Search Results (68,777)

Background: Left ventricular non-compaction cardiomyopathy (LVNC) is a congenital heart disease characterized by abnormal prenatal development of the left ventricle that has an aberrantly thick trabecular layer and a thinner compacted myocardial layer. However, the underlying molecular mechanisms of LVNC regulated by mitochondrial phosphatase genes remain largely unresolved. Methods: We generated a mouse model with cardiac-specific deletion (CKO) of Ptpmt1, a type of mitochondrial phosphatase gene, using the αMHC-Cre, and investigated the effects of cardiac-specific Ptpmt1 deficiency on cardiac development. Morphological, histological, and immunofluorescent analyses were conducted in Ptpmt1 CKO and littermate controls. A transcriptional atlas was identified by RNA sequencing (RNA-seq) analysis. Results: We found that CKO mice were born at the Mendelian ratio with normal body weights. However, most of the CKO mice died within 24 h after birth, developing spontaneous ventricular tachycardia. Morphological and histological analysis further revealed that newborn CKO mice developed an LVNC phenotype, evidenced by a thicker trabecular layer and a thinner myocardium layer, when compared with the littermate control. We then examined the embryonic hearts and found that such an LVNC phenotype could also be observed in CKO hearts at E15.5 but not at E13.5. We also performed the EdU incorporation assay and demonstrated that cardiac cell proliferation in both myocardium and trabecular layers was significantly reduced in CKO hearts at E15.5, which is also consistent with the dysregulation of genes associated with heart development and cardiomyocyte proliferation in CKO hearts at the same stage, as revealed by both the transcriptome analysis and the quantitative real-time PCR. Deletion of Ptpmt1 in mouse cardiomyocytes also induced an increase in phosphorylated eIF2α and ATF4 levels, indicating a mitochondrial stress response in CKO hearts. Conclusions: Our results demonstrated that Ptpmt1 may play an essential role in regulating left ventricular compaction during mouse heart development. Full article

BeiDou navigation satellite system (BDS) precise point positioning (PPP)-B2b has significant potential for application in meteorological fields, such as standalone water vapor monitoring in depopulated area without Internet. In this study, the practical ability of water vapor monitoring using the BDS PPP-B2b service is illustrated through a continuously operated water vapor monitoring system in Wuhan, China, with a 25-day experiment in 2025. Original observations from the Global Positioning System (GPS) and BDS are collected and processed in the near real-time (NRT) mode using ephemeris from the PPP-B2b service. Precipitable water vapor PWV monitored with B2b ephemeris are evaluated with radiosonde and ERA5 reanalysis, respectively. Taking PWV from radiosonde observations as the reference, RMS of PWV based on B2b ephemeris varies from 3.71 to 4.66 mm for different satellite combinations. While those values are with a range from 3.95 to 4.55 mm when compared with ERA5 reanalysis. These values are similar to those processed with the real-time ephemeris from the China Academy of Science (CAS). In general, this study demonstrates that the practical accuracy of water vapor monitored based on the BDS PPP-B2b service can meet the basic demand for operational meteorology for the first time. This will provide a scientific reference for its wide promotion to meteorological applications in the near future. Full article

Optimization has always been viewed as a central component of many electrical engineering techniques, where it involves designing a complex system with various constraints and competing objectives. The method described in this work proposes a hybrid quantum–classical evolutionary optimization algorithm targeting high-frequency electromagnetic problems. A genetic algorithm with a quantum selection operator that applies high selection pressure while preserving selection diversity is introduced. This change means that stagnation can be reduced without compromising the speed of convergence. This was used on both real quantum hardware as well as quantum simulators. The results demonstrate that the performance of the real quantum devices was deteriorated by the noise in these devices and that simulators would be a useful option. We provide a description of the operation of the proposed evolutionary optimization method with mathematical benchmarks and electromagnetic design problems that show that it outperforms conventional evolutionary algorithms in terms of convergence behavior and robustness. Full article

The dynamic development of distributed sources (mainly RES) contributes to the emergence of, among others, balance and overload problems. For this reason, many RES do not receive conditions for connection to the power grid in Poland. Operators sometimes extend permits based on the possibility of periodic power reduction in RES in the event of the problems mentioned above. Before making a decision, investors, for economic reasons, need information on the probability of annual power reduction in their potential installation. Analyses that allow one to determine such a probability require knowledge of the reliability indicators of transmission lines and transformers, as well as failure removal times. The article analyses the available literature on the annual risk of outages of these elements and methods to determine the appropriate reliability indicators. Example calculations were performed for two networks (test and real). The values of indicators and times that can be used in practice were indicated. The unique contribution of this article lies not only in the comprehensive comparison of current, relevant transmission line and transformer reliability analysis methods but also in developing the first reliability indices for the Polish power system in more than 30 years. It is based on the relationships presented in the article and their comparison with results reported in the international literature. Full article

Expansive soils, characterized by the presence of surface and subsurface cracks, over-consolidation, and swell-shrink properties, present significant challenges to slope stability in geotechnical engineering. Despite extensive research, preventing geohazards associated with expansive soils remains unresolved. This study investigates shallow sliding failures in slopes of highly expansive soils induced by rainfall, using model tests to explore deformation and mechanical behavior under cyclic wetting and drying conditions, focusing on the interaction between soil properties and environmental factors. Model tests were conducted in a wedge-shaped box filled with Nanyang expansive clay from Henan, China, which is classified as high-plasticity clay (CH) according to the Unified Soil Classification System (USCS). The soil was compacted in four layers to maintain a 1:2 slope ratio (i.e., 1 vertical to 2 horizontal), which reflects typical expansive soil slope configurations observed in the field. Monitoring devices, including moisture sensors, pressure transducers, and displacement sensors, recorded changes in soil moisture, stress, and deformation. A static treatment phase allowed natural crack development to simulate real-world conditions. Key findings revealed that shear failure propagated along pre-existing cracks and weak structural discontinuities, supporting the progressive failure theory in shallow sliding. Cracks significantly influenced water infiltration, creating localized stress concentrations and deformation. Atmospheric conditions and wet-dry cycles were crucial, as increased moisture content reduced soil suction and weakened the slope’s strength. These results enhance understanding of expansive soil slope failure mechanisms and provide a theoretical foundation for developing improved stabilization techniques. Full article

Alzheimer’s disease (AD) is a neurodegenerative condition in which amyloid-beta (Aβ) plaques trigger oxidative stress (OS) and neuroinflammation, causing memory loss. OS and neurodegeneration can also be caused by reactive astrocytes, thereby promoting AD via toxic metabolite accumulation in the astrocytic urea cycle. However, the effect of molecular hydrogen (H₂) on this cycle remains unknown. Therefore, we investigated whether H₂ treatment could reduce OS-induced neurodegeneration and memory loss. 5xFAD (n = 14) and wild-type (n = 15) mice were randomized into four groups and treated with either 3% hydrogen gas (H₂) or vehicle for 60 days. Cognitive behaviors were evaluated using the Morris water maze and Y-maze tests. In addition, we used biochemical assays to measure ammonia and hydrogen peroxide (H₂O₂) levels in the hippocampi of the mice and AβO-treated primary mouse astrocytes. Aβ, γ-aminobutyric acid (GABA), and the expression of inflammatory markers were evaluated using immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR). We observed that H₂ treatment significantly prevented cognitive deficits, oxidative stress, the accumulation of toxic metabolites, and the increase in inflammatory markers in 5xFAD mice. These results suggest that H₂ therapy can mitigate toxic metabolites in the astrocytic urea cycle, thereby reducing neurodegeneration and memory loss in AD. Full article

This work investigates, for the first time, the application of a modified natural magnetite material with 35% of lanthanum for phosphorus (P) recovery from synthetic and actual wastewater under both static (batch) and dynamic (continuous stirred tank reactor (CSTR)) conditions. The characterization results showed that the natural feedstock mainly comprises magnetite and kaolinite. Moreover, the lanthanum-modified magnetite (La-MM) exhibited more enhanced textural, structural, and surface chemistry properties than the natural feedstock. In particular, its surface area (82.7 m² g⁻¹) and total pore volume (0.160 cm³ g⁻¹) were higher by 86.6% and 255.5%, respectively. The La-MM efficiently recovered P in batch mode under diverse experimental settings with an adsorption capacity of 50.7 mg g⁻¹, which is significantly greater than that of various engineered materials. It also maintained high efficiency even when used for the treatment of actual wastewater, with an adsorption capacity of 47.3 mg g⁻¹. In CSTR mode, the amount of P recovered from synthetic solutions and real wastewater decreased to 33.8 and 10.2 mg g⁻¹, respectively, due to the limited contact time. The phosphorus recovery process involves mainly electrostatic attraction over a wide pH interval, complexation, and precipitation as lanthanum phosphates. This investigation indicates that lanthanum-modified natural feedstocks from magnetite deposits can be regarded as promising materials for P recovery from aqueous solutions. Full article

Resin Transfer Molding (RTM) is a widely used composite manufacturing process where liquid resin is injected into a closed mold filled with a fibrous preform. By applying this process, large pieces with complex shapes can be produced on an industrial scale, presenting excellent properties and quality. A true physical phenomenon occurring in the RTM process, especially when using vegetable fibers, is related to the absorption of resin by the fiber during the infiltration process. The real effect is related to the slowdown in the advance of the fluid flow front, increasing the mold filling time. This phenomenon is little explored in the literature, especially for non-isothermal conditions. In this sense, this paper does a numerical study of the liquid injection process in a closed and heated mold. The proposed mathematical modeling considers the radial, three-dimensional, and transient flow, variable injection pressure, and fluid viscosity, including the effect of liquid fluid absorption by the reinforcement (fiber). Simulations were carried out using Computational Fluid Dynamic tools. The numerical results of the filling time were compared with experimental results, and a good approximation was obtained. Further, the pressure, temperature, velocity, and volumetric fraction fields, as well as the transient history of the fluid front position and injection fluid volumetric flow rate, are presented and analyzed. Full article

Amomum villosum Lour., a perennial medicinal plant in the Zingiber genus, usually requires approximately 3–4 years of vegetative growth from seed germination to first fruiting, resulting in high initial investment costs and a prolonged revenue cycle, which pose significant challenges to the industry’s sustainable development. Our research team observed a distinct premature fruiting phenomenon in A. villosum. We investigated the regulatory mechanisms underlying premature fruiting in A. villosum by identifying the key differentially expressed genes (DEGs) and metabolic pathways governing the premature fruiting (Precocious) and typical plants (CK) of the ‘Yunsha No.8’ cultivar. Transcriptomic sequencing (RNA-seq) and bioinformatic analyses were performed using the DNBSEQ^TM platform. The sequencing generated 29.0 gigabases (Gb) of clean data, and 115,965 unigenes were identified, with an average length of 1368 bp. Based on the sequencing results, 1545 DEGs were identified. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were annotated for these DEGs. This study identifies phytohormone signaling, carbohydrate and lipid metabolism, and polysaccharide degradation as critical pathways controlling premature fruiting in A. villosum. Six randomly selected DEGs were validated using real-time fluorescence quantitative polymerase chain reaction (RT-qPCR), and the results corroborated the transcriptome data, confirming their reliability. This study lays the foundation for the elucidation of the molecular mechanisms and metabolic pathways driving premature fruiting in A. villosum. Full article

Accurate extraction of representative operating conditions is crucial for optimizing systems in renewable energy applications. This study proposes a novel framework that combines the Parzen window estimation method, ideal for nonparametric modeling of wind, solar, and load datasets, with a game theory-based time scale selection mechanism. The novelty of this work lies in integrating probabilistic density modeling with multi-indicator evaluation to derive realistic operational profiles. We first validate the superiority of the Parzen window approach over traditional Weibull and Beta distributions in estimating wind and solar probability density functions. In addition, we analyze the influence of key meteorological parameters such as wind direction, temperature, and solar irradiance on energy production. Using three evaluation metrics, the main result shows that a 3-day representative time scale offers optimal accuracy when determined through game theory methods. Validation with real-world data from Inner Mongolia confirms the robustness of the proposed method, yielding low errors in wind, solar, and load profiles. This study contributes a novel 3-day typical profile extraction method validated on real meteorological data, providing a data-driven foundation for optimizing energy storage systems under renewable uncertainty. This framework supports energy sustainability by ensuring realistic modeling under renewable intermittency. Full article

The increasing deployment of photovoltaic (PV) systems necessitates robust fault detection mechanisms to ensure operational reliability and safety. Conventional approaches, however, struggle in complex industrial environments characterized by high noise, data incompleteness, and class imbalance. This study proposes an innovative Advanced CNN-BiLSTM architecture integrating multi-scale feature extraction with hierarchical attention to enhance PV fault detection. The proposed framework employs four parallel CNN branches with kernel sizes of 3, 7, 15, and 31 to capture temporal patterns across various time scales. These features are then integrated by an adaptive feature fusion network that utilizes multi-head attention. A two-layer bidirectional LSTM with temporal attention mechanism processes the fused features for final classification. Comprehensive evaluation on the GPVS-Faults dataset using a progressive difficulty validation framework demonstrates exceptional performance improvements. Under extreme industrial conditions, the proposed method achieves 83.25% accuracy, representing a substantial 119.48% relative improvement over baseline CNN-BiLSTM (37.93%). Ablation studies reveal that the multi-scale CNN contributes 28.0% of the total performance improvement, while adaptive feature fusion accounts for 22.0%. Furthermore, the proposed method demonstrates superior robustness under severe noise (σ = 0.20), high levels of missing data (15%), and significant outlier contamination (8%). These characteristics make the architecture highly suitable for real-world industrial deployment and establish a new paradigm for temporal feature fusion in renewable energy fault detection. Full article

The United States leads in corn production and consumption in the world with an estimated USD 50 billion per year. There is a pressing need for the development of novel and efficient techniques aimed at enhancing the identification and eradication of weeds in a manner that is both environmentally sustainable and economically advantageous. Weed classification for autonomous agricultural robots is a challenging task for a single-camera-based system due to noise, vibration, and occlusion. To address this issue, we present a multi-camera-based system with decision-level sensor fusion to improve the limitations of a single-camera-based system in this paper. This study involves the utilization of a convolutional neural network (CNN) that was pre-trained on the ImageNet dataset. The CNN subsequently underwent re-training using a limited weed dataset to facilitate the classification of three distinct weed species: Xanthium strumarium (Common Cocklebur), Amaranthus retroflexus (Redroot Pigweed), and Ambrosia trifida (Giant Ragweed). These weed species are frequently encountered within corn fields. The test results showed that the re-trained VGG16 with a transfer-learning-based classifier exhibited acceptable accuracy (99% training, 97% validation, 94% testing accuracy) and inference time for weed classification from the video feed was suitable for real-time implementation. But the accuracy of CNN-based classification from video feed from a single camera was found to deteriorate due to noise, vibration, and partial occlusion of weeds. Test results from a single-camera video feed show that weed classification accuracy is not always accurate for the spray system of an agricultural robot (AgBot). To improve the accuracy of the weed classification system and to overcome the shortcomings of single-sensor-based classification from CNN, an improved Dempster–Shafer (DS)-based decision-level multi-sensor fusion algorithm was developed and implemented. The proposed algorithm offers improvement on the CNN-based weed classification when the weed is partially occluded. This algorithm can also detect if a sensor is faulty within an array of sensors and improves the overall classification accuracy by penalizing the evidence from a faulty sensor. Overall, the proposed fusion algorithm showed robust results in challenging scenarios, overcoming the limitations of a single-sensor-based system. Full article

The increasing presence of ECs in aquatic environments has drawn significant attention to the need for innovative, accessible, and sustainable solutions in wastewater treatment. This review provides a comprehensive overview of the use of agricultural residues—often discarded and undervalued—as raw materials for the development of efficient bioadsorbents. Based on a wide range of recent studies, this work presents various types of materials, such as rice husks, sugarcane bagasse, and açaí seeds, that can be transformed through thermal and chemical treatments into advanced bioadsorbents capable of removing pharmaceuticals, pesticides, dyes, and in some cases, even addressing highly persistent pollutants such as PFASs. The main objectives of this review are to (1) assess agricultural-residue-derived bioadsorbents for the removal of ECs; (2) examine physical and chemical modification techniques that enhance adsorption performance; (3) evaluate their scalability and applicability in real-world treatment systems. The review also highlights key adsorption mechanisms—such as π–π interactions, hydrogen bonding, and ion exchange—alongside the influence of parameters like pH and ionic strength. The review also explores the kinetic, isothermal, and thermodynamic aspects of the adsorption processes, highlighting both the efficiency and reusability potential of these materials. This work uniquely integrates microwave-assisted pyrolysis, magnetic functionalization, and hybrid systems, offering a roadmap for sustainable water remediation. Finally, comparative performance analyses, applications using real wastewater, regeneration strategies, and the integration of these bioadsorbents into continuous treatment systems are presented, reinforcing their promising role in advancing sustainable water remediation technologies. Full article

House price prediction is crucial in real estate for informed decision-making. This paper presents an automated prediction system that combines genetic algorithms (GA) for feature optimization and Analysis of Variance (ANOVA) for statistical analysis. We apply and compare five ensemble machine learning (ML) models, namely Extreme Gradient Boosting Regression (XGBR), random forest regression (RFR), Categorical Boosting Regression (CBR), Adaptive Boosting Regression (ADBR), and Gradient Boosted Decision Trees Regression (GBDTR), on a comprehensive dataset. We used a dataset with 1000 samples with eight features and a secondary dataset for external validation with 3865 samples. Our integrated approach identifies Categorical Boosting with GA (CBRGA) as the best performer, achieving an R² of 0.9973 and outperforming existing state-of-the-art methods. ANOVA-based analysis further enhances model interpretability and performance by isolating key factors such as square footage and lot size. To ensure robustness and transparency, we conduct 10-fold cross-validation and employ explainable AI techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), providing insights into model decision-making and feature importance. Full article

The real-time acquisition of an accurate underwater sound velocity profile (SSP) is crucial for tracking the propagation trajectory of underwater acoustic signals, making it play a key role in ocean communication positioning. SSPs can be directly measured by instruments or inverted leveraging sound field data. Although measurement techniques provide a good accuracy, they are constrained by limited spatial coverage and require a substantial time investment. The inversion method based on the real-time measurement of acoustic field data improves operational efficiency but loses the accuracy of SSP estimation and suffers from limited spatial applicability due to its stringent requirements for ocean observation infrastructures. To achieve accurate long-term ocean SSP estimation independent of real-time underwater data measurements, we propose a semi-transformer neural network (STNet) specifically designed for simulating sound velocity distribution patterns from the perspective of time series prediction. The proposed network architecture incorporates an optimized self-attention mechanism to effectively capture long-range temporal dependencies within historical sound velocity time-series data, facilitating an accurate estimation of current SSPs or prediction of future SSPs. Through the architectural optimization of the transformer framework and integration of a time encoding mechanism, STNet could effectively improve computational efficiency. For long-term forecasting (using the Pacific Ocean as a case study), STNet achieved an annual average RMSE of 0.5811 m/s, outperforming the best baseline model, H-LSTM, by 26%. In short-term forecasting for the South China Sea, STNet further reduced the RMSE to 0.1385 m/s, demonstrating a 51% improvement over H-LSTM. Comparative experimental results revealed that STNet outperformed state-of-the-art models in predictive accuracy and maintained good computational efficiency, demonstrating its potential for enabling accurate long-term full-depth ocean SSP forecasting. Full article