Search Results (335)

Multiphoton endomicroscopy (MPEM) has recently become a key development in optical biomedical diagnostics, providing histologically relevant in vivo images that are eliminating both the need for tissue damage during biopsy sampling and the need for dye injections. Due to its ability to visualize structures at the epithelial, extracellular matrix, and subcellular levels, MPEM offers a promising diagnostic method for precancerous conditions and early forms of gastrointestinal (GI) cancer. The high specificity of multiphoton signals—the two-photon fluorescence response of endogenous fluorophores (NADH, FAD), the second-harmonic generation signal from collagen, and others—makes this method a promising alternative to both traditional histology and confocal endoscopy, enabling real-time assessment of metabolic status, intestinal epithelial cell status, and stromal remodeling. Despite the promising prospects of multiphoton microscopy, its practical implementation is progressing extremely slowly. The main factors here include the difficulty of delivering ultrashort pulses with high peak power, which is necessary for multiphoton excitation (MPE), and obtaining these pulses at the required wavelengths to activate the autofluorescence mechanism. One of the most promising solutions is the use of specialized multimode optical fibers that can both induce beam self-cleaning (BSC), which allows for the formation of a stable beam profile close to the fundamental mode, and significantly broaden the optical spectrum, which can ultimately cover the entire region of interest. This review presents the biophysical foundations of multiphoton microscopy of GI tissue, existing endoscopic architectures for MPE, and an analysis of the potential for using novel nonlinear effects in multimode optical fibers, such as the BSC effect and supercontinuum generation. It is concluded that the use of optical fibers in which the listed effects are realized in the tracts of multiphoton endomicroscopes can become a key step in the creation of a new generation of high-resolution instruments for the early detection of malignant neoplasms of the GI tract. Full article

Marine ecosystems represent a largely untapped reservoir of bioactive compounds with significant pharmacological potential. This study aimed to evaluate the therapeutic properties of ethanol extracts from four marine species: Padina australis, Spatoglossum asperum, Holothuria (Halodeima) atra, and Hypnea valentiae. Phytochemical screening, along with a comprehensive series of in vitro, in vivo, and in silico assays, was performed to evaluate the extracts’ pharmacological activities, including antioxidant potential (2,2-diphenyl-1-picrylhydrazyl assay), anti-inflammatory effect (carrageenan-induced paw edema method), analgesic activity (acetic acid-induced writhing and tail immersion tests), and anti-arthritic efficacy (protein denaturation assay). The extracts were found to be rich in flavonoids, tannins, alkaloids, saponins, glycosides, and phenolic compounds, which may underlie the observed bioactivities. In the acetic acid–induced writhing test, Hypnea valentiae at 400 mg/kg exhibited the highest peripheral analgesic activity, producing 82.51% inhibition of writhing (p < 0.001). In the tail immersion assay, Padina australis at doses of 200 and 400 mg/kg showed significant central analgesic effects, as evidenced by increased latency time and percentage of maximum possible effect (MPE). In the carrageenan-induced paw edema model, several treatment groups, including Padina australis, Hypnea valentiae, Spatoglossum asperum, and Holothuria atra, at both tested doses showed marked suppression of inflammation, with some groups achieving complete inhibition (100%; p < 0.001) at 120 min. The ethanol extract of Holothuria atra exhibited the strongest antioxidant and anti-arthritic activities, with an IC₅₀ value of 88.39 µg/mL in the DPPH assay and 81.35% inhibition of protein denaturation. Additionally, the compounds derived from the four marine species exhibited significant binding affinity to the selected target receptors, thereby validating the experimental findings. The marine species studied possess multifaceted pharmacological properties, supporting their potential as natural sources for developing therapeutic agents supporting the blue economy. Further studies are recommended to isolate active compounds and elucidate underlying mechanisms to support future drug development efforts. Full article

Polymer composites have opened a novel innovation phase in additive manufacturing (AM), and now lightweight, high-strength, and geometrical advanced components with tailored functionalities can be produced. The present study introduces advances in polymer composite materials and their integration into AM processes, particularly in rapidly growing industries such as aerospace, automotive, biomedical, and electronics. The embedding of cutting-edge reinforcement materials, such as nanoparticles, carbon fibers, and natural fibers, into polymer matrices enhances mechanical, thermal, electrical, and multifunctional properties. These material developments are combined with advanced fabrication techniques, including multi-material printing, in situ curing, and functionally graded manufacturing, which achieves accurate regulation of microstructures and properties. Furthermore, high-impact innovations such as smart polymer composites with self-healing or stimuli-responsive behaviors, the growing shift toward sustainable, bio-based composite alternatives, are driving progress. Despite significant advances, challenges remain in interfacial bonding, printability, process repeatability, and long-term durability. This review offers a comprehensive overview of current advancements and outlines future directions in polymer composite–based AM. Full article

Fifth-generation (5G) wireless networks have been widely deployed across various applications, including indoor positioning. This paper presents a model for 3D indoor localization of an unmanned aerial vehicle (UAV) using 5G millimeter-wave technology. Wireless InSite software is used to simulate a real-world environment and extract channel state information from multiple 5G next-generation NodeBs (gNBs), which is then used to generate channel frequency response (CFR) images. These images are employed in a fingerprinting method, where a deep convolutional neural network is trained for accurate position prediction. The model is trained across multiple scenarios involving changes in the number of gNBs, receiver positions, and spacing. In all scenarios, the model is tested using a UAV flying along a trajectory at variable speed. It is shown that a mean positioning error (MPE) of 0.36 m in 2D and 0.43 m in 3D is achieved when twelve gNBs with receivers spaced at 0.25 m are used. In addition, the corresponding root mean square error (RMSE) values of 0.32 m (2D) and 0.33 m (3D) further confirm the stability of the localization performance by indicating a low dispersion of positioning errors. This demonstrates that high positioning accuracy is feasible, even when synchronization errors and hardware imperfections exist. Full article

Plantain (Musa paradisiaca L.) is a tropical monocotyledonous, succulent plant of the Musaceae family commonly grown for food in the tropical regions of the African, Asian, and South American continents, where its parts are also sought for ethnomedicinal purposes in the treatment of burns, inflammation, and diabetes, among others. In the present preliminary exploratory study, the ethanol extract of the underutilized Musa paradisiaca peel (MPE) was evaluated for its in vitro inhibitory effects on α-amylase and α-glucosidase, as well as its in vivo hypoglycemic activity and potential biochemical toxicity. MPE (100, 200, 400 mg/kg) was orally administered to normal experimental rats for 30 days, following which the lipid profile, antioxidant status, and serum/tissue indices of hepatic, renal, and cardiac functions were evaluated. MPE produced significant inhibition (p < 0.05) of α-amylase (37%) and α-glucosidase (46%) at 120 µg/mL in vitro. The effect was lower than that of acarbose (IC₅₀ = 44.4 ± 1.14 and 15.60 ± 0.01 µg/mL, respectively). A modest blood glucose-lowering effect of MPE was observed at the highest tested dose (400 mg/kg) following subacute oral administration. During this treatment period, no biochemical alterations of toxicological importance were caused by MPE, as the organ–body weight ratio and serum/tissue indicators of organ function/damage were not adversely altered. In conclusion, MPE demonstrated inhibitory activity against both α-amylase and α-glucosidase, which may contribute to its potential hypoglycemic effects. Additionally, the findings indicate that the peel extract is non-toxic in rats following sub-acute administration at doses up to 400 mg/kg body weight. Further studies involving diabetic models and chronic exposure will substantiate and extend these preliminary observations. Full article

This study analyzed the photosynthetic traits of 74 sugarcane genotypes using PAM-2500 and SPAD instruments over three years. Our findings revealed significant variations in photosynthetic characteristics among different genotypes and ratoon years, highlighting the complex interplay between genotype and ratoon age. Notably, the heritability of these traits ranged from 0.70 to 0.86, indicating a strong genetic influence. Through principal component analysis, we identified three critical aspects of photosynthesis: efficiency and light utilization, electron transfer and reaction center status, and chlorophyll content, which collectively accounted for 99.9% of the observed variance. The germplasms were categorized into three efficiency groups—high, moderate, and low—based on their photosynthetic performance. Among these, 45 genotypes were classified as High Photosynthetic Efficiency (HPE), 19 as Moderate Photosynthetic Efficiency (MPE), and 10 as Low Photosynthetic Efficiency (LPE). Importantly, germplasms with high photosynthetic efficiency correlated with increased stalk weight and sucrose content, suggesting potential targets for breeding programs. These findings establish a quantitative framework linking photosynthetic performance with agronomic outputs, providing breeders with measurable selection criteria for developing next-generation sugarcane cultivars optimized for both biomass and sucrose production. Full article

This study examined the impact of various treatments on the storage quality of sugar-cored ‘Fuji’ apples, with the objective of establishing a theoretical foundation for extending the retention period of these apples and augmenting their commercial worth. This experiment utilized three distinct treatments for sugar-cored ‘Fuji’ apples, with the combination of 1-methylcyclopropene and polyethylene self-sealing bag treatment (1-M+PE) demonstrating the most effective prolongation of the storage duration for the sugar-cored apples. The 1-M+PE treatment significantly mitigated the reduction of sugar-cored in ‘Fuji’ apples and extended the onset of cellular rupture, postponing the loss of firmness and preserving the concentrations of sorbitol and sucrose throughout the storage duration. The 1-M+PE treatment effectively prolonged the storage duration of sugar-cored apples in cold storage. ‘Fuji’ apples subjected to 1-M+PE treatment were stored in cold storage for 120 days. The sugar-cored apple rate was 38.9%. The firmness was 14.8% greater than that of the control group. The soluble solid concentration in the sugar-cored part was 3.83% higher than that of the control group. The reducing sugar content in the sugar-cored part was 16.2% higher than that of the control group, and the titratable acid content in the sugar-cored part was 1.86 times greater than that of the control group. The correlation study of the indicators during the storage period revealed a robust association between the rate of sugar-cored apple and the content of sorbitol, potassium, phosphorus, and calcium. Experimental findings demonstrate that the concurrent application of 1-M+PE significantly inhibits the disappearance of sugar-cored. Full article

Introduction: Malignant pericardial effusion (MPE) represents a relatively rare complication in various types of solid tumors. Its management is often challenging. One solution can be represented by surgical approaches, including a pericardio-peritoneal window (PPW), which allows draining the fluid into the abdominal cavity. The aim of this study is to investigate the efficacy and long-term outcomes of the PPW procedure as a definitive therapeutic strategy for MPE. Materials and methods: We retrospectively and prospectively observed pre-, peri-, and postoperative data of patients undergoing pericardio-peritoneal window creation from 2010 to December 2023 at the European Institute of Oncology (IEO), including the surgical procedures needed, total and specific postoperative complications, 30-day mortality rate, relapse rate, and the treatment of possible relapses. Results: A total of 44 consecutive patients underwent a pericardio-peritoneal window. In 28 patients (63.8%) PPW was associated with mono or bilateral videothoracoscopy for pleural biopsies/talc poudrage. In 23 cases, pre-operative percutaneous pericardial drainage (usually 1–2 days before surgery) was performed. No intraoperative deaths were observed. The 30-day mortality was 9% (four patients). We observed pericardial effusion recurrence in three patients at two months and in five patients at six months. In only two cases we treated this condition because of a pre-tamponade condition, treated by percutaneous pericardial drainage. The success rate of the PPW regarding pericardial relapse requiring further procedures was 95.5%. Conclusions: Patients presenting with a favorable short-term prognosis benefit from the pericardio-peritoneal window as a safe and effective method for resolving malignant pericardial effusion. Conversely, pericardial drainage is recommended as the most appropriate therapy for those with a less favorable prognosis. Full article

This article presents an experimental and statistical investigation of how optical magnification influences calibration constants, measurement results, and uncertainty in a digital optical microscope. Measurements were performed on reference gauge blocks with nominal lengths from 1.0 mm to 1.5 mm at five magnification levels (1×–5×) to quantify the effect of magnification on dimensional accuracy. A combined statistical methodology integrating non-parametric hypothesis testing and bootstrap-based uncertainty analysis was developed to evaluate data distributions and validate the use of a normal coverage factor (k = 2) for expanded uncertainty. The results showed that magnification has a statistically significant effect on the measured lengths for most standards, with the smallest combined standard uncertainty achieved at approximately 4× magnification. The uncertainty budget analysis revealed that the dominant component arises from the microscope’s declared Maximum Permissible Error (MPE), while type A and reference-standard components contribute only marginally. All expanded uncertainties remained within the declared MPE limits, confirming the reliability and traceability of the measurement process. Practical recommendations were proposed for selecting optimal magnification and for implementing calibration verification procedures at each zoom level. The presented methodology provides a validated framework for minimizing uncertainty in image-based dimensional measurements using digital optical microscopes. Full article

Large background noise, difficulty in feature extraction, and low parameter-optimization efficiency of diagnosis models are key challenges in rolling bearing fault diagnosis. To address these issues, this paper proposes a fault diagnosis framework that combines Singular Spectrum Decomposition (SSD) with a Multi-Strategy Enhanced Cuckoo Search (MS-CS) algorithm to optimize an Extreme Learning Machine (ELM). First, the raw vibration signal is decomposed via SSD and each intrinsic component’s energy contribution is computed; components whose cumulative energy exceeds 90% are retained and reconstructed, thereby effectively suppressing noise while preserving critical fault features. Next, Multiscale Permutation Entropy (MPE) is extracted from the reconstructed signal to form a high-discriminability feature set. To overcome the traditional Cuckoo Search algorithm’s tendency to become trapped in local optima and its slow convergence, Cauchy mutation and adaptive Levy flight strategies are introduced to enhance global exploration and local exploitation. Finally, the improved MS-CS algorithm is employed to optimize the ELM’s input weights and hidden-layer biases, yielding a high-precision diagnostic model. Experimental results on benchmark bearing data demonstrate an average fault recognition rate of 96%, representing improvements of 6.67% over the conventional CS-ELM and 18% over the unoptimized ELM. These findings confirm the proposed method’s effectiveness and robustness in practical engineering applications. Full article

Indoor localization remains a challenging task due to the unavailability of reliable global navigation satellite system (GNSS) signals in most indoor environments. One way to overcome this challenge is through visual–inertial odometry (VIO), which enables real-time pose estimation by fusing camera and inertial measurements. However, VIO suffers from performance degradation under high-speed motion and in poorly lit environments. In such scenarios, motion blur, sensor noise, and low temporal resolution reduce the accuracy and robustness of the estimated trajectory. To address these limitations, we propose a motion-aware event-based VIO (MA-EVIO) system that adaptively fuses asynchronous event data, frame-based imagery, and inertial measurements for robust and accurate pose estimation. MA-EVIO employs a hybrid tracking strategy combining sparse feature matching and direct photometric alignment. A key innovation is its motion-aware keyframe selection, which dynamically adjusts tracking parameters based on real-time motion classification and feature quality. This motion awareness also enables adaptive sensor fusion: during fast motion, the system prioritizes event data, while under slow or stable motion, it relies more on RGB frames and feature-based tracking. Experimental results on the DAVIS240c and VECtor benchmarks demonstrate that MA-EVIO outperforms state-of-the-art methods, achieving a lower mean position error (MPE) of 0.19 on DAVIS240c compared to 0.21 (EVI-SAM) and 0.24 (PL-EVIO), and superior performance on VECtor with MPE/mean rotation error (MRE) of 1.19%/1.28 deg/m versus 1.27%/1.42 deg/m (EVI-SAM) and 1.93%/1.56 deg/m (PL-EVIO). These results validate the effectiveness of MA-EVIO in challenging dynamic indoor environments. Full article

UAV laser scanning (UAV-LS) combines extensive scanning coverage with high point cloud density, enabling efficient and precise acquisition of key forest attributes. Based on field-measured data and UAV-LS data from 138 Chinese fir (Cunninghamia lanceolata) plantation plots in southern China, this study systematically developed growing stock volume (GSV) estimation models at both tree and stand levels. The models included base models (allometric models), linear models, dummy variable models incorporating age groups, and nonlinear mixed-effects models incorporating random effects (plot and area levels for the tree level, and only the area level for the stand level). The results showed the following: (1) Stand-level GSV prediction relied primarily on height metrics, achieving optimal performance through a combination of the 10th cumulative height percentile (AIH₁₀) and canopy cover (CC), both of which showed near-linear relationships with GSV; tree-level GSV was predicted by LiDAR-derived tree height (LH) and crown width (LCW), with LH explaining most variation. (2) Tree-level models achieved R² = 0.639–0.725 and RMSE = 0.050–0.058 m³, exhibiting larger individual prediction errors (mean percentage standard error, MPSE > 30%) with smaller aggregate prediction errors (mean prediction error, MPE < 1%); stand-level models reached R² = 0.785–0.879 and RMSE = 46.052–61.314 m³ ha⁻¹ while maintaining controlled errors across scales (MPE < 5%, MPSE < 20%). (3) At both the tree and stand levels, the nonlinear mixed-effects model outperformed the others, followed by the dummy variable model and the base model, with the linear model exhibiting the worst performance; area-level random effects primarily influenced the baseline value of tree-level GSV and the allometric relationship between stand-level GSV and AIH₁₀, whereas plot-level random effects affected the allometric relationships of tree-level GSV with LH and LCW. This study confirms the effectiveness of UAV-LS for large-scale forest resource monitoring, while underscoring the necessity of incorporating spatial heterogeneity in GSV estimation. Full article

This paper compares different point measurement strategies for spherical surfaces using a coordinate measuring machine. A total of 77 points were measured using the Sphere, Touch Point, Space Point, and Mid Point strategies, and approximately 2200 points were measured by scanning the surface with eight circles. Three probe configurations with different probe tip diameters were used for the measurements. The acquired data were processed with Calypso software, and the results for sphere diameters and centers were verified using the LSQ algorithms with the Kåsa and Newton methods, as well as Chebyshev fitting. Spherical shell half-widths and Root Mean Square Errors were used to evaluate accuracy. The results show that the sensor with $r_{\mathrm{probe}}=2.500557$ mm provides the smallest deviation from the certified value for both diameter and center position. The deviations remain smaller than the maximum permissible error of length measurement ${\mathrm{E}}_{0,\mathrm{MPE}}$. The scanning strategy does not consistently provide better results for either the diameter or the sphere center compared to point measurement. The results of the LSQ method in Calypso correspond to those obtained by the Kåsa/Newton methods (with results identical at the $\mathsf{\mu }$m scales), while the Calypso Minimum Feature evaluation method corresponds to the Chebyshev fit. Full article

Apple (Malus domestica) is a woody fruit tree belonging to the Rosaceae family, genus Malus. It has been widely reported that MYB transcription factors are critical regulators for the red color of apple peel by activating the expression of anthocyanin biogenesis genes. However, it is still not clear what the molecular mechanism for the yellow color of apple peel is. In order to investigate key genes and metabolites responsible for yellow coloration of apple peel, three strains of apples, “Venus Gold (Ype)” with yellow peel, “Yanfu8 (Mpe)” with medium red peel, and “Red love” with dark red peel, were selected in this study. Transcriptomic and metabolomic profiles were obtained for the peels of the three apple strains. After analyzing the transcriptomic profiles and being verified with qRT-PCR experiments, our results suggest that LAR1 is a critical gene for the yellow color of Ype peel. Analysis of metabolomic profiles revealed that the abundances of catechin and epicatechin in Ype peel were higher than those in Rpe, indicating an important reason for the yellow color of Ype peel. Furthermore, when comparing volatile metabolites from Ype, Mpe, and Rpe, hundreds of volatile metabolites show significantly different abundances, suggesting that apples with different peel colors have different odors. Our results uncover a novel metabolic mechanism for yellow coloration, where high expression of LAR1 promotes accumulation of catechin and epicatechin, providing the first integrated transcriptomic and metabolomic evidence for this pathway in apple peel. Full article

Centralized heating systems in severe cold regions suffer from widespread load estimation deviations due to architectural heterogeneity and a lack of construction drawings, leading to substantial energy waste. This study proposes a lightweight load calculation method that facilitates efficient calculation of heating loads for heterogeneous building clusters via spatial thermal pattern decoupling and matrix reorganization. First, a 3 × 3 load characteristic matrix is developed to characterize the spatial variation in thermal demand across different building positions (corner vs. intermediate units × top, middle, and bottom floors), revealing that corner units exhibit higher thermal loads than intermediate units, while top and bottom floors show significantly higher loads than middle floors. Second, two complementary matrices are established: the load characteristic matrix, which represents the building’s thermal behavior, and the structural feature matrix, which encodes the architectural configuration in terms of unit count (a) and floor count (b). Together, they enable rapid hourly load synthesis using only lightweight input parameters. The method is validated on 56 heterogeneous residential buildings in Northeast China. Using a decoupled 4U/6F standard model, the synthesized cluster heating load achieves an R² of 0.88, an RMSE of 24.15 GJ, a MAPE of 4.94%, and a Mean Percentage Error (MPE) of −0.82% against actual heating supply data, demonstrating high accuracy and negligible systematic bias—particularly during cold waves. This approach allows the seasonal variation in heat demand across an entire residential area to be estimated even in the absence of detailed construction drawings, offering practical guidance for operational heating management. Full article