Acculturation Stress, Psychological and Sociocultural Adjustment, and Development of American Adolescents: A Qualitative Study of Newton High School Exchange Students in China

Theories from the extant acculturation literature functioned to categorize international students’ adaptation experiences and predict their acculturation outcomes. Also, relevant studies focused mainly on students at the tertiary level. For adolescent students seeking self-development toward independence and autonomy, how they negotiated their identity challenges and tensions in a cross-cultural context, and how surrounding others in their socialization impacted on their psychosocial adjustment process and transformative experiences have not been actively explored. This qualitative study approached adolescent students’ acculturation as an integrated development and learning process to explore the effects of developmental and cultural factors on their cross-cultural adaptation, especially examined their homestay experiences and student-host family relationships. It revealed how the surrounding others, through social interactions, impacted students for possible behaviors changes. Particularly, through in-depth interviews, it provided an insider aspect of how daily interactions amplified students’ different expectation into confusion and misunderstanding, and how they negotiated and reconciled the confusion and misunderstanding to create meaningful everyday activities, and over time, their shifting behaviors ensued. It is hoped that by shedding some light on self-resilience of adolescent students, and revealing their acculturative stresses and help-seeking behaviors, their emotional and social needs in their adjustment process might be better served. Keywords Adolescent students, social interaction, psychosocial adjustmen

Combining Total Variation and Nonlocal Means Regularization for Edge Preserving Image Deconvolution

We propose a new edge preserving image deconvolution model by combining total variation and nonlocal means regularization. Natural images exhibit an high degree of redundancy. Using this redundancy, the nonlocal means regularization strategy is a good technique for detail preserving image restoration. In order to further improve the visual quality of the nonlocal means based algorithm, total variation is introduced to the model to better preserve edges. Then an efficient alternating minimization procedure is used to solve the model. Numerical experiments illustrate the effectiveness of the proposed algorithm

A New Noninterior Continuation Method for Solving a System of Equalities and Inequalities

By using slack variables and minimum function, we first reformulate the system of equalities and inequalities as a system of nonsmooth equations, and, using smoothing technique, we construct the smooth operator. A new noninterior continuation method is proposed to solve the system of smooth equations. It shows that any accumulation point of the iteration sequence generated by our algorithm is a solution of the system of equalities and inequalities. Some numerical experiments show the feasibility and efficiency of the algorithm

Characterization of cervid skin tissues with chronic wasting disease by Raman spectroscopy and machine learning

Chronic wasting disease (CWD) is a contagious neurological disease in cervids that belongs to transmissible spongiform encephalopathies (TSEs). Its spread has threatened the healthy growth of wild and farm-raised deer and resulted in adverse population-level impacts. It also raised concerns over the possibility of infecting human beings like bovine spongiform encephalopathy (BSE). CWD is a prion disease that may take as long as two years for visible signs of the disease to appear. Currently, diagnostic tests approved for official CWD are postmortem tests (immunohistochemistry (IHC) and ELISA) which are not suitable for in vivo diagnosis. Raman spectroscopy offers a potential approach to detect and diagnose CWD rapidly in real time as a first screen onsite. With the Raman spectral data, machine learning algorithms could be utilized to extract meaningful information to differentiate the spectroscopic features that underline the signatures associated with the diseases effectively, even with a low signal-to-noise ratio (SNR) Raman spectral data acquired with a portable Raman spectrometer. In this study, in order to evaluate the effectiveness of Raman spectroscopy on CWD diagnosis, Raman spectra were collected by a Raman microscope as well as a portable Raman spectrometer from cervid skin tissue samples collected from both healthy (i.e., control, CWD-negative) and diseased (i.e., CWD-positive) cervids. The spectral data were classified by two machine learning algorithms, support vector machine and artificial neural network. The results suggested that Raman spectroscopy in conjunction with Machine learning can indeed offer a rapid first screening for CWD, with the highest accuracy of 94.4%. It has the potential to become a useful tool for in-field diagnosis and detection of CWD

Guaranteed Cost Control for Multirate Networked Control Systems with Both Time-Delay and Packet-Dropout

Compared with traditional networked control systems, the sampling rates of the nodes are not the same in the multirate networked control systems (NCSs). This paper presents a new stabilization method for multirate NCSs. A multirate NCSs with simultaneous considering time-delay and packet-dropout is modeled as a time-varying sampling system with time-delay. The proposed Lyapunov function deceases at each input signal updating point, which is largely ignored in prior works. Sufficient condition for the stochastic mean-square stability of the multirate NCSs is given, and the cost function value is less than a bound. Numerical examples are presented to illustrate the effectiveness of the proposed control scheme