Cardiac arrhythmias, defined as irregular heartbeats caused by disrupted electrical signaling, remain a major clinical challenge worldwide. Pulsed field ablation (PFA) has recently emerged as a promising treatment due to its ability to selectively ablate arrhythmic tissue while minimizing injury to surrounding structures, offering advantages over thermal methods such as radiofrequency and cryoablation. PFA induces irreversible electroporation by delivering short, high-voltage pulses, leading to targeted cell death. Despite its recent FDA approval and encouraging clinical data, standardized electrode and waveform designs are still lacking to ensure consistent outcomes. This study employs a finite element method (FEM) integrated with a design of experiments (DOE) framework to optimize PFA electrode design. The coupled multiphysics model incorporates electrical, thermal, and fluid dynamic equations to capture the complex interactions during treatment. Using a Taguchi DOE approach, parametric studies were conducted to evaluate the effect of three critical variables in a bipolar electrode configuration (contact depth, active electrode length, and electrode diameter) on PFA outcomes. The computational model was validated against previously reported ex vivo data. Analysis of variance (ANOVA) was used to quantify the effect of each design variable on the irreversible ablation volume and maximum cardiac tissue temperature. Furthermore, simplified statistical correlations were developed to predict ablation volume within the studied design space, enabling rapid predictions without reliance on computationally expensive FEM simulations. These findings highlight the importance of computational modeling in advancing the preclinical development of PFA by providing actionable insights into electrode design strategies and their impact on treatment efficacy and safety.

Injection molding is a widely adopted manufacturing process for producing plastic parts with high precision and consistency. It is extensively used in industries such as automotive, medical devices, electronics, and consumer goods due to its ability to produce complex geometries at high volumes and low cost per part. This study investigates the influence of cooling channel and ambient room temperatures on the thermal behavior of the mold and the injected part. A numerical analysis was performed using the Finite Element method (FEM) software ANSYS Workbench 2025 R1, focusing on heat transfer and cooling performance within the mold system. The simulation model incorporated realistic boundary conditions, including varying ambient temperatures and cooling channel water temperatures, to evaluate their effects on the temperature of the injected part.

Linear relationships were identified between the input temperatures and the resulting temperature field in the injected part, indicating a predictable thermal response under controlled conditions. These findings suggest that both cooling channel and room temperatures can be strategically managed to optimize part quality and process efficiency. Future work may focus on developing analytical models to accurately predict the temperature evolution within the molded part, enabling faster process tuning and facilitating the evaluation of temperature influence with minimal computational resources.

Effective maintenance practices are critical for ensuring the availability and reliability of equipment used in Research, Development, and Innovation (R&D) laboratories. Frequent failures and lack of standardized maintenance procedures often compromise operational continuity and project schedules. The use of world-class maintenance metrics provides a structured approach to measure equipment performance and identify areas for improvement. Academic laboratories, however, face challenges in systematically applying these indicators. This study proposes and applies a methodology for determining world-class maintenance metrics in 3D printers used at the Manufacturing Laboratory of the School of Technology, Amazonas State University. The mixed-method research combined literature review, field data collection, and the calculation of indicators such as MTBF, MTTR, availability, reliability, and OEE using MCM software. Data collection included equipment mapping, operational records, and interviews with technicians and operators. Results showed that the analyzed 3D printers presented an availability of 86.96% and OEE of 72%, both below world-class benchmarks of 90% and 85%, respectively. Failures were primarily associated with calibration inconsistencies, extruder blockages, and first-layer adhesion issues. These findings highlight the need for implementing preventive and predictive maintenance routines, standardizing critical procedures, and training operators to reduce downtime. The methodology demonstrates how structured maintenance metrics can support strategic asset management and strengthen operational continuity in R&D environments.

Lithium-ion batteries (LiBs) are fundamental to modern energy systems, particularly in electric vehicle (EV) applications, due to their high energy density, long cycle life, and low self-discharge characteristics. Accurate State-of-Charge (SoC) estimation is essential for ensuring reliable performance, efficient energy usage, and the safety of Battery Management Systems (BMSs). However, the nonlinear and time-varying characteristics of LiBs, along with the difficulty in directly measuring internal states, pose significant challenges for parameter identification and SoC estimation. This study presents an advanced approach based on the Weighted Mean of Vectors optimization algorithm to simultaneously identify the unknown parameters of an extended Thevenin Equivalent Circuit Model (ECM) and estimate the SoC. Unlike previous methods that use static parameters for specific battery modes, the proposed technique accounts for dynamic changes during both charging and discharging operations. The algorithm demonstrates superior adaptability by continuously adjusting model parameters to reflect real-time battery behavior under varying operational conditions. The algorithm also models the relationship between SoC and open-circuit voltage (Voc) using data collected from real lithium-ion cells tested under a controlled load profile in the laboratory. This experimental validation ensures the practical applicability and robustness of the proposed methodology. The simulation results confirm the effectiveness and precision of the proposed approach, showing excellent agreement between measured and estimated values, with minimal errors in both voltage and SoC prediction. The enhanced accuracy achieved through this dynamic parameter identification framework represents a significant advancement in battery state estimation technology.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and neuronal dysfunction, representing a major global health challenge. Current pharmacological treatments provide only symptomatic relief and are often limited by side effects, prompting the search for natural compounds with neuroprotective potential. Mansonone G, a bioactive quinone isolated from Mansonia gagei, exhibits antioxidant and anti-inflammatory properties, which may contribute to the preservation of neuronal function. This study aimed to investigate the effects of Mansonone G on cognitive performance in zebrafish (Danio rerio) exposed to an AD-like experimental model. Zebrafish were assigned to six experimental groups: control (DMSO), galantamine, okadaic acid, and OKA combined with Mansonone G at concentrations of 1, 3, or 6 μg/L. Cognitive function was evaluated using the Y-maze test for spatial memory and locomotor activity and the Novel Object Recognition (NOR) test for recognition memory. Group differences were analyzed statistically, with pairwise comparisons conducted to determine significant effects. Exposure to OKA significantly impaired both spatial and recognition memory, whereas galantamine restored cognitive performance. Treatment with Mansonone G at 3 and 6 μg/L significantly improved cognitive outcomes, increasing exploration of the novel arm, enhancing preference for new objects, and stimulating locomotor activity. The 1 μg/L dose did not produce significant effects.
These findings indicate that Mansonone G mitigates OKA-induced cognitive deficits in zebrafish and highlight its potential as a natural neuroprotective agent for AD, supporting further preclinical investigations.

Artificial intelligence is having a revolutionary effect on diverse areas of research, such as proteins, drugs, and materials (in July of 2025, Google Scholar listed around eighty publications with “artificial intelligence” and “materials” in the title and dated from the current year), including prediction of new entities.

The discovery of new mineral species constitutes a more demanding challenge as these predicted new mineral species must have natural occurrences resulting from geological processes. There are some initial results, however, that are not especially impressive, as we will discuss.
We assess diverse instances of available generative artificial intelligence tools (Aria, ChatGPT, Claude, Copilot, Gemini, Grok, M365 Copilot, Meta AI, Perplexity, and YouChat) in relation to their usefulness in predicting new, undiscovered mineral species along the following main lines: the current state of the art in relation to confirmed predictions, and proposed methodologies of artificial intelligence (including potential limitations) for this goal. Special attention will be given to the issue of natural occurrence. Accordingly, we promote an evaluation of artificial intelligence potential by artificial intelligence tools.

Results are widely variable, with some generic answers, some problems with references, and some promising suggestions regarding the conditions under which the new mineral species could be found.

This research presents a study into the thermal stability of a MEMS vibrating ring gyroscope, specifically designed for space and harsh environmental applications. An innovative internal vibrating ring gyroscope design configuration was modelled and analysed using finite element analysis (FEA) in ANSYS R1 2023 software. The proposed design exhibited structural stability by incorporating sixteen support springs connected to the internal vibrating ring, and the whole structure is supported through externally placed anchors. The primary objective is to study the robustness of these gyroscopes under severe thermal fluctuations, ranging from −100 °C to 100 °C. The FEA results indicate that increasing the number of semicircular support springs significantly enhances the structural integrity and thermal performance of the gyroscopes. The proposed design presents the symmetric structure of the vibrating ring gyroscope that oscillates with identical wine glass mode shapes for driving and sensing resonance frequencies. Additionally, reduced thermal deformation, thermal stresses, and thermal strains compared to the traditional vibrating ring gyroscopes. These findings corroborate the effectiveness of the proposed internal design approach, confirming the suitability of MEMS vibrating ring gyroscopes for various applications, including aerospace, defense, and automotive industries. Overall, this research work provides valuable insight into optimizing MEMS vibrating ring gyroscope designs for high-performance and thermally stable inertial sensing applications.

The world suffers from serious environmental problems related to pollution and greenhouse gases, especially from the construction sector, known for its high level of energy consumption and carbon emission. To evaluate this, the authors aimed to demonstrate the benefits of ecological binders on the thermal characteristics of walls, encouraging constructors to adopt these binders in the building sector, which would significantly reduce energy consumption, pollution, and carbon emissions from construction activities.

This work has been divided into several sections. The first section consisted of a description of the walls studied and the materials used; the second one included a description of the thermal characteristics of modern and vernacular walls using the ecological binders plaster-cork and Tadelakt, in which thermal properties have been determined using the hot plate method. In addition, a study concerning the heat exchange of those walls using the ecological binders has been conducted to evaluate the effect of Tadelakt and plaster-cork on the thermal characteristics of walls. The final step involves a comparison between carbon emissions of the walls studied with and without those ecological binders. Results show some important findings about the thermal characteristics and the heat exchange of walls with the exterior air using the ecological binders. Moreover, the authors observe a significant carbon emission from walls using those ecological binders.

Molecular dynamics simulations decipher the structure–function relationship of a key bacterial amyloid peptide and its cytotoxic mutants. Experimentally, a Lys17Ala mutation reduces α-helicity and cytotoxicity, while an Asp13Ala mutation reduces helicity but paradoxically enhances toxic activity. To elucidate the underlying atomistic mechanisms, we performed extensive sampling for the wild-type (WT) peptide and both single-point mutants (Ala13, Ala17). Our computational strategy included twenty independent classical MD simulations (4 µs per variant) for robust sampling, supplemented by 400 ns of well-tempered metadynamics per variant to explore free energy landscapes and metastable states exhaustively.

Time-structured Independent Component Analysis (TICA) identified the dominant conformational states, confirming the experimental helical trend: WT > Ala17 > Ala13. Our results reveal that residue 17 is critical for C-terminal helix stabilization via specific intramolecular contacts. Conversely, the Ala13 mutation disrupts a key salt bridge, resulting in a pronounced increase in N-terminal flexibility. We propose that this enhanced conformational freedom and an altered amphipathic profile may promote deeper insertion into and more effective disruption of cell membranes, explaining the elevated cytotoxicity despite lower helicity.

This study provides the crucial mechanistic basis for the mutants' divergent behaviors, highlighting that while one residue acts as a primary structural stabilizer, the other serves as an electrostatic regulator. These detailed insights are crucial for understanding bacterial amyloid toxicity and can guide the rational development of novel anti-staphylococcal therapies.

Modern wastewater treatment plants face growing operational challenges due to increasingly variable influent compositions, stricter environmental regulations, and rising energy efficiency demands. This study provides a comprehensive evaluation of three advanced control strategies for optimizing wastewater treatment processes: conventional Proportional-Integral-Derivative (PID) control, fuzzy logic control, and the innovative Adaptive Neuro-Fuzzy Inference System (ANFIS). The research specifically focuses on addressing the critical need for intelligent systems capable of managing complex, non-linear relationships in key water quality parameters, particularly Total Dissolved Solids (TDSs) and water hardness concentrations. Through detailed MATLAB/Simulink simulations, we implemented each control methodology in a sophisticated wastewater treatment plant model that accurately replicates real-world operational conditions. The controller’s performance was rigorously assessed using multiple quantitative metrics: settling time, percentage overshoot, steady-state error, and energy consumption efficiency. Experimental results demonstrated that while the conventional PID controller achieved basic regulation, it exhibited significant limitations including 10% overshoot, prolonged 25-second settling time, and noticeable steady-state error. The fuzzy logic approach showed marked improvement, reducing overshoot to less than 1% and settling time to 13 seconds. The ANFIS controller outperformed both alternatives, delivering exceptional control precision with near-zero overshoot (0.2%), rapid 10-second response time, and complete elimination of steady-state error. Furthermore, the ANFIS system demonstrated superior adaptability to process variations while reducing energy consumption by 50% compared to traditional methods. These findings provide compelling empirical evidence that ANFIS-based control systems represent a transformative solution for next generation wastewater treatment infrastructure, offering unmatched performance in terms of both treatment quality and operational efficiency.