Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry

The petroleum industry is crucial for modern society, but the production process is complex and risky. During the production, accidents or failures, resulting from undesired production events, can cause severe environmental and economic damage. Previous studies have investigated machine learning (ML) methods for undesired event detection. However, the prediction of event probability in real-time was insufficiently addressed, which is essential since it is important to undertake early intervention when an event is expected to happen. This paper proposes two ML approaches, random forests and temporal convolutional networks, to detect undesired events in real-time. Results show that our approaches can effectively classify event types and predict the probability of their appearance, addressing the challenges uncovered in previous studies and providing a more effective solution for failure event management during the production.Comment: Paper accepted at PRICAI 2023 AI-Impact Trac

A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks

Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key enabler for envisioned 6G networks, for the purpose of improving the network capacity, coverage, efficiency, and security with low energy consumption and low hardware cost. However, integrating RISs into the existing infrastructure greatly increases the network management complexity, especially for controlling a significant number of RIS elements. To unleash the full potential of RISs, efficient optimization approaches are of great importance. This work provides a comprehensive survey on optimization techniques for RIS-aided wireless communications, including model-based, heuristic, and machine learning (ML) algorithms. In particular, we first summarize the problem formulations in the literature with diverse objectives and constraints, e.g., sum-rate maximization, power minimization, and imperfect channel state information constraints. Then, we introduce model-based algorithms that have been used in the literature, such as alternating optimization, the majorization-minimization method, and successive convex approximation. Next, heuristic optimization is discussed, which applies heuristic rules for obtaining low-complexity solutions. Moreover, we present state-of-the-art ML algorithms and applications towards RISs, i.e., supervised and unsupervised learning, reinforcement learning, federated learning, graph learning, transfer learning, and hierarchical learning-based approaches. Model-based, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques. Finally, we highlight RIS-aided applications towards 6G networks and identify future challenges.Comment: This paper has been accepted by IEEE Communications Surveys and Tutorial

Heuristic Algorithms for RIS-assisted Wireless Networks: Exploring Heuristic-aided Machine Learning

Reconfigurable intelligent surfaces (RISs) are a promising technology to enable smart radio environments. However, integrating RISs into wireless networks also leads to substantial complexity for network management. This work investigates heuristic algorithms and applications for optimizing RIS-aided wireless networks, including greedy algorithms, meta-heuristic algorithms, and matching theory. Moreover, we combine heuristic algorithms with machine learning (ML), and propose three heuristic-aided ML algorithms, namely heuristic deep reinforcement learning (DRL), heuristic-aided supervised learning, and heuristic hierarchical learning. Finally, a case study shows that heuristic DRL can achieve higher data rates and faster convergence than conventional DRL. This work aims to provide a new perspective for optimizing RIS-aided wireless networks by taking advantage of heuristic algorithms and ML

Surface-exposed loops L7 and L8 of Haemophilus (Glaesserella) parasuis OmpP2 contribute to the expression of proinflammatory cytokines in porcine alveolar macrophages

International audienceOuter membrane protein P2 (OmpP2) of the virulent Haemophilus (Glaesserella) parasuis has been shown to induce the release of proinflammatory cytokines. The OmpP2 protein is composed of eight or nine surface-exposed loops, but it is unclear which of them participates in the OmpP2-induced inflammatory response. In this study, we synthesized linear peptides corresponding to surface-exposed loops L1–L8 of OmpP2 from the virulent H. parasuis SC096 strain to stimulate porcine alveolar macrophages (PAMs) in vitro. We found that both L7 and L8 significantly upregulated the mRNA expression of interleukin (IL)-1α, IL-1β, IL-6, IL-8, IL-17, and IL-23 and the chemokines CCL-4 and CCL-5 in a time- and dose-dependent manner. Additionally, we constructed ompP2ΔLoop7 and ompP2ΔLoop8 mutant SC096 strains and extracted their native OmpP2 proteins to stimulate PAMs. These mutant proteins induced significantly less mRNA expression of inflammatory cytokines than SC096 OmpP2. Next, the amino acid sequences of L7 and L8 from 15 serovars of H. parasuis OmpP2 were aligned. These sequences were relatively conserved among the most virulent reference strains, suggesting that L7 and L8 are the most active peptides of the OmpP2 protein. Furthermore, L7 and L8 significantly upregulated the NF-κB and AP-1 activity levels based on luciferase reporter assays in a dose-dependent manner. Therefore, our results demonstrated that both surface-exposed loops L7 and L8 of H. parasuis OmpP2 induced the expression of proinflammatory cytokines possibly by activating the NF-κB and MAPK signalling pathways in cells infected by H. parasuis

Highly efficient blueish-green fluorescent OLEDs based on AIE liquid crystal molecules : From ingenious molecular design to multifunction materials

In order to seek the balance point between liquid crystallinity and high efficiency emission, two novel aggregation-induced emission-based (AIE) liquid crystal materials of TPE-PBN and TPE-2PBN, which contain a tetraphenylethene derivative as the emission core and a 4-cynobiphenyl moiety as the mesogenic unit, were designed and prepared. Both simple molecules showed a mesophase at high temperature as evidenced by polarised optical microscopy (POM), differential scanning calorimetry (DSC) and temperature-dependent X-ray diffraction (XRD). Simultaneously, TPE-PBN and TPE-2PBN presented clear AIE characteristics in the blueish-green region and achieved a high emission quantum efficiency of 71% and 83% in the solid state, respectively. Due to the self-assembly properties of thermotropic liquid crystals, both compounds showed higher hole mobilities in the annealed films than in pristine films. Employing TPE-PBN and TPE-2PBN as the emitting materials, both non-doped devices and doped devices were fabricated. The TPE-PBN-based doped OLEDs showed a better device performance with an external quantum efficiency (EQE) of 4.1% which is among the highest EQEs of blue AIE fluorescent OLEDs

Dendritic Polyglycerol-Conjugated Gold Nanostars for Metabolism Inhibition and Targeted Photothermal Therapy in Breast Cancer Stem Cells

Breast cancer stem cells (CSCs) are believed to be responsible for tumor initiation, invasion, metastasis, and recurrence, which lead to treatment failure. Thus, developing effective CSC-targeted therapeutic strategies is crucial for enhancing therapeutic efficacy. In this work, GNSs-dPG-3BP, TPP, and HA nanocomposite particles are developed by simultaneously conjugating hexokinase 2 (HK2) inhibitor 3-bromopyruvate (3BP), mitochondrial targeting molecule triphenyl phosphonium (TPP), and CSCs targeting agent hyaluronic acid (HA) onto gold nanostars-dendritic polyglycerol (GNSs-dPG) nanoplatforms for efficient eradication of CSCs. The nanocomposite particles possess good biocompatibility and exhibit superior mitochondrial-bound HK2 binding ability via 3BP to inhibit metabolism, and further induce cellular apoptosis by releasing the cytochrome c. Therefore, it enhanced the therapeutic efficacy of CSCs-specific targeted photothermal therapy (PTT), and achieved a synergistic effect for the eradication of breast CSCs. After administration of the synergistic treatment, the self-renewal of breast CSCs and the stemness gene expression are suppressed, CSC-driven mammosphere formation is diminished, the in vivo tumor growth is effectively inhibited, and CSCs are eradicated. Altogether, GNSs-dPG-3BP, TPP, and HA nanocomposite particles have been developed, which will provide a novel strategy for precise and highly efficient targeted eradication of CSCs

Near-infrared photoactivatable control of Ca signaling and optogenetic immunomodulation

The application of current channelrhodopsin-based optogenetic tools is limited by the lack of strict ion selectivity and the inability to extend the spectra sensitivity into the near-infrared (NIR) tissue transmissible range. Here we present an NIR-stimulable optogenetic platform (termed Opto-CRAC ) that selectively and remotely controls Ca2+ oscillations and Ca2+-responsive gene expression to regulate the function of non-excitable cells, including T lymphocytes, macrophages and dendritic cells. When coupled to upconversion nanoparticles, the optogenetic operation window is shifted from the visible range to NIR wavelengths to enable wireless photoactivation of Ca2+-dependent signaling and optogenetic modulation of immunoinflammatory responses. In a mouse model of melanoma by using ovalbumin as surrogate tumor antigen, Opto-CRAC has been shown to act as a genetically-encoded photoactivatable adjuvant to improve antigen-specific immune responses to specifically destruct tumor cells. Our study represents a solid step forward towards the goal of achieving remote control of Ca2+-modulated activities with tailored function