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
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
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
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
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
This paper studies a single degree of freedom system under free vibration and controlled by a general semiactive damping.A general integral of squared error is considered as the performance index.A one-time switching ...This paper studies a single degree of freedom system under free vibration and controlled by a general semiactive damping.A general integral of squared error is considered as the performance index.A one-time switching damping controller is proposed and optimized.The pontryagin maximum principle is used to prove that no other form of semi-active damping can provide the better performance than the proposed one-time switching damping.展开更多
This research studies the effect of Mn-Co-La_(2)O_(3) coating synthesized by the electrodeposition method on the oxidation resistance and electrical conductivity of the Crofer 22 APU stainless steel interconnect plate...This research studies the effect of Mn-Co-La_(2)O_(3) coating synthesized by the electrodeposition method on the oxidation resistance and electrical conductivity of the Crofer 22 APU stainless steel interconnect plates in solid oxide fuel cells.The test samples were characterized by a field emission scanning electron microscope(FESEM)equipped with energy dispersive spectroscopy(EDS),X-ray diffraction(XRD),and X-ray photoelectron spectroscopy(XPS).The oxidation kinetics of the coated and uncoated samples were studied by tracking their weight changes over time at 800°C,showing that the oxidation mechanism for all samples follows the parabolic law.Lower oxidation rate constant(k_(p))values of the coated sample compared with that of the uncoated one indicated a reduction in the oxidation rate of the steel substrate in the presence of the Mn-Co-La_(2)O_(3) coating.The examination of the cross-section of different samples after the isothermal oxidation for 500 h at 800°C exhibited that applying the composite coating leads to a decrease in the thickness of the chromia layer formed on the steel surface.Furthermore,under these conditions,the area-specific resistance(ASR)of the coated sample(13.11 mΩcm^(2))is significantly lower than that of the uncoated one(41.45 mΩcm^(2)).展开更多
Recently,there has been considerable interest in high-efficiency ultraviolet(UV)photodetectors for their potential practical uses.In this study,a high-quality UV photodetector was fabricated using a combination of Ag ...Recently,there has been considerable interest in high-efficiency ultraviolet(UV)photodetectors for their potential practical uses.In this study,a high-quality UV photodetector was fabricated using a combination of Ag and Au NPs with GaN film.The GaN film was deposited using sputtering technique,whereas Ag and Au films were grown using thermal evaporation technique.Ag-Au bimetallic nanoparticles were formed by treating them at the various annealing temperature to improve the interaction between light and the photoactive layers of the photodetectors.The optimal annealing temperature to achieve the best performance of a photodetector is 650℃.This led to a photoresponsivity of 98.5 A/W and the ON/OFF ratio of 705 at low bias voltage of 1 V.This work establishes the foundation for the advancement of high-performance UV photodetectors.展开更多
Analyzing physical activities through wearable devices is a promising research area for improving health assessment.This research focuses on the development of an affordable and real-time Human Activity Recognition(HA...Analyzing physical activities through wearable devices is a promising research area for improving health assessment.This research focuses on the development of an affordable and real-time Human Activity Recognition(HAR)system designed to operate on low-performance microcontrollers.The system utilizes data from a bodyworn accelerometer to recognize and classify human activities,providing a cost-effective,easy-to-use,and highly accurate solution.A key challenge addressed in this study is the execution of efficient motion recognition within a resource-constrained environment.The system employs a Random Forest(RF)classifier,which outperforms Gradient Boosting Decision Trees(GBDT),Support Vector Machines(SVM),and K-Nearest Neighbors(KNN)in terms of accuracy and computational efficiency.The proposed features Average absolute deviation(AAD),Standard deviation(STD),Interquartile range(IQR),Range,and Root mean square(RMS).The research has conducted numerous experiments and comparisons to establish optimal parameters for ensuring system effectiveness,including setting a sampling frequency of 50 Hz and selecting an 8-s window size with a 40%overlap between windows.Validation was conducted on both the WISDM public dataset and a self-collected dataset,focusing on five fundamental daily activities:Standing,Sitting,Jogging,Walking,and Walking the stairs.The results demonstrated high recognition accuracy,with the system achieving 96.7%on the WISDM dataset and 97.13%on the collected dataset.This research confirms the feasibility of deploying HAR systems on low-performance microcontrollers and highlights the system’s potential applications in patient support,rehabilitation,and elderly care.展开更多
The aromatic compounds,including o-xylene,m-xylene,p-xylene,and ethylbenzene,primarily originate from the catalytic reforming of crude oil,and have a wide variety of applications.However,because of similar physical an...The aromatic compounds,including o-xylene,m-xylene,p-xylene,and ethylbenzene,primarily originate from the catalytic reforming of crude oil,and have a wide variety of applications.However,because of similar physical and chemical properties,these compounds are difficult to be identified by gas chromatography(GC)without standard samples.With the development of modern nuclear magnetic resonance(NMR)techniques,NMR has emerged as a powerful and efficient tool for the rapid analysis of complex and crude mixtures without purification.In this study,the parameters of one-dimensional(1D)total correlation spectroscopy(TOCSY)NMR techniques,including 1D selective gradient TOCSY and 1D chemicalshift-selective filtration(CSSF)with TOCSY,were optimized to obtain comprehensive molecular structure information.The results indicate that the overlapped signals in NMR spectra of nonpolar aromatic compounds(including o-xylene,m-xylene,p-xylene and ethylbenzene),polar aromatic compounds(benzyl alcohol,benzaldehyde,benzoic acid),and aromatic compounds with additional conjugated bonds(styrene)can be resolved in 1D TOCSY.More importantly,full molecular structures can be clearly distinguished by setting appropriate mixing time in 1D TOCSY.This approach simplifies the NMR spectra,provides structural information of entire molecules,and can be applied for the analysis of other structural isomers.展开更多
Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from COVID-19.Methods:This study applied a cross-sectional approach within...Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from COVID-19.Methods:This study applied a cross-sectional approach within community settings in a southern province of Vietnam.A structured questionnaire featuring socio-demographic information and common long COVID symptoms was employed.Phi correlation coefficients assessed associations among pairs of long COVID symptoms.Additionally,multivariable logistic regression models were performed to investigate the risk factors of long COVID in recovered COVID-19 children and adolescents.Results:Among 422 participants,39.3%reported long COVID symptoms,with a prevalence of 45.2%(SD=0.5)in children and 22.2%(SD=0.4)in adolescents.Common symptoms reported were cough 34.6%(SD=0.5),fatigue 20.6%(SD=0.4),shortness of breath 10.9%(SD=0.3),and lack of appetite 6.6%(SD=0.3).Concerning risk factors of long COVID,a higher risk was observed among demographic groups,including girls(OR 1.25,95%CI 1.15-1.37;P<0.001,reference:boys),children compared to adolescents(OR 1.24,95%CI 1.12-1.37;P<0.001),overweight individuals(OR 1.14,95%CI 1.02-1.27;P=0.018,reference:healthy weight),and participants without any COVID-19 vaccination(OR 1.36,95%CI 1.20-1.54;P<0.001),or have received only one single dose(OR 1.35,95%CI 1.10-1.64;P=0.004)compared to those who have received two doses.Besides,patients with a COVID-19 treatment duration exceeding two weeks also had a higher risk of long COVID(OR 1.32,95%CI 1.09-1.60;P=0.003)than those who recovered less than seven days.Conclusions:The insights from this study provide crucial guidance for predicting the factors associated with the occurrence of long COVID in pediatric patients,contributing to strategic interventions aimed at mitigating the long COVID risks among children and adolescents in Vietnam.展开更多
The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication e...The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.展开更多
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.
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.
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%).
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.
基金supported by Vietnam Academy of Science and Technology(Grant No.VAST01.04/22-23)。
文摘This paper studies a single degree of freedom system under free vibration and controlled by a general semiactive damping.A general integral of squared error is considered as the performance index.A one-time switching damping controller is proposed and optimized.The pontryagin maximum principle is used to prove that no other form of semi-active damping can provide the better performance than the proposed one-time switching damping.
文摘This research studies the effect of Mn-Co-La_(2)O_(3) coating synthesized by the electrodeposition method on the oxidation resistance and electrical conductivity of the Crofer 22 APU stainless steel interconnect plates in solid oxide fuel cells.The test samples were characterized by a field emission scanning electron microscope(FESEM)equipped with energy dispersive spectroscopy(EDS),X-ray diffraction(XRD),and X-ray photoelectron spectroscopy(XPS).The oxidation kinetics of the coated and uncoated samples were studied by tracking their weight changes over time at 800°C,showing that the oxidation mechanism for all samples follows the parabolic law.Lower oxidation rate constant(k_(p))values of the coated sample compared with that of the uncoated one indicated a reduction in the oxidation rate of the steel substrate in the presence of the Mn-Co-La_(2)O_(3) coating.The examination of the cross-section of different samples after the isothermal oxidation for 500 h at 800°C exhibited that applying the composite coating leads to a decrease in the thickness of the chromia layer formed on the steel surface.Furthermore,under these conditions,the area-specific resistance(ASR)of the coated sample(13.11 mΩcm^(2))is significantly lower than that of the uncoated one(41.45 mΩcm^(2)).
基金supported by the Physics development program grant funded by Vietnam Academy of Science and Technology (VAST) (KHCBVL.06/24-25)support by the Korea Evaluation Institute of Industrial Technology (KEIT)grant funded by the Korean government (MOTIE) (No.RS-2022-00143570).
文摘Recently,there has been considerable interest in high-efficiency ultraviolet(UV)photodetectors for their potential practical uses.In this study,a high-quality UV photodetector was fabricated using a combination of Ag and Au NPs with GaN film.The GaN film was deposited using sputtering technique,whereas Ag and Au films were grown using thermal evaporation technique.Ag-Au bimetallic nanoparticles were formed by treating them at the various annealing temperature to improve the interaction between light and the photoactive layers of the photodetectors.The optimal annealing temperature to achieve the best performance of a photodetector is 650℃.This led to a photoresponsivity of 98.5 A/W and the ON/OFF ratio of 705 at low bias voltage of 1 V.This work establishes the foundation for the advancement of high-performance UV photodetectors.
基金Human activity data for the experiments were sourced from the Ethics Council for Grassroots Biomedical Research at Phenikaa University.The data collection adhered to Decision No.476/QD-DHP-HÐÐÐthe Ethics Council for Grassroots Biomedical Research at Phenikaa University(No.023.07.01/DHP-HÐÐÐ,2023 Dec).
文摘Analyzing physical activities through wearable devices is a promising research area for improving health assessment.This research focuses on the development of an affordable and real-time Human Activity Recognition(HAR)system designed to operate on low-performance microcontrollers.The system utilizes data from a bodyworn accelerometer to recognize and classify human activities,providing a cost-effective,easy-to-use,and highly accurate solution.A key challenge addressed in this study is the execution of efficient motion recognition within a resource-constrained environment.The system employs a Random Forest(RF)classifier,which outperforms Gradient Boosting Decision Trees(GBDT),Support Vector Machines(SVM),and K-Nearest Neighbors(KNN)in terms of accuracy and computational efficiency.The proposed features Average absolute deviation(AAD),Standard deviation(STD),Interquartile range(IQR),Range,and Root mean square(RMS).The research has conducted numerous experiments and comparisons to establish optimal parameters for ensuring system effectiveness,including setting a sampling frequency of 50 Hz and selecting an 8-s window size with a 40%overlap between windows.Validation was conducted on both the WISDM public dataset and a self-collected dataset,focusing on five fundamental daily activities:Standing,Sitting,Jogging,Walking,and Walking the stairs.The results demonstrated high recognition accuracy,with the system achieving 96.7%on the WISDM dataset and 97.13%on the collected dataset.This research confirms the feasibility of deploying HAR systems on low-performance microcontrollers and highlights the system’s potential applications in patient support,rehabilitation,and elderly care.
基金We thank the Natural Science Foundation of Shanxi Province(202103021224439)National Natural Science Foundation of China(22075308)for financial support.
文摘The aromatic compounds,including o-xylene,m-xylene,p-xylene,and ethylbenzene,primarily originate from the catalytic reforming of crude oil,and have a wide variety of applications.However,because of similar physical and chemical properties,these compounds are difficult to be identified by gas chromatography(GC)without standard samples.With the development of modern nuclear magnetic resonance(NMR)techniques,NMR has emerged as a powerful and efficient tool for the rapid analysis of complex and crude mixtures without purification.In this study,the parameters of one-dimensional(1D)total correlation spectroscopy(TOCSY)NMR techniques,including 1D selective gradient TOCSY and 1D chemicalshift-selective filtration(CSSF)with TOCSY,were optimized to obtain comprehensive molecular structure information.The results indicate that the overlapped signals in NMR spectra of nonpolar aromatic compounds(including o-xylene,m-xylene,p-xylene and ethylbenzene),polar aromatic compounds(benzyl alcohol,benzaldehyde,benzoic acid),and aromatic compounds with additional conjugated bonds(styrene)can be resolved in 1D TOCSY.More importantly,full molecular structures can be clearly distinguished by setting appropriate mixing time in 1D TOCSY.This approach simplifies the NMR spectra,provides structural information of entire molecules,and can be applied for the analysis of other structural isomers.
文摘Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from COVID-19.Methods:This study applied a cross-sectional approach within community settings in a southern province of Vietnam.A structured questionnaire featuring socio-demographic information and common long COVID symptoms was employed.Phi correlation coefficients assessed associations among pairs of long COVID symptoms.Additionally,multivariable logistic regression models were performed to investigate the risk factors of long COVID in recovered COVID-19 children and adolescents.Results:Among 422 participants,39.3%reported long COVID symptoms,with a prevalence of 45.2%(SD=0.5)in children and 22.2%(SD=0.4)in adolescents.Common symptoms reported were cough 34.6%(SD=0.5),fatigue 20.6%(SD=0.4),shortness of breath 10.9%(SD=0.3),and lack of appetite 6.6%(SD=0.3).Concerning risk factors of long COVID,a higher risk was observed among demographic groups,including girls(OR 1.25,95%CI 1.15-1.37;P<0.001,reference:boys),children compared to adolescents(OR 1.24,95%CI 1.12-1.37;P<0.001),overweight individuals(OR 1.14,95%CI 1.02-1.27;P=0.018,reference:healthy weight),and participants without any COVID-19 vaccination(OR 1.36,95%CI 1.20-1.54;P<0.001),or have received only one single dose(OR 1.35,95%CI 1.10-1.64;P=0.004)compared to those who have received two doses.Besides,patients with a COVID-19 treatment duration exceeding two weeks also had a higher risk of long COVID(OR 1.32,95%CI 1.09-1.60;P=0.003)than those who recovered less than seven days.Conclusions:The insights from this study provide crucial guidance for predicting the factors associated with the occurrence of long COVID in pediatric patients,contributing to strategic interventions aimed at mitigating the long COVID risks among children and adolescents in Vietnam.
文摘The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.
