Search Results (2,546)

The rapid expansion of Generative Artificial Intelligence (GAI) is transforming higher education systems, particularly public institutions seeking to advance toward smart governance models and digital transformation. In this context, digital teaching competence emerges as a strategic factor for the effective, ethical, and pedagogically sound adoption of these technologies. This study assesses the level of digital competence among public higher education faculty in Paraguay and examines its predictive capacity regarding the adoption of GAI tools using machine learning models. A nationwide quantitative study was conducted with a sample of 800 faculty members from public universities across Paraguay. Data were collected through a structured questionnaire based on international digital competence frameworks, incorporating additional variables such as attitudes toward GAI, technological experience, institutional infrastructure, and perceived organizational support. Data analysis involved the application of machine learning techniques, including Logistic Regression, Random Forest, and Gradient Boosting, to identify the variables with the strongest predictive power regarding faculty readiness and willingness to integrate GAI into teaching practices. Model performance was evaluated using metrics such as accuracy, F1-scores, and the AUC-ROC. The findings identify key predictors of technological readiness and structural gaps within Paraguay’s public higher education system. This research provides empirical evidence from Latin America on the factors influencing GAI adoption in public sector educational contexts and contributes to the design of educational policies aimed at fostering smart universities and digitally sustainable academic ecosystems. Full article

Efficiently and accurately converting floating-point numbers to decimal strings remains a fundamental challenge in numerical computation, data serialization, and human–computer interaction. While modern algorithms such as Ryū, Dragonbox, and Schubfach rigorously satisfy the Steele–White criteria for correctness and minimal output length, their performance is frequently constrained by branch mispredictions, high-precision multiplication overhead, and suboptimal utilization of instruction-level parallelism. This paper introduces xjb, a novel floating-point–string conversion algorithm derived from Schubfach that systematically overcomes these bottlenecks. By restructuring the core computation to reduce instruction dependencies, adopting branchless decision logic, and exploiting SIMD instruction sets for decimal-to-ASCII formatting, xjb delivers state-of-the-art throughput across diverse hardware platforms. The algorithm requires only a single 64-by-128-bit multiplication for IEEE 754 binary64 conversions and a single 64-by-64-bit multiplication for binary32, drastically decreasing arithmetic complexity. Extensive benchmarking on AMD R7-7840H and Apple M1/M5 processors demonstrates that xjb consistently outperforms leading contemporary implementations. Notably, on the Apple M5, xjb achieves speedups of approximately 20% and 136% for binary64 and binary32 conversions, respectively, when compared to the highly optimized zmij library. The algorithm is fully compliant with the Steele–White principle; exhaustive validation over the entire binary32 space and extensive random testing across the binary64 range confirm both its theoretical soundness and practical robustness. Full article

Continuous monitoring of pig behavior is essential for timely health management and welfare assessment in commercial production systems. Although vision-based methods have been widely studied, their practical application in commercial barns is often limited by variable lighting, frequent occlusion, and high stocking density. Acoustic sensing offers a non-contact alternative that is independent of lighting conditions; however, reliable behavior classification from pig vocalizations remains challenging in commercial environments because of background noise and temporal variability in sound patterns. In this study, an attention-guided acoustic framework, termed ATF-Conformer, was developed for pig vocalization classification under farm conditions. A five-class vocalization dataset was collected from finishing Landrace pigs and multiparous sows on a commercial farm, including cough, scream, estrus, feeding, and normal behavior sounds. The proposed framework combined spectrogram denoising with interactive attention to enhance behavior-related acoustic information, while a time-frequency-decoupled Conformer encoder was introduced to improve feature representation under noisy conditions. Final classification was performed using mask-based temporal pooling with an additive angular margin Softmax objective. In five-fold grouped cross-validation, ATF-Conformer achieved an accuracy of 97.34% ± 0.42 and outperformed several existing acoustic models across multiple evaluation metrics. A similar accuracy of 97.38% was obtained on an independent test set, indicating stable performance across datasets. These results suggest that the proposed method can support continuous, non-invasive pig vocalization-based behavior monitoring and may assist farm owners or workers in pen-level screening of frequent cough or abnormal vocal events, thereby supporting targeted on-site inspection in precision livestock farming. Full article

The nonlinear dynamic response of aerospace thin-walled structures in a post-buckling state under thermo-acoustic loads is critical for their design. This study investigates this phenomenon through integrated experimental and numerical approaches. Acoustic tests on thermally stressed flat plates yielded results in close agreement with finite element and reduced-order modal (FEM/ROM) simulations, with first-order frequency deviations within ±2 Hz and strain values of the same order of magnitude (10.7 µε vs. 9.5 µε at 50 °C). A key observation is the non-monotonic variation in the thermal modal frequency, which initially decreases then increases with the buckling coefficient, while dynamic strain data further validate the computational model. Comparative analysis of three Haynes 188 alloy geometries—flat plates, cylindrical shells, and spherical shells—reveals distinct behaviors rooted in their critical buckling temperatures (68.46 °C, 151.20 °C, and 698.28 °C, respectively): flat plates exhibit softening–hardening transitions with a frequency range of 491–624 Hz; cylindrical shells show irregular responses with a dramatic frequency drop from 1120 Hz to 360 Hz; and spherical shells maintain the highest stability and frequency range (1913–2109 Hz), governed by the buckling coefficient’s linear effect. Time-domain and probability density function (PDF) analyses elucidate the snap-through phenomena and the modulating roles of the buckling coefficient and sound pressure level (SPL). These findings underscore that geometric configuration and inherent stiffness are critical to post-buckling performance, providing a theoretical basis for designing aerospace components in extreme environments. Full article

This paper presents a modular system architecture and an automated signal processing pipeline designed to quantify neurophysiological relaxation responses to fragrance using consumer-grade wearable electroencephalography (EEG). By integrating real-time data streaming via Open Sound Control (OSC) with a high-performance backend, the platform enables objective assessment of olfactory stimuli through a reproducible Sleep Readiness Index (SRI) derived from spectral power shifts. To mitigate the signal quality constraints inherent in portable hardware, the framework utilizes a robust suite of engineering controls, including zero-phase filtering and automated artifact rejection, ensuring data integrity across short-window trials. Validation through construct-level analysis of public sleep datasets and synthetic sensitivity testing confirms the index’s directional reliability, while runtime benchmarking demonstrates sub-millisecond compute times suitable for interactive wellness applications. Ultimately, this framework provides a transparent, auditable engineering scaffold that replaces subjective self-reports with a standardized, within-session proxy metric for comparative fragrance evaluation. Full article

In historic districts, the audiovisual environment plays an important role in shaping both cultural expression and spatial experience. However, the influence of acoustic and visual environmental factors on perceived street vitality remains insufficiently understood. Taking the Shangxiahang Historic District in Fuzhou as a case study, this paper employs on-site sound pressure level measurements, panoramic visual data collection, questionnaire surveys, principal component analysis, correlation analysis, and multiple regression analysis to systematically examine the effects of acoustic and visual environmental factors on perceived street vitality. The results indicate that traditional cultural sounds and natural sounds have a significant positive impact on perceived street vitality, while construction noise and tour guide’s horn sound exhibit negative effects. Regarding the visual environment, street and alley spaces, traditional architecture, greenery, and the sky are all important factors in promoting perceived street vitality. Further regression analysis reveals that the perception rate of street and alley spaces has the strongest influence, followed by the perception rate of traditional architecture, the perceived frequency of folk activity sounds, preference for greenery, and the perception rate of the sky. These findings demonstrate that perceived street vitality in historic districts does not depend on a single environmental factor but rather arises from synergistic interaction between culturally meaningful acoustic cues and legible spatial forms. These results offer practical implications for multisensory design and vitality-oriented regeneration in historic districts. Full article

This paper addresses the challenge of accurately measuring liquid level interfaces in multiphase mixed media by proposing a detection method based on ultrasonic transmission. First, a mathematical model of the ultrasonic measurement system was established, and the acoustic field characteristics of transducers with different frequencies and diameters in slurry were simulated and analyzed to determine the optimal excitation frequency and probe diameter. On this basis, an echo sound pressure calculation model based on the side-incidence method was constructed, and a formula for calculating the liquid level interface height was derived. Finally, an experimental test platform with a multi-layer steel container was built to measure the propagation velocity, attenuation coefficient, and acoustic impedance coefficient of ultrasound in the slurry, verifying the feasibility of the liquid level interface measurement method. Full article

High-frequency transformer (HFT) noise is a pivotal indicator of equipment performance. To conduct a comprehensive evaluation, this study systematically performed testing and evaluation on the noise generated by a 70 kW HFT under no-load conditions. Acoustic data were collected using acoustic sensors and a head-and-torso simulator, followed by an analysis of noise characteristics focusing on the impacts of voltage levels and operating frequencies. A multi-dimensional evaluation of HFT noise was carried out using sound quality parameters to unravel its intrinsic attributes under electrical parameter excitation. The key findings are as follows: HFT noise exhibits steady-state time-domain behavior and distinct tonal frequency-domain features; the dominant frequency is twice the operating frequency, with prominent harmonics. The noise intensity increases with the voltage levels (~47.0 dB (A) at 200 V to ~72.0 dB (A) at 750 V at 5 kHz) but decreases with the operating frequencies (~82.0 dB (A) at 4 kHz to ~47.0 dB (A) at 10 kHz at 750 V). This study establishes correlations between the electrical parameters and sound quality metrics; the loudness, sharpness, tone-to-noise ratio and prominence ratio are sensitive to the electrical parameters of HFT. Single-frequency noise from HFT exhibits remarkable perceptual salience, exacerbating the perceived annoyance. Thus, HFT design should prioritize reducing single-frequency noise to alleviate such issues. Full article

This paper examines the subjective annoyance associated with shared-bike electronic alert sounds (SBeASs), an emerging urban noise source. A study was conducted by employing extensive questionnaire surveys and psychoacoustic experiments. A preliminary survey (N = 1340) indicated that 90.6% of participants reported being impacted by SBeASs, with pronounced effects on nighttime rest and daytime work efficiency. In this study, SBeAS samples were taken from three prominent Chinese bike-sharing brands: Hello Bike, Meituan Bike, and DiDi Bike. Under laboratory conditions, subjective annoyance assessments (N = 28) for SBeASs were conducted at controlled sound pressure levels (SPLs) ranging from 45 to 65 dBA, with occurrence frequencies of 1, 3, and 5 s. Simultaneously, annoyance assessments were also conducted for two reference noise types: traffic noise and street noise. The results indicated a notable increase in annoyance levels related to SBeASs with rising SPL and increased occurrence frequency. Minor variations in annoyance were identified among different bike-sharing brands, which can be attributed to their distinct acoustic features. When the SPL was above 55 dBA, the DiDi Bike SBeASs produced considerably higher annoyance than those of other brands. This can be attributed to its elevated low-frequency energy, loudness, and roughness. Moreover, individuals exhibiting increased sensitivity to noise reported notably higher annoyance ratings on the SBeAS scale (p = 0.019). Under low-SPL conditions (45–55 dBA), the annoyance attributed to frequent SBeASs can exceed that caused by traffic noise and street noise at comparable SPLs, highlighting the distinct disruptive impact of abrupt sound sources. Full article

Drone noise-induced human annoyance is emerging as one of the main barriers to socially acceptable large-scale urban air mobility (UAM) operations, which have the potential to revolutionize urban transportation systems in the next few decades. This paper investigates the state-of-the-art technology in the assessment of drone noise and its impact on individuals, focusing on measurement and evaluation methodologies, as well as subjective evaluations. Various acoustic metrics are reviewed to characterize drone noise, including sound pressure levels, spectral analysis, and psychoacoustic parameters such as loudness and annoyance. Preliminary experimental investigations to identify key frequencies and tonal components that significantly contribute to drone noise-induced public annoyance are also discussed. Interdisciplinary approaches integrating pure technical acoustics, human perception, and subjectivity emerge as promising solutions for a comprehensive understanding of drone noise effects. Finally, a preliminary framework for drone noise assessment towards noise-aware UAM operations is proposed. Full article

This study investigates how key milling parameters influence both cutting noise and surface quality during the machining of laminated bamboo lumber. Using a multifactorial optimal response surface methodology, the effects of fibre orientation (0–135°), spindle speed (7000–10,000 r/min), feed rate (0.5–2.0 m/min) and milling depth (0.5–2.0 mm) were quantified through 25 experimental runs. Cutting noise, measured as peak sound pressure level (SPL), ranged from 86.8 to 95.2 dB, increasing markedly with fibre angle, feed rate, and milling depth, but exhibiting a non-linear response to spindle speed. Surface roughness (S_a) varied from 2.6 to 11.7 µm and was most strongly governed by milling depth, followed by fibre orientation and feed rate, with a significant interaction between fibre orientation and spindle speed. Quadratic regression models demonstrated strong predictive performance (R² = 0.97 for SPL; R² = 0.85 for S_a). Based on the response surfaces, optimal low-noise, high-quality machining was achieved at moderate spindle speeds, low feed rates, and shallow milling depths. These findings provide a mechanistic basis for understanding noise–roughness coupling in bamboo machining and offer practical guidance for computer numerical control processing, tool selection, and industrial noise reduction strategies in bamboo manufacturing. Full article

Background: Dyslexia is a reading disorder that is associated with phonological processing and awareness difficulties. However, little is known about phonetic production in dyslexia. Whereas individual differences in speech sound perception were linked to native and foreign speech sound production in typical readers, this remains to be explored in dyslexia. Given the phonetic processing deficits frequently encountered in dyslexia, we aimed to pinpoint potential differences in the acoustic realization of native phonemic production in adults with dyslexia. Methods: Ten adults with dyslexia and ten age-matched typical readers produced 24 native-language minimal voiced–voiceless word pairs across three places of articulation (labial, dental, velar) in a reading task. Acoustic analyses addressed phonemic category size, between-category distance, and voice onset time (VOT). Pseudoword reading performance served as an index of phonological decoding ability. Results: For category size, we observed a trend-level group-by-type interaction (p = 0.059, η² = 0.04): both groups showed larger category sizes for voiced than voiceless consonants, but this difference was numerically larger in typical readers. Between-category distance showed a marginal group effect (p = 0.089, η² = 0.14), with larger differences between categories in dyslexia. VOT showed the expected effect of voicing, but no group differences. Conclusions: Our results indicate broadly preserved speech production in dyslexia, alongside subtle differences in category separation and size in dyslexia, marked by considerable inter-individual variability. Full article

This paper presents an integrated assessment of aircraft noise in flight and ground operations within the EFACA project, supporting the roadmap toward ACARE Flightpath-2050 noise goals. It summarizes required reductions, evaluates current technology readiness, and analyzes contributions from advanced propulsion concepts, propeller-noise modeling, and operational procedures. New seven-bladed propeller designs, validated through semi-empirical, analytical, and CAA methods, demonstrate substantial tonal-noise improvements, influencing the aircraft noise reductions by 2–4 dB depending on the fight stage, and during the ground operation by up to 5 dB. Full article

Existing studies of street-related emotional perception mainly rely on static scene evaluations, which cannot capture the cumulative effects of environmental exposure during continuous walking. To address this limitation, this study proposes a method for predicting pedestrian physiological responses in sequential audiovisual street environments. Four real-world walking routes were selected, with outbound and return directions treated as independent paths, yielding eight paths and 32 valid samples. EEG, ECG, sound pressure level, first-person video, and GPS data were synchronously collected to construct a 1 s multimodal time-series dataset. Pearson correlation, Kendall correlation, and mutual information analyses were used to examine linear, monotonic, and nonlinear relationships between environmental variables and physiological indicators, and the resulting weights were incorporated into a Long Short-Term Memory (LSTM) model for multi-step prediction. Visual elements and noise exposure were the main factors influencing physiological responses. Among the models, the mutual-information-weighted LSTM performed best, achieving an R² of 0.77 for heart rate variability (RMSSD), whereas prediction of the EEG ratio (β/α and θ/β) remained limited. An additional independent street sample outside the training set was then used to generate a dual-dimensional EEG-ECG physiological response map, demonstrating the model’s potential for identifying emotional risk segments and supporting street-level micro-renewal. Full article

Introduction: Workplace mobbing is a psychosocial risk factor associated with adverse mental health outcomes, including depression, anxiety, and suicidal ideation. Accurate assessment of this phenomenon is essential for both research and applied settings; however, validated brief instruments in Spanish remain limited. The Luxembourg Workplace Mobbing Scale (LWMS) is a short measure with sound psychometric properties that allows efficient evaluation of exposure to workplace mobbing. Objective: Translation and validation of the LWMS into Spanish in adults. Methods: A total of 345 adults (51.3% women) participated, completing a sociodemographic questionnaire and psychological instruments. Statistical analyses were conducted using structural equation modelling and item response theory. Results: The LWMS demonstrated adequate content validity; a unidimensional structure (CFI = 0.99, RMSEA = 0.04 [90% CI: 0.001, 0.092], SRMR = 0.02); and reliability (ω = 0.79, H = 0.86 and r_xx = 0.78). In addition, significant associations were found with depressive symptoms (r = 0.37, p = 0.001), generalised anxiety (r = 0.38, p = 0.001), and suicidal ideation (r = 0.27, p = 0.001). Item 2 showed the highest discrimination and information, and the scale proved to be accurate at higher levels of workplace mobbing. Conclusions: The Spanish version of the LWMS shows solid evidence of validity and reliability, supporting its use as a brief and precise instrument for assessing workplace mobbing in adult populations. Its strong psychometric performance and clinical relevance make it suitable for research, screening, and preventive interventions in occupational settings. Full article