Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Imperfection and Noise on the Image Reconstruction Through a Multimode Fiber with a Neural Network

Traditional medical endoscopes of single-mode fiber bundles have large diameters and are inconvenient to operate. In recent years, many methods, including artificial intelligence technology, for imaging using a single multimode fiber (MMF) have been proposed, greatly reducing the size of medical endoscopes. Herein, the effects of imperfection and noise of the received images on the image reconstruction through MMFs with neural networks are investigated. The random noise in image sensor, partially shading of the light path, random image distortion, and interference from environment lighting are considered. We find that the image reconstruction based on a neural network are immune to most of the imperfection and noise, while significantly degraded by several other noises.
A novel automated detection of electromagnetic fields due to lightning strokes

The development of a novel lightning detection system is presented. The system is based on the measurement of electromagnetic fields due to lightning return stroke channel, and offers distinct features, including distortionless signal amplification and intelligent triggering facility. The intelligent triggering unit is capable of rejecting unwanted transient signals such as switching signals, cloud-to-cloud lightning signals, etc. In order to calibrate the system, a laboratory lightning pulse-generator kit is developed. This calibration kit can generate simulated lightning signals in an electromagnetically polluted environment with noise and unwanted signals.
Impact of Author indexing from the Co-authorship Relation

As the the quality of research and spectrum of the publications are improving day by day, the citation parameter h-index [1] alone seems not sufficient to measure authors' performance. The citation is the relationship between two papers when one paper refers to another paper. In way to resolve the said issue, in this paper we have introduced a new author indexing parameter, $\mathbf{h}_{c}$ -index based on the influence of the co-authors' h-index level. Our newly introduced index $(\mathbf{h}_{c})$ looks through the co-authorship genealogy tree of an author and subsequently measures the related citation impacts created by every coauthors. This mechanism gives more accurate measurement for calculating author's citation index and the same we have observed in our experimental analysis, where we have seen substantial impact improvement while comparing actual h-index with $\mathbf{h}_{c}$ -index for top 10 authors from Web of Universities11https://www.webometrics. info/enlhlargerthan100 ranking.
Functional Surface Coatings on Orthodontic Appliances: Reviews of Friction Reduction, Antibacterial Properties, and Corrosion Resistance

Surface coating technology is an important way to improve the properties of orthodontic appliances, allowing for reduced friction, antibacterial properties, and enhanced corrosion resistance. It improves treatment efficiency, reduces side effects, and increases the safety and durability of orthodontic appliances. Existing functional coatings are prepared with suitable additional layers on the surface of the substrate to achieve the abovementioned modifications, and commonly used materials mainly include metal and metallic compound materials, carbon-based materials, polymers, and bioactive materials. In addition to single-use materials, metal-metal or metal-nonmetal materials can be combined. Methods of coating preparation include, but are not limited to, physical vapor deposition (PVD), chemical deposition, sol-gel dip coating, etc., with a variety of different conditions for preparing the coatings. In the reviewed studies, a wide variety of surface coatings were found to be effective. However, the present coating materials have not yet achieved a perfect combination of these three functions, and their safety and durability need further verification. This paper reviews and summarizes the effectiveness, advantages and disadvantages, and clinical perspectives of different coating materials for orthodontic appliances in terms of friction reduction, antibacterial properties, and enhanced corrosion resistance, and discusses more possibilities for follow-up studies as well as for clinical applications in detail.
Attention Multihop Graph and Multiscale Convolutional Fusion Network for Hyperspectral Image Classification

Convolutional neural networks (CNNs) for hyperspectral image (HSI) classification have generated good progress. Meanwhile, graph convolutional networks (GCNs) have also attracted considerable attention by using unlabeled data, broadly and explicitly exploiting correlations between adjacent parcels. However, the CNN with a fixed square convolution kernel is not flexible enough to deal with irregular patterns, while the GCN using the superpixel to reduce the number of nodes will lose the pixel-level features, and the features from the two networks are always partial. In this article, to make good use of the advantages of CNN and GCN, we propose a novel multiple feature fusion model termed attention multihop graph and multiscale convolutional fusion network (AMGCFN), which includes two subnetworks of multiscale fully CNN and multihop GCN to extract the multilevel information of HSI. Specifically, the multiscale fully CNN aims to comprehensively capture pixel-level features with different kernel sizes, and a multihead attention fusion module (MAFM) is used to fuse the multiscale pixel-level features. The multihop GCN systematically aggregates the multihop contextual information by applying multihop graphs on different layers to transform the relationships between nodes, and an MAFM is adopted to combine the multihop features. Finally, we design a cross-attention fusion module (CAFM) to adaptively fuse the features of two subnetworks. The AMGCFN makes full use of multiscale convolution and multihop graph features, which is conducive to the learning of multilevel contextual semantic features. Experimental results on three benchmark HSI datasets show that the AMGCFN has a better performance than a few state-of-the-art methods. Code: https://github.com/EdwardHaoz/IEEE_TGRS_AMGCFN.
Fast Determination of Transient Short-Circuit Current of PM Synchronous Machines via Magnetostatic Flux Maps

The three-phase symmetric short-circuit is a reference fault condition for the qualification of a newly designed permanent magnet (PM) synchronous machine against the risk of irreversible demagnetization. To date, the accurate determination of the peak transient short-circuit current condition requires coupled circuital and transient Finite Element Analysis (FEA) to properly account for magnetic saturation, and several simulations to determine the worst-case pre-fault conditions. This work presents a method for the fast evaluation of the transient short-circuit current of a PM synchronous machine by manipulation of extended flux linkage maps, obtained with magnetostatic FEA or experimental measures. Besides providing a fast and accessible computational tool, the flux-map based method gives insights into the effect of pre-fault conditions, showing that the higher the pre-fault torque and thus flux amplitude, the higher the transient peak current after a fault. Moreover, the paper shows that braking is a more severe pre-fault condition. Finally, the hyper-worst-case short circuit current is also defined as the magnetic property of the machine under test and computed by means of quick FEA iterations without the need for pre-determined flux-linkage maps. The proposed methods are validated against transient FEA using a commercial software and verified experimentally on a commercial motor for traction applications.
Deep Learning Network for Vision-Based UAV Recognition

Unmanned aerial vehicles (UAVs) could be used carelessly or intentionally, which poses a serious risk to aviation safety. This makes automated UAV detection and tracking a crucial component of aerial security systems. Radar, radio frequency, and visible- and thermal-infrared imaging are often used technologies for UAV detection. Deep neural networks (DNNs) for the image-based object recognition have recently made advancements that make it possible to leverage For this detecting and tracking challenge, visual data. Through the encoder-decoder structure network, the suggested technique first estimates the distribution line area. The interference is eliminated using sampling clustering and image processing operations. Finally, based on the discovered distribution line, the UAV direction of tracking of the power line is determined. The approach can achieve a frame inspection speed of over 78 ms, a heading error of deviation range of (−1.53°, 1.36°), and a tracking rate of almost 99%. The results of the network and inspection technique dataset test demonstrate that the approach presented in this research may be used to check UAV distribution lines efficiently, swiftly, and accurately.
DGD Tolerant Fast RSOP Tracking Scheme based on Multi-symbol Extended Kalman Filter in 16QAM Coherent Optical systems

This paper proposes a scheme of tracking fast rotation of the state of polarization (RSOP), which uses multi symbols to update the parameters of extended Kalman filter (EKF) and thus significantly improves differential group delay (DGD) tolerance. Simulation results of polarization division multiplexing 16-ary quadrature amplitude modulation (PDM16QAM) systems indicate that the proposed scheme has better DGD tolerance capability than the symbol-by-symbol traditional EKF algorithm, and it increases the maximum tolerance of DGD from 6ps to 14ps with the 9Mrad/s RSOP.
DPK: Deep Neural Network Approximation of the First Piola-Kirchhoff Stress

This paper presents a speciﬁc network architecture for approximation of the ﬁrst Piola-Kirchhoff stress. The neural network enables us to construct the constitutive relation based on both macroscopic observations and atomistic simulation data. In contrast to traditional deep learning models, this architecture is intrinsic symmetric, guarantees the frame-indifference and material-symmetry of stress. Speciﬁcally, we build the approximation network inspired by the Cauchy-Born rule and virial stress formula. Several numerical results and theory analyses are presented to illustrate the learnability and effectiveness of our network
Application of actor level social characteristic indicator selection for the precursory detection of bullies in online social networks

Bullying is a national problem for families, courts, schools, and the economy. Social, educational, and professional lives of victims are affected. Early detection of bullies mitigates destructive effects of bullying. Our previous research found, given specific characteristics of an actor, actor logics can be developed utilizing input from natural language processing and graph analysis. Given similar characteristics of cyberbullies, in this paper, we create specific actor logics and apply these to a select social media dataset for the purpose of rapid identification of cyberbullying.
Human appropriation of net primary production as driver of change in landscape‐scale vertebrate richness

Abstract Aim Land use is the most pervasive driver of biodiversity loss. Predicting its impact on species richness (SR) is often based on indicators of habitat loss. However, the degradation of habitats, especially through land‐use intensification, also affects species. Here, we evaluate whether an integrative metric of land‐use intensity, the human appropriation of net primary production, is correlated with the decline of SR in used landscapes across the globe. Location Global. Time period Present. Major taxa studied Birds, mammals and amphibians. Methods Based on species range maps (spatial resolution: 20 km × 20 km) and an area‐of‐habitat approach, we calibrated a “species–energy model” by correlating the SR of three groups of vertebrates with net primary production and biogeographical covariables in “wilderness” areas (i.e., those where available energy is assumed to be still at pristine levels). We used this model to project the difference between pristine SR and the SR corresponding to the energy remaining in used landscapes (i.e., SR loss expected owing to human energy extraction outside wilderness areas). We validated the projected species loss by comparison with the realized and impending loss reconstructed from habitat conversion and documented by national Red Lists. Results Species–energy models largely explained landscape‐scale variation of mapped SR in wilderness areas (adjusted R 2‐values: 0.79–0.93). Model‐based projections of SR loss were lower, on average, than reconstructed and documented ones, but the spatial patterns were correlated significantly, with stronger correlation in mammals (Pearson's r = 0.68) than in amphibians (r = 0.60) and birds (r = 0.57). Main conclusions Our results suggest that the human appropriation of net primary production is a useful indicator of heterotrophic species loss in used landscapes, hence we recommend its inclusion in models based on species–area relationships to improve predictions of land‐use‐driven biodiversity loss.
The Length of Hospital Stay of Patients with Venous Thromboembolism: A Cross-Sectional Study from Jordan

Background and Objectives: Venous thromboembolism is one of the leading causes of mortality and disability worldwide. Treatment with anticoagulation therapy is essential and requires a delicate approach to select the most appropriate option to improve patient outcomes, including the length of hospital stay (LOS). The aim of this study was to determine the LOS among patients with acute onset of VTE in several public hospitals in Jordan. Materials and Methods: In this study, we recruited hospitalized patients with a confirmed diagnosis of VTE. We reviewed the electronic medical records and charts of VTE admitted patients in addition to a detailed survey to collect the patients’ self-reported data. Hospital LOS was categorized into three levels: 1–3 days, 4–6 days, and ≥7 days. An ordered logistic regression model was used to study the significant predictors of LOS. Results: A total of 317 VTE patients were recruited, with 52.4% of them were male and 35.3% aged between 50 and 69 years. Most patients had a deep vein thrombosis (DVT) diagnosis (84.2%), and most of the VTE cases were admitted for the first-time (64.6%). The majority of the patients were smokers (57.2%), overweight/obese (66.3%), and hypertensive (59%). Most of the VTE patients received Warfarin overlapped with low molecular weight heparins as their treatment regimen (>70%). Almost half of the admitted VTE patients (45%) were hospitalized for at least 7 days. Longer LOS was significantly associated with hypertension. Conclusions: We recommend using therapies that have been proven to reduce hospital LOS, such as non-vitamin K antagonist oral anticoagulants or direct oral anticoagulants, to treat VTE patients in Jordan. Additionally, preventing and controlling comorbidities such as hypertension is essential.
Effects of dried chaya leaf meal inclusion in the diet on growth performance and blood profiles in Thai native chicken (Pradu Hangdum)

Objective: The objective of the present study was to evaluate the effects of using dried chaya leaf meal (DCLM) as a protein feed in a diet on growth performance, blood profiles, and carcass quality in a native Thai chicken. Materials and Methods: Eighty 14-day-old Pradu Hangdum chicks were divided into four groups with four replicates each: control (without DCLM), 10%, 20%, and 30% DCLM inclusion in mash feed. Growth performance was recorded weekly until 98 days of age. Blood profile, carcass quality, and visceral organ weight were measured at 98 days of age. Results: The 10%–30% DCLM inclusion in the diet did not affect feed intake or feed efficiency; however, the body weight gain of chicks decreased linearly with the increase in DCLM inclusion. The heterophils, eosinophils, and monocytes were linearly increased with the increasing DCLM levels in the groups. The serum blood chemistry did not differ among the groups, while the AST in 10% and 20% DCLM was lower than in the control. Increasing the level of DCLM in the chicken diet did not affect carcass quality. Conclusion: The DCLM can be used as a feed ingredient in Thai native chicken feed up to 20%.
Dark Sky Simulations Collaboration

The Dark Sky Simulations are an ongoing series of cosmological N-body simulations designed to provide a quantitative and accessible model of the evolution of the large-scale Universe. Cosmological simulations are the cornerstone of theoretical analysis of structure in the Universe from scales of kiloparsecs to gigaparsecs. Predictions from numerical models are critical to almost every aspect of the studies of dark matter and dark energy, due to the intrinsically non-linear gravitational evolution of matter. During the next few years, projects such as Pan-STARRS, the South Pole Telescope (SPT) and the Dark Energy Survey (DES) will measure the spatial distribution of large-scale structure in enormous volumes of space across billions of years of cosmic evolution. At the other extreme (sub-galactic and galactic scales from 100 parsecs to a megaparsec) understanding the distribution of dark matter within Milky Way type halos is necessary to interpret the results of Earth-based dark matter detection experiments. The revolutionary transformation of cosmology from a qualitative to a quantitative science has occurred over just the last twenty years. Driven by a diverse suite of observations, the parameters describing the large-scale Universe are now known to near 1% precision. Yet, the precise nature of dark matter and dark energy remain a mystery, and are unquestionably among the most important unsolved problems in physics. Advances in modeling must keep pace with observational advances if we are to understand the Universe which led to these observations. based on the widely-adopted yt project, we propose a far-reaching set of scienti(cid:12)c goals. to advance the state-of-the-art in domain decomposition and hierarchical tree-based computational techniques relevant to many simulation and data analysis problems. We will address a wide range of scienti(cid:12)cally relevant tests of the standard cosmological model, including measurements of cluster abundance, void statistics, baryon acoustic oscilla-tions, redshift-space distortions, velocity statistics and gravitational lensing. At small scales, we will test the abundance and central kinematics of the dwarf spheroidal galaxy satellites and related small-scale gravitational physics which determine the expected signal for dark matter detection experiments. Our simulations will produce an unprecedented suite of ac-curate and reliable halo, sub-halo and mock galaxy catalogs, which we will make publicly available.
Inflammatory Response and Activation of Coagulation after COVID-19 Infection

SARS-CoV-2 (COVID-19) infection is responsible for causing a disease with a wide spectrum of clinical presentations. Predisposition to thromboembolic disease due to excessive inflammation is also attributed to the disease. The objective of this study was to characterize the clinical and laboratory aspects of hospitalized patients, in addition to studying the pattern of serum cytokines, and associate them with the occurrence of thromboembolic events. Methodology: A retrospective cohort study with 97 COVID-19 patients hospitalized from April to August 2020 in the Triângulo Mineiro macro-region was carried out. A review of medical records was conducted to evaluate the clinical and laboratory aspects and the frequency of thrombosis, as well as the measurement of cytokines, in the groups that presented or did not present a thrombotic event. Results: There were seven confirmed cases of thrombotic occurrence in the cohort. A reduction in the time of prothrombin activity was observed in the group with thrombosis. Further, 27.8% of all patients had thrombocytopenia. In the group that had thrombotic events, the levels of IL1b, IL-10, and IL2 were higher (p < 0.05). Conclusions: In the studied sample, there was an increase in the inflammatory response in patients with thrombotic events, confirmed by the increase in cytokines. Furthermore, in this cohort, a link was observed between the IL-10 percentage and an increased chance of a thrombotic event.
Short-term power load forecasting based on similar day selection and improved LSTM

The electric power industry is the most important basic energy industry in the development of the national economy. The operation control and dispatch of the electric power system is of great significance in ensuring the planning of the electric power system, industrial development, economic operation and environmental protection. Short-term power load forecasting plays an important role in power system operation control and dispatch. This paper proposes a combined power load forecasting model based on similar day selection and improved LSTM. First, a combined similar day screening model based on grey relational analysis and cosine similarity is constructed, which makes up for the shortcomings of the single selection method. The training set and test set ensure the quality of the input data of the model; then use the single-feature time series training to increase the dropout layer of the LSTM model for power load prediction, reduce the resource consumption of training and prediction, and effectively alleviate the occurrence of overfitting. , to improve the robustness of the model. The prediction results of the two examples basically coincide with the real value in trend, which confirms that the similar daily screening and improved LSTM combined prediction model constructed in this paper can provide reliable support for power load forecasting and other time-series data forecasting applications.
Metaplectic stacks and vector-valued modular forms of half-integral weight

In this thesis we give a geometric theory of vector-valued modular forms attached to Weil representations, and in particular of modular forms of half-integral weight. More specifically, we construct over the integers a ‘metaplectic’ stack of elliptic curves and vector bundles Vm ⊗ ω over it, k ∈ Z, whose sections over the complex numbers give weight k/2 vectorvalued modular forms attached to rank 1 lattices with quadratic form x 7→ mx/2, for m ∈ 2Z>0. The metaplectic stack is the stack of elliptic curves endowed with a non-degenerate rank one quadratic form. It is canonically endowed with a square root ω of the Hodge bundle ω of the moduli stack of elliptic curves. The vector bundles Vm are obtained from the Schrödinger representations of Heisenberg groups of elliptic curves: though the Vm’s do not exist over the moduli stack of elliptic curves, we show that they can be defined over the metaplectic stack. We can then define q-expansions of vector-valued modular forms by pulling back sections of Vm ⊗ ω to Tate curves endowed with a quadratic form. We then find a canonical isomorphism between Vm ⊗ ω−1/2 and the ‘geometric’ representations Jm of Heisenberg groups, given by the sections of totally symmetric invertible sheaves of degree m on elliptic curves. Using this isomorphism, we are able to give entirely geometric constructions of the classical level m single-variable theta functions and their theta constants, and prove that they are indeed vector-valued modular forms of half-integral weight, in our algebro-geometric sense. We also compute their q-expansions and show that they agree with the classical analytic q-expansions. In the case m = 2, we obtain a geometric theory of modular forms of half-integral weight, as defined by Shimura. Finally we show that over the category of analytic spaces, the previous constructions recover the usual notions of vector-valued modular forms and modular forms of half-integral weight. In particular, the canonical isomorphism between Vm ⊗ ω−1/2 and Jm turns into a well-known theorem of Eichler and Zagier relating vector-valued modular forms to Jacobi forms, and can be used to give a purely geometric proof of the analytic transformation laws of single-variable theta functions.