Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative performance investigation of optimal controller for AGC of electric power generating systems

In this study, an attempt is made to present the application and comparative performance analysis of optimal control approach for automatic generation control (AGC) of electric power generating systems. Optimal controller is designed utilizing performance index minimization criterion. To conduct the study, various single and multi-area models with/without system nonlinearities from the literature are simulated under sudden load perturbation. In this comparative study, to corroborate the worth of optimal controller, the performance of optimal AGC controller is compared with that of I/PI controller optimized adopting recently published the best established techniques such as teacher learning based optimization (TLBO), differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), hybrid bacteria foraging optimization algorithm-PSO (hBFOA-PSO), craziness based PSO (CBPSO), firefly algorithm (FA), krill herd algorithm (KHA), moth-flame optimization (MFO), glow swarm optimization (GSO), simulated annealing (SA), bat algorithm (BA), stochastic fractal search (SFS) and hybrid SFSlocal unimodal sampling (hSFS-LUS) technique. The simulated results are compared in terms of settling time (ST), peak undershoot (PU)/overshoot (PO), various performance indices (PIs), minimum damping ratio (ξmin) and system eigenvalues. A sensitivity study is conducted to certify the robustness of optimal controller.
A comparative evaluation of the antimicrobial effect of chamomile, Aloe vera-green tea, and chlorhexidine mouthwashes on some oral bacterial species

Background: Plant compounds such as Aloe vera, green tea, and chamomile have been increasingly used in recent years to achieve oral health. This study aimed to determine the antimicrobial effect of chamomile (Matrika), A. vera-green tea, and chlorhexidine (CHX) mouthwashes on some oral bacterial species. Materials and Methods: This prospective experimental study investigated the antimicrobial properties of three mouthwashes, including chamomile (Matrika), A. vera -green tea, and CHX as well as distilled water as control on five bacterial species, including Streptococcus Oralis, Streptococcus sanguis, and Streptococcus mutans as primary colonizers and Porphyromonas gingivalis and Eikenella corrodens as secondary colonizers. Colony-forming unit was used to count the colonies and disc diffusion and well diffusion methods were used to measure the diameter of zone of inhibition. Data were analyzed by SPSS (version 22) software using descriptive statistics, ANOVA, Tukey, Kruskal–Wallis, and Mann–Whitney tests (α = 0.05). Results: CHX had a significantly higher antibacterial effect than the other two mouthwashes in all three methods (P < 0.001). Further, the herbal mouthwashes in all three methods had a statistically significant effect on the bacterial species (P < 0.001). A. vera-green tea mouthwash had a significantly higher effect than chamomile mouthwash (Matrika) on all bacterial species except for S. sanguis (P < 0.05). Conclusion: The findings showed that herbal mouthwashes had potentially antibacterial effects, but these effects were significantly lower than that of CHX. However, more clinical studies are needed to prove the current findings.
Asymptotic distribution of regression correlation coefficient for Poisson regression model

ABSTRACT This study treats an asymptotic distribution for measures of predictive power for generalized linear models (GLMs). We focus on the regression correlation coefficient (RCC) that is one of the measures of predictive power. The RCC, proposed by Zheng and Agresti is a population value and a generalization of the population value for the coefficient of determination. Therefore, the RCC is easy to interpret and familiar. Recently, Takahashi and Kurosawa provided an explicit form of the RCC and proposed a new RCC estimator for a Poisson regression model. They also showed the validity of the new estimator compared with other estimators. This study discusses the new statistical properties of the RCC for the Poisson regression model. Furthermore, we show an asymptotic normality of the RCC estimator.
Monitoring and simulation-based optimization of two multi-apartment NZEBs with heat pump, solar thermal and PV

For achieving carbon neutrality, new buildings must be built according to very high-performance standards, such that their energy needs can be covered predominantly by onsite renewables. Although the technical solutions for efficient envelopes are well established, heat pumps together with PV have proven to be a reliable HVAC system, the discussion is ongoing regarding the optimal system concept and design for NZEB. Furthermore, the real performance strongly depends on the design, implementation, commissioning and operation. Given the complexity of building and HVAC with the influence of climate and users, a benchmark for optimal operation requires the combination of monitoring analysis and validated component and system models. Different steps and aspects of the calibration and optimization process are presented. The model, representing a step towards a digital twin, was used to improve the operation and derive guidelines for future optimal NZEB HVAC design, commissioning and operation for maximum primary energy savings. HIGHLIGHTS Integrated simulation-based design, commissioning and optimization of Net-zero energy residential buildings Calibrated component as well as building and HVAC system simulation models Simulation assisted commissioning and optimization leads to relevant energy savings NZE Multi-Apartment Buildings possible with optimized system design and control Monthly evaluation of primary energy highlights the need for reduced winter energy demand GRAPHICAL ABSTRACT
Speed and Torque Estimation of BLDC using DTC and Sliding Mode Observer

This paper presents speed and torque estimation for Brushless DC (BLDC) motors with non-sinusoidal back electromotive force using six switch inverter and DTC technique. The 180 conduction mode is the more popular method used for three-phase drives but here we use two-phase conduction mode. A simple approach is discussed n how to reduce ripples in the estimated torque at low frequency operation. A simple look-up table at a pre-defined sampling time is used to select the inverter voltage space vectors and the quasi-wave current is obtained. Estimation of electromagnetic torque for BLDC drives is the key issue and so sensor-less control method are used. The sliding mode observer estimates the back-EMF and generates torque, as under sliding mode observer error equation is reduced and it makes stability easier. Only the measurements of the stator currents is used in the estimation of back-EMF waveform. The electromagnetic torque and the rotor speed is estimated using values from Sliding Mode Observer. Fuzzy Gain Scheduling method is used to tune the parameters because this scheme uses human expertise on PID gain scheduling can be represented in fuzzy rules. Furthermore, better control performance can be expected in the proposed method than that of the PID controllers with fixed parameters and the gains of the sliding mode observer are tuned manually. The effectiveness of the proposed scheme is verified by using simulation results. Keywords: BLDC, DTC, Sliding Mode Observer(SMO), PID Controller, Fuzzy Gain Scheduling, Estimated Rotor speed, Estimated Torque, Estimated Back-EMF.
NO WEAL WITHOUT WOE: IMPLEMENTATION OF PERSONAL DATA PROTECTION SYSTEMS AND CORPORATE VALUE

ABSTRACT The commercial exploitation of personal information has raised concerns regarding privacy, illegal data use, and information security, among others. Therefore, personal data protection systems (PDPS) play a significant role, and corporations are the primary enforcers of these systems’ regulation. However, PDPS require significant investment from companies, and there is no consensus regarding the economic outcomes of establishing these systems. This study investigates whether the establishment of PDPS affects short-term financial performance and long-term corporate value. After applying the propensity score matching method, a dataset comprising 912 firm-year observations of e-commerce companies listed on the Shanghai and Shenzhen Stock Exchanges from 2008 to 2020 was selected. The results show that PDPS implementation can improve a company’s short-term financial performance by a) exploring markets and strengthening internal control and b) increase long-term corporate value by strengthening corporate social responsibility. This study offers insights for companies to proactively implement PDPS and strengthen their management of personal data, thereby boosting the overall corporate value. In addition, this study can help governments to develop legislation on national information security and enhance international cooperation, especially for emerging markets.
Additional Value of PET and CT Image-Based Features in the Detection of Occult Lymph Node Metastases in Lung Cancer: A Systematic Review of the Literature

Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([18F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [18F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs.
Model-Based State-of-Charge and State-of-Health Estimation Algorithms Utilizing a New Free Lithium-Ion Battery Cell Dataset for Benchmarking Purposes

State estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore, this paper introduces, on the one hand, a new lithium-ion battery dataset with dynamic validation data over degradation and, on the other hand, a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman-filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The paper describes the dataset, the models, the parameterisation, the implementation of the state estimations, and their validation using parts of the dataset, resulting in SOC and SOH estimations over the entire battery life. The results show that the dataset can be used to extract parameters, design models based on it, and validate it with dynamically degraded battery cells. The work provides an approach and dataset for better performance evaluations, applicability, and reliability investigations.
Demonstration on range imaging of 256×256 pixels and 30 frames per second using short wavelength infrared pulsed time-of-flight laser sensor with linear array receiver

Abstract. We demonstrated the range imaging with high resolution of 256×256 pixels and high frame rate of 30 frames per second (fps) using a short wavelength infrared pulsed time-of-flight laser sensor, which is suitable for long range imaging. We additionally demonstrated the long range imaging of more than 1 km and wide field of view imaging of 12 deg× 4 deg, 768×256 pixels, and 10 fps. For these demonstrations, we developed the linear array devices of the aluminum indium arsenide avalanche photodiode array and silicon germanium bipolar complementary metal oxide semiconductor read-out integrated circuit array. We also deployed the flattop beam illumination optics with the beam division and recombination method and realized efficient line shape illumination.
DYMEMULP – Dynamic Model of Process Optimisation in Regional Logistics

The expansion of logistics requirements, limited space and strict requirements of generators of logistics requests (GLR) in terms of service quality complicate the supply of the region, resulting in the necessity to improve logistics models (MoL). Proximity to water, the presence of ports and piers along the coast, new eco vehicles and the development of cooperation between land and water transport are elements for improving the existing MoLs in an economically and environmentally acceptable way. Research on the development of an improved multi-echelon logistics network with variable terminals including the coordination and cooperation of a heterogeneous group of transport agencies for the realisation of goods flows represents an innovation in regional logistics (RL). This article presents an integrated MoL development process using dynamic optimisation with a focus on spatial, temporal, transport, economic and environmental components.
A novel unstructured mesh finite element method for solving the time-space fractional wave equation on a two-dimensional irregular convex domain

Abstract Most existing research on applying the finite element method to discretize space fractional operators is studied on regular domains using either uniform structured triangular meshes, or quadrilateral meshes. Since many practical problems involve irregular convex domains, such as the human brain or heart, which are difficult to partition well with a structured mesh, the existing finite element method using the structured mesh is less efficient. Research on the finite element method using a completely unstructured mesh on an irregular domain is of great significance. In this paper, a novel unstructured mesh finite element method is developed for solving the time-space fractional wave equation on a two-dimensional irregular convex domain. The novel unstructured mesh Galerkin finite element method is used to discretize in space and the Crank-Nicholson scheme is used to discretize the Caputo time fractional derivative. The implementation of the unstructured mesh Crank-Nicholson Galerkin method (CNGM) is detailed and the stability and convergence of the numerical scheme are analyzed. Numerical examples are presented to verify the theoretical analysis. To highlight the ability of the proposed unstructured mesh Galerkin finite element method, a comparison of the unstructured mesh with the structured mesh in the implementation of the numerical scheme is conducted. The proposed numerical method using an unstructured mesh is shown to be more effective and feasible for practical applications involving irregular convex domains.
Normative Values of Heart Rate Variability During Sleep in Indian Population

Heart rate variability (HRV) measurement is a noninvasive method to assess the autonomic nervous system. Therefore, conducting HRV measurements to asses many risk factors including cardiovascular is gaining popularity in athletic, non-athletic, and clinical populations. An electrocardiogram (ECG) is the most reliable tool to measure HRV, but using heart rate monitors like wristbands, watches, and chest bands are more useful during ambulatory or fitness-related activities. Studies have been conducted for the wake time HRV in various populations, but HRV measurements during sleep have not been conducted yet. The time domain of HRV quantifies the amount of variability in measurements of the inter-beat interval (IBI), which is the time period between successive heartbeats. This study analyzed time-domain sleep HRV in a large sample (n = 300) of a healthy young population. ECG of subjects were recorded during sleep using the Repose chest wearable. The ECG was preprocessed and analysed by Repose platform. Kubios standard software (version 3.5) was used to measure the sleep time HRV parameters. It was found that the HRV for 15-minute deep sleep window can reliably give a baseline data against which wake time HRV can be compared. It was also found that males have significantly (p<0.05) higher HRV compared to females. In conclusion, this study provided normative data for sleep time-domain HRV parameters and also reflected that females have higher sympathetic activity compared to males in the Indian population. These values will help compare the clinically or otherwise measured HRV to the benchmarks established by the study.
Dietary Palygorskite-Based Antibacterial Agent Supplementation as an Alternative to Antibiotics Improves Growth Performance, Blood Parameters, and Rumen Microbiota in Sheep

This research aimed to investigate the effects of a palygorskite-based antibacterial agent (PAA) as an alternative to antibiotics on growth performance, blood parameters, and rumen microbiota in sheep. A total of 120 sheep were randomly divided into five groups of six replicates with four sheep each. Sheep were fed a basal diet, an antibiotic diet supplemented with 500 g/t chlortetracycline (CTC), and a basal diet supplemented with 500, 1000, and 2000 g/t PAA for 80 d, respectively. Supplementation with 2000 g/t PAA and 500 g/t CTC increased the average daily gain (ADG) of sheep compared with the control group (p < 0.05). Diets supplemented with 2000 g/t PAA and 500 g/t CTC reduced (p < 0.05) the feed:gain ratio (F/G ratio) in the overall periods. Dietary supplementation with 1000 g/t PAA significantly increased albumin and total protein (p < 0.05). A significant positive correlation was found between growth hormone concentration and PAA supplementation (p < 0.05). In addition, compared to the control group, the CTC group had higher growth hormone concentration and lower lipopolysaccharide concentration (p < 0.05). No difference was observed between the five groups in terms of rumen fermentation characteristics (p > 0.05). At the phylum level, the relative abundance of Proteobacteria was lower in the PAA 2000 and CTC 500 groups than in the control and PAA 500 groups (p < 0.05). At the genus level, a significant decrease (p < 0.05) in the relative abundance of RuminococcaceaeUCG-010 was observed in the PAA 1000, PAA 2000, and CTC 500 groups compared with that in the control group. In addition, the relative abundance of Prevotella1 (p < 0.05) was higher in the PAA 2000 group than in the control group. These findings indicate that dietary supplementation with PAA has ameliorative effects on growth performance, blood parameters, and rumen microbiota, with an optimal dosage of 2000 g/t for sheep.
Implementation of Qurma Learning in Developing Speaking Skills in Fourth Grade Students at Islamic Elementary School (MI) Narrative Qur'an Lamongan

Speaking skill is a very important role in elementary school. Speaking skills are mechanistic, meaning that speaking skills are acquired through a continuous process of practice. The purpose of this research is to describe an implementation of Qur'an learning (Qur'an and meaning) in developing speaking skills in fourth-class students at Elementary school Islamic Narrative Qur'an Lamongan. This research used a qualitative approach with a qualitative descriptive research type. The data collection techniques in this research were interviews, observation, and documentation. This research used data analysis in the form of data reduction, data presentation, and conclusion. The validity of the data was tested using triangulation of sources and techniques. The results of the research indicate the implementation of Qurma learning at Elementary schools Islamic Narrative Qur'an Lamongan includes planning, implementing, and evaluating learning. The growth of speaking skills in students through learning Qurma is marking by choosing words that are easy to understand, placement of appropriate intonation, self-confidence, mastering the topic of the story being told, fluency and accuracy of speech, and courage.
Dual Word Embedding for Robust Unsupervised Bilingual Lexicon Induction

The word embedding models such as Word2vec and FastText simultaneously learn dual representations of input vectors and output vectors. In contrast, almost all existing unsupervised bilingual lexicon induction (UBLI) methods use only input vectors without utilizing output vectors. In this article, we propose a novel approach to making full use of both input and output vectors for more robust and strong UBLI. We discover the Common Difference Property that one orthogonal transformation can connect not only the input vectors of two languages but also the output vectors. Therefore, we can learn just one transformation to induce two different dictionaries from the input and output vectors, respectively. Between these two quite different dictionaries, a more accurate lexicon with less noise can be induced by taking the intersection of them in UBLI procedure. Extensive experiments show that our method achieves much more robust and strong results than state-of-the-art methods in distant language pairs, while reserving comparable performances in similar language pairs.
Recent Trends in Deep Learning Based Textual Emotion Cause Extraction

Emotion Cause Extraction Field (ECEF) focuses on the cause that triggers an emotion in a document. Traditional ECEF aims to extract the cause based on a given emotion while recent ECEF focuses more on extracting both the emotion and its corresponding cause. ECEF has attracted a lot of attention due to the significant developments in deep learning techniques, especially machine reading comprehension and neural network-based information retrieval. However, a comprehensive review of existing approaches and recent trends in ECEF is lacking. In this paper, we present a thorough survey to summarise existing methods for ECEF, including those for Emotion Cause Extraction (ECE), Emotion Cause Pair Extraction (ECPE), and Conversational Emotion Cause Extraction Field (CECEF). We also detail the widely used public datasets and discuss the limitations and prospects of existing methods in ECEF.