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This article analyses the maritime security pillar of India’s Indo-Pacific Outlook Initiative (IPOI) in Southeast Asia and its far-reaching impact on Vietnam. The study utilizes the analytical framework of neorealism, liberalism, and constructivism to examine the objectives and intentions of India’s IPOI and ASEAN’s AOIP in the context of strategic competition in the Indo-Pacific. The author’s findings reveal a significant convergence of strategic interests between IPOI and AOIP, which brings numerous and tremendous benefits to Vietnam, such as fostering the Vietnam-India comprehensive strategic partnership and equipping Vietnam to tackle challenges from the rise of China. The rise of China, strategic competition among major powers, and the increasing significance of the Indo-Pacific have spurred India and ASEAN to develop their visions to adapt to the Indo-Pacific concept. However, the implementation of IPOI in Southeast Asia still faces several limitations, primarily due to the divergent understanding and goals of IPOI, which places a premium on maritime security, and AOIP, which is more focused on naval cooperation. Furthermore, differences in the perceptions of India and ASEAN on maritime security also contribute to these limitations, highlighting the need for further research and development.

Positional data in team sports is key in evaluating the players’ individual and collective performances. When the sole source of data is a broadcast-like video of the game, an efficient video tracking method is required to generate this data. This article describes a framework that extracts individual soccer player positions on the field. It is based on two main components. As in broadcast-like videos of team sport games, the camera view moves to follow the action and a sport field registration method estimates the homography between the pitch and the frame space. Our method estimates the positions of key points sampled on the pitch thanks to an encoder–decoder architecture. The attention mechanisms of the encoder, based on a vision transformer, captures characteristic pitch features globally in the frames. A multiple person tracker generates tracklets in the frame space by associating, with bipartite matching, the player detections between the current and the previous frames thanks to Intersection-Over-Union and distance criteria. Tracklets are then iteratively merged with appearance criteria thanks to a re-identification model. This model is fine-tuned in a self-supervised way on the player thumbnails of the video sample to specifically recognize the fine identification details of each player. The player positions in the frames projected by the homographies allow the obtaining of the real position of the players on the pitch at every moment of the video. We experimentally evaluate our sport field registration method and our 2D player tracker on public datasets. We demonstrate that they both outperform previous works for most metrics. Our 2D player tracker was also awarded first place at the SoccerNet tracking challenge in 2022 and 2023.

Two ATP-binding cassette transporters, ABCB1/MDR1 and ABCG2/BCRP, are considered the most critical determinants for chemoresistance in hepatocellular carcinoma. However, their roles in the chemoresistance in liver cancer stem cells remain elusive. Here we explored the role of inhibition of MDR1 or ABCG2 in sensitizing liver cancer stem cells to doxorubicin, the most frequently used chemotherapeutic agent in treating liver cancer. We show that the inhibition of MDR1 or ABCG2 in Huh7 and PLC/PRF/5 cells using either pharmacological inhibitors or RNAi resulted in the elevated level of intracellular concentration of doxorubicin and the accompanied increased apoptosis as determined by confocal microscopy, high-performance liquid chromatography, flow cytometry, and annexin V assay. Notably, the inhibition of MDR1 or ABCG2 led to the reversal of the chemoresistance, as evident from the enhanced death of the chemoresistant liver cancer stem cells in tumorsphere-forming assays. Thus, the elevation of effective intracellular concentration of doxorubicin via the inhibition of MDR1 or ABCG2 represents a promising future strategy that transforms doxorubicin from a traditional chemotherapy agent into a robust killer of liver cancer stem cells for patients undergoing transarterial chemoembolization.

This paper presents an incremental learning method and system for autonomous robot navigation. The range finder laser sensor and online deep reinforcement learning are utilized for generating the navigation policy, which is effective for avoiding obstacles along the robot’s trajectories as well as for robot’s reaching the destination. An empirical experiment is conducted under simulation and real-world settings. Under the simulation environment, the results show that the proposed method can generate a highly effective navigation policy (more than 90% accuracy) after only 150k training iterations. Moreover, our system has slightly outperformed deep-Q, while having considerably surpassed Proximal Policy Optimization, two recent state-of-the art robot navigation systems. Finally, two experiments are performed to demonstrate the feasibility and effectiveness of our robot’s proposed navigation system in real-time under real-world settings.

Herein, Fe-doped C3N4 high-performance photocatalysts, synthesized by a facile and cost effective heat stirring method, were investigated systematically using powder X-ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscopy (SEM) and Brunauer–Emmett–Teller (BET) surface area measurement, X-ray photoelectron (XPS), UV–Vis diffusion reflectance (DRS) and photoluminescence (PL) spectroscopy. The results showed that Fe ions incorporated into a g-C3N4 nanosheet in both +3 and +2 oxidation states and in interstitial configuration. Absorption edge shifted slightly toward the red light along with an increase of absorbance in the wavelength range of 430–570 nm. Specific surface area increased with the incorporation of Fe into g-C3N4 lattice, reaching the highest value at the sample doped with 7 mol% Fe (FeCN7). A sharp decrease in PL intensity with increasing Fe content is an indirect evidence showing that electron-hole pair recombination rate decreased. Interestingly, Fe-doped g-C3N4 nanosheets present a superior photocatalytic activity compared to pure g-C3N4 in decomposing RhB solution. FeCN7 sample exhibits the highest photocatalytic efficiency, decomposing almost completely RhB 10 ppm solution after 30 min of xenon lamp illumination with a reaction rate approximately ten times greater than that of pure g-C3N4 nanosheet. This is in an agreement with the BET measurement and photoluminescence result which shows that FeCN7 possesses the largest specific surface area and low electron-hole recombination rate. The mechanism of photocatalytic enhancement is mainly explained through the charge transfer processes related to Fe2+/Fe3+ impurity in g-C3N4 crystal lattice.

We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks.

Tuberculosis continues to be a global health challenge. In this report, active case finding was more than twice as likely as standard passive case finding to identify tuberculosis among household contacts of index patients with smear-positive disease.

This paper presents an adaptive trajectory tracking neural network control using radial basis function (RBF) for an n -link robot manipulator with robust compensator to achieve the high-precision position tracking. One of the difficulties in designing a suitable control scheme which can achieve accurate trajectory tracking and good control performance is to guarantee the stability and robustness of control system, due to friction forces, external disturbances error, and parameter variations. To deal with this problem, the RBF network is investigated to the joint position control of an n -link robot manipulator. The RBF network is one approach which has shown a great promise in this sort of problems because of its fast learning algorithm and better approximation capabilities. The adaptive RBF network can effectively improve the control performance against large uncertainty of the system. The adaptive turning laws of network parameters are derived using the back-propagation algorithm and the Lyapunov stability theorem, so that the stability of the entire system and the convergence of the weight adaptation are guaranteed. In this control scheme, a robust compensator plays as an auxiliary controller to guarantee the stability and robustness under various environments such as the mass variation, the external disturbances, and modeling uncertainties. Finally, the simulation and experimental results in comparison with adaptive fuzzy and wavelet network control method are provided to verify the effectiveness of the proposed control methodology.

This study aims at empirically exploring the influence of sustainability practices on the finan cial performance of 116 listed Swedish companies in the year 2019. The research findings indicate a positive relationship between corporate sustainability and financial performance that is measured by earnings yield, return on asset, return on equity and return on capital employed. However, when it comes to a market-based financial measure, Tobin's Q, the result is inconclusive. Finally, to improve financial performance, firms are recommended to e ngaging in Dow Jones Sustainability Index, prepare their sustainability report in accordance with Global Reporting Initiative (GRI) Standards, improve their sustainable growth rate, as well as keep a high position in the corporate social responsibility ranking.

This paper introduces a Hexa parallel robot and obstacle collision detection method based on dynamic modeling and a computer vision system. The processes to deal with the collision issues refer to collision detection, collision isolation, and collision identification applied to the Hexa robot, respectively, in this paper. Initially, the configuration, kinematic and dynamic characteristics during movement trajectories of the Hexa parallel robot are analyzed to perform the knowledge extraction for the method. Next, a virtual force sensor is presented to estimate the collision detection signal created as a combination of the solution to the inverse dynamics and a low-pass filter. Then, a vision system consisting of dual-depth cameras is designed for obstacle isolation and determining the contact point location at the end-effector, an arm, and a rod of the Hexa robot. Finally, a recursive Newton-Euler algorithm is applied to compute contact forces caused by collision cases with the real-Hexa robot. Based on the experimental results, the force identification is compared to sensor forces for the performance evaluation of the proposed collision detection method.