Segmental atrophy(SA)of the liver is a rare,often underrecognized,benign condition that presents as a mass lesion,mimicking a neoplasm,which poses a significant diagnostic challenge.Given its rarity,only a limited num...Segmental atrophy(SA)of the liver is a rare,often underrecognized,benign condition that presents as a mass lesion,mimicking a neoplasm,which poses a significant diagnostic challenge.Given its rarity,only a limited number of case reports and series have been published,resulting in sparse literature on the entity.This review aims to summarize the clinicopathologic and diagnostic features of SA and thus improve its recognition.展开更多
When only a portion of the shield lining structures in a full-line tunnel are overloaded,their bearing and failure characteristics are significantly different from those in the full-line overloaded case.In existing st...When only a portion of the shield lining structures in a full-line tunnel are overloaded,their bearing and failure characteristics are significantly different from those in the full-line overloaded case.In existing studies,one or several segmental lining rings have been studied,with overload applied to selected lining rings to analyze the performance evolution of the lining structures;however,this approach fails to reveal the bearing and failure characteristics of shield lining rings under localized overload.To address this research gap,we employ 3D finite element modeling to investigate the mechanical performance and failure mechanisms of shield segmental linings under localized overload conditions,and compare the results with full-line overload scenarios.Additionally,the impact of reinforcing shield segmental linings with steel rings is studied to address issues arising from localized overloads.The results indicate that localized overloads lead to significant ring joint dislocation and higher stress on longitudinal bolts,potentially causing longitudinal bolt failure.Furthermore,the overall deformation of lining rings,segmental joint opening,and stress in circumferential bolts and steel bars is lower compared to full-line overloads.For the same overload level,the convergence deformation of the lining under full-line overload is 1.5 to 2.0 times higher than that under localized overload.For localized overload situations,a reinforcement scheme with steel rings spanning across two adjacent lining rings is more effective than installing steel rings within individual lining rings.This spanning ring reinforcement strategy not only enhances the structural rigidity of each ring,but also limits joint dislocation and reduces stress on longitudinal bolts,with the reduction in maximum ring joint dislocation ranging from 70%to 82%and the reduction in maximum longitudinal bolt stress ranging from 19%to 57%compared to reinforcement within rings.展开更多
Cage plus plate(CP)and zero-profile(Zero-P)devices are widely used in anterior cervical discectomy and fusion(ACDF).This study aimed to compare adjacent segment biomechanical changes after ACDF when using Zero-P devic...Cage plus plate(CP)and zero-profile(Zero-P)devices are widely used in anterior cervical discectomy and fusion(ACDF).This study aimed to compare adjacent segment biomechanical changes after ACDF when using Zero-P device and CP in different segments.First,complete C1—C7 cervical segments were constructed and validated.Meanwhile,four surgery models were developed by implanting the Zero-P device or CP into C4—C5 or C5—C6 segments based on the intact model.The segmental range of motion(ROM)and maximum value of the intradiscal pressure of the surgery models were compared with those of the intact model.The implantation of CP and Zero-P devices in C4—C5 segments decreased ROM by about 91.6%and 84.3%,respectively,and increased adjacent segment ROM by about 8.3%and 6.82%,respectively.The implantation of CP and Zero-P devices in C5—C6 segments decreased ROM by about 93.3%and 89.9%,respectively,while increasing adjacent segment ROM by about 4.9%and 4%,respectively.Furthermore,the implantation of CP and Zero-P devices increased the intradiscal pressure in the adjacent segments of C4—C5 segments by about 4.5%and 6.7%,respectively.The implantation of CP and Zero-P devices significantly increased the intradiscal pressure in the adjacent segments of C5—C6 by about 54.1%and 15.4%,respectively.In conclusion,CP and Zero-P fusion systems can significantly reduce the ROM of the fusion implant segment in ACDF while increasing the ROM and intradiscal pressure of adjacent segments.Results showed that Zero-P fusion system is the best choice for C5—C6 segmental ACDF.However,further studies are needed to select the most suitable cervical fusion system for C4—C5 segmental ACDF.Therefore,this study provides biomechanical recommendations for clinical surgery.展开更多
Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the...Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the monolithic counterpart,the sub-mirrors must maintain precise co-phasing.Piston error critically degrades segmented mirror imaging quality,necessitating efficient and precise detection.To ad-dress the limitations that the conventional circular-aperture diffraction with two-wavelength algorithm is sus-ceptible to decentration errors,and the traditional convolutional neural networks(CNNs)struggle to capture global features under large-range piston errors due to their restricted local receptive fields,this paper pro-poses a method that integrates extended Young’s interference principles with a Vision Transformer(ViT)to detect piston error.By suppressing decentration error interference through two symmetrically arranged aper-tures and extending the measurement range to±7.95μm via a two-wavelength(589 nm/600 nm)algorithm.This approach exploits ViT’s self-attention mechanism to model global characteristics of interference fringes.Unlike CNNs constrained by local convolutional kernels,the ViT significantly improves sensitivity to inter-ferogram periodicity.The simulation results demonstrate that the proposed method achieves a measurement accuracy of 5 nm(0.0083λ0)across the range of±7.95μm,while maintaining an accuracy exceeding 95%in the presence of Gaussian noise(SNR≥15 dB),Poisson noise(λ≥9 photons/pixel),and sub-mirror gap er-ror(Egap≤0.2)interference.Moreover,the detection speed shows significant improvement compared to the cross-correlation algorithm.This study establishes an accurate,robust framework for segmented mirror error detection,advancing high-precision astronomical observation.展开更多
Background:Reliable animal models are crucial to drug development for focal segmental glomerulosclerosis(FSGS),a rare kidney disease.Variability in success rates in literature and significant ethical concerns with ani...Background:Reliable animal models are crucial to drug development for focal segmental glomerulosclerosis(FSGS),a rare kidney disease.Variability in success rates in literature and significant ethical concerns with animal welfare necessitate further optimization of adriamycin(ADR)-induced FSGS model developed on BALB/c mice.Methods:High-performance liquid chromatography(HPLC)was used to assess ADR stability in water and upon light exposure.To identify the optimal ADR level,single intravenous ADR injections with dosing levels from 10 to 17 mg/kg body weight were administered to BALB/c mice to induce FSGS-like pathology.Body weight and proteinuria of FSGS mice were monitored and analyzed for FSGS model-associated morbidity.Animals were euthanized for hematological and kidney histological assessments 8 weeks post induction.To identify the suitable experiment time frame of the ADR-induced FSGS mouse model,a longitudinal study was performed,with an 11-week continuous monitoring of the symptoms.Results:ADR was found to be unstable in aqueous media and light sensitive.A dosing level of 10.5 mg/kg of ADR was optimal for consistent FSGS mouse model induction on BALB/c strain,characterized by minimal mortality and sustained FSGS-like symptoms.Findings from the longitudinal study suggest that 6 weeks post ADR induction may represent the peak of FSGS pathology severity in this mouse model.This time frame may be used for FSGS drug development projects.Conclusion:Based on the outcome from this study,we identified the optimal ADR dosing level and model testing duration.A standard operating procedure(SOP)for the ADR-induced FSGS mouse model was established to facilitate FSGS basic research and drug development.展开更多
Focal segmental glomerulosclerosis(FSGS)is a histological pattern of glomerular damage that significantly contributes to chronic kidney disease and end-stage renal disease.Its incidence is rising globally,necessitatin...Focal segmental glomerulosclerosis(FSGS)is a histological pattern of glomerular damage that significantly contributes to chronic kidney disease and end-stage renal disease.Its incidence is rising globally,necessitating timely and personalized management strategies.This paper aims to provide an updated overview of the pathophysiology,diagnosis,and therapeutic strategies for FSGS,emphasizing the importance of early interventions and tailored treatments.This editorial synthesizes key findings from recent literature to highlight advancements in understanding and managing FSGS.Emerging evidence supports the role of targeted therapies and personalized approaches in improving outcomes for FSGS patients.Advances include novel biomarkers,genetic testing,and innovative therapeutics such as transient receptor potential ion channel blockers and antisense oligonucleotides for apolipoprotein 1-related FSGS.Effective mana-gement of FSGS requires a combination of timely diagnosis,evidence-based therapeutic strategies,and ongoing research to optimize patient outcomes and address gaps in the current understanding of the disease.展开更多
This study aims to assess the comprehensive strengthening effect of a steel-ultra high performance concrete(UHPC)composite strengthening method.The axial force-moment interaction curve(N-M curve)was calculated in a no...This study aims to assess the comprehensive strengthening effect of a steel-ultra high performance concrete(UHPC)composite strengthening method.The axial force-moment interaction curve(N-M curve)was calculated in a novel way,using cross-sectional strains at ultimate states as well as real-time stress measurements for each material.The enclosed area of the N-M curve was defined as a comprehensive performance index for the system.We validate our approach with comparisons to numerical modeling and full-scale four-point bending experiments.Additionally,strengthening effects were compared for different sagging and hogging moments based on material stress responses,and the impact of various strengthening parameters was analyzed.We find that the N-M curve of the strengthened cross-section envelops that of the un-strengthened cross-section.Notably,improvements in flexural capacity are greater under sagging moments during the large eccentric failure stage,and greater under hogging moments during the small eccentric failure stage.This discrepancy is attributed to the strength utilization of strengthening materials.These findings provide a reference for understanding the strengthening effects and parameters of steel-UHPC composite.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and stru...Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.展开更多
Polyurethane elastomers exhibit high dielectric constants owing to their polar groups,and can be used as energy storage capacitors.Energy storage depends not only on the dielectric constant but also on the dielectric ...Polyurethane elastomers exhibit high dielectric constants owing to their polar groups,and can be used as energy storage capacitors.Energy storage depends not only on the dielectric constant but also on the dielectric loss.However,the relationship between chain structure and dielectric properties is not yet clear.Ketal-containing crosslinked polyurethane elastomers were prepared using cyclic ketal diol as a chain extender.The effect of the soft segment length on the dielectric properties and energy storage was investigated.The cause of the change in the dipolar polarization with the soft segment length was analyzed.As the soft segment length increased,the hard-soft hydrogen bonding decreased,whereas the hard-hard hydrogen bonding increased.Under the action of an electric field,the polar bonds in the ketal-containing polyurethane elastomer overcome the hydrogen bonding between hard-soft segments to produce polarization;meanwhile,they also experience crankshaft motions to generate polarization.The former has a relatively high relaxation activation energy of approximately 10-20 k J·mol^(-1),resulting in a large dielectric loss.The latter has a relatively low relaxation activation energy,approximately 0.7-1.7 kJ·mol^(-1),leading to low dielectric loss.As a result,the dielectric constant showed a decreasing trend,and the dielectric loss gradually decreased.This study provides a theoretical foundation for improving the dielectric properties of polyurethane elastomers.展开更多
Background:Diabetic macular edema is a prevalent retinal condition and a leading cause of visual impairment among diabetic patients’Early detection of affected areas is beneficial for effective diagnosis and treatmen...Background:Diabetic macular edema is a prevalent retinal condition and a leading cause of visual impairment among diabetic patients’Early detection of affected areas is beneficial for effective diagnosis and treatment.Traditionally,diagnosis relies on optical coherence tomography imaging technology interpreted by ophthalmologists.However,this manual image interpretation is often slow and subjective.Therefore,developing automated segmentation for macular edema images is essential to enhance to improve the diagnosis efficiency and accuracy.Methods:In order to improve clinical diagnostic efficiency and accuracy,we proposed a SegNet network structure integrated with a convolutional block attention module(CBAM).This network introduces a multi-scale input module,the CBAM attention mechanism,and jump connection.The multi-scale input module enhances the network’s perceptual capabilities,while the lightweight CBAM effectively fuses relevant features across channels and spatial dimensions,allowing for better learning of varying information levels.Results:Experimental results demonstrate that the proposed network achieves an IoU of 80.127%and an accuracy of 99.162%.Compared to the traditional segmentation network,this model has fewer parameters,faster training and testing speed,and superior performance on semantic segmentation tasks,indicating its highly practical applicability.Conclusion:The C-SegNet proposed in this study enables accurate segmentation of Diabetic macular edema lesion images,which facilitates quicker diagnosis for healthcare professionals.展开更多
This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 20...This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.展开更多
Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the b...Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.展开更多
The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic ...The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic Seismic Hazard Analysis(PSHA)for this region is of significant importance for supporting seismic fortification in major engineering projects and formulating disaster prevention and mitigation policies.In this study,a composite seismic source model was constructed by integrating data on historical earthquakes,active faults,and paleoseismicity.Furthermore,a logic tree framework was employed to quantify epistemic uncertainties,enabling a systematic seismic hazard assessment of the region.To more accurately characterize the spatial heterogeneity of seismic activity,improvements were made to both the Circular Spatial Smoothing Model(CSSM)with a fixed radius and the Adaptive Spatial Smoothing Model(ASSM),with full consideration given to the spatiotemporal completeness of historical earthquake magnitudes.Regarding the CSSM,for scenarios involving small sample sizes in earthquake catalogs,the cross-validation method proposed in this study demonstrated higher robustness than the maximum likelihood method in determining the optimal correlation distance.Performance evaluation results indicate that while both models effectively characterize seismic activity,the ASSM exhibits superior overall predictive performance compared to the CSSM,owing to its ability to adaptively adjust the smoothing radius according to seismic density.Significant discrepancies were observed in the Peak Ground Acceleration(PGA)results calculated with a 10%probability of exceedance in 50 years across different combinations of seismic source models.The single spatially smoothed point-source model yielded a maximum PGA of approximately 0.52 g,with high-value areas concentrated near historical epicenters,thereby significantly underestimating the hazard associated with major fault zones.When combined with the simple fault-source model,the maximum PGA increased to 0.8 g,with high-value zones exhibiting a zonal distribution along faults;however,the risk remained underestimated for faults with low slip rates that are nevertheless approaching their recurrence cycles.Following the introduction of the time-dependent characteristic fault-source model,local PGA values for faults in the middle-to-late stages of their recurrence cycles increased by a factor of 2 to 7 compared to the single model.These results demonstrate that the characteristic fault-source model reasonably delineates the time-dependence of large earthquake recurrence,thereby providing a more accurate assessment of imminent seismic risks.By comprehensively applying the improved spatially smoothed pointsource model,the simple fault-source model,and the characteristic fault-source model,the following faults within the region were identified as having high seismic hazard:the Huangxianggou,Zhangxian,and Tianshui segments of the Xiqinling northern edge fault;the Maqin-Maqu segment of the Dongkunlun fault;the Longriqu fault;the Maoergai fault;the Elashan fault;the Riyueshan fault;the eastern segment of the Lenglongling fault;the Maxianshan segment of the Maxianshan northern Margin fault;and the Maomaoshan-Jinqianghe segment of the Laohushan-Maomaoshan fault.As these faults are located within seismic gaps or are approaching the recurrence periods of large earthquakes,they should be prioritized for current and future seismic monitoring as well as disaster prevention and mitigation efforts.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
In recent years, precast segmental concrete bridge columns became prevalent because of the benefits of accelerated construction, low environmental impact, high quality and low life cycle costs. The lack of a detailed ...In recent years, precast segmental concrete bridge columns became prevalent because of the benefits of accelerated construction, low environmental impact, high quality and low life cycle costs. The lack of a detailed configuration and appropriate design procedure to ensure a comparable performance with monolithic construction has impeded this structural system from being widely used in areas of high seismicity. In this study, precast segmental bridge column cyclic loading tests were conducted to investigate the performance of unbonded post-tensioned segmental bridge columns. One monolithic and two precast segmental columns were tested. The preeast segmental column exhibited minor damage and small residual displacement after the maximum 7% cyclic drift; energy dissipation （ED） can be enhanced byadding ED bars. The experimental results were modeled by a simplified pushover method （SPOM）, as well as a fiber model （FIBM） finite element method. Forty-five cases of columns with different aspect ratios, axial load ratios and ED bar ratios were analyzed with the SPOM and FIBM, respectively. Using these parametric results, a simplified design method was suggested by regressive analysis. Satisfactory correlation was found between the experimental results and the simplified design method for preeast segmental columns with different design parameters.展开更多
AIM: To evaluate the results of segmental duodenectomy (SD) and pancreaticoduodenectomy (PD) for duodenal gastrointestinal stromal tumor (GIST) and help clinicians with surgical management. METHODS: All patients who u...AIM: To evaluate the results of segmental duodenectomy (SD) and pancreaticoduodenectomy (PD) for duodenal gastrointestinal stromal tumor (GIST) and help clinicians with surgical management. METHODS: All patients who underwent surgery for non-metastatic GIST of the duodenum in a single institution since 2000 were prospectively followed up. Seven patients (median age 51 years, range: 41-73 years) were enrolled: five underwent SD and two underwent PD. RESULTS: All the patients had a complete resection (R0), with no postoperative morbidity and mortality. Among the SD group, GIST was classified as low risk in two patients, intermediate risk in two, and high risk in one, according to the Fletcher scale, (vs two high risk patients in the PD group). With a median followup of 41 (18-85) mo, disease-free survival (DFS) rateswere 100% after SD and 0% after PD (P < 0.05). The median DFS was 13 mo in the PD group. CONCLUSION: Whenever associated with clear surgical margins, SD is a reliable and curative option for most duodenal GISTs, and is compatible with longterm DFS.展开更多
AIM: To study the feasibility and safety of middle segmental pancreatectomy (MSP) compared with pancreaticoduodenectomy (PD) and extended distal pancreatectomy (EDP). METHODS: We studied retrospectively 36 cases that ...AIM: To study the feasibility and safety of middle segmental pancreatectomy (MSP) compared with pancreaticoduodenectomy (PD) and extended distal pancreatectomy (EDP). METHODS: We studied retrospectively 36 cases that underwent MSP, 44 patients who underwent PD, and 26 who underwent EDP with benign or low-grade malignant lesions in the mid-portion of the pancreas, between April 2003 and December 2009 in Ruijin Hospital. The perioperative outcomes and long-term outcomes of MSP were compared with those of EDP and PD. Periop-erative outcomes included operative time, intraoperative hemorrhage, transfusion, pancreatic fistula, intraabdominal abscess/infection, postoperative bleeding, reoperation, mortality, and postoperative hospital time. Long-term outcomes, including tumor recurrence, newonset diabetes mellitus (DM), and pancreatic exocrine insufficiency, were evaluated. RESULTS: Intraoperative hemorrhage was 316.1 ± 309.6, 852.2 ± 877.8 and 526.9 ± 414.5 mL for the MSP, PD and EDP groups, respectively (P < 0.05). The mean postoperative daily fasting blood glucose level was significantly lower in the MSP group than in the EDP group (6.3 ± 1.5 mmol/L vs 7.3 ± 1.5 mmol/L, P < 0.05). The rate of pancreatic fistula was higher in the MSP group than in the PD group (42% vs 20.5%, P = 0.039), all of the fistulas after MSP corresponded to grade A (9/15) or B (6/15) and were sealed following conservative treatment. There was no significant difference in the mean postoperative hospital stay between the MSP group and the other two groups. After a mean follow-up of 44 mo, no tumor recurrences were found, only one patient (2.8%) in the MSP group vs five (21.7%) in the EDP group developed new-onset insulin-dependent DM postoperatively (P = 0.029). Moreover, significantly fewer patients in the MSP group than in the PD (0% vs 33.3%, P < 0.001) and EDP (0% vs 21.7%, P = 0.007) required enzyme substitution. CONCLUSION: MSP is a safe and organ-preserving option for benign or low-grade malignant lesions in the neck and proximal body of the pancreas.展开更多
文摘Segmental atrophy(SA)of the liver is a rare,often underrecognized,benign condition that presents as a mass lesion,mimicking a neoplasm,which poses a significant diagnostic challenge.Given its rarity,only a limited number of case reports and series have been published,resulting in sparse literature on the entity.This review aims to summarize the clinicopathologic and diagnostic features of SA and thus improve its recognition.
基金supported by the National Natural Science Foundation of China(No.52008308).
文摘When only a portion of the shield lining structures in a full-line tunnel are overloaded,their bearing and failure characteristics are significantly different from those in the full-line overloaded case.In existing studies,one or several segmental lining rings have been studied,with overload applied to selected lining rings to analyze the performance evolution of the lining structures;however,this approach fails to reveal the bearing and failure characteristics of shield lining rings under localized overload.To address this research gap,we employ 3D finite element modeling to investigate the mechanical performance and failure mechanisms of shield segmental linings under localized overload conditions,and compare the results with full-line overload scenarios.Additionally,the impact of reinforcing shield segmental linings with steel rings is studied to address issues arising from localized overloads.The results indicate that localized overloads lead to significant ring joint dislocation and higher stress on longitudinal bolts,potentially causing longitudinal bolt failure.Furthermore,the overall deformation of lining rings,segmental joint opening,and stress in circumferential bolts and steel bars is lower compared to full-line overloads.For the same overload level,the convergence deformation of the lining under full-line overload is 1.5 to 2.0 times higher than that under localized overload.For localized overload situations,a reinforcement scheme with steel rings spanning across two adjacent lining rings is more effective than installing steel rings within individual lining rings.This spanning ring reinforcement strategy not only enhances the structural rigidity of each ring,but also limits joint dislocation and reduces stress on longitudinal bolts,with the reduction in maximum ring joint dislocation ranging from 70%to 82%and the reduction in maximum longitudinal bolt stress ranging from 19%to 57%compared to reinforcement within rings.
基金the National Natural Science Foundation of China(Nos.32260235 and 82260446)。
文摘Cage plus plate(CP)and zero-profile(Zero-P)devices are widely used in anterior cervical discectomy and fusion(ACDF).This study aimed to compare adjacent segment biomechanical changes after ACDF when using Zero-P device and CP in different segments.First,complete C1—C7 cervical segments were constructed and validated.Meanwhile,four surgery models were developed by implanting the Zero-P device or CP into C4—C5 or C5—C6 segments based on the intact model.The segmental range of motion(ROM)and maximum value of the intradiscal pressure of the surgery models were compared with those of the intact model.The implantation of CP and Zero-P devices in C4—C5 segments decreased ROM by about 91.6%and 84.3%,respectively,and increased adjacent segment ROM by about 8.3%and 6.82%,respectively.The implantation of CP and Zero-P devices in C5—C6 segments decreased ROM by about 93.3%and 89.9%,respectively,while increasing adjacent segment ROM by about 4.9%and 4%,respectively.Furthermore,the implantation of CP and Zero-P devices increased the intradiscal pressure in the adjacent segments of C4—C5 segments by about 4.5%and 6.7%,respectively.The implantation of CP and Zero-P devices significantly increased the intradiscal pressure in the adjacent segments of C5—C6 by about 54.1%and 15.4%,respectively.In conclusion,CP and Zero-P fusion systems can significantly reduce the ROM of the fusion implant segment in ACDF while increasing the ROM and intradiscal pressure of adjacent segments.Results showed that Zero-P fusion system is the best choice for C5—C6 segmental ACDF.However,further studies are needed to select the most suitable cervical fusion system for C4—C5 segmental ACDF.Therefore,this study provides biomechanical recommendations for clinical surgery.
文摘Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the monolithic counterpart,the sub-mirrors must maintain precise co-phasing.Piston error critically degrades segmented mirror imaging quality,necessitating efficient and precise detection.To ad-dress the limitations that the conventional circular-aperture diffraction with two-wavelength algorithm is sus-ceptible to decentration errors,and the traditional convolutional neural networks(CNNs)struggle to capture global features under large-range piston errors due to their restricted local receptive fields,this paper pro-poses a method that integrates extended Young’s interference principles with a Vision Transformer(ViT)to detect piston error.By suppressing decentration error interference through two symmetrically arranged aper-tures and extending the measurement range to±7.95μm via a two-wavelength(589 nm/600 nm)algorithm.This approach exploits ViT’s self-attention mechanism to model global characteristics of interference fringes.Unlike CNNs constrained by local convolutional kernels,the ViT significantly improves sensitivity to inter-ferogram periodicity.The simulation results demonstrate that the proposed method achieves a measurement accuracy of 5 nm(0.0083λ0)across the range of±7.95μm,while maintaining an accuracy exceeding 95%in the presence of Gaussian noise(SNR≥15 dB),Poisson noise(λ≥9 photons/pixel),and sub-mirror gap er-ror(Egap≤0.2)interference.Moreover,the detection speed shows significant improvement compared to the cross-correlation algorithm.This study establishes an accurate,robust framework for segmented mirror error detection,advancing high-precision astronomical observation.
基金United States Department of Defense Office of the Congressionally Directed Medical Research Programs(CDMRP),Grant/Award Number:W81XWH-22-1-0176。
文摘Background:Reliable animal models are crucial to drug development for focal segmental glomerulosclerosis(FSGS),a rare kidney disease.Variability in success rates in literature and significant ethical concerns with animal welfare necessitate further optimization of adriamycin(ADR)-induced FSGS model developed on BALB/c mice.Methods:High-performance liquid chromatography(HPLC)was used to assess ADR stability in water and upon light exposure.To identify the optimal ADR level,single intravenous ADR injections with dosing levels from 10 to 17 mg/kg body weight were administered to BALB/c mice to induce FSGS-like pathology.Body weight and proteinuria of FSGS mice were monitored and analyzed for FSGS model-associated morbidity.Animals were euthanized for hematological and kidney histological assessments 8 weeks post induction.To identify the suitable experiment time frame of the ADR-induced FSGS mouse model,a longitudinal study was performed,with an 11-week continuous monitoring of the symptoms.Results:ADR was found to be unstable in aqueous media and light sensitive.A dosing level of 10.5 mg/kg of ADR was optimal for consistent FSGS mouse model induction on BALB/c strain,characterized by minimal mortality and sustained FSGS-like symptoms.Findings from the longitudinal study suggest that 6 weeks post ADR induction may represent the peak of FSGS pathology severity in this mouse model.This time frame may be used for FSGS drug development projects.Conclusion:Based on the outcome from this study,we identified the optimal ADR dosing level and model testing duration.A standard operating procedure(SOP)for the ADR-induced FSGS mouse model was established to facilitate FSGS basic research and drug development.
文摘Focal segmental glomerulosclerosis(FSGS)is a histological pattern of glomerular damage that significantly contributes to chronic kidney disease and end-stage renal disease.Its incidence is rising globally,necessitating timely and personalized management strategies.This paper aims to provide an updated overview of the pathophysiology,diagnosis,and therapeutic strategies for FSGS,emphasizing the importance of early interventions and tailored treatments.This editorial synthesizes key findings from recent literature to highlight advancements in understanding and managing FSGS.Emerging evidence supports the role of targeted therapies and personalized approaches in improving outcomes for FSGS patients.Advances include novel biomarkers,genetic testing,and innovative therapeutics such as transient receptor potential ion channel blockers and antisense oligonucleotides for apolipoprotein 1-related FSGS.Effective mana-gement of FSGS requires a combination of timely diagnosis,evidence-based therapeutic strategies,and ongoing research to optimize patient outcomes and address gaps in the current understanding of the disease.
基金supported by the National Natural Science Foundation of China(Nos.51938005,52090082,and 52378395)the National Key Research and Development Program of China(No.2023YFB2604402).
文摘This study aims to assess the comprehensive strengthening effect of a steel-ultra high performance concrete(UHPC)composite strengthening method.The axial force-moment interaction curve(N-M curve)was calculated in a novel way,using cross-sectional strains at ultimate states as well as real-time stress measurements for each material.The enclosed area of the N-M curve was defined as a comprehensive performance index for the system.We validate our approach with comparisons to numerical modeling and full-scale four-point bending experiments.Additionally,strengthening effects were compared for different sagging and hogging moments based on material stress responses,and the impact of various strengthening parameters was analyzed.We find that the N-M curve of the strengthened cross-section envelops that of the un-strengthened cross-section.Notably,improvements in flexural capacity are greater under sagging moments during the large eccentric failure stage,and greater under hogging moments during the small eccentric failure stage.This discrepancy is attributed to the strength utilization of strengthening materials.These findings provide a reference for understanding the strengthening effects and parameters of steel-UHPC composite.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
文摘Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.
基金financially supported by the Hubei Key Laboratory of Pollutant Analysis&Reuse Technology(No.PA230102)。
文摘Polyurethane elastomers exhibit high dielectric constants owing to their polar groups,and can be used as energy storage capacitors.Energy storage depends not only on the dielectric constant but also on the dielectric loss.However,the relationship between chain structure and dielectric properties is not yet clear.Ketal-containing crosslinked polyurethane elastomers were prepared using cyclic ketal diol as a chain extender.The effect of the soft segment length on the dielectric properties and energy storage was investigated.The cause of the change in the dipolar polarization with the soft segment length was analyzed.As the soft segment length increased,the hard-soft hydrogen bonding decreased,whereas the hard-hard hydrogen bonding increased.Under the action of an electric field,the polar bonds in the ketal-containing polyurethane elastomer overcome the hydrogen bonding between hard-soft segments to produce polarization;meanwhile,they also experience crankshaft motions to generate polarization.The former has a relatively high relaxation activation energy of approximately 10-20 k J·mol^(-1),resulting in a large dielectric loss.The latter has a relatively low relaxation activation energy,approximately 0.7-1.7 kJ·mol^(-1),leading to low dielectric loss.As a result,the dielectric constant showed a decreasing trend,and the dielectric loss gradually decreased.This study provides a theoretical foundation for improving the dielectric properties of polyurethane elastomers.
基金supported by the Guangdong Pharmaceutical University 2024 Higher Education Research Projects(GKP202403,GMP202402)the Guangdong Pharmaceutical University College Students’Innovation and Entrepreneurship Training Programs(Grant No.202504302033,202504302034,202504302036,and 202504302244).
文摘Background:Diabetic macular edema is a prevalent retinal condition and a leading cause of visual impairment among diabetic patients’Early detection of affected areas is beneficial for effective diagnosis and treatment.Traditionally,diagnosis relies on optical coherence tomography imaging technology interpreted by ophthalmologists.However,this manual image interpretation is often slow and subjective.Therefore,developing automated segmentation for macular edema images is essential to enhance to improve the diagnosis efficiency and accuracy.Methods:In order to improve clinical diagnostic efficiency and accuracy,we proposed a SegNet network structure integrated with a convolutional block attention module(CBAM).This network introduces a multi-scale input module,the CBAM attention mechanism,and jump connection.The multi-scale input module enhances the network’s perceptual capabilities,while the lightweight CBAM effectively fuses relevant features across channels and spatial dimensions,allowing for better learning of varying information levels.Results:Experimental results demonstrate that the proposed network achieves an IoU of 80.127%and an accuracy of 99.162%.Compared to the traditional segmentation network,this model has fewer parameters,faster training and testing speed,and superior performance on semantic segmentation tasks,indicating its highly practical applicability.Conclusion:The C-SegNet proposed in this study enables accurate segmentation of Diabetic macular edema lesion images,which facilitates quicker diagnosis for healthcare professionals.
文摘This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Metaverse Support Program to Nurture the Best Talents(IITP-2024-RS-2023-00254529)grant funded by the Korea government(MSIT).
文摘Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.
基金supported by the National Key R&D Program of China(No.2022YFC3003502).
文摘The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic Seismic Hazard Analysis(PSHA)for this region is of significant importance for supporting seismic fortification in major engineering projects and formulating disaster prevention and mitigation policies.In this study,a composite seismic source model was constructed by integrating data on historical earthquakes,active faults,and paleoseismicity.Furthermore,a logic tree framework was employed to quantify epistemic uncertainties,enabling a systematic seismic hazard assessment of the region.To more accurately characterize the spatial heterogeneity of seismic activity,improvements were made to both the Circular Spatial Smoothing Model(CSSM)with a fixed radius and the Adaptive Spatial Smoothing Model(ASSM),with full consideration given to the spatiotemporal completeness of historical earthquake magnitudes.Regarding the CSSM,for scenarios involving small sample sizes in earthquake catalogs,the cross-validation method proposed in this study demonstrated higher robustness than the maximum likelihood method in determining the optimal correlation distance.Performance evaluation results indicate that while both models effectively characterize seismic activity,the ASSM exhibits superior overall predictive performance compared to the CSSM,owing to its ability to adaptively adjust the smoothing radius according to seismic density.Significant discrepancies were observed in the Peak Ground Acceleration(PGA)results calculated with a 10%probability of exceedance in 50 years across different combinations of seismic source models.The single spatially smoothed point-source model yielded a maximum PGA of approximately 0.52 g,with high-value areas concentrated near historical epicenters,thereby significantly underestimating the hazard associated with major fault zones.When combined with the simple fault-source model,the maximum PGA increased to 0.8 g,with high-value zones exhibiting a zonal distribution along faults;however,the risk remained underestimated for faults with low slip rates that are nevertheless approaching their recurrence cycles.Following the introduction of the time-dependent characteristic fault-source model,local PGA values for faults in the middle-to-late stages of their recurrence cycles increased by a factor of 2 to 7 compared to the single model.These results demonstrate that the characteristic fault-source model reasonably delineates the time-dependence of large earthquake recurrence,thereby providing a more accurate assessment of imminent seismic risks.By comprehensively applying the improved spatially smoothed pointsource model,the simple fault-source model,and the characteristic fault-source model,the following faults within the region were identified as having high seismic hazard:the Huangxianggou,Zhangxian,and Tianshui segments of the Xiqinling northern edge fault;the Maqin-Maqu segment of the Dongkunlun fault;the Longriqu fault;the Maoergai fault;the Elashan fault;the Riyueshan fault;the eastern segment of the Lenglongling fault;the Maxianshan segment of the Maxianshan northern Margin fault;and the Maomaoshan-Jinqianghe segment of the Laohushan-Maomaoshan fault.As these faults are located within seismic gaps or are approaching the recurrence periods of large earthquakes,they should be prioritized for current and future seismic monitoring as well as disaster prevention and mitigation efforts.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
基金Supported by the National Natural Science Foundation （51208268）the Pubiic-Welfare-Technology Research Industry Project of Science Technology Department of Zhejiang （2011 C21078） the Social Development Project of Science Technology Bureau of Ningbo （2011CS0017）.
基金National Natural Science Foundation of China under Grants Nos.51208268 and 51178429K.C.Wong Magna Fund in Ningbo University+1 种基金Transportation Science and Technology Project of Ningbo City under Grant No.201507Natural Science Foundation of Ningbo City under Grant No.2015A610293
文摘In recent years, precast segmental concrete bridge columns became prevalent because of the benefits of accelerated construction, low environmental impact, high quality and low life cycle costs. The lack of a detailed configuration and appropriate design procedure to ensure a comparable performance with monolithic construction has impeded this structural system from being widely used in areas of high seismicity. In this study, precast segmental bridge column cyclic loading tests were conducted to investigate the performance of unbonded post-tensioned segmental bridge columns. One monolithic and two precast segmental columns were tested. The preeast segmental column exhibited minor damage and small residual displacement after the maximum 7% cyclic drift; energy dissipation （ED） can be enhanced byadding ED bars. The experimental results were modeled by a simplified pushover method （SPOM）, as well as a fiber model （FIBM） finite element method. Forty-five cases of columns with different aspect ratios, axial load ratios and ED bar ratios were analyzed with the SPOM and FIBM, respectively. Using these parametric results, a simplified design method was suggested by regressive analysis. Satisfactory correlation was found between the experimental results and the simplified design method for preeast segmental columns with different design parameters.
文摘AIM: To evaluate the results of segmental duodenectomy (SD) and pancreaticoduodenectomy (PD) for duodenal gastrointestinal stromal tumor (GIST) and help clinicians with surgical management. METHODS: All patients who underwent surgery for non-metastatic GIST of the duodenum in a single institution since 2000 were prospectively followed up. Seven patients (median age 51 years, range: 41-73 years) were enrolled: five underwent SD and two underwent PD. RESULTS: All the patients had a complete resection (R0), with no postoperative morbidity and mortality. Among the SD group, GIST was classified as low risk in two patients, intermediate risk in two, and high risk in one, according to the Fletcher scale, (vs two high risk patients in the PD group). With a median followup of 41 (18-85) mo, disease-free survival (DFS) rateswere 100% after SD and 0% after PD (P < 0.05). The median DFS was 13 mo in the PD group. CONCLUSION: Whenever associated with clear surgical margins, SD is a reliable and curative option for most duodenal GISTs, and is compatible with longterm DFS.
基金Supported by The Ministry of Health Sector Funds of China,No. 201002020
文摘AIM: To study the feasibility and safety of middle segmental pancreatectomy (MSP) compared with pancreaticoduodenectomy (PD) and extended distal pancreatectomy (EDP). METHODS: We studied retrospectively 36 cases that underwent MSP, 44 patients who underwent PD, and 26 who underwent EDP with benign or low-grade malignant lesions in the mid-portion of the pancreas, between April 2003 and December 2009 in Ruijin Hospital. The perioperative outcomes and long-term outcomes of MSP were compared with those of EDP and PD. Periop-erative outcomes included operative time, intraoperative hemorrhage, transfusion, pancreatic fistula, intraabdominal abscess/infection, postoperative bleeding, reoperation, mortality, and postoperative hospital time. Long-term outcomes, including tumor recurrence, newonset diabetes mellitus (DM), and pancreatic exocrine insufficiency, were evaluated. RESULTS: Intraoperative hemorrhage was 316.1 ± 309.6, 852.2 ± 877.8 and 526.9 ± 414.5 mL for the MSP, PD and EDP groups, respectively (P < 0.05). The mean postoperative daily fasting blood glucose level was significantly lower in the MSP group than in the EDP group (6.3 ± 1.5 mmol/L vs 7.3 ± 1.5 mmol/L, P < 0.05). The rate of pancreatic fistula was higher in the MSP group than in the PD group (42% vs 20.5%, P = 0.039), all of the fistulas after MSP corresponded to grade A (9/15) or B (6/15) and were sealed following conservative treatment. There was no significant difference in the mean postoperative hospital stay between the MSP group and the other two groups. After a mean follow-up of 44 mo, no tumor recurrences were found, only one patient (2.8%) in the MSP group vs five (21.7%) in the EDP group developed new-onset insulin-dependent DM postoperatively (P = 0.029). Moreover, significantly fewer patients in the MSP group than in the PD (0% vs 33.3%, P < 0.001) and EDP (0% vs 21.7%, P = 0.007) required enzyme substitution. CONCLUSION: MSP is a safe and organ-preserving option for benign or low-grade malignant lesions in the neck and proximal body of the pancreas.
