and unmanned aerial vehicle data are used for the development of a numerical model whose results feed into the custom-architecture neural network, which then provides information about on the complex relationships between the creep characteristics and soil displacements. By utilizing InSAR and GPS monitoring data, particle swarm algorithm identifies the most probable set of Burger’s creep parameters, eventually providing a reliable estimation of the long-term behavior of soft soils. The validation of methodology is conducted for the Oostmolendijk embankment in the Netherlands, constructed on the soft clay and peat layers. The validation results show that the application of the proposed methodology, which relies on multisensor data, can overcome the high cost and long duration issues of laboratory tests for the determination of the creep parameters and can provide reliable estimates of the long-term behavior of geotechnical structures constructed on soft soils.
Should We Cut the Cards? Assessing the Influence of “Take 5” Pre-Task Risk Assessments on Safety
Authors: Havinga Jop , Shire Mohammed Ibrahim , Rae Andrew
Published: April 2022

This paper describes and analyses a particular safety practice, the written pre-task risk assessment, commonly referred to as a “Take 5”. The paper draws on data from a trial at a major infrastructure construction project. We conducted interviews and field observations during alternating periods of enforced Take 5 usage, optional Take 5 usage, and banned Take 5 usage. These data, along with evidence from other field studies, were analysed using the method of Functional Interrogation. We found no evidence to support any of the purported mechanisms by which Take 5 might be effective in reducing the risk of workplace accidents. Take 5 does not improve the planning of work, enhance worker heedfulness while conducting work, educate workers about hazards, or assist with organisational awareness and management of hazards. Whilst some workers believe that Take 5 may sometimes be effective, this belief is subject to the “Not for Me” effect, where Take 5 is always believed to be helpful for someone else, at some other time. The adoption and use of Take 5 is most likely to be an adaptive response by individuals and organisations to existing structural pressures. Take 5 provides a social defence, creating an auditable trail of safety work that may reduce anxiety in the present, and deflect blame in the future. Take 5 also serves a signalling function, allowing workers and companies to appear diligent about safety.
Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy
Authors: Guido Giuseppe , Shaffiee Haghshenas Sina , Shaffiee Haghshenas Sami , Vitale Alessandro , Astarita Vittorio , Park Yongjin , Geem Zong Woo
Published: April 2022

The evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, the evaluation and analysis of important contributing factors affecting the number of vehicles involved in crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms, including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA), are employed. Hence, the number of vehicles involved in an accident is considered to be the output, and the seven factors affecting transport safety, including Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS), and Annual average daily traffic (AADT) of rural roads in Cosenza, southern Italy, are selected as the inputs. In this study, 564 data sets from rural areas were investigated, and the relevant, effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation in rural areas. The results obtained demonstrated that the “Type of accident” has the highest level and “Location” has the lowest importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.
Outline and Impact of Firms Related to the Third Industrial Revolution: Investigation through Big Data
Authors: Belarouci Matthieu
Published: April 2022

This article is focusing on the dynamism of the Third Industrial Revolution (TIR) in the region Hauts-de-France between 2013 and 2018. Region Hauts-de-France has been a forerunner in setting up a proactive policy in favour of the TIR. It provides a relevant and suitable context for the identification of TIR activities. We assess the job dynamism of the TIR through the implementation of big data methods for the identification of the firms involved in the TIR activities and the collection of firm microlevel data. We provide evidence of the strong dynamism of the TIR activities in a context of weak regional dynamics. We show that the growth in employment arises mainly from renewable energies, positive energy buildings, circular economy and energy efficiency. Future researches are encouraged to investigate the quality of employment and to question the distinctive characteristics of the firms involved in TIR.
‘Rorting the System’: Police Detectives, Diversity, and Workplace Advantage
Authors: Miles-Johnson Toby , Linklater Kate
Published: April 2022

Internal workplace practices and policies in policing are based on a notion of fairness and equal opportunity. Yet police organizations are frequently criticized for discriminatory policing practices, unfair and biased workplace practices, and poor interpersonal treatment of officers. Whilst there is a wide body of research examining diversity in relation to external police practices, there is a lack of knowledge regarding diversity and internal workplace practices; particularly from the perspective of police detectives who often have more substantial policing experience and longer employment histories than other non-commissioned officers. Contributing new findings to the extant policing literature, this research analyzes data collected from interviews with twenty police detectives working in one of the largest Australian police organizations. It suggests that police detectives in this study have negative perceptions of diversity, and associate diversity with unfair advantages in the workplace. In Australian culture, the phrase ‘rorting the system’ is an informal expression used to describe individuals or groups of people who take unfair advantage of a public service or workplace policy to change their circumstances. The findings suggest that detectives in this study believe diversity enables some officers to take advantage of workplace policy and ‘rort’ the system.
Exploring Operational Procedures to Assess Ecosystem Services at Farm Level, including the Role of Soil Health
Authors: Bouma Johan , de Haan Janjo , Dekkers Maria-Franca S.
Published: April 2022

Reaching the land-related UN Sustainable Development Goals (SDGs) and similar goals articulated by the EU Green Deal (GD) by 2030 presents a major challenge and requires a pragmatic approach focused on joint learning by land users (mostly farmers), researchers and other stakeholders in “Living Labs” and system experiments at experimental farms of research organizations. Defining specific indicators and thresholds for ecosystem services in line with land-related SDGs is crucial to establish “Lighthouses” that can act as inspiring examples if they meet the various thresholds. This exploratory paper discusses indicators and thresholds for an arable farm operating on marine, calcareous light clay soils in the Netherlands. Studies of a system experiment are used to discuss and test operational methodology to be widely applied when characterizing many “Living Labs” in future, as planned by the European Union. The important role of soils in contributing to ecosystem services is discussed in terms of soil health. Recommendations are made for innovative methodology to be associated with all land-related SDGs. Satisfying the thresholds of ecosystem services, which will vary by soil type, region and farm type, can be the basis for farm subsidies, such as the Common Agricultural Policy (CAP). Research on Living Labs and in system experiments has to be judged by different criteria than those associated with traditional linear research. The important contributions of soils to achieve ecosystem services are framed in terms of soil health and are the most effective way to promote soil science in a by now widely desired inter- and transdisciplinary context.
Training Monitoring in Sports: It Is Time to Embrace Cognitive Demand
Authors: Perrey Stéphane
Published: April 2022

Appropriate training burden monitoring is still a challenge for the support staff, athletes, and coaches. Extensive research has been done in recent years that proposes several external and internal indicators. Among all measurements, the importance of cognitive factors has been indicated but has never been really considered in the training monitoring process. While there is strong evidence supporting the use of cognitive demand indicators in cognitive neuroscience, their importance in training monitoring for multiple sports settings must be better emphasized. The aims of this scoping review are to (1) provide an overview of the cognitive demand concept beside the physical demand in training; (2) highlight the current methods for assessing cognitive demand in an applied setting to sports in part through a neuroergonomics approach; (3) show how cognitive demand metrics can be exploited and applied to our better understanding of fatigue, sport injury, overtraining and individual performance capabilities. This review highlights also the potential new ways of brain imaging approaches for monitoring in situ. While assessment of cognitive demand is still in its infancy in sport, it may represent a very fruitful approach if applied with rigorous protocols and deep knowledge of both the neurobehavioral and cognitive aspects. It is time now to consider the cognitive demand to avoid underestimating the total training burden and its management.
RSEI-Based Modeling of Ecological Security and Its Spatial Impacts on Soil Quality: A Case Study of Dayu, China
Authors: Su Xiaoxia , Wu Jing , Li Pengshuo , Li Renjie , Cheng Penggen
Published: April 2022

Rapid urbanization and industrialization have brought serious threats to urban ecological security, which refers to the health and integrity of urban ecosystems. By collecting multi-source data in the modeling of the ecological security pattern, we used the remote sensing ecological index (RSEI) to identify the ecological sources (ESOs), and applied five indicators to construct the resistance surface, including land-use type, normalized vegetation index (NDVI), normalized building index (NDBI), slope, and digital elevation model (DEM). Based on the ESOs and ecological resistance surface, we calculated the cost distance of each pixel to the nearest ESO using the minimum cumulative resistance model. With the natural breakpoint method, we classified the cost distance into five levels, and constructed the ecological security pattern of Dayu. In Dayu, there were areas of at least 40% with stable ecological security. We identified 39, 31, and 43 ESOs of Dayu in 2012, 2016, and 2020, respectively. During 2012 to 2016, the number of medium ESOs decreased from 16 to 5, and the number of small ESOs increased from 13 to 26. From 2016 to 2020, the number of medium-sized ESOs increased from 5 to 18, and the number of small-sized ESOs decreased from 26 to 20. The percentage of the Level-5 (the worst) ecological security was 5.84% in 2012, 6.80% in 2016, and 4.42% in 2020. The ecological security was negatively correlated with the intensity of the human activities and varied significantly in different towns. The soil quality was positively consistent with the ecological security, and the urbanization caused damage to the soil security. A few suggestions were finally provided for decision-makers to improve the ecological environments and the soil quality.
Global Mangrove Deforestation and Its Interacting Social-Ecological Drivers: A Systematic Review and Synthesis
Authors: Bhowmik Avit K. , Padmanaban Rajchandar , Cabral Pedro , Romeiras Maria M.
Published: April 2022

Globally, mangrove forests are substantially declining, and a globally synthesized database containing the drivers of deforestation and drivers’ interactions is scarce. Here, we synthesized the key social-ecological drivers of global mangrove deforestation by reviewing about two hundred published scientific studies over the last four decades (from 1980 to 2021). Our focus was on both natural and anthropogenic drivers with their gradual and abrupt impacts and on their geographic coverage of effects, and how these drivers interact. We also summarized the patterns of global mangrove coverage decline between 1990 and 2020 and identified the threatened mangrove species. Our consolidated studies reported an 8600 km2 decline in the global mangrove coverage between 1990 and 2020, with the highest decline occurring in South and Southeast Asia (3870 km2). We could identify 11 threatened mangrove species, two of which are critically endangered (Sonneratia griffithii and Bruguiera hainseii). Our reviewed studies pointed to aquaculture and agriculture as the predominant driver of global mangrove deforestation though their impacts varied across global regions. Gradual climate variations, i.e., sea-level rise, long-term precipitation, and temperature changes and driven coastline erosion, salinity intrusion and acidity at coasts, constitute the second major group of drivers. Our findings underline a strong interaction across natural and anthropogenic drivers, with the strongest interaction between the driver groups aquaculture and agriculture and industrialization and pollution. Our results suggest prioritizing globally coordinated empirical studies linking drivers and mangrove deforestation and global development of policies for mangrove conservation.
Learning to Succeed? Interplay between Ethnic Identity, National Identity, and Students’ Perception on Social Mobility in a Xinjiang Class School of China
Authors: Yuan Zhenjie , Xie Yulin , Li Jun (Justin) , Li Jie , Yang Rong
Published: April 2022

While studies on students’ ethnic identity, national identity and social mobility gained relatively good research development in Western societies, little attention has been given to other social contexts, particularly non-Western societies like China. A questionnaire (N = 570, aged 14 to 20) was conducted in a Xinjiang Class school in Southern China to examine the nexus of students’ ethnic identity, national identity and perception on social mobility. The Xinjiang Class is currently one of the most iconic preferential educational policies recruiting students from ethnic groups in Xinjiang (an ethnic minority-concentrated border region) and offering them senior secondary education in select Han-centric, senior-secondary schools in China’s central and east coast provinces. The results demonstrate that the strengthening effect of students’ ethnic identity on national identity is not obvious; students’ strong national identity contributes to their positive perception on upward social mobility. However, the widely accepted viewpoint that students’ ethnic identity has a negative influence on perception of social mobility cannot be fully supported in this case. This study enriches the extant literature by providing a combined model to explore the nuanced mechanisms between ethnic identity, national identity and students’ perception on social mobility in a multi-ethnic society and by helping to unveil the identity politics unfolded in current China’s educational sector.
Creating a Rainbow for Sustainability: The Case of Sustainable Beef
Authors: Lynch Richard , Henchion Maeve , Hyland John J. , Gutiérrez José A.
Published: April 2022

Sustainability is a complex, multi-dimensional issue that requires contributions from diverse disciplines, perspectives, and actors. Research and innovation are recognised as having the potential to help address some of the trade-offs and synergies associated with sustainability, and interactive innovation in particular offers many advantages. The beef sector has faced significant sustainability challenges in recent times, with criticisms relating to greenhouse gas (GHG) production, biodiversity, water quality, human health, and animal welfare, along with economic challenges to the viability of the sector. Furthermore, the low level of adoption of solutions proposed by research to address these challenges indicates the need for a pan-European multi-actor network to produce actionable and usable information to support European beef farmers. Drawing on practice-based research, the purpose of this exploratory paper was to examine how interactive innovation can be supported in a sector that, to date, has been the focus of limited interactive innovation efforts. It concluded that a “rainbow” of actors and diverse knowledges, along with different types of innovation intermediaries, can enhance the sustainability of the beef sector.
Criteria for Smart City Identification: A Systematic Literature Review
Authors: Dashkevych Oleg , Portnov Boris A.
Published: April 2022

The transition towards greater smartness is an emerging trend in the development of modern cities. This transition manifests itself in the widespread adoption of information and communication technologies (ICTs), cloud computing, Internet of Things (IoT), and other technological tools aimed at improving the level of city smartness. Although numerous studies have focused on the smart city (SC) phenomenon, knowledge about empirical criteria that can be used to define a city as “smart” and to measure the degree of a city’s “smartness” remains limited. The present study aims to bridge this knowledge gap by a systematic literature review of recent studies, in which various empirical criteria are used for SC identification. The study helps to identify a total of 48 SC identification metrics, which are further split into three main categories—smart digital technology, living conditions, and environmental (ecological) sustainability. Among these groups of criteria, the “smart digital technology” group of metrics appears to be the most popular, while criteria pertinent to “ecological sustainability” are applied considerably less often. As the study also reveals, only about half of the criteria used by empirical studies for SC identification actually relate to urban residents’ needs, with the rest being general technological measures. Therefore, for a balanced SC assessment, we suggest a ranking system based on the nine most important metrics, which equally represent all the main aspects of the SC phenomenon while placing an emphasis on the improvement of the quality of life of local residents. The proposed system is applied to several major cities across the globe to demonstrate its use and usefulness.
Characteristics and Causes of Construction Accidents in a Large-Scale Development Project
Authors: Chan Albert P. C. , Yang Yang , Choi Tracy N. Y. , Nwaogu Janet Mayowa
Published: April 2022

The airport at the Kai Tak district was relocated to Chek Lap Kok in 1998. The Hong Kong Government endeavors to redevelop Kai Tak into a mixed-use community. A total of eight fatal construction accidents have happened since the Kai Tak Development (KTD) commenced in 2013, and seven of them occurred between 2020 and 2021. The alarming figures call for an in-depth investigation of fatal accidents, hence the aim of the current research. Detailed accident investigation reports that outlined accident causation and preventive measures for the eight accidents were collected. With a detailed examination of validity and reliability, the modified loss causation model was applied to analyze the situational variables, incident sequences, and causes of accidents. The results showed that “fall” and “struck by” were the most common accidents in the KTD. Several risk factors for fatal accidents were identified, including “aging workers”, “new to a construction site”, “ethnic minority”, “illegal worker”, “working on weekends”, and “subcontracting companies”. Preventive measures were offered to help government authorities and construction practitioners enhance the safety performance of the ongoing KTD projects. This study contributes to the knowledge of construction safety by identifying safety issues of mega interfacing projects. The practice of learning from accidents should be promoted in order to prevent similar accidents from occurring again.