Usage Scenarios Research Articles

Efficient Motion Primitives-Based Trajectory Planning for UAVs in the Presence of Obstacles

The achievement of full autonomy in Unmanned Aerial Vehicles (UAVs) is significantly dependent on effective motion planning. Specifically, it is crucial to plan collision-free trajectories for smooth transitions from initial to final configurations. However, finding a solution executable by the actual system adds complexity: the planned motion must be dynamically feasible. This involves meeting rigorous criteria, including vehicle dynamics, input constraints, and state constraints. This work addresses optimal kinodynamic motion planning for UAVs in the presence of obstacles by employing a hybrid technique instead of conventional search-based or direct trajectory optimization approaches. This technique involves precomputing a library of motion primitives by solving several Two-Point-Boundary-Value Problems (TPBVP) offline. This library is then repeatedly used online within a graph-search framework. Moreover, to make the method computationally tractable, continuity between consecutive motion primitives is enforced only on a subset of the state variables. This approach is compared with a state-of-the-art quadrotor-tailored search-based approach, which generates motion primitives online through control input discretization and forward propagation of the dynamic equations of a simplified model. The effectiveness of both methods is assessed through simulations and real-world experiments, demonstrating their ability to generate resolution-complete, resolution-optimal, collision-free, and dynamically feasible trajectories. Finally, a comparative analysis highlights the advantages, disadvantages, and optimal usage scenarios for each approach.

RouteVis: Quantitative Visual Analytics of Various Factors to Understand Route Choice Preferences

AbstractAnalyzing the preference of route choice not only facilitates the understanding of individuals' decision‐making behavior, but also provides valuable information for improving traffic management strategies. As the layout of the road network, the variability of individual preferences and the spatial distribution of origins and destinations all play a role in route choice, it is a great challenge to reveal the interplay of such numerous complex factors. In this paper, we propose RouteVis, an interactive visual analytics system that enables traffic analysts to gain insight into what factors drive individuals to choose a specific route. To uncover the relationship between route choice and influencing factors, we design a quantitative analytical framework that supports analysts in conducting closed‐loop analysis of various factors, i.e., data preprocessing, route identification, and the quantification of influence and contribution. Furthermore, given the multidimensional and spatio‐temporal characteristics of the analysis results, we customize a set of coordinated views and visual designs to provide an intuitive presentation of the factors affecting people's travels, thus freeing analysts from tedious repetitive tasks and significantly enhancing work efficiency. Two typical usage scenarios and expert feedback on the system's functionality demonstrate that RouteVis can greatly enhance the capabilities of understanding the travel status.

AVA: Towards Autonomous Visualization Agents through Visual Perception‐Driven Decision‐Making

AbstractWith recent advances in multi‐modal foundation models, the previously text‐only large language models (LLM) have evolved to incorporate visual input, opening up unprecedented opportunities for various applications in visualization. Compared to existing work on LLM‐based visualization works that generate and control visualization with textual input and output only, the proposed approach explores the utilization of the visual processing ability of multi‐modal LLMs to develop Autonomous Visualization Agents (AVAs) that can evaluate the generated visualization and iterate on the result to accomplish user‐defined objectives defined through natural language. We propose the first framework for the design of AVAs and present several usage scenarios intended to demonstrate the general applicability of the proposed paradigm. Our preliminary exploration and proof‐of‐concept agents suggest that this approach can be widely applicable whenever the choices of appropriate visualization parameters require the interpretation of previous visual output. Our study indicates that AVAs represent a general paradigm for designing intelligent visualization systems that can achieve high‐level visualization goals, which pave the way for developing expert‐level visualization agents in the future.

Moderating AC Usage Can Reduce Thermal Disparity between Indoor and Outdoor Environments.

In the context of escalating urban heat events due to climate change, air conditioning (AC) has become a critical factor in maintaining indoor thermal comfort. Yet the usage of AC can also exacerbate outdoor heat stress and burden the electricity system, and there is little scientific knowledge regarding how to balance these conflicting goals. To address this issue, we established a coupled modeling approach, integrating the Weather Research and Forecasting model with the building energy model (WRF_BEP + BEM), and designed multiple AC usage scenarios. We selected Chongqing, China's fourth-largest megacity, as our study area due to its significant socioeconomic importance, the severity of extreme heat events, and the uniqueness of its energy infrastructure. Our analysis reveals that AC systems can substantially reduce indoor temperatures by up to 18 °C; however, it also identifies substantial nighttime warming (2-2.5 °C) and a decline in thermal comfort. Particularly for high-density neighborhoods, when we increase 2 °C indoors, the outdoor temperature can be alleviated by up to 1 °C. Besides, despite the limited capacity to regulate peak electricity demand, we identified that reducing the spatial cooled fraction, increasing targeted indoor temperature by 2 °C, and implementing temporal AC schedules can effectively lower energy consumption in high-density neighborhoods, especially the reduction of spatial cooled fraction (up to 50%). Considering the substantial demand for cooling energy, it is imperative to carefully assess the adequacy and continuity of backup energy sources. The study underscores the urgency of reassessing energy resilience and advocates for addressing the thermal equity between indoor and outdoor environments, contributing to the development of a sustainable and just urban climate strategy in an era of intensifying heat events.

High-Performance Computing Storage Performance and Design Patterns—Btrfs and ZFS Performance for Different Use Cases

Filesystems are essential components in contemporary computer systems that organize and manage data. Their performance is crucial in various applications, from web servers to data storage systems. This paper helps to pick the suitable filesystem by comparing btrfs with ZFS by considering multiple situations and applications, ranging from sequential and random performance in the most common use cases to extreme use cases like high-performance computing (HPC). It showcases each option’s benefits and drawbacks, considering different usage scenarios. The performance of btrfs and ZFS will be evaluated through rigorous testing. They will assess their capabilities in handling huge files, managing numerous small files, and the speed of data read and write across varied usage levels. The analysis indicates no definitive answer; the selection of the optimal filesystem is contingent upon individual data-access requirements.

Screen‐shooting resistant robust document watermarking in the Discrete Fourier Transform domain

AbstractMetaverse's augmented reality (AR) function allows virtual information to be seamlessly superimposed onto real scenes through the camera of a head‐mounted device. However, this raises concerns about privacy protection and copyright authentication when transmitting cross‐media information. Additionally, there is a risk of secret information leakage due to screen candid shooting in the real world. Ensuring information security and copyright authentication in case of unauthorized screen capturing is crucial. To prevent information loss and interference from cross‐media transfer between screens and cameras, we implement digital watermarking for copyright protection. We have proposed an innovative framework for automatic document watermarking that can resist screen‐shooting. Our approach involves embedding a ring watermark in the document underlay. On the extraction side, the watermark extraction process is divided into three key steps: automatic location, automatic correction, and automatic extraction. First, the document image is located in the covert photography. Then, perspective correction is performed based on the text line features of the document. Finally, the watermark information is extracted by combining the ring watermark features. Our method is capable of automatically extracting watermarks from covert photography while considering aspects such as concealment, robustness, and visual quality. The watermark is embedded in the document underlay, which ensures good visual quality and does not affect the normal reading and editing. We also propose various embedding strength schemes that can adapt to different usage scenarios, providing resistance to screen‐shooting or screenshot attacks, as well as various noise attacks. Through extensive experiments, we have demonstrated the feasibility of the proposed automated framework and the robustness of the watermarking algorithm, as well as the superiority and broad application prospects of our method.

CoInsight: Visual Storytelling for Hierarchical Tables With Connected Insights.

Extracting data insights and generating visual data stories from tabular data are critical parts of data analysis. However, most existing studies primarily focus on tabular data stored as flat tables, typically without leveraging the relations between cells in the headers of hierarchical tables. When properly used, rich table headers can enable the extraction of many additional data stories. To assist analysts in visual data storytelling, an approach is needed to organize these data insights efficiently. In this work, we propose CoInsight, a system to facilitate visual storytelling for hierarchical tables by connecting insights. CoInsight extracts data insights from hierarchical tables and builds insight relations according to the structure of table headers. It further visualizes related data insights using a nested graph with edge bundling. We evaluate the CoInsight system through a usage scenario and a user experiment. The results demonstrate the utility and usability of CoInsight for converting data insights in hierarchical tables into visual data stories.

Cefiderocol for treating severe aerobic Gram-negative bacterial infections: technology evaluation to inform a novel subscription-style payment model.

To limit the use of antimicrobials without disincentivising the development of novel antimicrobials, there is interest in establishing innovative models that fund antimicrobials based on an evaluation of their value as opposed to the volumes used. The aim of this project was to evaluate the population-level health benefit of cefiderocol in the NHS in England, for the treatment of severe aerobic Gram-negative bacterial infections when used within its licensed indications. The results were used to inform the National Institute for Health and Care Excellence guidance in support of commercial discussions regarding contract value between the manufacturer and NHS England. The health benefit of cefiderocol was first derived for a series of high-value clinical scenarios. These represented uses that were expected to have a significant impact on patients' mortality risks and health-related quality of life. The clinical effectiveness of cefiderocol relative to its comparators was estimated by synthesising evidence on susceptibility of the pathogens of interest to the antimicrobials in a network meta-analysis. Patient-level costs and health outcomes of cefiderocol under various usage scenarios compared with alternative management strategies were quantified using decision modelling. Results were reported as incremental net health effects expressed in quality-adjusted life-years, which were scaled to 20-year population values using infection number forecasts based on data from Public Health England. The outcomes estimated for the high-value clinical scenarios were extrapolated to other expected uses for cefiderocol. Among Enterobacterales isolates with the metallo-beta-lactamase resistance mechanism, the base-case network meta-analysis found that cefiderocol was associated with a lower susceptibility relative to colistin (odds ratio 0.32, 95% credible intervals 0.04 to 2.47), but the result was not statistically significant. The other treatments were also associated with lower susceptibility than colistin, but the results were not statistically significant. In the metallo-beta-lactamase Pseudomonas aeruginosa base-case network meta-analysis, cefiderocol was associated with a lower susceptibility relative to colistin (odds ratio 0.44, 95% credible intervals 0.03 to 3.94), but the result was not statistically significant. The other treatments were associated with no susceptibility. In the base case, patient-level benefit of cefiderocol was between 0.02 and 0.15 quality-adjusted life-years, depending on the site of infection, the pathogen and the usage scenario. There was a high degree of uncertainty surrounding the benefits of cefiderocol across all subgroups. There was substantial uncertainty in the number of infections that are suitable for treatment with cefiderocol, so population-level results are presented for a range of scenarios for the current infection numbers, the expected increases in infections over time and rates of emergence of resistance. The population-level benefits varied substantially across the base-case scenarios, from 896 to 3559 quality-adjusted life-years over 20 years. This work has provided quantitative estimates of the value of cefiderocol within its areas of expected usage within the NHS. Given existing evidence, the estimates of the value of cefiderocol are highly uncertain. Future evaluations of antimicrobials would benefit from improvements to NHS data linkages; research to support appropriate synthesis of susceptibility studies; and application of routine data and decision modelling to assess enablement value. No registration of this study was undertaken. This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment Policy Research Programme (NIHR award ref: NIHR135591), conducted through the Policy Research Unit in Economic Methods of Evaluation in Health and Social Care Interventions, PR-PRU-1217-20401, and is published in full in Health Technology Assessment; Vol. 28, No. 28. See the NIHR Funding and Awards website for further award information.

Expert Consensus on Big Data Collection of Skin and Appendage Disease Phenotypes in Chinese.

The collection of big data on skin and appendage phenotypes has revolutionized the field of personalized diagnosis and treatment by enabling the evaluation of individual characteristics and early detection of abnormalities. To establish a standardized system for collecting and measuring big data on phenotypes, a systematic categorization of measurement entries has been undertaken, accompanied by recommendations on measurement entries, environmental equipment requirements, and collection processes, tailored to the needs of different usage scenarios. Specific collection sites have also been recommended based on different index characteristics. A multi-center, multi-regional collaboration has been initiated to collect big date on phenotypes of healthy and diseased skin in the Chinese population. This data will be correlated with patient disease information, exploring the factors influencing skin phenotype, analyzing the phenotypic data features that can predict prognosis, and ultimately promoting the exploration of the pathophysiology and pathogenesis of skin diseases and therapeutic approaches. Non-invasive skin measurement robots are also in development. This consensus aims to provide a reference for the study of phenomics and the standardization of phenotypic measurements of skin and appendages in China.

Development of aggressive driving conditions for light-duty fuel cell vehicles in Beijing

Abstract As the main technology route of new energy vehicles, the reliability and durability of fuel cell vehicles have gradually become the focus of researchers. Based on the actual road driving data of fuel cell light-duty vehicles in Beijing, this paper screened out the aggressive driving segments of different speed intervals through the RPA95% quantile curve and constructed a library of aggressive driving conditions of fuel cell vehicles, based on this library, we developed the aggressive driving conditions of fuel cell vehicles that meet the actual driving scenarios of users. The driving conditions have a deviation of no more than 8% from the corresponding library in the three key indexes of average speed, average acceleration, and average deceleration. In addition, this paper also compared the differences between the constructed aggressive driving condition and the CLTC-P condition, which verifies the necessity of the aggressive driving condition in the durability test of fuel cell vehicles. So, this condition can be used as the basic condition for the subsequent durability test of fuel cell vehicles. It will help enterprises shorten the test cycle and reduce the test cost. It can also support the formulation of the durability test specification for the subsequent fuel cell vehicles. At the same time, it can also support the formulation of subsequent durability test specifications for fuel cell vehicles.

Low-Voltage IGZO Field-Effect Ultraviolet Photodiode

Abstract In the era of Internet of Things (IoTs), an energy-efficient ultraviolet (UV) photodetector (PD) is highly desirable considering the massive usage scenarios such as environmental sterilization, fire alarm and corona discharge monitoring. So far, common self-powered UV PDs are mainly based on metal-semiconductor hetero-structures or p–n heterojunctions, where the limited intrinsic built-in electric field restricts further enhancement of the photoresponsivity. In this work, an extremely low-voltage field-effect UV PD is proposed using a gate-drain shorted amorphous IGZO (a-IGZO) thin film transistor (TFT) architecture. A combined investigation of the experimental measurements and technology computer-aided design (TCAD) simulations suggests that the reverse current (I R) of field-effect diode (FED) is highly related with the threshold voltage (V th) of the parental TFT, implying an enhancement-mode TFT is preferable to fabricate the field-effect UV PD with low dark current. Driven by a low bias of −0.1 V, decent UV response has been realized including large UV/visible (R 300/R 550) rejection ratio (1.9 × 103), low dark current (1.15 × 10−12 A) as well as high photo-to-dark current ratio (PDCR, ∼ 103) and responsivity (1.89 A/W). This field-effect photodiode provides a new platform to construct UV PDs with well-balanced photoresponse performance at a low bias, which is attractive for designs of large-scale smart sensor networks with high energy efficiency.

Research on design thinking workshop based on protocol analysis: A case study of library usage scenarios

Design thinking has a broad range of applications beyond the design field, such as in libraries. However, significant differences remain between designers and non-designers in using design thinking tools for innovation. This study investigated these distinctions by examining the design thinking process. In a design thinking workshop focused on library usage, we recruited three groups of participants consisting of both designers and non-designers, who were assigned the same tasks and conducted similar activities. Through protocol analysis, significant cognitive differences between designers and non-designers in the design thinking process were found to involve information intention, long-term memory, working memory, creative thinking, and externalization. We concluded that the involvement of a designer is optimal for non-designers when using design thinking for innovation, particularly during the problem-framing stage. Additionally, we summarized the challenges faced by non-designers in utilizing design thinking for innovation and provided recommendations for improving design thinking workshops, including considerations for time management and staff allocation.

Energy storage peak-shaving demand prediction based on an improved neural network with hidden layers

Abstract The result shows that the BP neural network has a very low convergent speed for load prediction, which leads to local optimization. This paper presents a modified GA for the optimization of the threshold value, the initial weight, and the addition of the smoothing coefficient. In addition, it improves the BP neural network’s adaptive learning speed and further improves the efficiency of the search. The proposed model is characterized by high convergent speed and global space retrieval capability. To validate the validity of the modified GA-BP model, the prediction of power load in some regions is made. Compared with the BP neural network forecast algorithm, this method has better performance in terms of prediction accuracy and takes less time in terms of prediction time than the BP neural network. In parallel, the realization of multiple sources of electricity from wind, solar, and heat storage is an important way to deal with large-scale new grid connections. Starting from an improvement in the efficiency of wind and PV generation, it is also possible to build a joint optimization scheduling model for wind, solar, and heat storage. The purpose of taking the minimum net load fluctuation is for optimization. Taking the typical output day of user scenarios as the optimization scenario, the scheduling strategies of wind power, PV, thermal units, and energy storage systems are introduced and simulated on the platform. It is proved that the proposed model can decrease the reduction ratio and the peak and valley load.

Emotional design of traditional Chinese cultural creative products based on user demand

Mortice-tenons are the main structural forms of ancient buildings, furniture, and woodwork in China. They are the inheritance of China's craft and cultural spirit, follow the principles of mechanics and have strong practicability. How-ever, mortice-tenons have gradually disappeared from modern life, mainly be-cause they cannot meet the specific needs of users. The purpose of this study is to establish a set of mortice-tenons structure innovation and product design for modern people. By analyzing the types and structural characteristics of mortice-tenon structure, based on the user's needs, using modern creative design methods, and combining with practice, we can verify how to further improve the market acceptance of mortice-tenon structure innovation prod-ucts, constantly explore the combination of modern technology and material experiments, and explore the emotional design of China traditional cultural creative products. Finally, it is proposed that further research is needed in this field to fully understand the user's behavior of using mortice-tenon structure innovation products, and at the same time, mortice-tenon structure innova-tion is applied to daily necessities and furniture products to meet the needs of a wider range of user scenarios.

Experimental evaluation of performance parameters of a domestic freezer under door opening conditions

The current study focuses on a comprehensive experimental assessment of the cooling system parameters of a domestic-type upright freezer that is equipped with shelves and test packages under periodic door opening conditions, referring to real user scenarios. The influence of varying ambient relative humidity levels as well as the load state of the cabinet are taken into account. The study reveals that both heat and moisture have a crucial effect on the average air temperature inside the cabinet, the temperature of the walls, the transient evaporation and condensation pressure behaviour of the refrigerant, as well as the compressor power, the total operation ratio of the compressor, and the overall amount of energy consumption. The specific performance experiments are conducted at 32 °C ambient and 35 %, 65 %, and 85 % relative humidity conditions with unloaded, half-loaded, and full-loaded cases. The obtained data and results are evaluated between frosting, influenced by altering ambient humidity levels, and cabinet load state under periodic door openings where the variation in energy consumption is clearly measured. Consequently, the amounts of frost accumulation in the cabinet under half and full loaded conditions at 32 °C an 85 % relative humidity conditions are observed to be about 220 g and 270 g, respectively, followed by regular door openings and closings which result in a raise in total energy consumption rate of 7.6 % and 11.2 % compared to the period before the door opening. The frost accumulation in the full loaded condition at 32 °C and 85 % RH is found to be 2.4 times greater than the unloaded condition with a relative humidity (RH) of 35 % whereas the energy consumption is 3.7 times higher.

Cloud storage cost: a taxonomy and survey

Cloud service providers offer application providers with virtually infinite storage and computing resources, while providing cost-efficiency and various other quality of service (QoS) properties through a storage-as-a-service (StaaS) approach. Organizations also use multi-cloud or hybrid solutions by combining multiple public and/or private cloud service providers to avoid vendor lock-in, achieve high availability and performance, and optimise cost. Indeed cost is one of the important factors for organizations while adopting cloud storage; however, cloud storage providers offer complex pricing policies, including the actual storage cost and the cost related to additional services (e.g., network usage cost). In this article, we provide a detailed taxonomy of cloud storage cost and a taxonomy of other QoS elements, such as network performance, availability, and reliability. We also discuss various cost trade-offs, including storage and computation, storage and cache, and storage and network. Finally, we provide a cost comparison across different storage providers under different contexts and a set of user scenarios to demonstrate the complexity of cost structure and discuss existing literature for cloud storage selection and cost optimization. We aim that the work presented in this article will provide decision-makers and researchers focusing on cloud storage selection for data placement, cost modelling, and cost optimization with a better understanding and insights regarding the elements contributing to the storage cost and this complex problem domain.

Development and Validation of Cloud Applications Usability Development Scale

Cloud computing has revolutionized technology and education by providing scalable virtual resources accessible via the internet. This study emphasizes the importance of usability in cloud services, discussing various scales and metrics while highlighting their strengths and weaknesses. It underscores the necessity for a comprehensive usability scale that integrates both subjective and objective measures to suit diverse user scenarios and populations. The article introduces the Cloud Applications/Services Usability Development Scale, comprising five usability dimensions: Capable, Personal, Reliable, Valuable, and Secure. To validate the instrument, factor analysis was performed on 28 items for each construct, utilizing Principal Component Analysis and Varimax Rotation. The Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test were applied to assess sampling adequacy. Construct validity and reliability were scrutinized using AMOS 21, a statistical analysis software for structural equation modeling (SEM). A confirmation factor analysis was employed to assess both converging and discriminating validity. A survey conducted among actual cloud application users, specifically Google Drive users in Lahore’s public libraries, demonstrated the strong construct validity and reliability of the developed instrument. The Cloud Applications/Services Usability Development Scale proved effective in evaluating the usability of cloud-based applications. This study not only contributes valuable insights to the development and validation of usability measurement tools for cloud applications but also addresses a significant gap in local and international literature. Furthermore, it serves as a model for creating and validating measurement tools for cloud services, offering practical implications for usability testing and methodological advancements in the field of cloud computing.

Advanced analysis of operating parameters utilizing big data to improve building cooling equipment energy efficiency standards

The rapid increase in building energy consumption has highlighted the critical contribution of building cooling/heating equipment, including room air conditioners and variable refrigerant flow (VRF) systems. However, existing energy efficiency standards for such equipment often specify testing conditions that differ from real-world usage scenarios and are unsupported by in-depth investigations, resulting in evaluations of equipment performance that do not align with actual performance. Therefore, the increasing availability of large-scale real-time monitoring datasets was leveraged in this study to develop an innovative technical approach to derive a more accurate description of VRF operation-related parameters. This approach was applied to inform suggestions for the enhancement of energy efficiency standards for VRF devices by collecting and preprocessing data, extracting operating parameters, conducting data analysis and comparisons, and discussing potential applications. The results of this study indicate that the type of building significantly influences the actual performance of a VRF and imply that large-scale monitoring data can provide a foundation for the revision of building equipment energy efficiency standards as well as future investigations of building equipment energy policies.

ECSkin: Tessellating Electrochromic Films for Reconfigurable On-skin Displays

Emerging electrochromic (EC) materials have advanced the frontier of thin-film, low-power, and non-emissive display technologies. While suitable for wearable or textile-based applications, current EC display systems are manufactured in fixed, pre-designed patterns that hinder the potential of reconfigurable display technologies desired by on-skin interactions. To realize the customizable and scalable EC display for skin wear, this paper introduces ECSkin, a construction toolkit composed of modular EC films. Our approach enables reconfigurable designs that display customized patterns by arranging combinations of premade EC modules. An ECSkin device can pixelate patterns and expand the display area through tessellating congruent modules. We present the fabrication of flexible EC display modules with accessible materials and tools. We performed technical evaluations to characterize the electrochromic performance and conducted user evaluations to verify the toolkit's usability and feasibility. Two example applications demonstrate the adaptiveness of the modular display on different body locations and user scenarios.

TeleAware Robot: Designing Awareness-augmented Telepresence Robot for Remote Collaborative Locomotion

Telepresence robots can be used to support users to navigate an environment remotely and share the visiting experience with their social partners. Although such systems allow users to see and hear the remote environment and communicate with their partners via live video feed, this does not provide enough awareness of the environment and their remote partner's activities. In this paper, we introduce an awareness framework for collaborative locomotion in scenarios of onsite and remote users visiting a place together. From an observational study of small groups of people visiting exhibitions, we derived four design goals for enhancing the environmental and social awareness between social partners, and developed a set of awareness-enhancing techniques to add to a standard telepresence robot - named TeleAware robot. Through a controlled experiment simulating a guided exhibition visiting task, TeleAware robot showed the ability to lower the workload, facilitate closer social proximity, and improve mutual awareness and social presence compared with the standard one. We discuss the impact of mobility and roles of local and remote users, and provide insights for the future design of awareness-enhancing telepresence robot systems that facilitate collaborative locomotion.