Explore the vast range of titles available.

This paper investigates why industrial firms conduct Corporate Venture Capital (CVC) investment in entrepreneurial companies. I test alternative views on CVC by exploiting the entry, investment, and termination decisions of CVC divisions. CVC entry concentrates in firms that experience deteriorations of internal innovation. At the investment stage, CVCs select startups with a similar technological focus but that have a non-overlapping knowledge base, and they integrate technologies generated from these ventures that create strategic value. CVCs are terminated when parent firms’ innovation recovers. Overall, the strategic desire to fix innovation weaknesses after adverse shocks motivates firms to adopt CVCs.

This paper combines the nine-factor model with the construction of university think tanks, establishes the strategic positioning of university think tanks, analyzes the methods and measures of cultivating leading talents and innovative talents in university think tanks and classifies the influence of university think tanks into decision-making influence, elite influence and popular influence. Based on the analysis of big data and concerning the fuzzy hierarchical analysis method, the evaluation model of information service quality of university think tanks has been established. The evaluation indexes of the information platform of university think tanks are divided into four indexes: service resources, service content, webpage technology, and service effect. According to the constructed evaluation indexes, two-by-two comparisons are made between each index to obtain the score of the importance of each index. Based on fuzzy mathematics, the qualitative evaluation can be transformed into quantitative evaluation according to the principle of affiliation degree to arrive at the evaluation grade of the information platform capacity of university think tanks and innovative talents.

Carbon supported intermetallic compound nanoparticles with high activity and stability are promising cathodic catalysts for oxygen reduction reaction in proton-exchange-membrane fuel cells. However, the synthesis of intermetallic catalysts suffers from large diffusion barrier for atom ordering, resulting in low ordering degree and limited performance. We demonstrate a low-melting-point metal doping strategy for the synthesis of highly ordered L1 0 -type M-doped PtCo (M = Ga, Pb, Sb, Cu) intermetallic catalysts. We find that the ordering degree of the M-doped PtCo catalysts increases with the decrease of melting point of M. Theoretic studies reveal that the low-melting-point metal doping can decrease the energy barrier for atom diffusion. The prepared highly ordered Ga-doped PtCo catalyst exhibits a large mass activity of 1.07 A mg Pt −1 at 0.9 V in H 2 -O 2 fuel cells and a rated power density of 1.05 W cm −2 in H 2 -air fuel cells, with a Pt loading of 0.075 mg Pt  cm −2 . The development of highly ordered intermetallic catalyst for oxygen reduction reactions suffers from large diffusion barrier for atom ordering. Here, the authors use a low melting-point metal doping strategy to synthesize a series of highly ordered metal-doped platinum–cobalt alloy fuel cell catalysts.

Wave–particle duality epitomizes the counterintuitive character of quantum physics. A striking illustration is the quantum delayed-choice experiment, which is based on Wheeler’s classic delayed-choice gedanken experiment, but with the addition of a quantum-controlled device enabling wave-to-particle transitions. Here, we realize a quantum delayed-choice experiment in which we control the wave and the particle states of photons and particularly the phase between them, thus directly establishing the created quantum nature of the wave–particle. We generate three-photon entangled states and inject one photon into a Mach–Zehnder interferometer embedded in a 186-m-long two-photon Hong–Ou–Mandel interferometer. The third photon is sent 141 m away from the interferometers and remotely prepares a two-photon quantum gate according to independent active choices under Einstein locality conditions. We realize transitions between wave and particle states in both classical and quantum scenarios, and therefore tests of the complementarity principle that go fundamentally beyond earlier implementations.The quantum-delayed choice experiment is implemented with multiple entangled photons under Einstein’s locality condition. The wave–particle quantum superposition is realized by controlling the relative phase between the wave and particle states.

Background Increasing number of studies have demonstrated certain patterns of microbial changes in gynecological diseases; however, the interaction between them remains unclear. To evaluate the consistency or specificity across multiple studies on different gynecological diseases and microbial alterations at different sites of the body (gut and genital tract), we conducted a systematic review and meta-analysis. Methods We searched PubMed, Embase, Web of Science, and Cochrane Library up to December 5, 2022(PROSPERO: CRD42023400205). Eligible studies focused on gynecological diseases in adult women, applied next-generation sequencing on microbiome, and reported outcomes including alpha or beta diversity or relative abundance. The random-effects model on standardized mean difference (SMD) was conducted using the inverse-variance method for alpha diversity indices. Results Of 3327 unique articles, 87 eligible studies were included. Significant decreases were found in gut microbiome of patients versus controls (observed species SMD=-0.35; 95%CI, -0.62 to -0.09; Shannon index SMD=-0.23; 95%CI, -0.40 to -0.06), whereas significant increases were observed in vaginal microbiome (Chao1 SMD = 1.15; 95%CI, 0.74 to 1.56; Shannon index SMD = 0.51; 95%CI, 0.16 to 0.86). Most studies of different diagnostic categories showed no significant differences in beta diversity. Disease specificity was observed, but almost all the changes were only replicated in three studies, except for the increased Aerococcus in bacterial vaginosis (BV). Patients with major gynecological diseases shared the enrichment of Prevotella and depletion of Lactobacillus , and an overlap in microbes was implied between BV, cervical intraepithelial neoplasia, and cervical cancer. Conclusions These findings demonstrated an association between alterations in gut and genital microbiota and gynecological diseases. The most observed results were shared alterations across diseases rather than disease-specific alterations. Therefore, further investigation is required to identify specific biomarkers for diagnosis and treatment in the future.

Since substations are key parts of power transmission, ensuring the safety of substations involves monitoring whether the substation equipment is in a normal state. Oil leakage detection is one of the necessary daily tasks of substation inspection robots, which can immediately find out whether there is oil leakage in the equipment in operation so as to ensure the service life of the equipment and maintain the safe and stable operation of the system. At present, there are still some challenges in oil leakage detection in substation equipment: there is a lack of a more accurate method of detecting oil leakage in small objects, and there is no combination of intelligent inspection robots to assist substation inspection workers in judging oil leakage accidents. To address these issues, this paper proposes a small object detection method for oil leakage defects in substations. This paper proposes a small object detection method for oil leakage defects in substations, which is based on the feature extraction network Resnet-101 of the Faster-RCNN model for improvement. In order to decrease the loss of information in the original image, especially for small objects, this method is developed by canceling the downsampling operation and replacing the large convolutional kernel with a small convolutional kernel. In addition, the method proposed in this paper is combined with an intelligent inspection robot, and an oil leakage decision-making scheme is designed, which can provide substation equipment oil leakage maintenance recommendations for substation workers to deal with oil leakage accidents. Finally, the experimental validation of real substation oil leakage image collection is carried out by the intelligent inspection robot equipped with a camera. The experimental results show that the proposed FRRNet101-c model in this paper has the best performance for oil leakage detection in substation equipment compared with several baseline models, improving the Mean Average Precision (mAP) by 6.3%, especially in detecting small objects, which has improved by 12%.

• Sessile plants constantly experience environmental stresses in nature. They must have evolved effective mechanisms to balance growth with stress response. Here we report the MADS-box transcription factor AGL16 acting as a negative regulator in stress response in Arabidopsis. • Loss-of-AGL16 confers resistance to salt stress in seed germination, root elongation and soil-grown plants, while elevated AGL16 expression confers the opposite phenotypes compared with wild-type. However, the sensitivity to abscisic acid (ABA) in seed germination is inversely correlated with AGL16 expression levels. • Transcriptomic comparison revealed that the improved salt resistance of agl16 mutants was largely attributed to enhanced expression of stress-responsive transcriptional factors and the genes involved in ABA signalling and ion homeostasis. We further demonstrated that AGL16 directly binds to the CArG motifs in the promoter of HKT1;1, HsfA6a and MYB102 and represses their expression. Genetic analyses with double mutants also support that HsfA6a and MYB102 are target genes of AGL16. • Taken together, our results show that AGL16 acts as a negative regulator transcriptionally suppressing key components in the stress response and may play a role in balancing stress response with growth.

In the big data context, decision makers usually face the problem of evaluating environmental efficiencies of a massive number of decision making units (DMUs) using the data envelopment analysis (DEA) method. However, standard implementations of the traditional DEA calculation process will consume much time when the data set is very large. To eliminate this limitation of DEA applied to big data, firstly, the slacks-based measure (SBM) model is extended considering undesirable outputs and the variable returns to scale (VRS) assumption for environmental efficiency evaluation of the DMUs. Then, an approach comprised of two algorithms is proposed for environmental efficiency evaluation when the number of DMUs is massive. The set of DMUs is partitioned into subsets, a technique which facilitates the application of a parallel computing mechanism. Algorithm 1 can be used for identifying the environment efficient DMUs in any DMU set. Further, Algorithm 2 (a parallel computing algorithm) shows how to use the proposed model and Algorithm 1 in parallel to find the environmental efficiencies of all DMUs. A simulation shows that the parallel computing design helps to significantly reduce calculation time when completing environmental efficiency evaluation tasks with large data sets, compared with using the traditional calculation processes. Finally, the proposed approach is applied to do environmental efficiency analysis of transportation systems.

Traditional theories and methods for comprehensive environmental performance evaluation are challenged by the appearance of big data because of its large quantity, high velocity, and high diversity, even though big data is defective in accuracy and stability. In this paper, we first review the literature on environmental performance evaluation, including evaluation theories, the methods of data envelopment analysis, and the technologies and applications of life cycle assessment and the ecological footprint. Then, we present the theories and technologies regarding big data and the opportunities and applications for these in related areas, followed by a discussion on problems and challenges. The latest advances in environmental management based on big data technologies are summarized. Finally, conclusions are put forward that the feasibility, reliability, and stability of existing theories and methodologies should be thoroughly validated before they can be successfully applied to evaluate environmental performance in practice and provide scientific basis and guidance to formulate environmental protection policies.

When ischemic stroke occurs, oxygen and energy depletion triggers a cascade of events, including inflammatory responses, glutamate excitotoxicity, oxidative stress, and apoptosis that result in a profound brain injury. The inflammatory response contributes to secondary neuronal damage, which exerts a substantial impact on both acute ischemic injury and the chronic recovery of the brain function. Microglia are the resident immune cells in the brain that constantly monitor brain microenvironment under normal conditions. Once ischemia occurs, microglia are activated to produce both detrimental and neuroprotective mediators, and the balance of the two counteracting mediators determines the fate of injured neurons. The activation of microglia is defined as either classic (M1) or alternative (M2): M1 microglia secrete pro-inflammatory cytokines (TNFα, IL-23, IL-1β, IL-12, etc ) and exacerbate neuronal injury, whereas the M2 phenotype promotes anti-inflammatory responses that are reparative. It has important translational value to regulate M1/M2 microglial activation to minimize the detrimental effects and/or maximize the protective role. Here, we discuss various regulators of microglia/macrophage activation and the interaction between microglia and neurons in the context of ischemic stroke.