On the Synthesis of the Astronomically Elusive 1-Ethynyl-3-Silacyclopropenylidene (c-SiC4H2) Molecule in Circumstellar Envelopes of Carbon-rich Asymptotic Giant Branch Stars and Its Potential Role in the Formation of the Silicon Tetracarbide Chain (SiC4)

Organosilicon molecules such as silicon carbide (SiC), silicon dicarbide (c-SiC2), silicon tricarbide (c-SiC3), and silicon tetracarbide (SiC4) represent basic molecular building blocks connected to the growth of silicon-carbide dust grains in the outflow of circumstellar envelopes of carbon-rich asymptotic giant branch (AGB) stars. Yet, the fundamental mechanisms of the formation of silicon carbides and of the early processes that initiate the coupling of silicon-carbon bonds in circumstellar envelopes have remained obscure. Here, we reveal in a crossed molecular beam experiment contemplated with ab initio electronic calculations that the astronomically elusive 1-ethynyl-3-silacyclopropenylidene molecule (c-SiC4H2, Cs, X1A′) can be synthesized via a single-collision event through the barrierless reaction of the silylidyne radical (SiH) with diacetylene (C4H2). This system represents a benchmark of a previously overlooked class of reactions, in which the silicon-carbon bond coupling can be initiated by a barrierless and overall exoergic reaction between the simplest silicon-bearing radical (silylidyne) and a highly hydrogen-deficient hydrocarbon (diacetylene) in the inner circumstellar envelopes of evolved carbon-rich stars such as IRC+10216. Considering that organosilicon molecules like 1-ethynyl-3-silacyclopropenylidene might be ultimately photolyzed to bare carbon-silicon clusters like the linear silicon tetracarbide (SiC4), hydrogenated silicon-carbon clusters might represent the missing link eventually connecting simple molecular precursors such as silane (SiH4) to the population of silicon-carbide based interstellar grains ejected from carbon-rich AGB stars into the interstellar medium

AdaRec: Adaptive Sequential Recommendation for Reinforcing Long-term User Engagement

Growing attention has been paid to Reinforcement Learning (RL) algorithms when optimizing long-term user engagement in sequential recommendation tasks. One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies. When formulated as a Markov Decision Process (MDP), the dynamics and reward functions of the recommendation system are continuously affected by these changes. Existing RL algorithms for recommendation systems will suffer from distribution shift and struggle to adapt in such an MDP. In this paper, we introduce a novel paradigm called Adaptive Sequential Recommendation (AdaRec) to address this issue. AdaRec proposes a new distance-based representation loss to extract latent information from users' interaction trajectories. Such information reflects how RL policy fits to current user behavior patterns, and helps the policy to identify subtle changes in the recommendation system. To make rapid adaptation to these changes, AdaRec encourages exploration with the idea of optimism under uncertainty. The exploration is further guarded by zero-order action optimization to ensure stable recommendation quality in complicated environments. We conduct extensive empirical analyses in both simulator-based and live sequential recommendation tasks, where AdaRec exhibits superior long-term performance compared to all baseline algorithms.Comment: Preprint. Under Revie

Gas-phase synthesis of silaformaldehyde (h2sio) and hydroxysilylene (hsioh) in outflows of oxygen-rich asymptotic giant branch stars

Silicon- and oxygen-containing species such as silicon monoxide (SiO) and silicon dioxide (SiO2) represent basic molecular building blocks connected to the growth of silicate grains in outflows of oxygen-rich asymptotic giant branch (AGB) stars like R Doradus. Yet the fundamental mechanisms of the formation of silicate grains and the early processes that initiate the coupling of the silicon with the oxygen chemistries in circumstellar envelopes have remained obscure. Here, in a crossed molecular beams experiment combined with ab initio electronic structure calculations, we reveal that at least the d2-silaformaldehyde (D2SiO) and d2-hydroxysilylene (DSiOD) molecules -proxies for the astronomically elusive silaformaldehyde (H2SiO) and hydroxysilylene (HSiOH) molecules-can be synthesized via the reaction of the D1-silylidyne radical (SiD; X2π) with D2-water (D2O) under single-collision conditions. This system represents a benchmark of a previously overlooked class of reactions, in which the silicon- oxygen bond coupling can be initiated by a reaction between the simplest silicon-bearing radical (silylidyne) and one of the most abundant species in the circumstellar envelopes of evolved oxygen-rich AGB stars (water). As supported by novel astrochemical modeling, considering that silicon- and oxygen-containing species like H2SiO and HSiOH might be photolyzed easily, they ultimately connect to simple molecular precursors such as SiO that drive a chain of reactions conceivably forming higher molecular weight silicon oxides and, ultimately, a population of silicates at high temperatures

Low-temperature gas-phase formation of indene in the interstellar medium

Polycyclic aromatic hydrocarbons (PAHs) are fundamental molecular building blocks of fullerenes and carbonaceous nanostructures in the interstellar medium and in combustion systems. However, an understanding of the formation of aromatic molecules carrying five-membered rings—the essential building block of nonplanar PAHs—is still in its infancy. Exploiting crossed molecular beam experiments augmented by electronic structure calculations and astrochemical modeling, we reveal an unusual pathway leading to the formation of indene (C9H8)—the prototype aromatic molecule with a five-membered ring—via a barrierless bimolecular reaction involving the simplest organic radical—methylidyne (CH)—and styrene (C6H5C2H3) through the hitherto elusive methylidyne addition–cyclization–aromatization (MACA) mechanism. Through extensive structural reorganization of the carbon backbone, the incorporation of a five-membered ring may eventually lead to three-dimensional PAHs such as corannulene (C20H10) along with fullerenes (C60, C70), thus offering a new concept on the low-temperature chemistry of carbon in our galaxy

Disrupted Asymmetry of Inter- and Intra-Hemispheric Functional Connectivity at Rest in Medication-Free Obsessive-Compulsive Disorder

Disrupted functional asymmetry of cerebral hemispheres may be altered in patients with obsessive-compulsive disorder (OCD). However, little is known about whether anomalous brain asymmetries originate from inter- and/or intra-hemispheric functional connectivity (FC) at rest in OCD. In this study, resting-state functional magnetic resonance imaging was applied to 40 medication-free patients with OCD and 38 gender-, age-, and education-matched healthy controls (HCs). Data were analyzed using the parameter of asymmetry (PAS) and support vector machine methods. Patients with OCD showed significantly increased PAS in the left posterior cingulate cortex, left precentral gyrus/postcentral gyrus, and right inferior occipital gyrus and decreased PAS in the left dorsolateral prefrontal cortex (DLPFC), bilateral middle cingulate cortex (MCC), left inferior parietal lobule, and left cerebellum Crus I. A negative correlation was found between decreased PAS in the left DLPFC and Yale–Brown Obsessive-compulsive Scale compulsive behavior scores in the patients. Furthermore, decreased PAS in the bilateral MCC could be used to distinguish OCD from HCs with a sensitivity of 87.50%, an accuracy of 88.46%, and a specificity of 89.47%. These results highlighted the contribution of disrupted asymmetry of intra-hemispheric FC within and outside the cortico-striato-thalamocortical circuits at rest in the pathophysiology of OCD, and reduced intra-hemispheric FC in the bilateral MCC may serve as a potential biomarker to classify individuals with OCD from HCs

Effect of Particle Size on Current-Carrying Friction and Wear Properties of Copper-Graphite Composites by Spark Plasma Sintering

Copper-graphite composites were prepared by spark plasma sintering (SPS) with copper powder and copper-coated graphite powder. The effect of particle size of raw material powder on the current-carrying friction properties of copper-graphite composites was studied. The results show that the friction coefficient of the composites decreased with the decrease of the particle size of copper-coated graphite powder, the friction coefficient of the composites increased with the decrease of the particle size of the copper powder, the wear rate of the composites increased with the decrease of the particle size of the copper-coated graphite powder, and the wear rate of the composites increased significantly with the decrease of the particle size of the copper-coated graphite powder. The current carrying properties of composites with different particle size ratios and QCr0.5 pairs are good and fluctuate little. The current-carrying friction properties of 150 μm copper powder and 75 μm copper-coated graphite powder were found to be the best. The wear surface could be divided into mechanical wear area and arc erosion area. The main area of arc erosion was less than 15% of the total area, and it was mainly distributed in the friction outlet area. The main forms of mechanical wear included furrow, rolling deformation, cold welding, and tearing, among other forms. Graphite film was formed on the surface. The surface quality of the composite prepared by 150 μm copper powder and 75 μm copper-coated graphite powder was the best, the Sa was 3.22 μm, rolling deformation was the most adequate, no large tear pit and furrow appeared, and the carbon content on the worn surface was much higher than that in the composite. The behavior of arc erosion was mainly melting and splashing, and the particle size of the original powder had little effect on it

Online Bayesian Data Fusion in Environment Monitoring Sensor Networks

Assuring reliable data collection in environment monitoring sensor network is a major design challenge. This paper gives a novel Bayesian model to reliably monitor physical phenomenon. We briefly review the errors on the data transfer channel between the sensor quantifying the physical phenomenon and the fusion node, and a discrete K -ary input and K -ary output channel is presented to model the data transfer channel, where K is the number of quantification levels at the sensor. Then, discrete time series models are used to estimate the mean value of the physical phenomenon, and the estimation error is modeled as a Gaussian process. Finally, based on the transition probability of the proposed data transfer channel and the probability of the estimated value transited to specific quantification levels, the level with the maximum posterior probability is decided to be the current value of the physical phenomenon. Evaluations based on real sensor data show that significant gain can be achieved by the proposed algorithms in environment monitoring sensor networks compared with channel-unaware algorithms