The Role of Individual Variables, Organizational Variables and Moral Intensity Dimensions in Libyan Management Accountants’ Ethical Decision Making

This study investigates the association of a broad set of variables with the ethical decision making of management accountants in Libya. Adopting a cross-sectional methodology, a questionnaire including four different ethical scenarios was used to gather data from 229 participants. For each scenario, ethical decision making was examined in terms of the recognition, judgment and intention stages of Rest’s model. A significant relationship was found between ethical recognition and ethical judgment and also between ethical judgment and ethical intention, but ethical recognition did not significantly predict ethical intention—thus providing support for Rest’s model. Organizational variables, age and educational level yielded few significant results. The lack of significance for codes of ethics might reflect their relative lack of development in Libya, in which case Libyan companies should pay attention to their content and how they are supported, especially in the light of the under-development of the accounting profession in Libya. Few significant results were also found for gender, but where they were found, males showed more ethical characteristics than females. This unusual result reinforces the dangers of gender stereotyping in business. Personal moral philosophy and moral intensity dimensions were generally found to be significant predictors of the three stages of ethical decision making studied. One implication of this is to give more attention to ethics in accounting education, making the connections between accounting practice and (in Libya) Islam. Overall, this study not only adds to the available empirical evidence on factors affecting ethical decision making, notably examining three stages of Rest’s model, but also offers rare insights into the ethical views of practising management accountants and provides a benchmark for future studies of ethical decision making in Muslim majority countries and other parts of the developing world

What factors are associated with adolescents\u27 school break time physical activity and sedentary time?

Purpose Adolescents\u27 physical activity levels during school break time are low and understanding correlates of physical activity and sedentary time in this context is important. This study investigated cross-sectional and longitudinal associations between a range of individual, behavioural, social and policy/organisational correlates and objectively measured school break time physical activity and sedentary time.Methods In 2006, 146 adolescents (50% males; mean age = 14.1&plusmn;0.6 years) completed a questionnaire and wore an accelerometer for &ge;3 school days. Time spent engaged in sedentary, light (LPA) and moderate-to-vigorous physical activity (MVPA) during school break times (recess and lunchtime) were calculated using existing cut-points. Measures were repeated in 2008 among 111 adolescents. Multilevel models examined cross-sectional and longitudinal associations.Results Bringing in equipment was cross-sectionally associated with 3.2% more MVPA during break times. Females engaged in 5.1% more sedentary time than males, whilst older adolescents engaged in less MVPA than younger adolescents. Few longitudinal associations were observed. Adolescents who brought sports equipment to school engaged in 7.2% less LPA during break times two years later compared to those who did not bring equipment to school.Conclusion These data suggest that providing equipment and reducing restrictions on bringing in sports equipment to school may promote physical activity during school recess. Strategies targeting females\u27 and older adolescents\u27, in particular, are warranted.<br /

Risk factors for anxiety and depression among pregnant women during COVID-19 pandemic-Results of a web-based multinational cross-sectional study

Objective To assess risk factors for anxiety and depression among pregnant women during the COVID-19 pandemic using Mind-COVID, a prospective cross-sectional study that compares outcomes in middle-income economies and high-income economies. Methods A total of 7102 pregnant women from 12 high-income economies and nine middle-income economies were included. The web-based survey used two standardized instruments, General Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9). Result Pregnant women in high-income economies reported higher PHQ-9 (0.18 standard deviation [SD], P < 0.001) and GAD-7 (0.08 SD, P = 0.005) scores than those living in middle-income economies. Multivariate regression analysis showed that increasing PHQ-9 and GAD-7 scales were associated with mental health problems during pregnancy and the need for psychiatric treatment before pregnancy. PHQ-9 was associated with a feeling of burden related to restrictions in social distancing, and access to leisure activities. GAD-7 scores were associated with a pregnancy-related complication, fear of adverse outcomes in children related to COVID-19, and feeling of burden related to finances. Conclusions According to this study, the imposed public health measures and hospital restrictions have left pregnant women more vulnerable during these difficult times. Adequate partner and family support during pregnancy and childbirth can be one of the most important protective factors against anxiety and depression, regardless of national economic status

Modeling the Instantaneous Pressure–Volume Relation of the Left Ventricle: A Comparison of Six Models

Simulations are useful to study the heart’s ability to generate flow and the interaction between contractility and loading conditions. The left ventricular pressure–volume (PV) relation has been shown to be nonlinear, but it is unknown whether a linear model is accurate enough for simulations. Six models were fitted to the PV-data measured in five sheep and the estimated parameters were used to simulate PV-loops. Simulated and measured PV-loops were compared with the Akaike information criterion (AIC) and the Hamming distance, a measure for geometric shape similarity. The compared models were: a time-varying elastance model with fixed volume intercept (LinFix); a time-varying elastance model with varying volume intercept (LinFree); a Langewouter’s pressure-dependent elasticity model (Langew); a sigmoidal model (Sigm); a time-varying elastance model with a systolic flow-dependent resistance (Shroff) and a model with a linear systolic and an exponential diastolic relation (Burkh). Overall, the best model is LinFree (lowest AIC), closely followed by Langew. The remaining models rank: Sigm, Shroff, LinFix and Burkh. If only the shape of the PV-loops is important, all models perform nearly identically (Hamming distance between 20 and 23%). For realistic simulation of the instantaneous PV-relation a linear model suffices

Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works

An Analysis and Improvement of the Predictive Control Integrating Component

integrator wind-up and, therefore, it is recommended that separate weighting be used with a modified integrating component predictive controller. The separate weighting also improves the designers intuition with respect to tuning the controller, significantly reducing the time required to generate desired closed loop responses. References Clarke, D. W., and Mohtadi, C, 1987, &quot;Properties of Generalized Predictive Control,&quot; World Congress IFAC, Munich. Cutler, C. R., and Ramaker, B. L., 1979, &quot;Dynamic Matrix Control-A Computer Control Algorithm,&quot; A.I.Ch.E., 86th National Meeting, Apr. Kurfess, T. R., Whitney, D. E., and Brown, M. L., 1988, &quot;Verification of a Dynamic Grinding Model,&quot; ASME JOURNAL OF DYNAMIC SYSTEMS, MEAS-UREMENT, AND CONTROL, Dec., Vol. 110, Kurfess, T. R., 1989 &quot;Predictive Control of a Robotic Weld Bead Grinding System,&quot; Ph.D. thesis, MIT Department of Mechanical Engineering. Kurfess, T. R., and Whitney, D. E., 1989, &quot;Predictive Control of a Robotic Grinding System,&quot; Proceedings of the NMTBA Eastern Manufacturing Technology Conference, Hartford, CT, Oct. Kurfess, T. R., Whitney, D. E., 1989, &quot;An Analysis and Improvement of the Predictive Control Integrating Component,&quot; ASME JOURNAL OF DYNAMIC SYS-TEMS, MEASUREMENT, AND CONTROL, submitted Dec. Kwakernaak, H., and Sivan, R., 1972 Introduction The usefulness of observers for real-time state estimation of linear dynamic systems based on measured system outputs is well known. Procedures for designing observers Another approach to robust state estimation has centered upon the fact that the estimated state is often used for feedback control. Hence, the criterion for observer design in these cases is to reduce the effect of modeling errors on the controlled system response. The work of The current work on robust state estimation using observers is motivated by the need to estimate pressure and temperature fields in thermoplastic injection molding processes, based on a few measurement locations in the mold cavity. Robustness of the estimate to errors in the process model is essential for this application given the complexity of the process. The initial use of the estimated pressure and temperature fields is for more effective process monitoring rather than for feedback control. The robustness of the state estimates obtained using observers, in the presence of system modeling error, is examined in this paper following the procedure of Determination of State Estimation Error Bound • Consider the linear time-invariant system described by x{t)=Ax(t) + Bu(t) y(t)=Cx(t) (1) subject to the initial condition x(0) = x 0 where A, B, and C are (nxn), (nxp), and (mxn) matrices, respectively, and x(t), u{t), and y(t) are («xl), (pxl) and (m x 1) vectors, respectively. A full order observer is designed Copyright © 1993 by ASME based on this model to estimate the state x(t). The observer is described by x(t) =AJt(t) +B c u(t)+L(y(t) -y(t)) y(t)=Cx(t) (2) subject to the initial condition Note that modeling errors are permitted only in the A and B matrices and not in the C matrix. Let the estimation error be defined by Manipulation of subject to the initial condition e(0) = x(0)-x(0) = e 0 (5) The eigenvalues of the augmented system described by (1) and (4) are those of A and F c . We assume that the input u{f) is bounded in magnitude and that all the eigenvalues of A have negative real parts, thus ensuring that the estimation error is bounded if all the eigenvalues of F c also have negative real parts. The solution of where M being the modal matrix corresponding to F c and A a diagonal matrix with the eigenvalues of F c as the diagonal elements. Extension of the results obtained here to the case of repeated eigenvalues is relatively straightforward. Taking norms of both sides of Eq. (6), we get C[ being the real part of the observer pole farthest to the right in the complex plane, assumed to be negative here. Id represents the Euclidean norm of any (n x 1) vector v and IIP! represents the spectral norm of any (n x ri) matrix P above. Also, k(M) is the condition number of the (n x ri) matrix M and is equal to IIMII. HAT 1 ! Note that the expression within curly brackets on the right hand side of Eq. (7) depends on the observer eigenvalues and not on the eigenvectors associates with these eigenvalues. The dependence of the state estimation error bound on these eigenvectors is solely via the condition number k(M) of the modal matrix corresponding to F c . Therefore, for competing observer designs with the same eigenvalues, the only difference is in the modal matrix M. The other terms within the curly brackets would be identical for such competing designs. Equation The result obtained here that the eigenvectors corresponding to the observer eigenvalues be chosen to be as nearly mutually orthogonal as possible to reduce the norm of the state estimation error seems to be a natural extension of a result obtained by The suggested observer design guideline does not address the issue of observer eigenvalue selection despite the fact that eigenvalue selection affects the estimation error. Thus, selection of observer eigenvalues without reference to consequences for estimation error may well lead to more robust observer designs being overlooked. Futhermore, Eq. (7) provides only a bound on the estimation error norm. Therefore, it is possible that even if two observer designs differ only in their eigenvector selections, the actual state estimation error norm may in some cases be lower for the design which yields a higher value of k(M) and hence of the error bound. This is less likely to occur, however, if the difference in the values of k(M) for the competing designs is large. Finally, the results obtained here are valid only for cases where the C matrix is known exactly. The procedure for eigenvector selection and observer gain computation follows that of D&apos;Azzo and Houpis (1988). Since the eigenvectors and reciprocal eigenvectors of a matrix are known to be mutually orthogonal, the procedure begins with selection of the reciprocal eigenvectors of F c to be as nearly orthogonal as possible and normalized to have Euclidean norms of unity. S(\ i ) = (A c T -\ i IC T ) for the n specified eigenvalues of F c . At this point in the observer design, the available freedom in eigenvector assignment is used to obtain as nearly mutually orthogonal a set of reciprocal eigenvectors as is possible. The observer gain matrix is then given by Example of Observer Design Consider one dimensional heat conduction in a bar insulated at both ends, governed by the equation where c is the thermal diffusivity of the bar and u(r, t) is the temperature at the location r and time t. It is assumed here that two temperature sensors are located on the bar, one at each end. Using the two measurements provided by the sensors, we need to estimate the temperature distribution in the bar. It is also assumed that the initial temperature distribution in the bar may be unknown. A third order lumped parameter approximation of the distributed parameter system is developed using the modal expansion method. This lumped parameter model is described in a normalized form by The elements of x are the normalized weighting factors on the responses of the corresponding modes, c&apos; is a normalized version of c. It is assumed that the actual value of c&apos; is 0.11, while for observer design, a value of 0.09 is assumed, indicating about 18 percent error. The elements of the C matrix depend only on the boundary conditions and the form of the partial differential Eq. and yields a condition number of the modal matrix of F c , after equilibration, of 3.43. In design 2, the reciprocal eigenvectors are chosen to get a poorer condition number of the modal matrix of F c , equal to 31.44. The observer gain matrix for this design is given by It should be noted here, as an indication of the restricted nature of the results of There is no guarantee, however, that the norm of the state estimation error will always be lower if the observer is designed as indicated here. In fact, if the initial state estimation error vector is dominated by one component, or if the errors in some of the parameters of the A and B matrices are dominant over the others, the relationship between the state estimation error norms may not be the same as the relationship between the error bounds indicated by Eq. Conclusions In this paper, we have derived an expression for an upper bound on the norm of the estimation error for an observer, in the presence of errors in the system A and B matrices and in the estimated initial conditions. It is shown that, in designing observers for multi-output systems using eigenstructure assignment, if the eigenvectors of the F c matrix are chosen to be as nearly mutually orthogonal as possible, a smaller bound on the state estimation error is obtained and thus may lead to more accurate state estimation. This is demonstrated by means of an example. The approach presented seems most appropriate in the absence of any a priori information on the initial state or the nature of the modeling errors. References Introduction This paper is concerned with the problem of identifying the input-output relationship of an unknown nonlinear dynamical system. Classical adaptive control of deterministic linear systems whose state variables are not all observed makes use of the separation principle (Narendra and Annaswamy, 1989) which says, in effect, that the problems of constructing an observer and parameter estimator can be considered separately. When the system is not observable it is not possible to construct an observer to recover the full state. Furthermore, when the system is nonlinear the separation principle no longer applies, and hence conventional adaptive identification and control techniques offer little hope of effective control of partially observed nonlinear systems. In this paper we show that these difficulties can be avoided by using neural networks instead. Neural networks are already successfully applied in control theory and system identification. In a recent paper, Narandra and Parthasarathy (1990) formalized a unified approach to solving nonlinear identification and control problems using multilayered neural networks. Chen (1990) applied multilayer neural network to nonlinear self-tuning tracking problems. Chu et al. (1990) implemented a Hopfield network on identifying time-varying linear systems. Various learning architectures for training neural net controller are outlined in Psaltis et al. (1988) and some interesting applications of neural networks in adaptive control can be found in Goldenthal an