Optimal Strategies of Constrained Repeated Games

We examine a specific framework of a repeated game that asks for the player to repeatedly choose between a constant and a two-point distribution, where the two-point distribution has higher variance and expected value. The revenue is persistently subject to a linear minimum constraint. Under these conditions, we seek to maximize the expected value of $$\left(\sum\limits_{i=1}^T X_i\right)\cdot I\left(\left\{\sum\limits_{i=1}^t X_i> f(t)\right\}_ {1\leq t\leq T}\right)$$ We are able to give specific asymptotic results on the best adaptive and non-adaptive strategies for this game and solve it completely for linear $f(\cdot)$. In doing so we draw upon methods from random walks and calculus and even find a novel variant of the zero-one law

Classification of gait phases based on a machine learning approach using muscle synergy

The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection

Three-Dimensional Printing of Natural Materials Involving Loess-Based Composite Materials Designed for Ecofriendly Applications

In this work, loess-based materials were designed based on a multicomponent composite materials system for ecofriendly natural three-dimensional (3D) printing involving quick lime, gypsum, and water. The 3D printing process was monitored as a function of gypsum content; in terms of mechanical strength and electrical resistance, in the cube-shaped bulk form. After initial optimization, the 3D printing composition was refined to provide improved printability in a 3D printing system. The optimal 3D fabrication allowed for reproducible printing of rectangular columns and cubes. The development of 3D printing materials was scrutinized using a multitude of physicochemical probing tools, including X-ray diffraction for phase identification, impedance spectroscopy to monitor setting behaviors, and mercury intrusion porosimetry to extract the pore structure of loess-based composite materials. Additionally, the setting behavior in the loess-based composite materials was analyzed by investigating the formation of gypsum hydrates induced by chemical reaction between quick lime and water. This setting reaction provides reasonable mechanical strength that is sufficient to print loess-based pastes via 3D printing. Such mechanical strength allows utilization of robotic 3D printing applications that can be used to fabricate ecofriendly structures

Blocking β1/β2-Adrenergic Signaling Reduces Dietary Fat Absorption by Suppressing Expression of Pancreatic Lipase in High Fat-Fed Mice

We investigated whether β-adrenergic antagonists attenuates dietary fat absorption through the regulation of pancreatic lipase (PNLIP) expression in pancreatic acinar cells in the context of high fat diet feeding. Male six-week-old C57BL/6 mice were assigned into an ad libitum fed control diet (CON) and a high fat diet (HIGH). Within each diet group, subgroups of mice were treated with vehicle (VEH) or propranolol, a β-adrenergic antagonist (BB). Over 12 weeks, body weight gain observed in HIGHVEH was mitigated in HIGHBB (+103% vs. +72%). Increase in fecal fat amount observed in HIGHVEH was further increased in HIGHBB. Increase in PNLIP expressions observed in HIGHVEH pancreatic tissues was abolished in HIGHBB. PNLIP expression in mouse primary pancreatic acinar cells and 266-6 cell lines increased with isoproterenol treatment, which was blocked by propranolol. Isoproterenol increased PNLIP expression in a cAMP/protein kinase A/ cyclic AMP response element binding protein (CREB)-dependent manner. CREB directly bound to the CRE on the mouse PNLIP promoter and transactivated PNLIP expression. These results suggest that sympathetic activation increases dietary fat absorption through the upregulation of PNLIP expression and that a β-adrenergic antagonist attenuates obesity development partly through the downregulation of PNLIP expression and inhibition of dietary fat absorption in the context of high fat diet feeding

Electrical/Mechanical Monitoring of Shape Memory Alloy Reinforcing Fibers Obtained by Pullout Tests in SMA/Cement Composite Materials

Self-healing is an essential property of smart concrete structures. In contrast to other structural metals, shape memory alloys (SMAs) offer two unique effects: shape memory effects, and superelastic effects. Composites composed of SMA wires and conventional cements can overcome the mechanical weaknesses associated with tensile fractures in conventional concretes. Under specialized environments, the material interface between the cementitious component and the SMA materials plays an important role in achieving the enhanced mechanical performance and robustness of the SMA/cement interface. This material interface is traditionally evaluated in terms of mechanical aspects, i.e., strain–stress characteristics. However, the current work attempts to simultaneously characterize the mechanical load-displacement relationships synchronized with impedance spectroscopy as a function of displacement. Frequency-dependent impedance spectroscopy is tested as an in situ monitoring tool for structural variations in smart composites composed of non-conducting cementitious materials and conducting metals. The artificial geometry change in the SMA wires is associated with an improved anchoring action that is compatible with the smallest variation in resistance compared with prismatic SMA wires embedded into a cement matrix. The significant increase in resistance is interpreted to be associated with the slip of the SMA fibers following the elastic deformation and the debonding of the SMA fiber/matrix

Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints

Ginsenoside Rb2 suppresses the glutamate-mediated oxidative stress and neuronal cell death in HT22 cells

Background: The objective of our study was to analyze the neuroprotective effects of ginsenoside derivatives Rb1, Rb2, Rc, Rd, Rg1, and Rg3 against glutamate-mediated neurotoxicity in HT22 hippocampal mouse neuron cells. Methods: The neuroprotective effect of ginsenosides were evaluated by measuring cell viability. Protein expressions of mitogen-activated protein kinase (MAPK), Bcl2, Bax, and apoptosis-inducing factor (AIF) were determined by Western blot analysis. The occurrence of apoptotic and death cells was determined by flow cytometry. Cellular level of Ca2+ and reactive oxygen species (ROS) levels were evaluated by image analysis using the fluorescent probes Fluor-3 and 2′,7′-dichlorodihydrofluorescein diacetate, respectively. In vivo efficacy of neuroprotection was evaluated using the Mongolian gerbil of ischemic brain injury model. Result: Reduction of cell viability by glutamate (5 mM) was significantly suppressed by treatment with ginsenoside Rb2. Phosphorylation of MAPKs, Bax, and nuclear AIF was gradually increased by treatment with 5 mM of glutamate and decreased by co-treatment with Rb2. The occurrence of apoptotic cells was decreased by treatment with Rb2 (25.7 μM). Cellular Ca2+ and ROS levels were decreased in the presence of Rb2, and in vivo data indicated that Rb2 treatment (10 mg/kg) significantly diminished the number of degenerated neurons. Conclusion: Our results suggest that Rb2 possesses neuroprotective properties that suppress glutamate-induced neurotoxicity. The molecular mechanism of Rb2 is by suppressing the MAPKs activity and AIF translocation. Keywords: Ginsenoside Rb2, Neurotoxicity, MAPK, Reactive oxygen specie