Violating the normality assumption is possible in an analysis of longitudinal data characterized by skewness and multiple modes. To model the random effects in simplex mixed-effects models, we use the centered Dirichlet process mixture model (CDPMM) in this paper. genetic code We leverage the block Gibbs sampler and the Metropolis-Hastings algorithm to expand the Bayesian Lasso (BLasso), enabling simultaneous estimation of target parameters and selection of important covariates exhibiting nonzero effects within semiparametric simplex mixed-effects models. To showcase the proposed methodologies, both simulated scenarios and a real-world example are presented and analyzed.
Edge computing, an emerging paradigm in computing, substantially amplifies the collaborative potential within server networks. By drawing upon the resources available around users, the system promptly addresses task requests from the terminal devices. Task offloading is a frequently employed solution for optimizing task execution performance within edge networks. However, the specific features of edge networks, notably the random access by mobile devices, generate unforeseen complexities for offloading tasks in a mobile edge network. This paper details a trajectory prediction model for moving targets in edge networks, independently of historical user paths representing habitual movement patterns. Our proposed mobility-aware task offloading strategy leverages a trajectory prediction model and parallel task execution mechanisms for parallelization. Our edge network experiments, utilizing the EUA dataset, gauged the prediction model's hit ratio, network bandwidth, and task execution efficiency. Empirical findings demonstrated that our model significantly outperforms random, non-position-based parallel and non-parallel strategy-driven position prediction. At speeds below 1296 meters per second, the task offloading hit rate, closely tied to user movement speed, usually surpasses the 80% mark. In parallel, the bandwidth usage is markedly connected to the degree of parallel processing tasks and the count of services running on the network's servers. Parallel network operations exhibit substantial bandwidth optimization, surpassing non-parallel designs by over eight times as the number of concurrent processes increases.
In order to predict missing links in networks, classical link prediction techniques primarily make use of node information and the network's structural features. Yet, the challenge of accessing vertex information in real-world networks, exemplified by social networks, persists. Consequently, link prediction methods rooted in topological structures are commonly heuristic, predominantly considering shared neighbors, node degrees, and paths, ultimately failing to encapsulate the entire topological context. Network embedding models have proven efficient in link prediction over recent years, but this efficiency unfortunately comes at the cost of interpretability. To solve these issues, this paper introduces a novel link prediction methodology dependent on an optimized vertex collocation profile (OVCP). The topology of vertices was first represented by proposing the 7-subgraph topology. Following this, OVCP uniquely addresses any 7-node subgraph, resulting in the generation of interpretable feature vectors for the associated vertices. A classification model employing OVCP features was used to predict links, and then the network was divided into multiple, smaller communities by the overlapping community detection algorithm, resulting in a substantial reduction in the complexity of our proposed method. Results from the experiments reveal that the proposed method exhibits a highly promising performance against traditional link prediction methods, and its interpretability surpasses that of network-embedding-based techniques.
Long-block-length, rate-compatible low-density parity-check (LDPC) codes are fundamentally conceived to effectively address the substantial inconsistencies in quantum channel noise and exceptionally low signal-to-noise ratios observed within the realm of continuous-variable quantum key distribution (CV-QKD). Hardware and secret key resources are inevitably taxed when implementing rate-compatible methods for CV-QKD. Employing a single check matrix, we propose a design standard for rate-compatible LDPC codes that accounts for the full range of SNRs. We achieve high reconciliation efficiency (91.8%) in continuous-variable quantum key distribution information reconciliation, facilitated by this extended block length LDPC code, with improvements in hardware processing speed and frame error rate reduction compared to other existing schemes. A remarkable practical secret key rate and a long transmission distance can be attained using our proposed LDPC code, especially in an extremely unstable communication channel.
Significant attention has been given to machine learning techniques in financial fields, driven by the progress in quantitative finance and attracting researchers, investors, and traders alike. Nevertheless, the study of stock index spot-futures arbitrage remains relatively underdeveloped in its research efforts. Furthermore, existing work predominantly takes a retrospective approach, neglecting the anticipatory identification of arbitrage possibilities. Using machine learning models trained on historical high-frequency data, this research anticipates arbitrage opportunities in spot and futures contracts for the China Security Index (CSI) 300, thereby addressing the existing disparity. Using econometric models, the existence of spot-futures arbitrage opportunities is determined. Exchange-Traded-Fund (ETF) portfolios are designed to react in sync with the CSI 300 index, resulting in minimal tracking error. A derived strategy, consisting of non-arbitrage intervals and timing indicators for unwinding positions, proved profitable in a backtesting environment. HLA-mediated immunity mutations To predict the acquired indicator in forecasting, four machine learning approaches are employed: Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Long Short-Term Memory neural network (LSTM). Each algorithm's performance is scrutinized and compared across two different measurements. Assessing error involves analyzing the Root-Mean-Squared Error (RMSE), the Mean Absolute Percentage Error (MAPE), and the measure of fit, denoted by R-squared. Yet another metric for return is a function of the trade's yield and the number of arbitrage opportunities identified and capitalized upon. To complete the analysis, the performance heterogeneity of bull and bear markets is scrutinized. The LSTM algorithm's performance, measured over the entire time period, demonstrates the strongest results among all algorithms, showing an RMSE of 0.000813, a MAPE of 0.70%, an R-squared of 92.09%, and an arbitrage return of 58.18%. In the context of varied market scenarios, specifically during both bull and bear phases, though abbreviated, LASSO frequently outperforms.
A thermodynamic analysis, coupled with Large Eddy Simulation (LES), was conducted on the components of an Organic Rankine Cycle (ORC), including the boiler, evaporator, turbine, pump, and condenser. learn more To heat the butane evaporator, the petroleum coke burner provided the necessary heat flux. In the organic Rankine cycle (ORC), the high-boiling-point fluid phenyl-naphthalene finds practical application. The safety of heating the butane stream is enhanced by the use of a high-boiling liquid, which helps prevent steam explosions from occurring. The exergy efficiency of this is unparalleled. Featuring non-corrosive properties, and highly stable, and flammable, this material exhibits the following traits. To model pet-coke combustion and compute the Heat Release Rate (HRR), Fire Dynamics Simulator (FDS) software was employed. The boiler houses 2-Phenylnaphthalene with a maximal temperature drastically less than its boiling point of 600 Kelvin. Using the THERMOPTIM thermodynamic code, the enthalpy, entropy, and specific volume needed to calculate heat rates and power output were determined. The enhanced safety of the proposed ORC design is noteworthy. The petroleum coke burner's flame and the separated flammable butane contribute to this outcome. The suggested ORC system conforms to the two foundational laws of thermodynamics. Calculations reveal a net power output of 3260 kW. Our findings regarding net power are well-supported by the established data in the literature. An impressive 180% thermal efficiency is exhibited by the ORC.
Regarding the finite-time synchronization (FNTS) problem, a class of delayed fractional-order fully complex-valued dynamic networks (FFCDNs), featuring internal delay and both non-delayed and delayed couplings, is analyzed by directly constructing Lyapunov functions, a method distinct from the conventional decomposition into real-valued networks. For the first time, a complex-valued mixed-delay fractional-order mathematical model is established, where the external coupling matrices are unrestricted in terms of identity, symmetry, or irreducibility. To address the restricted scope of a single controller, two delay-dependent controllers are created, each built upon a distinct norm. One leverages the complex-valued quadratic norm, while the other is formulated from the absolute values of the real and imaginary parts, thereby bolstering synchronization control efficiency. In addition, the analyses of the relationships between the fractional order of the system, the fractional-order power law, and the settling time (ST) are performed. The proposed control method's performance and applicability are evaluated through numerical simulation.
A method for extracting composite-fault signal features, operating under low signal-to-noise ratios and intricate noise patterns, is presented. This method leverages phase-space reconstruction and maximum correlation Renyi entropy deconvolution. Employing Rényi entropy as the performance metric, facilitating an advantageous balance between resistance to intermittent noise and sensitivity to faults, the noise reduction and decomposition attributes of singular value decomposition are leveraged and integrated into the feature extraction process of composite fault signals via maximum correlation Rényi entropy deconvolution.