Search Results (51,301)

The high performance of optical components is contingent upon the quality of their optical surfaces, thereby imposing elevated standards on the methodologies employed for their fabrication. This study involved experimental research on freeform optical surface elements of polymethyl methacrylate with nano-surface roughness. In this study, the effects of machining parameters of ultra-precision slow tool servo turning on the surface roughness of different types of areas of freeform optical surfaces in the finishing stage were analysed. Based on the analysis of ultra-precision turning test results for freeform optical surfaces, a novel evaluation method for surface quality is proposed to assess the overall uniformity of surface quality across the entire freeform optical surface. Building upon this proposed evaluation method for overall surface quality uniformity, the processing method of high-quality freeform optical surfaces is studied. The results show that in the finishing stage, the radial feed rate exerts the greatest influence on the surface roughness of the freeform optical surface, especially the surface roughness of the concave surface area. This will exacerbate the surface quality inhomogeneity of the freeform optical surface. Based on the analysis results, optimal machining parameters were selected for processing trials. Concurrently, additional machining tests were conducted to further validate the influence of radial feed rate. Ultimately, a nano-scale PMMA freeform optical surface with uniform overall surface quality was achieved. The variation in surface roughness in different regions of the optical freeform is regulated to stabilise within 2 nm on the surface of polymethyl methacrylate. The overall uniformity of surface quality across the entire freeform optical surface was maintained at a high level. Full article

Accurate detection of pressure-relief boreholes is crucial for evaluating drilling quality and monitoring safety in coal mine roadways. Nevertheless, the highly challenging underground environment—characterized by insufficient lighting, severe dust and water mist disturbances, and frequent occlusions—poses substantial difficulties for current object detection approaches, particularly in identifying small-scale and low-visibility targets. To effectively tackle these issues, a lightweight and robust detection framework, referred to as YOLO-DFBL, is developed using the YOLOv11n architecture. The proposed approach incorporates a DualConv-based lightweight convolution module to optimize the efficiency of feature extraction, a Frequency Spectrum Dynamic Aggregation (FSDA) module for noise-robust enhancement, and a Biformer (Bi-level Routing Transformer)-based routing attention mechanism for improved long-range dependency modeling. In addition, a Lightweight Shared Convolution Head (LSCH) is incorporated to effectively decrease the overall model complexity. Experimental results on a real coal mine roadway dataset demonstrate that YOLO-DFBL achieves an mAP@50:95 of 78.9%, with a compact model size of 1.94 M parameters, a computational complexity of 4.7 GFLOPs, and an inference speed of 157.3 FPS, demonstrating superior accuracy–efficiency trade-offs compared with representative lightweight YOLO variants and classical detectors. Field experiments under challenging low-illumination and occlusion environments confirm the robustness of the proposed approach in real mining scenarios. The developed method enables reliable visual perception for underground drilling equipment and facilitates safer and more intelligent operations in coal mine engineering. Full article

Background: This study evaluated time-dependent changes in anchor hole width (AHW) and their association with postoperative medial meniscus extrusion (MME) in patients undergoing open-wedge high tibial osteotomy (OWHTO) with medial meniscus posterior root tear (MMPRT) repair and meniscal centralization. Methods: Thirty knees treated with combined OWHTO and MMPRT repair using the centralization technique were retrospectively reviewed. MRI, CT, and second-look arthroscopy were performed preoperatively and postoperatively. AHW of the MMPRT anchor and two centralization anchors (midbody and midbody–posterior, M-anchor and MP-anchor) were measured on multiplanar reconstruction CT images at 1, 3, and 6 months, and 1 year, and their correlations with postoperative MME were analyzed. Results: AHW increased up to 3 months and gradually decreased with surrounding sclerosis by 1 year. The M-anchor showed significantly greater mediolateral (ML) expansion than the MP-anchor and demonstrated a moderate positive correlation between 1-year AHW and MME (r ≈ 0.5, p < 0.01). Second-look arthroscopy confirmed a 90% healing rate of the repaired root. Conclusions: Although OWHTO combined with MMPRT repair and centralization achieved favorable root healing, postoperative MME progression was not fully prevented. Time-dependent ML anchor hole expansion around the M-anchor may indicate persistent micromotion, elongation of the meniscotibial ligament, and degenerative stretch of the repaired meniscus following healing, suggesting that even after successful root healing, ML motion remains difficult to control, highlighting the need for biomechanically optimized fixation. Full article

Background/Objectives: Accurate classification of primary bone tumors from radiographic images is essential for early diagnosis, appropriate treatment planning, and informed clinical decision-making. While deep convolutional neural networks (CNNs) have shown strong performance in medical image analysis, their high computational complexity often limits real-world clinical deployment. This study aims to develop a lightweight yet highly accurate model for multi-class bone tumor classification. Methods: We propose Bone-CNN, a computationally efficient CNN architecture specifically designed for radiograph-based classification of primary bone tumors. The model was evaluated using the publicly available Figshare Radiograph Dataset of Primary Bone Tumors, which includes nine distinct tumor classes ranging from benign to malignant lesions and originates from multiple imaging centres. Performance was assessed through extensive experiments and compared against established baseline models, including DenseNet121, EfficientNet-B0, and MobileNetV2. Results: Bone-CNN achieved a test accuracy of 96.52% and a macro-AUC of 0.9989, outperforming all baseline architectures. Both quantitative and qualitative evaluations, including confusion matrices and ROC curve analyses, demonstrated robust and reliable discrimination between challenging tumor subtypes. Conclusions: The results indicate that Bone-CNN offers an excellent balance between accuracy and computational efficiency. Its strong performance and lightweight design highlight its suitability for clinical deployment, supporting effective and scalable radiograph-based assessment of primary bone tumors. Full article

Heterogeneous electronic medical record (EMR) systems and institution-specific data structures continue to limit interoperability and large-scale utilization of healthcare data. Although Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) has been adopted as an international standard, existing conversion approaches often require extensive preprocessing, high implementation costs, or deep system-specific expertise, restricting their applicability, particularly in small and medium-sized hospitals. To address these constraints, we propose a lightweight EMR-to-HL7 FHIR conversion library optimized for small- and medium-sized healthcare providers that operate with limited system resources. Methods: The library adopts a modular architecture comprising data preprocessing, reference management, structural transformation using transform maps, terminology translation, and validation modules. The proposed approach was implemented using the HL7 Application Programming Interface (HAPI) FHIR and evaluated with anonymized EMR data extracted from multiple hospitals in South Korea, with performance and validation results compared against a conventional HAPI FHIR client-based conversion method. Results: This study proposes a standardized FHIR-based medical data conversion library that enables the efficient transformation of diverse EMR data structures into interoperable FHIR. The proposed library achieved approximately 30% lower single-request conversion latency compared to a conventional HAPI FHIR client-based conversion pipeline under identical hardware and runtime conditions. Conclusions: The proposed conversion method provides a lightweight and adaptable solution for EMR-to-FHIR transformation, improving interoperability with reduced implementation effort and supporting scalable medical data exchange across diverse healthcare environments. Full article

The task of identifying high-value projects from vast investment portfolios presents a major challenge in the construction industry, particularly within the energy sector, where decision-making carries high financial and operational stakes. This complexity is driven by both the volume and heterogeneity of project documentation, as well as the multidimensional criteria used to assess project value. Despite this, research gaps remain: large language models (LLMs) as pretrained transformer encoder models are underutilized in construction project selection, especially in domains where investment precision is paramount. Existing methodologies have largely focused on multi-criteria decision-making (MCDM) frameworks, often neglecting the potential of LLMs to automate and enhance early-phase project evaluation. However, deploying LLMs for such tasks introduces high computational demands, particularly in privacy-sensitive, enterprise-level environments. This study investigates the application of the robustly optimized BERT model (RoBERTa) for identifying high-value energy infrastructure projects. Our dual objective is to (1) leverage RoBERTa’s pre-trained language architecture to extract key information from unstructured investment texts and (2) evaluate its effectiveness in enhancing project selection accuracy. We benchmark RoBERTa against several leading LLMs: BERT, DistilBERT (a distilled variant), ALBERT (a lightweight version), and XLNet (a generalized autoregressive model). All models achieved over 98% accuracy, validating their utility in this domain. RoBERTa outperformed its counterparts with an accuracy of 99.6%. DistilBERT was fastest (1025.17 s), while RoBERTa took 2060.29 s. XLNet was slowest at 4145.49 s. In conclusion, RoBERTa can be the preferred option when maximum accuracy is required, while DistilBERT can be a viable alternative under computational or resource constraints. Full article

Black currants (Ribes nigrum L.) and their hybrid berries are distinguished by their exceptionally high content levels of anthocyanin and vitamin C, major phytochemicals with health-promoting properties. This study was designed to substantially reduce the HPLC runtime required for black currant anthocyanin analysis and clarify how key determinants, including morphological traits (berry size and peel proportion), genetic variation across 12 cultivars, and cryogenic milling, affect anthocyanin accumulation and quantification. A rapid HPLC protocol was developed that achieves the high-resolution separation of four major and eight minor anthocyanins in black currant within a 10 min run, enabling efficient, high-throughput analysis, very important in long-term breeding programs due to the large number of genotypes. Cryogenic grinding substantially enhanced the extraction yield and reproducibility relative to just blending. Using the improved extraction and analysis method, a set of anthocyanin content-related morphologic berry traits was systematically evaluated, providing information directly relevant to future phenotyping and breeding efforts. Smaller black currant berries generally have higher total anthocyanin content than larger berries, and these morphological attributes are tightly linked to the genotype. Although a higher peel proportion was related to higher anthocyanin content within genotype, there was no global trend, and anthocyanin contents were similar in different size berry peels. 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

Considering the nonlinear trends, multi-scale variations, and capacity regeneration phenomena exhibited by battery capacity degradation under real-world conditions, accurately predicting its trajectory remains a critical challenge for ensuring the reliability and safety of electric vehicles. To address this, this study proposes a hybrid prediction framework based on Variational Mode Decomposition and a Transformer–Long Short-Term Memory architecture. Specifically, the proposed Variational Mode Decomposition–Transformer for Time Series–Long Short-Term Memory (VMD–TTS–LSTM) framework first decomposes the capacity sequence using Variational Mode Decomposition. The resulting modal components are then aggregated into high-frequency and low-frequency parts based on their frequency centroids, followed by targeted feature analysis for each part. Subsequently, a simplified Transformer encoder (Transformer for Time Series, TTS) is employed to model high-frequency fluctuations, while a Long Short-Term Memory (LSTM) network captures the long-term degradation trends. Evaluated on charging data from 20 commercial electric vehicles under a long-horizon setting of 20 input steps predicting 100 steps ahead, the proposed method achieves a mean absolute error of 0.9247 and a root mean square error of 1.0151, demonstrating improved accuracy and robustness. The results confirm that the proposed frequency-partitioned, heterogeneous modeling strategy provides a practical and effective solution for battery health prediction and energy management in real-world electric vehicle operation. Full article

Sustainable urban water system (UWS) management is vital for climate-resilient, resource-efficient cities. This study presents the first comprehensive life cycle assessment (LCA) of Seoul Metropolitan City (SMC)’s UWS, encompassing water abstraction, treatment, distribution, wastewater collection and treatment, and sludge management. Nine midpoint impact categories from ReCiPe 2016 (H) were analyzed to identify environmental hotspots and mitigation pathways. Results show that wastewater treatment dominates impacts, contributing 57.3% of global warming potential (GWP; 0.947 kg CO₂-eq per functional unit of 1 m³ of potable water supplied) and 71.1% of freshwater eutrophication (FE; 0.00066 kg P-eq/m³), driven by electricity use, sludge disposal, and direct CH₄/N₂O emissions. Electricity consumption is the leading driver across GWP, terrestrial acidification (TA), and fossil resource scarcity (FRS). Infrastructure construction notably influenced terrestrial ecotoxicity (TET) and human toxicity. Sensitivity analysis showed that SMC’s projected 2030 electricity mix could reduce GWP and FRS by up to 18%. Scenario evaluations revealed that sludge ash utilization in concrete and expanded wastewater reuse improve resource circularity, whereas biogas upgrading, solar generation, and heat recovery significantly lower GWP and FRS. The findings underscore the importance of energy decarbonization, resource recovery, and infrastructure longevity in achieving low-carbon and resource-efficient UWSs. This study offers a transferable framework for guiding sustainability transitions in rapidly urbanizing, energy-transitioning regions. Full article

Accurate indoor localization in 5G remains challenging due to multipath propagation, signal blockage, and limited bandwidth in frequency range 1 (FR1). This study evaluates attention-based recurrent neural networks for two-dimensional user equipment (UE) localization using only positioning reference signal (PRS) magnitude data. We compare five models on the xG-Loc dataset (InF-DH scenario at 3.5 GHz, 5 MHz bandwidth): a simple GRU (M1), a deeper GRU with dropout (M2), a GRU optimized via Optuna (M3), a stacked GRU with multi-head attention (M4), and a bidirectional GRU with attention (M5). Model performance is quantified using the area above the cumulative distribution function (CDF) curve (AAC) metric, where lower values indicate better localization accuracy. Attention-based models significantly outperform baselines, and M4 achieves the lowest AAC of 6.71 (17% reduction versus M1’s 8.09), while M5 attains an AAC of 6.90. Statistical analysis confirms that M4 and M5 significantly outperform M3 (ANOVA, p < 0.000001). Optimal performance emerges with moderate numbers of time steps (TS ≈ 500 to 2500), with performance plateauing and degrading at higher values. These findings demonstrate that attention mechanisms substantially enhance 5G indoor localization accuracy using only PRS magnitudes, and that automated hyperparameter optimization improves model robustness. Full article

Pseudolite (PL) positioning technology is one of the effective methods to achieve high-precision indoor positioning. The Distributed Array Pseudolite System (DAPLS) is a ground-based augmentation architecture designed to provide high-precision positioning in GNSS-denied or indoor environments. However, maintaining the stability and integrity of pseudolite signals in distributed deployments remains a significant challenge. To address this, a Pseudolite Monitoring Station (PMS) was developed for real-time signal observation, performance evaluation, and anomaly detection. The proposed PMS integrates a multi-channel front-end, signal-processing engine, and monitoring algorithms capable of continuous assessment across three hierarchical levels: Signal Quality Monitoring (SQM), Receiver Processing Monitoring (RPM), and Measurement Quality Monitoring (MQM). To integrate multi-domain monitoring results, a Composite Quality Index (CQI) model is introduced, combining normalized sub-scores through weighted fusion to reflect overall system integrity. A comprehensive Signal Quality Assessment (SQA) framework is further introduced, including four dimensions of evaluation: constellation status, time reference, spatial coordinate reference, and signal anomaly detection. An indoor DAPLS experiment was conducted within a laboratory-level test field. The system comprised three pseudolite transmitter arrays (six transmitters each) and a central monitoring station. Experimental results showed stable synchronization within ±5 ns, coordinate accuracy within 0.2 m, and consistently high signal quality. The monitoring station effectively detected minor signal distortions and synchronization deviations, confirming its diagnostic precision and robustness. This study demonstrates a complete monitoring and evaluation framework for DAPLS, enabling both system-level quality assurance and signal integrity monitoring. The proposed PMS and SQA methods provide essential tools for future deployment of pseudolite-based indoor positioning and timing systems. Full article

The efficacy of Underwater Camouflaged Object Detection (UCOD) is fundamentally constrained by severe boundary ambiguity, where biological mimicry blends targets into complex backgrounds and aquatic optical degradation erodes edge details. We propose a lightweight boundary perception detector named CAR-YOLO (Camouflage Ambiguity Resolution YOLO). Specifically, a frequency-domain dual-path mechanism (FRM-DWT/EG-IWT) leverages selective wavelet aggregation and dynamic injection to recover high-frequency edges. Subsequently, these high-frequency cues are synergized with low-frequency semantic information via the Low-level Adaptive Fusion (LAF) module. To further address noisy samples, an Uncertainty Calibration Head (UCH) refines supervision via prediction consistency. Finally, we constructed specialized datasets based on public data for training and evaluation, including UCOD10K and UWB-COT220. On UCOD10K, CAR-YOLO achieves 27.1% mAP50–95, surpassing several state-of-the-art (SOTA) methods while reducing parameters from 2.58 M to 2.43 M and GFLOPs from 6.3 to 5.9. On the challenging UWB-COT220 benchmark, the model attains 30.7% mAP50–95, marking a 7.7-point improvement over YOLOv11. Furthermore, cross-domain experiments on UODD demonstrate strong generalization. These results indicate that CAR-YOLO effectively mitigates boundary ambiguity, achieving an optimal balance between accuracy, robustness, and efficiency. Full article

The increasing resistance of agricultural pests to conventional pesticides necessitates the development of alternative biological control strategies. This study evaluated the acaricidal potential of two Photorhabdus luminescens strains (0805-P2R and the newly isolated 2103-RUVI) against the spider mite Tetranychus truncatus. Culture conditions were optimized using a Taguchi L9(3⁴) design to maximize growth, protease activity, and acaricidal efficacy. The optimized medium for strain 2103-RUVI achieved 90% mortality against T. truncatus at 72 h, compared to 83% for strain 0805-P2R under equivalent conditions. Genomic analysis identified putative phosphoporin PhoE genes exclusively in 2103-RUVI, which may contribute to its enhanced virulence, although this association remains correlative and requires functional validation. Histopathological observations confirmed severe tissue disruption in treated mites. Comparative analysis demonstrated complex, strain-specific relationships among bacterial growth, enzyme activity, and acaricidal effects. These results highlight the potential of the P. luminescens strain 2103-RUVI as an effective biocontrol agent, providing insights for its application in sustainable integrated pest management programs. Full article

Existing structural stress is an important parameter for evaluating the current state of a structure. In order to improve the accuracy of in situ stress measurement in the field, this paper proposes an in situ stress measurement method for existing structures, which combines Digital Image Correlation (DIC) technology with the drilling stress relief method. The method utilizes DIC technology to monitor the local displacement or strain caused by stress release from the drilled hole in real time, and further inverts the in situ stress state of the structure based on this data. First, the principle and specific implementation process of the method are introduced. Then, finite element simulations are used to analyze the influence of factors such as size effects, drill hole diameter, drill hole depth, and initial stress magnitude on the measurement results. Finally, experimental validation of the method’s effectiveness is conducted. The results show that the in situ stress measurement method based on the combination of DIC and stress relief has good application effects and prospects in the stress analysis of existing structures. The accuracy and effectiveness of the method are influenced by factors such as specimen size, drill hole diameter, drill hole depth, and stress magnitude. In practical engineering, a comprehensive evaluation should be made, considering the precision of DIC testing and the magnitude of in situ stress, to select appropriate drilling parameters and measurement ranges. During the subsequent stress inversion process, a size calibration factors is applied to adjust the theoretical results, significantly improving the method’s applicability under finite size conditions, and achieving good results. This research provides important references for the stress testing and evaluation of existing structures with finite sizes. Full article