Cycles through 4 vertices in 3-connected graphs

AbstractS.C. Locke proposed a question: If G is a 3-connected graph with minimum degree d and X is a set of 4 vertices on a cycle in G, must G have a cycle through X with length at least min{2d,|V(G)|}? In this paper, we answer this question

Augmenting intrinsic fenton-like activities of MOF-derived catalysts via N-molecule-assisted self-catalyzed carbonization

To overcome the ever-growing organic pollutions in the water system, abundant efforts have been dedicated to fabricating efficient Fenton-like carbon catalysts. However, the rational design of carbon catalysts with high intrinsic activity remains a long-term goal. Herein, we report a new N-molecule-assisted self-catalytic carbonization process in augmenting the intrinsic Fenton-like activity of metal–organic-framework-derived carbon hybrids. During carbonization, the N-molecules provide alkane/ammonia gases and the formed iron nanocrystals act as the in situ catalysts, which result in the elaborated formation of carbon nanotubes (in situ chemical vapor deposition from alkane/iron catalysts) and micro-/meso-porous structures (ammonia gas etching). The obtained catalysts exhibited with abundant Fe/Fe–Nx/pyridinic-N active species, micro-/meso-porous structures, and conductive carbon nanotubes. Consequently, the catalysts exhibit high efficiency toward the degradation of different organic pollutions, such as bisphenol A, methylene blue, and tetracycline. This study not only creates a new pathway for achieving highly active Fenton-like carbon catalysts but also takes a step toward the customized production of advanced carbon hybrids for diverse energy and environmental applications

Dopamine D2-receptor neurons in nucleus accumbens regulate sevoflurane anesthesia in mice

IntroductionThe mechanism of general anesthesia remains elusive. In recent years, numerous investigations have indicated that its mode of action is closely associated with the sleep-wake pathway. As a result, this study aimed to explore the involvement of dopamine D2 receptor (D2R) expressing neurons located in the nucleus accumbens (NAc), a critical nucleus governing sleep-wake regulation, in sevoflurane anesthesia.MethodsThis exploration was carried out using calcium fiber photometry and optogenetics technology, while utilizing cortical electroencephalogram (EEG), loss of righting reflex (LORR), and recovery of righting reflex (RORR) as experimental indicators.ResultsThe findings from calcium fiber photometry revealed a decrease in the activity of NAcD2R neurons during the induction phase of sevoflurane anesthesia, with subsequent recovery observed during the anesthesia’s emergence phase. Moreover, the activation of NAcD2R neurons through optogenetics technology led to a reduction in the anesthesia induction process and an extension of the arousal process in mice. Conversely, the inhibition of these neurons resulted in the opposite effect. Furthermore, the activation of NAcD2R neurons projecting into the ventral pallidum (VP) via optogenetics demonstrated a shortened induction time for mice under sevoflurane anesthesia.DiscussionIn conclusion, our research outcomes suggest that NAcD2R neurons play a promotive role in the sevoflurane general anesthesia process in mice, and their activation can reduce the induction time of anesthesia via the ventral pallidum (VP)

The Inception of Housing Pathways in Urban China: The Declining Household Formation of Young Adults from 2011 to 2017

journal articleThe homeownership rate of young adults has surged to an unprecedented level in urban China, despite rising housing prices and significant rural-urban migration. A trend analysis of nationally representative microdata shows that household formation is the missing link in the paradox and that many young adults aged 18-44 have failed to form independent households from 2011 to 2017, thereby delaying the start of their housing pathways. When factors such as socioeconomic and institutional attributes are controlled for, age differences in household formation decrease as expected. However, the age differences grow surprisingly larger over the study period, reflective of reform-induced changes in resource allocation. Further analysis demonstrates significant heterogeneity in headship status. While local young adults are squeezing into homeownership, migrants are overrepresented in the relatively stunted rental sector. Thus, while migration has brought newcomers to urban China and kept the headship rates from falling even further, institutional barriers have blocked migrants' housing pathways. Overall, the pace of change is breathtaking. There is a growing divergence in young adults' housing pathways, which depends on the timing of market entry, institutional attributes, housing prices, and personal income

Do Honored Cities Achieve a Sustainable Development? A Quasi-Natural Experimental Study Based on “National Civilized City” Campaign in China

As a new model of urban governance with Chinese characteristics, the national honored cities from city evaluation competitions, represented by the “National Civilized City” campaign, has always been popular among Chinese cities. Can the honored cities of the campaigns achieve sustainable development, and how? Based on the five concepts of sustainable development, which are innovation, coordination, green, openness and sharing, this study sets up a comprehensive index to measure the sustainability of the growth of a city. Then, the data of 242 Chinese cities from 2011 to 2019 and the difference-in-differences (DID) approach are used to evaluate the impacts of the Civilized City honored in the campaigns on the sustainability of growth. The results show that: (1) the “Civilized City” honor promotes sustainable development in Chinese cities; (2) the mechanism analysis shows that the “Civilized City” honor contributes to the sustainability of growth by effectively promoting the level of industrial agglomeration in cities; (3) further heterogeneity analysis shows that the effect of the “Civilized City” honor on the sustainability of growth varies by city size, the administrative level and the location of the city. By providing the evidence of economic effects of the “Civilized City” honor, this research rationalizes the city campaigns run by the Chinese government and provides important enlightenment for the continuous improvement of the selection mechanism of the national honored cities to promote sustainable development

Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN) based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD) algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity). In order to examine the advantages of the proposed model, four popular artificial intelligence methods—the backward propagation neural network (BPNN), the generalized radial basis function neural network (GRBFNN), the extreme learning machine (ELM), and the support vector regressor (SVR) are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption prediction