Miracle-dz/UniFAR-FTUs huggingface dataset:
Grid Interconnection of Solar and Battery System Using an Asymmetrical T-Type Multilevel Inverter to improve Conversion Efficiency with Reduced THD

A three-phase dual-port T-type asymmetrical multilevel inverter (ASMLI) using two sources, solar forming the high voltage level and the battery forming the low voltage level, is considered for grid interconnection. A vertical shifted SPWM is used for the ASMLI circuit. A transformerless system for grid interconnection is achieved for a 100-kW power range. A well-designed boost converter and a Buck/Boost converter is used on the front side of the inverter. Design of battery charge controller and its controlling logic are done and its SOC is found to be efficient during charging and discharging conditions. A closed-loop control using PQ theory is implemented for obtaining power balance at 0.7 modulation index. The THD of the current harmonics in the system is observed to be 0.01% and voltage harmonics is 0.029% which are well within the permissible limits of IEEE-519 standard. The power balance is found to be good between the inverter, load, and the grid during load disconnection for a period of 0.15s. A comparison of THD’s, voltage, current stresses on the switches, and conduction losses is also presented for a single-phase system with respect to a two-level inverter which shows improved efficiency and low THD. Hence this system can be proposed for use in grid interconnection with renewable energy sources.
Crystal structure of poly-[μ-1,4-bis(2-ethylbenzimidazol-1-ylmethyl)benzene-κ2N:N′)-bis(μ4-4,4′-sulfonyldibenzoato-κ4O:O′:O′′:O′′′)dicadmium(II)] monohydrate, C52H42N4O14Cd2

Abstract C52H42N4O14Cd2, triclinic, P1̅ (no. 2), a = 9.0684(18) Å, b = 12.585(3) Å, c = 12.697(3) Å, α = 101.14(3)°, β = 106.98(3)°, γ = 102.06(3)°, V = 1304.3(5) Å3, Z = 1, Rgt(F) = 0.0276, wRref(F2) = 0.0853, T = 293(2) K.
Automatic Wireless Nurse Caller

The nurse caller device is used as a special communication device between the patient and the nurse within the hospital area as a means of speeding the nurse's time response in providing immediate care to the patient. The designed wireless-based nurse caller device made installation easier and neater. The remote used a Bluetooth module MH-10 connected to the ATMega8 microcontroller as the sender and receiver. The data process using a microcontroller ATMega8 produced characters on the LCD, turned on the LED, and activated the buzzer to call the nurse. The results of the test on the device showed that the farthest distance taken by the HM-10 Bluetooth module in the open area (outdoor) was about 45 meters, and the closed area (indoor) was about 20 meters.
Implicit Contact Model for Discrete Elastic Rods in Knot Tying

Rod–rod contact is critical in simulating knots and tangles. To simulate contact, typically a contact force is applied to enforce nonpenetration condition. This force is often applied explicitly (Euler forward). At every time-step in a dynamic simulation, the equations of motions are solved over and over again until the right amount of contact force successfully imposes the nonpenetration condition. There are two drawbacks: (1) Explicit implementation brings numerical convergence issues. (2) Solving equations of motion iteratively to find this right contact force slows down the simulation. In this article, we propose a simple, efficient, and fully implicit contact model with high convergence properties. This model is shown to be capable of taking large time-steps without forfeiting accuracy during knot tying simulations when compared to previous methods. We introduce a new contact potential, based on a smoothed segment–segment distance function, that is an analytic function of the four endpoints of the two contacting edges. Since this contact potential is differentiable, we can incorporate its force (negative gradient of the energy) and Jacobian (negative Hessian of the energy) into the elastic rod simulation.
Modelling of cutting parameters for Nilo 36 superalloy with machine learning methods and developing an interactive interface

* Corresponding author. Tel. : +90 248-213-2700; Fax: +90-248-213-2704 E-mail addresses: gbasmaci@mehmetakif.edu. tr (G.Basmaci), ismailkirbas@mehmetakif.edu. tr (I.,Kırbas), muay@marmara.edu. tr (M.Ay) ORCID: 0000-0003-4818-3160 (G.Basmaci), 0000-0002-1206-8294 (I,Kırbas) , 0000-0002-7672-1846 (M. Ay) DOI: 10.35860/iarej.805124 This article is licensed under the CC BY-NC 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/). Research Article
The stability control for isolated wind‐diesel power system based on the cross coupling effect model

In the isolated wind-diesel hybrid power system, the system voltage and frequency may be affected simultaneously by the ﬂuctuated wind turbine output power and variable load, which can make the system crash in extreme cases without control strategy. Therefore, in order to reduce the voltage and frequency deviation caused by system power ﬂuctuation, both the battery and super twisting algorithm sliding mode state observer (STAT-COM) are equipped to adjust the active power and reactive power for load power demand in the isolated small-scale power system. Then, the system mathematical model is established which considers the cross-coupling effect exist in the small-scale power system. In order to improve the control effect of battery and STATCOM, the uniﬁed control strategy is designed for battery and STATCOM by using the super twisting sliding mode state observer and the adaptive sliding mode controller according to the constructed cross-coupling effect model. Furthermore, the parameters of controller are further optimized
Laser scattering by submicron droplets formed during the electrical explosion of thin metal wires

This paper presents the results of studying dispersed media formation during the electrical explosion of thin metal wires in vacuum by using low-current generators (∼1–10 kA). Particular attention is paid to the analysis of the composition and structure of the corresponding explosion products as well as to the problem of their visualization using simultaneous laser interferometry and shadow imaging at two wavelengths (1.064 µm and 0.532 µm). Our findings indicate the important role in the visualization of the explosion products that belongs to multiple scattering by submicron droplets of dense condensed matter, which are mixed with metal vapor. The hypothesis on the existence of submicron droplets in the products of exploding metal wires correlates with the results obtained by soft x-ray radiography combined with a laser probing technique. Taking into account the multiple scattering by submicron droplets, it is possible to significantly clarify the parameters of the explosion products visualized via laser probing techniques as well as to gain a deeper insight into the physics behind the electrical wire explosion.
A Study on the Efficacy of Deep Reinforcement Learning for Intrusion Detection

The world has witnessed a fast-paced digital transformation in the past decade, giving rise to all-connected environments. While the increasingly widespread availability of networks has benefited many aspects of our lives, providing the necessary infrastructure for smart autonomous systems, it has also created a large cyber attack surface. This has made real-time network intrusion detection a significant component of any computerized system. With the advances in computer hardware architectures with fast, high-volume data processing capabilities and the developments in the field of artificial intelligence, deep learning has emerged as a significant aid for achieving accurate intrusion detection, especially for zero-day attacks. In this paper, we propose a deep reinforcement learning-based approach for network intrusion detection and demonstrate its efficacy using two publicly available intrusion detection datasets, namely NSL-KDD and UNSW-NB15. The experiment results suggest that deep reinforcement learning has significant potential to provide effective intrusion detection in the increasingly complex networks of the future.
Mechanical Response of Aluminosilicate Nanotubes under Compression

The mechanical response of aluminosilicate nanotubes (imogolite) under compression is investigated by means of classical molecular dynamics simulations using the CLAYFF potential. Using the uncompressed length L0, when there is no strain, to describe each nanotube, we have found that imogolite tends to coil for L0 > 15 nm to prevent the failure of the nanotube. The simulation also shows that the nanotubes do not break under stress for L0 ≥ 100 nm. Hence, the imogolite is a very flexible nanotube, which has been observed with lengths up to 500 nm. Finally, we can report a Young modulus of the order of 200 GPa, which is relatively independent of L0.
Anti-Resonance Frequency Control by Span-Tuning Dynamic Vibration Absorber for Brush Cutter Vibration Reduction

Significant handle vibrations often occur during mowing operation even with anti-vibration type brush cutters. This is often caused by combined-bending natural mode of the main pipe and driveshaft which is mainly excited by cutting head rotational force. In this study, we focused on the placement span of the rubber bushings that support the driveshaft to suppress this kind of resonance. More specifically, we have designed a new component, called a span-tuning dynamic vibration absorber (ST-DVA), which utilizes the bending mode of the driveshaft that is determined by the placement span of rubber bushings. Analysis results of the finite element method (FEM) showed that the ST-DVA generated anti-resonance at a specific point on the main pipe under the first-order inertial force of the cutting head. We also succeeded in controlling anti-resonance frequency under the excitation. In actual measurements at the target frequency, handle vibration of the first-order component of the cutting head could be reduced by 51% and overall handle vibration could be reduced by 49% compared with those produced via equal-span rubber bushing placement. Hence, our study provides a design method that makes it possible to utilize the driveshaft, which a primary brush cutter component, as a dynamic vibration absorber by altering the placement span of the rubber bushings.
WEIR-P: An Information Extraction Pipeline for the Wastewater Domain

<p>Urbanization has been an increasing trend over the past century (UN, 2018) and city managers have had to constantly extend water access and sanitation services to new peripheral areas. Originally these networks were installed, operated, and repaired by their owners (Rogers et al. 2012). However, as concessions were increasingly granted to private companies and new tenders requested regularly by public authorities, archives were sometimes misplaced and event logs were lost. Thus, part of the networks&#8217; operational history was thought to be permanently erased. The advent of Web big data and text-mining techniques may offer the possibility of recovering some of this knowledge by crawling secondary information sources, i.e. documents available on the Web. Thus, insight might be gained on the wastewater collection scheme, the treatment processes, the network&#8217;s geometry and events (accidents, shortages) which may have affected these facilities and amenities. The primary aim of the <strong>"Megadata, Linked Data and Data Mining for Wastewater Networks" (MeDo) project </strong>(http://webmedo.msem.univ-montp2.fr/? page_id=223&lang=en), is to develop resources for text mining and information extraction in the wastewater domain. We developed a specific Natural Language Processing (NLP) pipeline named <strong>WEIR-P (WastewatEr InfoRmation extraction Platform)</strong> which allows users to retrieve relevant documents for a given network, process them to extract potentially new information, assess this information also by using an interactive visualization and add it to a pre-existing knowledge base. The system identifies the entities and relations to be extracted from texts, pertaining network information, wastewater treatment, accidents and works, organizations, spatio-temporal information, measures and water quality. We present and evaluate the first version of the NLP system. The preliminary results obtained on the Montpellier corpus (1,557 HTML and PDF documents in French) are encouraging and show how a mix of Machine Learning approaches and rule-based techniques can be used to extract useful information and reconstruct the various phases of the extension of a given wastewater network. While the NLP and Information Extraction (IE) methods used are state of the art, the novelty of our work lies in their adaptation to the domain, and in particular in the wastewater management conceptual model, which defines the relations between entities. </p>
An enhancement of mammogram images for breast cancer classification using artificial neural networks

Breast cancer is the most driving reason for death in women in both developed and developing nations. For the plan of effective classification of a system, the selection of features method must be used to decrease irregularity part in mammogram images. The proposed approach is used to crop the region of interests (ROIs) manually. Based on that number of features are extracted. In this proposed method a novel hybrid optimum feature selection (HOFS) method is used to find out the significant features to reach maximum accuracy for this classification. A number of selected features is applied to train the neural network. In this proposed method accessible informational index from the mini–mammographic image analysis society (MIAS) database was used. The classification of this mammogram database involved a neural networks classifier which attained an accuracy of 99.7% with a sensitivity of 99.5%, and specificity of 100% as the area under the curve (AUC) is 0.9975 and matthew’s correlation coefficient (MCC) represents a binary class value which reached the value of 0.9931. It can be useful in a computer-aided diagnosis system (CAD) framework to help the radiologist in analyzing breast cancer. Results achieved with the proposed method are better compared to recent work.
Motivation to Teach: A Mixed Method Study

The present study aimed to investigate the motivation to teach of the teachers and to determine the factors that affect motivation to teach. Convergent parallel mixed design was used in the study. The study sample included 157 teachers who were selected with the convenience sampling method. A data collection instrument that consisted of three sections was used to collect the study data. In the first part of the instrument, questions about personal variables and school stakeholders (administrator, colleague, student and parents) are included. In the second part, "Teaching Motivation Scale" developed by Kauffman, Yilmaz-Soylu and Duke (2011) and adapted into Turkish by Ayik, Atas-Akdemir and Secer (2015) takes place. In the third part, there are two open-ended questions created by the researcher. Descriptive statistics, independent samples t-test, Mann-Whitney U test, one-way analysis of variance, and content analysis were used to analyze the study data. In the study, it was observed that quantitative and qualitative findings were generally consistent. It was determined that attitudes and behavior of school administrators, students, colleagues, and parents towards the teachers and attitudes and behavior of the students towards the course were the factors that affected the motivation to teach of the teachers. It was determined that the marital status and professional seniority of the teachers did not affect the motivation to teach of the teachers. While quantitative findings demonstrated that gender affected extrinsic motivation to teach, there was no qualitative evidence that gender affected motivation to teach. In the study, various recommendations such as supporting teachers, appreciation of their work, and showing respect were presented in the study to improve the motivation to teach of the teachers.
Performance analysis of OFDM-IM scheme under STO and CFO

In this letter, performance analysis of orthogonal frequency division multiplexing with index modulation (OFDM-IM) is presented in term of bit error rate (BERs). The analysis considers its performance under two impairments, symbol time offset (STO) and carrier frequency offset (CFO) in frequency-selective fading channel. As orthogonal multicarrier system, OFDM-IM is subject to both inter-symbol interference (ISI) and inter-carrier interference (ICI) in a frequency-selective fading channel. OFDM-IM is a new multicarrier communication system, where the active subcarriers indices are used to carry additional bits of information. In general, in the previous existing works, OFDM-IM are evaluated only for near-ideal communication scenarios by only incorporating the CFO factor. In this work, the OFDM-IM performance is investigated and compared with conventional OFDM in the presence of two impairments, STO and CFO. Simulation results show that OFDM-IM outperforms the conventional OFDM with the presence of STO and CFO, especially at high SNR areas.
Etude ab-initio des phénomènes spintronique dans les jonctions tunnel magnétiques et le graphène

Phenomenes de spintronique dans les jonctions tunnel magnetiques et des films minces sont tres prometteurs des deux points fondamentaux et l'application de vue. Elles sont basees sur l'exploration de spin d'electron en plus de sa charge et comprennent intercalaire couplage d'echange (CEI), l'anisotropie magnetique perpendiculaire (PMA), geante (GMR) et magnetoresistance tunnel (TMR), Couple de transfert de spin (STT), Spin effet Hall (SHE) et meme induire du magnetisme dans les elements non compris d graphene. Cette these comprendra premiers principes des etudes de phenomenes spintronique qui ont ete d'un grand interet recemment. La premiere partie est consacree a intercalaire couplage d'echange a travers les materiaux isolants dont le MgO, SrTiO3, GaAs et ZnSe. La deuxieme partie comprendra des etudes ab initio d'anisotropie magnetique perpendiculaire au Fe | interfaces MgO et MTJ y compris le mecanisme et sa correlation avec le spin Bloch symetrie Etat fonde de filtrage. Dans les enquetes troisieme partie de l'anisotropie magnetique et la fonction de travail dans les Co | interfaces graphene seront presentes. Ensuite, il sera montre possibilite d'induire et d'optimiser le magnetisme intrinseque dans nanomeshes graphene. Derniere partie sera consacree a l'induction de polarisation de spin et le reglage de Dirac point et ordre magnetique dans le graphene a l'aide d'effets de proximite magnetiques substrat.
Embolization of Duodenal Hematoma Post-Endoscopy in Noonan Syndrome: A Novel Management Approach

CASE REPORT A 6-year-old girl with Noonan Syndrome (confirmed SOS1 mutation), underwent elective upper gastrointestinal endoscopy for symptoms of gastro-oesophageal reflux disease. She had a normal aorta and no known bleeding diathesis. Endoscopy was undertaken with a 8.9 mm outer diameter pediatric gastroscope. Direct visualization was unremarkable, and 2 biopsies each were obtained from the proximal and distal esopha- gus and 3 from the second part of the duodenum (D2) with standard biopsy forceps (2.2 mm). The procedure was uneventful, and the patient discharged home that same morning. She presented to the Emergency Department 48 hours later with abdominal pain, vomiting, fever, and tachycardia. Abdominal examination revealed generalized tenderness without peritonism. Hemoglobin dropped from 116 g/L to 97 g/L, with a normal platelet count (216 × 10 9 /L) and normal coagulation profile (INR 1.2, pro-thrombin time 13 seconds, APTT 28 seconds, fibrinogen 2.7 g/L). D-dimer was elevated at 1.06. Electrolyte profile, liver biochemistry, and serum lipase were unremarkable, while lactate on venous blood gas was 0.5. Abdominal ultrasound revealed a heterogenous mass con- forming to the region of D2 tapering to the duodenojejunal flexure, where normal, collapsed jejunum was demonstrated. The mass mea- sured approximately 9 × 5 × 5 cm and appeared avascular centrally, consistent with duodenal hematoma. Furthermore, there was a mod- erate volume of free-fluid noted within the pelvis and iliac fossae. A computed tomography (CT) was then undertaken, which confirmed the presence of a
A Big Data Analysis Using Fuzzy Deep Convolution Network Based Model for Heart Disease Classification

Heart disease is a serious disease that causes sudden death among 80% of the people around the world. The traditional models performed predictive analytics using machine learning techniques to make a better decision. For better decision making in heart disease prediction, the big data analysis shows the great opportunities to predict the future health status from health parameters and provide best outcomes. However, the traditional decision making models had traffic data or contained noise and uncertainty was unpredictable as the data ambiguity emerged. In order to overcome such an issue, the big data is used to ensure the medical service which is mostly needed in a timely manner and for accurate diagnosis. The pre-processing of the medical data acquired from Cleveland heart disease UCI datasets has a vast number of attributes which helps to predict the heart disease. The data are contaminated with the noise and some of the data are missing, so the pre-processing using Min max Normalization is performed to remove contaminated noise acquired in the data which is taken from the UCI repository dataset. The proposed Fuzzy Deep Convolution Network (FDCN) permits the input features for fuzzification process that uses transformed features. The fuzzification process eliminates the redundant or irrelevant fuzzified features and overcomes the system complexity problems. The proposed FDCN obtains accuracy of 95.56 % and 92 % of F-score shows better results when compared with the existing KNN-DT, Naive Bayes, and Random Forest algorithms.
Tidal Current Energy Resources Assessment in the Patinti Strait, Indonesia

Indonesia is currently intensively developing its renewable energy resource and targets at least 23% by 2025. As an archipelago country, Indonesia has the potential to benefit from its abundant renewable energy resources from its offshore regions. However, the short tidal range of mixed semi-diurnal and the suitable tidal turbine capacity may hinder marine renewable energy development in Indonesian waters. This paper presents higher-order hydrodynamic numerical models to provide spatial information for tidal current resource assessment of the Patinti Strait. The present study applied the hydrographic and oceanographic method to produce input of the numerical model. Based on the selected simulation analysis, the highest current speed could be identified around Sabatang and Saleh Kecil Island with up to 2.5 m/s in P1 and 1.7 m/s in P4. Besides, the operational hours for the two observation points are 69% and 74.5%, respectively. The results indicate that this location is of prime interest for tidal turbine implementation as an energy source, for medium capacity (300 kW) and high capacity (1 MW).
Genetics of Epilepsy

Bottom line: Epilepsy is a chronic brain disease that is characterized by an enduring predisposition to recurrent unprovoked epileptic seizures. A single gene mutation causes epilepsy in about 30% of affected individuals. Genetic testing should be considered for individuals with red flags, including: epilepsy with accompanying dysmorphic features, intellectual disability, autism and/or cognitive regression; drug-refractory epilepsy; epilepsy with multiple neurological co-morbidities; or a positive family history. Consultation with an epileptologist and/or a clinical or biochemical geneticist may be warranted. Genetic testing may provide a specific diagnosis for the individual with epilepsy and may end the diagnostic odyssey. This may help to guide management, provide information on prognosis and more accurate recurrence risk counselling, and provide prenatal testing options. Importantly, a molecular diagnosis may bring closure for the family and help them to network with support groups.