Range-only Target Localisation using Geometrically Constrained Optimisation

The problem of optimal range-only localisation of a single target is of considerable interest in two-dimensional search radar networking. For coping with this problem, a range-only target localisation method using synchronous measurements from radars is presented in the real ellipsoidal earth model. In the relevant radar localisation scenario, geometric relationships between the target and three radars were formed. A set of localisation equations was derived on range error in such a scenario. Using these equations, the localisation task has been formulated as a nonlinear weighted least squares problem that can be performed using the Levenberg- Marquardt (LM) algorithm to provide the optimal estimate of the target’s position. To avoid the double value solutions and to accelerate the convergence speed for the LM algorithm, the initial value was approximately given according to observations from two radars. In addition, the relative validity has been defined to evaluate the performance of the proposed method. The performance of the proposed approach is evaluated using two simulation experiments and a real-test experiment, and it has been found to possess higher localisation accuracy than the other conventional method.Defence Science Journal, Vol. 65, No. 1, January 2015, pp.70-76, DOI:http://dx.doi.org/10.14429/dsj.65.547

Accelerating Discovery of Novel and Bioactive Ligands With Pharmacophore-Informed Generative Models

Deep generative models have gained significant advancements to accelerate drug discovery by generating bioactive chemicals against desired targets. Nevertheless, most generated compounds that have been validated for potent bioactivity often exhibit structural novelty levels that fall short of satisfaction, thereby providing limited inspiration to human medicinal chemists. The challenge faced by generative models lies in their ability to produce compounds that are both bioactive and novel, rather than merely making minor modifications to known actives present in the training set. Recognizing the utility of pharmacophores in facilitating scaffold hopping, we developed TransPharmer, an innovative generative model that integrates ligand-based interpretable pharmacophore fingerprints with generative pre-training transformer (GPT) for de novo molecule generation. TransPharmer demonstrates superior performance across tasks involving unconditioned distribution learning, de novo generation and scaffold elaboration under pharmacophoric constraints. Its distinct exploration mode within the local chemical space renders it particularly useful for scaffold hopping, producing compounds that are structurally novel while pharmaceutically related. The efficacy of TransPharmer is validated through two case studies involving the dopamine receptor D2 (DRD2) and polo-like kinase 1 (PLK1). Notably in the case of PLK1, three out of four synthesized designed compounds exhibit submicromolar activities, with the most potent one, IIP0943, demonstrating a potency of 5.1 nM. Featuring a new scaffold of 4-(benzo[b]thiophen-7-yloxy)pyrimidine, IIP0943 also exhibits high selectivity for PLK1. It was demonstrated that TransPharmer is a powerful tool for discovery of novel and bioactive ligands

Pre-disaster transmission maintenance scheduling considering network topology optimization

Several devastating experiences with extreme natural disasters demonstrate that improving power system resilience is becoming increasingly important. This paper proposes a pre-disaster transmission maintenance scheduling considering network topology optimization to ensure the power system economics before disasters and power system resilience during disasters. The transmission line fragility is distinguished and considered in the proposed optimization model to determine the maintenance scheduling of defective lines that minimizes load shedding during disasters. The proposed model is established as a tri-level optimization problem that is further reformulated to a bi-level problem utilizing duality theory. The column-and-constraint generation (C&CG) algorithm is employed to solve the equivalent robust optimization problem. Finally, the proposed model and its solution algorithm are implemented on the modified IEEE RTS-79 system. The significant cost savings and increased resilience illustrate the effectiveness of the proposed model

Role of hepatic stellate cells in liver ischemia-reperfusion injury

Liver ischemia-reperfusion injury (IRI) is a major complication of liver trauma, resection, and transplantation. IRI may lead to liver dysfunction and failure, but effective approach to address it is still lacking. To better understand the cellular and molecular mechanisms of liver IRI, functional roles of numerous cell types, including hepatocytes, Kupffer cells, neutrophils, and sinusoidal endothelial cells, have been intensively studied. In contrast, hepatic stellate cells (HSCs), which are well recognized by their essential functions in facilitating liver protection and repair, have gained less attention in their role in IRI. This review provides a comprehensive summary of the effects of HSCs on the injury stage of liver IRI and their associated molecular mechanisms. In addition, we discuss the regulation of liver repair and regeneration after IRI by HSCs. Finally, we highlight unanswered questions and future avenues of research regarding contributions of HSCs to IRI in the liver

A Secure User Anonymity-Preserving Biometrics and PUFs-Based Multi-Server Authentication Scheme With Key Agreement in 5G Networks

The 5G networks can provide high data rates, ultra-low latency and huge network capacity. In 5G networks environment, the popularity of the Internet of Things (IoT) has led to a rapid increase in the amount of data. Multi-server distributed cloud computing technology provides an excellent solution to alleviate network pressure caused by the rapid growth of data. However, this technology serves as a two-edged weapon, which not only makes various IoT applications possible, but also brings growing concerns for user privacy and ever pressing security challenges. To ensure the high security of 5G network-based applications, we design a secure user anonymity-preserving biometrics and PUFs-based multi-server authentication scheme with key agreement. In our method, we make full use of the inherent security features of user fingerprint and smart device PUF to design a secure multi-server authentication scheme with key agreement in 5G Networks. The proposed scheme is able to resist recognized attacks and its robustness has been verified by security analysis