Hasil untuk "Communities. Classes. Races"

Joanna Sławińska

Wkrótce.

Ely Mulyati, Anggi Purnama Sari Dewi, Andrian Noviandry et al.

This community service activity aims to empower the community in innovative and sustainable plastic waste management. The program is implemented through three main activities, namely socialization of plastic waste management and circular economy, training in making triangular bricks from plastic waste, and creating social media accounts to market Bank Sampah products. The brick-making process involves shredding plastic using a shredder, conducting a sieve analysis to ensure uniform particle size, then mixing it with sand and cement before molding it into a triangular shape. From the initial stage of production, 300 triangular bricks were successfully produced with lightweight and strong characteristics. Through this activity, the community not only gained technical skills but also digital promotion skills to expand their market reach. This program demonstrates that collaboration between universities and the community can yield concrete solutions for plastic waste management while supporting the implementation of a circular economy and sustainable development.

Kijin Sung, Kenta Hoshino, Akihiko Honda et al.

Drone racing involves high-speed navigation of three-dimensional paths, posing a substantial challenge in control engineering. This study presents a game-theoretic control framework, the nonlinear receding-horizon differential game (NRHDG), designed for competitive drone racing. NRHDG enhances robustness in adversarial settings by predicting and countering an opponent's worst-case behavior in real time. It extends standard nonlinear model predictive control (NMPC), which otherwise assumes a fixed opponent model. First, we develop a novel path-following formulation based on projection point dynamics, eliminating the need for costly distance minimization. Second, we propose a potential function that allows each drone to switch between overtaking and obstructing maneuvers based on real-time race situations. Third, we establish a new performance metric to evaluate NRHDG with NMPC under race scenarios. Simulation results demonstrate that NRHDG outperforms NMPC in terms of both overtaking efficiency and obstructing capabilities.

Onur Akgün

The coordination of multiple autonomous agents in high-speed, competitive environments represents a significant engineering challenge. This paper presents CRUISE (Curriculum-Based Iterative Self-Play for Scalable Multi-Drone Racing), a reinforcement learning framework designed to solve this challenge in the demanding domain of multi-drone racing. CRUISE overcomes key scalability limitations by synergistically combining a progressive difficulty curriculum with an efficient self-play mechanism to foster robust competitive behaviors. Validated in high-fidelity simulation with realistic quadrotor dynamics, the resulting policies significantly outperform both a standard reinforcement learning baseline and a state-of-the-art game-theoretic planner. CRUISE achieves nearly double the planner's mean racing speed, maintains high success rates, and demonstrates robust scalability as agent density increases. Ablation studies confirm that the curriculum structure is the critical component for this performance leap. By providing a scalable and effective training methodology, CRUISE advances the development of autonomous systems for dynamic, competitive tasks and serves as a blueprint for future real-world deployment.

Luca Tognoni, Neil Reichlin, Edoardo Ghignone et al.

Reactive controllers for autonomous racing avoid the computational overhead of full ee-Think-Act autonomy stacks by directly mapping sensor input to control actions, eliminating the need for localization and planning. A widely used reactive strategy is FTG, which identifies gaps in LiDAR range measurements and steers toward a chosen one. While effective on fully bounded circuits, FTG fails in scenarios with incomplete boundaries and is prone to driving into dead-ends, known as FTG-traps. This work presents DTR, a reactive controller that combines Delaunay triangulation, from raw LiDAR readings, with track boundary segmentation to extract a centerline while systematically avoiding FTG-traps. Compared to FTG, the proposed method achieves lap times that are 70\% faster and approaches the performance of map-dependent methods. With a latency of 8.95 ms and CPU usage of only 38.85\% on the robot's OBC, DTR is real-time capable and has been successfully deployed and evaluated in field experiments.

Mattia Piccinini, Aniello Mungiello, Georg Jank et al.

Autonomous racing has gained increasing attention in recent years, as a safe environment to accelerate the development of motion planning and control methods for autonomous driving. Deep learning models, predominantly based on neural networks (NNs), have demonstrated significant potential in modeling the vehicle dynamics and in performing various tasks in autonomous driving. However, their black-box nature is critical in the context of autonomous racing, where safety and robustness demand a thorough understanding of the decision-making algorithms. To address this challenge, this paper proposes MS-NN-steer, a new Model-Structured Neural Network for vehicle steering control, integrating the prior knowledge of the nonlinear vehicle dynamics into the neural architecture. The proposed controller is validated using real-world data from the Abu Dhabi Autonomous Racing League (A2RL) competition, with full-scale autonomous race cars. In comparison with general-purpose NNs, MS-NN-steer is shown to achieve better accuracy and generalization with small training datasets, while being less sensitive to the weights' initialization. Also, MS-NN-steer outperforms the steering controller used by the A2RL winning team. Our implementation is available open-source in a GitHub repository.

Razaz Waheeb Attar, Mohammad Habes, Ahlam Almusharraf et al.

By using advanced technologies and data analytics, smart cities can establish conditions that are both inclusive and accessible, addressing the distinctive needs of disabled people. This research aims to examine the benefits of smart city technologies and develop strategies for developing environments that serve the requirements of individuals with disabilities in Saudi Arabia. Using a sequential mixed method, the study uses the social disability model. The initial phase involves gathering quantitative data from 427 individuals with disabilities in Saudi Arabia. Further, qualitative data was obtained through semi-structured interviews with a sample of four professionals employed in Saudi smart city initiatives. Quantitative data is analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM), while qualitative data is analyzed using thematic analysis. Quantitative findings revealed the robustness of the measurement model, confirming the significant effects of Smart City Initiatives on Accessibility Enhancement, Inclusive Information, and Health and Wellbeing Improvement. The respondents indicated that they are satisfied with the initiatives and their effectiveness, providing them with equal services and opportunities without discrimination. The qualitative analysis further revealed themes, i.e., Technology Integration for Accessibility, Inclusive Design, Inclusive Planning for Health, and others. Participants indicated special consideration for implementing the designs and approaches to ensure inclusivity and availability of services to disabled people. Besides, implementing infrastructure and policies to ensure the health and wellbeing of disabled people also remained prevalent. Hence, it is concluded that smart city initiatives break obstacles and improve the wellbeing of individuals with disabilities. Improved healthcare services and inclusive urban planning highlight the transformative effect of these initiatives on health and wellbeing, promoting an equitable and sustainable services environment. Finally, research implications and limitations are discussed.

Narine A. Givargizova

<p>The article considers the problem of the relevance for the institute of mediation in Russia. Statistical data for the country concerning 2020 are presented and data for the Rostov region to the same period are analyzed. A comparative analysis is made with data for the same period in the Sverdlovsk region. The information and empirical base of the study consists of materials from: the website of the Judicial Department at the Supreme Court of the Russian Federation; the Association of Mediators of the Rostov region “Reconciliation”; “Reconciliation Rooms” in the Arbitration Court of the Sverdlovsk region. The methods of content analysis, statistical analysis, in-depth interviews, and a mass survey conducted among the employed population of the Rostov region aged 21 to 30 years were used. The study revealed: low awareness of respondents about the possibilities of using the mediation procedure, its structure and features; low level of modern mediation practice in the country. It is shown that in Russia today the courts are inundated with lawsuits, while mediation could well constructively solve many social problems. The necessity of creating favorable conditions for the development of mediation activity and its popularization is substantiated. The main reasons preventing the institution of mediation from becoming a popular activity among the population are identified: a low culture of conflict resolution, which in some cases leads to the avoidance of disputants from finding a suitable way to resolve it; a factor of insufficient confidence in the possibility of resolving&nbsp;a dispute involving a third party. It is concluded that the lack of trust is primarily due to insufficient awareness of the population about the essence of the mediation procedure, about the legal status of the mediation agreement, which in the case of notarization has the force of an executive document. The wider use of all types of media, the increase in online platforms with the location of mediation webinars conducted by experienced specialists in this field will help spread the practice of mediation and will contribute to its early institutionalization.</p>

Vaishali Sharma

Community food systems, exemplified by initia­tives like community grain banks (CGBs), play a crucial role in achieving Sustainable Development Goal 2 (SDG 2), which aims to achieve zero hunger and ensure food security by 2030. This paper draws upon a systematic review of the literature on CGBs to emphasize the relevance of community institutions in enhancing local food security. Adhering to Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, this paper reviews 16 aca­demic articles, two theses, and 19 online sources. The study reveals that CGBs offer immediate relief during food shortages, empower women, stabilize farmers’ income, reduce debt burdens, foster social trust, and enhance community resilience. This review highlights the need for international stake­holders to prioritize supporting CGBs to preserve the self-sustaining systems. Tailoring CGB designs to community-specific needs could significantly enhance local food security, offering actionable strategies to mitigate severe food insecurity globally and regionally.

Andrew Cinar, Forrest Laine

Two-vehicle racing is natural example of a competitive dynamic game. As with most dynamic games, there are many ways in which the underlying solution concept can be structured, resulting in different equilibrium concepts. The assumed solution concept influences the behaviors of two interacting players in racing. For example, blocking behavior emerges naturally in leader-follower play, but to achieve this in Nash play the costs would have to be chosen specifically to trigger this behavior. In this work, we develop a novel model for competitive two-player vehicle racing, represented as an equilibrium problem, complete with simplified aerodynamic drag and drafting effects, as well as position-dependent collision-avoidance responsibility. We use our model to explore how different solution concepts affect competitiveness. We develop a solution for bilevel optimization problems, enabling a large-scale empirical study comparing bilevel strategies (either as leader or follower), Nash equilibrium strategy and a single-player constant velocity baseline. We find the choice of strategies significantly affects competitive performance and safety.

Zhouheng Li, Bei Zhou, Cheng Hu et al.

The development of autonomous driving has boosted the research on autonomous racing. However, existing local trajectory planning methods have difficulty planning trajectories with optimal velocity profiles at racetracks with sharp corners, thus weakening the performance of autonomous racing. To address this problem, we propose a local trajectory planning method that integrates Velocity Prediction based on Model Predictive Contouring Control (VPMPCC). The optimal parameters of VPMPCC are learned through Bayesian Optimization (BO) based on a proposed novel Objective Function adapted to Racing (OFR). Specifically, VPMPCC achieves velocity prediction by encoding the racetrack as a reference velocity profile and incorporating it into the optimization problem. This method optimizes the velocity profile of local trajectories, especially at corners with significant curvature. The proposed OFR balances racing performance with vehicle safety, ensuring safe and efficient BO training. In the simulation, the number of training iterations for OFR-based BO is reduced by 42.86% compared to the state-of-the-art method. The optimal simulation-trained parameters are then applied to a real-world F1TENTH vehicle without retraining. During prolonged racing on a custom-built racetrack featuring significant sharp corners, the mean projected velocity of VPMPCC reaches 93.18% of the vehicle's handling limits. The released code is available at https://github.com/zhouhengli/VPMPCC.

Chelsea Rozanski, Michael Gavin

The food landscape of Calgary, Canada, is sown with an abundance of polycultures. Alongside place-specific Indigenous foodways are food rescue, banking, and hamper programs, food studies scholars, a City of Calgary food resilience plan, and a growing number of alternative food network producers. Within the local alternative food network, there has been a boom in advancing indoor growing for our colder climate, including container, aquaponic, vertical hydroponic, and greenhouse growing. Situated as an agrarian ethno­grapher and an urban regenerative farmer, we seek to highlight the viability of agricultural techniques that are in relation with the land to grow more socially and ecologically sustainable food and farm systems in and around Calgary. From this posi­tion, we formed a collaboration between the University of Calgary, Root and Regenerate Urban Farms, and the Young Agrarians to document the cultivation process for a production urban farm. Over the course of one growing season—May to September, 2021—we harvested approximately 7,000 lbs (3,175 kg) of produce across nine urban spaces totaling 0.26 acres. The 48 vegetable varie­ties were distributed to 35 community supported agriculture shareholders, weekly farmers market customers, restaurant chefs, and members of the YYC Growers and Distributors cooperative. More­over, we donated 765 lbs (347 kg) of surplus pro­duce to the Calgary Community Fridge, Calgary Food Bank, and the Alex Community Food Cen­tre, which work to mitigate food insecurity. Through a reflexive practitioner approach, our reflective essay discusses the benefits and limita­tions of Small Plot Intensive Farming methods and urban land-sharing strategies, as well as the viability of land-based urban agriculture in a rapidly chang­ing socio-ecological climate. Our paper also demonstrates the potential for transcending siloed approaches to knowledge-making vis-à-vis experi­ential learning partnerships between graduate student researchers, farmers, and agricultural organizations.

Mae Al-Ansari, Saud AlKhaled

In September 2015, the State of Kuwait signed the UN’s 2030 Agenda, committing to all 17 of its Sustainable Development Goals (SDGs), which include the building of sustainable cities and communities through initiatives such as social housing. In response, the New Kuwait Vision 2035 has witnessed a shift in approach to the social housing paradigm at the state level. This paper examines the status of recent social housing projects in Jaber Al-Ahmed City, Kuwait, through a critical, evidence-based analysis of the decision-making processes and urban-architectural products that shaped its development. The mixed-methods data for this case study were generated via archival research and semi-structured interviews supplemented with field observations to evaluate the local and international sustainability agendas implemented in the city as process and product. Principles of Sustainable Urban Forms are implemented for the evaluation. The article also presents evidence of local urban practices (resident appropriation and participation), legitimized environmental practices, and community wellbeing. It concludes with recommendations for resolving issues in the current processes around the design and implementation of sustainable urban forms to inform future social housing developments. These recommendations for sustainable social housing in Kuwait provide an opportunity to revisit and reconsider the core values of sustainability while adding to the multiplicity of its definitions. Although recent social housing projects in Kuwait may demonstrate an overall effective process-to-product procedure as a means of architectural production that addresses the country’s housing demand, important aspects remain in question with regard to sustainable built environments.

Chanyoung Jung, Andrea Finazzi, Hyunki Seong et al.

While the majority of autonomous driving research has concentrated on everyday driving scenarios, further safety and performance improvements of autonomous vehicles require a focus on extreme driving conditions. In this context, autonomous racing is a new area of research that has been attracting considerable interest recently. Due to the fact that a vehicle is driven by its perception, planning, and control limits during racing, numerous research and development issues arise. This paper provides a comprehensive overview of the autonomous racing system built by team KAIST for the Indy Autonomous Challenge (IAC). Our autonomy stack consists primarily of a multi-modal perception module, a high-speed overtaking planner, a resilient control stack, and a system status manager. We present the details of all components of our autonomy solution, including algorithms, implementation, and unit test results. In addition, this paper outlines the design principles and the results of a systematical analysis. Even though our design principles are derived from the unique application domain of autonomous racing, they can also be applied to a variety of safety-critical, high-cost-of-failure robotics applications. The proposed system was integrated into a full-scale autonomous race car (Dallara AV-21) and field-tested extensively. As a result, team KAIST was one of three teams who qualified and participated in the official IAC race events without any accidents. Our proposed autonomous system successfully completed all missions, including overtaking at speeds of around $220 km/h$ in the IAC@CES2022, the world's first autonomous 1:1 head-to-head race.

Mohamed Huti, Tiarna Lee, Elinor Sawyer et al.

Recent research has shown that artificial intelligence (AI) models can exhibit bias in performance when trained using data that are imbalanced by protected attribute(s). Most work to date has focused on deep learning models, but classical AI techniques that make use of hand-crafted features may also be susceptible to such bias. In this paper we investigate the potential for race bias in random forest (RF) models trained using radiomics features. Our application is prediction of tumour molecular subtype from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of breast cancer patients. Our results show that radiomics features derived from DCE-MRI data do contain race-identifiable information, and that RF models can be trained to predict White and Black race from these data with 60-70% accuracy, depending on the subset of features used. Furthermore, RF models trained to predict tumour molecular subtype using race-imbalanced data seem to produce biased behaviour, exhibiting better performance on test data from the race on which they were trained.

Julian Jeggle, Raphael Wittkowski

Recently, there has been much progress in the formulation and implementation of methods for generic many-particle simulations. These models, however, typically either do not utilize shared memory hardware or do not guarantee data-race freedom for arbitrary particle dynamics. Here, we present both a abstract formal model of particle dynamics and a corresponding domain-specific programming language that can guarantee data-race freedom. The design of both the model and the language are heavily inspired by the Rust programming language that enables data-race-free general-purpose parallel computation. We also present a method of preventing deadlocks within our model by a suitable graph representation of a particle simulation. Finally, we demonstrate the practicability of our model on a number of common numerical primitives from molecular dynamics.

Kartick Kolachala, Mohammed Ababneh, Roopa Vishwanathan

The Bitcoin scalability problem has led to the development of off-chain financial mechanisms such as payment channel networks (PCNs) which help users process transactions of varying amounts, including micro-payment transactions, without writing each transaction to the blockchain. Since PCNs only allow path-based transactions, effective, secure routing protocols that find a path between a sender and receiver are fundamental to PCN operations. In this paper, we propose RACED, a routing protocol that leverages the idea of Distributed Hash Tables (DHTs) to route transactions in PCNs in a fast and secure way. Our experiments on real-world transaction datasets show that RACED gives an average transaction success ratio of 98.74%, an average pathfinding time of 31.242 seconds, which is $1.65*10^3$, $1.8*10^3$, and $4*10^2$ times faster than three other recent routing protocols that offer comparable security/privacy properties. We rigorously analyze and prove the security of RACED in the Universal Composability framework.

Jonathan R. Anderson, M. J. Bloom, G. Y. Chen et al.

Background: The coronavirus disease 2019 (COVID-19) pandemic adversely disrupted university student educational experiences worldwide, with consequences that included increased stress levels and unhealthy sedentary behavior. Aim: This study aimed to quantify the degree of impact that COVID-19 had on the levels of physical activity and stress of university students by utilizing wearable fitness tracker data and standard stress survey instrument scores before and during the pandemic. Methods: We collected Fitbit heart rate and physical activity data, and the results of a modified Social Readjustment Rating Scale (SRRS) stress survey from 2,987 university students during the Fall 2019 (residential instruction; before COVID-19) and Fall 2020 (hybrid instruction; during COVID-19) semesters. Results: We found indicators of increased sedentary behavior during the pandemic. There was a significant decrease in both the levels of physical activity as measured by mean daily step count (↓636 steps/day; p = 1.04 · 10-9) and minutes spent in various heart rate zones (↓58 minutes/week; p = 2.20 · 10-16). We also found an increase in stressors during the pandemic, primarily from an increase in the number of students who experienced the “death of a close family member” (38.8%), with the number even higher for the population of students who opted to stay home and attend classes virtually (41.4%). Conclusions: This study quantifies the decrease in levels of physical activity and notes an increase in the number of students who experienced the death of a close family member, a known stressor, during the first year of the COVID-19 pandemic. These findings allow for more informed student-health-focused interventions related to the COVID-19 pandemic disruptions experienced by academic communities worldwide.

Front Matter

Jonathan Becker, Nadine Imholz, Luca Schwarzenbach et al.

Autonomous racing is a research field gaining large popularity, as it pushes autonomous driving algorithms to their limits and serves as a catalyst for general autonomous driving. For scaled autonomous racing platforms, the computational constraint and complexity often limit the use of Model Predictive Control (MPC). As a consequence, geometric controllers are the most frequently deployed controllers. They prove to be performant while yielding implementation and operational simplicity. Yet, they inherently lack the incorporation of model dynamics, thus limiting the race car to a velocity domain where tire slip can be neglected. This paper presents Model- and Acceleration-based Pursuit (MAP) a high-performance model-based trajectory tracking algorithm that preserves the simplicity of geometric approaches while leveraging tire dynamics. The proposed algorithm allows accurate tracking of a trajectory at unprecedented velocities compared to State-of-the-Art (SotA) geometric controllers. The MAP controller is experimentally validated and outperforms the reference geometric controller four-fold in terms of lateral tracking error, yielding a tracking error of 0.055m at tested speeds up to 11m/s.