The quality of surface water is rapidly changing due to climatic variations, natural processes, and anthropogenic activities. The objectives of this study were to classify and analyze the surface water quality of 12 m...The quality of surface water is rapidly changing due to climatic variations, natural processes, and anthropogenic activities. The objectives of this study were to classify and analyze the surface water quality of 12 major rivers of Alberta on the basis of 17 parameters during the period of five years (i.e., 2004-2008) using principal component analysis (PCA), total exceedance model and clustering technique. Seven major principal components (PCs) with variability of about 89% were identified. These PCs were the indicators of watershed geology, mineralization and anthropogenic activities related to land use/cover. The seven dominant parameters revealed from the seven PCs were total dissolved solids (TDS), true color (TC), pH, iron (Fe), fecal coliform (FC), dissolved oxygen (DO), and turbidity (TUR). The normalized data of dominant parameters were used to develop a model for obtaining total exceedance. The exceedance values acquired from the total exceedance model were used to determine the patterns for the development of five clusters. The performance of the clusters was compared with the classes obtained in Canadian Water Quality Index (CWQI). Cluster 1, cluster 2, cluster 3, cluster 4 and cluster 5 showed agreements of 85.71%, 83.54%, 90.22%, 80.74%, and 83.40% with their respective CWQI classes on the basis of the data for all rivers during 2004-2008. The water quality was deteriorated in growing season due to snow melting. This methodology could be applied to classify the raw surface water quality, analyze the spatio-temporal trends and study the impacts of the factors affecting the water quality anywhere in the world.展开更多
Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients a...Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.展开更多
Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satell...Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.展开更多
The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxi...The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxidized AgMgNi alloys,which were internally oxidized at 800℃ for 8 h under an oxy-gen atmosphere.We found that Mg-O clusters contributed to the hardening(138 HV)and strengthening(376.9 MPa)of the AgMg alloy through solid solution strengthening effects,albeit at the expense of duc-tility.To address this limitation,we introduced Ni nanoparticles into the AgMg alloy,resulting in signifi-cant grain refinement within its microstructure.Specifically,the grain size decreased from 67.2μm in the oxidized AgMg alloy to below 6.0μm in the oxidized AgMgNi alloy containing 0.3 wt%Ni.Consequently,the toughness increased significantly,rising from toughness value of 2177.9 MJ m^(-3) in the oxidized AgMg alloy to 6186.1 MJ m^(-3) in the oxidized AgMgNi alloy,representing a remarkable 2.8-fold enhancement.Furthermore,the internally oxidized AgMgNi alloy attained a strength of up to 387.6 MPa,comparable to that of the internally oxidized AgMg alloy,thereby demonstrating the successful realization of concurrent strengthening and toughening.These results collectively offer a novel approach for the design of high-performance alloys through the synergistic combination of cluster strengthening and grain refinement toughening.展开更多
In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in spec...In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.展开更多
A multi-phase heterogeneous FeCoNi-based high-entropy alloy is developed to overcome the trade-off between strength and ductility.By alloying with a small amount of Cu and employing a rapid recrystalliza-tion process,...A multi-phase heterogeneous FeCoNi-based high-entropy alloy is developed to overcome the trade-off between strength and ductility.By alloying with a small amount of Cu and employing a rapid recrystalliza-tion process,it exhibits a good combination of yield strength(roughly 1300 MPa)and ductility(approach-ing 20%).Firstly,a multi-phase heterogeneous structure is tailored ranging from nano to micron.Cu is efficiently precipitated as nanoscale clusters(4.2 nm),high-density cuboidal L1_(2) particles(20-40 nm)and L2_(1) particles(500-800 nm)are found to be embedded in the matrix and a bimodal heterogeneous grain structure(1-40μm)is constructed.Secondly,the introduction of Cu effectively suppresses the pre-cipitation of coarse L21 phase at grain boundaries,reducing its volume fraction by 80%and replaced by smaller-scale continuous precipitations within the grains.Thirdly,the high mixing enthalpy gap of Cu and the matrix leads to the formation of local chemical fluctuation and the consequential rugged topog-raphy in the matrix,which result in retarded dislocation motion and promotes dislocation plugging and interlocking during strain,enhancing yield stress and work hardening rate.This study provides a valuable perspective to enhance strength and ductility via enlarged local chemical fluctuation-tailored multi-phase heterogeneous structures.展开更多
Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subse...Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.展开更多
By systematically reviewing the development status of global carbon dioxide capture,utilization and storage(CCUS)cluster,and comparing domestic and international CCUS industrial models and successful experiences,this ...By systematically reviewing the development status of global carbon dioxide capture,utilization and storage(CCUS)cluster,and comparing domestic and international CCUS industrial models and successful experiences,this study explores the challenges and strategies for the scaled development of the CCUS industry of China.Globally,the CCUS industry has entered a phase of scaled and clustered development.North America has established a system of key technologies in large-scale CO_(2) capture,long-distance pipeline transmission,pipeline network optimization,and large-scale CO_(2) flooding for enhanced oil recovery(CO_(2)-EOR),with relatively mature cluster development and a gradual shift in industrial model from CO_(2)-EOR to geological storage.The CCUS industry of China has developed rapidly across all segments but remains in the early stage of cluster development,facing challenges such as absent business model,insufficient policy support,and technological gaps in core areas.China needs to improve the policy support system to boost enterprises participation across the entire industrial chain,strengthen top-level design and medium-to long-term planning to accelerate demonstration projects construction for whole-process CCUS clusters,advance for a full-chain technological system,including low-cost capture,pipeline optimization and EOR/storage integration technologies,and strengthen personnel training,strengthen discipline construction and university-enterprise research cooperation.展开更多
Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold...Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed.In this study,we constructed the largest c-MET dataset,which included 2,278 molecules with different struc-tures,by inhibiting the half maximal inhibitory concentration(IC_(50))of kinase activity.No significant differences in drug-like properties were observed between active molecules(1,228)and inactive mol-ecules(1,050),including chemical space coverage,physicochemical properties,and absorption,distri-bution,metabolism,excretion,and toxicity(ADMET)profiles.The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding(t-SNE)high-dimensional data.Further clustering and chemical space networks(CSNs)analyses revealed commonly used scaffolds for c-MET inhibitors,such as M5,M7,and M8.Activity cliffs and structural alerts were used to reveal“dead ends”and“safe bets”for c-MET,as well as dominant structural fragments consisting of pyr-idazinones,triazoles,and pyrazines.Finally,the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules,including at least three aromatic het-erocycles,five aromatic nitrogen atoms,and eight nitrogeneoxygen atoms.Overall,our analyses revealed potential structure-activity relationship(SAR)patterns for c-MET inhibitors,which can inform the screening of new compounds and guide future optimization efforts.展开更多
Thermally activated delayed fluorescence(TADF)materials driven by a through-space charge transfer(TSCT)mechanism have garnered wide interest.However,access of TSCT-TADF molecules with longwavelength emission remains a...Thermally activated delayed fluorescence(TADF)materials driven by a through-space charge transfer(TSCT)mechanism have garnered wide interest.However,access of TSCT-TADF molecules with longwavelength emission remains a formidable challenge.In this study,we introduce a novel V-type DA-D-A’emitter,Trz-mCzCbCz,by using a carborane scaffold.This design strategically incorporates carbazole(Cz)and 2,4,6-triphenyl-1,3,5-triazine(Trz)as donor and acceptor moieties,respectively.Theoretical calculations alongside experimental validations affirm the typical TSCT-TADF characteristics of this luminogen.Owing to the unique structural and electronic attributes of carboranes,Trz-mCzCbCz exhibits an orange-red emission,markedly diverging from the traditional blue-to-green emissions observed in classical Cz and Trz-based TADF molecules.Moreover,bright emission in aggregates was observed for Trz-mCzCbCz with absolute photoluminescence quantum yield(PLQY)of up to 88.8%.As such,we have successfully fabricated five organic light-emitting diodes(OLEDs)by utilizing Trz-mCzCbCz as the emitting layer.It is important to note that both the reverse intersystem crossing process and the TADF properties are profoundly influenced by host materials.The fabricated OLED devices reached a maximum external quantum efficiency(EQE)of 12.7%,with an emission peak at 592 nm.This represents the highest recorded efficiency for TSCT-TADF OLEDs employing carborane derivatives as emitting layers.展开更多
For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Veh...For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Vehicles(SRVs)into CP networks,which is called SRV-aided CP.However,the CP system may split into several sub-clusters that cannot be connected with each other in dense urban environments,in which the sub-clusters with few SRVs will suffer from degradation of CP performance.Since Unmanned Aerial Vehicles(UAVs)have been widely used to aid vehicular communications,we intend to utilize UAVs to assist sub-clusters in CP.In this paper,a UAV-aided CP network is constructed to fully utilize information from SRVs.First,the inter-node connection structure among the UAV and vehicles is designed to share available information from SRVs.After that,the clustering optimization strategy is proposed,in which the UAV cooperates with the high-precision sub-cluster to obtain available information from SRVs,and then broadcasts this positioning-related information to other low-precision sub-clusters.Finally,the Locally-Centralized Factor Graph Optimization(LC-FGO)algorithm is designed to fuse positioning information from cooperators.Simulation results indicate that the positioning accuracy of the CP system could be improved by fully utilizing positioning-related information from SRVs.展开更多
Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power o...Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.展开更多
Key technologies that make productivity increase are revealed through analyzing the best practices and production data in major shale basins of North America.Trends of the key technologies and optimization designs for...Key technologies that make productivity increase are revealed through analyzing the best practices and production data in major shale basins of North America.Trends of the key technologies and optimization designs for shale oil and gas development are summarized and analyzed based on drilling and completion operations and well data.These technologies mainly include:(1)Optimizing well design and hydraulic fracturing design,including reducing cluster spacing,increasing proppant and fracturing fluid volumes,optimizing horizontal well lateral length and fracture stage length.The most effective method is to reduce cluster spacing to an optimized length.The second most effective method is to optimally increase proppant volumes.(2)Placing horizontal wells in the sweet spots and drilling the wells parallel or close to the minimum horizontal stress direction.(3)Using cube development with optimized well spacing to maximize resource recovery and reduce well interferences.Plus,in-situ stress impacts on hydraulic fracture propagation and hydrocarbon production are addressed.Determination of formation breakdown pressure is studied by considering the impacts of in-situ stresses,drilling and perforation directions.Whether or not the hydraulic fracturing can generate orthogonal fracture networks is also discussed.The key technologies and optimization design parameters proposed in this paper can be applied to guide new well placement,drilling and completion designs,and hydraulic fracture operations to increase productivity.展开更多
BACKGROUND Dermatofibrosarcoma protuberans(DFSP)is a rare,low-grade,locally aggressive cutaneous sarcoma.DFSP in the periocular region is exceedingly rare,leading to diagnostic and surgical challenges due to anatomica...BACKGROUND Dermatofibrosarcoma protuberans(DFSP)is a rare,low-grade,locally aggressive cutaneous sarcoma.DFSP in the periocular region is exceedingly rare,leading to diagnostic and surgical challenges due to anatomical constraints in the periocular region.Precise diagnosis is essential to guide appropriate surgical management and prevent recurrence.CASE SUMMARY A 32-year-old female presented with a recurrent tumor in the medial canthus,previously diagnosed as a solitary fibrous tumor in an outside institution.After complete radiological and systemic workup,she was scheduled for a wide local excision followed by reconstruction after getting tumor clear margins on frozen section.Histopathology confirmed DFSP,characterized by storiform spindle cell proliferation,diffuse cluster of differentiation 34 positivity,and signal transducer and activator of transcription 6 negativity.CONCLUSION This case highlights the challenges in the diagnostic and surgical management of DFSP in periocular tumors.Comprehensive surgical excision with appropriate reconstruction is critical for achieving oncological control while preserving aesthetics and function.展开更多
In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper prese...In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.展开更多
Materials showing metallophilic interactions continue to attract considerable theoretical and experimental attention largely because of their unusual and unanticipated photophysical behavior as well as their unique st...Materials showing metallophilic interactions continue to attract considerable theoretical and experimental attention largely because of their unusual and unanticipated photophysical behavior as well as their unique stimuli-responsive behavior in an aggregate or solid state.Metallophilic interactions are mostly found between metals with either identical(d^(10)–d^(10))or different(s^(2)–d^(8),d^(8)–d^(10))configurations.Among various metallophilic interactions,aurophilic interactions(Au⋯Au)are well-known and widely reported.In this study,a new phosphorescent gold(I)complex,[(CF_(3)Ph)_(3)PAuC≡CPh](TPPGPA)was reported.展开更多
Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the...Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.展开更多
In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment techni...In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment technique known as ultrasonic vibration rapid processing(UVRP),which enables the formation of high-density strong magnetic α-Fe clusters,thereby enhancing the soft magnetic properties of Fe_(78)Si(13)B_(9) amorphous alloy ribbon.展开更多
Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as...Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as the strongly enhanced synergistic interactions between Pt and Co atoms,the obtained Pt-on-Co/C400 catalysts exhib-ited excellent catalytic activity toward the hydrolysis of ammonia borane with an extremely high turnover frequency(TOF)value of 3022 min^(-1)at 303 K.Durability test indicated that the obtained Pt-on-Co/C400 catalysts possessed high catalytic stability,and there were no changes in the catalyst structures and catalytic activities after 10 cycles.展开更多
文摘The quality of surface water is rapidly changing due to climatic variations, natural processes, and anthropogenic activities. The objectives of this study were to classify and analyze the surface water quality of 12 major rivers of Alberta on the basis of 17 parameters during the period of five years (i.e., 2004-2008) using principal component analysis (PCA), total exceedance model and clustering technique. Seven major principal components (PCs) with variability of about 89% were identified. These PCs were the indicators of watershed geology, mineralization and anthropogenic activities related to land use/cover. The seven dominant parameters revealed from the seven PCs were total dissolved solids (TDS), true color (TC), pH, iron (Fe), fecal coliform (FC), dissolved oxygen (DO), and turbidity (TUR). The normalized data of dominant parameters were used to develop a model for obtaining total exceedance. The exceedance values acquired from the total exceedance model were used to determine the patterns for the development of five clusters. The performance of the clusters was compared with the classes obtained in Canadian Water Quality Index (CWQI). Cluster 1, cluster 2, cluster 3, cluster 4 and cluster 5 showed agreements of 85.71%, 83.54%, 90.22%, 80.74%, and 83.40% with their respective CWQI classes on the basis of the data for all rivers during 2004-2008. The water quality was deteriorated in growing season due to snow melting. This methodology could be applied to classify the raw surface water quality, analyze the spatio-temporal trends and study the impacts of the factors affecting the water quality anywhere in the world.
基金supported by the Foundation of President of Hebei University(XZJJ202303).
文摘Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.
文摘Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.
基金supported by the National Natural Science Foundation of China(Nos.51977027 and 51967008)the Scientific and Technological Project of Yunnan Precious Metals Lab-oratory(Nos.YPML-2023050250 and YPML-2022050206).
文摘The pursuit of Ag-based alloys with both high strength and toughness has posed a longstanding chal-lenge.In this study,we investigated the cluster strengthening and grain refinement toughening mecha-nisms in fully oxidized AgMgNi alloys,which were internally oxidized at 800℃ for 8 h under an oxy-gen atmosphere.We found that Mg-O clusters contributed to the hardening(138 HV)and strengthening(376.9 MPa)of the AgMg alloy through solid solution strengthening effects,albeit at the expense of duc-tility.To address this limitation,we introduced Ni nanoparticles into the AgMg alloy,resulting in signifi-cant grain refinement within its microstructure.Specifically,the grain size decreased from 67.2μm in the oxidized AgMg alloy to below 6.0μm in the oxidized AgMgNi alloy containing 0.3 wt%Ni.Consequently,the toughness increased significantly,rising from toughness value of 2177.9 MJ m^(-3) in the oxidized AgMg alloy to 6186.1 MJ m^(-3) in the oxidized AgMgNi alloy,representing a remarkable 2.8-fold enhancement.Furthermore,the internally oxidized AgMgNi alloy attained a strength of up to 387.6 MPa,comparable to that of the internally oxidized AgMg alloy,thereby demonstrating the successful realization of concurrent strengthening and toughening.These results collectively offer a novel approach for the design of high-performance alloys through the synergistic combination of cluster strengthening and grain refinement toughening.
文摘In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.
基金financial support from the National Natural Science Foundation of China(Nos.52104306,52274301,52334009)the Aeronautical Science Foundation of China(No.2023Z0530S6005)+3 种基金the National Key Research and Development Program of China(No.2023YFB3712401)the Science and Technology Commission of Shanghai Municipality(No.21DZ1208900)the Academician Workstation of Kunming University of Science and Technology(2024),the Ningbo Yongjiang Talent-Introduction Programme(No.2022A-023-C)the Zhejiang Phenomenological Materials Technology Co.,Ltd.,China.
文摘A multi-phase heterogeneous FeCoNi-based high-entropy alloy is developed to overcome the trade-off between strength and ductility.By alloying with a small amount of Cu and employing a rapid recrystalliza-tion process,it exhibits a good combination of yield strength(roughly 1300 MPa)and ductility(approach-ing 20%).Firstly,a multi-phase heterogeneous structure is tailored ranging from nano to micron.Cu is efficiently precipitated as nanoscale clusters(4.2 nm),high-density cuboidal L1_(2) particles(20-40 nm)and L2_(1) particles(500-800 nm)are found to be embedded in the matrix and a bimodal heterogeneous grain structure(1-40μm)is constructed.Secondly,the introduction of Cu effectively suppresses the pre-cipitation of coarse L21 phase at grain boundaries,reducing its volume fraction by 80%and replaced by smaller-scale continuous precipitations within the grains.Thirdly,the high mixing enthalpy gap of Cu and the matrix leads to the formation of local chemical fluctuation and the consequential rugged topog-raphy in the matrix,which result in retarded dislocation motion and promotes dislocation plugging and interlocking during strain,enhancing yield stress and work hardening rate.This study provides a valuable perspective to enhance strength and ductility via enlarged local chemical fluctuation-tailored multi-phase heterogeneous structures.
基金supported in part by NIH grants R01NS39600,U01MH114829RF1MH128693(to GAA)。
文摘Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.
基金Supported by the PetroChina Science and Technology Major Project(2021ZZ01-05)Hainan Merit-based Recruitment Project(ZDYF2024SHFZ147)National Natural Science Foundation of China(NNSC)Project(52474033)。
文摘By systematically reviewing the development status of global carbon dioxide capture,utilization and storage(CCUS)cluster,and comparing domestic and international CCUS industrial models and successful experiences,this study explores the challenges and strategies for the scaled development of the CCUS industry of China.Globally,the CCUS industry has entered a phase of scaled and clustered development.North America has established a system of key technologies in large-scale CO_(2) capture,long-distance pipeline transmission,pipeline network optimization,and large-scale CO_(2) flooding for enhanced oil recovery(CO_(2)-EOR),with relatively mature cluster development and a gradual shift in industrial model from CO_(2)-EOR to geological storage.The CCUS industry of China has developed rapidly across all segments but remains in the early stage of cluster development,facing challenges such as absent business model,insufficient policy support,and technological gaps in core areas.China needs to improve the policy support system to boost enterprises participation across the entire industrial chain,strengthen top-level design and medium-to long-term planning to accelerate demonstration projects construction for whole-process CCUS clusters,advance for a full-chain technological system,including low-cost capture,pipeline optimization and EOR/storage integration technologies,and strengthen personnel training,strengthen discipline construction and university-enterprise research cooperation.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82173699 and 32200531)Shanghai Jiao Tong University Trans-Med Awards Research,China(STAR Project No.:20230101)Shanghai Science and Technol-ogy Commission,China(Grant No.:23DZ2290600).
文摘Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed.In this study,we constructed the largest c-MET dataset,which included 2,278 molecules with different struc-tures,by inhibiting the half maximal inhibitory concentration(IC_(50))of kinase activity.No significant differences in drug-like properties were observed between active molecules(1,228)and inactive mol-ecules(1,050),including chemical space coverage,physicochemical properties,and absorption,distri-bution,metabolism,excretion,and toxicity(ADMET)profiles.The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding(t-SNE)high-dimensional data.Further clustering and chemical space networks(CSNs)analyses revealed commonly used scaffolds for c-MET inhibitors,such as M5,M7,and M8.Activity cliffs and structural alerts were used to reveal“dead ends”and“safe bets”for c-MET,as well as dominant structural fragments consisting of pyr-idazinones,triazoles,and pyrazines.Finally,the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules,including at least three aromatic het-erocycles,five aromatic nitrogen atoms,and eight nitrogeneoxygen atoms.Overall,our analyses revealed potential structure-activity relationship(SAR)patterns for c-MET inhibitors,which can inform the screening of new compounds and guide future optimization efforts.
基金supported by the Natural Science Foundation of Jiangsu Province(No.BZ2022007)the National Natural Science Foundation of China(No.92261202)+1 种基金the Ministry of Science and Technology of the People’s Republic of China(No.2021YFE0114800)the Ministry of Science and Higher Education of the Russian Federation(No.075-15-2021-1027).
文摘Thermally activated delayed fluorescence(TADF)materials driven by a through-space charge transfer(TSCT)mechanism have garnered wide interest.However,access of TSCT-TADF molecules with longwavelength emission remains a formidable challenge.In this study,we introduce a novel V-type DA-D-A’emitter,Trz-mCzCbCz,by using a carborane scaffold.This design strategically incorporates carbazole(Cz)and 2,4,6-triphenyl-1,3,5-triazine(Trz)as donor and acceptor moieties,respectively.Theoretical calculations alongside experimental validations affirm the typical TSCT-TADF characteristics of this luminogen.Owing to the unique structural and electronic attributes of carboranes,Trz-mCzCbCz exhibits an orange-red emission,markedly diverging from the traditional blue-to-green emissions observed in classical Cz and Trz-based TADF molecules.Moreover,bright emission in aggregates was observed for Trz-mCzCbCz with absolute photoluminescence quantum yield(PLQY)of up to 88.8%.As such,we have successfully fabricated five organic light-emitting diodes(OLEDs)by utilizing Trz-mCzCbCz as the emitting layer.It is important to note that both the reverse intersystem crossing process and the TADF properties are profoundly influenced by host materials.The fabricated OLED devices reached a maximum external quantum efficiency(EQE)of 12.7%,with an emission peak at 592 nm.This represents the highest recorded efficiency for TSCT-TADF OLEDs employing carborane derivatives as emitting layers.
基金supported by the National Natural Science Foundation of China(No.62271399)the National Key Research and Development Program of China(No.2022YFB1807102)。
文摘For multi-vehicle networks,Cooperative Positioning(CP)technique has become a promising way to enhance vehicle positioning accuracy.Especially,the CP performance could be further improved by introducing Sensor-Rich Vehicles(SRVs)into CP networks,which is called SRV-aided CP.However,the CP system may split into several sub-clusters that cannot be connected with each other in dense urban environments,in which the sub-clusters with few SRVs will suffer from degradation of CP performance.Since Unmanned Aerial Vehicles(UAVs)have been widely used to aid vehicular communications,we intend to utilize UAVs to assist sub-clusters in CP.In this paper,a UAV-aided CP network is constructed to fully utilize information from SRVs.First,the inter-node connection structure among the UAV and vehicles is designed to share available information from SRVs.After that,the clustering optimization strategy is proposed,in which the UAV cooperates with the high-precision sub-cluster to obtain available information from SRVs,and then broadcasts this positioning-related information to other low-precision sub-clusters.Finally,the Locally-Centralized Factor Graph Optimization(LC-FGO)algorithm is designed to fuse positioning information from cooperators.Simulation results indicate that the positioning accuracy of the CP system could be improved by fully utilizing positioning-related information from SRVs.
基金funded by the“Research and Application Project of Collaborative Optimization Control Technology for Distribution Station Area for High Proportion Distributed PV Consumption(4000-202318079A-1-1-ZN)”of the Headquarters of the State Grid Corporation.
文摘Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.
文摘Key technologies that make productivity increase are revealed through analyzing the best practices and production data in major shale basins of North America.Trends of the key technologies and optimization designs for shale oil and gas development are summarized and analyzed based on drilling and completion operations and well data.These technologies mainly include:(1)Optimizing well design and hydraulic fracturing design,including reducing cluster spacing,increasing proppant and fracturing fluid volumes,optimizing horizontal well lateral length and fracture stage length.The most effective method is to reduce cluster spacing to an optimized length.The second most effective method is to optimally increase proppant volumes.(2)Placing horizontal wells in the sweet spots and drilling the wells parallel or close to the minimum horizontal stress direction.(3)Using cube development with optimized well spacing to maximize resource recovery and reduce well interferences.Plus,in-situ stress impacts on hydraulic fracture propagation and hydrocarbon production are addressed.Determination of formation breakdown pressure is studied by considering the impacts of in-situ stresses,drilling and perforation directions.Whether or not the hydraulic fracturing can generate orthogonal fracture networks is also discussed.The key technologies and optimization design parameters proposed in this paper can be applied to guide new well placement,drilling and completion designs,and hydraulic fracture operations to increase productivity.
文摘BACKGROUND Dermatofibrosarcoma protuberans(DFSP)is a rare,low-grade,locally aggressive cutaneous sarcoma.DFSP in the periocular region is exceedingly rare,leading to diagnostic and surgical challenges due to anatomical constraints in the periocular region.Precise diagnosis is essential to guide appropriate surgical management and prevent recurrence.CASE SUMMARY A 32-year-old female presented with a recurrent tumor in the medial canthus,previously diagnosed as a solitary fibrous tumor in an outside institution.After complete radiological and systemic workup,she was scheduled for a wide local excision followed by reconstruction after getting tumor clear margins on frozen section.Histopathology confirmed DFSP,characterized by storiform spindle cell proliferation,diffuse cluster of differentiation 34 positivity,and signal transducer and activator of transcription 6 negativity.CONCLUSION This case highlights the challenges in the diagnostic and surgical management of DFSP in periocular tumors.Comprehensive surgical excision with appropriate reconstruction is critical for achieving oncological control while preserving aesthetics and function.
基金the National Natural Science Foundation of China(No.62063006)to the Guangxi Natural Science Foundation under Grant(Nos.2023GXNSFAA026025,AA24010001)+3 种基金to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2023RY018)to the Special Guangxi Industry and Information Technology Department,Textile and Pharmaceutical Division(ID:2021 No.231)to the Special Research Project of Hechi University(ID:2021GCC028)to the Key Laboratory of AI and Information Processing,Education Department of Guangxi Zhuang Autonomous Region(Hechi University),No.2024GXZDSY009。
文摘In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.
基金supported by the National Natural Science Foundation of China(no.21788102)the Natural Science Foundation of Guangdong Province(nos.2019B121205002 and 2019B030301003)+1 种基金the Research Grants Council of Hong Kong(nos.16305618,16305518,16304819,C6009-17G,and C6014-20W)the Innovation and Technology Commission(no.ITC-CNERC14SC01).
文摘Materials showing metallophilic interactions continue to attract considerable theoretical and experimental attention largely because of their unusual and unanticipated photophysical behavior as well as their unique stimuli-responsive behavior in an aggregate or solid state.Metallophilic interactions are mostly found between metals with either identical(d^(10)–d^(10))or different(s^(2)–d^(8),d^(8)–d^(10))configurations.Among various metallophilic interactions,aurophilic interactions(Au⋯Au)are well-known and widely reported.In this study,a new phosphorescent gold(I)complex,[(CF_(3)Ph)_(3)PAuC≡CPh](TPPGPA)was reported.
基金supported by the Spanish Ministry of Science and Innovation under Projects PID2022-137680OB-C32 and PID2022-139187OB-I00.
文摘Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.
基金supported by the Major Science and Technology Project of Zhongshan City(No.2022AJ004)the Key Basic and Applied Research Program of Guangdong Province(Nos.2019B030302010 and 2022B1515120082)Guangdong Science and Technology Innovation Project(No.2021TX06C111).
文摘In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment technique known as ultrasonic vibration rapid processing(UVRP),which enables the formation of high-density strong magnetic α-Fe clusters,thereby enhancing the soft magnetic properties of Fe_(78)Si(13)B_(9) amorphous alloy ribbon.
文摘Ultrafine,highly dispersed Pt clusters were immobilized onto the Co nanoparticle surfaces by one-step pyrolysis of the precursor Pt(Ⅱ)-encapsulating Co-MOF-74.Owing to the small size effects of Pt clusters as well as the strongly enhanced synergistic interactions between Pt and Co atoms,the obtained Pt-on-Co/C400 catalysts exhib-ited excellent catalytic activity toward the hydrolysis of ammonia borane with an extremely high turnover frequency(TOF)value of 3022 min^(-1)at 303 K.Durability test indicated that the obtained Pt-on-Co/C400 catalysts possessed high catalytic stability,and there were no changes in the catalyst structures and catalytic activities after 10 cycles.
