Dynamic microphysiological system chip platform for high-throughput,customizable,and multi-dimensional drug screening

Spheroids and organoids have attracted significant attention as innovative models for disease modeling and drug screening.By employing diverse types of spheroids or organoids,it is feasible to establish microphysiological systems that enhance the precision of disease modeling and offer more dependable and comprehensive drug screening.High-throughput microphysiological systems that support optional,parallel testing of multiple drugs have promising applications in personalized medical treatment and drug research.However,establishing such a system is highly challenging and requires a multidisciplinary approach.This study introduces a dynamic Microphysiological System Chip Platform(MSCP)with multiple functional microstructures that encompass the mentioned advantages.We developed a high-throughput lung cancer spheroids model and an intestine-liverheart-lung cancer microphysiological system for conducting parallel testing on four anti-lung cancer drugs,demonstrating the feasibility of the MSCP.This microphysiological system combines microscale and macroscale biomimetics to enable a comprehensive assessment of drug efficacy and side effects.Moreover,the microphysiological system enables evaluation of the real pharmacological effect of drug molecules reaching the target lesion after absorption by normal organs through fluid-based physiological communication.The MSCP could serves as a valuable platform for microphysiological system research,making significant contributions to disease modeling,drug development,and personalized medical treatment.

Effect of preload forces on multidimensional signal dynamic behaviours for battery early safety warning

Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three characteristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4°C,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module validation.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.

Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks

Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.

Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates

This paper investigates the problem of collecting multidimensional data throughout time(i.e.,longitudinal studies)for the fundamental task of frequency estimation under Local Differential Privacy(LDP)guarantees.Contrary to frequency estimation of a single attribute,the multidimensional aspect demands particular attention to the privacy budget.Besides,when collecting user statistics longitudinally,privacy progressively degrades.Indeed,the“multiple”settings in combination(i.e.,many attributes and several collections throughout time)impose several challenges,for which this paper proposes the first solution for frequency estimates under LDP.To tackle these issues,we extend the analysis of three state-of-the-art LDP protocols(Generalized Randomized Response–GRR,Optimized Unary Encoding–OUE,and Symmetric Unary Encoding–SUE)for both longitudinal and multidimensional data collections.While the known literature uses OUE and SUE for two rounds of sanitization(a.k.a.memoization),i.e.,L-OUE and L-SUE,respectively,we analytically and experimentally show that starting with OUE and then with SUE provides higher data utility(i.e.,L-OSUE).Also,for attributes with small domain sizes,we propose Longitudinal GRR(L-GRR),which provides higher utility than the other protocols based on unary encoding.Last,we also propose a new solution named Adaptive LDP for LOngitudinal and Multidimensional FREquency Estimates(ALLOMFREE),which randomly samples a single attribute to be sent with the whole privacy budget and adaptively selects the optimal protocol,i.e.,either L-GRR or L-OSUE.As shown in the results,ALLOMFREE consistently and considerably outperforms the state-of-the-art L-SUE and L-OUE protocols in the quality of the frequency estimates.

The Static Stability Region of an Integrated Electricity-Gas System Considering Voltage and Gas Pressure

In an integrated electricity-gas system(IEGS),load fluctuations affect not only the voltage in the power system but also the gas pressure in the natural gas system.The static voltage stability region(SVSR)method is a tool for analyzing the overall static voltage stability in a power system.However,in an IEGS,the SVSR boundary may be overly optimistic because the gas pressure may collapse before the voltage collapses.Thus,the SVSR method cannot be directly applied to an IEGS.In this paper,the concept of the SVSR is extended to the IEGS-static stability region(SSR)while considering voltage and gas pressure.First,criteria for static gas pressure stability in a natural gas system are proposed,based on the static voltage stability criteria in a power system.Then,the IEGS-SSR is defined as a set of active power injections that satisfies multi-energy flow(MEF)equations and static voltage and gas pressure stability constraints in the active power injection space of natural gas-fired generator units(NGUs).To determine the IEGSSSR,a continuation MEF(CMEF)method is employed to trace the boundary point in one specific NGU scheduling direction.A multidimensional hyperplane sampling method is also proposed to sample the NGU scheduling directions evenly.The obtained boundary points are further used to form the IEGSSSR in three-dimensional(3D)space via a Delaunay triangulation hypersurface fitting method.Finally,the numerical results of typical case studies are presented to demonstrate that the proposed method can effectively form the IEGS-SSR,providing a tool for IEGS online monitoring and dispatching.

Multivariate form of Hermite sampling series

In this paper,we establish a new multivariate Hermite sampling series involving samples from the function itself and its mixed and non-mixed partial derivatives of arbitrary order.This multivariate form of Hermite sampling will be valid for some classes of multivariate entire functions,satisfying certain growth conditions.We will show that many known results included in Commun Korean Math Soc,2002,17:731-740,Turk J Math,2017,41:387-403 and Filomat,2020,34:3339-3347 are special cases of our results.Moreover,we estimate the truncation error of this sampling based on localized sampling without decay assumption.Illustrative examples are also presented.

RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

AAK Theorem and a Design of Multidimensional BIBO Stable Filters

Rational approximation theory occupies a significant place in signal processing and systems theory. This research paper proposes an optimal design of BIBO stable multidimensional Infinite Impulse Response filters with a realizable (rational) transfer function thanks to the Adamjan, Arov and Krein (AAK) theorem. It is well known that the one dimensional AAK results give the best approximation of a polynomial as a rational function in the Hankel semi norm. We suppose that the Hankel matrix associated to the transfer function has a finite rank.

iDEAL-CIO: Instant Digital Express Advocated “Magic Lamp” for Cloud Intelligence Outlet

Instant Digital Express iDEAL-CIO The “Magic Lamp” for Cloud Intelligence Outlet, which has been recommended, combines innovations to satisfy modern users’ needs and efficiently sift through the ever-expanding amount of intelligent content stored in the cloud. One such innovation introduces a ground-breaking concept to remove superfluous and outdated sequential search patterns that overwhelm the user and computer in order to better serve the user in an eclectic & elastic and multidimensional approach to finding, grouping, assimilation, organizing, and delivering archival content. The cloud intelligence outlet (CIO) is presented in this article as the perfect magic lamp option for quick digital express advocacy. The grouping, indexing, folding, and targeting (GIFT) method of multidimensional online synthetic/analytical intelligent content (MOSAIC) for adaptive intelligence is the fundamental intelligent aggregation and automated process of the Magic Lamp. Three perspectives above this new ideal framework are available to observe: The Magic Lamp proposes contextual and multiple analytical tracks to improve cloud intelligence services conceptually. Technically speaking, MOSAIC combines domain-specific services for a wide range of international users, and through the usage of Cloud Intelligence Outlet, GIFT operationally activates grouping, indexing, folding, and targeting to promote decent experience and in-depth research on target for users’ wants. Because of this, iDEAL-CIO works in tandem with cloud extraction, digital transformation, and archival loading to provide improved service through the readily accessible cloud intelligence outlet.

The Role and Place of Artificial Neural Network Architectures Structural Redundancy in the Input Data Prototypes and Generalization Development

Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.

Multidimensional Perspectives on the Teaching and Practice of Typography Design

With the deepening of the curriculum teaching reform in colleges and universities,the teaching reform of art design professional courses is imperative.The study found that the current art design professional font design course teaching there are more solidified teaching methods,students operating ability is relatively homogenised,the teaching content to the"westernised"based,resulting in poor adaptability of students and other prominent problems.This paper starts from the"three effects"of"effect","efficiency"and"effectiveness",and adopts a multi-dimensional and emotional thinking perspective to carry out the curriculum of typeface design.This paper starts from the"three effects"of"effect","efficiency"and"effectiveness",and adopts a multi-dimensional and emotional perspective of thinking to carry out the teaching and practice of typeface design in order to strengthen the visual information of Chinese culture,and at the same time,to explore the creative potential of the students,so as to improve the students'innovation and creativity.

A Corpus-Based Comparative Multidimensional Analysis of the Two English Translations of Luoyang Jialan Ji

By using the MD approach, this study aims at making a comparison between the lexical features in W. J. F. Jenner’s and Wang Yitong’s translations of Luoyang Jialan Ji (547 AD) from a multidimensional perspective. Statistics show that both the two translations belong to the General Narrative Exposition Register. However, there are significant discrepancies between the two versions in Dimension 1 “Involved versus Informational Production”, Dimension 4 “Overt Expression of Persuasion” and Dimension 5 “Abstract versus Non-Abstract Information”. The differences in Dimension 2 “Narrative versus Non-Narrative Concerns” and Dimension 3 “Explicit versus Situation-Dependent Reference” are not that significant. It is found that in Dimension 1, the information density of Wang’s version is much higher than that of Jenner’s. In Dimension 4, Wang’s version contains more overt persuasive effort than Jenner’s. In Dimension 5, the positive and negative contrast between the two versions indicates that Wang’s version provides information in a more abstract way, whereas Jenner’s in a relatively non-abstract way. Combined with typical examples in the two translation versions, this study is hoped to help better understand the translations of Luoyang Jialan Ji and conduct future studies on the translation styles of the two translators.

Interactive Trajectory Star Coordinates i-tStar and Its Extension i-tStar(3D)

There are many sources of geographic big data,and most of them come from heterogeneous environments.The data sources obtained in this case contain attribute information of different spatial scales,different time scales and different complexity levels.It is worth noting that the emergence of new high-dimensional trajectory data types and the increasing number of details are becoming more difficult.In this case,visualizing high-dimensional spatiotemporal trajectory data is extremely challenging.Therefore,i-tStar and its extension i-tStar(3D)proposed,a trajectory behavior feature formoving objects that are integrated into a view with less effort to display and extract spatiotemporal conditions,and evaluate our approach through case studies of an open-pit mine truck dataset.The experimental results show that this method is easier to mine the interaction behavior of multi-attribute trajectory data and the correlation and influence of various indicators of moving objects.

Multidimensional defects tailoring local electron and Mg^(2+) diffusion channels for boosting magnesium storage performance of WO_(3)/MoO_(2)

Defect engineering presents great promise in addressing lower specific capacity,sluggish diffusion kinetics and poor cycling life issues in energy storage devices.Herein,multidimensional(0D/2D/3D) structural defects are constructed in WO_(3)/MoO_(2) simultaneously via competing for and sharing with O atoms during simple hydrothermal process.OD and 2D defects tailor local electron,activating more sites and generating built-in electric fields to yield ion reservoir,meanwhile,3D defect owning lower anisotropic property tailors Mg^(2+) diffusion channels to fully exploit Mg^(2+) adsorbed sites induced by OD and 2D defects,enhance the kinetics and maintain structural stability.Benefitted from synergistic effect of 0D/2D/3D structural defects,the designed WO_(3)/MoO_(2) shows the higher specific capacity(112.8 mA h g^(-1) at 50 mA g^(-1) with average attenuation rate per cycle of 0.068%),superior rate capability and excellent cycling stability(specific capacity retention of 80% after 1500 cycles at 1000 mA g^(-1)).This strategy provides design ideas of introducing multidimensional structural defects for tailoring local electron and microstructure to improve energy storage property.

Mapping species assemblages of tropical forests at different hierarchical levels based on multivariate regression trees

Background: Vegetation distribution maps are of great significance for nature protection and management. In diverse tropical forests, accurate spatial mapping of vegetation types is challenging;the high species diversity and abundance of rare species challenge classification concepts, while remote sensing signals may not vary systematically with species composition, complicating the technical capability for delineating vegetation types in the landscape.Methods: We used a combination of field-based compositional data and their relations to environmental variables to predict the distribution of forest types in the Wuzhishan National Natural Reserve(WNNR), Hainan Island,China, using multivariate regression trees(MRT). The MRT was based on arboreal vegetation composition in 132plots of 20 m×20 m with a regular spacing of 1 km. Apart from the MRT, non-metric multidimensional scaling(NMDS) was used to evaluate vegetation-environment relationships.Results: The MRT model worked best when using 14 key environmental variables including topography, climate,latitude and soil, although the difference with the simpler model including only topographical variables was small. The full model classified the 132 plots into 3 vegetation types, 6 formation groups, 20 formations and 65associations at different hierarchical syntaxonomic levels. This model was the basis for forest vegetation maps for the WNNR. MRT and NMDS showed that elevation was the main driving force for the distribution of vegetation types and formation groups. Climate, latitude, and soil(especially available P), together with topographic variables, all influenced the distribution of formations and associations.Conclusions: While elevation determines forest-type distributions, lower-level syntaxonomic forest classes respond to the topographic diversity typical for mountains. Apart from providing the first detailed forest vegetation map for any part of WNNR, we show how, in spite of limitations, MRT with existing environmental data can be a useful method for mapping diverse and remote tropical forests.

Soil water content and nitrogen differentially correlate with multidimensional leaf traits of two temperate broadleaf species

The variation and correlation of leaf economics and vein traits are crucial for predicting plant ecological strategies under different environmental changes.However,correlations between these two suites of traits and abiotic factors such as soil water and nitrogen content remain ambiguous.We measured leaf economics and vein traits as well as soil water and nitrogen content for two different shade-tolerant species(Betula platyphylla and Acer mono)in four mixed broadleaved-Korean pine(Pinus koraiensis)forests along a latitudinal gradient in Northeast China.We found that leaf economics traits and vein traits were decoupled in shade-intolerant species,Betula platphylla,but significantly coupled in a shadetolerant species,A.mono.We found stronger correlations among leaf traits in the shade tolerant species than in the shade intolerant species.Furthermore,leaf economic traits were positively correlated with the soil water gradient for both species,whereas vein traits were positively correlated with soil water gradient for the shade intolerant species but negatively correlated in the shade tolerant species.Although economic traits were positively correlated with soil nitrogen gradient in shade intolerant species but not correlated in shade tolerant species,vein traits were negatively correlated with soil nitrogen gradient in shade tolerant species but not correlated in shade intolerant species.Our study provides evidence for distinct correlations between leaf economics and vein traits and local abiotic factors of species differing in light demands.We recommend that the ecological significance of shade tolerance be considered for species when evaluating ecosystem functions and predicting plant responses to environmental changes.

Dimensionality reduction model based on integer planning for the analysis of key indicators affecting life expectancy

Purpose:Exploring a dimensionality reduction model that can adeptly eliminate outliers and select the appropriate number of clusters is of profound theoretical and practical importance.Additionally,the interpretability of these models presents a persistent challenge.Design/methodology/approach:This paper proposes two innovative dimensionality reduction models based on integer programming(DRMBIP).These models assess compactness through the correlation of each indicator with its class center,while separation is evaluated by the correlation between different class centers.In contrast to DRMBIP-p,the DRMBIP-v considers the threshold parameter as a variable aiming to optimally balances both compactness and separation.Findings:This study,getting data from the Global Health Observatory(GHO),investigates 141 indicators that influence life expectancy.The findings reveal that DRMBIP-p effectively reduces the dimensionality of data,ensuring compactness.It also maintains compatibility with other models.Additionally,DRMBIP-v finds the optimal result,showing exceptional separation.Visualization of the results reveals that all classes have a high compactness.Research limitations:The DRMBIP-p requires the input of the correlation threshold parameter,which plays a pivotal role in the effectiveness of the final dimensionality reduction results.In the DRMBIP-v,modifying the threshold parameter to variable potentially emphasizes either separation or compactness.This necessitates an artificial adjustment to the overflow component within the objective function.Practical implications:The DRMBIP presented in this paper is adept at uncovering the primary geometric structures within high-dimensional indicators.Validated by life expectancy data,this paper demonstrates potential to assist data miners with the reduction of data dimensions.Originality/value:To our knowledge,this is the first time that integer programming has been used to build a dimensionality reduction model with indicator filtering.It not only has applications in life expectancy,but also has obvious advantages in data mining work that requires precise class centers.

Two-Dimensional Riemann Problems:Transonic Shock Waves and Free Boundary Problems

We are concerned with global solutions of multidimensional(M-D)Riemann problems for nonlinear hyperbolic systems of conservation laws,focusing on their global configurations and structures.We present some recent developments in the rigorous analysis of two-dimensional(2-D)Riemann problems involving transonic shock waves through several prototypes of hyperbolic systems of conservation laws and discuss some further M-D Riemann problems and related problems for nonlinear partial differential equations.In particular,we present four different 2-D Riemann problems through these prototypes of hyperbolic systems and show how these Riemann problems can be reformulated/solved as free boundary problems with transonic shock waves as free boundaries for the corresponding nonlinear conservation laws of mixed elliptic-hyperbolic type and related nonlinear partial differential equations.

Psychometric Properties among Japanese Women Based on the Multidimensional Assessment of Women’s Experience of Childbirth

Aims/Background: Perceived experiences of childbirth are important for mothers as they affect their self-esteem, mother-child interactions, subsequent mother-child bonding, and their desire for another child. This study examined the factor structure and construct validity of Salmon and Drew’s Multidimensional Assessment of Women’s Experience of Childbirth (MAWEC) in Japanese. Design/Methods: This study conducted a questionnaire survey using the Japanese version of the MAWEC and other variables with 759 women with infants in Japan. The participants’ mean (standard deviation [SD], range) age was 31.9 (5.3, 18 - 46) years. A total of 353 (46.5%) participants were primiparous and 384 (50.6%) were multiparous, and 22 (2.9%) did not answer. The data were randomly divided into two groups for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) and separately analyzed the positive- and negative-worded items. I conducted EFA using the maximum-likelihood method with PROMAX rotation from one-through two- and three-factor structures. Different factor structure models were compared in terms of CFAs using, as indicator of goodness-of-fit, chi-square/df, comparative fit index (CFI), and root mean square of error approximation (RMSEA). Results: This study showed that a bifactor model for the MAWEC with four subscales (positive emotion, positive coping, negative emotion, and negative coping) fit the data the best. These four subscales were associated with demographic, obstetric, neonatal, and feeding characteristics. For example, primiparas were more vulnerable to coping aspects of perceived childbirth experiences, including sense of mastery over the delivery process and ability to enjoy the delivery. Gestational age at birth was associated with emotional aspects of perceived childbirth experiences. Conclusion: The Japanese version of the MAWEC consists of four aspects regarding perceived childbirth experience. Perinatal health professionals may examine women’s childbirth experiences from these four perspectives. .

On the Role of Security in the System of Personal Values:Are Conservatives Prioritizing Security?

Security is a value contained in most theories of personal values.Yet,while the relations among the most basic values are clear and reliable,the role of security in the system of values remains ambiguous.People striving for security are often also striving for tradition and conformity but sometimes they are focusing more on other values(such as health values,for example).Based on eight representative surveys(N=24,000)in several German cities between 1998 and 2022,the author shows that when measuring security without suggesting a particular meaning of this notion,security takes a relatively central position within the system of values and their components(shown by multidimensional scaling,MDS).People striving for security also emphasize the importance of law and order,working hard,and having a good family life as guiding principles in their lives.Conformity is not that important for them,and having an exciting life is even negatively correlated.Age has little impact on the MDS structure of values and their components,even though people exhibit substantial changes in the relative weights they assign to many values as they get older.