No abstract

Atomic doping of clusters is known as an effective approach to stabilize or modify the structures and properties of resulting doped clusters. We herein report the effect of manganese (Mn) doping on the structure evolution of small-sized boron (B) clusters. The global minimum structures of both neutral and charged Mn doped B cluster [Formula: see text] (n  =  10-20 and Q  =  0, ±1) have been proposed through extensive first-principles swarm-intelligence based structure searches. It is found that Mn doping has significantly modified the grow behaviors of B clusters, leading to two novel structural transitions from planar to tubular and then to cage-like B structures in both neutral and charged species. Half-sandwich-type structures are most favorable for small [Formula: see text] (n  ⩽  13) clusters and gradually transform to Mn-centered double-ring tubular structures at [Formula: see text] clusters with superior thermodynamic stabilities compared with their neighbors. Most strikingly, endohedral cages become the ground-state structures for larger [Formula: see text] (n  ⩾  19) clusters, among which [Formula: see text] adopts a highly symmetric structure with superior thermodynamic stability and a large HOMO-LUMO gap of 4.53 eV. The unique stability of the endohedral [Formula: see text] cage is attributed to the geometric fit and formation of 18-electron closed-shell configuration. The results significantly advance our understanding about the structure and bonding of B-based clusters and strongly suggest transition-metal doping as a viable route to synthesize intriguing B-based nanomaterials.

Honeybees form societies in which thousands of members integrate their behaviours to act as a single functional unit. We have little knowledge on how the collaborative features are regulated by workers’ activities because we lack methods that enable collection of simultaneous and continuous behavioural information for each worker bee. In this study, we introduce the Bee Behavioral Annotation System (BBAS), which enables the automated detection of bees’ behaviours in small observation hives. Continuous information on position and orientation were obtained by marking worker bees with 2D barcodes in a small observation hive. We computed behavioural and social features from the tracking information to train a behaviour classifier for encounter behaviours (interaction of workers via antennation) using a machine learning-based system. The classifier correctly detected 93% of the encounter behaviours in a group of bees, whereas 13% of the falsely classified behaviours were unrelated to encounter behaviours. The possibility of building accurate classifiers for automatically annotating behaviours may allow for the examination of individual behaviours of worker bees in the social environments of small observation hives. We envisage that BBAS will be a powerful tool for detecting the effects of experimental manipulation of social attributes and sub-lethal effects of pesticides on behaviour.

New-generation farmers have become a vital force for entrepreneurship in their hometowns. To better promote farmers to start businesses, it’s important to know about their quality of work life. Based on a survey of the quality of work life and entrepreneurship will of farmers from three cities and eight counties in Shandong province of China, this paper analyzed the effects of farmers’ quality of work life on their entrepreneurship will in their hometowns using a Logistic regression model. Our findings show that farmers have a relatively low cognition level of their quality of work life, and their interpersonal relationship, work characteristics, material security, and family demands have significant effects on their entrepreneurship will. According to the findings, this paper proposed some suggestions for promoting farmers’ entrepreneurship will in their hometowns from the perspectives of organizational management, extrinsic entrepreneurship stimulus, and internal demand.