Miracle-dz/UniFAR-FTUs huggingface dataset:
1SAX J1324.4–6200: a possible low‐mass X‐ray binary pulsar

We present results of a long ASCA observation made on 2000 February 2, of the X-ray pulsar 1SAX 1324.4-6200, in which the pulse period P s p i n was found to be 171.245 ′ 0.012 s, and a possible orbital period P o r b of27 ′ 1 h was discovered. P s p i n is significantly larger than the pulse period determined from the previous observations with BeppoSAX (1997 August) and ASCA (1994 August), and the three period values spread over a period of 5.5 yr agree well with a linear spin-down of the pulsar. The best-fitting rate of spin-down, P, is +5.2063 ′ 0.0005 x 10 - 9 s s - 1 , and P/P is +3.04 × 10 - 1 1 s - 1 , or +9.59 x 10 - 4 yr - 1 . However, we cannot rule out the possibility that the overall trend is of spin-up, and the observations represent fluctuations of spin-down. The ASCA X-ray spectrum of 1SAX 1324.4-6200 is rather featureless with an upper limit for a narrow iron line at 6.4 keV of 80 eV. The possible nature of the pulsar is discussed, the non-transient nature making it unlikely to be a Be system. It is possible that it is a wind-fed supergiant; however, this would require the overall trend in period evolution to be that of spin-up. If the tentative orbital period is real, the supergiant has to be lobe-filling. In fact, the observational evidence is more consistent with the object being a low-mass X-ray binary (LMXB) pulsar, including both spin and orbital periods, and the 5.5-yr apparent duration of spin-down. In this case, the object would join the small number of LMXB pulsars known.
A ZEROONE PROGRAMMING MODEL FOR RNA STRUCTURES WITH ARCLENGTH≥4

In this paper, we consider RNA structures with arc-length 4  . First, we represent these structures as matrix models and zero-one linearprogramming problems. Then, we obtain an optimal solution for this problemusing an implicit enumeration method. The optimal solution corresponds toan RNA structure with the maximum number of hydrogen bonds.
PM2.5 Concentration Forecasting Using Weighted Bi-LSTM and Random Forest Feature Importance-Based Feature Selection

Particulate matter (PM) in the air can cause various health problems and diseases in humans. In particular, the smaller size of PM2.5 enable them to penetrate deep into the lungs, causing severe health impacts. Exposure to PM2.5 can result in respiratory, cardiovascular, and allergic diseases, and prolonged exposure has also been linked to an increased risk of cancer, including lung cancer. Therefore, forecasting the PM2.5 concentration in the surrounding is crucial for preventing these adverse health effects. This paper proposes a method for forecasting the PM2.5 concentration after 1 h using bidirectional long short-term memory (Bi-LSTM). The proposed method involves selecting input variables based on the feature importance calculated by random forest, classifying the data to assign weight variables to reduce bias, and forecasting the PM2.5 concentration using Bi-LSTM. To compare the performance of the proposed method, two case studies were conducted. First, a comparison of forecasting performance according to preprocessing. Second, forecasting performance between deep learning (long short-term memory, gated recurrent unit, and Bi-LSTM) and conventional machine learning models (multi-layer perceptron, support vector machine, decision tree, and random forest). In case study 1, The proposed method shows that the performance indices (RMSE: 3.98%p, MAE: 5.87%p, RRMSE: 3.96%p, and R2:0.72%p) are improved because weights are given according to the input variables before the forecasting is performed. In case study 2, we show that Bi-LSTM, which considers both directions (forward and backward), can effectively forecast when compared to conventional models (RMSE: 2.70, MAE: 0.84, RRMSE: 1.97, R2: 0.16). Therefore, it is shown that the proposed method can effectively forecast PM2.5 even if the data in the high-concentration section is insufficient.
Guided Bone Regeneration Using a Novel Magnesium Membrane: A Literature Review and a Report of Two Cases in Humans

Guided bone regeneration (GBR) is a common procedure used to rebuild dimensional changes in the alveolar ridge that occur after extraction. In GBR, membranes are used to separate the bone defect from the underlying soft tissue. To overcome the shortcomings of commonly used membranes in GBR, a new resorbable magnesium membrane has been developed. A literature search was performed via MEDLINE, Scopus, Web of Science and PubMed in February 2023 for research on magnesium barrier membranes. Of the 78 records reviewed, 16 studies met the inclusion criteria and were analyzed. In addition, this paper reports two cases where GBR was performed using a magnesium membrane and magnesium fixation system with immediate and delayed implant placement. No adverse reactions to the biomaterials were detected, and the membrane was completely resorbed after healing. The resorbable fixation screws used in both cases held the membranes in place during bone formation and were completely resorbed. Therefore, the pure magnesium membrane and magnesium fixation screws were found to be excellent biomaterials for GBR, which supports the findings of the literature review.
A Machine Learning-Based Surrogate Finite Element Model for Estimating Dynamic Response of Mechanical Systems

An efficient approach for improving the predictive understanding of dynamic mechanical system variability is developed in this work. The approach requires low model assessment time through the fitting of surrogate models. ML-based surrogate algorithms for finite element analysis (FEA) are developed in this study to accelerate FEA and prevent rerunning complex simulations. The research begins with an overview of the recent novelties in ML algorithms applied to finite element (FE) and other physics-based computational schemes. To predict the time-varying response variables, that is, the displacement of a two-dimensional truss structure, a surrogate FE technique based on ML algorithms is developed. In this work, several ML regression algorithms, including decision trees (DTs) and deep neural networks, are developed, and their efficacies are compared. In this study, the ML-based surrogate FE models are able to effectively predict the response of the truss structure in two dimensions over the entire structure. Extreme gradient-boosting DTs provide more precise outcomes and outperform other ML algorithms.
Exploring the Cholesterol-Modifying Abilities of Lactobacilli Cells in Digestive Models and Dairy Products

This study aimed to investigate the ability of lactic acid bacteria to remove cholesterol in simulated gastric and intestinal fluids. The findings showed that the amount of cholesterol removed was dependent on the biomass, viability, and bacterial strain. Some cholesterol binding was stable and not released during gastrointestinal transit. The presence of cholesterol affected the fatty acid profile of bacterial cells, potentially influencing their metabolism and functioning. However, adding cholesterol did not significantly impact the survival of lactic acid bacteria during gastrointestinal transit. Storage time, passage, and bacterial culture type did not show significant effects on cholesterol content in fermented dairy products. Variations in cell survival were observed among lactic acid bacteria strains in simulated gastric and intestinal fluids, depending on the environment. Higher milk protein content was found to be more protective for bacterial cells during gastrointestinal transit than fat content. Future research should aim to better understand the impact of cholesterol on lactic acid bacteria metabolism and identify potential health benefits.
Zinc: an undervalued microelement in research and treatment

Recent years have seen a growing interest in a healthy lifestyle, particularly nutrition. An important component of a balanced diet is the microelement content. Zinc is the second most abundant trace element, after iron. It has antioxidant and immunomodulatory functions, and plays important roles in the pathogenesis of various diseases, including dermatoses. Individuals with a zinc deficiency may present with nonspecific erythematous, pustular, erosive, and bullous lesions as well as alopecia, nail dystrophy, and a variety of systemic symptoms. Any individual assessment of zinc levels should consider risk factors for deficiency, clinical symptoms, type of diet, and results of laboratory analyses. Recent research has shed light on the systemic and topical effects of zinc, indicating the value of its supplementation for many conditions.
Championship-Level Play of Domineering

Domineering is a game played on subsets of the square lattice by two players, who alternately remove connected two-square regions (dominoes) from play. Left may only place dominoes vertically; Right must play horizontally. The normal win-condition applies, so that the first player unable to move loses. It is difficult to analyse general Domineering positions, even for quite small boards. One way to gain insight into the nature of the problem is to watch actual games between expert players. To determine who were the strongest players available, an open-registration tournament was held. To insure that the players gave proper consideration to their play, a prize of $500 was awarded the winner. The finalists were Dan Calistrate of Calgary and David Wolfe of Berkeley. The format for the final was to play two games, each player taking the first turn once; if the series was split, two more games would be played, and so on until one player won both games of a round. As in chess, it would be expected that one set of pieces would provide an advantage, therefore winning with the favoured set would be like holding serve in tennis. In the event, the first round produced two first-player wins, and Calistrate won both second-round games. An 8×8 board was selected as large enough to be beyond the range of current analysis, and therefore apt to provide an interesting game. It seems quite likely that analysis of the general position is genuinely hard, so that the solution of the 8 × 8 board would simply necessitate a move to 9 × 9 or 10 × 10 to retain interest. Because of symmetry, the square board must be either a first-player or a second-player win (as opposed to a win for Left or a win for Right). Indeed, because of this symmetry, we can always orient the board so that the player who
Density dependence of seed dispersal and fecundity profoundly alters the spread dynamics of plant populations

Plant population spread has fundamental ecological and evolutionary importance. Both determinants of plant population spread, fecundity and dispersal, can be density‐dependent, which should cause feedback between population densities and spread dynamics. Yet it is poorly understood how density‐dependence affects key characteristics of spread: spread rate at which the location of the furthest forward individual moves, edge depth (the geographical area over which individuals contribute to spread) and population continuity (occupancy of the spreading population). We present a general modelling framework for analysing the effects of density‐dependent fecundity and dispersal on population spread and parameterize this framework with experimental data from a common‐garden experiment using five wind‐dispersed plant species grown at different densities. Our model shows that density‐dependent fecundity and dispersal strongly affect all three population spread characteristics for both exponential and lognormal dispersal kernels. Spread rate and edge depth are strongly correlated but show weaker correlations with population continuity. Positive density‐dependence of fecundity increases all three spread characteristics. Increasingly positive density‐dependence of dispersal increases spread rate and edge depth but generally decreases population continuity. Density‐dependent fecundity and dispersal are largely additive in their effect on spread characteristics. For population continuity, the joint effects of density‐dependent fecundity and dispersal are somewhat contingent on the dispersal kernel. The common‐garden experiment and the experimentally parameterized mechanistic dispersal model revealed density‐dependent fecundity and dispersal across study species. All study species exhibited negatively density‐dependent fecundity, but they differed qualitatively in the density‐dependence of dispersal distance and probability of long‐distance dispersal. The negative density‐dependence of fecundity and dispersal found for three species reinforced each other in reducing spread rate and edge depth. The positively density‐dependent dispersal found for two species markedly increased spread rate and edge depth. Population continuity was hardly affected by population density in all study species except Crepis sancta in which it was strongly reduced by negatively density‐dependent fecundity. Synthesis. Density‐dependent fecundity and seed dispersal profoundly alter population spread. In particular, positively density‐dependent dispersal should promote the spread and genetic diversity of plant populations migrating under climate change but also complicate the control of invasive species.
Adenovirus as a Vector and Oncolytic Virus

Adenoviral vectors, both oncolytic viruses and gene delivery vectors, are among the earliest approved and commercialised vectors for gene therapy. Adenoviruses have high cytotoxicity and immunogenicity. Therefore, lentiviruses or adeno-associated viruses as viral vectors and herpes simplex virus as an oncolytic virus have recently drawn attention. Thus, adenoviral vectors are often considered relatively obsolete. However, their high cargo limit and transduction efficiency are significant advantages over newer viral vectors. This review provides an overview of the new-generation adenoviral vectors. In addition, we describe the modification of the fiber knob region that enhances affinity of adenoviral vectors for cancer cells and the utilisation of cancer-cell-specific promoters to suppress expression of unwanted transgenes in non-malignant tissues.
Formulation, Optimization and Evaluation of Controlled Release Alginate Microspheres Using Synergy Gum Blends

The microspheres were prepared by ionic cross-linking technique. Chemical reaction between sodium alginate and calcium chloride to form calcium alginate was utilized for microspheres. For slowing the rate of release from microspheres the hydrophilic polymer Locust bean gum and xanthan gum and their combinations was added in different concentration so that the drug will be release constantly for 12hrs. The formulation prepared showed the angle of repose within acceptable range having a good flow property. The drug entrapment efficacy of all the formulation was in the range of 90.6 – 98.9%. The drug entrapment efficacy of microspheres increases with increase in concentration of hydrophilic gums. Diclofenac sodium in vitro release from microspheres was studied. The microspheres containing 2.5% of Locust bean gum: xanthan gum (6:4) shows 98.8% drug release. The values of coefficient correlation (r) were calculated and were found to be linear for first order release as compare to zero order release. Scanning electron microscopy of selected formulation indicated that the microspheres are spherical in shape and smooth surface. Stability studies reveled that polymers used were stable and compatible with the drug and there is no significant effect on physical characteristics, drug content and dissolution profile of the microsphere.
Climate Change and Hydrological Response in the Ranwu Lake Basin of Southeastern Tibet Plateau

It is of great practical significance to accurately distinguish the different water supply sources of rivers and lakes under climate change for regional water resources utilization. This study examines the impact of climate change on the hydrological processes of the Ranwu Lake basin in the southeastern Tibet Plateau. The authors used China Meteorological Forcing Dataset (CMFD) historical data, CanESM5′s future climate predictor, and the SPHY model to analyze trends in temperature, precipitation, and water supply sources in the basin. The study found that warming in the basin was higher than that in the Tibet Plateau, with high-altitude areas and winter showing more significant warming. From 1998 to 2018, precipitation in the basin showed a trend of fluctuation and decline. The study also found that glacial meltwater accounted for the majority of total runoff in the basin (54.13%), while snow meltwater, rainfall, and baseflow accounted for about 22.98%, 11.84%, and 11.06%, respectively, on average in recent years. The total runoff in the Ranwu Lake Basin will continue to decrease due to the accelerating retreat of glaciers, with the hydrological process transforming from being dominated by glacier processes to rain–snow processes. The study also predicts that three-quarters of glaciers in the basin will vanish within the next forty years, and by 2100, only around 20% of glaciers will remain.
Matrix Forms of Gradient Descent Algorithms Applied to Restoration of Blurred Images

Iterative gradient descent algorithms to solve algebraic linear equations are well known in the literature. These algorithms can be interpreted as dynamical closed loop control systems, where the step sizes are the control variables that can be optimally calculated by the Liapunov Control Functions (CLF) and Liapunov Optimizing Control (LOC) methods. In this paper matrix versions of gradient descent algorithms are deduced, including an unpublished matrix form of the Barzilai-Borwein algorithm. The step sizes (control variables) are also calculated by CLF/LOC. The main utility of these matrix algorithms is in image deblurring in the cases where the matrix that represents the blurring process is too large to be stored in the memory of the majority of conventional computational systems.
Brain tumor classification based on deep CNN and modified butterfly optimization algorithm

ABSTRACT There is an emerging need for medical imaging data to provide timely diagnosis. The segmentation of brain tumours from Magnetic Resonance Imaging (MRI) is of great significance for planning treatment. However, mechanising the process with different imaging conditions and accuracy is a significant challenge due to variations in tumour structures. An effective optimisation-driven classifier is developed for brain tumour detection to increase accuracy. The proposed Modified Butterfly Optimization Algorithm (MBOA) is employed for training the Deep Convolutional Neural Network (DCNN) for brain tumour detection. The input MRI image is pre-processed using a Gaussian filter to eliminate noises. The pre-processed output is fed to segmentation, wherein the U-Net model is adapted for generating the segments. The extraction of statistical and texture features is done for tumour region classification. The classification of tumours is done with DCNN, wherein the weights are optimally tuned using the proposed MBOA. The experimental result demonstrates that the developed method achieved better.
Emerging Evidence for the Use of Antidiabetic Drugs, Glucagon-like Peptide 1 Receptor Agonists, for the Treatment of Alzheimer’s Disease

From an epidemiological and pathophysiological point of view, Alzheimer’s disease (AD) and type 2 diabetes (T2DM) should be considered 'sister' diseases. T2DM significantly increases the risk of developing AD, and the mechanisms of neuronal degeneration themselves worsen peripheral glucose metabolism in multiple ways. The pathophysiological links between the two diseases, particularly cerebral insulin resistance, which causes neuronal degeneration, are so close that AD is sometimes referred to as 'type 3 diabetes'. Although the latest news on the therapeutic front for AD is encouraging, no treatment has been shown to halt disease progression permanently. At best, the treatments slow down the progression; at worst, they are inactive, or cause worrying side effects, preventing their use on a larger scale. Therefore, it appears logical that optimizing the metabolic milieu through preventive or curative measures can also slow down the cerebral degeneration that characterizes AD. Among the different classes of hypoglycaemic drugs, glucagon-like peptide 1 receptor agonists, which are widely used in the treatment of T2DM, were shown to slow down, or even prevent, neuronal degeneration. Data from animal, preclinical, clinical phase II, cohort and large cardiovascular outcomes studies are encouraging. Of course, randomized clinical phase III studies, which are on-going, will be essential to verify this hypothesis. Thus, for once, there is hope for slowing down the neurodegenerative processes associated with diabetes, and that hope is the focus of this review.
An Analytic Expression for the Tritium Burnup Fraction in Burning-Plasma Devices

Abstract The tritium burnup fraction fburnup can strongly affect the design of a fusion reactor since it influences the size of the tritium reprocessing plant, the on-site tritium inventory, and hence, the licensing requirements and cost of the entire plant. In this paper a simple analytic expression for fburnup is derived and then applied to typical parameters proposed for three possible fusion devices: ARIES-AT, FDF, and ITER. We find that for these parameters the burnup fraction is most strongly affected by the global recycling coefficient (through the global replacement time) and the fueling efficiency. The latter term may be the most easily influenced by plant design, such as by high-field-side pellet injection, for example. Because of the hotter edge plasmas in these devices compared to present-day tokamaks, the recycling coefficient will be lower, reducing the tritium burnup fraction. While this may not adversely affect ITER, which is limited to 400-s pulses for the inductive scenario, the tritium reprocessing for nearly continuous operation of devices such as ARIES-AT must be carefully considered in the overall plant design.
Multi-Scale Feature Interaction Network for Remote Sensing Change Detection

Change detection (CD) is an important remote sensing (RS) data analysis technology. Existing remote sensing change detection (RS-CD) technologies cannot fully consider situations where pixels between bitemporal images do not correspond well on a one-to-one basis due to factors such as seasonal changes and lighting conditions. Existing networks construct two identical feature extraction branches through convolution, which share weights. The two branches work independently and do not merge until the feature mapping is sent to the decoder head. This results in a lack of feature information interaction between the two images. So, directing attention to the change area is of research interest. In complex backgrounds, the loss of edge details is very important. Therefore, this paper proposes a new CD algorithm that extracts multi-scale feature information through the backbone network in the coding stage. According to the task characteristics of CD, two submodules (the Feature Interaction Module and Detail Feature Guidance Module) are designed to make the feature information between the bitemporal RS images fully interact. Thus, the edge details are restored to the greatest extent while fully paying attention to the change areas. Finally, in the decoding stage, the feature information of different levels is fully used for fusion and decoding operations. We build a new CD dataset to further verify and test the model’s performance. The generalization and robustness of the model are further verified by using two open datasets. However, due to the relatively simple construction of the model, it cannot handle the task of multi-classification CD well. Therefore, further research on multi-classification CD algorithms is recommended. Moreover, due to the high production cost of CD datasets and the difficulty in obtaining them in practical tasks, future research will look into semi-supervised or unsupervised related CD algorithms.