Objective:To assess prenatal Bisphenol A(BPA)exposure levels and explore their preliminary associations with maternal and fetal characteristics in a population from Northeastern Yunnan.Methods:A cross-sectional analys...Objective:To assess prenatal Bisphenol A(BPA)exposure levels and explore their preliminary associations with maternal and fetal characteristics in a population from Northeastern Yunnan.Methods:A cross-sectional analysis was performed using data and urine samples from 70 pregnant women in their third trimester recruited at Qujing Central Hospital.Urinary BPA was measured by HPLC-MS/MS.Participants were stratified into high and low BPA exposure groups based on the median concentration.Results:BPA was detected in all samples(100%)with a median concentration of 2.41μg/L(IQR:0.68-4.96).The high BPA exposure group(≥2.41μg/L)had a significantly higher proportion of gestational diabetes mellitus(GDM)(42.9%vs.17.1%,p=0.021)and a lower median fetal birth weight(3250 g vs.3450 g,p=0.048)compared to the low exposure group.Conclusion:This pilot study reveals ubiquitous BPA exposure in pregnant women from Northeastern Yunnan.The observed preliminary associations with GDM and reduced fetal birth weight warrant further investigation in larger,longitudinal studies.展开更多
Objective This study aimed to determine the temporal trends in sleep duration among Chinese adults.Methods In this series of repeated nationally representative cross-sectional surveys(China Chronic Disease and Risk Fa...Objective This study aimed to determine the temporal trends in sleep duration among Chinese adults.Methods In this series of repeated nationally representative cross-sectional surveys(China Chronic Disease and Risk Factors Surveillance)conducted between 2010 and 2018,a total of 645,420 adult participants(97,741 in 2010;175,749 in 2013;187,777 in 2015;and 184,153 in 2018)were included in the trend analysis.Linear and logistic regression models were utilized to assess trends in sleep duration.Results In 2018,the estimated overall mean sleep duration among the Chinese adult population was7.58(SD,1.45)hours per day,with no significant trend from 2010.A significant increase in short sleep duration(≤6 hours)was observed in the total population,from 15.3%(95%CI:14.1%–16.5%)in 2010 to18.5%(95%CI:17.7%–19.3%)in 2018(P<0.001).Similarly,the trend in long sleep duration(>9 hours)was also significant,increasing in weighted prevalence from 7.2%(95%CI:6.3%–8.1%)in 2010 to 9.0%(95%CI:8.2%–9.9%)in 2018(P<0.001).Conclusion The prevalence of both short and long sleep durations significantly increased among Chinese adults from 2010 to 2018,highlighting the urgency of health initiatives to promote optimal sleep duration in China.展开更多
To advance the theoretical understanding,technological development,and field application of electric charge induction for monitoring rock deformation and failure,this study investigates the induced electric charge gen...To advance the theoretical understanding,technological development,and field application of electric charge induction for monitoring rock deformation and failure,this study investigates the induced electric charge generated during the deformation and failure of igneous rocks.The charge originates mainly from a combination of electrical polarization and triboelectric effects.Through laboratory experiments,we analyzed the time-frequency evolution of induced electric charge signals and identified relevant monitoring parameters.An online downhole electric charge induction monitoring system was developed and validated in the field.Experimental results show that the dominant frequency range of induced electric charge signals generated during igneous rock deformation and failure lies between 0 and 23 Hz,and a low-pass finite impulse response(FIR)filter effectively suppresses noise.Optimal sensor distances for monitoring cubic and cylindrical specimens were determined to be 17 mm and 13 mm,respectively.We proposed early warning indicators,including the maximum absolute value of the induced electric charge,the arithmetic mean value,the distribution dispersion coefficient,and the cumulative sum value.In field application,time-domain curves and spatial distribution charts of these warning indicators correspond well with changes in abutment stress ahead of the mining face,offering indirect insights into local stress evolution.This research provides technical and equipment support for the application of electric charge induction technology to monitoring and early warning of coal bursts.展开更多
A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without in...A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.展开更多
The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warni...The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.展开更多
Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting s...Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting software,and the physical system may not be able to be protected.In this paper,a nonintrusive virtual machine(VM)-based runtime protection framework is provided to protect the physical system with the isolated IoT services as a controlling means.Compared with existing solutions,the framework gets inconsistent and untrusted observation knowledge from multiple observation sources,and enforces property policies concurrently and incrementally in a competing-game way to avoid compositional problems.In addition,the monitoring is implemented without any modification to the protected system.Experiments are conducted to validate the proposed techniques.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
Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-in...Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.展开更多
Flexible and wearable sensors offer immense potential for rehabilitation medicine,but most rely solely on electrical signals,lacking real-time visual feedback and limiting trainee's interactivity.Inspired by the s...Flexible and wearable sensors offer immense potential for rehabilitation medicine,but most rely solely on electrical signals,lacking real-time visual feedback and limiting trainee's interactivity.Inspired by the structural coloration of Cyanocitta stelleri feathers,we developed a dual-mode sensor by utilizing black conductive polymer hydrogel(CPH)-enhanced structural color strategy.This sensor integrates a hydroxypropyl cellulose(HPC)-based structural color interface with a designed CPH sensing component.Highly visible light-absorbing CPH(absorption rate>88%)serves as the critical substrate for enhancing structural color performance.By absorbing incoherent scattered light and suppressing background interference,it significantly enhances the saturation of structural color,thereby achieving a high contrast index of 4.92.Unlike the faint and hardly visible structural colors on non-black substrates,the HPC on CPH displays vivid,highly perceptible colors and desirable mechanochromic behavior.Moreover,the CPH acts as a flexible sensing element,fortified by hydrogen and coordination bond networks,and exhibits exceptional electromechanical properties,including 867.1 kPa tensile strength,strain sensitivity(gauge factor of 4.24),and outstanding durability(over 4400 cycles).Compared to traditional single-mode sensors,the integrated sensor provides real-time visual and digital dual feedback,enhancing the accuracy and interactivity of rehabilitation assessments.This technology holds promise for advancing next-generation rehabilitation medicine.展开更多
The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show...The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show clinical potential,their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity(R^(2)>0.99 up to 1 MPa)in conventional designs.Inspired by the tactile sensing mechanism of human skin,where dermal stratification enables wide-range pressure adaptation and ion-channelregulated signaling maintains linear electrical responses,we developed a dual-mechanism flexible iontronic pressure sensor(FIPS).This innovative design synergistically combines two bioinspired components:interdigitated fabric microstructures enabling pressure-proportional contact area expansion(αP1/3)and iontronic film facilitating self-adaptive ion concentration modulation(αP^(2/3)),which together generate a linear capacitance-pressure response(CαP).The FIPS achieves breakthrough performance:242 kPa^(-1)sensitivity with 0.997linearity across 0-1 MPa,yielding a record linear sensing factor(LSF=242,000).The design is validated across various substrates and ionic materials,demonstrating its versatility.Finally,the FIPS-driven design enables a smart insole demonstrating 1.8%error in tibial load assessment during gait analysis,outperforming nonlinear counterparts(6.5%error)in early fracture-risk prediction.The biomimetic design framework establishes a universal approach for developing high-performance linear sensors,establishing generalized principles for medical-grade wearable devices.展开更多
The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on th...The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on the Dagangshan high-arch dam during its normal water storage operating period to assess potential damage.The study analyzes the MS characteristics of the dam during the Luding earthquake(Ms=6.8).A framework for constructing a damage driven DT model of a high-arch dam is proposed.The DT model is capable of self-updating its mechanical parameters based on MS data.Seismic response calculations are conducted utilizing cloud computing,allowing for the direct presentation of results within the DT model.The results indicate a high-risk area of the Dagangshan arch dam,characterized by significantMS deformation,primarily centered on the arch crown beam.This zone encompasses dam sections Nos.5-6,10-11,13-16,and 19-20,all located above 1030 m elevation.Under seismic loading,the arch dam exhibits a back-and-forth movement along the river,ultimately reaching a stable state.Following the earthquake,the stress state of the dam does not experience substantial changes.The average relative error between numerical results and measured peak ground acceleration values is 17%when considering the cumulative effect of damage,compared to 36%when neglecting this effect.This study presents a more reliable approach for assessing the state of dams.展开更多
The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process ...The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process conditions can lead to degradation in layer quality and non-uniformity,highlighting the need for real-time monitoring to improve overall quality and efficiency.In this study,an AI-based monitoring system was developed to evaluate layer width and assess quality in real time.Three deep learning models Faster Region-based Convolutional Neural Network(R-CNN),You Only Look Once version 8(YOLOv8),and Single Shot MultiBox Detector(SSD)were compared,and YOLOv8 was ultimately selected for its superior speed,flexibility,and scalability.The selected model was integrated into a user-friendly interface.To verify the reliability of the system,bead width control experiments were conducted,which identified feed speed and extrusion speed as the key process parameters.Accordingly,a Central Composite Design(CCD)experimental plan with 13 conditions was applied to evaluate layer width and validate the system’s reliability.Finally,the proposed system was applied to the additive manufacturing of an aerospace component,where it successfully detected bead width deviations during printing and enabled stable fabrication with a maximum geometric deviation of approximately 6 mm.These findings demonstrate the critical role of real-time monitoring of layer width and quality in improving process stability and final product quality in composite material additive manufacturing.展开更多
The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable pr...The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.展开更多
The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties ...The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties of organic coatings.This study compared a bare epoxy coating with one containing zinc phosphate corrosion inhibitors,both applied on ACM sensors,to observe their corrosion protection properties over time.Coatings with artificial damage via scratches were exposed to immersion and alternating dry and wet environments,which allowed for monitoring galvanic corrosion currents in real-time.Throughout the corrosion tests,the ACM currents of the zinc phosphate/epoxy coating were considerably lower than those of the blank epoxy coating.The trend in ACM current variations closely matched the results obtained from regular electrochemical tests and surface analysis.This alignment highlights the potential of the ACM technique in evaluating the corrosion protection capabilities of organic coatings.Compared with the blank epoxy coating,the zinc phosphate/epoxy coating showed much-decreased ACM current values that confirmed the effective inhibition of zinc phosphate against steel corrosion beneath the damaged coating.展开更多
Objective The relationship between non-high-density lipoprotein(NHDL)cholesterol to high-density lipoprotein cholesterol(HDL-C)ratio(NHHR)and stoke remains unknown.This study aimed to evaluate the association between ...Objective The relationship between non-high-density lipoprotein(NHDL)cholesterol to high-density lipoprotein cholesterol(HDL-C)ratio(NHHR)and stoke remains unknown.This study aimed to evaluate the association between the adult NHHR and stroke occurrence in the United States of America(USA).Methods To clarify the relationship between the NHHR and stroke risk,this study used a multivariable logistic regression model and a restricted cubic spline(RCS)model to investigate the association between the NHHR and stroke,and data from the National Health and Nutrition Examination Survey(NHANES)from 2005 to 2018.Subgroup and sensitivity analyses were conducted to test the robustness of the results.Results This study included 29,928 adult participants,of which 1,165 participants had a history of stroke.Logistic regression analysis of variables demonstrated a positive association between NHHR and stroke(OR 1.24,95%CI:1.03-1.50,P=0.026).Compared with the lowest reference group of NHHR,participants in the second,third,and fourth quartile had a significantly increased risk of stroke after full adjustments(OR:1.35,95%CI:1.08-1.69)(OR:1.83,95%CI:1.42-2.36)(OR:2.04,95%CI:1.50-2.79).In the total population,a nonlinear dose-response relationship was observed between the NHHR and stroke risk(P non-linearity=0.002).This association remained significant in several subgroup analyses.Further investigation of the NHHR may enhance our understanding of stroke prevention and treatment.Conclusion Our findings suggest a positive correlation between the NHHR and an increased prevalence of stroke,potentially serving as a novel predictive factor for stroke.Timely intervention and management of the NHHR may effectively mitigate stroke occurrence.Prospective studies are required to validate this association and further explore the underlying biological mechanisms.展开更多
The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly comple...The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.展开更多
Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wound dressings.However,certain challenges,including surgical difficu...Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wound dressings.However,certain challenges,including surgical difficulty,lengthy recovery times,and a high recurrence rate persist.Conductive hydrogel dressings with combined monitoring and therapeutic properties have strong advantages in promoting wound healing due to the stimulation of endogenous current on wounds and are the focus of recent advancements.Therefore,this review introduces the mechanism of conductive hydrogel used for wound monitoring and healing,the materials selection of conductive hydrogel dressings used for wound monitoring,focuses on the conductive hydrogel sensor to monitor the output categories of wound status signals,proving invaluable for non-invasive,real-time evaluation of wound condition to encourage wound healing.Notably,the research of artificial intelligence(AI)model based on sensor derived data to predict the wound healing state,AI makes use of this abundant data set to forecast and optimize the trajectory of tissue regeneration and assess the stage of wound healing.Finally,refractory wounds including pressure ulcers,diabetes ulcers and articular wounds,and the corresponding wound monitoring and healing process are discussed in detail.This manuscript supports the growth of clinically linked disciplines and offers motivation to researchers working in the multidisciplinary field of conductive hydrogel dressings.展开更多
OBJECTIVE:To investigate the difference in gut microbiota between population with damp-heat constitution(DHC)and balanced constitution(BC).METHODS:A multi-centered cross-sectional casecontrol study was conducted,which...OBJECTIVE:To investigate the difference in gut microbiota between population with damp-heat constitution(DHC)and balanced constitution(BC).METHODS:A multi-centered cross-sectional casecontrol study was conducted,which included 249 participants with damp-heat constitution or balanced constitution.Baseline information of participants was collected,and stool samples were collected for gut microbiota analysis.Principal coordinate analysis,linear discriminant analysis effect size analysis,receiver operating characteristic,random forest model,and phylogenetic investigation of communities by reconstruction of unobserved states methods were used to reveal the relationship between gut microbiota and the damp-heat constitution.RESULTS:Compared to those in the BC group,the richness and diversity of the microbiota,specifically those of several short-chain fatty acid producing genera such as Barnesiella,Coprobacter,and Butyricimonas,were significantly decreased in the DHC group.Regarding biological functions,flavonoid biosynthesis,propanoate metabolism,and nucleotide sugar metabolism were suppressed,while arachidonic acid metabolism and glutathione metabolism were enriched in the DHC group.Finally,a classifier based on the microbiota was constructed to discriminate between the DHC and BC populations.CONCLUSION:The gut microbiota of the DHC population exhibits significantly reduced diversity and is closely related to inflammation,metabolic disorders,and liver steatosis,which is consistent with clinical observations,thus serving as a potential diagnostic tool for traditional Chinese medicine constitution discrimination.展开更多
Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze we...Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.展开更多
Objective:Postpartum nutrition plays a critical role in maternal recovery and long-term health.However,the nutritional status of working mothers in the postpartum period remains understudied.This study aimed to assess...Objective:Postpartum nutrition plays a critical role in maternal recovery and long-term health.However,the nutritional status of working mothers in the postpartum period remains understudied.This study aimed to assess the dietary quality of postpartum women in urban Beijing,identify occupational-related factors influencing their diet,and explore potential interventions to improve maternal nutrition during the postpartum period.Methods:In this cross-sectional analysis,554 women one year after delivery were recruited from ten community health centers.Sociodemographic,occupational and postpartum care variables were collected via questionnaire.Dietary intake over the preceding year was assessed using a food frequency questionnaire.The modified dietary balance index for postpartum women were used for dietary quality assessment.Results:The study revealed severe dietary imbalances among postpartum women,characterized by excessive consumption of cereals,eggs,and meats,while their intake of vegetables,fruits,and dairy products was inadequate.According to dietary balance index for postpartum women,66.25%of mothers showed varying degrees of excessive intake.45.31%of mothers experienced varying levels of insufficient intake,with only 19.86%of participants having a relatively balanced diet.Occupational differences were observed,with women in the commercial employment group showing higher levels of excessive food intake.The analysis of influencing factors showed that family monthly income,maternity leave,and postpartum care significantly affected the dietary quality.Conclusions:Postpartum women in Beijing experience widespread dietary imbalances,with both excesses and deficiencies.Occupational context and related factors significantly shape diet quality.These findings highlight the need for targeted nutritional interventions tailored to the specific challenges of different occupational groups.展开更多
文摘Objective:To assess prenatal Bisphenol A(BPA)exposure levels and explore their preliminary associations with maternal and fetal characteristics in a population from Northeastern Yunnan.Methods:A cross-sectional analysis was performed using data and urine samples from 70 pregnant women in their third trimester recruited at Qujing Central Hospital.Urinary BPA was measured by HPLC-MS/MS.Participants were stratified into high and low BPA exposure groups based on the median concentration.Results:BPA was detected in all samples(100%)with a median concentration of 2.41μg/L(IQR:0.68-4.96).The high BPA exposure group(≥2.41μg/L)had a significantly higher proportion of gestational diabetes mellitus(GDM)(42.9%vs.17.1%,p=0.021)and a lower median fetal birth weight(3250 g vs.3450 g,p=0.048)compared to the low exposure group.Conclusion:This pilot study reveals ubiquitous BPA exposure in pregnant women from Northeastern Yunnan.The observed preliminary associations with GDM and reduced fetal birth weight warrant further investigation in larger,longitudinal studies.
基金supported by the National Natural Science Foundation of China(82341245,82371491)the Chinese Central Government(Key Project of Public Health Program)the National Key Research and Development Program of China(2018YFC1311706,2018YFC1311702)。
文摘Objective This study aimed to determine the temporal trends in sleep duration among Chinese adults.Methods In this series of repeated nationally representative cross-sectional surveys(China Chronic Disease and Risk Factors Surveillance)conducted between 2010 and 2018,a total of 645,420 adult participants(97,741 in 2010;175,749 in 2013;187,777 in 2015;and 184,153 in 2018)were included in the trend analysis.Linear and logistic regression models were utilized to assess trends in sleep duration.Results In 2018,the estimated overall mean sleep duration among the Chinese adult population was7.58(SD,1.45)hours per day,with no significant trend from 2010.A significant increase in short sleep duration(≤6 hours)was observed in the total population,from 15.3%(95%CI:14.1%–16.5%)in 2010 to18.5%(95%CI:17.7%–19.3%)in 2018(P<0.001).Similarly,the trend in long sleep duration(>9 hours)was also significant,increasing in weighted prevalence from 7.2%(95%CI:6.3%–8.1%)in 2010 to 9.0%(95%CI:8.2%–9.9%)in 2018(P<0.001).Conclusion The prevalence of both short and long sleep durations significantly increased among Chinese adults from 2010 to 2018,highlighting the urgency of health initiatives to promote optimal sleep duration in China.
基金supported by the National Key Research and Development Project of the National Natural Science Foundation of China(Grant No.2022YFC3004605)the National Natural Science Foundation of China Youth Science Fund(Grant No.52104087).
文摘To advance the theoretical understanding,technological development,and field application of electric charge induction for monitoring rock deformation and failure,this study investigates the induced electric charge generated during the deformation and failure of igneous rocks.The charge originates mainly from a combination of electrical polarization and triboelectric effects.Through laboratory experiments,we analyzed the time-frequency evolution of induced electric charge signals and identified relevant monitoring parameters.An online downhole electric charge induction monitoring system was developed and validated in the field.Experimental results show that the dominant frequency range of induced electric charge signals generated during igneous rock deformation and failure lies between 0 and 23 Hz,and a low-pass finite impulse response(FIR)filter effectively suppresses noise.Optimal sensor distances for monitoring cubic and cylindrical specimens were determined to be 17 mm and 13 mm,respectively.We proposed early warning indicators,including the maximum absolute value of the induced electric charge,the arithmetic mean value,the distribution dispersion coefficient,and the cumulative sum value.In field application,time-domain curves and spatial distribution charts of these warning indicators correspond well with changes in abutment stress ahead of the mining face,offering indirect insights into local stress evolution.This research provides technical and equipment support for the application of electric charge induction technology to monitoring and early warning of coal bursts.
基金supported by the National Natural Science Foundation of China(22074072,22274083,52376199)the Shandong Provincial Natural Science Foundation(ZR2023LZY005)+1 种基金the Exploration Project of the State Key Laboratory of BioFibers and EcoTextiles of Qingdao University(TSKT202101)the Fundamental Research Funds for the Central Universities(2022BLRD13,2023BLRD01).
文摘A rapidly growing field is piezoresistive sensor for accurate respiration rate monitoring to suppress the worldwide respiratory illness.However,a large neglected issue is the sensing durability and accuracy without interference since the expiratory pressure always coupled with external humidity and temperature variations,as well as mechanical motion artifacts.Herein,a robust and biodegradable piezoresistive sensor is reported that consists of heterogeneous MXene/cellulose-gelation sensing layer and Ag-based interdigital electrode,featuring customizable cylindrical interface arrangement and compact hierarchical laminated architecture for collectively regulating the piezoresistive response and mechanical robustness,thereby realizing the long-term breath-induced pressure detection.Notably,molecular dynamics simulations reveal the frequent angle inversion and reorientation of MXene/cellulose in vacuum filtration,driven by shear forces and interfacial interactions,which facilitate the establishment of hydrogen bonds and optimize the architecture design in sensing layer.The resultant sensor delivers unprecedented collection features of superior stability for off-axis deformation(0-120°,~2.8×10^(-3) A)and sensing accuracy without crosstalk(humidity 50%-100%and temperature 30-80).Besides,the sensor-embedded mask together with machine learning models is achieved to train and classify the respiration status for volunteers with different ages(average prediction accuracy~90%).It is envisioned that the customizable architecture design and sensor paradigm will shed light on the advanced stability of sustainable electronics and pave the way for the commercial application in respiratory monitory.
基金support from the Sichuan Science and Technology Program(Grant No.2023NSFSC0812).
文摘The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.
基金supported by the National Key Research and Development Program of China under grant 2022YFF0902701the National Natural Science Foundation of China under grant U21A20468,61972043,61921003+1 种基金Zhejiang Lab under grant 2021PD0AB 02the Fundamental Research Funds for the Central Universities under grant 2020XD-A07-1.
文摘Ensuring an information fabric safe is critical and mandatory.For its related Internet of Things(IoT)service system running on the open Internet,existing host-based monitoring methods may fail due to only inspecting software,and the physical system may not be able to be protected.In this paper,a nonintrusive virtual machine(VM)-based runtime protection framework is provided to protect the physical system with the isolated IoT services as a controlling means.Compared with existing solutions,the framework gets inconsistent and untrusted observation knowledge from multiple observation sources,and enforces property policies concurrently and incrementally in a competing-game way to avoid compositional problems.In addition,the monitoring is implemented without any modification to the protected system.Experiments are conducted to validate the proposed techniques.
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.
文摘Diabetes mellitus represents a major global health issue,driving the need for noninvasive alternatives to traditional blood glucose monitoring methods.Recent advancements in wearable technology have introduced skin-interfaced biosensors capable of analyzing sweat and skin biomarkers,providing innovative solutions for diabetes diagnosis and monitoring.This review comprehensively discusses the current developments in noninvasive wearable biosensors,emphasizing simultaneous detection of biochemical biomarkers(such as glucose,cortisol,lactate,branched-chain amino acids,and cytokines)and physiological signals(including heart rate,blood pressure,and sweat rate)for accurate,personalized diabetes management.We explore innovations in multimodal sensor design,materials science,biorecognition elements,and integration techniques,highlighting the importance of advanced data analytics,artificial intelligence-driven predictive algorithms,and closed-loop therapeutic systems.Additionally,the review addresses ongoing challenges in biomarker validation,sensor stability,user compliance,data privacy,and regulatory considerations.A holistic,multimodal approach enabled by these next-generation wearable biosensors holds significant potential for improving patient outcomes and facilitating proactive healthcare interventions in diabetes management.
基金supported by the Science and Technology Development Fund,Macao SAR(0065/2023/AFJ,0116/2022/A3)the National Natural Science Foundation of China(52402166)+4 种基金the Natural Science Foundation of Guangdong Province(2025A1515011120)the Australian Research Council(DE220100154)the financial support from the Science and Technology Development Fund(FDCT),Macao SAR(No.0149/2022/A),and(No.0046/2024/AFJ)Guangdong Science and Technology Department(2023QN10C305)for this workthe financial support from the National Natural Science Foundation of China(Grant No.22305185)。
文摘Flexible and wearable sensors offer immense potential for rehabilitation medicine,but most rely solely on electrical signals,lacking real-time visual feedback and limiting trainee's interactivity.Inspired by the structural coloration of Cyanocitta stelleri feathers,we developed a dual-mode sensor by utilizing black conductive polymer hydrogel(CPH)-enhanced structural color strategy.This sensor integrates a hydroxypropyl cellulose(HPC)-based structural color interface with a designed CPH sensing component.Highly visible light-absorbing CPH(absorption rate>88%)serves as the critical substrate for enhancing structural color performance.By absorbing incoherent scattered light and suppressing background interference,it significantly enhances the saturation of structural color,thereby achieving a high contrast index of 4.92.Unlike the faint and hardly visible structural colors on non-black substrates,the HPC on CPH displays vivid,highly perceptible colors and desirable mechanochromic behavior.Moreover,the CPH acts as a flexible sensing element,fortified by hydrogen and coordination bond networks,and exhibits exceptional electromechanical properties,including 867.1 kPa tensile strength,strain sensitivity(gauge factor of 4.24),and outstanding durability(over 4400 cycles).Compared to traditional single-mode sensors,the integrated sensor provides real-time visual and digital dual feedback,enhancing the accuracy and interactivity of rehabilitation assessments.This technology holds promise for advancing next-generation rehabilitation medicine.
基金supported by the National Natural Science Foundation of China(NSFC 52175281,52475315)Youth Innovation Promotion Association of CAS(2021382)。
文摘The growing prevalence of exercise-induced tibial stress fractures demands wearable sensors capable of monitoring dynamic musculoskeletal loads with medical-grade precision.While flexible pressure-sensing insoles show clinical potential,their development has been hindered by the intrinsic trade-off between high sensitivity and full-range linearity(R^(2)>0.99 up to 1 MPa)in conventional designs.Inspired by the tactile sensing mechanism of human skin,where dermal stratification enables wide-range pressure adaptation and ion-channelregulated signaling maintains linear electrical responses,we developed a dual-mechanism flexible iontronic pressure sensor(FIPS).This innovative design synergistically combines two bioinspired components:interdigitated fabric microstructures enabling pressure-proportional contact area expansion(αP1/3)and iontronic film facilitating self-adaptive ion concentration modulation(αP^(2/3)),which together generate a linear capacitance-pressure response(CαP).The FIPS achieves breakthrough performance:242 kPa^(-1)sensitivity with 0.997linearity across 0-1 MPa,yielding a record linear sensing factor(LSF=242,000).The design is validated across various substrates and ionic materials,demonstrating its versatility.Finally,the FIPS-driven design enables a smart insole demonstrating 1.8%error in tibial load assessment during gait analysis,outperforming nonlinear counterparts(6.5%error)in early fracture-risk prediction.The biomimetic design framework establishes a universal approach for developing high-performance linear sensors,establishing generalized principles for medical-grade wearable devices.
基金supported by the National Natural Science Foundation of China(Grant Nos.52379098 and 42122052)the Liaoning XingLiao Talent Program(Grant No.XLYC2203008).
文摘The integration of digital twin(DT)technology with microseismic(MS)monitoring for evaluating the dynamic response of high-arch dams remains under-explored.This paper investigates the application of MS monitoring on the Dagangshan high-arch dam during its normal water storage operating period to assess potential damage.The study analyzes the MS characteristics of the dam during the Luding earthquake(Ms=6.8).A framework for constructing a damage driven DT model of a high-arch dam is proposed.The DT model is capable of self-updating its mechanical parameters based on MS data.Seismic response calculations are conducted utilizing cloud computing,allowing for the direct presentation of results within the DT model.The results indicate a high-risk area of the Dagangshan arch dam,characterized by significantMS deformation,primarily centered on the arch crown beam.This zone encompasses dam sections Nos.5-6,10-11,13-16,and 19-20,all located above 1030 m elevation.Under seismic loading,the arch dam exhibits a back-and-forth movement along the river,ultimately reaching a stable state.Following the earthquake,the stress state of the dam does not experience substantial changes.The average relative error between numerical results and measured peak ground acceleration values is 17%when considering the cumulative effect of damage,compared to 36%when neglecting this effect.This study presents a more reliable approach for assessing the state of dams.
基金support of the Korea Institute of Industrial Technol-ogy as“Development of a remote manufacturing system for high-risk,high-difficulty pipe production processes”(kitech EH-25-0004)supported by the Technology Innovation Program(or Industrial Strategic Technology Development Program)(RS-2023–00237714+2 种基金Development of Dynamic Metrology Tool for CMP Process StabilizationRS-2025–02634755Development of Real-Time Electrical Fire Prevention System Technology Reflecting the Characteristics of Traditional Markets)funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea).
文摘The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process conditions can lead to degradation in layer quality and non-uniformity,highlighting the need for real-time monitoring to improve overall quality and efficiency.In this study,an AI-based monitoring system was developed to evaluate layer width and assess quality in real time.Three deep learning models Faster Region-based Convolutional Neural Network(R-CNN),You Only Look Once version 8(YOLOv8),and Single Shot MultiBox Detector(SSD)were compared,and YOLOv8 was ultimately selected for its superior speed,flexibility,and scalability.The selected model was integrated into a user-friendly interface.To verify the reliability of the system,bead width control experiments were conducted,which identified feed speed and extrusion speed as the key process parameters.Accordingly,a Central Composite Design(CCD)experimental plan with 13 conditions was applied to evaluate layer width and validate the system’s reliability.Finally,the proposed system was applied to the additive manufacturing of an aerospace component,where it successfully detected bead width deviations during printing and enabled stable fabrication with a maximum geometric deviation of approximately 6 mm.These findings demonstrate the critical role of real-time monitoring of layer width and quality in improving process stability and final product quality in composite material additive manufacturing.
基金supported by the National Science and Technology Council,Taiwan,with grant numbers NSTC 114-2622-8-992-007-TD1 and 112-2811-E-992-003-MY3.
文摘The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.
基金financially supported by the National Natural Science Foundation of China(No.52371049)the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(YESS,No.2020QNRC001)the National Science and Technology Resources Investigation Program of China(Nos.2021FY100603 and 2019FY101404)。
文摘The atmospheric corrosion monitoring(ACM)technique has been widely employed to track the real-time corrosion behavior of metal materials.However,limited studies have applied ACM to the corrosion protection properties of organic coatings.This study compared a bare epoxy coating with one containing zinc phosphate corrosion inhibitors,both applied on ACM sensors,to observe their corrosion protection properties over time.Coatings with artificial damage via scratches were exposed to immersion and alternating dry and wet environments,which allowed for monitoring galvanic corrosion currents in real-time.Throughout the corrosion tests,the ACM currents of the zinc phosphate/epoxy coating were considerably lower than those of the blank epoxy coating.The trend in ACM current variations closely matched the results obtained from regular electrochemical tests and surface analysis.This alignment highlights the potential of the ACM technique in evaluating the corrosion protection capabilities of organic coatings.Compared with the blank epoxy coating,the zinc phosphate/epoxy coating showed much-decreased ACM current values that confirmed the effective inhibition of zinc phosphate against steel corrosion beneath the damaged coating.
文摘Objective The relationship between non-high-density lipoprotein(NHDL)cholesterol to high-density lipoprotein cholesterol(HDL-C)ratio(NHHR)and stoke remains unknown.This study aimed to evaluate the association between the adult NHHR and stroke occurrence in the United States of America(USA).Methods To clarify the relationship between the NHHR and stroke risk,this study used a multivariable logistic regression model and a restricted cubic spline(RCS)model to investigate the association between the NHHR and stroke,and data from the National Health and Nutrition Examination Survey(NHANES)from 2005 to 2018.Subgroup and sensitivity analyses were conducted to test the robustness of the results.Results This study included 29,928 adult participants,of which 1,165 participants had a history of stroke.Logistic regression analysis of variables demonstrated a positive association between NHHR and stroke(OR 1.24,95%CI:1.03-1.50,P=0.026).Compared with the lowest reference group of NHHR,participants in the second,third,and fourth quartile had a significantly increased risk of stroke after full adjustments(OR:1.35,95%CI:1.08-1.69)(OR:1.83,95%CI:1.42-2.36)(OR:2.04,95%CI:1.50-2.79).In the total population,a nonlinear dose-response relationship was observed between the NHHR and stroke risk(P non-linearity=0.002).This association remained significant in several subgroup analyses.Further investigation of the NHHR may enhance our understanding of stroke prevention and treatment.Conclusion Our findings suggest a positive correlation between the NHHR and an increased prevalence of stroke,potentially serving as a novel predictive factor for stroke.Timely intervention and management of the NHHR may effectively mitigate stroke occurrence.Prospective studies are required to validate this association and further explore the underlying biological mechanisms.
基金financially supported by Guangdong Province Basic and Applied Basic Research Fund Project(Grant No.2022B1515250009)Liaoning Provincial Natural Science Foundation-Doctoral Research Start-up Fund Project(Grant No.2024-BSBA-05)+1 种基金Major Science and Technology Innovation Project in Shandong Province(Grant No.2024CXGC010803)the National Natural Science Foundation of China(Grant Nos.52271269 and 12302147).
文摘The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.
基金supports received from Scientific Research Fund of Liaoning Province Education Department(Grant No.JYTQN 2023025)Scientific Research Fund of Liaoning Province Education Department(Grant No.JYTQN 2023025)+3 种基金the Natural Science Foundation of Liaoning Province(Grant No.2024-MS-075)the National Natural Science Foundation of China(32201179)National Key R&D Program of China(2023YFC2508200)Liaoning Provincial Natural Science Foundation Joint Fund(General Support Program Project)(2023-MSBA-093).
文摘Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wound dressings.However,certain challenges,including surgical difficulty,lengthy recovery times,and a high recurrence rate persist.Conductive hydrogel dressings with combined monitoring and therapeutic properties have strong advantages in promoting wound healing due to the stimulation of endogenous current on wounds and are the focus of recent advancements.Therefore,this review introduces the mechanism of conductive hydrogel used for wound monitoring and healing,the materials selection of conductive hydrogel dressings used for wound monitoring,focuses on the conductive hydrogel sensor to monitor the output categories of wound status signals,proving invaluable for non-invasive,real-time evaluation of wound condition to encourage wound healing.Notably,the research of artificial intelligence(AI)model based on sensor derived data to predict the wound healing state,AI makes use of this abundant data set to forecast and optimize the trajectory of tissue regeneration and assess the stage of wound healing.Finally,refractory wounds including pressure ulcers,diabetes ulcers and articular wounds,and the corresponding wound monitoring and healing process are discussed in detail.This manuscript supports the growth of clinically linked disciplines and offers motivation to researchers working in the multidisciplinary field of conductive hydrogel dressings.
基金National Nonprofit Institute Research Grant for the Institute of Basic Theory for Chinese Medicine,China Academy of Chinese Medical Sciences:Mechanism of Regulating Phlegm-Dampness Constitution to Prevent Metabolic Diseases based on Gut Microbiota-host DNA Methylation(No.YZ-202151)。
文摘OBJECTIVE:To investigate the difference in gut microbiota between population with damp-heat constitution(DHC)and balanced constitution(BC).METHODS:A multi-centered cross-sectional casecontrol study was conducted,which included 249 participants with damp-heat constitution or balanced constitution.Baseline information of participants was collected,and stool samples were collected for gut microbiota analysis.Principal coordinate analysis,linear discriminant analysis effect size analysis,receiver operating characteristic,random forest model,and phylogenetic investigation of communities by reconstruction of unobserved states methods were used to reveal the relationship between gut microbiota and the damp-heat constitution.RESULTS:Compared to those in the BC group,the richness and diversity of the microbiota,specifically those of several short-chain fatty acid producing genera such as Barnesiella,Coprobacter,and Butyricimonas,were significantly decreased in the DHC group.Regarding biological functions,flavonoid biosynthesis,propanoate metabolism,and nucleotide sugar metabolism were suppressed,while arachidonic acid metabolism and glutathione metabolism were enriched in the DHC group.Finally,a classifier based on the microbiota was constructed to discriminate between the DHC and BC populations.CONCLUSION:The gut microbiota of the DHC population exhibits significantly reduced diversity and is closely related to inflammation,metabolic disorders,and liver steatosis,which is consistent with clinical observations,thus serving as a potential diagnostic tool for traditional Chinese medicine constitution discrimination.
基金supported by the National Natural Science Foundation of China(No.51605054).
文摘Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.
基金supported by an Innovation Fund for Medical Sciences of the Chinese Academy of Medical Sciences (Grant No.2019-I2M-2-007).
文摘Objective:Postpartum nutrition plays a critical role in maternal recovery and long-term health.However,the nutritional status of working mothers in the postpartum period remains understudied.This study aimed to assess the dietary quality of postpartum women in urban Beijing,identify occupational-related factors influencing their diet,and explore potential interventions to improve maternal nutrition during the postpartum period.Methods:In this cross-sectional analysis,554 women one year after delivery were recruited from ten community health centers.Sociodemographic,occupational and postpartum care variables were collected via questionnaire.Dietary intake over the preceding year was assessed using a food frequency questionnaire.The modified dietary balance index for postpartum women were used for dietary quality assessment.Results:The study revealed severe dietary imbalances among postpartum women,characterized by excessive consumption of cereals,eggs,and meats,while their intake of vegetables,fruits,and dairy products was inadequate.According to dietary balance index for postpartum women,66.25%of mothers showed varying degrees of excessive intake.45.31%of mothers experienced varying levels of insufficient intake,with only 19.86%of participants having a relatively balanced diet.Occupational differences were observed,with women in the commercial employment group showing higher levels of excessive food intake.The analysis of influencing factors showed that family monthly income,maternity leave,and postpartum care significantly affected the dietary quality.Conclusions:Postpartum women in Beijing experience widespread dietary imbalances,with both excesses and deficiencies.Occupational context and related factors significantly shape diet quality.These findings highlight the need for targeted nutritional interventions tailored to the specific challenges of different occupational groups.
