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A grey box framework is applied to model ship maneuvering by using a reference model (RM) and a support vector machine (SVM) (RM-SVM). First, the nonlinear characteristics of the target ship are determined using the RM and the similarity rule. Then, the linear SVM adaptively fits the errors between acceleration variables of RM and target ship. Finally, the accelerations of the target ship are predicted using RM and linear SVM. The parameters of the RM are known and conveniently acquired, thus avoiding the modeling process. The SVM has the advantages of fast training, quick simulation, and no overfitting. Testing and validation are conducted using the ship model test data. The test case reveals the practicability of the RF-SVM based modeling method, while the validation cases confirm the generalization ability of the grey box framework.

Progressive cerebral infarctions increase mortality and functional disability through mechanisms which have yet to be completely understood. The goal of this study was to explore the dynamic changes of serum C-reactive protein (CRP), fibrinogen (FIB) and D-dimer (D-D) in order to better characterize progressive cerebral infarction.

Timber real estate investment trusts (REITs) are companies that own and manage timberland and generate revenue by harvesting and selling timber or other forest-related products. Due to their popularity with investors, timber REITs in the United States have attracted growing research interest in the recent decades. This necessitates a review of existing knowledge on the evolution of timber REITs and their financial performance over the years. In this review, we summarize the history and development of timber REITs, discuss the tax policies applicable to timber REIT growth and their implications. We also review past studies that focused on the financial performance of timber REITs and synthesize the methodologies used in those studies. At the end, we posit the possibility of consolidation waves in the timber industry and identify some opportunities for future research. This review can shed some new light on the evolution of public timber REITs and their financial performance. Key words: forest finance, real estate, timber industry, timberland investment, timber taxation. Les societes de gestion d'actifs forestiers (SGAF) sont des compagnies qui possedent et amenagent des territoires forestiers dans le but de generer des revenus grace a la vente de bois ou d'autres produits de la foret. En raison de leur popularite aupres des investisseurs, les SGAF aux Etats-Unis ont suscite un interet de recherche croissant au cours des dernieres decennies. Cela exige un recensement des connaissances existantes en lien avec l'evolution des SGAF et leur performance financiere au fil des annees. Dans cette revue, nous resumons l'historique et le developpement des SGAF, et discutons des politiques fiscales applicables a la croissance des SGAF et de leurs consequences. Nous passons egalement en revue les etudes anterieures axees sur la performance financiere des SGAF et nous synthetisons les methodologies utilisees dans ces etudes. En conclusion, nous avancons la possibilite qu'il y ait des vagues de consolidation dans l'industrie du bois. Nous presentons aussi les opportunites pour des recherches futures. Cette revue peut apporter un eclairage nouveau sur l'evolution des SGAF publiques et leur performance financiere. [Traduit par la Redaction] Mots-cles: finances forestieres, immobilier, industrie forestiere, investissement forestier, taxation du bois., 1. Introduction In the United States (US), forestland comprises about 310 million ha, representing 34% of the total land area in the country. Of which, about 208 million ha are [...]

As the nonlinear and coupling characteristics of autonomous underwater vehicles (AUVs) are the challenges for motion modeling, the nonparametric identification method is proposed based on dung beetle optimization (DBO) and deep temporal convolutional networks (DTCNs). First, the improved wavelet threshold is utilized to select the optimal threshold and wavelet basis functions, and the raw model test data are denoising. Second, the bidirectional temporal convolutional networks, the bidirectional gated recurrent unit, and the attention mechanism are used to achieve the nonlinear nonparametric model of the AUV motion. And the hyperparameters are optimized by the DBO. Finally, the lazy-search-based path planning and the line-of-sight-based path following control are used for the proposed AUV model. The simulation shows that the prediction accuracy of the DBO-DTCN is better than other artificial intelligence methods and mechanical models, and the path following of AUV is feasible. The methods proposed in this paper can provide an effective strategy for AUV modeling, searching, and rescue cruising. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to establish and validate a diagnostic nomogram for identifying false positives in the Xpert MTB/RIF (Xpert) for detection of rifampicin resistance (RIF-R). Patients and Methods: In this retrospective study, we collected basic patient characteristics and various clinical information from the electronic medical record database. Patients were randomly divided into training and validation groups in a 7:3 ratio. LASSO regression was used to screen variables and construct a diagnostic nomogram. The ROC curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results: A total of 384 patients were included in the study, with 268 and 116 patients in the training and validation cohorts, respectively. Finally, probe mutations and probe delay were identified as the independent influencing factors. Using the mutation of probe E as a reference, probes A or C (OR = 51.07, P< 0.001), probe D (OR = 7.48, P< 0.001), and multiple probes (OR = 4.42, P=0.029) were identified as factors influencing false positives in Xpert for detection of RIF-R. Taking probe delay ΔCT < 4 as a reference, ΔCT (4– 5.9) (OR = 17.06, P=0.005) and ΔCT (6– 7.9) (OR = 36.67, P< 0.001) were noted to be the factors influencing false positives in Xpert for detection of RIF-R. Based on these two variables, we constructed a diagnostic nomogram. The area under the curve of the nomogram model was 0.847 and 0.850 for the training and validation groups, respectively. The calibration curves were consistent. The DCA revealed that the model achieved the greatest net benefit when the threshold probability was set between 6% and 71% in the training cohort and 6% and 70% in the validation cohort. Conclusion: The nomogram constructed can identify false positives in Xpert for detection of RIF-R and provides basis for clinicians to formulate diagnosis and treatment plans. [ABSTRACT FROM AUTHOR]