bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Interannual Rainfall Runoff Equation Derived from Ol’Dekop’s Model Using Artificial Neural Networks
Authors: Mimeche Omar , Aieb Amir , Liotta Antonio , Madani Khodir
Published: June 2022

In water resources management, modeling water balance factors is necessary to control dams, agriculture, irrigation, and also to provide water supply for drinking and industries. Generally, conceptual and physical models present challenges to find more hydro-climatic parameters, which show good performance in the assessment of runoff in different climatic regions. Accordingly, a dynamic and reliable model is proposed to estimate inter-annual rainfall-runoff in five climatic regions of northern Algeria. This is a new improvement of Ol’Dekop’s equation, which models the residual values obtained between real and predicted data using artificial neuron networks (ANNs), namely by ANN1 and ANN2 sub-models. In this work, a set of climatic and geographical variables, obtained from 16 basins, which are inter-annual rainfall (IAR), watershed area (S), and watercourse (WC), were used as input data in the first model. Further, the ANN1 output results and De Martonne index (I) were classified, and were then processed by ANN2 to further increase reliability, and make the model more dynamic and unaffected by the climatic characteristic of the area. The final model proved the best performance in the entire region compared to a set of parametric and non-parametric water balance models used in this study, where the R2Adj obtained from each test gave values between 0.9103 and 0.9923.
Development of a Vision-Guided Shared-Control System for Assistive Robotic Manipulators
Authors: Ding Dan , Styler Breelyn , Chung Cheng-Shiu , Houriet Alexander
Published: June 2022

Assistive robotic manipulators (ARMs) provide a potential solution to mitigating the difficulties and lost independence associated with manipulation deficits in individuals with upper-limb impairments. However, achieving efficient control of an ARM can be a challenge due to the multiple degrees of freedom (DoFs) of an ARM that need to be controlled. This study describes the development of a vision-guided shared-control (VGS) system and how it is applied to a multi-step drinking task. The VGS control allows the user to control the gross motion of the ARM via teleoperation and commands the ARM to autonomously perform fine manipulation. A bench-top test of the autonomous actions showed that success rates for different subtasks ranged from 80% to 100%. An evaluation with three test pilots showed that the overall task performance, in terms of success rate, task completion time, and joystick mode-switch frequency, was better with VGS than with teleoperation. Similar trends were observed with a case participant with a spinal cord injury. While his performance was better and he perceived a smaller workload with VGS, his perceived usability for VGS and teleoperation was similar. More work is needed to further improve and test VGS on participants with disabilities.
A New Image Grating Sensor for Linear Displacement Measurement and Its Error Analysis
Authors: Cheng Fang , Zhou Dongfang , Yu Qing , Tjahjowidodo Tegoeh
Published: June 2022

To improve the accuracy of the current vision-based linear displacement measurement in a large range, a new type of linear displacement sensing system, namely, image grating, is proposed in this paper. The proposed system included a patterned glass plate attached to the moving object and an ultra-low distortion lens for high-accuracy image matching. A DFT local up-sampling phase correlation method was adopted to obtain the sub-pixel translation of the patterns onto the target plate. Multiple sets of stripe patterns with different designs were located on the glass plate to expand the measurement range, based on the principle of phase correlation. In order to improve the measurement accuracy, the main errors of the image grating system were analyzed, and the nonlinear error compensation was completed based on the dynamic calibration of the pixel equivalent. The measurement results, after the error compensation, showed that the total error of the proposed system was less than 2.5 μm in the range of 60 mm, and the repeatability was within 0.16 μm, as quantified by standard deviation.
Distributed Optical Fiber Sensors for Monitoring of Civil Engineering Structures
Authors: Kishida Kinzo , Imai Michio , Kawabata Junichi , Guzik Artur
Published: June 2022

Distributed Fiber Optics Sensing (DFOS) is a mature technology, with known, tested, verified, and even certified performance of various interrogators and measurement methods, which include Distributed Temperature Sensing (DTS), Distributed Temperature-Strain Sensing (DTSS), and Distributed Acoustic Sensing (DAS). This paper reviews recent progress in two critical areas of DFOS implementation in large scale civil engineering structures. First is the substantial improvement in sensing accuracy achieved by replacing Brillouin scattering-based measurements with its Rayleigh counterpart. The second is progress in acquisition speed and robustness, as now engineers can observe parameters of interest in real-time, and make informed, operational decisions regarding quality and safety. This received a high valuation from field engineers when used during the construction stage of the project. Furthermore, this change in the use of DFOS in civil engineering greatly increases the practical possibility of installing FO cables permanently. The same FO cables can be later used for long-term monitoring, during maintenance periods throughout the structure’s lifetime. To illustrate these two advances, we present a comparison between Brillouin and Rayleigh scattering measurements, and their accuracy, and highlight the importance of temperature and strain separation. We also present several important applications in large scale civil engineering infrastructure projects.
Resistive-Based Gas Sensors Using Quantum Dots: A Review
Authors: Mirzaei Ali , Kordrostami Zoheir , Shahbaz Mehrdad , Kim Jin-Young , Kim Hyoun Woo , Kim Sang Sub
Published: June 2022

Quantum dots (QDs) are used progressively in sensing areas because of their special electrical properties due to their extremely small size. This paper discusses the gas sensing features of QD-based resistive sensors. Different types of pristine, doped, composite, and noble metal decorated QDs are discussed. In particular, the review focus primarily on the sensing mechanisms suggested for these gas sensors. QDs show a high sensing performance at generally low temperatures owing to their extremely small sizes, making them promising materials for the realization of reliable and high-output gas-sensing devices.
Real-Time Lidar Odometry and Mapping with Loop Closure
Authors: Liu Yonghui , Zhang Weimin , Li Fangxing , Zuo Zhengqing , Huang Qiang
Published: June 2022

Real-time performance and global consistency are extremely important in Simultaneous Localization and Mapping (SLAM) problems. Classic lidar-based SLAM systems often consist of front-end odometry and back-end pose optimization. However, due to expensive computation, it is often difficult to achieve loop-closure detection without compromising the real-time performance of the odometry. We propose a SLAM system where scan-to-submap-based local lidar odometry and global pose optimization based on submap construction as well as loop-closure detection are designed as separated from each other. In our work, extracted edge and surface feature points are inserted into two consecutive feature submaps and added to the pose graph prepared for loop-closure detection and global pose optimization. In addition, a submap is added to the pose graph for global data association when it is marked as in a finished state. In particular, a method to filter out false loops is proposed to accelerate the construction of constraints in the pose graph. The proposed method is evaluated on public datasets and achieves competitive performance with pose estimation frequency over 15 Hz in local lidar odometry and low drift in global consistency.
Test Case Prioritization, Selection, and Reduction Using Improved Quantum-Behaved Particle Swarm Optimization
Authors: Bajaj Anu , Abraham Ajith , Ratnoo Saroj , Gabralla Lubna Abdelkareim
Published: June 2022

The emerging areas of IoT and sensor networks bring lots of software applications on a daily basis. To keep up with the ever-changing expectations of clients and the competitive market, the software must be updated. The changes may cause unintended consequences, necessitating retesting, i.e., regression testing, before being released. The efficiency and efficacy of regression testing techniques can be improved with the use of optimization approaches. This paper proposes an improved quantum-behaved particle swarm optimization approach for regression testing. The algorithm is improved by employing a fix-up mechanism to perform perturbation for the combinatorial TCP problem. Second, the dynamic contraction-expansion coefficient is used to accelerate the convergence. It is followed by an adaptive test case selection strategy to choose the modification-revealing test cases. Finally, the superfluous test cases are removed. Furthermore, the algorithm’s robustness is analyzed for fault as well as statement coverage. The empirical results reveal that the proposed algorithm performs better than the Genetic Algorithm, Bat Algorithm, Grey Wolf Optimization, Particle Swarm Optimization and its variants for prioritizing test cases. The findings show that inclusivity, test selection percentage and cost reduction percentages are higher in the case of fault coverage compared to statement coverage but at the cost of high fault detection loss (approx. 7%) at the test case reduction stage.
An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate Measurements
Authors: Rusch Alexander , Rösgen Thomas
Published: June 2022

The COVID-19 pandemic has emphasized the need for infection risk analysis and assessment of ventilation systems in indoor environments based on air quality criteria. In this context, simulations and direct measurements of CO2 concentrations as a proxy for exhaled air can help to shed light on potential aerosol pathways. While the former typically lack accurate boundary conditions as well as spatially and temporally resolved validation data, currently existing measurement systems often probe rooms in non-ideal, single locations. Addressing both of these issues, a large and flexible wireless array of 50 embedded sensor units is presented that provides indoor climate metrics with configurable spatial and temporal resolutions at a sensor response time of 20 s. Augmented by an anchorless self-localization capability, three-dimensional air quality maps are reconstructed up to a mean 3D Euclidean error of 0.21 m. Driven by resolution, ease of use, and fault tolerance requirements, the system has proven itself in day-to-day use at ETH Zurich, where topologically differing auditoria (at-grade, sloped) were investigated under real occupancy conditions. The corresponding results indicate significant spatial and temporal variations in the indoor climate rendering large sensor arrays essential for accurate room assessments. Even in well-ventilated auditoria, cleanout time constants exceeded 30 min.
Analysis of the Conflict between Car Commuter’s Route Choice Habitual Behavior and Traffic Information Search Behavior
Authors: Liu Kai
Published: June 2022

Motivated by the conflict between travelers’ habitual choice behavior and traffic information search behavior, in this paper, a behavioral experiment under different types of traffic information (i.e., per-trip traffic information and en-route traffic information) was designed to obtain data regarding car commuters’ daily route choices. Based on the observed data, participants’ route choices, habit strength, response time, and information search behaviors were analyzed. It is concluded that, in the beginning, the traffic information had a great influence on the habit participants’ route choices, let them think more, and made most of them switch from habit route to the best route (as recommended by traffic information); however, as time went on, the impact of traffic information declined, and several features of habits, such as automatically responding and repeated behavior, would reappear in some participants’ decision-making. Meanwhile, the different way of traffic information search behaviors (i.e., in active performance or in passive reception) could cause different information compliance ratios. These results would help to understand the interrelationship between car commuters’ daily route choice behaviors and traffic information search behaviors in short-term and in long-term, respectively, and provide an interesting starting point for the development of practical traffic information issuing strategies to enhance the impact of traffic information to alleviate traffic congestion during morning commuting.
Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study
Authors: den Hartog Dylan , van der Krogt Marjolein M. , van der Burg Sven , Aleo Ignazio , Gijsbers Johannes , Bonouvrié Laura A. , Harlaar Jaap , Buizer Annemieke I. , Haberfehlner Helga
Published: June 2022

Accurate and reliable measurement of the severity of dystonia is essential for the indication, evaluation, monitoring and fine-tuning of treatments. Assessment of dystonia in children and adolescents with dyskinetic cerebral palsy (CP) is now commonly performed by visual evaluation either directly in the doctor’s office or from video recordings using standardized scales. Both methods lack objectivity and require much time and effort of clinical experts. Only a snapshot of the severity of dyskinetic movements (i.e., choreoathetosis and dystonia) is captured, and they are known to fluctuate over time and can increase with fatigue, pain, stress or emotions, which likely happens in a clinical environment. The goal of this study was to investigate whether it is feasible to use home-based measurements to assess and evaluate the severity of dystonia using smartphone-coupled inertial sensors and machine learning. Video and sensor data during both active and rest situations from 12 patients were collected outside a clinical setting. Three clinicians analyzed the videos and clinically scored the dystonia of the extremities on a 0–4 scale, following the definition of amplitude of the Dyskinesia Impairment Scale. The clinical scores and the sensor data were coupled to train different machine learning models using cross-validation. The average F1 scores (0.67 ± 0.19 for lower extremities and 0.68 ± 0.14 for upper extremities) in independent test datasets indicate that it is possible to detected dystonia automatically using individually trained models. The predictions could complement standard dyskinetic CP measures by providing frequent, objective, real-world assessments that could enhance clinical care. A generalized model, trained with data from other subjects, shows lower F1 scores (0.45 for lower extremities and 0.34 for upper extremities), likely due to a lack of training data and dissimilarities between subjects. However, the generalized model is reasonably able to distinguish between high and lower scores. Future research should focus on gathering more high-quality data and study how the models perform over the whole day.
Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence
Authors: Meddage D. P. P. , Ekanayake I. U. , Herath Sumudu , Gobirahavan R. , Muttil Nitin , Rathnayake Upaka
Published: June 2022

Predicting the bulk-average velocity (UB) in open channels with rigid vegetation is complicated due to the non-linear nature of the parameters. Despite their higher accuracy, existing regression models fail to highlight the feature importance or causality of the respective predictions. Therefore, we propose a method to predict UB and the friction factor in the surface layer (fS) using tree-based machine learning (ML) models (decision tree, extra tree, and XGBoost). Further, Shapley Additive exPlanation (SHAP) was used to interpret the ML predictions. The comparison emphasized that the XGBoost model is superior in predicting UB (R = 0.984) and fS (R = 0.92) relative to the existing regression models. SHAP revealed the underlying reasoning behind predictions, the dependence of predictions, and feature importance. Interestingly, SHAP adheres to what is generally observed in complex flow behavior, thus, improving trust in predictions.
P4DM: Measure the Link Delay with P4
Authors: Al Sadi Amir , Berardi Davide , Callegati Franco , Melis Andrea , Prandini Marco
Published: June 2022

Network management strategies depend on a timely and accurate knowledge of the network performance measures. Among these, one of the most relevant is the delay of the links, which unfortunately is not easy to measure with accuracy, especially when considering multi-hop paths. This is a classical networking problem, for which several solutions have been proposed. Nonetheless, we argue in this manuscript that there is still some room for improving accuracy and effectiveness in the measurement. This paper proposes a new solution based on the exploitation of the P4 data plane programming language. The basic idea is to handle lightweight probe packets that are forged ad-hoc at the edge of a link and processed at the other edge. Hosts generate the probe packets that are then exploited by the P4 programs in the switches to implement the measure. This approach provides an accurate and reliable measure of the link transit time, also effective in multi-hop links. In this latter case, we show that the measurement is not influenced much by the packet loss when the network is overloaded, thus providing more reliable results with respect to more conventional tools such as the classical ping utility. The manuscript explains the proposed P4 solution; then, it provides a comparison with several other approaches found in the literature, showing that outperform most of them, and finally show the behavior of the proposed methodology when facing a multi hop network path on a congested network to prove its robustness.
Frequency-Based Representation of Massive Alerts and Combination of Indicators by Heterogeneous Intrusion Detection Systems for Anomaly Detection
Authors: Park Hyunjae , Choi Young-June
Published: June 2022

Although the application of a wide range of sensors has been generalized through the development of technology, the processing of massive alerts generated through data analysis and monitoring remains a challenge. This problem is also found in cyber security because the intrusion detection system (IDS) produces a tremendous number of alerts. Massive alerts not only significantly increase resources for analysis, but also make it difficult to analyze the overall situation of the system. In order to handle massive alerts, we propose using an indicator as a frequency-based representation. The proposed indicator is generated from categorical parameters of alerts that occur within a unit time utilizing frequency and is used for situational awareness with machine learning to detect whether there is a threat or not. The advantage of using indicators is that they can determine the situation for a period without analyzing individual alerts, which helps security experts to recognize the situation in the system and focus on targets that require in-depth analysis. In addition, the conversion from the categorical parameters which is highly related to analysis to numeric parameter allows for applying machine learning. For performance evaluation, we collect data from an HAI testbed similar to real critical infrastructure and conduct experiments using indicators and XGBoost, a classification machine learning algorithm against five famous vulnerability attacks. Consequently, we show that the proposed method can detect attacks with more than 90 percent accuracy, and the performance is enhanced using heterogeneous intrusion detection systems.
Monitoring and Visualization of Crystallization Processes Using Electrical Resistance Tomography: CaCO3 and Sucrose Crystallization Case Studies
Authors: Rao Guruprasad , Aghajanian Soheil , Zhang Yuchong , Jackowska-Strumiłło Lidia , Koiranen Tuomas , Fjeld Morten
Published: June 2022

In the current research work, electrical resistance tomography (ERT) was employed for monitoring and visualization of crystallization processes. A first-of-its-kind MATLAB-based interactive GUI application “ERT-Vis” is presented. Two case studies involving varied crystallization methods were undertaken. The experiments were designed and performed involving calcium carbonate reactive (precipitative) crystallization for the high conductivity solution-solute media, and the cooling crystallization of sucrose representing the lower conductivity solution–solute combination. The software successfully provided key insights regarding the process in both crystallization systems. It could detect and separate the solid concentration distributions in the low as well as high conductivity solutions using the visual analytics tools provided. The performance and utility of the software were studied using a software evaluation case study involving domain experts. Participant feedback indicated that ERT-Vis software helps by reconstructing images instantaneously, interactively visualizing, and evaluating the output of the crystallization process monitoring data.
Real-Life Application of a Wearable Device towards Injury Prevention in Tennis: A Single-Case Study
Authors: Kramberger Iztok , Filipčič Aleš , Germič Aleš , Kos Marko
Published: June 2022

The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment.
Recent Advances in Bipedal Walking Robots: Review of Gait, Drive, Sensors and Control Systems
Authors: Mikolajczyk Tadeusz , Mikołajewska Emilia , Al-Shuka Hayder F. N. , Malinowski Tomasz , Kłodowski Adam , Pimenov Danil Yurievich , Paczkowski Tomasz , Hu Fuwen , Giasin Khaled , Mikołajewski Dariusz , Macko Marek
Published: June 2022

Currently, there is an intensive development of bipedal walking robots. The most known solutions are based on the use of the principles of human gait created in nature during evolution. Modernbipedal robots are also based on the locomotion manners of birds. This review presents the current state of the art of bipedal walking robots based on natural bipedal movements (human and bird) as well as on innovative synthetic solutions. Firstly, an overview of the scientific analysis of human gait is provided as a basis for the design of bipedal robots. The full human gait cycle that consists of two main phases is analysed and the attention is paid to the problem of balance and stability, especially in the single support phase when the bipedal movement is unstable. The influences of passive or active gait on energy demand are also discussed. Most studies are explored based on the zero moment. Furthermore, a review of the knowledge on the specific locomotor characteristics of birds, whose kinematics are derived from dinosaurs and provide them with both walking and running abilities, is presented. Secondly, many types of bipedal robot solutions are reviewed, which include nature-inspired robots (human-like and birdlike robots) and innovative robots using new heuristic, synthetic ideas for locomotion. Totally 45 robotic solutions are gathered by thebibliographic search method. Atlas was mentioned as one of the most perfect human-like robots, while the birdlike robot cases were Cassie and Digit. Innovative robots are presented, such asslider robot without knees, robots with rotating feet (3 and 4 degrees of freedom), and the hybrid robot Leo, which can walk on surfaces and fly. In particular, the paper describes in detail the robots’ propulsion systems (electric, hydraulic), the structure of the lower limb (serial, parallel, mixed mechanisms), the types and structures of control and sensor systems, and the energy efficiency of the robots. Terrain roughness recognition systems using different sensor systems based on light detection and ranging or multiple cameras are introduced. A comparison of performance, control and sensor systems, drive systems, and achievements of known human-like and birdlike robots is provided. Thirdly, for the first time, the review comments on the future of bipedal robots in relation to the concepts of conventional (natural bipedal) and synthetic unconventional gait. We critically assess and compare prospective directions for further research that involve the development of navigation systems, artificial intelligence, collaboration with humans, areas for the development of bipedal robot applications in everyday life, therapy, and industry.
Frequency Sweep Keying CDMA for Reducing Ultrasonic Crosstalk
Authors: Park Ga-Rin , Park Sang-Ho , Baek Kwang-Ryul
Published: June 2022

Various sensors are embedded in automobiles to implement intelligent safety technologies such as autonomous driving and front–rear collision avoidance technology. In particular, ultrasonic sensors have been used in the past because they have an accuracy of centimeters to sub-centimeters in air despite their low cost and low hardware complexity. Recently, the crosstalk problem between ultrasonic sensors has been raised because the number of ultrasonic sensors in the unit space has increased as the number of vehicles increases. Various studies have been conducted to solve the crosstalk, but a demodulation error occurs when signals overlap. Therefore, in this paper, we propose a method that is robust to ultrasonic signal overlap, is robust even at shorter code length, and has reduced time of flight (TOF) error compared to the existing method by applying frequency sweep keying modulation based on code division multiple access (CDMA). As a result of the experiment, the code was detected accurately regardless of the overlap ratio of the two signals, and it was robust even in situations where the power of the two signals was different. In addition, it shows an accurate TOF estimation even if the ID code length is shorter than the existing on–off-keying, frequency shift keying, and phase shift keying methods.
WASH and Health in Sindhupalchowk District of Nepal after the Gorkha Earthquake
Authors: Shrestha Salina , Malla Rabin , Kazama Futaba
Published: June 2022

An earthquake of magnitude 7.8 MW and 6.8 MW struck Nepal on 25 April and 12 May, 2015, respectively, which caused massive damage. In such crises, understanding the water, sanitation, and hygiene (WASH) situation is of paramount importance. Therefore, we aimed to assess the WASH situation and its impact on health, particularly in the Sindhupalchowk district. A questionnaire survey and microbial analysis of water samples were conducted. Descriptive statistics and parametric and non-parametric statistical tests were employed. The results revealed that 97.1% of water samples from the source during the pre-monsoon season and 98.5% during the monsoon season had fecal contamination. Similarly, 92.8% of water samples during the pre-monsoon season and 96.7% during the monsoon season at point of use (PoU) had fecal contamination. Furthermore, water consumption was comparatively less during the pre-monsoon season. The increase in water consumption improved hygiene behavior and lowered the prevalence of waterborne diseases. Similarly, less water consumption affected water handling behavior; for example, the cleaning interval of storage vessels was less frequent. An increase in cleaning interval resulted in fecal contamination of water at PoU. The findings of this study can be useful in the review of existing WASH policy and plans and integration with the disaster management plan for disaster risk reduction.
Evaluating ‘Health in All Policies’ in Norwegian Municipalities
Authors: Synnevåg Ellen Strøm
Published: June 2022

The Health in All Policies (HiAP) approach has emerged as a central strategy for promoting health at local, regional, and state levels in different countries. Representing a complex and complicated strategy, evaluations of HIAP at the local level are scarce, and scholars call for more knowledge and critical discussions regarding how to evaluate at this level. In this conceptual paper, I discuss how summative and formative evaluation approaches might supplement each other when evaluating HiAP at the local level. First, I discuss the possibilities of using summative and formative evaluation of HiAP. Further, I discuss how formative-dialogue research might represent possibilities for evaluation by combining the two approaches. Finally, I ask if there has been a shift in the Norwegian evaluation discourse, from the promotion of summative evaluation alone to a combination of both summative and formative methods.
What Works? How Combining Equal Opportunity and Work–Life Measures Relates to the Within-Firm Gender Wage Gap
Authors: Marx Charlotte K. , Diewald Martin
Published: June 2022

In this paper, we investigate how organizational equal opportunity and work–life measures when combined are associated with the gender wage gap within large German establishments. By looking at both kinds of measures as parts of a comprehensive personnel strategy, we provide a novel way to distinguish between a career-supportive implementation of work–life measures versus implementation as a compensating differential. Using a linked employer–employee dataset, we performed a cross-sectional multilevel regression analysis with fixed effects for 6439 respondents within 122 establishments. The results indicate that work–life measures that support employees in their parenthood responsibilities are linked to a significantly higher GWG, particularly for parents. Our results indicate that the implementation of work–life measures is used as compensating differentials, primarily for mothers. Particularly, this can be found when looking at the combination of multiple measures. In combination with equal opportunity measures for women, work–life measures that highlight the long-term absence from the workplace are associated with a higher GWG for parents.
Barriers That Keep Vulnerable People as NEETs
Authors: Pesquera Alonso Carlos , Iniesta Martínez Almudena , Muñoz Sánchez Práxedes
Published: June 2022

The rates of young people Not in Education, Employment or Training (NEET) in the Mediterranean European Economic Area (MED EEA) are high. Hence, the European Union and national governments have developed and implemented different policies aimed to tackle the NEET situation. In this article, we try to identify and understand the most relevant barriers that keep vulnerable people as NEETs. We focused on youth as being at the highest risk of becoming NEETs: migrant women aged 25–29. By using semi-structured interviews and focus groups with key stakeholders and NEETs in the Spanish region of Murcia, we collected their views on and experiences with different programs and strategies. We conclude that this group is exposed to additional barriers due to the intersectionality of their characteristics. There are structural and contextual aspects, such as motherhood, a lack of social networks, or not knowing the language, which NEET policies do not address. We highlight the need of (i) improving the adaptiveness of relevant policies by being tailored to specific problems NEETs face, (ii) expanding the focus of NEET policies, and (iii) including the regional perspective in NEET policy design. In our comprehensive approach, we stress that the NEET policy alone cannot solve the NEET problem.
Educating in History: Thinking Historically through Historical Reenactment
Authors: González-González José-Manuel , Franco-Calvo Jesús-Gerardo , Español-Solana Darío
Published: June 2022

This paper aimed to identify trends in the scientific literature that relate the link between two concepts: historical thinking and historical reenactment. The definition of both concepts and their commonalities were examined. Convinced that History instruction and Heritage education could improve new methods and techniques, and aware of the benefits of reenactments in active learning and participation in and outside the classroom, we came to the obvious conclusion that merging both aspects is a must and should be disseminated. We also analyzed the presence of second-order concepts in reenactment practices and how they are addressed by actors and spectators. Reenactments foster the acquisition of critical thinking by citizens through education; their quality, however, must be improved through research and didactics—didactics based on reenactment that help us value the past and the traces still present in local areas. Local and global identity and heritage, emotions, reproduction of objects, the use of sources, relevance, empathy, multiperspectives, causation, communication, the relationship between past and present, and the sustainable economy proposed by the 2030 Agenda, are all aspects that should take center stage in turning this phenomenon into a living and lasting history as an experience.
International Health Cooperation in the Post-Pandemic Era: Possibilities for and Limitations of Middle Powers in International Cooperation
Authors: Kim Yongmin , Park Youngdeuk
Published: June 2022

The COVID-19 pandemic has left international cooperation and liberalistic values in crisis. As liberalism’s downfall is widely discussed, international collaborations like the European Union are criticised for their inability to operate adequately during the pandemic. The four examples in this paper are middle power countries (South Korea, Japan, Taiwan, and Singapore) in terms of economic scale and influence. The purpose of this study was to uncover possibilities for and limitations of these middle powers within international cooperative efforts during and after the pandemic. The unknown factor is the path the post-pandemic world will follow. Will nations focus on independent survival? Or will international cooperation shape the new world? Globalisation already seems to have progressed too far for the national egoism of the great powers to prevail. Even if face-to-face is replaced by virtual and offline meetings move online, the social nature of humans remains unchanged, and international cooperation remains valid. The four middle power countries in Asia, which are included among the most economically successful countries, are important to international society based on their relatively excellent quarantine performance. What is important in the diplomacy of middle power countries is not traditional security and hard power but the soft power of international law, human rights, health security, and international cooperation.
Mental Toughness Development via Military-Style Training in the NCAA: A Three-Phase, Mixed-Method Study of the Perspectives of Strength and Conditioning Coaches
Authors: Stamatis Andreas , Morgan Grant B. , Nyamaruze Patrick , Koutakis Panagiotis
Published: June 2022

Sport cultures transmit values for anticipated conduct. Recent events have resulted in injuries/deaths of National Collegiate Athletic Association (NCAA) student-athletes, usually during off-season football training. Through media reports, strength and conditioning coaches (SCC) have been allegedly involved by incorporating military-style training (MST). Mental toughness (MT) has been associated with hypermasculine subcultures in sports. For the first time, perceptions of collegiate SCCs were chosen to contribute to the development of cultural best practices in sports, via a multiphase mixed-method design (Phase 1, n = 465; Phase 2, n = 72; Phase 3, n = 99). Quantitative and qualitative data were collected aiming to confirm and explore the use of MST in the NCAA, its connection to SCCs, its association with MT development, and the role of the media. MST is uncommon in the NCAA. MST takes place mostly during the off-season in the form of physical, in-scope protocols while football is the most common sport. MST promotes MT. The recent media backlash is considered unfounded. Cultures promoted by SCCs do not indicate conformity of student-athletes to unethical/unhealthy expectations. Future sport psychology research and practice should continue to prioritize culture, cultural identities, and physical and mental well-being.
SCS 4th Annual Conference: Strength and Conditioning for Human Performance, Porto, Portugal, 2021
Authors: Alcaraz Pedro E. , Freitas Tomás T. , Marín-Cascales Elena , Blazevich Anthony J. , Oliveira José , Soares Susana , Vilas-Boas João P.
Published: June 2022

On behalf of the Strength & Conditioning Society (SCS) and the Faculty of Sport of the University of Porto, we are pleased to present the abstracts of the SCS 4th Annual Conference: Strength and Conditioning for Human Performance, which took place in, Porto, Portugal, on 12–13 November 2021. The event was a success with invited sessions from renowned international and national speakers on a myriad of topics related to strength and conditioning and its application to health and sports performance, such as agility training and testing, high-intensity interval training in chronic conditions, hamstring strain injuries in soccer, and the utilization of surface electromyography (EMG) decomposition for assessing human performance, among others. During the Conference there were also different practical workshops on (1) velocity-based training; (2) performance testing and athlete monitoring using force platforms; (3) 3D kinematics tracking and flow force assessment in aquatic sports; (4) the application of inertial sensors for physical performance testing; (5) muscle fiber recruitment, force production, and energy expenditure in progressive bicycle testing; (6) EMG decomposition, motor-units recruitment, and muscle contraction modes; and (7) recovery strategies in team-sport athletes. Researchers and academics were able to present their latest findings by submitting the abstracts that compose this Conference Report.
Live Sharing of Learning Activities on E-Books for Enhanced Learning in Online Classes
Authors: Taniguchi Yuta , Owatari Takuro , Minematsu Tsubasa , Okubo Fumiya , Shimada Atsushi
Published: June 2022

While positive effects of imitating other learners have been reported, the recent increases in the number of online classes have seriously limited opportunities to learn how others are learning. Providing information about others’ learning activities through dashboards could be a solution, but few studies have targeted learning activities on e-textbook systems; it remains unclear what information representations would be useful and how they would affect learning. We developed a dashboard system that enables live sharing of students’ learning activities on e-textbooks. An experiment was conducted applying the dashboard in an online class to evaluate its impact. The results of questionnaires and quizzes were analyzed along with learning activities on the e-textbook system. From the questionnaire results, the most useful feedback types were identified. Regarding the impact on learning, the study found that a higher percentage of students who used the dashboard followed the progress of the class than those who did not. The study also found that students who used the dashboard were more likely to achieve higher quiz scores than those who did not. This study is the first to reveal what specific feedback is useful and to successfully investigate the impact of its use on learning.
Prediction of International Roughness Index Based on Stacking Fusion Model
Authors: Luo Zhiyuan , Wang Hui , Li Shenglin
Published: June 2022

Pavement performance prediction is necessary for road maintenance and repair (M&R) management and plans. The accuracy of performance prediction affects the allocation of maintenance funds. The international roughness index (IRI) is essential for evaluating pavement performance. In this study, using the road pavement data of LTPP (Long-Term Pavement Performance), we screened the feature parameters used for IRI prediction using the mean decrease impurity (MDI) based on random forest (RF). The effectiveness of this feature selection method was proven suitable. The prediction accuracies of four promising prediction models were compared, including Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR). The two integrated learning algorithms, GBDT and XGBoost, performed well in prediction. GBDT performs best with the lowest root mean square error (RMSE) of 0.096 and the lowest mean absolute error (MAE) of 6.2% and the coefficient of determination (R2) reaching 0.974. However, the prediction accuracy varies in numerical intervals, with some deviations. The stacking fusion model with a powerful generalization capability is proposed to build a new prediction model using GBDT and XGBoost as the base learners and bagging as the meta-learners. The R2, RMSE, and MAE of the stacking fusion model are 0.996, 0.040, and 1.3%, which further improves the prediction accuracy and verifies the superiority of this fusion model in pavement performance prediction. Besides, the prediction accuracy is generally consistent across different numerical intervals.
Heritage Building Preservation in the Process of Sustainable Urban Development: The Case of Brasov Medieval City, Romania
Authors: Bogdan Alexandru , Chambre Dorina , Copolovici Dana Maria , Bungau Tudor , Bungau Constantin C. , Copolovici Lucian
Published: June 2022

This paper aims to present a comprehensive review of the literature on the definition and development of the concepts of heritage and sustainability. The harmful effects of various pollutants on the materials widely used in the construction of monuments/buildings, which are part of the national and international cultural heritage, are also highlighted. In addition, the paper draws attention to modern techniques for investigating the composition and diagnosis of the alteration of buildings materials with the focus on stone, limestone, and mortars/concrete. The present research also emphasizes that in the case of heritage buildings, different skills are needed not only related to heritage conservation and rehabilitation, but also skills related to heritage planning processes, and to sustainable constructions. For exemplification, the manuscript proposes specific conservation principles based on the case of Brasov city, located in the heart of Romania and being par excellence a medieval town with representative buildings for that period.
Customer Perception and Its Influence on the Financial Performance in the Ecuadorian Banking Environment
Authors: Tulcanaza-Prieto Ana Belen , Aguilar-Rodríguez Iliana E. , Lee Chang Won
Published: June 2022

This study examines the relationship between customer perception and financial corporate performance in the Ecuadorian banking environment. A self-designed online questionnaire was carried out to gather information regarding customer perception factors (tangibility, trust and service guarantee, empathy, customer satisfaction, and customer loyalty), while the financial data of the Ecuadorian banks were attained from their annual financial indicator reports (financial efficiency and liquidity). A total of 243 questionnaires were recollected and 219 were considered as the final valid data. SPSS 26 was utilized for data analyses. Structural equation modeling was used to test the hypotheses. Our findings revealed that customer perception has a positive and significant (at least at the 5% level) impact on the financial performance of banks. Similarly, customer loyalty is influenced by tangibility, trust and service guarantee, empathy, and customer satisfaction. Study results are mostly consistent with the banking environment of other countries, especially Nigeria, and Scandinavian nations. Bank managers might always prioritize the customer perception of the bank due to it being considered a strong predictor of the bank’s financial performance. This study provides a complete statistical and econometrical model with tangible and intangible factors of customer perception (qualitative variables) and it includes financial records (quantitative variables). The main limitation of our study is the calculation of the customer perception index because Ecuadorian institutions do not calculate it and thus, we estimate the index in our model. For future research, the suggestion is that a corporate governance index with a customer perception component is included to improve the model.
Recovery of Carbon and Vegetation Diversity 23 Years after Fire in a Tropical Dryland Forest of Indonesia
Authors: Adinugroho Wahyu Catur , Prasetyo Lilik Budi , Kusmana Cecep , Krisnawati Haruni , Weston Christopher J. , Volkova Liubov
Published: June 2022

Understanding the recovery rate of forest carbon stocks and biodiversity after disturbance, including fire, is vital for developing effective climate-change-mitigation policies and actions. In this study, live and dead carbon stocks aboveground, belowground, and in the soil to a 30 cm depth, as well as tree and shrub species diversity, were measured in a tropical lowland dry forest, 23 years after a fire in 1998, for comparison with adjacent unburned reference forests. The results showed that 23 years since the fire was insufficient, in this case, to recover live forest carbon and plant species diversity, to the level of the reference forests. The total carbon stock, in the recovering 23-year-old forest, was 199 Mg C ha−1 or about 90% of the unburned forest (220 Mg C ha−1), mainly due to the contribution of coarse woody debris and an increase in the 5–10 cm soil horizon’s organic carbon, in the burned forest. The carbon held in the live biomass of the recovering forest (79 Mg C ha−1) was just over half the 146 Mg C ha−1 of the reference forest. Based on a biomass mean annual increment of 6.24 ± 1.59 Mg ha−1 yr−1, about 46 ± 17 years would be required for the aboveground live biomass to recover to equivalence with the reference forest. In total, 176 plant species were recorded in the 23-year post-fire forest, compared with 216 in the unburned reference forest. The pioneer species Macaranga gigantea dominated in the 23-year post-fire forest, which was yet to regain the similar stand structural and compositional elements as those found in the adjacent unburned reference forest.