bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Dynamic Plantar Pressure and Influence of Clinical-Functional Measures on Their Performance in Subjects with Bimalleolar Ankle Fracture at 6 and 12 Months Post-Surgery
Authors: Fernández-Gorgojo Mario , Salas-Gómez Diana , Sánchez-Juan Pascual , Laguna-Bercero Esther , Pérez-Núñez María Isabel
Published: April 2023

Recovery after ankle fracture surgery can be slow and even present functional deficits in the long term, so it is essential to monitor the rehabilitation process objectively and detect which parameters are recovered earlier or later. The aim of this study was (1) to evaluate dynamic plantar pressure and functional status in patients with bimalleolar ankle fracture 6 and 12 months after surgery, and (2) to study their degree of correlation with previously collected clinical variables. Twenty-two subjects with bimalleolar ankle fractures and eleven healthy subjects were included in the study. Data collection was performed at 6 and 12 months after surgery and included clinical measurements (ankle dorsiflexion range of motion and bimalleolar/calf circumference), functional scales (AOFAS and OMAS), and dynamic plantar pressure analysis. The main results found in plantar pressure were a lower mean/peak plantar pressure, as well as a lower contact time at 6 and 12 months with respect to the healthy leg and control group and only the control group, respectively (effect size 0.63 ≤ d ≤ 0.97). Furthermore, in the ankle fracture group there is a moderate negative correlation (−0.435 ≤ r ≤ 0.674) between plantar pressures (average and peak) with bimalleolar and calf circumference. The AOFAS and OMAS scale scores increased at 12 months to 84.4 and 80.0 points, respectively. Despite the evident improvement one year after surgery, data collected using the pressure platform and functional scales suggest that recovery is not yet complete.
An Investigation on the Possible Application Areas of Low-Cost PM Sensors for Air Quality Monitoring
Authors: Suriano Domenico , Prato Mario
Published: April 2023

In recent years, the availability on the market of low-cost sensors (LCSs) and low-cost monitors (LCMs) for air quality monitoring has attracted the interest of scientists, communities, and professionals. Although the scientific community has raised concerns about their data quality, they are still considered a possible alternative to regulatory monitoring stations due to their cheapness, compactness, and lack of maintenance costs. Several studies have performed independent evaluations to investigate their performance, but a comparison of the results is difficult due to the different test conditions and metrics adopted. The U.S. Environmental Protection Agency (EPA) tried to provide a tool for assessing the possible uses of LCSs or LCMs by publishing guidelines to assign suitable application areas for each of them on the basis of the mean normalized bias (MNB) and coefficient of variance (CV) indicators. Until today, very few studies have analyzed LCS performance by referring to the EPA guidelines. This research aimed to understand the performance and the possible application areas of two PM sensor models (PMS5003 and SPS30) on the basis of the EPA guidelines. We computed the R2, RMSE, MAE, MNB, CV, and other performance indicators and found that the coefficient of determination (R2) ranged from 0.55 to 0.61, while the root mean squared error (RMSE) ranged from 11.02 µg/m3 to 12.09 µg/m3. Moreover, the application of a correction factor to include the humidity effect produced an improvement in the performance of the PMS5003 sensor models. We also found that, based on the MNB and CV values, the EPA guidelines assigned the SPS30 sensors to the “informal information about the presence of the pollutant” application area (Tier I), while PMS5003 sensors were assigned to the “supplemental monitoring of regulatory networks” area (Tier III). Although the usefulness of the EPA guidelines is acknowledged, it appears that improvements are necessary to increase their effectiveness.
Experimental Exploration of Multilevel Human Pain Assessment Using Blood Volume Pulse (BVP) Signals
Authors: Khan Muhammad Umar , Aziz Sumair , Hirachan Niraj , Joseph Calvin , Li Jasper , Fernandez-Rojas Raul
Published: April 2023

Critically ill patients often lack cognitive or communicative functions, making it challenging to assess their pain levels using self-reporting mechanisms. There is an urgent need for an accurate system that can assess pain levels without relying on patient-reported information. Blood volume pulse (BVP) is a relatively unexplored physiological measure with the potential to assess pain levels. This study aims to develop an accurate pain intensity classification system based on BVP signals through comprehensive experimental analysis. Twenty-two healthy subjects participated in the study, in which we analyzed the classification performance of BVP signals for various pain intensities using time, frequency, and morphological features through fourteen different machine learning classifiers. Three experiments were conducted using leave-one-subject-out cross-validation to better examine the hidden signatures of BVP signals for pain level classification. The results of the experiments showed that BVP signals combined with machine learning can provide an objective and quantitative evaluation of pain levels in clinical settings. Specifically, no pain and high pain BVP signals were classified with 96.6% accuracy, 100% sensitivity, and 91.6% specificity using a combination of time, frequency, and morphological features with artificial neural networks (ANNs). The classification of no pain and low pain BVP signals yielded 83.3% accuracy using a combination of time and morphological features with the AdaBoost classifier. Finally, the multi-class experiment, which classified no pain, low pain, and high pain, achieved 69% overall accuracy using a combination of time and morphological features with ANN. In conclusion, the experimental results suggest that BVP signals combined with machine learning can offer an objective and reliable assessment of pain levels in clinical settings.
Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics
Authors: Lee Yongkuk , Pokharel Suresh , Muslim Asra Al , KC Dukka B. , Lee Kyoung Hag , Yeo Woon-Hong
Published: April 2023

Older adults are more vulnerable to falling due to normal changes due to aging, and their falls are a serious medical risk with high healthcare and societal costs. However, there is a lack of automatic fall detection systems for older adults. This paper reports (1) a wireless, flexible, skin-wearable electronic device for both accurate motion sensing and user comfort, and (2) a deep learning-based classification algorithm for reliable fall detection of older adults. The cost-effective skin-wearable motion monitoring device is designed and fabricated using thin copper films. It includes a six-axis motion sensor and is directly laminated on the skin without adhesives for the collection of accurate motion data. To study accurate fall detection using the proposed device, different deep learning models, body locations for the device placement, and input datasets are investigated using motion data based on various human activities. Our results indicate the optimal location to place the device is the chest, achieving accuracy of more than 98% for falls with motion data from older adults. Moreover, our results suggest a large motion dataset directly collected from older adults is essential to improve the accuracy of fall detection for the older adult population.
Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning
Authors: Almadhor Ahmad , Sampedro Gabriel Avelino , Abisado Mideth , Abbas Sidra , Kim Ye-Jin , Khan Muhammad Attique , Baili Jamel , Cha Jae-Hyuk
Published: April 2023

With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track health status using adequate user data. Insufficient data is accessible, however, due to privacy concerns, making it challenging to use Artificial Intelligence (AI) models in the medical industry. This research aims to preserve the privacy of patient data while classifying wearable-based electrodermal activities. We propose a Federated Learning (FL) based approach using a Deep Neural Network (DNN) model. For experimentation, we use the Wearable Stress and Affect Detection (WESAD) dataset, which includes five data states: transient, baseline, stress, amusement, and meditation. We transform this raw dataset into a suitable form for the proposed methodology using the Synthetic Minority Oversampling Technique (SMOTE) and min-max normalization pre-processing methods. In the FL-based technique, the DNN algorithm is trained on the dataset individually after receiving model updates from two clients. To decrease the over-fitting effect, every client analyses the results three times. Accuracies, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values are evaluated for each client. The experimental result shows the effectiveness of the federated learning-based technique on a DNN, reaching 86.82% accuracy while also providing privacy to the patient’s data. Using the FL-based DNN model over a WESAD dataset improves the detection accuracy compared to the previous studies while also providing the privacy of patient data.
A Time-of-Flight Image Sensor Using 8-Tap P-N Junction Demodulator Pixels
Authors: Miyazawa Ryosuke , Shirakawa Yuya , Mars Kamel , Yasutomi Keita , Kagawa Keiichiro , Aoyama Satoshi , Kawahito Shoji
Published: April 2023

This paper presents a time-of-flight image sensor based on 8-Tap P-N junction demodulator (PND) pixels, which is designed for hybrid-type short-pulse (SP)-based ToF measurements under strong ambient light. The 8-tap demodulator implemented with multiple p-n junctions used for modulating the electric potential to transfer photoelectrons to eight charge-sensing nodes and charge drains has an advantage of high-speed demodulation in large photosensitive areas. The ToF image sensor implemented using 0.11 µm CIS technology, consisting of an 120 (H) × 60 (V) image array of the 8-tap PND pixels, successfully works with eight consecutive time-gating windows with the gating width of 10 ns and demonstrates for the first time that long-range (>10 m) ToF measurements under high ambient light are realized using single-frame signals only, which is essential for motion-artifact-free ToF measurements. This paper also presents an improved depth-adaptive time-gating-number assignment (DATA) technique for extending the depth range while having ambient-light canceling capability and a nonlinearity error correction technique. By applying these techniques to the implemented image sensor chip, hybrid-type single-frame ToF measurements with depth precision of maximally 16.4 cm (1.4% of the maximum range) and the maximum non-linearity error of 0.6% for the full-scale depth range of 1.0–11.5 m and operations under direct-sunlight-level ambient light (80 klux) have been realized. The depth linearity achieved in this work is 2.5 times better than that of the state-of-the-art 4-tap hybrid-type ToF image sensor.
Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm
Authors: Si Qing , Li Changyong
Published: April 2023

An improved whale optimization algorithm is proposed to solve the problems of the original algorithm in indoor robot path planning, which has slow convergence speed, poor path finding ability, low efficiency, and is easily prone to falling into the local shortest path problem. First, an improved logistic chaotic mapping is applied to enrich the initial population of whales and improve the global search capability of the algorithm. Second, a nonlinear convergence factor is introduced, and the equilibrium parameter A is changed to balance the global and local search capabilities of the algorithm and improve the search efficiency. Finally, the fused Corsi variance and weighting strategy perturbs the location of the whales to improve the path quality. The improved logical whale optimization algorithm (ILWOA) is compared with the WOA and four other improved whale optimization algorithms through eight test functions and three raster map environments for experiments. The results show that ILWOA has better convergence and merit-seeking ability in the test function. In the path planning experiments, the results are better than other algorithms when comparing three evaluation criteria, which verifies that the path quality, merit-seeking ability, and robustness of ILWOA in path planning are improved.
A Novel Fiber-Optic Ice Sensor to Identify Ice Types Based on Total Reflection
Authors: Zhang Chi , Xiao Chunhua , Li Shaorong , Guo Xiaowei , Wang Qi , He Yizhou , Lv Huiyan , Yan Hongkai , Liu Dongan
Published: April 2023

To address the issues of not accurately identifying ice types and thickness in current fiber-optic ice sensors, in this paper, we design a novel fiber-optic ice sensor based on the reflected light intensity modulation method and total reflection principle. The performance of the fiber-optic ice sensor was simulated by ray tracing. The low-temperature icing tests validated the performance of the fiber-optic ice sensor. It is shown that the ice sensor can detect different ice types and the thickness from 0.5 to 5 mm at temperatures of −5 °C, −20 °C, and −40 °C. The maximum measurement error is 0.283 mm. The proposed ice sensor provides promising applications in aircraft and wind turbine icing detection.
Technique-Dependent Relationship between Local Ski Bending Curvature, Roll Angle and Radial Force in Alpine Skiing
Authors: Thorwartl Christoph , Tschepp Andreas , Lasshofer Michael , Holzer Helmut , Zirkl Martin , Hammer Matthias , Stadlober Barbara , Stöggl Thomas
Published: April 2023

Skiing technique, and performance are impacted by the interplay between ski and snow. The resulting deformation characteristics of the ski, both temporally and segmentally, are indicative of the unique multi-faceted nature of this process. Recently, a PyzoFlex® ski prototype was presented for measuring the local ski curvature (w″), demonstrating high reliability and validity. The value of w″ increases as a result of enlargement of the roll angle (RA) and the radial force (RF) and consequently minimizes the radius of the turn, preventing skidding. This study aims to analyze segmental w″ differences along the ski, as well as to investigate the relationship among segmental w″, RA, and RF for both the inner and outer skis and for different skiing techniques (carving and parallel ski steering). A skier performed 24 carving and 24 parallel ski steering turns, during which a sensor insole was placed in the boot to determine RA and RF, and six PyzoFlex® sensors were used to measure the w″ progression along the left ski (w1−6″). All data were time normalized over a left-right turn combination. Correlation analysis using Pearson’s correlation coefficient (r) was conducted on the mean values of RA, RF, and segmental w1−6″ for different turn phases [initiation, center of mass direction change I (COM DC I), center of mass direction change II (COM DC II), completion]. The results of the study indicate that, regardless of the skiing technique, the correlation between the two rear sensors (L2 vs. L3) and the three front sensors (L4 vs. L5, L4 vs. L6, L5 vs. L6) was mostly high (r > 0.50) to very high (r > 0.70). During carving turns, the correlation between w″ of the rear (w1−3″) and that of front sensors (w4−6″) of the outer ski was low (ranging between −0.21 and 0.22) with the exception of high correlations during COM DC II (r = 0.51–0.54). In contrast, for parallel ski steering, the r between the w″ of the front and rear sensors was mostly high to very high, especially for COM DC I and II (r = 0.48–0.85). Further, a high to very high correlation (r ranging between 0.55 and 0.83) among RF, RA, and w″ of the two sensors located behind the binding (w2″,w3″) in COM DC I and II for the outer ski during carving was found. However, the values of r were low to moderate (r = 0.04–0.47) during parallel ski steering. It can be concluded that homogeneous ski deflection along the ski is an oversimplified picture, as the w″ pattern differs not only temporally but also segmentally, depending on the employed technique and turn phase. In carving, the rear segment of the outer ski is considered to have a pivotal role for creating a clean and precise turn on the edge.
Integrating Visual and Network Data with Deep Learning for Streaming Video Quality Assessment
Authors: Margetis George , Tsagkatakis Grigorios , Stamou Stefania , Stephanidis Constantine
Published: April 2023

Existing video Quality-of-Experience (QoE) metrics rely on the decoded video for the estimation. In this work, we explore how the overall viewer experience, quantified via the QoE score, can be automatically derived using only information available before and during the transmission of videos, on the server side. To validate the merits of the proposed scheme, we consider a dataset of videos encoded and streamed under different conditions and train a novel deep learning architecture for estimating the QoE of the decoded video. The major novelty of our work is the exploitation and demonstration of cutting-edge deep learning techniques in automatically estimating video QoE scores. Our work significantly extends the existing approach for estimating the QoE in video streaming services by combining visual information and network conditions.
A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment
Authors: Jung Sylvain , de l’Escalopier Nicolas , Oudre Laurent , Truong Charles , Dorveaux Eric , Gorintin Louis , Ricard Damien
Published: April 2023

This paper presents a novel approach to creating a graphical summary of a subject’s activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time series collected while monitoring patients in Semi Free-Living Environments are often long and complex, our contribution relies on an innovative pipeline of signal processing methods and machine learning algorithms. Once learned, the graphical representation is able to sum up all activities present in the data and can quickly be applied to newly acquired time series. In a nutshell, raw data from inertial measurement units are first segmented into homogeneous regimes with an adaptive change-point detection procedure, then each segment is automatically labeled. Then, features are extracted from each regime, and lastly, a score is computed using these features. The final visual summary is constructed from the scores of the activities and their comparisons to healthy models. This graphical output is a detailed, adaptive, and structured visualization that helps better understand the salient events in a complex gait protocol.
A Portable Multi-Modal Cushion for Continuous Monitoring of a Driver’s Vital Signs
Authors: Linschmann Onno , Uguz Durmus Umutcan , Romanski Bianca , Baarlink Immo , Gunaratne Pujitha , Leonhardt Steffen , Walter Marian , Lueken Markus
Published: April 2023

With higher levels of automation in vehicles, the need for robust driver monitoring systems increases, since it must be ensured that the driver can intervene at any moment. Drowsiness, stress and alcohol are still the main sources of driver distraction. However, physiological problems such as heart attacks and strokes also exhibit a significant risk for driver safety, especially with respect to the ageing population. In this paper, a portable cushion with four sensor units with multiple measurement modalities is presented. Capacitive electrocardiography, reflective photophlethysmography, magnetic induction measurement and seismocardiography are performed with the embedded sensors. The device can monitor the heart and respiratory rates of a vehicle driver. The promising results of the first proof-of-concept study with twenty participants in a driving simulator not only demonstrate the accuracy of the heart (above 70% of medical-grade heart rate estimations according to IEC 60601-2-27) and respiratory rate measurements (around 30% with errors below 2 BPM), but also that the cushion might be useful to monitor morphological changes in the capacitive electrocardiogram in some cases. The measurements can potentially be used to detect drowsiness and stress and thus the fitness of the driver, since heart rate variability and breathing rate variability can be captured. They are also useful for the early prediction of cardiovascular diseases, one of the main reasons for premature death. The data are publicly available in the UnoVis dataset.
Gemellar Competition as a Key Component in Seed–Seedling Transition of Handroanthus chrysotrichus (Mart. ex A. DC.) Mattos (Bignoniaceae)
Authors: Mendes-Rodrigues Clesnan , Caetano Ana Paula de Souza , Sampaio Diana Salles , Pereira Janser Moura , Ribeiro-Oliveira João Paulo , Oliveira Paulo Eugênio , Ranal Marli A.
Published: April 2023

The occurrence of more than one embryo per seed (polyembryony) is common among angiosperms; however, there are gaps in the knowledge of its effects on the early stages of plant development. In this context, we study the effects of polyembryony and intraspecific variability in gemellar competition during the seed–seedling transition in Neotropical Handroanthus chrysotricus (Bignoniaceae). We used seeds from five cultivated trees in an urban environment inserted in a biodiversity hotspot (Cerrado). Embryo mass, seed germination, seedling emergence and seedling morphometry were evaluated. We did not find intraspecific variability in seed germination, seedling emergence or the mean number of embryos and seedlings per seed. On the other hand, intraspecific variability was observed during the transition from embryo to seedling. When only one seedling emerged from a seed, the seed–seedling transition was more asynchronous than when more seedlings emerged from one seed (with higher uncertainty and a longer time to emergence of the last seedling). The mass of embryos and seedlings decreased with the increase in the number of embryos in a seed, reinforcing the occurrence of gemellar competition. However, the total mass of embryos per seed was similar. The increase in seedlings per seed also decreased the morphometric measurements of each one. A positive morphometric aspect of the emergence of two seedlings per seed was that they had the highest total seedling mass, evidencing the positive Allee effect. Polyembryony had both positive and negative effects on seed germination and seedling morphology in the species, which helps to understand how this phenomenon acts on seed biology and plant establishment.
An Enjoyable Workplace Combined Exercise Program for Health Promotion in Trained Employees: Yoga, Pilates, and Circuit Strength Training
Authors: Karatrantou Konstantina , Batatolis Christos , Chatzigiannis Petros , Vasilopoulou Theodora , Melissopoulou Anastasia , Ioakimidis Panagiotis , Gerodimos Vassilis
Published: April 2023

Corporate wellness has become an important public health priority through the designing and implementation of different workplace exercise interventions. The objectives of this study were to investigate: (a) the effectiveness of a 4-month workplace combined yoga, Pilates, and circuit strength training program (outside work shift) on health indices, functional capacity, and physical fitness in office employees; and (b) the employees’ enjoyment following the program. Fifty physically active office employees (26–55 years old) were equally divided into training (TG) and control groups (CG). The TG followed a 4-month (3 times/week, 50–60 min/training) combined yoga, Pilates, and circuit strength training program. Health indices (body composition, body mass, circumferences, musculoskeletal pains), functional capacity (flexibility, balance), and physical fitness (strength, aerobic capacity) were measured before and after the 4-month time period. After the completion of the program, the TG participants’ enjoyment was assessed. The TG significantly improved (p < 0.001) all health, functional capacity (11.3–82.0%), and physical fitness indices (33.9%), except for aerobic capacity, which did not change (p > 0.05). Furthermore, a great percentage of employees (84%) reported high levels of enjoyment. This program could be effectively and safely used in workplace settings as an enjoyable intervention to improve specific health, functional capacity, and physical fitness indices in office employees.
EWM-FCE-ODM-Based Evaluation of Smart Community Construction: From the Perspective of Residents’ Sense of Gain
Authors: Dong Fang , Yin Jiyao , Xiang Jirubin , Chang Zhangyu , Gu Tiantian , Han Feihu
Published: April 2023

As a crucial paradigm for addressing urbanization-related problems, smart community construction is in full swing, and its goal is to enhance residents’ sense of gain. Prior studies have not been able to account for all aspects of smart community construction, especially the evaluation tools from the perspective of residents’ sense of gain. Therefore, this paper seeks to establish a comprehensive evaluation framework for residents’ sense of gain in the smart community through the integrated method, which includes the entropy weight method (EWM), the fuzzy comprehensive evaluation (FCE), and the obstacle degree model (ODM). For the purpose of verifying the feasibility of the evaluation framework, 31 smart communities in 6 Chinese cities (Shenzhen City, Putian City, Huizhou City, Dongguan City, Zhengzhou City, and Luoyang City) were selected. The results indicated that the weight of “Cultural activities for the elderly” indicator is the highest while the “Overall design” indicator is the lowest. In addition, Putian City had the best performance, but Shenzhen City ranked last among the six cities. Moreover, among the 31 communities, the Fengshan community in Putian City performed the best while the Xinglong community in Luoyang City performed the worst. Several suggestions are proposed to improve residents’ sense of gain in smart communities, such as enhancing the quality of healthcare services, meeting the needs of the elderly through multiple channels, and enriching business services. This study not only innovates the evaluation method of smart community construction from the perspective of residents’ sense of gain but also provides suggestions for promoting the sustainable development of the smart community and enabling residents to feel more satisfied.
Towards Carbon Neutrality: A Comprehensive Analysis on Total Factor Carbon Productivity of the Yellow River Basin, China
Authors: Ma Mingjuan , Ke Shuifa , Li Qiang , Wu Yaqi
Published: April 2023

Increasing total factor carbon productivity (TFCP) is crucial to mitigate global climate change and achieve carbon neutrality target. The Yellow River Basin is a critical energy area in China, but its TFCP is relatively low, which results in particularly prominent environmental problems. This paper investigates TFCP using MCPI, Global Moran’s I and kernel density estimation based on panel data of the 9 provinces along this vast basin in 2007–2017. The results demonstrate that: the average value of TFCP fluctuates around 1 and overall TFCP evolution exhibits significant spatial aggregation effect, and technological progress is the dominant impetus for TFCP growth. At regional level, regional heterogeneities of TFCP change and its dynamics exactly exist, with Qinghai the lowest performance and Shandong the highest performance. Moreover, global Moran’s I index reflects there is a significant positive spatial correlation between provincial TFCP, and cumulative TFCP takes on a certain degree of club convergence features. Furthermore, specific and targeted recommendations have drawn from this paper, in particular for the Yellow River Basin, to increase TFCP and achieve sustainable development in the long run.
The Impact of IoT Characteristics, Cultural Factors and Safety Concerns on Consumer Purchase Intention of Green Electronic Products
Authors: Mal Hoshiar , Nehra Nagendra Singh
Published: April 2023

This study examines the impact of IoT characteristics, key cultural dimensions, and safety concerns on consumer green purchase intentions. The research employed a survey approach, collecting responses from 278 consumers. Structural equation modeling (SEM) was used to test the hypotheses. The finding of the study indicates that IoT characteristics (IoTC), environmental concerns (EC), collectivism (GW), and individualism (ID) have a positive effect with attitude toward green purchasing (AGP), which further affect green purchase intentions (GPI). Attitude toward green purchasing (AGP) mediates the relationship between IoTC, safety concerns (SC), collectivism (GW), individualism (ID), and green purchase intentions (GPI). The study’s findings help us understand how IoTC, social concerns, and cultural factors affect consumer green purchase intention.
Selecting Geological Formations for CO2 Storage: A Comparative Rating System
Authors: Rasool Muhammad Hammad , Ahmad Maqsood , Ayoub Muhammad
Published: April 2023

Underground storage of carbon dioxide (CO2) in geological formations plays a vital role in carbon capture and storage (CCS) technology. It involves capturing CO2 emissions from industrial processes and power generation and storing them underground, thereby reducing greenhouse gas emissions and curbing the impact of climate change. This review paper features a comparative analysis of CO2 storage in deep saline aquifers, depleted reservoirs, coal seams, basaltic formations and clastic formations. The comparison has been drawn based upon seven factors carefully selected from the literature, i.e., safety, storage capacity, injection rates, efficiency, residual trapping, containment and integrity and potential to improve, and all of these factors have been rated from low (1) to high (5) based upon their individual traits. Based upon these factors, an overall M.H. rating system has been developed to categorize geological formations for CO2 storage and it is observed that deep water aquifers and basaltic formations are the most effective options for CO2 storage. Lastly, a detailed way forward has been suggested, which can help researchers and policymakers to find more viable ways to enhance the efficiency of CO2 storage in various geological formations.
Experimental Study of the Effect of Tack Coats on Interlayer Bond Strength of Pavement
Authors: Ali Mohammed H. , Khalil Amjad H. , Wang Yu
Published: April 2023

The performance and lifetime of the flexible asphalt pavement are mainly dependent on the interfacial bond strength between layer courses. To enhance the bond between layers, adhesive materials, such as tack coats, are used. The tack coat itself is a bituminous material, which is applied on an existing relatively non-absorbent surface to ensure a strong bond between the old and newly paved layer. The primary objective of this study was to evaluate the effects of various types of tack coat materials on interlayer bond strength and to determine the optimal application rate for each type. The tack coat types used in this paper were RC-70, RC-250, and CSS-1h. Both laboratory-prepared and field-constructed hot mix asphalt concrete pavements using the tack coats were tested for the binding strength between the layers. A direct shear test was used for the testing. The results obtained from the study showed that the optimum application rate for RC-70 was 0.1 L/m2, and for RC-250, it was 0.2 L/m2, while the optimum application rate for CSS-1h was 0.1 L/m2. From the field test, the optimum application rate of the RC-250 tack coat was 0.1 L/m2.
Microalgal Growth and Nutrient Removal Efficiency in Non-Sterilised Primary Domestic Wastewater
Authors: Tan Yeong Hwang , Chai Mee Kin , Na Ji Yu , Wong Ling Shing
Published: April 2023

Microalgae biomass can produce high quantities of biochemicals that can be used in various applications such as biodiesel, biogas, and aquaculture feed. The potential of sterilizing wastewater for microalgae-based wastewater treatment on a lab scale is well introduced. However, the operation cost for large-scale microalgae cultivation in wastewater treatment plants is high if using sterilising wastewater as the growth medium. The present study aimed to evaluate the growth of Scenedesmus sp., Chlorococcum aquaticum, Ankistrodesmus augustus, and Haematococcus pluvialis in non-sterilised domestic wastewater and their potential for pollutant removal in wastewater. The microalgae were cultivated in different concentrations of non-sterilised domestic wastewater, collected from a primary wastewater plant of a national sewerage company in Malaysia. Each species’ capacity for growth and the removal of pollutants were assessed. The results showed that the cell density, maximum biomass productivity, and biomass concentration of H. pluvialis, Scenedesmus sp., and C. aquaticum in 100% wastewater were significantly higher than the standard medium. Higher biomass concentration was obtained from H. pluvialis and C. aquaticum in 100% wastewater (815 g/L and 775.83 mg/L); nevertheless, Scenedesmus sp. in 100% wastewater yielded the highest specific growth rate (0.798 d−1) and the maximum biomass productivity (99.33 mg/L/day). Scenedesmus sp. in 100% wastewater also achieved better removal efficiency of total nitrogen (TN), total phosphorus (TP), and ammonia (N-NH4) with more than 90%. All tested microalgae species successfully remove nitrogen, ammonium, and phosphorus and reach the concentration limits set by the Department of the Environment, Malaysia. This study demonstrated that microalgae can grow well in non-sterilised domestic wastewater while simultaneously removing nitrogen and phosphorus effectively.
Assistance Needed for Increasing Knowledge of HACCP Food Safety Principles for Organic Sector in Selected EU Countries
Authors: Allam Mohamed , Bazok Renata , Bordewick-Dell Ursula , Czarniecka-Skubina Ewa , Kazimierczak Renata , Laikoja Katrin , Luik Anne , Fuka Mirna Mrkonjić , Muleo Rosario , Peetsmann Elen , Petroselli Verdiana , Roasto Mati , Średnicka-Tober Dominika , Veith Michelle , Mancinelli Roberto , Trafialek Joanna
Published: April 2023

There are numerous food safety aspects that must be taken into consideration by organic food producers and processors to ensure the safety and quality of their products. The application of Hazard Analysis and Critical Control Point (HACCP) principles, together with the implementation of good hygiene practices (GHP), ensures that food safety and process hygiene criteria are met. This study was based on a survey conducted among 316 producers and processors representing the organic food sector in five European countries (Croatia, Estonia, Germany, Italy and Poland). The knowledge and experience of organic food operators with HACCP systems were evaluated. Moreover, their needs and expectations towards assistance (training, guidance materials) that could improve the level of knowledge and compliance with respective food safety rules and regulations were assessed. The need for support on a number of issues related to food safety and guidance documents on the application of HACCP principles were also identified. This study provides highlights of the application of HACCP principles, with particular focus on identifying priorities and needs for two types of food operators (food producers and food processors). Although most of the surveyed food operators were confirmed to have basic knowledge regarding the HACCP system, there was a lack of understanding of the system principles. The needs and expectations varied among the studied countries and types of organic operators. Recognition of the full potential of the HACCP system requires assistance, particularly in the preparation of documents and records. Further research is needed to understand the implications of these findings and to identify effective strategies to improve the HACCP knowledge of food operators.
Sense of Place Attitudes on Quality of Life during the COVID-19 Pandemic: The Case of Iranian Residents in Hungary
Authors: Mousazadeh Hossein , Ghorbani Amir , Azadi Hossein , Almani Farahnaz Akbarzadeh , Mosazadeh Hasan , Zhu Kai , Dávid Lóránt Dénes
Published: April 2023

Limited research has investigated the impact of immigrants’ sense of place (SOP) attitudes on their quality of life (QOL) outcomes, especially during crises such as the COVID-19 pandemic. The present study aimed to fill this gap by examining the effect of SOP attitudes and health-related QOL (HQOL) on 120 Iranian citizens residing in Budapest, Hungary, during the pandemic. Using social media platforms, an online survey was conducted between March and July 2020. The findings of this study revealed that SOP elements, such as connection to sites, location identification, and location reliance, are susceptible to change and rely on QOL during the pandemic. The study also highlighted that Budapest has the potential to become a desirable destination for future immigrants. Overall, this study contributes to the literature on the relationship between SOP attitudes and QOL outcomes among immigrants during a crisis. It provides valuable insights for policymakers and practitioners to improve the SOP and QOL of immigrants living in cities affected by pandemics or other crises. This study provides insights into the influence of a sense of place on the quality of life of Iranian immigrants by presenting a model and highlighting significant results in a selected community.
A Survey of Electric-Scooter Riders’ Route Choice, Safety Perception, and Helmet Use
Authors: Sievert Kelsey , Roen Madeleine , Craig Curtis M. , Morris Nichole L.
Published: April 2023

This study investigated electric-scooter (e-scooter) rider behaviors and preferences to inform ways to increase safety for e-scooter riders. Data was collected from 329 e-scooter riders via two online and one in-person survey. Survey questions considered rider roadway infrastructure preferences, safety perceptions, and helmet-wearing behavior. Protected bike lanes were more commonly indicated as the safest infrastructure (62.4%) but were less likely to be the most preferred infrastructure (49.7%). Sidewalks were better matched between riders, indicating them as their preferred riding infrastructure (22.7%) and the perceived safest riding infrastructure (24.5%). Riders had low feelings of safety and preference for riding on major/neighborhood streets or on unprotected bike lanes. Riders reported significant concern about being hit by a moving vehicle, running into a pothole/rough roadway, and running into a moving vehicle. In line with the Theory of Planned Behavior, a significant relationship was found between the frequency of riding and helmet-wearing behavior, with more frequent riders being more likely to wear helmets. Findings suggest that existing roadway infrastructure may pose safety challenges and encourage rider-selected workarounds. Public policy may consider emphasizing protected bicycle lane development, rather than helmet mandates, to support e-scooter riding safety for all vulnerable road users.
Sustainable Road Pothole Detection: A Crowdsourcing Based Multi-Sensors Fusion Approach
Authors: Xin Hanyu , Ye Yin , Na Xiaoxiang , Hu Huan , Wang Gaoang , Wu Chao , Hu Simon
Published: April 2023

Real-time road quality monitoring, involves using technologies to collect data on the conditions of the road, including information on potholes, cracks, and other defects. This information can help to improve safety for drivers and reduce costs associated with road damage. Traditional methods are time-consuming and expensive, leading to limited spatial coverage and delayed responses to road conditions. With the widespread use of smartphones and ubiquitous computing technologies, data can be collected from built-in sensors of mobile phones and in-vehicle video, on a large scale. This has raised the question of how these data can be used for road pothole detection and has significant practical relevance. Current methods either use acceleration sequence classification techniques, or image recognition techniques based on deep learning. However, accelerometer-based detection has limited coverage and is sensitive to the driving speed, while image recognition-based detection is highly affected by ambient light. To address these issues, this study proposes a method that utilizes the fusion of accelerometer data and in-vehicle video data, which is uploaded by the participating users. The preprocessed accelerometer data and intercepted video frames, were then encoded into real-valued vectors, and projected into the public space. A deep learning-based training approach was used to learn from the public space and identify road anomalies. Spatial density-based clustering was implemented in a multi-vehicle scenario, to improve reliability and optimize detection results. The performance of the model is evaluated with confusion matrix-based classification metrics. Real-world vehicle experiments are carried out, and the results demonstrate that the proposed method can improve accuracy by 6% compared to the traditional method. Consequently, the proposed method provides a novel approach for large-scale pavement anomaly detection.
Injury-Based Surrogate Resilience Measure: Assessing the Post-Crash Traffic Resilience of the Urban Roadway Tunnels
Authors: Jiang Chenming , He Junliang , Zhu Shengxue , Zhang Wenbo , Li Gen , Xu Weikun
Published: April 2023

Crash injuries not only result in huge property damages, physical distress, and loss of lives, but arouse a reduction in roadway capacity and delay the recovery progress of traffic to normality. To assess the resilience of post-crash tunnel traffic, two novel concepts, i.e., surrogate resilience measure (SRM) and injury-based resilience (IR), were proposed in this study. As a special kind of semi-closed infrastructure, urban tunnels are more vulnerable to traffic crashes and injuries than regular roadways. To assess the IR of the post-crash roadway tunnel traffic system, an over-one-year accident dataset comprising 8621 crashes in urban roadway tunnels in Shanghai, China was utilized. A total of 34 variables from 11 factors were selected to establish the IR assessment indicator system. Methodologically, to tackle the skewness issue in the dataset, a binary skewed logit (Scobit) model was found to be superior to a conventional logistic model and subsequently adopted for further analysis. The estimated results showed that 15 variables were identified to be significant in assessing the IR of the roadway tunnels in Shanghai. Finally, the formula for calculating the IR levels of post-crash traffic systems in tunnels was given and would be a helpful tool to mitigate potential trends in crash-related resilience deterioration. The findings of this study have implications for bridging the gap between conventional traffic safety research and system resilience modeling.
Career Planning Indicators of Successful TVET Entrepreneurs
Authors: Muridan Natasha Dora , Rasul Mohamad Sattar , Yasin Ruhizan Mohamad , Nor Ahmad Rosli Mohd , Rauf Rose Amnah Abd. , Jalaludin Nur Atiqah
Published: April 2023

The absence of career planning is closely linked to low income, severe work environments, lack of health facilities, low life expectancy, and unemployment risks. (1) Background: career planning is the basis of individual career development. The aim of this article is to identify the career planning indicators of high-income entrepreneurs who are graduates with a technical and vocational education and training [TVET] background. (2) Methods: this study, which used a modified Delphi technique, was conducted using two rounds of surveys among 10 TVET graduates and entrepreneurs with high incomes in order to obtain the career planning indicators on how they achieved a high income. All of them have a revenue of above RM 5000 a month, which is considered to be a high-income status according to Bank Negara Malaysia’s (BNM) Payment System and Financial Stability Report 2018. (3) Results: the findings have shown two indicators to get a high consensus, which are focus and self-efficacy in career. In fact, assigning a specific career objective and striving to achieve high salary are crucial and must be within entrepreneurs’ skillset (realistic). (4) Conclusions: the objective can be modified to imply the self-efficacy of the students in optimizing career plans that are suitable to the personality, beliefs, and the self-motivation of the entrepreneur.
Comparative Life Cycle Assessment and Cost Analysis of the Production of Ti6Al4V-TiC Metal–Matrix Composite Powder by High-Energy Ball Milling and Ti6Al4V Powder by Gas Atomization
Authors: Santiago-Herrera Mario , Ibáñez Jesús , De Pamphilis Marco , Alegre Jesús Manuel , Tamayo-Ramos Juan Antonio , Martel-Martín Sonia , Barros Rocío
Published: April 2023

Environmental awareness and the necessary reduction in costs in industrial processes has facilitated the development of novel techniques such as Additive Manufacturing, decreasing the amount of raw materials and energy needed. The longing for improved materials with different and enhanced properties has resulted in research efforts in the Metal Matrix Composites field. These two novelties combined minimise environmental impacts and costs without compromising technical properties. Two technologies can feed Additive Manufacturing techniques with metallic powder: Gas Atomization and High Energy Ball Milling. This study provides a comparative Life Cycle Assessment of these technologies to produce one kilogram of metallic powder for the Directed Energy Deposition technique: a Ti6Al4V alloy, and a Ti6Al4V-TiC Metal–Matrix Composite, respectively. The LCA methodology is according to ISO 14040:2006, and large amounts of information on the use of raw materials, energy consumption, and environmental impacts is provided. Different impact categories following the Environmental Footprint methodology were analysed, showing a big difference between both technologies, with an 87.8% reduction of kg CO2 eq. emitted by High Energy Ball Milling in comparison with Gas Atomization. In addition, an economic analysis was performed, addressing the viability perspective and decision making and showing a 17.2% cost reduction in the conventional process.
Technological Affordance and the Realities of Citizen Science Projects Developed in Challenging Territories
Authors: Grigoletto Fábio , de Oliveira Fernanda Antunes , Momesso Caio Caradi , Nehemy Ibrahim Kamel Rodrigues , Junior João Emílio de Almeida , São Pedro Vinícius de Avelar , Greco Roberto , Alves Mário Aquino , Edwards Tim
Published: April 2023

Citizen science has established itself as an important approach to the co-production of knowledge and public participation in scientific research. Combined with digital technologies and online tools, the approach has been celebrated as a path toward the democratization of science. However, only a few studies have investigated the role digital technologies play in shaping interactions between people and nature. Additionally, the role of context in shaping online and face-to-face participation in citizen science projects has yet to receive much attention. This article takes a citizen science initiative in the Brazilian Atlantic Forest in the state of São Paulo as an illustrative case of the emergence of unanticipated consequences of digital technologies. The emergence of a socio-material practice of animal identification through a popular instant-messaging app is described, allowing a better understanding of the role of digital technologies and the context framing citizen participation in challenging environments.