bluuebunny/crossref_metadata_2025 huggingface dataset:
Strategies from A Multi-National Sample of Electroconvulsive Therapy (ECT) Services: Managing Anesthesia for ECT during the COVID-19 Pandemic
Authors: Sarma Shanthi , Branjerdporn Grace , McCosker Laura , Kenworthy Sean , Ryan Leanne , Dong Vanessa , Martin Donel , O’Shea Halia , Loo Colleen
Published: November 2022

Electroconvulsive therapy (ECT) is important in the management of severe, treatment-resistant, and life-threatening psychiatric illness. Anesthesia supports the clinical efficacy and tolerability of ECT. The COVID-19 pandemic has significantly disrupted ECT services, including anesthesia. This study documents strategies for managing ECT anesthesia during the pandemic. Data were collected between March and November 2021, using a mixed-methods, cross-sectional, electronic survey. Clinical directors in ECT services, their delegates, and anesthetists worldwide participated. One hundred and twelve participants provided quantitative responses to the survey. Of these, 23.4% were anesthetists, and the remainder were ECT clinical directors. Most participants were from Australia, New Zealand, North America, and Europe. Most were located in a public hospital, in a metropolitan region, and in a ‘medium/high-risk’ COVID-19 hotspot. Half of the participants reported their services made changes to ECT anesthetic technique during the pandemic. Services introduced strategies associated with anesthetic induction, ventilation, use of laryngeal mask airways, staffing, medications, plastic barriers to separate staff from patients, and the location of extubation and recovery. This is the first multi-national, mixed-methods study to investigate ECT anesthesia practices during the COVID-19 pandemic. The results are vital to inform practice during the next waves of COVID-19 infection, ensuring patients continue to receive ECT.
Identity Styles and Readiness to Enter into Interreligious Dialogue: The Moderating Function of Religiosity
Authors: Rydz Elżbieta , Romaneczko Jakub
Published: November 2022

The aim of the study was to research moderating function of religiosity in the relationship between identity styles and the readiness to enter into interreligious dialogue in people in early, middle, and late adulthood. The result of the identity integration processes is the achievement by an adult of a specific identity style, characterized by varying levels of openness to new information and experiences, including in the area of recognized values, especially religious ones. Previous studies have addressed the problem of the relationship between identity styles and religiosity, while a clear research gap can be noticed in the search for connections between identity styles and openness to religious diversity, religious tolerance and readiness to enter into interreligious dialogue. However, relationships between identity styles and personality openness to experience have been found, which refers to, inter alia, to tolerance and exploration of the unknown. It has been shown that the informational identity style is positively associated with openness to experience, as well as with caring, the need to know, and openness to ideas. Contemporary theoretical models emphasize that the religiosity of an individual can act as a contextual framework that gives meaning and directs the individual in their interpretation, understanding, and response to life experiences. In this paper, it was assumed that the religiosity of an individual may play a moderating role in the relationship between identity styles and the readiness to enter into interreligious dialogue. The study was conducted using the Test of readiness to enter into interreligious dialogue (TGDM), Identity Style Inventory (ISI-5) and The Duke University religion index (DUREL). The study involved 450 people in early, middle, and late adulthood, maintaining the gender ratio. Statistically significant positive correlations were obtained between informational identity style and all dimensions of readiness to enter interreligious dialogue and a negative correlation of this style with personal barriers to dialogue symmetry. The moderating function of religiosity for the relationship between identity styles and readiness to enter interreligious dialogue was confirmed for three relationships.
The Demons of Judas and Mary Magdalene in Medieval Art
Authors: Monzón Pertejo Elena , Bernad López Victoria
Published: November 2022

There are few specific studies on the demonic possession of Judas and Mary Magdalene, especially as regards the representation of these demons in medieval art. This article analyses the matter in order to subsequently carry out a comparative analysis of the two characters and thus respond to both the general and specific objectives put forward: the reason for the difference in quantity in the representations of Judas with his demons compared to Magdalena; the type of demons represented; their possible meanings; and some considerations related to gender issues. The analysis has been carried out with a cultural perspective, comparing images with texts, as well as putting these materials into context. Taking all of this into account, it is shown that the main cause behind the quantitative difference in the images of the two characters lies in their subsequent fates: Magdalene, exorcised, becomes an example of repentance, confession, and penance for the faithful, whereas Judas is condemned and never abandoned by the devil. The reason for the choice of Judas and Mary Magdalene is that they are two of the most important characters in the New Testament to have suffered from demonic possession, though there are also depictions of different exorcisms performed by Jesus. Furthermore, the antagonism of these characters forms a key feature in both art and religion in the medieval West, as demonstrated at the end of this article.
The Epistemic Status of Mystical Experience in Ibn ʻArabī’s Legal Reasoning
Authors: Lala Ismail
Published: November 2022

Arguably the most influential Sufi thinker in Islam, Muḥyī l-Dīn Ibn ʻArabī (d. 638/1240), views revelatory knowledge and mystical experience, what he terms ‘spiritual unveiling’ (kashf), as a form of continuing divine revelation that is bequeathed to the spiritual elite or saints (awliyā’). As the self-proclaimed ‘Seal of Saints’ (Khātam al-awliyā’), who is the mystical heir to the wisdom of Muḥammad, the ‘Seal of Prophets’ (Khātam al-anbiyā’), Ibn ʻArabī has a unique method of deriving legal rulings. Not only does he emphasise the inner aspect (bāṭin) of all rituals and forms of worship, like many of his sufi counterparts; he, rather uniquely, extracts legal rulings from mystical experience. This study investigates the importance of revelatory experience and spiritual unveiling in the thought of Ibn ʻArabī and his followers. It then looks at what role these play in Ibn ʻArabī’s jurisprudence and, specifically, how he determines that hands should be raised (rafʻ al-yadayn) during formal prayer (ṣalāt) because he was commanded to by the Prophet Muḥammad in a mystical vision. By considering this issue, the deeper question of the epistemic status of mystical experience for Ibn ʻArabī, and the intricate interplay between mystical experience and textual evidence in his thought is explored.
Online Religious Involvement, Spiritual Support, Depression, and Anxiety during the COVID-19 Pandemic
Authors: Yoo Jieun
Published: November 2022

A growing body of research has shown that religious involvement is related to individuals’ mental health outcomes. This study aimed to understand how online religious involvement impacts depression and anxiety mediated by spiritual support among Korean adults during the COVID-19 pandemic using structural equation modeling. The study also examined gender differences in the latent variables and pathways using a latent mean analysis and multigroup structural analysis. The results showed that Korean men had greater online religious involvement and spiritual support and less depression and anxiety than women. Further, it was found that the relationship between online religious involvement and depression was fully mediated by spiritual support only for Korean men, which suggests the importance of online religious involvement and spiritual support in predicting depressive symptoms during a pandemic.
Economic Development Leads to Stronger Support among Religious Individuals for Clerical Influence in Politics
Authors: Lu Yun
Published: November 2022

The secularization paradigm suggests that economic development should lead to the decline of clerical influence in politics. However, this suggestion is challenged by the fact that there is widespread support for clerics who try to transform politics according to religious precepts in many developed societies. In order to address this question, the present study examines how national economic development moderates the relationship between individual-level religiosity and attitudes towards clerical influence in politics, using the fourth and fifth waves of the World Values Survey data from 54 national samples. Multilevel regression models show that, first, religious individuals are more likely to support clerics’ political influence than nonreligious individuals. Moreover, the effects of individual religiosity in this regard depend on the economic context. In less developed societies, there is no significant difference between religious and nonreligious individuals regarding their attitudes toward clerical influence in politics. However, religious individuals become more supportive of clerical influence in politics in more developed countries.
Hyperspectral Imaging Zero-Shot Learning for Remote Marine Litter Detection and Classification
Authors: Freitas Sara , Silva Hugo , Silva Eduardo
Published: November 2022

This paper addresses the development of a novel zero-shot learning method for remote marine litter hyperspectral imaging data classification. The work consisted of using an airborne acquired marine litter hyperspectral imaging dataset that contains data about different plastic targets and other materials and assessing the viability of detecting and classifying plastic materials without knowing their exact spectral response in an unsupervised manner. The classification of the marine litter samples was divided into known and unknown classes, i.e., classes that were hidden from the dataset during the training phase. The obtained results show a marine litter automated detection for all the classes, including (in the worst case of an unknown class) a precision rate over 56% and an overall accuracy of 98.71%.
Deep Learning Method of Landslide Inventory Map with Imbalanced Samples in Optical Remote Sensing
Authors: Chen Xuerong , Zhao Chaoying , Xi Jiangbo , Lu Zhong , Ji Shunping , Chen Liquan
Published: November 2022

Landslide inventory mapping (LIM) is a key prerequisite for landslide susceptibility evaluation and disaster mitigation. It aims to record the location, size, and extent of landslides in each map scale. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), have been increasingly applied to landslide detection using remote sensing images in recent decades. However, their limitations have impeded their wide application. Furthermore, despite the widespread use of deep learning algorithms in remote sensing, for LIM, deep learning algorithms are limited to less unbalanced landslide samples. To this end, in this study, full convolution networks with focus loss (FCN-FL) were adopted to map historical landslides in regions with imbalanced samples using an improved symmetrically connected full convolution network and focus loss function to increase the feature level and reduce the contribution of the background loss value. In addition, K-fold cross-validation training models (FCN-FLK) were used to improve data utilization and model robustness. Results showed that the recall rate, F1-score, and mIoU of the model were improved by 0.08, 0.09, and 0.15, respectively, compared to FCN. It also demonstrated advantages over U-Net and SegNet. The results prove that the method proposed in this study can solve the problem of imbalanced sample in landslide inventory mapping. This research provides a reference for addressing imbalanced samples in the deep learning of LIM.
Improvement and Application of a GAN Model for Time Series Image Prediction—A Case Study of Time Series Satellite Cloud Images
Authors: Wang Ruifu , Teng Dongdong , Yu Wenqing , Zhang Xi , Zhu Jinshan
Published: November 2022

Predicting the shape evolution and movement of remote sensing satellite cloud images is a difficult task requiring the effective monitoring and rapid prediction of thunderstorms, gales, rainstorms, and other disastrous weather conditions. We proposed a generative adversarial network (GAN) model for time series satellite cloud image prediction in this research. Taking time series information as the constraint condition and abandoning the assumption of linear and stable changes in cloud clusters in traditional methods, the GAN model is used to automatically learn the data feature distribution of satellite cloud images and predict time series cloud images in the future. Through comparative experiments and analysis, the Mish activation function is selected for integration into the model. On this basis, three improvement measures are proposed: (1) The Wasserstein distance is used to ensure the normal update of the GAN model parameters; (2) establish a multiscale network structure to improve the long-term performance of model prediction; (3) combined image gradient difference loss (GDL) to improve the sharpness of prediction cloud images. The experimental results showed that for the prediction cloud images of the next four times, compared with the unimproved Mish-GAN model, the improved GDL-GAN model improves the PSNR and SSIM by 0.44 and 0.02 on average, and decreases the MAE and RMSE by 18.84% and 7.60% on average. It is proven that the improved GDL-GAN model can maintain good visualization effects while keeping the overall changes and movement trends of the prediction cloud images relatively accurate, which is helpful to achieve more accurate weather forecast. The cooperation ability of satellite cloud images in disastrous weather forecasting and early warning is enhanced.
Evolution Simulation and Risk Analysis of Land Use Functions and Structures in Ecologically Fragile Watersheds
Authors: Wang Yafei , He Yao , Li Jiuyi , Jiang Yazhen
Published: November 2022

The evolution of land use functions and structures in ecologically fragile watersheds have a direct impact on regional food security and sustainable ecological service supply. Previous studies that quantify and simulate land degradation in ecologically fragile areas from the perspective of long-term time series and the spatial structure of watersheds are rare. This paper takes the Huangshui Basin of the Qinghai-Tibet Plateau in China as a case study and proposes a long-time series evolution and scenario simulation method for land use function using the Google Earth Engine platform, which realizes the simulation of land use function and structure in ecologically fragile areas by space–time cube segmentation and integrated forest-based prediction. This allows the analysis of land degradation in terms of food security and ecological service degradation. The results show that: (1) the land use function and structure evolution of the Huangshui watershed from 1990 to 2020 have a significant temporospatial variation. In the midstream region, the construction land expanded 151.84% from 1990 to 2004, driven by urbanization and western development policy; in the middle and downstream region, the loss of farmland was nearly 12.68% from 1995 to 2005 due to the combined influence of the policy of returning farmland to forest and urban expansion. (2) By 2035, the construction land in the watershed will be further expanded by 28.47%, and the expansion intensity will be close to the threshold in the upstream and midstream areas and will continue to increase by 33.53% over 2020 in downstream areas. (3) The evolution of land use function and structure will further induce land degradation, causing a 15.30% loss of farmland and 114.20 km2 of occupation of ecologically vulnerable areas, seriously threatening food security and ecological protection. Accordingly, this paper proposes policy suggestions to strengthen the spatial regulation for land degradation areas and the coordination of upstream, midstream, and downstream development.
Embedded Feature Selection and Machine Learning Methods for Flash Flood Susceptibility-Mapping in the Mainstream Songhua River Basin, China
Authors: Li Jianuo , Zhang Hongyan , Zhao Jianjun , Guo Xiaoyi , Rihan Wu , Deng Guorong
Published: November 2022

Mapping flash flood susceptibility is effective for mitigating the negative impacts of flash floods. However, a variety of conditioning factors have been used to generate susceptibility maps in various studies. In this study, we proposed combining logistic regression (LR) and random forest (RF) models with embedded feature selection (EFS) to filter specific feature sets for the two models and map flash flood susceptibility in the mainstream basin of the Songhua River. According to the EFS results, the optimized feature sets included 32 and 28 features for the LR and RF models, respectively, and the composition of the two optimal feature sets was similar and distinct. Overall, the relevant vegetation cover and river features exhibit relatively high effects overall for flash floods in the study area. The LR and RF models provided accurate and reliable flash flood susceptibility maps (FFSMs). The RF model (accuracy = 0.8834, area under the curve (AUC) = 0.9486) provided a better prediction capacity than the LR model (accuracy = 0.8634, AUC = 0.9277). Flash flood-prone areas are mainly distributed in the south and southwest and areas close to rivers. The results obtained in this study is useful for flash flood prevention and control projects.
Unbiasing the Estimation of Chlorophyll from Hyperspectral Images: A Benchmark Dataset, Validation Procedure and Baseline Results
Authors: Ruszczak Bogdan , Wijata Agata M. , Nalepa Jakub
Published: November 2022

Recent advancements in hyperspectral remote sensing bring exciting opportunities for various domains. Precision agriculture is one of the most widely-researched examples here, as it can benefit from the non-invasiveness and enormous scalability of the Earth observation solutions. In this paper, we focus on estimating the chlorophyll level in leaves using hyperspectral images—capturing this information may help farmers optimize their agricultural practices and is pivotal in planning the plants’ treatment procedures. Although there are machine learning algorithms for this task, they are often validated over private datasets; therefore, their performance and generalization capabilities are virtually impossible to compare. We tackle this issue and introduce an open dataset including the hyperspectral and in situ ground-truth data, together with a validation procedure which is suggested to follow while investigating the emerging approaches for chlorophyll analysis with the use of our dataset. The experiments not only provided the solid baseline results obtained using 15 machine learning models over the introduced training-test dataset splits but also showed that it is possible to substantially improve the capabilities of the basic data-driven models. We believe that our work can become an important step toward standardizing the way the community validates algorithms for estimating chlorophyll-related parameters, and may be pivotal in consolidating the state of the art in the field by providing a clear and fair way of comparing new techniques over real data.
Impact of the Management Scale on the Technical Efficiency of Forest Vegetation Carbon Sequestration: A Case Study of State-Owned Forestry Enterprises in Northeast China
Authors: Liu Shuohua , Liu Xiefei , Ding Zhenmin , Yao Shunbo
Published: November 2022

Improving the technical efficiency of forest vegetation carbon sequestration is an effective way to accelerate the pace and reduce the cost of carbon neutrality in China. Therefore, it is particularly important to explore the technical efficiency, influencing factors, and optimization paths of forest vegetation carbon sequestration. This work uses a 21-year panel data set (2000–2020) of 87 state-owned forestry enterprises (SOFEs) in Northeast China and combines geographic information system (GIS) and remote sensing (RS) technology. First, stochastic frontier analysis (SFA) was used to quantitatively analyze the technical efficiency of forest vegetation carbon sequestration in different SOFEs during different periods. Then, the individual fixed-effects model was used to examine the factors influencing technical efficiency under the control of climate factors. Finally, the panel threshold model was used to determine the impact of different management scales on the technical efficiency of forest vegetation carbon sequestration. The main results were as follows: technological progress can effectively reduce forestry investment and improve the technical efficiency of forest vegetation carbon sequestration production. There was technological progress in forest vegetation carbon sequestration production during the study period, but the rate of technological progress showed a decreasing trend. Forest management scale, total output value, employee wages, precipitation, and sun duration had a significant positive impact, whereas wood production had a significant negative impact on the technical efficiency of carbon sequestration. The impact of different management scales on the technical efficiency of carbon sequestration is highly heterogeneous. The study established an analytical framework for researching the technical efficiency and optimization of forest vegetation carbon sequestration, providing a theoretical and practical basis for forest management.
Refined Aboveground Biomass Estimation of Moso Bamboo Forest Using Culm Lengths Extracted from TLS Point Cloud
Authors: Jiang Rui , Lin Jiayuan , Li Tianxi
Published: November 2022

Bamboo forest is a special forest type, and its aboveground biomass (AGB) is a key indicator of its carbon sequestration capacity and ecosystem productivity. Due to its complex canopy structure and particular growth pattern, the AGBs of individual bamboos that were estimated using traditional remotely sensed data are of relatively low accuracy. In recent years, the point cloud data scanned by terrestrial laser scanners (TLS) offer the possibility for more accurate estimations of bamboo AGB. However, bamboo culms tend to have various bending degrees during the growth process, which causes the AGB estimated on culm height (H) to be generally less than the true value. In this paper, taking one sample plot of the Moso bamboo forest in Hutou Village, Chongqing, China as the study site, we employed a TLS to acquire the point cloud data. The layer-wise distance discrimination method was first developed to accurately segment individual bamboos from the dense stand. Next, the diameter at breast height (DBH) and culm length (L) of an individual bamboo were precisely extracted by fitting the cross-section circle and constructing the longitudinal axis of the bamboo culm, respectively. Lastly, the AGBs of the Moso bamboos in the study site were separately calculated using the allometric equations with the DBH and L as predictor variables. As results, the precision of the complete bamboo segmentation was 90.4%; the absolute error (AE) of the extracted DBHs ranged from −1.22 cm to 0.88 cm (R2 = 0.93, RMSE = 0.40 cm); the AE of the extracted Hs varied from –0.77 m to 1.02 m (R2 = 0.91, RMSE = 0.45 m); and the AE of the extracted Ls varied from −1.08 m to 0.77 m (R2 = 0.95, RMSE = 0.23 m). The total estimated AGB of the Moso bamboos in the sample plot increased by 2.85%, from 680.40 kg on H to 696.36 kg on L. These measurements demonstrated the unique benefits of the TLS-acquired point cloud in characterizing the structural parameters of Moso bamboos and estimating their AGBs with high accuracy.
Spatio-Temporal Multi-Scale Analysis of Landscape Ecological Risk in Minjiang River Basin Based on Adaptive Cycle
Authors: Bao Tiantian , Wang Ruifan , Song Linghan , Liu Xiaojie , Zhong Shuangwen , Liu Jian , Yu Kunyong , Wang Fan
Published: November 2022

Landscape ecological security is an environmental requirement for social and economic development. Understanding the dynamic mechanisms of landscape change and the associated ecological risks in regional socioecological systems is necessary for promoting regional sustainable development. Using the Minjiang River Basin as the research area, the Google Earth Engine platform, random forest (RF) model, and FLUS model were employed for land use classification and future multi-scenario prediction. Multisource remote sensing data were used to establish a three-dimensional evaluation index system for an adaptive cycle. Additionally, the “potential-connection-resilience” framework was adopted to explore the spatial and temporal variations in landscape ecological risk in the basin from 2001 to 2035 under different administrative scales and development scenarios. The results showed that from 2001 to 2020, the building and forest areas increased significantly, whereas grassland and plowland areas decreased significantly. Moreover, the spatial fragmentation of the watershed improved significantly with the transformation of large amounts of grassland into forests. The construction area continued to expand in 2035 under different scenarios. Under the economic development scenario, the grassland and plowland areas decreased considerably, but the forest area increased slowly. Under the ecological protection scenario, the expansion of land use was restrained, and the reduction rate of grassland and cultivated land was moderated. From 2001 to 2020, the overall ecological risk was at a medium-low level and showed a decreasing trend, and the fragmentation degree of the forest had a significant impact on ecological risk. By 2035, landscape ecological risks increased under different development scenarios, and construction land expansion had become the dominant factor affecting the risk level. By evaluating the distribution and development trend of ecologically high-risk areas in the Minjiang River Basin, the results of this study provide basic support for the rational planning of land resources in the basin and decision making for future sustainable development efforts.
Analysis of NO2 and O3 Total Columns from DOAS Zenith-Sky Measurements in South Italy
Authors: Pettinari Paolo , Donateo Antonio , Papandrea Enzo , Bortoli Daniele , Pappaccogli Gianluca , Castelli Elisa
Published: November 2022

The Gas Absorption Spectrometer Correlating Optical Difference—New Generation 4 (GASCOD/NG4) is a multi-axis differential optical absorption spectroscopy (MAX-DOAS) instrument which measures diffuse solar spectra at the Environmental-Climate Observatory (ECO) of the Italian research institute CNR-ISAC, near Lecce. The high-resolution spectra measured in zenith-sky configuration were used to retrieve the NO2 and O3 vertical column densities (VCDs) from March 2017 to November 2019. These good-quality data, proven by the comparison with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) satellite measurements, were used to characterize the ECO site by exploiting the sinergy with in situ NO2 and O3 concentrations and meteorological data. Although stratospheric processes seem to be the main forces behind the NO2 and O3 VCDs seasonal trends, diurnal variabilities revealed the presence of a tropospheric signal in the NO2 VCDs, which had significant lower values during Sundays. Comparison with wind data acquired at the ECO observatory, at 20 m above the ground, revealed how NO2 VCDs are influenced by both tropospheric local production and transport from the nearby city of Lecce. On the other hand, no significant tropospheric signal was contained in the O3 VCDs.
Effect of Growth Hormone Exon-5 Polymorphism on Growth Traits, Body Measurements, Slaughter and Carcass Characteristics, and Meat Quality in Meat-Type Lambs in Turkey
Authors: Esen Vasfiye Kader , Elmacı Cengiz
Published: November 2022

This study examined the relationship between GHE5 polymorphisms and growth and carcass characteristics of meat-type sheep breeds reared in Turkey. A total of 202 lambs were tested, consisting of five breeds. By SSCP analysis and nucleotide sequencing, 14 nucleotide changes (12 substitutions and two deletions) were identified in four variants of GHE5. In the coding region of GHE5, five substitutions occur, including c.1588C>Y(C/T) (Ala160Val), c.1603A>M(A/C), c.1604G>S(G/C) (Lys165Thr), c.1606A>W(A/T) (Gln166Leu), and c.1664C>Y(C/T). P3 female and P1 male lambs had the highest rump height at weaning, whereas P3 females and P2 males had the highest chest depth (p < 0.05). At yearling, P1 variant lambs have longer body length (BL; p < 0.05), wider leg circumferences, and thinner cannon bone perimeter (CBP) (p > 0.01), in contrast to P2 variant lambs, which have a shorter BL and thicker CBP. Furthermore, P2 had a greater percentage of neck, shoulder, and leg, P1 had a greater percentage of loin, and P3 had a greater percentage of rack, but there was no significant difference between them. A marker-assisted selection approach can be used to improve sheep carcass quality traits by taking advantage of the nucleotide substitutions found on GHE5 and the detected differences between variants.
Photothermal Self-Excitation of a Phase-Controlled Microcantilever for Viscosity or Viscoelasticity Sensing
Authors: Mouro João , Paoletti Paolo , Sartore Marco , Vassalli Massimo , Tiribilli Bruno
Published: November 2022

This work presents a feedback closed-loop platform to be used for viscosity or viscoelasticity sensing of Newtonian or non-Newtonian fluids. The system consists of a photothermally excited microcantilever working in a digital Phase-Locked Loop, in which the phase between the excitation signal to the cantilever and the reference demodulating signals is chosen and imposed in the loop. General analytical models to describe the frequency and amplitude of oscillation of the cantilever immersed in viscous and viscoelastic fluids are derived and validated against experiments. In particular, the sensitivity of the sensor to variations of viscosity of Newtonian fluids, or to variations of elastic/viscous modulus of non-Newtonian fluids, are studied. Interestingly, it is demonstrated the possibility of controlling the sensitivity of the system to variations of these parameters by choosing the appropriate imposed phase in the loop. A working point with maximum sensitivity can be used for real-time detection of small changes of rheological parameters with low-noise and fast-transient response. Conversely, a working point with zero sensitivity to variations of rheological parameters can be potentially used to decouple the effect of simultaneous external factors acting on the resonator.
Research on the Applicability of Touchscreens in Manned/Unmanned Aerial Vehicle Cooperative Missions
Authors: Xue Hongjun , Zhang Qingpeng , Zhang Xiaoyan
Published: November 2022

The suitability of touchscreens for human–computer interaction in manned/unmanned aerial vehicle cooperative missions remains uncertain, especially in situations that are time-sensitive with variations in difficulty levels. The purpose of this study is to determine the feasibility of touchscreen applications in manned/unmanned aerial vehicle cooperative missions and the magnitude of the effects of time pressure and task difficulty. In contrast to previous studies, a combination of performance and perceptual load measures was used to divide errors into disposition errors, undetected errors, and miscalculation errors to explore specific error mechanisms, set up typical manned/unmanned aerial vehicle cooperative human–computer interaction tasks, and set up antecedent features for potential factors. Thirty subjects participated in an experiment that required the use of touchscreens or keyboards to perform a human–computer interaction task in a simulated manned/unmanned aerial vehicle cooperative mission. Experiments were set at three task difficulties: low, medium, and high, and were matched to a set time pressure or no time pressure for two seconds for low difficulty, three seconds for medium difficulty, and four seconds for high difficulty. The results showed that the touchscreens improved the participants’ response speed at a time pressure of 2 s or less compared with the use of a general input device; however, the task error rate also increased significantly. The higher the task difficulty was, the worse the performance was and the greater the perceived workload of the participants. The application of touchscreens in dynamic environments subjected the participants to greater physical demands. The performance of participants using a keyboard was no better than that when touchscreens were used during the experiment. Moreover, touchscreens did not significantly improve participant performance. The results support the possibility of using touchscreens in manned/unmanned aerial vehicle cooperative missions.
Deep Learning-Based Intelligent Forklift Cargo Accurate Transfer System
Authors: Ren Jie , Pan Yusu , Yao Pantao , Hu Yicheng , Gao Wang , Xue Zhenfeng
Published: November 2022

In this research, we present an intelligent forklift cargo precision transfer system to address the issue of poor pallet docking accuracy and low recognition rate when using current techniques. The technology is primarily used to automatically check if there is any pallet that need to be transported. The intelligent forklift is then sent to the area of the target pallet after being recognized. Images of the pallets are then collected using the forklift’s camera, and a deep learning-based recognition algorithm is used to calculate the precise position of the pallets. Finally, the forklift is controlled by a high-precision control algorithm to insert the pallet in the exact location. This system creatively introduces the small target detection into the pallet target recognition system, which greatly improves the recognition rate of the system. The application of Yolov5 into the pallet positional calculation makes the coverage and recognition accuracy of the algorithm improved. In comparison with the prior approach, this system’s identification rate and accuracy are substantially higher, and it requires fewer sensors and indications to help with deployment. We have collected a significant amount of real data in order to confirm the system’s viability and stability. Among them, the accuracy of pallet docking is evaluated 1000 times, and the inaccuracy is kept to a maximum of 6 mm. The recognition rate of pallet recognition is above 99.5% in 7 days of continuous trials.
Framework for Vehicle Make and Model Recognition—A New Large-Scale Dataset and an Efficient Two-Branch–Two-Stage Deep Learning Architecture
Authors: Lyu Yangxintong , Schiopu Ionut , Cornelis Bruno , Munteanu Adrian
Published: November 2022

In recent years, Vehicle Make and Model Recognition (VMMR) has attracted a lot of attention as it plays a crucial role in Intelligent Transportation Systems (ITS). Accurate and efficient VMMR systems are required in real-world applications including intelligent surveillance and autonomous driving. The paper introduces a new large-scale dataset and a novel deep learning paradigm for VMMR. A new large-scale dataset dubbed Diverse large-scale VMM (DVMM) is proposed collecting image-samples with the most popular vehicle brands operating in Europe. A novel VMMR framework is proposed which follows a two-branch architecture performing make and model recognition respectively. A two-stage training procedure and a novel decision module are proposed to process the make and model predictions and compute the final model prediction. In addition, a novel metric based on the true positive rate is proposed to compare classification confusion of the proposed 2B–2S and the baseline methods. A complex experimental validation is carried out, demonstrating the generality, diversity, and practicality of the proposed DVMM dataset. The experimental results show that the proposed framework provides 93.95% accuracy over the more diverse DVMM dataset and 95.85% accuracy over traditional VMMR datasets. The proposed two-branch approach outperforms the conventional one-branch approach for VMMR over small-, medium-, and large-scale datasets by providing lower vehicle model confusion and reduced inter-make ambiguity. The paper demonstrates the advantages of the proposed two-branch VMMR paradigm in terms of robustness and lower confusion relative to single-branch designs.
Seizure Detection: A Low Computational Effective Approach without Classification Methods
Authors: Sreenivasan Neethu , Gargiulo Gaetano D. , Gunawardana Upul , Naik Ganesh , Nikpour Armin
Published: November 2022

Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpretation. The traditional seizure-detection method of professional review of long-term EEG signals is an expensive, time-consuming, and challenging task. To reduce the complexity and cost of the task, researchers have developed several seizure-detection approaches, primarily focusing on classification systems and spectral feature extraction. While these methods can achieve high/optimal performances, the system may require retraining and following up with the feature extraction for each new patient, thus making it impractical for real-world applications. Herein, we present a straightforward manual/automated detection system based on the simple seizure feature amplification analysis to minimize these practical difficulties. Our algorithm (a simplified version is available as additional material), borrowing from the telecommunication discipline, treats the seizure as the carrier of information and tunes filters to this specific bandwidth, yielding a viable, computationally inexpensive solution. Manual tests gave 93% sensitivity and 96% specificity at a false detection rate of 0.04/h. Automated analyses showed 88% and 97% sensitivity and specificity, respectively. Moreover, our proposed method can accurately detect seizure locations within the brain. In summary, the proposed method has excellent potential, does not require training on new patient data, and can aid in the localization of seizure focus/origin.
GNSS Based Low-Cost Magnetometer Calibration
Authors: Andel Ján , Šimák Vojtech , Kanálikova Alžbeta , Pirník Rastislav
Published: November 2022

With the development of MEMS sensors, the magnetometer has increasingly become a part of various wearable devices. The magnetometer measures the intensity of the magnetic field in all three axes, resulting in a 3D vector—direction and power. Calibration must be done before using a magnetometer, especially in wearable electronics, due to the low quality of the sensor and high proximity to other electromagnetic emission sources. Several magnetometer calibration algorithms exist in the literature, with most of them requiring multi-sided rotation. However, such calibration is highly impractical when the sensor is mounted on larger objects, e.g., vehicles, which cannot easily be rotated. Vehicles contain a large amount of ferromagnetic soft and hard material that affects the measured magnetic field. A magnetometer can be useful for an INS system in a car as long as it does not drift over time. This article describes how to calibrate a magnetometer using the GNSS motion vector. The calibration is performed using data from the initial section of the vehicle’s trajectory. The quality of the calibration is then validated using the remaining section of the trajectory, comparing the deviation between the azimuth obtained by GNSS and by the calibrated magnetometer. Based on the azimuth and speed of the vehicle, we predicted the position of the vehicle and plotted the prediction on the map. The experiment showed that such calibration is functional. The uncalibrated data were unusable due to the strong effect of ferromagnetic soft and hard materials in the vehicle.
Fast Analysis of Caffeic Acid-Related Molecules in Instant Coffee by Reusable Sonogel–Carbon Electrodes
Authors: Pigani Laura , Rioli Cristina , Zanfrognini Barbara , García-Guzmán Juan José , Palacios-Santander José Maria , Cubillana-Aguilera Laura María
Published: November 2022

Reusable Sonogel–Carbon electrodes containing carbon black (SNGC-CB) have been used for the electrochemical analysis of caffeic acid (CA) in real matrices. Measurements were firstly performed in standard solutions, in which SNGC-CB electrodes allowed the electrochemical determination of CA with high sensitivity and low limit of detection, equal to 0.76 μM. The presence of CB nanostructures in the formulation led to improved performances with respect to pristine SNGC electrodes. Then, measurements were performed in four instant coffees of different brands. A comparison between the results obtained by electrochemical, chromatographic and spectroscopic methods showed that SBGC-CB electrodes represent a simple and economic tool for the rapid assessment of caffeic acid-related molecules in instant coffees.
The Potential of Biochar as N Carrier to Recover N from Wastewater for Reuse in Planting Soil: Adsorption Capacity and Bioavailability Analysis
Authors: Yu Yingliang , Yang Bei , Petropoulos Evangelos , Duan Jingjing , Yang Linzhang , Xue Lihong
Published: November 2022

Recovering nitrogen (N) from agricultural wastewater for reuse in planting fields is a more sustainable and economical strategy to limit N pollution than using conventional treatments. Hereby, regular biochar produced by wheat straw pyrolysis and Mg-modified biochar were used as the N carriers to assess inorganic-N adsorption from simulated agricultural wastewater and the potential for reuse of the carried N in a planting system. The results showed that biochar materials have different affinities towards inorganic-N types. The amount of biochar carried-N increased with the increase in inorganic-N concentration and reached 4.44 mg/g as the maximum. The biochar carried ~4 mg/g of inorganic N substituting nearly 40% of N fertilizer following a 1% w/w addition rate for vegetable planting. After a trial season, 34.7–42.7% of the carried N from biochar was assimilated by the plant, 45.9–53.7% was retained by the soil, and only about 10% was lost. In comparison to the condition with all N inputs from chemical fertilizer, the addition of part of N by the N–biochar matrix significantly reduced the N loss by improving the plant N uptake or increasing the N content in the soil. This study demonstrates that biochar materials could be used as N carriers to recover N from wastewater for reuse in soil, carrier stability, and bioavailability preservation.
Western US Basque-American e-Diaspora: Action Research in California, Idaho, and Nevada
Authors: Calzada Igor , Arranz Iker
Published: November 2022

Basque settlement increased in the western states of the US decades ago, particularly in California, Idaho, and Nevada. Alongside this migration phenomenon, Basque Studies programs have been emerging at the University of Nevada, Reno (UNR), Boise State University (BSU), and California State University, Bakersfield (CSUB), particularly in the humanities, including history, anthropology, linguistics, and literature. The impact of the pandemic in Basque e-Diasporic communities in California, Idaho, and Nevada, and, consequently, the deep digitalization process being undertaken at the abovementioned universities, has resulted in an increasing demand for an articulated strategy in community engagement through action research. To respond to this timely challenge, the article suggests a need for a transition towards a Social Science transdisciplinary roadmap to support Basque e-diasporic communities. Basque Studies programs have the potential to act as a transformational policy driver through their virtual connections with the Basque Country and key homeland institutions. This article explores this necessary transition through action research by acknowledging the potential for the three abovementioned US states and the Basque Country to set up a transformational e-Diaspora.
Correction: McBurnie et al. Training Management of the Elite Adolescent Soccer Player throughout Maturation. Sports 2021, 9, 170
Authors: McBurnie Alistair J. , Dos’Santos Thomas , Johnson David , Leng Edward
Published: November 2022

The authors wish to make the following corrections to this paper [...]
Are Laundry Balls a Sustainable Washing Option for Consumers? Investigating the Effect of Laundry Balls on Microfiber Pollution through the Lens of Cradle-to-Cradle Design Model
Authors: Salahuddin Mir , Lee Young-A
Published: November 2022

Laundry balls are promoted as a sustainable washing option because they do not contain chemical elements, unlike laundry detergent, which causes water pollution. However, little research exists to showcase the impact of laundry balls on microfiber pollution during home laundering. Using a cradle-to-cradle framework, this study aimed to investigate the effect of laundry balls on microfiber shedding during the home laundering process of synthetic clothing with different washing cycles. A factorial experimental design, consisting of 4 (washing options) × 3 (washing cycles) × 2 (repetitions) was used for this study. The ANOVA test was applied using SAS software. The findings revealed that different laundry balls including a ceramic laundry ball, lint remover laundry ball, and scrubbing laundry ball, did not reduce microfiber shedding of synthetic clothing. In fact, the use of the ceramic laundry ball accelerated microfiber shedding. It also presented the effects of different washing cycles on microfiber shedding of synthetic clothing. The study findings point towards the need to educate consumers about choosing the right washing options to minimize microfiber shedding. The findings also have implications for the industry and researchers to develop more sustainable washing options to reduce negative environmental effects by controlling microfiber pollution in home laundering.
End-of-Life Vehicle Management Systems in Major Automotive Production Bases in Southeast Asia: A Review
Authors: Jamaluddin Faridzah , Saibani Nizaroyani , Mohd Pisal Siti Maisarah , Wahab Dzuraidah Abd , Hishamuddin Hawa , Sajuri Zainuddin , Khalid Rasyikah Md
Published: November 2022

End-of-life vehicle (ELV) management is becoming increasingly important as the automotive industry is one of the world’s most critical sectors and is rapidly expanding. ELV management is essential for environmental protection and wellbeing under Sustainable Development Goal (SDG) 3, provides more jobs under SDG 8, and promotes a circular economy under SDG 11. All these factors contribute to the importance of ELV management as a research topic. Today, ELV management is well-positioned and an emerging research area particularly on ELV recycling systems in Europe, Japan, Korea, Taiwan and China. This paper aims to provide a review of ELV management in Malaysia, Thailand and Indonesia, which are the major automotive production bases in Southeast Asia, but which lack formal ELV recycling policies. Towards this end, the researchers have adopted a qualitative study in which document research has been used to analyze existing scientific studies and other published sources. The findings reveal that immature ELV management systems in Malaysia, Thailand and Indonesia are the reason that no specific ELV laws and regulations exist, as well as why there are no precise ELV statistics. Nevertheless, there have been growing concerns about ELV management, as evidenced by environmental regulations and programs aimed at better management of ELV recycling in these countries. These findings will assist the respective authorities in formulating specific laws and regulations that will ensure sustainable management of ELV and will also ensure a better life and economy for society and for industry.