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Objective: Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease with treatment manifestations that can cause changes in appearance, including skin rashes, alopecia, vitiligo, and scars. SLE has been shown to adversely impact body image outcomes, and previous research has identified that greater disease activity is associated with worse body image outcomes which, in turn, are associated with greater depressive symptoms. For patients with SLE who also experience significant pain, poor body image outcomes may further compromise wellbeing and lead to greater depressive symptoms. The role of pain in body image has not been explored in SLE. Thus, the present study examined whether body image (specifically, body image-related quality of life) serves as a mediator of the relationship between pain and depressive symptoms among patients with SLE. Methods: Multiple mediation analysis was used to examine the hypothesis that body image-related quality of life mediates the relationship between pain and depressive symptoms in a sample of patients with SLE (N = 135) from an urban region in Los Angeles, California. Results: The sample was predominately female (92.6%) with a mean disease duration of approximately 17 years. Approximately one-quarter of the sample had elevated depressive symptoms. Body image-related quality of life was a significant mediator in the relationship between pain and depressive symptoms. The model accounted for 51% of the total variance in depressive symptoms (R 2 = 0.51). Conclusion: This cross-sectional study suggested that body image-related quality of life may mediate the effects of pain on depressive symptoms among patients with SLE. [ABSTRACT FROM AUTHOR]

Objective Helplessness is a relevant construct in systemic lupus erythematosus (SLE), an unpredictable chronic illness with no known cure characterized by relapsing and remitting features. However, no measure of helplessness has been validated in this population. The present study examined the structural validity, reliability, and convergent validity of the Arthritis Helplessness Index, a measure initially developed for rheumatoid arthritis populations, in a sample of patients with SLE. Methods Patients with SLE (N = 136) receiving medical care at a private hospital completed the Arthritis Helplessness Index and other self-report measures. The structural validity of the Arthritis Helplessness Index was examined using confirmatory factor analysis. Internal consistency reliability was evaluated with Cronbach’s coefficient alpha. Pearson product–moment correlations were used to examine convergent validity with measures of depression, anxiety and mastery. Results The five-item Arthritis Helplessness Index–Helplessness measure demonstrated a tenable factor structure (comparative fit index 0.98, root mean square error of approximation 0.06, standardized root mean residual 0.04). Internal consistency reliability was fair (α = 0.69). Convergent validity was evidenced by significant correlations with measures of depression, anxiety and mastery. Conclusion The five-item Arthritis Helplessness Index–Helplessness scale can confidently be used as a measure of helplessness in SLE. [ABSTRACT FROM AUTHOR]

This study assesses the progression of damage occurring on glass fiber reinforced polyester composite specimens using acoustic emission (AE) parameters. Its aims are to improve understanding of the particular characteristics of AE signals; and also to determine the relationship between AE signals and the failure of the material. Time and frequency domain trends were analysed at four different applied loads (60.97, 67.75, 74.52 and 81.30 MPa) representing 45–60% of the ultimate tensile strength of material. The relevant AE parameters were analysed both in the early stages of the test and as the material neared the fracture zone. The results showed a high degree of correlation between the root mean square and number of hits AE values and the number of cycles to failure, of 92.99 and 92.19%, respectively. This correlation, as well as AE basic parameters, suggests that AE can be a valuable tool to predict the fatigue life and detect the onset of damage in such composite materials. [ABSTRACT FROM AUTHOR]

• Comparison of ensemble learning methods namely, XGboost, LightGBM, and CatBoost and their accuracy in damage detection of composites. • Using Unsupervised learning methods to predict damage and AE parameter. • Different methodologies for damage detection and characterization of AE parameters. • Using SHAP values to select AE key features and K-means algorithms were employed to classify damage severity. • The accuracy and the reliability of various machine learning techniques in predicting fatigue life of composite materials using acoustic emission. Fatigue strength is one of the most important properties of composite materials because it directly relates to their lifespan. Acoustic emission (AE) is a passive structural health monitoring (SHM) technique that provides real-time damage detection based on stress waves generated by cracks in the structure. This study evaluates the damage progression on glass fiber reinforced polyester composite specimens using different approaches of machine learning. Different methodologies for damage detection and characterization of AE parameters are presented. Three different ensemble learning methods namely, XGboost, LightGBM, and CatBoost were chosen to predict damages and AE parameters. SHAP values were used to select AE key features and K-means algorithms were employed to classify damage severity. The accuracy of these approaches demonstrates the reliability of various machine learning techniques in predicting the fatigue life of composite materials using acoustic emission. [ABSTRACT FROM AUTHOR]

Anopheles (Cellia) stephensi Liston 1901 is known as an Asian malaria vector. Three biological forms, namely “mysorensis”, “intermediate”, and “type” have been earlier reported in this species. Nevertheless, the present morphological and molecular information is insufficient to diagnose these forms. During this investigation, An. stephensi biological forms were morphologically identified and sequenced for odorant-binding protein 1 ( Obp1 ) gene. Also, intron I sequences were used to construct phylogenetic trees. Despite nucleotide sequence variation in exon of AsteObp1 , nearly 100% identity was observed at the amino acid level among the three biological forms. In order to overcome difficulties in using egg morphology characters, intron I sequences of An. stephensi Obp1 opens new molecular way to the identification of the main Asian malaria vector biological forms. However, multidisciplinary studies are needed to establish the taxonomic status of An. stephensi . [ABSTRACT FROM AUTHOR]

Shape optimization of structures with frequency constraints is a highly nonlinear dynamic optimization problem. In order to deal with this type of optimization problem, efficient optimization algorithms should be employed. The main objective of the present study is to propose an efficient harmony search (HS)-based algorithm for solving the shape optimization problem of pin-jointed structures subject to multiple natural frequency constraints. In the proposed algorithm an enhanced version of HS is employed in the framework of the sequential unconstrained minimization technique. The efficiency of the presented sequential harmony search (SHS) algorithm is illustrated through several benchmark optimization examples and the results are compared to those of different optimization techniques. The numerical results demonstrate the computational advantages of the SHS in shape optimization of structures for frequency constraints. [ABSTRACT FROM AUTHOR]

Abstract: An efficient methodology is presented to achieve optimal design of structures for earthquake loading. In this methodology a combination of wavelet transforms, neural networks and evolutionary algorithms are employed. The stochastic nature of the evolutionary algorithms makes the slow convergence. Specially, when earthquake induced loads are taken into account. To reduce the computational burden, a discrete wavelet transform is used by means of which the number of points in the earthquake record is decreased. Then, by using a surrogate model, the dynamic responses of the structures are predicted. In order to investigate the efficiency of the proposed methodology, two structures are designed for optimal weight. The numerical results demonstrate the computational advantages of the proposed hybrid methodology to optimal dynamic design of structures. [ABSTRACT FROM AUTHOR]

An efficient structural optimisation method based on a combination of simultaneous perturbation stochastic approximation (SPSA) and particle swarm optimisation (PSO) is presented. This new method is called the SPSA-PSO method. The operation of SPSA-PSO includes three phases. In the first phase, a preliminary optimisation is accomplished using the SPSA. In the second phase, an efficient initial swarm is produced using the first phase results. In the last phase, the PSO is employed to find the optimum design using the efficient initial swarm. Various benchmark truss examples are considered to demonstrate the effectiveness of the suggested method. The numerical results indicate that the SPSA-PSO is a powerful and computationally efficient method for solving the structural optimisation problems when comparing with other methods found in the literature. [ABSTRACT FROM AUTHOR]

Abstract: The main aim of this study is to propose advanced soft computing techniques for the optimal seismic design of real steel structures subjected to natural ground motion records. For the solution of the optimization problem an efficient combination of the particle swarm optimization (PSO) and adaptive virtual sub-population (AVSP) algorithms is proposed. Also an efficient combination of the adaptive neuro-fuzzy inference system (ANFIS), wavelet transforms (WT) and radial basis function (RBF) neural networks, termed as fuzzy wavelet radial basis function (FWRBF), is proposed to accurately predict the structural responses. The numerical results demonstrate the computational advantages of the proposed methodology. [Copyright &y& Elsevier]

An efficient method is proposed to find optimal shape of arch dams subjected to response spectrum loading. The optimization is performed by a combination of simultaneous perturbation stochastic approximation (SPSA) and genetic algorithm (GA) methods. This new method is called simultaneous perturbation genetic algorithm (SPGA). Operation of SPGA includes three phases. In the first phase, a preliminary optimization is accomplished using SPSA. In the second phase, an optimal initial population is produced using the first phase results. In the last phase, GA is employed to find optimum design using the optimal initial population. The numerical results reveal the robustness and high performance of the proposed method for optimum shape design of arch dams. The optimum design obtained by SPGA is compared with those of SPSA and GA. It is demonstrated that the SPGA converges to better solution compared to SPSA and GA by spending lower computational cost. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a combination of genetic algorithm (GA) and neural networks (NN) is proposed to find the optimal weight of structures subject to multiple natural frequency constraints. The optimization is carried out by an evolutionary algorithm using discrete design variables. The evolutionary algorithm employed in this investigation is virtual sub-population (VSP) method. To reduce the computational time of optimization process, the natural frequencies of structures are evaluated using properly trained radial basis function (RBF) and wavelet radial basis function (WRBF) neural networks. In the WRBF neural network, the activation function of hidden layer neurons is substituted with a type of wavelet functions. In this new network, the position and dilation of the wavelet are fixed and only the weights are optimized. The numerical results demonstrate the robustness and high performance of the suggested methods for structural optimization with frequency constraints. It is found that the best results are obtained by VSP method using WRBF network. [Copyright &y& Elsevier]

dynamic analysis of structures for earthquake induced loads is very expensive in terms of the computational burden. In this study, to reduce the computational effort a new neural system which is called parallel wavelet back propagation (PWBP) neural networks has been introduced. Training of PWBP is implemented in two phases. In the first phase, the input space is classified by using competitive neural networks. In the second phase, one distinct WBP neural network is trained for each class. Comparison the numerical results obtained by PWBP with the corresponding ones obtained by the single WBP neural network reveals the better performance generality of PWBP. [ABSTRACT FROM AUTHOR]

Objective: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can result in disability and psychological distress. Although pain has been associated with depressive symptomatology and stress in SLE, a paucity of theoretical models have been used to explain the relationship between pain and psychological distress in this population. Thus, the present study examined helplessness as a mediator of the relationship between pain and psychological distress among patients with SLE. Methods: Multiple mediation analysis was used to examine the hypothesis that learned helplessness mediates the relationship between pain and symptoms of anxiety, depression, and stress in a sample of patients with SLE (N=136) receiving medical care at Cedars Sinai Medical Center. Results: The mean score on the Helplessness subscale was 14.5 (SD=5.4). Helplessness fully mediated the relationship between pain vitality and symptoms of anxiety (BCa 95% CI (-0.073, -0.015)), depression (BCa 95% CI (-0.502, -0.212)), and stress (BCa 95% CI (-0.063, -0.027)). Conclusion: Participants reported a high level of perceived inability to control one's disease. Helplessness fully mediated the relationship between pain and measures of anxiety, depression, and perceived stress among patients with SLE. [ABSTRACT FROM AUTHOR]

Purpose: Our aim was to determine association of vesicoureteral reflux (VUR) and idiopathic hypercalciuria in children with recurrent and single episode of urinary tract infection (UTI). Materials and Methods: The study group consisted of 45 children with VUR and recurrent UTI, and 2 control groups: 45 normal healthy children (control group 1) and 45 children with VUR and single episode of UTI (control group 2). Idiopathic hypercalciuria was defined as urine calcium to creatinine ratio more than 0.8 (mg/mg) in infants younger than 1 year old, and more than 0.2 (mg/mg) in older children (without any detectable causes for hypercalciuria). Results: The study group consisted of 26 (57.8%) girls and 19 (42.2%) boys, with the mean age of 41.14 +/- 22.1 months. Nine (20%) subjects had hypercalciuria. The control group 1 composed of 22 (48.9%) girls and 23 (51.1%) boys, with the mean age of 43.98 +/- 16.23 months. In this group, 6 subjects (13.3%) with hypercalciuria were detected. The control group 2 composed of 23 (51.1%) girls and 22 (48.9%) boys, with the mean age of 39.96 +/- 24.2 months. In group 2, 7 subjects (15.6%) with hypercalciuria were detected. Conclusion: Comparison between such results was not statistically significant. Despite reports of different studies about accompanying of hypercalciuria with recurrent UTI with or without anatomical abnormalities, according to the present study, idiopathic hypercalciuria is not a major contributing factor to recurrent UTI in children with VUR. [ABSTRACT FROM AUTHOR]

An efficient methodology is proposed to find the optimum shape of arch dams considering fluid-structure interaction subject to earthquake loading. The earthquake load is considered by time variant ground acceleration applied in the upstream-downstream direction of the arch dam. The optimization is carried out by particle swarm optimization, employing real values of design variables. To reduce the computational cost of the optimization process, two strategies are adopted. In the first strategy, the most influential design variables on arch-dam response from original variables are selected using an adaptive neuro-fuzzy inference system. In the second, arch-dam response is predicted by a properly trained wavelet radial basis function neural network employing the influential design variables as the inputs. In order to assess the effectiveness of the suggested methodology, a real arch dam is considered as a test example. The numerical results demonstrate the computational advantages of the proposed methodology for the optimal design of arch dams. [ABSTRACT FROM AUTHOR]