Hydrogen‐Bonding‐Assisted Intermolecular Charge Transfer: A New Strategy to Design Single‐Component White‐Light‐Emitting Materials

This study reveals the mechanism of the dual‐emission properties for asymmetrical diphenylsulfone and diphenylketone derivatives. A series of asymmetrical diphenylketone and diphenylsulfone derivatives with dual‐emission properties are designed and synthesized. By single crystal structure analyses, various photophysical studies, and 2D ¹H–¹H NOSEY NMR studies, the lower energy emission bands in the dual‐emission spectra are successfully assigned to hydrogen‐bonding‐assisted intermolecular charge transfer emission. The emission properties of these compounds can easily be tuned in both solid state and solution state by destroying or strengthening the intermolecular hydrogen bonding. In addition, thermally activated delayed fluorescence characteristics for the intermolecular charge transfer emissions are also observed. The control of the intermolecular and intramolecular charge transfers serves as the basis for the generation of the white‐light emission. For compound CPzPO, nearly pure white‐light emission with CIE coordinates of (0.31, 0.32) is easily achieved by precipitation from dichloromethane and hexane mixed solvent system. These results clearly give an insight into the dual‐emission properties and provide a rational strategy for the design and synthesis of single‐component white‐light‐emitting materials and mechanoresponsive light‐emitting materials.     

Entropy-based measures for quantifying sleep-stage transition dynamics: relationship to sleep fragmentation and daytime sleepiness

We present two novel entropy-based measures that quantify sleep-stage transition dynamics (sleep structure) from polysomnogram derived hypnograms: Walsh spectral entropy (WSE) and Haar spectral entropy (HSE). These measures quantify patterns of temporal regularity of a categorical time series without requiring numerical encoding (scaling) of the (categorical) sleep stages. Additionally, we show that conditional entropy (CE) is well suited for quantifying predictability of the hypnogram. The relationship of those measures with traditional sleep fragmentation indices (arousal index, total sleep time, and sleep efficiency) is explored for a 394 participant sample of the Cleveland Family Study, an epidemiologic study in which standardized single-night polysomnogram data were collected. The new entropy-based sleep structure measures (WSE, HSE, and CE) are positively correlated (moderate to weak) with the traditional sleep fragmentation indices. Because the sleep structure measures developed in this paper provide direct information related to temporal patterns of sleep that is not contained in traditional sleep fragmentation measures, the correlation between these new alternative sleep structure measures and the traditional sleep fragmentation measures is less important. Our goal is not to develop alternative measures that correlate highly with traditional measures of sleep fragmentation, but rather to provide methods to quantify sleep structure by examining other (e.g., dynamic sleep-stage transition) properties of the hypnogram. Additionally, the relationship of the new entropy-based and traditional measures with daytime sleepiness as quantified by the Epworth sleepiness scale (ESS) is investigated. Multiple linear regression analysis shows that WSE has a stronger relationship with ESS than the traditional measures, even after both are adjusted for common confounders (age, race, gender, and body mass index). This further suggests that the entropy-based measures, especially WSE, are capturing additional temporal patterns of sleep not captured in the traditional sleep fragmentation measures, and have a relationship with daytime sleepiness.     

Advancing Tailored Health Communication: A Persuasion and Message Effects Perspective

Tailored health communication (THC) is any combination of information and behavior change strategies intended to reach one specific person based on information unique to that person, related to the outcome of interest, and derived from an individual assessment. THCs have been studied as a means to facilitate behavior change by influencing some key intermediate steps that precede the behavioral outcome. These include the extent to which people attend to communications, think about them, find them relevant and salient, and intend to take action. Evidence shows that THCs have achieved modest success in changing a number of cancer-related behaviors, including smoking, diet, exercise, and cancer screening. However, it is likely that THCs could be more effective if they were developed with a greater understanding of message effects and what we refer to as the behavioral pathway. Instead of using unidimensional approaches to influence behavior change, a message effects approach would help researchers identify key leverage points for impact on such intermediate outcomes as persuasion and yielding. Such a strategy also might be used to determine when THCs are the preferred approach and when generic, targeted, or combinations of THCs and targeted communications might be appropriate. Viewing THCs from the perspective of the behavioral pathway might indicate use of different messages, sources, and formats to influence different people at different points on the pathway. We provide a brief history of THCs and suggest how integrating a broader perspective of health behavior and health communication theories could enrich THCs.     

Voltage pulses change neural interface properties and improve unit recordings with chronically implanted microelectrodes

Current neuroprosthetic systems based on electrophysiological recording have an extended, yet finite working lifetime. Some posited lifetime-extension solutions involve improving device biocompatibility or suppressing host immune responses. Our objective was to test an alternative solution comprised of applying a voltage pulse to a microelectrode site, herein termed "rejuvenation." Previously, investigators have reported preliminary electrophysiological results by utilizing a similar voltage pulse. In this study we sought to further explore this phenomenon via two methods: 1) electrophysiology; 2) an equivalent circuit model applied to impedance spectroscopy data. The experiments were conducted via chronically implanted silicon-substrate iridium microelectrode arrays in the rat cortex. Rejuvenation voltages resulted in increased unit recording signal-to-noise ratios (10%/spl plusmn/2%), with a maximal increase of 195% from 3.74 to 11.02. Rejuvenation also reduced the electrode site impedances at 1 kHz (67%/spl plusmn/2%). Neither the impedance nor recording properties of the electrodes changed on neighboring microelectrode sites that were not rejuvenated. In the equivalent circuit model, we found a transient increase in conductivity, the majority of which corresponded to a decrease in the tissue resistance component (44%/spl plusmn/7%). These findings suggest that rejuvenation may be an intervention strategy to prolong the functional lifetime of chronically implanted microelectrodes.     

Hierarchical statistical analysis of complex analog and mixed-signal systems

With increasing process parameter variations in nanometre regime, circuits and systems encounter significant performance variations and therefore statistical analysis has become increasingly important. For complex analog and mixed-signal circuits and systems, efficient yet accurate statistical analysis has been a challenge mainly due to significant simulation and modelling time. In the past years, there have been various approaches proposed for statistical analysis of analog and mixed-signal circuits. A recent work is reported to address statistical analysis for continuous-time Delta-Sigma modulators. In this article, we generalise that method and present a hierarchical method for efficient statistical analysis of complex analog and mixed-signal circuits while maintaining reasonable accuracy. At circuit level, we use the response surface modelling method to extract quadratic models of circuit-level performance parameters in terms of process parameters. Then at system level, we use behavioural models and apply the Monte-Carlo method for statistical evaluation of system performance parameters. We illustrate and validate the method on a continuous-time Delta–Sigma modulator and an analog filter.     

Fast Ion Transport in Li-Rich Alloy Anode for High-Energy-Density All Solid-State Lithium Metal Batteries

All-solid-state Li batteries (ASSLBs) with solid-polymer electrolytes are considered promising battery systems to achieve improved safety and high energy density. However, Li dendrite formation at the Li anode under high charging current density/capacity has limited their development. To tackle the issue, Li-metal alloying has been proposed as an alternative strategy to suppress Li dendrite growth in ASSLBs. One drawback of alloying is the relatively lower operating cell voltages, which will inevitably lower energy density compared to cells with pure Li anode. Herein, a Li-rich Li₁₃In₃ alloy electrode (LiRLIA) is proposed, where the Li₁₃In₃ alloy scaffold guides Li nucleation and hinders Li dendrite formation. Meanwhile, the free Li can recover Li's potential and facilitate fast charge transfer kinetics to realize high-energy-density ASSLBs. Benefitting from the stronger adsorption energy and lower diffusion energy barrier of Li on a Li₁₃In₃ substrate, Li prefers to deposit in the 3D Li₁₃In₃ scaffold selectively. Therefore, the Li–Li symmetric cell constructed with LiRLIA can operate at a high current density/capacity of 5 mA cm⁻²/5 mAh cm⁻² for almost 1000 h.     

A multiple circular path convolution neural network system for detection of mammographic masses

A multiple circular path convolution neural network (MCPCNN) architecture specifically designed for the analysis of tumor and tumor-like structures has been constructed. We first divided each suspected tumor area into sectors and computed the defined mass features for each sector independently. These sector features were used on the input layer and were coordinated by convolution kernels of different sizes that propagated signals to the second layer in the neural network system. The convolution kernels were trained, as required, by presenting the training cases to the neural network. In this study, randomly selected mammograms were processed by a dual morphological enhancement technique. Radiodense areas were isolated and were delineated using a region growing algorithm. The boundary of each region of interest was then divided into 36 sectors using 36 equi-angular dividers radiated from the center of the region. A total of 144 Breast Imaging-Reporting and Data System-based features (i.e., four features per sector for 36 sectors) were computed as input values for the evaluation of this newly invented neural network system. The overall performance was 0.78-0.80 for the areas (Az) under the receiver operating characteristic curves using the conventional feed-forward neural network in the detection of mammographic masses. The performance was markedly improved with Az values ranging from 0.84 to 0.89 using the MCPCNN. This paper does not intend to claim the best mass detection system. Instead it reports a potentially better neural network structure for analyzing a set of the mass features defined by an investigator.