Modelling of Violent Water Wave Propagation and Impact by Incompressible SPH with First-Order Consistent Kernel Interpolation Scheme

The Smoothed Particle Hydrodynamics (SPH) method has proven to have great potential in dealing with the wave–structure interactions since it can deal with the large amplitude and breaking waves and easily captures the free surface. The paper will adopt an incompressible SPH (ISPH) approach to simulate the wave propagation and impact, in which the fluid pressure is solved using a pressure Poisson equation and thus more stable and accurate pressure fields can be obtained. The focus of the study is on comparing three different pressure gradient calculation models in SPH and proposing the most efficient first-order consistent kernel interpolation (C1_KI) numerical scheme for modelling violent wave impact. The improvement of the model is validated by the benchmark dam break flows and laboratory wave propagation and impact experiments

Penerapan Pendekatan Pengajaran Terbalik (Reciprocal Teaching) Untuk Meningkatkan Kemandirian Belajar Biologi Siswa Kelas Vii-g SMP N 5 Karanganyar Tahun Pelajaran 2010/ 2011

– The objective of this study is to improve student independence in learning biology by implementing Inverted Teaching Approach (Reciprocal Teaching) on Environmental Management material. This research is a classroom action research. This research was conducted in two cycles. Each cycle consisted of planning, implementation of the action,observation, and reflection. The subjects of the study were VII-G class students of SMP Negeri 5 Karanganyar in the academic year of 2010/2011. The number of the students was 32. The technique and instrumen of collectiing data were questionnaire, observation, and interviews. The technique of analyzing data was descriptive analysis techniques. Triangulation technique was used in data validation. The results proved that by implementing Inverted Teaching Approach (Reciprocal Teaching) students\u27 independence in learning biology enhanced. It is based on the results of questionnaires, observations and interviews. The questionnaire of students\u27 learning independence showed that the mean percentage of students\u27 achievement in each indicator in pre-cycle, cycle I, and cycle II was 67.97%, 72.55%, and 77.58% respectively. The observation of students\u27 learning independence showed that the mean percentage of students\u27 achievement in each indicator in pre-cycle, cycle I, and cycle II was 39.68%, 67.5%, and 80.62% respectively. It can be concluded that the implementation of Inverted Teaching Approach (Reciprocal Teaching) can enhance students learning independence

Absolute Oxygenation Metabolism Measurements Using Magnetic Resonance Imaging

Cerebral oxygen metabolism plays a critical role in maintaining normal function of the brain. It is the primary energy source to sustain neuronal functions. Abnormalities in oxygen metabolism occur in various neuro-pathologic conditions such as ischemic stroke, cerebral trauma, cancer, Alzheimer’s disease and shock. Therefore, the ability to quantitatively measure tissue oxygenation and oxygen metabolism is essential to the understanding of pathophysiology and treatment of various diseases. The focus of this review is to provide an introduction of various blood oxygenation level dependent (BOLD) contrast methods for absolute measurements of tissue oxygenation, including both magnitude and phase image based approaches. The advantages and disadvantages of each method are discussed

Conservative State Value Estimation for Offline Reinforcement Learning

Offline reinforcement learning faces a significant challenge of value over-estimation due to the distributional drift between the dataset and the current learned policy, leading to learning failure in practice. The common approach is to incorporate a penalty term to reward or value estimation in the Bellman iterations. Meanwhile, to avoid extrapolation on out-of-distribution (OOD) states and actions, existing methods focus on conservative Q-function estimation. In this paper, we propose Conservative State Value Estimation (CSVE), a new approach that learns conservative V-function via directly imposing penalty on OOD states. Compared to prior work, CSVE allows more effective in-data policy optimization with conservative value guarantees. Further, we apply CSVE and develop a practical actor-critic algorithm in which the critic does the conservative value estimation by additionally sampling and penalizing the states \emph{around} the dataset, and the actor applies advantage weighted updates extended with state exploration to improve the policy. We evaluate in classic continual control tasks of D4RL, showing that our method performs better than the conservative Q-function learning methods and is strongly competitive among recent SOTA methods

Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances

Fairness testing aims at mitigating unintended discrimination in the decision-making process of data-driven AI systems. Individual discrimination may occur when an AI model makes different decisions for two distinct individuals who are distinguishable solely according to protected attributes, such as age and race. Such instances reveal biased AI behaviour, and are called Individual Discriminatory Instances (IDIs). In this paper, we propose an approach for the selection of the initial seeds to generate IDIs for fairness testing. Previous studies mainly used random initial seeds to this end. However this phase is crucial, as these seeds are the basis of the follow-up IDIs generation. We dubbed our proposed seed selection approach I&D. It generates a large number of initial IDIs exhibiting a great diversity, aiming at improving the overall performance of fairness testing. Our empirical study reveal that I&D is able to produce a larger number of IDIs with respect to four state-of-the-art seed generation approaches, generating 1.68X more IDIs on average. Moreover, we compare the use of I&D to train machine learning models and find that using I&D reduces the number of remaining IDIs by 29% when compared to the state-of-the-art, thus indicating that I&D is effective for improving model fairnessComment: 19 pages, 7 figure

Balance Reward and Safety Optimization for Safe Reinforcement Learning: A Perspective of Gradient Manipulation

Ensuring the safety of Reinforcement Learning (RL) is crucial for its deployment in real-world applications. Nevertheless, managing the trade-off between reward and safety during exploration presents a significant challenge. Improving reward performance through policy adjustments may adversely affect safety performance. In this study, we aim to address this conflicting relation by leveraging the theory of gradient manipulation. Initially, we analyze the conflict between reward and safety gradients. Subsequently, we tackle the balance between reward and safety optimization by proposing a soft switching policy optimization method, for which we provide convergence analysis. Based on our theoretical examination, we provide a safe RL framework to overcome the aforementioned challenge, and we develop a Safety-MuJoCo Benchmark to assess the performance of safe RL algorithms. Finally, we evaluate the effectiveness of our method on the Safety-MuJoCo Benchmark and a popular safe RL benchmark, Omnisafe. Experimental results demonstrate that our algorithms outperform several state-of-the-art baselines in terms of balancing reward and safety optimization

Elevated international normalized ratio contributes to poor prognosis in patients with traumatic lung injury

ObjectiveTo investigate the pivotal determinants contributing to the adverse prognosis in patients afflicted with traumatic lung injury (TLI), with an aim to mitigate the elevated mortality rate associated with this condition.MethodsA retrospective analysis was carried out on 106 TLI patients who were admitted to the intensive care unit of a comprehensive hospital from March 2018 to November 2022. The patients were categorized into two groups based on their 28-day outcome: the survival group (n = 88) and the death group (n = 18). Random forest model, least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) were utilized to pinpoint the primary factors linked to poor prognosis in TLI patients. The Receiver Operating Characteristic (ROC) curve analysis was utilized to ascertain the predictive value of INR in forecasting the prognosis of TLI patients. Based on the cut-off value of INR, patients were categorized into two groups: INR ≥ 1.36 group (n = 35) and INR < 1.36 group (n = 71). The 28-day survival rate was then compared using Kaplan–Meier analysis.ResultsRandom forest model, LASSO, and SVM-RFE jointly identified International standardization ratio (INR) as a risk factor for TLI patients. The area under the ROC curve for INR in predicting the 28-day mortality of TLI patients was 0.826 (95% CI 0.733–0.938), with a cut-off value of 1.36. The 28-day mortality risk for TLI patients with an INR ≥ 1.36 was 8.5 times higher than those with an INR < 1.36.ConclusionTraumatic lung injury patients with elevated INR have a poor prognosis. An INR of ≥1.36 can be used as an early warning indicator for patients with traumatic lung injury