1,721,026 research outputs found
Local Search Strategies for Multi-Objective Flowshop Scheduling: Introducing Pareto Late Acceptance Hill Climbing
No full textWe present the Pareto Late Acceptance Hill Climbing algorithm, a multi-objective optimization algorithm based on the Late Acceptance Hill Climbing. We propose an initial experimental analysis of its behavior applying it to different formulations of the bi-objective Permutation Flowshop Scheduling Problem
An Empirical Analysis of Tabu Lists
No full textMetaheuristics, such as tabu search, simulated annealing, and ant colony optimization, have demonstrated remarkable success in solving combinatorial optimization problems across diverse domains. Despite their efficacy, the lack of understanding of why these metaheuristics work well has sparked criticism, emphasizing the need for a deeper exploration of their components. This paper focuses on the tabu list component within tabu search, aiming to unravel its relative importance in influencing overall algorithmic performance. We employ a white-box framework to investigate various methods for handling the tabu list, including short-term and long-term strategies. We conduct experiments to compare the performance of different tabu list strategies using a well-known benchmark problem, the Permutation Flow Shop Scheduling Problem. The results show that the tabu list component does not significantly differ from the final result. Nevertheless, the strategies exhibit diverse search trajectories related to distinct prohibition structures
Exploring the Potential of JuLeS: A White Box Framework for Local Search Metaheuristics
No full textThe JuLeS framework (short for Julia Local Search)1 is a versatile and highly customizable platform for quickly creating Local Search solvers. Developed using the Julia programming language, JuLeS enables efficient implementation of solvers, facilitates the development of new meta-heuristic algorithms, and easily integrates with existing tools. The overhead of the framework with respect to existing C++ frameworks is moderate, but it benefits from a lower programming effort and integrability into the Julia ecosystem. The design, architecture, and features of JuLeS are explained and its effectiveness is demonstrated through three use cases that highlight its design achievements
Distance and spectral power profile shaping using machine learning enabled Raman amplifiers
Full text linkWe propose a Convolutional Neural Network (CNN) to learn the mapping between the 2D power profiles, (distance and frequency), and the Raman pumps. Using the CNN, the pump powers and wavelengths for arbitrary 2D profiles can be determined with high accuracy
Click mechanism for racing car self-levelling flap
No full textThis paper presents a simulation and experimental study on a passive “click” mechanism, which is designed to adapt the angle of incidence of a racing car flap along a circuit to have high down force when the car undertakes low speed corners and low drag force when the car sprints along the straights. The mechanism is composed by four rigid linkages connected via flexible pivot junctions. The paper first provides a parametric study that shows how the stiffness of the flexible pivot junctions, the length of the linkages and the rest angles of the linkages influence the typical N-shaped resistant moment–joint rotation function, which is at the basis of the clicking effect. The parametric study is then used to design a mechanism, which is characterised by a stable high pitch equilibrium configuration only and maintains the N-shaped moment-rotation feature such that the flap snaps from high-to-low pitch and vice versa at given critical speeds during acceleration and deceleration of the car. Finally, the dynamic response of a flap mounted on the proposed monostable clicking joint is analysed in detail and contrasted with that of a flap fixed on classical passive joints built with a stiff spring or a soft pre-loaded spring
Experimental Validation of Spectral-Spatial Power Evolution Design Using Raman Amplifiers
Full text linkWe experimentally validate a machine learning-enabled Raman amplification framework, capable of jointly shaping the signal power evolution in two domains: frequency and fiber distance. The proposed experiment addresses the amplification in the whole C-band, by optimizing four first-order counter-propagating Raman pumps
Inverse design of a Raman amplifier in frequency and distance domains using convolutional neural networks
Full text linkWe present a convolutional neural network architecture for inverse Raman amplifier design. This model aims at finding the pump powers and wavelengths required for a target signal power evolution in both distance along the fiber and in frequency. Using the proposed framework, the prediction of the pump configuration required to achieve a target power profile is demonstrated numerically with high accuracy in C-band considering both counter-propagating and bidirectional pumping schemes. For a distributed Raman amplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54, and 0.64 dB) and standard deviation set (0.62, 0.43, and 0.38 dB) of the maximum test error are obtained numerically employing two and three counter-, and four bidirectional propagating pumps, respectively
Experimental Characterization of Raman Amplifier Optimization through Inverse System Design
Full text linkOptical communication systems are always evolving to support the need for ever-increasing transmission rates. This demand is supported by the growth in complexity of communication systems which are moving towards ultra-wideband transmission and space-division multiplexing. Both directions will challenge the design, modeling, and optimization of devices, subsystems, and full systems. Amplification is a key functionality to support this growth and in this context, we recently demonstrated a versatile machine learning framework for designing and modeling Raman amplifiers with arbitrary gains. In this article, we perform a thorough experimental characterization of such machine learning framework. The applicability of the proposed approach, as well as its ability to accurately provide flat and tilted gain-profiles, are tested on several practical fiber types, showing errors below 0.5 dB. Moreover, as channel power optimization is heavily employed to further enhance the transmission rate, the tolerance of the framework to variations in the input signal spectral profile is investigated. Results show that the inverse design can provide highly accurate gain-profile adjustments for different input signal power profiles even not considering this information during the training phase
Modeling Doxorubicin Treatment Effect in Multiple Myeloma
No full textDoxorubicin (DOXO) is commonly employed as chemotherapy drug to treat several kinds of cancer, including multiple myeloma (MM). Hence, a full characterization of DOXO pharmacokinetics/pharmacodynamics (PK/PD) is essential to maximize its efficacy while minimizing possible side effects. We recently proposed a mathematical model of DOXO PK in MM cells. Thus, as a natural succession, here we aim at modeling DOXO PD to describe its effects on MM cells. We monitored in vitro the MM cell proliferation in eight 2-week experiments, one under untreated conditions (control) and seven after a 3-h administration of DOXO at different concentrations (from 15 nM to 900 nM). A logistic growth and treatment response model, accounting for both cell proliferation and death rates, was developed and identified on each experiment to fit the collected cell count time series. Results show that the proposed model is able to describe cellular growth both in untreated conditions and after DOXO treatment. We also propose a simple model to characterize the relationship between the administered DOXO amount and the corresponding drug-induced cell death rate parameter. The proposed model is perfectly scalable to obtain a more precise descriptive implementation but also to develop a predictive framework that could lead to advancements in the current MM treatment paradigms. © 2023 Convegno Nazionale di Bioingegneria. All rights reserve
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