Recent advances and prospect in immune microenvironment and its mechanisms of function in head and neck squamous cell carcinoma

Head and neck cancer (HNC) remains a significant cause of morbidity and mortality. The most prevalent pathology among HNC is head and neck squamous cell carcinoma (HNSCC). The tumor microenvironment (TME) encompasses the components surrounding tumor cells, including immune cells, stromal cells, extracellular matrix (ECM), blood and lymph vessels. Strategies targeting the TME have yielded significant outcomes. Thus, further exploration of the interactions between TME components is crucial. This review discussed recent advances in cytotoxic T lymphocytes (CTL), CD4+ T lymphocytes, regulatory T cells (Treg), myeloid-derived suppressor cells (MDSC), natural killer (NK) cells and tumor-associated macrophages (TAM) in HNSCC TME. The article summarized herein primarily focused on restoring the activity of anti-tumor cells and eliminating the immunosuppressive effects of Treg and so on, to provide new insights for more effective HNSCC therapy

A multimodal psychological, physiological and behavioural dataset for human emotions in driving tasks

Human emotions are integral to daily tasks, and driving is now a typical daily task. Creating a multi-modal human emotion dataset in driving tasks is an essential step in human emotion studies. we conducted three experiments to collect multimodal psychological, physiological and behavioural dataset for human emotions (PPB-Emo). In Experiment I, 27 participants were recruited, the in-depth interview method was employed to explore the driver’s viewpoints on driving scenarios that induce different emotions. For Experiment II, 409 participants were recruited, a questionnaire survey was conducted to obtain driving scenarios information that induces human drivers to produce specific emotions, and the results were used as the basis for selecting video-audio stimulus materials. In Experiment III, 40 participants were recruited, and the psychological data and physiological data, as well as their behavioural data were collected of all participants in 280 times driving tasks. The PPB-Emo dataset will largely support the analysis of human emotion in driving tasks. Moreover, The PPB-Emo dataset will also benefit human emotion research in other daily tasks

Modelling and Analysis of Power-Regenerating Potential for High-Speed Train Suspensions

Sustainable technologies in transport systems have attracted significant research efforts over the last two decades. One area of interest is self-powered devices, which reduce system integration complexity and cost with an undoubtedly great potential for improving adaptability and developing sustainability in railway transport systems. One potential solution is a regenerative suspension system, which enables the suspension movements and dissipated energy to be converted into useful electricity. This paper explores the application of hydraulic–electromagnetic regenerative dampers (HERDs) under realistic railway operating conditions for a high-speed train (HST). A vehicle-track-coupled dynamics model is employed to evaluate the regenerative power potential of an HST suspension over a range of operating conditions. The work considers typical route curvature and track irregularity of a high-speed line and speed profile. It was found that power could be regenerated at a level of up to 5–30 W and 5–45 W per generation unit when fitted to the primary and secondary dampers, respectively. Such power-regeneration levels were adequate to supply a variety of low-power-consumption onboard components such as warning lights and wireless sensors. Further analysis of the carbody loading level also was carried out. The analysis revealed that, in the case of a high-speed journey, poor track geometry, low curvature, and reduced carbody weight increased the quantity of regenerative energy harvested by the HERDs. It was concluded that a suitable HERD design could be achieved that could facilitate the development of a smart railway damper that includes both self-sensing and power-generation functions

Deep reinforcement learning for secrecy energy efficiency maximization in RIS-assisted networks

This paper investigates the deep reinforcement learning (DRL) for maximization of the secrecy energy efficiency (SEE) in reconfigurable intelligent surface (RIS)-assisted networks. An SEE maximization problem is formulated under constraints of the rate requirement of each (legitimate) user, the power budget of the transmitter and the discrete phase shift coefficient of each reflecting element at the RIS by jointly optimizing the beamforming vectors for users and the artificial noise vectors for eavesdroppers as well as the phase shift matrix. The considered problem is first reformulated into a Markov decision process with the designed state space, action space and reward function, and then solved under a proximal policy optimization (PPO) framework. Numerical results are provided to evaluate the optimality, the generalization performance and the running time of proposed PPO-based algorithm.Info-communications Media Development Authority (IMDA)National Research Foundation (NRF)This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant 2021RC204, in part by the National Natural Science Foundation of China (NSFC) under Grants 62101025 and 62221001, in part by the China Postdoctoral Science Foundation under Grants BX2021031 and 2021M690342, and in part by Beijing Nova Program under Grant Z211100002121139. The work of Dusit Niyato was supported in part by the National Research Foundation, Singapore and Infocomm Media Development Authority through the Future Communications Research Development Programme, and in part by the DSO National Laboratories through AI Singapore Programme under AISG Award AISG2-RP-2020-019), under Energy Research Test-Bed and Industry Partnership Funding Initiative, part of the Energy Grid (EG) 2.0 Programme, and under DesCartes and the Campus for Research Excellence and Technological Enterprise (CREATE) programme

Study of the mechanism for improving green pellet performance with compound binders

In this study, the characters of bentonite organic binders were investigated by analytical methods, including X-ray diffraction, zeta potential, and scanning electron microscope. The results showed that the surfaces and the edges of montmorillonite layers reacted with carboxyl groups or oxhydryl groups of expanded starch or carboxymethyl cellulose (CMC), enhancing the surface electronegativity of the bentonite. The addition of organic binders reduced the contact angle between the bentonite and magnetite and enhanced the hydrophilicity of bentonite surfaces. Based on the polymer chains of organic binders, the house-of-cards structure formed by a compound binder was more stable than when formed by bentonite alone. The non-stripped layers in bentonite were peeled off under the action of chemical bonds. At the same time, improved bentonite dispersion in pellets was observed, with decreased bentonite particle size and increased surface area. The solid bridges produced by the reaction between magnetite and Mg2+ in the montmorillonite layer restrained pellet expansion, which thus improved the decrepitation temperature during the heating process