Miracle-dz/UniFAR-FTUs huggingface dataset:
Extração de características em imagens para reconhecimento de padrões

Este artigo apresenta uma investigação dos algoritmos de segmentação e extração de características de imagens de modo a permitir o reconhecimento objetos. Um conjunto de imagens foram obtidas de um ranário e utilizadas no experimento. O experimento consistiu em utilizar um algoritmo para segmentação e extração de características e realizar um estudo comparativo dos resultados de três algoritmos de classificação. O algoritmo Random Forest obteve maior índice de instâncias classificadas corretamente.
Similarity coefficient-based cell formation method considering operation sequence with repeated operations

Cell formation is a crucial problem in the design of cellular manufacturing systems and has a decisive effect on productivity and cost. The similarity coefficient method is the most widely used to solve this problem because of its flexibility and computational efficiency. Although various similarity coefficient methods have been developed in the literature, few of these methods consider the factor of operation sequence with repeated operations, which may cause more material intercell movements. To fill this research gap, this study presents an improved similarity coefficient method for designing machine groups in the first stage. Subsequently, a modified part assignment strategy is proposed for forming cells to achieve a higher compatibility with the improved similarity coefficient method. Finally, problems from previous studies are employed to demonstrate the performance of the proposed method. Comparison results testify that the proposed method is sensitive and efficient for solving the cell formation problem.
Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree

Abstract Flash floods pose a major challenge in various regions of the world, causing serious damage to life and property. Here we investigated the Izvorul Dorului river basin from Romania, to identify slope surfaces with a high potential for flash-flood employing a combination of fuzzy logic algorithm with the following four machine learning models: classification and regression tree, deep learning neural network, XGBoost and naïve Bayes. Ten flash-flood predictors were used as independent variables to determine the flash-flood potential index. As a dependent variable, we used areas with ttorrential phenomena divided into training (70%) and validating data set (30%). Predictive ability and the degree of correlation between factors were assessed through the correlation-based feature selection (CFS) method and through the confusion matrix, respectively. In the training phase, all ensemble models yielded good and very good accuracies of over 84%. The spatialization of flash-flood potential index (FFPI) over the study area showed that high and very high values of flash-flood potential occur in the northern half of the region and occupy the following weights within the study area: 53.11% (FFPI Fuzzy-CART), 45.09% (Fuzzy-DLNN), 45.58% (Fuzzy-NB) and 44.85% (Fuzzy-XGBoost). The validation of the results was done through the ROC curve method. Thus, according to success rate, Fuzzy-XGBoost (AUC = 0.886) is the best model, while in terms of prediction rate, the ideal one is Fuzzy-DLNN (AUC = 0.84). The novelty of this work is the application of the four ensemble models in evaluating this natural hazard.
Araucaria angustifolia (bert.) Otto kuntze): Comparative evaluation of phenolic composition, antioxidant and antimicrobial activities of seeds cooking water

Araucaria angustifolia var. angustifolia and Araucaria angustifolia var. indehiscens, seeds (common pinhão and monkey pinhão) are consumed after cooking, coats and water represent waste. In this work, pinhão was submitted to different cooking conditions, the water extracts were analyzed to determine and identify their phenolic compounds content, antioxidant activity and antimicrobial potential. Cooking for 45 minutes without addition of sodium chloride resulted in residual water with highest content of phenolic compounds and antioxidant activity (DPPH and FRAP), both for the common pinhão and for the monkey pinhão. Protocatecuic acid, vanillin and coniferaldehyde were identified and quantified by HPLC-ESI-MS/MS as the most prevalent phenolic compounds. No antimicrobial potential was observed against Salmonella enterica Typhimurium (ATCC 14028), Escherichia coli (ATCC 25922), Staphylococcus aureus (ATCC 25923) and Bacillus cereus (ATCC 11778).
Potassium sources impact on cassava plant productivity, quality and mineral composition

Abstract In the mineral nutrition of plants, potassium is considered “the element of quality” of harvested products, and its lack or excess can cause decreased or loss of product quality. In this sense, the aim of the present study was to evaluate the productivity, root quality and mineral nutrient levels of the Pretinha cassava cultivar according to potassium sources and doses. Two sources (potassium chloride and potassium sulfate) and five K2O doses (0, 30, 60, 90 and 120 kg of K2O ha−1) were tested, in a randomized block design, with five repetitions. After 11 months of sowing, five plants were manually harvested from the useful area of each plot and the number, diameter, firmness, root moisture and productivity were determined, in addition to the nutritional contents in different plant organs (leaf, stem and root and bark). Fertilization with potassium sulfate promotes higher root productivity, higher P and S levels in leaves and increased N and K levels in root barks. Potassium fertilization promotes a greater number of roots per plant, larger root diameter and greater external firmness, increased K content in plant organs and decreased Ca content in cassava leaves. The use of potassium chloride also increases Cl levels in cassava leaves.
Application of Pattern Language for Game Design in Pedagogy and Design Practice

Existing implementations of game design patterns have largely been confined to theoretical or research settings. Weaknesses in these implementations have prevented game design patterns from being properly evaluated as an educational and practical development tool. This paper examines these weaknesses, describes a method of developing and applying patterns that overcome the weaknesses, and evaluates use of the method for game design education and practice. Weaknesses in existing pattern implementations are: omission of design problems, presumption of functional completeness at the level of pattern languages, narrow topical focus, and lack of a concise, repeatable method for pattern production. Several features of the proposed method were specifically built to address these weaknesses, namely the pattern template, the process for connecting patterns into a language and assessing the language’s scope, a rubric for assessing pattern confidence and interconnectivity confidence, and pattern-building exercises. This method was applied in a classroom setting. Results, as assessed by the evaluation of student work, suggest that creating patterns/pattern languages is an effective pedagogical approach. De-signs produced using designer-created patterns closely align with existing design theory and are clearly understood by students. The above results may indicate that the path to gaining wider acceptance of pattern theory as a design framework within game design is not to produce a universal pattern language but to facilitate the creation of case-specific languages, by students and professional designers, that use a shared ontology and thus can be combined easily to solve the diverse sets of problems faced by these groups.
Jusbrasil e os Desafios Tecnológicos para Facilitar e Aprimorar o Acesso à Justiça

Este artigo apresenta uma breve introdução a alguns dos desafios tecnológicos enfrentados na empresa Jusbrasil em sua missão de facilitar o acesso à justiça no Brasil. A Jusbrasil busca unir direito e tecnologia para que a justiça ultrapasse as fronteiras dos tribunais e chegue às casas de qualquer cidadão ou cidadã. Milhões de pessoas acessam a plataforma da empresa atualmente, havendo acesso de mais de 900 mil advogados. Por outro lado, nossa base de informação possui bilhões de documentos contendo artefatos relacionados ao direito no Brasil. Essa base de informação traz oportunidades para o desenvolvimento de produtos inteligentes, tais como sistemas de busca eficazes e eficientes, ferramentas de estruturação e tratamento da informação, mineração de dados e sistemas de recomendação, dentre outros. Neste trabalho, discutimos e apresentamos para a comunidade acadêmica alguns dos desafios tecnológicos enfrentados pela empresa no dia-a-dia.
Assessments of Solar, Thermal and Net Irradiance from Simple Solar Geometry and Routine Meteorological Measurements in the Pannonian Basin

In this paper, we discussed several different procedures for calculating irradiation from routine weather measurements and observations. There are between four and eight frequently used parameterizations of radiation balance components in meteorological preprocessors, and we investigated them. First of all, the estimated and measured solar and net irradiance were compared. Afterwards, the estimated and measured longwave irradiance were investigated. Then, we recalculated the net irradiance from the sum of global solar irradiance, longwave downwelling irradiance, reflect solar irradiance and upwelling longwave irradiance. Statistical estimates of the described methods were also recalculated compared with each shortwave and longwave radiation budget component measured separately with WMO first-class radiation instruments (Kipp&Zonen CMP6 and CMP11 and CGR3 and CGR4) in the Agrometeorological Observatory Debrecen, Hungary during one-year time period. Finally, we compared the calculated and measured values for longer periods (2008–2010 and 2008–2017) through statistical errors. The suggested parameterizations of the net radiation based on the separately parameterized all radiation balance components were: Foken’s calculation for clear sky solar global irradiance, Beljaars and Bosveld parameterization for albedo, Dilley and O’Brien methodology for the clear sky incoming longwave (LW) irradiance and Holstlag and Van Ulden cloudiness correction for all sky incoming LW and for the LW outgoing irradiance.
A Compact Implantable MIMO Antenna for High-Data-Rate Biotelemetry Applications

This article presents a low-profile multiple-input–multiple-output (MIMO) implantable antenna system, including a capsule endoscopy and a scalp implantation at medical implant communication service (MICS) band (402–405 MHz), and industrial, scientific, and medical (ISM) band (433.1–438.8 MHz). It consists of two elements with an edge-to-edge gap of <inline-formula> <tex-math notation="LaTeX">$0.0018\lambda _{g}$ </tex-math></inline-formula> and overall dimensions of <inline-formula> <tex-math notation="LaTeX">$\pi \times (5.65)^{2} \times 0.13$ </tex-math></inline-formula> mm<sup>3</sup> and is placed on a 0.13 mm-thick RO3010 (<inline-formula> <tex-math notation="LaTeX">$\epsilon _{r} = 10.2$ </tex-math></inline-formula> and tan<inline-formula> <tex-math notation="LaTeX">$\delta = 0.0022$ </tex-math></inline-formula>) substrate. The antenna is integrated with batteries, sensors, and electronic components in two different types of implantable devices (capsule and flat-type devices). The simulation result shows that the antenna has a fractional bandwidth (FBW) of 33.9%, a maximum realized gain of −30 dBi, and isolation of more than 26 dB. The fabricated prototype is measured in a saline solution and minced pork meat. The measured results are found in good accordance with simulations. The antenna parameters are calculated and showed a significant independence between the radiators. A link budget is estimated to be 78 Mb/s for a distance of 20 m. Thus, the proposed antenna is considered as a promising solution for high-data-rate medical applications such as capsule endoscopy, live surgeries, and other biomedical applications where the high data rate is required.
Statistical Modeling and Monitoring of Geometrical Deviations in Complex Shapes With Application to Additive Manufacturing

Abstract The industrial development of new production processes like additive manufacturing (AM) is making available novel types of complex shapes that go beyond traditionally manufactured geometries and 2.5D free-form surfaces. New challenges must be faced to characterize, model and monitor the natural variability of such complex shapes, since previously proposed methods based on parametric models are not applicable. The present study proposes a methodology that applies to complex shapes represented in the form of triangulated meshes, which is the current standard for AM data format. The method combines a novel bi-directional way to model the deviation between the reconstructed geometry (e.g., via X-ray computed tomography) and the nominal geometry (i.e., the originating 3D model) with a profile monitoring approach for the detection of out-of-control shapes. A paradigmatic example consisting of an egg-shaped trabecular shell representative of real parts produced via AM is used to illustrate the methodology and to test its effectiveness in detecting real geometrical distortions.
Fractal Structures in a Binary Schwarzschild Black Hole System

In open Hamiltonian systems, the dynamics underlying chaotic scattering presents a number of fractal structures. One example is the de  ection of light rays produced by a pair of supermassive (Schwarzschild) black holes. A light ray approaching this system can diverge to in  nity, fall down into one of the black holes or can orbit around the black hole pair as periodic orbits. Using a two-dimensional area-preserving map proposed by Moura and Letelier, we investigated the escape basins and their fractal boundaries, using the corresponding basin and basin boundary entropies. We also varied the distance between the black holes, showing how it impacts in the complexity of the basin structure.
Quasi‐Stationary Intense Rainstorms Spread Across Europe Under Climate Change

Under climate change, increases in precipitation extremes are expected due to higher atmospheric moisture. However, the total precipitation in an event also depends on the condensation rate, precipitation efficiency, and duration. Here, a new approach following an “ingredients‐based methodology” from severe weather forecasting identifies important aspects of the heavy precipitation response to climate change, relevant from an impacts perspective and hitherto largely neglected. Using 2.2 km climate simulations, we show that a future increase in precipitation extremes across Europe occurs, not only because of higher moisture and updraft velocities, but also due to slower storm movement, increasing local duration. Environments with extreme precipitation potential are 7× more frequent than today by 2100, while the figure for quasi‐stationary ones is 11× (14× for land). We find that a future reduction in storm speeds, possibly through Arctic Amplification, could enhance event accumulations and flood risk beyond expectations from studies focusing on precipitation rates.
Oleanolic acid mediated the proliferation and invasion of U251 glioma cells and promoted their apoptosis through the IKK-β, MAPK3, and MAPK4 signaling pathway

ObjectTo investigate the effects of Oleanolic acid (OA) on proliferation, apoptosis, migration, and invasion of human glioma cell U251, as well as IKK-β and MAPK signaling pathways.MethodsThe binding of OA to IKK-β and MAPK signaling pathway essential proteins IKK-β, MAPK3, and MAPK4 was analyzed by molecular docking technique. U251 cells were treated with different concentrations of OA. The proliferation and apoptosis rates of U251 cells were detected by CCK-8 assay, MTT assay, cell cloning assay, and AnnexinⅤ FITC/PI double staining assay. Transwell chamber assay was used to detect migration and invasion of U251 cells. Finally, Western blotting was used to detect the protein expression levels of IKK-β, MAPK3, and MAPK4 in U251 cells treated with OA.ResultsThe results of molecular docking showed that OA could stably bind to IKK-β, MAPK3, and MAPK4 proteins. OA could not only effectively inhibit the proliferation and induce apoptosis of U251 cells (P < 0.05), but also significantly inhibit the invasion of U251 cells (P < 0.05). Western blot assay confirmed that OA could dramatically inhibit the protein expression levels of IKK-β, MAPK3, and MAPK4 in U251 cells (P < 0.05). ConclusionsOA may inhibit the proliferation, migration, and invasion of glioma U251 cells by binding key molecules of the IKK-β signaling pathway and essential target proteins of MAPK3 and MAPK4 in the MAPK signaling pathway.
On Contributions of Multiscale Dynamic Processes to the Steric Height in the Northeastern South China Sea as Revealed by Moored Observations

Based on 2‐year moored measurements in the northeastern South China Sea, contributions of multiscale dynamic processes to steric height (SH) at 60 m are quantified. It shows that on average, root‐mean‐squared (RMS) SHs of mesoscales, submesoscales, diurnal and semidiurnal internal tides (ITs), and supertidal internal gravity waves (IGWs) are 7.56, 1.01, 1.19, 2.84, and 1.46 cm, respectively, with their respective relative contributions of 53.8%, 7.2%, 8.5%, 20.2%, and 10.4%. The SHs of ITs and supertidal IGWs are dominated by stationary and nonstationary components, respectively. Seasonally, mesoscales and submesoscales show larger RMS SHs in winter than summer but the opposite occurs for ITs and supertidal IGWs. Although the RMS SH of submesoscales exceeds nonstationary ITs in winter, it is much smaller than the sum of nonstationary ITs and supertidal IGWs. Therefore, to detect submesoscales using SWOT data, approaches to remove the SHs of nonstationary ITs and supertidal IGWs are called for.
Bonding features in Appel's salt from the orbital-free quantum crystallographic perspective

Bonding properties in the crystal of 4,5-dichloro-l,2,3-dithiazolium chloride (Appel's salt) were studied using a combination of single-crystal high-resolution X-ray diffraction data and the orbital-free quantum crystallography approach. A QTAIM-based topological model shows the proximity of S—C and S—N bonds to the sesquialteral type and establishes the low S—S bond order in the l,2,3-dithiazolium heterocycle. It is found that the electrostatic potential carries the traces of a common positive area on the junction of interatomic zero-flux surfaces of S1 and S2 atomic basins; meanwhile the exchange energy density per particle shows perfectly here two separate minima through which the two bond paths run. Thus, the pair intermolecular interactions Cl−. S1 and Cl−. S2 formed by the common chloride anion placed near the center of the S—S bond are categorized as chalcogen bonds.
A Self-Tuning S/S Compensation WPT System Without Parameter Recognition

In the application of wireless power transfer system, parameter drift of resonant tank is a general situation which induces natural frequency of primary side or secondary side deviates from switching frequency. This frequency deviation phenomenon will deteriorate load-independent constant current characteristics and decrease output power capacity for an S/S compensation system. This article illustrates the problems caused by frequency deviation through output characteristic analysis and addresses them by a self-tuning method based on switched capacitors (SCs). The proposed self-tuning method requires no resonant parameters such as compensation capacitance and coil self-inductance in advance. Primary and secondary natural frequencies can be both tuned to switching frequency continuously by SCs. The proposed method is verified by a simulation and a 500 W S/S compensation experimental prototype. After completion of self-tuning process, load-independent constant current characteristic is recovered and the output power capacity is boosted, which are in consistent with the theoretical analysis of system output characteristics. The proposed method is also applicable to LCC/S and LCC/LCC topologies.
Causal Machine Learning and Business Decision Making

Causal knowledge is critical for strategic and organizational decision-making. By contrast, standard machine learning approaches remain purely correlational and prediction-based, rendering them unsuitable for addressing a wide variety of managerial decision problems. Taking a mixed-methods approach, which relies on multiple sources, including semi-structured interviews with data scientists and senior decision-makers, as well as quantitative survey data, this study argues that causality is a critical boundary condition for the application of machine learning in a business analytical context. It highlights the crucial role of theory in causal inference and offers a new perspective on human-machine interaction for data-augmented decision making.
Resistance of alkali activated concretes to sulphate attack

The article highlights the resistance of two types of alkali activated material (AAM) concretes to sulphate attack. AAM considered were made of 100% ground granulated blast furnace slag (GGBS) and a blend of GGBS and pulverised fuel ash (PFA). Sodium hydroxide and sodium silicate based activator was used for this study. The material presented here is part of a wider study into Alkali Activated Materials to characterise them for their durability performance. Although it is accepted that AAM based concretes had superior strength characteristics to Portland cement based concretes, there is still debate over the durability of AAM. The mass loss, strength and expansion of four mixes were studied for a period of 56 days. Concrete cubes were exposed to water, magnesium sulphate, sodium sulphate and calcium sulphate solutions and a sulphuric acid solution. Mass and ultrasonic pulse velocity across each sample were measured once a week. For studying expansion concrete prisms were immersed in water and the salt solutions and changes to the length of each sample was measured every week. Dry prisms kept in ideal condition were used as a reference. For the first 56 days of immersion the Blended samples appeared to perform better than the GGBS samples.
Robust Meteorological Drought Prediction Using Antecedent SST Fluctuations and Machine Learning

While reliable drought prediction is fundamental for drought mitigation and water resources management, it is still a challenge to develop robust drought prediction models due to complex local hydro‐climatic conditions and various predictors. Sea surface temperature (SST) is considered as the fundamental predictor to develop drought prediction models. However, traditional models usually extract SST signals from one or several specific sea zones within a given time span, which limits full use of SST signals for drought prediction. Here, we introduce a new meteorological drought prediction approach by using the antecedent SST fluctuation pattern (ASFP) and machine learning techniques (e.g., support vector regression (SVR), random forest (RF), and extreme learning machine (ELM)). Three models (i.e., ASFP‐SVR, ASFP‐ELM, and ASFP‐RF) are developed for ensemble, probability, and deterministic drought predictions. The Colorado, Danube, Orange, and Pearl River basins with frequent droughts over different continents are selected, as the cases, where standardized precipitation evapotranspiration index (SPEI) are predicted at the 1° × 1° resolution with 1‐ and 3‐month lead times. Results show that the ASFP‐ELM model can effectively predict space‐time evolutions of drought events with satisfactory skills, outperforming the ASFP‐SVR and ASFP‐RF models. Our study has potential to provide a reliable tool for drought prediction, which further supports the development of drought early warning systems.