King University

Fear causes fleeing and thereby saves lives: this exemplifies a popular and common sense but increasingly untenable view that the direct causation of behavior is the primary function of emotion. Instead, the authors develop a theory of emotion as a feedback system whose influence on behavior is typically indirect. By providing feedback and stimulating retrospective appraisal of actions, conscious emotional states can promote learning and alter guidelines for future behavior. Behavior may also be chosen to pursue (or avoid) anticipated emotional outcomes. Rapid, automatic affective responses, in contrast to the full-blown conscious emotions, may inform cognition and behavioral choice and thereby help guide current behavior. The automatic affective responses may also remind the person of past emotional outcomes and provide useful guides as to what emotional outcomes may be anticipated in the present. To justify replacing the direct causation model with the feedback model, the authors review a large body of empirical findings.

We developed a model in which leader-member exchange mediated between perceived transformational leadership behaviors and followers' task performance and organizational citizenship behaviors. Our sample comprised 162 leader-follower dyads within organizations situated throughout the People's Republic of China. We showed that leader-member exchange fully mediated between transformational leadership and task performance as well as organizational citizenship behaviors. Implications for the theory and practice of leadership are discussed, and future research directions offered.

The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic.

Water is the most abundant liquid on earth and also the substance with the largest number of anomalies in its properties. It is a prerequisite for life and as such a most important subject of current research in chemical physics and physical chemistry. In spite of its simplicity as a liquid, it has an enormously rich phase diagram where different types of ices, amorphous phases, and anomalies disclose a path that points to unique thermodynamics of its supercooled liquid state that still hides many unraveled secrets. In this review we describe the behavior of water in the regime from ambient conditions to the deeply supercooled region. The review describes simulations and experiments on this anomalous liquid. Several scenarios have been proposed to explain the anomalous properties that become strongly enhanced in the supercooled region. Among those, the second critical-point scenario has been investigated extensively, and at present most experimental evidence point to this scenario. Starting from very low temperatures, a coexistence line between a high-density amorphous phase and a low-density amorphous phase would continue in a coexistence line between a high-density and a low-density liquid phase terminating in a liquid-liquid critical point, LLCP. On approaching this LLCP from the one-phase region, a crossover in thermodynamics and dynamics can be found. This is discussed based on a picture of a temperature-dependent balance between a high-density liquid and a low-density liquid favored by, respectively, entropy and enthalpy, leading to a consistent picture of the thermodynamics of bulk water. Ice nucleation is also discussed, since this is what severely impedes experimental investigation of the vicinity of the proposed LLCP. Experimental investigation of stretched water, i.e., water at negative pressure, gives access to a different regime of the complex water diagram. Different ways to inhibit crystallization through confinement and aqueous solutions are discussed through results from experiments and simulations using the most sophisticated and advanced techniques. These findings represent tiles of a global picture that still needs to be completed. Some of the possible experimental lines of research that are essential to complete this picture are explored.

Microbes appear in every corner of human life, and microbes affect every aspect of human life. The human oral cavity contains a number of different habitats. Synergy and interaction of variable oral microorganisms help human body against invasion of undesirable stimulation outside. However, imbalance of microbial flora contributes to oral diseases and systemic diseases. Oral microbiomes play an important role in the human microbial community and human health. The use of recently developed molecular methods has greatly expanded our knowledge of the composition and function of the oral microbiome in health and disease. Studies in oral microbiomes and their interactions with microbiomes in variable body sites and variable health condition are critical in our cognition of our body and how to make effect on human health improvement.

MXenes are a growing family of two-dimensional transition metal carbides and/or nitrides that are densely stacked into macroscopically layered films and have been considered for applications such as flexible electromagnetic interference (EMI) shielding materials. However, the mechanical and electrical reliabilities of titanium carbide MXene films are affected by voids in their structure. We applied sequential bridging of hydrogen and covalent bonding agents to induce the densification of MXene films and removal of the voids, leading to highly compact MXene films. The obtained MXene films show high tensile strength, in combination with high toughness, electrical conductivity, and EMI shielding capability. Our high-performance MXene films are scalable, providing an avenue for assembling other two-dimensional platelets into high-performance films.

As organizational environments become increasingly dynamic, complex, and competitive, leaders are likely to face intensified contradictory, or seemingly paradoxical, demands. We develop the construct of “paradoxical leader behavior” in people management, which refers to seemingly competing, yet interrelated, behaviors to meet structural and follower demands simultaneously and over time. In Study 1, we develop a measure of paradoxical leader behavior in people management using five samples from China. Confirmatory factor analyses support a multidimensional measure of paradoxical leader behavior with five dimensions: (1) combining self-centeredness with other-centeredness; (2) maintaining both distance and closeness; (3) treating subordinates uniformly, while allowing individualization; (4) enforcing work requirements, while allowing flexibility; and (5) maintaining decision control, while allowing autonomy. In Study 2, we examine the antecedents and consequences of paradoxical leader behavior in people management with a field sample of 76 supervisors and 516 subordinates from 6 firms. We find that the extent to which supervisors engage in holistic thinking and have integrative complexity is positively related to their paradoxical behavior in managing people, which, in turn, is associated with increased proficiency, adaptivity, and proactivity among subordinates.

Aptamers are oligonucleotide sequences with a length of about 25-80 bases which have abilities to bind to specific target molecules that rival those of monoclonal antibodies. They are attracting great attention in diverse clinical translations on account of their various advantages, including prolonged storage life, little batch-to-batch differences, very low immunogenicity, and feasibility of chemical modifications for enhancing stability, prolonging the half-life in serum, and targeted delivery. In this Review, we demonstrate the emerging aptamer discovery technologies in developing advanced techniques for producing aptamers with high performance consistently and efficiently as well as requiring less cost and resources but offering a great chance of success. Further, the diverse modifications of aptamers for therapeutic applications including therapeutic agents, aptamer-drug conjugates, and targeted delivery materials are comprehensively summarized.

Video anomaly detection under weak labels is formulated as a typical multiple-instance learning problem in previous works. In this paper, we provide a new perspective, i.e., a supervised learning task under noisy labels. In such a viewpoint, as long as cleaning away label noise, we can directly apply fully supervised action classifiers to weakly supervised anomaly detection, and take maximum advantage of these well-developed classifiers. For this purpose, we devise a graph convolutional network to correct noisy labels. Based upon feature similarity and temporal consistency, our network propagates supervisory signals from high-confidence snippets to low-confidence ones. In this manner, the network is capable of providing cleaned supervision for action classifiers. During the test phase, we only need to obtain snippet-wise predictions from the action classifier without any extra post-processing. Extensive experiments on 3 datasets at different scales with 2 types of action classifiers demonstrate the efficacy of our method. Remarkably, we obtain the frame-level AUC score of 82.12% on UCF-Crime.

Advances in the synthesis and scalable manufacturing of single-walled carbon nanotubes (SWCNTs) remain critical to realizing many important commercial applications. Here we review recent breakthroughs in the synthesis of SWCNTs and highlight key ongoing research areas and challenges. A few key applications that capitalize on the properties of SWCNTs are also reviewed with respect to the recent synthesis breakthroughs and ways in which synthesis science can enable advances in these applications. While the primary focus of this review is on the science framework of SWCNT growth, we draw connections to mechanisms underlying the synthesis of other 1D and 2D materials such as boron nitride nanotubes and graphene.

Abstract Recently, two-dimensional (2D) atomic sheets have inspired new ideas in nanoscience including topologically protected charge transport,1, 2 spatially separated excitons,3 and strongly anisotropic heat transport.4 Here, we report the intriguing observation of stable nonvolatile resistance switching (NVRS) in single-layer atomic sheets sandwiched between metal electrodes. NVRS is observed in the prototypical semiconducting (MX2, M = Mo, W; and X = S, Se) transitional metal dichalcogenides (TMDs),5 which alludes to the universality of this phenomenon in TMD monolayers and offers forming-free switching. This observation of NVRS phenomenon, widely attributed to ionic diffusion, filament, and interfacial redox in bulk oxides and electrolytes,6−9 inspires new studies on defects, ion transport, and energetics at the sharp interfaces between atomically thin sheets and conducting electrodes. Our findings overturn the contemporary thinking that nonvolatile switching is not scalable to subnanometre owing to leakage currents.10 Emerging device concepts in nonvolatile flexible memory fabrics, and brain-inspired (neuromorphic) computing could benefit substantially from the wide 2D materials design space. A new major application, zero-static power radio frequency (RF) switching, is demonstrated with a monolayer switch operating to 50 GHz.

Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the artificial intelligence community. However, different from a natural language sentence, a program contains rich, explicit, and complicated structural information. Hence, traditional NLP models may be inappropriate for programs. In this paper, we propose a novel tree-based convolutional neural network (TBCNN) for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information. TBCNN is a generic architecture for programming language processing; our experiments show its effectiveness in two different program analysis tasks: classifying programs according to functionality, and detecting code snippets of certain patterns. TBCNN outperforms baseline methods, including several neural models for NLP.

We propose a novel multi-grained attention network (MGAN) model for aspect level sentiment classification.Existing approaches mostly adopt coarse-grained attention mechanism, which may bring information loss if the aspect has multiple words or larger context.We propose a fine-grained attention mechanism, which can capture the word-level interaction between aspect and context.And then we leverage the fine-grained and coarsegrained attention mechanisms to compose the MGAN framework.Moreover, unlike previous works which train each aspect with its context separately, we design an aspect alignment loss to depict the aspect-level interactions among the aspects that have the same context.We evaluate the proposed approach on three datasets: laptop and restaurant are from SemEval 2014, and the last one is a twitter dataset.Experimental results show that the multi-grained attention network consistently outperforms the state-of-the-art methods on all three datasets.We also conduct experiments to evaluate the effectiveness of aspect alignment loss, which indicates the aspect-level interactions can bring extra useful information and further improve the performance.

Abstract Metal–organic frameworks (MOFs) have emerged as desirable cross-functional platforms for electrochemical and photochemical energy conversion and storage (ECS) systems owing to their highly ordered and tunable compositions and structures. In this Review, we present engineering principles promoting the electro-/photochemical performance of MOF-based materials for ECS by component design and nanostructuring. Through the discussion of the engineering strategies of pristine MOFs, MOF composites, and their derivatives for ECS, the superiority and composition–structure–activity relationships of the engineered MOF-based materials with advanced components and nanostructures will be clarified. Finally, we provide a concluding discussion on the challenges and direction of future development in this emerging area of MOF-based materials for ECS.

Long noncoding RNAs (lncRNAs) represent a big category of noncoding RNA molecules, and increasing studies have shown that they play important roles in various critical biological processes. They show a diversity of functions through diverse mechanisms, among which regulating RNA molecules is one of the most popular ones. Given the big number of lncRNAs, it becomes urgent and important to predict the RNA targets of lncRNAs in a large scale for the comprehensive understanding of lncRNA functions and action mechanisms. Although several methods have been developed to predict RNA-RNA interactions, none of them can be used to predict the RNA targets of lncRNAs in a large scale. Here we presented a tool, LncTar, which shows the ability to efficiently predict the RNA targets of lncRNAs in a large scale. To test the accuracy of LncTar, we applied it to 10 experimentally supported lncRNA-mRNA interactions. As a result, LncTar successfully predicted 8 (80%) of the 10 lncRNA-mRNA pairs, suggesting that LncTar has a reliable accuracy. Finally, we believe that LncTar could be an efficient tool for the fast identification of the RNA targets of lncRNAs. LncTar is freely available at http://www.cuilab.cn/lnctar.

Organic-inorganic metal halide perovskite solar cells were fabricated by laminating films of a carbon nanotube (CNT) network onto a CH3NH3PbI3 substrate as a hole collector, bypassing the energy-consuming vacuum process of metal deposition. In the absence of an organic hole-transporting material and metal contact, CH3NH3PbI3 and CNTs formed a solar cell with an efficiency of up to 6.87%. The CH3NH3PbI3/CNTs solar cells were semitransparent and showed photovoltaic output with dual side illuminations due to the transparency of the CNT electrode. Adding spiro-OMeTAD to the CNT network forms a composite electrode that improved the efficiency to 9.90% due to the enhanced hole extraction and reduced recombination in solar cells. The interfacial charge transfer and transport in solar cells were investigated through photoluminescence and impedance measurements. The flexible and transparent CNT network film shows great potential for realizing flexible and semitransparent perovskite solar cells.

Severe acute respiratory syndrome coronavirus (SARS-CoV) is the pathogen of SARS, which caused a global panic in 2003. We describe here the screening of Chinese herbal medicine-based, novel small molecules that bind avidly with the surface spike protein of SARS-CoV and thus can interfere with the entry of the virus to its host cells. We achieved this by using a two-step screening method consisting of frontal affinity chromatography-mass spectrometry coupled with a viral infection assay based on a human immunodeficiency virus (HIV)-luc/SARS pseudotyped virus. Two small molecules, tetra-O-galloyl-beta-D-glucose (TGG) and luteolin, were identified, whose anti-SARS-CoV activities were confirmed by using a wild-type SARS-CoV infection system. TGG exhibits prominent anti-SARS-CoV activity with a 50% effective concentration of 4.5 microM and a selective index of 240.0. The two-step screening method described here yielded several small molecules that can be used for developing new classes of anti-SARS-CoV drugs and is potentially useful for the high-throughput screening of drugs inhibiting the entry of HIV, hepatitis C virus, and other insidious viruses into their host cells.

Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stochasticity of human behavior. Our approach models these interactions and constraints jointly within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context. The model decodes recurrently to multiple agents' future trajectories, using adversarial loss to learn stochastic predictions. Experiments on both highway driving and pedestrian crowd datasets show that the model achieves state-of-the-art prediction accuracy.

The article focuses on the effects of economic globalization on the field of management science. The continued growth of international business enterprises means that a corresponding importance must be placed on research into their management problems and practice. This obvious fact has been recognized, and there has been a large increase in research in this area published in the leading scholarly periodicals of the discipline. There is, however, a worrisome tendency for researchers to employ the paradigms of research into North American, primarily U.S. management. This ignores the differing cultural, social, and economic ideas and practices persons of different nations bring to the management of the same international operation. Research into these businesses requires a pluralistic approach if it is to accurately reflect their real world operations.

What is the interrelationship among formal institutions, social networks, and new venture growth? Drawing on the theory of institutional polycentrism and social network theory, we examine this question using data on 637 entrepreneurs from four different countries. We find the confluence of weak and inefficient formal institutions to be associated with a larger number of structural holes in entrepreneurial social networks. While the effect of this institutional order on the revenue growth of new ventures is negative, a network's structural holes have a positive effect on revenue growth. Furthermore, the positive effect of structural holes on revenue growth is stronger in an environment with a more adverse institutional order (i.e., weaker and more inefficient institutions). The contributions and implications of these findings are discussed.