Search Results (1,170)

Articulated arm coordinate measuring machines are designed for in situ use directly in manufacturing environments, enabling efficient dimensional control outside of climate-controlled laboratories. This study investigates the influence of ambient temperature variation on the accuracy of length measurements performed with the Hexagon Absolute Arm 8312. The experiment was carried out in a laboratory setting simulating typical shop floor conditions through controlled temperature changes in the range of approximately 20–31 °C. A calibrated steel gauge block was used as a reference standard, allowing separation of the influence of the measuring system from that of the measured object. The results showed that the gauge block length changed in line with the expected thermal expansion, while the articulated arm coordinate measuring machine exhibited only a minor residual thermal drift and stable performance. The experiment also revealed a constant measurement offset of approximately 22 µm, likely due to calibration deviation. As part of the study, an uncertainty budget was developed, taking into account all relevant sources of influence and enabling a more realistic estimation of accuracy under operational conditions. The study confirms that modern carbon composite articulated arm coordinate measuring machines with integrated compensation can maintain stable measurement behavior even under fluctuating temperatures in controlled environments. Full article

Background/Objectives: Chemically or genetically detoxified pertussis toxin (PTx) is a crucial antigen component of the acellular pertussis vaccine. Chemical detoxification using glutaraldehyde generally causes significant structural changes to the toxin. However, how these structural changes in PTx affect its antigenic properties remains unclear. Additionally, there is limited knowledge regarding how many alterations in antigenic properties impact immunogenicity. Methods: To investigate the impact of structural changes on antigenic properties, we developed a sandwich ELISA to quantify the neutralizing epitopes on PTx. Subsequently, we analyzed different PTx toxoid (PTd) preparations with the assay. Additionally, we assessed the immunogenicity of various acellular pertussis vaccine candidates containing these PTd preparations. Finally, the assay was applied to evaluate the consistency of commercial batches of PTx and PTd intermediates. Results: The assay demonstrated reasonable specificity, accuracy, and precision, and it was sensitive enough to quantify variations in neutralizing epitopes among different PTd samples that shared the same protein concentration. Importantly, we found a positive correlation between the number of neutralizing epitopes in detoxified PTx and its immunogenicity, indicating that the amount of neutralizing epitopes present determines the immunogenicity of glutaraldehyde-inactivated PTx. Moreover, commercial batches of PTx and PTd intermediates exhibited minor variations in neutralizing epitopes. Conclusions: These findings have significant implications for developing acellular pertussis vaccines as they highlight the importance of preserving the neutralizing epitopes of PTx during detoxification to ensure the vaccine’s effectiveness. This assay is also valuable for the quality control of PTd as it more accurately represents the actual antigenic changes of PTx. Full article

Background/Objective: Dry eye syndrome affects 16 million Americans with USD 52 billion in annual healthcare costs. With large language models (LLMs) increasingly used for healthcare information, understanding their performance in delivering equitable dry eye guidance across diverse populations is critical. This study aims to evaluate and compare five major LLMs (Grok, ChatGPT, Gemini, Claude.ai, and Meta AI) regarding dry eye syndrome information delivery across different demographic groups. Methods: LLMs were queried using standardized prompts simulating a 62-year-old patient with dry eye symptoms across four demographic categories (White, Black, East Asian, and Hispanic males and females). Responses were analyzed for word count, readability, cultural sensitivity scores (0–3 scale), keyword coverage, and response times. Results: Significant variations existed across LLMs. Word counts ranged from 32 to 346 words, with Gemini being the most comprehensive (653.8 ± 96.2 words) and Claude.ai being the most concise (207.6 ± 10.8 words). Cultural sensitivity scores revealed Grok demonstrated highest awareness for minority populations (scoring 3 for Black and Hispanic demographics), while Meta AI showed minimal cultural tailoring (0.5 ± 0.5). All models recommended specialist consultation, but medical term coverage varied significantly. Response times ranged from 7.41 s (Meta AI) to 25.32 s (Gemini). Conclusions: While all LLMs provided appropriate referral recommendations, substantial disparities exist in cultural sensitivity, content depth, and information delivery across demographic groups. No LLM consistently addressed the full spectrum of dry eye causes across all demographics. These findings underscore the importance for physician oversight and standardization in AI-generated healthcare information to ensure equitable access and prevent care delays. Full article

Speaker burn-in is a controversial practice in the audio world, based on the belief that new devices reach optimal performance only after a certain period of use. Supporters claim it improves component flexibility, reduces initial distortion, and enhances sound quality—especially in the low-frequency range. Critics, however, emphasize the lack of scientific evidence for audible changes and point to the placebo effect in subjective listening tests. They argue that modern manufacturing and strict quality control minimize differences between new and “burned-in” devices. This study cites a standard describing a preliminary burn-in procedure, specifying the exact conditions and duration required. Objective tests revealed slight changes in speaker impedance and amplitude response after burn-in, but these differences are inaudible to the average listener. Notably, significant variation was observed between speakers of the same series, attributed to production line tolerances rather than use-related changes. The study also explored aging processes in speaker materials to better understand potential long-term effects. However, subjective listening tests showed that listeners rated the sound consistently across all test cases, regardless of whether the speaker had undergone burn-in. Overall, while minor physical changes may occur, their audible impact is negligible, especially for non-expert users. Full article

In industrial systems, sensors often classify collected time series data for incipient fault diagnosis. However, time series data from sensors during the initial stages of a fault often exhibits minor fluctuation characteristics. Existing time series classification (TSC) methods struggle to achieve high classification accuracy when these minor fluctuations serve as the primary distinguishing feature. This limitation arises because the low-amplitude variations of these fluctuations, compared with trends, lead the classifier to prioritize and learn trend features while ignoring the minor fluctuations crucial for accurate classification. To address this challenge, this paper proposes a novel graph-based time series classification framework, termed MFSI-TSC. MFSI-TSC first extracts the trend component of the raw time series. Subsequently, both the trend series and the raw series are represented as graphs by extracting the “visible relationship” of the series. By performing a subtraction operation between these graphs, the framework isolates the differential information arising from the minor fluctuations. The subtracted graph effectively captures minor fluctuations by highlighting topological variations, thereby making them more distinguishable. Furthermore, the framework incorporates optimizations to reduce computational complexity, facilitating its deployment in resource-constrained sensor systems. Finally, empirical evaluation of MFSI-TSC on both real-world and publicly available datasets demonstrates its effectiveness. Compared with ten benchmark methods, MFSI-TSC exhibits both high accuracy and computational efficiency, making it more suitable for deployment in sensor systems to complete incipient fault detection tasks. Full article

In the present study, two novel donor–acceptor (D–A) conjugated copolymers, PIDTBDI and PIDTTBDI, were successfully synthesized via Stille coupling polymerization. These alternating copolymers incorporate indacenodithiophene and indacenodithienothiophene as donor units, coupled with benzothiadiazole dicarboxylic imide as the electron-deficient acceptor unit. The influence of extended conjugation on the structural, optical, thermal, and electrochemical properties of the copolymers was systematically investigated and confirmed by density functional theory (DFT). XRD analysis confirmed that both polymers are amorphous. Thermogravimetric analysis revealed that both materials possess excellent thermal stability, with decomposition temperatures exceeding 270 °C. The theoretical and experimental values of the energy gap confirmed the thermal stability of the studied polymers. The molecular weight was determined to be 10,673 Da for PIDTBDI and 7149 Da for PIDTTBDI. Despite the variation in molecular weight, both copolymers exhibited comparable optical and electrochemical bandgaps of approximately 1.57 and 1.69 eV, respectively. Electrochemical measurements showed that PIDTBDI has a HOMO energy level of −5.30 eV and a LUMO level of −3.61 eV, while PIDTTBDI displays HOMO and LUMO levels of −5.28 eV and −3.59 eV, respectively. These results indicate that minor structural differences can considerably affect the electronic characteristics of the polymers, thus altering their overall efficacy in solar cell applications. Full article

In this article, we investigate the fractional soliton solutions as well as the chaotic analysis of the fourth-order nonlinear Ablowitz–Kaup–Newell–Segur wave equation. This model is considered an intriguing high-order nonlinear partial differential equation that integrates additional spatial and dispersive effects to extend the concepts to more intricate wave dynamics, relevant in engineering and science for understanding complex phenomena. To examine the solitary wave solutions of the proposed model, we employ sophisticated analytical techniques, including the generalized projective Riccati equation method, the new improved generalized exponential rational function method, and the modified F-expansion method, along with mathematical simulations, to obtain a deeper insight into wave propagation. To explore desirable soliton solutions, the nonlinear partial differential equation is converted into its respective ordinary differential equations by wave transforms utilizing $\beta$-fractional derivatives. Further, the solutions in the forms of bright, dark, singular, combined, and complex solitons are secured. Various physical parameter values and arrangements are employed to investigate the soliton solutions of the system. Variations in parameter values result in specific behaviors of the solutions, which we illustrate via various types of visualizations. Additionally, a key aspect of this research involves analyzing the chaotic behavior of the governing model. A perturbed version of the system is derived and then analyzed using chaos detection techniques such as power spectrum analysis, Poincaré return maps, and basin attractor visualization. The study of nonlinear dynamics reveals the system’s sensitivity to initial conditions and its dependence on time-decay effects. This indicates that the system exhibits chaotic behavior under perturbations, where even minor variations in the starting conditions can lead to drastically different outcomes as time progresses. Such behavior underscores the complexity and unpredictability inherent in the system, highlighting the importance of understanding its chaotic dynamics. This study evaluates the effectiveness of currently employed methodologies and elucidates the specific behaviors of the system’s nonlinear dynamics, thus providing new insights into the field of high-dimensional nonlinear scientific wave phenomena. The results demonstrate the effectiveness and versatility of the approach used to address complex nonlinear partial differential equations. Full article

The extensive diversity of volatile organic compounds, along with their minor structural variations, presents significant challenges in the development of chemosensory-based biosensors. Previously, we generated sensor cells expressing insect odorant receptors (ORs) in Sf21 cells, demonstrating their potential as cell-based odorant sensor elements. However, it remains unclear whether the selectivity of cells expressing ORs in vitro for diverse compounds aligns with the receptor’s in vivo performance, aside from the response to target compounds. To address this, we assessed the ligand responses of sensor cells expressing ORs from Drosophila melanogaster using a high-throughput calcium imaging system. Our results demonstrate that in vitro receptor responses exhibit ligand selectivity comparable to in vivo conditions across different chemical categories. Broadly tuned OR-expressing sensor cells (Or13a, Or47a, and Or98a) displayed differential affinities, whereas the narrowly tuned Or56a-expressing sensor cells selectively responded to geosmin. Moreover, cell responses varied with subtle differences in chemical structure, including carbon chain length and functional group positioning. These findings provide valuable insights into insect OR–ligand interactions in vitro, demonstrating that receptor selectivity in sensor cells closely mirrors in vivo conditions. In addition to this consistency, our results highlight the subtle ligand differentiation capabilities of sensor cells enabling fluorescence-based visualization of receptor–ligand interactions. Full article

The Life Cycle Cost Assessment (LCCA) methodology is widely used to determine the optimal thickness of thermal insulation for walls and roofs. The results depend on several factors, such as the degree day calculations method, the ambient or sol–air temperature, base temperature variations, and the heat capacity of the thermal envelope elements. This study aims to analyze the impact of solar radiation on mass walls with different orientations in five cities in northern Chile, which have severe solar climates. The goal is to determine the optimal thickness of expanded polystyrene insulation using the LCCA method, considering solar radiation, a varying base temperature, and validating results by analyzing the energy demand for heating and cooling of a typical house. The findings show that excluding solar radiation in the LCCA methodology can lead to an underestimation of the optimal insulation thickness by 21–39% for walls in northern Chile. It was also found that using variable monthly threshold temperatures for heating and cooling based on the adaptive thermal comfort model results in a slight underestimation (1–3%) of the optimal thickness compared to a constant annual temperature. An energy simulation of a typical house in five cities in northern Chile showed that neglecting the effect of solar radiation when determining the thermal insulation thickness for the studied wall can lead to a minor increase in heating and cooling energy demand, ranging from approximately 1% to 9%. However, this study emphasizes the importance of applying optimal insulation thickness for cities with more continental climates like Santiago and Calama, where the heating demand is higher than cooling. Full article

The increasing incorporation of renewable energy into power grids has significantly reduced system inertia and damping, posing challenges to frequency stability and power quality. To address this issue, an adaptive virtual synchronous generator (VSG) control strategy is proposed, which dynamically adjusts virtual inertia and damping in response to real-time frequency variations. Virtual inertia is modulated by an exponential function according to the frequency variation rate, while damping is regulated via a hyperbolic tangent function, enabling minor support during small disturbances and robust compensation during severe events. Control parameters are optimized using an enhanced particle swarm optimization (PSO) algorithm based on a composite performance index that accounts for frequency deviation, overshoot, settling time, and power tracking error. Simulation results in MATLAB/Simulink under step changes, load fluctuations, and single-phase faults demonstrate that the proposed method reduces the frequency deviation by over 26.15% compared to fixed-parameter and threshold-based adaptive VSG methods, effectively suppresses power overshoot, and eliminates secondary oscillations. The proposed approach significantly enhances grid transient stability and demonstrates strong potential for application in power systems with high levels of renewable energy integration. Full article

Dens invaginatus is a developmental dental anomaly characterized by the infolding of the enamel organ into the dental papilla during early odontogenesis. This process leads to a broad spectrum of anatomical variations, ranging from minor enamel-lined pits confined to the crown to deep invaginations extending through the root, occasionally communicating with periodontal or periapical tissues. The internal complexity of affected teeth presents diagnostic and therapeutic challenges, particularly in severe forms that mimic root canal systems or are associated with pulpal or periapical pathology. Maxillary lateral incisors are most frequently affected, likely due to their unique developmental timeline and morphological susceptibility. Although various classification systems have been proposed, Oehlers’ classification remains the most clinically relevant due to its simplicity and correlation with treatment complexity. Recent advances in diagnostic imaging, especially cone beam computed tomography (CBCT), have revolutionized the identification and classification of these anomalies. CBCT-based adaptations of Oehlers’ classification allow for the precise assessment of invagination extent and pulpal involvement, facilitating improved treatment planning. Contemporary therapeutic strategies now include calcium-silicate-based cement sealing materials, endodontic microsurgery for inaccessible anatomy, and regenerative endodontic procedures for immature teeth with necrotic pulps. Emerging developments in artificial intelligence, genetic research, and tissue engineering promise to further refine diagnostic capabilities and treatment options. Early detection remains critical to prevent complications such as pulpal necrosis or apical disease. A multidisciplinary, image-guided, and patient-centered approach is essential for optimizing clinical outcomes in cases of dens invaginatus. Full article

The Citrus tristeza virus (CTV), a member of the Closterovirus genus, is considered a serious threat to citrus trees grafted onto sour orange (SO) rootstock. In the Mediterranean area, the most prevalent CTV strains are VT and T30. The VT strain includes both mild and severe isolates, some of them associated with seedling yellows (SY) syndrome. Mild CTV-VT isolates that do not induce SY symptoms (no-SY) show minor variations in their Orf1a, p23, and p33 genes, with a single nucleotide polymorphism at position 161 of the p23 gene. These isolates can repress superinfection with homologous severe isolates. The aim of this study was to investigate the mechanism of cross-protection by means of biological indexing, real-time RT-PCR high-resolution melting (HRM), and p23 gene amplicon sequencing. Four no-SY CTV-VT isolates were inoculated onto SO seedlings and Hamlin sweet orange trees grafted on SO. These plants were later challenged with two homologous CTV-VT SY isolates and remained asymptomatic. The biological evaluation of the infection process in superinfected plants was investigated via inoculation of the bark on SO seedlings that were also asymptomatic. A parallel HRM analysis of midvein RNA extracts revealed that the melting temperature (Tm) of the no-SY isolates was statistically lower than that of the SY isolates. The Tm values of RNAs extracts from superinfected plants were not statistically different from those of the no-SY isolates. This suggests that the SY isolates failed to establish infection or replicate in plants pre-inoculated with no-SY isolates. This blockage of replication resembles superinfection exclusion, with attractive perspectives to prevent SY damage in field applications. Full article

Machine learning (ML) techniques are becoming increasingly valuable for modeling the transport of pollutants in plant systems. However, two challenges (small sample sizes and a lack of quantitative calculation functions) remain when using ML to predict migration in hydroponic systems. For the bioaccumulation of per- and polyfluoroalkyl substances, we studied the key factors and quantitative calculation equations based on data augmentation, ML, and symbolic regression. First, feature expansion was performed on the input data after data preprocessing; the most important step was data augmentation. The original training set was expanded nine times by combining the synthetic minority oversampling technique and a variational autoencoder. Subsequently, the four ML models were applied to the test set to predict the selected output parameters. Categorical boosting (CatBoost) had the highest prediction accuracy (R² = 0.83). The Shapley Additive Explanation values indicated that molecular weight and exposure time were the most important parameters. We applied three symbolic regression models to obtain accurate prediction equations based on the original and augmented data. Based on augmented data, the high-dimensional sparse interaction equation exhibited the highest accuracy (R² = 0.776). Our results indicate that this method could provide crucial insights into absorption and accumulation in plant roots. Full article

Regulating lamprey populations is crucial for maintaining ecological equilibrium. However, the unique sex determination process of lampreys is constrained by multiple factors, complicating intuitive analysis of population dynamics and their impact on the natural environment. This study employed a two-species competition mechanism to elucidate the factors influencing sex ratios and their mechanistic effects on lamprey population size. Using the Lotka–Volterra equations, we investigated how sex ratios affect trophic levels both upstream and downstream of lampreys in the food web. A logistic population growth model was applied to assess the impact of sex ratio variations on symbiotic parasitic species, while the Analytic Hierarchy Process (AHP) was utilized to explore the dynamic relationship between sex ratio changes and ecosystem stability. To validate model efficacy, we manipulated temperature and food availability under controlled disturbance conditions, analyzing temporal variations in lamprey population size across different disturbance intensities to evaluate model sensitivity. The findings indicate that the variable sex ratio’s benefit is in facilitating the lampreys’ population’s enhanced adaptation to environmental shifts. The coexisting species exhibit a similar pattern of population alteration as the lampreys, albeit with a minor delay. A definitive link between the quantity of lampreys and the parasitic species is absent. A male ratio of 0.6 optimally contributes to the ecosystem’s equilibrium. Over time, the configuration of our model’s parameters proves to be sensible. This research provides robust theoretical support for developing scientific strategies to regulate lamprey populations. Full article

Global warming is driving significant changes in aquatic ecosystems, where temperature fluctuations influence biological processes across multiple levels of organisation. As ectothermic organisms, fish are particularly susceptible, with even minor thermal shifts affecting their metabolism, behaviour, and overall fitness. Understanding these responses is essential for evaluating the ecological and evolutionary consequences of climate change. This study investigates the effects of acute (4-day) and chronic (21-day) exposure to three temperature regimes—18 °C (low), 26 °C (control), and 34 °C (high)—on the spatio-temporal shoaling behaviour of adult zebrafish (Danio rerio). Groups of four fish were tested for six minutes in water maintained at the same temperature as their prior acclimation. Shoaling behaviour was assessed by analysing shoal structure—encompassing shoal dimensions and cohesion—as well as spatial positioning. Parameters measured included inter-fish distance, shoal volume, shoal area, homogeneity index, distance to the centroid, and the shoal’s vertical and horizontal distribution. Results revealed complex behavioural changes influenced by both temperature and duration of exposure. At 18 °C, zebrafish showed a marked preference for the bottom zone and exhibited no significant temporal modulation in exploratory behaviour—patterns indicative of heightened anxiety-like responses. In contrast, exposure to 34 °C resulted in increased shoal cohesion, particularly under chronic conditions, and a progressive increase in environmental exploration over the six-minute test period. This enhancement in exploratory activity was especially evident when compared to the first minute of the test and was characterised by greater vertical movement—reflected in the increased use of the upper zone—and broader horizontal exploration, including more frequent occupation of peripheral areas. These findings align with previous research linking thermal variation to neurobiological and proteomic alterations in zebrafish. By elucidating how temperature modulates social behaviour in ectotherms, this study offers valuable insights into the potential behavioural impacts of climate change on aquatic ecosystems. Full article