Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy cl...Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.展开更多
In the context of the continuous deepening of the“Double Reduction”policy and the growing demand for quality education,leveled mathematics readers,as an emerging form of publishing that integrates subject education ...In the context of the continuous deepening of the“Double Reduction”policy and the growing demand for quality education,leveled mathematics readers,as an emerging form of publishing that integrates subject education and reading experience,face challenges such as unclear leveling logic,insufficient functional support,and weak user engagement.This paper introduces the 4V marketing theory and constructs an analytical framework from four dimensions:differentiation,functionality,added value,and resonance.Two representative products,“Climbing Mathematics”and“Spark Mathematics,”are selected for a typical case comparison to identify their strengths and weaknesses in content design,service systems,and brand operation,and to extract transferable strategic elements.The study finds that the user-value-oriented strategy based on the 4V model can effectively address the core issues in the market promotion and user relationship building of leveled mathematics readers,providing practical paths and theoretical support for educational publishing institutions to achieve product innovation and brand upgrading in this niche field.展开更多
Selecting the embryo with the highest implantation potential is a top priority in in-vitro fertilization(IVF)centers.Few studies have explored the relationship between day 5 blastocyst morphokinetics and implantation ...Selecting the embryo with the highest implantation potential is a top priority in in-vitro fertilization(IVF)centers.Few studies have explored the relationship between day 5 blastocyst morphokinetics and implantation outcomes[1].Despite numerous time-lapse studies,the findings often conflict due to differences in patient demographics,lab conditions,and protocols,such as oxygen concentration[2].Thus,there is ongoing debate regarding which parameters are most predictive of implantation.展开更多
This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors.Data collection and behavioral analysis are achieved using m...This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors.Data collection and behavioral analysis are achieved using machine learning(ML)algorithms through accelerometer sensors.However,behavioral analysis poses challenges due to the complexity of cow activities.The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints.Shorter windows may lack sufficient information,reducing algorithm performance.Additionally,the sensor’s position on the cowsmay shift during practical use,altering the collected accelerometer data.This study addresses these challenges by employing a 3-s data window to analyze cow behaviors,specifically Feeding,Lying,Standing,and Walking.Data synchronization between accelerometer sensors placed on the neck and leg compensates for the lack of information in short data windows.Features such as the Vector of Dynamic Body Acceleration(VeDBA),Mean,Variance,and Kurtosis are utilized alongside the Decision Tree(DT)algorithm to address energy efficiency and ensure computational effectiveness.This study also evaluates the impact of sensor misalignment on behavior classification.Simulated datasets with varying levels of sensor misalignment were created,and the system’s classification accuracy exceeded 0.95 for the four behaviors across all datasets(including original and simulated misalignment datasets).Sensitivity(Sen)and PPV for all datasets were above 0.9.The study provides farmers and the dairy industry with a practical,energy-efficient system for continuously monitoring cattle behavior to enhance herd productivity while reducing labor costs.展开更多
This study presents CGB-Net,a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer,with direct applicability to gastroesophagea...This study presents CGB-Net,a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer,with direct applicability to gastroesophageal reflux disease(GERD)monitoring.Unlike conventional approaches limited to four basic postures,CGB-Net enables fine-grained classification of twelve clinically relevant sleep positions,providing enhanced resolution for personalized health assessment.The architecture introduces a unique integration of three complementary components:1D Convolutional Neural Networks(1D-CNN)for efficient local spatial feature extraction,Gated Recurrent Units(GRU)to capture short-termtemporal dependencieswith reduced computational complexity,and Bidirectional Long Short-Term Memory(Bi-LSTM)networks for modeling long-term temporal context in both forward and backward directions.This complementary integration allows the model to better represent dynamic and contextual information inherent in the sensor data,surpassing the performance of simpler or previously published hybrid models.Experiments were conducted on a benchmark dataset consisting of 18 volunteers(age range:19–24 years,mean 20.56±1.1 years;height 164.78±8.18 cm;weight 55.39±8.30 kg;BMI 20.24±2.04),monitored via a single abdominal accelerometer.A subjectindependent evaluation protocol with multiple random splits was employed to ensure robustness and generalizability.The proposed model achieves an average Accuracy of 87.60% and F1-score of 83.38%,both reported with standard deviations over multiple runs,outperforming several baseline and state-of-the-art methods.By releasing the dataset publicly and detailing themodel design,this work aims to facilitate reproducibility and advance research in sleep posture classification for clinical applications.展开更多
BACKGROUND The pathogenesis of non-ampullary duodenal epithelial tumors(NADETs)is not fully understood.NADETs that express gastric-type mucin phenotypes(GNADETs)are noteworthy because of their high malignancy.Gastric ...BACKGROUND The pathogenesis of non-ampullary duodenal epithelial tumors(NADETs)is not fully understood.NADETs that express gastric-type mucin phenotypes(GNADETs)are noteworthy because of their high malignancy.Gastric foveolar metaplasia,from which G-NADETs originate,protects the duodenal mucosa from gastric acidity.As gastric acid secretion is affected by endoscopic gastric mucosal atrophy(EGMA),we hypothesized that EGMA would be associated with GNADETs.AIM To evaluate the association between EGMA and the occurrence of G-NADETs.METHODS This cross-sectional retrospective study investigated the relationship between EGMA and NADETs in 134 patients.The duodenum was divided into parts 1(bulb),2(superior duodenal angle to the papilla),and 3(anal side of the papilla to the horizontal part).The effects of gastric acidity and presence of Brunner’s glands were considered.EGMA was divided into types C(no or mild atrophy)and O(severe atrophy).Mucin phenotype expressions in NADETs were divided into gastric,intestinal,gastrointestinal,and unclassifiable.RESULTS When NADETs were classified according to EGMA,105 were classified as type C and 29 as type O.G-NADETs were present in 11.9%(16 cases)of all cases,and all 16 cases were of type C.Among G-NADETs,93.8%(15 cases)were present in part 1 or 2.There was an association between G-NADETs and type C in part 1,and 50.0%(eight of 16 cases)of G-NADETs were associated with a current or previous Helicobacter pylori infection status.Additionally,all eight cases occurred in part 1.CONCLUSION G-NADETs were significantly associated with type C.Gastric acidity and Brunner's gland growth may be associated with G-NADETs.展开更多
Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data,guided by active learning,to enhance classification accuracy,particularly in complex and ambiguous datasets.Alth...Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data,guided by active learning,to enhance classification accuracy,particularly in complex and ambiguous datasets.Although several active semi-supervised fuzzy clustering methods have been developed previously,they typically face significant limitations,including high computational complexity,sensitivity to initial cluster centroids,and difficulties in accurately managing boundary clusters where data points often overlap among multiple clusters.This study introduces a novel Active Semi-Supervised Fuzzy Clustering algorithm specifically designed to identify,analyze,and correct misclassified boundary elements.By strategically utilizing labeled data through active learning,our method improves the robustness and precision of cluster boundary assignments.Extensive experimental evaluations conducted on three types of datasets—including benchmark UCI datasets,synthetic data with controlled boundary overlap,and satellite imagery—demonstrate that our proposed approach achieves superior performance in terms of clustering accuracy and robustness compared to existing active semi-supervised fuzzy clustering methods.The results confirm the effectiveness and practicality of our method in handling real-world scenarios where precise cluster boundaries are critical.展开更多
Self-assembled monolayers(SAMs)are widely used as hole transport materials in inverted perovskite solar cells,offering low parasitic absorption and suitability for semitransparent and tandem solar cells.While SAMs hav...Self-assembled monolayers(SAMs)are widely used as hole transport materials in inverted perovskite solar cells,offering low parasitic absorption and suitability for semitransparent and tandem solar cells.While SAMs have shown to be promising in small-area devices(≤1 cm^(2)),their application in larger areas has been limited by a lack of knowledge regarding alternative deposition methods beyond the common spin-coating approach.Here,we compare spin-coating and upscalable methods such as thermal evaporation and spray-coating for[2-(9H-carbazol-9-yl)ethyl]phosphonic acid(2PACz),one of the most common carbazole-based SAMs.The impact of these deposition methods on the device performance is investigated,revealing that the spray-coating technique yields higher device performance.Furthermore,our work provides guidelines for the deposition of SAM materials for the fabrication of perovskite solar modules.In addition,we provide an extensive characterization of 2PACz films focusing on thermal evaporation and spray-coating methods,which allow for thicker 2PACz deposition.It is found that the optimal 2PACz deposition conditions corresponding to the highest device performances do not always correlate with the monolayer characteristics.展开更多
Two new species of Leptobrachella are described from Vietnam based on morphological differences and genetic divergences in 16S rRNA mitochondrial gene sequences.The new taxa are distinguished from each other and from ...Two new species of Leptobrachella are described from Vietnam based on morphological differences and genetic divergences in 16S rRNA mitochondrial gene sequences.The new taxa are distinguished from each other and from other species of the genus Leptobrachella in body size,head width/length ratio,tympanum morphology,dorsal skin texture,the presence/absence of fringes on toes,color of dorsal and ventral body,and iris color.The two new species are also divergent from each other and from other congeners by a 4.14% or greater uncorrected genetic distance.Leptobrachella batxatensis sp.nov.is genetically closest to L.shiwandashanensis and L.wuhuangmontis from China.Leptobrachella duyenae sp.nov.is genetically closest to L.bidoupensis from Vietnam with strong nodal support from both BI and ML analyses(1.0/99%).展开更多
Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While s...Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While semi-supervised fuzzy clustering(SSFC)methods leverage limited labeled data to enhance accuracy,they remain highly susceptible to inappropriate or mislabeled prior knowledge,especially in noisy or overlapping datasets where cluster boundaries are ambiguous.To enhance the effectiveness of clustering algorithms,it is essential to leverage labeled data while ensuring the safety of the previous knowledge.Existing solutions,such as the Trusted Safe Semi-Supervised Fuzzy Clustering Method(TS3FCM),struggle with random centroid initialization,fixed neighbor radius formulas,and handling outliers or noise at cluster overlaps.A new framework called Active Safe Semi-Supervised Fuzzy Clustering with Pairwise Constraints Based on Cluster Boundary(AS3FCPC)is proposed in this paper to deal with these problems.It does this by combining pairwise constraints and active learning.AS3FCPC uses active learning to query only the most informative data instances close to the cluster boundaries.It also uses pairwise constraints to enforce the cluster structure,which makes the system more accurate and robust.Extensive test results on diverse datasets,including challenging noisy and overlapping scenarios,demonstrate that AS3FCPC consistently achieves superior performance compared to state-of-the-art methods like TS3FCM and other baselines,especially when the data is noisy and overlaps.This significant improvement underscores AS3FCPC’s potential for reliable and accurate semisupervised fuzzy clustering in complex,real-world applications,particularly by effectively managing mislabeled data and ambiguous cluster boundaries.展开更多
Gallium oxide(Ga_(2)O_(3))is an ultra-wide bandgap semiconductor with excellent potential for high-power and ultraviolet optoelectronic device applications.High-performance Ga_(2)O_(3)-based high-power devices rely he...Gallium oxide(Ga_(2)O_(3))is an ultra-wide bandgap semiconductor with excellent potential for high-power and ultraviolet optoelectronic device applications.High-performance Ga_(2)O_(3)-based high-power devices rely heavily on precise processing,especially in wafer dicing.Laser stealth dicing(LSD)is an innova-tive laser technology that utilizes a focused laser to create subsurface modifications in the wafer without surface damage.LSD has broad application prospects in the field of semiconductor precision processing.In this work,the idea of achieving high-quality dicing ofβ-Ga_(2)O_(3) wafers via LSD was proposed.A com-bination of atomistic simulations and experiments was used to understand the underlying mechanism of LSD ofβ-Ga_(2)O_(3) wafers.On the one hand,the laser loading and fracture process ofβ-Ga_(2)O_(3) wafers were simulated using molecular dynamics(MD)methods as well as a machine learning potential.The effects of single-pulse energy on LSD were analyzed through the lattice residual pressure,the final total energy of the system,the internal atomic strain,and the maximum stress value during uniaxial tension.On the other hand,based on the MD simulations,LSD was successfully performed onβ-Ga_(2)O_(3) wafers along three main crystal planes in the laboratory,resulting in good surface quality.This work not only provides profound optimization strategies for the LSD process ofβ-Ga_(2)O_(3),establishing the foundation for high-quality dicing ofβ-Ga_(2)O_(3) wafers,but also verifies the accuracy of MD simulations in predict-ing trends related to the LSD,offering a potential approach for high-quality dicing of other materials in future research.展开更多
Global climate change,along with the rapid increase of the population,has put significant pressure on water security.A water reservoir is an effective solution for adjusting and ensuring water supply.In particular,the...Global climate change,along with the rapid increase of the population,has put significant pressure on water security.A water reservoir is an effective solution for adjusting and ensuring water supply.In particular,the reservoir water level is an essential physical indicator for the reservoirs.Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies.In recent years,deep learning models have been widely applied to solve forecasting problems.In this study,we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,ConvLSTM,and linear interpolation to predict reservoir water levels.It utilizes data from Sentinel-2 satellite images,generated from visible spectrum bands(Red-Blue-Green)to reconstruct true-color reservoir images.Adam is used as the optimization algorithm with the loss function being MSE(Mean Squared Error)to evaluate the model’s error during training.We implemented and validated the proposed model using Sentinel-2 satellite imagery for the An Khe reservoir in Vietnam.To assess its performance,we also conducted comparative experiments with other related models,including SegNet_ConvLSTM and UNet_ConvLSTM,on the same dataset.The model performances were validated using k-fold cross-validation and ANOVA analysis.The experimental results demonstrate that the YOLOv9_ConvLSTM model outperforms the compared models.It has been seen that the proposed approach serves as a valuable tool for reservoir water level forecasting using satellite imagery that contributes to effective water resource management.展开更多
The Tien's Mountain Stream Snake,Opisthotropis daovantieni Orlov, Darevsky, and Murphy, 1998, has been represented solely by its type series, with no additional specimens reported in the past two decades. As a res...The Tien's Mountain Stream Snake,Opisthotropis daovantieni Orlov, Darevsky, and Murphy, 1998, has been represented solely by its type series, with no additional specimens reported in the past two decades. As a result, limited data exist and O. daovantieni remains one of the least studied members of its genus. Based on a re-examination of the type series, analysis of newly collected topotypic specimens, and a review of museum collections, this study provides an updated and comprehensive morphological characterization of O. daovantieni including detailed descriptions of hemipenial morphology, revised diagnostic characters,phylogenetic positioning, and ecological insights.Based on morphological comparisons with congeners, we also define the informal Opisthotropis spenceri group to facilitate future taxonomic work. In addition, this study documents a previously unreported defensive behavior involving tail-poking,observed in the field and thus far unique within the genus Opisthotropis.展开更多
The early stages of crystallization and occurrence of surface wrinkling were investigated using poly(butadiene)-block-poly(ε-caprolactone)with an ordered lamellar structure.Direct evidence has demonstrated that surfa...The early stages of crystallization and occurrence of surface wrinkling were investigated using poly(butadiene)-block-poly(ε-caprolactone)with an ordered lamellar structure.Direct evidence has demonstrated that surface wrinkling precedes nucleation and crystal growth.This study examined the relationship between surface wrinkling,nucleation,and the formation of crystalline supramolecular structures using atomic force microscopy(AFM)and X-ray scattering measurements.Surface wrinkling is attributed to curving induced by accumulated stresses,including residual stress from the sample preparation and thermal stress during cooling.These stresses cause large-scale material flow and corresponding changes in the molecular conformations,potentially reducing the nucleation barrier.This hypothesis is supported by the rapid crystal growth observed following the spread of surface wrinkles.Additionally,the surface curving of the polymer thin film creates local minima of the free energy,facilitating nucleation.The nuclei subsequently grow into crystalline supramolecular structures by incorporating polymer molecules from the melt.This mechanism highlights the role of localized structural inhomogeneity in the early stages of crystallization and provides new insights into structure formation processes.展开更多
The magnetic fields and dynamical processes in the solar polar regions play a crucial role in the solar magnetic cycle and in supplying mass and energy to the fast solar wind,ultimately being vital in controlling sola...The magnetic fields and dynamical processes in the solar polar regions play a crucial role in the solar magnetic cycle and in supplying mass and energy to the fast solar wind,ultimately being vital in controlling solar activities and driving space weather.Despite numerous efforts to explore these regions,to date no imaging observations of the Sun's poles have been achieved from vantage points out of the ecliptic plane,leaving their behavior and evolution poorly understood.This observation gap has left three top-level scientific questions unanswered:How does the solar dynamo work and drive the solar magnetic cycle?What drives the fast solar wind?How do space weather processes globally originate from the Sun and propagate throughout the solar system?The Solar Polarorbit Observatory(SPO)mission,a solar polar exploration spacecraft,is proposed to address these three unanswered scientific questions by imaging the Sun's poles from high heliolatitudes.In order to achieve its scientific goals,SPO will carry six remote-sensing and four in-situ instruments to measure the vector magnetic fields and Doppler velocity fields in the photosphere,to observe the Sun in the extreme ultraviolet,X-ray,and radio wavelengths,to image the corona and the heliosphere up to 45 R_(s),and to perform in-situ detection of magnetic fields,and low-and high-energy particles in the solar wind.The SPO mission is capable of providing critical vector magnetic fields and Doppler velocities of the polar regions to advance our understanding of the origin of the solar magnetic cycle,providing unprecedented imaging observations of the solar poles alongside in-situ measurements of charged particles and magnetic fields from high heliolatitudes to unveil the mass and energy supply that drive the fast solar wind,and providing observational constraints for improving our ability to model and predict the three-dimensional(3D)structures and propagation of space weather events.展开更多
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex,high-dimensional data that single-view methods cannot capture.Traditional fuzzy clustering techniques,such as Fuzzy C-Means(FCM),face significant challenges in handling uncertainty and the dependencies between different views.To overcome these limitations,we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data,aiming to enhance clustering accuracy and robustness,termed Multi-view Picture Fuzzy Clustering(MPFC).In particular,the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels:membership degrees,neutral degrees,and refusal degrees.This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models.Meanwhile,dual-anchor graphs exploit the similarity relationships between data points and integrate information across views.This combination improves stability,scalability,and robustness when handling noisy and heterogeneous data.Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency,outperforming traditional methods.Specifically,the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets,attaining a Purity(PUR)score of 0.6440 and an Accuracy(ACC)score of 0.6213 for the 3 Sources dataset,underscoring its robustness and efficiency.The proposed approach significantly contributes to fields such as pattern recognition,multi-view relational data analysis,and large-scale clustering problems.Future work will focus on extending the method for semi-supervised multi-view clustering,aiming to enhance adaptability,scalability,and performance in real-world applications.
文摘In the context of the continuous deepening of the“Double Reduction”policy and the growing demand for quality education,leveled mathematics readers,as an emerging form of publishing that integrates subject education and reading experience,face challenges such as unclear leveling logic,insufficient functional support,and weak user engagement.This paper introduces the 4V marketing theory and constructs an analytical framework from four dimensions:differentiation,functionality,added value,and resonance.Two representative products,“Climbing Mathematics”and“Spark Mathematics,”are selected for a typical case comparison to identify their strengths and weaknesses in content design,service systems,and brand operation,and to extract transferable strategic elements.The study finds that the user-value-oriented strategy based on the 4V model can effectively address the core issues in the market promotion and user relationship building of leveled mathematics readers,providing practical paths and theoretical support for educational publishing institutions to achieve product innovation and brand upgrading in this niche field.
文摘Selecting the embryo with the highest implantation potential is a top priority in in-vitro fertilization(IVF)centers.Few studies have explored the relationship between day 5 blastocyst morphokinetics and implantation outcomes[1].Despite numerous time-lapse studies,the findings often conflict due to differences in patient demographics,lab conditions,and protocols,such as oxygen concentration[2].Thus,there is ongoing debate regarding which parameters are most predictive of implantation.
基金funded by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under grant number:02/2022/TN.
文摘This study focuses on the design and validation of a behavior classification system for cattle using behavioral data collected through accelerometer sensors.Data collection and behavioral analysis are achieved using machine learning(ML)algorithms through accelerometer sensors.However,behavioral analysis poses challenges due to the complexity of cow activities.The task becomes more challenging in a real-time behavioral analysis system with the requirement for shorter data windows and energy constraints.Shorter windows may lack sufficient information,reducing algorithm performance.Additionally,the sensor’s position on the cowsmay shift during practical use,altering the collected accelerometer data.This study addresses these challenges by employing a 3-s data window to analyze cow behaviors,specifically Feeding,Lying,Standing,and Walking.Data synchronization between accelerometer sensors placed on the neck and leg compensates for the lack of information in short data windows.Features such as the Vector of Dynamic Body Acceleration(VeDBA),Mean,Variance,and Kurtosis are utilized alongside the Decision Tree(DT)algorithm to address energy efficiency and ensure computational effectiveness.This study also evaluates the impact of sensor misalignment on behavior classification.Simulated datasets with varying levels of sensor misalignment were created,and the system’s classification accuracy exceeded 0.95 for the four behaviors across all datasets(including original and simulated misalignment datasets).Sensitivity(Sen)and PPV for all datasets were above 0.9.The study provides farmers and the dairy industry with a practical,energy-efficient system for continuously monitoring cattle behavior to enhance herd productivity while reducing labor costs.
基金funded by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under grant number:NCUD.02-2024.11.
文摘This study presents CGB-Net,a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer,with direct applicability to gastroesophageal reflux disease(GERD)monitoring.Unlike conventional approaches limited to four basic postures,CGB-Net enables fine-grained classification of twelve clinically relevant sleep positions,providing enhanced resolution for personalized health assessment.The architecture introduces a unique integration of three complementary components:1D Convolutional Neural Networks(1D-CNN)for efficient local spatial feature extraction,Gated Recurrent Units(GRU)to capture short-termtemporal dependencieswith reduced computational complexity,and Bidirectional Long Short-Term Memory(Bi-LSTM)networks for modeling long-term temporal context in both forward and backward directions.This complementary integration allows the model to better represent dynamic and contextual information inherent in the sensor data,surpassing the performance of simpler or previously published hybrid models.Experiments were conducted on a benchmark dataset consisting of 18 volunteers(age range:19–24 years,mean 20.56±1.1 years;height 164.78±8.18 cm;weight 55.39±8.30 kg;BMI 20.24±2.04),monitored via a single abdominal accelerometer.A subjectindependent evaluation protocol with multiple random splits was employed to ensure robustness and generalizability.The proposed model achieves an average Accuracy of 87.60% and F1-score of 83.38%,both reported with standard deviations over multiple runs,outperforming several baseline and state-of-the-art methods.By releasing the dataset publicly and detailing themodel design,this work aims to facilitate reproducibility and advance research in sleep posture classification for clinical applications.
文摘BACKGROUND The pathogenesis of non-ampullary duodenal epithelial tumors(NADETs)is not fully understood.NADETs that express gastric-type mucin phenotypes(GNADETs)are noteworthy because of their high malignancy.Gastric foveolar metaplasia,from which G-NADETs originate,protects the duodenal mucosa from gastric acidity.As gastric acid secretion is affected by endoscopic gastric mucosal atrophy(EGMA),we hypothesized that EGMA would be associated with GNADETs.AIM To evaluate the association between EGMA and the occurrence of G-NADETs.METHODS This cross-sectional retrospective study investigated the relationship between EGMA and NADETs in 134 patients.The duodenum was divided into parts 1(bulb),2(superior duodenal angle to the papilla),and 3(anal side of the papilla to the horizontal part).The effects of gastric acidity and presence of Brunner’s glands were considered.EGMA was divided into types C(no or mild atrophy)and O(severe atrophy).Mucin phenotype expressions in NADETs were divided into gastric,intestinal,gastrointestinal,and unclassifiable.RESULTS When NADETs were classified according to EGMA,105 were classified as type C and 29 as type O.G-NADETs were present in 11.9%(16 cases)of all cases,and all 16 cases were of type C.Among G-NADETs,93.8%(15 cases)were present in part 1 or 2.There was an association between G-NADETs and type C in part 1,and 50.0%(eight of 16 cases)of G-NADETs were associated with a current or previous Helicobacter pylori infection status.Additionally,all eight cases occurred in part 1.CONCLUSION G-NADETs were significantly associated with type C.Gastric acidity and Brunner's gland growth may be associated with G-NADETs.
文摘Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data,guided by active learning,to enhance classification accuracy,particularly in complex and ambiguous datasets.Although several active semi-supervised fuzzy clustering methods have been developed previously,they typically face significant limitations,including high computational complexity,sensitivity to initial cluster centroids,and difficulties in accurately managing boundary clusters where data points often overlap among multiple clusters.This study introduces a novel Active Semi-Supervised Fuzzy Clustering algorithm specifically designed to identify,analyze,and correct misclassified boundary elements.By strategically utilizing labeled data through active learning,our method improves the robustness and precision of cluster boundary assignments.Extensive experimental evaluations conducted on three types of datasets—including benchmark UCI datasets,synthetic data with controlled boundary overlap,and satellite imagery—demonstrate that our proposed approach achieves superior performance in terms of clustering accuracy and robustness compared to existing active semi-supervised fuzzy clustering methods.The results confirm the effectiveness and practicality of our method in handling real-world scenarios where precise cluster boundaries are critical.
基金supported by funding from the Energy Materials and Surface Sciences Unit of the Okinawa Institute of Science and Technology Graduate University,the OIST R&D Cluster Research Program,the OIST Proof of Concept(POC)Program,the JSPS KAKENHI Grant Number JP21F21754 and Alexander von Humboldt Foundation。
文摘Self-assembled monolayers(SAMs)are widely used as hole transport materials in inverted perovskite solar cells,offering low parasitic absorption and suitability for semitransparent and tandem solar cells.While SAMs have shown to be promising in small-area devices(≤1 cm^(2)),their application in larger areas has been limited by a lack of knowledge regarding alternative deposition methods beyond the common spin-coating approach.Here,we compare spin-coating and upscalable methods such as thermal evaporation and spray-coating for[2-(9H-carbazol-9-yl)ethyl]phosphonic acid(2PACz),one of the most common carbazole-based SAMs.The impact of these deposition methods on the device performance is investigated,revealing that the spray-coating technique yields higher device performance.Furthermore,our work provides guidelines for the deposition of SAM materials for the fabrication of perovskite solar modules.In addition,we provide an extensive characterization of 2PACz films focusing on thermal evaporation and spray-coating methods,which allow for thicker 2PACz deposition.It is found that the optimal 2PACz deposition conditions corresponding to the highest device performances do not always correlate with the monolayer characteristics.
基金supported by the National Foundation for Science and Technology Development (NAFOSTED,106.05-2021.19)The CAS President’s International Fellowship Initiative (PIFI 2023VBC0022) supported C.V.HOANG as a Visiting Scientist in China+1 种基金Field surveys in Vietnam were partially supported by Project to Build National Forest Resources Museum Networkpartially supported by Ideal Wild and the Rufford Foundation (grant No.43835-1) to C.V.HOANG。
文摘Two new species of Leptobrachella are described from Vietnam based on morphological differences and genetic divergences in 16S rRNA mitochondrial gene sequences.The new taxa are distinguished from each other and from other species of the genus Leptobrachella in body size,head width/length ratio,tympanum morphology,dorsal skin texture,the presence/absence of fringes on toes,color of dorsal and ventral body,and iris color.The two new species are also divergent from each other and from other congeners by a 4.14% or greater uncorrected genetic distance.Leptobrachella batxatensis sp.nov.is genetically closest to L.shiwandashanensis and L.wuhuangmontis from China.Leptobrachella duyenae sp.nov.is genetically closest to L.bidoupensis from Vietnam with strong nodal support from both BI and ML analyses(1.0/99%).
文摘Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance.This method effectively integrates prior knowledge using pre-labeled data.While semi-supervised fuzzy clustering(SSFC)methods leverage limited labeled data to enhance accuracy,they remain highly susceptible to inappropriate or mislabeled prior knowledge,especially in noisy or overlapping datasets where cluster boundaries are ambiguous.To enhance the effectiveness of clustering algorithms,it is essential to leverage labeled data while ensuring the safety of the previous knowledge.Existing solutions,such as the Trusted Safe Semi-Supervised Fuzzy Clustering Method(TS3FCM),struggle with random centroid initialization,fixed neighbor radius formulas,and handling outliers or noise at cluster overlaps.A new framework called Active Safe Semi-Supervised Fuzzy Clustering with Pairwise Constraints Based on Cluster Boundary(AS3FCPC)is proposed in this paper to deal with these problems.It does this by combining pairwise constraints and active learning.AS3FCPC uses active learning to query only the most informative data instances close to the cluster boundaries.It also uses pairwise constraints to enforce the cluster structure,which makes the system more accurate and robust.Extensive test results on diverse datasets,including challenging noisy and overlapping scenarios,demonstrate that AS3FCPC consistently achieves superior performance compared to state-of-the-art methods like TS3FCM and other baselines,especially when the data is noisy and overlaps.This significant improvement underscores AS3FCPC’s potential for reliable and accurate semisupervised fuzzy clustering in complex,real-world applications,particularly by effectively managing mislabeled data and ambiguous cluster boundaries.
基金financially supported by the National Nat-ural Science Foundation of China(Nos.92473102,62004141,and 52202045)the Knowledge Innovation Program of Wuhan-Shuguang(Nos.2023010201020243,and 2023010201020255)+4 种基金the Major Program(JD)of Hubei Province(No.2023BAA009)the Shenzhen Science and Technology Program(No.JCYJ20240813175906008)the Fundamental Research Funds for the Central Universities(Nos.2042023kf0112,and 2042022kf1028)the Open Fund of Hubei Key Laboratory of Electronic Manufacturing and Packaging Integration(Wuhan University)(Nos.EMPI2024014,EMPI2024021,and EMPI2023027)the China Scholarship Council(No.202206275005).
文摘Gallium oxide(Ga_(2)O_(3))is an ultra-wide bandgap semiconductor with excellent potential for high-power and ultraviolet optoelectronic device applications.High-performance Ga_(2)O_(3)-based high-power devices rely heavily on precise processing,especially in wafer dicing.Laser stealth dicing(LSD)is an innova-tive laser technology that utilizes a focused laser to create subsurface modifications in the wafer without surface damage.LSD has broad application prospects in the field of semiconductor precision processing.In this work,the idea of achieving high-quality dicing ofβ-Ga_(2)O_(3) wafers via LSD was proposed.A com-bination of atomistic simulations and experiments was used to understand the underlying mechanism of LSD ofβ-Ga_(2)O_(3) wafers.On the one hand,the laser loading and fracture process ofβ-Ga_(2)O_(3) wafers were simulated using molecular dynamics(MD)methods as well as a machine learning potential.The effects of single-pulse energy on LSD were analyzed through the lattice residual pressure,the final total energy of the system,the internal atomic strain,and the maximum stress value during uniaxial tension.On the other hand,based on the MD simulations,LSD was successfully performed onβ-Ga_(2)O_(3) wafers along three main crystal planes in the laboratory,resulting in good surface quality.This work not only provides profound optimization strategies for the LSD process ofβ-Ga_(2)O_(3),establishing the foundation for high-quality dicing ofβ-Ga_(2)O_(3) wafers,but also verifies the accuracy of MD simulations in predict-ing trends related to the LSD,offering a potential approach for high-quality dicing of other materials in future research.
基金funded by International School,Vietnam National University,Hanoi(VNU-IS)under project number CS.2023-10.
文摘Global climate change,along with the rapid increase of the population,has put significant pressure on water security.A water reservoir is an effective solution for adjusting and ensuring water supply.In particular,the reservoir water level is an essential physical indicator for the reservoirs.Forecasting the reservoir water level effectively assists the managers in making decisions and plans related to reservoir management policies.In recent years,deep learning models have been widely applied to solve forecasting problems.In this study,we propose a novel hybrid deep learning model namely the YOLOv9_ConvLSTM that integrates YOLOv9,ConvLSTM,and linear interpolation to predict reservoir water levels.It utilizes data from Sentinel-2 satellite images,generated from visible spectrum bands(Red-Blue-Green)to reconstruct true-color reservoir images.Adam is used as the optimization algorithm with the loss function being MSE(Mean Squared Error)to evaluate the model’s error during training.We implemented and validated the proposed model using Sentinel-2 satellite imagery for the An Khe reservoir in Vietnam.To assess its performance,we also conducted comparative experiments with other related models,including SegNet_ConvLSTM and UNet_ConvLSTM,on the same dataset.The model performances were validated using k-fold cross-validation and ANOVA analysis.The experimental results demonstrate that the YOLOv9_ConvLSTM model outperforms the compared models.It has been seen that the proposed approach serves as a valuable tool for reservoir water level forecasting using satellite imagery that contributes to effective water resource management.
基金supported by the National Natural Science Foundation of China(32300370, 32200363)International Partnership Program of Chinese Academy of Sciences (071GJHZ2023041MI),Biological Resources Programme, Chinese Academy of Sciences (KFJ-BRP-017-65, KFJ-BRP017-086, CAS-TAX-24-051, CAS-TAX-24-052)+2 种基金China Postdoctoral Science Foundation (2023M743416)Natural Science Foundation of Sichuan Province (No. 2023NSFSC1155)partially supported by the Vietnam Academy of Science and Technology (CT0000.03/25-27) to NTT。
文摘The Tien's Mountain Stream Snake,Opisthotropis daovantieni Orlov, Darevsky, and Murphy, 1998, has been represented solely by its type series, with no additional specimens reported in the past two decades. As a result, limited data exist and O. daovantieni remains one of the least studied members of its genus. Based on a re-examination of the type series, analysis of newly collected topotypic specimens, and a review of museum collections, this study provides an updated and comprehensive morphological characterization of O. daovantieni including detailed descriptions of hemipenial morphology, revised diagnostic characters,phylogenetic positioning, and ecological insights.Based on morphological comparisons with congeners, we also define the informal Opisthotropis spenceri group to facilitate future taxonomic work. In addition, this study documents a previously unreported defensive behavior involving tail-poking,observed in the field and thus far unique within the genus Opisthotropis.
基金the National Natural Science Foundation of China(Nos.U2032101 and 11905306)the National Key Research and Development Project of China(No.2022YFB2402602).
文摘The early stages of crystallization and occurrence of surface wrinkling were investigated using poly(butadiene)-block-poly(ε-caprolactone)with an ordered lamellar structure.Direct evidence has demonstrated that surface wrinkling precedes nucleation and crystal growth.This study examined the relationship between surface wrinkling,nucleation,and the formation of crystalline supramolecular structures using atomic force microscopy(AFM)and X-ray scattering measurements.Surface wrinkling is attributed to curving induced by accumulated stresses,including residual stress from the sample preparation and thermal stress during cooling.These stresses cause large-scale material flow and corresponding changes in the molecular conformations,potentially reducing the nucleation barrier.This hypothesis is supported by the rapid crystal growth observed following the spread of surface wrinkles.Additionally,the surface curving of the polymer thin film creates local minima of the free energy,facilitating nucleation.The nuclei subsequently grow into crystalline supramolecular structures by incorporating polymer molecules from the melt.This mechanism highlights the role of localized structural inhomogeneity in the early stages of crystallization and provides new insights into structure formation processes.
文摘The magnetic fields and dynamical processes in the solar polar regions play a crucial role in the solar magnetic cycle and in supplying mass and energy to the fast solar wind,ultimately being vital in controlling solar activities and driving space weather.Despite numerous efforts to explore these regions,to date no imaging observations of the Sun's poles have been achieved from vantage points out of the ecliptic plane,leaving their behavior and evolution poorly understood.This observation gap has left three top-level scientific questions unanswered:How does the solar dynamo work and drive the solar magnetic cycle?What drives the fast solar wind?How do space weather processes globally originate from the Sun and propagate throughout the solar system?The Solar Polarorbit Observatory(SPO)mission,a solar polar exploration spacecraft,is proposed to address these three unanswered scientific questions by imaging the Sun's poles from high heliolatitudes.In order to achieve its scientific goals,SPO will carry six remote-sensing and four in-situ instruments to measure the vector magnetic fields and Doppler velocity fields in the photosphere,to observe the Sun in the extreme ultraviolet,X-ray,and radio wavelengths,to image the corona and the heliosphere up to 45 R_(s),and to perform in-situ detection of magnetic fields,and low-and high-energy particles in the solar wind.The SPO mission is capable of providing critical vector magnetic fields and Doppler velocities of the polar regions to advance our understanding of the origin of the solar magnetic cycle,providing unprecedented imaging observations of the solar poles alongside in-situ measurements of charged particles and magnetic fields from high heliolatitudes to unveil the mass and energy supply that drive the fast solar wind,and providing observational constraints for improving our ability to model and predict the three-dimensional(3D)structures and propagation of space weather events.
