Search Results (570)

For the high-voltage AC port of power electronic transformer (HVAC-PET) with three-phase independent DC buses on the low-voltage side, a decoupling control strategy, concerning the influence of grid voltage imbalance, three-phase active-load imbalance, and high-order harmonic distortion, is proposed in this paper to simultaneously realize the functions of active power control, reactive power compensation, and active power filtering. In the outer power control loop, according to the distribution rule of decoupled average active power components in three phases, stability control for the sum of cluster average active power flows is realized by injecting positive-sequence active current, so as to control the average cluster voltage (i.e., the average of all the DC-link capacitor voltages), and by injecting negative-sequence current, the cluster average active power flows can be controlled individually to balance the three cluster voltages (i.e., the average of the DC-link capacitor voltages in each cluster). The negative-sequence reactive power component is considered to realize the reactive power compensation. In the inner current control loop, the fundamental and high-order harmonic components are uniformly controlled in the positive-sequence dq frame using the PI + VPIs (vector proportional integral) controller, and the harmonic filtering function is realized while the fundamental positive-sequence current is adjusted. Experiments performed on the 380 V/50 kVA laboratory HVAC-PET verify the effectiveness of the proposed control strategy. Full article

We evaluated the inclusion of hydrolyzed feather meal (HFM) as a partial replacement for fishmeal in diets for Macrobrachium rosenbergii post-larvae (PL) over a 32-day nursery feeding trial. Five experimental diets with increasing HFM levels (control, 1.5%, 3.0%, 4.5%, and 6.0%) were tested. Survival rates ranged from 73.3 ± 5.44% to 83.3 ± 3.84% without significant differences among groups. Dietary HFM inclusion levels above 3.0% significantly improved prawn performance, including final weight (up to 2.18-fold higher than control), length (1.13-fold), antenna length (1.18-fold), biomass gain (2.14-fold), and feed conversion ratio (1.59-fold lower). Prawn-fed diets at 6.0% HFM showed the highest performance among all experimental groups. No significant effects were observed on antioxidant biomarkers or digestive enzymes in prawns hepatopancreas, which suggests no imbalance in the antioxidant system or impairment of digestive function. Likewise, carcass proximate composition remained stable across experimental groups. These findings suggest that HFM at 3.0–6.0% dietary inclusion levels is a potential alternative to fishmeal in nursery-phase diets for M. rosernbergii PL, promoting prawn growth and welfare and maintaining health and carcass quality. Notably, to the best of our knowledge, this is the first study demonstrating the potential effective use of HFM in feeding the nursery phase of M. rosernbergii. Full article

CT scanners are essential tools in modern medical imaging. Sudden failures of their X-ray tubes can lead to equipment downtime, affecting healthcare services and patient diagnosis. However, existing prediction methods based on a single model struggle to adapt to the multi-stage variation characteristics of tube lifespan and have limited modeling capabilities for temporal features. To address these issues, this paper proposes an intelligent prediction architecture for CT tubes’ remaining useful life based on a dual-branch neural network. This architecture consists of two specialized branches: a residual self-attention BiLSTM (RSA-BiLSTM) and a multi-layer dilation temporal convolutional network (D-TCN). The RSA-BiLSTM branch extracts multi-scale features and also enhances the long-term dependency modeling capability for temporal data. The D-TCN branch captures multi-scale temporal features through multi-layer dilated convolutions, effectively handling non-linear changes in the degradation phase. Furthermore, a dynamic phase detector is applied to integrate the prediction results from both branches. In terms of optimization strategy, a dynamically weighted triplet mixed loss function is designed to adjust the weight ratios of different prediction tasks, effectively solving the problems of sample imbalance and uneven prediction accuracy. Experimental results using leave-one-out cross-validation (LOOCV) on six different CT tube datasets show that the proposed method achieved significant advantages over five comparison models, with an average MSE of 2.92, MAE of 0.46, and R² of 0.77. The LOOCV strategy ensures robust evaluation by testing each tube dataset independently while training on the remaining five, providing reliable generalization assessment across different CT equipment. Ablation experiments further confirmed that the collaborative design of multiple components is significant for improving the accuracy of X-ray tubes remaining life prediction. Full article

Understanding root decomposition dynamics is essential to address declining carbon sequestration and nutrient imbalances in monoculture plantations. This study elucidates how forest gaps regulate Pinus massoniana root decomposition through comparative methodological analysis, providing theoretical foundations for near-natural forest management and carbon–nitrogen cycle optimization in plantations. The results showed the following: (1) Root decomposition was significantly accelerated by the in situ soil litterbag method (ISLM) versus the traditional litterbag method (LM) (decomposition rate (k) = 0.459 vs. 0.188), reducing the 95% decomposition time (T_0.95) by nearly nine years (6.53 years vs. 15.95 years). ISLM concurrently elevated the root potassium concentration and reconfigured the relationships between root decomposition and soil nutrients. (2) Lower-order roots (orders 1–3) decomposed significantly faster than higher-order roots (orders 4–5) (k = 0.455 vs. 0.193). This disparity was amplified under ISLM (lower-/higher-order root k ratio = 4.1) but diminished or reversed under LM (lower-/higher-order root k ratio = 0.8). (3) Forest gaps regulated decomposition through temporal phase interactions, accelerating decomposition initially (0–360 days) while inhibiting it later (360–720 days), particularly for higher-order roots. Notably, forest gap effects fundamentally reversed between methodologies (slight promotion under LM vs. significant inhibition under ISLM). Our study reveals that conventional LM may obscure genuine ecological interactions during root decomposition, confirms lower-order roots as rapid nutrient-cycling pathways, provides crucial methodological corrections for plantation nutrient models, and advances theoretical foundations for precision management of P. massoniana plantations. Full article

One concern in the yearly re-formulation of influenza vaccines is the time-consuming manufacturing of vaccine potency reagents, particularly for emergency responses. The continuous evaluation of modern techniques such as reversed-phase (RP) chromatography is an asset for streamlining this process. One challenge with RP methods, however, is the need to re-optimize methods for antigens that show poor separation, which can be highly dependent on analyst experience and available data. In this study, we leveraged a large RP dataset of influenza antigens to explore machine learning (ML) approaches of classifying challenging separations for computer-assisted method re-optimization across years, products, and analysts. Methods: To address recurring chromatographic issues—such as poor resolution, strain co-elution, and signal absence—we applied data augmentation techniques to correct class imbalance and trained multiple supervised ML classifiers to distinguish between these peak profiles. Results: With data augmentation, several ML models demonstrated promising accuracy in classifying chromatographic profiles according to the provided labels. These models effectively distinguished patterns indicative of separation issues in real-world data. Conclusions Our findings highlight the potential of ML as a computer assisted tool in the evaluation of vaccine quality, offering a scalable and objective approach to chromatogram classification. By reducing reliance on manual interpretation, ML can expedite the optimization of analytical methods, which is particularly needed for rapid responses. Future research involving larger, inter-laboratory datasets will further elucidate the utility of ML in vaccine analysis. Full article

Background/Objectives: The novel compound 221s (2,9), derived from danshensu and ACEI-active proline, exhibits antihypertensive effects (50/35 mmHg SBP/DBP reduction in SHRs) with potential cough mitigation. However, its excretion kinetics remain unstudied. This study investigates 221s (2,9) elimination in rats to bridge this knowledge gap. Methods: Excretion of unchanged 221s (2,9) was quantified in urine, feces, and bile of Sprague-Dawley rats after oral administration (30 mg/kg). Concentrations of unchanged 221s (2,9) in all matrices were quantified using developed UPLC-MS/MS that underwent methodological validation. Excretion amount, excretion velocity, and accumulative excretion rate of 221s (2,9) were calculated. Results: Urinary excretion exhibited rapid elimination kinetics, reaching peak cumulative excretion rates (138.81 ± 15.56 ng/h) at 8 h post-dosing and plateauing by 48 h (cumulative excretion: 1479.81 ± 155.7 ng). Fecal excretion displayed an accelerated elimination phase between 4 and 8 h (excretion rate: 7994.29 ± 953.75 ng/h), followed by a sustained slow-release phase, culminating in a cumulative output of 36,726.31 ± 5507 ng at 48 h. Biliary excretion was minimal and ceased entirely by 24 h. Notably, total recovery of unchanged drug across all matrices remained below 1% (urine: 0.020 ± 0.021%; feces: 0.73 ± 0.069%; bile: 0.00044 ± 0.00002%) at 72 h. Conclusions: This study provides the first definitive excretion data for 221s (2,9). Quantitative analysis via a validated UPLC-MS/MS method revealed that fecal excretion is the principal elimination pathway for unchanged 221s (2,9) in rats, with direct excretion of the parent compound accounting for <1% of the administered dose over 72 h. Future studies will employ extended pharmacokinetic monitoring and concurrent UPLC-MS/MS analysis of the parent drug and phase II conjugates to resolve the observed mass imbalance and elucidate contributions to total elimination. Full article

Endometrial cancer (EC) is the most common gynecological malignancy in developed countries. Risk factors for EC include metabolic alterations (obesity, metabolic syndrome, insulin resistance), hormonal imbalance, age at menopause, reproductive factors, and inherited conditions, such as Lynch syndrome. For the inherited forms, several genes had been implicated in EC occurrence and development, such as POLE, MLH1, TP53, PTEN, PIK3CA, PIK3R1, CTNNB1, ARID1A, PPP2R1A, and FBXW7, all mutated at high frequency in EC patients. However, gene function impairment is not necessarily caused by mutations in the coding sequence of these and other genes. Gene function alteration may also occur through post-transcriptional control of messenger RNA translation, frequently caused by microRNA action, but transcriptional impairment also has a profound impact. Here, we review how chromatin modifications change the expression of genes whose impaired function is directly related to EC etiopathogenesis. Chromatin modification plays a central role in EC. The modification of chromatin structure alters the accessibility of genes to transcription factors and other regulatory proteins, thus altering the intracellular protein amount. Thus, DNA structural alterations may impair gene function as profoundly as mutations in the coding sequences. Hence, its central importance is in the diagnostic and prognostic evaluation of EC patients, with the caveat that chromatin alteration is often difficult to identify and needs investigations that are specific and not broadly used in common clinical practice. The different phases of the healthy endometrium menstrual cycle are characterized by differential gene expression, which, in turn, is also regulated through epigenetic mechanisms involving DNA methylation, histone post-translational modifications, and non-coding RNA action. From a medicolegal and policy-making perspective, the implications of using epigenetics in cancer care are briefly explored as well. Epigenetics in endometrial cancer is not only a topic of biomedical interest but also a crossroads between science, ethics, law, and public health, requiring integrated approaches and careful regulation. Full article

In the deep integration process between digital infrastructure and new economic forms, structural imbalance between the evolution rate of cloud storage technology and the growth rate of data-sharing demands has caused systemic security vulnerabilities such as blurred data sovereignty boundaries and nonlinear surges in privacy leakage risks. Existing academic research indicates current proxy re-encryption schemes remain insufficient for cloud access control scenarios characterized by diversified user requirements and personalized permission management, thus failing to fulfill the security needs of emerging computing paradigms. To resolve these issues, a revocable attribute-based proxy re-encryption scheme supporting policy-hiding is proposed. Data owners encrypt data and upload it to the blockchain while concealing attribute values within attribute-based encryption access policies, effectively preventing sensitive information leaks and achieving fine-grained secure data sharing. Simultaneously, proxy re-encryption technology enables verifiable outsourcing of complex computations. Furthermore, the SM3 (SM3 Cryptographic Hash Algorithm) hash function is embedded in user private key generation, and key updates are executed using fresh random factors to revoke malicious users. Ultimately, the scheme proves indistinguishability under chosen-plaintext attacks for specific access structures in the standard model. Experimental simulations confirm that compared with existing schemes, this solution delivers higher execution efficiency in both encryption/decryption and revocation phases. Full article

With the digital economy continuing to integrate deeply into the agricultural sector, agricultural digital transformation has emerged as a pivotal driver of rural revitalization and the development of a robust agricultural economy. Although existing studies have affirmed the positive role of agricultural digital transformation in promoting rural development and enhancing agricultural efficiency, its spatiotemporal evolution patterns, regional disparities, and underlying driving factors have not yet been systematically and thoroughly investigated. This study seeks to fill that gap. Based on provincial panel data from China spanning 2011 to 2023, this study employs the Theil index, kernel density estimation, Moran’s index, and quantile regression to systematically assess the spatiotemporal dynamics and driving factors of agricultural digital transformation at both national and regional levels. The results reveal a steady overall improvement in agricultural digital transformation, yet regional development imbalances remain prominent, with a shift from inter-regional disparities to intra-regional disparities over time. The four major regions exhibit a stratified evolutionary trajectory marked by internal differentiation: the eastern region retains its lead, while central and western regions show potential for catch-up, and the northeastern region faces a “balance trap.” Economic development foundation, human capital quality, and policy environment support are identified as the core driving forces of transformation, while other factors demonstrate pronounced regional and phase-specific variability. This study not only deepens theoretical understanding of the uneven development and driving logic of agricultural digital transformation but also provides empirical evidence to support policy optimization and promote more balanced and sustainable development in the agricultural sector. Full article

The non-uniform thermal distribution in the active neutral-point clamped (ANPC) topology causes significant thermal gradients during high-power operation, restricting its use in large-capacity power conversion systems like variable-speed pumped storage. This study introduces a novel hybrid fundamental frequency modulation strategy. Through a dynamic allocation mechanism based on a reference signal, this strategy alternates inner and outer power switches at the fundamental frequency, ensuring balanced switching frequency across devices. Consequently, it effectively mitigates the inherent loss imbalance in conventional ANPC topologies. Quantitative analysis using a power device loss model shows that, compared to conventional carrier phase-shift modulation, the proposed method reduces total system losses by 39.98% and improves the loss-balancing index by 18.27% over inner-switch fundamental frequency modulation. A multidimensional validation framework, including an MW-level hardware platform, numerical simulations, and test data, was established. The results confirm the proposed strategy’s effectiveness in improving power device thermal balance. Full article

Ergonomic risk classification during manual lifting tasks is crucial for the prevention of workplace injuries. This study addresses the challenge of classifying lifting task risk levels (low, medium, and high risk, labeled as 0, 1, and 2) using multi-modal time-series data comprising raw facial landmarks and bio-signals (electrocardiography [ECG] and electrodermal activity [EDA]). Classifying such data presents inherent challenges due to multi-source information, temporal dynamics, and class imbalance. To overcome these challenges, this paper proposes a Multi-Adaptive Functional Neural Network (Multi-AdaFNN), a novel method that integrates functional data analysis with deep learning techniques. The proposed model introduces a novel adaptive basis layer composed of micro-networks tailored to each individual time-series feature, enabling end-to-end learning of discriminative temporal patterns directly from raw data. The Multi-AdaFNN approach was evaluated across five distinct dataset configurations: (1) facial landmarks only, (2) bio-signals only, (3) full fusion of all available features, (4) a reduced-dimensionality set of 12 selected facial landmark trajectories, and (5) the same reduced set combined with bio-signals. Performance was rigorously assessed using 100 independent stratified splits (70% training and 30% testing) and optimized via a weighted cross-entropy loss function to manage class imbalance effectively. The results demonstrated that the integrated approach, fusing facial landmarks and bio-signals, achieved the highest classification accuracy and robustness. Furthermore, the adaptive basis functions revealed specific phases within lifting tasks critical for risk prediction. These findings underscore the efficacy and transparency of the Multi-AdaFNN framework for multi-modal ergonomic risk assessment, highlighting its potential for real-time monitoring and proactive injury prevention in industrial environments. Full article

During the long-term preservation of murals, the degradation of mural image information poses significant challenges to the restoration and conservation of world cultural heritage. Currently, mural conservation scholars focus on image segmentation techniques for mural restoration and protection. However, existing image segmentation methods suffer from suboptimal segmentation quality. To improve mural image segmentation, this study proposes an efficient mural image segmentation method termed Adaptive Nonlinear Bernstein-guided Parrot Optimizer (ANBPO) by integrating an adaptive learning strategy, a nonlinear factor, and a third-order Bernstein-guided strategy into the Parrot Optimizer (PO). In ANBPO, First, to address PO’s limited global exploration capability, the adaptive learning strategy is introduced. By considering individual information disparities and learning behaviors, this strategy effectively enhances the algorithm’s global exploration, enabling a thorough search of the solution space. Second, to mitigate the imbalance between PO’s global exploration and local exploitation phases, the nonlinear factor is proposed. Leveraging its adaptability and nonlinear curve characteristics, this factor improves the algorithm’s ability to escape local optimal segmentation thresholds. Finally, to overcome PO’s inadequate local exploitation capability, the third-order Bernstein-guided strategy is introduced. By incorporating the weighted properties of third-order Bernstein polynomials, this strategy comprehensively evaluates individuals with diverse characteristics, thereby enhancing the precision of mural image segmentation. ANBPO was applied to segment twelve mural images. The results demonstrate that, compared to competing algorithms, ANBPO achieves a 91.6% win rate in fitness function values while outperforming them by 67.6%, 69.4%, and 69.7% in PSNR, SSIM, and FSIM metrics, respectively. These results confirm that the ANBPO algorithm can effectively segment mural images while preserving the original feature information. Thus, it can be regarded as an efficient mural image segmentation algorithm. Full article

We investigate how collisional interactions between the condensate and the thermal cloud influence the distinct dynamical regimes (Josephson plasma, phase-slip-induced dissipative regime, and macroscopic quantum self-trapping) emerging in ultracold atomic Josephson junctions at non-zero subcritical temperatures. Specifically, we discuss how the self-consistent dynamical inclusion of collisional processes facilitating the exchange of particles between the condensate and the thermal cloud impacts both the condensate and the thermal currents, demonstrating that their relative importance depends on the system’s dynamical regime. Our study is performed within the full context of the Zaremba–Nikuni–Griffin (ZNG) formalism, which couples a dissipative Gross–Pitaevskii equation for the condensate dynamics to a quantum Boltzmann equation with collisional terms for the thermal cloud. In the Josephson plasma oscillation and vortex-induced dissipative regimes, collisions markedly alter dynamics at intermediate-to-high temperatures, amplifying damping in the condensate imbalance mode and inducing measurable frequency shifts. In the self-trapping regime, collisions destabilize the system even at low temperatures, prompting a transition to Josephson-like dynamics on a temperature-dependent timescale. Our results show the interplay between coherence, dissipation, and thermal effects in a Bose–Einstein condensate at a finite temperature, providing a framework for tailoring Josephson junction dynamics in experimentally accessible regimes. Full article

Considering the lightweight requirement of modular multilevel converter (MMC), the implementation of arm multiplexing significantly improves submodule utilization and achieves remarkable lightweight performance. However, the challenges of overvoltage and energy imbalance during pole-to-ground fault still exist. To address these issues, this paper proposes a hybrid half-wave alternating MMC (HHA-MMC) and presents its fault ride-through strategy. First, a transient equivalent model based on topology and operation principles is established to analyze fault characteristics. Depending on the arm’s alternative multiplexing feature, the half-wave shift non-blocking fault ride-through strategy is proposed to eliminate system overvoltage and fault current. Furthermore, to eliminate energy imbalance caused by asymmetric operation during non-blocking transients, dual-modulation energy balancing control based on the third-harmonic current and the phase-shifted angle is introduced. This strategy ensures capacitor voltage balance while maintaining 50% rated power transmission during the fault period. Finally, simulations and experiments demonstrate that the lightweight HHA-MMC successfully accomplishes non-blocking pole-to-ground fault ride-through with balanced arm energy distribution, effectively enhancing power supply reliability. Full article

Food security constitutes a fundamental pillar of future sustainable development. A systematic evaluation of the impact of urban expansion on food security is critical to advancing the United Nations Sustainable Development Goals (SDGs), particularly “Zero Hunger” (SDG 2). Drawing on bibliographic data from the Web of Science Core Collection, this study employs the bibliometrix package in R to conduct a comprehensive bibliometric analysis of the literature on the “urban expansion–food security” nexus spanning from 1982 to 2024. The analysis focuses on knowledge production, collaborative structures, and thematic research trends. The results indicate the following: (1) The publication trajectory in this field exhibits a generally increasing trend with three distinct phases: an incubation period (1982–2000), a development phase (2001–2014), and a phase of rapid growth (2015–2024). Land Use Policy stands out as the most influential journal in the domain, with an average citation rate of 43.5 per article. (2) China and the United States are the leading contributors in terms of publication output, with 3491 and 1359 articles, respectively. However, their international collaboration rates remain relatively modest (0.19 and 0.35) and considerably lower than those observed for the United Kingdom (0.84) and Germany (0.76), suggesting significant potential for enhanced global research cooperation. (3) The major research hotspots cluster around four core areas: urban expansion and land use dynamics, agricultural systems and food security, environmental and climate change, and socio-economic and policy drivers. These focal areas reflect a high degree of interdisciplinary integration, particularly involving land system science, agroecology, and socio-economic studies. Collectively, the field has established a relatively robust academic network and coherent knowledge framework. Nonetheless, it still confronts several limitations, including geographical imbalances, fragmented research scales, and methodological heterogeneity. Future efforts should emphasize cross-regional, interdisciplinary, and multi-scalar integration to strengthen the systematic understanding of urban expansion–food security interactions, thereby informing global strategies for sustainable development. Full article