Search Results (216)

This study investigates consonant devoicing in Brazilian Portuguese (BP), in order to assess whether an ongoing sound change is taking place. We examine plural forms consisting of a stop consonant followed by a word-final sibilant, such as in redes [hedz] ~ [heds] ~ [hets] and sedes [sɛdz] ~ [sɛds] ~ [sɛts], focusing on the emergence of voiceless sibilants before word-initial vowels (e.g., redes amarelas, ‘yellow hammocks’). If sibilants remain voiceless despite a following vowel, this challenges the expected regressive voicing assimilation in BP and raises the question of the conditions under which this devoicing occurs. Data were collected through recordings of oral production from twenty Brazilian speakers, using reading and picture naming tasks. Sibilant voicing was quantified using harmonics-to-noise ratio (HNR). A linear mixed-effects model—including random intercepts and slopes for both speakers and words—reveals that sibilants are significantly more voiced before a vowel than before a pause, but this voicing is substantially reduced when the sibilant is preceded by voiceless consonants. These findings indicate an ongoing devoicing process at pre-vocalic word boundaries in BP, affecting clusters [pz, tz, kz] and [bz, dz, gz] alike. Spectrographic analyses indicate that not only the sibilants but also their preceding stop may exhibit devoicing. Moreover, minimal-pair considerations suggest that speakers potentially maintain sibilant voicing in certain lexical items to preserve intelligibility (e.g., gra[dz] ‘grades’ and se[dz] ‘headquarters’ vs. grá[ts] ‘free’ and se[ts] ‘sets’). Drawing on Exemplar Theory, we propose a competition between the influence of the phonological environment and word-final devoicing: sibilants are sometimes voiced due to a following vowel (e.g., botes argentinos [bɔtz ah.ʒẽ.’tʃi.nus] ‘Argentine boats’), but they often emerge as voiceless due to consonantal devoicing (e.g., [bɔts ah.ʒẽ.’tʃi.nus]), resulting in both expected and unexpected forms. We suggest that fine phonetic detail, whether associated with allophonic or emergent sound patterns, contributes to the construction of phonological representations. Full article

Erosion poses a significant threat to infrastructure and ecosystems on coastlines worldwide. Public infrastructure such as US 101—a critical conduit linking coastal communities and renowned destinations—can be costly to maintain due to erosion hazards. Erosion is episodic and varies both spatially and temporarily; hence, forecasting erosion patterns to identify vulnerable infrastructure is immensely challenging. This study presents an innovative Geographic Information Systems (GIS) algorithm to forecast sea cliff erosion progression utilizing imagery datasets (hereafter referred to as ‘rasters’). This approach is demonstrated for an approximately 300 m segment of sea cliffs near Spencer Creek Bridge in Beverly Beach State Park, Oregon, USA. First, Digital Elevation Model (DEM) rasters are created from multiple epochs of terrestrial lidar point clouds using two approaches: Triangular Irregular Networks (TINs) and Empirical Bayesian Kriging (EBK). These DEMs were integrated into a multidimensional raster to generate trend rasters. Based on these trend rasters, forecast DEMs were created based on several different combinations of training and forecast epochs. The forecast DEMs were evaluated against the original lidar data, to calculate residuals to determine optimal model parameters. It was revealed that four combinations warrant particular attention: EBK with harmonic and linear regression of trend rasters, and TIN with harmonic and linear regression of trend rasters. These methods demonstrate consistent decreases in residuals as the number of epochs used for interpolation increases. Under these circumstances, it is expected that the forecasting DEMs will exhibit residuals lower than 10 cm. This outcome is contingent on the condition that the time between the epochs used for prediction and the forecasted epochs does not increase. Full article

Background: The registry-based collection of detailed cancer and late effect (LE) data in childhood and adolescent cancer (CAC) is rarely explored. Aim: We aimed to provide an overview of CAC registration practices in Europe and share a Slovenian example. Methods: We distributed a questionnaire among European cancer registries on disease, treatment and LE registration and present the system at the Slovenian Cancer Registry along with an example of retrospectively collected LE data from a cohort of central nervous system tumour survivors from 1983 to 2000. Kaplan–Meier and Cox regression were used to calculate the LE incidence. Results: Out of 27 responding registries, over 80% registered cancer type, vital status, death and second primary cancer data. Less than 20% registered cumulative doses of radiation and systemic therapy or progressions. Only three registered LEs. The obstacles in setting up LE collection in registries are a lack of standardization in the variable sets, definitions and methods of collection. In the retrospective cohort, neurological and endocrine LEs were most common. Females had a higher risk of endocrine LEs (HR of 1.89; 95% CI of 1.08–3.31), while patients treated with radiotherapy had higher risks of endocrine (3.47; 1.80–6.69), musculoskeletal and skin LEs (3.16; 1.60–6.26) and second primary cancers (2.85; 1.18–6.75). Conclusions: Standardization and harmonization are necessary to promote detailed CAC and LE registration. Full article

The prediction of tidal fields is crucial in coastal and marine hydrodynamic analyses, particularly in complex tidal environments, as it plays an essential role in disaster warning and fisheries management. However, monitoring the entire tidal field is impractical, and harmonic analysis and numerical simulation methods continue to face challenges in accuracy and efficiency for large-scale predictions. To address these issues, this paper proposes a tidal field prediction method based on Long Short-Term Memory (LSTM) networks. A physics-based hydrodynamic model is established, and the numerical model is validated using observational data from multiple sites in the study area. The accuracy is quantified using performance indicators such as root mean square error (RMSE) and correlation coefficients. The validated numerical model is then used to generate a high-quality comprehensive dataset. An LSTM-based model is then developed to predict tidal fields in a semi-closed marine bay. The performance of the LSTM-based model is compared with models developed using Transformer, Random Forest, and KNN regression methods. The results demonstrate that the LSTM-based model surpasses the other machine learning models in prediction accuracy, with a notable advantage in handling time series field data. This study introduces new ideas and technical approaches for rapid tidal field prediction, overcoming the limitations of traditional methods and providing robust support for coastal disaster prevention, resource management, and environmental protection. Full article

Soil organic carbon (SOC) is a crucial component for investigating carbon cycling and global climate change. Accurate data exhibiting the temporal and spatial distributions of SOC are very important for determining the soil carbon sequestration potential and formulating climate strategies. An important scheme of mapping SOC is to establish a link between environmental factors and SOC via different methods. The Shiyang River Basin is the third largest inland river basin in the Hexi Corridor, which has closed geographical conditions and a relatively independent carbon cycle system, making it an ideal area for carbon cycle research in arid areas. In this study, 65 SOC samples were collected and 21 environmental factors were assessed from 2011 to 2021 in the Shiyang River Basin. The linear regression (LR) method and two machine learning methods, i.e., support vector machine regression (SVR) and random forest (RF), are applied to estimate the spatial distribution of SOC. RF is slightly better than SVR because of its advantages in the comparison of classification. When latitude, slope, and the normalized vegetation index (NDVI) are used as predictor variables, the best SOC performance is shown. Compared with the Harmonized World Soil Database (HWSD), the optimal scheme improved the accuracy of the SOC significantly. Finally, the spatial distribution of SOC tended to increase, with a total increase of 135.94 g/kg across the whole basin. The northwestern part of the middle basin decreased by 2.82% because of industrial activities. The SOC in Minqin County increased by approximately 62.77% from 2011 to 2021. Thus, the variability of the spatial SOC increased. This study provides a theoretical basis for the spatial and temporal distributions of SOC in inland river basins. In addition, this study can also provide effective and scientific suggestions for carbon projects, offer a key scientific basis for understanding the carbon cycle, and support global climate change adaptation and mitigation strategies. Full article

Chemical graph theory connects the analysis of molecular structures with mathematical graph theory, allowing for the prediction of chemical and physical properties through the use of topological indices. Among these, the recently introduced Harmonic–Arithmetic (HA) index, proposed by Abeer M. Albalahi et al. in 2023, offers a novel method to quantify molecular and graph structures. It is defined as $HA\left(G\right)={\sum }_{\mu \omega \in E\left(G\right)}{\displaystyle \frac{4d_G\left(\mu \right)d_G\left(\omega \right)}{{(d_G\left(\mu \right)+d_G\left(\omega \right))}^2}}$, where $d_G\left(\mu \right)$ and $d_G\left(\omega \right)$ are degrees of nodes $\mu$ and $\omega$ in G. In this paper, the HA index examines the bounds for a tree T of order n, with a maximum degree $∆$. The application of the HA index extends to QSPR/QSAR analyses, where topological indices play a crucial role in predicting the relationship between molecular structures and physicochemical properties, such as in Parkinson’s, disease-related antibiotics by calculating their topological indices and analyzing them using QSPR models. Comparative analyses were performed between linear regression models and curvilinear-approach quadratic and cubic regression models to identify the minimal RMSE and enhance predictive accuracy for physicochemical properties. The results demonstrate that the HA index effectively connects mathematical graph theory with molecular characterization, offering reliable predictions, dependable bounds for tree graphs, and meaningful insights into molecular properties. These findings highlight the HA index’s potential as a versatile and innovative tool in advancing chemical graph theory and its applications to real-world problems in chemistry. Full article

Background: This study aimed to explore the potential associations between voice metrics of patients with PD and their motor symptoms. Methods: Motor and vocal data including the Unified Parkinson’s Disease Rating Scale part III (UPDRS-III), Harmonic–Noise Ratio (HNR), jitter, shimmer, and smoothed cepstral peak prominence (CPPS) were analyzed through exploratory correlations followed by univariate linear regression analyses. We employed these four voice metrics as independent variables and the total and sub-scores of the UPDRS-III as dependent variables. Results: Thirteen subjects were included, 76% males and 24% females, with a mean age of 62.9 ± 10.1 years, and a median Hoehn and Yahr stage of 2.3 ± 0.7. The regression analysis showed that CPPS is associated with posture (UPDRS-III posture scores: β = −0.196; F = 10.0; p = 0.01; R² = 0.50) and UPDRS-III postural stability scores (β = −0.130; F = 5.57; p = 0.04; R² = 0.35). Additionally, the associations between CPPS and rapid alternating movement (β = −0.297; p = 0.07), rigidity (β= −0.36; p = 0.11), and body bradykinesia (β = −0.16; p = 0.13) showed a trend towards significance. Conclusion: These findings highlight the potential role of CPPS as a predictor of postural impairments secondary to PD, emphasizing the need for further investigation. Full article

Climate change caused by greenhouse gas (GHG) emissions is an escalating global issue, with the transportation sector being a significant contributor, accounting for approximately a quarter of all energy-related GHG emissions. In the transportation sector, vehicle emissions testing is a key part of ensuring compliance with environmental regulations. The Vehicle Certification Agency (VCA) of the UK plays a pivotal role in certifying vehicles for compliance with emissions and safety standards. One of the primary methods employed by the VCA to measure vehicle emissions for light-duty vehicles is the Worldwide Harmonized Light Vehicles Test Procedure (WLTP). The WLTP is a global standard for testing vehicle emissions and fuel consumption, and sensors are crucial in ensuring accurate, real-time data collection in laboratories. Using the data collected by the VCA, regression machine learning models were trained to predict CO₂ emissions in light-duty vehicles. Among six regression models tested, the Decision Tree Regression model achieved the highest accuracy, with a Mean Absolute Error (MAE) of 2.20 and a Mean Absolute Percentage Error (MAPE) of 1.69%. It was then deployed as a web application that provides users with accurate CO₂ emission estimates for vehicles, enabling informed decisions to reduce GHG emissions. This research demonstrates the efficacy of machine learning and AI-driven approaches in fostering sustainability within the transportation sector. Full article

The high voltage, current and harmonic distortion in high-voltage cable metal sheaths cause cable insulation faults. The SSBLR (Sectional Solid Bonding with Inductance (L) and Resistance) method was designed as a new cable grounding method to prevent insulation faults. SSBLR was optimized using multi-objective optimization (MOP) with the prediction method (PM) to minimize these factors. The Pareto optimal method was used for MOP. The artificial neural network, hybrid artificial neural network and regression methods were used as the PM. When the artificial neural network–genetic algorithm hybrid method was used as the PM, and the genetic algorithm was used as the optimization method, the voltage and current were significantly reduced in the metal sheath of the cable. Full article

This study investigates the integration of non-economic policies into the framework for assessing macroeconomic coherence as applied by the Chinese government, with a particular focus on green policies. We examine the impact of non-economic factors on social disagreement and investor disagreement (expectations), and how these influences interact with macroeconomic regulation, employing both empirical evidence and dynamic stochastic general equilibrium (DSGE) theoretical models. In the basic analysis section, we merge statistical data on social divergence with policy implementation, utilizing multiple regression and deep neural network models. Our findings provide direct evidence that non-economic policies significantly regulate social sentiment. Additionally, theoretical analyses using contagion models, grounded in real textual data on social and investor divergence, demonstrate that expectations of social sentiment can ultimately affect economic variables. In the extended analysis, we enhance the classic DSGE model to delineate the pathways through which non-economic policies impact the macroeconomy. Drawing from our analyses, we propose specific optimization measures for non-economic policies. The results indicate that targeted policy optimization can effectively manage social disagreement, thereby shaping expectations and harmonizing non-economic with economic policy initiatives. This alignment enhances the coherence of macroeconomic policy interventions. The innovative contribution of this study lies in its provision of both theoretical and empirical evidence supporting the formulation of non-economic policies for the first time, alongside specific recommendations for improving the consistency of macroeconomic policies. Full article

In this work, a novel method of viscosity measurement is proposed using a device comprising a compliant mechanism, a vibration source, and a piezoelectric sensor. The vibration source creates linear harmonic vibrations in the compliant mechanism suspended in the liquid, and the acceleration response of the mechanism is measured using the piezoelectric sensor. The vibration source is located in the central mass of the compliant mechanism, which is designed to have the necessary directional stiffness. As the mechanism vibrates, the links in the mechanism undergo damping due to the shearing action of the fluid because of its viscosity. A series of viscosity measurements are carried out with the use of water–glycerol solutions such that the acceleration of the mass is influenced by the fluid’s viscosity. During the working of the device, the mechanism is immersed in the liquid whose viscosity is to be measured. The acceleration response of the mass is recorded as time domain data using NI Lab View hardware and software, which are used to train a machine learning model. Later, a regression-based machine learning model is used for the estimation of dynamic viscosity for the given acceleration input. Experiments are performed with the prototype device using the water–glycerol solution within a viscosity ranging from 10 cP to 60 cP. The proposed sensor can be used for in-line measurements or used as a handheld instrument for quick measurements. The machine learning model achieved a high level of accuracy, evidenced by an R-squared value of 0.99, indicating that it explains 99% of the variance in the data. Full article

This study investigates the impact of energy intensity and CO₂ emissions on economic growth in Gulf Cooperation Council (GCC) countries, aiming to understand the interplay between energy consumption, environmental sustainability, and economic performance. We analyze data from 1990 to 2023 across six GCC countries. The study employs the fixed effects model, random effects model, and pooled regression model to examine the relationships between energy intensity, CO₂ emissions, and GDP growth, controlling for factors such as foreign direct investment, trade openness, population, unemployment, and urbanization. Our findings reveal a significant negative impact of energy intensity on economic growth, and an increase in energy intensity is associated with a decrease of approximately 0.2969 units in GDP, indicating that higher energy consumption per unit of output hinders economic performance. While CO₂ emissions positively affect growth in GCC countries, a one-unit increase in CO₂ emissions is associated with an increase of approximately 0.3961 units in GDP. The study emphasizes the necessity for GCC countries to adopt sustainable energy practices to reduce energy intensity and boost economic growth. By aligning economic strategies with environmental sustainability goals, these nations can achieve long-term growth while effectively addressing the challenges of climate change. This research contributes to the ongoing discourse on sustainable development in the region and underscores the importance of harmonizing economic growth strategies with environmental objectives. Full article

Background: Testosterone is an androgenic hormone that plays important roles in both males and females. The circulating levels of total testosterone vary from 1 to 1480 ng/dL. High-throughput immunoassays often lack accuracy in lower concentration ranges (below 100 ng/dL), particularly when used for females or children. To address this limitation, we developed a total testosterone LC-MS/MS assay on three instruments. Methods: Sample preparation began with the dilution and conditioning of 200 µL of serum. A supported liquid extraction cartridge was used to extract the analyte from biological matrices. Chromatographic separation was achieved using a C18 column with a runtime of 5 min per sample. This assay was validated on a Triple Quad 6500 and an API 4500 instrument. Results: Method validation was carried out according to the CLSI C62-ED2 guideline and our hospital protocol. The within-day coefficient of variation (CV) was less than 10% and the between-day CV was less than 15%. The assay had a limit of quantitation of 0.5 ng/dL with an analyte measure range of 2–1200 ng/dL. A comparison using Deming regression and Bland–Altman plots showed that this assay correlated well with a reference method. The results from the API 4500 and an Orbitrap were consistent with those from the TQ 6500. Both serum-separator tubes (BD) and serum-activator tubes were found to be suitable. Conclusions: We successfully developed and validated a robust total testosterone LC-MS/MS assay for routine clinical testing. This assay was harmonized across two triple quadrupole instruments and one high-resolution mass spectrometer. Full article

How do transformers model physics? Do transformers model systems with interpretable analytical solutions or do they create an “alien physics” that is difficult for humans to decipher? We have taken a step towards demystifying this larger puzzle by investigating the simple harmonic oscillator (SHO), $\ddot{x}+2\gamma \dot{x}+{\omega }_0^{2}x=0$, one of the most fundamental systems in physics. Our goal was to identify the methods transformers use to model the SHO, and to do so we hypothesized and evaluated possible methods by analyzing the encoding of these methods’ intermediates. We developed four criteria for the use of a method within the simple test bed of linear regression, where our method was $y=wx$ and our intermediate was w: (1) Can the intermediate be predicted from hidden states? (2) Is the intermediate’s encoding quality correlated with the model performance? (3) Can the majority of variance in hidden states be explained by the intermediate? (4) Can we intervene on hidden states to produce predictable outcomes? Armed with these two correlational (1,2), weak causal (3), and strong causal (4) criteria, we determined that transformers use known numerical methods to model the trajectories of the simple harmonic oscillator, specifically, the matrix exponential method. Our analysis framework can conveniently extend to high-dimensional linear systems and nonlinear systems, which we hope will help reveal the “world model” hidden in transformers. Full article

In recent decades, the integrity and security of the ecosystem in the Sanjiang Plain have faced severe challenges due to land reclamation. Understanding the impact of paddy field expansion on regional ecosystem services (ESs), as well as revealing the trade-offs and synergies (TOS) between these services to achieve optimal resource allocation, has become an urgent issue to address. This study employs the InVEST model to map the spatial and temporal dynamics of five key ESs, while the Optimal Parameter Geodetector (OPGD) identifies primary drivers of these changes. Correlation analysis and Geographically Weighted Regression (GWR) reveal intricate TOS among ESs at multiple scales. Additionally, the Partial Least Squares-Structural Equation Model (PLS-SEM) elucidates the direct impacts of paddy field expansion on ESs. The main findings include the following: (1) The paddy field area in the Sanjiang Plain increased from 5775 km² to 18,773.41 km² from 1990 to 2020, an increase of 12,998.41 km² in 40 years. And the area of other land use types has generally decreased. (2) Overall, ESs showed a recovery trend, with carbon storage (CS) and habitat quality (HQ) initially decreasing but later improving, and consistent increases were observed in soil conservation, water yield (WY), and food production (FP). Paddy fields, drylands, forests, and wetlands were the main ES providers, with soil type, topography, and NDVI emerging as the main influencing factors. (3) Distinct correlations among ESs, where CS shows synergies with HQ and SC, while trade-offs are noted between CS and both WY and FP. These TOS demonstrate significant spatial heterogeneity and scale effects across subregions. (4) Paddy field expansion enhances regional SC, WY, and FP, but negatively affects CS and HQ. These insights offer a scientific basis for harmonizing agricultural development with ecological conservation, enriching our understanding of ES interrelationships, and guiding sustainable ecosystem management and policymaking. Full article