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Data transformation is an essential step in data science. While experts primarily use programming to transform their data, there is an increasing need to support non-programmers with user interface-based tools. With the rapid development in interaction techniques and computing environments, we report our empirical findings about the effects of interaction techniques and environments on performing data transformation tasks. Specifically, we studied the potential benefits of direct interaction and virtual reality (VR) for data transformation. We compared gesture interaction versus a standard WIMP user interface, each on the desktop and in VR. With the tested data and tasks, we found time performance was similar between desktop and VR. Meanwhile, VR demonstrates preliminary evidence to better support provenance and sense-making throughout the data transformation process. Our exploration of performing data transformation in VR also provides initial affirmation for enabling an iterative and fully immersive data science workflow.

To realize an initial objective of the negative ion-based neutral beam injection (N-NBI) at the Comprehensive Research Facility for Fusion Technology (CRAFT) test facility, which targets an H0beam power of 2 MW at an energy of 200–400 keV and a pulse duration of 100 s, it is crucial to study the cesium dynamics of the negative ion source. Here a numerical simulation program CSFC3D is developed and applied to simulate the distribution and time dynamics of cesium during short pulses. The calculations show that most of the cesium on the plasma grid (PG) area originates from the release of cesium that is accumulated within the ion source in the plasma phase. Increasing the wall temperature reduces the loss of cesium on the wall of the ion source. Furthermore, the thickness of the cesium monolayer is directly influenced by the PG temperature. Both simulated and experimental results demonstrate that maintaining the PG temperature between 180 °C and 200 °C is essential for enhancing the performance of the ion source and optimizing the cesium behavior.

Labels are widely used in augmented reality (AR) to display digital information. Ensuring the readability of AR labels requires placing them in an occlusion-free manner while keeping visual links legible, especially when multiple labels exist in the scene. Although existing optimization-based methods, such as force-based methods, are effective in managing AR labels in static scenarios, they often struggle in dynamic scenarios with constantly moving objects. This is due to their focus on generating layouts optimal for the current moment, neglecting future moments and leading to sub-optimal or unstable layouts over time. In this work, we present RL-LABEL, a deep reinforcement learning-based method intended for managing the placement of AR labels in scenarios involving moving objects. RL-LABEL considers both the current and predicted future states of objects and labels, such as positions and velocities, as well as the user's viewpoint, to make informed decisions about label placement. It balances the trade-offs between immediate and long-term objectives. We tested RL-LABEL in simulated AR scenarios on two real-world datasets, showing that it effectively learns the decision-making process for long-term optimization, outperforming two baselines (i.e., no view management and a force-based method) by minimizing label occlusions, line intersections, and label movement distance. Additionally, a user study involving 18 participants indicates that, within our simulated environment, RL-LABEL excels over the baselines in aiding users to identify, compare, and summarize data on labels in dynamic scenes.

Pickering emulsions were prepared by phacoemulsification in an ice water bath with squalene as the oil phase and an aluminum adjuvant as the particle stabilizer. The effects of formulation and process conditions on the size and distribution of the Pickering emulsions were investigated. Pickering emulsions prepared under the optimal prescription and process conditions were mixed with a peptide antigen to obtain a peptide vaccine. The optimal prescription and process condition of the Pickering emulsion is as follows: squalene as the oil phase, ultra-pure water as the water phase with 5 mg/mL aluminum adjuvant, and an ultrasonication time of 4 min at 200 W power. BALB/c mice were immunized with the peptide vaccine, and the ability of the Pickering emulsion as an immunological adjuvant to improve the efficacy of the peptide vaccine was evaluated. Under optimal conditions, a Pickering emulsion with a small particle size (430.8 nm), uniform distribution (polydispersion index of 16.9%), and zeta potential of 31.5 mV, was obtained. Immunological results showed that the serum specific antibody level in the vaccinated group reached 1×104after three immunizations. The proportion of CD4+T cells and CD4/CD8 cells was significantly higher (P<0.05) in the vaccinated groups than the blank control group. Further, cytokine (TNF-α) secretion decreased in the aluminum adjuvant and Pickering emulsion groups but increased in the Freund's adjuvant group. All three vaccinated groups of mice exhibited low but detectable levels of IFN-γsecretion.

Augmented Reality (AR) embeds digital information into objects of the physical world. Data can be shown in-situ, thereby enabling real-time visual comparisons and object search in real-life user tasks, such as comparing products and looking up scores in a sports game. While there have been studies on designing AR interfaces for situated information retrieval, there has only been limited research on AR object labeling for visual search tasks in the spatial environment. In this article, we identify and categorize different design aspects in AR label design and report on a formal user study on labels for out-of-view objects to support visual search tasks in AR. We design three visualization techniques for out-of-view object labeling in AR, which respectively encode the relative physical position (height-encoded), the rotational direction (angle-encoded), and the label values (value-encoded) of the objects. We further implement two traditional in-view object labeling techniques, where labels are placed either next to the respective objects (situated) or at the edge of the AR FoV (boundary). We evaluate these five different label conditions in three visual search tasks for static objects. Our study shows that out-of-view object labels are beneficial when searching for objects outside the FoV, spatial orientation, and when comparing multiple spatially sparse objects. Angle-encoded labels with directional cues of the surrounding objects have the overall best performance with the highest user satisfaction. We discuss the implications of our findings for future immersive AR interface design.

Sports game data is becoming increasingly complex, often consisting of multivariate data such as player performance stats, historical team records, and athletes' positional tracking information. While numerous visual analytics systems have been developed for sports analysts to derive insights, few tools target fans to improve their understanding and engagement of sports data during live games. By presenting extra data in the actual game views, embedded visualization has the potential to enhance fans' game-viewing experience. However, little is known about how to design such kinds of visualizations embedded into live games. In this work, we present a user-centered design study of developing interactive embedded visualizations for basketball fans to improve their live game-watching experiences. We first conducted a formative study to characterize basketball fans' in-game analysis behaviors and tasks. Based on our findings, we propose a design framework to inform the design of embedded visualizations based on specific data-seeking contexts. Following the design framework, we present five novel embedded visualization designs targeting five representative contexts identified by the fans, including shooting, offense, defense, player evaluation, and team comparison. We then developed Omnioculars, an interactive basketball game-viewing prototype that features the proposed embedded visualizations for fans' in-game data analysis. We evaluated Omnioculars in a simulated basketball game with basketball fans. The study results suggest that our design supports personalized in-game data analysis and enhances game understanding and engagement.

Most sports visualizations rely on a combination of spatial, highly temporal, and user-centric data, making sports a challenging target for visualization. Emerging technologies, such as augmented and mixed reality (AR/XR), have brought exciting opportunities along with new challenges for sports visualization. We share our experience working with sports domain experts and present lessons learned from conducting visualization research in SportsXR. In our previous work, we have targeted different types of users in sports, including athletes, game analysts, and fans. Each user group has unique design constraints and requirements, such as obtaining real-time visual feedback in training, automating the low-level video analysis workflow, or personalizing embedded visualizations for live game data analysis. In this article, we synthesize our best practices and pitfalls we identified while working on SportsXR. We highlight lessons learned in working with sports domain experts in designing and evaluating sports visualizations and in working with emerging AR/XR technologies. We envision that sports visualization research will benefit the larger visualization community through its unique challenges and opportunities for immersive and situated analytics.

The switching overvoltage due to the operation of the switching device is considered to be one of the important research contents during the design phase of a high-power supply system based on thyristor rectifier topology in the comprehensive research facility for fusion technology (CRAFT). Switching peak overvoltage is suppressed by resistors–capacitors (RC) snubber circuit generally; however, the traditional RC design often relies on some simple empirical formulas and blind amplification margins, which is unable to meet the precision requirements of engineering design. All the work that has been done is based on the cophase counter parallel connection structure of the converter module, and a new strategy is proposed to optimize the RC snubber circuit in order to suppress the operating peak overvoltage better. First, the no-load transformer breaking overvoltage is analyzed, then RC parameters are designed based on a new strategy. Compared with traditional methods, the RC parameters selected by this strategy are more accurate, which meets the accuracy requirements of engineering design and suppresses switching better than before.

In order to solve the problem of switching overvoltage for CRAFT, an optimal design is put forward to suppress overvoltage, which aims to enable the converter thyristor work normal. When the vacuum circuit breaker on the grid side of CRAFT rectifier transformer, an overvoltage appeared on the secondary side of the transformer. Compared the conventional way to suppress overvoltage, the overvoltage can be limited effectively by accurate calculation, as a result, the switching voltage is improved. In addition, the problem of electric elements waste can be avoided. All the work has been done provides a useful reference in optimization design of suppression switching overvoltage for CRAFT, which can protect the power supply and maintain the system run normal.

Clubroot, caused by Plasmodiophora brassicae, is an important disease of canola and other brassica crops. Recommending improved management strategies may be guided by an evolving understanding of host and pathogen biology. In order to better understand infection thresholds, clubroot-susceptible canola plants (cultivar Westar) were inoculated with resting spores of P. brassicaein a soil inoculation assay and a plant inoculation assay. In the soil inoculation assay, seven-day-old seedlings were transplanted into soil containing clubroot resting spores at a final concentration of each of the 10× serial dilutions from 1 to 1 × 106spores/mL soil. In the plant inoculation assay, seven-day-old seedlings were inoculated with 1-µL aliquots of a resting spore suspension at a concentration of each of the 10× serial dilutions from 1 × 103to 1 × 107spores/mL, and after 48 h, the inoculated seedlings were transplanted into soil. After 42 days, clubroot development, incidence and severity were visually assessed. From the soil inoculation assay, clubroot was observed to establish in the soil containing as low as 1 spore/mL soil of resting spores. From the plant inoculation assay, very low clubroot incidence was observed when the plants were inoculated with ≤ 100 resting spores total. Our results indicated the urgent need for more sensitive methods of clubroot diagnosis and emphasized the difficulty of the creation of P. brassicaesingle-spore isolates.

The metaverse is a visual world that blends the physical world and digital world. At present, the development of the metaverse is still in the early stage, and there lacks a framework for the visual construction and exploration of the metaverse. In this paper, we propose a framework that summarizes how graphics, interaction, and visualization techniques support the visual construction of the metaverse and user-centric exploration. We introduce three kinds of visual elements that compose the metaverse and the two graphical construction methods in a pipeline. We propose a taxonomy of interaction technologies based on interaction tasks, user actions, feedback and various sensory channels, and a taxonomy of visualization techniques that assist user awareness. Current potential applications and future opportunities are discussed in the context of visual construction and exploration of the metaverse. We hope this paper can provide a stepping stone for further research in the area of graphics, interaction and visualization in the metaverse.

Deubiquitinases (DUBs) regulate diverse biological processes and represent a novel class of drug targets. However, the biological function of only a small fraction of DUBs, especially in adaptive immune response regulation, is well-defined. In this study, we identified DUB ubiquitin-specific peptidase 12 (USP12) as a critical regulator of CD4+T cell activation. USP12 plays an intrinsic role in promoting the CD4+T cell phenotype, including differentiation, activation, and proliferation. Although USP12-deficient CD4+T cells protected mice from autoimmune diseases, the immune response against bacterial infection was subdued. USP12 stabilized B cell lymphoma/leukemia 10 (BCL10) by deubiquitinating, and thereby activated the NF-κB signaling pathway. Interestingly, this USP12 regulatory mechanism was identified in CD4+T cells, but not in CD8+T cells. Our study results showed that USP12 activated CD4+T cell signaling, and targeting USP12 might help develop therapeutic interventions for treating inflammatory diseases or pathogen infections.

A survey was conducted in southern Alberta, Canada, to collect Fusarium stalk rot and Fusarium head blight (FHB) disease samples from corn and wheat fields, respectively, with an aim to understand the diversity in the population of Fusarium graminearum. From 77 wheat and 80 corn samples, 79 isolates of F. graminearumwere obtained. These 79 isolates were evaluated for their growth rate, sporulation ability, pathogenicity and trichothecene genotype. Eighty-two percent of the isolates were recovered from corn, which suggests corn residue may be better suited than wheat for perpetuating the FHB pathogen. Three genotypes with potential of producing 3-ADON, 15-ADON and NIV were present in the population, and among them, 15-ADON producing genotype was predominant, and 3-ADON genotype produced more spores than the 15-ADON genotype. The proportion of 3-ADON genotype is increasing overtime in Alberta. The impact of increasing population of 3-ADON genotype is worthy of to be monitored for the management of FHB. There were variations in the population of F. graminearumisolates for growth rate, sporulation and pathogenicity but revealed no association among the parameters suggesting other factors like host susceptibility and environment play a significant role in the development of FHB.

Innate immune signaling in macrophages during viral infection is regulated by ISGylation, the covalent attachment of the ubiquitin-like protein interferon-stimulated gene 15 (ISG15) to protein targets. Here, we explored the role of ISGylation in the macrophage response to infection with Mycobacterium tuberculosis. In human and mouse macrophages, the E3 ubiquitin ligases HERC5 and mHERC6, respectively, mediated the ISGylation of the phosphatase PTEN, which promoted its degradation. The decreased abundance of PTEN led to an increase in the activity of the PI3K-AKT signaling pathway, which stimulated the synthesis of proinflammatory cytokines. Bacterial growth was increased in culture and in vivo when human or mouse macrophages were deficient in the major E3 ISG15 ligase. The findings expand the role of ISGylation in macrophages to antibacterial immunity and suggest that HERC5 signaling may be a candidate target for adjunct host-directed therapy in patients with tuberculosis.

An air conditioning system plays an important role in regulating the indoor environment and improving the indoor thermal comfort. However, there exists hysteresis of the temperature and humidity regulating system of an air conditioning system. The coupling between temperature and humidity control circuits leads to more energy consumption of the air conditioning system. To solve this issue, an improved feed-forward decoupling model with Smith predictor was designed. The simulation results indicate that it can eliminate the coupling between temperature and humidity regulating circuits. This model can have less time-delay, decoupling between temperature and humidity control circuits. Compared with the conventional method, this decoupling model with Smith predictor has the advantages of timely dynamic response, quick increasing time and less overshoot.

With the continuous development of the global new energy industry, lithium-ion batteries as new energy and the heart of intelligent manufacturing have attracted much attention. But the performance of lithium-ion battery systems has been deteriorating for a long time. An accurate assessment of its state of health (SOH) can effectively avoid unnecessary loss of lithium-ion batteries due to unexpected failure. Therefore, a method for evaluating the SOH of deep Gaussian process regression (DGPR) lithium-ion batteries based on the Gaussian process and deep network is presented. Firstly, the heterogeneous features reflecting the SOH of lithium-ion batteries were extracted from the aspects of charging and discharging time, the peak of incremental capacity curve (ICC), internal resistance, and energy. Then, through grey correlation analysis (GRA), significant features are introduced into the DGPR model to establish the SOH estimation method for lithium-ion batteries. Finally, the data sets provided by CALCE and NASA are used as an experimental object to compare with different data-driven models to verify the accuracy, reliability and applicability of the proposed models. The experimental results show that the method proposed in this paper has high accuracy in SOH estimation, RMSE is less than 0.7 %, and R2is more than 98.2 % in each lithium-ion battery. It shows that this method can provide a reliable basis for the health management of lithium-ion batteries.