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Purpose Mindfulness, while being suggested as an important psychological cognitive capability of customers, has received insufficient attention in studies of transformative services characterized by challenging cocreation behaviors. It is unclear about the contributions of mindfulness to customers’ cocreation and transformative outcomes. This study aims to investigate the direct, indirect, mediating and moderating relationships to explain how mindfulness sustains cocreation effort, increases perceived service value and ultimately enhances the diffusion from the service value to customer well-being. Design/methodology/approach A structural model was developed and tested using the CB-SEM method. Data were surveyed from two transformative service industries, yoga training and higher education (N = 283 and 273 cases, respectively). Findings Customer mindfulness has a positive relationship with cocreation effort, which in turn positively associates with perceived value. Additionally, mindfulness has a direct relationship with perceived value, which then is the full mediator in the relationships between mindfulness, cocreation effort and life satisfaction. Mindfulness also moderates the transformation from service value (immediate outcome) to life satisfaction (long-term outcome). Practical implications Transformative service providers and policymakers should acknowledge and develop strategies to cultivate customers’ mindfulness, which subsequently fosters their value cocreation effort and enhances their well-being. Originality/value This research puts forward the concept of mindfulness, a trainable cognitive capability of customers, and shows its importance in transformative service cocreation. This paper provides a full structural mechanism explaining how mindfulness helps cocreate a transformative service and diffuse its immediate value to customer life satisfaction.

The evaluation of the morphology and organization of collagen fibers is critical in understanding wound healing and tissue remodeling after a thermal injury of the skin. However, histological analysis conducted by pathologists is often labor-intensive and limited to qualitative evaluations and scoring within a narrow field of view. In this study, we propose a convolutional neural network (CNN) model to classify Masson’s trichrome (MT)-stained histology images of burn-induced scar tissue and to characterize the microstructures of normal tissue and scar tissue in a quantitative manner. The scar tissue is created on in vivo rodent models and prepared for MT-stained histology slides after wound healing. A CNN model is developed, trained, and tested with various sizes of the histology images for classification and characterization. The proposed model classifies both normal tissue (i.e., without burn, as the control) and scar tissue at various scales with over 97% accuracy. The features acquired from the proposed CNN model visually characterizes the density and directional variance of the collagen fibers distributed in the dermal layers from whole histology images. The proposed deep learning technique can provide an objective and reliable method to rapidly assess and quantify wound repair and remodeling.

Sleeplessness and driver’s drowsiness is one of the reasons leading to accidents. Many studies as well as applied products have been recently developed and integrated into car. However, almost research concentrated on eyes-movement and pupillary stretch. Especially, mobile Electroencephalography (EEG) measurement device becomes new trend thanks to its convenience and affordable price. This research focused on using portable EEG device—EPOC Emotiv for detecting sleep-onset by analyzing power spectrum after filtering frequency band of brain waves. The processed features become the input of Support Vector Machine (SVM) classification and the prerequisite for real-time drowsiness detection. The changes of vigilance state classified by SVM in analysis show the result with over 70% data samples, reliably used in driving safety system in the future.

The reaction mechanism of Si electrodeposition in the ionic liquid trimethyl- n -hexylammonium bis(trifluoromethylsulfonyl) imide (TMHATFSI) with SiCl 4 was investigated using an electrochemical quartz crystal microbalance (EQCM) and X-ray photoelectron spectroscopy (XPS). The deposited films were further characterized by Raman spectroscopy. The EQCM method measured the frequency change in situ during electrodeposition, whereas XPS provided the Si mass concentration in the electrodeposited films. By combining these results, the mass change due to SiCl 4 reduction was estimated. Then, the current efficiency for Si electrodeposition at various potentials was evaluated. The calculated current efficiency was almost 100% when it was assumed that SiCl 4 reduction progressed by four-electron electroreduction. The XPS and Raman results showed that the deposited films were composed primarily of amorphous Si with Si–Si bonds. Crystalline Si thin films were obtained after annealing in an Ar gas stream.

Three new enantiopure aryl-thioureas have been synthesized, N-(4-X-phenyl)-N-[1(S)-1-phenylethyl]thiourea, X= Cl, Br, and NO2 (compounds 1-3, respectively). Large single crystals of up to 0.5 cm(3) were grown from methanol/ethanol solutions. Molecular structures were derived from X-ray diffraction studies and the crystal morphology was compared to calculations employing the Bravais-Friedel, Donnay-Harker model. Molecular packing was further studied with Hirshfeld surface calculations. Semi-empirical classical model calculations of refractive indices, optical rotation and the electro-optic effect were performed with OPTACT on the basis of experimentally determined refractive indices. Compound 3 (space group P 1 (No. 1)) was estimated to possess a large electro-optic coefficient r(333) of approximately 30 pm/V, whereas 1 and 2 (space Group P 2(1) (No. 4) exhibit much smaller effects.