Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification and damping of low-frequency oscillations based on WAMS data and the revisited residue method – part I

The results of low-frequency oscillations identification in the Republic of Kazakhstan power grid by using a Wide Area Measurement System are presented and an algorithm for damping low-frequency oscillations is proposed in this paper. Analysis of weakly damped inter-area low-frequency oscillations revealed a constant mode with a frequency range of 0.3‒0.4 Hz. It was determined that at these low-frequency oscillations, the amplitude of active power fluctuations along the transmission line was 150 MW with a duration of 9 minutes. The modal analysis calculation of the Republic of Kazakhstan power system model in the «DigSilent Power Factory» software shows the dangerous low-frequency oscillation modes having a damping ratio is 2.2 % and an eigenfrequency 0.328 Hz. These oscillation modes identified by the real data and in the developed model indicate the incorrect tuning of power system stabilizer parameters at power plants. It is necessary to retune the power system stabilizer parameters whenever changing the system’s and mode’s configurations. An analysis of existing power system stabilizer tuning methods was performed, and revisited residue method was determined as sufficiently effective. Thus, the developed algorithm for identification and damping of low-frequency oscillation consists of three tasks. The first task is data collection from the Wide Area Measurement System and Supervisory Control and Data Acquisition system and updating the calculation model based on the current status of equipment (generators, transformers, transmission lines, etc.). The second task is the identification of dangerous electromechanical oscillations and modal analysis based on information obtained in real-time. The third task is tuning the power system stabilizer parameters for damping dangerous low-frequency oscillation modes based on the revisited residue method
Novel Indoline Spiropyrans Based on Human Hormones β-Estradiol and Estrone: Synthesis, Structure, Chromogenic and Cytotoxic Properties

The introduction of a switchable function into the structure of a bioactive compound can endow it with unique capabilities for regulating biological activity under the influence of various types of external stimuli, which makes such hybrid compounds promising objects for photopharmacology, targeted drug delivery and bio-imaging. This work is devoted to the synthesis and study of new spirocyclic derivatives of important human hormones—β-estradiol and estrone—possessing a wide range of biological activities. The obtained hybrid compounds represent an indoline spiropyrans family, a widely known class of organic photochromic compounds. The structure of the compounds was confirmed by 1H and 13C NMR, IR, HRMS and single-crystal X-ray analysis. The intermolecular interactions in the crystals of spiropyran (3) were defined by Hirshfeld surfaces and 2D fingerprint plots, which were successfully acquired from CrystalExplorer (v21.5). All target hybrids demonstrated pronounced activity in the visible region of the spectrum. The mechanisms of thermal isomerization processes of spiropyrans and their protonated merocyanine forms were studied by DFT methods, which revealed the energetic advantage of the protonation process with the formation of a β-cisoid CCCH conformer at the first stage and its further isomerization to more stable β-transoid forms. The proposed mechanism of acidochromic transformation was confirmed by the additional NMR study data that allowed for the detecting of the intermediate CCCH isomer. The study of the short-term cytotoxicity of new spirocyclic derivatives of estrogens and their 2-formyl-precursors was performed on the HeLa cell model. The precursors and spiropyrans differed in toxicity, suggesting their variable applicability in novel anti-cancer technologies.
Profiling the Urobiota in a Pediatric Population with Neurogenic Bladder Secondary to Spinal Dysraphism

The human bladder has been long thought to be sterile until that, only in the last decade, advances in molecular biology have shown that the human urinary tract is populated with microorganisms. The relationship between the urobiota and the development of urinary tract disorders is now of great interest. Patients with spina bifida (SB) can be born with (or develop over time) neurological deficits due to damaged nerves that originate in the lower part of the spinal cord, including the neurogenic bladder. This condition represents a predisposing factor for urinary tract infections so that the most frequently used approach to treat patients with neurogenic bladder is based on clean intermittent catheterization (CIC). In this study, we analyzed the urobiota composition in a pediatric cohort of patients with SB compared to healthy controls, as well as the urobiota characteristics based on whether patients received CIC or not.
Comparison of the Effectiveness of Interpersonal Counseling and Interpersonal Psychotherapy in Emotional Expression, Social Skills, and Depression Symptoms in Students

Background: Depression symptoms are among the most common psychological problems in students. Short-term treatments are important in preventing depression from turning into a disorder. Objectives: The current study aimed to investigate the effectiveness of interpersonal counseling (IPC) in depression symptoms, emotional expression, and social skills of students in comparison to interpersonal psychotherapy (IPT). Methods: A pretest and posttest design with follow-up was used in this study, with two experimental groups and a control group conducted in Mashhad, Iran, in 2021. A total of 51 subjects who scored 14 or higher on the Beck Depression Inventory-Second Edition (BDI-II) were randomly assigned to two experimental and control groups. Finally, the information obtained from 41 subjects was analyzed. The experimental groups underwent 7 sessions of 45 minutes of IPC and 12 sessions of 90 minutes of IPT every week. At the beginning of the study, after the end of the intervention, and after one- and three-month follow-up periods, all the subjects were evaluated with the BDI-II, emotional expressiveness, and social skills questionnaires. The findings were analyzed using the repeated measurement method and Kruskal-Wallis and Mann-Whitney U tests. All the analyses were carried out with SPSS software (version 23). Results: Both IPC and IPT treatments were effective in depression symptoms (M: 31.76, 20.41, 22.94, and 24), emotional expression (M: 32.94, 40.26, 38.47, and 37.23), and social skills (M: 224.9, 265.2, 254.4, and 253.7) (P < 0.05). The comparison of the two treatments showed no significant difference in the variables (P > 0.05). Conclusions: The findings of the present study can be considered a useful step in the field of short-term and effective interventions.
3D Robust Gait Recognition Based on Manifold Learning

Aiming at the situation that many approaches for gait recognition are based on a single camera, an approach of gait recognition based on stereo vision is proposed. Firstly, 3D coordinates of human body contour are gotten by stereo matching. Then, 3D body contour descriptor (3D-BCD) is constructed to get the gait feature of human. The noise and glitch are eliminated by noise-eliminated method. Thus, manifold learning (Laplacian Eigenmaps) is used for dimensionality reduction. The nearest neighbor classifier (NN) and the nearest neighbor classifier about template (TNN) are used for classifying category. Finally, a series of experiment results on the stereo gait database of PRLABⅡ and the irregular test stereo gait dataset ExN proved out the high correct classification rate and the strong robustness of the proposed approach. And the approach is not related with the distance between camera and the walking path. Moreover, it has stronger robustness with the incomplete gait sequences, the changes of human behavior, the changes of apparels, and carrying a bag.
Extended Propagator Method for Direction Estimation of Noncircular Signals

Noncircular signals are widely used in modern communication systems.The performance of classical high resolution al- gorithms for noncircular signals is not so good because the noncircular information is not used.A new direction finding algorithm called Extended Propagator Method (EPM) for noncircular signals is proposed in this paper.It can detect more signals than sensors and has better performance (in terms of resolution and angular precision) than OPM (orthonormal-PM) and MUSIC algorithms.Meanwhile it has the advantages of PM which needs no ED or SVD.The mean square error (MSE) performance of EPM is analyzed and the closed form expression of MSE is given in this paper.The computational complexity of EPM is analyzed also in this paper.Simulation results show the better performance of EPM than OPM and MUSIC algorithms and the accordance between the theoretical results and the simulation results.
Double-Diffusive Convection in a Porous Medium, Nonlinear Stability, and the Brinkman Effect

This paper establishes a nonlinear energy stability theory for the double-diffusive convection in a porous medium when both viscosity and thermal expansion coefficient are allowed to vary with temperature. After presenting a nonlinear stability theorem, a variational problem is formulated and the numerical solutions via the compoound matrix method are carried out. It is noted that higher values of the thermal coefficient and higher values of the viscosity ratio have the effect of delaying the onset of convection.
Application of curvelet transform for denoising of CT images

The paper presents a method of noise reduction in CT images by the curvelet transform. Noise affects the ability to visualize pathologic qualities and the living tissues structure in CT. Noise in CT images depends on the amount of discrete x-ray photons reaching the detector. In the CT images, noise is responsible for visibility reduction the low contrast areas and objects. Noisy picture may not be properly interpreted by a physician, especially for the case of detection of pathological changes in tissues. The tests were performed with the Shepp-Logan test image with additive Gaussian noise.
Difficult-to-Treat Pathogens: A Review on the Management of Multidrug-Resistant Staphylococcus epidermidis

Multidrug-resistant Staphylococcus epidermidis (MDRSE) is responsible for difficult-to-treat infections in humans and hospital-acquired-infections. This review discusses the epidemiology, microbiology, diagnosis, and treatment of MDRSE infection and identifies knowledge gaps. By using the search term “pan resistant Staphylococcus epidermidis” OR “multi-drug resistant Staphylococcus epidermidis” OR “multidrug-resistant lineages of Staphylococcus epidermidis”, a total of 64 records have been identified from various previously published studies. The proportion of methicillin resistance in S. epidermidis has been reported to be as high as 92%. Several studies across the world have aimed to detect the main phylogenetic lineages and antibiotically resistant genes through culture, mass spectrometry, and genomic analysis. Molecular biology tools are now available for the identification of S. epidermidis and its drug resistance mechanisms, especially in blood cultures. However, understanding the distinction between a simple colonization and a bloodstream infection (BSI) caused by S. epidermidis is still a challenge for clinicians. Some important parameters to keep in mind are the number of positive samples, the symptoms and signs of the patient, the comorbidities of the patient, the presence of central venous catheter (CVC) or other medical device, and the resistance phenotype of the organism. The agent of choice for empiric parenteral therapy is vancomycin. Other treatment options, depending on different clinical settings, may include teicoplanin, daptomycin, oxazolidinones, long-acting lipoglycopeptides, and ceftaroline. For patients with S. epidermidis infections associated with the presence of an indwelling device, assessment regarding whether the device warrants removal is an important component of management. This study provides an overview of the MDRSE infection. Further studies are needed to explore and establish the most correct form of management of this infection.
The Reliability of the Modified Fels Knee Skeletal Maturity System

Background: The recently described Modified Fels knee skeletal maturity system (mFels) has proven utility in prediction of ultimate lower extremity length in modern pediatric patients. mFels users evaluate chronological age, sex, and 7 anteroposterior knee radiographic parameters to produce a skeletal age estimate. We developed a free mobile application to minimize the learning curve of mFels radiographic parameter evaluation. We sought to identify the reliability of mFels for new users. Methods: Five pediatric orthopaedic surgeons, 5 orthopaedic surgery residents, 3 pediatric orthopaedic nurse practitioners, and 5 medical students completely naïve to mFels each evaluated a set of 20 pediatric anteroposterior knee radiographs with the assistance of the (What’s the Skeletal Maturity? ) mobile application. They were not provided any guidance beyond the instructions and examples embedded in the app. The results of their radiographic evaluations and skeletal age estimates were compared with those of the mFels app developers. Results: Averaging across participant groups, inter-rater reliability for each mFels parameter ranged from 0.73 to 0.91. Inter-rater reliability of skeletal age estimates was 0.98. Regardless of group, steady proficiency was reached by the seventh radiograph measured. Conclusions: mFels is a reliable means of skeletal maturity evaluation. No special instruction is necessary for first time users at any level to utilize the (What’s the Skeletal Maturity? ) mobile application, and proficiency in skeletal age estimation is obtained by the seventh radiograph. Level of Evidence: Level II.
Reduction of Class Activation Uncertainty with Background Information

Multitask learning is a popular approach to training high-performing neural networks with improved generalization. In this paper, we propose a background class to achieve improved generalization at a lower computation compared to multitask learning to help researchers and organizations with limited computation power. We also present a methodology for selecting background images and discuss potential future improvements. We apply our approach to several datasets and achieved improved generalization with much lower computation. We also investigate class activation mappings (CAMs) of the trained model and observed the tendency towards looking at a bigger picture in a few class classification problems with the proposed model training methodology. Applying transformer with the proposed background class, we receive state-of-the-art (SOTA) performance on STL-10, Caltech-101, and CINIC-10 datasets. Example scripts are available in the `CAM' folder of the following GitHub Repository: github.com/dipuk0506/UQ
Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation

A surge of interest has emerged in weakly supervised semantic segmentation due to its remarkable efficiency in recent years. Existing approaches based on transformers mainly focus on exploring the affinity matrix to boost CAMs with global relationships. While in this work, we first perform a scrupulous examination towards the impact of successive affinity matrices and discover that they possess an inclination toward sparsification as the network approaches convergence, hence disclosing a manifestation of over-smoothing. Besides, it has been observed that enhanced attention maps tend to evince a substantial amount of extraneous background noise in deeper layers. Drawing upon this, we posit a daring conjecture that the undisciplined over-smoothing phenomenon introduces a noteworthy quantity of semantically irrelevant background noise, causing performance degradation. To alleviate this issue, we propose a novel perspective that highlights the objects of interest by investigating the regions of the trait, thereby fostering an extensive comprehension of the successive affinity matrix. Consequently, we suggest an adaptive re-activation mechanism (AReAM) that alleviates the issue of incomplete attention within the object and the unbounded background noise. AReAM accomplishes this by supervising high-level attention with shallow affinity matrices, yielding promising results. Exhaustive experiments conducted on the commonly used dataset manifest that segmentation results can be greatly improved through our proposed AReAM, which imposes restrictions on each affinity matrix in deep layers to make it attentive to semantic regions.
Curating corpora with classifiers: A case study of clean energy sentiment online

Well curated, large-scale corpora of social media posts containing broad public opinion offer an alternative data source to complement traditional surveys. While surveys are effective at collecting representative samples and are capable of achieving high accuracy, they can be both expensive to run and lag public opinion by days or weeks. Both of these drawbacks could be overcome with a real-time, high volume data stream and fast analysis pipeline. A central challenge in orchestrating such a data pipeline is devising an effective method for rapidly selecting the best corpus of relevant documents for analysis. Querying with keywords alone often includes irrelevant documents that are not easily disambiguated with bag-of-words natural language processing methods. Here, we explore methods of corpus curation to filter irrelevant tweets using pre-trained transformer-based models, fine-tuned for our binary classification task on hand-labeled tweets. We are able to achieve F1 scores of up to 0.95. The low cost and high performance of fine-tuning such a model suggests that our approach could be of broad benefit as a pre-processing step for social media datasets with uncertain corpus boundaries.
Online Gesture Recognition using Transformer and Natural Language Processing

The Transformer architecture is shown to provide a powerful machine transduction framework for online handwritten gestures corresponding to glyph strokes of natural language sentences. The attention mechanism is successfully used to create latent representations of an end-to-end encoder-decoder model, solving multi-level segmentation while also learning some language features and syntax rules. The additional use of a large decoding space with some learned Byte-Pair-Encoding (BPE) is shown to provide robustness to ablated inputs and syntax rules. The encoder stack was directly fed with spatio-temporal data tokens potentially forming an infinitely large input vocabulary, an approach that finds applications beyond that of this work. Encoder transfer learning capabilities is also demonstrated on several languages resulting in faster optimisation and shared parameters. A new supervised dataset of online handwriting gestures suitable for generic handwriting recognition tasks was used to successfully train a small transformer model to an average normalised Levenshtein accuracy of 96% on English or German sentences and 94% in French.
Shared Latent Space by Both Languages in Non-Autoregressive Neural Machine Translation

Latent variable modeling in non-autoregressive neural machine translation (NAT) is a promising approach to mitigate the multimodality problem. In the previous works, they added an auxiliary model to estimate the posterior distribution of the latent variable conditioned on the source and target sentences. However, it causes several disadvantages, such as redundant information extraction in the latent variable, increasing parameters, and a tendency to ignore a part of the information from the inputs. In this paper, we propose a new latent variable modeling that is based on a dual reconstruction perspective and an advanced hierarchical latent modeling approach. Our proposed method, {\em LadderNMT}, shares a latent space across both languages so that it hypothetically alleviates or solves the above disadvantages. Experimental results quantitatively and qualitatively demonstrate that our proposed latent variable modeling learns an advantageous latent space and significantly improves translation quality in WMT translation tasks.
Personalized crime location prediction

Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper, we present CrimeTracer1, a personalized random walk-based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behaviour of known offenders within their activity spaces. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently select targets in or near places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CrimeTracer outperforms all other methods used for location recommendation we evaluate here.
Robust Model Predictive Techno-Economic Control of Active Distribution Networks

Stochastic controllers are perceived as a promising solution for techno-economic operation of distribution networks having higher generation uncertainties at large penetration of renewables. These controllers are supported by forecasters capable of predicting generation uncertainty by means of lower/upper bounds rather than by probability density function (PDF). Hence, the stochastic controller assumes a suitable PDF for scenario creation and optimization, requiring validation of the assumption. To effectively bridge the forecaster’s capability and resolve the assumption issues, the paper proposes a robust model prediction-based techno-economic controller, which essentially utilizes only the lower/upper bounds of the forecast, eliminating the necessity of PDF. Both discrete and continuous control resources such as tap-changers and DERs are utilized for regulating the lower/upper bounds of the network states and robustly minimizing the cost of energy import. The proposed controller is implemented for UKGDS network and validated by comparing performance at various confidence levels of lower/upper bound forecast.
HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration

Feature Descriptors and Detectors are two main components of feature-based point cloud registration. However, little attention has been drawn to the explicit representation of local and global semantics in the learning of descriptors and detectors. In this paper, we present a framework that explicitly extracts dual-level descriptors and detectors and performs coarse-to-fine matching with them. First, to explicitly learn local and global semantics, we propose a hierarchical contrastive learning strategy, training the robust matching ability of high-level descriptors, and refining the local feature space using low-level descriptors. Furthermore, we propose to learn dual-level saliency maps that extract two groups of keypoints in two different senses. To overcome the weak supervision of binary matchability labels, we propose a ranking strategy to label the significance ranking of keypoints, and thus provide more fine-grained supervision signals. Finally, we propose a global-to-local matching scheme to obtain robust and accurate correspondences by leveraging the complementary dual-level features. Quantitative experiments on 3DMatch and KITTI odometry datasets show that our method achieves robust and accurate point cloud registration and outperforms recent keypoint-based methods. [code release]