Highly Stretchable and Transparent Ionogels as Nonvolatile Conductors for Dielectric Elastomer Transducers

Large deformation of soft materials is harnessed to provide functions in the nascent field of soft machines. This paper describes a new class of systems enabled by highly stretchable, transparent, stable ionogels. We synthesize an ionogel by polymerizing acrylic acid in ionic liquid 1-ethyl-3-methylimidazolium ethylsulfate ([C2mim][EtSO4]). The ionogel exhibits desired attributes of adequate conductivity (0.22 S m−1), low elastic modulus (∼3 kPa), large rupturing stretch (∼4.6), and negligible hysteresis and degradation after cyclic stretches of large amplitude. Using the ionogel and a dielectric elastomer, we fabricate electromechanical transducers that achieve a voltage-induced areal strain of 140%. The ionogel is somewhat hygroscopic, but the transducers remain stable after a million cycles of excitation in a dry oven and in air. The transparency of the ionogels enable the transducers with conductors placed in the path of light, and the nonvolatility of the ionogels enable the transducers to be used in open air.Engineering and Applied Science

Network characteristics of the youth’s insomnia and emotional symptoms and their gender differences

ObjectiveTo explore the association between sleep disorders and symptoms of depression and anxiety in the youth and to analyze the influence of gender factors.MethodsUsing the Mental Health Status Survey Questionnaire for Adolescent Students compiled by Professor Maosheng Ran, a survey was conducted and 7247 valid responses were collected (valid response rate of 79.11%). Integrating the Insomnia Severity Index(ISI), Patient Health Questionnaire(PHQ-9), and Generalized Anxiety Disorder Scale(GAD-7), network analysis was employed to assess the network structure, symptom associations, and gender differences related to insomnia, depression, and anxiety among youth.ResultsIn the network of insomnia, depression, and anxiety symptoms among youth, the highest strength centrality values were observed for “excessive worry,” “fatigue,” “sleep dissatisfaction,” and “distress caused by sleep difficulties.” Five bridge symptoms were identified: “fatigue,” “nervousness,” “suicidal ideation,” “motor,” and “guilt.” Significant differences in network structures existed between genders, specifically in network invariance (M = 0.909, p = 0.025) and global strength (males = 75.155, females = 70.527; S = 4.628, p = 0.041). Additionally, males showed significantly higher bridge strength in “anhedonia” than females (p = 0.044).ConclusionsThis study revealed that insomnia, anxiety, and depression symptoms among youth are closely interconnected. Core symptoms such as “excessive worry” and “sleep dissatisfaction,” along with bridge symptoms like “fatigue,” “nervousness,” and “suicidal ideation,” represent potential intervention targets, with fatigue playing a dual role in the network. Males require particular attention regarding the intervention of “anhedonia.” Targeted improvement of these key symptoms may help break the cycle of comorbidity and provide precise directions for mental health interventions among young people

Scaling Machine Learning in Practice

In recent years, machine learning has become pervasive, powering algorithmic clin-icians, translators, and world-beating go masters. As practitioners build on this success, they repeatedly observe that scale–data, model size, compute–is critical. However, scale is now a challenge in and of itself; simple tasks such as gathering data become formidable, even prohibitive. In this thesis, we discuss techniques for addressing scale in three areas: 1. Differentiable reinforcement learning for physical devices: Reinforce- ment learning has emerged as a potential strategy for machines to make deci- sions in complex, dynamic environments. However, successful demonstrations have required vast experience to learn an optimal policy, making real-world physical applications particularly challenging. We present a method that uses limited experience to learn a differentiable simulator of a physical system and then uses gradient methods on the simulator to learn a state-of-the-art policy for controlling that system. 2. Practical optimization for deep learning: Optimization is an essential as- pect of deep learning. However, while a constellation of optimization algorithms dot the literature, the low burden of proof and empirical nature of deep learning has led practitioners to rely on defaults (i.e., Adagrad, Adam) rather than view optimization as a lever for progress. To rigorously test ideas in optimization, we introduce a comprehensive benchmark that currently includes 8 deep learning workloads and rules for training procedures, computational budget, and eval- uation. We also use the benchmark to evaluate new optimization results and re-evaluate existing ones. 3. Scaling computer systems via thread scheduling: Large global-scale ap- plications are expensive and complex to operate let alone optimize. As a result, many simple parameters that govern important behaviors of these systems are simply set once and never touched again. However, we show that these param- eters present low-hanging fruit for significant efficiency improvements

Guidelines for Therapeutic Drug Monitoring of Vancomycin: A Systematic Review

<div><p>Background and Objective</p><p>Despite the availability of clinical practice guidelines (CPGs) for therapeutic drug monitoring (TDM) of vancomycin, vancomycin serum concentrations still do not reach therapeutic concentrations in many patients. Thus, we sought to systematically review the quality and consistency of recommendations for an international cohort of CPGs regarding vancomycin TDM.</p><p>Methods</p><p>PubMed, Embase, guidelines' websites and Google were searched for CPGs for vancomycin TDM. Two independent assessors rated the quality of each CPG using the Appraisal of Guidelines for Research & Evaluation II (AGREEII) instrument and data were independently extracted.</p><p>Results</p><p>Twelve guidelines were evaluated and the overall quality of guidelines for vancomycin TDM was moderate. The highest score was recorded in the domain of clarity of presentation, and the lowest score was recorded in the domain of rigor of development and stakeholder involvement. The specific recommendations for vancomycin TDM were moderately consistent and guidelines varied in trough concentration monitoring, frequency of TDM, and serum concentration targets.</p><p>Conclusion</p><p>The overall guideline quality for vancomycin TDM was not optimal and effort is needed to improve guideline quality, especially in the domain of rigor of development and stakeholder involvement.</p></div

Organic liquid-crystal devices based on ionic conductors

We use hydrogels to drive liquid crystals, achieving stretchable electrooptics. The device performance was maintained under a biaxial stretch of 1.5.</p

AGREE II domain-standardized scores for CPGs on vancomycin TDM.

<p>AGREE II domain-standardized scores for CPGs on vancomycin TDM.</p

Ag-decorated ZnO nanorods prepared by photochemical deposition and their high selectivity to ethanol using conducting oxide electrodes

A novel gas sensor structure consisting of LaNiO3 thin film electrodes and gas sensitive Ag nanoparticle-decorated ZnO nanorods is presented in this work.</p

Recommendations from CPGs.

<p>NR: not reported.</p>a<p>Higher levels may be required in specific situations as directed by the microbiologist.</p

Flow chart for the systematic review.

<p>Flow chart for the systematic review.</p

Characteristics of clinical practice guideline.

<p>AME: American; ASHP: American Society of Health-System Pharmacists; IDSA: Infectious Diseases Society of America; SIDP: Society of Infectious Diseases Pharmacists; LOS: Los Angeles; VAGLAHS: VA Greater Los Angeles Healthcare System; JAP: Japanese; JSC: Japanese Society of Chemotherapy; JSTDM: Japanese Society of Therapeutic Drug Monitoring. VAN: Vancouver; VCH: Vancouver Costal Health; PHC: Providence Health Care; AHS: ALB: Alberta; Alberta Health Services; NHS: National Health Services; File NHS ADTC: File National Health Services Board Area Drugs and Therapeutics Committee; CAL: Calderdale; CHNHS: Calderdale and Huddersfield NHS; DEV: Devon; RDENHS: Royal Devon and Exeter NHS; COR: Cornwall; RCHNHS: Royal Cornwall Hospitals NHS; BAT: Bath; RUHBNHS: Royal United Hospitals Bath NHS; SAP: Scottish Antimicrobial Prescribing; SAPG: Scottish Antimicrobial Prescribing Group; WOR: Worcestershire; WAHNHS: Worcestershire Acute Hosptials NHS; NR: not reported.</p