Search Results (20,971)

The study addresses the critical issue of accurately predicting ammonia nitrogen (NH₃-N) concentration in a sequencing batch reactor (SBR) system, achieving reduced consumption through automatic control technology. NH₃-N concentration serves as a key indicator of treatment efficiency and environmental impact; however, its complex dynamics and the scarcity of measurements pose significant challenges for accurate prediction. To tackle this problem, an innovative Transformer-long short-term memory (Transformer-LSTM) network model was proposed, which effectively integrates the strengths of both Transformer and LSTM architectures. The Transformer component excels at capturing long-range dependencies, while the LSTM component is adept at modeling sequential patterns. The innovation of the proposed methodology resides in the incorporation of dissolved oxygen (DO), electrical conductivity (EC), and oxidation-reduction potential (ORP) as input variables, along with their respective rate of change and cumulative value. This strategic selection of input features enhances the traditional utilization of water quality indicators and offers a more comprehensive dataset for prediction, ultimately improving model accuracy and reliability. Experimental validation on NH₃-N datasets from the SBR system reveals that the proposed model significantly outperforms existing advanced methods in terms of root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Furthermore, by integrating real-time sensor data with the Transformer-LSTM network and automatic control, substantial improvements in water treatment processes were achieved, resulting in a 26.9% reduction in energy or time consumption compared with traditional fixed processing cycles. This methodology provides an accurate and reliable tool for predicting NH₃-N concentrations, contributing significantly to the sustainability of water treatment and ensuring compliance with emission standards. Full article

This study investigates the bending behavior of steel-ribbed composite slabs for a 500 kV substation project in China through numerical simulation. The unidirectional bending performance of the slab was first analyzed and validated against theoretical calculations. After that, the bidirectional bending performance of double-spliced and triple-spliced composite slabs were evaluated against the monolithic slab, followed by a parametric analysis to identify the influence of key factors. The results indicate that the steel-ribbed composite slabs feature high cracking strength, post-crack stiffness, bearing capacity, and commendable ductility under both unidirectional and bidirectional loading conditions. Under unidirectional loading, the ultimate capacity of the slab reaches 57–58 kN/m². Under bidirectional loading, the cracking load and bearing capacity of the dense-splicing composite slabs increase by more than 60% compared with unidirectional loading. Composite and monolithic slabs exhibit similar crack patterns and ultimate capacities under bidirectional loading; however, the presence of splicing joints results in a slight increase in the ultimate deflection of the double-spliced and triple-spliced composite slabs by 7.53% and 7.75% compared with that of the monolithic slab. The ratio of prestressing steel is identified as the most critical parameter for failure control, followed by the concrete strength. When the strength of the joint-connecting rebars exceeds 235 MPa and the diameter is greater than 4 mm, transversal force transfer across the joints is reliable. This paper provides valuable insights and practical guidance for the prefabricated construction of substations. Full article

Bacterial biofilms play an important role in the pathogenesis of infectious diseases but are also very relevant in other fields such as the food industry. This fact has led to an increased focus on the early identification of these structures as prophylaxes to prevent biofilm-related contaminations or infections. One of the objectives of the present study was to assess the effectiveness of NIR (Near Infrared) spectroscopy in the detection and differentiation of biofilms from different bacterial species, namely Staphylococcus epidermidis, Staphylococcus aureus, Enterococcus faecium, Salmonella Typhymurium, Escherichia coli, Listeria monocytogenes, and Lactiplantibacillus plantarum. Additionally, we aimed to examine the capability of this technology to specifically identify S. aureus biofilms on glass surfaces commonly used as storage containers and processing equipment. We developed a detailed methodology for data acquisition and processing that takes into consideration the biochemical composition of these biofilms. To improve the quality of the spectral data, SNV (Standard Normal Variate) and Savitzky–Golay filters were applied, which correct systematic variations and eliminate random noise, followed by an exploratory analysis that revealed significant spectral differences in the NIR range. Then, we performed principal component analysis (PCA) to reduce data dimensionality and, subsequently, a Random Forest discriminant statistical analysis was used to classify biofilms accurately and reliably. The samples were organized into two groups, a control set and a test set, for the purpose of performing a comparative analysis. Model validation yielded an accuracy of 80.00% in the first analysis (detection and differentiation of biofilm) and 93.75% in the second (identification of biofilm on glass surfaces), thus demonstrating the efficacy of the proposed method. These results demonstrate that this technique is effective and reliable, indicating great potential for its application in the field of biofilm detection. Full article

Teachers’ sense of efficacy (TSE) is a crucial construct for evaluating the quality of pre-service teachers. While the Teachers’ Sense of Efficacy Scale (TSES) is the most widely used and promising instrument for measuring TSE, there is no existing literature assessing the appropriateness of the TSES for early childhood pre-service teachers in China. This study aimed to adapt the English version of the TSES for the Chinese early childhood education contexts, testing its factor structure, validity, measurement invariance, and reliability. The sample included 402 participants in China. The TSES was translated into Chinese using the standard back-to-back translation method. CFA results indicated that the TSES is best represented by a modified three-factor model, demonstrating strong preliminary, overall, and internal structure fit. The concurrent validity, convergent validity, criterion-related validity, internal consistency reliability, and composite reliability of the Chinese version of the TSES were robust. The measurement invariance across age and college year was also confirmed. This study addresses a gap in the literature by providing robust empirical evidence on the factor structure, validity, measurement invariance, and reliability of the Chinese version of the TSES for early childhood pre-service teachers, thereby enhancing understanding of TSE in Chinese-speaking Confucian culture and in early childhood education contexts. Full article

This paper presents a new model-independent constraint on the Hubble constant ($H_0$) by anchoring relative distances from Type Ia supernovae (SNe Ia) observations to absolute distance measurements from time-delay strong Gravitational Lensing (SGL) systems. The approach only uses the validity of the cosmic distance duality relation (CDDR) to derive constraints on $H_0$. By using Gaussian Process (GP) regression to reconstruct the unanchored luminosity distance from the Pantheon+ compilation to match the time-delay angular diameter distance at the redshift of the lenses, one yields a value of $H_0=75.57\pm 4.415$ km/s/Mpc at a 68% confidence level. The result aligns well with the local estimate from Cepheid variables within the $1\sigma$ confidence region, indicating consistency with late-universe probes. Full article

Background/Objectives: Despite widespread use in clinical and athletic settings, validity of handgrip strength (HGS) as a surrogate for maximal strength remains debated, particularly regarding how testing posture influences its predictive value. Moreover, while HGS is frequently considered a marker of ‘total strength’, this term is often vaguely defined, lacking a clear, performance-based framework. Therefore, this study investigates HGS as a potential surrogate measure for one-repetition maximum (1RM) performances in key compound lifts via back squat (BS), bench press (BP), deadlift (DL), and total (TOT), while accounting for variations in testing posture. Methods: Two distinct testing conditions were used to account for postural influences: Experiment 1 implemented high-output standing HGS (HGS_STAND) in 22 recreationally trained males [Wilks Score: 318.51 ± 44.61 au] vs. Experiment 2, which included low-output seated HGS (HGS_SIT) in 22 competitive powerlifters [409.86 ± 46.76 au], with all testing immediately followed by 1RM assessment. Results: Correlational analyses identified the strongest association between HGS_STAND and 1RM DL (r = 0.693, BF₁₀ = 106.42), whereas HGS_SIT exhibited the strongest relationship with 1RM BP (r = 0.732, BF₁₀ = 291.32). Postural effects had a significant impact on HGS outcomes (p < 0.001, η² = 0.413), with HGS_STAND producing higher outputs than HGS_SIT despite lower absolute strength 1RM capabilities. Conclusions: These findings emphasize the role of biomechanical specificity and neuromuscular engagement in grip strength assessments, indicating that HGS can function as a practical surrogate for maximal strength, though its predictive value depends on posture. Strength practitioners, sport scientists, and clinicians should consider these confounding factors when implementing HGS-based monitoring strategies. Full article

Flowers are one of the most important organs in plants. Their development serves as a key indicator of the transition from vegetative to reproductive growth and is regulated by various internal signals and environmental factors. NAC (NAM, ATAF, CUC) transcription factors (TFs) play a crucial regulatory role in floral organ development; however, research on the analysis and identification of the NAC TF family in Chinese cabbage (Brassica rapa L.) remains limited. In this study, we performed a comprehensive genome-wide analysis of NACs in B. rapa and identified 279 members of the BrNAC gene family. Their physicochemical properties, domain structure, collinearity relation, and cis-regulatory elements were evaluated. Phylogenetic analysis indicates that NAC proteins from Arabidopsis, B. rapa, B. oleracea, and B. nigra can be classified into seven distinct clades. BrNACs exhibit a tissue-specific expression, and nine BrNACs being specifically expressed in the inflorescence. Furthermore, nine flower-related BrNACs were selected for RT-qPCR analysis to validate their expression profiles. BrNAC2s has been cloned to investigate their subcellular localization, and examine the expression patterns of their promoters in Arabidopsis inflorescences. BrNAC2a and BrNAC2c are highly expressed in stamens while BrNAC2b exhibits elevated expression in pistils and pedicel. Collectively, our findings enhance the understanding of the BrNAC family and provide a foundation for future studies on the molecular mechanisms of BrNACs in floral development. Full article

Battery cell voltage is an important evaluation index for electric vehicle condition estimation and one of the main monitoring parameters of the battery management system, and accurate voltage prediction is crucial for electric vehicle battery failure warning. Therefore, this paper proposes a novel hybrid gated recurrent unit and long short-term memory (GRU-LSTM) neural network to predict electric vehicle lithium-ion battery cell voltage. Firstly, Pearson coefficient correlation analysis is carried out to determine the input parameters of the neural network by analyzing the influence factors of the voltage parameters, and the hyperparameters of the neural network are determined through cross-validation to construct the lithium-ion battery single-unit voltage prediction model based on GRU-LSTM. Secondly, the voltage prediction accuracy and robustness of the GRU-LSTM model are verified by training the historical data of real vehicles in spring, summer, fall, and winter, combined with four different error indicators. Finally, the feasibility of the proposed method is verified by designing hierarchical warning rules based on the prediction data to realize the accurate warning of multiple voltage anomalies. Full article

To address the issue of insufficient reliability of fiber optic sensing networks in complex environments, this study proposes a self-diagnosis and self-healing method based on intelligent algorithms. This method integrates redundant fiber paths and a fault detection mechanism, enabling rapid data transmission recovery through redundant paths during network faults, ensuring the stable operation of the monitoring system. Unlike traditional self-diagnosis techniques that rely on an optical time domain reflectometer, the proposed self-diagnosis algorithm utilizes data structure analysis, significantly reducing dependence on costly equipment and improving self-diagnosis efficiency. On the hardware front, a light switch driving device that does not require an external power source has been developed, expanding the application scenarios of optical switches and enhancing system adaptability and ease of operation. In the experiments, three fiber optic sensing network topologies—redundant ring structure, redundant dual-ring structure, and redundant mesh structure—are constructed for testing. The results show that the average self-diagnosis time is 0.1257 s, and the self-healing time is 0.5364 s, validating the efficiency and practicality of the proposed method. Furthermore, this study also proposes a robustness evaluation model based on sensor perception ability and coverage uniformity indicators, providing a theoretical basis for the self-healing capability of fiber optic sensing networks. This model aids in network topology optimization and fault recovery strategy design, contributing to the improvement of the stability and reliability of fiber optic sensing networks in practical applications. Full article

This study evaluated the impact of a proprietary blend of fruit and vegetable fibers rich in naturally occurring bound polyphenols (commercially marketed as NatureKnit^TM), compared to purified fibers (inulin and psyllium), on the human gut microbiome using the validated M-SHIME^® gastrointestinal model. A short-term single-stage colonic M-SHIME^® experiment (with fecal inoculum from three healthy human donors) was used to evaluate the test products compared to a negative control. Samples were assessed for pH, gas pressure, short-chain fatty acid (SCFA) production, lactate, and ammonium from 0 h to 48 h. Microbial community composition was assessed at 0 h (negative control only), 24 h, and 48 h (lumen) or 48 h (mucosal). All test products were fermented well in the colon as demonstrated by decreases in pH and increases in gas pressure over time; these changes occurred faster with the purified fibers, whereas NatureKnit™ demonstrated slow, steady changes, potentially indicating a gentler fermentation process. SCFA production significantly increased over the course of the 48 h experiment with all test products versus negative control. SCFA production was significantly greater with NatureKnit™ versus the purified fibers. Shifts in the microbial community composition were observed with all test products versus negative control. At the conclusion of the 48 h experiment, the absolute bacterial abundance and the richness of observed bacterial taxa in the lumen compartment was significantly greater with NatureKnit™ compared with inulin, psyllium, and negative control. Overall, NatureKnit™ demonstrated greater or similar prebiotic effects on study measures compared with established prebiotic fibers. Full article

In the face of rapid progress in heritage preservation and cultural tourism integration, landscape planning in historic cities is pivotal to showcasing regional identities and disseminating cultural value. However, the complexity of cultural characteristic identification and the imbalance in planning often restrict the progress of urban development. Additionally, existing studies predominantly rely on subjective methods and focus on a single cultural attribute, highlighting the urgent need for research on diversified cultural perception. Using Jingdezhen, a renowned historic cultural city, as an example, this study introduces a multi-label deep learning approach to examine cultural perceptions in tourism heritage landscapes. Leveraging social media big data and an optimized ResNet-50 model, a framework encompassing artifacts, production, folk, and living culture was constructed and integrated with ArcGIS spatial analysis and diversity indices. The results show: (1) The multi-label classification model achieves 92.35% accuracy, validating its potential; (2) Heritage landscapes exhibit a “material-dominated, intangible-weak” structure, with artifacts culture as the main component; (3) Cultural perception intensity is unevenly distributed, with core areas demonstrating higher recognition and diversity; (4) Diversity indices suggest that comprehensive venues display stronger cultural balance, whereas specialized ones reveal marked cultural singularity, indicating a need for improved integration across sites. This research expands the use of multi-label deep learning in tourism heritage studies and offers practical guidance for global heritage sites tackling mass tourism. Full article

Background/Objectives: BuZhong Yiqi Formula (BZYQF) has significant ameliorative effects on type 2 diabetes mellitus (T2DM). However, its efficacy in alleviating the hyposalivation caused by T2DM needs to be confirmed, and its mechanism is unclear. Methods: Network pharmacology and molecular docking were combined to analyze the molecular mechanism by which BZYQF alleviates T2DM-caused hyposalivation. A T2DM rat model was induced to evaluate the efficacy of BZYQF. The total saliva before and after acid stimulation was collected to determine the salivary flow rate and salivary alpha-amylase (sAA) activity. The parotid (PG) and submandibular glands (SMG) of experimental rats were removed to perform histopathology observation, biochemical indicator determination, and expression detection of signaling molecules in the salivary secretion pathway. Results: The present study screened out 1014 potential targets of BZYQF regarding the treatment of T2DM. These targets were mainly involved in the formation of the receptor complex, exercising the neurotransmitter receptor activity and regulating secretion. They were significantly enriched in the salivary secretion pathway of β1-AR/PKA/AMY1 and CHRM3/IP3R/AQP5. Furthermore, in BZYQF, nine validated compounds were able to dock into the active site of β1-AR, and three validated compounds were able to dock into the active site of CHRM3. Animal experiments confirmed that BZYQF significantly reduces fasting blood glucose, total cholesterol and triglyceride levels; enhances insulin level and HOMA-IS (p < 0.05); and increases salivary flow rate (Basal: increase from 21.04 ± 14.31 to 42.65 ± 8.84 μL/min, effect size of Cohen’s d = 6.80, p = 0.0078; Stimulated: increase from 36.88 ± 17.48 to 72.63 ± 17.67 μL/min, effect size of Cohen’s d = 7.61, p = 0.0025) and sAA activity (Basal: increase from 0.68 ± 0.32 to 2.17 ± 0.77 U/mL, effect size of Cohen’s d = 9.49, p = 0.0027; Stimulated: increase from 1.15 ± 0.77 to 4.80 ± 1.26 U/mL, effect size of Cohen’s d = 13.10, p = 0.0001) in basal and stimulated saliva in T2DM rats. Further mechanistic studies revealed that BZYQF reduces glucose and lipid accumulation, enhances acetylcholine content, improves pathological lesions and inflammation, and significantly increases the expression of salivary secretion pathway signaling molecules, including PKA, IP3R, β1-AR, AQP5, CHRM3, and AMY1 in the PG and SMG of T2DM rats (p < 0.05). Conclusions: The present study demonstrated that BZYQF is able to alleviate T2DM-caused hyposalivation by improving glucose metabolism and activating the salivary secretion pathway in the PG and SMG of T2DM rats. This study might provide a novel rationale and treatment strategy for BZYQF in diabetes-induced hyposalivation in a clinical setting. Full article

This study integrates morphometric analysis, remote sensing, and GIS with the analytical hierarchical process (AHP) to identify high potential groundwater recharge areas in Wadi Abadi, Egyptian Eastern Desert, supporting sustainable water resource management. Groundwater recharge primarily comes from rainfall and Nile River water, particularly for Quaternary aquifers. The analysis focused on the Quaternary and Nubian Sandstone aquifers, evaluating 16 influencing parameters, including elevation, slope, rainfall, lithology, soil type, and land use/land cover (LULC). The drainage network was derived from a 30 m-resolution Digital Elevation Model (DEM). ArcGIS 10.8 was used to classify the basin into 13 sub-basins, with layers reclassified and weighted using a raster calculator. The groundwater potential map revealed that 24.95% and 29.87% of the area fall into very low and moderate potential categories, respectively, while low, high, and very high potential zones account for 18.62%, 17.65%, and 8.91%. Data from 41 observation wells were used to verify the potential groundwater resources. In this study, the ROC curve was applied to assess the accuracy of the GWPZ models generated through different methods. The validation results indicated that approximately 87% of the wells corresponded accurately with the designated zones on the GWPZ map, confirming its reliability. Over-pumping in the southwest has significantly lowered water levels in the Quaternary aquifer. This study provides a systematic approach for identifying groundwater recharge zones, offering insights that can support resource allocation, well placement, and aquifer sustainability in arid regions. This study also underscores the importance of recharge assessment for shallow aquifers, even in hyper-arid environments. Full article

In the wave of digital transformation and Industry 4.0, accurate time series forecasting has become critical across industries such as manufacturing, energy, and finance. However, while deep learning models offer high predictive accuracy, their lack of interpretability often undermines decision-makers’ trust. This study proposes a linear time series model architecture based on seasonal decomposition. The model effectively captures trends and seasonality using an additive decomposition, chosen based on initial data visualization, indicating stable seasonal variations. An augmented feature generator is introduced to enhance predictive performance by generating features such as differences, rolling statistics, and moving averages. Furthermore, we propose a gradient-based feature importance method to improve interpretability and implement a gradient feature elimination algorithm to reduce noise and enhance model accuracy. The approach is validated on multiple datasets, including order demand, energy load, and solar radiation, demonstrating its applicability to diverse time series forecasting tasks. Full article

Dimensional quality inspection of prefabricated components is crucial for ensuring building quality and safety. Currently, manual measurement methods are predominantly used in dimensional quality inspection of prefabricated components, which are both time-consuming and labor-intensive, constraining production efficiency. This study thus developed a non-contact image measurement system using an innovative three-dimensional (3D) matrix camera, which automatically performed dimensional quality inspection, utilizing technologies such as a parallel optical axis four-camera matrix imaging and machine learning algorithms. Compared to traditional techniques, this system exhibited enhanced adaptability to the manufacturing process of prefabricated components, along with desirable accuracy and efficiency. Building upon a comprehensive literature review, the hardware constituents of the 3D matrix camera image measurement system were meticulously introduced, followed by the underlying principles and implementations of data acquisition, processing and comparison methods, including parallel optical axis four-camera matrix imaging, automatic stitching algorithms for 3D point clouds, feature recognition algorithms, and matching principles. The feasibility of the proposed system was validated through a case study analysis. The application results indicated that the system was capable of automatically performing non-contact measurements of dimensional deviations in prefabricated components with an accuracy of ±3 mm, thereby enhancing production quality. Full article