Mobile phone addiction is a universal phenomenon that has attracted a lot of attention in recent years. Previous researches revealed a significant relation between adverse childhood experiences (ACEs) and addiction. This study further investigated the association between ACEs and mobile phone addiction, and the mediating effects of attachment styles and interpersonal relationships. The cross-sectional design and multiple questionnaires, namely, the Revised Adverse Childhood Experience Questionnaire, the Mobile Phone Addiction Index, the Revised Adult Attachment Scale (AAS), and the Interpersonal Relationship Comprehensive Diagnostic Scale (IRCDS) were used in the sample of 345 university students. Correlation analysis revealed that adverse childhood experience, attachment anxiety, attachment avoidance, interpersonal relationship, and mobile phone addiction were significantly positively correlated with each other. Results of regression analysis showed that attachment style and interpersonal relationship played multiple mediation roles in the association between adverse childhood experience and mobile phone addiction. That is, (1) adverse childhood experience was positively related to mobile phone addiction, (2) both attachment anxiety and interpersonal relationship played partial and parallel mediating roles between adverse childhood experience and mobile phone addiction, and (3) attachment anxiety/avoidance and interpersonal relationship mediated the relationship between adverse childhood experience and mobile phone addiction sequentially. These results indicated that mobile phone addiction among college students who had adverse childhood experience can be relieved by way of the remission of attachment anxiety, reduction of attachment avoidance, and improvement of interpersonal relationship.

The first examples of an optically active Birch reduced tertiary phosphine, viz. (R(P))-(cyclohexa-2,5-dienyl)(3-pentyl)phenylphosphine, and successful hydrophosphination of the related racemic ligand (±)-(cyclohexa-2,5-dienyl)(2-propyl)phenylphosphine with PHPh(2) in the presence of KOBu(t) in thf to give a 1,2-cyclohexenebis(tertiary phosphine), viz. (±)-1,2-C(6)H(8)(PPh(2))(PPhPr(i)), are described; as confirmed by crystal structure determinations of [SP-4-4-(S(P),S)]-chloro[(cyclohexa-2,5-dienyl)(3-pentyl)phenylphosphine][2-{1-(dimethylamino)ethyl}phenyl-C,N]palladium(II) and [SP-4-3-(±)]-dimethyl[(1-diphenylphosphino)(2-isopropylphenylphosphino)cyclohexene]platinum(II).

Obtaining all feasible parameters of the proportional-integral-differential (PID) controller is the key goal in uncertain systems. This paper proposes a graphical tuning method based on an internal model control (IMC) strategy for uncertain systems with time delay. Specifically, the Kharitonov theorem is introduced first to simplify the uncertain system into 32 polynomials. Then, for each polynomial, the IMC structure is applied to reduce the tuning parameters of the PID controller in order to rapidly determine the controller parameters. Finally, the maximum sensitivity (Ms) is used to further guarantee the controlled system with a certain robustness and dynamic performance, which can portray constant gain margin and phase margin boundaries, and can even determine the range of parameters of the proposed IMC filter. Three example results from simulations are presented to demonstrate the effectiveness and applicability of the proposed method.

A hybrid model, which is composed of an autoregressive moving-average (ARMA) filter and a feedforward neural network (FNN), is proposed to increase prediction accuracy and to reduce learning time for the estimation of thermal deformations in a machine tool. The ARMA filter is used to yield state variables which establish the relationship between the present and past states of thermal deformations for the reservation of the influences of past temperatures and deformation. Otherwise, the quantity of FNN inputs is very vast because of the data needed for the non-linear system. These state variables, which are estimated by past measured temperatures and past estimated deformation, serve as inputs of the FNN. The algorithms of this hybrid model are presented and verified by the experimental results; also, the prediction accuracy is compared with the ARMA and FNN independently for the same learning iterations.

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