ReBandit: Random Effects Based Online RL Algorithm for Reducing Cannabis Use.

The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, especially among emerging adults (EAs; ages 18-25), addressing cannabis use and CUD remains a pivotal objective within the 2030 United Nations Agenda for Sustainable Development Goals (SDG). In this work, we develop an online reinforcement learning (RL) algorithm called reBandit which will be utilized in a mobile health study to deliver personalized mobile health interventions aimed at reducing cannabis use among EAs.

Online learning in bandits with predicted context.

We consider the contextual bandit problem where at each time, the agent only has access to a noisy version of the context and the error variance (or an estimator of this variance). This setting is motivated by a wide range of applications where the true context for decision-making is unobserved, and only a prediction of the context by a potentially complex machine learning algorithm is available. When the context error is non-vanishing, classical bandit algorithms fail to achieve sublinear regret.

A mobile health intervention for emerging adults with regular cannabis use: A micro-randomized pilot trial design protocol.

Background: Emerging adult (EA) cannabis use is associated with increased risk for health consequences. Just-in-time adaptive interventions (JITAIs) provide potential for preventing the escalation and consequences of cannabis use. Powered by mobile devices, JITAIs use decision rules that take the person's state and context as input, and output a recommended intervention (e.

Solution Blow Spinning of the Benzimidazole-Containing Aramid Nanofibers for Separators of Lithium-Ion Batteries.

Nanofibers based on high-performance polymers are much highlighted in recent studies toward advanced lithium-ion batteries. Herein, we demonstrate one scalable poly(ethylene oxide) (PEO)-assisted solution blow spinning strategy for the preparation of heterocyclic aramid (HA) nanofibers of poly(-phenylene-benzimidazole-terephthalamide). The incorporation of PEO is essential to improve the spinnability of the HA solution achieved directly through the low-temperature-solution copolymerization process.

Communication-Efficient Accurate Statistical Estimation.

When the data are stored in a distributed manner, direct applications of traditional statistical inference procedures are often prohibitive due to communication costs and privacy concerns. This paper develops and investigates two Communication-Efficient Accurate Statistical Estimators (CEASE), implemented through iterative algorithms for distributed optimization. In each iteration, node machines carry out computation in parallel and communicate with the central processor, which then broadcasts aggregated information to node machines for new updates.

Recent Advances in Self-Assembly and Application of -Aramids.

Poly(-phenylene terephthalamide) (PPTA) is one kind of lyotropic liquid crystal polymer. Kevlar fibers performed from PPTA are widely used in many fields due to their superior mechanical properties resulting from their highly oriented macromolecular structure. However, the "infusible and insoluble" characteristic of PPTA gives rise to its poor processability, which limits its scope of application.

First Report of Tobacco Mosaic Virus Infecting Papaya in China.

Papaya (Carica papaya. L) is widely cultivated in tropical and subtropical regions of China and has high nutritional and medicinal values. More than 11 species of papaya viruses have been recorded in the world, but the most destructive one for papaya production in China is papaya ringspot virus (PRSV) (Li, 2019).

Comparative transcriptome analysis revealed resistance differences of Cavendish bananas to Fusarium oxysporum f.sp. cubense race1 and race4.

Background: Banana Fusarium wilt is a devastating disease of bananas caused by Fusarium oxysporum f. sp. cubense (Foc) and is a serious threat to the global banana industry.

Predictive factors of progression in renal function after unilateral nephrectomy in renal malignancy.

Purpose: This study aimed to investigate the predictors of renal function changes after unilateral nephrectomy in renal malignancy.

Methods: The clinical data from patients who were pathologically diagnosed with primary renal cancer with preoperative eGFR≥60 mL/min/1.73 m2 and had accepted the unilateral nephrectomy for renal malignant tumors were retrospectively analysed.