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From arsenal delivery to rescue missions, unmanned aerial vehicles (UAVs) are playing a crucial role in various fields, which brings the need for continuous evolution of system identification techniques to develop sophisticated mathematical models for effective flight control. In this paper, a novel parameter estimation technique based on filter error method (FEM) augmented with particle swarm optimisation (PSO) is developed and implemented to estimate the longitudinal and lateral-directional aerodynamic, stability and control derivatives of fixed-wing UAVs. The FEM used in the estimation technique is based on the steady-state extended Kalman filter, where the maximum likelihood cost function is minimised separately using a randomised solution search algorithm, PSO and the proposed method is termed FEM-PSO. A sufficient number of compatible flight data sets were generated using two cropped delta wing UAVs, namely CDFP and CDRW, which are used to analyse the applicability of the proposed estimation method. A comparison has been made between the parameter estimates obtained using the proposed method and the computationally intensive conventional FEM. It is observed that most of the FEM-PSO estimates are consistent with wind tunnel and conventional FEM estimates. It is also noticed that estimates of crucial aerodynamic derivatives ${C_{{L_\alpha }}},\;{C_{{m_\alpha }}},\;{C_{{Y_\beta }}},\;{C_{{l_\beta }}}$ and ${C_{{n_\beta }}}$ obtained using FEM-PSO are having relative offsets of 2.5%, 1.5%, 6.5%, 3.4% and 7.6% w.r.t. wind tunnel values for CDFP, and 1.4%, 1.9%, 0.1%, 9.6% and 7.5% w.r.t. wind tunnel values for CDRW. Despite having slightly higher Cramer-Rao Lower Bounds of estimated aerodynamic derivatives using the FEM-PSO method, the simulated responses have a relative error of less than 0.10% w.r.t. measured flight data. A proof-of-match exercise is also conducted to ascertain the efficacy of the estimates obtained using the proposed method. The degree of effectiveness of the FEM-PSO method is comparable with conventional FEM.
Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS.
Methods
Self-identifying black and white American women and men (n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (⩾33, Impact of Events Scale – Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample).
Results
Twenty-five percent (n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 ± 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms.
Conclusions
These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.
This book is primarily about prevention; its emphasis is on interventions that can be done at the time of cancer diagnosis – modifications of treatment and techniques for storing gametes, tissues or embryos for future use. By contrast, this chapter explores options open to cancer survivors after treatment has been completed. If preventive treatment was successful, either through medical interventions such as using less gonadotoxic regimens, fertility-sparing surgery, oophoropexy or gonadoprotective adjuncts like GnRH agonists, normal fertility has been preserved. Other survivors may be able to conceive using the gametes, embryos or tissue that was obtained and cryopreserved before their gonadotoxic treatment(s). However, in some cases, fertility preservation may not have been possible before treatment or, alternatively, the cryopreserved gametes, embryos or tissue may not have resulted in a successful pregnancy. This chapter provides insight into the fertility management of cancer survivors with compromised or absent ovarian function, who do not have cryopreserved gametes, embryos, or ovarian tissue.
The transplantation of endocrine organs can be regarded as the oldest form of transplantation in modern medical history. By the end of the nineteenth and beginning of the twentieth centuries, a large research focus was set on endocrine transplantations. Before the complex endocrine secretion and function was even understood, researchers attempted to cure endocrine diseases and infertility through transplantation of the endocrine glands and gonads. Hence, most endocrine organs have been transplanted in that period, including the thyroid [1], the adrenal gland [2], the testis [3] and the ovary [4]. Even though the principles of transplant rejection have not been understood at that time, researchers already noticed successful transplantations almost exclusively in experiments with autografts. The first published allogeneic ovarian transplantations in animals have been performed by Paul Bert in the sixties of the nineteenth century [5].