A hybrid deep learning approach to solve optimal power flow problem in hybrid renewable energy systems.

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model's better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

Mating with Multi-Armed Bandits: Reinforcement Learning Models of Human Mate Search.

Mate choice requires navigating an exploration-exploitation trade-off. Successful mate choice requires choosing partners who have preferred qualities; but time spent determining one partner's qualities could have been spent exploring for potentially superior alternatives. Here I argue that this dilemma can be modeled in a reinforcement learning framework as a multi-armed bandit problem. Moreover, using agent-based models and a sample of k = 522 real-world romantic dyads, I show that a reciprocity-weighted Thompson sampling algorithm performs well both in guiding mate search in noisy search environments and in reproducing the mate choices of real-world participants. These results provide a formal model of the understudied psychology of human mate search. They additionally offer implications for our understanding of person perception and mate choice.

Experimental study on the effect of freeze-thaw cycles to the cohesion and moisture content of geogrid reinforced silty clay.

The freezing and thawing cycle is one of the primary causes of damage and instability to buildings in seasonal frost regions. During this process, the mechanical properties of soil are affected, leading to settlement, cracking, or deformation of infrastructure. Mitigating or reducing the occurrence of building frost damage in seasonal frost regions has become an important subject of study. Freeze-thaw (F-T) action will influence the distribution of moisture inside the reinforced soil and change the strength of thawing soil, which is closely related to the main influencing factors, such as initial moisture content, compaction degree, reinforced spacing, number of freeze-thaw cycles (FTC), freezing temperature, and effective vertical stress. Cohesion is an important index to determine the shear strength of clay, which is important to analyze the change in cohesion after F-T. Meanwhile, cohesion is closely related to soil moisture content. This study conducted orthogonal experiments on these primary influencing factors (6 factors at 5 levels) through FTC tests, triaxial tests, and moisture content tests to determine the undrained cohesion and moisture content of the clay after FTC, thereby establishing the influence of reinforcement on soil strength under freeze-thaw conditions. Based on the experimental results, SPSS software was used to fit the regression equations of undrained cohesion and moisture content expressed by the main influencing factors at different heights of the clay. Optimization options for the main influencing factors were obtained with Matlab software when the highest undrained cohesion values 6.8, 10.6, 8.9 kPa and lowest moisture content values 24.0%, 24.3%, 26.2% appeared in upper, middle and lower parts of the testing clay structure respectively, in conditions of - 15 °C freezing temperature and 5 times FTC. And determined the optimal combinations of moisture content, reinforcement spacing, compaction density, and vertical load at different heights. Decreasing reinforced spacing in silty clay was beneficial for liquid underwater seepage after F-T. The redistribution of internal moisture in the soil sample strengthened its undrained cohesion, thereby increasing the soil's shear strength. Comparing reinforcement conditions at different locations, it was found that when there were 3 layers of reinforcement with a spacing of 150 mm between them, this spacing was optimal. It played a significant role in improving the soil's shear strength and enhancing its bearing capacity. For reinforced clay itself, the order of the main factors influencing the undrained cohesion of soil after F-T, from high to low, was initial moisture content, reinforced spacing, and compaction degree.

Field study on vibration characteristics of geocell-reinforced aeolian sand subgrades.

Constructing highways in deserts is expensive due to the difficulty of acquiring materials; utilizing aeolian sand effectively has become a problem, especially in the Xinjiang region, where the desert widely occurs. This paper aims to investigate the vibration response of a geocell-reinforced aeolian sand subgrade under traffic loading based on field tests of highways in deserts. The vibration acceleration response of geocell-reinforced aeolian sand and gravelly soil upper roadbed structures is tested. The field test results illustrate the effects of dynamic loading on geocell-reinforced aeolian sand roadbeds, and the thickness substitution ratio between geocell-reinforced aeolian sand roadbeds and conventional gravelly soil roadbeds is determined and verified based on the vibration acceleration monitoring values. The results show that the vibration response induced by the test vehicle is concentrated within the 30 Hz frequency band, and the higher the vibration frequency, the faster the vertical decay in the road. The vibration damping capacity of the reinforced aeolian sand roadbed is better than that of the gravelly soil roadbed; when replacing the gravelly soil roadbed with the reinforced aeolian sand roadbed, the substitution ratio is 0.31-0.42. It is verified that half thickness of gravel soil on roadbeds can be replaced by geocell-reinforced aeolian sand under different working conditions. The results of this study can provide reference data for the design of highway subgrades in deserts.

Delayed reinforcement learning converges to intermittent control for human quiet stance.

The neural control of human quiet stance remains controversial, with classic views suggesting a limited role of the brain and recent findings conversely indicating direct cortical control of muscles during upright posture. Conceptual neural feedback control models have been proposed and tested against experimental evidence. The most renowned model is the continuous impedance control model. However, when time delays are included in this model to simulate neural transmission, the continuous controller becomes unstable. Another model, the intermittent control model, assumes that the central nervous system (CNS) activates muscles intermittently, and not continuously, to counteract gravitational torque. In this study, a delayed reinforcement learning algorithm was developed to seek optimal control policy to balance a one-segment inverted pendulum model representing the human body. According to this approach, there was no a-priori strategy imposed on the controller but rather the optimal strategy emerged from the reward-based learning. The simulation results indicated that the optimal neural controller exhibits intermittent, and not continuous, characteristics, in agreement with the possibility that the CNS intermittently provides neural feedback torque to maintain an upright posture.

Soybean seed pest damage detection method based on spatial frequency domain imaging combined with RL-SVM.

Soybean seeds are susceptible to damage from the Riptortus pedestris, which is a significant factor affecting the quality of soybean seeds. Currently, manual screening methods for soybean seeds are limited to visual inspection, making it difficult to identify seeds that are phenotypically defect-free but have been punctured by stink bugs on the sub-surface. To facilitate the convenient and efficient identification of healthy soybean seeds, this paper proposes a soybean seed pest detection method based on spatial frequency domain imaging combined with RL-SVM. Firstly, soybean optical data is obtained using single integration sphere technique, and the vigor index of soybean seeds is obtained through germination experiments. Then, based on the above two data items using feature extraction algorithms (the successive projections algorithm and the competitive adaptive reweighted sampling algorithm), the characteristic wavelengths of soybeans are identified. Subsequently, the spatial frequency domain imaging technique is used to obtain the sub-surface images of soybean seeds in a forward manner, and the optical coefficients such as the reduced scattering coefficient µ ' s and absorption coefficient µ a of soybean seeds are inverted. Finally, RL-MLR, RL-GRNN, and RL-SVM prediction models are established based on the ratio of the area of insect-damaged sub-surface to the entire seed, soybean varieties, and µ a at three wavelengths (502 nm, 813 nm, and 712 nm) for predicting and identifying soybean the stinging and sucking pest damage levels of soybean seeds. The experimental results show that the spatial frequency domain imaging technique yields small errors in the optical coefficients of soybean seeds, with errors of less than 15% for µ a and less than 10% for µ ' s . After parameter adjustment through reinforcement learning, the Macro-Recall metrics of each model have improved by 10%-15%, and the RL-SVM model achieves a high Macro-Recall value of 0.9635 for classifying the pest damage levels of soybean seeds.

Deep reinforcement learning in radiation therapy planning optimization: A comprehensive review.

PURPOSE: The formulation and optimization of radiation therapy plans are complex and time-consuming processes that heavily rely on the expertise of medical physicists. Consequently, there is an urgent need for automated optimization methods. Recent advancements in reinforcement learning, particularly deep reinforcement learning (DRL), show great promise for automating radiotherapy planning. This review summarizes the current state of DRL applications in this field, evaluates their effectiveness, and identifies challenges and future directions. METHODS: A systematic search was conducted in Google Scholar, PubMed, IEEE Xplore, and Scopus using keywords such as "deep reinforcement learning", "radiation therapy", and "treatment planning". The extracted data were synthesized for an overview and critical analysis. RESULTS: The application of deep reinforcement learning in radiation therapy plan optimization can generally be divided into three categories: optimizing treatment planning parameters, directly optimizing machine parameters, and adaptive radiotherapy. From the perspective of disease sites, DRL has been applied to cervical cancer, prostate cancer, vestibular schwannoma, and lung cancer. Regarding types of radiation therapy, it has been used in HDRBT, IMRT, SBRT, VMAT, GK, and Cyberknife. CONCLUSIONS: Deep reinforcement learning technology has played a significant role in advancing the automated optimization of radiation therapy plans. However, there is still a considerable gap before it can be widely applied in clinical settings due to three main reasons: inefficiency, limited methods for quality assessment, and poor interpretability. To address these challenges, significant research opportunities exist in the future, such as constructing evaluators, parallelized training, and exploring continuous action spaces.

Fish-inspired tracking of underwater turbulent plumes.

Autonomous ocean-exploring vehicles have begun to take advantage of onboard sensor measurements of water properties such as salinity and temperature to locate oceanic features in real time. Such targeted sampling strategies enable more rapid study of ocean environments by actively steering towards areas of high scientific value. Inspired by the ability of aquatic animals to navigate via flow sensing, this work investigates hydrodynamic cues for accomplishing targeted sampling using a palm-sized robotic swimmer. As proof-of-concept analogy for tracking hydrothermal vent plumes in the ocean, the robot is tasked with locating the center of turbulent jet flows in a 13,000-liter water tank using data from onboard pressure sensors. To learn a navigation strategy, we first implemented RL on a simulated version of the robot navigating in proximity to turbulent jets. After training, the RL algorithm discovered an effective strategy for locating the jets by following transverse velocity gradients sensed by pressure sensors located on opposite sides of the robot. When implemented on the physical robot, this gradient following strategy enabled the robot to successfully locate the turbulent plumes at more than twice the rate of random searching. Additionally, we found that navigation performance improved as the distance between the pressure sensors increased, which can inform the design of distributed flow sensors in ocean robots. Our results demonstrate the effectiveness and limits of flow-based navigation for autonomously locating hydrodynamic features of interest.

Negative allosteric modulation of CB1 cannabinoid receptor signalling decreases intravenous morphine self-administration and relapse in mice.

The endocannabinoid system interacts with the reward system to modulate responsiveness to natural reinforcers, as well as drugs of abuse. Previous preclinical studies suggested that direct blockade of CB1 cannabinoid receptors (CB1R) could be leveraged as a potential pharmacological approach to treat substance use disorder, but this strategy failed during clinical trials due to severe psychiatric side effects. Alternative strategies have emerged to circumvent the side effects of direct CB1 binding through the development of allosteric modulators. We hypothesized that negative allosteric modulation of CB1R signalling would reduce the reinforcing properties of morphine and decrease behaviours associated with opioid misuse. By employing intravenous self-administration in mice, we studied the effects of GAT358, a functionally-biased CB1R negative allosteric modulator (NAM), on morphine intake, relapse-like behaviour and motivation to work for morphine infusions. GAT358 reduced morphine infusion intake during the maintenance phase of morphine self-administration under a fixed ratio 1 schedule of reinforcement. GAT358 also decreased morphine-seeking behaviour after forced abstinence. Moreover, GAT358 dose dependently decreased the motivation to obtain morphine infusions under a progressive ratio schedule of reinforcement. Strikingly, GAT358 did not affect the motivation to work for food rewards in an identical progressive ratio task, suggesting that the effect of GAT358 in decreasing opioid self-administration was reward specific. Furthermore, GAT58 did not produce motor ataxia in the rotarod test. Our results suggest that CB1R NAMs reduced the reinforcing properties of morphine and could represent a viable therapeutic route to safely decrease misuse of opioids.

Optimal Inference of Hidden Markov Models Through Expert-Acquired Data.

This paper focuses on inferring a general class of hidden Markov models (HMMs) using data acquired from experts. Expert-acquired data contain decisions/actions made by humans/users for various objectives, such as navigation data reflecting drivers' behavior, cybersecurity data carrying defender decisions, and biological data containing the biologist's actions (e.g., interventions and experiments). Conventional inference methods rely on temporal changes in data without accounting for expert knowledge. This paper incorporates expert knowledge into the inference of HMMs by modeling expert behavior as an imperfect reinforcement learning agent. The proposed method optimally quantifies experts' perceptions about the system model, which, alongside the temporal changes in data, contributes to the inference process. The proposed inference method is derived through a combination of dynamic programming and optimal recursive Bayesian estimation. The applicability of this method is demonstrated to a wide range of inference criteria, such as maximum likelihood and maximum a posteriori. The performance of the proposed method is investigated through a comprehensive numerical experiment using a benchmark problem and biological networks.

Maternal psychopathology is differentially associated with adolescent offspring neural response to reward given offspring ADHD risk.

Reinforcement sensitivity is a hypothesized attention-deficit/hyperactivity disorder (ADHD) intermediate phenotype but its role in transgenerational transmission of ADHD-linked psychopathology risk is largely unknown. We examined, in a carefully phenotyped, N = 123 sample of adolescents (Mage = 15.27 years, SD = 0.984; 61.78% boys), whether (1) parental psychopathology is differentially associated with fMRI-indexed neural response to reward receipt and (2) both maternal and paternal psychopathology are associated with neural response to reward; across adolescents at-risk for and not at-risk for ADHD. Indices of parental psychopathology were differentially associated with adolescent offspring neural response to reward such that across measures, parental psychopathology was negatively or not associated with offspring superior frontal gyrus (SFG) response to reward receipt in adolescents at-risk for ADHD, but parental psychopathology was positively associated with offspring SFG response in adolescents not at-risk. Further, across measures, greater maternal psychopathology was associated with blunted adolescent SFG response to reward in adolescents at-risk for ADHD whereas greater maternal externalizing problems were linked to enhanced adolescent SFG response in adolescents not at-risk. Across measures, paternal psychopathology was not associated with adolescent response to reward, in either group. ADHD risk confers differential reward-related susceptibility to the effects of parental psychopathology. Results also show this association is nonspecific in terms of parental psychopathology type but is specific to maternal psychopathology.

Polybenzimidazole-Reinforced Terphenylene Anion Exchange Water Electrolysis Membranes.

Anion exchange membrane water electrolysis (AEMWE) for hydrogen production combines the advantages of proton exchange membrane water electrolysis and alkaline water electrolysis. Several strategies have been adopted to improve the performance of AEMWE and to obtain membranes with high hydroxide ion conductivity, low gas permeation, and high durability. In this work AEMs reinforced with poly[2,2'-(p-oxydiphenylene)-5,5'-benzimidazole] (PBIO) polymer fibres have been developed. A fibre web of PBIO prepared by electrospinning was impregnated into the poly(terphenylene) mTPN ionomer. The membranes are strengthened by the formation of a strong surface interaction between the reinforcement and the ionomer and by the expansion of the reinforcement over the membrane thickness. The hydroxide ion conductivity, thermal stability, dimensional swelling, mechanical properties, and hydrogen crossover of the reinforced membranes were compared with the characteristics of the non-reinforced counterpart. The incorporation of PBIO nanofibre reinforcement into the membrane reduced hydrogen crossover and improved tensile properties, without affecting hydroxide conductivity. PBIO-reinforced mTPN membrane was assessed in a PGM-free 5 cm2 AEMWE single cell using NiFe oxide anode and NiMo cathode catalysts, at a cell temperature of 50 °C and with 1 M KOH fed to the anode. The performance of the cell increased continuously over the 260 hours test period, reaching 2.06 V at 1.0 A cm-2.

Fabrication, microstructure, mechanical properties and wear behavior of Al/Cu-SiCw composite.

This research investigates the production of Cu-SiCw composites by using gas pressure infiltration method and the production of Al/Cu-SiCw composite by using the cold rolling method. The layers were bonded by three rolling reductions of 40 %, 50 %, and 60 %. The microstructures of composites before and after roll bonding were discussed based on SEM and EBSD images. Less agglomeration of whiskers was seen after higher rolling reductions which indicates better distribution of reinforcement in copper. In addition, no decomposition and reaction were observed in Cu-SiCw. The effect of rolling reductions on hardness, wear behavior, and tensile properties was also investigated. Hardness, yield, and ultimate strengths increased at higher rolling reductions. The yield and ultimate strengths increased from 118 MPa to 227 MPa after a 40 % rolling reduction to 150 MPa and 263 MPa after a 60 % rolling reduction. The measured friction coefficient and mass loss showed better wear resistance of composites at higher rolling reductions because the layers became hardened. The mass loss decreases from 6.46 mg after a 40 % rolling reduction to 5.42 mg after a 60 % rolling reduction. Worn surfaces based on SEM images showed shallower grooves and less remaining debris.

Towards reconciling usability and usefulness of policy explanations for sequential decision-making systems.

Safefy-critical domains often employ autonomous agents which follow a sequential decision-making setup, whereby the agent follows a policy to dictate the appropriate action at each step. AI-practitioners often employ reinforcement learning algorithms to allow an agent to find the best policy. However, sequential systems often lack clear and immediate signs of wrong actions, with consequences visible only in hindsight, making it difficult to humans to understand system failure. In reinforcement learning, this is referred to as the credit assignment problem. To effectively collaborate with an autonomous system, particularly in a safety-critical setting, explanations should enable a user to better understand the policy of the agent and predict system behavior so that users are cognizant of potential failures and these failures can be diagnosed and mitigated. However, humans are diverse and have innate biases or preferences which may enhance or impair the utility of a policy explanation of a sequential agent. Therefore, in this paper, we designed and conducted human-subjects experiment to identify the factors which influence the perceived usability with the objective usefulness of policy explanations for reinforcement learning agents in a sequential setting. Our study had two factors: the modality of policy explanation shown to the user (Tree, Text, Modified Text, and Programs) and the "first impression" of the agent, i.e., whether the user saw the agent succeed or fail in the introductory calibration video. Our findings characterize a preference-performance tradeoff wherein participants perceived language-based policy explanations to be significantly more useable; however, participants were better able to objectively predict the agent's behavior when provided an explanation in the form of a decision tree. Our results demonstrate that user-specific factors, such as computer science experience (p < 0.05), and situational factors, such as watching agent crash (p < 0.05), can significantly impact the perception and usefulness of the explanation. This research provides key insights to alleviate prevalent issues regarding innapropriate compliance and reliance, which are exponentially more detrimental in safety-critical settings, providing a path forward for XAI developers for future work on policy-explanations.

Adaptive satellite attitude control for varying masses using deep reinforcement learning.

Traditional spacecraft attitude control often relies heavily on the dimension and mass information of the spacecraft. In active debris removal scenarios, these characteristics cannot be known beforehand because the debris can take any shape or mass. Additionally, it is not possible to measure the mass of the combined system of satellite and debris object in orbit. Therefore, it is crucial to develop an adaptive satellite attitude control that can extract mass information about the satellite system from other measurements. The authors propose using deep reinforcement learning (DRL) algorithms, employing stacked observations to handle widely varying masses. The satellite is simulated in Basilisk software, and the control performance is assessed using Monte Carlo simulations. The results demonstrate the benefits of DRL with stacked observations compared to a classical proportional-integral-derivative (PID) controller for the spacecraft attitude control. The algorithm is able to adapt, especially in scenarios with changing physical properties.

Evaluation of reinforcement learning in transformer-based molecular design.

Designing compounds with a range of desirable properties is a fundamental challenge in drug discovery. In pre-clinical early drug discovery, novel compounds are often designed based on an already existing promising starting compound through structural modifications for further property optimization. Recently, transformer-based deep learning models have been explored for the task of molecular optimization by training on pairs of similar molecules. This provides a starting point for generating similar molecules to a given input molecule, but has limited flexibility regarding user-defined property profiles. Here, we evaluate the effect of reinforcement learning on transformer-based molecular generative models. The generative model can be considered as a pre-trained model with knowledge of the chemical space close to an input compound, while reinforcement learning can be viewed as a tuning phase, steering the model towards chemical space with user-specific desirable properties. The evaluation of two distinct tasks-molecular optimization and scaffold discovery-suggest that reinforcement learning could guide the transformer-based generative model towards the generation of more compounds of interest. Additionally, the impact of pre-trained models, learning steps and learning rates are investigated.Scientific contributionOur study investigates the effect of reinforcement learning on a transformer-based generative model initially trained for generating molecules similar to starting molecules. The reinforcement learning framework is applied to facilitate multiparameter optimisation of starting molecules. This approach allows for more flexibility for optimizing user-specific property profiles and helps finding more ideas of interest.

Rigdelet neural network and improved partial reinforcement effect optimizer for music genre classification from sound spectrum images.

In this paper, a new approach has been introduced for classifying the music genres. The proposed approach involves transforming an audio signal into a unified representation known as a sound spectrum, from which texture features have been extracted using an enhanced Rigdelet Neural Network (RNN). Additionally, the RNN has been optimized using an improved version of the partial reinforcement effect optimizer (IPREO) that effectively avoids local optima and enhances the RNN's generalization capability. The GTZAN dataset has been utilized in experiments to assess the effectiveness of the proposed RNN/IPREO model for music genre classification. The results show an impressive accuracy of 92 % by incorporating a combination of spectral centroid, Mel-spectrogram, and Mel-frequency cepstral coefficients (MFCCs) as features. This performance significantly outperformed K-Means (58 %) and Support Vector Machines (up to 68 %). Furthermore, the RNN/IPREO model outshined various deep learning architectures such as Neural Networks (65 %), RNNs (84 %), CNNs (88 %), DNNs (86 %), VGG-16 (91 %), and ResNet-50 (90 %). It is worth noting that the RNN/IPREO model was able to achieve comparable results to well-known deep models like VGG-16, ResNet-50, and RNN-LSTM, sometimes even surpassing their scores. This highlights the strength of its hybrid CNN-Bi-directional RNN design in conjunction with the IPREO parameter optimization algorithm for extracting intricate and sequential auditory data.

Suture reinforcement using a modified cyanoacrylate glue to prevent anastomotic leak in colorectal surgery: a prospective multicentre randomized trial : The Rectal Anastomotic seaL (ReAL) trial.

BACKGROUND: Anastomotic leakage (AL) is the most frequent life-threating complication following colorectal surgery. Several attempts have been made to prevent AL. This prospective, randomized, multicentre trial aimed to evaluate the safety and efficacy of nebulised modified cyanoacrylate in preventing AL after rectal surgery. METHODS: Patients submitted to colorectal surgery for carcinoma of the high-medium rectum across five high-volume centres between June 2021 and January 2023 entered the study and were randomized into group A (anastomotic reinforcement with cyanoacrylate) and group B (no reinforcement) and followed up for 30 days. Anastomotic reinforcement was performed via nebulisation of 1 mL of a modified cyanoacrylate glue. Preoperative features and intraoperative and postoperative results were recorded and compared. The study was registered at ClinicalTrials.gov (ID number NCT03941938). RESULTS: Out of 152 patients, 133 (control group, n = 72; cyanoacrylate group, n = 61) completed the follow-up. ALs were detected in nine patients (12.5%) in the control group (four grade B and five grade C) and in four patients (6.6%), in the cyanoacrylate group (three grade B and one grade C); however, despite this trend, the differences were not statistically significant (p = 0.36). However, Clavien-Dindo complications grade > 2 were significantly higher in the control group (12.5% vs. 3.3%, p = 0.04). No adverse effects related to the glue application were reported. CONCLUSION: The role of modified cyanoacrylate application in AL prevention remains unclear. However its use to seal colorectal anastomoses is safe and could help to reduce severe postoperative complications.

Deep reinforcement learning models in auction item price prediction: an optimisation study of a cross-interval quotation strategy.

In the contemporary digitalization landscape and technological advancement, the auction industry undergoes a metamorphosis, assuming a pivotal role as a transactional paradigm. Functioning as a mechanism for pricing commodities or services, the procedural intricacies and efficiency of auctions directly influence market dynamics and participant engagement. Harnessing the advancing capabilities of artificial intelligence (AI) technology, the auction sector proactively integrates AI methodologies to augment efficacy and enrich user interactions. This study delves into the intricacies of the price prediction challenge within the auction domain, introducing a sophisticated RL-GRU framework for price interval analysis. The framework commences by adeptly conducting quantitative feature extraction of commodities through GRU, subsequently orchestrating dynamic interactions within the model's environment via reinforcement learning techniques. Ultimately, it accomplishes the task of interval division and recognition of auction commodity prices through a discerning classification module. Demonstrating precision exceeding 90% across publicly available and internally curated datasets within five intervals and exhibiting superior performance within eight intervals, this framework contributes valuable technical insights for future endeavours in auction price interval prediction challenges.

Evaluation of microhardness, degree of conversion, and abrasion resistance of nanoglass and multiwalled carbon nanotubes reinforced three-dimensionally printed denture base resins.

PURPOSE: To assess the effect of nanoglass (NG) particles and multiwalled carbon nanotubes' (MWCNTs) addition on Vickers hardness (VH), degree of conversion (DC), and abrasion resistance of 3D-printed denture base resin. MATERIALS AND METHODS: 3D-printed denture base resin was reinforced using silanized NG and MWCNTs to obtain four groups: Control, 0.25 wt% NG reinforced resin, 0.25 wt% MWCNTs reinforced resin, and a combination group of 0.25 wt% of both fillers. All specimens (N = 176) were tested before and after thermal aging (600 cycles) for VH (n = 22), DC, and abrasion resistance (n = 22). Abrasion resistance specimens were subjected to 60,000 brushing strokes, and then assessed for surface roughness (Ra) and weight loss. Specimens were then scanned with a benchtop scanner before and after abrasion to produce a color map of topographical changes from superimposed images. Data were analyzed using ANOVA tests followed by Tukey post hoc test. Kruskal-Wallis test was used to compare percent change among groups, followed by Dunn post hoc test (α = 0.05). RESULTS: The interaction between nanofiller content and thermal cycling displayed a significant effect on VH and DC. The 0.25% NG expressed the highest VH before aging but revealed the highest percent decrease after aging. Nanofiller content, thermal aging, and brushing displayed a significant interaction impact on the Ra values. CONCLUSIONS: The addition of nanofillers resulted in an overall improvement in resin microhardness and abrasion resistance. The 0.25% MWCNTs group revealed the lowest Ra with the least percent change in VH and DC, while the combination one displayed the least change in weight.