Miracle-dz/UniFAR-FTUs huggingface dataset:
Oppositional Jaya Algorithm With Distance-Adaptive Coefficient in Solving Directional Over Current Relays Coordination Problem

The model of directional over current relays (DOCRs) coordination is considered as an optimization problem. It is generally formulated as linear programming (LP), non-linear programming (NLP) and mixed integer non-linear programming (MINLP), according to the nature of the design variables. For each kind of formulation, the main goal is to minimize the summation of operating times of primary relays, by setting optimal values for decision variables as time dial setting (TDS) and pickup current setting (IP) or plug setting (PS). In this paper, we proposed an oppositional Jaya (OJaya) algorithm with distance-adaptive coefficient (DAC), to effectively solve the DOCRs coordination problem. Firstly, by oppositional learning (OL), the searching space of Jaya is expanded and the diversity of its population is strengthened; secondly, by DAC, the population’s trends of running towards the best position and escaping from the worst position is accelerated. The performance of OJaya is evaluated by 3-bus, 8-bus, 9-bus and 15-bus testing systems, in aspects of convergence rate, objective function value, robustness and computation efficiency. The results indicate the effectiveness and superiority of OJaya in solving DOCRs coordination problems compared with standard Jaya.
Optimization of Dialysis Modality Transitions for Improved Patient Care

Purpose of review: Initial and subsequent modality decisions are important, impacting both clinical outcomes and quality of life. Transition from chronic kidney disease to dialysis and between dialysis modalities are periods were patients may be especially vulnerable. Reviewing our current knowledge surrounding these critical periods and identifying areas for future research may allow us to develop dialysis strategies beneficial to patients. Sources of information: We searched the electronic database PubMed and queried Google Scholar for English peer-reviewed articles using appropriate keywords (non-exhaustive list): dialysis transitions, peritoneal dialysis, home hemodialysis, integrated care pathway, and health-related quality of life. Primary sources were accessed whenever possible. Methods: In this narrative review, we aim to expose the controversies surrounding home-dialysis first strategies and examine the evidence underpinning home-dialysis first strategies as well as home-to-home and home-to-in-center transitions. Key findings: Diverse factors must be taken into consideration when choosing initial and subsequent dialysis modalities. Given the limitations of available data (and lack of convincing benefit or detriment of one modality over the other), patient-centered considerations may prime over suspected mortality benefits of one modality or another. Limitations: Available data stem almost exclusively from retrospective and observational studies, often using large national and international databases, susceptible to bias. Furthermore, this is a narrative review which takes into account the views and opinions of the authors, especially as it pertains to optimal dialysis pathways. Implications: Emphasis must be placed on individual patient goals and preferences during modality selection while planning ahead to achieve timely and appropriate transitions limiting discomfort and anxiety for patients. Further research is required to ascertain specific interventions which may be beneficial to patients.
Investigation of Complexity and Regulatory Role of Physiological Activities During a Pacing Exercise

Existing physiological control fatigue models propose that there may be a regulator in the central nervous system which modulates our daily physical activity. Within limits, this regulator ensures physical activity is completed without physiological system failure through interactive communications between the peripheral systems and the central systems. The ability of the central nervous system to regulate exercise is vital to optimise sport performance when severe intensity exercise might be required for prolonged or frequent periods. Based on mathematical models, this investigation explores the complex relationship between some of the mechanisms controlling physical activity and behaviour. In order to analyse the system control mechanisms, heart rate, volume of oxygen consumption and power output were measured for a well-trained male cyclist. Using power spectrum analysis, fractal analysis and continuous wavelet transforms, we show that the system control mechanisms regulating physiological systems, have distinct complexity. Moreover, the potential central controller uses specific frequency bands simultaneously to control and communicate with the various physiological systems. We show that pacing trials are regulated by different physiological systems.
Ray-tracing based channel modeling for the simulation of the performance of visible light communication in an indoor environment

Ray-tracing based channel modeling is a powerful tool for the determination of the channel characteristics of VLC settings. In order to overcome the huge computational effort that is necessary in this regard, new strategies for less time-consuming ray-tracing based channel models are essential. Here we introduce a channel model that is based on optical simulations using non-sequential ray-tracing in combination with selfdeveloped algorithms in MATLAB. In addition, we discuss an approach, which allows for a time-effective determination of the dependence of the receiver position on the VLC characteristics along the diagonal of the floor of a room. Raytracing information of each ray impinging on a diagonal stripe (with a width of $\sim1.41$ cm) is exported to MATLAB for further analysis. By doing so, 26 different receiver positions along the diagonal axis of a room can be investigated using only one raytracing simulation run instead of repeating ray-tracing simulations for each of the 26 receiver positions. The error made by this simulation approach turns out to be negligible because of the small ratio between the area or the receiver stripe and the area of the whole floor.
Question Answering Systems With Deep Learning-Based Symbolic Processing

The authors propose methods to learn symbolic processing with deep learning and to build question answering systems by means of learned models. Symbolic processing, performed by the Prolog processing systems which execute unification, resolution, and list operations, is learned by a combination of deep learning models, Neural Machine Translation (NMT) and Word2Vec training. To our knowledge, the implementation of a Prolog-like processing system using deep learning is a new experiment that has not been conducted in the past. The results of their experiments revealed that the proposed methods are superior to the conventional methods because symbolic processing (1) has rich representations, (2) can interpret inputs even if they include unknown symbols, and (3) can be learned with a small amount of training data. In particular (2), handling of unknown data, which is a major task in artificial intelligence research, is solved using Word2Vec. Furthermore, question answering systems can be built from knowledge bases written in Prolog with learned symbolic processing, which, with conventional methods, is extremely difficult to accomplish. Their proposed systems can not only answer questions through powerful inferences by utilizing facts that harbor unknown data but also have the potential to build knowledge bases from a large amount of data, including unknown data, on the Web. The proposed systems are a completely new trial, there is no state-of-the-art methods in the sense of “newest”. Therefore, to evaluate their efficiency, they are compared with the most traditional and robust system i.e., the Prolog system. This is new research that encompasses the subjects of conventional artificial intelligence and neural network, and their systems have higher potential to build applications such as FAQ chatbots, decision support systems and energy-efficient estimation using a large amount of information on the Web. Mining hidden information through these applications will provide great value.
Hardware Efficient Integer Discrete Cosine Transform for Efficient Image/Video Compression

With the introduction of high efficiency video coding (HEVC) standard which provides super compression efficiency, there has been a lot of research works on integer transform matrices that can provide good approximation to the discrete cosine transform (DCT) used in HEVC. Not only maintaining the coding performance, the hardware and power of the circuit to implement the derived integer DCT (Int-DCT) needs to be minimized. To address these multiple design considerations, a new multi-objective optimization algorithm is proposed in this paper to search for efficient Int-DCT matrix, which has the coding performance as close as possible to the transform in HEVC but implemented with reduced hardware and power. Experimental results show that the approximated Int-DCT matrix generated by the proposed algorithm can achieve almost the same coding performance as the transforms in HEVC measured in terms of BjØntegaard Delta rate. Meanwhile, the experiments demonstrate that the proposed 16-point Int-DCT can produce at least 15.5% and 26.8% lower circuit area in FPGA and ASIC respectively, compared with other state-of-the-art Int-DCT realizations which can provide similar coding performance.
LSTM Neural Network Based Math Information Retrieval

The work presented in this paper ascertains role of Long Sort-Term Memory (LSTM) neural network in Math Information Retrieval (MIR). Motivated from promising performances of the LSTM for sequence-to-sequence tasks, an LSTM based Formula Entailment (LFE) module is implemented for recognizing entailment between mathematical user query and document formulae. The LFE module is trained and validated using a symbol level Math Formula Entailment (MENTAIL) dataset. The relevance of a document is determined by the fraction of document formulae which entail the user query. A reasonable score of 0.45 for the P_5 evaluation measure substantiates competence of the implemented MIR system in retrieving relevant documents corresponding to a mathematical user query.
An Industrial Robot-Based Rehabilitation System for Bilateral Exercises

Robot-assisted rehabilitation devices can provide intensive and precise task-based training that differs from clinician-facilitated manual therapy. However, industrial robots are still rarely used in rehabilitation, especially in bilateral exercises. The main purpose of this research is to develop and evaluate the functionality of a bilateral upper-limb rehabilitation system based on two modern industrial robots. A ‘patient-cooperative’ control strategy is developed based on an adaptive admittance controller, which can take into account patients’ voluntary efforts. Three bilateral training protocols (passive, active, and self) are also proposed based on the system and the control strategy. Experimental results from 10 healthy subjects show that the proposed system can provide reliable bilateral exercises: the mean RMS values for the master error and the master-slave error are all less than 1.00 mm and 1.15 mm respectively, and the mean max absolute values for the master error and the master-slave error are no greater than 6.11 mm and 6.73 mm respectively. Meanwhile, the experimental results also confirm that the recalculated desired trajectory can present the voluntary efforts of subjects. These experimental findings suggest that industrial robots can be used in bilateral rehabilitation training, and also highlight the potential applications of the proposed system in further clinical practices.
Investigating Factors Contributing to Injury Severity in Work Zone Rear-End Crashes

Egypt’s National Road Project is a large infrastructure project which presently aims to upgrade 2500 kilometers of road networks as well as construct 4000 kilometers of new roads to meet today’s need. Increasing the amount of highway construction results in a significant increase in the number of work zones and therefore a rise in more hazardous conditions. This paper aims to investigate and identify the significant contributing factors for work zone rear-end crash severity, by applying a factor analysis method and an ordered probit model to analyze data on the Egyptian long-term highway work zone projects during the period of 2010 to 2016. The main and common factors which influence crashes are identified by factor analysis, while the probit model is developed based on the scores of factor analysis to examine how those factors contribute to crashes. The results show that the most influential factors which contribute to crashes are gender, road character, number of lane closures, and age. Furthermore, five common factors significantly affect the severity of work zone rear-end crashes, whose accumulated contribution percentage is 65.92%.
Determinants of E-Government Use in Developing Countries: The Influence of Privacy and Security Concerns

There has been growing concern about privacy and security risks towards electronic-government (e-government) services adoption. Though there are positive results of e- government, there are still other contestable challenges that hamper success of e-government services. While many of the challenges have received considerable attention, there is still little to no firm research on others such as privacy and security risks, effects of infrastructure both in urban and rural settings. Other concerns that have received little consideration are how for instance; e-government serves as a function of perceived usefulness, ease of use, perceived benefit, as well as cultural dimensions and demographic constructs in South Africa. Guided by technology acceptance model, privacy calculus, Hofstede cultural theory and institutional logic theory, the current research sought to examine determinants of e- government use in developing countries. Anchored upon the aforementioned theories and background, the current study proposed three recommendations as potential value chain, derived from e-government service in response to citizens (end- user) support, government and community of stakeholders.
Dielectrophoretic Microfluidic Chip Integrated With Liquid Metal Electrode for Red Blood Cell Stretching Manipulation

Cellular mechanical properties are closely related to cell physiological functions and status, and their analysis and measurement help understand cell mechanism. In this study, a microfluidic platform was built to measure the mechanical properties of cells by using dielectrophoretic (DEP) force. The electrodes generally used to stretch cells are made of indium tin oxide, Au, and Pt, which have inherent disadvantages. In this paper, galinstan alloy liquid metal was first introduced as microelectrode to form non-uniform electric filed for red blood cell stretching manipulation. The liquid metal microelectrode is easy to manufacture, low in price, stable at high voltage, and reusable. An effective microfluidic chip integrated with liquid metal electrode was designed and simulated, and a series of experiments to capture and stretch red blood cells was performed. The length of the red blood cells increased from <inline-formula> <tex-math notation="LaTeX">$6~\mu \text{m}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$8~\mu \text{m}$ </tex-math></inline-formula> under the DEP force from 0 pN to 103 pN. This work also revealed the potential use of liquid metal as microelectrode to manipulate the microparticles and cells in a microfluidic chip.
Laparoscopic cholecystectomy in liver cirrhosis patients: An Egyptian experience

Aim: Cirrhosis represents a common histological pathway for a wide variety of chronic liver diseases. Hepatitis C virus (HCV) is the most important cause of liver cirrhosis in Egypt. Although cirrhosis has been regarded as a relative contraindication for laparoscopic cholecystectomy (LC) as a result of bleeding complications and subsequent liver failure, several reports support the safety of LC in selected patients. This was a prospective study to evaluate the efficacy and safety of LC in cirrhotic patients.
Fine-tuning of Pre-trained DCNN for Gastritis Detection from Gastric X-ray Images

This paper presents a detection method of gastritis from gastric X-ray images using fine-tuning techniques. With the development of deep convolutional neural networks (DCNNs), DCNN-based methods have achieved more accurate performance than conventional machine learning methods using hand-crafted features in the field of medical image analysis. However, lack of training images often occurs in clinical situations even though DCNNs require a large amount of training images to avoid overfitting. Therefore, the proposed method aims to consider the clinical situations that a limited amount of the training images are available. By fine-tuning a DCNN pre-trained with a large amount of annotated natural images, we avoid overfitting and realize accurate detection of the gastritis with a small amount of the training images.
Scattering of E-Polarized Electromagnetic Wave by Slotted Cylinder with Coaxially Enclosed Dielectric-Coated Cylinder

The boundary-value method is applied to the problem of electromagnetic scattering of a plane E-polarized wave by a slotted circular cylinder with a coaxially enclosed solid circular cylinder coated with dielectric; both cylinders are perfectly conducting. The solution is obtained in the form of a system of linear algebraic equations. An approximate analytical solution in long-wavelength range for the case of narrow slot opening is formulated. Using both the analytical and the accurate numerical solutions the scattering cross sections of the structure are analyzed for a selection of sample configurations, including the effects of a finite thickness of the slotted cylinder’s wall. Shown that the scattering from such structure exhibits resonance features, while in long-wavelength range it is closely matching the scattering from an equivalent solid circular cylinder.
An Advanced Dice Similarity Measure of Fuzzy Numbers and Its Application in Multicriteria Decision Making

Due to presence of uncertainty decision making process becomes more complicated. The similarity of fuzzy numbers which deals with such uncertainties plays again an important role in the process. Similarity of fuzzy numbers is basically necessary to take the appropriate decision by a decision maker in any real world environment. A few numbers of similarity measures have been encountered in recent decades. But, despite of advantages of the recent approaches, they have some drawbacks/shorcomings. In this connection, this paper presents a general dice similarity measure. The results obtained from this approach are found to be more efficient in comparison to the other ranking methods and outperform in all situations. Comparison of the present method with existing works has been made which established novelty and validity. Furthermore, the approach has been applied in decision making problem and the results obtained by the approach absolutely conform with human intuitions as well as analytical results.
The Toowoomba infant feeding support service project: Report on phase 1 - a longitudinal needs analysis of breastfeeding behaviours and supports in the Toowoomba region

This study represents a needs analysis of breastfeeding issues in the Darling Downs region of Queensland. It involved a longitudinal study of regional, rural and remotely located mothers giving birth in the public and private hopsitals in Toowoomba. In line with gaps in knowledge identified in the literature, the aims of the project were to: ascertain the breastfeeding initiation rates and breastfeeding duration for mothers in the sample; determine if these rates were affected by rurality or hospital type; identify variables statistically related to predication of initiation of breastfeeding and breastfeeding duration; determine the adequacy of information provided to mothers by hospital staff about breastfeeding and postnatal supports; and identify the availability, utilisation, and perceived helpfulness of postnatal breastfeeding services and supports in the region.
Retinal Layer Segmentation in Optical Coherence Tomography Images

The four major causes of blindness are age-related diseases, out of which three affects the retina. These diseases, i.e., glaucoma, diabetic retinopathy, and age-related macular degeneration, require life-long treatment and cause irreversible blindness. Conversely, early diagnosis has been shown to curtail or prevent blindness and visual impairments. A critical element of the clinical diagnosis is the analysis of individual retinal layer properties, as the manifestation of the dominant eye diseases has been shown to correlate with structural changes to the retinal layers. Regrettably, manual segmentation is dependent on the ophthalmologist’s level of expertise, and currently becoming impractical due to advancement in imaging modalities. Inherently, much research on computer-aided diagnostic methods is conducted to aid in extracting useful layer information from these images, which were inaccessible without these techniques. However, speckle noise and intensity inhomogeneity remain a challenge with a detrimental effect on the performance of automated methods. In this paper, we propose a method comprising of fuzzy image processing techniques and graph-cut methods to robustly segment optical coherence tomography (OCT) into five (5) distinct layers. Notably, the method establishes a specific region of interest to suppress the interference of speckle noise, while Fuzzy C-means is utilized to build data terms for better integration into the continuous max-flow to handle inhomogeneity. The method is evaluated on 225 OCT B-scan images, and promising experimental results were achieved. The method will allow for early diagnosis of major eye diseases by providing the basic, yet critical layer information necessary for an effective eye examination.
Pharmacognostical review of Urtica dioica L.

Urtica dioica L. belongs to the family Urticaceae, is a perennial herb commonly known as ‘stinging nettle’. This herb is found in many South Asian Countries, Indian subcontinent and has been known in the world as a medicinal herb for a long time. U. dioica is widely used by the traditional medicinal practitioners for curing various diseases such as nephritis, haematuria, jaundice, menorrhagia, arthritis and rheumatism. Phytochemical studies revealed the presence of many valuable chemical compounds like phytosterols, saponins, flavanoids, tannins, proteins and amino acids. The plant also has been used as food, fiber, paint, manure and cosmetics. U. dioica has been reported to have various pharmacological activities like antibacterial, antioxidant, analgesic, anti‑inflammatory, antiviral, immunomodulatory, hepatoprotective, anti‑colitis and anticancer effects. The current review summarizes published information about the ethnopharmacology, phytochemistry, biological activities and toxicological reports of U. dioica. The present review summarizes all the research work carried out on this plant in order to provide updated information for future works. Key words: Urtica dioica L. , Ethanomedicinal uses, phytochemistry, pharmacology, toxicology
Full Wave Simulation of a Vertically Polarized Transverse Electromagnetic Mode Cell

Vertically Polarized Transverse Electromagnetic Mode (VTEM) cells may be used for immunity tests in vehicles. The tests are performed according to the ISO 11451-2 Standard. This work material describes a full wave approach for simulation of a VTEM cell. The investigation is performed in two stages. Firstly, the field uniformity and vertical polarization of the empty cell are verified. Secondly, the impact of the vehicle presence on the field structure inside the cell is examined. The simulation results are compared to measurements.