Background/Objectives: Alzheimer’s disease (AD) is a progressive, multifactorial neurodegenerative condition characterized by neurofibrillary tangles, neuronal loss, cognitive impairment, and accumulation of β-amyloid plaques. Considering the limitations of current treatments, which present adverse effects and only alleviate symptoms without modifying disease progression, there is an urgent need for new therapeutic approaches. This study aimed to investigate the neuroprotective potential of synthetic derivatives of (1E,4E)-1,5-bis[(het)aryl]penta-1,4-dien-3-ones, focusing on the inhibition of the cholinesterase enzymes acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), targets directly related to the cholinergic deficit observed in AD. Methods: The compounds were initially synthesized by aldol condensation reactions, with subsequent physicochemical characterization. They were then subjected to in silico assays that demonstrated high binding affinity to the active sites of AChE and BChE. The derivatives were evaluated in vitro for their inhibitory activity on these enzymes and in vivo in an experimental model of AD induced by streptozotocin in Wistar rats. Results: The synthesized derivatives showed favorable predicted interactions with the active sites of AChE and BChE, supporting their potential as cholinesterase inhibitors. In vitro assays demonstrated inhibitory activity against both enzymes, with selected derivatives showing improved activity compared with the parent scaffold. In the in vivo model, treatment with the selected compounds was associated with neuroprotective effects, suggesting preservation of nervous tissue integrity under AD-like conditions. Conclusions: These findings indicate that derivatives may represent promising candidates for further investigation as multitarget agents for AD. The study reinforces the relevance of integrating organic synthesis, molecular modeling, enzymatic assays, and in vivo evaluation in the search for new therapeutic strategies for neurodegenerative diseases.

Alzheimer's disease requires therapeutic strategies targeting multiple pathological mechanisms. This study investigates the neuroprotective potential of quercetin using an integrated approach combining in silico modeling and in vivo validation. Computational analyses examined the binding behavior of quercetin toward three key enzymes implicated in disease pathophysiology: acetylcholinesterase, butyrylcholinesterase, and beta-secretase 1. Molecular dynamics simulations reveal consistent interaction patterns and stable binding profiles across independent trajectories. For experimental validation, Wistar rats with surgically induced Alzheimer's disease were orally treated with quercetin (30 mg/kg) for 5 weeks. Histological and immunohistochemical analyses of the hippocampus and subventricular zone evaluated neuronal density and astrocytic activation using glial fibrillary acidic protein and vimentin markers. Computational results supported the multitarget potential of quercetin through stable enzyme interactions. Consistently, in vivo assays demonstrated increased neuronal density and reduced astrocytic marker expression, suggesting a protective modulation of neuroinflammatory processes. These findings highlight quercetin as a promising scaffold for multitarget therapeutic strategies aimed at mitigating the progression of Alzheimer's disease.

This paper introduces a novel evolving fuzzy classifier that begins with no initial structure and develops incrementally through a participatory learning-based clustering algorithm. It employs multivariable Gaussian membership functions for rule antecedents and class outputs for consequents. The classifier’s learning algorithm is designed to adjust dynamically by creating, merging, deleting, and updating clusters and rules. Uniquely, it features a ‘procrastination’ approach where clusters are initially formed in a disabled state to robustly manage outliers and ensure only representative data influence the model. Clusters are refined based on compatibility measures using the Mahalanobis distance, with adjustments to learning rates influenced by the nature of incoming data-slowing for anomalies and accelerating for typical inputs. This mechanism enhances adaptability and model accuracy, distinguishing it from existing fuzzy classifiers. Comparative analyses on binary and multiclass tasks demonstrate superior or competitive performance, underscoring the classifier’s innovative approach to evolving fuzzy classification.

We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robustness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to capture drifts. The classifier evolves from scratch, incorporates new classes on the fly, and performs local incremental feature weighting. As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals. Emotion recognition is crucial for enhancing the realism and interactivity of computer systems. The challenge lies exactly in developing high-performance algorithms capable of effectively managing individual differences and non-stationarities in physiological data without relying on subject-specific information. We extract features from the Fourier spectrum of EEG signals obtained from 28 individuals engaged in playing computer games – a public dataset. Each game elicits a different predominant emotion: boredom, calmness, horror, or joy. We analyze individual electrodes, time window lengths, and frequency bands to assess the accuracy and interpretability of resulting user-independent neural models. The findings indicate that both brain hemispheres assist classification, especially electrodes on the temporal (T8) and parietal (P7) areas, alongside contributions from frontal and occipital electrodes. While patterns may manifest in any band, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz) bands, in this order, exhibited higher correspondence with the emotion classes. The eGNN-C+ demonstrates effectiveness in learning EEG data. It achieves an accuracy of 81.7% and a 0.002933 interpretability using 10-second time windows, even in face of a highly-stochastic time-varying 4-class classification problem.

This research investigates the impact of women's involvement in STEM education through the introductory programming and robotics course offered by the Computing Department at CEFET-MG. The course aims to foster logical reasoning, creativity, and computational thinking among students in the final years of public elementary schools. The study gathered data on the participation of women as students, monitors, and researchers from 2016 to 2024. These data were organized to create graphs supporting quantitative and qualitative analyses. The results show that including women researchers in the course, which began in 2022, has positively affected young women's participation. The lack of women in the department's faculty until 2022 highlights the importance of recent efforts to promote gender diversity within the course and the wider academic environment, promoting positive practices for advancing gender equality in fields where disparities persist.

This paper presents a novel approach, dubbed Falcon-GA, for rule extraction in a Fuzzy Adaptive Learning Control Network (FALCON) using a Genetic Algorithm (GA). The FALCON-GA combines multiple techniques to establish the relationships and connections among fuzzy rules, including the use of a GA for rule extraction and a Gradient-based method for fine-tuning the membership function parameters. The learning algorithm of FALCON-GA incorporates three key components: the ART (Adaptive Resonance Theory) clustering algorithm for initial membership function identification, the Genetic Algorithm for rule extraction, and the Gradient method for adjusting membership function parameters. Moreover, FALCON-GA offers flexibility by allowing the incorporation of different rule types within the FALCON architecture, making it flexible and expansible. The proposed model has been evaluated in various forecasting problems reported in the literature and compared to alternative models. Computational experiments demonstrate the effectiveness of FALCON-GA in forecasting tasks and reveal significant performance improvements compared to the original FALCON. These results indicate that Genetic Algorithms efficiently extract rules for Fuzzy Adaptive Learning Control Networks.

This paper introduces a new multiclass classifier called the evolving Fuzzy Classifier (eFC). Starting its knowledge base from scratch, the eFC structure evolves based on a clustering algorithm that can add, merge, delete, or update clusters (= rules) simultaneously while providing class predictions. The procedure to add clusters uses the procrastination idea to prevent outliers from affecting the quality of learning. Two pruning mechanisms are used to maintain a concise and compact structure. In the first, redundant clusters are merged based on a similarity measure, and in the second, obsolete and unrepresentative clusters are excluded based on an inactivity strategy. The center of the clusters is adjusted based on the mean value of the attributes. The eFC model was evaluated and compared with state-of-the-art evolving fuzzy systems on 8 randomly selected data streams from the UCI and Kaggle repositories. The experimental results indicate that the eFC outperforms or is at least comparable to alternative state-of-the-art models. Specifically, the eFC achieved an average accuracy of 7% to 37% higher than the competing classifiers. The results and comparisons demonstrate that the eFC is a promising alternative for classification tasks in non-stationary environments, offering good accuracy, a compact structure, low computational cost, and efficient processing time.

Alzheimer’s disease causes chronic neurodegeneration and is the leading cause of dementia in the world. The causes of this disease are not fully understood but seem to involve two essential cerebral pathways: cholinergic and amyloid. The simultaneous inhibition of AChE, BuChE, and BACE-1, essential enzymes involved in those pathways, is a promising therapeutic approach to treat the symptoms and, hopefully, also halt the disease progression. This study sought to identify triple enzymatic inhibitors based on stereo-electronic requirements deduced from molecular modeling of AChE, BuChE, and BACE-1 active sites. A pharmacophore model was built, displaying four hydrophobic centers, three hydrogen bond acceptors, and one positively charged nitrogen, and used to prioritize molecules found in virtual libraries. Compounds showing adequate overlapping rates with the pharmacophore were subjected to molecular docking against the three enzymes and those with an adequate docking score (n = 12) were evaluated for physicochemical and toxicological parameters and commercial availability. The structure exhibiting the greatest inhibitory potential against all three enzymes was subjected to molecular dynamics simulations (100 ns) to assess the stability of the inhibitor-enzyme systems. The results of this in silico approach indicate ZINC1733 can be a potential multi-target inhibitor of AChE, BuChE, and BACE-1, and future enzymatic assays are planned to validate those results.

Drug discovery and repositioning are important processes for the pharmaceutical industry. These processes demand a high investment in resources and are time-consuming. Several strategies have been used to address this problem, including computer-aided drug design (CADD). Among CADD approaches, it is essential to highlight virtual screening (VS), an in silico approach based on computer simulation that can select organic molecules toward the therapeutic targets of interest. The techniques applied by VS are based on the structure of ligands (LBVS), receptors (SBVS), or fragments (FBVS). Regardless of the type of VS to be applied, they can be divided into categories depending on the used algorithms: similarity-based, quantitative, machine learning, meta-heuristics, and other algorithms. Each category has its objectives, advantages, and disadvantages. This review presents an overview of the algorithms used in VS, describing them and showing their use in drug design and their contribution to the drug development process.

This work presents an approach to taxi demand forecasting and classification‎. ‎The proposed approach uses historical data from taxi rides and meteorological data‎. ‎The Kruskal-Wallis variable ranking method is used to identify the most relevant variables‎. ‎The selected variables are used as input to an evolving fuzzy system to perform the prediction‎. ‎Once the forecast is made‎, ‎the demand results are classified by value ranges‎. ‎Those ranges are also identified by colors that compose a heatmap‎, ‎displayed at each time interval‎. ‎In this work‎, ‎to perform the prediction‎, ‎four evolving systems are evaluated‎: ‎Autonomous Learning Multi-Model (ALMMo); evolving Multivariable Gaussian Fuzzy Modeling System (eMG); evolving Fuzzy with Multivariable Gaussian Participatory Learning and Recursive Maximum Correntropy eFCE and; evolving Neo-Fuzzy Neuron (eNFN)‎. ‎Computational experiments were carried out to evaluate the evolving systems in predicting Pick-Up and Drop-Off‎, ‎at intervals of 15 and 30 minutes‎, ‎for 86 zones in New York‎, ‎covering the period from 01/01/2018 to 31‎/ ‎10/2018‎. ‎The results obtained by the evolving systems are compared with each other and state of the art‎. ‎Among the evolving models‎, ‎ALMMo presented the best results compared to the state of the art and other evolving models‎. ‎Performance obtained by the evolving models suggests that the proposed approach is promising an alternative to forecasting and classifying passenger demand‎.

This article describes the experience of an Intro-duction to Programming and Robotics course offered to the community since 2016 for elementary school students with no prior exposure to programming. The objective of the course is to foster logical and computational reasoning and creativity and attract potential talents to the field of computer science. The course was designed to be a blended methodology aimed at developing active methodologies such as flipped classrooms, maker culture, and gamification from a Freirean perspective. It seeks to promote critical thinking, student empowerment, and meaningful learning experiences. The achieved results of the course encompass student education, skill development, scientific dissemination, and integration between the school and the community.

This work introduces an approach for making decisions on buying and selling stocks in the Brazilian Stock Exchange to maximize profits in each operation. The proposed approach was built using the Neo-Fuzzy-Neuron (NFN) network to predict the future value of stocks and the Hurwicz criterion for decision analysis under risk and uncertainty, considering different degrees of optimism and pessimism. The approach was applied to Petrobras stocks (PETR4), and the results obtained were compared with the ”buy and hold”strategy. The computational results and comparisons suggest that the proposed approach is promising and provides a significant return on investment

This paper introduces a prediction model based on machine learning techniques for dimensional control in the manufacturing process of side flange bearing housings, according to the technical standard DIN 31693. The process is implemented in a journal-bearing manufacturing industry positioned among the three brands with the highest participation in the international market in 2023. The manufacturing process consists of rigid machining processes composed of a universal horizontal machining center and dimensional control composed of a coordinate measuring machine. After machining, the part is measured, and its dimensional report is generated. Qualified professionals use deviations obtained from this report to support the decision-making. The method used is based on the holistic monitoring of the surface geometry of the machined part. The approach used to compensate for dimensional deviations is based on monitoring and modeling the total deviation. In this context, the effects of all sources of systematic errors are compensated regardless of their origin. The heuristic is used for the steps that make up the decision-making process. The way to implement the predictive model in the production line is based on the interaction between human and machine experience. This paper proposes using the regression decision trees for defining the displacement parameters of the machining center axes from the dimensional results of housings obtained in the coordinate measuring machine. The model is validated if the mean absolute error is less than or equal to 0.003 mm. A comparison between an assembled model is performed to verify the performance between different predictive models.

Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive loss of cognitive functions, and it is the most prevalent type of dementia worldwide, accounting for 60 to 70% of cases. The pathogenesis of AD seems to involve three main factors: deficiency in cholinergic transmission, formation of extracellular deposits of β-amyloid peptide, and accumulation of deposits of a phosphorylated form of the TAU protein. The currently available drugs are prescribed for symptomatic treatment and present adverse effects such as hepatotoxicity, hypertension, and weight loss. There is urgency in finding new drugs capable of preventing the progress of the disease, controlling the symptoms, and increasing the survival of patients with AD. This study aims to present new multipurpose compounds capable of simultaneously inhibiting acetylcholinesterase (AChE), butyrylcholinesterase (BChE)—responsible for recycling acetylcholine in the synaptic cleft—and beta-secretase 1 (BACE-1)—responsible for the generation of amyloid-β plaques. AChE, BChE, and BACE-1 are currently considered the best targets for the treatment of patients with AD. Virtual hierarchical screening based on a pharmacophoric model for BACE-1 inhibitors and a dual pharmacophoric model for AChE and BChE inhibitors were used to filter 214,446 molecules by QFITBACE > 0 and QFITDUAL > 56.34. The molecules selected in this first round were subjected to molecular docking studies with the three targets and further evaluated for their physicochemical and toxicological properties. Three structures: ZINC45068352, ZINC03873986, and ZINC71787288 were selected as good fits for the pharmacophore models, with ZINC03873986 being ultimately prioritized for validation through activity testing and synthesis of derivatives for SAR studies.

Private transport has become a viable and increasingly popular alternative to urban transportation. However, with this growth, an old and recurring problem becomes more latent: the relationship between passenger demands and taxi supply. This problem suggests the creation and use of techniques which make it possible to reduce the gap between the demand for taxi passengers and the effective contingent of vehicles needed to meet this demand. This work introduces a new approach to forecasting and classifying taxi passengers' demands. The proposed approach uses historical data from taxi rides and meteorological data. The Kruskal-Wallis method identifies the most relevant variables, and an evolving fuzzy system performs demand forecasting/classification. Five evolving systems are evaluated with our approach: Autonomous Learning Multi-Model (ALMMo), evolving Multivariable Gaussian Fuzzy System (eMG), evolving Fuzzy with Multivariable Gaussian Participatory Learning and Recursive Maximum Correntropy (eFCE), evolving Fuzzy with Multivariable Gaussian Participatory Learning and Multi-Innovations RecursiveWeighted Least Squares (eFMI), and evolving Neo-Fuzzy Neuron (eNFN). In addition, computational experiments using real-world data were conducted to evaluate and compare the performance of the proposed approach. The results revealed that it obtained performance superior or comparable to state-of-the-art ones. Therefore, the experimental results suggest that the proposed approach is promising as an alternative for forecasting and classifying taxi passenger demand.

This paper proposes a new evolving fuzzy model constructed with an unsupervised recursive clustering algorithm with participatory learning and multivariable Gaussian membership functions. The proposed model, called evolving fuzzy with multivariable Gaussian participatory learning and multi-innovations recursive weighted least squares, uses first-order Takagi-Sugeno functional rules. The rules are extracted by the clustering algorithm that can add a new cluster, delete, merge or update existing clusters. The clusters are created using a compatibility measure and an arousal mechanism. The compatibility measure is computed by Euclidian or Mahalanobis distance according to the cluster's number of samples. The clusters exclusion method combines age and population to exclude inactive clusters. Redundant clusters are merged if there is a noticeable overlap between two clusters. The consequent parameters are updated by a multi-innovations weighted least squares recursive algorithm. The performance of the eFMI is evaluated and compared with alternative state-of-the-art evolving models in times series forecasting and non-linear system identification problems. Computational experiments and comparisons suggest the proposed model performs better or comparable than the alternative evolving models.

Alzheimer's disease (AD) is a neurodegenerative pathology responsible for 70% of dementia cases worldwide. Despite its relevance, the few drugs available for the treatment of this disease offer only symptomatic relief, with limited efficacy and serious adverse effects. The most accepted hypothesis about the pathogenesis involves the aggregation and deposition of beta-amyloid peptides, mainly in the cerebral cortex and hippocampus, through the catalytic action of beta-secretase 1 (BACE-1), making this enzyme a promising target for the development of new drugs. In order to prioritize candidates for BACE-1 inhibitors, a hierarchical virtual screening by pharmacophore model and molecular docking was performed against the 216,833 molecules contained in several databases. Our previously built pharmacophore model was used for the first filtering step, which resulted in the selection of 399 molecules. The remaining molecules were filtered through molecular docking with GOLD 5.4.0. In this step, molecules with scoring values greater than the mean plus standard deviation were evaluated for commercial availability and absence of asymmetric centers. Four molecules were selected and evaluated for mutagenic potential by the AMES test with the help of the pkCSM server. Finally, they were tested against the descriptors on Lipinski and Veber rules, and ZINC01589617 (QFIT = 56.52/Score = 44.95) satisfied all requirements, being subjected to molecular dynamics simulations (t = 100 ns) in order to obtain robust data on the mode of bonding and profile of intermolecular interactions. Those in silico strategies demonstrated that ZINC01589617 is a potential candidate for biological tests. Communicated by Ramaswamy H. Sarma

Evolving models have shown great success in processing non-stationary data that change their characteristics over time. Motivated by elaborating a high-performance model for data classification, the present work proposes a new evolving fuzzy classifier. The proposed model, named evolving Fuzzy Mean Classifier (eFMC), has a low computational cost and is autonomous, i.e., no has user-defined parameters. The eFMC is based on fuzzy clustering structures, where the membership degree between the samples and the clusters is used to obtain the output. In the proposed approach, each class is represented by a cluster, and new clusters are created whenever a new class is discovered. The centers of the clusters are updated through the sample’s means calculated incrementally. Computational experiments were carried out to evaluate and compare the performance of the eFMC in terms of accuracy and processing time. Experimental results and comparisons against alternative state-of-the-art evolving classifiers show that the eFMC is accurate and fast, characteristics essential for adaptive classifiers, especially in online and real-time environments.

DNA is a molecular target for the treatment of several diseases, including cancer, but there are few docking methodologies exploring the interactions between nucleic acids with DNA intercalating agents. Different docking methodologies, such as AutoDock Vina, DOCK 6, and Consensus, implemented into Molecular Architect (MolAr), were evaluated for their ability to analyze those interactions, considering visual inspection, redocking, and ROC curve. Ligands were refined by Parametric Method 7 (PM7), and ligands and decoys were docked into the minor DNA groove (PDB code: 1VZK). As a result, the area under the ROC curve (AUC-ROC) was 0.98, 0.88, and 0.99 for AutoDock Vina, DOCK 6, and Consensus methodologies, respectively. In addition, we proposed a machine learning model to determine the experimental ∆Tm value, which found a 0.84 R2 score. Finally, the selected ligands mono imidazole lexitropsin (42), netropsin (45), and N,N′-(1H-pyrrole-2,5-diyldi-4,1-phenylene)dibenzenecarboximidamide (51) were submitted to Molecular Dynamic Simulations (MD) through NAMD software to evaluate their equilibrium binding pose into the groove. In conclusion, the use of MolAr improves the docking results obtained with other methodologies, is a suitable methodology to use in the DNA system and was proven to be a valuable tool to estimate the ∆Tm experimental values of DNA intercalating agents.