Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilizing the L-curve criterion for the inverse magnetostatic problem of Hall drift current estimation

Aiming at achieving the in-orbit diagnostic of Hall drift current, this study focuses on estimation through the indirect measurement methodology using a magnetic sensor array. It elaborates on the application of a pseudo-seminorm defined for the Hall drift current solution to address the inverse magnetostatic problems, which are formulated with a two-dimensional Tikhonov regularization constraint, and thereby offering a systematic approach to select regularization parameters. Our investigation discusses factors influencing the formation of the L-curve and the accuracy of the resultant solution obtained via the L-curve criterion. The results reveal that the formation of the defined pseudo-seminorm of the Hall drift current solution in the semi-logarithmic coordinate system is independent of the number of calibrating current elements or the number of magnetic sensors. This effectively resolves the issue of failing to generate an L-curve during regularization parameter selection. Furthermore, the study indicates that expanding the number of calibrating current elements—essentially increasing the unknown variables in the inverse magnetostatic equations—contributes to a significant enhancement in the accuracy of Hall drift current solutions. It also has extensibility to be applied to other areas where the contactless current measuring is required.
To study the importance of pyrroloquinoline quinone in pet nutrition and health care

Pyrroloquinoline quinone (PQQ) is a highly functional oxidoreductase coenzyme, which has a variety of physiological functions such as regulating the level of free radicals in the body, antioxidant and promoting nerve growth factor regeneration. PQQ playes a significant role in disease prevention and treatment, which has attracted wide attention in recent years. PQQ can protect the brain nerve and intestinal nerve of pets, reduce stress and anti-aging, and also promote nutrient absorption and improve immunity. Mammals can only take PQQ through external supplements and cannot synthesize it themselves. Therefore, the application of PQQ supplements in pet breeding can play a significant positive effect. Based on the studies supplementing PQQ in weaned rats and pigs, this paper will explain the necessity of PQQ for mammals, and discuss the feasibility of strengthening the use of PQQ supplement in pet nutrition and health care, and advocate further research on this by pet-related enterprises, which may open up new markets.
Impact of Users’ Comprehension of the Privacy Policy of FemTech Apps on their Information Disclosure Intentions: The Mediating Effects of Privacy Fatigue and Privacy Data Control

FemTech stands for female technology, which encompasses a wide range of software, products, and services designed to improve women's health through technology. Personal data used in this type of app are intimate, which highlights the importance of privacy, and therefore, comprehensibility of the privacy policies of such apps. This study explores the effect of user comprehension of privacy policies on their information disclosure intention through mediators such as privacy fatigue and privacy data control, which have rarely been studied. A scenario-based questionnaire was used to collect data from 236 females and SMART PLS 4.0 was used to conduct the analysis. Findings indicate that both mediators have an indirect-only mediation effect; however, the direction of the impact of privacy fatigue on disclosure intention was opposite to what was hypothesised. Privacy data control was found to be the stronger mediator. This study entails implications for privacy policymakers and mobile health application providers.
Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews

Annotation and analysis of online customer reviews were identified as significant problems in various domains, including business intelligence, marketing, and e-governance. In the last decade, various approaches based on topic modeling have been developed to solve this problem. The known solutions, however, often only work well on content with static topics. As a result, it is challenging to analyze customer reviews that include dynamic and constantly expanding collections of short and noisy texts. A method was proposed to handle such dynamic content. The proposed system applied a dynamic topic model using BERTopic to monitor topics and word evolution over time. It would help decide when the topic model needs to be retrained to capture emerging topics. Several experiments were conducted to test the practicality and effectiveness of the proposed framework. It demonstrated how a dynamic topic model could handle the emergence of new and over-time-correlated topics in customer review data. As a result, improved performance was achieved compared to the baseline static topic model, with 25% of new segmented texts discovered using the dynamic topic model. Experimental results have, therefore, convincingly demonstrated that the proposed framework can be used in practice to develop automatic review annotation tools.
Effect of Sn-incorporation on the Structural and Optical Properties of ZnO Thin Films Prepared by Sol-Gel Method

In this research, thin films of ZnO doped with Sn were prepared through the sol-gel method. The effects of Sn dopant on optical, morphological and structural studies were investigated. All of the films in this work showed favored orientation along the (002) plane, according to XRD analysis. It was observed by SEM analysis that the study Sn morphological and structural studies were investigated. From AFM pictures it is clear that as the doping concentration rises, the size of the grains on the surface of thin films decreases.The existence of Zn, O and Sn was verified by the EDAX analysis. The Ultraviolet-visible spectroscope was utilized to examine the optical characteristics. That band gap increased with the dopant material percent increased.
Review of ion imprinted polymers nanofiber with technology electrospinning: An advanced materials for removal of heavy metal ions

The existence of essential heavy metal ions that pollute the environment has become a substantial concern for countries worldwide due to their ability to damage ecosystems and harm living things. Therefore, research in the field of materials science to eliminate the presence of heavy metal ions that pollute the environment, such as Fe (III), Pb (II), Ni (II), Hg (II), Cd (II), Cr (VI), Ru (III), and As (III) need to be developed. Ion Imprinted polymers nanofiber (IIPs-NF) is one of the advanced materials with the ability to determine and remove heavy metal ions containing a high degree of selectivity and stability. The use of conjugate electrospinning technology in producing nanofiber (NF) can expand the surface of the material and the recognition sites for target ion elements. This research is a literature review of scientific articles on the development of ion imprinted polymers (IIPs) intelligent NF materials that utilize electrospinning technology. It discusses initial research in the preparation of IIPs samples, the principles of the synthesis method, the characterization analysis that needs to be conducted and the adsorption research of the IIPs-NF adsorption applications. The results showed that modification of the IIPs material with electrospun conjugation can increase the adsorption capacity and have several advantages such as high stability, biocompatibility and a better regeneration. Therefore, this material has great potential to become an advanced device that plays a role in overcoming harmful metal ions.
High-throughput computational screening and in vitro evaluation identifies 5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl) phenyl]-1H-isoindole-1,3(2H)-dione (C3), as a novel EGFR—HER2 dual inhibitor in gastric tumors

Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation, adhesion, angiogenesis, and metastasis. Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations. Hence, dual inhibition strategies are recommended to increase potency and reduce cytotoxicity. In this study, we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities. Diversity-based High-throughput Virtual Screening (D-HTVS) was used to screen the whole ChemBridge small molecular library against EGFR and HER2. The atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand complexes. EGFR/HER2 kinase enzymes, KATOIII, and Snu-5 cells were used for in vitro validations. The atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules, identified compound C3 (5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl) phenyl]-1H-isoindole-1,3(2H)-dione) to have a good affinity for both EGFR and HER2. The predicted compound, C3, was promising with better binding energy, good binding pose, and optimum interactions with the EGFR and HER2 residues. C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM, respectively. The GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM, respectively. Based on these findings, we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase, and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects, though testing in higher models with pharmacokinetic approach is required.
Study of the Transmission and Reflection of Electron Waves through a Bridge System Consisting of Quan-tum Dots Using the Tight-Binding Approximation

In this study, we treat tunneling similarly to the dispersion problem where the wave incident on the barrier is partly transmitted and partly reflected. The transport potential will be related to conduction using the tight-binding model, the steady-state formula, and the Landauer relationship. The tunnel was processed using a bridge model. We studied the effect of changing the size of the quantum dots that make the bridge on the probability of transmission and reflection, the effect of changing the number of quantum dots on them. The transmission and reflection spectrums were compared as functions of the system's energy spectrum. We also noticed the effect of the transmission and reflection spectrum on the conductivity of the system. The results that have been reached, and will contribute significantly to the manufacture of nano-devices in the not-too-distant ture.
Transcriptome Profiling of Oncorhynchus mykiss Infected with Low or Highly Pathogenic Viral Hemorrhagic Septicemia Virus (VHSV)

The rainbow trout (Oncorhynchus mykiss) is the most important produced species in freshwater within the European Union, usually reared in intensive farming systems. This species is highly susceptible to viral hemorrhagic septicemia (VHS), a severe systemic disease widespread globally throughout the world. Viral hemorrhagic septicemia virus (VHSV) is the etiological agent and, recently, three classes of VHSV virulence (high, moderate, and low) have been proposed based on the mortality rates, which are strictly dependent on the viral strain. The molecular mechanisms that regulate VHSV virulence and the stimulated gene responses in the host during infection are not completely unveiled. While some preliminary transcriptomic studies have been reported in other fish species, to date there are no publications on rainbow trout. Herein, we report the first time-course RNA sequencing analysis on rainbow trout juveniles experimentally infected with high and low VHSV pathogenic Italian strains. Transcriptome analysis was performed on head kidney samples collected at different time points (1, 2, and 5 days post infection). A large set of notable genes were found to be differentially expressed (DEGs) in all the challenged groups (e.s. trim63a, acod1, cox-2, skia, hipk1, cx35.4, ins, mtnr1a, tlr3, tlr7, mda5, lgp2). Moreover, the number of DEGs progressively increased especially during time with a greater amount found in the group infected with the high VHSV virulent strain. The gene ontology (GO) enrichment analysis highlighted that functions related to inflammation were modulated in rainbow trout during the first days of VHSV infection, regardless of the pathogenicity of the strain. While some functions showed slight differences in enrichments between the two infected groups, others appeared more exclusively modulated in the group challenged with the highly pathogenic strain.
Influence of partial migration and environmental change on the population dynamics of white perch (Morone americana) within the Hudson River Estuary

Title of Document: INFLUENCE OF PARTIAL MIGRATION AND ENVIRONMENTAL CHANGE ON THE POPULATION DYNAMICS OF WHITE PERCH (MORONE AMERICANA) WITHIN THE HUDSON RIVER ESTUARY Brian Kenneth Gallagher, Master of Science, 2016 Directed By: Professor David H. Secor Marine Estuarine Environmental Sciences White perch are a common estuarine fish that exhibit a complex life cycle, characterized by partial migration and sensitivity to ecosystem characteristics. Field collections, otolith microchemistry, otolith microstructure and environmental and biological monitoring data were used to investigate the prevalence of partial migration and environmental influence across life history stages of white perch within the Hudson River. Otolith strontium:calcium profiles identified a freshwater resident contingent and a migratory contingent which utilized brackish habitats. Demographic analyses indicated that migratory fish hatched earlier and experienced cooler temperatures as larvae compared to residents, and subsequently grew faster as juveniles and adults. Comparisons of early-life characteristics before (1974-1991) and after (1992-2013) the invasion of zebra mussels indicated that juvenile abundance became more sensitive to densitydependence and freshwater flow after the invasion, while growth and spatial distribution changed little. Partial migration and environmental sensitivity will shape Hudson River white perch responses to future climate change. INFLUENCE OF PARTIAL MIGRATION AND ENVIRONMENTAL CHANGE ON THE POPULATION DYNAMICS OF WHITE PERCH (MORONE AMERICANA) WITHIN THE HUDSON RIVER ESTUARY
Contributions of Air‐Sea Coupling to Northward‐Propagating Intraseasonal Convection Over the South China Sea

Northward‐propagating convection associated with intraseasonal oscillation (ISO) over the South China Sea (SCS) is closely linked to the evolution of the East China rainy season. In this study, we quantitatively examined the contribution of air‐sea coupling to northward propagation of intraseasonal convection over the SCS based on observational diagnosis and model experiments. While oceanic feedback plays a limited role in northward propagation of Indian Ocean and western Pacific ISO, its positive effect on SCS ISO propagation is much larger and nearly comparable with that of atmospheric internal dynamics over the central SCS. Warm intraseasonal sea‐surface temperature (SST) anomalies ahead of the convection induce boundary‐layer convergence and surface turbulent fluxes, largely favoring growth of moist static energy to the north of the ISO convection. Conversely, weak negative SST anomalies induce divergence and reduced fluxes over the convective center. The underlying SST anomalies strongly promote northward propagation of SCS ISO, but destroy the maintenance of deep convection. We then verified these oceanic feedbacks using atmosphere‐only (SPA) and atmosphere–ocean‐mixed‐layer coupled (SPK) configurations of the Super‐Parameterized Community Atmospheric Model. The SPA simulations produced stronger intraseasonal convection but weaker propagation signals than the SPK simulations, confirming the positive contribution of intraseasonal SST to ISO propagation observed over the SCS.
Safety-Guaranteed Learning-Based Flocking Control Design

This letter aims to develop a new learning-based flocking control framework that ensures inter-agent free collision. To achieve this goal, a leader-following flocking control based on a deep Q-network (DQN) is designed to comply with the three Reynolds’ flocking rules. However, due to the inherent conflict between the navigation attraction and inter-agent repulsion in the leader-following flocking scenario, there exists a potential risk of inter-agent collisions, particularly with limited training episodes. Failure to prevent such collision not only caused penalties in training but could lead to damage when the proposed control framework is executed on hardware. To address this issue, a control barrier function (CBF) is incorporated into the learning strategy to ensure collision-free flocking behavior. Moreover, the proposed learning framework with CBF enhances training efficiency and reduces the complexity of reward function design and tuning. Simulation results demonstrate the effectiveness and benefits of the proposed learning methodology and control framework.
Characterisation of Staphylococcus aureus strain causing severe respiratory disease in rabbits

Staphylococcus aureus is acknowledged as one of the important pathogens isolated from humans and animals. However, the S. aureus causing severe respiratory diseases in rabbits have not been well characterised. A S. aureus named FZHW001, isolated from the lungs of dead rabbits with severe respiratory disease, was characterised by artificial infection of rabbits, detection of virulence factors, multi-locus sequencing typing and antimicrobial susceptibility test. The FZHW001 infected rabbits showed identical respiratory symptoms to those of naturally infected ones, and the isolate could spread through directed contact among rabbits. The isolate was typed into clonal complex 121 and carried 7 of 13 tested virulence factors. Furthermore, the isolate was identified to be methicillin-susceptible S. aureus and was susceptible to 7 of 12 tested antibiotics. This study first describes the characteristics of S. aureus isolated from rabbits causing severe respiratory disease, which will help in further understanding the pathogenic mechanisms of S. aureus in rabbits.
An Effective Double Acknowldgement Scheme for Intrusion Detection in Mobile Ad Hoc Networks

The hegira to wireless network from wired network has been a worldwide trend in the past few years. The mobility and scalability brought by a wireless network made it possible in many application. Mobile Ad Hoc Network (MANET) is one of the most predominant and idiosyncratic applications. On the contrary to accustomed network architecture, MANET does not require a fixed network infrastructure, every single node works as Transceiver. Nodes communicate directly with each other when they are both within the same communication range. Otherwise , they reckon on their neighbours to relay messages. The Selfconfiguring ability of nodes in MANET made it popular among critical mission application like military use or emergency recovery. However, the open medium and wide distribution of nodes make MANET impuissant to rancorous attackers. In this case, it is pivotal to develop efficient encroachment-detection mechanisms to protect MANET from attacks. To adjust to such trend , we strongly believe that it is vital to address its potential security issues. In this paper, we propose and implement a new encroachment-detection system named Enhanced Adaptive ACKnowledgment (EAACK) specially designed for MANETSS . Compared to coeval approaches, EAACK demonstrates higher rancorous behaviour-detection ratesin certain circumsatances while does not greatly affect the network performances KeywordsEaack ,manet ,rancorous attacks, relay messages.
Strain dependent lateral reaction mechanism of piles beneath embankments

Piled abutments for approach embankments or piles designed to resist moving slopes are consid- ered to be one of the most typical soil-structure interaction problems. A number of ambiguities, in the design and construction of these structures mostly arise due to the difficulty to determine the load transfer mechanism devel- oped between the soil and the structure. Since the soil displacements are the cause for the forces and moments induced on the piles the problem may be tackled in terms of the free-field soil displacements. If this is the case, representative solutions can only be achieved by avoiding the poor estimates for free-field soil movements. Bearing this in mind measurements of an instrumented full-scale trial embankment are used in this paper. The method proposed in this paper facilitates a transformation process which links "soil to soil" free-field behaviour to "soil to pile" behaviour in the presence of piles.
Placing bounds on extreme temperature response of maize

Plant water availability is a key factor that determines maize yield response to excess heat. Lack of available data has limited researchers’ ability to estimate this relationship at regional and global scales. Using a new soil moisture data set developed by running a crop growth simulator over historical data we demonstrate how current estimates of maize yield sensitivity to high temperature are misleading. We develop an empirical model relating observed yields to climate variables and soil moisture in a high maize production region in the United States to develop bounds on yield sensitivity to high temperatures. For the portion of the region with a relatively long growing season, yield reduction per °C is 10% for high water availability and 32.5% for low water availability. Where the growing season is shorter, yield reduction per °C is 6% for high water availability and 27% for low water availability. These results indicate the importance of using both water availability and temperature to model crop yield response to explain future climate change on crop yields.
Batch correction of single-cell sequencing data via an autoencoder architecture

Abstract Motivation Technical differences between gene expression sequencing experiments can cause variations in the data in the form of batch effect biases. These do not represent true biological variations between samples and can lead to false conclusions or hinder the ability to integrate multiple datasets. Since there is a growing need for the joint analysis of single-cell sequencing datasets from different sources, there is also a need to correct the resulting batch effects while maintaining the true biological variations in the data. Results We developed a semi-supervised deep learning architecture called Autoencoder-based Batch Correction (ABC) for integrating single-cell sequencing datasets. Our method removes batch effects through a guided process of data compression using supervised cell type classifier branches for biological signal retention. It aligns the different batches using an adversarial training approach. We comprehensively evaluate the performance of our method using four single-cell sequencing datasets and multiple measures for batch effect removal and biological variation conservation. ABC outperforms 10 state-of-the-art methods for this task including Seurat, scGen, ComBat, scanorama, scVI, scANVI, AutoClass, Harmony, scDREAMER, and CLEAR, correcting various types of batch effects while preserving intricate biological variations.