bluuebunny/crossref_metadata_2025 huggingface dataset:
Urban Intersection Classification: A Comparative Analysis
Authors: Ballardini Augusto Luis , Hernández Saz Álvaro , Carrasco Limeros Sandra , Lorenzo Javier , Parra Alonso Ignacio , Hernández Parra Noelia , García Daza Iván , Sotelo Miguel Ángel
Published: September 2021

Understanding the scene in front of a vehicle is crucial for self-driving vehicles and Advanced Driver Assistance Systems, and in urban scenarios, intersection areas are one of the most critical, concentrating between 20% to 25% of road fatalities. This research presents a thorough investigation on the detection and classification of urban intersections as seen from onboard front-facing cameras. Different methodologies aimed at classifying intersection geometries have been assessed to provide a comprehensive evaluation of state-of-the-art techniques based on Deep Neural Network (DNN) approaches, including single-frame approaches and temporal integration schemes. A detailed analysis of most popular datasets previously used for the application together with a comparison with ad hoc recorded sequences revealed that the performances strongly depend on the field of view of the camera rather than other characteristics or temporal-integrating techniques. Due to the scarcity of training data, a new dataset is created by performing data augmentation from real-world data through a Generative Adversarial Network (GAN) to increase generalizability as well as to test the influence of data quality. Despite being in the relatively early stages, mainly due to the lack of intersection datasets oriented to the problem, an extensive experimental activity has been performed to analyze the individual performance of each proposed systems.
Design of a Digital 3D Model of Transport–Technological Environment of Open-Pit Mines Based on the Common Use of Telemetric and Geospatial Information
Authors: Temkin Igor , Myaskov Alexander , Deryabin Sergey , Konov Iliya , Ivannikov Alexander
Published: September 2021

This article is devoted to the issues of processing and analysis of heterogeneous information related to the functioning of mining transport equipment, which becomes available for analysis within the framework of modern technological operations control systems in open-pit mines. These issues are very relevant to robotized technological operations. The paper gives a brief overview of the modern landscape of the autonomous haulage systems management problems, the features of the platform approach to solving the problem of managing unmanned transport and technological processes in open pits are considered. The concept of an agent-based approach to the modeling of an open-pit mining is described in detail on the basis of the interaction of three systems: technical, infrastructural–technological, and geostructural. Some features of the developed platform architecture integration of heterogeneous information are discussed. The principles of information integration are considered in detail when constructing a dynamic 3D model (digital twin) of infrastructure and technological system elements using large arrays of telemetric data. The results of building digital models of open-pit technological roads are presented. The resulting models are comparatively analyzed in the process of optimizing of the interaction of technical autonomous mobile agents and elements of technological infrastructure.
Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy
Authors: Menevseoglu Ahmed , Aykas Didem P. , Hatta-Sakoda Beatriz , Toledo-Herrera Victor Hugo , Rodriguez-Saona Luis E.
Published: September 2021

Handheld Raman and portable FT-IR spectroscopy devices were evaluated for fast and non-invasive determination of methanol and ethanol levels in Peruvian Pisco. Commercial Peruvian Pisco (n = 171) samples were kindly provided by the UNALM Alliance for Research in Alcohol and its Derivatives (Lima, Peru) and supplemented by purchases at grocery and online stores. Pisco spectra were collected on handheld Raman spectrometers equipped with either a 1064 nm or a 785 nm excitation laser and a portable infrared unit operating in transmission mode. The alcohol levels were determined by GC–MS. Calibration models used partial least-squares regression (PLSR) to develop prediction algorithms. GC–MS data revealed that 10% of Pisco samples had ethanol levels lower than 38%, indicating possible water dilution. Methanol levels ranged from 10 to 130 mg/100 mL, well below the maximum levels allowed for fruit brandies. Handheld Raman equipped with a 1064 nm excitation laser gave the best results for determining ethanol (SEP = 1.2%; RPre = 0.95) and methanol (SEP = 1.8 mg/100 mL; RPre = 0.93). Randomly selected Pisco samples were spiked with methanol (75 to 2800 mg/100 mL), and their Raman spectra were collected through their genuine commercial bottles. The prediction models gave an excellent performance (SEP = 98 mg/100 mL; RPre = 0.97), allowing for the non-destructive and non-contact determination of methanol and ethanol concentrations without opening the bottles.
An Approach to Steady-State Power Transformer Modeling Considering Direct Current Resistance Test Measurements
Authors: Pires Corrêa Henrique , Henrique Teles Vieira Flávio
Published: September 2021

Measurements obtained in transformer tests are routinely used for computing associated steady-state model parameters, which can then be used for load flow simulation and other modeling applications. The short circuit and open circuit tests are most commonly performed with this purpose, allowing estimation of series and parallel branch transformer parameters. In this study, an extended model is proposed which does not employ the usually assumed cantilever circuit approximation and explicitly accounts for transformer connection resistance. An estimation of the proposed model parameters is enabled by usage of additional measurements yielded by the direct current (DC) resistance test. The proposed approach is validated by means of an experiment carried out on a real distribution power transformer, whose results demonstrate that the proposed model and parameter computation approach effectively decompose total transformer resistance into winding and contact components. Furthermore, the numerical results show that contact resistance is not negligible especially for low voltage windings, which reinforces the usefulness of the proposed model in providing detailed modeling of transformer resistances.
Effects of ECG Data Length on Heart Rate Variability among Young Healthy Adults
Authors: Chou En-Fan , Khine Michelle , Lockhart Thurmon , Soangra Rahul
Published: September 2021

The relationship between the robustness of HRV derived by linear and nonlinear methods to the required minimum data lengths has yet to be well understood. The normal electrocardiography (ECG) data of 14 healthy volunteers were applied to 34 HRV measures using various data lengths, and compared with the most prolonged (2000 R peaks or 750 s) by using the Mann–Whitney U test, to determine the 0.05 level of significance. We found that SDNN, RMSSD, pNN50, normalized LF, the ratio of LF and HF, and SD1 of the Poincaré plot could be adequately computed by small data size (60–100 R peaks). In addition, parameters of RQA did not show any significant differences among 60 and 750 s. However, longer data length (1000 R peaks) is recommended to calculate most other measures. The DFA and Lyapunov exponent might require an even longer data length to show robust results. Conclusions: Our work suggests the optimal minimum data sizes for different HRV measures which can potentially improve the efficiency and save the time and effort for both patients and medical care providers.
Electrochemical Deposition Treatment (EDT) as a Comprehensive Rehabilitation Method for Corrosion-Induced Deterioration in Concrete with Various Severity Levels
Authors: Kim Je-Kyoung , Yee Jurng-Jae , Kee Seong-Hoon
Published: September 2021

The primary purposes of this study are to investigate the feasibility of electrochemical deposition treatment (EDT) as a comprehensive rehabilitation method for corrosion-induced deterioration in reinforced concrete with various severity levels, and to propose a guideline for the determination of critical factors to advance EDT. This study includes three experimental phases, each of which simulates the initiation (de-passivation), propagation (high corrosion activity), and acceleration (formation of a surface-breaking crack) periods of corrosion-induced deterioration. After completion of a series of accelerated corrosion tests, damaged concrete samples with different severity levels are rehabilitated by a series of EDT processes using a MgCl2 solution in an electrolyte. The main variables for this experiment are the concentration levels (0, 0.3, 1.0 and 3.0 M) of a MgCl2 solution for test phase 1, charging time (0, 2, and 7 days) in EDT for test phase 2, and configuration of pre- and post-treatment processes in EDT for test phase 3. The rehabilitation performance of EDT is evaluated by analyzing the AC impedance properties of the steel-and-concrete interface using electrochemical impedance spectroscopy (EIS) for the test phases 1 and 2, and microscopic alternation in concrete cracks using optical microscopic image and SEM/EDX. It is demonstrated that EDT is an effective method for preventing and mitigating corrosion-induced deterioration in the initiation and rust propagation periods of corrosion and for repairing (closing and filling) a corrosion-induced surface-breaking crack in the acceleration phase of corrosion. Corrosion-resistant performance of concrete increases as the concentration levels of a MgCl2 solution in an electrolyte increases and as the charging time in EDT increases. In addition, a post-treatment process (applying a NaOH solution) after the electrochemical deposition process significantly improves crack-repairing performance of EDT.
Improving the Event-Based Classification Accuracy in Pit-Drilling Operations: An Application by Neural Networks and Median Filtering of the Acceleration Input Signal Data
Authors: Castro Pérez Sarahi Nicole , Borz Stelian Alexandru
Published: September 2021

Forestry is a complex economic sector which is relying on resource and process monitoring data. Most of the forest operations such as planting and harvesting are supported by the use of tools and machines, and their monitoring has been traditionally done by the use of pen-and-paper time studies. Nevertheless, modern data collection and analysis methods involving different kinds of platforms and machine learning techniques have been studied lately with the aim of easing the data management process. By their outcomes, improvements are still needed to reach a close to 100% activity recognition, which may depend on several factors such as the type of monitored process and the characteristics of the signals used as inputs. In this paper, we test, thought a case study on mechanized pit-drilling operations, the potential of digital signal processing techniques combined with Artificial Neural Networks (ANNs) in improving the event-based classification accuracy in the time domain. Signal processing was implemented by the means of median filtering of triaxial accelerometer data (window sizes of 3, 5, and up to 21 observations collected at 1 Hz) while the ANNs were subjected to the regularization hyperparameter’s tunning. An acceleration signal processed by a median filter with a window size of 3 observations and fed into an ANN set to learn and generalize by a regularization parameter of α = 0.01 has been found to be the best strategy in improving the event-based classification accuracy (improvements of 1% to 8% in classification accuracy depending on the type of event in question). Improvement of classification accuracy by signal filtering and ANN tuning may depend largely on the type of monitored process and its outcomes in terms of event duration; therefore, other monitoring applications may need particular designs of signal processing and ANN tuning.
Research on Coil Impedance of Self-Inductive Displacement Sensor Considering Core Eddy Current
Authors: Ren Zongqiang , Li Hongwei , Yu Wentao
Published: September 2021

The inductive displacement sensor is widely used in active magnetic bearing (AMB) systems to detect rotor displacement in real time, and the performance of the sensor directly affects the performance of AMB. At present, most theoretical studies on the working principle of inductive displacement sensor are based on a traditional mathematical model, ignoring the influence of the core magnetic resistance and core eddy current, which will lead to a certain error between the theoretical analysis of the sensor output characteristics and the actual situation. In this regard, based on the theory of electromagnetic field and circuit, an improved theoretical model of the inductive sensor was established in this paper by introducing the complex permeability, by which the influence of core eddy current on magnetic field can be taken into account. In order to verify the improved model, an eight-pole radial self-inductive displacement sensor with an air gap of 1 mm was designed. Then the electromagnetic field of the designed sensor was simulated by a finite element software and the GW LCR-6100 measuring instrument was used to measure the changes of the inductance and resistance of the designed sensor core coils with the rotor displacement at 20–100 kHz. The results demonstrated that there is a good linear relationship between the impedance change of the sensor coils and the rotor displacement within the measurement range of −0.4 ~ +0.4 mm. At the same time, compared with the traditional model, the sensitivity of the improved theoretical model is closer to the results from FEM and experiment, and the accuracy of the sensitivity of the improved theoretical model can be approximately doubled, despite there are certain differences with the experimental situation. Therefore, the improved theoretical model considering complex permeability is of great significance for studying the influence of core eddy current on the coil impedance of sensor.
Development of a Finger-Ring-Shaped Hybrid Smart Stethoscope for Automatic S1 and S2 Heart Sound Identification
Authors: Lee Soomin , Wei Qun , Park Heejoon , Na Yuri , Jeong Donghwa , Lim Hongjoon
Published: September 2021

Cardiac auscultation is one of the most popular diagnosis approaches to determine cardiovascular status based on listening to heart sounds with a stethoscope. However, heart sounds can be masked by visceral sounds such as organ movement and breathing, and a doctor’s level of experience can more seriously affect the accuracy of auscultation results. To improve the accuracy of auscultation, and to allow nonmedical staff to conduct cardiac auscultation anywhere and anytime, a hybrid-type personal smart stethoscope with an automatic heart sound analysis function is presented in this paper. The device was designed with a folding finger-ring shape that can be worn on the finger and placed on the chest to measure photoplethysmogram (PPG) signals and acquire the heart sound simultaneously. The measured heart sounds are detected as phonocardiogram (PCG) signals, and the boundaries of the heart sound variation and the peaks of the PPG signal are detected in preprocessing by an advanced Shannon entropy envelope. According to the relationship between PCG and PPG signals, an automatic heart sound analysis algorithm based on calculating the time interval between the first and second heart sounds (S1, S2) and the peak of the PPG was developed and implemented via the manufactured prototype device. The prototype device underwent accuracy and usability testing with 20 young adults, and the experimental results showed that the proposed smart stethoscope could satisfactorily collect the heart sounds and PPG signals. In addition, within the developed algorithm, the device was as accurate in start-points of heart sound detection as professional physiological signal-acquisition systems. Furthermore, the experimental results demonstrated that the device was able to identify S1 and S2 heart sounds automatically with high accuracy.
Voltammetric Determination of Levodopa Using Mesoporous Carbon—Modified Screen-Printed Carbon Sensors
Authors: Dăscălescu Dorin , Apetrei Constantin
Published: September 2021

Levodopa is a precursor of dopamine, having important beneficial effects in the treatment of Parkinson’s disease. In this study, levodopa was accurately detected by means of cyclic voltammetry using carbon-based (C-SPCE), mesoporous carbon (MC-SPCE) and ordered mesoporous carbon (OMC-SPCE)-modified screen-printed sensors. Screen-printed carbon sensors were initially used for the electrochemical detection of levodopa in a 10−3 M solution at pH 7.0. The mesoporous carbon with an organized structure led to better electroanalysis results and to lower detection and quantification limits of the OMC-SPCE sensor as compared to the other two studied sensors. The range of linearity obtained and the low values of the detection (0.290 µM) and quantification (0.966 µM) limit demonstrate the high sensitivity and accuracy of the method for the determination of levodopa in real samples. Therefore, levodopa was detected by means of OMC-SPCE in three dietary supplements produced by different manufacturers and having various concentrations of the active compound, levodopa. The results obtained by cyclic voltammetry were compared with those obtained by using the FTIR method and no significant differences were observed. OMC-SPCE proved to be stable, and the electrochemical responses did not vary by more than 3% in repeated immersions in a solution with the same concentration of levodopa. In addition, the interfering compounds did not significantly influence the peaks related to the presence of levodopa in the solution to be analyzed.
Extension of the Rigid-Constraint Method for the Heuristic Suboptimal Parameter Tuning to Ten Sensor Fusion Algorithms Using Inertial and Magnetic Sensing
Authors: Caruso Marco , Sabatini Angelo Maria , Knaflitz Marco , Della Croce Ugo , Cereatti Andrea
Published: September 2021

The orientation of a magneto-inertial measurement unit can be estimated using a sensor fusion algorithm (SFA). However, orientation accuracy is greatly affected by the choice of the SFA parameter values which represents one of the most critical steps. A commonly adopted approach is to fine-tune parameter values to minimize the difference between estimated and true orientation. However, this can only be implemented within the laboratory setting by requiring the use of a concurrent gold-standard technology. To overcome this limitation, a Rigid-Constraint Method (RCM) was proposed to estimate suboptimal parameter values without relying on any orientation reference. The RCM method effectiveness was successfully tested on a single-parameter SFA, with an average error increase with respect to the optimal of 1.5 deg. In this work, the applicability of the RCM was evaluated on 10 popular SFAs with multiple parameters under different experimental scenarios. The average residual between the optimal and suboptimal errors amounted to 0.6 deg with a maximum of 3.7 deg. These encouraging results suggest the possibility to properly tune a generic SFA on different scenarios without using any reference. The synchronized dataset also including the optical data and the SFA codes are available online.
Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach
Authors: Brophy Eoin , De Vos Maarten , Boylan Geraldine , Ward Tomás
Published: September 2021

Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.
On the Unification of Common Actigraphic Data Scoring Algorithms
Authors: Biegański Piotr , Stróż Anna , Dovgialo Marian , Duszyk-Bogorodzka Anna , Durka Piotr
Published: September 2021

Actigraphy is a well-known, inexpensive method to investigate human movement patterns. Sleep and circadian rhythm studies are among the most popular applications of actigraphy. In this study, we investigate seven common sleep-wake scoring algorithms designed for actigraphic data, namely Cole-Kripke algorithm, two versions of Sadeh algorithm, Sazonov algorithm, Webster algorithm, UCSD algorithm and Scripps Clinic algorithm. We propose a unified mathematical framework describing five of them. One of the observed novelties is that five of these algorithms are in fact equivalent to low-pass FIR filters with very similar characteristics. We also provide explanations about the role of some factors defining these algorithms, as none were given by their Authors who followed empirical procedures. Proposed framework provides a robust mathematical description of discussed algorithms, which for the first time allows one to fully understand their operation and basics.
Transfer Learning in Wastewater Treatment Plant Control Design: From Conventional to Long Short-Term Memory-Based Controllers
Authors: Pisa Ivan , Morell Antoni , Vilanova Ramón , Vicario Jose Lopez
Published: September 2021

In the last decade, industrial environments have been experiencing a change in their control processes. It is more frequent that control strategies adopt Artificial Neural Networks (ANNs) to support control operations, or even as the main control structure. Thus, control structures can be directly obtained from input and output measurements without requiring a huge knowledge of the processes under control. However, ANNs have to be designed, implemented, and trained, which can become complex and time-demanding processes. This can be alleviated by means of Transfer Learning (TL) methodologies, where the knowledge obtained from a unique ANN is transferred to the remaining nets reducing the ANN design time. From the control viewpoint, the first ANN can be easily obtained and then transferred to the remaining control loops. In this manuscript, the application of TL methodologies to design and implement the control loops of a Wastewater Treatment Plant (WWTP) is analysed. Results show that the adoption of this TL-based methodology allows the development of new control loops without requiring a huge knowledge of the processes under control. Besides, a wide improvement in terms of the control performance with respect to conventional control structures is also obtained. For instance, results have shown that less oscillations in the tracking of desired set-points are produced by achieving improvements in the Integrated Absolute Error and Integrated Square Error which go from 40.17% to 94.29% and from 34.27% to 99.71%, respectively.
The Implementation and Evaluation of Individual Preference in Robot Facial Expression Based on Emotion Estimation Using Biological Signals
Authors: Sripian Peeraya , Anuardi Muhammad Nur Adilin Mohd , Yu Jiawei , Sugaya Midori
Published: September 2021

Recently, robot services have been widely applied in many fields. To provide optimum service, it is essential to maintain good acceptance of the robot for more effective interaction with users. Previously, we attempted to implement facial expressions by synchronizing an estimated human emotion on the face of a robot. The results revealed that the robot could present different perceptions according to individual preferences. In this study, we considered individual differences to improve the acceptance of the robot by changing the robot’s expression according to the emotion of its interacting partner. The emotion was estimated using biological signals, and the robot changed its expression according to three conditions: synchronized with the estimated emotion, inversely synchronized, and a funny expression. During the experiment, the participants provided feedback regarding the robot’s expression by choosing whether they “like” or “dislike” the expression. We investigated individual differences in the acceptance of the robot expression using the Semantic Differential scale method. In addition, logistic regression was used to create a classification model by considering individual differences based on the biological data and feedback from each participant. We found that the robot expression based on inverse synchronization when the participants felt a negative emotion could result in impression differences among individuals. Then, the robot’s expression was determined based on the classification model, and the Semantic Differential scale on the impression of the robot was compared with the three conditions. Overall, we found that the participants were most accepting when the robot expression was calculated using the proposed personalized method.
Preparation of Hydroxytyrosol by Acid Hydrolysis from Olive Leaves
Authors: Wang Yujie , Luo Siyuan , Luo Jieling , Qu Jipeng , Feng Shiling , Chen Tao , Zhou Lijun , Yuan Ming , Yang Hongyu , Li Tian , Lan Bo , Ding Chunbang
Published: September 2021

Hydroxytyrosol (HT) is the main bioactive compound in olive leaves. However, olive leaves contain a lower level of HT and the extraction process of HT was rarely optimized. In this study, compared with two extraction methods, ultrasound was found to have a positive effect on improving the yield of HT. Therefore, ultrasound was used to assist hydrolysis of hydrochloric acid to extract HT from olive leaves. Response surface method and macroporous resins were applied to optimize the extraction process as well as enrichment of HT. The results showed that ultrasonic extraction time had a significant effect on the yield and the optimal extraction conditions were obtained: ultrasonic time was 120 min, hydrochloric acid concentration was 1.60 mol/L and the liquid-to-material ratio was 60.00 mL/g. Under the optimal extraction condition, the yield of HT was 14.11 ± 0.12 mg/g. NKA-Ⅱ macroporous resin was proved to be a suitable resin to enrich HT from extraction solution. The optimal condition for enriching HY was 250 mL of loading solution at the flow rate of 1.5 mL/min with 40 mL volume of 75% ethanol–eluent at a flow rate of 1.0 mL/min. The concentration of HT changed from 2.27% to 9.25% after enrichment by macroporous resin.
Application of Deep Learning on UAV-Based Aerial Images for Flood Detection
Authors: Munawar Hafiz Suliman , Ullah Fahim , Qayyum Siddra , Heravi Amirhossein
Published: September 2021

Floods are one of the most fatal and devastating disasters, instigating an immense loss of human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is a need to develop and implement real-time flood management systems that could instantly detect flooded regions to initiate relief activities as early as possible. Current imaging systems, relying on satellites, have demonstrated low accuracy and delayed response, making them unreliable and impractical to be used in emergency responses to natural disasters such as flooding. This research employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can identify inundated areas from aerial images. The Haar cascade classifier was explored in the case study to detect landmarks such as roads and buildings from the aerial images captured by UAVs and identify flooded areas. The extracted landmarks are added to the training dataset that is used to train a deep learning algorithm. Experimental results show that buildings and roads can be detected from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded in classifying flooded and non-flooded regions from the input case study images. The system has shown promising results on test images belonging to both pre- and post-flood classes. The flood relief and rescue workers can quickly locate flooded regions and rescue stranded people using this system. Such real-time flood inundation systems will help transform the disaster management systems in line with modern smart cities initiatives.
Seeking Evidence of The MAGA Cult and Trump Derangement Syndrome: An Examination of (A)symmetric Political Bias
Authors: Franks Andrew S. , Hesami Farhang
Published: September 2021

Three studies sought to explore the existence of (a)symmetric bias regarding Donald Trump. In Study 1, participants read one of three statements expressing different degrees of favorability toward electing the President of the United States via a National Popular Vote attributed to Trump or an anonymous source. In Study 2, participants read one of two statements either favoring or disfavoring the name change of the Washington NFL franchise, and the statement was attributed to either Trump or an anonymous source. In Study 3, Trump and Biden voters were asked to rate their support or opposition to counting all the votes in battleground states when continued counting was expected to either help Trump or Biden. Results for all three studies supported the asymmetric bias hypothesis. Trump supporters consistently showed bias in favor of the interests and ostensible positions of Trump, whereas Trump’s detractors did not show an opposing bias.
Social Representations of Children and Parents in Parliamentary-Committee Debates about the Inclusion of Child Psychological Maltreatment in the Quebec Youth Protection Act
Authors: Plante Nathalie , Negura Lilian
Published: September 2021

Child psychological maltreatment (CPM) was incorporated into the Quebec Youth Protection Act (YPA) in 2006. At that time, various civil-society actors were invited to present to Parliament their views on these legislative changes. The objective of this article is to document the social representations mobilized by the stakeholders in the parliamentary committee in relation to the inclusion of CPM in the Quebec YPA. After explaining our research objectives, questions, and methodology, we will discuss our results, in particular about the distinctive nature of children as a representational object. This specificity will be analyzed in order to better understand the type of communication it generates and the corresponding hegemonic representation of parents. Specifically, implications related to the representational dynamics identified are discussed in relation to our collective capacity (or incapacity) to debate sensitive issues such as child abuse.
Perceived Trust in Public Authorities Nine Months after the COVID-19 Outbreak: A Cross-National Study
Authors: Price Daicia , Bonsaksen Tore , Ruffolo Mary , Leung Janni , Chiu Vivian , Thygesen Hilde , Schoultz Mariyana , Geirdal Amy Ostertun
Published: September 2021

This study aimed to examine the perceived trust in information provided by public authorities and financial measures put in place to address the impact of COVID-19. Using a cross-national approach among four Western countries—the United States of America, Norway, Australia, and the United Kingdom—provides an analysis of responses related to trust and how they were associated with age group, gender, education level, employment status, size of place of residence, infection status, and social media use. When controlling for all included variables in logistic regression analyses, the likelihood of having trust in the public authorities’ information was higher for women, those with higher levels of education, and those living in urban areas. Being infected with the coronavirus, and spending more time daily on social media, were both associated with lower likelihood of reporting trust in information. Although policies implemented to respond to economic concerns varied cross-nationally, higher age, identifying as female, being employed, living in a city, no COVID-19 infection experience and lower levels of social media usage were associated with a higher likelihood of trusting in the financial measures put in place to counteract the economic effects of COVID-19.
A Machine-Learning Classification Tree Model of Perceived Organizational Performance in U.S. Federal Government Health Agencies
Authors: Kang In-Gu , Kim Nayoung , Loh Wei-Yin , Bichelmeyer Barbara A.
Published: September 2021

Perceived organizational performance (POP) is an important factor that influences employees’ attitudes and behaviors such as retention and turnover, which in turn improve or impede organizational sustainability. The current study aims to identify interaction patterns of risk factors that differentiate public health and human services employees who perceived their agency performance as low. The 2018 Federal Employee Viewpoint Survey (FEVS), a nationally representative sample of U.S. federal government employees, was used for this study. The study included 43,029 federal employees (weighted n = 75,706) among 10 sub-agencies in the public health and human services sector. The machine-learning classification decision-tree modeling identified several tree-splitting variables and classified 33 subgroups of employees with 2 high-risk, 6 moderate-risk and 25 low-risk subgroups of POP. The important variables predicting POP included performance-oriented culture, organizational satisfaction, organizational procedural justice, task-oriented leadership, work security and safety, and employees’ commitment to their agency, and important variables interacted with one another in predicting risks of POP. Complex interaction patterns in high- and moderate-risk subgroups, the importance of a machine-learning approach to sustainable human resource management in industry 4.0, and the limitations and future research are discussed.
Ecological Risk Assessment of Heavy Metals in Adjoining Sediment of River Ecosystem
Authors: Singh Bhanu Pratap , Choudhury Moharana , Samanta Palas , Gaur Monu , Kumar Maniram
Published: September 2021

The present study was focused on heavy metal distribution patterns and the associated ecological risk assessment in the adjoining sediment of the Hindon River in Muzaffarnagar Region (U.P.), India. Lead (Pb), zinc (Zn), copper (Cu), cadmium (Cd), nickel (Ni), iron (Fe), aluminum (Al), sodium (Na), and potassium (K) were estimated from six sediment samples (Atali A and B, Budhana A and B, and Titavi A and B). The concentration of the heavy metals Zn, Pb, Cu, Ni, and Cd ranged from 25.5–74.7 mg kg−1, 29.8–40.6 mg kg−1, 7.0–29.2 mg kg−1, 14.7–21.8 mg kg−1, and 0.96–1.2 mg kg−1, respectively and followed the sequence Zn > Pb > Cu > Ni > Cd, while major elements followed the sequence Na > Fe > Al > K. The enrichment factor (EF) and geo-accumulation index (Igeo) revealed that Atali A showed the highest enrichment and followed the sequence Zn > Cu > Pb > Ni > Cd. Contamination factor (CF) and contamination degree (CD) depicted that all of the sites (except Titavi B) were moderately to considerably contaminated. The highest degree of contamination (CF, CD, and PLI, pollution load index) was observed at Titavi A followed by Atali A and Budhana A. Eco-toxicological risk assessment (RI) indicated that the sites were moderately contaminated, predominantly by Ni and Pb and Zn. The results revealed that the metal contamination in sediment is alarming and might pose an adverse threat to ecosystem health.
A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications
Authors: Jamal Saif , Tan Nadia M. L. , Pasupuleti Jagadeesh
Published: September 2021

In the past few years, the application and research community has expressed a lot of interest in managing energy and power while using distributed generation systems. Electricity generation and its usage coordination are vital aspects of energy efficiency that can help in saving energy, decreasing energy costs, and fulfilling global emission objectives. Owing to the relevance of the topic, here, the researchers have presented a comparative and critical review of recent developments in the fields of energy management systems (EMSs) and power management systems (PMSs). Furthermore, the researchers also reviewed the various EMS and PMS methods that could be used for reviewing microgrid (MG) and nanogrid (NG) systems. The EMS for MG and NG systems helps in addressing important economic objectives like minimisation of operational costs after optimising the fuel costs, emission costs, and battery degradation costs, while also improving the life of the MG devices. Alternatively, the PMS helps in addressing technical objectives like improving the stability, flexibility, reliability, and quality of MG and NG systems. The researchers have also discussed the drawbacks and challenges affecting the widespread application of EMSs and PMSs.
Plant Species Composition and the Perception of the Afforestation in Urban Public Green Spaces in a Municipality in Eastern Brazilian Amazon
Authors: dos Santos Ediane Bó , Nogueira Fernanda Mayara , Talgatti Dávia Marciana
Published: September 2021

The plant composition is a fundamental element in public green spaces, improving the environment and people’s quality of life. The executing of floristic inventories can contribute to better understanding and management of these spaces. Here, we sought to know the plants used in the afforestation of the main public green spaces in the town of Oriximiná, eastern Brazilian Amazon, as well as to perform a brief analysis of the population’s perception regarding the afforestation of these environments. The plants were collected and identified using specific literature and dichotomous keys, in addition to consultations in virtual herbariums. The analysis of the population’s perception took place through interviews, with questionnaires collected in each public green space. We registered 1616 individuals from the flora of the squares, distributed in 16 families, 24 genera, and 28 species. Exotic plants are predominant in number species and of individuals. The interviewees demonstrated that they are aware of the importance of plants in the squares and providing shade stands out as the most cited benefit. The afforestation of the squares shows some irregularities, highlighting the need for better planning which includes the use of native species and the participation of the local community in the management of these green areas. Finally, we provide a list of native species, naturally occurring in the Amazon region, that can be used in urban afforestation.
Trends and Challenges in Regional Life Cycle Management: A Bibliometric Analysis
Authors: Bezama Alberto , Mittelstädt Nora , Thrän Daniela , Balkau Fritz
Published: September 2021

Life cycle management has become increasingly important in the formulation of regional sustainable development policies and programs, while research on and the application of life cycle assessment (LCA) methodologies are predominantly conducted in a business context or for materials and product-related issues. In light of the growing importance of regional policymaking, this article discusses the current scientific landscape of regional life cycle management approaches. It conducts a bibliometric analysis using the Scopus database and integrates a co-word analysis to highlight important linkages. Our analysis reveals that an increasing number of papers have been published on regional-level LCAs in recent years; however, they remain focused on issues related to the traditional materials-oriented aspects. Conversely, little research appears to be conducted on regional life cycle management issues and activities. The research is strongly centralized, with a clustering of regional LCA studies within a relatively small group of countries, institutes, journals and authors. Despite a rapid increase in recently published papers, our analysis reveals a gap in research and knowledge transfer of regional LCA results to public policymakers and regional planners.
An Integrated Energy Simulation Model of a Compressed Air System for Sustainable Manufacturing: A Time-Discretized Approach
Authors: Vyas Vansh , Jeon Hyun-woo , Wang Chao
Published: September 2021

A compressed air system (CAS) is one of the most common and energy-consuming systems in manufacturing. To practice more economically and environmentally sustainable manufacturing, manufacturers need ways to reduce the energy costs and carbon footprint, resulting from a CAS in their production systems. While preliminary energy studies on a CAS and on machining processes are available separately, existing research studies rarely analyze energy costs using a tool that considers both a CAS and production systems. Therefore, in this study, we propose an energy simulation tool that combines a CAS and a production system to evaluate the effects of a CAS and production parameters on energy consumption and costs at a factory level. In particular, we develop a time-discretized algorithm for simulating a CAS to accurately consider the dynamics of CAS parameters such as pressure and flow rate. From 48 simulation case studies, we show that changes in a CAS such as proper HP sizing, a reduction in compressed air leaks, and a decrease in the discharge pressure can increase productivity and reduce energy costs by up to 11%. The simulation analysis from this study suggests a way to help manufacturers and researchers find more sustainable ways to achieve energy-efficient configurations for production systems including a CAS.
Assessment of the Diversity of Large Tree Species in Rapidly Urbanizing Areas along the Chao Phraya River Rim, Central Thailand
Authors: Asanok Lamthai , Kamyo Torlarp , Norsaengsri Monthon , Yotapakdee Teeka , Navakam Suwit
Published: September 2021

Urban trees provide numerous ecosystem services in cities such as pollution absorption and reduced urban heat island intensity, energy use, and mental fatigue. Understanding urban tree species diversity can enhance tree planning and management in rapidly urbanizing areas. However, few studies have examined the effects of urbanization on urban tree diversity in Thailand. This study assessed the diversity of large trees in urban landscapes including 11 cities along the 372 km Chao Phraya River Rim in central Thailand. Species diversity, importance value, and distribution were evaluated in each city. Our survey documented 987 large trees belonging to 65 species, 48 genera, and 31 families. The dominant species of Dipterocarpus alatus and Hopea odorata had the highest importance and relative abundance. The highest abundance of large trees was seen in the rural city of Sing Buri, while large urban cities such as Ayutthaya and Bangkok exhibited the highest species diversity. Detrended correspondence analysis indicated that the diversity of unique species was high in large urban cities, while dominant and common species were ubiquitous in rural cities. These findings suggest that large trees are few in number but exhibit high species diversity in large rapidly urbanizing cities. Therefore, preserving good site conditions is critical for the survival of large trees in urban cities. Heritage tree registration may aid preservation efforts and enhance the benefits of these large trees in rapidly urbanizing areas.
Impact of Ventilation Strategy on the Transmission of Outdoor Pollutants into Indoor Environment Using CFD
Authors: Mohammadi Murtaza , Calautit John
Published: September 2021

The transition to remote working due to the pandemic has accentuated the importance of clean indoor air, as people spend a significant portion of their time indoors. Amongst the various determinants of indoor air quality, outdoor pollution is a significant source. While conventional studies have certainly helped to quantify the long-term personal exposure to pollutants and assess their health impact, they have not paid special attention to the mechanism of transmission of pollutants between the two environments. Nevertheless, the quantification of infiltration is essential to determine the contribution of ambient pollutants in indoor air quality and its determinants. This study evaluates the transmission of outdoor pollutants into the indoor environment using 3D computational fluid dynamics modelling with a pollution dispersion model. Naturally ventilated buildings next to an urban canyon were modelled and simulated using Ansys Fluent and validated against wind tunnel results from the Concentration Data of Street Canyons database. The model consisted of two buildings of three storeys each, located on either side of a road. Two line-source pollutants were placed in the street, representing traffic emissions. Three internal rooms were selected and modelled on each floor and implemented with various ventilation strategies. Results indicate that for a canyon with an aspect ratio of 1, indoor spaces in upstream buildings are usually less polluted than downstream ones. Although within the canyon, pollution is 2–3 times higher near the upstream building. Cross ventilation can minimise or prevent infiltration of road-side pollutants into indoor spaces, while also assisting in the dispersion of ambient pollutants. The critical configuration, in terms of air quality, is single-sided ventilation from the canyon. This significantly increases indoor pollutant concentration regardless of the building location. The study reveals that multiple factors determine the indoor–outdoor links, and thorough indexing and understanding of the processes can help designers and urban planners in regulating urban configuration and geometries for improved indoor air quality. Future works should look at investigating the influence of indoor emissions and the effects of different seasons.