Truly optimal semi-active damping to control free vibration of a single degree of freedom system

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.

Qualitative analysis of aromatic compounds via 1D TOCSY techniques

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.

Prevalence and risk factors associated with long COVID symptoms in children and adolescents in a southern province of Vietnam

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.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

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 R2 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.

Comparative Chemical Research in Essential Oils from Six Apiaceae Species Growing in the Northern Region of Vietnam

Our study aimed to compare the essential oil(EO)concentration and composition of several Apiaceae species growing in the Northern region of Vietnam.The yields of EOs from materials ranged from 0.03%(root EO of Angelica acutiloba and aerial parts EO of Heracleum bivittatum)-0.27%(leaf EO of Xyloselinum vietnamense).Gas chromatography-mass spectrometry(GC-MS)allowed the identification of 74 components in the EOs of six Apiaceae species,making up 94.4%–100.0%of the oils.In EO from Angelica acutiloba,(Z)-ligustilide accounted for an extremely large proportion(94.9%).EO of Angelica pubescens was dominated by six character-istic components includingα-pinene(21.5%),β-phellandrene(18.1%),p-cymene(12.2%),3-methylnonane(8.7%),o-cymene(8.1%),and D-sylvestrene(6.2%).The EO from Cryptotaenia japonica was characterized by high amounts ofα-selinene(48.7%),β-selinene(23.7%),and trans-β-farnesene(5.4%).The EOs from leaves and stems of Xyloselinum vietnamense were characterized by high concentrations of sabinene(69.8%and 33.8%),4-terpineol(8.7%and 7.4%)andβ-pinene(4.0%and 6.5%)while EOs from aerial parts and root of Xylo-selinum leonidii comprise four characteristic monoterpenes includingα-pinene(28.2%and 52.8%),β-pinene(7.9%and 10.3%),β-phellandrene(7.6%and 15.3%),and sabinene(3.0%and 4.1%).Additionally,cryptone is also one of the major components in the EO of Xyloselinum leonidii(13.2%in the aerial parts oil and 2.8%in the root oil).In the EOs isolated from the aerial parts and root of Heracleum bivittatum,α-pinene(22.5%and 70.2%)andβ-pinene(43.2%and 20.0%)were the predominant monoterpenes.Sabinene appeared in the EO from aerial parts of Heracleum bivittatum with a relatively high concentration(13.5%)while bornyl acetate(5.1%)was also one of the main components in the EO from its aerial parts but was not detected in other Apiaceae species in the present study.These databases help identify and control the quality of plant material studied from the family Apiaceae growing in Vietnam.

United States Healthcare Data Breaches: Insights for NIST SP 800-66 Revision 2 from a Review of the NIST SP 800-66 Revision 1

Healthcare security and privacy breaches are occurring in the United States (US), and increased substantially during the pandemic. This paper reviews the National Institute of Standards and Technology (NIST) publication base as an effective solution. The NIST Special Publication 800-66 Revision 1 was an essential standard in US healthcare, which was withdrawn in February 2024 and superseded by SP 800-66 Revision 2. This review investigates the academic papers concerning the application of the NIST SP 800-66 Revision 1 standard in the US healthcare literature. A systematic review method was used in this study to determine current knowledge gaps of the SP 800-66 Revision 1. Some limitations were employed in the search to enforce validity. A total of eleven articles were found eligible for the study. Consequently, this study suggests the necessity for additional academic papers pertaining to SP 800-66 Revision 2 in the US healthcare literature. In turn, it will enhance awareness of safeguarding electronic protected health information (ePHI), help to mitigate potential future risks, and eventually reduce breaches.

Research on Innovation Strategy of Supply Chain Management of Agricultural Enterprises Under the Background of Big Data

With the rapid development and widespread application of Big Data technology, the supply chain management of agricultural products enterprises is facing unprecedented reform and challenges. This study takes the perspective of Big Data technology and collects relevant information on the application of supply chain management in 100 agricultural product enterprises through a survey questionnaire. The study found that the use of Big Data can effectively improve the accuracy of demand forecasting, inventory management efficiency, optimize logistics costs, improve supplier management efficiency, enhance the overall level of supply chain management of enterprises, and propose innovative strategies for supply chain management of agricultural products enterprises based on this. Big Data technology brings a new solution for agricultural products enterprises to enhance their supply chain management level, making the supply chain smarter and more efficient.

Research on the Problems and Countermeasures of Monetary Capital Internal Control in Small and Medium-Sized Enterprises

Small and medium-sized enterprises(SMEs)constitute the primary drivers of production within the nation’s existing enterprise landscape.They represent the most dynamic segment of the national economy and play a pivotal role in supporting economic growth,fostering employment,and enhancing people’s livelihoods.However,despite their significant and extensive organizational structures,only a fraction of these companies have established internal control systems,and even fewer possess robust ones.Building upon this premise and considering the prevailing circumstances of SMEs,this paper undertakes an empirical analysis of monetary fund management within this sector.It delves into the operational intricacies,exploring how monetary funds should be effectively implemented to ensure their safety and integrity.Furthermore,it proposes viable strategies to enhance the circulation efficiency of monetary funds,thereby maximizing benefits for enterprises.Addressing these challenges is crucial for SME managers striving to achieve sustainable profit growth and navigate the complexities of financial management.

The ALMA-QUARKS Survey.Ⅰ.Survey Description and Data Reduction

This paper presents an overview of the QUARKS survey,which stands for Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures."The QUARKS survey is observing139 massive clumps covered by 156 pointings at Atacama Large Millimeter/submillimeter Array(ALMA)Band 6(λ~1.3 mm).In conjunction with data obtained from the ALMA-ATOMS survey at Band 3(λ~3 mm),QUARKS aims to carry out an unbiased statistical investigation of massive star formation process within protoclusters down to a scale of 1000 au.This overview paper describes the observations and data reduction of the QUARKS survey,and gives a first look at an exemplar source,the mini-starburst Sgr B2(M).The wide-b and width(7.5 GHz)and high-angular-resolution(~0."3)observations of the QUARKS survey allow for the resolution of much more compact cores than those could be done by the ATOMS survey,and to detect previously unrevealed fainter filamentary structures.The spectral windows cover transitions of species including CO,SO,N_(2)D^(+),SiO,H_(30)α,H_(2)CO,CH_(3)CN,and many other complex organic molecules,tracing gas components with different temperatures and spatial extents.QUARKS aims to deepen our understanding of several scientific topics of massive star formation,such as the mass transport within protoclusters by(hub-)filamentary structures,the existence of massive starless cores,the physical and chemical properties of dense cores within protoclusters,and the feedback from already formed high-mass young protostars.

The ALMA-QUARKS Survey.Ⅱ.The ACA 1.3 mm Continuum Source Catalog and the Assembly of Dense Gas in Massive Star-Forming Clumps

Leveraging the high resolution,sensitivity,and wide frequency coverage of the Atacama Large Millimeter/submillimeter Array(ALMA),the QUARKS survey,standing for“Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures”,is observing 139 massive starforming clumps at ALMA Band 6(λ~1.3 mm).This paper introduces the Atacama Compact Array(ACA)7 m data of the QUARKS survey,describing the ACA observations and data reduction.Combining multiwavelength data,we provide the first edition of QUARKS atlas,offering insights into the multiscale and multiphase interstellar medium in high-mass star formation.The ACA 1.3 mm catalog includes 207 continuum sources that are called ACA sources.Their gas kinetic temperatures are estimated using three formaldehyde transitions with a non-LTE radiation transfer model,and the mass and density are derived from a dust emission model.The ACA sources are massive(16–84 percentile values of 6–160 M_(⊙)),gravity-dominated(M∝R^(1.1))fragments within massive clumps,with supersonic turbulence(M>1)and embedded star-forming protoclusters.We find a linear correlation between the masses of the fragments and the massive clumps,with a ratio of 6%between the two.When considering fragments as representative of dense gas,the ratio indicates a dense gas fraction(DGF)of 6%,although with a wide scatter ranging from 1%to 10%.If we consider the QUARKS massive clumps to be what is observed at various scales,then the size-independent DGF indicates a self-similar fragmentation or collapsing mode in protocluster formation.With the ACA data over four orders of magnitude of luminosity-to-mass ratio(L/M),we find that the DGF increases significantly with L/M,which indicates clump evolutionary stage.We observed a limited fragmentation at the subclump scale,which can be explained by a dynamic global collapse process.

Universal Three-Party Quantum Secret Sharing Against Collective Noise

we present a robust and universal quantum secret sharing protocol with four-qubit decoherence-free （DF） states against collective noise. The transmission＇s safety is ensured by the nonorthogonality of the noiseless states traveling on the quantum channel. Although this scheme uses entangled states for encoding, only single-particle product measurements are required.

Cosmological constraints on the Undulant Universe

We use the redshift Hubble parameter H（z） data derived from relative galaxy ages, distant type Ia supernovae （SNe Ia）, the Baryonic Acoustic Oscillation （BAO） peak, and the Cosmic Microwave Background （CMB） shift parameter data, to constrain cosmological parameters in the Undulant Universe. We marginalize the like- lihood functions over h by integrating the probability density 19 ∝ e-x^2/2. By using a Markov Chain Monte Carlo （MCMC） technique, we obtain the best fitting results and give the confidence regions in the b - Ωm0 plane. Then we compare their constraints. Our results show that the H（z） data play a similar role with the SNe Ia data in cosmological study. By presenting the independent and joint constraints, we find that the BAO and CMB data play very important roles in breaking the degeneracy compared with the H（z） and SNe Ia data alone. Combined with the BAO or CMB data, one can remarkably improve the constraints. The SNe Ia data sets constrain Ωm0 much tighter than the H（z） data sets, but the H（z） data sets constrain b much tighter than the SNe Ia data sets. All these results show that the Undulant Universe approaches the ACDM model. We expect more H（z） data to constrain cosmological parameters in the future.

Comparison of differential evolution, particle swarm optimization,quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states

Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution(DE), particle swarm optimization(PSO), quantum-behaved particle swarm optimization(QPSO), and quantum evolutionary algorithm(QEA).We compare their control performance and point out their differences. By sampling and learning for uncertain quantum systems, the robustness of control pulses found by these four algorithms is also demonstrated and compared. The resulting research shows that the QPSO nearly outperforms the other three algorithms for all the performance criteria considered.This conclusion provides an important reference for solving complex quantum control problems by optimization algorithms and makes the QPSO be a powerful optimization tool.

Embryonic, genetic and clinical outcomes of fresh versus vitrified oocyte: A retrospective cohort study

Objective:To compare embryonic development,ploidy status and clinical outcomes between fresh and frozen-thawed oocytes.Methods:This retrospective cohort study evaluated 83 fertilization cycles including both fresh and frozen oocytes from 79 patients at the HP Fertility Center of Hai Phong International Hospital of Obstetrics and Pediatrics in Vietnam.The patient underwent several ovarian stimulation cycles to accumulate a certain number of oocytes that would be vitrified.In the last oocyte retrieval,all patient’s oocytes including both frozen and fresh would be fertilized.The outcomes included the rates of oocyte survival,cleavage embryo,blastocyst,ploidy status,pregnancy,biochemical pregnancy and clinical pregnancy.Results:The oocyte survival rate after thawing was 96.5%.No statistically significant difference was found when comparing fresh and frozen oocytes regarding fertilization rate(78.1%vs.75.5%,P=0.461),usable cleavage embryo rate(86.9%vs.87.2%,P=0.916)but usable blastocyst rate was found higher statistically in the frozen oocyte group(44.4%vs.54.0%,P=0.049).The percentages of euploid,aneuploid and mosaic embryos between the fresh group and the vitrified group had no significant differences(33.8%vs.31.6%,P=0.682;51.0%vs.54.2%,P=0.569;15.2%vs.12.4%,P=0.787;respectively).The rates of pregnancy,biochemical pregnancy and clinical pregnancy had no statistical difference(68.8%vs.64.8%,P=0.764;12.5%vs.3.6%,P=0.258;37.5%vs.46.4%,P=0.565).17 Mature oocytes are the minimum to have at least one euploid embryo.Conclusions:Oocyte vitrification does not affect embryonic,genetic and clinical results.The number of mature oocytes should be considered for fertilization in some cases.

Historical development of karst evergreen broadleaved forests in East Asia has shaped the evolution of a hemiparasitic genus Brandisia(Orobanchaceae)

Brandisia is a shrubby genus of about eight species distributed basically in East Asian evergreen broadleaved forests(EBLFs),with distribution centers in the karst regions of Yunnan,Guizhou,and Guangxi in southwestern China.Based on the hemiparasitic and more or less liana habits of this genus,we hypothesized that its evolution and distribution were shaped by the development of EBLFs there.To test our hypothesis,the most comprehensive phylogenies of Brandisia hitherto were constructed based on plastome and nuclear loci(nrDNA,PHYA and PHYB);then divergence time and ancestral areas were inferred using the combined nuclear loci dataset.Phylogenetic analyses reconfirmed that Brandisia is a member of Orobanchaceae,with unstable placements caused by nuclear-plastid incongruences.Within Brandisia,three major clades were well supported,corresponding to the three subgenera based on morphology.Brandisia was inferred to have originated in the early Oligocene(32.69 Mya)in the Eastern Himalayas-SW China,followed by diversification in the early Miocene(19.45 Mya)in karst EBLFs.The differentiation dates of Brandisia were consistent with the origin of keystone species of EBLFs in this region(e.g.,Fagaceae,Lauraceae,Theaceae,and Magnoliaceae)and the colonization of other characteristic groups(e.g.,Gesneriaceae and Mahonia).These findings indicate that the distribution and evolution of Brandisia were facilitated by the rise of the karst EBLFs in East Asia.In addition,the woody and parasitic habits,and pollination characteristics of Brandisia may also be the important factors affecting its speciation and dispersal.

Parameter Identification in Traveling Wave Solutions of a Modified Fisher’s Equation

In this work, we focus on the inverse problem of determining the parameters in a partial differential equation from given numerical solutions. For this purpose, we consider a modified Fisher’s equation that includes a relaxation time in relating the flux to the gradient of the density and an added cubic non-linearity. We show that such equations still possess traveling wave solutions by using standard methods for nonlinear dynamical systems in which fixed points in the phase plane are found and their stability characteristics are classified. A heteroclinic orbit in the phase plane connecting a saddle point to a node represents the traveling wave solution. We then design parameter estimation/discovery algorithms for this system including a few based on machine learning methods and compare their performance.

Dynamics and Equilibria of N Point Charges on a 2D Ellipse or a 3D Ellipsoid

We consider the so-called Thomson problem which refers to finding the equilibrium distribution of a finite number of mutually repelling point charges on the surface of a sphere, but for the case where the sphere is replaced by a spheroid or ellipsoid. To get started, we first consider the problem in two dimensions, with point charges on circles (for which the equilibrium distribution is intuitively obvious) and ellipses. We then generalize the approach to the three-dimensional case of an ellipsoid. The method we use is to begin with a random distribution of charges on the surface and allow each point charge to move tangentially to the surface due to the sum of all Coulomb forces it feels from the other charges. Deriving the proper equations of motion requires using a projection operator to project the total force on each point charge onto the tangent plane of the surface. The position vectors then evolve and find their final equilibrium distribution naturally. For the case of ellipses and ellipsoids or spheroids, we find that multiple distinct equilibria are possible for certain numbers of charges, depending on the starting conditions. We characterize these based on their total potential energies. Some of the equilibria found turn out to represent local minima in the potential energy landscape, while others represent the global minimum. We devise a method based on comparing the moment-of-inertia tensors of the final configurations to distinguish them from one another.

Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

Clustering is a crucial method for deciphering data structure and producing new information.Due to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize clustering for cognitive research.Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties.Noisy data can lead to incorrect object recognition and inference.This research aims to innovate a novel clustering approach,named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering(PNTS3FCM),to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set(PFS)and Neutrosophic Set(NS).Our contribution is to propose a new optimization model with four essential components:clustering,outlier removal,safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data.The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods,standard Picture fuzzy clustering(FC-PFS)and Confidence-weighted safe semi-supervised clustering(CS3FCM)on benchmark UCI datasets.The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.

A Novel Incremental Attribute Reduction Algorithm Based on Intuitionistic Fuzzy Partition Distance

Attribute reduction,also known as feature selection,for decision information systems is one of the most pivotal issues in machine learning and data mining.Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problemof attribute reduction.Unfortunately,the intuitionistic fuzzy sets based methods have not received much interest,while these methods are well-known as a very powerful approach to noisy decision tables,i.e.,data tables with the low initial classification accuracy.Therefore,this paper provides a novel incremental attribute reductionmethod to dealmore effectivelywith noisy decision tables,especially for highdimensional ones.In particular,we define a new reduct and then design an original attribute reduction method based on the distance measure between two intuitionistic fuzzy partitions.It should be noted that the intuitionistic fuzzypartitiondistance iswell-knownas aneffectivemeasure todetermine important attributes.More interestingly,an incremental formula is also developed to quickly compute the intuitionistic fuzzy partition distance in case when the decision table increases in the number of objects.This formula is then applied to construct an incremental attribute reduction algorithm for handling such dynamic tables.Besides,some experiments are conducted on real datasets to show that our method is far superior to the fuzzy rough set based methods in terms of the size of reduct and the classification accuracy.