bluuebunny/crossref_metadata_2025 huggingface dataset:
The Quantum Regime Operation of Beam Splitters and Interference Filters
Authors: Vatarescu Andre
Published: April 2023

The presence of quantum Rayleigh scattering, or spontaneous emission, inside a dielectric medium such as a beam splitter or an interferometric filter prevents a single photon from propagating in a straight line. Modelling a beam splitter by means of a unitary transformation is physically meaningless because of the loss of photons. Additional missing elements from the conventional theory are the quantum Rayleigh-stimulated emission, which can form groups of photons of the same frequency, and the unavoidable parametric amplification of single photons in the original parame-tric crystal. An interference filter disturbs, through multiple internal reflections, the original stream of single photons, thereby confirming the existence of groups of photons being spread out to lengthen the coherence time. The approach of modelling individual, single measurements with probability amplitudes of a statistical ensemble leads to counterintuitive explanations of the experimental outcomes and should be replaced with pure states describing instantaneous measurements whose values are afterwards averaged.
Biocatalytic Profiling of Free and Immobilized Partially Purified Alkaline Protease from an Autochthonous Bacillus aryabhattai Ab15-ES
Authors: Adetunji Adegoke Isiaka , Olaniran Ademola Olufolahan
Published: April 2023

Partially purified alkaline protease produced by an indigenous bacterial strain, Bacillus aryabhattai Ab15-ES, was insolubilized in alginate beads using an entrapment technique. Maximum entrapped enzyme activities of 68.76% and 71.06% were recorded at optimum conditions of 2% (w/v) sodium alginate and 0.3 M calcium chloride. Biochemical profiling of free and immobilized proteases was investigated by determining their activity and stability as well as kinetic properties. Both enzyme preparations exhibited maximum activity at the optimum pH and temperature of 8.0 and 50 °C, respectively. However, in comparison to the free enzyme, the immobilized protease showed improved pH stability at 8.0–9.0 and thermal stability at 40–50 °C. In addition, the entrapped protease exhibited a higher Vmax and increased affinity to the substrate (1.65-fold) than the soluble enzyme. The immobilized protease was found to be more stable than the free enzyme, retaining 80.88% and 38.37% of its initial activity when stored at 4 °C and 25 °C, respectively, for 30 d. After repeated use seven times, the protease entrapped in alginate beads maintained 32.93% of its original activity. These findings suggest the efficacy and sustainability of the developed immobilized catalytic system for various biotechnological applications.
Formalism versus Purposivism in Islamic Jurisprudence: The Case of Islamic Finance Law
Authors: Abedifar Pejman
Published: April 2023

This manuscript critically discusses the current implications of the scriptural injunctions against gharar and maysir. It elaborates how overlooking the features of the contemporary world and adopting a formalistic approach in Islamic jurisprudence have led to absurdity in the implication of the doctrines of gharar and maysir for Muslims’ financial activities. The manuscript also underscores the necessity of adopting the maqāsid approach (purposivism) in Islamic jurisprudence. It propounds that the cogent concern of the injunctions could have been an initiative for Islamic scholars to establish an advanced contract law and to promote transparency in economic activities if a maqāsid approach had been adopted in Islamic jurisprudence.
Simulation of Diffuse Solar Radiation with Tree-Based Evolutionary Hybrid Models and Satellite Data
Authors: Zhao Shuting , Xiang Youzhen , Wu Lifeng , Liu Xiaoqiang , Dong Jianhua , Zhang Fucang , Li Zhijun , Cui Yaokui
Published: April 2023

Diffuse solar radiation (Rd) provides basic data for designing and optimizing solar energy systems. Owing to the notable unavailability in many regions of the world, Rd is traditionally estimated by models through other easily available meteorological factors. However, in the absence of ground weather station data, such models often need to be supplemented according to satellite remote sensing data. The performance of Himawari-7 satellite inversion of Rd was evaluated in the study, and hybrid models were established (XGBoost_DE, XGBoost_FPA, XGBoost_GOA, and XGBoost_GWO), so as to improve the satellite data and achieve a better utilization effect. The meteorological data of 14 Rd stations in mainland China from 2011 to 2015 were used. Four input combinations (L1–L4) and eight input combinations (S1–S8) of meteorological factors corresponding to satellite remote sensing data were used for model simulation, while two optimal combinations (S7 and S8) were selected for cross-station application. The results revealed that the accuracy of Himawari-7 satellite Rd data was low, with RMSE, R2, MAE, and MBE values of 2.498 MJ·m−2·d−1, 0.617, 1.799 MJ·m−2·d−1, and 0.323 MJ·m−2·d−1, respectively. The performance of these coupled models based on satellite data was significantly improved. The RMSE and MAE values increased by 15.5% and 9.4%, respectively, while the R2 value decreased by 10.9 %. Compared with others based on satellite data, the XGBoost_GOA model exhibited optimal performance. The mean values of RMSE, R2, and MAE were 1.63 MJ·m−2·d−1, 0.76 and 1.21 MJ·m−2·d−1, respectively. The XGBoost_GWO model exhibited optimal performance in the cross-station application, and the average RMSE value was reduced by 2.3–10.5% compared with the other models. The meteorological factors input by the models exhibited different levels of significance in different scenarios. Rd_s was the main meteorological parameter that affected the model based on satellite data, while RH exhibited a significant improvement in the XGBoost_FPA and XGBoost_GWO models based on ground weather stations data. Accordingly, the present authors believe that the XGBoost_GOA model has excellent ability for simulating Rd, while the XGBoost_GWO model allows for cross-station simulation of Rd from satellite data.
Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems
Authors: Demeyer Séverine , Kristoffersen Samuel K. , Le Pichon Alexis , Larsonnier Franck , Fischer Nicolas
Published: April 2023

To improve the confidence and quality of measurements produced by regional and international infrasound monitoring networks, this work investigates a methodology for propagating uncertainty associated with on-site measurement systems. We focus on the propagation of sensor calibration uncertainties. The proposed approach is applied to synthetic infrasound signals with known back azimuth and trace velocity, recorded at the array elements. Relevant input uncertainties are investigated for propagation targeting the incoming signals (noise), instrumentation (microbarometers, calibration system, wind noise reduction system), and the time-delay-of-arrival (TDOA) model (frequency band). Uncertainty propagation is performed using the Monte Carlo method to obtain the corresponding uncertainties of the relevant output quantities of interest, namely back azimuth and trace velocity. The results indicate that, at high frequencies, large sensor uncertainties are acceptable. However, at low frequencies (<0.1 Hz), even a 2∘ sensor phase uncertainty can lead to errors in the back azimuth of up to 5∘ and errors in the trace velocity of 20 m/s.
Combining an Eddy Detection Algorithm with In-Situ Measurements to Study North Brazil Current Rings
Authors: Subirade Corentin , L’Hégaret Pierre , Speich Sabrina , Laxenaire Rémi , Karstensen Johannes , Carton Xavier
Published: April 2023

North Brazil Current (NBC) rings are believed to play a key role in the Atlantic Ocean circulation and climate. Here, we use a new collection of high-resolution in-situ observations acquired during the EUREC4A-OA field experiment together with satellite altimetry to define, with unprecedented detail, the structure and evolution of these eddies. In-situ observations reveal a more complex structure than previously documented. In particular, we highlight a measurable impact of the Amazon outflow in creating a barrier layer over a large portion of the eddies. We show that this unprecedented data set allows us to estimate the accuracy of satellite altimetry gridded fields. The geostrophic velocities derived from satellite altimetry turn out to be considerably lower (up to 50% in amplitude) than the values measured by current meters. However, eddy properties as detected by TOEddies, a newly developed algorithm show to be relatively precise. For example, the eddy center and maximum azimuthal velocity contour fall within 25 ± 5 km and 16 ± 9 km, respectively, from the in-situ observed values. We apply TOEddies to 27 years of satellite altimetry to investigate the generic NBC rings behavior. We found a mean generation rate of 4.5 ± 1.1 rings per year, and a strong seasonal cycle in all eddy properties.
Potential for Peatland Water Table Depth Monitoring Using Sentinel-1 SAR Backscatter: Case Study of Forsinard Flows, Scotland, UK
Authors: Toca Linda , Artz Rebekka R. E. , Smart Catherine , Quaife Tristan , Morrison Keith , Gimona Alessandro , Hughes Robert , Hancock Mark H. , Klein Daniela
Published: April 2023

Peatland restoration has become a common land-use management practice in recent years, with the water table depth (WTD) being one of the key monitoring elements, where it is used as a proxy for various ecosystem functions. Regular, uninterrupted, and spatially representative WTD data in situ can be difficult to collect, and therefore, remotely sensed data offer an attractive alternative for landscape-scale monitoring. In this study, we illustrate the application of Sentinel-1 SAR backscatter for water table depth monitoring in near-natural and restored blanket bogs in the Flow Country of northern Scotland. Among the study sites, the near-natural peatlands presented the smallest fluctuations in the WTD (with depths typically between 0 and 15 cm) and had the most stable radar signal throughout the year (~3 to 4 dB amplitude). Previously drained and afforested peatlands undergoing restoration management were found to have higher WTD fluctuations (depths up to 35 cm), which were also reflected in higher shifts in the radar backscatter (up to a ~6 dB difference within a year). Sites where more advanced restoration methods have been applied, however, were associated with shallower water table depths and smoother surfaces. Three models—simple linear regression, multiple linear regression, and the random forest model—were evaluated for their potential to predict water table dynamics in peatlands using Sentinel-1 SAR backscatter. The random forest model was found to be the most suited, with the highest correlation scores, lowest RMSE values, and overall good temporal fit (R2 = 0.66, RMSE = 2.1 cm), and multiple linear regression came in a close second (R2 = 0.59, RMSE = 4.5 cm). The impact of standing water, terrain ruggedness, and the ridge and furrow aspect on the model correlation scores was tested but found not to have a statistically significant influence. We propose that this approach, using Sentinel-1 and random forest models to predict the WTD, has strong potential and should be tested in a wider range of peatland sites.
Radiometric Terrain Correction Method Based on RPC Model for Polarimetric SAR Data
Authors: Zhao Lei , Chen Erxue , Li Zengyuan , Fan Yaxiong , Xu Kunpeng
Published: April 2023

Radiometric terrain correction (RTC) is an important preprocessing step for synthetic aperture radar (SAR) data application in mountainous areas. At present, the RTC processing of SAR depends on the Range Doppler (RD) positioning model. However, the solution of this model has a high threshold for ordinary remote sensing technicians. To solve this problem, we propose an RTC method based on the rational polynomial coefficient (RPC) model, which is widely used in optical remote sensing and is simpler and more practical than the RD model. China’s GF-3 polarimetric SAR data were used to verify the proposed method. The experimental results showed that the RTC method based on RPC is effective and can achieve better correction effects on the premise of reducing the complexity of the algorithm. The correction effect based on the RPC model can be similar to that based on the RD model. The proposed approach can realize the correction of 4~5 dB terrain radiation distortion to a 0.5 dB level.
A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang’e-2 Lunar Microwave Sounder (CELMS) Data
Authors: Li Yu , Yuan Zifeng , Meng Zhiguo , Ping Jinsong , Zhang Yuanzhi
Published: April 2023

The brightness temperature (TB) features extracted from Chang’e-2 Lunar Microwave Sounder (CELMS) data represent the passive microwave thermal emission (MTE) from the lunar regolith at different depths. However, there have been few studies assessing the importance and contribution of each TB feature for mapping mare basalt units. In this study, a unified framework of TB features analysis is proposed through a case study of Mare Fecunditatis, which is a large basalt basin on the eastern nearside of the Moon. Firstly, TB maps are generated from original CELMS data. Next, all TB features are evaluated systematically using a range of analytical approaches. The Pearson coefficient is used to compute the correlation of features and basalt classes. Two distance metrics, normalized distance and J-S divergence, are selected to measure the discrimination of basalt units by each TB feature. Their contributions to basalt classification are quantitatively evaluated by the ReliefF method and out-of-bag (OOB) importance index. Then, principal component analysis (PCA) is applied to reduce the dimension of TB features and analyze the feature space. Finally, a new geological map of Mare Fecunditatis is generated using CELMS data based on a random forest (RF) classifier. The results will be of great significance in utilizing CELMS data more widely as an additional tool to study the geological structure of the lunar basalt basin.
Deep Learning-Based Framework for Soil Moisture Content Retrieval of Bare Soil from Satellite Data
Authors: Dabboor Mohammed , Atteia Ghada , Meshoul Souham , Alayed Walaa
Published: April 2023

Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in a variety of remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep learning (DL) is a subfield of ML that uses models with complex structures to solve prediction problems with higher performance than traditional ML. In this study, a framework based on DL was developed for SMC retrieval. For this purpose, a sample dataset was built, which included synthetic aperture radar (SAR) backscattering, radar incidence angle, and ground truth data. Herein, the performance of five optimized ML prediction models was evaluated in terms of soil moisture prediction. However, to boost the prediction performance of these models, a DL-based data augmentation technique was implemented to create a reconstructed version of the available dataset. This includes building a sparse autoencoder DL network for data reconstruction. The Bayesian optimization strategy was employed for fine-tuning the hyperparameters of the ML models in order to improve their prediction performance. The results of our study highlighted the improved performance of the five ML prediction models with augmented data. The Gaussian process regression (GPR) showed the best prediction performance with 4.05% RMSE and 0.81 R2 on a 10% independent test subset.
Correction of Dual-PRF Velocity for Operational S-Band Doppler Weather Radar
Authors: Park So-Yeon , Jung Sung-Hwa , Kim Kwang-Ho
Published: April 2023

The Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has been providing three-dimensional radar wind fields based on the “WInd Synthesis System using Doppler Measurements (WISSDOM)” in real time since February 2019. Its accuracy is significantly affected by the quality of the Doppler velocity, such as velocity aliasing. For the de-aliasing of Doppler velocity, the dual-pulse repetition frequency (dual-PRF) technique is commonly utilized for commercial Doppler weather radar. The Doppler weather radars of the KMA have extended their Nyquist velocity up to 132 m s−1 using a dual PRF of 5:4. However, the dual-PRF technique produces significant noise and loss of radial velocity. Therefore, we developed a technique for noise cancelation and recovery of radial velocity to improve the quality of WISSDOM wind fields. The proposed approach identifies and removes speckles of abnormal radial velocity by comparing the sign of the median radial velocity with the surrounding radar bins. We then recovered the eliminated radial velocity using median interpolation. To recover the losses of radial velocity over a wide area using the dual-PRF technique, we used the Velocity Azimuth Display curve-fitting technique. These techniques are straightforward, preserve spatial gradients, and suppress local extrema. We tested this technique, verified its performance, and applied it to the operational radar quality control system of the WRC from August 2021. We concluded that the process helps improve the quality of the radial velocity and the accuracy of the WISSDOM wind fields.
Mattress-Based Non-Influencing Sleep Apnea Monitoring System
Authors: Qi Pengjia , Gong Shuaikui , Jiang Nan , Dai Yanyun , Yang Jiafeng , Jiang Lurong , Tong Jijun
Published: April 2023

A mattress-type non-influencing sleep apnea monitoring system was designed to detect sleep apnea-hypopnea syndrome (SAHS). The pressure signals generated during sleep on the mattress were collected, and ballistocardiogram (BCG) and respiratory signals were extracted from the original signals. In the experiment, wavelet transform (WT) was used to reduce noise and decompose and reconstruct the signal to eliminate the influence of interference noise, which can directly and accurately separate the BCG signal and respiratory signal. In feature extraction, based on the five features commonly used in SAHS, an innovative respiratory waveform similarity feature was proposed in this work for the first time. In the SAHS detection, the binomial logistic regression was used to determine the sleep apnea symptoms in the signal segment. Simulation and experimental results showed that the device, algorithm, and system designed in this work were effective methods to detect, diagnose, and assist the diagnosis of SAHS.
Design and Development of a Family of Integrated Devices to Monitor Animal Movement in the Wild
Authors: Kazimierski Laila Daniela , Oliva Trevisan Andrés , Kubisch Erika , Laneri Karina , Catalano Nicolás
Published: April 2023

Monitoring the tortoise Chelonoidis chilensis in the wild, currently in a vulnerable state of conservation in southern Argentina, is essential to gather movement information to elaborate guidelines for the species preservation. We present here the electronic circuit design as well as the associated firmware for animal monitoring that was entirely designed by our interdisciplinary research team to allow the extension of device features in the future. Our development stands out for being a family of low-cost and low-power devices, that could be easily adaptable to other species and contexts. Each device is composed of a sub 1 GHz radiofrequency IoT-compatible transceiver, a global navigation satellite system (GNSS) receiver, a magnetometer, and temperature and inertial sensors. The device does not exceed 5% of the animal’s weight to avoid disturbance in their behavior. The board was designed to work as a monitoring device as well as a collecting data station and a tracker, by adding only small pieces of hardware. We performed field measurements to assess the autonomy and range of the radiofrequency link, as well as the power consumption and the associated positioning error. We report those values and discuss the device’s limitations and advantages. The weight of the PCB including battery and GNSS receiver is 44.9 g, its dimensions are 48.7 mm × 63.7 mm, and it has an autonomy that can vary between a week and a month, depending on the sampling rates of the sensors and the rate of the RF signal and that of the GNSS receiver. The characterization of the device parameters will favor the open use of this development by other research groups working on similar projects.
Pose and Focal Length Estimation Using Two Vanishing Points with Known Camera Position
Authors: Guo Kai , Cao Rui , Tian Ye , Ji Binyuan , Dong Xuefeng , Li Xuyang
Published: April 2023

This paper proposes a new pose and focal length estimation method using two vanishing points and a known camera position. A vanishing point can determine the unit direction vector of the corresponding parallel lines in the camera frame, and as input, the unit direction vector of the corresponding parallel lines in the world frame is also known. Hence, the two units of direction vectors in camera and world frames, respectively, can be transformed into each other only through the rotation matrix that contains all the information of the camera pose. Then, two transformations can be obtained because there are two vanishing points. The two transformations of the unit direction vectors can be regarded as transformations of 3D points whose coordinates are the values of the corresponding unit direction vectors. The key point in this paper is that our problem with vanishing points is converted to rigid body transformation with 3D–3D point correspondences, which is the usual form in the PnP (perspective-n-point) problem. Additionally, this point simplifies our problem of pose estimation. In addition, in the camera frame, the camera position and two vanishing points can form two lines, respectively, and the angle between the two lines is equal to the angle between the corresponding two sets of parallel lines in the world frame. When using this geometric constraint, the focal length can be estimated quickly. The solutions of pose and focal length are both unique. The experiments show that our proposed method has good performances in numerical stability, noise sensitivity and computational speed with synthetic data and real scenarios and also has strong robustness to camera position noise.
A Crash Data Analysis through a Comparative Application of Regression and Neural Network Models
Authors: Mussone Lorenzo , Alizadeh Meinagh Mohammadamin
Published: April 2023

One way to reduce road crashes is to determine the main influential factors among a long list that are attributable to driver behavior, environmental conditions, vehicle features, road type, and traffic signs. Hence, selecting the best modelling tool for extracting the relations between crash factors and their outcomes is a crucial task. To analyze the road crash data of Milan City, Italy, gathered between 2014–2017, this study used artificial neural networks (ANNs), generalized linear mixed-effects (GLME), multinomial regression (MNR), and general nonlinear regression (NLM), as the modelling tools. The data set contained 35,182 records of road crashes with injuries or fatalities. The findings showed that unbalanced and incomplete data sets had an impact on outcome performance, and data treatment methods could help overcome this problem. Age and gender were the most influential recurrent factors in crashes. Additionally, ANNs demonstrated a superior capability to approximate complicated relationships between an input and output better than the other regression models. However, they cannot provide an analytical formulation, but can be used as a baseline for other regression models. Due to this, GLME and MNR were utilized to gather information regarding the analytical framework of the model, that aimed to construct a particular NLM.
Sources of Wellbeing Amongst Saudi Arabian Women Academic Leaders: An Explorative Study
Authors: Alanazi Rasis , Alkouatli Claire
Published: April 2023

Although Saudi Arabian women increasingly attain leadership positions in a national reform movement, few studies have examined their wellbeing during this time of cultural change. Contributing to filling this gap, we engaged ten Saudi women academic leaders in semi-structured interviews, inquiring into their perspectives on wellbeing. Three thematic sources of wellbeing—ecological building blocks, spiritual wellsprings, and eudaimonic motivations—highlight that these Saudi women leaders are drawing from varied sources, from skydiving or culturally unique iterations of ‘me time’ to socio-spiritual activities and relationships. The women identified stresses including balancing ageing parents and young children with their high-pressure positions and co-workers with outdated mindsets. Describing their work as social contributions, acts of worship, and charitable offerings of knowledge, the women’s voices counter stereotypes while illuminating culturally specific sources of wellbeing across life domains.
The Bioeconomy and Food Systems Transformation
Authors: Trigo Eduardo , Chavarria Hugo , Pray Carl , Smyth Stuart J. , Torroba Agustin , Wesseler Justus , Zilberman David , Martinez Juan
Published: April 2023

While the global number of people experiencing food insecurity remains stubbornly high, innovations have been increasingly adopted that are contributing to ensure that food systems are as resilient and flexible as they can possibly be. Bioeconomy and biotechnology innovations have contributed to improving rural development and food production. Genomic knowledge is an important part of innovative bioeconomy and biotechnology research as it is applied to increase the efficiency of crops, animals, biofuel, bioplastics and bioenergy production. This allows food systems to transform to be more sustainable and equitable, providing healthy, nutritious food, while creating livelihood opportunities and reducing negative impacts. This article highlights the beneficial impacts of innovative bioeconomy and biotechnology products in technologies, particularly as they relate to the Americas.
The Impact of Socioeconomic Status, Perceived Threat and Healthism on Vaccine Hesitancy
Authors: Kirbiš Andrej
Published: April 2023

The ideology of healthism and low perceptions of the threat of vaccine-preventable diseases may explain the positive link between socioeconomic status (SES) and vaccine hesitancy in high-income countries. The present study aimed to examine the effect of three measures of SES (education, income and family economic status), the perceived threat of infectious diseases and two dimensions of healthism (personal responsibility for own health and distrust in healthcare institutions) on vaccine hesitancy, adjusting for sociodemographic variables. A cross-sectional quantitative study was performed in 2019. Non-probability sampling was employed by sending invitations to respondents over the age of 18 to participate in the study. The snowball technique was used, employing e-mails and digital social networks (Facebook, Twitter and Instagram). Data from 661 respondents were collected via 1 ka.si, an online survey tool. Multivariate regression analysis indicated that vaccine hesitancy was significantly more likely to be expressed by women (β = 0.09; p < 0.001), high-income respondents (β = 0.09; p < 0.01), those who have lower perceptions of the threat of vaccine-preventable diseases (β = 0.39; p < 0.001) and those scoring high on two healthism measures (expressing high perceived control of their own health (β = 0.18; p < 0.001) and high distrust in the Slovenian healthcare system and institutions (β = 0.37; p < 0.001)). The findings indicate that among the examined predictors, low perceived threat of vaccine-preventable diseases and low trust in the healthcare system are among the strongest predictors of vaccine hesitancy among the Slovenian public. Policymakers, physicians and other healthcare workers should be especially attentive to the public’s and patients’ perceptions of the risk of infectious diseases and distrust in medical institutions, including during doctor–patient communication and through public health campaigns and policies.
Sewage Sludge Management and Application in the Form of Sustainable Fertilizer
Authors: Sugurbekova Gulnar , Nagyzbekkyzy Elvira , Sarsenova Ainur , Danlybayeva Gaziza , Anuarbekova Sandugash , Kudaibergenova Rabiga , Frochot Céline , Acherar Samir , Zhatkanbayev Yerlan , Moldagulova Nazira
Published: April 2023

One of the most pressing environmental problems worldwide is sewage sludge (SS) management. Every year, wastewater volume increases and thus, the amount of SS produced increases as well. The disposal of SS in landfills, as practiced in many countries, is not a sustainable solution. Instead, SS, rich in organic matter and other nutrients, can be used as an alternative soil additive or fertilizer. The properties of these materials depend on their chemical composition and the method of treatment. Experience from a number of countries, such as the US and Europe, has shown that SS can be transformed from a waste into a valuable resource, provided that the final product fulfils the relevant regulatory standards. This review examines the sustainable conversion of SS to sustainable fertilizers, the impact on waste minimization, and the potential benefits in agriculture.
A Hybrid Approach for the Assessment of Risk Spillover to ESG Investment in Financial Networks
Authors: Li Lei , Qin Kun , Wu Desheng
Published: April 2023

In this paper, we present a framework for evaluating risk contagion by merging financial networks with machine learning techniques. The framework begins with building a financial network model based on the inter-institutional correlation network, followed by analyzing the structure and overall value changes of the financial network under the stress of a liquidation shock. We then examine the network’s evolution over time. We also use three machine learning techniques to assess the abnormal volatility of important financial institutions in the financial network. Finally, we evaluate the spillover effects of risk volatility in financial networks on ESG investments. The findings suggest that the financial network becomes more robust as the connections among financial institutions become more intricate. This leads to an improvement in the ability of the financial network to withstand systemic risk events. Overall, our study provides evidence of the negative impact of risk spillovers in financial networks on ESG investments, highlighting the need for a more sustainable and resilient financial system. This innovative framework combining financial network modeling and machine learning prediction provides a deeper understanding of the evolution of financial networks and a more accurate evaluation of abnormal volatility in financial networks.
“Sailing Together in the Storm”: Chinese EFL Teachers’ Trajectory of Interpersonal Emotion Regulation towards Well-Being
Authors: Xiao Jing , Tian Guoxiu
Published: April 2023

Teaching is an emotion-laden process during which teachers are inevitably confronted with emotional disturbance. This study examines how Chinese EFL teachers transform emotional disturbance into well-being through interpersonal emotion regulation (IER). Previous research has examined teacher emotion regulation at an individual level, yet the interpersonal perspective has not been sufficiently adopted. In order to have an in-depth investigation of teachers’ IER, three Chinese EFL teachers were selected; qualitative data were collected from narrative reflections, interviews, class observation, and field notes. The findings reveal that (1) EFL teachers’ trajectory of IER moves from deconstruction of emotional blocks in companionship and re-construction of emotional strength through trust to co-construction of emotional pedagogy with dialogues; (2) Intrinsic IER of teachers’ own emotion and extrinsic IER of students’ emotion were not separate but dynamically interacted; (3) Effective IER entails the interplay between teachers’ readiness for reflective inquiry and learning peers’ capacity for emotional support; (4) Teachers’ IER is interwoven with ethical development. The study also suggests that effective teacher IER contributes to their professional learning in terms of triggering the revisitation of teaching conceptions and educational beliefs to better serve students’ learning. This study sheds light on the construction of a social support system for the sustainability of teachers’ emotional well-being as well as their professional development.
A Fractional-Order Density-Dependent Mathematical Model to Find the Better Strain of Wolbachia
Authors: Joseph Dianavinnarasi , Ramachandran Raja , Alzabut Jehad , Jose Sayooj Aby , Khan Hasib
Published: April 2023

The primary objective of the current study was to create a mathematical model utilizing fractional-order calculus for the purpose of analyzing the symmetrical characteristics of Wolbachia dissemination among Aedesaegypti mosquitoes. We investigated various strains of Wolbachia to determine the most sustainable one through predicting their dynamics. Wolbachia is an effective tool for controlling mosquito-borne diseases, and several strains have been tested in laboratories and released into outbreak locations. This study aimed to determine the symmetrical features of the most efficient strain from a mathematical perspective. This was accomplished by integrating a density-dependent death rate and the rate of cytoplasmic incompatibility (CI) into the model to examine the spread of Wolbachia and non-Wolbachia mosquitoes. The fractional-order mathematical model developed here is physically meaningful and was assessed for equilibrium points in the presence and absence of disease. Eight equilibrium points were determined, and their local and global stability were determined using the Routh–Hurwitz criterion and linear matrix inequality theory. The basic reproduction number was calculated using the next-generation matrix method. The research also involved conducting numerical simulations to evaluate the behavior of the basic reproduction number for different equilibrium points and identify the optimal CI value for reducing disease spread.
An Efficient Analytical Approach to Investigate Fractional Caudrey–Dodd–Gibbon Equations with Non-Singular Kernel Derivatives
Authors: Fathima Dowlath , Alahmadi Reham A. , Khan Adnan , Akhter Afroza , Ganie Abdul Hamid
Published: April 2023

Fractional calculus is at this time an area where many models are still being developed, explored, and used in real-world applications in many branches of science and engineering where non-locality plays a key role. Although many wonderful discoveries have already been reported by researchers in important monographs and review articles, there is still a great deal of non-local phenomena that have not been studied and are only waiting to be explored. As a result, we can continually learn about new applications and aspects of fractional modelling. In this study, a precise and analytical method with non-singular kernel derivatives is used to solve the Caudrey–Dodd–Gibbon (CDG) model, a modification of the fifth-order KdV equation (fKdV). The fractional derivative is taken into account by the Caputo–Fabrizio (CF) derivative and the Atangana–Baleanu derivative in the Caputo sense (ABC). This model illustrates the propagation of magneto-acoustic, shallow-water, and gravity–capillary waves in a plasma medium. The dynamic behaviour of the acquired solutions has been represented in a number of two- and three-dimensional figures. A number of simulations are also performed to demonstrate how the resulting solutions physically behave with respect to fractional order. The significance of the current research is that new solutions are obtained by using a strong analytical approach. Utilizing a fractional derivative operator to solve equivalent models is another benefit of this approach. The results of the present work have similar aspects to the symmetry of partial differential equations.
Effect of Abiotic Conditions on Growth, Mycotoxin Production, and Gene Expression by Fusarium fujikuroi Species Complex Strains from Maize
Authors: Dong Ting , Qiao Shouning , Xu Jianhong , Shi Jianrong , Qiu Jianbo , Ma Guizhen
Published: April 2023

Fusarium fujikuroi species complex (FFSC) strains are a major concern for food quantity and quality due to their strong ability to synthesize mycotoxins. The effects of interacting conditions of water activity, temperature, and incubation time on the growth rate, toxin production, and expression level of biosynthetic genes were examined. High temperature and water availability increased fungal growth. Higher water activity was in favor of toxin accumulation. The maximum amounts of fusaric acid (FA) and fumonisin B1 (FB1) were usually observed at 20–25 °C. F. andiyazi could produce a higher content of moniliformin (MON) in the cool environment than F. fujikuroi. The expression profile of biosynthetic genes under environmental conditions varied wildly; it was suggested that these genes might be expressed in a strain-dependent manner. FB1 concentration was positively related to the expression of FUM1, while a similar correlation of FUB8 and FUB12 with FA production could be observed in F. andiyazi, F. fujikuroi, and F. subglutinans. This study provides useful information in the monitoring and prevention of such toxins entering the maize production chain.
Subchronic Microcystin-LR Aggravates Colorectal Inflammatory Response and Barrier Disruption via Raf/ERK Signaling Pathway in Obese Mice
Authors: Yang Yue , Zheng Shuilin , Chu Hanyu , Du Can , Chen Mengshi , Emran Mohammed Y. , Chen Jihua , Yang Fei , Tian Li
Published: April 2023

Microcystin-LR (MC-LR) is an extremely poisonous cyanotoxin that poses a threat to ecosystems and human health. MC-LR has been reported as an enterotoxin. The objective of this study was to determine the effect and the mechanism of subchronic MC-LR toxicity on preexisting diet-induced colorectal damage. C57BL/6J mice were given either a regular diet or a high-fat diet (HFD) for 8 weeks. After 8 weeks of feeding, animals were supplied with vehicle or 120 μg/L MC-LR via drinking water for another 8 weeks, and their colorectal were stained with H&E to detect microstructural alterations. Compared with the CT group, the HFD and MC-LR + HFD-treatment group induced a significant weight gain in the mice. Histopathological findings showed that the HFD- and MC-LR + HFD-treatment groups caused epithelial barrier disruption and infiltration of inflammatory cells. The HFD- and MC-LR + HFD-treatment groups raised the levels of inflammation mediator factors and decreased the expression of tight junction-related factors compared to the CT group. The expression levels of p-Raf/Raf and p-ERK/ERK in the HFD- and MC-LR + HFD-treatment groups were significantly increased compared with the CT group. Additionally, treated with MC-LR + HFD, the colorectal injury was further aggravated compared with the HFD-treatment group. These findings suggest that by stimulating the Raf/ERK signaling pathway, MC-LR may cause colorectal inflammation and barrier disruption. This study suggests that MC-LR treatment may exacerbate the colorectal toxicity caused by an HFD. These findings offer unique insights into the consequences and harmful mechanisms of MC-LR and provide strategies for preventing and treating intestinal disorders.
Anti-Metalloproteases: Production and Characterization of Polyclonal IgG Anti-F2 Fraction Antibodies Purified from the Venom of the Snake Bitis arietans
Authors: Godoi Kemily Stephanie de , Guidolin Felipe Raimondi , Portaro Fernanda Calheta Vieira , Spencer Patrick Jack , da Silva Wilmar Dias
Published: April 2023

Bitis arietans is a medically important snake found in Sub-Saharan Africa. The envenomation is characterized by local and systemic effects, and the lack of antivenoms aggravates the treatment. This study aimed to identify venom toxins and develop antitoxins. The F2 fraction obtained from Bitis arietans venom (BaV) demonstrated the presence of several proteins in its composition, including metalloproteases. Titration assays carried out together with the immunization of mice demonstrated the development of anti-F2 fraction antibodies by the animals. The determination of the affinity of antibodies against different Bitis venoms was evaluated, revealing that only BaV had peptides recognized by anti-F2 fraction antibodies. In vivo analyses demonstrated the hemorrhagic capacity of the venom and the effectiveness of the antibodies in inhibiting up to 80% of the hemorrhage and 0% of the lethality caused by BaV. Together, the data indicate: (1) the prevalence of proteins that influence hemostasis and envenomation; (2) the effectiveness of antibodies in inhibiting specific activities of BaV; and (3) isolation and characterization of toxins can become crucial steps in the development of new alternative treatments. Thus, the results obtained help in understanding the envenoming mechanism and may be useful for the study of new complementary therapies.
Proteomic Analysis, Immuno-Specificity and Neutralization Efficacy of Pakistani Viper Antivenom (PVAV), a Bivalent Anti-Viperid Antivenom Produced in Pakistan
Authors: Lim Andy Shing Seng , Tan Kae Yi , Quraishi Naeem H. , Farooque Saud , Khoso Zahoor Ahmed , Ratanabanangkoon Kavi , Tan Choo Hock
Published: April 2023

Snakebite envenoming is a neglected tropical disease prevalent in South Asia. In Pakistan, antivenoms are commonly imported from India despite the controversy over their effectiveness. To solve the problem, the locals have developed the Pakistani Viper Antivenom (PVAV), raised against Sochurek’s Saw-scaled Viper (Echis carinatus sochureki) and Russell’s Viper (Daboia russelii) of Pakistani origin. This study is set to evaluate the composition purity, immuno-specificity and neutralization efficacy of PVAV. Chromatographic and electrophoretic profiling coupled with proteomic mass spectrometry analysis showed PVAV containing high-purity immunoglobulin G with minimum impurities, notably the absence of serum albumin. PVAV is highly immuno-specific toward the venoms of the two vipers and Echis carinatus multisquamatus, which are indigenous to Pakistan. Its immunoreactivity, however, reduces toward the venoms of other Echis carinatus subspecies and D. russelii from South India as well as Sri Lanka. Meanwhile, its non-specific binding activities for the venoms of Hump-nosed Pit Vipers, Indian Cobras and kraits were extremely low. In the neutralization study, PVAV effectively mitigated the hemotoxic and lethal effects of the Pakistani viper venoms, tested in vitro and in vivo. Together, the findings suggest the potential utility of PVAV as a new domestic antivenom for the treatment of viperid envenoming in Pakistan.
SARS-CoV-2-Induced Myocarditis: A State-of-the-Art Review
Authors: Nappi Francesco , Avtaar Singh Sanjeet Singh
Published: April 2023

In this review, we investigated whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can directly cause myocarditis with severe myocardial damage induced by viral particles. A review of the major data published from 2020 to 2022 was performed by consulting the major databases alongside first-hand experiences that emerged from the cardiac biopsies and autopsy examinations of patients who died of SARS-CoV-2 infections. From this study, a significantly large amount of data suggests that the Dallas criteria were met in a residual percentage of patients, demonstrating that SARS-CoV-2 myocarditis was a rare clinical and pathological entity that occurred in a small percentage of subjects. All cases described here were highly selected and subjected to autopsies or endomyocardial biopsies (EMBs). The most important discovery, through the detection of the SARS-CoV-2 genome using the polymerase chain reaction, consisted in the presence of the viral genome in the lung tissue of most of the patients who died from COVID-19. However, the discovery of the SARS-CoV-2 viral genome was a rare event in cardiac tissue from autopsy findings of patients who died of myocarditis It is important to emphasize that myocardial inflammation alone, as promoted by macrophages and T cell infiltrations, can be observed in noninfectious deaths and COVID-19 cases, but the extent of each cause is varied, and in neither case have such findings been reported to support clinically relevant myocarditis. Therefore, in the different infected vs. non-infected samples examined, none of our findings provide a definitive histochemical assessment for the diagnosis of myocarditis in the majority of cases evaluated. We report evidence suggesting an extremely low frequency of viral myocarditis that has also been associated with unclear therapeutic implications. These two key factors strongly point towards the use of an endomyocardial biopsy to irrefutably reach a diagnosis of viral myocarditis in the context of COVID-19.
Hydropower Dam Development and Fish Biodiversity in the Mekong River Basin: A Review
Authors: Baird Ian G. , Hogan Zeb S.
Published: April 2023

Over the last few decades, considerable concern has been expressed about the threat of Mekong River Basin hydropower dams to a range of important freshwater riverine fisheries, particularly for fish that seasonally migrate long distances. However, much less attention has been given to the threat of hydropower dams to fish biodiversity in the high-diversity Mekong River Basin, the focus of this paper. Through reviewing the existing state of knowledge regarding Mekong River Basin fish biodiversity, and threats to it, we argue that even though no species are definitively known to have been extirpated from the Mekong River Basin to date, hydropower dam development and various other developments nevertheless pose a serious threat to fish biodiversity. Indeed, dams typically significantly block fish migrations and fish larvae distribution, cause river fragmentation, fundamentally alter river hydrology, and change water quality, all factors that have the potential to intersect with each other and lead to significant species extirpation and extinction, or in some cases, functional extinction, when a small population remains but the important larger population is lost permanently. The circumstances are further exacerbated by the lag time between impact and when that impact becomes evident, cumulative impacts, a lack of consistent data collection, including the collection of base-line data, and insufficient post-project research related to biodiversity. We contend that much more could and should be done to ensure that the Mekong River basin’s exceptional fish biodiversity is not variously diminished and destroyed during the coming years.