No abstract

Semiconducting materials are central to the development of high-performance electronics that are capable of dissolving completely when immersed in aqueous solutions, groundwater, or biofluids, for applications in temporary biomedical implants, environmentally degradable sensors, and other systems. The results reported here include comprehensive studies of the dissolution by hydrolysis of polycrystalline silicon, amorphous silicon, silicon-germanium, and germanium in aqueous solutions of various pH values and temperatures. In vitro cellular toxicity evaluations demonstrate the biocompatibility of the materials and end products of dissolution, thereby supporting their potential for use in biodegradable electronics. A fully dissolvable thin-film solar cell illustrates the ability to integrate these semiconductors into functional systems.

Research has shown that parents with higher socioeconomic status provide more resources to their children during childhood and adolescence. The authors asked whether similar effects associated with parental socioeconomic position are extended to adult children. Middle-aged parents (N = 633) from the Family Exchanges Study reported support they provided to their grown children and coresidence with grown children (N = 1,384). Parents with higher income provided more emotional and material support to the average children. Grown children of parents with less education were more likely to coreside with them. Parental resources (e.g., being married) and demands (e.g., family size) explained these patterns. Of interest is that lower income parents provided more total support to all children (except total financial support). Lower income families may experience a double jeopardy; each grown child receives less support on average, but parents exert greater efforts providing more total support to all their children.

Many caregivers do not have an accurate depiction of the IWD's values, yet, caregivers will become the surrogate decision makers for IWDs as dementia progresses. These findings indicate the need for assessments of values and preferences in care and to develop programs that assess values, consider the caregiver's beliefs about care, and improve communication within the dyad in the early stages of dementia.

Total exposure to stressors and stress appraisals decreased significantly over time on ADS days compared with non-ADS days. Most of this difference was accounted by the time the person with dementia was away from the caregiver, but there were also significant reductions in behavioral problems during the evening and improved sleep immediately following ADS use. DISCUSSION. ADS use lowered caregivers' exposure to stressors and may improve behavior and sleep for people with dementia on days they have ADS. The study highlights how a within-person design can identify the effects of an intermittent intervention, such as ADS.

The findings demonstrate that stressors on caregivers are partly lowered, and affect is improved on ADS days, which may provide protection against the effects of chronic stress associated with caregiving.

The sensitivity of searches for astrophysical transients in data from the Laser Interferometer Gravitational-wave Observatory (LIGO) is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high enough rate such that accidental coincidence across multiple detectors is non-negligible. These ''glitches'' can easily be mistaken for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational waves. We apply machine-learning algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. Noise sources may produce artifacts in these auxiliary channels as well as the gravitational-wave channel. The number of auxiliarychannel parameters describing these disturbances may also be extremely large; high dimensionality is an area where MLAs are particularly well suited. We demonstrate the feasibility and applicability of three different MLAs: artificial neural networks, support vector machines, and random forests. These classifiers identify and remove a substantial fraction of the glitches present in two different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth-science-run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar performance to the benchmark algorithm, the ordered veto list, which is optimized to detect pairwise correlations between transients in LIGO auxiliary channels and glitches in the gravitational-wave data. This suggests that most of the useful information currently extracted from the auxiliary channels is already described by this model. Future performance gains are thus likely to involve additional sources of information, rather than improvements in the classification algorithms themselves. We discuss several plausible sources of such new information as well as the ways of propagating it through the classifiers into gravitational-wave searches.

No abstract