Search Results (892)

Background: Perioperative outcomes in pelvic organ prolapse (POP) surgery remain difficult to predict due to substantial heterogeneity in both surgical techniques and patient characteristics. Existing studies typically evaluate these factors in isolation, limiting their ability to support individualized risk stratification. This study introduces a stratified analytical framework to disentangle the relative impact of procedural and patient-related determinants across common vaginal reconstructive approaches. Methods: A retrospective cohort of 376 women undergoing POP surgery between 2020 and 2025 was analyzed. Patients were stratified into three procedure groups: sacrospinous fixation with mid-urethral sling (SFM + TOT/TVT), anterior and posterior repair with sling (A&P + TOT/TVT), and isolated anterior and posterior repair (A&P alone). Key outcomes included intraoperative blood loss, length of hospitalization, postoperative hospital stay and catheterization time. Within-group predictors were assessed using stratified odds ratios and synthesized via a random-effects model. Results: Procedure type was consistently associated with recovery-related outcomes, although it explained only a modest proportion of outcome variability. Patients undergoing A&P repair exhibited significantly prolonged hospitalization (8.00 vs. 6.29 and 6.94 days), postoperative recovery (4.99 vs. 3.48 and 4.17 days), and catheterization duration (3.31 vs. 2.33 and 2.86 days) (all p < 0.001). In contrast, intraoperative blood loss was primarily driven by patient-specific factors, including concomitant hysterectomy, prolapse severity, obesity, age, and obstetric history. Prolonged hospitalization was strongly associated with combined procedural complexity and clinical burden, while catheterization duration was influenced by postoperative complications and parity. Conclusions: This study demonstrates that perioperative outcomes in POP surgery arise from distinct and interacting domains: procedural factors predominantly shape recovery trajectories, whereas patient characteristics govern intraoperative risk. The proposed stratified random-effects framework enables integrated evaluation across heterogeneous surgical groups and provides an exploratory basis for identifying domains that may inform future individualized perioperative risk models. Full article

Background/Objectives: Patellar chondromalacia (PC) is a clinical condition characterized by early cartilage degeneration in the patellofemoral joint. This study aimed to investigate the relationship between the lateral-to-medial femoral condyle length ratio (LFCL/MFCL) measured on standard anteroposterior (AP) knee radiographs and the presence of PC. Methods: A retrospective analysis was conducted on patients who presented with anterior knee pain between 2020 and 2024. PC was diagnosed using magnetic resonance imaging (MRI). The LFCL/MFCL ratio was measured on plain radiographs. Additional morphological condylar parameters were evaluated on MRI. Symptom severity was assessed using the Kujala score. Statistical analyses included t-tests, correlation analysis, logistic regression, and receiver operating characteristic (ROC) curve analysis. Results: A total of 100 patients (50 with PC, 50 controls) were included. The LFCL/MFCL ratio was significantly higher in the PC group compared to controls (1.24 ± 0.19 vs. 1.08 ± 0.15, p = 0.002). A negative correlation was found between the LFCL/MFCL ratio and Kujala score (r = −0.322, p = 0.029). Other MRI-based parameters did not show statistically significant differences. In logistic regression, the LFCL/MFCL ratio was identified as an independent predictor of PC (p = 0.01). ROC analysis yielded an AUC of 0.743 (95% CI: 0.643–0.842). Conclusions: The LFCL/MFCL ratio, which can be easily measured on plain radiographs, may serve as a simple, cost-effective, and reproducible parameter to aid in the diagnosis of patellar chondromalacia. Further prospective studies with larger sample sizes are needed to validate this finding. Full article

Background and Objectives: This study was conducted to investigate the effects of fragment size on clinical and radiological outcomes and the development of complications during the surgical treatment of pediatric lateral condyle fractures. Materials and Methods: This retrospective cohort study evaluated data from 47 pediatric patients with lateral condyle fractures, including demographic information, fracture type, range of motion, complications, radiographic assessments, capitellum size, and presence of a lateral bump. Clinical evaluations were performed using the Hardacre functional classification. To objectively investigate the effect of fragment size on prognosis, the total anterior–posterior (AP) and lateral fragment areas, as well as the corresponding AP and lateral capitellum areas (to provide an individualized constant for ratio calculation), were measured. The fractured fragment/capitellum area ratio was then calculated by dividing the fragment area by the respective capitellum area. All statistical analyses were conducted using a two-tailed significance threshold of p < 0.05, and corresponding 95% confidence intervals were calculated where applicable. Results: After a mean follow-up of 66 months (range: 12–142 months), no significant association was identified between the fractured fragment/capitellum area ratio and either the range of motion (p > 0.05) or the presence of a palpable lateral bump (p > 0.05). In contrast, a higher fractured fragment/capitellum area ratio was found to be significantly associated with the presence of radiographic complications (p = 0.01) and a larger final capitellar area (p = 0.02). Conclusions: The fractured fragment/capitellum area ratio, a newly defined parameter for quantifying initial fracture size in pediatric lateral condylar fractures, demonstrated no measurable effects on clinical outcomes, the presence of a lateral bump, or range of motion; however, it was significantly associated with the development of radiological complications and may represent an important predictor of subsequent capitellar hypertrophy. Level of Evidence: This study corresponds to Level III—retrospective cohort study. Full article

To enable the deployment of road distress detection models on resource-constrained embedded platforms, this paper presents a compression case study based on the LRDD-YOLOv8n detector designed for real-time 1080p video input. Layer-adaptive magnitude-based pruning (LAMP) was integrated with channel-wise knowledge distillation. First, LAMP performs structured pruning adaptive global sparsity allocation to reduce parameters and FLOPs. Then, a larger teacher model (LRDD-YOLOv8s) with high structural similarity guides the pruned student to recover feature representations. Compared to the baseline LRDD-YOLOv8n (64.4% mAP@0.5, 2.02 M parameters, 5.9G FLOPs, and 55.5 ms GPU inference time on Jetson TX2), our compressed model under a 1/1.4 target compression ratio achieves a mAP@0.5 of 65.1% (an slight accuracy increment of 0.7%), while reducing parameters by 36.1% (to 1.29 M) and FLOPs by 30.5% (to 4.1 G). Deployed on the BOXER-8120AI edge platform (Jetson TX2), the optimized model achieves an average inference time of 48.3 ms per frame (a 13.0% latency reduction compared to the baseline model). In addition, a 20 FPS detection rate was sustained under the 30 FPS maximum hardware acquisition limit of the industrial camera stream. Kinematic and geometric analysis validates that this processing rate utilizes 66.7% of all physically available frames and establishes a 95.4% consecutive frame-to-frame spatial overlap at typical inspection vehicle speeds (40–60 km/h). Full article

The utilization of saline–alkali lands and the competition between medicinal plants and grain crops are urgent issues. This study aimed to evaluate the effects of combined biochar and phosphogypsum application on soil physicochemical properties, microbial communities, and safflower growth, yield, and bioactive component accumulation in moderately saline–alkali soil of western Jilin, and to identify key soil factors driving these responses. To achieve this, outdoor pot experiments were conducted using safflower (Carthamus tinctorius L.), with the application of 1% biochar + 1% phosphogypsum to moderately saline–alkali soil. The results showed that the amendment significantly reduced bulk density (BD), pH, sodium adsorption ratio (SAR), total alkalinity (TA), and exchangeable sodium percentage (ESP), while increasing soil water content (SWC), soil organic matter (SOM), nitrogen, phosphorus, potassium, and beneficial ions. Soil sucrase, urease, alkaline phosphatase, and catalase activities were enhanced. Copiotrophic taxa (Pseudomonadota, Sphingomonas, Vicinamibacter) increased, whereas oligotrophic taxa (Gemmatimonadetes, Longimicrobium, Luteitalea) decreased, with stronger effects on bacteria than fungi. Safflower growth indices improved; leaf Na⁺/K⁺ ratio, superoxide radicals, and malondialdehyde decreased; and soluble protein, proline, and antioxidant enzyme activities increased. Bioactive components (hydroxysafflor yellow A, kaempferol) and yield reached 1.41%, 0.056%, and 343.23 mg/plant, representing 1.74–27.68-fold increases over moderate and mild saline–alkali soils. Correlation analysis identified SOM, total nitrogen (TN), available phosphorus (AP), BD, SWC, pH, SAR, TA, and ESP as key factors. In conclusion, co-application of 1% biochar and 1% phosphogypsum improves soil physicochemical and microbial properties, alleviates saline–alkali stress, and enhances safflower quality and yield. Full article

As climate change progresses, the frequency of wildfires has increased dramatically, causing severe ecological damage. Timely and accurate fire detection has emerged as a pressing concern. This paper explores the effects of different parameters on model performance based on four datasets, namely the FIRE Dataset, Home Fire Dataset, Forest Fire Dataset and Wildfire Prediction Dataset. Meanwhile, by adopting the strategy of dataset fusion and progressive ablation, the contribution of each individual dataset is quantitatively analyzed. This work introduced the YOLOv11s architecture for the task of fire and smoke identification and conducted comparative experiments with several representative YOLO-series detectors. Extensive experimental findings fully verify that the presented YOLOv11s model displays superior performance in inference efficiency, detection precision, and model lightweight characteristics. Experimental findings demonstrate that the YOLOv11s framework designed in this study realizes optimal model compression efficiency and the lowest inference delay, processing each frame at a speed of merely 3.5 ms, with a detection precision of 93.2%, coupled with a recall ratio of 88.9%, a mean average precision (mAP) of up to 93.1%, and a corresponding F1-score of 91%. The proposed model is easy to deploy on the RK3588 platform. Field tests on the board have verified that the frame rate and peak memory usage meet the requirements for long-term, uninterrupted monitoring applications. Full article

This systematic research was conducted as the first comprehensive scientific analysis of ancient lime plaster samples from Anuradhapura, a World Heritage Site in Sri Lanka. Five ancient heritage sites from 1st to 10th Century AD, covering two stupa domes: Abhayagiri (AP01) and Jethavana (AP02), Monk residence building near Ruwanweliseya Stupa (AP03), Deeghapashan Rock Shelter Building of Abhayagiri Monastery Complex (AP04), and Vessagiriya Rock Shelter wall lime Plaster (AP05) were examined by employing Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray fluorescence (XRF), thermogravimetric analysis (TGA), optical microscopy (OM), scanning electron microscopy (SEM) and gas chromatography-mass spectrometry (GC-MS). The current work investigated the composition, mineralogical and microstructural properties, binding media, and organic additives. Our findings indicate that calcareous lime from seashells and river sand are the main raw materials, with ratios of 1:2.7, 1:2.0, 1:2.4, 1:4.4, and 1:3.7 for the AP01, AP02, AP03, AP04, and AP05 samples, respectively. Data also suggest that plant-based materials, mainly wood apple wax, along with nanoscale fibrous materials, were used as the main additives to enhance the properties of lime plasters. This study provides insights into the raw materials, their mixing ratios, and the techniques employed in the lime plastering of ancient Anuradhapura City, and serves as a scientific reference for the conservation and restoration of ancient buildings resilient to climate change. Full article

To address the issue of CO₂ leakage induced by microcracks in cement sheaths during geological CO₂ storage, this study developed a multi-crosslinked CO₂-responsive preformed particle gel (MCCR-PPG) system. Using vinyl silica nanoparticles (VSNPs) as nano-crosslinkers and reinforcing agents, combined with CO₂-responsive monomers, sodium alginate, organic crosslinkers, and ionic crosslinkers, an intelligent plugging material with a quadruple crosslinking network was constructed. The optimal formulation was determined through single-factor experiments: the molar ratio of DMAA, VIM, and NVP was 2:2:1; the dosages of crosslinker MBA and initiator APS were each 0.5% of the total monomer molar amount; the concentration of CaCl₂ solution was 0.1 mol/L; and the VSNP content was 1 wt%. The results showed that the equilibrium swelling ratio of MCCR-PPGs in CO₂ acidic solution reached 3200%, which was 4.27 times that in deionized water, demonstrating excellent CO₂ responsiveness. Fracture plugging experiments further confirmed that the swollen gel formed a stable barrier within fractures, effectively preventing CO₂ channeling with a breakthrough pressure differential of 2.008 MPa, indicating excellent plugging performance. This study provides a critical material solution for wellbore integrity in CCUS applications and holds significant engineering value for preventing CO₂ leakage and ensuring storage safety. Full article

Locally sourced roughages constitute the dietary foundation of donkey production in northern China, yet their fermentation behavior in the donkey hindgut remains poorly characterized. The present study employed in vitro batch cultures to compare the dry matter disappearance (IVDMD), gas production (GP) kinetics and short-chain volatile fatty acid (VFA) profiles of five locally available feedstuffs—peanut vine (PNV), soybean straw (SBS), wheat shell (WS), reed grass (RG) and bamboo leaf (BL)—when incubated separately with cecal or colonic microbial inocula obtained from Dezhou donkeys. After 40 h incubation, both IVDMD and total VFA concentrations ranked identically across the two hindgut segments: PNV > SBS > WS > RG > BL (p < 0.05), and all indices were consistently higher in the cecal than in the colonic fermentation system (p < 0.05). The asymptotic gas production (A) and the time required to reach half of A (T₁/₂) followed the same ranking as IVDMD (p < 0.01), indicating that feedstuffs with greater fermentable substrate availability sustained fermentation for longer periods. In contrast, the fractional gas production rate (c) and the average gas production rate (AGPR) in RG and BL exceeded those of PNV, SBS, and WS under cecal incubation (p < 0.05), reflecting rapid utilization of a small pool of readily fermentable components in these fibrous substrates. Regarding VFA stoichiometry, BL yielded the highest molar proportion of acetate and PNV the lowest in the colonic system (p < 0.05), whereas the propionate proportion followed the order PNV > SBS > WS > RG > BL (p < 0.01). Consequently, the acetate-to-propionate (A:P) ratio and the non-glucogenic-to-glucogenic (NGR) ratio were highest in BL (p < 0.05). The molar proportions of butyrate and branched-chain VFAs (BCVFAs) in WS, RG, and BL were greater than those in PNV (p < 0.05). Collectively, the five feedstuffs differed markedly in their fermentability, kinetic behavior, and VFA yield profiles, reflecting distinct energy-supply potentials for the donkey host. PNV and SBS exhibited superior overall in vitro fermentation performance and are therefore recommended as preferred roughage sources, whereas BL and RG may serve complementary roles by supporting hindgut epithelial health through elevated butyrate production. These findings provide a mechanistic basis for the rational selection and combination of locally sourced roughages to optimize feeding strategies and improve feed-use efficiency in donkey production. Full article

Structural planes widely developed in slope rock masses are key geological elements governing deformation, failure modes and engineering stability. Traditional manual logging suffers from low efficiency, high safety risks and inadequate data integrity, failing to meet large-scale and refined survey needs. This paper proposes a cross-modal collaborative recognition system for slope discontinuities. The principal methodological contribution is the cross-modal ROI-guidance mechanism itself: 2D detection bounding boxes are back-projected through pixel-to-point-cloud registration to construct region-of-interest constraints in 3D space, transforming intractable global blind-search segmentation into localized oriented analysis within bounded volumes—to the best of the authors’ knowledge, the first systematic establishment of such a “visual detection → ROI-guided 3D analysis” framework for slope discontinuity characterization. Within this paradigm, established modules are adapted to the discontinuity recognition task rather than newly invented: channel attention, bidirectional multi-scale fusion and angle-aware regression are integrated into the detection backbone to address the weak texture contrast, large-scale span and extreme aspect-ratio morphology of discontinuity targets, while a PCA–DBSCAN–RANSAC cascade operating within the ROI volumes extracts dip direction, dip angle, spacing and trace length. Validated on two typical slopes in Hunan Province, the improved network achieves a mAP@0.5 of 89.4%, the average IoU of point cloud segmentation is 82.6–86.3%, the dip angle RMSE is 2.46° and the spacing average relative error is 6.8%. The full workflow takes about 86 min, a 19.5-fold efficiency gain over manual methods, and provides an automated pipeline from heterogeneous remote sensing data to engineering-usable structural parameters. The resulting outputs are organized in a tabular schema compatible with mainstream discrete-element software such as 3DEC and UDEC, where they serve as geometric inputs to downstream stability modelling once site-specific mechanical calibration is performed. The two-site validation reported here should accordingly be read as a proof of operational feasibility within the limestone and sandstone–mudstone envelope examined, with broader deployment to other lithologies identified as the natural next phase of evaluation. Full article

Detecting hail from remote sensing observations remains challenging because hail develops rapidly and its signatures may appear at different levels within a storm. Ground-based radar and geostationary meteorological satellites are the two primary observing systems for this task, yet their observations are often spatially misaligned. Satellite measurements mainly characterize the thermal structure near the cloud top, whereas radar observations capture the lower-level precipitation core. This mismatch is further exacerbated by satellite parallax, namely the apparent horizontal shift of high cloud tops caused by the oblique viewing geometry of a geostationary satellite, together with the vertical tilt of convective storms. Existing joint methods generally combine satellite cloud-top information with radar precipitation information directly, without explicitly correcting the spatial displacement, which limits detection accuracy. To address this issue, we propose HailDeformer, a deep learning framework that first aligns satellite and radar features through a bidirectional deformable cross-attention module equipped with a position-wise confidence gate and optimized with smoothness, contrastive alignment, and observation-structure consistency losses, and then refines the fused representation using an inter-scale attention module and a wavelet-guided refinement module. Experiments on a four-region dataset from China show that HailDeformer consistently outperforms Direct Fusion, Manual Weighting, Cross-Attention Fusion, and Optical Flow Alignment, achieving a mean Average Precision at IoU 0.5 (mAP@0.5) of 0.916, an F1 score of 0.864, a Critical Success Index (CSI) of 0.760, and the lowest False Alarm Ratio (FAR) of 0.149. Ablation studies further confirm that all proposed modules and associated constraints contribute to the overall performance, with the alignment module providing the largest improvement. Additional evaluations demonstrate that HailDeformer remains effective throughout storm evolution and under challenging observational conditions. Full article

Plant detritus plays a pivotal role in regulating soil nutrient dynamics within forest ecosystems. Understanding short-to-medium-term responses of soil-available nitrogen (AN) and phosphorus (AP) to altered detritus inputs is important for forest nutrient management. In this study, we investigated the effects of changing detritus inputs on soil AN and AP in two representative forest types in Northeast China—Korean pine (Pinus koraiensis Siebold et Zucc.) forest (KP) and Aspen (Populus ussuriensis Kom.)−birch (Betula platyphylla Sukaczev) forest (AB). Using the detritus input and removal treatments (DIRTs) method, we established six experimental treatments and measured soil ammonium nitrogen (NH₄⁺-N), soil nitrate nitrogen (NO₃⁻-N), and AP contents monthly from May to October. The results showed that significant differences in NH₄⁺-N, NO₃⁻-N, and AP contents were observed among treatments. Under the six DIRTs, the fluctuation ranges of NH₄⁺-N, NO₃⁻-N, and AP contents in KP soil were 1.16–12.52 mg/kg, 7.34–35.40 mg/kg, and 9.63–31.72 mg/kg, respectively. For AB soil, the fluctuation ranges of the above three nutrients under the six DIRTs were 2.94–13.17 mg/kg, 3.45–28.47 mg/kg, and 1.77–25.60 mg/kg, respectively. Root treatments exerted stronger effects on AN and AP than litter: root exclusion generally reduced NH₄⁺-N but increased NO₃⁻-N and AP, with the direction and magnitude of the response to this treatment varying with month and forest type, whereas litter treatments showed no consistent trends. The soil-available N:P ratio was lower in the KP forest than in the AB forest; root exclusion significantly reduced the N:P ratio in the AB forest but had no significant effect on that in the KP forest. In terms of seasonal dynamics, the study found that AN peaked in May and AP in July. In conclusion, these findings reflect the short-to-medium-term effects of plant detritus, forest type, and month on soil-available nitrogen and phosphorus, providing scientific insights into how detritus changes alter soil nutrients in temperate forests. Full article

We study the cooperation for the joint unicast and multicast (JUMC) transmission system through a full-duplex (FD) decode-and-forward (DaF) mode relay and propose sub-optimal beamforming schemes for this cooperative JUMC FD relay channel. The beamforming vectors at the access point (AP) and at the full-duplex relay (FDR) are optimized with the metric based on the end-to-end information rate. The cooperation lets the user terminals (UT) outside of the direct coverage of the AP to be served by JUMC from the AP, and hence, the focus of this paper is on the sum rate resulting from the cooperation. As a reference scheme, a zero-forcing-based (ZF) beamforming algorithm is proposed, which suppresses the self-interference (SI) at the FDR perfectly. The SI at the FDR and the minimum operation for the signal-to-interference power ratios (SINR) at involved nodes of the DaF protocol are leveraged in designing and optimizing the second beamforming algorithm, which is the regularized beamforming scheme, since it allows an optimal amount of the SI at the FDR. This algorithm relies on the iterative applications of a quadratically constrained quadratic problem (QCQP) in its central part, while a few one-dimensional searches are running for the optimal SI levels for the individual rates. We consider three different scenarios depending on the existence of an FDR unicast message and the multiple UTs in the coverage of FDR for the application of the proposed algorithms, with some necessary modifications. Corroborating simulation results are presented to show the strengths and weaknesses of the proposed algorithms for the cooperative JUMC system. Full article

Frequency-hopping (FH) signals appear as small rectangular pulses in time-frequency spectrograms. At low signal-to-noise ratios (SNRs), noise along the frequency axis, caused by short-time Fourier transform (STFT) spectral leakage, blurs pulse boundaries, while the varying scales of hop rectangles exceed the capacity of a single receptive field. This paper presents YOLO11-FH, a modified YOLO11 detector that introduces two signal-processing-motivated modules. A FreqSmoothBlock (FSB) uses a $(3,1)$ depthwise convolution to smooth exclusively along the frequency axis, while adding only $5C$ parameters. A TFMultiResBlock (TFMRB) fuses three parallel dilated convolution branches (dilation rates of 1, 2, and 3) to cover different hop scales, replacing a heavier C3k2 module. The detection head is further simplified by halving the Bottleneck repeat count and disabling the deep submodule at the P5 scale. On a simulated FH dataset (SNRs ranging from $-15$ dB to $-10$ dB, five jamming types), YOLO11-FH achieves 96.04% mean average precision (mAP)@0.5 and 76.18% mAP@0.5:0.95, outperforming the YOLO11n baseline by 0.95 and 2.91 percentage points (pp) with 2.9% fewer parameters. Full article

p-nitrophenol (p-NP) is a pollutant with environmental persistence, bioaccumulation potential, and significant health risks, and is widely dispersed in wastewater, so efficient removal of p-NP is imperative. Among the various methods, the catalytic reduction of p-NP to p-aminophenol (p-AP) using sodium borohydride (NaBH₄) is a particularly promising one and, herein, catalysts play a crucial role. Among the various metals, Au shows unique catalytic activity for p-NP reduction. However, nanosized Au often exhibit limited activity and stability due to their high surface free energy. To address this challenge, we designed and synthesized Au-SnO_x hybrid nanoparticles confined within hollow mesoporous silica nanoreactors (Au-SnO_x@hm-SiO₂) via a soft-template-assisted co-adsorption strategy. The resulting bimetallic Au-SnO_x@hm-SiO₂ nanoreactor showed significantly enhanced catalytic activity toward the NaBH₄-mediated reduction of p-nitrophenol (p-NP) compared with its monometallic Au@hm-SiO₂ counterpart, owing to the synergistic effect between Au and SnO_x. Among various Au/Sn ratios, the catalyst with an Au/Sn molar ratio of 1:0.1 demonstrated the highest activity, achieving complete conversion of p-NP within 5 min at a p-NP/Au molar ratio of 529:1—a tenfold improvement over Au@hm-SiO₂. Moreover, the catalyst maintained high efficiency over six consecutive cycles, with only slight deactivation, benefiting from the protective silica shell. Full article