bluuebunny/crossref_metadata_2025 huggingface dataset:
Smart Watch Versus Classic Receivers: Static Validity of Three GPS Devices in Different Types of Built Environments
Authors: Vorlíček Michal , Stewart Tom , Schipperijn Jasper , Burian Jaroslav , Rubín Lukáš , Dygrýn Jan , Mitáš Josef , Duncan Scott
Published: November 2021

In order to study the relationship between human physical activity and the design of the built environment, it is important to measure the location of human movement accurately. In this study, we compared an inexpensive GPS receiver (Holux RCV-3000) and a frequently used Garmin Forerunner 35 smart watch, with a device that has been validated and recommended for physical activity research (Qstarz BT-Q1000XT). These instruments were placed on six geodetic points, which represented a range of different environments (e.g., residential, open space, park). The coordinates recorded by each device were compared with the known coordinates of the geodetic points. There were no differences in accuracy among the three devices when averaged across the six sites. However, the Garmin was more accurate in the city center and the Holux was more accurate in the park and housing estate areas compared to the other devices. We consider the location accuracy of the Holux and the Garmin to be comparable to that of the Qstarz. Therefore, we consider these devices to be suitable instruments for locating physical activity. Researchers must also consider other differences among these devices (such as battery life) when determining if they are suitable for their research studies.
The Utility of DSRC and V2X in Road Safety Applications and Intelligent Parking: Similarities, Differences, and the Future of Vehicular Communication
Authors: Zadobrischi Eduard , Dimian Mihai , Negru Mihai
Published: November 2021

As the technological advancement in the automotive field increases and the complexity of vehicle and infrastructure applications is extremely high, new directions and approaches are needed in this field. Supporting and developing vehicular applications dedicated to road safety by analyzing the current behavior of existing networks in various forms is imperative. This paper studies and implements a DSRC-type communications infrastructure that receives a set of controllable and adjustable indicators, which can provide messages to network drivers in a timely manner. The implementation is based on the 802.11p protocol and initially addresses pedestrian infrastructure or pedestrian safety, controlled areas, and perimeters that allow intelligent communications. The design and setting of the communication parameters in the lower layer of the DSRC stack for vehicle applications are part of this work, aspects that are also relevant in the case of autonomous vehicles.
SIMpLE: A Mobile Cloud-Based System for Health Monitoring of People with ALS
Authors: Palumbo Arrigo , Ielpo Nicola , Calabrese Barbara , Corchiola Domenico , Garropoli Remo , Gramigna Vera , Perri Giovanni
Published: November 2021

Adopting telemonitoring services during the pandemic for people affected by chronic disease is fundamental to ensure access to health care services avoiding the risk of COVID-19 infection. Among chronic diseases, Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s disease, is a progressive neurodegenerative disease of adulthood, caused by the loss of spinal, bulbar and cortical motor neurons, which leads to paralysis of the voluntary muscles and, also, involves respiratory ones. Therefore, remote monitoring and teleconsulting are essential services for ALS patients with limited mobility, as the disease progresses, and for those living far from ALS centres and hospitals. In addition, the COVID 19 pandemic has increased the need to remotely provide the best care to patients, avoiding infection during ALS centre visits. The paper illustrates an innovative, secure medical monitoring and teleconsultation mobile cloud-based system for disabled people, such as those with ALS (Amyotrophic Lateral Sclerosis). The design aims to remotely monitor biosignals, such as ECG (electrocardiographic) and EMG (electromyographic) signals of ALS patients in order to prevent complications related to the pathology.
A Systematic Design Optimization Approach for Multiphysics MEMS Devices Based on Combined Computer Experiments and Gaussian Process Modelling
Authors: Saghir Shayaan , Saleem Muhammad Mubasher , Hamza Amir , Riaz Kashif , Iqbal Sohail , Shakoor Rana Iqtidar
Published: November 2021

This paper presents a systematic and efficient design approach for the two degree-of-freedom (2-DoF) capacitive microelectromechanical systems (MEMS) accelerometer by using combined design and analysis of computer experiments (DACE) and Gaussian process (GP) modelling. Multiple output responses of the MEMS accelerometer including natural frequency, proof mass displacement, pull-in voltage, capacitance change, and Brownian noise equivalent acceleration (BNEA) are optimized simultaneously with respect to the geometric design parameters, environmental conditions, and microfabrication process constraints. The sampling design space is created using DACE based Latin hypercube sampling (LHS) technique and corresponding output responses are obtained using multiphysics coupled field electro–thermal–structural interaction based finite element method (FEM) simulations. The metamodels for the individual output responses are obtained using statistical GP analysis. The developed metamodels not only allowed to analyze the effect of individual design parameters on an output response, but to also study the interaction of the design parameters. An objective function, considering the performance requirements of the MEMS accelerometer, is defined and simultaneous multi-objective optimization of the output responses, with respect to the design parameters, is carried out by using a combined gradient descent algorithm and desirability function approach. The accuracy of the optimization prediction is validated using FEM simulations. The behavioral model of the final optimized MEMS accelerometer design is integrated with the readout electronics in the simulation environment and voltage sensitivity is obtained. The results show that the combined DACE and GP based design methodology can be an efficient technique for the design space exploration and optimization of multiphysics MEMS devices at the design phase of their development cycle.
Automated Assessment of the Quality of Phonocardographic Recordings through Signal-to-Noise Ratio for Home Monitoring Applications
Authors: Giordano Noemi , Rosati Samanta , Knaflitz Marco
Published: November 2021

The signal quality limits the applicability of phonocardiography at the patients’ domicile. This work proposes the signal-to-noise ratio of the recorded signal as its main quality metrics. Moreover, we define the minimum acceptable values of the signal-to-noise ratio that warrantee an accuracy of the derived parameters acceptable in clinics. We considered 25 original heart sounds recordings, which we corrupted by adding noise to decrease their signal-to-noise ratio. We found that a signal-to-noise ratio equal to or higher than 14 dB warrants an uncertainty of the estimate of the valve closure latencies below 1 ms. This accuracy is higher than that required by most clinical applications. We validated the proposed method against a public database, obtaining results comparable to those obtained on our sample population. In conclusion, we defined (a) the signal-to-noise ratio of the phonocardiographic signal as the preferred metric to evaluate its quality and (b) the minimum values of the signal-to-noise ratio required to obtain an uncertainty of the latency of heart sound components compatible with clinical applications. We believe these results are crucial for the development of home monitoring systems aimed at preventing acute episodes of heart failure and that can be safely operated by naïve users.
A 16-Channel Dipole Antenna Array for Human Head Magnetic Resonance Imaging at 10.5 Tesla
Authors: Woo Myung Kyun , DelaBarre Lance , Waks Matt , Radder Jerahmie , Choi Uk-Su , Lagore Russell , Ugurbil Kamil , Adriany Gregor
Published: November 2021

For ultra-high field and frequency (UHF) magnetic resonance imaging (MRI), the associated short wavelengths in biological tissues leads to penetration and homogeneity issues at 10.5 tesla (T) and require antenna transmit arrays for efficiently generated 447 MHz B1+ fields (defined as the transmit radiofrequency (RF) magnetic field generated by RF coils). Previously, we evaluated a 16-channel combined loop + dipole antenna (LD) 10.5 T head array. While the LD array configuration did not achieve the desired B1+ efficiency, it showed an improvement of the specific absorption rate (SAR) efficiency compared to the separate 8-channel loop and separate 8-channel dipole antenna arrays at 10.5 T. Here we compare a 16-channel dipole antenna array with a 16-channel LD array of the same dimensions to evaluate B1+ efficiency, 10 g SAR, and SAR efficiency. The 16-channel dipole antenna array achieved a 24% increase in B1+ efficiency in the electromagnetic simulation and MR experiment compared to the LD array, as measured in the central region of a phantom. Based on the simulation results with a human model, we estimate that a 16-channel dipole antenna array for human brain imaging can increase B1+ efficiency by 15% with similar SAR efficiency compared to a 16-channel LD head array.
Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling
Authors: Hajra Sujoy Ghosh , Liu Careesa C. , Fickling Shaun D. , Pawlowski Gabriela M. , Song Xiaowei , D’Arcy Ryan C. N.
Published: November 2021

Background: Electroencephalography (EEG)-derived event-related potentials (ERPs) provide information about a variety of brain functions, but often suffer from low inherent signal-to-noise ratio (SNR). To overcome the low SNR, techniques that pool data from multiple sensors have been applied. However, such pooling implicitly assumes that the SNR among sensors is equal, which is not necessarily valid. This study presents a novel approach for signal pooling that accounts for differential SNR among sensors. Methods: The new technique involves pooling together signals from multiple EEG channels weighted by their respective SNRs relative to the overall SNR of all channels. We compared ERP responses derived using this new technique with those derived using both individual channels as well as traditional averaged-based channel pooling. The outcomes were evaluated in both simulated data and real data from healthy adult volunteers (n = 37). Responses corresponding to a range of ERP components indexing auditory sensation (N100), attention (P300) and language processing (N400) were evaluated. Results: Simulation results demonstrate that, compared to traditional pooling technique, the new SNR-weighted channel pooling technique improved ERP response effect size in cases of unequal noise among channels (p’s < 0.001). Similarly, results from real-world experimental data showed that the new technique resulted in significantly greater ERP effect sizes compared to either traditional pooling or individual channel approach for all three ERP components (p’s < 0.001). Furthermore, the new channel pooling approach also resulted in larger ERP signal amplitudes as well as greater differences among experimental conditions (p’s < 0.001). Conclusion: These results suggest that the new technique improves the capture of ERP responses relative to traditional techniques. As such, SNR-weighted channel pooling can further enable widespread applications of ERP techniques, especially those that require rapid assessments in noisy out-of-laboratory environments.
Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review
Authors: Agarwal Deevyankar , Marques Gonçalo , de la Torre-Díez Isabel , Franco Martin Manuel A. , García Zapiraín Begoña , Martín Rodríguez Francisco
Published: November 2021

Alzheimer’s disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.
Combining Indoor Positioning Using Wi-Fi Round Trip Time with Dust Measurement in the Field of Occupational Health
Authors: Ando Hajime , Sekoguchi Shingo , Ikegami Kazunori , Yoshitake Hidetaka , Baba Hiroka , Myojo Toshihiko , Ogami Akira
Published: November 2021

Monitoring of personal exposure to hazardous substances has garnered increasing attention over the past few years. However, no straightforward and exact indoor positioning technique has been available until the recent discovery of Wi-Fi round trip time (Wi-Fi RTT). In this study, we investigated the possibility of using a combination of Wi-Fi RTT for indoor positioning and a wearable particle monitor (WPM) to observe dust concentration during walking in a simulated factory. Ultrasonic humidifiers were used to spray sodium chloride solution inside the factory. The measurements were recorded three times on different routes (Experiments A, B, and C). The error percentages, i.e., measurements that were outside the expected measurement area, were 7% (49 s/700 s) in Experiment A, 2.3% (15 s/660 s) in Experiment B, and 7.8% (50 s/645 s) in Experiment C. The dust measurements were also recorded without any obstruction. A heat map was created based on the results from both measured values. Wi-Fi RTT proved useful for computing the indoor position with high accuracy, suggesting the applicability of the proposed methodology for occupational health monitoring.
Performance Improvement of Residue-Free Graphene Field-Effect Transistor Using Au-Assisted Transfer Method
Authors: Jang Yamujin , Seo Young-Min , Jang Hyeon-Sik , Heo Keun , Whang Dongmok
Published: November 2021

We report a novel graphene transfer technique for fabricating graphene field-effect transistors (FETs) that avoids detrimental organic contamination on a graphene surface. Instead of using an organic supporting film like poly(methyl methacrylate) (PMMA) for graphene transfer, Au film is directly deposited on the as-grown graphene substrate. Graphene FETs fabricated using the established organic film transfer method are easily contaminated by organic residues, while Au film protects graphene channels from these contaminants. In addition, this method can also simplify the device fabrication process, as the Au film acts as an electrode. We successfully fabricated graphene FETs with a clean surface and improved electrical properties using this Au-assisted transfer method.
Cognitive Based Authentication Protocol for Distributed Data and Web Technologies
Authors: Krzyworzeka Natalia , Ogiela Lidia , Ogiela Marek R.
Published: November 2021

The objective of the verification process, besides guaranteeing security, is also to be effective and robust. This means that the login should take as little time as possible, and each time allow for a successful authentication of the authorised account. In recent years, however, online users have been experiencing more and more issues with recalling their own passwords on the spot. According to research done in 2017 by LastPass on its employees, the number of personal accounts assigned to one business user currently exceeds 191 profiles and keeps growing. Remembering these many passwords, especially to applications which are not used every week, seems to be impossible without storing them either on paper, in a password manager, or saved in a file somewhere on a PC. In this article a new verification model using a Google Street View image as well as the user’s personal experience and knowledge will be presented. The purpose of this scheme is to assure secure verification by creating longer passwords as well as delivering a ‘password reminder’ already embedded into the login scheme.
Coastal Waste Detection Based on Deep Convolutional Neural Networks
Authors: Ren Chengjuan , Jung Hyunjun , Lee Sukhoon , Jeong Dongwon
Published: November 2021

Coastal waste not only has a seriously destructive effect on human life and marine ecosystems, but it also poses a long-term economic and environmental threat. To solve the issues of a poor manual coastal waste sorting environment, such as low sorting efficiency and heavy tasks, we develop a novel deep convolutional neural network by combining several strategies to realize intelligent waste recognition and classification based on the state-of-the-art Faster R-CNN framework. Firstly, to effectively detect small objects, we consider multiple-scale fusion to get rich semantic information from the shallower feature map. Secondly, RoI Align is introduced to solve positioning deviation caused by the regions of interest pooling. Moreover, it is necessary to correct key parameters and take on data augmentation to improve model performance. Besides, we create a new waste object dataset, named IST-Waste, which is made publicly to facilitate future research in this field. As a consequence, the experiment shows that the algorithm’s mAP reaches 83%. Detection performance is significantly better than Faster R-CNN and SSD. Thus, the developed scheme achieves higher accuracy and better performance against the state-of-the-art alternative.
Spatiotemporal Characteristics of Event-Related Potentials Triggered by Unexpected Events during Simulated Driving and Influence of Vigilance
Authors: Seo Pukyeong , Kim Hyun , Kim Kyung Hwan
Published: November 2021

We investigated the spatiotemporal characteristics of brain activity due to sudden events during monotonous driving and how it changes with vigilance level. Two types of sudden events, emergency stop and car drifting, were presented using driving simulator, and event-related potentials (ERPs) were measured. From the ERPs of both types of events, an early component representing sensory information processing and a late component were observed. The early component was expected to represent sensory information processing, which corresponded to visual and somatosensory/vestibular information processing for the sudden stop and lane departure tasks, respectively. The late components showed spatiotemporal characteristics of the well-known P300 component for both types of events. Common characteristic brain activities occurred in response to sudden events, regardless of the type. The modulation of brain activity due to the vigilance level also shared common characteristics between the two types. We expect that our results will contribute to the development of an effective means to assist drivers’ reactions to ambulatory situations.
Modeling of a Generic Edge Computing Application Design
Authors: Roig Pedro Juan , Alcaraz Salvador , Gilly Katja , Bernad Cristina , Juiz Carlos
Published: November 2021

Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently. As the importance of such scenarios is growing by the day, we propose to undertake two different kind of models, such as an algebraic models, with a process algebra called ACP and a coding model with a modeling language called Promela. Both approaches have been used to build models considering an edge infrastructure with a cloud backup, which has been further extended with the addition of extra fog nodes, and after having applied the proper verification techniques, they have all been duly verified. Specifically, a generic edge computing design has been specified in an algebraic manner with ACP, being followed by its corresponding algebraic verification, whereas it has also been specified by means of Promela code, which has been verified by means of the model checker Spin.
Human Activity Recognition: A Dynamic Inductive Bias Selection Perspective
Authors: Hamidi Massinissa , Osmani Aomar
Published: November 2021

In this article, we study activity recognition in the context of sensor-rich environments. In these environments, many different constraints arise at various levels during the data generation process, such as the intrinsic characteristics of the sensing devices, their energy and computational constraints, and their collective (collaborative) dimension. These constraints have a fundamental impact on the final activity recognition models as the quality of the data, its availability, and its reliability, among other things, are not ensured during model deployment in real-world configurations. Current approaches for activity recognition rely on the activity recognition chain which defines several steps that the sensed data undergo: This is an inductive process that involves exploring a hypothesis space to find a theory able to explain the observations. For activity recognition to be effective and robust, this inductive process must consider the constraints at all levels and model them explicitly. Whether it is a bias related to sensor measurement, transmission protocol, sensor deployment topology, heterogeneity, dynamicity, or stochastic effects, it is essential to understand their substantial impact on the quality of the data and ultimately on activity recognition models. This study highlights the need to exhibit the different types of biases arising in real situations so that machine learning models, e.g., can adapt to the dynamicity of these environments, resist sensor failures, and follow the evolution of the sensors’ topology. We propose a metamodeling approach in which these biases are specified as hyperparameters that can control the structure of the activity recognition models. Via these hyperparameters, it becomes easier to optimize the inductive processes, reason about them, and incorporate additional knowledge. It also provides a principled strategy to adapt the models to the evolutions of the environment. We illustrate our approach on the SHL dataset, which features motion sensor data for a set of human activities collected in real conditions. The obtained results make a case for the proposed metamodeling approach; noticeably, the robustness gains achieved when the deployed models are confronted with the evolution of the initial sensing configurations. The trade-offs exhibited and the broader implications of the proposed approach are discussed with alternative techniques to encode and incorporate knowledge into activity recognition models.
Compressive Sensing-Based SAR Image Reconstruction from Sparse Radar Sensor Data Acquisition in Automotive FMCW Radar System
Authors: Lee Seongwook , Jung Yunho , Lee Myeongjin , Lee Wookyung
Published: November 2021

In this paper, we propose a method for reconstructing synthetic aperture radar (SAR) images by applying a compressive sensing (CS) technique to sparsely acquired radar sensor data. In general, SAR image reconstruction algorithms require radar sensor data acquired at regular spatial intervals. However, when the speed of the radar-equipped platform is not constant, it is difficult to consistently perform regular data acquisitions. Therefore, we used the CS-based signal recovery method to efficiently reconstruct SAR images even when regular data acquisition was not performed. In the proposed method, we used the l1-norm minimization to overcome the non-uniform data acquisition problem, which replaced the Fourier transform and inverse Fourier transform in the conventional SAR image reconstruction method. In addition, to reduce the phase distortion of the recovered signal, the proposed method was applied to each of the in-phase and quadrature components of the acquired radar sensor data. To evaluate the performance of the proposed method, we conducted experiments using an automotive frequency-modulated continuous wave radar sensor. Then, the quality of the SAR image reconstructed with data acquired at regular intervals was compared with the quality of images reconstructed with data acquired at non-uniform intervals. Using the proposed method, even if only 70% of the regularly acquired radar sensor data was used, a SAR image having a correlation of 0.83 could be reconstructed.
A Tuning Fork Frequency Up-Conversion Energy Harvester
Authors: Wu Qinghe , Gao Shiqiao , Jin Lei , Zhang Xiyang , Yin Zuozong , Wang Caifeng
Published: November 2021

In this paper, a novel tuning fork structure for self-frequency up-conversion is proposed. The structure has an in-phase vibration mode and an anti-phase vibration mode. The in-phase vibration mode is used to sense the environment vibration, and the anti-phase vibration mode is used for energy conversion and power generation. The low-frequency energy collection and the high-frequency energy conversion can be achieved simultaneously. Theoretical and experimental results show that the tuning fork frequency up-conversion energy harvester has excellent performance. This structure provides the energy harvester with excellent output power in a low-frequency vibration environment. At the resonant frequency of 7.3 Hz under 0.7 g acceleration, the peak voltage is 41.8 V and the peak power is 8.74 mW. The tuning fork frequency up-conversion energy harvester causes the humidity sensor to work stably. The structure has the potential to power wireless sensor nodes or to be used as a small portable vibration storage device, especially suitable for the monitoring of the environment related to human movement.
COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion
Authors: Khan Muhammad , Alhaisoni Majed , Tariq Usman , Hussain Nazar , Majid Abdul , Damaševičius Robertas , Maskeliūnas Rytis
Published: November 2021

In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach—parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.
Research Progress on Quality Control Methods for Xiaochaihu Preparations
Authors: Xu Guangzheng , Wang Hui , Deng Yingqian , Xie Keyi , Zhao Weibo , Gong Xingchu
Published: November 2021

Xiaochaihu (XCH) is a classic Chinese medicine formula. XCH tablet, XCH granule, XCH capsule, and XCH effervescent tablet are included in the Chinese Pharmacopoeia. In this review, the formula and quality standards of XCH preparations at home and abroad were compared. The differences in manufacturing process of XCH preparations are discussed. The progress of research on the qualitative identification, quantitative detection and fingerprint chromatogram/specific chromatogram of XCH preparations was reviewed. The characteristic components of Pinelliae Rhizoma Praeparatum Cum Zingibere Et Alumine and Jujubae Fructus was rarely analyzed for XCH preparations. It is suggested that the specificity of drug quality detection methods should be improved. Considering drug safety and drug efficacy, it is suggested to set the upper and lower limits of the content of saikosaponins. The standards for heavy metals and other limited items for XCH preparations are also suggested to be set.
21-Day Stress Detox: Open Trial of a Universal Well-Being Chatbot for Young Adults
Authors: Williams Ruth , Hopkins Sarah , Frampton Chris , Holt-Quick Chester , Merry Sally Nicola , Stasiak Karolina
Published: November 2021

There has been a lot of interest in digital mental health interventions but adherence to online programmes has been less than optimal. Chatbots that mimic brief conversations may be a more engaging and acceptable mode of delivery. We developed a chatbot, called 21-Day Stress Detox, to deliver stress management techniques for young adults. The purpose of the study was to explore the feasibility, acceptability, and potential efficacy of this low-intensity digital mental health intervention in a non-clinical population of young adults. The content was derived from cognitive behavioural therapy (CBT) and included evidence-informed elements such as mindfulness and gratitude journaling. It was delivered over 21 daily sessions using the Facebook Messenger platform. Each session was intended to last about 5–7 min and included text, animated GIFs, relaxation tracks and reflective exercises. We conducted an open single-arm trial collecting app usage through passive data collection as well as self-rated satisfaction and qualitative (open-ended) feedback. Efficacy was assessed via outcome measures of well-being (World Health Organisation (Five) Well-being Index; WHO-5; and Personal Well-being Measure; ONS4); stress (Perceived Stress Scale–10 item version; PSS-10); and anxiety (Generalized Anxiety Disorder 7-item scale; GAD-7). One hundred and ten of the 124 participants who completed baseline commenced the chatbot and 64 returned the post-intervention assessment. Eighty-one percent were female and 51% were first year students. Forty-five percent were NZ European and 41% were Asian. Mean engagement was 11 days out 21 days (SD = 7.8). Most (81%) found the chatbot easy to use. Sixty-three percent rated their satisfaction as 7 out of 10 or higher. Qualitative feedback revealed that convenience and relatable content were the most valued features. There was a statistically significant improvement on the WHO-5 of 7.38 (SD = 15.07; p < 0.001) and a mean reduction on the PSS-10 of 1.77 (SD = 4.69; p = 0.004) equating to effect sizes of 0.49 and 0.38, respectively. Those who were clinically anxious at baseline (n = 25) experienced a greater reduction of GAD-7 symptoms than those (n = 39) who started the study without clinical anxiety (−1.56, SD = 3.31 vs. 0.67, SD = 3.30; p = 0.011). Using a chatbot to deliver universal psychological support appears to be feasible, acceptable, have good levels of engagement, and lead to significant improvements in well-being and stress. Future iterations of the chatbot should involve a more personalised content.
Guilt and the Consumption of Products with an Unhealthy Image
Authors: Yang Jaeho , Kim Bokyeong
Published: November 2021

The purpose of this study was to investigate the effects of nutrition labeling on consumers’ guilt when they consumed products with an unhealthy image. The first investigation was conducted to examine direct effect according to the types of nutrition labeling (adding healthy ingredients/reducing unhealthy ingredients) by independent samples t-test. The findings show that consumers felt less guilty when the number of unhealthy ingredients was reduced, compared to when healthy ingredients were added. The second investigation tested the effects of goal activation types (utilitarian/hedonic) and nutrition labeling on consumers’ guilt by independent samples t-test and ANOVA. Goal activation was added as an independent variable, and hypothetical scenarios and stimulants were used. The experiment was designed with a focus on goal activation (utilitarian/hedonic) × nutrition labeling (adding healthy ingredient/reducing unhealthy ingredient). The findings reveal that the utilitarian goal activation group felt less guilty about the products with the reduction in the number of unhealthy ingredients than the products with the addition of healthy ingredients. The hedonic goal activation group felt less guilty about the products with the addition of positive nutrients compared to the products with the reduction in the number of negative nutrients. The third investigation tested consumer guilt in situations that create anxiety about food by independent samples t-test and ANOVA, based on Study 2. Unlike in Study 2, there was no interaction effect between product-nutrition labelling and goal activation. These results suggest that, when consumer anxiety increases, in relation to unhealthy foods that already invoke guilt and anxiety, the motivation—namely, goal activation when consuming food—plays a main role in causing anxiety, regardless of nutrition. Based on these experimental results, the investigator discussed the academic and practical implications of the present study. Finally, a couple of proposals were made for the direction of future study.
The Study Tour in China: An Emerging Mode for Practical Education
Authors: Sun Fu-Rong , Xu Tian-Zi
Published: November 2021

The study tour is an effective mode for students to learn in an effective way. For decades, it gradually has become an emerging mode for practical education in China, underpinned by policies, funds, technology, and human resources. This study aims to showcase how the curriculum of the study tour can currently be operated, including goals, content, teachers, and evaluation, as well as what the potential barriers are. Both qualitative and quantitative methods were used in the study through organizing site investigation and distributing questionnaires. The data were from 122 institutes of study tour in Zhejiang Province as the outstanding example. According to the results, we find that even though there are numerous study tour courses, the syllabi are not rigorous and the professional literacy of the teaching staff is low. Additionally, most study tour activities are carried out with lecturing or sightseeing, yet without practice, inquiry, or operation. Therefore, with the study tour being increasingly emphasized, more professional collaboration with schools and universities should be put on the agenda to enhance the prevailing curriculum.
Applicable Smart City Strategies to Ensure Energy Efficiency and Renewable Energy Integration in Poor Cities: Kabul Case Study
Authors: Sabory Najib Rahman , Senjyu Tomonobu , Danish Mir Sayed Shah , Hosham Ayaz , Noorzada Ajmal , Amiri Ahmad Shahpoor , Muhammdi Zabihullah
Published: November 2021

A smart city is fundamentally intended to reduce the consumption of resources and optimize efficiencies. In almost any area, efficiency results in energy saving, reduced energy intensity, sustainable economic development, enhanced productivity, a protected environment, and most importantly, cooperation with the climate change battle. Although budget, technology, and the required infrastructure are major constraints for poor cities to achieve smart and sustainable city goals, the benefits of smart cities are multiple for poor cities compared to developing and developed cities. Poor cities achieve improved living environments, security, safety, economic development, governance, and quality of life in addition to achieving sustainable energy goals, and this study seeks to identify those smart renewable energy and energy efficiency strategies that are economically feasible and technically applicable in poor cities. The findings of this research would help poor and low-income, developing cities take the initial steps towards becoming smart cities by applying smart, innovative, and economically feasible sustainable energy projects and initiatives. As a result, these cities will be able to enhance their environment, economy, and employment by transitioning to smart ones.
Toward Sustainable Cementitious Radioactive Waste Forms: Immobilization of Problematic Operational Wastes
Authors: Abdel Rahman Rehab O. , Ojovan Michael I.
Published: November 2021

Developing effective radioactive waste management practices is essential for ensuring the sustainability of the nuclear industry. The immobilization of radioactive wastes is one of the main activities conducted during the management of these wastes; it aims to produce a durable waste form that has sustainable performance over long periods of time. In this work, the challenges that face the design of durable cementitious waste forms are addressed for problematic operational wastes. In this respect, the problematic characteristics of evaporator concentrates, spent ion exchangers, and organic liquid wastes are overviewed, and the factors that affect the durability of their cementitious waste forms are identified. A summary of potential conventional and innovative cementitious matrices is presented by reviewing the cementation practices in national programs and recent research devoted to developing durable matrices. Finally, a guide to optimize the mix design of these waste forms was proposed that includes the selection of the testing procedure, factors that affect the waste form performance, and the optimization technique. This guide was presented with special focus on leaching tests, which are a means to test the stabilization performance of nuclear waste forms.
An Agile System to Enhance Productivity through a Modified Value Stream Mapping Approach in Industry 4.0: A Novel Approach
Authors: Tripathi Varun , Chattopadhyaya Somnath , Bhadauria Alok , Sharma Shubham , Li Changhe , Pimenov Danil Yurievich , Giasin Khaled , Singh Sunpreet , Gautam Girish Dutt
Published: November 2021

Worldwide, industries are emphasizing the development of an agile system to sustain higher productivity, which can be applied to ensure improvements in all production conditions in industry 4.0. In the present scenario, several methods are used for improvements in production, such as value stream mapping, kaizen, just in time, Kanban, and total productive maintenance, etc., The objective of the present research article is to produce an agile system to sustain improvements in productivity through a methodology coupled with value stream mapping in industry 4.0. Value stream mapping is a lean-based method and is used for the maximization of productivity by the elimination of non-value-added activities. The proposed methodology has been validated by productivity enhancements achieved in a case study of the earthmoving machinery manufacturing sector. The study establishes that the proposed methodology would encourage industry personnel during decision-making processes, which would lead to improvements in production in industry 4.0.
Stakeholders’ Opinions towards Water-Related Forests Ecosystem Services in Selected Southeast European Countries (Federation of Bosnia and Herzegovina, Croatia, Slovenia and Serbia)
Authors: Vuletić Dijana , Ostoić Silvija Krajter , Báliková Klára , Avdibegović Mersudin , Potočki Kristina , Pezdevšek Malovrh Špela , Posavec Stjepan , Stojnić Srđan , Paletto Alessandro
Published: November 2021

Even though water-related forest ecosystem services are important for forestry and water management sectors, they have different definitions and are regulated differently in each sector, which makes them poorly recognized. How stakeholders from two main sectors (forestry and water management) perceive the importance of water-related forest ecosystem services, the trade-offs between ecosystem services and the effectiveness and implementation of payments schemes related to forest water ecosystem services were our areas of interest. We have conduct surveys with different groups of stakeholders from both sectors in four selected countries (the Federation of Bosnia and Herzegovina, Croatia, Slovenia and Serbia) with a lot of similarities and the potential to learn from each other. The results show that in spite of the spotted differences among analyzed countries, there is a high level of agreement among respondents on all investigated aspects. In addition, even though different payment schemes exist in three of four countries, stakeholders are rarely aware of their existence, or it is better to say that they do not recognize them as payment schemes for ecosystem services because of their names and definitions, which do not clearly define ecosystem services. Mostly, they use bundled services and non-voluntary payments and are designed and implemented by the states. Due to the strong role of states and the low transparency in the existing schemes, we looked at possible conditions reflected through stakeholders’ opinions for overcoming that obstacle for the development of new payment schemes. We found that there is a high level of acceptance of payments schemes as more effective than “command and control” schemes and of the involvement of other stakeholders in decision-making processes as those conditions that can positively influence development of new payment schemes in all four countries. These results give us hope that in spite of the strong role of the state in selected countries, the role of stakeholders will be more acknowledged and, by that, the future schemes will be more harmonized among the sectors and their goals and needs, contributing to its effectiveness as well.
Factors Influencing Curriculum Leadership of Primary and Secondary School Teachers from the Perspective of Field Dynamic Theory: An Empirical Investigation in China
Authors: Chen Junyuan , Yang Yanru , Xu Fenghua , Xu Wenzhe , Zhang Xiaolin , Wang Yao , Zhang Yishi
Published: November 2021

Teacher curriculum leadership is in urgent demand to promote the sustainable advancement of curriculum reform, and an important guarantee for the sustainable development of students. It is of important theoretical and practical significance to clarify the influencing factors of the use and the development of teachers’ curriculum leadership. Based on Lewin’s field dynamic theory, this study conducts a multiple linear regression analysis on the data of 19,521 primary and secondary school teachers in 20 provinces of China, and investigates the influencing factors of teacher curriculum leadership from individual and school fields. The results show that individual field factors are the driving force for teachers to exert and develop curriculum leadership. When teachers have internal leading motivation, the school environment becomes an important inducing force. This finding confirms the realistic rationality of the field dynamic theory, and provides a clear direction for formulating relevant policies and practical plans for enhancing teacher curriculum leadership. That is, it is necessary to fully stimulate the internal motivation of teachers for curriculum leadership, but also to create a school environment for teachers to exercise and develop curriculum leadership.
Risk Governance and Sustainability: A Scientometric Analysis and Literature Review
Authors: Li Huijie , Li Jie
Published: November 2021

The outbreak of COVID-19 evoked a heated discussion of its drivers and extensive impacts on achieving sustainable development goals. Considering the deepening global interconnectedness and complex human–environment interactions, it calls for a clarity of the two concepts of risk governance and sustainability and their relationships. In this paper, a comprehensive review was provided based on scientometric analysis. A total number of 1156 published papers were studied and a considerable increase of interest in this line of research was found. The research outputs show the interdisciplinary feature of this field but with a focus on environmental issues. The journal “Sustainability” was found to be the most productive journal. Geographic and institutional focus on the line of research were also visualized. Five salient research themes were identified as follows: (1) Resilience and adaptation to climate change; (2) Urban risk governance and sustainability; (3) Environmental governance and transformation; (4) Collaborative governance and policy integration; and (5) Corporate governance and sustainability. This paper provides insights into the heterogeneity of the risk governance and sustainability research. Additionally, the study unveiled the implicit relationship linking risk governance and sustainability: risk governance can be a process of participation and coordination, and a means of coping with the uncertainty and complexity to achieve sustainable outcomes. On the other hand, risk governance is a constant aim to be optimized in the process of sustainable development.
Development of Graphite- and Graphene Reinforced Styrene-Butadiene Rubber
Authors: Zoltán Pirityi Dávid , Kornél Pölöskei
Published: November 2021

The environmental impact of rubber waste can be reduced by extending the lifetime of rubber products. It can be achieved by developing graphene/rubber nanocomposites with good abrasion resistance. In this paper, we investigated how rubber mixing technologies influence the mechanical properties of rubber. We added various amounts (0, 1, 5 and 10 phr) of graphite and graphene to rubber mixtures using a two-roll mill, an internal mixer, a single- and a twin-screw extruder. We performed tensile, tear strength and Shore A hardness tests on the vulcanisates and analysed their fracture surfaces with a scanning electron microscope. Our results show that graphene had a better reinforcing effect than graphite. Rubber mixing via extrusion may contribute to more severe polymer degradation, though their reproducibility is better than that achieved on a two-roll mill or in an internal mixer.