Search Results (37)

Radio occultation (RO) has become a key technique for monitoring the ionosphere by deriving electron density (Ne) profiles and total electron content (TEC) from GNSS signals. This study assesses the newly deployed PlanetiQ GNOMES constellation by validating its ionospheric Ne profiles and profile-based TEC against collocated measurements from ionosondes and the COSMIC-2 mission under both quiet and disturbed geomagnetic conditions. Data matching for the statistical validation uses conservative spatial thresholds of less than 1° in latitude and longitude and temporal limits of 30 min for ionosondes and 1 h for COSMIC-2, supported by a dedicated sensitivity analysis, whereas storm-time case studies apply tighter temporal collocation and explicit control of the ray path geometry. Quantitative agreement is evaluated using root mean square error (RMSE), mean and absolute mean differences, correlation coefficients, regression analysis, and normalized percentage differences for key F-layer parameters, including the maximum Ne of the F2 layer (NmF2), the peak height of the F2 layer (hmF2), and the critical frequency of the F2 layer (foF2), along with altitude-dependent Ne profiles. PlanetiQ shows strong consistency with ionosonde profiles, with RMSE ranging from 2.94 × 10⁴ to 2.76 × 10⁵ el/cm³, correlations typically exceeding 0.90, and normalized absolute mean differences often near or below about 10–20%, although lower correlations of about 0.31 and 0.69 are found at Poker Flat and Awase, respectively, reflecting complex local structures and regional variability. Comparisons with COSMIC-2 during quiet conditions yield RMSE values between 7.06 × 10⁴ and 2.16 × 10⁵ el/cm³, correlations from 0.94 to 0.99, and percentage differences that generally remain within a few tens of percent, while storm-time analyses show RMSE between 1.12 × 10⁵ and 3.70 × 10⁵ el/cm³ with correlations from 0.80 to 0.99, confirming robust agreement across a wide range of geophysical conditions. Regression results demonstrate slopes near 1.00 and correlation coefficients above 0.90 for NmF2 and foF2 between PlanetiQ and both ionosondes and COSMIC-2, whereas hmF2 exhibits larger scatter, particularly during geomagnetic disturbances; additional binning by spatial and temporal separation indicates that temporal mismatches generally have a stronger impact on discrepancies than horizontal distance. Overall, the results demonstrate that PlanetiQ ionospheric RO data provide accurate and consistent measurements of key ionospheric parameters, comparable to those from COSMIC-2 and ionosondes, and can reliably complement existing observing systems for monitoring ionospheric variability and space-weather impacts. Full article

In complex task environments in both nature and human society, structuralviolations of expectation (VoE) occur with non-trivial frequency. Agents that are designed to operate in such environments must be capable of open-world learning (OWL), defined as the ability to detect and accommodate out-of-distribution inputs, as well as more complex structural VoEs, without requiring extensive and offline re-training. Until recently, OWL research was relatively constrained and limited to areas such as anomaly detection and concept drift. More recently, agent-based OWL research has witnessed much interest from across the community. To support this research, not just for developing OWL algorithms, but also evaluating them, there is a need for multi-agent environments where structural VoEs can be generated, and controlled experiments can be run with relative ease. To address this need, we propose a resource called PokerOWL, a platform that is supported on the Gymnasium infrastructure, which is extensively used in the reinforcement learning and AI game-playing communities. PokerOWL supports both a rich VoE generator and a graphical interface for facilitating development and evaluation of OWL methods. Using an extensive set of experiments and a Poker-playing agent based on Deep Q-Networks, we use PokerOWL to demonstrate how even state-of-the-art agents can struggle to generalize to novel situations without additional OWL capabilities. Full article

The NA64 experiment at the CERN H4 beamline recently started a high-energy positron-beam program to search for light dark matter particles through a thick-target, missing-energy measurement. To fulfill the energy resolution requirement of the physics measurement ${\sigma }_E/E\simeq 2.5\%/\sqrt{E\left[\mathrm{GeV}\right]}\oplus 0.5\%$ and cope with the constraints and performance requests of the NA64 setup, a new high-resolution homogeneous electromagnetic calorimeter PKR-CAL has been designed. The detector is based on PbWO₄ crystals, each read by multiple SiPM sensors to maximize the light collection. The PKR-CAL design has been optimized to mitigate and control unavoidable SiPM saturation effects at high light levels, as well as to minimize the gain fluctuations induced by instantaneous variations of the H4 beam intensity. The R&D program culminated in the construction of a small-scale prototype, POKERINO. In this work, we present the results from the experimental characterization campaign of the POKERINO, aiming at demonstrating that the obtained performances are compatible with the application requirements. Full article

There has been significant recent progress in algorithms for approximation of Nash equilibrium in large two-player zero-sum imperfect-information games and exact computation of Nash equilibrium in multiplayer normal-form games. While counterfactual regret minimization and fictitious play are scalable to large games and have convergence guarantees in two-player zero-sum games, they do not guarantee convergence to Nash equilibrium in multiplayer games. We present an approach for exact computation of Nash equilibrium in multiplayer imperfect-information games that solves a quadratically-constrained program based on a nonlinear complementarity problem formulation from the sequence-form game representation. This approach capitalizes on recent advances for solving nonconvex quadratic programs. Our algorithm is able to quickly solve three-player Kuhn poker after removal of dominated actions. Of the available algorithms in the Gambit software suite, only the logit quantal response approach is successfully able to solve the game; however, the approach takes longer than our algorithm and also involves a degree of approximation. Our formulation also leads to a new approach for computing Nash equilibrium in multiplayer normal-form games which we demonstrate to outperform a previous quadratically-constrained program formulation. Full article

This paper proposes a hybrid collaborative framework to optimize technology selection in Small and Medium-sized Enterprises (SMEs) by integrating machine learning (ML) predictions with Planning Poker, consensus-based estimation technique used in agile software development. Addressing known challenges such as cognitive bias, resource constraints, and the need for inclusive decision-making, the proposed model combines data-driven suitability analysis with stakeholder-driven consensus. ML generates quantitative, criterion-wise suitability scores based on historical SME data, providing transparent baselines for evaluation. Stakeholders independently assess candidate technologies using Planning Poker, and their consensus is blended with ML predictions through a flexible weighting mechanism. An illustrative case study on CRM tool selection illustrates the framework’s practical advantages: improved decision accuracy, transparency, and greater stakeholder engagement. The methodology is iterative, allowing for continuous learning and adaptation as new data emerges. This dual approach ensures that technology adoption decisions in SMEs are both empirically validated and contextually robust, offering a significant improvement over traditional, siloed methods. Full article

In the age of big data, multimedia communication is becoming one of the most important communication methods. Therefore, it is important to overcome the challenge of processing numerous images safely and efficiently. In this paper, a 3D cross-image encryption scheme is proposed based on the Rubik’s Cube–poker model. The proposed scheme has the following properties: Firstly, it has flexible input image requirements to improve its applicability. Secondly, it has a strong ability to recover from data loss and retains important information in the reconstructed image. Finally, it achieves a lower time cost. Under the same input conditions in the grayscale color space, our proposed scheme achieves an execution time of 0.62 s, which is significantly lower than that of other schemes. The simulation results confirm the correctness, security, and robustness of the scheme. Full article

Low-temperature cracking is a primary failure mode of asphalt pavement. The poker chip test provides a straightforward and efficient approach to simulating the film state of asphalt binders in asphalt structures. By measuring the tensile strength and ultimate tensile strain of the binder film, this test can effectively evaluate the cracking resistance and ductility of asphalt binders. Accordingly, this study employed the poker chip test to analyze the evolutions of low-temperature cracking resistance under various aging levels. To ensure the reliability of tensile strength and ultimate tensile strain, a Pearson correlation analysis was conducted between the two indicators and the traditional low-temperature performance evaluation indicators: stiffness modulus, creep rate, and the Glover-Rowe (G-R) parameter. The results indicate that the tensile strength and ultimate tensile strain of styrene–butadiene–styrene (SBS)-modified asphalt are higher than those of 70# base asphalt at the same aging level. With increasing aging time, the tensile strength of both SBS-modified asphalt and 70# base asphalt increases, while the ultimate tensile strain decreases. Additionally, the tensile strength and ultimate tensile strain are sensitive to changes in asphalt binder types and aging levels. They have a good linear correlation with stiffness modulus and creep rate, with correlation coefficients exceeding 0.9. Due to the distinct characteristics represented, the correlation between the two indicators and the G-R parameter is relatively weaker, with correlation coefficients exceeding 0.7. The findings of this study demonstrate that tensile strength and ultimate tensile strain are effective indicators for assessing the low-temperature performance of asphalt binders. They can serve as substitute indicators of stiffness modulus and creep rate, respectively. Full article

Policy space response oracles (PSRO) is an important algorithmic framework for approximating Nash equilibria in two-player zero-sum games. Enhancing policy diversity has been shown to improve the performance of PSRO in this approximation process significantly. However, existing diversity metrics are often prone to redundancy, which can hinder optimal strategy convergence. In this paper, we introduce the policy similarity measure (PSM), a novel approach that combines Gaussian and cosine similarity measures to assess policy similarity. We further incorporate the PSM into the PSRO framework as a regularization term, effectively fostering a more diverse policy population. We demonstrate the effectiveness of our method in two distinct game environments: a non-transitive mixture model and Leduc poker. The experimental results show that the PSM-augmented PSRO outperforms baseline methods in reducing exploitability by approximately 7% and exhibits greater policy diversity in visual analysis. Ablation studies further validate the benefits of combining Gaussian and cosine similarities in cultivating more diverse policy sets. This work provides a valuable method for measuring and improving the policy diversity in two-player zero-sum games. Full article

The impact of immediate emotions on human decision-making has been extensively studied; however, their role within human–machine interactive systems remains underexplored. This study investigates how emotional states influence decision-making behaviors in such systems by introducing a novel two-tiered progressive inquiry model—environment–emotion and emotion–decision. Using Kuhn poker as an experimental context, we developed an intelligent decision support system based on Counterfactual Regret Minimization algorithms to provide real-time strategic advice to participants. Our findings reveal that positive emotional states lead individuals to be more risk-seeking and less inclined to collaborate with the AI-driven system, whereas negative emotional states result in risk-averse tendencies and higher compliance with system recommendations. By systematically uncovering the causal mechanisms through which environmental factors regulate emotions and subsequently affect decision-making, this research provides critical theoretical and empirical insights for optimizing human–machine interaction design. Full article

Effort estimation is critical in software engineering to assess the resources needed for development tasks and to enable realistic commitments in agile iterations. This study investigates whether generative AI tools, which are transforming various aspects of software development, can improve effort estimation efficiency. A controlled experiment was conducted in which development teams upgraded an existing information system, with the experimental group using the generative-AI-based tool GitLab Duo for estimation and the control group using conventional methods (e.g., planning poker or analogy-based planning). Results show that while generative-AI-based estimation tools achieved only 16% accuracy—currently insufficient for industry standards—they offered valuable support for task breakdown and iteration planning. Participants noted that a combination of conventional methods and AI-based tools could offer enhanced accuracy and efficiency in future planning. Full article

Collaborative learning has been widely studied in higher education and beyond, suggesting that collaboration in small groups can be effective for promoting deeper learning, enhancing engagement and motivation, and improving a range of cognitive and social outcomes. The study presented in this paper compared different forms of human and robot facilitation in the game of planning poker, designed as a collaborative activity in the undergraduate course on agile project management. Planning poker is a consensus-based game for relative estimation in teams. Team members collaboratively estimate effort for a set of project tasks. In our study, student teams played the game of planning poker to estimate the effort required for project tasks by comparing task effort relative to one another. In this within- and between-subjects study, forty-nine students in eight teams participated in two out of four conditions. The four conditions differed in respect to the form of human and/or robot facilitation. Teams 1–4 participated in conditions C1 human online and C3 unsupervised robot, while teams 5–8 participated in conditions C2 human face to face and C4 supervised robot co-facilitation. While planning poker was facilitated by a human teacher in conditions C1 and C2, the NAO robot facilitated the game-play in conditions C3 and C4. In C4, the robot facilitation was supervised by a human teacher. The study compared these four forms of facilitation and explored the effects of the type of facilitation on the facilitator’s competence (FC), learning experience (LX), and learning outcomes (LO). The results based on the data from an online survey indicated a number of significant differences across conditions. While the facilitator’s competence and learning outcomes were rated higher in human (C1, C2) compared to robot (C3, C4) conditions, participants in the supervised robot condition (C4) experienced higher levels of focus, motivation, and relevance and a greater sense of control and sense of success, and rated their cognitive learning outcomes and the willingness to apply what was learned higher than in other conditions. These results indicate that human supervision during robot-led facilitation in collaborative learning (e.g., providing hints and situational information on demand) can be beneficial for learning experience and outcomes as it allows synergies to be created between human expertise and flexibility and the consistency of the robotic assistance. Full article

Compaction quality significantly influences the strength and durability of concrete in structures. Under-compacting can retain entrapped air, reducing strength, while over-compacting can lead to segregation, creating local variances in strength distribution and modulus of elasticity in the concrete structure. This study examines the widely adopted concept that compaction is optimal when bubbles cease to emerge on the concrete surface. We recorded the surface activity of six comparable concrete specimens during the compaction process using a 4K video camera. Four specimens were compacted using a table vibrator and two with a poker vibrator. From the video frames, we isolated the bubbles for analysis, employing digital image processing techniques to distinguish newly risen bubbles per frame. It was found that the bubbles continuously rose to the surface in all specimens throughout the compaction process, suggesting a need for extended compaction, with some specimens showing a slow in the rate of the bubbles’ emergence. However, upon examining the segregation levels, it was discovered that all the specimens were segregated, some severely, despite the continued bubble emergence. These findings undermine the reliability of using bubble emergence as a principle to stop compaction and support the need for developing online measurement tools for evaluating compaction quality. Full article

For processing streaming events from a Dynamic Vision Sensor camera, two types of neural networks can be considered. One are spiking neural networks, where simple spike-based computation is suitable for low-power consumption, but the discontinuity in spikes can make the training complicated in terms of hardware. The other one are digital Complementary Metal Oxide Semiconductor (CMOS)-based neural networks that can be trained directly using the normal backpropagation algorithm. However, the hardware and energy overhead can be significantly large, because all streaming events must be accumulated and converted into histogram data, which requires a large amount of memory such as SRAM. In this paper, to combine the spike-based operation with the normal backpropagation algorithm, memristor–CMOS hybrid circuits are proposed for implementing event-driven neural networks in hardware. The proposed hybrid circuits are composed of input neurons, synaptic crossbars, hidden/output neurons, and a neural network’s controller. Firstly, the input neurons perform preprocessing for the DVS camera’s events. The events are converted to histogram data using very simple memristor-based latches in the input neurons. After preprocessing the events, the converted histogram data are delivered to an ANN implemented using synaptic memristor crossbars. The memristor crossbars can perform low-power Multiply–Accumulate (MAC) calculations according to the memristor’s current–voltage relationship. The hidden and output neurons can convert the crossbar’s column currents to the output voltages according to the Rectified Linear Unit (ReLU) activation function. The neural network’s controller adjusts the MAC calculation frequency according to the workload of the event computation. Moreover, the controller can disable the MAC calculation clock automatically to minimize unnecessary power consumption. The proposed hybrid circuits have been verified by circuit simulation for several event-based datasets such as POKER-DVS and MNIST-DVS. The circuit simulation results indicate that the neural network’s performance proposed in this paper is degraded by as low as 0.5% while saving as much as 79% in power consumption for POKER-DVS. The recognition rate of the proposed scheme is lower by 0.75% compared to the conventional one, for the MNIST-DVS dataset. In spite of this little loss, the power consumption can be reduced by as much as 75% for the proposed scheme. Full article

Oral infections occur due to contact between biofilm rich in Candida albicans formed on the inner surface of complete dentures and the mucosa. This study investigated historical advances in the prevention and treatment of oral mucosal infection and identified gaps in the literature. Bibliographic research was conducted, looking at PubMed, Embase, Web of Science, and Scopus, where 935 articles were found. After removing duplicates and excluding articles by reading the title and abstract, 131 articles were selected for full reading and 104 articles were included. Another 38 articles were added from the gray literature. This review followed the PRISMA-ScR guidelines. The historical period described ranges from 1969 to 2023, in which, during the 21st century, in vitro and in vivo studies became more common and, from 2010 to 2023, the number of randomized controlled trials increased. Among the various approaches tested are the incorporation of antimicrobial products into prosthetic materials, the improvement of oral and denture hygiene protocols, the development of synthetic and natural products for the chemical control of microorganisms, and intervention with local or systemic antimicrobial agents. Studies report good results with brushing combined with sodium hypochlorite, and new disinfectant solutions and products incorporated into prosthetic materials are promising. Full article

Poker is a game of skill, much like chess or go, but distinct as an incomplete information game. Substantial work has been done to understand human play in poker, as well as the optimal strategies in poker. Evolutionary game theory provides another avenue to study poker by considering overarching strategies, namely rational and random play. In this work, a population of poker playing agents is instantiated to play the preflop portion of Texas Hold’em poker, with learning and strategy revision occurring over the course of the simulation. This paper aims to investigate the influence of learning dynamics on dominant strategies in poker, an area that has yet to be investigated. Our findings show that rational play emerges as the dominant strategy when loss aversion is included in the learning model, not when winning and magnitude of win are of the only considerations. The implications of our findings extend to the modeling of sub-optimal human poker play and the development of optimal poker agents. Full article