From MFN to SFN: Performance Prediction Through Machine Learning

In the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model's training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R2) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97

Perivascular Expression and Potent Vasoconstrictor Effect of Dynorphin A in Cerebral Arteries

BACKGROUND: Numerous literary data indicate that dynorphin A (DYN-A) has a significant impact on cerebral circulation, especially under pathophysiological conditions, but its potential direct influence on the tone of cerebral vessels is obscure. The aim of the present study was threefold: 1) to clarify if DYN-A is present in cerebral vessels, 2) to determine if it exerts any direct effect on cerebrovascular tone, and if so, 3) to analyze the role of κ-opiate receptors in mediating the effect. METHODOLOGY/PRINCIPAL FINDINGS: Immunohistochemical analysis revealed the expression of DYN-A in perivascular nerves of rat pial arteries as well as in both rat and human intraparenchymal vessels of the cerebral cortex. In isolated rat basilar and middle cerebral arteries (BAs and MCAs) DYN-A (1-13) and DYN-A (1-17) but not DYN-A (1-8) or dynorphin B (DYN-B) induced strong vasoconstriction in micromolar concentrations. The maximal effects, compared to a reference contraction induced by 124 mM K(+), were 115±6% and 104±10% in BAs and 113±3% and 125±9% in MCAs for 10 µM of DYN-A (1-13) and DYN-A (1-17), respectively. The vasoconstrictor effects of DYN-A (1-13) could be inhibited but not abolished by both the κ-opiate receptor antagonist nor-Binaltorphimine dihydrochloride (NORBI) and blockade of G(i/o)-protein mediated signaling by pertussis toxin. Finally, des-Tyr(1) DYN-A (2-13), which reportedly fails to activate κ-opiate receptors, induced vasoconstriction of 45±11% in BAs and 50±5% in MCAs at 10 µM, which effects were resistant to NORBI. CONCLUSION/SIGNIFICANCE: DYN-A is present in rat and human cerebral perivascular nerves and induces sustained contraction of rat cerebral arteries. This vasoconstrictor effect is only partly mediated by κ-opiate receptors and heterotrimeric G(i/o)-proteins. To our knowledge our present findings are the first to indicate that DYN-A has a direct cerebral vasoconstrictor effect and that a dynorphin-induced vascular action may be, at least in part, independent of κ-opiate receptors

Deep Reinforcement Learning for Dynamic Radio Access Selection over Future Wireless Networks

Despite the fifth-generation (5G) of mobile communication systems being at its initial stage, the research community has started to focus on its successor. The sixth-generation (6G) is expected to provide massive scale communication and always-on intelligent connectivity, enabling several emerging applications with stricter quality of service (QoS) requirements. Softwarization technologies, network slicing paradigm, and artificial intelligence (AI) will be critical pieces of 6G to manage ultra-dense heterogeneous environments composed of terrestrial and non-terrestrial networks. This work aims to find the most efficient combination of access network and network slices (NSs) in 6G heterogeneous scenarios to satisfy the user petition and maximize the QoS. We propose a Dynamic Radio Access Network Selection (DRANS) algorithm based on Deep-Reinforcement Learning (DRL) as a suitable method to handle the constant changes in network conditions and the diversity of users' demands. We address the DRL problem by considering an adaptation of the DQN approach termed Double Deep Q-Network (DDQN). The proposal is evaluated through numerical simulations, focusing on the effective utilization of network resources and the convergence rate

LPA(1) receptor-mediated thromboxane A(2) release is responsible for lysophosphatidic acid-induced vascular smooth muscle contraction

Lysophosphatidic acid (LPA) has been recognized recently as an endothelium-dependent vasodilator, but several lines of evidence indicate that it may also stimulate vascular smooth muscle cells (VSMCs), thereby contributing to vasoregulation and remodeling. In the present study, mRNA expression of all 6 LPA receptor genes was detected in murine aortic VSMCs, with the highest levels of LPA(1), LPA(2), LPA(4), and LPA(6). In endothelium-denuded thoracic aorta (TA) and abdominal aorta (AA) segments, 1-oleoyl-LPA and the LPA(1–3) agonist VPC31143 induced dose-dependent vasoconstriction. VPC31143-induced AA contraction was sensitive to pertussis toxin (PTX), the LPA(1&3) antagonist Ki16425, and genetic deletion of LPA(1) but not that of LPA(2) or inhibition of LPA(3), by diacylglycerol pyrophosphate. Surprisingly, vasoconstriction was also diminished in vessels lacking cyclooxygenase-1 [COX1 knockout (KO)] or the thromboxane prostanoid (TP) receptor (TP KO). VPC31143 increased thromboxane A(2) (TXA(2)) release from TA of wild-type, TP-KO, and LPA(2)-KO mice but not from LPA(1)-KO or COX1-KO mice, and PTX blocked this effect. Our findings indicate that LPA causes vasoconstriction in VSMCs, mediated by LPA(1)-, G(i)-, and COX1-dependent autocrine/paracrine TXA(2) release and consequent TP activation. We propose that this new-found interaction between the LPA/LPA(1) and TXA(2)/TP pathways plays significant roles in vasoregulation, hemostasis, thrombosis, and vascular remodeling.—Dancs, P. T., Ruisanchez, E., Balogh, A., Panta, C. R., Miklós, Z., Nüsing, R. M., Aoki, J., Chun, J., Offermanns, S., Tigyi, G., Benyó, Z. LPA(1) receptor-mediated thromboxane A(2) release is responsible for lysophosphatidic acid-induced vascular smooth muscle contraction