Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-term Decomposition Method for Loss Allocation in Distribution Systems Considering Load Power Factor

Abstract The customers of distribution system are generally encouraged to maintain better power factor through suitable tariff structures. The feeder power loss allocation is a part of tariff. The feeder power losses also depend upon power factor of loads. Therefore, loss allocation method should incorporate suitable rewarding and penalizing strategy for better and poor factors loads. This paper proposes a new distribution loss allocation method that considers load power factor to allocate losses in a more practical way. The proposed method employs bifurcation of cross terms of branch power loss among contributing nodes. The method provides loss allocation to system nodes in such a way that it provides rebates/penalties against variation in load power factor only to the concerned nodes. The proposed method is applied to 30-bus and 33-bus test distribution system and compared with other established methods. The analysis of the application results highlights the importance of the proposed method.
BRCA1-associated R-loop affects transcription and differentiation in breast luminal epithelial cells

Abstract BRCA1-associated basal-like breast cancer originates from luminal progenitor cells. Breast epithelial cells from cancer-free BRCA1 mutation carriers are defective in luminal differentiation. However, how BRCA1 deficiency leads to lineage-specific differentiation defect is not clear. BRCA1 is implicated in resolving R-loops, DNA-RNA hybrid structures associated with genome instability and transcriptional regulation. We recently showed that R-loops are preferentially accumulated in breast luminal epithelial cells of BRCA1 mutation carriers. Here, we interrogate the impact of a BRCA1 mutation-associated R-loop located in a putative transcriptional enhancer upstream of the ERα-encoding ESR1 gene. Genetic ablation confirms the relevance of this R-loop-containing region to enhancer-promoter interactions and transcriptional activation of the corresponding neighboring genes, including ESR1, CCDC170 and RMND1. BRCA1 knockdown in ERα+ luminal breast cancer cells increases intensity of this R-loop and reduces transcription of its neighboring genes. The deleterious effect of BRCA1 depletion on transcription is mitigated by ectopic expression of R-loop-removing RNase H1. Furthermore, RNase H1 overexpression in primary breast cells from BRCA1 mutation carriers results in a shift from luminal progenitor cells to mature luminal cells. Our findings suggest that BRCA1-dependent R-loop mitigation contributes to luminal cell-specific transcription and differentiation, which could in turn suppress BRCA1-associated tumorigenesis.
On the Brink of Disaster Vulnerability to Mental Disorders

The notions of at-risk and subthreshold conditions are increasingly discussed in psychiatry to describe mild, brief, or otherwise atypical syndromes that fail to meet the criteria for clinical relevance. However, the concept of vulnerability is still underexplored in philosophy of psychiatry. This article discusses psychiatric vulnerability to clarify some conceptual issues about the various factors contributing to vulnerability, the notions of risk and protection, and the idea that there are multiple ways of crossing the threshold to clinical relevance. My goal is to lay the groundwork for a finer-grained discussion on psychiatric vulnerability that reflects the complex nature of mental conditions and illustrates the kind of thinking needed in clinical practice.
Morphological leaf characteristics of selected trees and its adsorbent capacity at particulate matter (PM 10 ) in Cagayan de Oro City, Philippines

The study aimed to give information about the use of trees in helping the environment particularly to the quality of air. Leaves of selected tree species planted along the busy streets in Cagayan de Oro City, Philippines were analyzed in terms of its surface area and its ability to adsorb PM 10 . Results revealed that G. arborea Roxb. exhibits the greatest leaf surface area based from the method UTHSCA software. Further, results of the study showed that among the four plant species sampled and analyzed along the road of Brgy. Kauswagan , the leaves of G. arborea Roxb. , adsorbed the highest amount of PM 10 while in Masterson Avenue, M. calabura Linn. showed the highest rate of adsorption capacity compared to other three species sampled and analyzed. Two- way ANOVA revealed that there are no significant differences between the two busiest streets and among the four species sampled and analyzed. Keywords: PM 10 , Surface leaf area, Adsorption, Air quality
A Dynamic Pricing Strategy for Vehicle Assisted Mobile Edge Computing Systems

The idle computing resources of parked vehicles could be utilized to improve performance by assisting task executions in mobile edge computing (MEC) systems. As a result, the owner of a vehicle could be compensated, resulting in a win-win situation. A dynamic pricing strategy is proposed to minimize the average cost of the MEC system under the constraints on quality of service by adjusting the price constantly based on the current system state. To do so, a cost minimization problem is solved to obtain the optimal dynamic pricing strategy efficiently. Finally, the optimization results are validated with extensive simulations.
Energy Management System and Its Implementation in Smart Grid using Renewable Energy Resources

Due to the excess usage of non-renewable resource based fuels such as petrol, diesel, coal and gas have left their natural reservoirs in a drastically depleted state. Now, mankind is finding new ways to make use of the renewable resources for the energy consumption purposes. In this paper, we have designed a smart grid which consists of PV array (solar energy resource), Fuel Cell, Battery Storage system and a fuzzy logic based Energy Management System.
An Efficient Residual Learning Neural Network for Hyperspectral Image Superresolution

Deep learning, especially a discriminative model for image reconstruction, has shown great potential for single image superresolution (SR) of hyperspectral images (HSI). For HSI SR task, it is crucial to predicting each pixel according to the surrounding context, exploiting both spatial and spectral correlation information simultaneously. In this paper, an efficient three-dimensional (3-D) HSI SR convolution neural network (CNN) based on residual learning is proposed. The network builds convolutional layers in low-resolution (LR) space and extracts the features along both spatial and spectral dimensions using 3-D dilated kernel. Then, 3-D deconvolution is employed at the last layer, which enlarges the image to the desired size. By employing multibranch and multiscale fusion in the architecture, the network can learn a better and more complex LR to high-resolution mapping. The overall network combines the global with local residual learning to reduce training difficulty and improve the performance. The design philosophy of our model is to find the best tradeoff between performance and computational cost. We train the network in an end-to-end fashion, and the experimental results of the quantitative and qualitative evaluation show that our proposed method yields satisfactory SR performance.
On the Low-Frequency Noise Characterization of Z2-FET Devices

This paper addresses the low-frequency noise characterization of Z2-FET structures. These double-gated p-i-n diode devices have been fabricated at STMicroelectronics in an ultrathin body and box (UTBB) 28-nm FDSOI technology and designed to operate as 1T-DRAM memory cells, although other applications, as for example electro static discharge (ESD) protection, have been reported. The experimentally extracted power spectral density of current reveals that the high-diode series resistance, carrier number fluctuations due to oxide traps, and gate leakage current are the main noise contributors at high-current regimes. These mechanisms are expected to contribute to the degradation of cell variability and retention time. Higher flicker noise levels have been reported when increasing the vertical electric field. A simple model considering the contribution of the main noise sources is proposed.
Premenstrual syndrome in Anand District, Gujarat: A cross-sectional survey

Aim of Study: We assessed the prevalence and severity of premenstrual syndrome (PMS) in adolescents and identified probable associations of PMS and premenstrual dysphoric disorder (PMDD) with age, locality, food habits, obesity, stress, genetic influence, menorrhagia and dysmenorrhoea. Methods: Cross-sectional study in schools of Anand District in State of Gujarat, India. We conducted the study in 1702 girls in the age group of 8–23 years who had achieved menarche. Main Outcome Measures: Prevalence of PMS and PMDD using the self-administered Premenstrual Symptoms Screening Tool for Adolescents (PSST-A). Results: The prevalence of moderate to severe PMS was 19.3% and PMDD was 4.6%. Almost all (94.8%) girls had at least one PMS symptom with 65.7% having moderate to severe symptoms. We found dysmenorrhoea in 71.2% girls and menorrhagia in 15.2%. Physical symptoms were reported by 53.5%, disruption of daily activities by 41.7%, while 25.1% had to miss school/college. Majority (81.3%) felt that PMS was a normal part of menstruation and 53.0% reported moderate to severe stress. Multivariate logistic regression model revealed older age, dysmenorrhoea, menorrhagia, high levels of stress and PMS in mother to be significantly associated with PMS. In addition to these, lower age at menarche and junk food significantly contributed to PMDD. Conclusion: Prevalence of moderate to severe PMS and PMDD in this population falls within the range reported elsewhere. PMS/PMDD affects the lives of many, significantly reducing their efficiency and worsening the quality of life.
Attack-State Estimation for Cyber-Physical Systems: A Graph Theory Perspective

Cyber-physical systems in nowadays are more vulnerable to be attacked by malicious attackers, which are generally out of touched by the users or organizers of the underlying system, due to the widespread usage on the decentralized algorithms. In current paper, we investigate the attacked state recovery problem by virtue of the graph theory perspective. We especially show that a state could be corrected even though the support of the attacked vector is larger than half of the corrupted sensor nodes, which is crucial for the sparse signal recovery problem that is extensively investigated in area of compressed sensing in the literature. Additively, two numerical examples are presented to support the theoretical results.
Least Angle Regression Coarsening in Bootstrap Algebraic Multigrid

The bootstrap algebraic multigrid framework allows for the adaptive construction of algebraic multigrid methods in situations where geometric multigrid methods are not known or not available at all. While there has been some work on adaptive coarsening in this framework in terms of algebraic distances, coarsening is the part of the adaptive bootstrap setup that is least developed. In this paper we try to close this gap by introducing an adaptive coarsening scheme that views interpolation as a local regression problem. In fact the bootstrap algebraic multigrid setup can be understood as a machine learning ansatz that learns the nature of smooth error by local regression. In order to turn this idea into a practical method we modify least squares interpolation to both avoid overfitting of the data and to recover a sparse response that can be used to extract information about the coupling strength amongst variables like in classical algebraic multigrid. In order to improve the so-found coarse grid we propose a post-processing to ensure stability of the resulting least squares interpolation operator. We conclude with numerical experiments that show the viability of the chosen approach.
Detecting False Data Injection Attacks on Modern Power Systems Based on Jensen-Shannon Distance

The modern power system is the integration and development of the physical grid and the information system. However, state estimation in the modern power system faces the rising threat of false data injection attacks, which lead to malfunction of the relay protection, dispatcher's misjudgment or faulty operation. In this paper, we propose an improved detection method to detect false data injection attacks based on the Jensen-Shannon distance. This method tracks the dynamics of measurement variations and calculates the distance between two probability distributions derived from real-time measurement variations and historical measurement variations. When false data is injected into the modern power system, the real-time probability distributions will deviate from the historical ones, thus leading to a larger Jansen-Shannon distance. The proposed method is simulated on the IEEE14-bus system using real load data. Test results demonstrate that our method can accurately detect most of the attacks, and has good stability as well as practicability.
Exploring Popularity Predictability of Online Videos With Fourier Transform

The prediction of video popularity is of significant importance to online video service providers in terms of resource provisioning, online advertisement, and video recommendation. Traditional approaches normally utilize videos’ historical popularity traces to make such a prediction. However, it is still uncertain whether the future popularity of a video is sure to be associated with its past popularity. In this paper, we explore the problem of video popularity predictability by analyzing videos’ view count traces in the frequency domain with Fourier transform. We observe that sharp turns (e.g., peaks and valleys) of view count traces cause the inaccuracy in popularity prediction, which can be seized and quantified by high-frequency components in the frequency domain. Based on the ratio of high-frequency energy, videos can be classified as the fluctuating group, which is hard for prediction, and the smooth group, which is friendly for prediction. The result is further verified via experiments with state-of-the-art predictive algorithms. Inspired by our findings, we propose a strategy to improve prediction performance by removing out-of-date traces before each sharp turn because it is highly possible that the popularity evolution trend has been altered at each sharp turn. To the end, we compare the prediction issue between videos and microblogs. Surprisingly, most microblog traces are smooth. We conjecture that video providers’ recommendation and promotion strategies are prone to causing sharp turns in view count traces. In contrast, there is no such initiative counterpart on microblog platforms changing trace evolution of microblogs frequently.
Power Quality Enhancement to Sensitive Loads with PV Based Microgrid System

Now a days not only emphasis is given on reliability of energy sources but also on cleanliness on energy sources. Microgrid based distribution energy resources are used for sensitive loads to improve the reliability of system. In current scenario of competitive energy market, photo voltaic (PV) based renewable energy resources are getting attention for low maintenance, pollution free and low running cost. So, PV based microgrid system is optimal solution for different distribution energy resources. Hysteresis current control & SPWM voltage controller is used in the proposed system for different modes of operation. The performance of this model has been studied in grid connected mode and under islanded mode. The effectiveness of proposed control strategies is studied in simulation for different grid connected modes and different loading conditions.
Sample Size Planning for Detecting Mediation Effects: A Power Analysis Procedure Considering Uncertainty in Effect Size Estimates

Abstract When planning mediation studies, researchers are often interested in the sample size needed to achieve adequate power for testing mediation. Power depends on population effect sizes, which are unknown in practice. In conventional power analysis, effect size estimates, however, are often used as population values, which could result in underpowered studies. Uncertainty in effect size estimates has been considered in other sample size planning contexts (e.g., t-test, ANOVA), but has not been handled properly for planning mediation studies. In the current study, we proposed an easy-to-use sample size planning method for testing mediation with uncertainty in effect size estimates considered. We conducted simulation studies to demonstrate the impact of uncertainty in effect size estimates on power of testing mediation, and to provide sample size suggestions under different levels of uncertainty. Empirical examples were provided to illustrate the application of our method. R functions and a web application were developed to facilitate implementation.
On consistent nonlinear analysis of soil–structure interaction problems

Abstract Nonlinear soil–linear structure computational strategy is commonly accepted in the community of geotechnical engineers using advanced finite element software for solving complex soil–structure interaction problems. However, further design procedure of the structural elements is carried out using increased values of the computed elastic stress resultants. It is absolutely not clear whether this method is conservative and, therefore, whether safe or not. To tackle this problem, a fully consistent nonlinear analysis of a deep excavation protected by the diaphragm wall is analysed here. The subsoil is modelled using the Hardening Soil model, while reinforced concrete is modelled using the modified Lee–Fenves model enhanced by the Eurocode 2 (EC2)-compatible creep module, developed by the author. It is shown that the commonly used nonlinear soil–linear structure computational strategy may yield insufficient amount of reinforcement from the ultimate limit state (ULS) and serviceability limit state (SLS) points of view. A consistent and conservative method of combining fully nonlinear analysis and the rules imposed by the EC2 is proposed.
On Energy Participation Factors in Sub-Gramians Method

Early detection of the threat of a cascading failure is encouraged to use the immune information system, the main subsystem of the which is a virtual stability analyzer. We propose to implement the virtual analyzer by usage of the linearized models of energy systems and apparatus Lyapunov direct method, based on the study of matrix Lyapunov equation solutions, called gramians. The functional of stability loss risk is defined as the square of H2 - norm of the system matrix transfer function, which is proportional to the energy of the system. We propose to use the functional of a risk assessment through thespectral decomposition of energy participation factors for the evaluating of the energy accumulated in dominant eigenvalues of the system. For sub-gramians method the energy participation factors are defined as a partion derivative of the functional regarding to the chosen parameter. In comparison with the method of a modal analysis the proposed approach allows to evaluate the impact of synergies between weak-stable eigenvalues and their combinations to the system stability loss risk, and also to detect the potential source of stability loss.
A Beam Guardrail Detection Algorithm Using Lidar for Intelligent Vehicle

In the paper, a beam guardrail detection algorithm using Lidar for intelligent vehicle is proposed. The proposed algorithm consists of feature extraction and clustering steps. The layered feature of Lidar scanning data is used to segment the point cloud into lines. Then, two novel features, which are corner feature and height feature, are developed to extract the beam guardrail points. Therefore, a clustering algorithm is used to remove some noise extracted points. To evaluate the performance of the proposed algorithm, a performance measure is defined. Computer simulations demonstrate that the proposed detection algorithm has improved performance compared to conventional methods and the proposed performance measure is meaningful and explicit.
A Stochastic Parallel Gradient Descent Algorithem for Pedestrian Re-identification

Pedestrians re-identification is a hot topic in the field of computer vision. Convolutional neural network(CNN) has achieved a good recognition effect in pedestrian re-identification. However, CNN is computationally intensive because of the vast pedestrian data and the depth of CNN training. As the requirement of higher accuracy, the training always takes days and even week. In this paper, we proposed a parallel stochastic gradient descent(SGD) algorithm, where five-hierarchy parallel structure sets up blocks based on pedestrian attributes. Moreover, the interval for updating parameters is analyzed to optimize parameter selections. Momentum and adaptive adjustment of the learning rate are also adopted in training. Our experiments results show that this parallelization method successfully speeds up the training process by five times and surpasses state-of-the-art in accuracy as well.
Dermoscopy of lichen planus: Vascular and Wickham striae variations in the skin of colour

Diagnosis and management of lichen planus (LP) and other inflammatory dermatoses in dark-skinned patients is challenging for many dermatologists, especially in Western countries. Indeed, the subspecialty of skin of colour dermatology has been proposed as an official part of the training curriculum and continuous medical education for both trainees and qualified dermatologists. Dermoscopy helps in the diagnosis of LP in fair skin, but specific dermoscopic studies in patients with skin of colour are lacking. The aim of our study was to investigate dermoscopic LP features in darkskinned patients.
A Frequency Approach to Creation of Executable File Signatures for their Identification

The paper presents methods of executable file signature creation based on frequency distributions of their informative features to be applied for program identification. Identification here should be understood as a process of file recognition by establishing its coincidence with a particular program. A new approach to creation of the archive of program signatures, both in terms of byte-frequency distribution of a program's binary code, and in terms of frequency distribution of assembler commands in their disassembler codes, is presented. The new method of executable file identification is offered and the results of experiments on their identification using a statistical criterion of φ * -Fisher and analysis of the slope are provided. The proposed method can be used to audit data-storage medium.
Target of Rapamycin kinase: central regulatory hub for plant growth and metabolism

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Target of Rapamycin kinase: central regulatory hub for plant growth and metabolism Lyubov A. Ryabova, Christophe Robaglia, Christian Meyer