Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Method for Diagnosing Breast Cancer using Firefly Algorithm and Fuzzy Rule based Classification

Predicting and diagnosing various diseases are two effective components in medical science. Recently, with increasing expansion of science, using the decision support system can help significantly to the therapeutic policies of physician. For this purpose, by using artificial intelligent systems in this study, we are trying to predict and diagnose the breast cancer which is one of the most prevalent cancers among the women. In this study, three datasets of Wisconsin cancer diagnosis were used which were obtained by FNA test. Process of breast cancer diagnosis was investigated by a new approach. With the help of fuzzy rule based classification systems, datasets were classified. Rule weight learning algorithm has been used to improve the classification system which minimizing the classification error on the training data using the rule weight adjustment. An integrated method was also presented to reduce the dimensions of datasets features using the firefly algorithm and statistical repair mechanism. Comparing the performance of the aforementioned method with other ones shows the desired performance and high accuracy of the proposed method.
A Soft-Switching Step-Down PFC Converter with High Power Factor Using Auxiliary Flyback Circuit

In this paper, a new soft-switching bridgeless single-phase power factor correction converter is introduced and analyzed. Employing an auxiliary flyback circuit, existing dead angle in the input current is eliminated while softswitching is achieved. The flyback converter processes only small amount of output power. Therefore, conduction losses of its bridge diode and semiconductor devices are low. Also, the auxiliary flyback converter provides soft-switching condition for buck switches. All semiconductors are turned on and off under soft-switching condition. In addition to reduction of switching losses, diode's reverse-recovery problems are eliminated due to soft-switching condition. The proposed approach takes advantage of operating under discontinuous conduction mode to shape the input current into sinusoidal form inherently. Experimental results are reported for a 120-W prototype at 110 Vrms input and 48-V output. The results demonstrate efficiency of 93% at nominal load condition. Furthermore, the input current harmonics fulfill IEC61000-3-2 (Class D) standard as well.
Adaptive Neural Integral Full-Order Terminal Sliding Mode Control for an Uncertain Nonlinear System

This paper reports the design of a control system for a class of general nonlinear second-order systems. The significant problems of singularity and chattering phenomenon, which limit the use of the conventional terminal sliding mode control (TSMC) in real applications due to the order of the sliding surface, need to be addressed. In addition, the effects of disturbances and uncertainties need to be removed, and the response rates are increased. Therefore, the integral full-order terminal sliding mode (IFOTSM) surface was proposed. To track the specified trajectories with high accuracy, a control approach is developed for the class of general nonlinear second-order systems by utilizing an IFOTSM surface and an adaptive compensator. The unknown dynamic model is derived based on a radial basis function neural network (RBFNN). Consequently, our controller provides good performance with minimum position errors, robustness against uncertainties, and work without a precise dynamic model. The simulated examples were performed to analyze the effectiveness of the control approach for position pathway tracking control of a 2-DOF parallel manipulator.
Aggregatibacter actinomycetemcomitans (Aa) Under the Radar: Myths and Misunderstandings of Aa and Its Role in Aggressive Periodontitis

Aggregatibacter actinomycetemcomitans (Aa) is a low-abundance Gram-negative oral pathobiont that is highly associated with a silent but aggressive orphan disease that results in periodontitis and tooth loss in adolescents of African heritage. For the most part Aa conducts its business by utilizing strategies allowing it to conceal itself below the radar of the host mucosal immune defense system. A great deal of misinformation has been conveyed with respect to Aa biology in health and disease. The purpose of this review is to present misconceptions about Aa and the strategies that it uses to colonize, survive, and evade the host. In the process Aa manages to undermine host mucosal defenses and contribute to disease initiation. This review will present clinical observational, molecular, and interventional studies that illustrate genetic, phenotypic, and biogeographical tactics that have been recently clarified and demonstrate how Aa survives and suppresses host mucosal defenses to take part in disease pathogenesis. At one point in time Aa was considered to be the causative agent of Localized Aggressive Periodontitis. Currently, it is most accurate to look at Aa as a community activist and necessary partner of a pathogenic consortium that suppresses the initial host response so as to encourage overgrowth of its partners. The data for Aa's activist role stems from molecular genetic studies complemented by experimental animal investigations that demonstrate how Aa establishes a habitat (housing), nutritional sustenance in that habitat (food), and biogeographical mobilization and/or relocation from its initial habitat (transportation). In this manner Aa can transfer to a protected but vulnerable domain (pocket or sulcus) where its community activism is most useful. Aa's “strategy” includes obtaining housing, food, and transportation at no cost to its partners challenging the economic theory that “there ain't no such thing as a free lunch. ” This “strategy” illustrates how co-evolution can promote Aa's survival, on one hand, and overgrowth of community members, on the other, which can result in local host dysbiosis and susceptibility to infection.
Text Classification Based on Latent Semantic Indexing and Graph Embedding

Text classification has become a key technology for processing and organizing large amounts of documents. In order to improve the performance of text classification, we propose a method based on latent semantic indexing and graph embedding technology. The method transforms original document space into semantic feature space by means of latent semantic indexing, and then graph embedding in this space is employed to construct an adjacent graph that reflects semantic similarity between documents and its complementary graph which reflects non-neighboring relationship. The proposed method tries to obtain the global structure and the local structure of documents. Using the $k$ nearest neighbor classifier on 20-Newsgroups dataset, the experimental results show that this method can improve the performance of text classification.
Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network

Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on. Accurately forecasting periodic data such as electricity can greatly improve the reliability of forecasting tasks in engineering applications. Time series forecasting problems are often modeled using deep learning methods. However, the deep information of sequences and dependencies among multiple variables are not fully utilized in existing methods. Therefore, a multivariate time series deep spatiotemporal forecasting model with a graph neural network (MDST-GNN) is proposed to solve the existing shortcomings and improve the accuracy of periodic data prediction in this paper. This model integrates a graph neural network and deep spatiotemporal information. It comprises four modules: graph learning, temporal convolution, graph convolution, and down-sampling convolution. The graph learning module extracts dependencies between variables. The temporal convolution module abstracts the time information of each variable sequence. The graph convolution is used for the fusion of the graph structure and the information of the temporal convolution module. An attention mechanism is presented to filter information in the graph convolution module. The down-sampling convolution module extracts deep spatiotemporal information with different sparsities. To verify the effectiveness of the model, experiments are carried out on four datasets. Experimental results show that the proposed model outperforms the current state-of-the-art baseline methods. The effectiveness of the module for solving the problem of dependencies and deep information is verified by ablation experiments.
Current mHealth e Dutch Dementia Care: A Guide o Action!

Objective: To report underlying factors that hinder or advance mHealth implementation and use in Dutch dementia care. Methods: 44 dementia experts (healthcare professionals, informal caregivers, dementia case managers, and researchers) completed a questionnaire as part of a wider Delphi study to share their experiences with, thoughts on, and proposals for mHealth use in Dutch dementia care. A SWOT-framework is used to categorize the experiences, thoughts and proposals. Results: Four strengths, twelve weaknesses, thirteen opportunities and six threats for the current use of mHealth in Dutch dementia care were identified. Conclusion: Identified weaknesses highlight the importance for a guide for action for everyone involved in design and implementing mHealth for older adults with dementia. It is critical to raise awareness of mHealth’s availability, improve its design, and continue to address the needs of older adults with dementia.
Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM

In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm−1 using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor c and the kernel function parameter g in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%.
Optimal Dynamic Capacity Allocation for High Throughput Satellite Communications Systems

In this letter, a simple engineering scheme for achieving optimal dynamic capacity allocation in geostationary and medium Earth orbit multi-beam high throughput satellite systems is presented. Exploiting smart gateway diversity setup and considering users’ requested and gateways’ offered capacities, we propose a theoretically optimal, based on Monge arrays, capacity allocation scheme for minimization of both system’s capacity losses and rate matching performance metrics. This scheme has low complexity considering the appropriate sorting of system’s offered and requested capacities. Finally, simulation results also confirm its optimality by presenting its identical performance with the much more complicated and time consuming exhaustive mechanism.
Relationship between the expressions of PD-L1 and tumour-associated fibroblasts in gastric cancer

Abstract Previous studies have focused on the changes of tumour cells in immune escape, and less is known about the effect of tumour microenvironment (TME) on immune escape. Tumour-associated fibroblasts (TAF) is an important part of the TME and has special physiological and biochemical characteristics, but the specific mechanism has not been clarified. In order to investigate the effect of TAF on the expression of PD-L1 in gastric cancer cells, gastric cancer cell lines MNK45, SGC7901 were non-contact co-culturing with TAF 1, 3 and 7 d via transwell. PD-L1 mRNA and protein expression were detected using qRT-PCR and FCM. Then, 95 cases of gastric cancer tissues were selected and evaluated PD-L1 and TAF expressions by immunohistochemical examination. The results showed that the mRNA and protein expression of PD-L1 in the experiment group were significantly higher than that in the control group. PD-L1 expression was associated with massive lymphocyte infiltration, diffuse/mixed histology and intratumoral TAFs in gastric cancers. In conclusion, TAFs promoted the growth in gastric cancer cell lines by increased the PD-L1 expression.
A Review on Machine Translation in Indian Languages

Machine translation (MT) is considered an important task that can be used to attain information from documents written in different languages. In the current paper, we discuss different approaches of MT, the problems faced in MT in Indian languages, the limitations of some of the current existing MT systems, and present a review of the work that has been done until now in MT in Indian language perspective.
Comparison of Attention and Meditation Based Mobile Applications by Using EEG Signals

Brain Computer Interface (BCI) is a technology in which we control the machines with the help of the brain waves, thus the brain waves play a major role. The attention and meditation levels are very helpful in many aspects to know the strength of the attention of the children to the lecture and also used for the medication for the people with ADHD. Many applications (apps) are developed which give the visual representation of the brain waves like graphical representation, charts and also gives the values of different frequencies of the brain waves which are very helpful in the field of technology, medication and for designing games. There are many apps which are not supported by all the devices and many apps are not free of cost. In this paper, we are going to suggest and compare some application software which is freely available on the android Google store which displays the attention level or meditation levels or both and the features of the apps and calculate the accuracy or performance of the apps.
Optogenetic Methods to Investigate Brain Alterations in Preclinical Models

Investigating the neuronal dynamics supporting brain functions and understanding how the alterations in these mechanisms result in pathological conditions represents a fundamental challenge. Preclinical research on model organisms allows for a multiscale and multiparametric analysis in vivo of the neuronal mechanisms and holds the potential for better linking the symptoms of a neurological disorder to the underlying cellular and circuit alterations, eventually leading to the identification of therapeutic/rescue strategies. In recent years, brain research in model organisms has taken advantage, along with other techniques, of the development and continuous refinement of methods that use light and optical approaches to reconstruct the activity of brain circuits at the cellular and system levels, and to probe the impact of the different neuronal components in the observed dynamics. These tools, combining low-invasiveness of optical approaches with the power of genetic engineering, are currently revolutionizing the way, the scale and the perspective of investigating brain diseases. The aim of this review is to describe how brain functions can be investigated with optical approaches currently available and to illustrate how these techniques have been adopted to study pathological alterations of brain physiology.
Obstacle circumvention by automated guided vehicles in industrial environments

The intralogistics of companies has been the subject of improvement and increased efficiency. The Automated Guided Vehicle (AGV, plural AGVs) has been used for decades instead of having manual labour transporting products or material around the site. The demand for these types of robots are increasing, and their autonomy is continuously developed. There is a wish to increase autonomy such as avoiding obstructions or collaboration between robots. Robotics research has proven that there are numerous ways to get a robot to overtake an obstacle. How should it be made in an industrial environment? These are uncontrolled environments with other workers present who should not be injured or even startled by AGVs crawling up behind them. This master thesis aims to develop a functional way to increase autonomy in industrial AGVs by enabling them to circumvent obstacles that block their intended path. The result is promising but raises the question of when more safety scanners is needed.
Caracterização de painéis de partículas de média densidade feitos com resina poliuretana monocomponente à base de mamona

Resumo Dentre os fatores envolvidos na fabricação dos painéis destaca-se o tipo adesivo, em que o uso de novos pode resultar em boas propriedades aos materiais fabricados. Nesse contexto, esta pesquisa objetivou avaliar propriedades físicas e mecânicas de painéis de partículas fabricados com madeiras de Pinus sp. e de Eucalyptus grandis e unidas com resina poliuretana monocomponente derivada do óleo de mamona, cujo desenvolvimento desse adesivo vem sendo sistematicamente aperfeiçoado por diversos fabricantes. Foram produzidos dez painéis para cada espécie de madeira, ambos fabricados sobre as mesmas condições experimentais: 640 g de partículas (teor de umidade próximo de 10%), teor de adesivo de 15% sobre a massa de partículas, 10 min de tempo de prensagem a 100 ºC sob pressão de 4,0 MPa, consistindo nos mesmos parâmetros utilizados na fabricação de painéis de partículas de outras pesquisas, mas com o uso da resina bicomponente à base de mamona. Os painéis foram caracterizados segundo premissas e métodos de cálculo da norma brasileira NBR 14810-2 (2013), sendo utilizadas também normas internacionais para a verificação do requisitos. Com exceção da resistência ao arrancamento de parafuso, as demais propriedades obtidas superaram os requisitos dos documentos normativos utilizados.
Detection of Schizophrenia Spectrum Disorders Using Saliency Maps

Modern medical diagnostic systems have greatly contributed to the increase in survival rate of patients suffering from illnesses and to lengthening of average lifespan. Some diagnostic devices, such as Magnetic Resonance, can detect illnesses that may not exhibit any symptoms yet and medical diagnostic equipment can furthermore significantly aid in confirmation of suspected, otherwise undetectable diagnoses. One of the medical fields lacking automated diagnostic tools is psychopathology, where a mental disorder presence is typically established from observations of classified symptoms and structured systematic interviews. The goal of our paper was to design and test a reliable and automated diagnostic method for detection of Schizophrenia spectrum disorders. The proposed method utilizes eye-tracker and Rorschach Inkblot Test to create a visual attention saliency map of observed subjects. These maps are then processed and analyzed using Digital Image Processing and statistical methods to determine, whether the subject exhibits signs of schizophrenia or not. The proposed approach is based on a trained classifier which separates incoming data into either healthy or schizophrenic patients. Of course, these results are only indicative and the final diagnosis rests in the hands of qualified specialists. High correlation of the proposed system, s diagnosis and real clinical diagnosis however proves applicability of the proposed concepts as reliable supplementary tool for Schizophrenia detection.
A linear algebra controller based on reduced order models applied to trajectory tracking for mobile robots: an experimental validation

A linear algebra controller (LACr) based on an empirical linear model of the system is presented in this paper. The controller design is based on a first order plus dead time (FOPDT) model and can be tuned using the characteristic parameters obtained from the reaction curve. In previous studies, the versatility of this proposed controller was tested by simulations, proving be an alternative to control many kinds of processes. In this paper, the proposed controller is implemented for trajectory tracking using a real mobile robot platform. The performance results are compared against a PI controller using the ISE performance index to measure it.
Film Mulching with Low Phosphorus Application Improves Soil Organic Carbon and Its Decomposability in a Semiarid Agroecosystem

Soil organic carbon (SOC) storage and decomposability are crucial for soil quality. Film mulching and phosphorus (P) application are important agricultural practices on the semiarid Loess Plateau. This study analyzed the combined effects of film mulching and P application on SOC, its fractions, and mineralization kinetics under alfalfa (Medicago sativa L.). The six-year field experiment incorporated randomized blocks of split-plot design with two mulching treatments (no film mulching with flat planting and film mulching with ridges and furrows) as main plots and four P levels (P0: 0 kg ha−1, P1: 9.73 kg ha−1, P2: 19.3 kg ha−1, P3: 28.9 kg ha−1) as subplots. Mulching increased SOC content, SOC fractions (light and heavy fraction organic C, microbial biomass C, and dissolved organic C), and mineralization. After six years, mulching increased SOC content by 2.18, 2.60, 2.37, and 0.17 g kg−1 at P0, P1, P2, and P3, relative to no mulching. With increasing P levels, SOC fractions and mineralization increased under no mulching but increased initially and then decreased under mulching. P1 with mulching displayed the highest SOC utilization efficiency and stability. Kinetic models divided SOC into an active and a slow SOC pool, with the latter showing the lowest decomposability and highest stability in P1 with mulching. Overall, film mulching with a low P level, especially 11.9 kg ha−1 P fertilizer, promoted SOC storage under alfalfa on the semiarid Loess Plateau due to the high SOC content with high C utilization efficiency and stability and low decomposability.
The effect of network topology on the control traffic in distributed SDN

Software Defined Networking (SDN) has the promise of flexible routing, traffic management and service provisioning in communication networks. To allow SDN based networks scale in size, the control architecture needs to be distributed, which in turn requires the introduction of controller to controller communication. This is needed to ensure that the distributed controllers have the same understanding about the underlaying network and can make consistent local decisions. In this paper we evaluate the volume of the emerging control traffic, considering a distributed controller architecture based on ONOS and OpenFlow. We show that the control traffic increases drastically with the number of controllers, as well as with the size of the underlaying network. We evaluate topologies forming regular and random graphs, and conclude that the type of the topology influences the traffic volume significantly, while the network density has less significant effect. We show that the control traffic is significant even if the number of controllers is selected such that the control traffic is minimized, and we argue that further optimization of ONOS is needed to trade off control traffic load and consistency in the network views.