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In recent years, the global adoption of electric vehicles (EVs) has surged, prompting a corresponding rise in the installation of charging stations. This proliferation has underscored the importance of expediting the deployment of charging infrastructure. Both academia and industry have thus devoted to addressing the charging station location problem (CSLP) to streamline this process. However, prevailing algorithms addressing CSLP are hampered by restrictive assumptions and computational overhead, leading to a dearth of comprehensive evaluations in the spatiotemporal dimensions. Consequently, their practical viability is restricted. Moreover, the placement of charging stations exerts a significant impact on both the road network and the power grid, which necessitates the evaluation of the potential post-deployment impacts on these interconnected networks holistically. In this study, we propose CSLens, a visual analytics system designed to inform charging station deployment decisions through the lens of coupled transportation and power networks. CSLens offers multiple visualizations and interactive features, empowering users to delve into the existing charging station layout, explore alternative deployment solutions, and assess the ensuring impact. To validate the efficacy of CSLens, we conducted two case studies and engaged in interviews with domain experts. Through these efforts, we substantiated the usability and practical utility of CSLens in enhancing the decision-making process surrounding charging station deployment. Our findings underscore CSLens's potential to serve as a valuable asset in navigating the complexities of charging infrastructure planning.

Promoting the development of electric vehicles requires the widespread deployment of charging infrastructure, which poses numerous technical and financial constraints. Despite extensive research focusing on optimizing charging station locations, few studies have accounted for charging station utilization and the factors that influence it. This study aims to evaluate charging station operations and explore charging station utilization to inform planning and facilitate better utilization of funds for expanding charging infrastructure. We present EVCSeer, a visual analytics system that utilizes representative predictive models and well-designed visualizations to analyze factors affecting charging station utilization, compare deployment strategies, and optimize utilization. The system also enables "what-if" analysis of charging station deployments. Two case studies, expert interviews, and a qualitative user study support the validity and usefulness of EVCSeer.

The issue of traffic congestion poses a significant obstacle to the development of global cities. One promising solution to tackle this problem is intelligent traffic signal control (TSC). Recently, TSC strategies leveraging reinforcement learning (RL) have garnered attention among researchers. However, the evaluation of these models has primarily relied on fixed metrics like reward and queue length. This limited evaluation approach provides only a narrow view of the model's decision-making process, impeding its practical implementation. Moreover, effective TSC necessitates coordinated actions across multiple intersections. Existing visual analysis solutions fall short when applied in multi-agent settings. In this study, we delve into the challenge of interpretability in multi-agent reinforcement learning (MARL), particularly within the context of TSC. We propose MARLens, a visual analytics system tailored to understand MARL-based TSC. Our system serves as a versatile platform for both RL and TSC researchers. It empowers them to explore the model's features from various perspectives, revealing its decision-making processes and shedding light on interactions among different agents. To facilitate quick identification of critical states, we have devised multiple visualization views, complemented by a traffic simulation module that allows users to replay specific training scenarios. To validate the utility of our proposed system, we present three comprehensive case studies, incorporate insights from domain experts through interviews, and conduct a user study. These collective efforts underscore the feasibility and effectiveness of MARLens in enhancing our understanding of MARL-based TSC systems and pave the way for more informed and efficient traffic management strategies.

High-performance perovskite solar cells (PSCs) typically require interfacial passivation, yet this is challenging for the buried interface, owing to the dissolution of passivation agents during the deposition of perovskites. Here, this limitation is overcome with in situ buried-interface passivation-achieved via directly adding a cyanoacrylic-acid-based molecular additive, namely BT-T, into the perovskite precursor solution. Classical and ab initio molecular dynamics simulations reveal that BT-T spontaneously may self-assemble at the buried interface during the formation of the perovskite layer on a nickel oxide hole-transporting layer. The preferential buried-interface passivation results in facilitated hole transfer and suppressed charge recombination. In addition, residual BT-T molecules in the perovskite layer enhance its stability and homogeneity. A power-conversion efficiency (PCE) of 23.48% for 1.0 cm 2 inverted-structure PSCs is reported. The encapsulated PSC retains 95.4% of its initial PCE following 1960 h maximum-power-point tracking under continuous light illumination at 65 °C (i.e., ISOS-L-2I protocol). The demonstration of operating-stable PSCs under accelerated ageing conditions represents a step closer to the commercialization of this emerging technology., (© 2023 Wiley‐VCH GmbH.)

Appropriate gestures can enhance message delivery and audience engagement in both daily communication and public presentations. In this article, we contribute a visual analytic approach that assists professional public speaking coaches in improving their practice of gesture training through analyzing presentation videos. Manually checking and exploring gesture usage in the presentation videos is often tedious and time-consuming. There lacks an efficient method to help users conduct gesture exploration, which is challenging due to the intrinsically temporal evolution of gestures and their complex correlation to speech content. In this article, we propose GestureLens, a visual analytics system to facilitate gesture-based and content-based exploration of gesture usage in presentation videos. Specifically, the exploration view enables users to obtain a quick overview of the spatial and temporal distributions of gestures. The dynamic hand movements are firstly aggregated through a heatmap in the gesture space for uncovering spatial patterns, and then decomposed into two mutually perpendicular timelines for revealing temporal patterns. The relation view allows users to explicitly explore the correlation between speech content and gestures by enabling linked analysis and intuitive glyph designs. The video view and dynamic view show the context and overall dynamic movement of the selected gestures, respectively. Two usage scenarios and expert interviews with professional presentation coaches demonstrate the effectiveness and usefulness of GestureLens in facilitating gesture exploration and analysis of presentation videos.

High-quality perovskite light harvesters and robust organic hole extraction layers are essential for achieving high-performing perovskite solar cells (PSCs). We introduce a phosphonic acid-functionalized fullerene derivative in mixed-cation perovskites as a grain boundary modulator to consolidate the crystal structure, which enhances the tolerance of the film against illumination, heat, and moisture. We also developed a redox-active radical polymer, poly(oxoammonium salt), that can effectively p-dope the hole-transporting material by hole injection and that also mitigates lithium ion diffusion. Power conversion efficiencies of 23.5% for 1-square-centimeter mixed-cation-anion PSCs and 21.4% for 17.1-square-centimeter minimodules were achieved. The PSCs retained 95.5% of their initial efficiencies after 3265 hours at maximum power point tracking under continuous 1-sun illumination at 70° ± 5°C.

Despite being a critical communication skill, grasping humor is challenging-a successful use of humor requires a mixture of both engaging content build-up and an appropriate vocal delivery (e.g., pause). Prior studies on computational humor emphasize the textual and audio features immediately next to the punchline, yet overlooking longer-term context setup. Moreover, the theories are usually too abstract for understanding each concrete humor snippet. To fill in the gap, we develop DeHumor, a visual analytical system for analyzing humorous behaviors in public speaking. To intuitively reveal the building blocks of each concrete example, DeHumor decomposes each humorous video into multimodal features and provides inline annotations of them on the video script. In particular, to better capture the build-ups, we introduce content repetition as a complement to features introduced in theories of computational humor and visualize them in a context linking graph. To help users locate the punchlines that have the desired features to learn, we summarize the content (with keywords) and humor feature statistics on an augmented time matrix. With case studies on stand-up comedy shows and TED talks, we show that DeHumor is able to highlight various building blocks of humor examples. In addition, expert interviews with communication coaches and humor researchers demonstrate the effectiveness of DeHumor for multimodal humor analysis of speech content and vocal delivery.

Analyzing students' emotions from classroom videos can help both teachers and parents quickly know the engagement of students in class. The availability of high-definition cameras creates opportunities to record class scenes. However, watching videos is time-consuming, and it is challenging to gain a quick overview of the emotion distribution and find abnormal emotions. In this article, we propose EmotionCues, a visual analytics system to easily analyze classroom videos from the perspective of emotion summary and detailed analysis, which integrates emotion recognition algorithms with visualizations. It consists of three coordinated views: a summary view depicting the overall emotions and their dynamic evolution, a character view presenting the detailed emotion status of an individual, and a video view enhancing the video analysis with further details. Considering the possible inaccuracy of emotion recognition, we also explore several factors affecting the emotion analysis, such as face size and occlusion. They provide hints for inferring the possible inaccuracy and the corresponding reasons. Two use cases and interviews with end users and domain experts are conducted to show that the proposed system could be useful and effective for analyzing emotions in the classroom videos.

Perovskite solar cells (PSCs) have rapidly developed and achieved power conversion efficiencies of over 20% with diverse technical routes. Particularly, planar-structured PSCs can be fabricated with low-temperature (≤150 °C) solution-based processes, which is energy efficient and compatible with flexible substrates. Here, the efficiency and stability of planar PSCs are enhanced by improving the interface contact between the SnO 2 electron-transport layer (ETL) and the perovskite layer. A biological polymer (heparin potassium, HP) is introduced to regulate the arrangement of SnO 2 nanocrystals, and induce vertically aligned crystal growth of perovskites on top. Correspondingly, SnO 2 -HP-based devices can demonstrate an average efficiency of 23.03% on rigid substrates with enhanced open-circuit voltage (V OC ) of 1.162 V and high reproducibility. Attributed to the strengthened interface binding, the devices obtain high operational stability, retaining 97% of their initial performance (power conversion efficiency, PCE > 22%) after 1000 h operation at their maximum power point under 1 sun illumination. Besides, the HP-modified SnO 2 ETL exhibits promising potential for application in flexible and large-area devices., (© 2020 Wiley-VCH GmbH.)

The long-term stability of perovskite solar cells (PSCs) remains an issue impeding their commercialization. Generally, polycrystalline perovskite thin films have many defects on the grain boundaries, which affect the optoelectronic performance and stability of the devices under moisture, heat, illumination, and the presence of an electric field condition. The O-donor Lewis base is often employed to regulate the performance of PSCs such as carbonyl and carboxyl compounds. Herein, we have developed a concept of radical molecular modulation using the O-donor group for high-performance perovskite photovoltaic devices. The judiciously designed radical modulators 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO), which located at the perovskite grain boundary through interaction with the perovskite surface sites, effectively passivated the surface defects while templating the formation of large grain crystal and high-quality perovskite thin films. Accordingly, the optimized TEMPO-modulated PSCs achieved a power conversion efficiency of 20.73% with superior stability. This work makes an important contribution for exploring the effect of radical in perovskites to improve the performance of PSCs and other optoelectronic devices., (Copyright © 2020 Peng, Zheng, Zhang, You, Li, Zhao, Zhang, Luo, Zeng and Li.)

Emotions play a key role in human communication and public presentations. Human emotions are usually expressed through multiple modalities. Therefore, exploring multimodal emotions and their coherence is of great value for understanding emotional expressions in presentations and improving presentation skills. However, manually watching and studying presentation videos is often tedious and time-consuming. There is a lack of tool support to help conduct an efficient and in-depth multi-level analysis. Thus, in this paper, we introduce EmoCo, an interactive visual analytics system to facilitate efficient analysis of emotion coherence across facial, text, and audio modalities in presentation videos. Our visualization system features a channel coherence view and a sentence clustering view that together enable users to obtain a quick overview of emotion coherence and its temporal evolution. In addition, a detail view and word view enable detailed exploration and comparison from the sentence level and word level, respectively. We thoroughly evaluate the proposed system and visualization techniques through two usage scenarios based on TED Talk videos and interviews with two domain experts. The results demonstrate the effectiveness of our system in gaining insights into emotion coherence in presentations.

Ether-based electrolytes are commonly used in Li-O 2 batteries (LOBs) because of their relatively high stability. But they are still prone to be attacked by superoxides or singlet oxygen via hydrogen abstract reactions, which leads to performance decaying during long-term operation. Herein we propose a methylated cyclic ether, 2,2,4,4,5,5-hexamethyl-1,3-dioxolane (HMD), as a stable electrolyte solvent for LOBs. Such a compound does not contain any hydrogen atoms on the alpha-carbon of the ether, and thus avoids hydrogen abstraction reactions. As the result, this solvent exhibits excellent stability with the presence of superoxide or singlet oxygen. In addition the CO 2 evolution during charge process is prohibited. The LOB with HMD-based electrolyte was able to run up to 157 cycles, 4 times more than with 1,3-dioxolane (DOL) or 1,2-dimethoxyethane (DME) based electrolytes., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A thiol-promoted site-specific addition of 1,3-dioxolane to imines through a radical chain process is described. This process represents a metal-free and redox-neutral way to convert inexpensive materials to a broad range of protected α-amino aldehydes in good to excellent yields using only a catalytic amount of radical precursor. Control experiments revealed that both the thiol and a small amount of oxygen from air are indispensable to the success of this reaction.

Understanding co-occurrence in urban human mobility (i.e. people from two regions visit an urban place during the same time span) is of great value in a variety of applications, such as urban planning, business intelligence, social behavior analysis, as well as containing contagious diseases. In recent years, the widespread use of mobile phones brings an unprecedented opportunity to capture large-scale and fine-grained data to study co-occurrence in human mobility. However, due to the lack of systematic and efficient methods, it is challenging for analysts to carry out in-depth analyses and extract valuable information. In this paper, we present TelCoVis, an interactive visual analytics system, which helps analysts leverage their domain knowledge to gain insight into the co-occurrence in urban human mobility based on telco data. Our system integrates visualization techniques with new designs and combines them in a novel way to enhance analysts' perception for a comprehensive exploration. In addition, we propose to study the correlations in co-occurrence (i.e. people from multiple regions visit different places during the same time span) by means of biclustering techniques that allow analysts to better explore coordinated relationships among different regions and identify interesting patterns. The case studies based on a real-world dataset and interviews with domain experts have demonstrated the effectiveness of our system in gaining insights into co-occurrence and facilitating various analytical tasks.