Pioneering initiatives in Yunnan Province are leveraging intangible cultural heritage to foster practical skills and create flexible employment,opening a sustainable path to prosperity and rural revitalization for peo...Pioneering initiatives in Yunnan Province are leveraging intangible cultural heritage to foster practical skills and create flexible employment,opening a sustainable path to prosperity and rural revitalization for people with disabilities.ACROSS China,provinces and municipalities have adopted a range of initiatives,based on local conditions to facilitate employment for people with disabilities.In southwest China’s Yunnan Province,home to many ethnic minorities,efforts have focused on integrating employment for disabled people with rural revitalization.展开更多
Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a ...Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.展开更多
Real-time assessment of slope reinforcements to diagnose their state in all stages of service life is imperative for prompt evaluation of slope stability and establishing an efficient early warning(EW)system.Many poin...Real-time assessment of slope reinforcements to diagnose their state in all stages of service life is imperative for prompt evaluation of slope stability and establishing an efficient early warning(EW)system.Many point-based monitoring instruments have been used in the last few decades.However,these sensors suffer from a particular risk of detection failures and practical limitations.Fibre-optic sensing(FOS)technologies have been developed,tested,and validated across various geoengineering applications,including slope monitoring,as they offer exceptional advantages,such as high data-carrying capacity,precise mapping of physical parameters,durability,and immunity to electromagnetic interference.The deformation of rock/soil causes the deformation and fracture of reinforcement materials,which are subsequently transferred to the encapsulated fibre-optic(FO)sensors,providing valuable information on reinforcements'safety state and performance for early failure detection.This paper is devoted to critically analysing the application of cutting-edge FOS technologies for slope reinforcement monitoring.Firstly,a concise overview of the fundamental principles underlying discrete and distributed FOS methods is provided.The key considerations for selecting FO cables and the appropriate packaging techniques necessary to withstand the challenges posed by complex geological environments are also summarised.We delve into the details of three distinct cable installation techniques within slope reinforcement components:surface bonding,slot embedment,and clamping.The recent advancements in FOS methods for monitoring slope reinforcements such as rock bolts,soil nails,anti-slide piles,geosynthetics,and retaining walls are extensively reviewed.The paper addresses this novel sensing technique's challenges and comprehensively explores its prospects.This review is anticipated to be a valuable resource for geoengineers and researchers involved in slope monitoring through FOS technology,offering insightful perspectives and guidance.展开更多
The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(...The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(Kuala Lumpur)as the study area,the slope characteristics of geometrical parameters are obtained from a multidisciplinary approach(consisting of geological,geotechnical,and remote sensing analyses).18 factors,including rock strength,rock quality designation(RQD),joint spacing,continuity,openness,roughness,filling,weathering,water seepage,temperature,vegetation index,water index,and orientation,are selected to construct model input variables while the factor of safety(FOS)functions as an output.The area under the curve(AUC)value of the receiver operating characteristic(ROC)curve is obtained with precision and accuracy and used to analyse the predictive model ability.With a large training set and predicted parameters,an area under the ROC curve(the AUC)of 0.95 is achieved.A precision score of 0.88 is obtained,indicating that the model has a low false positive rate and correctly identifies a substantial number of true positives.The findings emphasise the importance of using a variety of terrain characteristics and different approaches to characterise the rock slope.展开更多
相较于传统的两步法,动力学一步法能充分利用观测数据的原始信息,理论上可获得更合理的时变重力场产品,同时也因其涉及的参数维度更多样、函数模型更复杂,一直是当前研究的热点和难点.本文研究并实现了动力学一步法恢复时变重力场,给出...相较于传统的两步法,动力学一步法能充分利用观测数据的原始信息,理论上可获得更合理的时变重力场产品,同时也因其涉及的参数维度更多样、函数模型更复杂,一直是当前研究的热点和难点.本文研究并实现了动力学一步法恢复时变重力场,给出了合理的数据处理策略,而后基于GRACE-FO(GRACE Follow-On)星载GPS数据和KBR(K/Ka Band Ranging)距离变率数据反演了2021—2022年60阶全球月时变重力场模型.对于一步法中诸多技术细节,本文重点分析了先验权和经验参数对轨道确定和模型反演的影响,研究表明:当采用30 s采样率的GPS数据时,需适当对GPS数据降权,以免引入过多噪声,码伪距、载波相位和KBR距离变率数据的先验权比应为1:104:1014;为了保证轨道和模型质量,在反演过程中有必要引入经验参数以吸收残余的摄动力误差,相较其他经验参数(分段周期经验加速度、几何经验参数),分段常经验加速度在保证定轨精度的同时可更有效地吸收模型中的噪声.此外,在采用相同动力学参数配置时,动力学一步法反演的时变重力场模型无论是与官方模型的一致性还是内符合精度均优于两步法.最后,综合评估了整个时间跨度的轨道和时变重力场模型质量,结果显示,动力学一步法确定的轨道可满足厘米级需求,双星的卫星激光测距残差标准差均为1.6 cm,重力场模型与官方机构CSR(Center for Space Research)、JPL(Jet Propulsion Laboratory)、GFZ(GeoForschungsZentrum Potsdam)最新发布的RL06.1模型一致性较好,在保留完整时变信号特征的前提下,噪声表现与CSR模型相当,优于JPL、GFZ模型.展开更多
文摘Pioneering initiatives in Yunnan Province are leveraging intangible cultural heritage to foster practical skills and create flexible employment,opening a sustainable path to prosperity and rural revitalization for people with disabilities.ACROSS China,provinces and municipalities have adopted a range of initiatives,based on local conditions to facilitate employment for people with disabilities.In southwest China’s Yunnan Province,home to many ethnic minorities,efforts have focused on integrating employment for disabled people with rural revitalization.
文摘Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes.This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network(CNN)with a Bidirectional Long Short-Term Memory(BiLSTM)architecture,optimized using the Firefly Optimization algorithm(FO).The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data,capturing both local patterns and sequential dependencies in diagnostic features,while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance.The approach is evaluated on two benchmark biomedical datasets:one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities.Experimental results demonstrate that the proposed method outperforms standard deep learning models,including CNN,LSTM,BiLSTM,and CNN-LSTM hybrids,significantly.The CNNBiLSTM-FO model achieves an accuracy of 98.55%for bone cancer detection and 96.04%for marrow abnormality classification.The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision,recall,F1-score,and AUC.The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness.This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.
基金funding support from JSPS KAKENHI(Grant Nos.21H01593 and 21K18794)through Tetsuya KogureThis work was also partially supported by the Sasakawa Scientific Research Grant(2023e2026)from the Japan Science Society(JSS)through Ashis Acharya.
文摘Real-time assessment of slope reinforcements to diagnose their state in all stages of service life is imperative for prompt evaluation of slope stability and establishing an efficient early warning(EW)system.Many point-based monitoring instruments have been used in the last few decades.However,these sensors suffer from a particular risk of detection failures and practical limitations.Fibre-optic sensing(FOS)technologies have been developed,tested,and validated across various geoengineering applications,including slope monitoring,as they offer exceptional advantages,such as high data-carrying capacity,precise mapping of physical parameters,durability,and immunity to electromagnetic interference.The deformation of rock/soil causes the deformation and fracture of reinforcement materials,which are subsequently transferred to the encapsulated fibre-optic(FO)sensors,providing valuable information on reinforcements'safety state and performance for early failure detection.This paper is devoted to critically analysing the application of cutting-edge FOS technologies for slope reinforcement monitoring.Firstly,a concise overview of the fundamental principles underlying discrete and distributed FOS methods is provided.The key considerations for selecting FO cables and the appropriate packaging techniques necessary to withstand the challenges posed by complex geological environments are also summarised.We delve into the details of three distinct cable installation techniques within slope reinforcement components:surface bonding,slot embedment,and clamping.The recent advancements in FOS methods for monitoring slope reinforcements such as rock bolts,soil nails,anti-slide piles,geosynthetics,and retaining walls are extensively reviewed.The paper addresses this novel sensing technique's challenges and comprehensively explores its prospects.This review is anticipated to be a valuable resource for geoengineers and researchers involved in slope monitoring through FOS technology,offering insightful perspectives and guidance.
基金support in providing the data and the Universiti Teknologi Malaysia supported this work under UTM Flagship CoE/RG-Coe/RG 5.2:Evaluating Surface PGA with Global Ground Motion Site Response Analyses for the highest seismic activity location in Peninsular Malaysia(Q.J130000.5022.10G47)Universiti Teknologi Malaysia-Earthquake Hazard Assessment in Peninsular Malaysia Using Probabilistic Seismic Hazard Analysis(PSHA)Method(Q.J130000.21A2.06E9).
文摘The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(Kuala Lumpur)as the study area,the slope characteristics of geometrical parameters are obtained from a multidisciplinary approach(consisting of geological,geotechnical,and remote sensing analyses).18 factors,including rock strength,rock quality designation(RQD),joint spacing,continuity,openness,roughness,filling,weathering,water seepage,temperature,vegetation index,water index,and orientation,are selected to construct model input variables while the factor of safety(FOS)functions as an output.The area under the curve(AUC)value of the receiver operating characteristic(ROC)curve is obtained with precision and accuracy and used to analyse the predictive model ability.With a large training set and predicted parameters,an area under the ROC curve(the AUC)of 0.95 is achieved.A precision score of 0.88 is obtained,indicating that the model has a low false positive rate and correctly identifies a substantial number of true positives.The findings emphasise the importance of using a variety of terrain characteristics and different approaches to characterise the rock slope.
文摘相较于传统的两步法,动力学一步法能充分利用观测数据的原始信息,理论上可获得更合理的时变重力场产品,同时也因其涉及的参数维度更多样、函数模型更复杂,一直是当前研究的热点和难点.本文研究并实现了动力学一步法恢复时变重力场,给出了合理的数据处理策略,而后基于GRACE-FO(GRACE Follow-On)星载GPS数据和KBR(K/Ka Band Ranging)距离变率数据反演了2021—2022年60阶全球月时变重力场模型.对于一步法中诸多技术细节,本文重点分析了先验权和经验参数对轨道确定和模型反演的影响,研究表明:当采用30 s采样率的GPS数据时,需适当对GPS数据降权,以免引入过多噪声,码伪距、载波相位和KBR距离变率数据的先验权比应为1:104:1014;为了保证轨道和模型质量,在反演过程中有必要引入经验参数以吸收残余的摄动力误差,相较其他经验参数(分段周期经验加速度、几何经验参数),分段常经验加速度在保证定轨精度的同时可更有效地吸收模型中的噪声.此外,在采用相同动力学参数配置时,动力学一步法反演的时变重力场模型无论是与官方模型的一致性还是内符合精度均优于两步法.最后,综合评估了整个时间跨度的轨道和时变重力场模型质量,结果显示,动力学一步法确定的轨道可满足厘米级需求,双星的卫星激光测距残差标准差均为1.6 cm,重力场模型与官方机构CSR(Center for Space Research)、JPL(Jet Propulsion Laboratory)、GFZ(GeoForschungsZentrum Potsdam)最新发布的RL06.1模型一致性较好,在保留完整时变信号特征的前提下,噪声表现与CSR模型相当,优于JPL、GFZ模型.
