Search Results (7,529)

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking control strategy is developed to maximize kinetic energy recovery using an induction motor, efficiently distributing the recovered energy between the UC and battery. Additionally, a power flow management approach is introduced for both motoring (discharge) and braking (charge) operations via bidirectional buck–boost DC-DC converters. In discharge mode, an optimal distribution factor is dynamically adjusted to balance power delivery between the battery and UC, maximizing efficiency. During charging, a DC link voltage control mechanism prioritizes UC charging over the battery, reducing stress and enhancing energy recovery efficiency. The proposed EMS is validated through simulations and experiments, demonstrating significant improvements in vehicle acceleration, energy efficiency, and battery lifespan. Full article

The optimization of high-frequency circuits remains a computationally intensive task due to the need for repeated high-fidelity electromagnetic (EM) simulations. To address this challenge, we propose a novel integration of machine learning-generated inverse maps within the space mapping (SM) optimization framework to significantly accelerate circuit optimization while maintaining high accuracy. The proposed approach leverages Bayesian Neural Networks (BNNs) and surrogate modeling techniques to construct an inverse mapping function that directly predicts design parameters from target performance metrics, bypassing iterative forward simulations. The methodology was validated using a low-pass filter optimization scenario, where the inverse surrogate model was trained using electromagnetic simulations from COMSOL Multiphysics 2024 r6.3 and optimized using MATLAB R2024b r24.2 trust region algorithm. Experimental results demonstrate that our approach reduces the number of high-fidelity simulations by over 80% compared to conventional SM techniques while achieving high accuracy with a mean absolute error (MAE) of 0.0262 (0.47%). Additionally, convergence efficiency was significantly improved, with the inverse surrogate model requiring only 31 coarse model simulations, compared to 580 in traditional SM. These findings demonstrate that machine learning-driven inverse surrogate modeling significantly reduces computational overhead, accelerates optimization, and enhances the accuracy of high-frequency circuit design. This approach offers a promising alternative to traditional SM methods, paving the way for more efficient RF and microwave circuit design workflows. Full article

The low-cost Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS) is widely used in autonomous vehicles for positioning and navigation. Initial alignment is a critical stage for SINS operations, and the alignment time and accuracy directly affect the SINS navigation performance. To address the issue that low-cost SINS/GNSS cannot effectively achieve rapid and high-accuracy alignment in complex environments that contain noise and external interference, an adaptive multiple backtracking robust alignment method is proposed. The sliding window that constructs observation and reference vectors is established, which effectively avoids the accumulation of sensor errors during the full integration process. A new observation vector based on the magnitude matching is then constructed to effectively reduce the effect of outliers on the alignment process. An adaptive multiple backtracking method is designed in which the window size can be dynamically adjusted based on the innovation gradient; thus, the alignment time can be significantly shortened. Furthermore, the modified variational Bayesian Kalman filter (VBKF) that accurately adjusts the measurement noise covariance matrix is proposed, and the Expectation–Maximization (EM) algorithm is employed to refine the prior parameter of the predicted error covariance matrix. Simulation and experimental results demonstrate that the proposed method significantly reduces alignment time and improves alignment accuracy. Taking heading error as the critical evaluation indicator, the proposed method achieves rapid alignment within 120 s and maintains a stable error below 1.2° after 80 s, yielding an improvement of over 63% compared to the backtracking-based Kalman filter (BKF) method and over 57% compared to the fuzzy adaptive KF (FAKF) method. Full article

Mangoes are an important part of Hainan’s tropical characteristic agriculture. In response to the requirements of building an ecological civilization pilot demonstration zone in Hainan, China, green and sustainable development will be the future development trend of the mango planting system. However, the economic benefits and environmental impact during its planting and management process remain unclear. This paper combines emergy, life cycle assessment (LCA), and economic analysis to compare the system sustainability, environmental impact, and economic benefits of the traditional mango cultivation system (TM) in Dongfang City, Hainan Province, and the early-maturing mango cultivation system (EM) in Sanya City. The emergy evaluation results show that the total emergy input of EM (1.37 × 10¹⁶ sej ha⁻¹) was higher than that of TM (1.32 × 10¹⁶ sej ha⁻¹). From the perspective of the emergy index, compared with TM, EM exerted less pressure on the local environment and has better stability and sustainability. This was due to the higher input of renewable resources in EM. The LCA results showed that based on mass as the functional unit, the potential environmental impact of the EM is relatively high, and its total environmental impact index was 18.67–33.19% higher than that of the TM. Fertilizer input and On-Farm emissions were the main factors causing environmental consequences. Choosing alternative fertilizers that have a smaller impact on the environment may effectively reduce the environmental impact of the system. The economic analysis results showed that due to the higher selling price of early-maturing mango, the total profit and cost–benefit ratio of the EM have increased by 55.84% and 36.87%, respectively, compared with the TM. These results indicated that EM in Sanya City can enhance environmental sustainability and boost producers’ annual income, but attention should be paid to the negative environmental impact of excessive fertilizer input. These findings offer insights into optimizing agricultural inputs for Hainan mango production to mitigate multiple environmental impacts while enhancing economic benefits, aiming to provide theoretical support for promoting the sustainable development of the Hainan mango industry. Full article

Change point models are widely used in medical and epidemiological studies to capture the threshold effects of continuous covariates on health outcomes. These threshold effects represent critical points at which the relationship between biomarkers or risk factors and disease risk shifts, often reflecting underlying biological mechanisms or clinically relevant intervention points. While most existing methods focus on right-censored data, interval censoring is common in large-scale clinical trials and follow-up studies, where the exact event times are not observed but are known to fall within time intervals. In this paper, we propose a semiparametric transformation model with an unknown change point for interval-censored data. The model allows flexible transformation functions, including the proportional hazards and proportional odds models, and it accommodates both main effects and their interactions with the threshold variable. Model parameters are estimated via the EM algorithm, with the change point identified through a profile likelihood approach using grid search. We establish the asymptotic properties of the proposed estimators and evaluate their finite-sample performance through extensive simulations, showing good accuracy and coverage properties. The method is further illustrated through an application to the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial data. Full article

Railway systems occupy a predominant role in urban transport, providing efficient, high-capacity mobility. Progress in rail transport allows fast traveling, whilst environmental concerns and CO₂ emissions are on the rise. The integration of railway systems with renewable energy source (RES)-based stations presents a promising avenue to improve the sustainability, reliability, and efficiency of urban transport networks. A storage system is needed to both ensure a continuous power supply and meet train demand at the station. Batteries (BTs) offer high energy density, while supercapacitors (SCs) offer both a large number of charge and discharge cycles, and high-power density. This paper proposes a hybrid RES (photovoltaic and wind), combined with batteries and supercapacitors constituting the hybrid energy storage system (HESS). One major drawback of trains is the long charging time required in stations, so they have been fitted with SCs to allow them to charge up quickly. A new fuzzy energy management strategy (F-EMS) is proposed. This supervision strategy optimizes the power flow between renewable energy sources, HESS, and trains. DC bus voltage regulation is involved, maintaining BT and SC charging levels within acceptable ranges. The simulation results, carried out using MATLAB/Simulink, demonstrate the effectiveness of the suggested fuzzy energy management strategy for various production conditions and train demand. Full article

The rapid evolution of Industry 4.0 technologies has resulted in a scenario in which collaborative networks are essential to overcome the challenges related to their implementation. However, the frameworks to guide such collaborations remain underexplored. This study addresses this gap by proposing Business Rules and Goals Models to operationalize Industry 4.0 solutions through enterprise collaboration. Using the For Enterprise Modeling (4EM) method, the research integrates qualitative insights from expert opinions, including interviews with 12 professionals (academics, industry professionals, and consultants) from Brazilian manufacturing sectors. The Goals Model identifies five main objectives—competitiveness, efficiency, flexibility, interoperability, and real-time collaboration—while the Business Rules Model outlines 18 actionable recommendations, such as investing in digital infrastructure, upskilling employees, and standardizing information technology systems. The results reveal that cultural resistance, limited resources, and knowledge gaps are critical barriers, while interoperability and stakeholder integration emerge as enablers of digital transformation. The study concludes that successfully adopting Industry 4.0 requires technological investments, organizational alignment, structured governance, and collaborative ecosystems. These models provide a practical roadmap for companies navigating the complexities of Industry 4.0, emphasizing adaptability and cross-functional synergy. The research contributes to the literature on collaborative networks by connecting theoretical frameworks with actionable enterprise-level strategies. Full article

The Golgi of goblet cells represents a specialized machine for mucin glycosylation. This process occurs in a specialized form of the secretory pathway, which remains poorly examined. Here, using high-resolution three-dimensional electron microscopy (EM), EM tomography, serial block face scanning EM (SBF-SEM) and immune EM we analyzed the secretory pathway in goblet cells and revealed that COPII-coated buds on the endoplasmic reticulum (ER) are extremely rare. The ERES vesicles with dimensions typical for the COPII-dependent vesicles were not found. The Golgi is formed by a single cisterna organized in a spiral with characteristics of the cycloid surface. This ribbon has a shape of a cup with irregular perforations. The Golgi cup is filled with secretory granules (SGs) containing glycosylated mucins. Their diameter is close to 1 µm. The cup is connected with ER exit sites (ERESs) with temporal bead-like connections, which are observed mostly near the craters observed at the externally located cis surface of the cup. The craters represent conus-like cavities formed by aligned holes of gradually decreasing diameters through the first three Golgi cisternae. These craters are localized directly opposite the ERES. Clusters of the 52 nm vesicles are visible between Golgi cisternae and between SGs. The accumulation of mucin, started in the fourth cisternal layer, induces distensions of the cisternal lumen. The thickness of these distensions gradually increases in size through the next cisternal layers. The spherical distensions are observed at the edges of the Golgi cup, where they fuse with SGs and detach from the cisternae. After the fusion of SGs located just below the apical plasma membrane (APM) with APM, mucus is secreted. The content of this SG becomes less osmiophilic and the excessive surface area of the APM is formed. This membrane is eliminated through the detachment of bubbles filled with another SG and surrounded with a double membrane or by collapse of the empty SG and transformation of the double membrane lacking a visible lumen into multilayered organelles, which move to the cell basis and are secreted into the intercellular space where the processes of dendritic cells are localized. These data are evaluated from the point of view of existing models of intracellular transport. Full article

The use of low-resolution analog-to-digital converters (ADCs) in receivers has emerged as an effective solution for reducing power consumption in millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) systems. However, low-resolution ADCs also pose significant challenges for channel estimation. To address this issue, we propose a joint uplink/downlink (UL/DL) channel estimation algorithm that utilizes the spatial reciprocity of frequency division duplex (FDD) to improve the estimation of quantized UL channels. Quantified UL/DL channels are concentrated at the BS for joint estimation. This estimation problem is regarded as a compressed sensing problem with finite bits, which has led to the development of expectation-maximization-based quantitative generalized approximate messaging (EM-QGAMP) algorithms. In the expected step, QGAMP is used for posterior estimation of sparse channel coefficients, and the block maximization minimization (MM) algorithm is introduced in the maximization step to improve the estimation accuracy. Finally, simulation results verified the robustness of the proposed EM-QGAMP algorithm, and the proposed algorithm’s NMSE (normalized mean squared error) outperforms traditional methods by over 90% and recent state-of-the-art techniques by 30%. Full article

Extracellular vesicles (EVs), nanoscale membrane-enclosed particles, are natural carriers of proteins and nucleic acids. Microalgae are widely used as a source of bioactive substances in the food and cosmetic industries and definitely have a potential to be used as the producers of EVs for biomedical applications. In this study, the extracellular vesicles isolated from the culture medium of two unicellular microalgae, Chlamydomonas reinhardtii (Chlamy-EVs) and Parachlorella kessleri (Chlore-EVs), were characterized by atomic force microscopy (AFM), cryo-electronic microscopy (cryo-EM), and nanoparticle tracking analysis (NTA). The biocompatibility with human cells in vitro (HEK-293T, DF-2 and A172) and biodistribution in mouse organs and tissues in vivo were tested for both microalgal EVs. An exogenous therapeutic protein, human heat shock protein 70 (HSP70), was successfully loaded to Chlamy- and Chlore-EVs, and its efficient delivery to human glioma and colon carcinoma cell lines has been confirmed. Additionally, in order to search for potential therapeutic biomolecules within the EVs, their proteomes have been characterized. A total of 105 proteins were identified for Chlamy-EVs and 33 for Chlore-EVs. The presence of superoxide dismutase and catalase in the Chlamy-EV constituents allows for considering them as antioxidant agents. The effective delivery of exogenous cargo to human cells and the possibility of the particle yield optimization by varying the microalgae growth conditions make them favorable producers of EVs for biotechnology and biomedical application. Full article

Accurate delineation of within-field management zones (MZs) is essential for implementing precision agriculture, particularly in spatially heterogeneous environments. This study evaluates the spatiotemporal consistency and practical value of MZs derived from three complementary data sources: electromagnetic conductivity (EM38-MK2), basic soil chemical properties (pH, humus, P₂O₅, K₂O, nitrogen), and vegetation/surface indices (NDVI, SAVI, LCI, BSI) derived from Sentinel-2 imagery. Using kriging, fuzzy k-means clustering, percentile-based classification, and Weighted Overlay Analysis (WOA), MZs were generated for a five-year period (2018–2022), with 2–8 zone classes. Stability and agreement were assessed using the Cohen Kappa, Jaccard, and Dice coefficients on systematic grid samples. Results showed that EM38-MK2 and humus-weighted BSP data produced the most consistent zones (Kappa > 0.90). Sentinel-2 indices demonstrated strong alignment with subsurface data (r > 0.85), offering a low-cost alternative in data-scarce settings. Optimal zoning was achieved with 3–4 classes, balancing spatial coherence and interpretability. These findings underscore the importance of multi-source data integration for robust and scalable MZ delineation and offer actionable guidelines for both data-rich and resource-limited farming systems. This approach promotes sustainable agriculture by improving input efficiency and allowing for targeted, site-specific field management. Full article

Dietary tannin supplementation represents a potential strategy to modulate rumen fermentation and enhance lactation performance in dairy cows, though responses remain inconsistent. A 21-day feeding trial was conducted to evaluate the effect of chestnut tannin (CNT) extract on the enteric methane emissions (EME), blood metabolites, and milk production traits in mid-lactation dairy cows. Thirty-six Holstein cows were allocated to three homogeneous treatment groups: control (CNT₀, 0 g/d CNT), CNT₄₀ (40 g/d CNT), and CNT₈₀ (80 g/d CNT). Measurements of EME, dry matter intake (DMI), milk yield (MY), and blood and milk parameters were carried out pre- and post-21-day supplementation period. Compared with the no-additive group, the CNT extract reduced methane production, methane yield, and methane intensity in CNT₄₀ and CNT₈₀ (p < 0.001). CNT₄₀ and CNT₈₀ cows exhibited lower blood urea nitrogen (p = 0.019 and p = 0.002) and elevated serum insulin (p = 0.003 and p < 0.001) and growth hormone concentrations (p = 0.046 and p = 0.034), coinciding with reduced aspartate aminotransferase (p = 0.016 and p = 0.045), and lactate dehydrogenase (p = 0.011 and p = 0.008) activities compared to control. However, CNT₈₀ had higher circulating NEFA and BHBA than CNT₀ (p = 0.003 and p = 0.004) and CNT₄₀ (p = 0.035 and p = 0.019). The blood glucose, albumin, and total bilirubin concentrations were not affected. MY and fat- and protein-corrected milk (FPCM), MY/DMI, and FPCM/DMI were higher in both CNT₄₀ (p = 0.004, p = 0.003, p = 0.014, p = 0.010) and CNT₈₀ (p = 0.002, p = 0.003, p = 0.008, p = 0.013) cows compared with controls. Feeding CNT₈₀ resulted in higher protein content (p = 0.015) but lower fat percentage in milk (p = 0.004) compared to CNT₀. Milk urea nitrogen and somatic cell counts were significantly lower in both CNT₄₀ (p < 0.001, p = 0.009) and CNT₈₀ (p < 0.001 for both) compared to CNT₀, while milk lactose did not differ between treatments. These findings demonstrate that chestnut tannin extract effectively mitigates EME while enhancing lactation performance in mid-lactation dairy cows. Full article

Sorghum is an important cereal crop. The maintenance of leaf color significantly influences sorghum growth and development. Although the mechanisms of leaf color mutation have been well studied in many plants, those in sorghum remain largely unclear. Here, we identified a sorghum gradient-pale-green leaf mutant (sbgpgl1) from the ethyl methanesulfonate (EMS) mutagenesis mutant library. Phenotypic, photosynthesis-related parameter, ion content, transcriptome, and metabolome analyses were performed on wild-type BTx623 and the sbgpgl1 mutant at the heading stage, revealing changes in several agronomic traits and physiological indicators. Compared with BTx623, sbgpgl1 showed less height, with a smaller length and width of leaf and panicle. The overall Chl a and Chl b contents in sbgpgl1 were lower than those in BTx623. The net photosynthetic rate, stomatal conductance, and transpiration rate were significantly reduced in sbgpgl1 compared to BTx623. The content of copper (Cu), zinc (Zn), and manganese (Mn) was considerably lower in sbgpgl1 leaves than in BTx623. A total of 4469 differentially expressed genes (DEGs) and 775 differentially accumulated metabolites (DAMs) were identified by RNA-seq and UPLC-MS/MS. The results showed that sbgpgl1 primarily influenced sorghum metabolism by regulating metabolic pathways and the biosynthesis of secondary metabolites, especially flavonoids and phenolic acids, resulting in the gradient-pale-green leaf phenotype. These findings reveal key genes and metabolites involved on a molecular basis in physiological variations of the sorghum leaf color mutant. Full article

AAA+ ATPases are ring-shaped hexameric protein complexes that operate as elaborate macromolecular motors, driving a variety of ATP-dependent cellular processes. AAA+ ATPases undergo large-scale conformational changes that lead to the conversion of chemical energy from ATP into mechanical work to perform a wide range of functions, such as unfolding and translocation of the protein substrate inside a proteolysis chamber of an AAA+-associated protease. Despite extensive biochemical studies on these macromolecular assemblies, the mechanism of substrate unfolding and degradation has long remained elusive. Indeed, until recently, structural characterization of AAA+ protease complexes remained hampered by the size and complexity of the machinery, harboring multiple protein subunits acting together to process proteins to be degraded. Additionally, the major structural rearrangements involved in the mechanism of this complex represent a crucial challenge for structural biology. Here, we report the main advances in deciphering molecular details of the proteolytic reaction performed by AAA+ proteases, based on the remarkable progress in structural biology techniques. Particular emphasis is placed on the latest findings from high-resolution structural analysis of the ClpXP proteolytic complex, using crystallographic and cryo-EM investigations. In addition, this review presents some additional dynamic information obtained using solution-state NMR. This information provides molecular details that help to explain the protein degradation process by such molecular machines. Full article

Background/Objectives: mRNA vaccines introduced during the COVID-19 pandemic were a significant step forward in the rapid development and deployment of vaccines in a global pandemic context. These vaccines showed good protective efficacy, but—due to limited breadth of the immune response—they required frequent boosters with manufactured spike sequences that often lagged behind the circulating strains. In order to enhance the breadth, durability, and magnitude of immune responses, we studied the effect of combining priming with an RNA vaccine technology with boosting with protein/adjuvant using a TLR4-agonist based adjuvant. Methods: Specifically, four proprietary adjuvants (EmT4^TM, LiT4Q^TM, MiT4^TM, and AlT4^TM) were investigated in combination with multiple modes of SARS-CoV-2 vaccination (protein, peptide, RNA) for their effectiveness in boosting antibody responses to SARS-CoV-2 spike protein in murine models. Results: Results showed significant improvement in immune response strength and breadth—especially against more distant SARS-CoV-2 variants such as Omicron—when adjuvants were used in combination with boosters following an RNA vaccine prime. Conclusions: The use of novel TLR4 adjuvants in combination with protein or RNA vaccinations presents a promising strategy for improving the efficacy of vaccines in the event of future pandemics, by leveraging rapid response using an RNA vaccine prime and following up with protein/adjuvant-based vaccines to enhance the breadth of immunity. Full article