Search Results (7,277)

Background/Objectives: Increasing popularity, convenience, and access to processed foods are shifting the composition of dietary intake from whole to ultra-processed foods (UPF). This study aimed to assess the association between periconceptional UPF consumption and the incidence of adverse pregnancy outcomes (APOs). Methods: This was a secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). Patients were excluded if they were missing periconceptional diet data or if their pregnancy ended before 20 weeks. Food Frequency Questionnaire items were categorized using the NOVA Scale to calculate the proportion of total energy intake comprised of UPF (% kcal/day). Bivariate and multivariate analyses examined the relationships between UPF intake and preterm birth, hypertensive disorders of pregnancy (HDP), gestational diabetes (GDM), small-for-gestational-age (SGA) infants, large-for-gestational-age (LGA) infants, and fetal or neonatal demise. Results: A total of 6693 participants were included in the analysis. The sample was predominantly White (78%) and not Hispanic (84%), and a majority of participants had commercial insurance (76%). UPF accounted for an average of 51.3 ± 12.7% of participants’ daily total energy intake. Mean UPF intake was higher among patients who identified as Black or non-Hispanic, patients with public insurance, less than a high school education, a household income below the federal poverty level (all p-values < 0.001), patients with chronic hypertension (p = 0.02), and patients who delivered vaginally (p = 0.002). Patients with preterm birth, HDP, SGA infants, and fetal or neonatal demise all had significantly higher proportions of daily UPF intake compared to patients without these adverse outcomes. After adjusting for potential confounders, higher UPF intake remained significantly associated with preterm birth (AOR 1.11, 95% CI 1.02–1.21) and HDP (AOR 1.05, 95% CI 1.001–1.11). Conclusions: On average, more than half of participants’ daily energy intake was from UPF, and higher UPF intake correlated with several adverse pregnancy outcomes. Future efforts should focus on improving nutritional literacy regarding UPF consumption in pregnancy. Full article

The initiation of debris flows in mountainous areas is dynamically influenced by multiple factors, including rainfall intensity, duration, and antecedent rainfall conditions. Traditional static threshold methods struggle to adapt to these dynamic environmental conditions. To address this issue, this paper proposes a dynamic threshold determination method for the critical rainfall triggering debris flows in mountainous regions. Firstly, high-risk areas are identified based on the frequency ratio model, and the effective rainfall is quantified using the Crozier model. Subsequently, a combination of dynamic variables, such as soil saturation and safety factor, is constructed, and the Jensen–Shannon (JS) divergence is introduced for sensitivity screening to select the most relevant variables. These optimized variables are then fed into an LSTM-TCN (Long Short-Term Memory-Temporal Convolutional Network) framework to extract temporal features and predict the probability of debris flow occurrence time. Finally, real-time threshold determination is achieved by integrating the absolute rainfall energy with a dynamic threshold model. Test results demonstrate that this method can effectively quantify the dynamic nature of rainfall across different regions, screen key variables, and achieve threshold determination with high coverage (average of 0.978) and precise interval width (average of 0.023). This approach provides a more accurate and adaptive means of predicting and managing debris flow risks in mountainous areas, enhancing our ability to respond to these natural hazards in a timely and effective manner. Full article

The potential for ‘Hass’ avocado production is predicted to increase with climate warming in New Zealand, a country where avocado orchards often lack irrigation because of a cooler and wetter climate compared to most other major growing regions. However, intermittent summer droughts are also predicted to increase in frequency and intensity. This study assessed the effects of summer soil water deficits on fruit growth of ‘Hass’ avocado in the Bay of Plenty, New Zealand, by comparing irrigated and non-irrigated treatments. Rainfall was variable over the three years of the study (2016–17, 2017–18, and 2018–19), but each summer there was a dry period without any rainfall for 2–3 weeks that decreased soil water content in the non-irrigated treatment. Fruit number and final yields were highly variable between trees and years, an effect of variable fruit set during the spring flowering period, and were not affected by the irrigation treatments because soil water deficits did not occur until later, during the summer. Increasing tree crop load caused decreasing individual fruit weight and dry matter content at harvest. However, in the year with the highest average crop load a dry period occurred during early fruit development, and mean fruit weight at harvest was decreased by 26.4 g (10%) in the non-irrigated treatment, an effect that was only apparent after accounting for the effects of variable crop load. The trees responded to dry conditions by reducing stomatal conductance (g_s) by 20%, preventing midday leaf water potential (Ψ_leaf) from decreasing below −0.25 MPa. Irrigation of avocado under the conditions at this site is therefore recommended when soil tension decreases below −30 kPa at 30 cm depth, and adverse effects on fruit growth are likely when tension decreases below −50 kPa. Irregular bearing of avocado under New Zealand growing conditions causes highly variable crop loads that obscure economically significant effects of mild to moderate water deficits on fruit growth. However, irrigation is still an important consideration for avocado production under current growing conditions and is likely to become more important under future climate scenarios as the risk of summer droughts increases. Full article

Transport affordability defined as the share of household income devoted to transport expenditure is a key dimension of urban equity and social inclusion, particularly in contexts characterised by spatial inequality and income disparities. This study examines provincial variation in public transport affordability across South Africa using a hierarchical Bayesian regression framework applied to province–year data from 2015 to 2022 (n = 72). Affordability is operationalised as a transport cost burden, with higher values indicating a greater proportion of household income spent on transport, and is modelled as a function of household income, trip frequency, household population, and total provincial employment, with province-level random intercepts capturing unobserved regional heterogeneity. The results indicate that household income is negatively associated with transport cost burden, suggesting that provinces with higher average income devote a smaller share of income to transport and therefore experience better affordability. In contrast, household population and aggregate provincial employment are positively associated with transport cost burden, reflecting higher overall mobility and commuting demands in larger and more economically active provinces rather than improved affordability. Trip frequency shows no statistically meaningful association with affordability once household composition and income capacity are accounted for. After accounting for observed characteristics, between-province variation is limited, indicating that affordability dynamics are broadly similar across provinces over the study period. Methodologically, the hierarchical Bayesian framework enables partial pooling across provinces and supports probabilistic inference through credible intervals, thereby improving the stability of estimates in a small-sample multilevel context. While the analysis is associational rather than causal, the findings provide policy-relevant evidence for monitoring transport affordability, including benchmarking the prevalence of affordability burdens relative to the commonly used 10% threshold. Full article

Decarbonizing the transport sector is crucial for achieving global carbon peaking and carbon neutrality goals. Electric taxis (e-taxis), which play a vital role in urban public transportation, are central to this transition. However, their operational performance deteriorates significantly under extremely cold conditions. Existing planning models for charging infrastructure often overlook the impact of low temperatures, creating a critical research gap. To address this issue, we propose a novel planning framework using Urumqi, China (43.8° N, 87.6° E) as a case study. Urumqi is a major cold-region metropolis, where January temperatures regularly drop below $-20$ °C. Our methodology includes two key steps: integrating 412 driver questionnaires and 1.2 million high-resolution GPS trajectories to extract temperature-sensitive charging demand profiles; and incorporating these profiles into an integer linear programming (ILP) model to minimize lifecycle costs, considering climatic constraints, taxi operation patterns, and grid limitations. A key innovation is a temperature-correction coefficient, which dynamically adjusts vehicle energy consumption and driving range based on ambient temperature. Results show superiority over conventional (temperature-ignoring) and random plans: 14-fold lower annualized cost, 23-fold shorter average queuing time, 96.2% high-frequency demand coverage (+16.6%), and 78% charging station utilization (+50.0%). It achieves 29.8–32.3% cost savings at $-5$ °C (over 25.9% even at $-35$ °C) and scales stably for 5–50% e-taxi penetration, offering a transferable framework for cold-region e-taxi charging optimization. Full article

Despite reductions in sulphur and nitrogen emissions, lakes and streams in Europe and North America have shown only partial recovery from acidification. This study aims to assess the chemical and biological recovery of the upper stretch of the Litavka River, currently on of the most acidic stream in the Czech Republic. Water composition and macroinvertebrates were studied for 1999, 2010, and 2021, along with long-term data on hydrology and climate. Over these 22 years, concentrations of SO₄²⁻, base cations, conductivity, and toxic Al forms (Al_i) significantly decreased, but pH only increased from 4.2 to 4.3. Biological recovery was most evident during 1999–2010, with an increase in the number of taxa and the appearance of less acid-tolerant taxa such as stonefly Diura bicaudata and caddisfly Rhyacophila sp., mainly associated with decreased Al_i toxicity. Subsequently, however, despite continued chemical improvement, macroinvertebrate diversity decreased, and sensitive taxa were again absent in 2021. Average annual temperature increased by 2.4 °C over the past 50 years (1970–2020) while precipitation remained unchanged, resulting in significant aridification of the regional climate. We attribute the lack of biological recovery in 2021 to climate-related changes, including more frequent dry periods and floods. Although partial biological recovery of the river followed chemical recovery, the increasing frequency of hydrological extremes has likely become the main limiting factor. Full article

Subsurface temperature is an important parameter for characterizing oceanic physical processes, and accurate prediction of subsurface temperature is essential for understanding oceanic changes. Existing methods primarily focus on spatial modeling but offer limited characterization of the spatiotemporal structure and frequency features of sea temperature. They also suffer from restricted receptive fields and limited ability to model long-term dependencies. In this study, we propose a deep learning model named Fourier Window Transformer (FWinFormer), which integrates frequency-domain modeling to predict the three-dimensional subsurface temperature over the next 24 days. The model incorporates both temporal and frequency characteristics to enhance prediction accuracy. It consists of three modules: a Spatial Block Encoder, a Translator, and a Spatial Block Decoder. The spatial encoding and decoding modules are designed to extract spatial features, while the Translator models multi-scale temporal features based on the features extracted by the encoding and decoding modules. The input consists of 24 days of historical satellite observations, including sea-surface temperature (SST), salinity (SSS), eastward velocity (SSU), northward velocity (SSV) and height (SSH). We compared the model predictions with reanalysis data and evaluated performance from the perspectives of temporal evolution, spatial distribution, and vertical structure. Additionally, we validated the predicted temperatures against in situ observations. The results show that the model achieves strong and consistent performance across various temporal scales and spatial regions, with MAE, RMSE, and R² values of 0.529, 0.785, and 0.994, respectively, for the 24-day average prediction. Full article

Background/Objectives: This study aimed to use cluster analysis of healthcare utilization patterns to identify distinct clinical phenotypes in patients with comorbid chronic obstructive pulmonary disease (COPD) and atrial fibrillation (AF) and to assess their associations with demographic characteristics and clinical outcomes. Methods: A retrospective cohort study was conducted using data from 1247 patients with COPD and AF extracted from a regional medical information system (Lipetsk Region, period 2021–2025). The k-means algorithm was used to cluster patients based on the average number of medical encounters per three-character ICD-10 categories. Groups were compared using descriptive and analytical statistical methods with correction for multiple comparisons. Results: The k-means algorithm identified three distinct clusters (phenotypes), which differed significantly in demographics, comorbidity structure, and mortality. Cluster 1 (“High-frequency utilization phenotype”, 25.3%): characterized by high utilization for acute respiratory infections, metabolic, and urological diseases; demonstrated the lowest mortality (10.1%). Cluster 2 (“Cerebrovascular Phenotype”, 32.3%): characterized by chronic cerebrovascular pathology and its sequelae (codes I67, I69); had intermediate mortality (20.8%). Cluster 3 (“Low-frequency utilization phenotype”, 42.4%): distinguished by minimal utilization for “outpatient” reasons alongside the highest mortality (31.1%) and a high proportion of deaths from respiratory failure. Analysis within the deceased patient subgroup confirmed the persistence of specific utilization patterns for each phenotype right up until the fatal outcome. Conclusions: Cluster analysis of real-world clinical practice data identified three discrete phenotypes of patients with comorbid COPD and AF, which have fundamentally different clinical–behavioral trajectories and prognoses. These findings justify the need for differentiated organizational approaches, particularly the development of proactive strategies for the active detection and engagement in follow-up care of patients with the low-frequency utilization phenotype, which is associated with the worst outcomes. Full article

Studying the precipitation characteristics and extreme precipitation events in arid and semi-arid regions is of significant baseline value for optimizing water resource allocation and utilizing precipitation resources. Utilizing multi-scale ERA5 precipitation data from 1960 to 2023, this study focuses on the typical arid and semi-arid region of Ordos as the research area. Precipitation exceeding the 90th percentile was defined as extreme precipitation, and three indices—extreme precipitation amount (EPA), extreme precipitation frequency (EPF), and extreme precipitation proportion (EPP)—were used to investigate its characteristics in the study area. Additionally, three typical extreme precipitation events in recent years were analyzed to study the precipitation process of these typical events. The main results are as follows: The annual average precipitation in the study area ranges from 170.3 to 606.1 mm, with an average of 378.5 mm, which has been on a declining trend over the years, with an average annual decrease of 1.2 mm. Overall, 70% of the precipitation is concentrated in the months of June to September. The daily average of extreme precipitation in Ordos is 18.7 mm and the annual average number of extreme precipitation days ranges from 8 to 13 days, with an average annual number of extreme precipitation days being 11. Extreme precipitation accounts for more than 50% of the total precipitation. Among all areas analyzed, Jungar Banner demonstrates the greatest vulnerability to intense rainfall events. Typical extreme precipitation events in Ordos are characterized by short-duration heavy rainfall, with the rain peak ratio coefficients of the three events ranging from 0.62 to 0.72, exhibiting a distinct “post-peak” pattern. These findings provide scientific support for water resource management and disaster prevention strategies in arid and semi-arid regions. Full article

In recent years, unmanned aerial vehicle (UAV) technology has advanced rapidly, leading to its widespread deployment. However, this proliferation has been accompanied by a rise in unauthorized “black flight”, which poses a series of security risks to low-altitude airspace. Therefore, it is imperative to develop effective drone detection and identification techniques for airspace security management. This paper presents a radio frequency (RF)-based drone recognition method via variational mode decomposition (VMD) and multi-domain feature fusion. First, the collected RF signals exchanged between drones and their controllers are preprocessed using VMD. Subsequently, a multi-domain feature extraction method is introduced, which extracts time-domain, frequency-domain and time–frequency-domain features from the modes after VMD. To reduce feature dimensionality, a two-stage feature selection scheme based on ReliefF is then proposed. Finally, a support vector machine (SVM) is constructed for UAV classification. Experimental results on the open-source CardRF dataset show that the proposed method achieves superior performance compared to existing schemes, with an average identification accuracy of over 74.7% at SNRs greater than −10 dB. Full article

An innovative design for a dual-piezoelectric-layer MEMS hydrophone based on a composite film of scandium-doped aluminum nitride (Sc_0.2Al_0.8N) is presented. By designing the dual piezoelectric layer, the frequency response range has been expanded and the sensitivity of the device has been significantly enhanced. Meanwhile, doping with scandium can significantly increase the piezoelectric coefficient, enhancing the sensitivity. According to the standard underwater acoustic calibration test, the device exhibits an average sound pressure sensitivity of −162 dB (re: 1 V/μPa) across the 20 Hz–50 KHz frequency band and equivalent noise density of 47 dB (re: 1 μPa/√Hz) with a linearity of 99%. The experimental results show that the comprehensive performance of the dual-piezoelectric-layer hydrophone provides a new solution for underwater sensing and detection, and opens up a new path for the performance optimization of passive sonar systems. Full article

Legged robots inspired by animal locomotion require actuators with high power density, fast response, and robust force control, yet traditional valve-controlled hydraulic systems suffer from substantial energy losses and weak regeneration performance. Motivated by role allocation across gait phases in animal legs, where in-air positioning requires far less actuation effort than ground contact support and force modulation, this work proposes a novel gas–oil hybrid servo actuator, denoted GOhsa, for quadruped knee joints. GOhsa utilizes pre-charged high-pressure gas to pressurize hydraulic oil, converting the conventional dual-chamber pressure servo control into a single-chamber configuration while preserving the original piston stroke. This architecture enables bidirectional position–force control, enhances energy regeneration applicability, and improves operational efficiency. Theoretical modeling is conducted to analyze hydraulic stiffness and frequency-response characteristics, and a linearization-based force controller with dynamic compensation is developed to handle system nonlinearities. Experimental validation on a single-leg platform demonstrates significant energy-saving performance: under no-load conditions (simulating the swing phase), GOhsa achieves a maximum power reduction of 79.1%, with average reductions of 15.2% and 11.5% at inflation pressures of 3 MPa and 4 MPa, respectively. Under loaded conditions (simulating the stance phase), the maximum reduction reaches 28.0%, with average savings of 10.0% and 9.8%. Tracking accuracy is comparable to traditional actuators, with reduced maximum errors (13.7 mm/16.5 mm at 3 MPa; 15.0 mm/17.8 mm at 4 MPa) relative to the 16.6 mm and 18.1 mm errors of the conventional system, confirming improved motion stability under load. These results verify that GOhsa provides high control performance with markedly enhanced energy efficiency. Full article

High-frequency data collection in large-scale IoT sensing systems requires communication architectures that can maintain low latency and stable throughput as sensor density increases. Conventional RS485-based polling structures suffer from rapid performance degradation under multi-node and high-rate conditions due to their sequential communication model. In this study, we present a comparative performance analysis of parallel MQTT and RS485 communication architectures for high-frequency IoT sensing. The proposed parallel MQTT structure is implemented with topic-level parallelism, QoS-based reliability control, and non-blocking scheduling, and its performance is quantitatively evaluated under multi-sensor experimental conditions. Experimental results show that the parallel MQTT architecture achieves lower average latency (98 ms vs. 178 ms), higher message success rate (99.2% vs. 94.5%), and higher throughput compared to the RS485-based system. In addition, we analyze message loss behavior under high-frequency operation and examine how topic hierarchy and asynchronous processing affect broker-side congestion. Full article

Detecting precipitation patterns remains a central challenge in hydrological sciences due to the non-linear nature of atmospheric dynamics and the growing influence of climatic variability. This study investigates the evolution of a 64-year daily precipitation series (1961–2024) at the Tulcea meteorological station (Dobrogea, Romania) and introduces a novel adaptation of the Most Probable Precipitation Method (AMPPM), shifting its application from a regional spatial framework to a temporal one. Shannon Entropy is used as a measure of “climatic disorder.” Model evaluation incorporates Mean Error (ME), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), which here measure structural divergence rather than predictive accuracy. Results demonstrate that the Synthetic Representative Series (SRS) isolates the stable climatic signal, reducing the global coefficient of variation (cv (%)) to 70.96% and mitigating extreme skewness typical of coastal convective activity. Seasonal entropy analysis reveals divergence: winter entropy decreases through signal stabilization (minimum 2.00 bits in March), whereas July–October entropy increases, highlighting previously hidden high-frequency daily oscillations. The aggregated Tot_64 series achieves a final entropy of 2.75 bits, confirming a complex, multi-state daily precipitation process. MAE and RMSE values for the SRS (e.g., October: MAE = 1.20, RMSE = 4.53; Tot_64: MAE = 1.40, RMSE = 4.58) indicate that the SRS captures dominant precipitation patterns with minimal deviation, comparable to or better than the moving average approaches. Full article

Agricultural heritage is a cultural pillar of the Mediterranean region, where durum wheat plays a central role in traditional landscapes and food systems. Projected climate change is expected to alter crop productivity and place additional pressure on water resources. This study assesses future variability in durum wheat productivity and related implications for water resource management in Sardinia, Italy, where durum wheat is a major rainfed C3 crop. The AquaCrop-OpenSource model was calibrated to local conditions and applied to simulate historical (1950–2023) and near-future (2024–2050) scenarios using projections from seven climate models. Results indicate a modest increase in average yields under future conditions, accompanied by a higher frequency of crop failures. Elevated atmospheric CO₂ concentrations emerge as the primary driver of yield increases, while changes in precipitation represent the main limiting factor. The role of aid irrigation as an adaptation strategy to stabilize yields and enhance productivity was evaluated. Scenario analysis shows that aid irrigation aimed at preventing crop failure remains sustainable in the near future, requiring approximately 14–17% of current agricultural water use in Sardinia. In contrast, irrigation used to maximize productivity would increase water demand by more than 40%, intensifying competition for water resources. Full article