BACKGROUND Due to the increasing rate of thyroid nodules diagnosis,and the desire to avoid the unsightly cervical scar,remote thyroidectomies were invented and are increasingly performed.Transoral endoscopic thyroidec...BACKGROUND Due to the increasing rate of thyroid nodules diagnosis,and the desire to avoid the unsightly cervical scar,remote thyroidectomies were invented and are increasingly performed.Transoral endoscopic thyroidectomy vestibular approach and trans-areolar approaches(TAA)are the two most commonly used remote approaches.No previous meta-analysis has compared postoperative infections and swallowing difficulties among the two procedures.AIM To compared the same among patients undergoing lobectomy for unilateral thyroid carcinoma/benign thyroid nodule.METHODS We searched PubMed MEDLINE,Google Scholar,and Cochrane Library from the date of the first published article up to August 2025.The term used were transoral thyroidectomy vestibular approach,trans areolar thyroidectomy,scarless thyroidectomy,remote thyroidectomy,infections,postoperative,inflammation,dysphagia,and swallowing difficulties.We identified 130 studies,of them,30 full texts were screened and only six studies were included in the final meta-analysis.RESULTS Postoperative infections were not different between the two approaches,odd ratio=1.33,95%confidence interval:0.50-3.53,theχ2 was 1.92 and the P-value for overall effect of 0.57.Similarly,transient swallowing difficulty was not different between the two forms of surgery,with odd ratio=0.91,95%confidence interval:0.35-2.40;theχ2 was 1.32,and the P-value for overall effect of 0.85.CONCLUSION No significant statistical differences were evident between trans-oral endoscopic Mirghani H.Infections and swallowing difficulty in scarless thyroidectomy WJCC https://www.wjgnet.com 2 January 6,2026 Volume 14 Issue 1 thyroidectomy vestibular approach and trans-areolar approach regarding postoperative infection and transient swallowing difficulties.Further longer randomized trials are needed.展开更多
The photoacoustic imaging of lipid is intrinsically constrained by the feeble nature of endogenous lipid signals,posing a persistent sensitivity challenge that demands innovative solutions.Although adopting high-effic...The photoacoustic imaging of lipid is intrinsically constrained by the feeble nature of endogenous lipid signals,posing a persistent sensitivity challenge that demands innovative solutions.Although adopting high-efficiency excitation and detection elements may improve the imaging sensitivity to a certain extent,the application of the elements is inevitably subject to various limitations in practical applications,particularly during in vivo imaging and endoscopic imaging.In this study,we propose a multi-combinatorial approach to enhance the sensitivity of lipid photoacoustic imaging.The approach involves wavelet transform processing of one-dimensional A-line signals,gradient-based denoising of two-dimensional B-scan images,and finally,threedimensional spatial weighted averaging of the data processed by the previous two steps.This method not only significantly improves the signal-to-noise ratio(SNR)in distinguished feature regions of the image by around 10 dB,but also efficiently extracts weak signals with no distinct features in the original image.After processing with this method,the images acquired under single scanning were compared with those obtained under multiple scanning.The results showed highly consistent image features,with the structural similarity index increasing from 0.2 to 0.8,confirming the accuracy and reliability of the multi-combinatorial approach.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
The 17 Sustainable Development Goals(SDGs)for 2030,adopted by all United Nations member states in 2015,are facing a range of challenges.Factors such as climate change,regional conflicts and economic recession are havi...The 17 Sustainable Development Goals(SDGs)for 2030,adopted by all United Nations member states in 2015,are facing a range of challenges.Factors such as climate change,regional conflicts and economic recession are having a significant impact,particularly on global poverty governance.As a platform for dialogue,exchange and technical cooperation,the 2025 International Seminar on Global Poverty Reduction Partnerships was held in Beijing on 10 December 2025.展开更多
The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring suffi...The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.展开更多
Net primary production(NPP) is an indicator of rangeland ecosystem function. This research assessed the potential of the Carnegie Ames Stanford Approach(CASA) model for estimating NPP and its spatial and temporal chan...Net primary production(NPP) is an indicator of rangeland ecosystem function. This research assessed the potential of the Carnegie Ames Stanford Approach(CASA) model for estimating NPP and its spatial and temporal changes in semi-arid rangelands of Semirom County, Iran. Using CASA model, we estimated the NPP values based on monthly climate data and the normalized difference vegetation index(NDVI) obtained from the MODIS sensor. Regression analysis was then applied to compare the estimated production data with observed production data. The spatial and temporal changes in NPP and light utilization efficiency(LUE) were investigated in different rangeland vegetation types. The standardized precipitation index(SPI) was also calculated at different time scales and the correlation of SPI with NPP changes was determined. The results indicated that the estimated NPP values varied from 0.00 to 74.48 g C/(m^2·a). The observed and estimated NPP values had different correlations, depending on rangeland conditions and vegetation types. The highest and lowest correlations were respectively observed in Astragalus spp.-Agropyron spp. rangeland(R^2=0.75) with good condition and Gundelia spp.-Cousinia spp. rangeland(R^2=0.36) with poor and very poor conditions. The maximum and minimum LUE values were found in Astragalus spp.-Agropyron spp. rangeland(0.117 g C/MJ) with good condition and annual grassesannual forbs rangeland(0.010 g C/MJ), respectively. According to the correlations between SPI and NPP changes, the effects of drought periods on NPP depended on vegetation types and rangeland conditions. Annual plants had the highest drought sensitivity while shrubs exhibited the lowest drought sensitivity. The positive effects of wet periods on NPP were less evident in degraded areas where the destructive effects of drought were more prominent. Therefore, determining vegetation types and rangeland conditions is essential in NPP estimation. The findings of this study confirmed the potential of the CASA for estimating rangeland production. Therefore, the model output maps can be used to evaluate, monitor and optimize rangeland management in semi-arid rangelands of Iran where MODIS NPP products are not available.展开更多
植被净初级生产力(Net Primary Productivity,NPP)是反映陆地生态系统碳封存能力和环境变化的直接指标,受气候变化与人类活动的共同影响,且在不同地形上有分异性。然而,人类活动及地形对秦巴山区植被NPP变化的影响研究尚且不足。采用CAS...植被净初级生产力(Net Primary Productivity,NPP)是反映陆地生态系统碳封存能力和环境变化的直接指标,受气候变化与人类活动的共同影响,且在不同地形上有分异性。然而,人类活动及地形对秦巴山区植被NPP变化的影响研究尚且不足。采用CASA模型,综合利用线性趋势分析、转移矩阵和残差分析等方法研究了秦巴山区2001—2022年长时序NPP时空动态和地形效应,并进一步探讨了气候变化和人类活动对NPP变化的相对贡献率,主要结论如下:①秦巴山区2001—2022年的NPP空间分布表现为中间高,四周低,均值为585.11g C/m^(2),并以4.30g C m^(-2)a^(-1)的速度增加。②林地有最高的年NPP均值,而退耕还林区域具有最高的NPP增长速率(8.17g C m^(-2)a^(-1)),表明退耕还林是秦巴山区NPP增长的有效措施;③NPP随海拔和坡度变化具有明显的分异性。在海拔3400m以下,植被NPP随着高程的增加而增加,而当高程超过3400m时,植被NPP显著减少,坡度在10°—40°范围内植被NPP的多年均值和变化趋势较高;④秦巴山区NPP变化是气候变化和人类活动共同作用的结果,二者对NPP变化的相对贡献率分别为37.81%和62.19%,其中人类活动导致陇南等生态脆弱区NPP显著提高。展开更多
呼伦贝尔草原是著名的天然牧场,畜牧业也是呼伦贝尔草原地区的基础性产业,充分发挥畜牧业优势对于提高草原地区牧民收入有着重大意义。为获得长时序牧草产量数据,本文利用地理信息和遥感技术,基于CASA模型估算出呼伦贝尔市牧业五旗2001~...呼伦贝尔草原是著名的天然牧场,畜牧业也是呼伦贝尔草原地区的基础性产业,充分发挥畜牧业优势对于提高草原地区牧民收入有着重大意义。为获得长时序牧草产量数据,本文利用地理信息和遥感技术,基于CASA模型估算出呼伦贝尔市牧业五旗2001~2020年牧草生长季(5~8月)的牧草产量,并分别用各牧业旗生态观测站的实测产量数据对模拟产量数据进行精度验证。本研究首先阐释CASA模型的基本原理及模型框架,介绍CASA模型中估算牧草产量的两个关键参量:植被吸收的光合有效辐射和实际光能利用率。通过太阳总辐射和植物光合有效辐射吸收比例确定植被吸收的光合有效辐射,通过温度胁迫因子、水分胁迫因子、最大光能利用率确定实际光能利用率,最终计算出2001~2020年呼伦贝尔市牧业五旗牧草生长季的NPP。根据2001~2020年呼伦贝尔地区逐年的土地覆盖数据提取草原用地,经空间统计分析提取出牧业五旗的牧草生长季(5~8月)逐年平均NPP,进而转换成牧草产量。对比分析牧草估测产量与实测产量之间的相关性系数、均方根误差、平均绝对误差三种精度评价指标,验证CASA模型估产精度。实测产量值与估测产量值的相关性系数分布区间为0.76~0.82、均方根误差区间为20.42~24.21 g/m2、平均绝对误差区间为17.94~23.19 g/m2,三项误差指标均在合理误差范围内。结果表明CASA模型模拟出的牧草产量数据精度较高,可为呼伦贝尔畜牧业高质量发展提供技术支撑。Hulunbuir grassland is a famous natural pasture, and animal husbandry is also a basic industry in Hulunbuir grassland area, giving full play to the advantages of animal husbandry is of great significance to improve the income of herdsmen in the grassland area. In order to obtain the long time series pasture yield data, this paper estimates the pasture yield of the pasture growing season (May-August) of the five flags of Hulunbuir City pastoralism from 2001 to 2020 based on CASA model using geographic information and remote sensing technology, and verifies the accuracy of the simulated yield data with the measured yield data from the ecological observatory of each flag of pastoralism, respectively. In this study, the basic principles and model framework of CASA model were firstly explained, and two key parameters for estimating pasture yield in CASA model were introduced: photosynthetically active radiation absorbed by vegetation and actual light energy utilization rate. The photosynthetically active radiation absorbed by vegetation was determined by the ratio of total solar radiation and plant photosynthetically active radiation absorption, and the actual light energy utilization was determined by the temperature stress factor, moisture stress factor, and maximum light energy utilization, and the NPP of the growing season of forage grass in the five flags of pastoral industry in Hulunbuir City from 2001 to 2020 was finally calculated. Based on the year-by-year land-cover data of Hulunbuir area from 2001 to 2020, the NPP of the growing season of pasture grass was extracted. Grassland land was extracted, and the yearly average NPP of the pasture growing season (May-August) in the five banners of pastoralism was extracted by spatial statistical analysis, and then converted into pasture yield. The correlation coefficient, root-mean-square error, and average absolute error of three accuracy evaluation indexes between the estimated and measured pasture yield were comparatively analyzed to verify the accuracy of CASA model estimation. The distribution range of correlation coefficient between measured and estimated yield values was 0.76~0.82, the root mean square error range was 20.42~24.21 g/m2, and the average absolute error range was 17.94~23.19 g/m2, and the three kinds of error indexes were all within the reasonable error range. The results showed that the CASA model simulated pasture yield data with high accuracy, which can provide technical support for the high-quality development of Hulunbuir’s livestock industry.展开更多
Cardiovascular diseases(CVDs)remain the leading cause of morbidity and mortality worldwide,necessitating innovative diagnostic and prognostic strategies.Traditional biomarkers like C-reactive protein,uric acid,troponi...Cardiovascular diseases(CVDs)remain the leading cause of morbidity and mortality worldwide,necessitating innovative diagnostic and prognostic strategies.Traditional biomarkers like C-reactive protein,uric acid,troponin,and natriuretic peptides play crucial roles in CVD management,yet they are often limited by sensitivity and specificity constraints.This narrative review critically examines the emerging landscape of cardiac biomarkers and advocates for a multiple-marker approach to enhance early detection,prognosis,and risk stratification of CVD.In recent years,several novel biomarkers have shown promise in revolutionizing CVD diagnostics.Gamma-glutamyltransferase,microRNAs,endothelial microparticles,placental growth factor,trimethylamine N-oxide,retinol-binding protein 4,copeptin,heart-type fatty acid-binding protein,galectin-3,growth differentiation factor-15,soluble suppression of tumorigenicity 2,fibroblast growth factor 23,and adrenomedullin have emerged as significant indicators of CV health.These biomarkers provide insights into various pathophysiological processes,such as oxidative stress,endothelial dysfunction,inflammation,metabolic disturbances,and myocardial injury.The integration of these novel biomarkers with traditional ones offers a more comprehensive understanding of CVD mechanisms.This multiple-marker approach can improve diagnostic accuracy,allowing for better risk stratification and more personalized treatment strategies.This review underscores the need for continued research to validate the clinical utility of these biomarkers and their potential incorporation into routine clinical practice.By leveraging the strengths of both traditional and novel biomarkers,precise therapeutic plans can be developed,thereby improving the management and prognosis of patients with CVDs.The ongoing exploration and validation of these biomarkers are crucial for advancing CV care and addressing the limitations of current diagnostic tools.展开更多
For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies ...For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies bionic analytical framework based on the partial differential equation-ordinary differential equation(PDE-ODE)approach.Specifically,by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents,this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model,and that of sparsely distributed agents as several ODEs equivalent to the predator population models.The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE,thereby forming a PDE-ODE-based tracking error model for the considered MASs.Furthermore,by integrating adaptive neural control scheme with the aforementioned biological models,a“Flexible Neural Network”endowed with adaptive and self-stabilized capabilities is constructed,which acts upon the considered MASs,enabling their practical finite-time deployment.Finally,effectiveness of the developed approach is illustrated through a numerical example.展开更多
The progressive loss of dopaminergic neurons in affected patient brains is one of the pathological features of Parkinson's disease,the second most common human neurodegenerative disease.Although the detailed patho...The progressive loss of dopaminergic neurons in affected patient brains is one of the pathological features of Parkinson's disease,the second most common human neurodegenerative disease.Although the detailed pathogenesis accounting for dopaminergic neuron degeneration in Parkinson's disease is still unclear,the advancement of stem cell approaches has shown promise for Parkinson's disease research and therapy.The induced pluripotent stem cells have been commonly used to generate dopaminergic neurons,which has provided valuable insights to improve our understanding of Parkinson's disease pathogenesis and contributed to anti-Parkinson's disease therapies.The current review discusses the practical approaches and potential applications of induced pluripotent stem cell techniques for generating and differentiating dopaminergic neurons from induced pluripotent stem cells.The benefits of induced pluripotent stem cell-based research are highlighted.Various dopaminergic neuron differentiation protocols from induced pluripotent stem cells are compared.The emerging three-dimension-based brain organoid models compared with conventional two-dimensional cell culture are evaluated.Finally,limitations,challenges,and future directions of induced pluripotent stem cell–based approaches are analyzed and proposed,which will be significant to the future application of induced pluripotent stem cell-related techniques for Parkinson's disease.展开更多
Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensem...Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions.With the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data.Ensemble deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning algorithms.Although ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble learning.Ensemble deep learning has been successfully used in several areas,such as bioinformatics,finance,and health care.In this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug discovery.We cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also demonstrated.Furthermore,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and explored.Finally,future directions and opportunities for enhancing healthcare model performance are discussed.展开更多
This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal disease...This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.展开更多
文摘BACKGROUND Due to the increasing rate of thyroid nodules diagnosis,and the desire to avoid the unsightly cervical scar,remote thyroidectomies were invented and are increasingly performed.Transoral endoscopic thyroidectomy vestibular approach and trans-areolar approaches(TAA)are the two most commonly used remote approaches.No previous meta-analysis has compared postoperative infections and swallowing difficulties among the two procedures.AIM To compared the same among patients undergoing lobectomy for unilateral thyroid carcinoma/benign thyroid nodule.METHODS We searched PubMed MEDLINE,Google Scholar,and Cochrane Library from the date of the first published article up to August 2025.The term used were transoral thyroidectomy vestibular approach,trans areolar thyroidectomy,scarless thyroidectomy,remote thyroidectomy,infections,postoperative,inflammation,dysphagia,and swallowing difficulties.We identified 130 studies,of them,30 full texts were screened and only six studies were included in the final meta-analysis.RESULTS Postoperative infections were not different between the two approaches,odd ratio=1.33,95%confidence interval:0.50-3.53,theχ2 was 1.92 and the P-value for overall effect of 0.57.Similarly,transient swallowing difficulty was not different between the two forms of surgery,with odd ratio=0.91,95%confidence interval:0.35-2.40;theχ2 was 1.32,and the P-value for overall effect of 0.85.CONCLUSION No significant statistical differences were evident between trans-oral endoscopic Mirghani H.Infections and swallowing difficulty in scarless thyroidectomy WJCC https://www.wjgnet.com 2 January 6,2026 Volume 14 Issue 1 thyroidectomy vestibular approach and trans-areolar approach regarding postoperative infection and transient swallowing difficulties.Further longer randomized trials are needed.
基金supported by the National Key Research and Development Program of China(2022YFC2402400)the National Natural Science Foundation of China(82027803,62275062)+7 种基金the Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology(2020B121201010)the Shenzhen Science and Technology Innovation Committee under Grant(JCYJ20220818101417039)the Shenzhen Key Laboratory for Molecular lmaging(ZDSY20130401165820357)the Shenzhen Medical Research Fund(D2404002)the Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments(2023-SGTTXM-002 and 2024-SGTTXM-005)the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)(YDZX2023115)the Taishan Scholar Special Funding Project of Shandong Provinceand the Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai(ZL202402).
文摘The photoacoustic imaging of lipid is intrinsically constrained by the feeble nature of endogenous lipid signals,posing a persistent sensitivity challenge that demands innovative solutions.Although adopting high-efficiency excitation and detection elements may improve the imaging sensitivity to a certain extent,the application of the elements is inevitably subject to various limitations in practical applications,particularly during in vivo imaging and endoscopic imaging.In this study,we propose a multi-combinatorial approach to enhance the sensitivity of lipid photoacoustic imaging.The approach involves wavelet transform processing of one-dimensional A-line signals,gradient-based denoising of two-dimensional B-scan images,and finally,threedimensional spatial weighted averaging of the data processed by the previous two steps.This method not only significantly improves the signal-to-noise ratio(SNR)in distinguished feature regions of the image by around 10 dB,but also efficiently extracts weak signals with no distinct features in the original image.After processing with this method,the images acquired under single scanning were compared with those obtained under multiple scanning.The results showed highly consistent image features,with the structural similarity index increasing from 0.2 to 0.8,confirming the accuracy and reliability of the multi-combinatorial approach.
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.
文摘The 17 Sustainable Development Goals(SDGs)for 2030,adopted by all United Nations member states in 2015,are facing a range of challenges.Factors such as climate change,regional conflicts and economic recession are having a significant impact,particularly on global poverty governance.As a platform for dialogue,exchange and technical cooperation,the 2025 International Seminar on Global Poverty Reduction Partnerships was held in Beijing on 10 December 2025.
基金supported by the Program for NIM-Basic Research Business Expenses Key Field Program,China(No.AKYCX2315).
文摘The rapid identification of γ-emitting radionuclides with low activity levels in public areas is crucial for nuclear safety.However,classical methods rely on full-energy peaks in the integral spectrum,requiring sufficient count accumulation for evaluation,thereby limiting response time.The sequential Bayesian approach,which utilizes prior information and considers both photon energies and interarrival times,can significantly enhance the performance of radionuclides identification.This study proposes a theoretical optimization method for the traditional sequential Bayesian approach.Each photon is processed sequentially,and the corresponding posterior probability is updated in real time using a noninformative prior from the Bayesian theory.By comparing the posterior probabilities of the background and radionuclides based on the energy variance and time interval,the type of γ-rays can be identified(background characteristic γ-rays,Compton plateaus γ-rays,or radionuclide-specific characteristic γ-rays).By integrating the information from these multiple characteristic γ-rays,the presence and type of radionuclides were determined based on the final decision function and a set threshold.Based on theoretical research,verification experiments were conducted using a LaBr_(3)(Ce)detector in both low-and natural background radiation environments with typical radionuclides(^(137)Cs,^(60)Co,and ^(133)Ba).The results show that this approach can identify ^(137)Cs in 7.9 s and 8.5 s(source dose rate contribution:approximately 6.5×10^(−3)μGy/h),^(60)Co in 8.1 s and 9.8 s(approximately 4.8×10^(−2)μGy/h),and ^(133)Ba in 4.05 s and 5.99 s(approximately 3.4×10^(−2)μGy/h)under low and natural background radiation,respectively,with a miss rate below 0.01%.This demonstrates the effectiveness of the proposed approach for fast radionuclides identification,even at low activity levels and highlights its potential for enhancing public safety in diverse radiation environments.
文摘Net primary production(NPP) is an indicator of rangeland ecosystem function. This research assessed the potential of the Carnegie Ames Stanford Approach(CASA) model for estimating NPP and its spatial and temporal changes in semi-arid rangelands of Semirom County, Iran. Using CASA model, we estimated the NPP values based on monthly climate data and the normalized difference vegetation index(NDVI) obtained from the MODIS sensor. Regression analysis was then applied to compare the estimated production data with observed production data. The spatial and temporal changes in NPP and light utilization efficiency(LUE) were investigated in different rangeland vegetation types. The standardized precipitation index(SPI) was also calculated at different time scales and the correlation of SPI with NPP changes was determined. The results indicated that the estimated NPP values varied from 0.00 to 74.48 g C/(m^2·a). The observed and estimated NPP values had different correlations, depending on rangeland conditions and vegetation types. The highest and lowest correlations were respectively observed in Astragalus spp.-Agropyron spp. rangeland(R^2=0.75) with good condition and Gundelia spp.-Cousinia spp. rangeland(R^2=0.36) with poor and very poor conditions. The maximum and minimum LUE values were found in Astragalus spp.-Agropyron spp. rangeland(0.117 g C/MJ) with good condition and annual grassesannual forbs rangeland(0.010 g C/MJ), respectively. According to the correlations between SPI and NPP changes, the effects of drought periods on NPP depended on vegetation types and rangeland conditions. Annual plants had the highest drought sensitivity while shrubs exhibited the lowest drought sensitivity. The positive effects of wet periods on NPP were less evident in degraded areas where the destructive effects of drought were more prominent. Therefore, determining vegetation types and rangeland conditions is essential in NPP estimation. The findings of this study confirmed the potential of the CASA for estimating rangeland production. Therefore, the model output maps can be used to evaluate, monitor and optimize rangeland management in semi-arid rangelands of Iran where MODIS NPP products are not available.
文摘植被净初级生产力(Net Primary Productivity,NPP)是反映陆地生态系统碳封存能力和环境变化的直接指标,受气候变化与人类活动的共同影响,且在不同地形上有分异性。然而,人类活动及地形对秦巴山区植被NPP变化的影响研究尚且不足。采用CASA模型,综合利用线性趋势分析、转移矩阵和残差分析等方法研究了秦巴山区2001—2022年长时序NPP时空动态和地形效应,并进一步探讨了气候变化和人类活动对NPP变化的相对贡献率,主要结论如下:①秦巴山区2001—2022年的NPP空间分布表现为中间高,四周低,均值为585.11g C/m^(2),并以4.30g C m^(-2)a^(-1)的速度增加。②林地有最高的年NPP均值,而退耕还林区域具有最高的NPP增长速率(8.17g C m^(-2)a^(-1)),表明退耕还林是秦巴山区NPP增长的有效措施;③NPP随海拔和坡度变化具有明显的分异性。在海拔3400m以下,植被NPP随着高程的增加而增加,而当高程超过3400m时,植被NPP显著减少,坡度在10°—40°范围内植被NPP的多年均值和变化趋势较高;④秦巴山区NPP变化是气候变化和人类活动共同作用的结果,二者对NPP变化的相对贡献率分别为37.81%和62.19%,其中人类活动导致陇南等生态脆弱区NPP显著提高。
文摘呼伦贝尔草原是著名的天然牧场,畜牧业也是呼伦贝尔草原地区的基础性产业,充分发挥畜牧业优势对于提高草原地区牧民收入有着重大意义。为获得长时序牧草产量数据,本文利用地理信息和遥感技术,基于CASA模型估算出呼伦贝尔市牧业五旗2001~2020年牧草生长季(5~8月)的牧草产量,并分别用各牧业旗生态观测站的实测产量数据对模拟产量数据进行精度验证。本研究首先阐释CASA模型的基本原理及模型框架,介绍CASA模型中估算牧草产量的两个关键参量:植被吸收的光合有效辐射和实际光能利用率。通过太阳总辐射和植物光合有效辐射吸收比例确定植被吸收的光合有效辐射,通过温度胁迫因子、水分胁迫因子、最大光能利用率确定实际光能利用率,最终计算出2001~2020年呼伦贝尔市牧业五旗牧草生长季的NPP。根据2001~2020年呼伦贝尔地区逐年的土地覆盖数据提取草原用地,经空间统计分析提取出牧业五旗的牧草生长季(5~8月)逐年平均NPP,进而转换成牧草产量。对比分析牧草估测产量与实测产量之间的相关性系数、均方根误差、平均绝对误差三种精度评价指标,验证CASA模型估产精度。实测产量值与估测产量值的相关性系数分布区间为0.76~0.82、均方根误差区间为20.42~24.21 g/m2、平均绝对误差区间为17.94~23.19 g/m2,三项误差指标均在合理误差范围内。结果表明CASA模型模拟出的牧草产量数据精度较高,可为呼伦贝尔畜牧业高质量发展提供技术支撑。Hulunbuir grassland is a famous natural pasture, and animal husbandry is also a basic industry in Hulunbuir grassland area, giving full play to the advantages of animal husbandry is of great significance to improve the income of herdsmen in the grassland area. In order to obtain the long time series pasture yield data, this paper estimates the pasture yield of the pasture growing season (May-August) of the five flags of Hulunbuir City pastoralism from 2001 to 2020 based on CASA model using geographic information and remote sensing technology, and verifies the accuracy of the simulated yield data with the measured yield data from the ecological observatory of each flag of pastoralism, respectively. In this study, the basic principles and model framework of CASA model were firstly explained, and two key parameters for estimating pasture yield in CASA model were introduced: photosynthetically active radiation absorbed by vegetation and actual light energy utilization rate. The photosynthetically active radiation absorbed by vegetation was determined by the ratio of total solar radiation and plant photosynthetically active radiation absorption, and the actual light energy utilization was determined by the temperature stress factor, moisture stress factor, and maximum light energy utilization, and the NPP of the growing season of forage grass in the five flags of pastoral industry in Hulunbuir City from 2001 to 2020 was finally calculated. Based on the year-by-year land-cover data of Hulunbuir area from 2001 to 2020, the NPP of the growing season of pasture grass was extracted. Grassland land was extracted, and the yearly average NPP of the pasture growing season (May-August) in the five banners of pastoralism was extracted by spatial statistical analysis, and then converted into pasture yield. The correlation coefficient, root-mean-square error, and average absolute error of three accuracy evaluation indexes between the estimated and measured pasture yield were comparatively analyzed to verify the accuracy of CASA model estimation. The distribution range of correlation coefficient between measured and estimated yield values was 0.76~0.82, the root mean square error range was 20.42~24.21 g/m2, and the average absolute error range was 17.94~23.19 g/m2, and the three kinds of error indexes were all within the reasonable error range. The results showed that the CASA model simulated pasture yield data with high accuracy, which can provide technical support for the high-quality development of Hulunbuir’s livestock industry.
文摘Cardiovascular diseases(CVDs)remain the leading cause of morbidity and mortality worldwide,necessitating innovative diagnostic and prognostic strategies.Traditional biomarkers like C-reactive protein,uric acid,troponin,and natriuretic peptides play crucial roles in CVD management,yet they are often limited by sensitivity and specificity constraints.This narrative review critically examines the emerging landscape of cardiac biomarkers and advocates for a multiple-marker approach to enhance early detection,prognosis,and risk stratification of CVD.In recent years,several novel biomarkers have shown promise in revolutionizing CVD diagnostics.Gamma-glutamyltransferase,microRNAs,endothelial microparticles,placental growth factor,trimethylamine N-oxide,retinol-binding protein 4,copeptin,heart-type fatty acid-binding protein,galectin-3,growth differentiation factor-15,soluble suppression of tumorigenicity 2,fibroblast growth factor 23,and adrenomedullin have emerged as significant indicators of CV health.These biomarkers provide insights into various pathophysiological processes,such as oxidative stress,endothelial dysfunction,inflammation,metabolic disturbances,and myocardial injury.The integration of these novel biomarkers with traditional ones offers a more comprehensive understanding of CVD mechanisms.This multiple-marker approach can improve diagnostic accuracy,allowing for better risk stratification and more personalized treatment strategies.This review underscores the need for continued research to validate the clinical utility of these biomarkers and their potential incorporation into routine clinical practice.By leveraging the strengths of both traditional and novel biomarkers,precise therapeutic plans can be developed,thereby improving the management and prognosis of patients with CVDs.The ongoing exploration and validation of these biomarkers are crucial for advancing CV care and addressing the limitations of current diagnostic tools.
基金The National Key R&D Program of China(2021ZD0201300)the National Natural Science Foundation of China(624B2058,U1913602 and 61936004)+1 种基金the Innovation Group Project of the National Natural Science Foundation of China(61821003)the 111 Project on Computational Intelligence and Intelligent Control(B18024).
文摘For large-scale heterogeneous multi-agent systems(MASs)with characteristics of dense-sparse mixed distribution,this paper investigates the practical finite-time deployment problem by establishing a novel crossspecies bionic analytical framework based on the partial differential equation-ordinary differential equation(PDE-ODE)approach.Specifically,by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents,this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model,and that of sparsely distributed agents as several ODEs equivalent to the predator population models.The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE,thereby forming a PDE-ODE-based tracking error model for the considered MASs.Furthermore,by integrating adaptive neural control scheme with the aforementioned biological models,a“Flexible Neural Network”endowed with adaptive and self-stabilized capabilities is constructed,which acts upon the considered MASs,enabling their practical finite-time deployment.Finally,effectiveness of the developed approach is illustrated through a numerical example.
基金supported by Singapore National Medical Research Council(NMRC)grants,including CS-IRG,HLCA2022(to ZDZ),STaR,OF LCG 000207(to EKT)a Clinical Translational Research Programme in Parkinson's DiseaseDuke-Duke-NUS collaboration pilot grant(to ZDZ)。
文摘The progressive loss of dopaminergic neurons in affected patient brains is one of the pathological features of Parkinson's disease,the second most common human neurodegenerative disease.Although the detailed pathogenesis accounting for dopaminergic neuron degeneration in Parkinson's disease is still unclear,the advancement of stem cell approaches has shown promise for Parkinson's disease research and therapy.The induced pluripotent stem cells have been commonly used to generate dopaminergic neurons,which has provided valuable insights to improve our understanding of Parkinson's disease pathogenesis and contributed to anti-Parkinson's disease therapies.The current review discusses the practical approaches and potential applications of induced pluripotent stem cell techniques for generating and differentiating dopaminergic neurons from induced pluripotent stem cells.The benefits of induced pluripotent stem cell-based research are highlighted.Various dopaminergic neuron differentiation protocols from induced pluripotent stem cells are compared.The emerging three-dimension-based brain organoid models compared with conventional two-dimensional cell culture are evaluated.Finally,limitations,challenges,and future directions of induced pluripotent stem cell–based approaches are analyzed and proposed,which will be significant to the future application of induced pluripotent stem cell-related techniques for Parkinson's disease.
基金funded by Taif University,Saudi Arabia,project No.(TU-DSPP-2024-263).
文摘Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions.With the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data.Ensemble deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning algorithms.Although ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble learning.Ensemble deep learning has been successfully used in several areas,such as bioinformatics,finance,and health care.In this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug discovery.We cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also demonstrated.Furthermore,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and explored.Finally,future directions and opportunities for enhancing healthcare model performance are discussed.
基金Supported by National Research Foundation of Korea,No.NRF-2021S1A5A8062526.
文摘This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.
