bluuebunny/crossref_metadata_2025 huggingface dataset:
Rethinking Terms: Dohā, Vajra-, and Caryāgīti
Authors: Schott Julian
Published: August 2023

Dohās, vajragīti, and caryāgīti are key terms associated with the poetic writings of the Mahāsiddhas. This study focuses on Apabhraṃśa dohās, their commentaries, Tibetan translations, and collections containing them, shedding light on previously neglected aspects of this text type. By investigating the historical and original contexts of these three terms and comparing them to their later applications in traditional contexts and academia, this paper argues against the prevailing notion that they are genetically distinct and that this text type is primarily defined by orality and spontaneity. Consequently, it challenges the romanticized myth of certain origin narratives, such as student–teacher encounters. Instead, this brief presentation demonstrates that the often-repeated stereotypical definitions of these terms should be largely rejected, as they are merely different labels for the same text type with blurred and ill-defined subcategories. The analysis of primary sources reveals that various facets, e.g., compilation (an important but neglected aspect), go beyond the strongly emphasized oral component of this text type, thereby leading to the inaccurate definitions of the terms. In conclusion, intertextuality, compilation, and assigned authorship are crucial yet overlooked elements in defining the text type and understanding its function.
MURM: Utilization of Multi-Views for Goal-Conditioned Reinforcement Learning in Robotic Manipulation
Authors: Jang Seongwon , Jeong Hyemi , Yang Hyunseok
Published: August 2023

We present a novel framework, multi-view unified reinforcement learning for robotic manipulation (MURM), which efficiently utilizes multiple camera views to train a goal-conditioned policy for a robot to perform complex tasks. The MURM framework consists of three main phases: (i) demo collection from an expert, (ii) representation learning, and (iii) offline reinforcement learning. In the demo collection phase, we design a scripted expert policy that uses privileged information, such as Cartesian coordinates of a target and goal, to solve the tasks. We add noise to the expert policy to provide sufficient interactive information about the environment, as well as suboptimal behavioral trajectories. We designed three tasks in a Pybullet simulation environment, including placing an object in a desired goal position and picking up various objects that are randomly positioned in the environment. In the representation learning phase, we use a vector-quantized variational autoencoder (VQVAE) to learn a more structured latent representation that makes it feasible to train for RL compared to high-dimensional raw images. We train VQVAE models for each distinct camera view and define the best viewpoint settings for training. In the offline reinforcement learning phase, we use the Implicit Q-learning (IQL) algorithm as our baseline and introduce a separated Q-functions method and dropout method that can be implemented in multi-view settings to train the goal-conditioned policy with supervised goal images. We conduct experiments in simulation and show that the single-view baseline fails to solve complex tasks, whereas MURM is successful.
Plot-Scale Irrigation Dates and Amount Detection Using Surface Soil Moisture Derived from Sentinel-1 SAR Data in the Optirrig Crop Model
Authors: Hamze Mohamad , Cheviron Bruno , Baghdadi Nicolas , Courault Dominique , Zribi Mehrez
Published: August 2023

This study aimed to develop an approach using Sentinel-1 synthetic aperture radar (SAR) data and the Optirrig crop growth and irrigation model to detect irrigation dates and amounts for maize crops in the Occitanie region, Southern France. The surface soil moisture (SSM) derived from SAR data was analyzed for changes indicating irrigation events at the plot scale in four reference plots located in Montpellier (P1) and Tarbes (P2, P3, and P4). As rain most likely covers several square kilometers, while irrigation is decided at the plot scale, a difference between SSM signals at the grid scale (10 km × 10 km) and plot scale is a clear indication of a recent irrigation event. Its date and amount are then sought by forcing irrigation dates and amounts in Optirrig, selecting the most relevant (date, amount) combination from an appropriate criterion. As the observed SSM values hold for a depth of a few centimeters, while the modeled SSM values hold for exactly 10 cm, the best irrigation combination is the one that gives similar relative changes in SSM values rather than similar SSM values. The irrigation dates were detected with an overall accuracy (recall) of 86.2% and a precision of 85.7%, and thus, with relatively low numbers of missed or false irrigation detections, respectively. The performance of the method in detecting seasonal irrigation amounts varied with climatic conditions. For the P1 plot in the semi-arid climate of Montpellier, the mean absolute error percentage (MAE%) was 16.4%, showing a higher efficiency when compared with the humid climate of Tarbes (P2, P3, and P4 plots), where a higher MAE% of 50% was recorded, indicating a larger discrepancy between the detected and actual irrigation amounts. The limitations of the proposed method can be attributed to the characteristics of the Sentinel-1 constellation, including its 6-day revisit time and signal penetration challenges in dense maize cover, as well as the mismatch between the parameterization of Optirrig for SSM simulations and the actual irrigation practices followed by farmers. Despite these weaknesses, the results demonstrated the relevance of combining Optirrig and S1 SAR-derived SSM data for field-scale detection of irrigation dates and, potentially, irrigation amounts.
A Novel 3D ArcSAR Sensing System Applied to Unmanned Ground Vehicles
Authors: Hua Yangsheng , Wang Jian , Feng Dong , Huang Xiaotao
Published: August 2023

Microwave radar has advantages in detection accuracy and robustness, and it is an area of active research in unmanned ground vehicles. However, the existing conventional automotive corner radar, which employs real-aperture antenna arrays, has limitations in terms of observable angle and azimuthal resolution. This paper proposes a novel 3D ArcSAR method to address this issue, which combines rotational synthetic aperture radar (SAR) and direction estimation algorithms. The method aims to reconstruct 3D images of 360° scenes and offers distinctive advantages in both azimuthal and altitudinal sensing. Nevertheless, due to the unique structural characteristics of vehicle SAR, it is limited to receiving only a single snapshot signal for 3D sensing. We propose a resolution algorithm based on ArcSAR and the iterative adaptive approach (IAA) to resolve the limitation. Furthermore, the errors in altitude angle estimation of the proposed algorithm and conventional algorithms are analyzed under various conditions, including different target spacing and signal-to-noise ratio (SNR). Finally, we design and implement a prototype of the 3D ArcSAR sensing system, which utilizes a millimeter-wave MIMO radar system and a rotating scanning mechanical system. The experimental results obtained from this prototype effectively validate the effectiveness of the proposed method.
An Application of Machine-Learning Model for Analyzing the Impact of Land-Use Change on Surface Water Resources in Gauteng Province, South Africa
Authors: Gidey Eskinder , Mhangara Paidamwoyo
Published: August 2023

The change in land-use diversity is attributed to the anthropogenic factors sustaining life. The surface water bodies and other crucial natural resources in the study area are being depleted at an alarming rate. This study explored the implications of the changing land-use diversity on surface water resources by using a random forest (RF) classifier machine-learning algorithm and remote-sensing models in Gauteng Province, South Africa. Landsat datasets from 1993 to 2022 were used and processed in the Google Earth Engine (GEE) platform, using the RF classifier. The results indicate nine land-use diversity classes having increased and decreased tendencies, with high F-score values ranging from 72.3% to 100%. In GP, the spatial coverage of BL has shrunk by 100.4 km2 every year over the past three decades. Similarly, BuA exhibits an annual decreasing rate of 42.4 km2 due to the effect of dense vegetation coverage within the same land use type. Meanwhile, water bodies, marine quarries, arable lands, grasslands, shrublands, dense forests, and wetlands were expanded annually by 1.3, 2.3, 2.9, 5.6, 11.2, 29.6, and 89.5 km2, respectively. The surface water content level of the study area has been poor throughout the study years. The MNDWI and NDWI values have a stronger Pearson correlation at a radius of 5 km (r = 0.60, p = 0.000, n = 87,260) than at 10 and 15 km. This research is essential to improve current land-use planning and surface water management techniques to reduce the environmental impacts of land-use change.
A Novel Assessment of the Surface Heat Flux Role in Radon (Rn-222) Gas Flow within Subsurface Geological Porous Media
Authors: Benkovitz Ayelet , Zafrir Hovav , Reuveni Yuval
Published: August 2023

At present, Rn subsurface flow can be described only by diffusion and advection transportation models within porous media that currently exist. Even though the temperature is a strong driving force in climate and gas thermodynamics, the impact of the surface heating is missing from all gas flow models within geological porous media. In this work, it is shown that heating the ground surface by the sun, every day up to a maximum temperature at noon, creates a downward vertical temperature gradient related to the constant temperature in the upper shallow layer whose measured thickness is several meters. Undersurface, the Rn gas in the porous media is propelled in nonlinear dependency by the surface temperature gradient to flow downward, up to a measured depth of 100 m, revealing a daily periodicity with time delay depending on depth, similar to the diurnal cycle of the surface temperature. Moreover, regression analysis applied with the data implies a non-linear relationship between Rn and the temporal surface temperature. The relationship is non-linear and the best fit for it from a thermodynamic point of view is an exponential dependency. From now on, it will be possible according to the model to predict and extract, if required, by the time series of the surface-measured parameters (the ambient temperature and pressure), the semi-diurnal, diurnal, multiday, and seasonal Rn temporal variation at a shallow depth.
Virtual Metrology Filter-Based Algorithms for Estimating Constant Ocean Current Velocity
Authors: Huang Yongjiang , Liu Xixiang , Shao Qiantong , Wang Zixuan
Published: August 2023

The strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation system are widely used for autonomous underwater vehicles (AUVs). Whereas DVL works in the water tracking mode, the velocity provided by DVL is relative to the current layer and cannot be directly used to suppress the divergence of SINS errors. Therefore, the estimation and compensation of the ocean current velocity play an essential role in improving navigation positioning accuracy. In recent works, ocean currents are considered constant over a short term in small areas. In the common KF algorithm with the ocean current as a state vector, the current velocity cannot be estimated because the current velocity and the SINS velocity error are coupled. In this paper, two virtual metrology filter (VMF) methods are proposed for estimating the velocity of ocean currents based on the properties that the currents remain unchanged at the adjacent moments. New measurement equations are constructed to decouple the current velocity and the SINS velocity error, respectively. Simulations and lake tests show that both proposed methods are effective in estimating the current velocity, and each has its advantages in estimating the ocean current velocity or the misalignment angle.
Spatial and Temporal Patterns of Ecosystem Services and Trade-Offs/Synergies in Wujiang River Basin, China
Authors: Yang Junyi , Li Junsheng , Fu Gang , Liu Bo , Pan Libo , Hao Haojing , Guan Xiao
Published: August 2023

Analysis of the relationships among ecosystem services (ESs) can help ensure that benefits from ecosystems are consistent over time. This study explored the spatial and temporal patterns of water supply (WS), grain supply (GS), carbon storage (CS), water conservation (WC), soil conservation (SC), and habitat quality (HQ) in the Wujiang River Basin (WJRB) from 2000 to 2020 and the trade-off/synergy relationships of ESs. The ESs in the WJRB are downstream > midstream > upstream in space, with the greatest increase and decrease in the upstream and midstream temporal dimensions, respectively. The WS, WC, and SC underwent a trend shift in 2005 due to climatic influences, whereas GS and HQ underwent a trend shift in 2010 due to human social development. GS formed a trade-off with other ESs in the spatial pattern, whereas WS formed a trade-off with CS and WC in the temporal dimension. Adjusting the GS spatial pattern reduces the trade-off between ESs in the spatial pattern, allowing for focusing on monitoring soil and water erosion-prone areas to prevent extensive soil erosion during heavy precipitation years; this reduces the trade-off between ESs in the time dimension in the WJRB. This provides a theoretical basis for achieving high-quality WJRB development.
Research on the Efficiency of Bridge Crack Detection by Coupling Deep Learning Frameworks with Convolutional Neural Networks
Authors: Ma Kaifeng , Meng Xiang , Hao Mengshu , Huang Guiping , Hu Qingfeng , He Peipei
Published: August 2023

Bridge crack detection based on deep learning is a research area of great interest and difficulty in the field of bridge health detection. This study aimed to investigate the effectiveness of coupling a deep learning framework (DLF) with a convolutional neural network (CNN) for bridge crack detection. A dataset consisting of 2068 bridge crack images was randomly split into training, verification, and testing sets with a ratio of 8:1:1, respectively. Several CNN models, including Faster R-CNN, Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO)-v5(x), U-Net, and Pyramid Scene Parsing Network (PSPNet), were used to conduct experiments using the PyTorch, TensorFlow2, and Keras frameworks. The experimental results show that the Harmonic Mean (F1) values of the detection results of the Faster R-CNN and SSD models under the Keras framework are relatively large (0.76 and 0.67, respectively, in the object detection model). The YOLO-v5(x) model of the TensorFlow2 framework achieved the highest F1 value of 0.67. In semantic segmentation models, the U-Net model achieved the highest detection result accuracy (AC) value of 98.37% under the PyTorch framework. The PSPNet model achieved the highest AC value of 97.86% under the TensorFlow2 framework. These experimental results provide optimal coupling efficiency parameters of a DLF and CNN for bridge crack detection. A more accurate and efficient DLF and CNN model for bridge crack detection has been obtained, which has significant practical application value.
A Novel Two-Stage Induced Deep Learning System for Classifying Similar Drugs with Diverse Packaging
Authors: You Yu-Sin , Lin Yu-Shiang
Published: August 2023

Dispensing errors play a crucial role in various medical errors, unfortunately emerging as the third leading cause of death in the United States. This alarming statistic has spurred the World Health Organization (WHO) into action, leading to the initiation of the Medication Without Harm Campaign. The primary objective of this campaign is to prevent dispensing errors from occurring and ensure patient safety. Due to the rapid development of deep learning technology, there has been a significant increase in the development of automatic dispensing systems based on deep learning classification to avoid dispensing errors. However, most previous studies have focused on developing deep learning classification systems for unpackaged pills or drugs with the same type of packaging. However, in the actual dispensing process, thousands of similar drugs with diverse packaging within a healthcare facility greatly increase the risk of dispensing errors. In this study, we proposed a novel two-stage induced deep learning (TSIDL)-based system to classify similar drugs with diverse packaging efficiently. The results demonstrate that the proposed TSIDL method outperforms state-of-the-art CNN models in all classification metrics. It achieved a state-of-the-art classification accuracy of 99.39%. Moreover, this study also demonstrated that the TSIDL method achieved an inference time of only 3.12 ms per image. These results highlight the potential of real-time classification for similar drugs with diverse packaging and their applications in future dispensing systems, which can prevent dispensing errors from occurring and ensure patient safety efficiently.
Cross-Domain Sentiment Analysis Based on Feature Projection and Multi-Source Attention in IoT
Authors: Kong Yeqiu , Xu Zhongwei , Mei Meng
Published: August 2023

Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models.
To Bag or Not to Bag? How AudioMoth-Based Passive Acoustic Monitoring Is Impacted by Protective Coverings
Authors: Osborne Patrick E. , Alvares-Sanches Tatiana , White Paul R.
Published: August 2023

Bare board AudioMoth recorders offer a low-cost, open-source solution to passive acoustic monitoring (PAM) but need protecting in an enclosure. We were concerned that the choice of enclosure may alter the spectral characteristics of recordings. We focus on polythene bags as the simplest enclosure and assess how their use affects acoustic metrics. Using an anechoic chamber, a series of pure sinusoidal tones from 100 Hz to 20 kHz were recorded on 10 AudioMoth devices and a calibrated Class 1 sound level meter. The recordings were made on bare board AudioMoth devices, as well as after covering them with different bags. Linear phase finite impulse response filters were designed to replicate the frequency response functions between the incident pressure wave and the recorded signals. We applied these filters to ~1000 sound recordings to assess the effects of the AudioMoth and the bags on 19 acoustic metrics. While bare board AudioMoth showed very consistent spectral responses with accentuation in the higher frequencies, bag enclosures led to significant and erratic attenuation inconsistent between frequencies. Few acoustic metrics were insensitive to this uncertainty, rendering index comparisons unreliable. Biases due to enclosures on PAM devices may need to be considered when choosing appropriate acoustic indices for ecological studies. Archived recordings without adequate metadata may potentially produce biased acoustic index values and should be treated cautiously.
Modified Nonlinear Hysteresis Approach for a Tactile Sensor
Authors: Abdul-Hussain Gasak , Holderbaum William , Theodoridis Theodoros , Wei Guowu
Published: August 2023

Soft tactile sensors based on piezoresistive materials have large-area sensing applications. However, their accuracy is often affected by hysteresis which poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fiber-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units were designed. These sensor units underwent force sequences to collect corresponding output resistance. A backpropagation network was trained using these sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network’s parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor’s full-scale output to 13.5%. This improvement established the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for the measurement and control of force, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Though the complete elimination of hysteresis in tactile sensors may not be feasible, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.
Determining the Hydrophobicity Index of Protected Amino Acids and Common Protecting Groups
Authors: Gavva Varshitha , Al Musaimi Othman , Bent Colin , Williams Daryl R.
Published: August 2023

Peptides are in great demand in the pharmaceutical arena and a majority of these peptides contain 20 or more amino acids. They are infrequently synthesised using the fragment condensation approach. A key limitation in adopting this approach more commonly is that protected peptide fragments with high purity are often required prior to the final condensation steps. It is hypothesized that understanding the hydrophobic nature of the protected amino acids will assist with designing optimal fragment purification processes when needed. Whilst a myriad of hydrophobicity indices are reported in the literature for unprotected amino acids, the literature lacks any data regarding the protected amino acids which form the key precursor for the fragment condensation task. In this current study, hydrophobicity indices for protected amino acids with common α-amino and sidechain protecting groups were experimentally determined. Different positions for each amino acid within the peptide chain were considered, namely at the C-terminal and N-terminal as well as internal positions. These data give deep insights on the hydrophobicity of each amino acid with respect to its position in the peptide chain. The data acquired in this research facilitated the prediction of the retention time of protected peptide fragments with an uncertainty of less than ±1.5%.
UHPLC-ToF-MS as a High-Resolution Mass Spectrometry Tool for Veterinary Drug Quantification in Milk
Authors: Leite Marta , Marques Ana Rita , Vila Pouca Ana Sofia , Barros Silvia Cruz , Barbosa Jorge , Ramos Fernando , Afonso Isabel Maria , Freitas Andreia
Published: August 2023

Milk is one of the most widely consumed foods in the world, despite the increasing consumption of plant-based alternatives. Although rich in nutrients and believed by consumers to be free of undesirable contaminants, milk, whether of animal or plant origin, is not always free from residues of chemical substances, including veterinary medicines. For instance, in intensive livestock production, antibiotics are often used to treat animals or, illicitly, to improve their growth performance, which can lead to their presence in the final food. Additionally, the continuous use of veterinary drugs in intensive animal production can lead to their occurrence in agricultural soils and therefore are absorbed by plants as another source of entering the food chain. An effective and accurate multi-detection quantitative screening method to analyze 89 antibiotics in milk was optimized by ultra-high-performance liquid chromatography coupled with a time-of-flight detector (UHPLC-ToF-MS) and further validated in accordance with the Commission Implementing Regulation (CIR) 808/2021 and the International Council for Harmonization (ICH) guidelines on the validation of analytical procedures. Apart from the specific parameters required by CIR 808/2021, the aim was to access the lower limits of the method, limits of detection (LoD) and quantification (LoQ), regardless of the maximum residue limits (MRLs) defined in the legislation. The method was then applied in the analysis of 32 supermarket samples, resulting in four positive findings, including one plant-based sample. The antibiotics found were from the macrolides and sulphonamides families. Nevertheless, the concentrations detected were below the established maximum residue level (MRL).
Impact of Cumin (Cuminum cyminum) Incorporation on the Generation of Heterocyclic Aromatic Amines in Meatballs
Authors: Ekiz Elif , Savaş Adem , Aoudeh Eyad , Elbir Zeynep , Oz Emel , Proestos Charalampos , Ahmad Naushad , Oz Fatih
Published: August 2023

In the current study, the impacts of using cumin (0.5% and 1%, w/w) in beef meatballs on heterocyclic aromatic amines (HAAs) formation and some quality characteristics when cooked at 150 and 250 °C were investigated. It was found that using of cumin at different ratios in beef meatballs showed a significant (p < 0.01) effect on pH, thiobarbituric acid reactive (TBARS) value, and total HAA. The cooking process significantly (p < 0.01) affected the meatballs’ water content, pH, and TBARS values, while the cooking loss and total HAA content of meatballs were significantly (p < 0.01) affected by cooking temperature. It was found that the cumin usage rate in meatballs increased the pH value, while it was found to decrease the TBARS value. As expected, the cooking process was associated with a reduction in water, while it was found to result in an increase in the pH and TBARS values. On the other hand, both cooking temperature and cumin addition to meatballs led to an increase in their total HAA content. Diverse samples exhibited detectable levels of IQx, MeIQx, MeIQ, and PhIP compounds. The presence of MeIQx compound was found in all samples, except for the control group cooked at 150 °C. Additionally, the use of 0.5% and 1% cumin in meatballs cooked at temperature of 250 °C had an inhibitory effect on MeIQx compound. Our results revealed that the incorporation of cumin in the meatballs resulted in an increase in the total HAA content, likely due to its prooxidant effect. However, it was found that the use of cumin at certain rates could reduce the formation of MeIQx compound with an antioxidant effect. However, in this study found that even if 100 g of meatballs with the highest concentration of total HAA were consumed, the maximum HAA exposure limit value (0–15 µg/day) was not exceeded.
Optimization of QuEChERS Method for Antibiotic Residue Analysis in Animal Foods via Response Surface Methodology
Authors: Wu Xiaoqiong , Lin Yun , Zhang Xiang , Ouyang Nan , Zhou Ying
Published: August 2023

The present study employed a modified QuEChERS method to systematically analyze the presence of fifteen quinolone and seven tetracycline antibiotic residues in local animal food. Additionally, a multi-level four-factor Box–Behnken design (BBD) within the framework of response surface methodology (RSM) was utilized to evaluate the factors impacting the detection efficiency of the sample pretreatment procedure. Optimization was performed via Design Expert® 10.0.3, and the factors, including the volume of the acetonitrile, the addition of formic acid, the duration of the extraction, and the addition of EDTA, were combined with experimental design until an optimal solution was reached. Finally, the sample was tested via ultra-high performance liquid chromatography-quadrupole-linear ion trap mass spectrometry (UPLC/MS/MS) in both multiple reaction monitoring (MRM) and enhanced product ion (EPI) scan modes on a QTRAP® 5500 instrument (AB SCIEX instruments, Framingham, MA, USA). The overall average recoveries from actual samples fortified with 22 antibiotics at three levels ranged from 73.8% to 98.5% based on the use of matrix-fortified calibration, with variations ranging from 5.80 to 12.4% (n = 6). The limits of detection and quantification were 0.3 μg kg−1 and 1.0 μg kg−1, respectively. Lastly, the modified method was applied to practical sample analysis in the daily risk monitoring and assessment of food safety with satisfactory stability and robustness.
“Scholar–Practitioners”, Reflexivity and the Illusio of the Field: Ethnography, Yoga Studies and the Social Scientific Study of Religion
Authors: Di Placido Matteo
Published: August 2023

This article dialogues with “yoga studies” and the social scientific study of religion (e.g., the sociology of religion and religious studies), arguing that both substantially neglect a thorough discussion of scholars’ engagement in the field despite being largely composed by “scholar–practitioners”. This is problematic from a methodological point of view as well as from an ethical perspective. Moving in the interstices between biographical reflections, critical social theory and methodological notes on embodied ethnographic research, I self-reflexively discuss my “shifting positionality” from devoted yoga practitioner to critical scholar, mapping the most significant turning points that I encountered during my research on the pedagogies of modern forms of yoga (2017–current). In so doing, I also discuss my overall positioning, participation and ethical reflections in relation to the main object of inquiry of my research. From this, I posit that the positions of scholar and practitioner are, at least in some cases, incommensurable, while the scholar–practitioner may also foster a unique way of knowing based on reflexivity as a living engagement and on the linkages between theory and practice from which there is much to gain.
Well-Being of Parents of Children with Disabilities—Does Employment Status Matter?
Authors: Tokić Andrea , Slišković Ana , Nikolić Ivanišević Matilda
Published: August 2023

Being a parent of a child with a disability brings with it special burdens, e.g. extraordinary caregiving responsibilities, which can have a negative impact on other areas of life and the well-being of parents in general. The aim of this study is to examine the differences in various aspects of well-being and social support between parents who are employed in addition to having caregiving responsibilities, unemployed parents, and parents who are formal caregivers (they receive financial support to care for their child). The online survey included 165 unemployed parents, 467 employed parents (273 full-time and 194 part-time), and 182 formal caregivers. Multivariate analyses of covariance (MANCOVAs) showed that both full-time and part-time employed parents had higher levels of life satisfaction, global health, and social support than unemployed parents, when controlled for the child’s level of disability and the parents’ level of education. Formal caregivers and unemployed parents did not differ significantly in the variables of well-being or social support. Our results suggest that a social measure introduced in Croatia to facilitate parents of children with disabilities to work have a positive impact on parental well-being, and that social support from society for unemployed parents and formal caregivers needs to be improved.
Are TikTok Algorithms Influencing Users’ Self-Perceived Identities and Personal Values? A Mini Review
Authors: Ionescu Claudiu Gabriel , Licu Monica
Published: August 2023

The use of TikTok is more widespread now than ever, and it has a big impact on users’ daily lives, with self-perceived identity and personal values being topics of interest in light of the algorithmically curated content. This mini-review summarizes current findings related to the TikTok algorithm, and the impact it has on self-perceived identity, personal values, or related concepts of the Self. We pass through the contents of algorithmic literacy and emphasize its importance along with users’ attitudes toward algorithms. In the first part of our results, we show conceptual models of algorithms like the crystal framework, platform spirit, and collective imaginaries. In the second part, we talk about the degree of impact a social media algorithm may exert over an individual’s sense of self, understanding how the algorithmized self and domesticated algorithm are trying to sum up the dual development of this relationship. In the end, with the concept of Personal Engagement and the role of cognitive biases, we summarize the current findings and discuss the questions that still need to be addressed. Performing research on the topic of social media, especially TikTok, poses ethical, cultural, and regulatory challenges for researchers. That is why we will discuss the main theoretical frameworks that were published with their attached current studies and their impact on the current theoretical models as well as the limitations within these studies. Finally, we discuss further topics of interest related to the subject and possible perspectives, as well as recommendations regarding future research in areas like impact on personal values and identity, cognitive biases, and algorithmic literacy.
Assessment of Natural Disasters Impact on Cultural Mayan Heritage Spaces in Remotes Villages of Guatemala: Case of Black Salt
Authors: Yon Secaida Luis Pablo , Mori Suguru , Nomura Rie
Published: August 2023

In the town of Sacapulas located in the mountainous country of Guatemala, there is a constant risk of natural disasters. Floods and landslides occur frequently, resulting in the loss of human lives and cultural aspects. Specifically, in the region, the creation of black salt is the most affected. This resource has been created since the time of the Mayans on the salt beach surrounding the town. However, from the 1940s onwards, this industry has shrunk, impacting the sustainability of indigenous people. After conducting several area and space analyses, it was found that the black salt beach has evolved considerably since the last research conducted in 2001. The shape of the space has been reduced, while the use of the area has been modified by the people of the town, who specifically use the hot springs located below the river shore of the beach. This new usage can coexist with the Salt making industry is only made by a few people now, there are few working in this industry, and they only work in the dry season. The result is an opportunity for economic growth and an increase in tourism if the area handled properly by managing the land and planning ahead.
Fejér-Type Inequalities for Harmonically Convex Functions and Related Results
Authors: Amer Latif Muhammad
Published: August 2023

In this paper, new Fejér-type inequalities for harmonically convex functions are obtained. Some mappings related to the Fejér-type inequalities for harmonically convex are defined. Properties of these mappings are discussed and, as a consequence, we obtain refinements of some known results.
Symmetry Analyses of Epidemiological Model for Monkeypox Virus with Atangana–Baleanu Fractional Derivative
Authors: Gunasekar Tharmalingam , Manikandan Shanmugam , Govindan Vediyappan , D Piriadarshani , Ahmad Junaid , Emam Walid , Al-Shbeil Isra
Published: August 2023

The monkeypox virus causes a respiratory illness called monkeypox, which belongs to the Poxviridae virus family and the Orthopoxvirus genus. Although initially endemic in Africa, it has recently become a global threat with cases worldwide. Using the Antangana–Baleanu fractional order approach, this study aims to propose a new monkeypox transmission model that represents the interaction between the infected human and rodent populations. An iterative method and the fixed-point theorem are used to prove the existence and uniqueness of the symmetry model’s system of solutions. It shows that the symmetry model has equilibrium points when there are epidemics and no diseases. As well as the local asymptotic stability of the disease-free equilibrium point, conditions for the endemic equilibrium point’s existence have also been demonstrated. For this purpose, the existence of optimal control is first ensured. The aim of the proposed optimal control problem is to minimize both the treatment and prevention costs, and the number of infected individuals. Optimal conditions are acquired Pontryagin’s maximum principle is used. Then, the stability of the symmetry model is discussed at monkeypox-free and endemic equilibrium points with treatment strategies to control the spread of the disease. Numerical simulations clearly show how necessary and successful the proposed combined control strategy is in preventing the disease from becoming epidemic.
Distribution of Essential and Toxic Elements in Pelecus cultratus Tissues and Risk Assessment for Consumer Health
Authors: Payuta Aleksandra Aleksandrovna , Flerova Ekaterina Aleksandrovna , Zaitseva Yulia Vladimirovna
Published: August 2023

Nowadays, the problem of inland water pollution is acute. It is caused by vast industrial growth and agricultural intensification. Concentrations of Cd, Pb, Zn, Cu, Mn, Fe, Mg, and Kwere determined in the muscles, liver, and gonads sabrefish from Rybinsk Reservoir areas with different anthropogenic loads. The tissue samples were analyzed by atomic absorption spectrometry. Heavy metals accumulated more intensively in the body of fish from more polluted areas of the reservoir. Among the analyzed elements, the maximum accumulation levels were found for K, Zn, and Fe and the minimum levels were observed for Cd and Pb. The gonads contained the largest concentration of Cd and Mn, the muscles contained the highest concentrations of Mg, and the other elements mainly accumulated in the liver of sabrefish. The THQ and HI values for all elements did not exceed 1, which suggests that there is no potential non-carcinogenic risk to human health. The target values of carcinogenic risk (TR) for cadmium ranged from 8.32 × 10−6 to 1.22 × 10−4 in the muscles. The increased content of cadmium in the gonads of sabrefish not only poses a risk to human health, but also to the reproduction of this species in the Rybinsk Reservoir.
SARS-CoV-2 in Domestic UK Cats from Alpha to Omicron: Swab Surveillance and Case Reports
Authors: Jones Sarah , Tyson Grace B. , Orton Richard J. , Smollett Katherine , Manna Federica , Kwok Kirsty , Suárez Nicolás M. , Logan Nicola , McDonald Michael , Bowie Andrea , Filipe Ana Da Silva , Willett Brian J. , Weir William , Hosie Margaret J.
Published: August 2023

Although domestic cats are susceptible to infection with SARS-CoV-2, the role of the virus in causing feline disease is less well defined. We conducted a large-scale study to identify SARS-CoV-2 infections in UK pet cats, using active and passive surveillance. Remnant feline respiratory swab samples, submitted for other pathogen testing between May 2021 and February 2023, were screened using RT-qPCR. In addition, we appealed to veterinarians for swab samples from cats suspected of having clinical SARS-CoV-2 infections. Bespoke testing for SARS-CoV-2 neutralising antibodies was also performed, on request, in suspected cases. One RT-qPCR-positive cat was identified by active surveillance (1/549, 0.18%), during the Delta wave (1/175, 0.57%). Passive surveillance detected one cat infected with the Alpha variant, and two of ten cats tested RT-qPCR-positive during the Delta wave. No cats tested RT-qPCR-positive after the emergence of Omicron BA.1 and its descendants although 374 were tested by active and eleven by passive surveillance. We describe four cases of SARS-CoV-2 infection in pet cats, identified by RT-qPCR and/or serology, that presented with a range of clinical signs, as well as their SARS-CoV-2 genome sequences. These cases demonstrate that, although uncommon in cats, a variety of clinical signs can occur.
Current Achievements and Future Prospects in Virus Elimination Technology for Functional Chrysanthemum
Authors: Gao Kang , Chen Qingbing , Pan Bo , Sun Yahui , Xu Yuran , Chen Dongliang , Liu Hua , Luo Chang , Chen Xi , Li Haiying , Huang Conglin
Published: August 2023

Chrysanthemum is an important functional plant that is used for food, medicine and tea. Functional chrysanthemums become infected with viruses all around the world, seriously lowering their quality and yield. Viral infection has become an important limiting factor in chrysanthemum production. Functional chrysanthemum is often propagated asexually by cutting during production, and viral infection of seedlings is becoming increasingly serious. Chrysanthemums can be infected by a variety of viruses causing different symptoms. With the development of biotechnology, virus detection and virus-free technologies for chrysanthemum seedlings are becoming increasingly effective. In this study, the common virus species, virus detection methods and virus-free technology of chrysanthemum infection are reviewed to provide a theoretical basis for virus prevention, treatment and elimination in functional chrysanthemum.
Detection of West Nile and Usutu Virus RNA in Autumn Season in Wild Avian Hosts in Northern Italy
Authors: Musto Carmela , Tamba Marco , Calzolari Mattia , Rossi Arianna , Grisendi Annalisa , Marzani Katia , Bonilauri Paolo , Delogu Mauro
Published: August 2023

West Nile (WNV) and Usutu (USUV) viruses are two mosquito-borne viruses belonging to the family Flaviviridae and genus Flavivirus. The natural transmission cycle of WNV and USUV involves mosquitoes and birds, while mammals are thought to be accidental hosts. The goal of this study was to report—in the context of “off-season monitoring” and passive surveillance—the detection of WNV and USUV RNA in wild birds. To this end, we analyzed biological samples of wild birds in Northern Italy, from October to May, hence outside of the regional monitoring period (June-September). The virological investigations for the detection of USUV and WNV RNA were performed using real-time PCR on frozen samples of the brain, myocardium, kidney, and spleen. In a total sample of 164 wild birds belonging to 27 different species, sequences of both viruses were detected: four birds (2.44%) were positive for WNV and five (3.05%) for USUV. Off-season infections of WNV and especially USUV are still widely discussed and only a few studies have been published to date. To the best of our knowledge, this study is the first report on the detection of USUV RNA until December 22nd. Although further studies are required, our results confirm the viral circulation out-of-season of Flavivirus in wild birds, suggesting reconsidering the epidemiological monitoring period based on each individual climate zone and taking into consideration global warming which will play an important role in the epidemiology of vector-borne diseases.
Influenza Vaccination during Pregnancy: A Descriptive Study of the Knowledge, Beliefs, and Practices of Mexican Gynecologists and Family Physicians
Authors: Lopatynsky-Reyes Erika Zoe , Chacon-Cruz Enrique , Greenberg Michael , Clemens Ralf , Costa Clemens Sue Ann
Published: August 2023

Background: Influenza in pregnancy is associated with elevated morbidity and mortality. Influenza vaccines are safe and effective in pregnancy. There are no Mexican surveys of physicians on knowledge, beliefs, and practices towards influenza and influenza immunization during pregnancy. Methods: A 32-question descriptive survey was conducted, addressing the general knowledge of influenza as well as beliefs and practices regarding influenza vaccination during pregnancy among Mexican physicians responsible for prenatal care, traditionally Obstetricians (OBGYNs) and Family Physicians (FPs). Results: A total of 206 surveys were available, 98 (47.6%) from OBGYNs and 108 (52.4%) from FPs, representing an estimated 2472 daily pregnancy consultations. In total, 54 of the 206 respondents (26.2%) were not aware that influenza is more severe during pregnancy, 106 of the 206 respondents (51.5%) ignored the potential side effects of influenza infection on the fetus, and 56.8% did not know when to vaccinate pregnant women. Pregnancy as a risk factor for developing influenza complications was only known by 99 of the 206 respondents (48.1%), and 6.1% believed that vaccination does not confer protection to the fetus. Conclusions: The current beliefs of Mexican OBGYNs and FPs for both influenza morbidity and mortality, and the importance of influenza vaccination during pregnancy are suboptimal. The drivers of these beliefs should be assessed to improve influenza vaccination recommendations, as knowledge alone is not sufficient.
Intramuscular Immunization with a Liposomal Multi-Epitope Chimeric Protein Induces Strong Cellular Immune Responses against Visceral Leishmaniasis
Authors: Agallou Maria , Margaroni Maritsa , Karagouni Evdokia
Published: August 2023

Control of the intracellular parasite Leishmania (L.) requires the activation of strong type 1 cellular immune responses. Towards this goal, in the present study, a multiepitope chimeric protein named LiChimera was encapsulated into cationic liposomes and its protective efficacy against experimental visceral leishmaniasis was investigated. Liposomal LiChimera conferred significant protection against L. infantum as evidenced by the significantly reduced parasite loads in the spleen and liver. Protection detected in Lipo:LiChimera-immunized mice was dependent on the differentiation of long-lasting cellular immune responses and particularly the induction of antigen-specific multifunctional memory CD4+ TH1 and CD8+ T cells that persisted during infection, as evidenced by the persistent high production of IFN-γ and IL-2 and proliferation activity. Notably, protected mice were also characterized by significantly low numbers of non-regulatory CD4+ T cells able to co-produce IFN-γ and IL-10, an important population for disease establishment, as compared to non-immunized control group. Collectively, these results demonstrate that cationic liposomes containing LiChimera can be considered an effective candidate vaccine against visceral leishmaniasis.