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BACKGROUND Tumour size and extrathyroidal extension (ETE) may impact papillary thyroid carcinoma (PTC) outcomes. We therefore examined the prognostic value of tumour size and ETE for predicting posttreatment recurrence in PTC patients. METHODS A total of 2,902 patients who underwent thyroidectomy for previously untreated T1-T3 PTC (7th edition American Joint Committee on Cancer) at our tertiary referral center were included. Univariate and multivariate Cox proportional hazard regression analyses were used to determine significant factors predictive of posttreatment recurrence-free survival (RFS). RESULTS In univariate analysis, tumour factors (including tumour size, multifocality, ETE, and lymphovascular invasion), nodal factors (including positive lymph node number, lymph node ratio, and extranodal extension), and MACIS (metastases, age, completeness of resection, invasion, and size) scores were significantly associated with RFS outcomes (P 4 cm (P  less then  0.001) and multifocality (P = 0.038) were the independent factors of RFS. Nodal factors and MACIS scores were also independent factors of RFS. CONCLUSION Tumour size impacts RFS after thyroidectomy in T1-T3 PTC patients. Wild cattle species, often considered less alluring than certain conservation-dependent species, have not attracted the same level of interest as the charismatic megafauna from the general public, private or corporate donors, and other funding agencies. Currently, most wild cattle populations are vulnerable or threatened with extinction. The implementation of reproductive technologies to maintain genetically healthy cattle populations in situ and ex situ has been considered for more than 30 years. Protocols developed for domestic cattle breeds have been used with some success in various wild cattle species. However, inherent differences in the natural life history of these species makes extrapolation of domestic cattle protocols difficult, and in some cases, minimally effective. Reproductive seasonality, driven by either photoperiod or nutritional resource availability, has significant influence on the success of assisted reproductive technologies (ARTs). This review focuses on the physiological processes that differ in breeding (ovulatory) and non-breeding (anovulatory) seasons in female cattle, and the potential methods used to overcome these challenges. Techniques to be discussed within the context of seasonality include estrus synchronization and ovulation induction, ovarian superstimulation, artificial insemination (AI), multiple ovulation embryo transfer (MOET), and ovum pick-up (OPU) with in vitro fertilization (IVF) and embryo transfer (ET). Pregnant women frequently take prescription and over the counter medications. The efficacy of medications is affected by the many physiological changes during pregnancy, and these events may be further impacted by genetic factors. Research on pharmacogenomic and pharmacokinetic influences on drug disposition during pregnancy has lagged behind other fields. Clinical investigators have demonstrated altered activity of several drug metabolizing enzymes during pregnancy. Emerging evidence also supports the influence of pharmacogenomic variability in drug response for many important classes of drugs commonly used in pregnancy. Prescribing medications during pregnancy requires an understanding of the substantial dynamic physiologic and metabolic changes that occur during gestation. Pharmacogenomics also contributes to the inter-individual variability in response to many medications, and more research is needed to understand how best to manage drug therapy in pregnant women. This paper deals with the general decay synchronization (GDS) and general decay H∞ synchronization (GDHS) problems for spatial diffusion coupled delayed reaction-diffusion neural networks (SDCDRDNNs) without and with uncertain parameters respectively. First, based on the ψ-type stability and ψ-type function, the concept of GDS is generalized to include general robust decay synchronization (GRDS) and GDHS. Then, by exploiting a nonlinear controller and different types of inequality techniques, some verifiably sufficient conditions ensuring the GDS and GDHS of SDCDRDNNs (without and with uncertain parameters) are derived. Finally, two simulative examples are provided to demonstrate the validity of the synchronization results obtained. In the article, several concise and efficient iterated posterior linearization filtering and smoothing methodologies are proposed for nonlinear systems with cross-correlated noises. Based on the Gaussian approximation (GA), the presented methods are derived via performing statistical linear regressions (SLRs) of the nonlinear state-space models w.r.t the current posterior distribution in an iterated way. Various posterior linearization methods can be developed by employing different approximation computation approaches for the Gaussian-weighted integrals encountered in SLRs. These new estimation methods enjoy not only the accuracy and robustness of the GA filter but also the lower computational complexity. Estimation performances of the designed methods are illustrated and compared with conventional estimation schemes by two common numerical examples. This paper describes consensus control of multi-agent systems (MAS) with input and communication delay in the frequency domain. Each agent of MAS is a linear continuous-time system. The considered linear dynamic model of each agent involves multiple input delays. An H2 controller is proposed for optimal performance and robustness of the MAS. The internal stability approach is employed to compute the H2 controller and the performance index of the overall MAS. A sufficient criterion is derived for gain and delay margin to reach convergence. The simulation paradigm shows the effectiveness of the proposed control scheme. Micro-grids consist of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. Micro-grids are small-scale networks at low voltage levels that are use to provide thermal and electrical loads of small locations where there is no access to the main electrical grid. see more Given the environmental and economic issues for these areas, micro-grids can be a good solution for energy production. In this paper, determining the size and location of optimal electrical energy storage systems is presented. In other side, a new method based on the cost benefit analysis for optimal sizing of an energy storage system in a microgrid (MG) is proposed. The uncertainties associated with renewable energy sources and the occurrence of defects in the grid connection network and the effect of the contribution of load responses in a micro-grid are taken into account. The combined system consists of wind turbines and fuel cells. Basically, wind power is not definitively available. The new proposed method is based on two-stage randomization design (TSRD) for modeling the effect of wind power uncertainty so that the predicted wind energy error is considered as the main random parameter in the model.