Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Analysis of Diabetes-Risk with Class Imbalance

Diabetes type 2 (T2DM) is a common chronic disease, increasingly leading to many complications and affecting vital organs. Hyperglycemia is the main characteristic caused by insufficient insulin secretion and poses a serious risk to human health. The objective is to construct a type-2 diabetes prediction model with high classification accuracy. Advanced machine learning and predictive model techniques are utilized to achieve cutting-edge techniques for the early diagnosis of diabetes. This paper proposes an efficient performance model to predict and classify the minority class of type-2 diabetes. The impact of oversampling and undersampling approaches to reduce the effect of an unbalanced class has been compared to classification performance algorithms. Synthetic Minority Oversampling (SMOTE) and Tomek-links techniques are applied and examined. The outcomes were then compared to the original unbalanced dataset using an artificial neural network (ANN) predictive model. The model is compared with other state-of-the-art classifiers such as support vector machine (SVM), random forest (RF), and decision tree (DT). The tuned model had the best accuracy of 92.2%. The experimental findings clearly manifest the improvement in accuracy and evaluation metrics in terms of AUC and F1-measure using the SMOTE oversampling strategy rather than the baseline and undersampling schemes. The study recommends adopting dynamic hyperparameter optimization to further improve accuracy.
Vibration Suppression for Two-Inertia System With Variable-Length Flexible Load Based on Neural Network Compensation Sliding Mode Controller and Angle-Independent Method

The two-inertia system with variable-length flexible load (TSVFL) is a typical dynamic model uncertain system. It is affected by transmission flexibility, flexible load length variation, and inaccurate friction torque, in which the flexible load will vibrate during rotation. In this study, a neural network compensation sliding mode control (SMC) strategy mixed with the angle-independent method (AIM) is proposed to suppress the vibration of the TSVFL. Among them, the AIM is proposed to design the fluctuating desired input of the motor. The vibration of the flexible load is offset by the speed fluctuation of the motor. The RBF neural networks are proposed to recognize the uncertain term in the TSVFL's dynamic model. First, the nonlinear mathematical model of the TSVFL is deduced based on the assumed mode method. Then, the Lyapunov stability theorem is proposed to design the weight coefficients’ adaptive law in neural networks and the robust term in the control law. Finally, simulation and physical experiments on output speed control are implemented. The results show that the proposed strategy is able to effectively decrease the error and weaken the vibration.
Schwinger's Quantum Action Principle. I. From Dirac's formulation through Feynman's path integrals to the Schwinger-Keldysh method

Starting from the earlier notions of stationary action prin- ciples, we show how Schwinger's Quantum Action Principle descended from Dirac's formulation, which independently led Feynman to his path-integral formulation of quantum mechanics. The connection be- tween the two is brought out, and applications are discussed. The Keldysh-Schwinger time-cycle method of extracting matrix elements is described. Part II will discuss the variational formulation of quantum electrodynamics and the development of source. theory.
Deep Neural Network for Lung Image Segmentation on Chest X-ray

COVID-19 patients require effective diagnostic methods, which are currently in short supply. In this study, we explained how to accurately identify the lung regions on the X-ray scans of such people’s lungs. Images from X-rays or CT scans are critical in the healthcare business. Image data categorization and segmentation algorithms have been developed to help doctors save time and reduce manual errors during the diagnosis. Over time, CNNs have consistently outperformed other image segmentation algorithms. Various architectures are presently based on CNNs such as ResNet, U-Net, VGG-16, etc. This paper merged the U-Net image segmentation and ResNet feature extraction networks to construct the ResUNet++ network. The paper’s novelty lies in the detailed discussion and implementation of the ResUNet++ architecture in lung image segmentation. In this research paper, we compared the ResUNet++ architecture with two other popular segmentation architectures. The ResNet residual block helps us in lowering the feature reduction issues. ResUNet++ performed well compared with the UNet and ResNet architectures by achieving high evaluation scores with the validation dice coefficient (96.36%), validation mean IoU (94.17%), and validation binary accuracy (98.07%). The novelty of this research paper lies in a detailed discussion of the UNet and ResUNet architectures and the implementation of ResUNet++ in lung images. As per our knowledge, until now, the ResUNet++ architecture has not been performed on lung image segmentation. We ran both the UNet and ResNet models for the same amount of epochs and found that the ResUNet++ architecture achieved higher accuracy with fewer epochs. In addition, the ResUNet model gave us higher accuracy (94%) than the UNet model (92%).
Optimal Scheduling of Hybrid Sustainable Energy Microgrid: A Case Study for a Resort in Sokhna, Egypt

This paper is focused on analyzing, investigating, and designing a hybrid energy system based on sustainable or renewable resources, namely solar and wind energy, in addition to using a diesel generator and battery storage to supply a small resort in Suez, Egypt. The resort is located in Sokhna, which is on the Suez gulf and is about 50 km from the Suez governorate and 100 km from Cairo, Egypt. The Sokhna coast has plenty of high solar radiation and wind energy all year. At the same time, the Egyptian government is building many wind and photovoltaic projects there. Thus, it is expected that it will be very economic to use solar and wind energy in that area to supply the resort. The optimal combinations of energy resources to meet the load demand under various scenarios are considered. The optimal mix of sources is investigated with and without the presence of the grid. The cropped outcomes show that the hybrid energy system, which is also in the presence of the grid, is a very economical solution that provides the resort with an acceptable energy cost. The cost of energies (CoEs) is equal to 0.0441 and 0.0443 $/kWh for cases 2 and 4 (with grid), respectively. However, the CoEs are equal to 0.141 and 0.134 $/kWh for cases 1 and 3 (without grid), respectively.
Scalarizing Functions in Bayesian Multiobjective Optimization

Scalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving computationally expensive multi-and many-objective optimization problems using Bayesian multiobjective optimization is scarce. Scalarizing functions can play a crucial role on the quality and number of evaluations required when doing the optimization. In this article, we compare 15 different scalarizing functions in the framework of Bayesian multiobjective optimization and build Gaussian process models on them. We use the expected improvement as infill criterion (or acquisition function) to update the models. In particular, we analyze the performance of different scalarizing functions on several benchmark problems with different number of objectives to be optimized. The review and experiments on different functions provide useful insights in using a scalarizing function, especially for problems with a large number of objectives.
MEG3 Expression Indicates Lymph Node Metastasis and Presence of Cancer-Associated Fibroblasts in Papillary Thyroid Cancer

Papillary thyroid cancer is the most common endocrine malignancy, occurring at an incidence rate of 12.9 per 100,000 in the US adult population. While the overall 10-year survival of PTC nears 95%, the presence of lymph node metastasis (LNM) or capsular invasion indicates the need for extensive neck dissection with possible adjuvant radioactive iodine therapy. While imaging modalities such as ultrasound and CT are currently in use for the detection of suspicious cervical lymph nodes, their sensitivities for tumor-positive nodes are low. Therefore, advancements in preoperative detection of LNM may optimize the surgical and medical management of patients with thyroid cancer. To this end, we analyzed bulk RNA-sequencing datasets to identify candidate markers highly predictive of LNM. We identified MEG3, a long-noncoding RNA previously described as a tumor suppressor when expressed in malignant cells, as highly associated with LNM tissue. Furthermore, the expression of MEG3 was highly predictive of tumor infiltration with cancer-associated fibroblasts, and single-cell RNA-sequencing data revealed the expression of MEG3 was isolated to cancer-associated fibroblasts (CAFs) in the most aggressive form of thyroid cancers. Our findings suggest that MEG3 expression, specifically in CAFs, is highly associated with LNM and may be a driver of aggressive disease.
Gravitational anomalies, entanglement entropy, and flat-space holography

We introduce a prescription to compute the entanglement entropy of Galilean conformal field theories by combining gravitational anomalies and an \. {I}n\"{o}n\"{u}-Wigner contraction. We find that our expression for the entanglement entropy in the thermal limit reproduces the Cardy formula for Galilean conformal field theories. Using this proposal, we calculate the entanglement entropy for a class of Galilean conformal field theories, which are believed to be dual to three-dimensional flat-space cosmological solutions. These geometries describe expanding (contracting) universes and can be viewed as the flat-space limit of rotating Ba\~nados-Teitelboim-Zanelli black holes. We show that our finding reduces, in the appropriate limits, to the results discussed in the literature and provide interpretations for the previously unexplored regimes, such as flat-space chiral gravity.
Research on COVID-19 pneumonia diagnosis based on chest x-ray (CXR) images using transformer and CNN

Since December 2019, some hospitals in Wuhan, Hubei Province have found many cases of unidentified pneumonia, which were later named COVID-19. In recent years, the disease has spread widely all over the world. Many researchers are trying many methods to make rapid discovery and diagnosis for COVID-19, which is important to facilitate the treatment of the disease and to reduce its transmission. In this study, we propose the comparison of methods for COVID-19 pneumonia diagnosis based on chest X-ray (CXR) images using Transformer and CNN. Both of them are two of the most commonly studied deep learning method. In this paper, all four models will be trained with the COVID-19 Chest X-ray Database and tested for the performance on the four-class classification. The results showed that Swin Transformer achieved the highest score in the comparison of accuracy as 0.916. Additionally, VGG as one of CNN architectures performs well on the recognition of COVID-19 positive. This study can help doctors reduce their workload and effectively improve the speed and accuracy of detection of COVID-19 and other viral pneumonia.
Potential of in vivo stress reporter models to reduce animal use and provide mechanistic insights in toxicity studies

Chemical risk assessment ensures protection from the toxic effects of drugs and manmade chemicals. To comply with regulatory guidance, studies in complex organisms are required, as well as mechanistic studies to establish the relevance of any toxicities observed to man. Although in vitro toxicity models are improving, in vivo studies remain central to this process. Such studies are invariably time-consuming and often involve large numbers of animals. New regulatory frameworks recommend the implementation of “smart” in vivo approaches to toxicity testing that can effectively assess safety for humans and comply with societal expectations for reduction in animal use. A major obstacle in reducing the animals required is the time-consuming and complexity of the pathological endpoints used as markers of toxicity. Such endpoints are prone to inter-animal variability, subjectivity and require harmonisation between testing sites. As a consequence, large numbers of animals per experimental group are required. To address this issue, we propose the implementation of sophisticated stress response reporter mice that we have developed. These reporter models provide early biomarkers of toxic potential in a highly reproducible manner at single-cell resolution, which can also be measured non-invasively and have been extensively validated in academic research as early biomarkers of stress responses for a wide range of chemicals at human-relevant exposures. In this report, we describe a new and previously generated models in our lab, provide the methodology required for their use and discuss how they have been used to inform on toxic risk (likelihood of chemical causing an adverse health effect). We propose our in vivo approach is more informative (refinement) and reduces the animal use (reduction) compared to traditional toxicity testing. These models could be incorporated into tiered toxicity testing and used in combination with in vitro assays to generate quantitative adverse outcome pathways and inform on toxic potential.
On the Origin of Orbital Period Change in WY Cancri: A Genuine Angular-Momentum Loss?

WY Cancri is a short-period ($P$=0.829 d) eclipsing RS Canum Venaticorum stars, and both components are late-type stars. Recently, observations provided by photometric observations and light time minima show that the orbital period of WY Cancri is experiencing a secular decrease at a rate of ${\rm d} P/{\rm d}t=-1.2\times10^{-7}\rm d yr^{-1}$. In this Letter, we attempt to investigate if the period change of WY Cancri can originate from the angular momentum loss. In calculation, we assume that this source has a high wind loss rate of $\sim10^{-10} \rm M_{\odot} yr^{-1}$. To account for the observation, magnetic braking demands a strong surface magnetic field of $\gtrsim 10000$ G like Ap/Bp stars. Furthermore, if this source may be surrounded by a circumbinary disk, and 6% of the wind loss feeds the disk, tidal torque between the disk and the binary can offer the observed angular momentum loss rate. Such a strong magnetic field or an extremely high wind input fraction seem to be highly unlikely.
LONG-LIVED SPIRAL STRUCTURE FOR GALAXIES WITH INTERMEDIATE-SIZE BULGES

Spiral structure in disk galaxies is modeled with nine collisionless N-body simulations including live disks, halos, and bulges with a range of masses. Two of these simulations make long-lasting and strong two-arm spiral wave modes that last for ∼5 Gyr with constant pattern speed. These two had a light stellar disk and the largest values of the Toomre Q parameter in the inner region at the time the spirals formed, suggesting the presence of a Q-barrier to wave propagation resulting from the bulge. The relative bulge mass in these cases is about 10%. Models with weak two-arm spirals had pattern speeds that followed the radial dependence of the Inner Lindblad Resonance.
Study on the value of multi-dimensional conformal radiotherapy and functional imaging in tumor bioimaging

Background To explore the diagnostic and therapeutic effects of multi-dimensional conformal radiotherapy (MD-CRT) combined with functional imaging in tumor bioimaging. Methods A total of 150 cases of patients with brain metastases in First Affiliated Hospital of Xi’an Jiaotong University between December 2020 and December 2021 were retrospectively selected as the research cohort. Participants underwent whole brain imaging guided by MD-CRT combined with volume assessment to assess the patient’s recent treatment effect, statistically acceptable dose irradiation treatment, and the incidence of adverse reactions. All patients were followed up to evaluate the long-term efficacy. Results Among the 150 patients, 24 cases were in complete remission, 72 cases were in partial remission, 36 cases were in a stable condition, 18 cases were in deterioration, and the treatment of 96 cases (64.00%) was deemed effective. All participants were followed up, the mean survival was (62.37±1.24) months, 96 cases (64.00%) survived, and 54 cases (36.00%) had died. The average dose was (62.09±3.94) Gy. In terms of adverse reactions: brain edema occurred in 57 patients, accounting for 38.00%. Conclusions The MD-CRT and functional imaging techniques for patients with metastatic tumor have high therapeutic accuracy, are associated with improved local control rate, prolonged survival, and cause little damage to normal tissues, with significant therapeutic effect, and can be widely used in clinical practice.
An Application of Hyperspectral Image Clustering Based on Texture-Aware Superpixel Technique in Deep Sea

This paper aims to study the application of hyperspectral technology in the classification of deep-sea manganese nodules. Considering the spectral spatial variation of hyperspectral images, the difficulty of label acquisition, and the inability to guarantee stable illumination in deep-sea environments. This paper proposes a local binary pattern manifold superpixel-based fuzzy clustering method (LMSLIC-FCM). Firstly, we introduce a uniform local binary pattern (ULBP) to design a superpixel algorithm (LMSLIC) that is insensitive to illumination and has texture perception. Secondly, the weighted feature and the mean feature are fused as the representative features of superpixels. Finally, it is fused with fuzzy clustering method (FCM) to obtain a superpixel-based clustering algorithm LMSLIC-FCM. To verify the feasibility of LMSLIC-FCM on deep-sea manganese nodule data, the experiments were conducted on three different types of manganese nodule data. The average identification rate of LMSLIC-FCM reached 83.8%, and the average true positive rate reached 93.3%, which was preferable to the previous algorithms. Therefore, LMSLIC-FCM is effective in the classification of manganese nodules.
Anomalous polymer dynamics is non-Markovian: memory effects and the generalized Langevin equation formulation

Any first course on polymer physics teaches that the dynamics of a tagged monomer of a polymer is anomalously subdiffusive, i.e., the mean-square displacement of a tagged monomer increases as tα for some α < 1 until the terminal relaxation time τ of the polymer. Beyond time τ the motion of the tagged monomer becomes diffusive. Classical examples of anomalous dynamics in polymer physics are single polymeric systems, such as phantom Rouse, self-avoiding Rouse, self-avoiding Zimm, reptation, translocation through a narrow pore in a membrane, and many-polymeric systems such as polymer melts. In this pedagogical paper I report that all these instances of anomalous dynamics in polymeric systems are robustly characterized by power-law memory kernels within a unified generalized Langevin equation (GLE) scheme, and therefore are non-Markovian. The exponents of the power-law memory kernels are related to the relaxation response of the polymers to local strains, and are derived from the equilibrium statistical physics of polymers. The anomalous dynamics of a tagged monomer of a polymer in these systems is then reproduced from the power-law memory kernels of the GLE via the fluctuation-dissipation theorem (FDT). Using this GLE formulation I further show that the characteristics of the drifts caused by a (weak) applied field on these polymeric systems are also obtained from the corresponding memory kernels.
Sunyaev–Zel’dovich observations of galaxy clusters out to the virial radius with the Arcminute Microkelvin Imager

We present observations using the Small Array of the Arcminute Microkelvin Imager (AMI; 14–18 GHz) of four Abell and three MACS clusters spanning 0.171–0.686 in redshift. We detect Sunyaev–Zel’dovich (SZ) signals in five of these without any attempt at source subtraction, although strong source contamination is present. With radio-source measurements from high-resolution observations, and under the assumptions of spherical β-model, isothermality and hydrostatic equilibrium, a Bayesian analysis of the data in the visibility plane detects extended SZ decrements in all seven clusters over and above receiver noise, radio sources and primary cosmic microwave background imprints. Formal Bayesian evidence ratios range from 1011:1 to 1043:1 for six of the clusters and 3000:1 for one with substantially fewer data than the others. We present posterior probability distributions for, e.g., total mass and gas fraction averaged over radii internal to which the mean overdensity is 1000, 500 and 200, r200 being the virial radius. Reaching r200 involves some extrapolation for the nearer clusters but not for the more distant ones. We find that our estimates of gas fraction are low (compared with most in the literature) and decrease with increasing radius. These results appear to be consistent with the notion that gas temperature in fact falls with distance (away from near the cluster centre) out to the virial radius.
Probing an X-Ray Flare Pattern in Mrk 421 Induced by Multiple Stationary Shocks: A Solution to the Bulk Lorentz Factor Crisis

The common observations of multiple radio VLBI stationary knots in high-frequency-peaked BL Lacs (HBLs) can be interpreted as multiple recollimation shocks accelerating particles along jets. This approach can resolve the so-called “bulk Lorentz factor crisis” of sources with a high Lorentz factor deduced from maximum γ–γ opacity and fast variability and apparently inconsistent slow/stationary radio knots. It also suggests that a unique pattern of the nonthermal emission variability should appear after each strong flare. Taking advantage of the 13 yr of observation of the HBL Mrk 421 by the X-ray Telescope on the Neil Gehrels Swift Observatory (Swift-XRT), we probe for such an intrinsic variability pattern. Its significance is then statistically estimated via comparisons with numerous similar simulated light curves. A suggested variability pattern is identified, consistent with a main flare emission zone located in the most upstream 15.3 GHz radio knot at 0.38 mas from the core. Subsequent flux excesses in the light curve are consistent with a perturbation crossing all of the downstream radio knots with a constant apparent speed of 45c. The significance of the observed variability pattern not arising from stochastic processes is found above three standard deviations, opening a promising path for further investigations in other blazars and with other energy bands. In addition to highlighting the role of stationary radio knots as high-energy particle accelerators in jets, the developed method allows estimates of the apparent speed and size of a jet perturbation without the need to directly observe any motion in jets.