BACKGROUND Gastrointestinal(GI)tumors are among the most prevalent malignancies,and surgical intervention remains a primary treatment modality.However,the complexity of GI surgery often leads to prolonged recovery and...BACKGROUND Gastrointestinal(GI)tumors are among the most prevalent malignancies,and surgical intervention remains a primary treatment modality.However,the complexity of GI surgery often leads to prolonged recovery and high postoperative complication rates,which threaten patient safety and functional outcomes.Enhanced recovery after surgery(ERAS)principles have been shown to improve perioperative outcomes through evidence-based,multidisciplinary care pathways.Despite its widespread adoption,there is a paucity of research focusing specifically on optimizing ERAS-guided nursing processes in the post-anesthesia care unit(PACU)and evaluating its impact on perioperative safety in patients undergoing GI tumor surgery.This study aimed to investigate whether an ERASbased PACU nursing protocol could enhance recovery,reduce complications,and improve patient safety in this surgical population.AIM To explore the impact of optimizing the recovery room nursing process based on ERAS on the perioperative safety of patients with GI tumors.METHODS A total of 260 patients with GI tumors who underwent elective surgeries under general anesthesia in our hospital from August 2023 to August 2025 and were then observed in the recovery unit(PACU)were selected.They were randomly divided into the observation group(the PACU nursing process was optimized based on ERAS)and the control group(the conventional PACU nursing process was adopted)by the random number grouping method,with 130 cases in each group.The time of gastric tube removal,urinary catheter removal,defecation time,hospital stay,time of leaving the room after tube removal,retention time in the recovery room,occurrence of complications,satisfaction and readmission rate were compared between the two groups after entering the room.Compare the occurrence of adverse events in the PACU nursing process between the two groups.RESULTS The time of gastric tube removal,urinary catheter removal,defecation time,hospital stay,retention time in the recovery room,total incidence of complications and readmission rate in the observation group were significantly lower than those in the control group,and the satisfaction rate was higher than that in the control group(P<0.05).The occurrence of adverse events in the PACU nursing process in the observation group was lower than that in the control group(P<0.05).CONCLUSION Optimizing the PACU nursing process based on ERAS can effectively accelerate the recovery process of patients undergoing GI tumor surgery,reduce adverse events,improve nursing satisfaction,and at the same time,lower the incidence of adverse events in the PACU nursing process,providing a more refined management basis for clinical practice.展开更多
Objective:To systematically explore the effectiveness of combining Enhanced Recovery After Surgery(ERAS)nursing and empathy intervention for postoperative patients with glioma.Methods:A total of 54 patients with gliom...Objective:To systematically explore the effectiveness of combining Enhanced Recovery After Surgery(ERAS)nursing and empathy intervention for postoperative patients with glioma.Methods:A total of 54 patients with glioma undergoing surgical treatment were selected for the study.The patients were admitted to the hospital between April 2023 and April 2025.The patients were divided into an observation group(n=27)and a control group(n=27)based on a random number table method.Relevant intervention indicators were compared between the two groups.Results:Compared with the control group,the postoperative recovery indicators in the observation group showed significant differences(P<0.05).After intervention,the scores of stress psychological indicators,FMA,NHISS,and ADL in the observation group were all better than those in the control group(P<0.05).The incidence of complications in the observation group was significantly lower than that in the control group(P<0.05).Conclusion:The combined application of empathy intervention and ERAS nursing effectively regulates the postoperative stress psychological state of patients with glioma,significantly improves their limb and neurological functions as well as daily living abilities,accelerates postoperative recovery,and reduces complications.This approach is feasible for wider implementation.展开更多
This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raph...This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.展开更多
Aiming at the problem of insufficient recognition of implicit variants by existing Chinese sensitive text detection methods,this paper proposes the IPKE-MoE framework,which consists of three parts,namely,a sensitive w...Aiming at the problem of insufficient recognition of implicit variants by existing Chinese sensitive text detection methods,this paper proposes the IPKE-MoE framework,which consists of three parts,namely,a sensitive word variant extraction framework,a sensitive word variant knowledge enhancement layer and a mixture-of-experts(MoE)classification layer.First,sensitive word variants are precisely extracted through dynamic iterative prompt templates and the context-aware capabilities of Large Language Models(LLMs).Next,the extracted variants are used to construct a knowledge enhancement layer for sensitive word variants based on RoCBert models.Specifically,after locating variants via n-gram algorithms,variant types are mapped to embedding vectors and fused with original word vectors.Finally,a mixture-of-experts(MoE)classification layer is designed(sensitive word,sentiment,and semantic experts),which decouples the relationship between sensitiveword existence and text toxicity throughmultiple experts.This framework effectively combines the comprehension ability of Large Language Models(LLMs)with the discriminative ability of smaller models.Our two experiments demonstrate that the sensitive word variant extraction framework based on dynamically iterated prompt templates outperforms other baseline prompt templates.TheRoCBert models incorporating the sensitive word variant knowledge enhancement layer and a mixture-of-experts(MoE)classification layer achieve superior classification performance compared to other baselines.展开更多
It is difficult to recover chrysocolla from sulfidation flotation which is closely related to the mineral surface composition.In this study,the effects of fluoride roasting on the surface composition of chrysocolla we...It is difficult to recover chrysocolla from sulfidation flotation which is closely related to the mineral surface composition.In this study,the effects of fluoride roasting on the surface composition of chrysocolla were investigated,its impact on sulfidation flotation was explored,and the mechanisms involved in both fluoride roasting and sulfidation flotation were discussed.With CaF_(2)as the roasting reagent,Na_(2)S·9H_(2)O as the sulfidation reagent,and sodium butyl xanthate(NaBX)as the collector,the results of the flotation experiments showed that fluoride roasting improved the floatability of chrysocolla,and the recovery rate increased from 16.87%to 82.74%.X-ray diffraction analysis revealed that after fluoride roasting,approximately all the Cu on the chrysocolla surface was exposed in the form of CuO,which could provide a basis for subsequent sulfidation flotation.The microscopy and elemental analyses revealed that large quantities of"pagoda-like"grains were observed on the sulfidation surface of the fluoride-roasted chrysocolla,indicating high crystallinity particles of copper sulfide.This suggests that the effect of sulfide formation on the chrysocolla surface was more pronounced.X-ray photoelectron spectroscopy revealed that fluoride roasting increased the relative contents of sulfur and copper on the surface and that both the Cu~+and polysulfide fractions on the surface of the minerals increased.This enhances the effect of sulfidation,which is conducive to flotation recovery.Therefore,fluoride roasting improved the effect of copper species transformation and sulfidation on the surface of chysocolla,promoted the adsorption of collectors,and improved the recovery of chrysocolla from sulfidation flotation.展开更多
To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC per...To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.展开更多
3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with m...3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.展开更多
With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper pro...With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.展开更多
Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(...Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems.展开更多
Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instabili...Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.展开更多
We found the qualitative study by Xu et al.on how patients feel about laparoscopic incisions under enhanced recovery after surgery(ERAS)protocols to be very interesting.1 Xu et al.carried out a qualitative study on pa...We found the qualitative study by Xu et al.on how patients feel about laparoscopic incisions under enhanced recovery after surgery(ERAS)protocols to be very interesting.1 Xu et al.carried out a qualitative study on patient experience with laparoscopic incisions under an ERAS protocol to highlight the problem of psychosocial and aesthetic concerns,which are often overlooked when planning surgical operations.This study,which involved semistructured interviews with sixteen people,aimed to narrow perioperative education and the decision-making process for incision site selection,thus making the processes more focused on patient priorities.The study is based on a timely but under-researched subject area;however,it is possible to outline four possible areas of improvement that would allow the study to be more transparent and,at the same time,more applicable to clinical practice.展开更多
AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited...AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.展开更多
The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To...The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.展开更多
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.展开更多
Low-salinity water injection has been utilized as a promising method for oil recovery in recent years. Low-salinity water flooding changes the ion composition or brine salinity for improving oil recovery. Recently, th...Low-salinity water injection has been utilized as a promising method for oil recovery in recent years. Low-salinity water flooding changes the ion composition or brine salinity for improving oil recovery. Recently, the application of nanoparticles with low-salinity water flooding has shown remarkable results in enhanced oil recovery(EOR). Many studies have been performed on the effect of nanofluids on EOR mechanisms. Their results showed that nanofluids can improve oil recovery when used in low-salinity water flooding. In this work, the effects of injection of low-salinity water and low-salinity nanofluid(prepared by adding SiO_2 nanoparticles to low-salinity water) on oil recovery were investigated. At first, the effects of ions were investigated with equal concentrations in low-salinity water flooding. The experimental results showed that the monovalent ions had better performance than the divalent ions because of them having more negative zeta potential and less ionic strength. Also, low-salinity water flooding recovered 6.1% original oil in place(OOIP) more than the high-salinity flooding. Contact angle measurements demonstrated that low-salinity water could reduce the contact angle between oil and water. Then in the second stage, experiments were continued by adding SiO_2 nanoparticles to the K+ solution which had the highest oil recovery at the first stage. The experimental results illustrated that the addition of Si02 nanoparticles up to 0.05 wt% increased oil recovery by about 4% OOIP more than the low-salinity water flooding.展开更多
Great efforts have been devoted to improve the photocatalytic activity of TiO2 in the visible light region. Rational design of the external structure and adjustment of intrinsic electronic status by impurity doping ar...Great efforts have been devoted to improve the photocatalytic activity of TiO2 in the visible light region. Rational design of the external structure and adjustment of intrinsic electronic status by impurity doping are two main effective ways to achieve this purpose. A facile onepot synthetic approach was developed to prepare C-doped hollow TiO2 spheres, which simultaneously realized these advantages. The synthesized TiO2 exhibits a mesoporous hollow spherical structure composed of fine nanocrystals, leading to high specific surface area（~180 m^2/g） and versatile porous texture. Carbonate-doping was achieved by a postthermal treatment at a relatively low temperature（200°C）, which makes the absorption edge red-shifted to the visible region of the solar spectrum. Concomitantly, Ti^（3＋） induced by C-doping also functions in improving the visible-light photocatalytic activity by reducing the band gap. There exists a synergistic effect from multiple stimulatives to enhance the photocatalytic effect of the prepared TiO2 catalyst. It is not out of expectation that the asprepared C-doped hollow TiO2 spheres exhibits an improved photocatalytic activity under visible light irradiation in organic pollutant degradation.展开更多
This research addresses the critical challenge of enhancing satellite images captured under low-light conditions,which suffer from severely degraded quality,including a lack of detail,poor contrast,and low usability.O...This research addresses the critical challenge of enhancing satellite images captured under low-light conditions,which suffer from severely degraded quality,including a lack of detail,poor contrast,and low usability.Overcoming this limitation is essential for maximizing the value of satellite imagery in downstream computer vision tasks(e.g.,spacecraft on-orbit connection,spacecraft surface repair,space debris capture)that rely on clear visual information.Our key novelty lies in an unsupervised generative adversarial network featuring two main contributions:(1)an improved U-Net(IU-Net)generator with multi-scale feature fusion in the contracting path for richer semantic feature extraction,and(2)a Global Illumination Attention Module(GIA)at the end of the contracting path to couple local and global information,significantly improving detail recovery and illumination adjustment.The proposed algorithm operates in an unsupervised manner.It is trained and evaluated on our self-constructed,unpaired Spacecraft Dataset for Detection,Enforcement,and Parts Recognition(SDDEP),designed specifically for low-light enhancement tasks.Extensive experiments demonstrate that our method outperforms the baseline EnlightenGAN,achieving improvements of 2.7%in structural similarity(SSIM),4.7%in peak signal-to-noise ratio(PSNR),6.3%in learning perceptual image patch similarity(LPIPS),and 53.2%in DeltaE 2000.Qualitatively,the enhanced images exhibit higher overall and local brightness,improved contrast,and more natural visual effects.展开更多
In recent years,production from tight oil reservoirs has increasingly supplemented production from conventional oil resources.Oil-wet formations account for a considerable proportion of tight oil reservoirs.Surfactant...In recent years,production from tight oil reservoirs has increasingly supplemented production from conventional oil resources.Oil-wet formations account for a considerable proportion of tight oil reservoirs.Surfactant can change wettability and reduce interfacial tension,thus resulting in a better oil recovery.In this manuscript,a nonionic surfactant was introduced for tight oil-wet reservoirs.The oil recovery in the oil-wet sandstone due to spontaneous imbibition was 8.59%lower than that of the waterwet sandstone due to surfactant.The 0.1%surfactant solution corresponded to the highest imbibition recovery rate of 27.02%from the oil-wet sample.With the surfactant treatment,the treated core quickly changed from weakly oil-wet to weakly water-wet.The capillary force acted as the driving force and promoted imbibition.The optimal surfactant adsorption quantity in the oil-wet sandstone was observed in the sample at concentrations ranging from 0.1%to 0.3%,which also corresponded to the highest oil recovery.Analysis of the inverse Bond number NB-1 suggested that the driving force was gravity for brine imbibition in the oil-wet cores and that it was capillary force for surfactant imbibition in the oil-wet cores.When the surfactant concentration was lower than the critical micelle concentration,the surfactant concentration was negatively correlated with the inverse Bond number and positively correlated with the oil recovery rate.When the surfactant concentration was higher than the critical micelle concentration,the oil recovery increased with a smaller interfacial tension.Nuclear magnetic resonance suggested that the movable pore and pore throat size in the oil-wet sample decreased from 0.363 mm in the untreated rock to 0.326 mm with the surfactant treatment,which indicated that the surfactant improved the flow capacity of the oil.The findings of this study can help to better understand the adsorption impact of surfactants on the characteristics of the oil/water and solid/liquid interfaces.The imbibition mechanism in oil-wet tight sandstone reservoirs was further revealed.These systematic approaches help to select appropriate surfactants for better recovery in oil-wet tight sandstone reservoirs through imbibition.展开更多
BACKGROUND Intravenous infusion is a common method of drug administration in clinical practice.Errors in any aspect of the infusion process,from the verification of medical orders,preparation of the drug solution,to i...BACKGROUND Intravenous infusion is a common method of drug administration in clinical practice.Errors in any aspect of the infusion process,from the verification of medical orders,preparation of the drug solution,to infusion by nursing staff,may cause adverse infusion events.AIM To analyzed the value of improving nursing measures and enhancing nursing management to reduce the occurrence of adverse events in pediatric infusion.METHODS The clinical data of 130 children who received an infusion in the pediatric department of our hospital from May 2020 to May 2021 were analyzed and divided into two groups according to the differences in nursing measures and nursing management:65 patients in the control group received conventional nursing and nursing management interventions,while 65 patients in the observation group received improved nursing measure interventions and enhanced nursing management.The occurrence of adverse events,compliance of children,satisfaction of children’s families,and complaints regarding the transfusion treatment were recorded in both groups.RESULTS The incidence of fluid extravasation and infusion set dislodgement in the observation group were 3.08%and 1.54%,respectively,which were significantly lower than 12.31%and 13.85%in the control group(P<0.05),while repeated punctures and medication addition errors in the observation group were 3.08%and 0.00%,respectively,which were lower than 9.23%and 3.08%in the control group,but there was no significant difference(P>0.05).The compliance rate of children in the observation group was 98.46%(64/65),which was significantly higher than 87.69%(57/65)in the control group,and the satisfaction rate of children’s families was 96.92%(63/65),which was significantly higher than 86.15%(56/65)in the control group(P<0.05).The observation group did not receive any complaints from the child’s family,whereas the control group received four complaints,two of which were due to the crying of the child caused by repeated punctures,one due to the poor attitude of the nurse,and one due to medication addition errors,with a cumulative complaint rate of 6.15%.The cumulative complaint rate of the observation group was significantly lower than that of the control group(P<0.05).CONCLUSION Improving nursing measures and enhancing nursing management can reduce the incidence of fluid extravasation and infusion set dislodgement in pediatric patients,improve children’s compliance and satisfaction of their families,and reduce family complaints.展开更多
基金Supported by 2025 Henan Medical Education Research Project,No.WJLX2025038.
文摘BACKGROUND Gastrointestinal(GI)tumors are among the most prevalent malignancies,and surgical intervention remains a primary treatment modality.However,the complexity of GI surgery often leads to prolonged recovery and high postoperative complication rates,which threaten patient safety and functional outcomes.Enhanced recovery after surgery(ERAS)principles have been shown to improve perioperative outcomes through evidence-based,multidisciplinary care pathways.Despite its widespread adoption,there is a paucity of research focusing specifically on optimizing ERAS-guided nursing processes in the post-anesthesia care unit(PACU)and evaluating its impact on perioperative safety in patients undergoing GI tumor surgery.This study aimed to investigate whether an ERASbased PACU nursing protocol could enhance recovery,reduce complications,and improve patient safety in this surgical population.AIM To explore the impact of optimizing the recovery room nursing process based on ERAS on the perioperative safety of patients with GI tumors.METHODS A total of 260 patients with GI tumors who underwent elective surgeries under general anesthesia in our hospital from August 2023 to August 2025 and were then observed in the recovery unit(PACU)were selected.They were randomly divided into the observation group(the PACU nursing process was optimized based on ERAS)and the control group(the conventional PACU nursing process was adopted)by the random number grouping method,with 130 cases in each group.The time of gastric tube removal,urinary catheter removal,defecation time,hospital stay,time of leaving the room after tube removal,retention time in the recovery room,occurrence of complications,satisfaction and readmission rate were compared between the two groups after entering the room.Compare the occurrence of adverse events in the PACU nursing process between the two groups.RESULTS The time of gastric tube removal,urinary catheter removal,defecation time,hospital stay,retention time in the recovery room,total incidence of complications and readmission rate in the observation group were significantly lower than those in the control group,and the satisfaction rate was higher than that in the control group(P<0.05).The occurrence of adverse events in the PACU nursing process in the observation group was lower than that in the control group(P<0.05).CONCLUSION Optimizing the PACU nursing process based on ERAS can effectively accelerate the recovery process of patients undergoing GI tumor surgery,reduce adverse events,improve nursing satisfaction,and at the same time,lower the incidence of adverse events in the PACU nursing process,providing a more refined management basis for clinical practice.
文摘Objective:To systematically explore the effectiveness of combining Enhanced Recovery After Surgery(ERAS)nursing and empathy intervention for postoperative patients with glioma.Methods:A total of 54 patients with glioma undergoing surgical treatment were selected for the study.The patients were admitted to the hospital between April 2023 and April 2025.The patients were divided into an observation group(n=27)and a control group(n=27)based on a random number table method.Relevant intervention indicators were compared between the two groups.Results:Compared with the control group,the postoperative recovery indicators in the observation group showed significant differences(P<0.05).After intervention,the scores of stress psychological indicators,FMA,NHISS,and ADL in the observation group were all better than those in the control group(P<0.05).The incidence of complications in the observation group was significantly lower than that in the control group(P<0.05).Conclusion:The combined application of empathy intervention and ERAS nursing effectively regulates the postoperative stress psychological state of patients with glioma,significantly improves their limb and neurological functions as well as daily living abilities,accelerates postoperative recovery,and reduces complications.This approach is feasible for wider implementation.
基金funded by the Deanship of Scientific Research and Libraries,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No.(RPFAP-82-1445)。
文摘This research proposes an improved Puma optimization algorithm(IPuma)as a novel dynamic recon-figuration tool for a photovoltaic(PV)array linked in total-cross-tied(TCT).The proposed algorithm utilizes the Newton-Raphson search rule(NRSR)to boost the exploration process,especially in search spaces with more local regions,and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma.The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems.It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions.The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms.In addition,the proposed IPuma is utilized to reconfigure a 9×9 PV array that operates under different shade patterns,such as lower triangular(LT),long wide(LW),and short wide(SW).In addition to other programmed approaches,such as the Whale optimization algorithm(WOA),grey wolf optimizer(GWO),Harris Hawks optimization(HHO),particle swarm optimization(PSO),gravitational search algorithm(GSA),biogeography-based optimization(BBO),sine cosine algorithm(SCA),equilibrium optimizer(EO),and original Puma,the indicated method is contrasted to the traditional configurations of TCT and Sudoku.In addition,the metrics of mismatch power loss,maximum efficiency improvement,efficiency improvement ratio,and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach.The proposed IPuma improved the generated power by 36.72%,28.03%,and 40.97%for SW,LW,and LT,respectively,outperforming the TCT configuration.In addition,it achieved the best maximum efficiency improvement among the algorithms considered,with 26.86%,21.89%,and 29.07%for the examined patterns.The results highlight the superiority and competence of the proposed approach in both convergence rates and stability,as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.
基金funded by the National Natural Science Foundation of China(Grant No.62441212)the Major Project of the Natural Science Foundation of Inner Mongolia(Grant No.2025ZD008).
文摘Aiming at the problem of insufficient recognition of implicit variants by existing Chinese sensitive text detection methods,this paper proposes the IPKE-MoE framework,which consists of three parts,namely,a sensitive word variant extraction framework,a sensitive word variant knowledge enhancement layer and a mixture-of-experts(MoE)classification layer.First,sensitive word variants are precisely extracted through dynamic iterative prompt templates and the context-aware capabilities of Large Language Models(LLMs).Next,the extracted variants are used to construct a knowledge enhancement layer for sensitive word variants based on RoCBert models.Specifically,after locating variants via n-gram algorithms,variant types are mapped to embedding vectors and fused with original word vectors.Finally,a mixture-of-experts(MoE)classification layer is designed(sensitive word,sentiment,and semantic experts),which decouples the relationship between sensitiveword existence and text toxicity throughmultiple experts.This framework effectively combines the comprehension ability of Large Language Models(LLMs)with the discriminative ability of smaller models.Our two experiments demonstrate that the sensitive word variant extraction framework based on dynamically iterated prompt templates outperforms other baseline prompt templates.TheRoCBert models incorporating the sensitive word variant knowledge enhancement layer and a mixture-of-experts(MoE)classification layer achieve superior classification performance compared to other baselines.
基金financially supported by the National Natural Science Foundation of China(No.52374259)the Open Fund of the State Key Laboratory of Mineral Processing Science and Technology,China(No.BGRIMM-KJSKL-2023-11)the Major Science and Technology Projects in Yunnan Province,China(No.202302 AF080004)。
文摘It is difficult to recover chrysocolla from sulfidation flotation which is closely related to the mineral surface composition.In this study,the effects of fluoride roasting on the surface composition of chrysocolla were investigated,its impact on sulfidation flotation was explored,and the mechanisms involved in both fluoride roasting and sulfidation flotation were discussed.With CaF_(2)as the roasting reagent,Na_(2)S·9H_(2)O as the sulfidation reagent,and sodium butyl xanthate(NaBX)as the collector,the results of the flotation experiments showed that fluoride roasting improved the floatability of chrysocolla,and the recovery rate increased from 16.87%to 82.74%.X-ray diffraction analysis revealed that after fluoride roasting,approximately all the Cu on the chrysocolla surface was exposed in the form of CuO,which could provide a basis for subsequent sulfidation flotation.The microscopy and elemental analyses revealed that large quantities of"pagoda-like"grains were observed on the sulfidation surface of the fluoride-roasted chrysocolla,indicating high crystallinity particles of copper sulfide.This suggests that the effect of sulfide formation on the chrysocolla surface was more pronounced.X-ray photoelectron spectroscopy revealed that fluoride roasting increased the relative contents of sulfur and copper on the surface and that both the Cu~+and polysulfide fractions on the surface of the minerals increased.This enhances the effect of sulfidation,which is conducive to flotation recovery.Therefore,fluoride roasting improved the effect of copper species transformation and sulfidation on the surface of chysocolla,promoted the adsorption of collectors,and improved the recovery of chrysocolla from sulfidation flotation.
基金supported by the Jiangsu Engineering Research Center of the Key Technology for Intelligent Manufacturing Equipment and the Suqian Key Laboratory of Intelligent Manufacturing(Grant No.M202108).
文摘To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.
基金supported by the National Natural Science Foundation of China(Grant Nos.52304139,52325403)the CCTEG Coal Mining Research Institute funding(Grant No.KCYJY-2024-MS-10).
文摘3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.
文摘With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application.
基金supported by the National Natural Science Foundation of China(Grant No.12402139,No.52368070)supported by Hainan Provincial Natural Science Foundation of China(Grant No.524QN223)+3 种基金Scientific Research Startup Foundation of Hainan University(Grant No.RZ2300002710)State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment,Dalian University of Technology(Grant No.GZ24107)the Horizontal Research Project(Grant No.HD-KYH-2024022)Innovative Research Projects for Postgraduate Students in Hainan Province(Grant No.Hys2025-217).
文摘Optimization problems are prevalent in various fields of science and engineering,with several real-world applications characterized by high dimensionality and complex search landscapes.Starfish optimization algorithm(SFOA)is a recently optimizer inspired by swarm intelligence,which is effective for numerical optimization,but it may encounter premature and local convergence for complex optimization problems.To address these challenges,this paper proposes the multi-strategy enhanced crested porcupine-starfish optimization algorithm(MCPSFOA).The core innovation of MCPSFOA lies in employing a hybrid strategy to improve SFOA,which integrates the exploratory mechanisms of SFOA with the diverse search capacity of the Crested Porcupine Optimizer(CPO).This synergy enhances MCPSFOA’s ability to navigate complex and multimodal search spaces.To further prevent premature convergence,MCPSFOA incorporates Lévy flight,leveraging its characteristic long and short jump patterns to enable large-scale exploration and escape from local optima.Subsequently,Gaussian mutation is applied for precise solution tuning,introducing controlled perturbations that enhance accuracy and mitigate the risk of insufficient exploitation.Notably,the population diversity enhancement mechanism periodically identifies and resets stagnant individuals,thereby consistently revitalizing population variety throughout the optimization process.MCPSFOA is rigorously evaluated on 24 classical benchmark functions(including high-dimensional cases),the CEC2017 suite,and the CEC2022 suite.MCPSFOA achieves superior overall performance with Friedman mean ranks of 2.208,2.310 and 2.417 on these benchmark functions,outperforming 11 state-of-the-art algorithms.Furthermore,the practical applicability of MCPSFOA is confirmed through its successful application to five engineering optimization cases,where it also yields excellent results.In conclusion,MCPSFOA is not only a highly effective and reliable optimizer for benchmark functions,but also a practical tool for solving real-world optimization problems.
基金Supported by the National Defense Basic Scientific Research Program of China.
文摘Amphibious vehicles are more prone to attitude instability compared to ships,making it crucial to develop effective methods for monitoring instability risks.However,large inclination events,which can lead to instability,occur frequently in both experimental and operational data.This infrequency causes events to be overlooked by existing prediction models,which lack the precision to accurately predict inclination attitudes in amphibious vehicles.To address this gap in predicting attitudes near extreme inclination points,this study introduces a novel loss function,termed generalized extreme value loss.Subsequently,a deep learning model for improved waterborne attitude prediction,termed iInformer,was developed using a Transformer-based approach.During the embedding phase,a text prototype is created based on the vehicle’s operation log data is constructed to help the model better understand the vehicle’s operating environment.Data segmentation techniques are used to highlight local data variation features.Furthermore,to mitigate issues related to poor convergence and slow training speeds caused by the extreme value loss function,a teacher forcing mechanism is integrated into the model,enhancing its convergence capabilities.Experimental results validate the effectiveness of the proposed method,demonstrating its ability to handle data imbalance challenges.Specifically,the model achieves over a 60%improvement in root mean square error under extreme value conditions,with significant improvements observed across additional metrics.
文摘We found the qualitative study by Xu et al.on how patients feel about laparoscopic incisions under enhanced recovery after surgery(ERAS)protocols to be very interesting.1 Xu et al.carried out a qualitative study on patient experience with laparoscopic incisions under an ERAS protocol to highlight the problem of psychosocial and aesthetic concerns,which are often overlooked when planning surgical operations.This study,which involved semistructured interviews with sixteen people,aimed to narrow perioperative education and the decision-making process for incision site selection,thus making the processes more focused on patient priorities.The study is based on a timely but under-researched subject area;however,it is possible to outline four possible areas of improvement that would allow the study to be more transparent and,at the same time,more applicable to clinical practice.
文摘AIM:To find the effective contrast enhancement method on retinal images for effective segmentation of retinal features.METHODS:A novel image preprocessing method that used neighbourhood-based improved contrast limited adaptive histogram equalization(NICLAHE)to improve retinal image contrast was suggested to aid in the accurate identification of retinal disorders and improve the visibility of fine retinal structures.Additionally,a minimal-order filter was applied to effectively denoise the images without compromising important retinal structures.The novel NICLAHE algorithm was inspired by the classical CLAHE algorithm,but enhanced it by selecting the clip limits and tile sized in a dynamical manner relative to the pixel values in an image as opposed to using fixed values.It was evaluated on the Drive and high-resolution fundus(HRF)datasets on conventional quality measures.RESULTS:The new proposed preprocessing technique was applied to two retinal image databases,Drive and HRF,with four quality metrics being,root mean square error(RMSE),peak signal to noise ratio(PSNR),root mean square contrast(RMSC),and overall contrast.The technique performed superiorly on both the data sets as compared to the traditional enhancement methods.In order to assess the compatibility of the method with automated diagnosis,a deep learning framework named ResNet was applied in the segmentation of retinal blood vessels.Sensitivity,specificity,precision and accuracy were used to analyse the performance.NICLAHE–enhanced images outperformed the traditional techniques on both the datasets with improved accuracy.CONCLUSION:NICLAHE provides better results than traditional methods with less error and improved contrastrelated values.These enhanced images are subsequently measured by sensitivity,specificity,precision,and accuracy,which yield a better result in both datasets.
文摘The internal flow fields within a three-dimensional inward-tunning combined inlet are extremely complex,especially during the engine mode transition,where the tunnel changes may impact the flow fields significantly.To develop an efficient flow field reconstruction model for this,we present an Improved Conditional Denoising Diffusion Generative Adversarial Network(ICDDGAN),which integrates Conditional Denoising Diffusion Probabilistic Models(CDDPMs)with Style GAN,and introduce a reconstruction discrimination mechanism and dynamic loss weight learning strategy.We establish the Mach number flow field dataset by numerical simulation at various backpressures for the mode transition process from turbine mode to ejector ramjet mode at Mach number 2.5.The proposed ICDDGAN model,given only sparse parameter information,can rapidly generate high-quality Mach number flow fields without a large number of samples for training.The results show that ICDDGAN is superior to CDDGAN in terms of training convergence and stability.Moreover,the interpolation and extrapolation test results during backpressure conditions show that ICDDGAN can accurately and quickly reconstruct Mach number fields at various tunnel slice shapes,with a Structural Similarity Index Measure(SSIM)of over 0.96 and a Mean-Square Error(MSE)of 0.035%to actual flow fields,reducing time costs by 7-8 orders of magnitude compared to Computational Fluid Dynamics(CFD)calculations.This can provide an efficient means for rapid computation of complex flow fields.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
文摘Low-salinity water injection has been utilized as a promising method for oil recovery in recent years. Low-salinity water flooding changes the ion composition or brine salinity for improving oil recovery. Recently, the application of nanoparticles with low-salinity water flooding has shown remarkable results in enhanced oil recovery(EOR). Many studies have been performed on the effect of nanofluids on EOR mechanisms. Their results showed that nanofluids can improve oil recovery when used in low-salinity water flooding. In this work, the effects of injection of low-salinity water and low-salinity nanofluid(prepared by adding SiO_2 nanoparticles to low-salinity water) on oil recovery were investigated. At first, the effects of ions were investigated with equal concentrations in low-salinity water flooding. The experimental results showed that the monovalent ions had better performance than the divalent ions because of them having more negative zeta potential and less ionic strength. Also, low-salinity water flooding recovered 6.1% original oil in place(OOIP) more than the high-salinity flooding. Contact angle measurements demonstrated that low-salinity water could reduce the contact angle between oil and water. Then in the second stage, experiments were continued by adding SiO_2 nanoparticles to the K+ solution which had the highest oil recovery at the first stage. The experimental results illustrated that the addition of Si02 nanoparticles up to 0.05 wt% increased oil recovery by about 4% OOIP more than the low-salinity water flooding.
基金supported by the National Natural Science Foundation of China(Nos.21677159,21522706,21677167)the National Basic Research Program of China(2011CB936001)the Thousand Young Talents Program of China
文摘Great efforts have been devoted to improve the photocatalytic activity of TiO2 in the visible light region. Rational design of the external structure and adjustment of intrinsic electronic status by impurity doping are two main effective ways to achieve this purpose. A facile onepot synthetic approach was developed to prepare C-doped hollow TiO2 spheres, which simultaneously realized these advantages. The synthesized TiO2 exhibits a mesoporous hollow spherical structure composed of fine nanocrystals, leading to high specific surface area（~180 m^2/g） and versatile porous texture. Carbonate-doping was achieved by a postthermal treatment at a relatively low temperature（200°C）, which makes the absorption edge red-shifted to the visible region of the solar spectrum. Concomitantly, Ti^（3＋） induced by C-doping also functions in improving the visible-light photocatalytic activity by reducing the band gap. There exists a synergistic effect from multiple stimulatives to enhance the photocatalytic effect of the prepared TiO2 catalyst. It is not out of expectation that the asprepared C-doped hollow TiO2 spheres exhibits an improved photocatalytic activity under visible light irradiation in organic pollutant degradation.
基金supported by Anhui Province University Key Science and Technology Project(2024AH053415)Anhui Province University Major Science and Technology Project(2024AH040229).
文摘This research addresses the critical challenge of enhancing satellite images captured under low-light conditions,which suffer from severely degraded quality,including a lack of detail,poor contrast,and low usability.Overcoming this limitation is essential for maximizing the value of satellite imagery in downstream computer vision tasks(e.g.,spacecraft on-orbit connection,spacecraft surface repair,space debris capture)that rely on clear visual information.Our key novelty lies in an unsupervised generative adversarial network featuring two main contributions:(1)an improved U-Net(IU-Net)generator with multi-scale feature fusion in the contracting path for richer semantic feature extraction,and(2)a Global Illumination Attention Module(GIA)at the end of the contracting path to couple local and global information,significantly improving detail recovery and illumination adjustment.The proposed algorithm operates in an unsupervised manner.It is trained and evaluated on our self-constructed,unpaired Spacecraft Dataset for Detection,Enforcement,and Parts Recognition(SDDEP),designed specifically for low-light enhancement tasks.Extensive experiments demonstrate that our method outperforms the baseline EnlightenGAN,achieving improvements of 2.7%in structural similarity(SSIM),4.7%in peak signal-to-noise ratio(PSNR),6.3%in learning perceptual image patch similarity(LPIPS),and 53.2%in DeltaE 2000.Qualitatively,the enhanced images exhibit higher overall and local brightness,improved contrast,and more natural visual effects.
基金financially supported by the National Key R&D Program of China(No.2019YFA0708700)National Science Fund of China(No.51804327,51834010)+1 种基金Climb Taishan Scholar Program in Shandong Province(No.tspd20161004)the Fundamental Research Funds for the Central Universities(No.18CX02026A,24720182026A)。
文摘In recent years,production from tight oil reservoirs has increasingly supplemented production from conventional oil resources.Oil-wet formations account for a considerable proportion of tight oil reservoirs.Surfactant can change wettability and reduce interfacial tension,thus resulting in a better oil recovery.In this manuscript,a nonionic surfactant was introduced for tight oil-wet reservoirs.The oil recovery in the oil-wet sandstone due to spontaneous imbibition was 8.59%lower than that of the waterwet sandstone due to surfactant.The 0.1%surfactant solution corresponded to the highest imbibition recovery rate of 27.02%from the oil-wet sample.With the surfactant treatment,the treated core quickly changed from weakly oil-wet to weakly water-wet.The capillary force acted as the driving force and promoted imbibition.The optimal surfactant adsorption quantity in the oil-wet sandstone was observed in the sample at concentrations ranging from 0.1%to 0.3%,which also corresponded to the highest oil recovery.Analysis of the inverse Bond number NB-1 suggested that the driving force was gravity for brine imbibition in the oil-wet cores and that it was capillary force for surfactant imbibition in the oil-wet cores.When the surfactant concentration was lower than the critical micelle concentration,the surfactant concentration was negatively correlated with the inverse Bond number and positively correlated with the oil recovery rate.When the surfactant concentration was higher than the critical micelle concentration,the oil recovery increased with a smaller interfacial tension.Nuclear magnetic resonance suggested that the movable pore and pore throat size in the oil-wet sample decreased from 0.363 mm in the untreated rock to 0.326 mm with the surfactant treatment,which indicated that the surfactant improved the flow capacity of the oil.The findings of this study can help to better understand the adsorption impact of surfactants on the characteristics of the oil/water and solid/liquid interfaces.The imbibition mechanism in oil-wet tight sandstone reservoirs was further revealed.These systematic approaches help to select appropriate surfactants for better recovery in oil-wet tight sandstone reservoirs through imbibition.
文摘BACKGROUND Intravenous infusion is a common method of drug administration in clinical practice.Errors in any aspect of the infusion process,from the verification of medical orders,preparation of the drug solution,to infusion by nursing staff,may cause adverse infusion events.AIM To analyzed the value of improving nursing measures and enhancing nursing management to reduce the occurrence of adverse events in pediatric infusion.METHODS The clinical data of 130 children who received an infusion in the pediatric department of our hospital from May 2020 to May 2021 were analyzed and divided into two groups according to the differences in nursing measures and nursing management:65 patients in the control group received conventional nursing and nursing management interventions,while 65 patients in the observation group received improved nursing measure interventions and enhanced nursing management.The occurrence of adverse events,compliance of children,satisfaction of children’s families,and complaints regarding the transfusion treatment were recorded in both groups.RESULTS The incidence of fluid extravasation and infusion set dislodgement in the observation group were 3.08%and 1.54%,respectively,which were significantly lower than 12.31%and 13.85%in the control group(P<0.05),while repeated punctures and medication addition errors in the observation group were 3.08%and 0.00%,respectively,which were lower than 9.23%and 3.08%in the control group,but there was no significant difference(P>0.05).The compliance rate of children in the observation group was 98.46%(64/65),which was significantly higher than 87.69%(57/65)in the control group,and the satisfaction rate of children’s families was 96.92%(63/65),which was significantly higher than 86.15%(56/65)in the control group(P<0.05).The observation group did not receive any complaints from the child’s family,whereas the control group received four complaints,two of which were due to the crying of the child caused by repeated punctures,one due to the poor attitude of the nurse,and one due to medication addition errors,with a cumulative complaint rate of 6.15%.The cumulative complaint rate of the observation group was significantly lower than that of the control group(P<0.05).CONCLUSION Improving nursing measures and enhancing nursing management can reduce the incidence of fluid extravasation and infusion set dislodgement in pediatric patients,improve children’s compliance and satisfaction of their families,and reduce family complaints.
