MSADCN:Multi-Scale Attentional Densely Connected Network for Automated Bone Age Assessment

Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

Study on the Measures of Connectedness Between Herds

Pig breeding is generally conducted among many herds, so EBV comparisons across populationsare necessary. Genetic connectedness is required for reliable between-farm animal EBV comparisons.Five quantitative overall connectedness measures among populations have been proposed so far,coefficient of connectedness(γ*), coefficient of determination (CD) and overall indices ofprecision, connectedness rating, number of direct genetic links between subpopulations due tocommon sires and dams (GLt), and average genetic covariance (AGC) are reviewed and theirproperties are discussed in this paper. It is recommended to use AGC at present for measuringgenetic connectedness between herds.

Effect of Genetic Connectedness on the Selection Results of Breeding Pigs

Two pig populations were simulated with Monte Carlo method; each consisted of 5 boars and 50 sows per generation. Genetic connectedness between herds was established by randomly selecting 1 or 2 boars from one population to mate sows of the other population. Breeding pigs were selected within populations according to animal model BLUP. The benefits of genetic connectedness between herds were examined. The results showed that, the average coefficients of inbreeding decreased, while the cumulative selection responses of populations increased, and the higher response occurred randomly in the two populations at generation 5 with the increase of the genetic connectedness between herds. Selection response was affected by genetic connectedness and trait heritability, the lower heritability and higher connectedness, the better selection results. When the number of exchanged litters between populations per generation was 6 litters, the selection results reached a reflection point; if the number of exchanged litters between populations increased further from this point, neither the increase of the cumulative selection responses nor the decrease of coefficients of inbreeding was significant.

Fast interactive segmentation algorithm of image sequences based on relative fuzzy connectedness

A fast interactive segmentation algorithm of image-sequences based on relative fuzzy connectedness is presented. In comparison with the original algorithm, the proposed one, with the same accuracy, accelerates the segmentation speed by three times for single image. Meanwhile, this fast segmentation algorithm is extended from single object to multiple objects and from single-image to image-sequences. Thus the segmentation of multiple objects from complex hackground and batch segmentation of image-sequences can be achieved. In addition, a post-processing scheme is incorporated in this algorithm, which extracts smooth edge with one-pixel-width for each segmented object. The experimental results illustrate that the proposed algorithm can obtain the object regions of interest from medical image or image-sequences as well as man-made images quickly and reliably with only a little interaction.

Connectedness of Cone-efficient Solution Set for Cone-quasiconvex Multiobjective Programming in Hausdorff Topological Vector Spaces

这份报纸在局部地凸的 Hausdorff 拓扑的向量空格为向量优化处理锥有效的解决方案集合的 connectedness。锥有效的答案集合的 connectedness 为在选择的一个紧缩的凸的集合上印射的连续一对一的 cone-quasiconvex 定义的 multiobjective 编程被证明。在证明期间，概括僵绳定理起一个关键作用。

Dynamic connectedness between stock markets in the presence of the COVID‑19 pandemic:does economic policy uncertainty matter?

This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty(EPU)in eight countries where COVID-19 was most widespread(China,Italy,France,Germany,Spain,Russia,the US,and the UK)by implementing the time-varying VAR(TVP-VAR)model for daily data over the period spanning from 01/01/2015 to 05/18/2020.Results showed that stock markets were highly connected during the entire period,but the dynamic spillovers reached unprecedented heights during the COVID-19 pandemic in the first quarter of 2020.Moreover,we found that the European stock markets(except Italy)transmitted more spillovers to all other stock markets than they received,primarily during the COVID-19 outbreak.Further analysis using a nonlinear framework showed that the dynamic connectedness was more pronounced for negative than for positive returns.Also,findings showed that the direction of the EPU effect on net connectedness changed during the pandemic onset,indicating that information spillovers from a given market may signal either good or bad news for other markets,depending on the prevailing economic situation.These results have important implications for individual investors,portfolio managers,policymakers,investment banks,and central banks.

Liquidity connectedness in cryptocurrency market

We examine the dynamics of liquidity connectedness in the cryptocurrency market.We use the connectedness models of Diebold and Yilmaz(Int J Forecast 28(1):57–66,2012)and Baruník and Křehlík(J Financ Econom 16(2):271–296,2018)on a sample of six major cryptocurrencies,namely,Bitcoin(BTC),Litecoin(LTC),Ethereum(ETH),Ripple(XRP),Monero(XMR),and Dash.Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies,whereas BTC and LTC play a significant role in connectedness magnitude.A distinct liquidity cluster is observed for BTC,LTC,and XRP,and ETH,XMR,and Dash also form another distinct liquidity cluster.The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium-and long-run time horizons.In the short run,BTC,LTC,and XRP are the leading contributor to liquidity shocks,whereas,in the long run,ETH assumes this role.Compared with the medium term,a tight liquidity clustering is found in the short and long terms.The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time,pointing to the possible effect of rising demand and higher acceptability for this unique asset.Furthermore,more pronounced liquidity connectedness patterns are observed over the short and long run,reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time–frequency connectedness.

Time–frequency co-movement and risk connectedness among cryptocurrencies:new evidence from the higher-order moments before and during the COVID-19 pandemic

Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management.However,most existing research focuses on the lower-order moment nexus(i.e.the return and volatility interactions).For the first time,this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains.We combine the realized moment measures and wavelet coherence,and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach(Chatziantoniou et al.in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach.Technical report,University of Pretoria,Department of Economics,2021)using intraday high-frequency data.The empirical results demonstrate that the comovement of realized volatility between BTC and other cryp-tocurrencies is stronger than that of the realized skewness,realized kurtosis,and signed jump variation.The comovements among cryptocurrencies are both time-dependent and frequency-dependent.Besides the volatility spillovers,the risk spillovers of high-order moments and jumps are also significant,although their magnitudes vary with moments,making them moment-dependent as well and are lower than volatility connectedness.Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term(1–7 days).Furthermore,the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic.Several practical implications are drawn for crypto investors,portfolio managers,regulators,and policymakers in optimizing their investment and risk management tactics.

Importance of “connectedness” between herds for effective across herd genetic evaluation

“Connectedness” is an essential component of genetic evaluations. The degree of connectedness affects the accuracy of comparing estimated breeding values (EBVs) from one herd or contemporary group to the other. It can be measured through Connectedness Rating (CR) which is based on variances and covariance among the estimates of contemporary group effects. A computing algorithm and a computer program for estimating CR is available. The minimum required level of connectedness depends upon the size of the contemporary groups, the level of accuracy and the residual variance. About 48% CR is required to detect differences between EBVs that are greater than 20% of the standard deviation in the trait, for group sizes of about 100 animals. Higher levels are necessary for smaller group sizes and for more accurate comparisons. Breeders participating in a common genetic evaluation program should therefore exchange their superior genetics and possibly use some common testing facilities for meaningful estimates of breeding values. Maintaining a good connectedness level will make the genetic evaluation program more useful for selection of superior breeding animals and achieving faster rate of genetic progress.

Comparison of Estimated Genetic Connectedness among Three Herds of Chinese Holstein Cattle

Estimating genetic connectedness among herds is important for the accuracy of dairy cattle genetic evaluation. When selecting between animals raised in different herds, the accuracy of their genetic evaluations can be influenced by the degree of connectedness among these herds. In this study, two methods were used to measure genetic connectedness, CR （genetic connectedness rating ） and GLt （total number of direct genetic links between group）, among herds from Beijing, Shanghai, and Tianjin. Genetic connectedness between the herds from Beijing and Tianjin was 23.95%, between Beijing and Shanghai was 17. 10%, and between Shanghai and Tianjin it was 14.28%. Genetic connectedness between herds from Beijing and Tianjin was the highest and that between Shanghai and Tianjin was the lowest. The correlation coefficient for the two methods was 0. 808. Some suggestions for improved genetic evaluation of dairy cattle were also discussed.

A Note on the Connectedness of the Invertible Group of a Nest Algebra

The connectedness of the invertibles question for arbitrary nest has been reduced to the case of the lower triangular operators with respect to a fixed orthonormal basis en for n 1. For each f ∈ H∞, let Tf be the Toeplitz operator. In this paper we prove that Tf can be connected to the identity through a path in the invertible group of the lower triangular operators if f satisfies certain conditions.

Is There an Association between Social Connectedness, Social Identity, Alcohol Consumption and Mental Health among Young University Students?

Social connectedness has been identified as a protective factor for a range of health issues however the literature is not conclusive. The high prevalence of hazardous alcohol consumption and mental health problems among university students along with the potential for the university as a setting for health promotion prompted this study. The study aims to explore the association between levels of alcohol consumption, mental health, social connectedness and social identity among university students. Online data were collected from a random sample of university undergraduate students (n = 2506) aged 18 - 24 years old. Outcomes were measured using the Alcohol Use Disorders Identification Test (AUDIT), the Kessler Psychological Distress Scale, Social Connectedness Scale, Social Identity Scale and measures of paid employment and study (hours), and participation in sports and other clubs. The majority of students had consumed alcohol in the last 12 months (87%). Of these students 38% reported to drink at hazardous levels (AUDIT ≥ 8). When all factors were considered: gender, living arrangements, being a domestic student, hours spent at work, participation in university and community sport, higher levels of psychological distress, higher levels of social connectedness, and lower levels of social identity were significant predictors of hazardous alcohol consumption. The finding highlights the need for the inclusion of integrated, multi-strategy health promotion interventions on campus. Further exploration of the associations between social connectedness and social identity as influences of health behaviors will better inform the development of targeted strategies for specific groups.

Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.

On the Behavior of Connectedness Properties in Isotonic Spaces under Perfect Mappings

The topological study of connectedness is heavily geometric or visual. Connectedness and connectedness-like properties play an important role in most topological characterization theorems, as well as in the study of obstructions to the extension of functions. In this paper, the behaviour of these properties in the realm of closure spaces is investigated using the class of perfect mappings. A perfect mapping is a type of map under which the image generally inherits the properties of the mapped space. It turns out that the general behaviour of connectedness properties in topological spaces extends to the class of isotone space.

An efficient microreactor with continuous serially connected micromixers for the synthesis of superparamagnetic magnetite nanoparticles

A new microreactor with continuous serially connected micromixers(CSCM)was tailored for the coprecipitation process to synthesize Fe_(3)O_(4) nanoparticles.Numerical simulation reveals that the two types of CSCM microchannels(V-typed and U-typed)proposed in this work exhibited markedly better mixing performances than the Zigzag and capillary microchannels due to the promotion of Dean vortices.Complete mixing was achieved in the V-typed microchannel in 2.7 s at an inlet Reynolds number of 27.Fe_(3)O_(4) nanoparticles synthesized in a planar glass microreactor with the V-typed microchannel,possessing an average size of 9.3 nm and exhibiting superparamagnetism,had obviously better dispersity and uniformity and higher crystallinity than those obtained in the capillary microreactor.The new CSCM microreactor developed in this work can act as a potent device to intensify the synthesis of similar inorganic nanoparticles via multistep chemical precipitation processes.

Campus Connectedness and Its Relationship to Stress, Anxiety and Depression among Nepalese Undergraduate Nursing Students

Background: Campus connectedness is a kind of social connectedness that determines the students’ perception of their belonging to the other populace of the campus that includes students, teachers, administrators, and other staff. Campus connectedness plays a significant role in determining stress, anxiety, and depression among students. The purpose of the study was to examine the relationship of campus connectedness to stress, anxiety, and depression among nursing students. Methods: This cross-sectional analytical study was conducted among 680 undergraduate nursing students from nine nursing colleges affiliated with a university in Kathmandu Valley. Data were collected from June 1, 2018, through July 10, 2018. Depression, Anxiety, Stress Scale and Campus Connectedness Scale were used to collect data. The data were analyzed using Statistical Package for Social Sciences version 23. A one-way multivariate analysis of variance (MANOVA) test was used to examine the relationship of campus connectedness to stress, anxiety, and depression. Discriminant analysis was done as a follow-up procedure to MANOVA. Results: Students had moderate to extremely severe levels of depression (51.7%), anxiety (72.9%) and stress (47%). High campus connectedness score was observed (M = 62.42;SD = 9.79). Statistically significant differences were found among the levels of campus connectedness on the outcome variables, Wilks’ lambda (λ) = 0.90, F(3, 676) = 24.56, p < 0.001. Depression demonstrated the strongest relationship with the discriminant function. Conclusions: There was a significant relationship of campus connectedness to stress, anxiety, and depression. Despite the high level of campus connectedness, students had moderate to severe levels of stress, anxiety, and depression. The campus adminstration must take measures and efforts to provide conducive environment and counseling services for the wellbeing of their students.

Computing Connected Resolvability of Graphs Using Binary Enhanced Harris Hawks Optimization

In this paper,we consider the NP-hard problem offinding the minimum connected resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the ver-tices in B.A resolving set B of G is connected if the subgraph B induced by B is a nontrivial connected subgraph of G.The cardinality of the minimal resolving set is the metric dimension of G and the cardinality of minimum connected resolving set is the connected metric dimension of G.The problem is solved heuristically by a binary version of an enhanced Harris Hawk Optimization(BEHHO)algorithm.This is thefirst attempt to determine the connected resolving set heuristically.BEHHO combines classical HHO with opposition-based learning,chaotic local search and is equipped with an S-shaped transfer function to convert the contin-uous variable into a binary one.The hawks of BEHHO are binary encoded and are used to represent which one of the vertices of a graph belongs to the connected resolving set.The feasibility is enforced by repairing hawks such that an addi-tional node selected from V\B is added to B up to obtain the connected resolving set.The proposed BEHHO algorithm is compared to binary Harris Hawk Optimi-zation(BHHO),binary opposition-based learning Harris Hawk Optimization(BOHHO),binary chaotic local search Harris Hawk Optimization(BCHHO)algorithms.Computational results confirm the superiority of the BEHHO for determining connected metric dimension.

The Connectedness of Quantales

connectedness 并且局部地的概念 connectedness 为右边的幂等 quantales 被介绍。连接 quantale 的一些性质被学习，然后连接 quantale 的相等的描述也被给。