Search Results (15,517)

(1) Background: Ultra-short-term photovoltaic (PV) power prediction is crucial for optimizing grid scheduling and enhancing energy utilization efficiency. Existing prediction methods face challenges of missing data, noise interference, and insufficient accuracy. (2) Methods: This study proposes a single-step hybrid neural network model integrating Temporal Convolutional Network (TCN), Temporal Shift Transformer (TST), and Bidirectional Gated Recurrent Unit (BiGRU) to achieve high-precision 15-minute-ahead PV power prediction, with a design aligned with symmetry principles. Data preprocessing uses Variational Mode Decomposition (VMD) and random forest interpolation to suppress noise and repair missing values. A symmetric parallel dual-branch feature extraction module is built: TCN-TST extracts local dynamics and long-term dependencies, while BiGRU captures global features. This symmetric structure matches the intra-day periodic symmetry of PV power (e.g., symmetric irradiance patterns around noon) and avoids bias from single-branch models. Tensor concatenation and an adaptive attention mechanism realize feature fusion and dynamic weighted output. (3) Results: Experiments on real data from a Xinjiang PV power station, with hyperparameter optimization (BiGRU units, activation function, TCN kernels, TST parameters), show that the model outperforms comparative models in MAE and R²—e.g., the MAE is 26.53% and 18.41% lower than that of TCN and Transforme. (4) Conclusions: The proposed method achieves a balance between accuracy and computational efficiency. It provides references for PV station operation, system scheduling, and grid stability. Full article

This study investigates the immunomodulatory effects of a well-characterized cannabidiol (CBD)-rich cannabis extract, CAN296, on T lymphocytes (T cells), particularly Cluster of Differentiation 4 (CD4⁺) helper and Cluster of Differentiation 8 (CD8⁺) cytotoxic subsets, by examining T-cell activation, cytokine secretion, and cytotoxic molecule expression in comparison with the conventional treatments dexamethasone (DEX) and tacrolimus (TAC). It addresses key processes involved in the formation of premalignant immune-mediated lesions, such as those seen in oral lichen planus (OLP) and oral manifestations of graft-versus-host disease (oGVHD). CD4⁺ and CD8⁺ T cells were isolated from healthy donors and assessed in vitro for T cell activation via CD69 expression, secreted tumor necrosis factor alpha (TNF-α) and interferon gamma (IFN-γ) levels according to enzyme-linked immunosorbent assay (ELISA), and cytotoxic molecule expression Granzyme B, Perforin, Fas Ligand (Fas-L) quantified by flow cytometry. Cells were treated with different doses of CAN296 (2, 4, 8 µg/mL), DEX (0.4, 4, 40 µg/mL), or TAC (0.1, 1, 10 ng/mL), and all parameters were compared to untreated controls. CAN296 significantly inhibited T cell activation, reducing CD69 expression in CD4⁺ T cells to 2–11% and in CD8⁺ T cells to 5–17%. It also markedly suppressed TNF-α secretion in CD4⁺ T cells at all concentrations (p < 0.0001). In CD8⁺ T cells, CAN296 led to a near-complete reduction in TNF-α and IFN-γ, leaving both cytokines barely detectable at all tested doses (p < 0.0001). The effect of cell inhibition was significantly more pronounced than that observed with DEX or TAC, displaying dose-dependent reductions. TAC inconsistently lowered TNF-α while paradoxically increasing IFN-γ at lower concentrations. Additionally, CAN296 consistently suppressed cytotoxic molecule expression, reducing Granzyme B by 81–82%, Perforin by 40–53%, and Fas-L by 40–44%. DEX showed variable effects on cytotoxic molecule expression. At the same time, TAC demonstrated inconsistent modulation of Perforin and Granzyme B. Overall, CAN296 outperformed DEX and TAC, demonstrating more potent and consistent immunomodulatory effects. CBD-rich cannabis extract, CAN296, exhibits potent immunomodulatory properties by effectively inhibiting T cell activation, lowering pro-inflammatory cytokines, and suppressing cytotoxic molecule expression. Its efficacy surpasses conventional therapies like DEX and TAC, offering a promising novel treatment modality for T cell-mediated disorders, including OLP and oGVHD. These findings support further development of CAN296 formulations to optimize dosing and delivery, followed by clinical trials to validate its therapeutic potential. Full article

The pollination of kiwiberry flowers is closely related to fruit growth, development, and yield. Rapid and precise identification of flowers under natural field conditions plays a key role in enhancing pollination efficiency and improving overall fruit quality. Flowers and buds are densely distributed, varying in size, and exhibiting similar colors. Complex backgrounds, lighting variations, and occlusion further challenge detection. To address these issues, the YOLO11s-RFBS model was proposed. The P5 detection head was replaced with P2 to improve the detection of densely distributed small flowers and buds. RFAConv was incorporated into the backbone to strengthen feature discrimination across multiple receptive field scales and to mitigate issues caused by parameter sharing. The C3k2-Faster module was designed to reduce redundant computation and improve feature extraction efficiency. A weighted bidirectional feature pyramid slim neck network was constructed with a compact architecture to achieve superior multi-scale feature fusion with minimal parameter usage. Experimental evaluations indicated that YOLO11s-RFBS reached a mAP@0.5 of 91.7%, outperforming YOLO11s by 2.7%, while simultaneously reducing the parameter count and model footprint by 33.3% and 31.8%, respectively. Compared with other mainstream models, it demonstrated superior comprehensive performance. Its detection speed exceeded 21 FPS in deployment, satisfying real-time requirements. In conclusion, YOLO11s-RFBS enables accurate and efficient detection of kiwiberry flowers and buds, supporting intelligent pollination robots. Full article

Ion-adsorption type rare earth element (IREE) deposits are critical strategic resources formed under strong lithological, geomorphological, and weathering controls. In Houaphanh Province, Laos, widespread granitic intrusions and tropical monsoon weathering provide favorable conditions for IREE mineralization; however, exploration is limited by rugged terrain, dense vegetation cover, and sparse geological data. This study integrates Landsat 9, ASTER multispectral, and digital elevation data to enhance IREE exploration. Band ratio and principal component analysis (PCA) were applied to extract lithological and alteration features, while six topographic parameters describing elevation, slope, relief amplitude, incision depth, surface roughness, and elevation variability were derived from ASTER GDEM data. These datasets were combined using a weighted overlay to delineate favorable geomorphic zones. Six prospectives zones were identified, and field verification at Nongkhang confirmed 19 IREE ore bodies. The results demonstrate that integrating spectral and topographic indicators significantly improves the accuracy for IREE prediction in tropical, densely vegetated regions, offering a transferable framework for similar geological settings worldwide. Full article

Background: Fibromyalgia (FM) is a chronic pain syndrome characterized by widespread musculoskeletal pain, fatigue, unrefreshed sleep, and cognitive disturbances. Despite extensive research, its pathophysiology remains incompletely understood, and there are no validated biomarkers for diagnosis. Videocapillaroscopy is a non-invasive imaging technique that enables detailed visualization of microvascular structures and may provide insights into microcirculatory alterations associated with FM. Methods: Thirty patients with FM and 30 healthy controls underwent oral videocapillaroscopy at four sites: right and left buccal mucosa and upper and lower labial mucosa. Quantitative parameters, including capillary caliber, density, and length, were extracted using a validated neural-network-based software, while qualitative parameters, including visibility, orientation, and the presence of microhemorrhages, were assessed by the operator. Results: Capillary length was significantly reduced in fibromyalgia patients (297.49 ± 26.82 µm) compared to healthy controls (324.43 ± 37.59 µm; p = 0.002), and capillary orientation differed significantly between groups (p < 0.05). Capillary caliber, density, and visibility did not show statistically significant differences. Conclusions: These findings indicate subtle microvascular alterations in the oral mucosa of patients with fibromyalgia. Although the observed changes are not sufficient for diagnostic purposes or early detection, they provide preliminary evidence that videocapillaroscopy can detect microvascular features associated with FM in the oral mucosa. Further studies with larger cohorts and longitudinal designs are warranted to clarify the clinical relevance of these observations and to explore their potential association with symptom severity or disease progression. Full article

Background/Objectives: The global increase in the consumption of ultra-processed foods (UPFs) parallels the rise in obesity and non-communicable chronic diseases. Although several large-scale studies associate UPF intake with adverse health outcomes, the biological mechanisms remain unclear. Epigenetic alterations, such as changes in DNA methylation, may represent a potential pathway by which diet influences metabolic health. The aim of this study was to investigate whether higher UPF consumption is associated with genome-wide DNA methylation patterns in women. Methods: This was a cross-sectional observational study with exploratory epigenetic analysis. We selected 30 women, who were divided into tertiles based on their UPF consumption (expressed as a percentage of total energy intake) according to the NOVA food classification system. Dietary intake was assessed using a three-day food record. Anthropometric data, body composition and laboratory parameters were evaluated. The analysis of DNA methylation was performed utilizing DNA extracted from peripheral blood leukocytes of participants in the first and third tertiles of UPF consumption. Genome-wide methylation patterns were performed using next-generation sequencing. Results: Participants had a median (IQR) age of 31 years (26.0–36.5) and a BMI of 24.7 (23.6–35.8) kg/m². For the epigenetic analyses, 15 women were included. Of the 30 women initially evaluated, 20 were included as they belonged to the first and third tertile of UPF consumption. Of these, five were excluded due to a low number of reads obtained by NGS. A total of 80 differentially methylated regions were identified between groups, most of which were hypomethylated in the high-UPF-intake group. Conclusions: High UPF consumption was associated with altered DNA methylation patterns, suggesting a potential epigenetic mechanism underlying the negative health effects of UPFs. This pilot study provides a model for future research with larger samples. Full article

Object detection technology plays a vital role in monitoring the growth status of aquaculture organisms and serves as a key enabler for the automated robotic capture of target species. Existing models for underwater biological detection often suffer from low accuracy and high model complexity. To address these limitations, we propose AOD-YOLO—an enhanced model based on YOLOv11s. The improvements are fourfold: First, the SPFE (Sobel and Pooling Feature Enhancement) module incorporates Sobel operators and pooling operations to effectively extract target edge information and global structural features, thereby strengthening feature representation. Second, the RGL (RepConv and Ghost Lightweight) module reduces redundancy in intermediate feature mappings of the convolutional neural network, decreasing parameter size and computational cost while further enhancing feature extraction capability through RepConv. Third, the MDCS (Multiple Dilated Convolution Sharing Module) module replaces the SPPF structure by integrating parameter-shared dilated convolutions, improving multi-scale target recognition. Finally, we upgrade the C2PSA module to C2PSA-M (Cascade Pyramid Spatial Attention—Mona) by integrating the Mona mechanism. This upgraded module introduces multi-cognitive filters to enhance visual signal processing and employs a distribution adaptation layer to optimize input information distribution. Experiments conducted on the URPC2020 and RUOD datasets demonstrate that AOD-YOLO achieves an accuracy of 86.6% on URPC2020, representing a 2.6% improvement over YOLOv11s, and 88.1% on RUOD, a 2.4% increase. Moreover, the model maintains relatively low complexity with only 8.73 M parameters and 21.4 GFLOPs computational cost. Experimental results show that our model achieves high accuracy for aquaculture targets while maintaining low complexity. This demonstrates its strong potential for reliable use in intelligent aquaculture monitoring systems. Full article

Dietary supplements provide essential nutrients and bioactive compounds that enhance health and traditional therapies. However, the quality and composition of these supplements can vary significantly, potentially containing inconsistent levels of active ingredients or undisclosed risk substances. Due to the current extensive industrial applications, bisphenols (BPs) and alkylphenols (APs) have become environmentally ubiquitous. Substantial evidence indicates that these compounds exhibit endocrine-disrupting properties, posing potential health risks to humans. The detection of trace-level BPs and APs in dietary supplements is critical. This study developed a supramolecular solvent (SUPRAS) from a water/THF/1-hexanol system under mild conditions for analyzing 19 BPs and APs in commercial botanical dietary supplements. After optimizing SUPRAS preparation and extraction parameters, we established a SUPRAS–HPLC–MS/MS method enabling one-step extraction/cleanup within 10 min for tablets, capsules, and oral liquids, with high sensitivity and simplicity. The method scored 0.71 (out of 1) on the AGREE metric, confirming its green profile. Detectable levels of bisphenol A (178.7–452.6 μg/kg) and 4-pentylphenol (145.3 μg/kg) in marketed products highlight potential health risks from botanical dietary supplement-derived exposure. Full article

Metal components such as rolling bearings are prone to wear, cracks, and defects in harsh environments and long-term use, leading to performance degradation and potential equipment failures. Therefore, detecting surface cracks and other defects in rolling bearings is of great significance for ensuring equipment reliability and safety. However, traditional signal decomposition methods like EEMD and FEEMD suffer from residual noise and mode mixing issues, while deconvolution algorithms such as CYCBD are sensitive to parameter settings and struggle in high-noise environments. To mitigate the susceptibility of fault signals to background noise interference, this paper proposes a fault feature extraction method based on fast complementary ensemble empirical mode decomposition (FCEEMD) and adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD). Firstly, we propose FCEEMD, which effectively eliminates the residual noise of ensemble empirical mode decomposition (EEMD) and fast ensemble empirical mode decomposition (FEEMD) by introducing paired white noise with opposite signs, solving the problems of traditional decomposition methods that are greatly affected by noise, having large reconstruction errors, and being high time-consuming. Subsequently, a new intrinsic mode function (IMF) screening index based on correlation coefficients and energy kurtosis is developed to effectively mitigate noise influence and enhance the quality of signal reconstruction. Secondly, the ACYCBD model is constructed, and the hidden periodic frequency is detected by the enhanced Hilbert phase synchronization (EHPS) estimator, which significantly enhances the extraction effect of the real periodic fault features in the noise. Finally, instantaneous energy tracking of bearing fault characteristic frequency is achieved through Teager energy operator demodulation, thereby accurately extracting fault state features. The experiment shows that the proposed method accurately extracts the fault characteristic frequencies of 164.062 Hz for inner ring faults and 105.469 Hz for outer ring faults, confirming its superior accuracy and efficiency in rolling bearing fault diagnosis. Full article

This research investigates the biosynthesis optimization of the red pigment prodigiosin produced by Serratia marcescens 11E through submerged fermentation utilizing an alternative cheese whey-based medium, focusing on process parameters and antimicrobial properties. Four types of whey sourced from a local dairy industry were characterized, and the fermentation conditions were optimized using Plackett–Burman and central composite design methodologies, yielding up to 1.43 g/L of prodigiosin under optimal conditions, 25 °C, 200 rpm, pH 7, and 48 h of dark incubation, with whey serving as the sole carbon source. Normalization to biomass yielded 110 mg of prodigiosin per gram of dried cell weight (post-optimization), enabling meaningful comparison with prior studies. Pigment extraction was performed with acidic methanol, and identity was confirmed by UV–Vis spectrophotometry and Fourier transform infrared spectroscopy (FTIR). The antimicrobial activity of the purified pigment was also evaluated. Although cheese whey has significant nutritional value, nearly half of the global production is discarded due to high treatment costs. This study demonstrates that whey can be repurposed as a sustainable and economical fermentation medium for pigment production, which is compatible with dairy plants. This makes it a promising solution to address the underutilization of whey by cheese local producers in Mexico. Prodigiosin has diverse industrial applications, including antimicrobial, insecticidal, and antioxidant properties. These findings highlight the potential for dairy waste valorization in a circular bioeconomy, reducing environmental impacts and promoting the creation of valuable bioproducts. Full article

Diabetes mellitus is largely driven by oxidative stress that disrupts insulin signaling, leading to failure in insulin-mediated glucose absorption. Exploration of natural bioactive compounds is fueled by their promising role in correcting redox imbalance. This study aims to investigate the antidiabetic effect of the methanolic extract of Praecitrullus fistulosus, potentially by transcriptional modulation in streptozotocin–nicotinamide-induced diabetic rats. Male Wistar albino rats (n = 36) were assigned to six groups: normal control; diabetic control; standard drug group; and three treatment groups receiving P. fistulosus extract orally at doses of 200, 400, and 600 mg/kg body weight, respectively, for 30 consecutive days. Diabetes was induced in all groups, except for normal control, by intraperitoneal co-administration of streptozotocin and nicotinamide. Nicotinamide (100 mg/kg) was injected 15 min prior to a single dose of streptozotocin (50 mg/kg). Baseline and endpoint assessments of weight and blood glucose levels were performed. Blood was processed to assess insulin-related indices, lipid profile, and oxidative stress markers. q-PCR and Western blotting were utilized to explore the underlying molecular mechanisms. The diabetic control-group rats exhibited impaired glucose tolerance due to the marked reduction in serum insulin levels, compromised β-cell function, and substantial rise in lipid profile and oxidative stress parameters. Oral administration of P. fistulosus methanolic extract effectively mitigated these alterations in a dose-dependent manner, accompanied by the upregulation of both gene and protein expression involved in the insulin-signaling cascade. Full article

This randomized, double-blind, placebo-controlled study investigated Ageratum conyzoides (A. conyzoides) for alleviating osteoarthritis symptoms and improving quality of life. Conducted in Australia between 2021 and 2024, the study included 70 adults aged ≥45 years with clinically diagnosed osteoarthritis. Participants consumed 250 mg of pyrrolizidine alkaloid-free A. conyzoides extract or a placebo daily for 12 weeks. Pain and function were assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) every three weeks. Secondary measures included pain assessed by the Visual Analogue Scale (VAS), the SF-36 quality-of-life questionnaire, inflammatory markers, and safety parameters. A. conyzoides supplementation resulted in significant reductions in total WOMAC scores at weeks 9 and 12 (p < 0.05) compared to placebo. VAS pain scores were significantly lower at weeks 9 and 12 (p < 0.05). SF-36 scores improved significantly in the pain and role limitations due to physical health domains (p < 0.05). Plasma inflammatory markers IL-6 and IL-8 showed significant reductions compared with placebo (p < 0.05). No between-group differences were observed for adverse events. These findings demonstrate that A. conyzoides supplementation is a safe and effective option for reducing osteoarthritis symptoms, with significant improvements observed in pain, function, and inflammatory markers. Full article

Recently, due to advancements in keratorefractive surgery, small incision lenticule extraction (SMILE) has become increasingly recognized as a top surgical technique for treating refractive defects. The technology employs a femtosecond laser to precisely incise a stromal lenticule, which is subsequently extracted through a small incision, thereby preserving the front and most rigid regions of the cornea with minimal damage. Despite the widespread recognition of SMILE for its safety, biomechanical stability, effectiveness, and predictability, studies consistently document occurrences of postoperative keratectasia and a notable reduction in corneal biomechanical stability following the surgery. Hence, it is imperative to conduct further research on the several parameters linked to corneal biomechanical stability following SMILE. This narrative review comprehensively synthesizes the current literature on this topic and examines the literature on the evaluation of corneal biomechanics before and after SMILE. It provides a thorough review of the fundamental principles of corneal biomechanics, measurement techniques, the impact of various keratorefractive surgeries on corneal biomechanics, and the mechanisms through which SMILE affects corneal biomechanics. Full article

With the development of cities, traffic congestion has become a common problem, which seriously affects the efficiency of motorway transport. This study proposed an improved ML-YOLO video data extraction model based on You Only Look Once (YOLOv8n) combined with the Deep Simple Online and real-time tracking (DeepSORT) algorithm, to classify the obtained Traffic Performance Index (TPI) into different congestion levels by extracting traffic flow parameters in real-time and combining with the K-means clustering algorithm. The Long Short-Term Memory Dropout (LSTM-Dropout) model and the emergency lane opening model were used to implement the road congestion warning successfully. The practicality and stability of the model were also verified by calculating the relative error between the predicted traffic flow parameters and the extracted parameters through the LSTM time series model. According to the model results, emergency lanes are opened when the motorway traffic TPI exceeds 0.17 and closed when below 0.17. This study provided a reasonable theoretical basis for motorway traffic managers to decide whether or not to open the emergency lane, effectively relieved motorway road congestion, improved efficiency of road traffic, and had important practical value and significance in reality. Full article

This paper proposes and implements a method to efficiently parallelize constraint solving in rigid body simulation using GPUs. Rigid body simulation is widely used in robot development, computer games, movies, and other fields, and there is a growing need for faster computation. As current computers are reaching their limits in terms of scale-up, such as clock frequency improvements, performance improvements are being sought through scale-out, which increases parallelism. However, rigid body simulation is difficult to parallelize efficiently due to its characteristics. This is because, unlike fluid or molecular physics simulations, where each particle or lattice can be independently extracted and processed, rigid bodies can interact with a large number of distant objects depending on the instance. This characteristic causes significant load imbalance, making it difficult to evenly distribute computational resources using simple methods such as spatial partitioning. Therefore, this paper proposes and implements a computational method that enables high-speed computation of large-scale scenes by hierarchically clustering rigid bodies based on their number and associating the hierarchy with the hardware structure of GPUs. In addition, to effectively utilize parallel computing resources, we considered a more relaxed parallelization condition for the conventional Gauss–Seidel block parallelization method and demonstrated that convergence is guaranteed. We investigated how speed and convergence performance change depending on how much computational cost is allocated to each hierarchy and discussed the desirable parameter settings. By conducting experiments comparing our method with several widely used software packages, we demonstrated that our approach enables calculations at speeds previously unattainable with existing techniques, while leveraging GPU computational resources to handle multiple rigid bodies simultaneously without significantly compromising accuracy. Full article