Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization-based open-loop selective harmonic elimination pulse width modulation for multilevel inverter flying capacitor voltage balance considering load power factor variation

In this article, Newton–Raphson optimization-based selective harmonic elimination pulse width modulation is investigated for open-loop control of flying capacitor multilevel inverter. Proposed control strategy has allowed giving up current and voltage measurement instrumentations and sensors used in case of closed loop–based control. Proposed control strategy generates accurate switching angles, capable to balance flying capacitor voltage and eliminate lower order harmonics considering load power factor variation. Algorithm performances are verified by simulation and experiments, and obtained results prove that proposed technique does effectively eliminate specific harmonics and balance capacitor voltages while getting inverter output voltage with desired number of levels.
Key Aspects of Maturity Assessment in Lean Construction

To achieve Lean Construction maturity in the management of the construction project is a continuous improvement process that occurs when the LC implementation is integrated into the life cycle of the construction project. The model for the evolution of Lean Construction maturity in the production management of construction projects, the SLCEM model, contains the standard with which each of the elements involved in the maturity of Lean Construction (LC) is evaluated in construction project management (CPM). Identifying the elements that show the extent of maturity and understanding how they relate is the main task for the development of the SLC-EModel. When evaluating the maturity elements, the local and global maturity indices explain the maturity of the CPM system. The indices are external references to start a cycle of continuous improvement for the evolution towards a higher level of maturity. This research presents a maturity assessment model and an evolution strategy to advance towards the highest possible level of LC maturity in the management of construction projects.
Transcriptional Modulation of Resistance against Xanthomonas oryzae pv. oryzae Korean Race K2 in japonica Rice

Bacterial blight is a common disease found in the rice-growing regions in the Korean peninsula. Identification of the gene network involved against Xanthomonas oryzae pv. oryzae Korean race K2 in popular japonica cultivars is essential in underpinning the molecular mechanism of resistance. A microarray of two popular Korean japonica rice cultivars, a bacterial blight susceptible Dongjin and resistant Jinbaek, was performed to investigate the transcripts of inducible genes at 48 h post-inoculation. A total of 771 differentially expressed genes were identified in Jinbaek, whereas 298 were found in Dongjin. The resistance observed in Jinbaek is likely participated by genes with predicted functions in transmembrane perception, intracellular signal transduction, and transcription activity. Moreover, the remarkable involvement of numerous WRKY proteins signifies orchestration of defense signals via robust cellular reprogramming, which leads to resistance. To discover genes essential to bacterial blight resistance in Jinbaek, 13 highly expressed genes encoding different protein classes were cloned and overexpressed in rice. Although none of the overexpression plants exhibited resistance comparable to Jinbaek, four candidate genes, including one twin-arginine translocation pathway signal (LOC_Os01g45640.1), one cytochrome p450 (LOC_Os09g10340.1), and two uncharacterized expressed protein (LOC_Os08g26230.4, LOC_Os09g04310.1) conferred partial resistance. However, of these four genes, only p450s have been reported to play an important role in the synthesis of plant defense compounds. These findings revealed the complexity of key immune signaling conduits critical to mounting a full defense against Xanthomonas. oryzae pv. oryzae race K2 in japonica rice.
Enabling Methodologies for Predictive Power System Resilience Analysis in the Presence of Extreme Wind Gusts

Modern power system operation should comply with strictly reliability and security constraints, which aim at guarantee the correct system operation also in the presence of severe internal and external disturbances. Amongst the possible phenomena perturbing correct system operation, the predictive assessment of the impacts induced by extreme weather events has been considered as one of the most critical issues to address, since they can induce multiple, and large-scale system contingencies. In this context, the development of new computing paradigms for resilience analysis has been recognized as a very promising research direction. To address this issue, this paper proposes two methodologies, which are based on Time Varying Markov Chain and Dynamic Bayesian Network, for assessing the system resilience against extreme wind gusts. The main difference between the proposed methodologies and the traditional solution techniques is the improved capability in modelling the occurrence of multiple component faults and repairing, which cannot be neglected in the presence of extreme events, as experienced worldwide by several Transmission System Operators. Several cases studies and benchmark comparisons are presented and discussed in order to demonstrate the effectiveness of the proposed methods in the task of assessing the power system resilience in realistic operation scenarios.
A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits

We propose a neural information processing sys-tem obtained by re-purposing the function of a biological neural circuit model to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans , we introduce ordinary neural circuits (ONCs), deﬁned as the model of biological neural circuits reparameterized for the control of alternative tasks. We ﬁrst demonstrate that ONCs realize networks with higher maximum ﬂow compared to arbitrary wired networks. We then learn instances of ONCs to control a series of robotic tasks, including the autonomous parking of a real-world rover robot. For reconﬁguration of the purpose of the neural circuit, we adopt a search-based optimization algorithm. Ordinary neural circuits perform on par and, in some cases, signiﬁcantly surpass the performance of contemporary deep learning models. ONC networks are compact, 77% sparser than their counterpart neural controllers, and their neural dynamics are fully interpretable at the cell-level.
Antimicrobial Effects of Grape and Pomegranate Waste Extracts against two Foodborne Pathogens

In recent years, increasing in the level of public health consciousness has led to a drastic decrease in the use of synthetic food preservatives. Therefore, there has been a growing demand for novel antimicrobials. Apart from that, finding low cost sources has been always an important issue for food marketers. The present study was designed to investigate in-vitro antibacterial activities of grape and pomegranate waste separately and in combination with broth dilution methods against two bacteria (Staphylococcus aureus and Escherichia coli). The utilized solvent, as an extractant, was hot water. The total phenolic contents of grape and pomegranate wastes were also evaluated. Results revealed that the hot-water extract of the mixed wastes was the most potent, with the MIC / MBC of 52 / 286 mg/ml against E. coli and 75/98 mg/ml against S. aureus. It was also observed that the pomegranate waste contained higher level of phenolic compounds (439±0.82 mg GAE/L) as compared to the grape waste (333.6±1.25 mg GAE/L). It also exhibited higher antibacterial activity (lower MIC and MBC against tested bacteria) than the extract of grape waste. These results suggest that by-products of grape and pomegranate that are inexpensive and available can be employed as a potential source of antibacterial compounds.
A Time Domain Method for Calculation of Heart Rate Variability

The significance of extracting the RR interval is to determine the length of heart beat interval. , since HRV is physiological phenomenon at which time interval between each heart beat varies inconsistently and therefore evaluated by measuring inter-beat time interval fluctuations. Hence a reliable and accurate QRS detection algorithm is required. Modified Pan Tompkins method is used for QRS and RR time interval detection. Calculation of VARIANCE (NN) and SDNN and of MITBIH ECG data comes to 1942and 44 indicates normal ANS regulating function.
Performance of Delay Tolerant Network Protocol in Smart City Scenario

The smart city is being developed in many big cities to support local governments for resources and infrastructure optimization in terms of enhancing services for the citizen. Large investment costs are required to implement a communication network for the smart city infrastructure (i.e. Internet of Things (IoT) devices) using 4G or Wi-Fi networks. This study aims to implement Delay Tolerant Network (DTN) as a low-cost alternative network for the smart city. We use Surabaya smart city plan scenario in which smart mobility services are provided in city transportation and infrastructure. The IoT devices are installed in numerous locations and continuously transmit messages to the monitoring server located in the city hall, bus and car employed as a DTN node. In this paper, as a preliminary research, we investigate the performance of well-known DTN routing protocols (Epidemic, Maxprop, and ProphetV2) with our proposed protocol called Spray and Hop Distance (SNHD). The evaluation is performed using the ONE simulator in terms of the number of delivered messages, the average latency, and the overhead ratio. The results show the performance of SNHD is better than the well-known DTN protocols in various conditions when the total size of delivered messages is large. Unfortunately, none of those routing protocols has a good performance in all of the scenario conditions. In the future, we desire to implement this system in real smart city system in Surabaya and do simulation with more realistic scenario such as real traffic-trace.
Vibration‐based motion generation without any sensor or microprocessor

This article describes a decentralized controller that generates periodic motion patterns of general mechanical systems with multiple actuators, and its extremely simple implementation. The controller synchronizes the coupled dynamics of an uncertain body, the environment, and the actuators to its natural vibration modes autonomously. The major contribution of this method is that it makes possible decentralized and adaptive motion generation with multiple actuators without any sensor, microprocessor, or high‐torque and high‐precision motor. The proposed implementation of the decentralized controller is composed of a crank arm and low‐torque DC motor, and we focus on the intrinsic dynamics of the DC motor. When a reaction torque from the environment is applied to the motor shaft, the rotational speed of the motor will be changed passively. In our approach, we exploit these dynamics as a feedback controller that adjusts the phase of the DC motor. Experiments and simulations validate the fundamental features of the proposed method with a spring‐mass system, and the effects of the initial phase difference and improvement of the convergence property are reported. Moreover, an application example with a nonlinear legged robot simulation is demonstrated.
Extracting interrogative intents and concepts from geo-analytic questions

Abstract. Understanding syntactic and semantic structure of geographic questions is a necessary step towards true geographic question-answering (GeoQA) machines. The empirical basis for the understanding of the capabilities expected from GeoQA systems are geographic question corpora. Available corpora in English have been mostly drawn from generic Web search logs or limited user studies, supporting the focus of GeoQA systems on retrieving factoids: factual knowledge about particular places and everyday processes. Yet, the majority of questions enquired about in the spatial sciences go beyond simple place facts, with more complex analytical intents informing the questions. In this paper, we introduce a new corpus of geo-analytic questions drawn from English textbooks and scientific articles. We analyse and compare this corpus with two general-purpose GeoQA corpora in terms of grammatical complexity and semantic concepts, using a new parsing method that allows us to differentiate and quantify patterns of a question’s intent.
Defect, transport, and dopant properties of andradite garnet Ca3Fe2Si3O12

There is growing interest to discover suitable calcium containing oxides that can be used as electrode materials in calcium ion batteries. A comprehensive computational investigation of ionic defects and Ca-ion diffusion in Ca-bearing oxide materials at the atomic level is important so as to predict their suitability for use in Ca-ion batteries. In this study, we apply atomistic simulation techniques to examine the energetics of defects, dopants, and Ca-ion diffusion in Ca3Fe2Si3O12. The calculations suggest that the Ca/Fe anti-site defect is the most favorable intrinsic defect causing such significant disorder, which would be sensitive to synthesis conditions. Diffusion of Ca2+ ions within Ca3Fe2Si3O12 is three-dimensional, with the activation energy of migration of 2.63 eV inferring slow ionic conductivity. The most favorable isovalent defects are Mn2+, Sc3+, and Ge4+ on Ca, Fe, and Si, respectively, for this process. The formation of extra calcium was considered to increase the capacity and diffusion of Ca in this material. It is found that Al3+ and Mn2+ are the candidate dopants on the Si and Fe sites, respectively, for this process and there is a reduction observed in the activation energies. The electronic structures of favorable dopant configurations are discussed using density functional theory simulations.
A Concise Audit on the Screening Models for the Pharmacological Evaluation of Type-II Anti-Diabetic Medications

Background: The frequency of diabetes all over the globe has grown exotically over the recent past decades, and the vogue will continue for the anticipated future. The development of microand macro vascular complications is the dominant concern related to diabetes, whose contribution is highly proportional to the morbidity and mortality of the population with the disease. Advancement of the disease from prediabetic state to overt diabetes and the development of complexity transpire over many years. Reckoning of interventions crafted to delay or prevent disease amelioration or complexity in humans also takes years and desires impregnable resources. Objective: None the less, for an animal model to have more convenience to the study of diabetes, the features of the animal model should portray the patho-physiology and natural history of diabetes or the model should flourish the complications of diabetes with an etiology congruent to that of the human condition. Use of the suitable animal model based on these homogeneities can provide much needed data on patho-physiological mechanisms operative in human type 2 diabetes mellitus. Methods: A literature search was overseen by serving different keywords like "Type-II Diabetes Mellitus", " Anti-diabetic ", "In-vitro models" and "In-vivo models". The search was made to order by implementing convenient filters with a desire to fetch the best suited articles to meet the objective of this review article. Result: There are different research and review papers based on the anti-diabetic screening models for the determination of anti-diabetic activity of new drug molecules.
Target Tracking Based on Deep Optimization Features

Target tracking algorithm based on deep learning generally adopts offline trained deep network to extract features, but with unsatisfactory separation effect of the target and the background. A target tracking algorithm based on deep optimization features is proposed to solve this problem. First, the Conv3-1 and Conv4-3 layers are selected from the offline trained convolutional neural network model VGGNet16 for basic feature extraction. Then the loss function is used to quantitatively describe the effectiveness of different filter positioning target in the basic feature layer. Finally, the selected high-efficiency filters are fused to extract deep optimization features and achieve effective tracking under the correlation filtering framework. The experimental results show that compared with other related algorithms, this algorithm can better recognize the target and present better results.
Integrated Versus Subordinated: Public Relations Application In Faith-Based Women Organization

This paper offers a critical interpretation of public relations (PR) generic principles application in faith-based women organization. In particular, this paper examines whether this organization should manage PR in separate management. This study takes up the PR application in the Central Board of Aisyiyah (CBA) since it subordinated the PR function to a secretary in the administrative period from 2010 down to date, whereas it engaged with PR as a single department on 2005-2010. This subordination turns into vice versa application of the generic principle because if the significant contribution of PR considered, its application pattern should be run from subordinative, as a part of another department, into an integrative management function. Therefore, this paper elaborates on the PR application in both periods and the factors working on it. The research method was a case study using the four models of PR, from Hunt and Grunig, as an analysis framework. The results indicate that there is no significant difference between the period of subordination and before. The factors that influenced its applications are knowledge and skill, shared expectations, participative culture, and organizational identity. Thus, the results have theoretical and practical implications for PR application in faith-based women organizations. Keywords: P ublic Relations Application, The Four Models, Aisyiyah, Faith-Based Wome n Organization.
On the regularization of convolutional kernel tensors in neural networks

ABSTRACT Convolutional neural network is an important model in deep learning, where a convolution operation can be represented by a tensor. To avoid exploding/vanishing gradient problems and to improve the generalizability of a neural network, it is desirable to have a convolution operation that nearly preserves the norm, or to have the singular values of the transformation matrix corresponding to the tensor bounded around 1. We propose a penalty function that can constrain the singular values of the transformation matrix to be around 1. We derive an algorithm to carry out the gradient descent minimization of this penalty function in terms of convolution kernel tensors. Numerical examples are presented to demonstrate the effectiveness of the method.
Variable selection in Gamma regression model using binary gray Wolf optimization algorithm

In the real life applications, large amounts of variables have been accumulated quickly. Selection of variables is a very useful tool for improving the prediction accuracy by identifying the most relative variables that related to the study. Gamma regression model is one of the most models that applied in several science fields. Gray Wolf optimization algorithm (GWO) is one of the proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this paper, chaotic GWO is proposed to perform variable selection for gamma regression model. The simulation studies and a real data application are used to evaluate the performance of our proposed procedure in terms of prediction accuracy and variable selection criteria. The obtained results demonstrated the efficiency of our proposed methods comparing with other popular methods.
Silver-containing bioactive glasses for tissue engineering applications

Abstract: Bioactive glasses have shown promising results for tissue engineering applications. Specific metallic ions can be used for doping these materials to induce enhanced biological performance. Silver is an element well-known for its antibacterial properties. This chapter present an overview of Ag-doped bioactive glasses including their synthesis, properties and applications in tissue engineering. Moreover, the reported effects of Ag-doping on the properties of bioactive glasses are discussed.
New Multi-expert System Based on LSSVM and FI

In order to make a diagnosis and obviate the fault more effectively, when complicated mechanical system goes wrong, combining the characteristics of support vector machine, fuzzy integral and expert system, this paper puts forward the integral structure and diagnosis model of multi-expert system based on LSSVM and FI. The model synthesizes the advantages of each algorithm. This method overcomes the insufficiency of traditional expert system such as lack of the mechanism of effective self-study and self-adapt, be difficult to resolve the nonlinear system fault problems. Therefore, it is a convenient way to resolve the multi-sign and multi-reason fault problems of complicated mechanical system.
Penicillin Acylase from Streptomyces lavendulae and Aculeacin A Acylase from Actinoplanes utahensis: Two Versatile Enzymes as Useful Tools for Quorum Quenching Processes

Many Gram-negative bacteria produce N-acyl-homoserine lactones (AHLs), quorum sensing (QS) molecules that can be enzymatically inactivated by quorum quenching (QQ) processes; this approach is considered an emerging antimicrobial alternative. In this study, kinetic parameters of several AHLs hydrolyzed by penicillin acylase from Streptomyces lavendulae (SlPA) and aculeacin A acylase from Actinoplanes utahensis (AuAAC) have been determined. Both enzymes catalyze efficiently the amide bond hydrolysis in AHLs with different acyl chain moieties (with or without 3-oxo modification) and exhibit a clear preference for AHLs with long acyl chains (C12-HSL > C14-HSL > C10-HSL > C8-HSL for SlPA, whereas C14-HSL > C12-HSL > C10-HSL > C8-HSL for AuAAC). Involvement of SlPA and AuAAC in QQ processes was demonstrated by Chromobacterium violaceum CV026-based bioassays and inhibition of biofilm formation by Pseudomonas aeruginosa, a process controlled by QS molecules, suggesting the application of these multifunctional enzymes as quorum quenching agents, this being the first time that quorum quenching activity was shown by an aculeacin A acylase. In addition, a phylogenetic study suggests that SlPA and AuAAC could be part of a new family of actinomycete acylases, with a preference for substrates with long aliphatic acyl chains, and likely involved in QQ processes.
Short-Term Load Forecasting for Spanish Insular Electric Systems

In any electric power system, the Transmission System Operator (TSO) requires the use of short-term load forecasting algorithms. These predictions are essential for appropriate planning of the energy resources and optimal coordination for the generation agents. This study focuses on the development of a prediction model to be applied to the ten main Spanish islands: seven insular systems in the Canary Islands, and three systems in the Balearic Islands. An exhaustive analysis is presented concerning both the estimation results and the forecasting accuracy, benchmarked against an alternative prediction software and a set of modified models. The developed models are currently being used by the Spanish TSO (Red Eléctrica de España, REE) to make hourly one-day-ahead forecasts of the electricity demand of insular systems.