Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Transformer Fault Identification Optimization Method Based on Mathematical Statistics

Most power transformer faults are caused by iron core and winding faults. At present, the method that is most widely used for transformer iron core and winding faults identification is the vibration analysis method. The vibration analysis method generally determines the degree of fault by analyzing the energy spectrum of the transformer vibration signal. However, the noise reduction step in this method is complicated and costly, and the effect of denoising needs to be further improved to make the fault identification results more accurate. In addition, it is difficult to perform an accurate determination of the early mild failure of the transformer due to the effect of noise on the results. This paper presents a novel mathematical statistics method based on the vibration signal to optimize the vibration analysis method for the short-circuit failure of the transformer winding. The proposed method was used for linear analysis of the transformer vibration signal with different degrees of short-circuit failure of the transformer winding. By comparing the slope value of the transformer vibration signal cumulative probability distribution curve and analyzing the energy spectrum of the signal, the degree of short-circuit failure of the transformer winding was identified quickly and accurately. This method also simplified the signal denoising process in transformer fault detection, improved the accuracy of fault detection, reduced the time of fault detection, and provided good predictability for early mild faults of the transformer, thereby reducing the hidden hazards of operating the power transformer. The proposed optimization procedure offers a new research idea in transformer fault identification.
Morphological characteristics of different accessions of Cynodon dactylon (L.) Pers. and physico-chemical properties of soil of their growing region in Bangladesh

Distribution as well as adaptation of Cynodon dactylon to different ecological zones of Bangladesh is mainly dependant on weather and adaphic factors of their respective habitats. Generally, it is a warm season perennial grass species that initiates growth in the vernal season and its growth continues rapidly when moisture is adequate and they find the alkaline clay soil as their habitat. From that point of view, this study was aimed at analysing the morphological variations of Cynodon dactylon ecotypes along with determining the physicochemical properties of soils from their particular habitats. A total of 19 ecotypes /accessions from four different zones of Bangladesh were collected along with the habitat soil and all of them were transplanted in experimentation field of Institute of Biological Sciences, University of Rajshahi, Bangladesh. All the accessions were established in 1.5 × 1.4 m plots separately. At maturity just after started flowering, the morphological data on 26 characters were recorded quantitatively and those were analysed statistically. Rhizomatous and stoloniferous nature was observed from randomly selected areas within the plot. Both significant and non-significant variations were found among the morphological characters. Most of the vegetative and reproductive characters were found to show significant variations among the accessions. In addition, the values obtained on physio-chemical properties of soil were tabulated and their magnitude were determined and interpreted following Fertilization Recommendation Guide. In respect of soil properties variation on morphological parameters of almost of all the accessions were observed. In this study, all the accessions were found to be adapted nicely in alkaline soil of the experimentation field and that might be due to their many physiological and biochemical mechanisms.
Dynamic Pricing with Money‐Back Guarantees

Money‐back guarantees (MBGs), which allow customers to return products that do not meet their expectations, are widely used in the retail industry. In this study, we study a retailer's MBG policy with dynamic pricing of limited inventory. A key decision for the retailer is to decide whether to offer MBGs. When the product can be returned instantly, we find that the optimal MBG policy is a simple threshold policy: given the inventory level, it is optimal to offer an MBG if and only if the remaining selling time is longer than a threshold. Moreover, the threshold is decreasing with the inventory level. We also address the problem of dynamic pricing with positive return times. Due to the complexity, we analyze the associated fluid model, which has an infinite number of constraints. We consider a series of relaxations that have a nested structure and use the Lagrangian approach to explicitly solve these relaxed problems. This allows us to develop an iterative approach that is guaranteed to solve the fluid model in finite iterations. Our numerical analysis shows that the deterministic solution is asymptotically optimal for the stochastic system.
Biomarkers of Oxidative with Colorectal Cancer

The number of colorectal cancer increases regularly every year in Algeria and precisely in Tlemcen. It is a disease involving several genetic, hormonal, professional, environmental, but also behavioral factors, namely nutrition, the objective is to evaluate the nutritional and lipid profile on the one hand and some oxidative parameters of And to determine the relationship that may exist between nutritional factors and oxidative stress in patients with this type of cancer.
Android Malware Detection Based on Static Behavior Feature Analysis

As an open source operating system, Android has groups of users and developers, which result in a great deal of Android application in app markets. But at the same time, app’s benign and malicious behavior cannot be screened, the number of new malware sample for Android platform and infected mobile phone are still soaring, Android malware detection is faced with a tough challenge. Thus this paper proposes an approach that analyze Android malware by extracting app’s static behavior, and information are captured include the use of permission, android’s components and API calls. Then we take the advantage of PCA to extract app’s principle behavior features. Finally, classifiers are trained by Android app dataset to validate the performance. Experiments show that the proposed method is capable of 97.4% true positive rate and 99.8% area under ROC, our lightweight classifiers can detect Android’s unknown malware effectively and accurately.
Real-time protocol stream migration

Method for the primary server monitor and migration to transmit the RTSP stream is provided. The backup server because the primary server to determine whether it is active, monitoring at least one primary server. If the primary server is determined to be inactive, the backup server takes over the connection to the non-active primary server was streaming data, then take over the transmission of the RTSP stream. The backup server can further determine the position in the file of the next data that needs to be transmitted.
A fitted deviating argument and interpolation scheme for the solution of singularly perturbed differential-difference equation having layers at both ends

In this paper , problem of singularly perturbed differential-difference equation having boundary layers at both ends is solved and analyzed numerically by fitted method. To do this, original problem is transformed into an asymptotically equivalent singularly perturbed differential equation by Taylor’s series expansion. By introducing deviating argument concept, SPDE is replaced by first order differential equation. Resulting equation having deviating argument is solved with proper choice of fitting factor and interpolation. To demonstrate the applicability of this numerical method, three test examples are solved and numerical results are compared with the available/exact results.
Simulation of the Super-scalar Processor Core Operation

The article considers the features of super-scalar processors, their way of performing several operations on several pairs of operands simultaneously. The research focuses on the organization of processor pipeline execution operation of several machine instructions in one processor core. The simulating kit was developed for better understanding of a processor core microarchitecture. It includes two parts: program and methodical recommendations with multiple task options. The simulating kit demonstrates the pipeline architecture consisting of two clusters: front-end and back-end and the principle of translating complex multi-cycle CISC-like instructions into simpler RISC-like micro-operations. The main types of machine instructions are considered: data transfer between registers and memory cells (four variations), data processing of couple of operands from registers and memory cells (four variations), conditional jump to the specified address. The program-simulator makes it possible to conduct a more detailed simulation of one of the three mechanisms for calculations accelerating in the processor core: multi-functional (super-scalar) processing, out-of-order processing, speculative instructions execution after the branch prediction. The simulating kit is used in educational process when training masters of Higher School of Economics National Research University.
CluSim: a python package for calculating clustering similarity

Clustering is a primary method to reveal the structure of data (Jain, Murty, & Flynn, 1999). To understand, evaluate, and leverage data clusterings, we need to quantitatively compare them. Clustering comparison is the basis for method evaluation, consensus clustering, and tracking the temporal evolution of clusters, among many other tasks. For instance, the evaluation of a clustering method is usually achieved by comparing the method’s result to a planted reference clustering, assuming that the more similar the method’s solution is to the reference clustering, the better the method. Despite the importance of clustering comparison, no consensus has been reached for a standardized assessment; each similarity measure rewards and penalizes different criteria, sometimes producing contradictory conclusions.
Validation of NP Fertilizer Rates on Medium Maturing Maize Varieties at Jimma, South Western Ethiopia

Maize is the primary staple crop in Ethiopia and plays an important role in the livelihood of the people of Ethiopia. However, its productivity is very low mainly due to low soil fertility and use approved maize varieties. After three years on farm field study of NP Fertilizer Rates on medium maturing maize varieties further evaluation and validation was conducted to determine optimum NP fertilizer rates and best medium maturing maize variety at Jimma Zone, Southwestern Ethiopia during 2019 main cropping season. The experiment had two N/P 2 O 5 ; 92/69 and 115/86 kg ha -1 and two medium maturing maize varieties. Each treatment was assigned to each plot in Randomized Complete Block Design (RCBD) in factorial arrangement. Data on the yield and yield components were subjected to ANOVA using SAS version 9.3. The highest grain yield 7350 kg ha -1 and above ground biomass 14.53 ton ha -1 were recorded from BH547 with application of 115/86 kg ha -1 N/P 2 O 5 fertilizer rate which was statistically at par with planting BH546 maize variety and application of 92/69 kg ha -1 N/P 2 O 5 fertilizer rate with the highest net benefit 41,855 Ethiopian Birr ha -1 . Therefore, a medium maturing maize variety BH546 with the application of 92/69 kg ha -1 N/P 2 O 5 fertilizer rates can be taken as optimal and recommended for farmers under rain fed condition in Omonada woreda of Jimma zone and other similar humid agro-ecologies of the southwestern Ethiopia.
Towards a Better Understanding of Applied Textual Entailment: Annotation and Evaluation of the RTE-2 Dataset

Applied textual entailment is a newly introduced generic empirical task that captures major semantic inferences across a wide spectrum of Natural Language Processing applications. In the present thesis we quest for a better understanding of the task by means of investigating a benchmark dataset for textual entailment, the dataset of the Second PASCAL Recognising Textual Entailment (RTE-2) Challenge. We propose a scheme for annotation of textual entailment, the Annotating RTE (ARTE) scheme, which models a range of diverse entailment mechanisms. The annotation of a considerable portion of the RTE-2 dataset following this scheme enables us an evaluation of the textual entailment data by gaining insights into the semantic-linguistic properties of the textual entailment phenomenon. Based on this evaluation, the thesis finally examines from various aspects the performance of the textual entailment systems participating in the RTE-2 Challenge, relative to different types of entailment. The methodology followed and the resulting observations make first steps towards a thorough analysis of systems’ performance, which is a key issue for the advancement of textual entailment technology.
Optimized frame detection technique in vehicle accident using deep learning

Video processing becomes one of the most popular and needed steps in machine leering. Todays, Cameras are installed in many places for many reasons including government services. One of the most applications for this concern is traffic police services. One of the main problems of using videos in machine learning application is the duration of the video; which is consuming time, paperwork and space in processing. This leads to increase the computation cost through a high number of frames. This paper proposes an algorithm to optimize videos duration using a Gaussian mixture model (GMM) method for real accident video. The Histogram of Gradient (HoG) has been used to extract the features of the video frames, a scratch CNN has been designed and conducted on two common datasets; Stanford Dogs Dataset (SDD) and Vehicle Make and Model Recognition Dataset (VMMRdb) in addition to a local dataset that created for this research. The experimental work is done in two ways, the first is after applying GMM, the finding revealed that the number of frames in the dataset was decreased by nearly 51%. The second is comparing the accuracy and complexity of these datasets has been done. Whereas the experimental results of accuracy illustrated for the proposed CNN, 85% on the local dataset, 85% on SDD Dataset and 86% on VMMRdb Dataset. However, applying GoogleNet and AlexNet on the same datasets achieved 82%, 79%, 80%, 83%, 81%, 83% respectively.
Analysis and Design of Controller for Level Process Control without Sensor

This paper deals with process control, generalized definition and modeling design of flow systems and control it based on compare to constant as principle of operation approach. After reviewing the operation of the plant, a compare system between water level and desire level is formulated to control flow rate. The scheme is then designed and tested by Matlab and Simulink Toolbox. Simulation is presented and shown that on/off switch with constraint is better and more versatile compare with conventional approach.
Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine

Machine learning (ML) applications in medicine represent an emerging field of research with the potential to revolutionize the field of radiation oncology, in particular. With the era of big data, the utilization of machine learning algorithms in radiation oncology research is growing fast with applications including patient diagnosis and staging of cancer, treatment simulation, treatment planning, treatment delivery, quality assurance, and treatment response and outcome predictions. In this chapter, we provide the interested reader with an overview of the ongoing advances and cutting-edge applications of state-of-the-art ML techniques in radiation oncology process from the radiotherapy workflow perspective, starting from patient’s diagnosis to follow-up. We present with discussion the areas where ML has presently been used and also areas where ML could be applied to improve the efficiency (i.e., optimizing and automating the clinical processes) and quality (i.e., potentials for decision-making support toward a practical application of precision medicine in radiation therapy) of patient care.
Demonstration of a p-Type Ferroelectric FET With Immediate Read-After-Write Capability

In this letter, p-type ferroelectric field-effect-transistors (FeFETs) based on HfO2 and embedded in GlobalFoundries 28 nm bulk high- ${k}$ metal gate (HKMG) technology (28SLPe) are systematically investigated and compared to their n-type counterparts. We show that the two device types, based on Si channel, exhibit a symmetric memory window (MW) and very similar switching behavior, yet profoundly different trapping kinetics. In contrast to n-FeFETs, p-FeFETs display a full MW immediately after the write operation and apparently no parasitic electron trapping. Based on this, we demonstrate an immediate read-after-write operation. Finally, we illustrate how slight structural changes of the gate stack, such as the introduction of the SiGe channel material, can enhance charge trapping and nullify the above-mentioned advantages.
A green approach for the biosynthesis of powdered silver nanoparticles using leaf extract of Trigonella foenum-graecum L.: Characterization and antibacterial evaluation

Silver nanoparticles (AgNPs) were synthesized using aqueous leaf extract of Trigonella foenumgraecum as reducing and stabilizing agent. The ultraviolet-visible spectrum showed absorption peak at 480 nm. XRD pattern indicates the formation of face-centered cubic structure of silver nanoparticles. FESEM images indicate the presence of spherical silver nanoparticles with the particle size of ~90 nm. FTIR indicates that the participation of different functional groups present in the biomolecules is responsible for both reducing and stabilizing the formation of nanoparticles. The synthesized AgNPs demonstrated positive antibacterial activity against two different foodborne pathogenic bacteria (Escherichia coli O157:H7 ATCC 35150, Staphylococcus aureus ATCC 13565) with zones of inhibition of 9.20 ± 0.18 and 9.34 ± 0.11, respectively and MIC and MBC of 100 and >100 μg/ml, respectively. The synthesized AgNPs could serve as a candidate for development of antibacterial drugs or additive in the food packaging system for its application in medicine, cosmetics and food sector industries.
When is the verb a potential gap site? The influence of filler maintenance on the active search for a gap

ABSTRACT Throughout an open filler-gap dependency, some features of the filler are actively maintained in working-memory, while others decay and are retrieved at the gap. The current study asks whether the availability of certain features of the filler (due to their maintenance) influences the parser’s attempt to posit a gap upon encountering a verb. We report the results of two self-paced reading experiments testing the hypothesis that maintained features guide active gap-filling. Experiment 1 used similarity-based interference to show that animacy is a maintained feature. Experiment 2 combined a filled-gap design and a plausibility manipulation, with violation of either the animacy requirement or some other selectional restriction of the verb, to test when an attempt to resolve a dependency is made despite poor fit between the verb and the filler. Results suggest that only verbs selecting an argument with features like those maintained in the filler’s representation trigger an attempt to resolve the dependency.
Effect of the host-specific toxin SnTOX3 from Stagonospora nodorum on ethylene signaling pathway regulation and redox-state in common wheat

The fungus Stagonospora nodorum Berk. is the causative agent of Septoria nodorum blotch (SNB) of wheat. The most important factors of Stagonospora nodorum virulence include numerous fungal necrotrophic effectors (NEs) encoded by SnTox genes. They interact with the matching products of host susceptibility genes (Snn). SnTox-Snn interactions are mirror images of classical gene-for-gene interactions and lead to the development of disease. We have studied the SnTox3-Snn3 interaction, resulting in the development of infection on leaves and formation of extensive lesions. The mechanism of SnTox3 action is likely to be linked to the regulation of redox metabolism and the influence on ethylene synthesis in the wheat plants, although the molecular mechanisms are not fully unveiled. To characterize the SnTox3-Snn3 interaction, we used S. nodorum isolates differing in the expression of the NEs genes SnTox3 (SnB (Tox3+), Sn4VD (Tox3–)) and two soft spring wheat (Triticum aestivum L.) cultivars, contrasting in resistance to the SNB agent and differing in the allelic composition of the susceptibility locus Snn3-B1: Kazakhstanskaya 10 (susceptible) and Omskaya 35 (resistant). We carried out a comparative assessment of the transcriptional activity patterns of genes responsible for ethylene biosynthesis (TaACS1, TaACО) and signaling pathway (TaEIL1, TaERF1) by real-time PCR and estimated the redox state of wheat plants infected with different isolates of S. nodorum by spectrometry. The induction of ethylene biosynthesis and signaling has been shown to result from gene-for-gene interaction between Snn3-B1 and SnTox3. The results of plant redox status estimation showed that ethylene inhibited accumulation of hydrogen peroxide in SnTox3-sensitive genotypes by regulating the operation of various pro-/antioxidant enzymes at the transcriptional and posttranslational levels. Our results suggest that NE SnTox3 influences ethylene biosynthesis and signaling, thereby regulating redox metabolism in infected wheat plants as necessary for successful host colonization at the initial phases of infection, which ultimately leads to extensive lesions due to fast pathogen reproduction.
Relation of RBH Auditory-Perceptual Scale to Acoustic and Electroglottographic Voice Analysis in Children With Vocal Nodules

The aim of this paper was to present an analysis of the feasibility of voice quality prediction on the roughness, breathiness, hoarseness (RBH) scale for children with vocal nodules on the basis of both acoustic parameters and electroglottographic (EGG) examination. The first step to achieve this goal involved the creation of a dedicated database, Voice Pathology Analysis Database (VPADB), containing voice recordings from patients, the EGG signal, medical diagnosis, and the classification of voice quality on the above-mentioned scale. The database also contains data concerning the patients’ age and sex. The next step involved performing statistical analyses to test the relationship between the values of objective parameters, such as peak slope and normalized amplitude quotient, and the classification of voice quality. The study made use of voice recordings of 57 patients with vocal nodules and 37 healthy individuals. The RBH classification was carried out by two independent voice specialists. It was found that speech signal parameters can be used to predict expert evaluation with regard to roughness and hoarseness.