Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Invertible Variable-degree Optical Aberrations Correction

Optical aberrations of optical systems cause significant degradation of imaging quality. Aberration correction by sophisticated lens designs and special glass materials generally incurs high cost of manufacturing and the increase in the weight of optical systems, thus recent work has shifted to aberration correction with deep learning-based post-processing. Though real-world optical aberrations vary in degree, existing methods cannot eliminate variable-degree aberrations well, especially for the severe degrees of degradation. Also, previous methods use a single feed-forward neural network and suffer from information loss in the output. To address the issues, we propose a novel aberration correction method with an invertible architecture by leveraging its information-lossless property. Within the architecture, we develop conditional invertible blocks to allow the processing of aberrations with variable degrees. Our method is evaluated on both a synthetic dataset from physics-based imaging simulation and a real captured dataset. Quantitative and qualitative experimental results demonstrate that our method outperforms compared methods in correcting variable-degree optical aberrations.
Anti-seizure medication exposure and the risk of dementia: A meta-analysis of observational studies

Objective There is growing evidence of a relationship between anti-seizure medication (ASM) use and the risk of dementia. This study examined this association using a meta-analysis approach. Methods PubMed, EMBASE, and Cochrane Library were systematically searched for peer-reviewed observational studies published up to February 2023. Study quality was evaluated using the Newcastle-Ottawa Scale, and an overall odds ratio (OR) was pooled using fixed or random-effects models. Results The analysis included 9 publications with 10 studies. The results showed that overall ASM exposure was associated with an increased risk of dementia [OR: 1.09, 95% confidence interval (CI): 1.03–1.15; P = 0.003] in general population. However, this association disappeared (OR: 1.02, 95% CI: 0.97–1.07; P = 0.361) when the study data adjusted for drug indications were pooled. Subgroup analysis based on individual drugs found only a positive association among those exposed to valproate, carbamazepine, and clonazepam. Furthermore, an increased risk was found in patients with bipolar disorder exposed to ASMs (OR: 1.43, 95% CI: 1.07–1.92; P = 0.015). Conclusions The statistically significant association between ASM and dementia in general population may be driven by unmeasured confounding or several individual first-generation ASMs. However, a higher risk of dementia was observed among bipolar disorder patients treated with ASMs. Given the few included studies and evidence of high heterogeneity, further larger, prospective studies that control for important confounders are needed to verify our findings.
Research on Wavelet Threshold Denoising and its Use in Biomedical Signals' Processing

In this paper, the theory of wavelet thresholding denoising is discussed. The different wavelet estimators are presented , including all kinds of threshold functions and threshold values. ECG signals are given as an example to validate the denoising technology's good effects. In the end , the future tend of wavelet denoising is pointed out.
An Asymptotic Preserving Numerical Scheme for Kinetic Equations in the Low Mach Number Limit

A numerical scheme for the nonstationary Boltzmann equation in the incompressible Navier--Stokes limit is developed. The scheme is induced by the asymptotic analysis of the Navier--Stokes limit for the Boltzmann equation. It works uniformly for all ranges of mean free paths. In the limit the scheme reduces to the Chorin projection method for the incompressible Navier--Stokes equation. Numerical results for different physical situations are shown and the uniform convergence of the scheme is established numerically.
Identification and analysis of lignin biosynthesis genes related to fruit ripening and stress response in banana (Musa acuminata L. AAA group, cv. Cavendish)

Background Lignin is a key component of the secondary cell wall of plants, providing mechanical support and facilitating water transport as well as having important impact effects in response to a variety of biological and abiotic stresses. Results In this study, we identified 104 genes from ten enzyme gene families related to lignin biosynthesis in Musa acuminata genome and found the number of MaCOMT gene family was the largest, while MaC3Hs had only two members. MaPALs retained the original members, and the number of Ma4CLs in lignin biosynthesis was significantly less than that of flavonoids. Segmental duplication existed in most gene families, except for MaC3Hs, and tandem duplication was the main way to expand the number of MaCOMTs. Moreover, the expression profiles of lignin biosynthesis genes during fruit development, postharvest ripening stages and under various abiotic and biological stresses were investigated using available RNA-sequencing data to obtain fruit ripening and stress response candidate genes. Finally, a co-expression network of lignin biosynthesis genes was constructed by weighted gene co-expression network analysis to elucidate the lignin biosynthesis genes that might participate in lignin biosynthesis in banana during development and in response to stresses. Conclusion This study systematically identified the lignin biosynthesis genes in the Musa acuminata genome, providing important candidate genes for further functional analysis. The identification of the major genes involved in lignin biosynthesis in banana provides the basis for the development of strategies to improve new banana varieties tolerant to biological and abiotic stresses with high yield and high quality.
Multi-Objective Unsupervised Band Selection Method for Hyperspectral Images Classification

With the increasing spectral dimension of hyperspectral images (HSI), how correctly choose bands based on band correlation and information has become more significant, but also complicated. Band selection is a combinatorial optimization problem, and intelligent optimization algorithms have been shown to be crucial in solving combinatorial optimization problems. However, major of them only use a single objective as the selection index, while neglecting the overall features of hyperspectral images, which may lead to inaccuracy in object detection. To tackle this, we propose a band selection method based on a multi-objective cuckoo search algorithm (MOCS) when constructing a multi-objective unsupervised band selection model based on the amount of information and correlation of the bands (MOCS-BS). Specifically, an adaptive strategy based on population crowding degree is first proposed to assist Lévy flight in overcoming the influence of the parameter constancy. Then, an information-sharing strategy based on grouping and crossover is designed to balance the search ability between global exploration and local exploitation, which can overcome the shortcomings caused by the lack of information interaction between individuals. Finally, the HSI classification experiments are performed by Random Forest and KNN classifiers based on the subset of bands selected by the proposed MOCS-BS method. The proposed method is compared with state-of-the-art algorithms including neighborhood grouping normalized matched filter (NGNMF) and multi-objective artificial bee colony with band selection (MABC-BS) on four HSI datasets. The experimental results demonstrate that MOCS-BS is more effective and robust than other methods.
Research on the Influence of E-Learning Quality on the Intention to Continue E-Learning: Evidence from SEM and fsQCA

This study explores how Chinese college students perceive the quality of e-learning and its impact on their adoption of online learning. The study develops an impact model of e-learning quality on continuous learning intention based on the stimulus-organism-response framework, technology acceptance model, and information system success model. The model is validated using a structural equation model (SEM) and fuzzy set qualitative comparative analysis (fsQCA) with data collected from 253 college students at a university in Eastern China. The SEM analysis shows that perceived ease of use and perceived usefulness positively influence e-learning continuance intention, while system quality and personalization have a positive impact on perceived ease of use, and learning community positively impacts perceived usefulness. The fsQCA analysis shows that college students’ willingness to continue e-learning is not solely dependent on a single factor of e-learning service quality but is also influenced by the interaction between various factors. Therefore, e-learning providers should take into account both external stimuli and internal perception factors when designing e-learning services. The findings of this study have practical implications for improving e-learning quality and enhancing the online learning experience. E-learning providers should consider the importance of system quality, personalization, and learning community in improving perceived ease of use and usefulness, which in turn can increase students’ intention to continue e-learning. The study also highlights the importance of considering the interaction between various factors that influence e-learning adoption rather than relying solely on individual factors.
MELTING OF THE EUCLIDEAN METRIC TO NEGATIVE SCALAR CURVATURE

We find a C ∞ -continuous path of Riemannian metrics gt on Rk, k � 3, for 0 � t � " for some number " > 0 with the following property: g0 is the Euclidean metric on Rk, the scalar curvatures of gt are strictly decreasing in t in the open unit ball and gt is isometric to the Euclidean metric in the complement of the ball. Furthermore we extend the discussion to the Fubini-Study metric in a similar way.
Exposure to Ionizing Radiation and Risk of Dementia: A Systematic Review and Meta-Analysis

The number of people living with dementia is rising globally as life expectancy increases. Dementia is a multifactorial disease. Due to the ubiquity of radiation exposure in medical and occupational settings, the potential association between radiation and dementia, and its subtypes (Alzheimer's and Parkinson's disease), is of particular importance. There has also been an increased interest in studying radiation induced dementia risks in connection with the long-term manned space travel proposed by The National Aeronautics and Space Administration (NASA). Our aim was to systematically review the literature on this topic, and use meta-analysis to generate a summary measure of association, assess publication bias and explore sources of heterogeneity across studies. We identified five types of exposed populations for this review: 1. survivors of atomic bombings in Japan; 2. patients treated with radiation therapy for cancer or other diseases; 3. occupationally exposed workers; 4. those exposed to environmental radiation; and 5. patients exposed to radiation from diagnostic radiation imaging procedures. We included studies that considered incident or mortality outcomes for dementia and its subtypes. Following PRISMA guidelines, we systematically searched the published literature indexed in PubMed between 2001 and 2022. We then abstracted the relevant articles, conducted a risk-of-bias assessment, and fit random effects models using the published risk estimates. After we applied our eligibility criteria, 18 studies were identified for review and retained for meta-analysis. For dementia (all subtypes), the summary relative risk was 1.11 (95% CI: 1.04, 1.18; P = 0.001) comparing individuals receiving 100 mSv of radiation to those with no exposure. The corresponding summary relative risk for Parkinson's disease incidence and mortality was 1.12 (95% CI 1.07, 1.17; P <0.001). Our results provide evidence that exposure to ionizing radiation increases the risk of dementia. However, our findings should be interpreted with caution due to the small number of included studies. Longitudinal studies with improved exposure characterization, incident outcomes, larger sample size, and the ability to adjust for effects of potential confounders are needed to better assess the possible causal link between ionizing radiation and dementia.
Phase reconstruction of the focused Gaussian beam with optical vortex

In the previous paper the new scanning technique was proposed. The sample was illuminated by a focused laser beam with an optical vortex. The vortex was introduced to the laser beam by vortex lens. When shifting the vortex lens the optical vortex in the focused beam moves and scans the sample. In order to use this new scanning technique for microscopic imaging a method for the focused vortex beam phase reconstruction is necessary. In this paper a fast and accurate method for phase reconstruction of the focused vortex beam is investigated. It is shown that the propose method is accurate enough to explore even small optical vortex shifts inside the focused beam.
Decision-making model for the effective e-services adoption in the Indian educational organizations

Due to the advances in wireless network environments, consumers/end-user behaviors continue to expand in cyberspace. Similarly, university students (i.e. universities’ consumers) can easily shift from one university to another. In recent years, decision-makers in educational organizations have faced multi-criteria decision making (MCDM) problems in e-service adoption in order to improve quality standards and maintain students’ retention in highly competitive education environments. Generally, many required criteria in MCDM cannot be evaluated accurately since accurate data cannot be obtained from the decision makers’ assessments. Thus, this research aims to propose a decision-making model for identifying the factors that highly impact on e-service adoption in educational organizations. This new model combined the fuzzy Decision MAking Trial and Evaluation Laboratory (fuzzy DEMATEL) and fuzzy Techniques for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) to weight the interactions among the factors which were defined from a comprehensive review of literature and to determine the relative importance of these factors. The findings from our new proposed model: fuzzy DEMATEL-TOPSIS showed that environmental factors are the most important for effective e-service adoption among educational organizations in India. The proposed decision making model could guide educational organizations to improve their decisions related to technology adoption in their organizations. The conclusions and practical insights gleaned from this research could also hopefully be useful to school authorities in assisting with the adoption, acceptance, and usage of e-services.
Hepatitis C Virus Micro-elimination Among People With HIV in San Diego: Are We on Track?

Abstract Background Rising incidence of hepatitis C virus (HCV) among people with HIV (PWH) in San Diego County (SDC) was reported. In 2018, the University of California San Diego (UCSD) launched a micro-elimination initiative among PWH, and in 2020 SDC launched an initiative to reduce HCV incidence by 80% across 2015–2030. We model the impact of observed treatment scale-up on HCV micro-elimination among PWH in SDC. Methods A model of HCV transmission among people who inject drugs (PWID) and men who have sex with men (MSM) was calibrated to SDC. The model was additionally stratified by age, gender, and HIV status. The model was calibrated to HCV viremia prevalence among PWH in 2010, 2018, and 2021 (42.1%, 18.5%, and 8.5%, respectively), and HCV seroprevalence among PWID aged 18–39 years, MSM, and MSM with HIV in 2015. We simulate treatment among PWH, weighted by UCSD Owen Clinic (reaching 26% of HCV-infected PWH) and non-UCSD treatment, calibrated to achieve the observed HCV viremia prevalence. We simulated HCV incidence with observed and further treatment scale-up (+/− risk reductions) among PWH. Results Observed treatment scale-up from 2018 to 2021 will reduce HCV incidence among PWH in SDC from a mean of 429 infections/year in 2015 to 159 infections/year in 2030. County-wide scale-up to the maximum treatment rate achieved at UCSD Owen Clinic (in 2021) will reduce incidence by 69%, missing the 80% incidence reduction target by 2030 unless accompanied by behavioral risk reductions. Conclusions As SDC progresses toward HCV micro-elimination among PWH, a comprehensive treatment and risk reduction approach is necessary to reach 2030 targets.
Interpretable Rule Mining for Real-Time ECG Anomaly Detection in IoT Edge Sensors

Electrocardiogram (ECG) analysis is widely used in the diagnosis of cardiovascular diseases. This article proposes an explainable rule-mining strategy for prioritizing abnormal class detection in ECG data. The proposed method utilizes a biased-trained artificial neural network (ANN) with input features derived from an ECG beat sequence and formulates a set of rules at each node of an on-demand tree-like search algorithm. The rule base at each node is derived from a linear combination of the most impactful features identified using gradient analysis in an ANN. The final derived model is an explainable rule-based system that detects abnormal heartbeats based on statistical and morphological features from ECG. The model achieves the target sensitivity, and accuracy with a low run time complexity through a comprehensive offline rule-mining process and is trained using the MIT-BIH Arrhythmia Database. The system achieves an accuracy of 93% with only nine nodes and a test sensitivity of 90% and 80%, respectively, for VEB and SVEB beat types, when tested on previously unseen ECG data from the INCART database. The model performance and complexity can be easily adjusted based on the real-time resource constraints of a wearable sensor. The model was deployed on an ARM Cortex M4-based embedded device and is shown to achieve a >50% reduction in sensor power consumption when only abnormal beats are wirelessly transmitted. That is, RF transmission is gated using the model output and transmission is disabled when the subject’s ECG is normal. The proposed technique is highly suited for healthcare applications because of its explainability, lower complexity, and real-time flexibility when deployed in the Internet of Things (IoT)-enabled wearable edge sensors.
Monitoring the Release of Methylglyoxal (MGO) from Honey and Honey-Based Formulations

Methylglyoxal (MGO) is considered to be one of the vital components responsible for the anti-bacterial activity of Leptospermum spp. (Manuka) honey. While many studies have demonstrated a dose-dependent antibacterial activity for MGO in vitro, from a therapeutic viewpoint, it is also important to confirm its release from Manuka honey and also from Manuka honey-based formulations. This study is the first to report on the release profile of MGO from five commercial products containing Manuka honey using a Franz diffusion cell and High-Performance Liquid Chromatography (HPLC) analysis. The release of MGO expressed as percentage release of MGO content at baseline was monitored over a 12 h period and found to be 99.49 and 98.05% from an artificial honey matrix and NZ Manuka honey, respectively. For the investigated formulations, a time-dependent % MGO release between 85% and 97.18% was noted over the 12 h study period.
Analysis of risk factors of hepatocellular carcinoma and establishment of a clinical prognosis model

Liver cancer is a common malignancy of the digestive system. Hepatocellular carcinoma (HCC) accounts for the most majority of these tumors and it has brought a heavy medical burden to underdeveloped countries and regions. Many factors affect the prognosis of HCC patients, however, there is no specific statistical model to predict the survival time of clinical patients. This study derived a risk factor signature of HCC and reliable clinical prediction model by statistically analyzing The Surveillance, Epidemiology, and End Results (SEER) database patient information using an open source package in the python environment.
An LCC–LCC Compensated WPT System With Switch-Controlled Capacitor for Improving Efficiency at Wide Output Voltages

In this article, a triple-phase-shift (TPS) control strategy combining a switch-controlled capacitor (SCC) is proposed for LCC–LCC compensated wireless power transfer (WPT) system to improve the overall efficiency at wide output voltages. The basic mathematical model of the system is first established, and the conditions for zero-voltage switching (ZVS) and load matching are described under TPS. Then, the mechanism of TPS reducing system efficiency at wide output voltages is revealed based on the established model. An SCC is employed to adjust the compensation capacitance to achieve a minimum circulating reactive power on both sides and reduce the rectifier input current. Finally, the impact of the compensation capacitance variations on the current, output power, and impedance are analyzed, based on which the optimal value of adjustable capacitance for maximum efficiency is derived by further considering the established time domain model. A comparative experiment is performed with traditional TPS and the proposed control technique, which shows that the system with the proposed control technique achieves a higher efficiency over the TPS within the entire power range.
Surface accuracy of a large-scale compact antenna test range considering mechanism, metrology and alignment

A large compact range (CR) having a width of 23 m and height of 16 m that will generate a Φ15 m quiet zone is presented. The antenna consists of 30 blocks and 76 serrated reflectors. Its mechanical accuracy is reflected in two aspects: surface precision and gap precision. In addition, the root-mean-square (RMS) surface accuracy should be less than or equal to 0.075 mm for achieving the highest operating frequency of 40 GHz, and the gaps between two segments should be controlled strictly to the tolerance of 0.4 ± 0.2 mm for avoiding gap diffraction and compensating for inter-block interference due to thermal deformation. The surface accuracy in terms of mechanical structure, metrology and alignment approach is very tight. First, a high-accuracy honeycomb sandwich panel, anisotropic back structure and spatial parallel adjustment mechanism are introduced, and the error contributions of these three mechanisms are 0.03 mm, 0.01 mm and 0.005 mm, respectively. Second, a measurement network based on laser tracker metrology was established, and the RMS error of the measurement system is controlled to 0.025 mm through the optimization of the measuring stations and weighted coordinate regression. Third, an original alignment approach that divides the entire assembly into three key phases by marked point edge-constrained surface is proposed. By performing a few iterations of onsite adjustment, the reflectors were aligned in the prescribed positions, and the gap quality was controlled effectively. Finally, the on-site alignment of the large CR is introduced. The final antenna surface RMS accuracy was up to 0.054 mm, and the gaps achieved the desired design index.
On the continuation of solutions of non-autonomous semilinear parabolic problems

Abstract We study non-autonomous parabolic equations with critical exponents in a scale of Banach spaces Eσ, σ ∈ [0,1 + μ). We consider a suitable E1+ε-solution and describe continuation properties of the solution. This concerns both a situation when the solution can be continued as an E1+ε-solution and a situation when the E1+ε-norm of the solution blows up, in which case a piecewise E1+ε-solution is constructed.