Search Results (68)

Traditional liquid electrolyte batteries face safety concerns such as leakage and flammability, while further optimization has reached a bottleneck. Solid electrolytes are therefore considered a promising solution. Here, a PEO–LiTFSI–LATP (PELT) composite electrolyte was developed by incorporating nanosized Li_1.3Al_0.3Ti_1.7(PO₄)₃ fillers into a polyethylene oxide matrix, effectively reducing crystallinity, enhancing mechanical robustness, and providing additional Li⁺ transport channels. The PELT electrolyte exhibited an electrochemical stability window of 4.9 V, an ionic conductivity of 1.2 × 10⁻⁴ S·cm⁻¹ at 60 °C, and a Li⁺ transference number (tLi+) of 0.46, supporting stable Li plating/stripping for over 600 h in symmetric batteries. More importantly, to address poor electrode–electrolyte contact in conventional layered cells, we proposed an integrated electrode–electrolyte architecture by in situ coating the PELT precursor directly onto LiFePO₄ cathodes. This design minimized interfacial impedance, improved ion transport, and enhanced electrochemical stability. The integrated PELT/LFP battery retained 74% of its capacity after 200 cycles at 1 A·g⁻¹ and showed superior rate capability compared with sandwich-type batteries. These results highlight that coupling LATP-enhanced polymer electrolytes with an integrated architecture is a promising pathway toward high-safety, high-performance solid-state lithium-ion batteries. Full article

With the continued growth in wind power installed capacity and electricity generation, accurate wind power forecasting has become increasingly critical for power system stability and economic operations. Currently, short-term wind power forecasting often employs deep learning models following modal decomposition of wind power time series. However, the optimal length of the time series used for decomposition remains unclear. To address this issue, this paper proposes a short-term wind power forecasting method that integrates improved modal decomposition with deep learning techniques. First, the historical wind power series is segmented using the Pruned Exact Linear Time (PELT) method. Next, the segmented series is decomposed using an enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) to extract multiple modal components. High-frequency oscillatory components are then further decomposed using Variational Mode Decomposition (VMD), and the resulting modes are clustered using the K-means algorithm. The reconstructed components are subsequently input into a Long Short-Term Memory (LSTM) network for prediction, and the final forecast is obtained by aggregating the outputs of the individual modes. The proposed method is validated using historical wind power data from a wind farm. Experimental results demonstrate that this approach enhances forecasting accuracy, supports grid power balance, and increases the economic benefits for wind farm operators in electricity markets. Full article

Distributed acoustic sensing shows great potential for pipeline monitoring. However, internally deployed and unfixed sensing cables are highly susceptible to disturbances from water flow noise, severely challenging impact source localization. This study proposes a novel two-step method to address this. The first step employs Variational Mode Decomposition (VMD) combined with Short-Time Energy Entropy (STEE) for the adaptive extraction of impact signal from noisy data. STEE is introduced as a stable metric to quantify signal impulsiveness and guides the selection of the relevant intrinsic mode function. The second step utilizes the Pruned Exact Linear Time (PELT) algorithm for accurate signal segmentation, followed by an unsupervised learning method combining Dynamic Time Warping (DTW) and clustering to identify the impact segment and precisely pick the arrival time based on shape similarity, overcoming the limitations of traditional pickers under conditions of complex noise. Field tests on an operational water pipeline validated the method, demonstrating the consistent localization of manual impacts with standard deviations typically between 1.4 m and 2.0 m, proving its efficacy under realistic noisy conditions. This approach offers a reliable framework for pipeline safety assessments under operational conditions. Full article

Main roads are usually equipped with traffic flow monitoring devices in the road network to record the traffic flow data of the main roads in real time. Three complex scenarios, i.e., Y-junctions, multi-lane merging, and signalized intersections, are considered in this paper by developing a novel modeling system that leverages only historical main-road data to reconstruct branch-road volumes and identify pivotal time points where instantaneous observations enable robust inference of period-aggregate traffic volumes. Four mathematical models (I–IV) are built using the data given in appendix, with performance quantified via error metrics (RMSE, MAE, MAPE) and stability indices (perturbation sensitivity index, structure similarity score). Finally, the significant traffic flow change points are further identified by the PELT algorithm. Full article

This work explores the application of Hidden Markov Models (HMMs) for the classification and reconstruction of corrosion mechanisms in the aerospace-grade aluminum alloy AA2055 from the signals obtained by electrochemical noise (EN) analysis. Using the PELT algorithm to segment the signal based on relevant changepoints, distinct corrosion states within the segments are isolated and identified, including general, localized, and mixed corrosion based on statistical signal features, which are used to create the probabilistic structure of HMMs through the initiation, transition, and emission matrices. This study utilized a dataset composed of five electrolyte groups, each containing ten EN signals with 1024 data points per signal, totaling 51,200 data points. The model demonstrates that even with variability in signal quality, meaningful reconstruction is achievable, especially when datasets include distinct transient behavior. Full article

This study was conducted on New Zealand White male and female rabbits over a period of 133 days to ascertain their potential growth rates, body composition for major body parts, and chemical makeup. A total of 220 New Zealand White rabbits, evenly distributed between males and females, were used for this study. One hundred rabbits for potential growth were weighed from day 14 to day 140, while twelve rabbits, six males and six females, were randomly selected at days 14, 21, 28, 35, 42, 56, 70, 84, 112, and 140 for carcass analysis. Although the rate of maturation was faster in females than in males, the Gompertz equation fitted separately to the growth data for males and females indicated that the body weights were similar throughout the trial (0.0243 vs. 0.0239), but males had a higher mature weight (315 g) than the females (309 g). Mature body protein weights averaged 1497 g in males and 843 g in females, and mature body lipid contents averaged 252 and 227 g, respectively. The rate of maturation per day of pelt-free body protein of males and females was 0.0103 and 0.0172, while that of body lipids was 0.0410 and 0.0471, respectively. Separate equations were required for males and females to describe the allometric relationship between protein and lipids in the pelt-free body. The rate of maturation of pelts in females was higher than in males (0.0249 vs. 0.0214/d), and the mature weight was lower (456 vs. 523 g, respectively). Full article

This study evaluated the effects of dietary lysine supplementation in low-protein diets on nutrient digestibility, nitrogen metabolism, growth performance, serum biomarkers, and pelt quality in female blue foxes (Alopex lagopus) during the fur-growing period. A total of 105 18-week-old female blue foxes were randomly assigned to seven groups (n = 15 per group). The control group received a standard-protein diet (28% dry matter, DM), while six experimental groups were fed low-protein diets (26% DM) supplemented with 0%, 0.2%, 0.4%, 0.6%, 0.8%, and 1.0% lysine, corresponding to total lysine levels of 0.75%, 0.95%, 1.15%, 1.35%, 1.55%, and 1.75% DM, respectively. Lysine supplementation at 1.35% and 1.55% DM significantly improved the digestibility of ether extract and amino acids, including aspartic acid, glycine, methionine, isoleucine, and tyrosine (p < 0.05). Nitrogen retention increased accordingly, indicating enhanced dietary utilization (p < 0.05). Daily weight gain, particularly from day 15 to day 30, was significantly higher in 1.15–1.55% lysine groups compared to low-lysine groups (p < 0.05), achieving growth performance comparable to the control (p > 0.05). Serum total protein and albumin concentration were significantly improved with increasing lysine levels in low-protein groups (p < 0.01), aligning with those of the control group (p > 0.05). Furthermore, high lysine supplementation significantly improved pelt quality, as evidenced by the increased underfur length and decreased guard hair/underfur in 1.35–1.75% DM (p < 0.05). These findings suggest that lysine supplementation in low-protein diets supports nutrient utilization, growth performance, and metabolic health status while reducing dietary protein content. The optimal dietary lysine range is 1.15% to 1.55% DM (corresponding to 0.4% to 0.8% in air-dry basis), with 1.35% DM (corresponding to 0.6% in air-dry basis) identified as the most suitable level for balancing growth, nitrogen excretion, and pelt quality in fur-growing female blue foxes. Full article

Background: Earthquake exposure has been linked with high rates of posttraumatic stress symptoms (PTSS) and comorbid conditions. Familial factors play critical roles in modulating these outcomes. This study examined youth trauma and parental influence following the twin earthquakes in Kefalonia, Greece, in 2014; Methods: A cross-sectional study was conducted with 502 adolescents (aged 11–18 years) and 474 parents from three regions categorized by proximity to the earthquake epicenter. Standardized self-report measures were administered. Data were analyzed using descriptive statistics, correlation analyses, and multiple hierarchical regression analyses to identify key predictors of adverse outcomes; Results: Among children, 5.2% exhibited probable PTSD, with girls reporting significantly higher symptom levels than boys. Higher earthquake exposure was associated with elevated PTSS and anxiety. In parents, 44.3% met criteria for probable PTSD, and those in the epicenter group reported significantly higher levels of stress, anxiety, and sleep disturbances. Earthquake exposure was identified as the strongest predictor of adverse outcomes, with parental psychopathology and diminished social support further contributing to increased symptom severity in children; Conclusions: The study demonstrates that both direct earthquake exposure and familial factors—particularly parental mental health and social support—play critical roles in shaping posttraumatic outcomes in youth, underscoring the need for integrated, family-centered mental health interventions in post-disaster settings. Full article

This study presents a novel method for detecting sleep posture changes—specifically tossing and turning—by monitoring variations in humidity using an array of humidity sensors embedded at regular intervals within a memory-foam pillow. Unlike previous approaches that rely primarily on temperature or pressure sensors, our method leverages the observation that humidity fluctuations are more pronounced during movement, enabling the more sensitive detection of posture changes. We demonstrate that dynamic patterns in humidity data correlate strongly with physical motion during sleep. To identify these transitions, we applied the Pruned Exact Linear Time (PELT) algorithm, which effectively segmented the time series based on abrupt changes in humidity. Furthermore, we converted humidity fluctuation curves into image representations and employed a transfer-learning-based model to classify sleep postures, achieving accurate recognition performance. Our findings highlight the potential of humidity sensing as a reliable modality for non-invasive sleep monitoring. In this study, we propose a novel method for detecting tossing and turning during sleep by analyzing changes in humidity captured by a linear array of sensors embedded in a memory foam pillow. Compared to temperature data, humidity data exhibited more significant fluctuations, which were leveraged to track head movement and infer sleep posture. We applied a rolling smoothing technique and quantified the cumulative deviation across sensors to identify posture transitions. Furthermore, the PELT algorithm was utilized for precise change-point detection. To classify sleep posture, we converted the humidity time series into images and implemented a transfer learning model using a Vision Transformer, achieving a classification accuracy of approximately 96%. Our results demonstrate the feasibility of a sleep posture analysis using only humidity data, offering a non-intrusive and effective approach for sleep monitoring. Full article

To improve the accuracy and robustness of bearing remaining useful life (RUL) prediction, this paper proposes a bearing RUL prediction method based on PELT state segmentation and time–frequency analysis, incorporating the Informer model for time-series modeling. First, the PELT (Pruned Exact Linear Time) algorithm is used to segment the vibration signals over the full life cycle of the bearing, accurately identifying critical degradation states and optimizing the stage division of the degradation process. Next, wavelet transform is applied to perform time–frequency analysis on the vibration signals, generating time–frequency spectrograms to comprehensively extract features in both the time and frequency domains. Finally, the extracted time–frequency features are used as input to predict the bearing RUL using the Informer model. As an efficient time-series prediction model, the Informer excels at handling long time series by leveraging a sparse self-attention mechanism to effectively capture the long-term dependencies in the signals. Experiments conducted on a publicly available dataset and comparisons with traditional methods demonstrate that the proposed method offers significant advantages in terms of prediction accuracy, computational efficiency, and robustness, making it more suitable for bearing health assessment and RUL prediction under complex working conditions. Full article

Due to the complex reservoir types and strong heterogeneity of fractured–vuggy reservoirs with aquifers, evaluating such reservoirs’ dynamic reserves and aquifer size is challenging. This paper established a segmented elastic-drive material balance equation based on the material balance principle by combining the functional relationships among the crude oil volume factor, crude oil compressibility, and formation pressure. The PELT algorithm was used to segment the water invasion stages, and nonlinear least squares fitting was employed to determine the rock compressibility, dynamic reserves, and aquifer size of fractured–vuggy reservoirs. This study shows that production in fractured–vuggy reservoirs with aquifers can be divided into three stages: no water invasion, initial water invasion, and full water invasion. Rock compressibility and dynamic reserves can be calculated using production data from the no water invasion stage, while the aquifer size can be determined from data in the water invasion stage. Influenced by connectivity and production regulations, aquifers may not be fully affected by pressure waves, causing the aquifer size to increase gradually until stabilization. Compared with numerical simulation data, the method presented in this paper achieves errors of 0.34%, 0.67%, and 1.19% for rock compressibility, dynamic reserves, and aquifer size, respectively. Full article

A popular belief for why polar bears have black skin is to increase solar heat gain from solar radiation that penetrates through a translucent fur layer made of unpigmented hollow hair. To examine the relative importance of skin color on solar heat gain, we measured thermal gradients, heat flux, and solar transmittance through a polar bear pelt under solar irradiation while thermally anchored to a temperature-controlled plate set to 33 °C. We found that for 60–70% of the dorsal region of the pelt where the fur layer is thickest, solar energy cannot reach the skin through the fur (solar transmittance ≤ 3.5 ± 0.2%) and therefore skin color does not meaningfully contribute to solar heat gain. In contrast, skin pigmentation was important in the remaining areas of the pelt that were covered with thinner fur. This information was used to select commercially available materials according to their solar optical properties to build biomimetic outdoor apparel with enhanced solar heat gain by a factor of 3 compared to standard outerwear constructions. Full article

Background: Fasting glucose is higher in pregnancies with obesity (OB); less is known about postprandial (PP) and nocturnal patterns when the diet is eucaloric and fixed or about the continuous-glucose-monitor (CGM) metrics that predict neonatal adiposity (NB%fat). We hypothesized that continuous glucose monitors (CGMs) would reveal higher glycemia in OB vs. normal weight (NW) during Early (14–16 weeks) and Later (26–28 weeks) gestation despite macronutrient-controlled eucaloric diets and elucidate unique predictors of NB%fat. Methods: In a prospective, parallel-group comparative study, a eucaloric diet (NW: 25 kcal/kg; OB: 30 kcal/kg) was provided (50% carbohydrate [20% simple/30% complex; of total calories], 35% fat, 15% protein) to Early and Later gestation groups wearing a blinded CGM for three days. CGM metrics (mean fasting; 1 h and 2 h PP; daytime and nocturnal glucose; percent time-in-range (%TIR: 63–140 mg/dL); PP excursions; and area-under-the-curve [AUC]) were interrogated between groups and as predictors of NB%fat by dual X-ray absorptiometry(DXA). Results: Fifty-four women with NW (BMI: 23 kg/m²; n = 27) and OB (BMI: 32; n = 27) provided their informed consent to participate. Early, the daytime glucose was higher in OB vs. NW (mean ± SEM) (91 ± 2 vs. 85 ± 2 mg/dL, p = 0.017), driven by 2 h PP glucose (95 ± 2 vs. 88 ± 2, p = 0.004). Later, those with OB exhibited higher nocturnal (89 ± 2 vs. 81 ± 2), daytime (95 ± 2 vs. 87 ± 2), 1 h (109 ± 3 vs. 98 ± 2), and 2 h PP (101 ± 3 vs. 92 ± 2) glucose (all p < 0.05) but no difference in %TIR (95–99%). Postprandial peak excursions for all meals were markedly blunted in both the Early (9–19 mg/dL) and Later (15–26 mg/dL). In OB, the Later group’s 24 h AUC was correlated with NB%fat (r = 0.534, p = 0.02). Despite similar weight gain, infants of OB had higher birthweight (3528 ± 107 vs. 3258 ± 74 g, p = 0.037); differences in NB%fat did not reach statistical significance (11.0 vs. 8.9%; p > 0.05). Conclusions: Despite macronutrient-controlled eucaloric diets, pregnancies with OB had higher glycemia Early and Later in gestation; the Later 24 h glucose AUC correlated with NB%fat. However, glycemic patterns were strikingly lower than current management targets. Full article

Deep-learning algorithms for cell segmentation typically require large data sets with high-quality annotations to be trained with. However, the annotation cost for obtaining such sets may prove to be prohibitively expensive. Our work aims to reduce the time necessary to create high-quality annotations of cell images by using a relatively small well-annotated data set for training a convolutional neural network to upgrade lower-quality annotations, produced at lower annotation costs. We investigate the performance of our solution when upgrading the annotation quality for labels affected by three types of annotation error: omission, inclusion, and bias. We observe that our method can upgrade annotations affected by high error levels from 0.3 to 0.9 Dice similarity with the ground-truth annotations. We also show that a relatively small well-annotated set enlarged with samples with upgraded annotations can be used to train better-performing cell segmentation networks compared to training only on the well-annotated set. Moreover, we present a use case where our solution can be successfully employed to increase the quality of the predictions of a segmentation network trained on just 10 annotated samples. Full article

Microglia activity can drive excessive synaptic loss during the prodromal phase of Alzheimer’s disease (AD) and is associated with lowered cyclic adenosine monophosphate (cAMP) due to cAMP phosphodiesterase 4B (PDE4B). This study aimed to investigate whether long-term inhibition of PDE4B by A33 (3 mg/kg/day) can prevent synapse loss and its associated cognitive decline in APPswe/PS1dE9 mice. This model is characterized by a chimeric mouse/human APP with the Swedish mutation and human PSEN1 lacking exon 9 (dE9), both under the control of the mouse prion protein promoter. The effects on cognitive function of prolonged A33 treatment from 20 days to 4 months of age, was assessed at 7–8 months. PDE4B inhibition significantly improved both the working and spatial memory of APPswe/PSdE9 mice after treatment ended. At the cellular level, in vitro inhibition of PDE4B induced microglial filopodia formation, suggesting that regulation of PDE4B activity can counteract microglia activation. Further research is needed to investigate if this could prevent microglia from adopting their ‘disease-associated microglia (DAM)’ phenotype in vivo. These findings support the possibility that PDE4B is a potential target in combating AD pathology and that early intervention using A33 may be a promising treatment strategy for AD. Full article