Search Results (39)

Amidst global urbanization and rising psychological stress, urban green spaces are increasingly recognized as critical infrastructure for sustainable urban development and public health. However, the mechanisms by which summer vegetation structure mediates both physiological and psychological restoration, and the interplay between these two dimensions, remain poorly understood. Understanding these mechanisms is essential for designing sustainable, health-promoting urban environments that can support growing urban populations in a warming climate. This study employed a controlled field experiment in Xi’an during summer to examine the effects of five vegetation structure types (Single-Layer Grassland, single-layer woodland, tree–shrub–grass composite woodland, tree–grass composite woodland, and a non-vegetated square) on university students’ physiological (heart rate variability) and psychological (perceived restorativeness and affective states) restoration. Following stress induction, 300 participants engaged with the green spaces through both quiet sitting and walking. The results revealed three key findings: (1) the tree–shrub–grass composite woodland consistently showed the most favorable trends other vegetation types across all psychological restoration dimensions, while also showing favorable trends in physiological recovery, underscoring the importance of structural complexity for restorative quality; (2) walking significantly enhanced physiological recovery compared to seated observation across all settings, confirming the role of physical activity as a critical activator of green space benefits; (3) correlation analysis identified a specific cross-system association: the R-R interval recovery value showed a weak but significant correlation with positive affect (PA) scores, suggesting that physiological calmness and positive emotional experience are linked, yet their weak coupling under short-term exposure indicates they may operate as parallel processes with distinct temporal dynamics. These findings indicate that the restorative potential of summer green spaces emerges from an integrated framework combining vegetation complexity and activity support. We propose that future sustainable landscape design should prioritize multi-layered vegetation structures as nature-based solutions that simultaneously enhance human well-being and urban resilience. These findings provide empirical evidence for integrating health-promoting green infrastructure into sustainable urban planning frameworks, supporting multiple Sustainable Development Goals (SDGs), including SDG 3 (Good Health and Well-being), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Full article

Objectives: To confirm the utility of barometric whole-body plethysmography (BWBP) as a non-invasive, clinical diagnostic test for brachycephalic obstructive airway syndrome (BOAS) in cats. Methods: Client-owned cats belonging to brachycephalic breeds were enrolled and classified into two clinical severity grades of upper airway obstruction (UAO). Brachycephalic cats with high-grade UAO severity (Brachy-H-UAO) represented those with clinically evident effects on clinical signs or physical examination findings, whereas brachycephalic cats with low-grade UAO severity (Brachy-L-UAO) represented those without clinically evident problems. A group of non-brachycephalic (NB) cats that were respiratory disease-free and with neither a history of cardiac or systemic diseases nor exposure to cigarette smoke was used as the control group. Cats were placed in the BWBP chamber, and breathing signals were obtained after an adaptation period in a quiet and silent environment. The ventilatory variables obtained were respiratory rate (RR; [bpm]), tidal and minute volume per kilogram bodyweight (MV/BW and TV/BW; [mL/kg]), inspiratory (Ti; [s]) and expiratory (Te; [s]) intervals, airway obstruction index enhanced pause (Penh), and peak inspiratory and expiratory flows per kilogram (PIF and PEF; [mL/s/kg]). Results: Forty-three client-owned cats (11 Brachy-H-UAO, 7 Brachy-L-UAO, and 25 NB) were included. Brachycephalic cats (Brachy-H-UAO: 311 mL/kg; Brachy-L-UAO: 253 mL/kg) showed significantly lower median MV/BW than NB cats (503 mL/kg) (p = 0.01). Brachy-H-UAO cats demonstrated significantly higher median PEF/PIF ratios (Brachy-H-UAO: 1.46, minimum–maximum 0.82–2.48; Brachy-L-UAO: 0.76, 0.52–1.11; NB: 0.73, 0.56–1.00) and Penh (Brachy-H-UAO: 2.37, minimum–maximum 0.57–23.82; Brachy-L-UAO: 0.57, 0.27–1.11; NB: 0.53, 0.21–0.68) than Brachy-L-UAO and NB cats (p < 0.001). No significant differences were observed among the three groups for RR, TV/BW, Ti, Te, or Te/Ti. Conclusions and Relevance: Cats affected by BOAS demonstrate impaired ventilatory function, with reduced minute ventilation and a distinctive flow pattern and parameters reflecting limited inspiratory flow and increased upper airway resistance. BWBP can serve as a useful tool to diagnose and characterize the severity of BOAS in cats. Full article

The global shipping industry is surging ahead, and with it, a quiet revolution is taking place on the water: marine lithium-ion batteries have emerged as a crucial clean energy carrier, powering everything from ferries to container ships. When these vessels dock, they increasingly rely on shore power charging systems to refuel—essentially, plugging in instead of idling on diesel. But predicting how much power they will need is not straightforward. Think about it: different ships, varying battery sizes, mixed charging technologies, and unpredictable port stays all come into play, creating a load profile that is random, uneven, and often concentrated—a real headache for grid planners. So how do you forecast something so inherently variable? This study turned to the Monte Carlo method, a probabilistic technique that thrives on uncertainty. Instead of seeking a single fixed answer, the model embraces randomness, feeding in real-world data on supply modes, vessel types, battery capacity, and operational hours. Through repeated random sampling and load simulation, it builds up a realistic picture of potential charging demand. We ran the numbers for a simulated fleet of 400 vessels, and the results speak for themselves: load factors landed at 0.35 for conventional AC shore power, 0.39 for high-voltage DC, 0.33 for renewable-based systems, 0.64 for smart microgrids, and 0.76 when energy storage joined the mix. Notice how storage and microgrids really smooth things out? What does this mean in practice? Well, it turns out that Monte Carlo is not just academically elegant, it is practically useful. By quantifying uncertainty and delivering load factors within confidence intervals, the method offers port operators something precious: a data-backed foundation for decision-making. Whether it is sizing infrastructure, designing tariff incentives, or weighing the grid impact of different shore power setups, this approach adds clarity. In the bigger picture, that kind of insight matters. As ports worldwide strive to support cleaner shipping and align with climate goals—China’s “dual carbon” ambition being a case in point—achieving a reliable handle on charging demand is not just technical; it is strategic. Here, probabilistic modeling shifts from a simulation exercise to a tangible tool for greener, more resilient port energy management. Full article

The excellent thermal performance, quiet operation, and fuel flexibility of free-piston Stirling machines enable their broad application potential in sectors such as aerospace, distributed power generation, and industrial waste heat utilization. The impact of structural parameters on the output characteristics of the free-piston Stirling engine was investigated using a parametric MATLAB model based on an isothermal thermodynamic approach. Parameters such as the dead volume ratios (χ_H, χ_K, χ_R), temperature ratio τ, sweep volume ratio k, piston phase angle a_dr, and minimum pressure angle θ were evaluated for their effects on the dimensionless power Z. The results indicate that the dead volume ratio in the cold space χ_K has the most significant influence on system performance, followed by the hot space χ_H, while the regenerator χ_R exhibits a comparatively weaker effect. All three parameters demonstrate the existence of optimal design intervals. The dimensionless power Z decreases monotonically with increasing dead volume ratio. Moreover, this decline is intensified at higher temperature ratios τ, indicating that the influence of dead volume becomes more significant under larger τ values. The interaction between these parameters can be described by $Z=0.0037{\tau }^2-0.0045\tau +0.0021$. An excessively large sweep volume ratio k tends to degrade the system’s output performance. An empirical correlation between k and the dimensionless power can be established as follows $Z=1.53(1-e^{-3.37k})+0.01$. A moderate increase in the piston phase angle a_dr and a reduction in the minimum pressure angle θ contribute to improved system performance by enlarging the p-v diagram area and enhancing the utilization of gas expansion. The relationship between a_dr and the dimensionless power Z follows a linear trend, expressed as $Z=0.341a_{dr}-0.2104$. A well-defined functional relationship exists between the minimum pressure angle θ and the dimensionless power output Z, which can be expressed as $Z=2.18\times {10}^{-4}{\theta }^2-0.0261\theta +0.7065$. A coupling regulation mechanism and design strategy have been developed to facilitate the coordinated optimization of multiple parameters in free-piston Stirling engines, which delivers theoretical guidance that is expected to support the engineering implementation of next-generation, high-performance Stirling technologies. Full article

Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite density measurements from the CHAMP, GRACE, and SWARM missions, coupled with MSIS-00-derived exospheric temperature (tinf) data. The technical approach features three key innovations: (1) spherical harmonic decomposition of T∞ using spatiotemporally orthogonal basis functions, (2) sPCA-based extraction of dominant modes from sparse orbital sampling data, and (3) neural network prediction of temporal coefficients with built-in uncertainty quantification. This integrated framework significantly enhances the temperature calculation module in MSIS-00 while providing probabilistic density estimates. Validation against SWARM-C measurements demonstrates superior performance, reducing mean absolute error (MAE) during quiet periods from MSIS-00’s 44.1% to 23.7%, with uncertainty bounds (1σ) achieving an MAE of 8.4%. The model’s dynamic confidence intervals enable rigorous probabilistic risk assessment for LEO satellite collision avoidance systems, representing a paradigm shift from deterministic to probabilistic modeling of thermospheric density. Full article

Background/Objectives: The Matrix Sentence Test is an audiological evaluation that quantifies the signal-to-noise ratio, expressed in decibels, at which the patient comprehends 50% of the words of a random sentence heard in noise. It is an effective and reliable tool for cochlear implant fitting and follow-up in both adults and children, demonstrating reliability upon repeated administration. A simplified model of the Matrix Sentence Test can be used in children. This study had two main objectives: first, to evaluate the Simplified Matrix Sentence Test for objectively estimating post-fitting CI performance; and second, to assess the influence of various demographic and device-related variables on the results. The variables of interest included gender, manufacturer, placement, microphone position, array position, score in pre-fitting speech audiometry in quiet, age at first implantation, age at test administration, and the interval between the first implant and the test administration. Methods: A retrospective study of pediatric patients with cochlear implants was performed. The inclusion criteria were patients aged 7–18 years, with a minimum of two years of cochlear implantation, adequate Italian language proficiency, and regular follow-up attendance. The subjects were administered the Simplified Matrix Sentence Test prior to and following map fitting by an experienced audiologist. Results: The study’s sample population included 51 patients who met the established inclusion criteria, with an average age of 13 years. In the preliminary SiIMax test, the average SNR for 50% sentence comprehension in noise was −0.83 ± 1.86 dB. Map adjustments included reductions or increases in comfort and threshold levels, modifications to multiple electrodes, or minor secondary changes. Approximately two days later, the second Simplified Matrix Sentence Test was administered. The average signal-to-noise for sentence comprehension was −2.05 ± 1.73 dB. Univariate and multivariate analyses revealed that no variable had a statistically significant impact on the results. Conclusions: The Simplified Matrix Sentence Test demonstrated universal applicability in compliant patients. Post-implant improvement appeared independent of patient demographics and device variables. Full article

Background: Accurate differentiation of Parkinson’s disease (PD) from healthy aging is crucial for timely intervention and effective management. Postural sway abnormalities are prominent motor features of PD. Quantitative stabilometry and machine learning (ML) offer a promising avenue for developing objective markers to support the diagnostic process. This study aimed to develop and validate high-performance ML models to classify individuals with PD and age-matched healthy older adults (HOAs) using a comprehensive set of stabilometric parameters. Methods: Thirty-seven HOAs (mean age 70 ± 6.8 years) and 26 individuals with idiopathic PD (Hoehn and Yahr stages 2–3, on medication; mean age 66 years ± 2.9 years), all aged 60–80 years, participated. Stabilometric data were collected using a force platform during quiet stance under eyes-open (EO) and eyes-closed (EC) conditions, from which 34 parameters reflecting the time- and frequency-domain characteristics of center-of-pressure (COP) sway were extracted. After data preprocessing, including mean imputation for missing values and feature scaling, three ML classifiers (Random Forest, Gradient Boosting, and Support Vector Machine) were hyperparameter-tuned using GridSearchCV with three-fold cross-validation. An ensemble voting classifier (soft voting) was constructed from these tuned models. Model performance was rigorously evaluated using 15 iterations of stratified train–test splits (70% train and 30% test) and an additional bootstrap procedure of 1000 iterations to derive reliable 95% confidence intervals (CIs). Results: Our optimized ensemble voting classifier achieved excellent discriminative power, distinguishing PD from HOAs with a mean accuracy of 0.91 (95% CI: 0.81–1.00) and a mean Area Under the ROC Curve (AUC ROC) of 0.97 (95% CI: 0.92–1.00). Importantly, feature analysis revealed that anteroposterior sway velocity with eyes open (V-AP) and total sway path with eyes closed (TOD_EC, calculated using COP displacement vectors from its mean position) are the most robust and non-invasive biomarkers for differentiating the groups. Conclusions: An ensemble ML approach leveraging stabilometric features provides a highly accurate, non-invasive method to distinguish PD from healthy aging and may augment clinical assessment and monitoring. Full article

Background/Objectives: Quality of work life (QWL) in the healthcare industry emerges as an important factor for enhancing positive and preventing negative work-related outcomes, including quiet quitting. The aim of the study was to investigate the impact of quality of work life on the indication of quiet quitting among healthcare professionals. Methods: A cross-sectional study design and convenience sampling method were applied. A minimum sample was estimated by applying Cochran’s formula with a 5% significance level and 95% confidence interval. The target population of the study consisted of healthcare professionals employed in public health organizations in central Serbia, with a total sample size of 647 respondents. Testing the relationship between determinants of quality of work life and quiet quitting was conducted through a structural equation modeling approach based on partial least squares (PLS-SEM). Results: The results indicate that psychological, physical, and cultural quality of work life have a significant impact on the manifestation of quiet quitting among healthcare professionals, especially among women. Conclusions: Findings suggest that social well-being is significant only for men in relation to quiet quitting. The findings reveal the elements of quality of work life are associated with the occurrence of quiet quitting among healthcare professionals, thus serving as a solid starting point for formulating effective human resource management strategies that can prevent negative consequences. Full article

When inter-arrival times between events follow an exponential distribution, this implies a Poisson frequency of events, as both models assume events occur independently and at a constant average rate. However, these assumptions are often violated in real-insurance applications. When the rate at which events occur changes over time, the exponential distribution becomes unsuitable. In this paper, we study the distribution of inter-arrival times of severe storms, which exhibit substantial variability, violating the assumption of a constant average rate. A new approach is proposed for modeling severe storm recurrence patterns using a finite mixture of log-normal distributions. This approach effectively captures both frequent, closely spaced storm events and extended quiet periods, addressing the inherent variability in inter-event durations. Parameter estimation is performed using the Expectation–Maximization algorithm, with model selection validated via the Bayesian information criterion (BIC). To complement the parametric approach, Kaplan–Meier survival analysis was employed to provide non-parametric insights into storm-free intervals. Additionally, a simulation-based framework estimates storm recurrence probabilities and assesses financial risks through probable maximum loss (PML) calculations. The proposed methodology is applied to the Billion-Dollar Weather and Climate Disasters dataset, compiled by the U.S. National Oceanic and Atmospheric Administration (NOAA). The results demonstrate the model’s effectiveness in predicting severe storm recurrence intervals, offering valuable tools for managing risk in the property and casualty insurance industry. Full article

Approximate entropy (ApEn) and sample entropy (SampEn) are statistical indices designed to quantify the regularity or predictability of time-series data. Although ApEn has been a prominent choice in analyzing non-linear data, it is currently unclear which method and parameter selection combination is optimal for its application in biomechanics. This research aimed to examine the differences between ApEn and SampEn related to center-of-pressure (COP) data during tandem standing balance tasks, while also changing the tolerance window, r. Six participants completed five, 30 s trials, feet-together and tandem standing with eyes open and eyes closed. COP data (fs = 60 Hz, downsampled from 1200 Hz) from ground reaction force platforms were collected. ApEn and SampEn were calculated using a constant vector length, i.e., m = 2, but differing values of r (tolerance window). For each of the participants, four separate one-way analysis of variance analyses (ANOVA) were conducted for ApEn and SampEn along the anterior–posterior (AP) and medial–lateral (ML) axes. Dunnett’s intervals were applied to the one-way ANOVA analyses to determine which tandem conditions differed significantly from the baseline condition. ApEn and SampEn provided comparable results in the predictability of patterns for different stability conditions, with increasing instability, i.e., tandem eyes closed postures, being associated with greater unpredictability. The selection of r had a relatively consistent effect on mean ApEn and SampEn values across r = 0.15 × SD to r = 0.25 × SD, where both entropy methods tended to decrease as r increased. Mean SampEn values were generally lower than ApEn values. The results suggest that both ApEn and SampEn indices demonstrated relative consistency and were equally effective in quantifying the level of the center-of-pressure signal regularity during quiet tandem standing postural balance tests. Full article

As a prevalent geological hazard in underground engineering, the accurate prediction of mine earthquakes is crucial for ensuring operational safety and enhancing mining efficiency. The deformation localization method effectively predicts the instability of disaster rocks, yet the timing of mine earthquakes remains understudied. This study established a correlation between rock deformation localization and seismic activity within mines through theoretical derivations. A predictive model algorithm for forecasting mine earthquake timing was developed based on Saito’s theory, integrating optics, acoustics, and mathematical modeling theories. The “quiet period” was identified as a significant precursor; thus, the model used the initiation of deformation localization to accurately predict rock failure. Using the model, a coal mine in Inner Mongolia was selected as a case study to predict a historical mining earthquake. The results indicated that the following: (1) Deformation localization and the “quiet period” of microseismic (MS) and acoustic emission (AE) activities were identified as two key pre-cursory indicators. The model utilized the initiation time of deformation localization and the inflection point of the “quiet period” in MS and AE activity as primary parameters. (2) For predicting rock failure times, the earliest prediction time deviates from the actual failure time by 143 s. The accuracy rate of predicted time points falling within a 90% confidence interval of the actual failure times is 100%. The model achieved 60% in forecasting the occurrence times of mine earthquakes. (3) The model’s prediction accuracy improved as the starting time parameter more closely approximated the actual initiation time of deformation localization, with the accuracy increasing from 0% to 100%. Full article

In many species of animals, males aggregate in particular locations and produce calls to attract searching females for reproduction. One striking feature of such choruses is synchronization. On a scale of hours, choruses are often concentrated at particular times of day, such as dawn or dusk. On a scale of seconds, males may also synchronize the rhythm of their calls with one another. While synchronized calling at this scale suggests benefits acting at the collective level, competitive interactions between males to attract females can also lead to synchronized calling as an epiphenomenon. Why males in some species synchronize the rhythm of their calls is still debated, and more work is needed to understand the evolution of this behavior. I investigated chorus organization in the Emerald cicada (Zammara smaragdula), a Neotropical forest cicada in southern Belize, to explore these issues. Choruses in this species occurred at dawn and dusk and, occasionally, during daytime. There was no evidence for synchronization in the rhythm of calls among males, as bouts of collective calling occurred after quiet intervals of variable rather than fixed durations. The temporal aggregation of calls in this species thus probably emerged from competitive interactions among males to attract females. The degree of temporal overlap in the calls of males during a chorus varied as a function of chorus phase and time of day, suggesting flexibility in chorus organization, perhaps in relation to temporal variations in factors such as the number of calling cicadas, the number of predators present or ambient temperature during a chorus. Full article

A novel tool utilizing machine learning techniques was designed to forecast ap index values for the next three consecutive days (24 values). The tool employs time series data from the 3 h ap index of solar cycles 23 and 24 to train the Long Short-Term Memory (LSTM) model, predicting ap index values for the next 72 h at three-hour intervals. During periods of quiet geomagnetic activity, the LSTM model’s performance is sufficient to yield favorable outcomes. Nevertheless, during geomagnetically disturbed conditions, such as geomagnetic storms of different levels, the model needs to be adapted in order to provide accurate ap index results. In particular, when coronal mass ejections occur, the ap Prediction tool is modulated by inserting predominant features of coronal mass ejections such as the date of the event, the estimated time of arrival and the linear speed. In the present work, this tool is described thoroughly; moreover, results for G2 and G3 geomagnetic storms are presented. Full article

This study introduced a depth-sensing-based approach with robust algorithms for tracking relative morphological changes in the chests of patients undergoing physical therapy. The problem that was addressed was the periodic change in morphological parameters induced by breathing, and since the recording was continuous, the parameters were extracted for the moments of maximum and minimum volumes of the chest (inspiration and expiration moments), and analyzed. The parameters were derived from morphological transverse cross-sections (CSs), which were extracted for the moments of maximal and minimal depth variations, and the reliability of the results was expressed through the coefficient of variation (CV) of the resulting curves. Across all subjects and levels of observed anatomy, the mean CV for CS depth values was smaller than 2%, and the mean CV of the CS area was smaller than 1%. To prove the reproducibility of measurements (extraction of morphological parameters), 10 subjects were recorded in two consecutive sessions with a short interval (2 weeks) where no changes in the monitored parameters were expected and statistical methods show that there was no statistically significant difference between the sessions, which confirms the reproducibility hypothesis. Additionally, based on the representative CSs for inspiration and expirations moments, chest mobility in quiet breathing was examined, and the statistical test showed no difference between the two sessions. The findings justify the proposed algorithm as a valuable tool for evaluating the impact of rehabilitation exercises on chest morphology. Full article

In previous research, we presented an apparatus designed for comprehensive and systematic surveillance of trees against borers. This apparatus entailed the insertion of an uncoated waveguide into the tree trunk, enabling the transmission of micro-vibrations generated by moving or digging larvae to a piezoelectric probe. Subsequent recordings were then transmitted at predetermined intervals to a server, where analysis was conducted manually to assess the infestation status of the tree. However, this method is hampered by significant limitations when scaling to monitor thousands of trees across extensive spatial domains. In this study, we address this challenge by integrating signal processing techniques capable of distinguishing vibrations attributable to borers from those originating externally to the tree. Our primary innovation involves quantifying the impulses resulting from the fracturing of wood fibers due to borer activity. The device employs criteria such as impulse duration and a strategy of waiting for periods of relative quietness before commencing the counting of impulses. Additionally, we provide an annotated large-scale database comprising laboratory and field vibrational recordings, which will facilitate further advancements in this research domain. Full article