Using Stigmergy to Solve Numerical Optimization Problems

The current methodology for designing highly efficient technological systems needs to choose the best combination of the parameters that affect the performance. In this paper we propose a promising optimization algorithm, referred to as the Multilevel Ant Stigmergy Algorithm (MASA), which exploits stigmergy in order to optimize multi-parameter functions. We evaluate the performance of the MASA and Differential Evolution -- one of the leading stochastic method for numerical optimization -- in terms of their applicability as numerical optimization techniques. The comparison is performed using several widely used benchmark functions with added noise

The role of radiotherapy in oligometastatic gastrointestinal cancers

Zdravljenje s stereotaktično radioterapijo (SBRT) se je v zadnjih letih tudi pri oligometastatskem gastrointestinalnem raku izkazalo za eno izmed pomembnejših možnosti lokalnega ablativnega zdravljenja, ki izboljšuje tako lokalno kontrolo, preživetje brez bolezni, kot tudi celokupno preživetje. Ob tem pa se je stereotaktično obsevanje izkazalo za varno metodo. Glede na lokacijo zasevkov so najpogosteje obsevani zasevki v jetrih in pljučih. Zaradi višje radiorezistentence zasevkov je potrebna večja izsevana doza (BED ≥ 100 Gy). Za čim manj stranskih učinkov pa je potrebna uporaba sistemov, ki zmanjšujejo gibanje tarče in povečujejo natančnost obsevanja. Večina dokazov do sedaj sicer temelji na retrospektivnih analizah in manjših prospektivnih študijah faze I in II. Rezultati prospektivnih študij faze III pa nam bodo v prihodnosti potrdili kdaj in kateri pacienti bodo imeli od zdravljenja z obsevanjem največjo korist.In recent years, treatment with stereotactic radiotherapy (SBRT) has proven to be one of the most important local ablative treatment options for oligometastatic gastrointestinal cancer, which improves both local control, disease-free survival, and overall survival. However, stereotactic radiation has proven to be a safe method. Depending on the location of the nodules, the most frequently irradiated nodules are in the liver and lungs. A higher radiation dose (BED ≥ 100 Gy) is required due to the higher radioresistance of the metastases. In order to minimize side effects, it is necessary to use systems that reduce the movement of the target and increase the accuracy of irradiation. Most of the evidence so far is based on retrospective analyzes and smaller prospective phase I and II studies. The results of prospective phase III studies will confirm in the future when and which patients will benefit the most from radiation treatment

A GPU-Based Parallel-Agent Optimization Approach for the Service Coverage Problem in UMTS Networks

In the context of coverage planning and control, the power of the common pilot channel signal determines the coverage area of a network cell. It also impacts the network capacity and thus the quality of service. We consider the problem of minimizing the total amount of pilot power subject to a full coverage constraint. Our optimization approach, based on parallel autonomous agents, gives very good solutions within an acceptable amount of time. The parallel implementation takes full advantage of GPU hardware in order to achieve impressive speed-up. We report the results of our experiments for three UMTS networks of different sizes based on a real network currently deployed in Slovenia

Security and Anonymity of Bitcoin Payments

This thesis presents the digital payment system Bitcoin. It describes the cryptography on which the system is based. Presented are the short history of the system and the principles of how it works. Highlighted are the state of anonymity that the system provides while making payments and the safety it guarantees while executing transactions. Within the context of anonymity, the state of masking the users identity within the payment system is described, together with warnings and mechanisms to better hide the users identity. Considering the safety of Bitcoin transactions, potential vulnerabilities of the system are listed. Exposed is a weakness, while accepting fast payments, that enables attackers the execution of the double-spending attack. Practically it is demonstrated, how to execute the attack without special tools, with the use of opensource software recomended and developed by the Bitcoin community

Security and Anonymity of Bitcoin Payments

This thesis presents the digital payment system Bitcoin. It describes the cryptography on which the system is based. Presented are the short history of the system and the principles of how it works. Highlighted are the state of anonymity that the system provides while making payments and the safety it guarantees while executing transactions. Within the context of anonymity, the state of masking the users identity within the payment system is described, together with warnings and mechanisms to better hide the users identity. Considering the safety of Bitcoin transactions, potential vulnerabilities of the system are listed. Exposed is a weakness, while accepting fast payments, that enables attackers the execution of the double-spending attack. Practically it is demonstrated, how to execute the attack without special tools, with the use of opensource software recomended and developed by the Bitcoin community

Metaheuristic approach to transportation scheduling in emergency situations

This paper compares two metaheuristic approaches in solving a constrained transportation scheduling problem, which can be found in transporting goods in emergency situations. We compared Greedy Search, Parameter-less Evolutionary Search and Ant-Stigmergy Algorithm. The transportation scheduling/allocation problem is NP-hard, and is applicable to different real-life situations with high frequency of loading and unloading operations; like in depots, warehouses and ports. To evaluate the efficiency of the presented approaches, they were tested with four tasks based on realistic data. Each task was evaluated using group and free transportation approach. The experiments proved that all tested algorithms are viable option in solving such scheduling problems, however some performing better than others on some tasks

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

<p>Abstract</p> <p>Background</p> <p>We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods.</p> <p>Results</p> <p>We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input.</p> <p>Conclusions</p> <p>Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and artificial data, for all observability scenarios considered, and for all amounts of noise added to the artificial data. In sum, the meta-heuristic methods considered are suitable for estimating the parameters in the ODE model of the dynamics of endocytosis under a range of conditions: With the model and conditions being representative of parameter estimation tasks in ODE models of biochemical systems, our results clearly highlight the promise of bio-inspired meta-heuristic methods for parameter estimation in dynamic system models within system biology.</p