Search Results (2,384)

Helicobacter pylori (H. pylori) is a Gram-negative, spiral-shaped bacterium that colonizes the human stomach and is linked to various gastroduodenal diseases. The severity of different clinical outcomes may be determined by the combination of virulence genes. The aim of this study was to assess the combinations of the cytotoxin-associated gene A (cagA), the vacuolating cytotoxin A gene (vacA), the outer inflammatory protein A gene (oipA), and the blood group antigen-binding adhesin gene (babA2) genotypes in H. pylori and their associations with the clinical outcomes of infection in patients from Central Brazil. This cross-sectional study included 106 patients who underwent endoscopy or gastrectomy. The presence and genotypes of H. pylori were confirmed using Polymerase Chain Reaction (PCR). Gastropathies were classified according to established severity criteria. Multivariate logistic regression and Venn diagrams were used to evaluate gene combinations. In this study, the infection prevalence was 65.1%. The cagA/vacA/oipA/babA2 combination showed a protective effect against erosive esophagitis (p = 0.002), erosive duodenitis (p = 0.003), and general duodenitis (p < 0.001). No significant association was observed between this gene combination and severe gastric diseases, although a trend toward protection against gastric atrophy was noted (p = 0.049). These findings suggest that the coexistence of cagA/vacA/oipA/babA2 may play a protective role against inflammatory lesions. Further studies should explore the functional role of these gene combinations, also considering the immunogenetic profile of the host. Full article

High-precision spiral trajectory tracking for aquaculture net-cage inspection is hindered by uncertain hydrodynamics, strong coupling, and time-varying disturbances acting on an underactuated autonomous underwater vehicle. This paper adapts and validates a model–data-driven learning-aided adaptive robust control strategy for the specific challenge of high-precision spiral trajectory tracking for aquaculture net-cage inspection. At the kinematic level, a serial iterative learning feedforward compensator is combined with a line-of-sight guidance law to form a feedforward-compensated guidance scheme that exploits task repeatability and reduces systematic tracking bias. At the dynamic level, an integrated adaptive robust controller employs projection-based, rate-limited recursive least-squares identification of hydrodynamic parameters, along with a composite feedback law that combines linear error feedback, a nonlinear robust term, and fast dynamic compensation to suppress lumped uncertainties arising from estimation error and external disturbances. A Lyapunov-based analysis establishes uniform ultimate boundedness of all closed-loop error signals. Simulations that emulate net-cage inspection show faster convergence, higher tracking accuracy, and stronger robustness than classical adaptive robust control and other baselines while maintaining bounded control effort. The results indicate a practical and effective route to improving the precision and reliability of autonomous net-cage inspection. Full article

Acoustic spiral wavefronts demonstrate linear phase directionality, facilitating precise azimuth estimation in underwater navigation through phase comparison with reference wavefronts characterized by constant phase directionality. The reliability of azimuth estimation depends on the phase directionality accuracy of both the spiral and reference wavefront sources. This study introduces a seven-element transmitting array, constructed using bender disk transducers, which is capable of generating both spiral and reference acoustic wavefronts with minimal phase directionality error. The array design was developed and evaluated using a point source array model and numerical simulations, followed by physical fabrication. To address the sensitivity of the phase–azimuth linearity to manufacturing imperfections in sound sources, a phase error compensation technique was implemented by adjusting the input signal parameters to the acoustic emitters. Experimental validation was conducted in an anechoic water tank, where both spiral and reference wavefronts were transmitted across multiple frequencies. The results reveal that the array prototype achieved sub-degree-level compensated phase directionality accuracy for both wavefront types at all the tested frequencies. Notably, at the resonance frequency of 7.3 kHz, the root-mean-square phase directionality error of the spiral wavefront was reduced to as low as 0.19°. Full article

Efficient production of high-quality cubical crushed stone is critical for road construction and concrete manufacturing. Conventional vibrating screens suffer from low cubicality and high energy consumption, limiting their applicability. We developed a novel spiral vibrating screen featuring a helical screening surface and adjustable oscillation parameters. Experimental studies were conducted on granite aggregates (5–20 mm) at vibration frequencies of 16–26 Hz and amplitudes of 1.5–4.0 mm to evaluate cubicality, screening efficiency, throughput, and energy consumption. Under optimal operating conditions (22 Hz, 3.0 mm amplitude), the prototype achieved 84–86% cubical particles, 93–95% screening efficiency, and specific energy consumption of 1.20 ± 0.05 kWh/t. Compared with conventional flat and drum screens, cubicality improved by 8–12 percentage points, while energy consumption decreased by up to 12%. The developed screen offers a scalable solution for producing high-quality cubical aggregates with lower energy demand and reduced clogging risks. These findings provide practical guidance for improving aggregate processing technologies. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function, often causing tremors and difficulty with movement control. A promising diagnostic method involves analyzing hand-drawn patterns, such as spirals and waves, which show characteristic distortions in individuals with PD. This study compares three computational approaches for classifying individuals as Parkinsonian or healthy based on drawing-derived features: (1) Large Language Models (LLMs), (2) traditional machine learning (ML) algorithms, and (3) a fuzzy ontology-based method using fuzzy sets and Fuzzy-OWL2. Each method offers unique strengths: LLMs leverage pre-trained knowledge for subtle pattern detection, ML algorithms excel in feature extraction and predictive accuracy, and fuzzy ontologies provide interpretable, logic-based reasoning under uncertainty. Using three structured handwriting datasets of varying complexity, we assessed performance in terms of accuracy, interpretability, and generalization. Among the approaches, the fuzzy ontology-based method showed the strongest performance on complex tasks, achieving a high F1-score, while ML models demonstrated strong generalization and LLMs offered a reliable, interpretable baseline. These findings suggest that combining symbolic and statistical AI may improve drawing-based PD diagnosis. Full article

To extend the time between the overhauls of helicopters, a novel collaborative methodology that takes into account uncertain misalignment errors by considering the shape and performance of the gear is built. Firstly, the digital characteristics of contact patterns, such as the reference point and direction angle, are extracted. Secondly, an optimization model calculates the equivalent misalignment by minimizing deviations in the reference point and direction angle between two contact patterns. This equivalent misalignment accounts for uncertainty misalignment errors introduced by complex gear support deformation. Thirdly, the ease-off is utilized to derive the pinion target surface that can sustain meshing performance under an equivalent misalignment, similar to the original gear in real conditions. This way it integrates with the optimization theory for flank reconstruction to redesign the pinion surface. Simulations reveal that the critical digital characteristics of the contact path on the original gear under the equivalent misalignment mirror those of the original gear in real conditions. Moreover, the surface parameters of the redesigned pinion result in an identical surface under a different equivalent misalignment, maintaining similar contact and dynamic performance. This proposed collaborative design approach, considering the shape and performance while accounting for uncertain misalignment errors through ease-off, greatly improves the gear transmission behavior. Full article

Reinforced concrete (RC) columns play a vital role in structural integrity, and accurately predicting their failure modes is essential for enhancing seismic safety and performance. This study explores the use of a supervised machine learning approach—specifically, an artificial neural network (ANN) model—to classify failure modes of RC columns. The model is trained using data from the well-established Pacific Earthquake Engineering Research Center (PEER) structural performance database, which contains results from over 400 cyclic lateral-load tests on RC columns. These tests encompass a wide range of column types, including those with spiral or circular hoop confinement, rectangular ties, and varying configurations of longitudinal reinforcement with or without lap splices at critical sections. The ANNs were evaluated using a randomly selected subset from the PEER database, achieving classification accuracies of 94% for rectangular columns and 95% for circular columns. Notably, in certain cases, the model’s predictions aligned with or exceeded the accuracy of traditional building code-based methods. These findings underscore the strong potential of machine learning—particularly ANNs—for reliably postdicting failure modes (even the brittle ones) in RC columns, signaling a promising advancement in the field of earthquake engineering. Full article

Converting biological strategies into practical design principles during the Discover–Abstract phase of the Biomimicry Design Spiral (BSD) presents a considerable obstacle, particularly for designers lacking a biological background. This research introduces a Retrieval-Augmented Generation (RAG) framework that combines a specialized AskNature database of 2106 documents with a locally executed Llama 3.1 large language model (LLM) to fill this void. The innovation of this study lies in integrating the BDS with a stage-specific RAG–LLM framework. Unlike BioTRIZ or SAPPhIRE, which require specialized expertise, our approach provides designers with semantically precise and biologically grounded strategies that can be directly translated into practical design principles. A quasi-experimental study with 30 industrial design students assessed three setups—LLM-only, RAG-Small, and RAG-Large—throughout six biomimicry design stages. Performance was assessed via expert evaluations of text and design concept quality, along with a review of retrieval diversity. Findings indicate that RAG-Large consistently yielded superior text quality in stages with high cognitive demands. It also retrieved a more varied array of high-specificity biological ideas and facilitated more coherent incorporation of functional, aesthetic, and semantic aspects in design results. This framework diminishes cognitive burden, boosts the relevance and originality of inspirations, and provides a reproducible, stage-specific AI assistance model for closing the knowledge translation gap in biomimicry design, though its current validation is limited to a small sample and a single task domain. Full article

This study focuses on the nonlinear partial differential equation known as the Lonngren wave equation, which plays a significant role in plasma physics, nonlinear wave propagation, and astrophysical research. By applying a suitable wave transformation, the nonlinear model is reduced to an ordinary differential equation. Analytical wave solutions of the Lonngren wave equation are then derived using the extended direct algebraic method. The physical behavior of these solutions is illustrated through 2D, 3D, and contour plots generated in Mathematica. Finally, the stability analysis of the Lonngren wave equation is discussed. Full article

This study presents an innovative strategy for the electrochemical degradation of methylene blue (MB) using 3D-printed helical anode electrodes fabricated from commercially available conductive Polylactic acid/carbon black (PLA/CB) filaments. The choice of PLA/CB is particularly significant, since the amorphous PLA matrix combined with a percolating carbon black network provides a biodegradable, low-cost, and chemically versatile polymer composite that can be transformed from a simple prototyping filament into a functional electrochemical platform. Through a combination of chemical/electrochemical activation and electrodeposition of copper nanoparticles (Cu NPs), the polymer electrodes were successfully converted into highly efficient catalytic platforms. Beyond material functionalization, the influence of electrode geometry was systematically investigated, comparing single-, double-, and triple-spiral helical configurations. The double-spiral geometry proved the most effective, offering the best balance between active surface area and electrolyte flow dynamics. Under mild conditions (2 V, pH 6, 0.1 M NaCl), the system achieved up to 97% MB removal, while also demonstrating remarkable stability and reusability over at least ten consecutive cycles. These results highlight the synergistic role of polymer chemistry, arrangement, and metal decoration, demonstrating how 3D printing can be a useful platform for the easy production of electrodes with different geometries, even starting from simple conductive filaments reused in sustainable and scalable functional materials for advanced wastewater treatment. Full article

High peak-hour energy consumption from air conditioning in commercial buildings creates significant operational costs and grid instability. This study experimentally investigates the thermo-economic performance of a vapor compression refrigeration system (VCR) ice storage system to address this challenge through load shifting. The methodology involved operating a custom test rig, featuring an insulated test chamber and an ice tank with a novel spiral evaporator, under an improved 8 h night charging and 9 h day discharge strategy. Results show the system consumed 5.44 kWh of electricity to store 7.70 kWh of thermal energy, achieving a charging Coefficient of Performance (COP) of 1.42. A total of 5.195 kWh of cooling was delivered with a discharge efficiency of 67.5%. The experimental cost analysis confirmed an approximate 20% operating cost advantage over conventional direct cooling. A simple payback assessment indicates strong sensitivity to tariff structures and annual operating days. This study concludes that the optimized Ice Storage System (ISS) is a technically viable and economically advantageous solution for managing peak cooling loads, providing a validated reference model and dataset for future work. Full article

Growing concerns about the energy crisis and global warming have driven interest in geothermal energy. This paper presents a numerical thermo-hydraulic comparison of spiral ground heat exchangers (SGHEs) in vertical and horizontal configurations. Numerical models were validated against experimental data using ANSYS Fluent 2023 R2. Five spiral pitch values were tested to analyze their impact on heat transfer rate (HTR), pressure drop, and total pipe length. Results showed that decreasing pitch increases HTR but significantly raises pressure drop and pipe length. Higher inlet fluid velocities also increased HTR but led to greater pressure drops. Under all tested conditions, the vertical configuration consistently outperformed the horizontal one, achieving up to 19.3% higher mean HTR. For both configurations, a 10 cm pitch provided the best balance between HTR, pressure drop, and pipe length. Increasing inlet velocity from 0.05 to 0.15 ms⁻¹ increased mean HTR by approximately 30% for both configurations. These findings offer practical guidance for selecting the most appropriate SGHE configuration for specific geothermal applications. Full article

In this paper, we integrate the concepts of spiral-like functions and Janowski-type functions within the framework of q-calculus to define new subclasses in the open unit disk. Our primary focus is on analyzing convolution conditions that form the foundation for further theoretical developments. The main contributions include establishing sufficient conditions and applying Robertson’s theorem. Furthermore, motivated by a recent definition, we propose analogous neighborhood concepts for the above-mentioned classes, and we explore the corresponding neighborhood results. Full article

Background/Objectives: Among different milling techniques, spiral air jet milling can produce finer particles without the use of solvents or additives, thereby improving the bioavailability and content uniformity of the final dosage form. However, milling can complicate downstream processability of active pharmaceutical ingredients (APIs) due to reduced bulk powder flowability and post-milling lump formation. Process settings are often optimized only for particle size reduction, without sufficient consideration of manufacturability, largely because of limited API availability and a lack of knowledge about influential material properties. This study aimed to investigate the impact of material properties and process settings on milling performance and downstream manufacturability. Methods: Four APIs, examined in a total of eight grades, were characterized for their bulk mechanical properties and compression energy parameters using a compaction simulator. These grades were subjected to milling experiments within a design-of-experiments framework. Statistical analyses were performed, and population balance models (PBMs) were developed and calibrated for each experiment to link material properties and process settings to milling outcomes. Results: A higher gas flow rate was identified as the most significant contributor to particle size reduction. The influence of mechanical properties, particularly Young’s modulus and Poisson’s ratio, was evident and correlated with unmilled particle sizes. PBM analyses showed that a higher gas feed rate decreased the critical particle size for breakage, while intrinsic mechanical properties affected the breakage rate function. Conclusions: By integrating material properties and process settings into PBM analyses, specific breakage mechanisms could be identified. These findings provide a framework for optimizing jet milling not only for particle size reduction but also for downstream processability of APIs. Full article

This paper investigates the influence of the internal concave surface structure of the stirring tool and welding parameters on the microstructure and mechanical properties of the T-joint. The analysis reveals that compared to the inner concave surface without spirals, T-joints welded by inner concave surfaces with spirals exhibit fewer welding defects. Meanwhile, the microscopic results showed that there is a welding juncture zone between the thermomechanical affected zone and the nugget zone, and a large number of θ’, T₁, and η’ phases precipitate in the nugget zone of the joint, which improves its strength and hardness. When welding speed v, rotational speed w and insertion depth h are 60 mm/min, 350 rpm, and 0.21 mm, respectively, the yield strength, the tensile strength, and the elongation of the T-joint reach their maximum values (352 MPa, 408 MPa and 5%), and the tensile strength represents 68.0% and 71.6% of the base materials, respectively. The fracture mechanism of the joint is a mode of ductile fracture. Furthermore, the T-joint exhibits a “W” and “Z” distribution pattern on both sides of the weld centerline B and A, respectively. Full article