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Neural radiance fields (NeRFs) leverage a neural representation to encode scenes, obtaining photorealistic rendering of novel views. However, NeRF has notable limitations. A significant drawback is that it does not capture surface geometry and only renders the object surface colors. Furthermore, the training of NeRF is exceedingly time-consuming. We propose Depth-NeRF as a solution to these issues. Specifically, our approach employs a fast depth completion algorithm to denoise and complete the depth maps generated by RGB-D cameras. These improved depth maps guide the sampling points of NeRF to be distributed closer to the scene’s surface, benefiting from dense depth information. Furthermore, we have optimized the network structure of NeRF and integrated depth information to constrain the optimization process, ensuring that the termination distribution of the ray is consistent with the scene’s geometry. Compared to NeRF, our method accelerates the training speed by 18%, and the rendered images achieve a higher PSNR than those obtained by mainstream methods. Additionally, there is a significant reduction in RMSE between the rendered scene depth and the ground truth depth, which indicates that our method can better capture the geometric information of the scene. With these improvements, we can train the NeRF model more efficiently and achieve more accurate rendering results.

The increasing global demand for renewable energy poses significant challenges to grid stability due to the fluctuation and unpredictability of photovoltaic (PV) power generation. To enhance the accuracy of short-term PV power prediction, this study proposes an innovative integrated model that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), optimized using the Starfish Optimization Algorithm (SFOA) and integrated with a multi-method data processing framework. To reduce input feature redundancy and improve prediction accuracy under different conditions, the K-means clustering algorithm is employed to classify past data into three typical weather scenarios. Empirical Mode Decomposition is utilized for multi-scale feature extraction, while Kernel Principal Component Analysis is applied to reduce data redundancy by extracting nonlinear principal components. A hybrid CNN-BiLSTM neural network is then constructed, with its hyperparameters optimized using SFOA to enhance feature extraction and sequence modeling capabilities. The experiments were carried out with historical data from a Chinese PV power station, and the results were compared with other existing prediction models. The results demonstrate that the Root Mean Square Error of PV power generation prediction for three scenarios are 9.8212, 12.4448, and 6.2017, respectively, outperforming all other comparative models.

The randomness and volatility of wind power pose significant challenges for short-term forecasting, requiring the model to capture both temporal dynamics and the spatial correlations among turbines. To address this issue, this paper proposes a Spatio-Temporal Adaptive Graph Convolutional Recurrent Network (STAGCRN). The proposed method dynamically constructs and updates the spatial relationship graph through node adaptive parameter learning (NAPL) and a data adaptive graph generation (DAGG) module, enabling more accurate modeling of spatio-temporal dependencies in wind power data. In addition, a spatio-temporal self-attention mechanism is introduced to enhance the model’s ability to capture both short-term fluctuations and long-term temporal patterns. By stacking multiple spatio-temporal adaptive graph convolutional recurrent layers, the model is capable of extracting complex nonlinear characteristics in wind power sequences. Experimental results based on real wind farm data demonstrate that the proposed method achieves significantly improved prediction accuracy and robustness compared with existing approaches in short-term wind power forecasting tasks.

Introduction: Sepsis is a clinical syndrome characterized by dysregulation of the host immune response due to infection, resulting in life-threatening organ damage. Despite active promotion and implementation of early preventative measures and bundle treatments, sepsis continues to exhibit high morbidity and mortality rates with no optimal pharmacological intervention available. Lobetyolin (LBT), the crucial component of polyacetylenes found in Codonopsis pilosula , has been scientifically proven to possess potent antioxidant and antitumor properties. However, its therapeutic potential for sepsis remains unknown. Methods: The mice received pretreatment with intraperitoneal injections of LBT, followed by injection with lipopolysaccharide (LPS) to induce sepsis. Peripheral blood samples were collected to detect TNF-α, IL-1β, and IL-6 levels. The survival status of different groups was recorded at various time intervals. RNA-Seq was utilized for the analysis of gene expression in peritoneal macrophages treated with LBT or LPS. Results: In this study, we observed a significant increase in the survival rate of mice pretreated with LBT in LPS induced sepsis mouse model. LBT demonstrated a remarkable reduction in the production of IL-6, TNF-α, and IL-1β in the serum, along with mitigated lung and liver tissue damage characterized by reduced inflammatory cell infiltration. Additionally, through RNA-seq analysis coupled with GO and KEGG analysis, it was revealed that LBT effectively suppressed genes associated with bacterium presence, cellular response to lipopolysaccharide stimulation, as well as cytokine-cytokine receptor interaction involving Cxcl10, Tgtp1, Gbp5, Tnf, Il1b and IRF7 specifically within macrophages. We also confirmed that LBT significantly downregulates the expression of IL-6, TNF-α, and IL-1β in macrophage activation induced by LPS. Discussion: Therefore, our findings demonstrated that LBT effectively inhibits the production of inflammatory cytokines (IL-6, TNF-α, and IL-1β) and mitigates sepsis induced by LPS through modulating macrophages' ability to generate these cytokines. These results suggest that LBT holds promise as a potential therapeutic agent for sepsis treatment.

The deployment of heterogeneous Automated Guided Vehicles (AGVs) in smart manufacturing requires control strategies that can accommodate distinct actuation characteristics and constraints. This paper proposes a Multi-Factor Coupled Parameter-Adaptive Model Predictive Control (MFCP-AMPC) framework. Unlike conventional approaches requiring vehicle-specific tuning, this framework unifies differential-drive, dual-steer, and mecanum-wheel platforms under a single parameter-varying state-space model that respects the specific actuation limits of each topology. A key contribution is the multi-factor coupling mechanism that dynamically adjusts the prediction horizon and weighting matrices based on path curvature, vehicle speed, and tracking error. Experiments on industrial AGV prototypes demonstrate that the framework achieves robust tracking precision under varying payloads. Crucially, by acknowledging physical limits, the framework achieves strict millimeter-level accuracy (RMSE < 7 mm) in quasi-static low-speed complex maneuvers (v≤0.3 m/s), and maintains highly competitive industrial precision (RMSE ≈ 15∼25 mm) under aggressive high-speed tracking (v≥1.0 m/s). Crucially, the proposed method significantly improves the control input smoothness (Smoothness Index > 0.75), thereby reducing mechanical wear and preventing actuator saturation. Real-time validation (12 ms average solve time on an Intel i7 IPC) confirms its suitability for resource-constrained industrial controllers.

The mechanism of Yiqi-Wenyang-Tiaoshen decoction (YWT) in treating cisplatin-induced acute kidney injury (AKI) remains unknown. This study identifies the key components of YWT and explores its therapeutic potential and mechanisms in a cisplatin-induced AKI rat model. UPLC-ESI-MS/MS was utilized for the identification of compounds present in both the aqueous extract of YWT and serum samples. The overlapping components were recognized as active constituents, followed by a network pharmacological analysis. A rat model of cisplatin-induced AKI was established, and comprehensive pathological analyses including HE, PAS, and electron microscopy, as well as biochemical assessments of serum Cre, BUN, IL-6, and TNF-α levels, were conducted. Western blotting was utilized to evaluate the expression levels of Caspase-3, Caspase-9, BAX, Bcl-2, and LC3 Ⅱ/Ⅰ. Using UPLC-ESI-MS/MS, we identified 182 compounds in the aqueous extract of YWT, 34 of which are confirmed to be absorbable into the bloodstream. Network pharmacological analysis suggests that YWT primarily acts by inhibiting apoptosis and activating autophagy. In the rat model, YWT significantly ameliorated renal pathology and electron microscopic features. Additionally, YWT mitigated body weight loss and renal hypertrophy while lowering serum creatinine and blood urea nitrogen levels. YWT alleviates AKI by suppressing apoptosis-related proteins such as Caspase-3, Caspase-9, and BAX, enhancing Bcl-2 expression, increasing the LC3 Ⅱ/Ⅰ ratio, and reducing p62, a marker of autophagy. This study confirms the therapeutic efficacy of YWT in cisplatin-induced AKI, potentially linked to its ability to inhibit apoptosis, activate autophagy, and mitigate mitochondrial damage.

The formation transformation in intelligent connected autonomous vehicles (CAVs) enhances platoon versatility and significantly improves traffic efficiency. Current formation control strategies for CAV platoons often focus on fixed formation scenarios. This paper proposes a three-layer architecture for platoon reconfiguration, encompassing discrete, offline, and online layers. CAV platoons utilize this architecture to transform their existing formation into a specified target formation from the Intelligent Transportation System (ITS). In the discrete layer, we propose a formation representation scheme and design A* and cooperative sorting algorithms to achieve the optimal intermediate formation sequence. Moving to the offline layer, we design a Signal Temporal Logic-based model predictive control algorithm (MPC). This algorithm plans continuous, dynamically feasible, and collision-free safe trajectories, which are stored in an offline trajectory database. In the online layer, we design a successive linearization-based MPC to track the offline trajectories in real-time traffic environments and accomplish the platoon reconfiguration task. We implement single-lane and multi-lane platoon reconfiguration tasks in the MATLAB platform, comparing them with two advanced platoon reconfiguration algorithms. The experimental results, demonstrating the effectiveness of the proposed approach, are presented and discussed.

Obesity is associated with adverse changes in brain structure and function, in part, through crosstalk between adipose tissue (AT) and the brain. AT releases small extracellular vesicles (sEV) that can cross the blood–brain barrier (BBB) and modulate multiple pathophysiological pathways, including BBB function; however, this has never been investigated. We characterized circulating adipose tissue‐derived sEV (sEVAT) in adults with overweight and obesity and examined their effects on the BBB. The impact of adiposity and weight loss on these outcomes was also examined. sEVAT were isolated from the plasma of 29 adults (79% male; 93% White; mean age 66.2 ± 7.0 years; mean body mass index 36.0 ± 6.8 kg/m2) randomized to cardiac rehabilitation (CR) alone or CR plus a behavioural weight loss intervention (CR+WL). Following characterization of sEVAT size, concentration and total protein content, we assessed their effect on BBB permeability using an in vitro model. hCMEC/D3 cells were treated with sEVAT, and transendothelial electrical resistance (TEER) was measured at 0, 24, 48 and 72 h. Our findings show that sEVAT treatment decreased TEER by 40%, with a significantly lower TEER at 72 h compared with controls (23.138 ± 1.209 vs. 28.724 ± 1.613 Ω cm2, p = 0.012). TEER was also lower in participants with higher body mass index and body fat. However, we found no difference in TEER between the CR and CR+WL groups and no significant intervention effects on sEVAT characteristics or TEER. In conclusion, higher plasma sEVAT concentrations in adults with overweight and obesity are associated with greater adiposity, which might contribute to reductions in BBB function. What is the central question of this study? Small extracellular vesicles secreted from adipose tissue (sEVAT) might impair blood–brain barrier function. We developed an in vitro model to assess the effect of sEVAT on the blood–brain barrier in adults with overweight and obesity randomized to one of two lifestyle interventions. What is the main finding and its importance? Treatment of hCMEC/D3 cells with sEVAT decreased transendothelial resistance in both groups, with no significant intervention‐related effects. Higher plasma sEVAT concentrations were associated with greater adiposity, suggesting that sEVAT might contribute to the adverse effects of obesity on brain health.

Acute kidney injury (AKI) is a serious disease with a high incidence and easy induction. The search for innovative biomarkers and treatment methods is of great significance for improving the prognosis of patients. Autophagy is closely related to the occurrence and development of AKI. This study aims to explore the role of autophagy-related genes (ARGs) as potential biomarkers and therapeutic targets in AKI. In this study, the gene microarray data of the GEO dataset were used to explore the molecular profile of AKI, and three machine learning algorithms were used to screen autophagy-related feature genes. To further validate the reliability of the screening results, we constructed a cisplatin-induced AKI rat model to validate potential biomarkers of machine learning screening. Machine learning analysis identified 17 differentially expressed ARGs and selected the core genes FIZ1 and FBXO21, with area under curve (AUC) values both exceeding 0.7 (95% CI [0.706-0.899]). Immune analysis revealed that the number of Mast cells resting significantly decreased in AKI samples compared to normal samples (  < 0.05). Electron microscopy observations of the cisplatin-induced AKI rat model indicated thickening of the basement membrane, fusion of foot processes, and swelling and rupture of mitochondria in the model group, suggesting a correlation between AKI and mitochondrial autophagy; Western blot results indicated a significant increase in the expression of FIZ1 and a significant decrease in FBXO21 in the AKI group (  < 0.01). The results of IHC staining were also consistent with those of Western blot results. This study highlights the significant role of ARGs in AKI and identifies FIZ1 and FBXO21 as promising biomarkers with high diagnostic potential, offering new insights into the molecular mechanisms underlying AKI.

The building-integrated photovoltaic (BIPV) system has advantages in construction and energy, but due to the use of flammable polymer packaging materials, it introduces complex fire safety-related challenges. Although polymer backboards are traditionally considered to be the main combustible components in photovoltaic modules, recent studies have shown that ethylene–vinyl acetate (EVA) packaging materials play a key role in the development of fires. This study investigated the fire behavior, optical properties and system-level fire effects of montmorillonite (MMT) nano-clay-modified EVA packaging materials. Through the 50 kW/m2 conical calorimeter test, optical transmittance measurement and the accelerated aging test, pure EVA and EVA containing 3% MMT were evaluated, and the measured fire parameters were further incorporated into the simplified BIPV cavity fire model. The results show that MMT modification reduces the peak heat release rate of EVA by about 30%, delays the ignition time, and increases the formation of carbides, while maintaining the optical transmittance of more than 88%. At the system level, the reduction in heat release leads to a decrease in the cavity temperature and delays the ignition of adjacent insulation materials. These findings establish a direct link between material-level fire behavior and the fire performance of BIPV systems, indicating that nano-clay-modified EVA is a feasible strategy that can improve the fire safety of BIPV systems integrated into the facade without compromising optical or durability requirements.