bluuebunny/crossref_metadata_2025 huggingface dataset:
Parent-Metabolite Pharmacokinetic Modeling of Formononetin and Its Active Metabolites in Rats after Oral Administration of Formononetin Formulations
Authors: Kim Ju Hee , Kang Dong Wook , Cho Seok-jin , Cho Hea-Young
Published: December 2022

Formononetin is a major isoflavone contained in propolis and is reported to exhibit various pharmacological effects. However, the use of formononetin in pharmaceutical industry is limited due to its low bioavailability and solubility. There had been several efforts on formononetin formulation development, but further study is required to acquire optimal formulation. The aim of this study is to conduct pharmacokinetic (PK) evaluations after the oral administration of three formononetin formulations (20 mg/kg) in male Sprague Dawley rats. Then, a parent-metabolite PK model for formononetin was developed and evaluated for the first time. To do this, a simultaneous analysis method for formononetin and its active metabolites, daidzein, dihydrodaidzein and equol in rat plasma was developed using ultra-performance liquid chromatography tandem mass spectrometry. The separation was performed using a gradient elution of water and acetonitrile and a Kinetex C18 column (2.1 mm × 100 mm, 1.7 µm particle size) at a temperature of 30 ± 5 °C. The simultaneous analytical method developed in this study was validated according to international guidance and was successfully applied for the pharmacokinetic study. The time-plasma concentrations of formononetin and daidzein were well described by a two-compartment model combined with a metabolite compartment. Additionally, plasma protein binding assay was conducted in male rat plasma. The findings from the study could be used as a fundamental for the future development of formononetin as a pharmaceutical product.
Delta Opioid Peptide Targets Brain Microvascular Endothelial Cells Reducing Apoptosis to Relieve Hypoxia-Ischemic/Reperfusion Injury
Authors: Zhang Ran , Chen Meixuan , Deng Zhongfang , Kong Lingchao , Shen Bing , Zhang Lesha
Published: December 2022

Stroke is one of the leading causes of death. (D-ala2, D-leu5) enkephalin (DADLE) is a synthetic peptide and highly selective delta opioid receptor (δOR) agonist that has exhibited protective properties in ischemia. However, the specific target and mechanism are still unclear. The present study explores the expression of δOR on brain microvascular endothelial cells (BMECs) and whether DADLE could relieve I/R-induced injury by reducing apoptosis. A lateral ventricular injection of DADLE for pretreatment, the neurofunctional behavior score, and TTC staining, were used to evaluate the protective effect of DADLE. Immunofluorescence technology was used to label different types of cells with apoptosis-positive signals to test co-localization status. Primary cultured BMECs were separated and treated with DADLE, accompanied by OGD/R. The CCK-8 test was conducted to evaluate cell viability and TdT-mediated dUTP Nick-end Labelling (TUNEL) staining to test apoptosis levels. The levels of apoptosis-related proteins were analyzed by Western blotting. The co-localization results showed that BMECs, but not astrocytes, microglia, or neurons, presented mostly TUNEL-positive signals, especially in the Dentate gyrus (DG) area of the hippocampus. Either activation of δORs on rats’ brains or primary BMECs mainly reduce cellular apoptosis and relieve the injury. Interference with the expression δOR could block this effect. DADLE also significantly increased levels of Bcl-2 and reduced levels of Bax. δOR’s expressions can be detected on the BMECs, but not on the HEK293 cells, by Western blotting and IFC. Therefore, DADLE exerts a cytoprotective effect, primarily under hypoxia-ischemic injury/reperfusion conditions, by targeting BMECs to inhibit apoptosis.
Prospects on Tuning Bioactive and Antimicrobial Denture Base Resin Materials: A Narrative Review
Authors: Al-Dulaijan Yousif A. , Balhaddad Abdulrahman A.
Published: December 2022

Denture base resin (DBR) materials are used in dentistry in constructing removable dentures and implant-supported prostheses. A plethora of evidence has demonstrated that DBR materials are associated with a high risk of denture stomatitis, a clinical complication where the soft oral tissues underneath the resin-based material are inflamed. The prevalence of denture stomatitis among denture wearers is high worldwide. Plaque accumulation and the infiltration of oral microbes into DBRs are among the main risk factors for denture stomatitis. The attachment of fungal species, mainly Candida albicans, to DBRs can irritate the underneath soft tissues, leading to the onset of the disease. As a result, several attempts were achieved to functionalize antimicrobial compounds and particles into DBRs to prevent microbial attachment. This review article explored the advanced approaches in designing bioactive and antimicrobial DBR materials. It was reported that using monomer mixtures, quaternary ammonium compounds (QACs), and organic and inorganic particles can suppress the growth of denture stomatitis-related pathogens. This paper also highlighted the importance of characterizing bioactive DBRs to be mechanically and physically sustainable. Future directions may implement a clinical translational model to attempt these materials inside the oral cavity.
Kenyan Catholics’ Religiosity and Understanding of Marriage on the Basis of Individuals Associated with Shalom Center in Mitunguu: Educational and Pastoral Perspective
Authors: Goleń Jacek , Kobak Jan , Kabala Florence , Szyszka Małgorzata
Published: December 2022

This article presents the results of research into religiosity and understanding of marriage among a selected group of young Catholics, all of whom are current students or graduates of Shalom Center in Mitunguu, Kenya. The goal of our study was to determine the correlations between the two variables so as to reach some conclusions and suggestions for religious education and pastoral care. The research made use of Stefan Huber’s Centrality of Religiosity Scale, while the respondents’ understanding of marriage was analyzed with the use of a questionnaire prepared by the authors of this study. As our research shows, one’s Catholic understanding of marriage increases alongside an increase in interest in religiosity and in one’s religious convictions, while it does not correlate—or does so only weakly—with a centrality of religiosity. Correlations with centrality occurred more often among men than among women. Mutual connections were most frequent among the youngest group of respondents, those who were up to 20 years of age and current students. This may indicate that religious formation and education of youth in the Shalom Center exerts a clear impact on shaping Catholic views on marriage. With the passing of time, traditional cultural precepts seem to gain prominence among the graduates.
Green Ganesha Chaturthi: The Ritualising and Materialising of a Green Hindu Identity and the Emerging of an Alternative Representation of Ganesha
Authors: De Koning Deborah
Published: December 2022

With the world facing an ecological crisis, Hindus are challenged to reflect on the ways they impact their environment. The last few decades witnessed a rise of theological reflections on Hindu traditions—especially scriptures and concepts (not least by western scholars)—that advocate environmentally friendly perspectives. This stands in sharp contrast with the multiple examples of how Hindu ritual practices cause harm to the environment. Ganesha Chaturthi is a festival that due to the public element of immersion of Ganesha idols, has led to severe pollution of waterbodies. Because of the attention that has been paid to this lately, the festival now calls for ecofriendly alternatives. This article analyses how recently, environmental awareness is ritualized and materialized in the festival of Ganesha Chaturthi. For this, fieldwork was conducted during Ganesha Chaturthi in Chennai and Mumbai in 2022. The focus of the article lies on the ecofriendly material and ritual innovations of the festival—for instance the variety of Ganeshas made of biodegradable materials—and the dynamics of interaction with (alleged) traditions to validate a Green Hindu identity in general and the development of a Green Ganesha in particular.
Is Sanctification Real? Empirical Evidence for and against Christian Moral Transformation
Authors: Launonen Lari
Published: December 2022

According to a widely held view of the New Testament teaching on sanctification; the Holy Spirit brings about a significant moral transformation in the character of every true believer. This claim about Christian moral transformation (CMT) has empirical implications. Thus, its truth can be evaluated from a scientific perspective. Sociological and psychological data on the relationship of religion and morality suggests that (Christian) religion is negatively correlated with undesirable moral behavior such as alcohol and substance abuse, criminal behavior, domestic abuse, and nonmarital sex, and positively correlated with desirable behavior such as helping. While these correlations are consistent with CMT, they do not necessarily serve as good evidence for it, for they can be more naturally explained by situational factors than by character differences between religious and nonreligious people. Some scientific findings serve as evidence against CMT. Religious believers engage in dishonest behavior and porn viewing at least as much as others. There is also a positive correlation between prejudice and conservative Christian beliefs. These correlations cannot be explained by situational factors as easily as the link between religion and desirable moral behavior. Sociological and psychological research; therefore, provides at least a mild challenge for the truth of CMT.
In Answer to the Pauline Principle: Consent, Logical Constraints, and Free Will
Authors: Coetsee Marilie
Published: December 2022

James Sterba uses the Pauline Principle to argue that the occurrence of significant, horrendous evils is logically incompatible with the existence of a good God. The Pauline Principle states that (as a rule) one must never do evil so that good may come from it, and according to Sterba, this principle implies that God may not permit significant evils even if that permission would be necessary to secure other, greater goods. By contrast, I argue that the occurrence of significant evils is logically compatible with the existence of a good God because victims of significant evils may themselves reasonably consent to their suffering. In particular, I argue that they may be able to accept their suffering if it turns out that there was no way for God to secure relevant greater goods (or prevent other, greater evils) except by way of allowing their suffering, and God also provides them with other compensating, heavenly comforts. After using this consent-based argument to address Sterba’s logical problem from evil, I briefly consider how this argument may also help address a related evidential problem from evil, which suggests that while it is possible that victims of significant evils would consent to their suffering, it is unlikely that they would do so. While I do not provide a definitive solution to this evidential problem of evil, I highlight one important example of a trade-off that God may need to make that would—along with the provision of compensating, heavenly comforts—potentially persuade victims of significant evils to consent to their suffering. Specifically, I argue that there may be a necessary trade-off that God needs to make between permitting significant evils (on the one hand) and protecting a certain, morally significant form of free will (on the other hand).
Convolution Neural Network with Laser-Induced Breakdown Spectroscopy as a Monitoring Tool for Laser Cleaning Process
Authors: Choi Soojin , Park Changkyoo
Published: December 2022

In this study, eight different painted stainless steel 304L specimens were laser-cleaned using different process parameters, such as laser power, scan speed, and the number of repetitions. Laser-induced breakdown spectroscopy (LIBS) was adopted as the monitoring tool for laser cleaning. Identification of LIBS spectra with similar chemical compositions is challenging. A convolutional neural network (CNN)-based deep learning method was developed for accurate and rapid analysis of LIBS spectra. By applying the LIBS-coupled CNN method, the classification CNN model accuracy of laser-cleaned specimens was 94.55%. Moreover, the LIBS spectrum analysis time was 0.09 s. The results verified the possibility of using the LIBS-coupled CNN method as an in-line tool for the laser cleaning process.
A Lorentz Force EMAT Design with Racetrack Coil and Periodic Permanent Magnets for Selective Enhancement of Ultrasonic Lamb Wave Generation
Authors: Guo Xinfeng , Zhu Wujun , Qiu Xunlin , Xiang Yanxun
Published: December 2022

This article proposes an electromagnetic acoustic transducer (EMAT) for selectively improving the purity and amplitude of ultrasonic Lamb waves in non-ferromagnetic plates. The developed EMAT consists of a racetrack coil and a group of periodic permanent magnets (PPMs). Two-dimensional finite element simulations and experiments are implemented to analyze the working mechanism and performance of the PPM EMAT. Thanks to the specific design, the eddy currents increase with increasing wire density and the directions of the magnetic fields and Lorentz forces alternate according to the polarities of the magnet units. Wires laid uniformly beneath the magnets, and the gaps between adjacent magnets generate tangential and normal Lorentz forces, resulting in-plane (IP) and out-of-plane (OP) displacements, respectively. The constructive interference occurs when the wavelength of the generated Lamb wave is twice the spacing of the magnets, leading to large amplitudes of the targeted ultrasonic Lamb waves. Therefore, the PPM EMAT is capable of generating pure symmetric or antisymmetric mode Lamb waves at respective frequencies. The results prove that the developed PPM EMAT can generate pure either S0 or A0 mode Lamb waves at respective frequencies. The increase in wire width and wire density further increases the signal amplitudes. Compared with the case of conventional meander-line-coil (MLC) EMAT, the amplitudes of the A0 and S0 mode Lamb waves of our PPM EMAT are increased to 880% and 328%, respectively.
Indoor Spatiotemporal Contact Analytics Using Landmark-Aided Pedestrian Dead Reckoning on Smartphones
Authors: Gao Lulu , Konomi Shin’ichi
Published: December 2022

Due to the prevalence of COVID-19, providing safe environments and reducing the risks of virus exposure play pivotal roles in our daily lives. Contact tracing is a well-established and widely-used approach to track and suppress the spread of viruses. Most digital contact tracing systems can detect direct face-to-face contact based on estimated proximity, without quantifying the exposed virus concentration. In particular, they rarely allow for quantitative analysis of indirect environmental exposure due to virus survival time in the air and constant airborne transmission. In this work, we propose an indoor spatiotemporal contact awareness framework (iSTCA), which explicitly considers the self-containing quantitative contact analytics approach with spatiotemporal information to provide accurate awareness of the virus quanta concentration in different origins at various times. Smartphone-based pedestrian dead reckoning (PDR) is employed to precisely detect the locations and trajectories for distance estimation and time assessment without the need to deploy extra infrastructure. The PDR technique we employ calibrates the accumulative error by identifying spatial landmarks automatically. We utilized a custom deep learning model composed of bidirectional long short-term memory (Bi-LSTM) and multi-head convolutional neural networks (CNNs) for extracting the local correlation and long-term dependency to recognize landmarks. By considering the spatial distance and time difference in an integrated manner, we can quantify the virus quanta concentration of the entire indoor environment at any time with all contributed virus particles. We conducted an extensive experiment based on practical scenarios to evaluate the performance of the proposed system, showing that the average positioning error is reduced to less than 0.7 m with high confidence and demonstrating the validity of our system for the virus quanta concentration quantification involving virus movement in a complex indoor environment.
Design, Fabrication and Characterisation of Multi-Parameter Optical Sensors Dedicated to E-Skin Applications
Authors: Fliegans Lionel , Troughton Joseph , Divay Valentin , Blayac Sylvain , Ramuz Marc
Published: December 2022

For many years there has been a strong research interest in soft electronics for artificial skin applications. However, one challenge with stretchable devices is the limited availability of high performance, stretchable, electrical conductors and semiconductors that remain stable under strain. Examples of such electronic skin require excessive amounts of wires to address each sensing element—compression force and strain—in a conventional matrix structure. Here, we present a new process for fabricating artificial skin consisting of an optical waveguide architecture, enabling wide ranging sensitivity to external mechanical compression and strain. The manufacturing process allows design of a fully stretchable polydimethylsiloxane elastomer waveguide with embedded gratings, replicated from low cost DVD-Rs. This optical artificial skin allows the detection of compression forces from 0 to 3.8 N with controllable sensitivity. It also permits monitoring of elongation deformations up to 135%. This type of stretchable optical sensor is highly robust, transparent, and presents a large sensing area while limiting the amount of wires connecting to the sensor. Thus, this optical artificial skin presents far superior mechanical properties compared to current electronic skin.
The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era
Authors: Passian Ali , Buchs Gilles , Seck Christopher M. , Marino Alberto M. , Peters Nicholas A.
Published: December 2022

Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.
A Multi-Flow Production Line for Sorting of Eggs Using Image Processing
Authors: Akkoyun Fatih , Ozcelik Adem , Arpaci Ibrahim , Erçetin Ali , Gucluer Sinan
Published: December 2022

In egg production facilities, the classification of eggs is carried out either manually or by using sophisticated systems such as load cells. However, there is a need for the classification of eggs to be carried out with faster and cheaper methods. In the agri-food industry, the use of image processing technology is continuously increasing due to the data processing speed and cost-effective solutions. In this study, an image processing approach was used to classify chicken eggs on an industrial roller conveyor line in real-time. A color camera was used to acquire images in an illumination cabinet on a motorized roller conveyor while eggs are moving on the movement halls. The system successfully operated for the grading of eggs in the industrial multi-flow production line in real-time. There were significant correlations among measured weights of the eggs after image processing. The coefficient of linear correlation (R2) between measured and actual weights was 0.95.
Comparison of Different Linewidth Measuring Methods for Narrow Linewidth Laser
Authors: Zheng Ziqi , Luo Qiaoxia , Wang Xian , Ma Xiaohui , Zhang Wei , Fang Wentan , Chen Xiaolin , Huang Song , Zhou Yong , Gao Weiqing
Published: December 2022

We experimentally demonstrate a fiber laser with different linewidths based on self-injection locking (SIL) and the stimulated Brillouin scattering effect. Based on the homemade fiber laser, the error origin, resolution, and applicable range of delayed self-heterodyne interferometry (DSHI), self-correlation envelope linewidth detection (SCELD) and Voigt fitting are investigated numerically and experimentally. The selection of the linewidth measuring method should meet the following conclusions: an approximately Lorentzian self-heterodyne spectrum without the pedestal and high-intensity sinusoidal jitter is a prerequisite for DSHI; the SCELD needs a suitable length of delay fiber for eliminating flicker noise and dark noise of the electrical spectrum analyzer; a non-Lorentzian self-heterodyne spectrum without a pedestal is an indispensable element for Voigt fitting. According to the experimental results, the laser Lorentzian linewidth of SIL changes from 1.7 kHz to 587 Hz under different injection powers. When the Brillouin erbium fiber laser is utilized, the Lorentzian linewidth is measured to be 60 ± 5 Hz.
Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks
Authors: Massimi Federica , Ferrara Pasquale , Benedetto Francesco
Published: December 2022

Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices’ learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.
Feasibility of a Novel Therapist-Assisted Feedback System for Gait Training in Parkinson’s Disease
Authors: Silva-Batista Carla , Harker Graham , Vitorio Rodrigo , Horak Fay B. , Carlson-Kuhta Patricia , Pearson Sean , VanDerwalker Jess , El-Gohary Mahmoud , Mancini Martina
Published: December 2022

We tested the feasibility of one session of treadmill training using a novel physical therapist assisted system (Mobility Rehab) using wearable sensors on the upper and lower limbs of 10 people with Parkinson’s disease (PD). Participants performed a 2-min walk overground before and after 15 min of treadmill training with Mobility Rehab, which included an electronic tablet (to visualize gait metrics) and five Opal sensors placed on both the wrists and feet and on the sternum area to measure gait and provide feedback on six gait metrics (foot-strike angle, trunk coronal range-of-motion (ROM), arm swing ROM, double-support duration, gait-cycle duration, and step asymmetry). The physical therapist used Mobility Rehab to select one or two gait metrics (from the six) to focus on during the treadmill training. Foot-strike angle (effect size (ES) = 0.56, 95% Confidence Interval (CI) = 0.14 to 0.97), trunk coronal RoM (ES = 1.39, 95% CI = 0.73 to 2.06), and arm swing RoM (ES = 1.64, 95% CI = 0.71 to 2.58) during overground walking showed significant and moderate-to-large ES following treadmill training with Mobility Rehab. Participants perceived moderate (60%) and excellent (30%) effects of Mobility Rehab on their gait. No adverse events were reported. One session of treadmill training with Mobility Rehab is feasible for people with mild-to-moderate PD.
Fruit Volume and Leaf-Area Determination of Cabbage by a Neural-Network-Based Instance Segmentation for Different Growth Stages
Authors: Lüling Nils , Reiser David , Straub Jonas , Stana Alexander , Griepentrog Hans W.
Published: December 2022

Fruit volume and leaf area are important indicators to draw conclusions about the growth condition of the plant. However, the current methods of manual measuring morphological plant properties, such as fruit volume and leaf area, are time consuming and mainly destructive. In this research, an image-based approach for the non-destructive determination of fruit volume and for the total leaf area over three growth stages for cabbage (brassica oleracea) is presented. For this purpose, a mask-region-based convolutional neural network (Mask R-CNN) based on a Resnet-101 backbone was trained to segment the cabbage fruit from the leaves and assign it to the corresponding plant. Combining the segmentation results with depth information through a structure-from-motion approach, the leaf length of single leaves, as well as the fruit volume of individual plants, can be calculated. The results indicated that even with a single RGB camera, the developed methods provided a mean accuracy of fruit volume of 87% and a mean accuracy of total leaf area of 90.9%, over three growth stages on an individual plant level.
Discovering Key Sub-Trajectories to Explain Traffic Prediction
Authors: Wang Hongjun , Fan Zipei , Chen Jiyuan , Zhang Lingyu , Song Xuan
Published: December 2022

Flow prediction has attracted extensive research attention; however, achieving reliable efficiency and interpretability from a unified model remains a challenging problem. In the literature, the Shapley method offers interpretable and explanatory insights for a unified framework for interpreting predictions. Nevertheless, using the Shapley value directly in traffic prediction results in certain issues. On the one hand, the correlation of positive and negative regions of fine-grained interpretation areas is difficult to understand. On the other hand, the Shapley method is an NP-hard problem with numerous possibilities for grid-based interpretation. Therefore, in this paper, we propose Trajectory Shapley, an approximate Shapley approach that functions by decomposing a flow tensor input with a multitude of trajectories and outputting the trajectories’ Shapley values in a specific region. However, the appearance of the trajectory is often random, leading to instability in interpreting results. Therefore, we propose a feature-based submodular algorithm to summarize the representative Shapley patterns. The summarization method can quickly generate the summary of Shapley distributions on overall trajectories so that users can understand the mechanisms of the deep model. Experimental results show that our algorithm can find multiple traffic trends from the different arterial roads and their Shapley distributions. Our approach was tested on real-world taxi trajectory datasets and exceeded explainable baseline models.
Improved Spatiotemporal Framework for Human Activity Recognition in Smart Environment
Authors: Salem Ziad , Weiss Andreas Peter
Published: December 2022

The rapid development of microsystems technology with the availability of various machine learning algorithms facilitates human activity recognition (HAR) and localization by low-cost and low-complexity systems in various applications related to industry 4.0, healthcare, ambient assisted living as well as tracking and navigation tasks. Previous work, which provided a spatiotemporal framework for HAR by fusing sensor data generated from an inertial measurement unit (IMU) with data obtained by an RGB photodiode for visible light sensing (VLS), already demonstrated promising results for real-time HAR and room identification. Based on these results, we extended the system by applying feature extraction methods of the time and frequency domain to improve considerably the correct determination of common human activities in industrial scenarios in combination with room localization. This increases the correct detection of activities to over 90% accuracy. Furthermore, it is demonstrated that this solution is applicable to real-world operating conditions in ambient light.
Positive Resources for Flourishing: The Effect of Courage, Self-Esteem, and Career Adaptability in Adolescence
Authors: Parola Anna , Marcionetti Jenny
Published: December 2022

Flourishing is defined as an optimal state of functioning in which individuals pursue their goals and aspirations. Hence, flourishing seems to be a protective factor for career transitions in adolescence. This study aimed to analyze the predicting role of self-esteem, courage, and the four career adaptability dimensions, i.e., concern, control, curiosity, and confidence, on flourishing. The sample consisted of 221 Italian adolescents attending the last year of middle school. The preliminary analyses showed gender differences in courage and flourishing, reporting females higher scores than males on both variables. The SEM path model showed that courage, self-esteem, and confidence predict flourishing, and suggested that confidence partially mediates the relationship between courage, self-esteem, and flourishing. Findings have also permitted us to draw practical implications for interventions in adolescence.
Experimental Investigation on the Shear Characteristics of Frozen Silty Clay and Grey Relational Analysis
Authors: Huang Wanjun , Mao Xuesong , Wu Qian , Zhang Jianxun
Published: December 2022

In the spring thawing season, the decrease in the strength of frozen soil caused by temperature may lead to slope instability. Therefore, researchers have studied the relationship between the temperature and strength of the freeze-thaw zone. However, previous studies have considered the impact of thawing temperature on the strength, but less research on shaping temperature. Therefore, direct shear tests were performed on specimens with different thawing temperatures (i.e., −5, −2 and 1 °C) and different water contents (i.e., 9, 16, and 23%) at different shaping temperatures (i.e., −2, −7 and −12 °C). The results indicated that: the stress-strain curves under different test conditions exhibited strain softening characteristics; shear strength and shear index decreased with increasing shaping temperature; the effect of cohesion on strength was greater than that of internal friction angle; the mechanical mechanism in the freeze-thaw zone was related to the unfrozen water content of the specimen. In addition, the grey correlation analysis showed that the water content had the greatest effect on the shear index, while the shaping temperature had the least effect on the shear index. The grey relational grade of shaping temperature was greater than 0.5, indicating that the effect of shaping temperature on strength was not negligible.
Improved Whale Optimization Algorithm for Solving Microgrid Operations Planning Problems
Authors: Liu Yixing , Yang Shaowen , Li Dongjie , Zhang Shouming
Published: December 2022

Microgrid operations planning is one of the keys to ensuring the safe and efficient outputs of distributed energy resources (DERs) and the stable operation of a power system in a microgrid (MG). In this study, for the symmetry in renewable energy and microgrid systems, and coordinated control based on a storage battery system, an MG dispatching model with DER conditions and integrated costs is established in grid-connected mode, on the basis of MG operation costs, interaction costs, and pollutant emissions costs. Moreover, an optimization objective for minimizing integrated costs is established. Therefore, based on the original whale optimization algorithm (WOA), an improved whale optimization algorithm (IWOA) with adaptive weight strategy and Levy flight trajectory is proposed in this paper, to solve the optimal operations planning problem of MGs. Finally, in computing comparisons with methods such as the genetic algorithm (GA), particle swarm algorithm (PSO), WOA, wild horse optimizer (WHO), and enhanced whale optimization algorithm (EWOA), the results show that the IWOA computation had lower integrated costs and higher operational efficiency. Moreover, it is verified that the IWOA performed better in solving the MG operations planning problem.
Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
Authors: Vijayan Darveen , Aziz Izzatdin
Published: December 2022

Clustering algorithms are commonly used in the mining of static data. Some examples include data mining for relationships between variables and data segmentation into components. The use of a clustering algorithm for real-time data is much less common. This is due to a variety of factors, including the algorithm’s high computation cost. In other words, the algorithm may be impractical for real-time or near-real-time implementation. Furthermore, clustering algorithms necessitate the tuning of hyperparameters in order to fit the dataset. In this paper, we approach clustering moving points using our proposed Adaptive Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, which is an implementation of an adaptive approach to building the minimum spanning tree. We switch between the Boruvka and the Prim algorithms as a means to build the minimum spanning tree, which is one of the most expensive components of the HDBSCAN. The Adaptive HDBSCAN yields an improvement in execution time by 5.31% without depreciating the accuracy of the algorithm. The motivation for this research stems from the desire to cluster moving points on video. Cameras are used to monitor crowds and improve public safety. We can identify potential risks due to overcrowding and movements of groups of people by understanding the movements and flow of crowds. Surveillance equipment combined with deep learning algorithms can assist in addressing this issue by detecting people or objects, and the Adaptive HDBSCAN is used to cluster these items in real time to generate information about the clusters.
Plasma Methionine and Clinical Severity in Nitrous Oxide Consumption
Authors: Gernez Emeline , Deheul Sylvie , Tard Céline , Joncquel Marie , Douillard Claire , Grzych Guillaume
Published: December 2022

In the last few years, there has been an increase in the recreational use of nitrous oxide (N2O), which can lead to neurological symptoms such as sensory or motor disorders. The literature links these symptoms to a functional inactivation of vitamin B12 by oxidation of its cobalt ion, which prevents the vitamin B12 from acting as a cofactor for methionine synthase. Thus, demyelination related to methionine deficiency could be responsible for the neurological disorders associated with N2O consumption, including the combined sclerosis of the spinal cord. We aimed to study the correlation between the plasma methionine levels and clinical severity observed in N2O users. We retrospectively collected clinical and biological data from 93 patients who chronically consumed N2O. The patients were divided into four groups based of the severity of their clinical symptoms (based on their Peripheral Neuropathy Disability (PND) score). The plasma amino acids measurement, including methionine, were performed systematically by liquid chromatography coupled with mass spectrometry. Plasma methionine is significantly correlated with the clinical severity (Spearman coefficient: −0.42; p-value < 10−5), however, the average methionine level in the four groups is within the physiological values (N: 16–23 µmol/L). There is a significant inverse correlation between plasma methionine and homocysteine (Spearman coefficient: −0.57; p-value < 10−9), which confirms the action of nitrous oxide on the methionine synthase. A decrease in plasma methionine cannot be imputed as the only mechanism involved in the pathophysiology of the neurological disorders in nitrous oxide consumption. In addition, there are few therapeutic indications for the use of methionine. Thus, we should be careful concerning the potential use of methionine in nitrous oxide consumption. As a consequence, other pathophysiological mechanisms probably need to be identified in order to find potential therapeutic targets.
Prevalence of JC and BK Polyomavirus Infection in Patients with Chronic Kidney Disease in the State of Pará, Brazil
Authors: da Costa Scheila do Socorro Vasconcelos Ávila , Monteiro Jacqueline Cortinhas , Viegas Ana Paula do Vale , de Sá Keyla Santos Guedes , da Cruz Silvia Regina , Lima Sandra Souza , Vallinoto Izaura Maria Vieira Cayres , Costa Igor Brasil , Vallinoto Antonio Carlos Rosário
Published: December 2022

The polyomaviruses that infect humans, JC virus (JCV) and BK virus (BKV), can establish persistent infections in the cells that make up the renal system, causing nephritis and BKV-associated nephropathy in up to 10% of renal transplant patients, and of these, 90% lose the graft and return for hemodialysis. This study aimed to determine the prevalence of polyomaviruses (PyV) in the population with chronic kidney disease (CKD), classified into three groups (conservative, dialysis, and transplanted) and a control group. Urine samples were collected from 290 individuals, including 202 patients with CKD and 88 from the control group. PyV screening was performed by PCR amplification of a fragment of the VP1 region, and the JCV and BKV species were distinguished through enzymatic digestion with the restriction endonuclease BamHI from the amplification of a TAg region. All amplification products were visualized on a 3% agarose gel. The prevalence of PyV infection was correlated with clinical-epidemiological variables using the chi-squared and Fisher’s exact tests. In the group with CKD, the prevalence of PyV was 30.2%, a higher rate being observed in conservative patients (36.66%; 22/60), followed by dialysis patients (30.48%; 25/82), and transplanted patients (20%; 12/60). In the control group, the prevalence was 46.59% (41/88). The differentiation between species revealed that JCV was present in 77.8% and BKV in 22.2% of the group with CKD. The prevalence of infection was higher in male patients (59.32%), whose most common pathology was systemic arterial hypertension (35.59%). In the group of transplanted patients, there was a statistically significant association between infection and the use of the immunosuppressant azathioprine (p = 0.015). The prevalence of PyV infection was higher in the control group than in the group with CKD, being predominant in males and in patients with systemic arterial hypertension.
Meteorological Data Fusion Approach for Modeling Crop Water Productivity Based on Ensemble Machine Learning
Authors: Elbeltagi Ahmed , Srivastava Aman , Kushwaha Nand Lal , Juhász Csaba , Tamás János , Nagy Attila
Published: December 2022

Crop water productivity modeling is an increasingly popular rapid decision making tool to optimize water resource management in agriculture for the decision makers. This work aimed to model, predict, and simulate the crop water productivity (CWP) for grain yields of both wheat and maize. Climate datasets were collected over the period from 1969 to 2019, including: mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (H), solar radiation (SR), sunshine hours (Ssh), wind speed (WS), and day length (DL). Five machine learning (ML) methods were applied, including random forest (RF), support vector regression (SVM), bagged trees (BT), boosted trees (BoT), and matern 5/2 Gaussian process (MG). Models implemented by MG, including Tmean, SR, WS, and DL (Model 3); Tmax, Tmin, Tmean, SR, Ssh, WS, H, and DL (Model 8); Tmean, and SR (Model 9), were found optimal (r2 = 0.85) for forecasting CWP for wheat. Moreover, results of CWP for maize showed that the BT model, a combination of SR, WS, H, and Tmin data, achieved a high correlation coefficient of 0.82 compared to others. The outcomes demonstrated several high performance ML-based alternative CWP estimation methods in case of limited climatic data supporting decision making for designers, developers, and managers of water resources.
Application of Tesla Valve’s Obstruction Characteristics to Reverse Fluid in Fish Migration
Authors: Zeng Guorui , Xu Maosen , Mou Jiegang , Hua Chenchen , Fan Chuanhao
Published: December 2022

More and more activities have caused significant damage to the river environment, among which a typical problem of blocked fish migration is constantly attracting people’s attention. Nowadays, fishways are essential hydraulic facilities to solve such problems. Although a different fishway has a particular blocking effect on the water flow, the flow velocity of the vital positions of fish migration in the fishway could still be relatively high locally, which may pose a certain challenge to the fish migration (the higher flow velocity could lead to the increase in migratory energy consumption of fish). Therefore, further exploration of fish passing facilities may be required. As a check valve without movable parts, the Tesla valve is expected to be used in fish passing facilities because of its substantial obstruction to the reverse flow of internal fluid. This paper conducted numerical simulation experiments on the fish passage pipeline designed based on Tesla valves using the RNG (renormalization group) k-ε model. Grass carp were selected as the primary analysis object, and the simulation results were analyzed from the perspective of turbulence characteristics. The results showed that the fish passage pipeline based on the T45-R Tesla valve was better than that on the GMF (Gamboa, Morris and Forster) Tesla valve in velocity control. The velocity at the vital position of T45-R internal fluid was about 20% lower than that of GMF. The results of the velocity cloud diagram showed apparent high-velocity and low-velocity areas in the fish passage pipeline designed based on the T45-R Tesla valve. The high-velocity area was the vital position for fish upstream, and the maximum velocity variation range in this area was 0.904~1.478 m/s. At the same time, the flow in the low-velocity area is almost static water. The analysis illustrated that the resulting velocity environment could provide conditions for grass carp to move upstream successfully. The results of turbulent kinetic energy inside the fish passage pipeline showed that the maximum value of turbulent kinetic energy was only about 0.043 m2/s2, which could be friendly for fish upstream. In addition, the results show that pressure-related problems could not seem to have an excessive impact on fish migration, such as causing damage. Overall, the results further studied the feasibility of using the Tesla valve as a fish passage pipeline.
Wastewater Management: From Ancient Greece to Modern Times and Future
Authors: Angelakis Andreas N. , Capodaglio Andrea G. , Dialynas Emmanuel G.
Published: December 2022

Current wastewater management paradigms favor centralized solutions, as taught in traditional engineering schools, which imply high capital costs, long-range water transfer, long and disruptive construction and highly trained operators. On the other hand, small decentralized systems are seldom considered even though they require lower capital costs, less disruptive infrastructure construction and allow for the maintenance of a closer, more sustainable water cycle. This manuscript starts with an extensive review of the long history of wastewater systems, from the Greek antiquity to the modern era. The use of natural and physical systems in history and their evolution into modern technology is also analyzed. Finally, future trends are considered with emphasis on technological adaptation and sustainability of decentralized systems, with a view that lessons that can be learned from history and past practices. The manuscript aims to provide a critical overview of water and wastewater management in view of the oncoming challenges of this sector.
A Discontinuous Modulation Method with Variable Clamping Interval Width of the Modular Multilevel Converter
Authors: Gu Xin , Ma Langang , Zhang Guozheng , Jin Xuefeng
Published: December 2022

The modular multilevel converter (MMC) has been widely used because of the advantages of easier expansion, lower harmonic distortion and higher output voltage level. If the MMC is adapted to high voltage application, more submodules need to be connected in series in each arm. Thus, the switching loss needs to be considered a key issue. A discontinuous modulation method with variable clamping interval width is proposed in this paper in order to reduce the switching loss under variable power factor conditions. Different widths of the clamping interval can be selected according to the requirements of the system. Meanwhile, the capacitor voltage ripple of the submodules can also be reduced. The feasibility and effectiveness of the proposed modulation method are verified under a RT-LAB rapid control prototype based MMC system.
Protection of the right of real servitude according to local and international legislation and practice
Authors: Kastriote Kastriote , Kuçi Hajredin
Published: December 2022

The right of servitude as a real right enjoys the same rights as other real rights. Among these rights is the protection of the right of servitude in case it is violated by persons who are not holders of this right. Based on the local legislation as well as that of other countries, the holder of the right of easement has the right to protect the right of easement in case that right is violated by someone else, and not only the holder of the right but also the owner of the servient property in case it finds that the holder of the right of servitude does not use the right that he has, or in the case that he finds that the holder of the right of servitude uses that right causing damage to the owner of the servient property! All this based on the judicial practice that the parties seek the protection of the right of real servitude, as well as the right holder and the owner of the servient property, we have tried to show through this paper in short points how a right can be protected of real servitude based on local law and that of international countries!