Robustness of Extended Field Cervical Target Optimization Techniques to Isocenter Offsets with a Prototype Fast Jawless Mlc System

A rigid body model and electronic serial portal imaging (EPI) data were used to generate the cumulative dose distribution for the rectum incorporating organ motion during IMRT. The impact of rectal motion was assessed via NTCP and TCP based on equivalent uniform dose per fraction (EUDf ). The rectal positional variations were measured fraction-to-fraction from MV EPI for 20 prostate patients implanted with gold seeds. Five-field (5F) and seven-field (7F) IMRT plans for prostate patients were constructed with prescribed dose 78 Gy/39 fractions using a Pinnacle3 treatment planning system. EUD increased in 45% of the patients with greater than 2.5% increase for 5% of the patients. While EUD decreased in 55% of the patients with greater than 2.5% decrease for 10% of the patients. The amplitudes of EUDf increase and decrease are correlated with the dose gradient. Higher dose gradients lead to higher rectal EUDf change. The rectal NTCP decreases for half of the patients and increases for the other for both 5F and 7F plans. The NTCP decreased with the increasing of dose gradient between the prostate and rectum for 5F and 7F IMRT plans. The correlation coefficient for the rectal NTCP and the dose gradient is - 0.71. The increase or decrease of rectal cumulative dose depends on the dose gradient, motion amplitude and frequency in AP direction. EUDf is a useful QA parameter for interpreting the biological impact of geometric uncertainties on the static dose distribution. Rectal NTCP is patient dependent and must be determined individually.

34. Optimization in HDR gynecological brachytherapy

The coupling of finite element simulations to mathematical optimization techniques has contributed significantly to product improvements and cost reductions in the metal forming industries. The next challenge is to bridge the gap between deterministic optimization techniques and the industrial need for robustness. This paper introduces a generally applicable strategy for modeling and efficiently solving robust optimization problems based on time consuming simulations. Noise variables and their effect on the responses are taken into account explicitly. The robust optimization strategy consists of four main stages: modeling, sensitivity analysis, robust optimization and sequential robust optimization. Use is made of a metamodel-based optimization approach to couple the computationally expensive finite element simulations with the robust optimization procedure. The initial metamodel approximation will only serve to find a first estimate of the robust optimum. Sequential optimization steps are subsequently applied to efficiently increase the accuracy of the response prediction at regions of interest containing the optimal robust design. The applicability of the proposed robust optimization strategy is demonstrated by the sequential robust optimization of an analytical test function and an industrial V-bending process. For the industrial application, several production trial runs have been performed to investigate and validate the robustness of the production process. For both applications, it is shown that the robust optimization strategy accounts for the effect of different sources of uncertainty onto the process responses in a very efficient manner. Moreover, application of the methodology to the industrial V-bending process results in valuable process insights and an improved robust process design.

Support Vector Machine is the most efficient algorithm in machine learning for classification problems. The data generated these days requires Multi-classification and SVM’s are used for doing the same. To improve the performance and reduce the time complexity of the multi-class SVM, different optimization techniques have been used. This project aims to compare these optimization techniques on SVM on different parameters like accuracy and time. The comparative analysis has been carried out on optimization techniques which are the current state-of-art; Sequential Minimum Optimization and Dual Decomposition.

Preventing execution of unauthorized software on a given computer plays a pivotal role in system security. The key problem is that although a program at the beginning of its execution can be verified as authentic, its execution flow can be redirected to externally injected malicious code us-ing, for example, a buffer overflow exploit. We introduce a novel, simplified, hardware-assisted in-trusion prevention platform. Our platform introduces over-lapping of program execution and MAC verification. It par-titions a program binary into blocks of instructions. Each block is signed using a keyed MAC that is attached as a footer to the block. When the control flow reaches a partic-ular block, its instructions are speculatively executed, while dedicated hardware verifies the attached MAC at run-time. The computation state is preserved during speculative exe-cution using a mediating buffer placed between the proces-sor and L1 data cache. Upon MAC verification, the results from this buffer are propagated externally. Central to this paper is the proposal of a novel optimization technique that initially identifies instructions that are likely to stall exe-cution, and reorders basic blocks within a given instruc-tion block to minimize the execution overhead. While the presented optimization technique is problem specific, it is flexible such that it can be adjusted for different optimiza-tion goals. Preliminary results showed that our optimiza-tion methods produced an average overhead reduction of 60 % on the SPEC2000 benchmark suite and Microsoft Vi-sual FoxPro. 1.

Purpose: To compare the dosimetric characteristics and treatment delivery efficiency of trigeminal neuralgia (TN) stereotactic radiosurgery (SRS) patients previously treated with a 6 MV-FFF (flattening filter-free; radiation beam obtained by removing the flattening filter) beam versus those re-planned with a 10 MV-FFF beam using a conical collimator on the TrueBeam Novalis STx linear accelerator. Methods: Eleven patients with TN previously treated with a 6 MV-FFF beam following the SRS protocol of 90 Gy in a single fraction were selected. Plans were recalculated using 10 MV-FFF beam, maintaining the same dose prescription and beam angle configuration used with 6 MV-FFF beam. The dose gradient, volumes receiving 20 and 10 Gy, maximum dose and dose to 10% of the brainstem were recorded for both the energies. Efficiency was assessed by the average monitor unit (MU) and time per arc. The 10 MV-FFF machine was configured in the treatment planning system (TPS) to measure the tissue phantom ratio (TPR), dose profiles and scatter factors using RAZOR, PTW-60012 diodes and EBT3 radiochromic films. Results: Compared to the 6 MV-FFF, the 10 MV-FFF plans exhibit average increments in dose gradient, volume of 20 Gy and volume of 10 Gy of 3.8, 17.1 and 17.8%, respectively. Average increases of 6.5 and 18.1% were obtained for maximum dose and dose to 10% of the brainstem, respectively. An average increase of 31 MU/arc was observed for the 10 MV-FFF plans, with a 40% reduction in treatment time per arc. The TPR for the 10 MV-FFF beams increased by 10%, and a penumbra width of 0.3 mm was observed. Scatter factor increments of 15, 13.5, 12.7 and 10.3% were observed for the 6 MV-FFF over the 10 MV-FFF for cones of 4, 5, 6 and 7 mm, respectively. Conclusions: In TN SRS, the utilisation of 10 MV-FFF beams reduces treatment duration but results in an increased brainstem radiation dose. To mitigate this increase in brainstem dose, it is necessary to adjust the isocentre position.

Control of PET-Positive Lymph Nodes Treated with Definitive Chemoradiation in Locally Advanced Cervical Cancer

When designing a fusion power plant, many first-of-a-kind components are required. This presents a large potential design space across as many dimensions as the component’s parameters. In addition, multiphysics, multiscale, high-fidelity simulations are required to reliably capture a component’s performance under given boundary conditions. Even with high performance computing (HPC) resources, it is not possible to fully explore a component’s design space. Thus, effective interpolation between data points via machine learning (ML) techniques is essential. With sequential learning engineering optimisation, ML techniques inform the selection of simulation parameters which give the highest expected improvement for the model: balancing exploitation of the current best design with exploration of uncertain areas in the design space. In this paper, the application of an ML-driven design of experiment procedure for the sequential learning engineering design optimisation of a fusion component is shown. A parameterised divertor monoblock is taken as a typical example of a fusion component requiring HPC simulation to model. The component’s geometry is then optimised using Bayesian optimisation, seeking the design which minimises the stress experienced by the component under operational conditions.

The aim of any atomic spectroscopist, as of any analytical chemist, is to develop robust and efficient measurement procedures. Experimental design, sequential optimisation and response surface methodology are three critical tools to extract maximum information from a limited number of systematised and planned sets of experimental trials. The basics of these methodologies are introduced here and some common strategies presented in a user‐friendly, practical way and their advantages and drawbacks explained, from full factorial designs to saturated designs, which allow minimum performance of experiments. Tagughi’s designs and their philosophy, optimisation techniques and main response surface designs are introduced as well. Several matrices of experiments are presented so that they can be used directly by the reader. In addition, an updated review of published works, with comments related to applications in atomic spectrometry, is included.

Most IMRT techniques have been designed to treat targets smaller than the field size of conventional linac accelerators. In order to overcome the field size restrictions in applying IMRT, we developed a two isocenter IMRT technique to treat long volume targets. The technique exploits an extended dose gradient throughout a junction region of 4–6 cm to minimize the impact of field match errors on a junction dose and manipulates the inverse planning and IMRT segments to fill in the dose gradient and achieve dose uniformity. Techniques for abutting both conventional fields with IMRT (‘Static + IMRT’) and IMRT fields (‘IMRT + IMRT’) using two separate isocenters have been developed. Five long volume sarcoma cases have been planned in Pinnacle (Philips, Madison, USA) using Elekta Synergy and Varian 2100EX linacs; two of the cases were clinically treated with this technique. Advantages were demonstrated with well-controlled junction target uniformity and tolerance to setup uncertainties. The junction target dose heterogeneity was controlled at a level of ±5%; for 3 mm setup errors at the field edges, the junction target dose changed less than 5% and the dose sparing to organs at risk (OARs) was maintained. Film measurements confirmed the treatment planning results.

Purpose: Although bedside imaging is among the most common radiographic exams (e.g., line and catheter placement in the ICU), poor image quality often challenges even conspicuous tasks. We hypothesize that dual-energy (DE) imaging using a novel wireless detector (with form factor equivalent to a film cassette) improves the conspicuity of interventional devices through material decomposition. This work optimizes the system configuration, acquisition techniques, and decomposition techniques for this novel bedside imaging technology. Method and Materials: A mobile x-ray system was integrated with a wireless DR detector (DRX-1) to permit DE image acquisition. Preliminary experimentation involved a benchtop and phantoms simulating bedside imaging. The contrast-to-noise ratio (CNR) was evaluated in DE images for common ICU imaging tasks. Measurements were performed as a function of antiscatter grid ratio (from gridless to 12:1), kVp pair (kVpLow = 40–90 kVp and kVpHigh = 90–140 kVp), and added filtration. Image quality was further improved by implementing linear and nonlinear noise-reduction algorithms. Expert radiologists assessed DR and DE imaging task performance in anthropomorphic phantoms implanted with interventional tools. Results: DE imaging significantly boosted conspicuity of interventional devices in bedside imaging tasks. Antiscatter grids increased CNR by ∼50% without dose increase. Optimal kVp pair was [50/90] kVp, with lower techniques distinct from previous studies attributed to high x-ray scatter in bedside configurations. Optimal filters were identified, with ∼0.2 mm Ag or Sn found optimal for the fixed-filter scenario. Noise-reduction algorithms preserved CNR at reduced imaging dose and indicate distinct processing for soft-tissue and metal decomposition. Qualitative reader studies show a dramatic boost in conspicuity for DE imaging compared to DR. Conclusion: Advances in portable DR enable superior bedside imaging through advanced DE imaging functionality. Optimization of DE imaging and decomposition techniques render interventional tools conspicuous compared to DR without increasing patient dose. Research funded by Carestream Health

Radial distribution systems (RDSs), which dominate electricity delivery in many developing countries, are inherently prone to high technical losses and poor voltage regulation due to long feeder lengths and predominantly inductive loads. Optimal capacitor placement has therefore emerged as a cost-effective strategy for reducing power losses and enhancing voltage profiles. This paper presents a structured narrative review of optimization-based capacitor placement approaches applied to RDSs, synthesizing analytical, heuristic, and metaheuristic methods reported in the literature. The review systematically examines commonly used power-flow techniques and compares major optimization algorithms, including Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, and hybrid frameworks, with respect to solution quality, convergence behavior, computational effort, and practical applicability. The synthesis reveals broad consensus that metaheuristic methods outperform deterministic approaches in complex, nonlinear networks, while also highlighting important trade-offs: algorithms offering marginally higher loss reduction often incur increased computational cost or parameter sensitivity. The review further identifies persistent gaps related to load variability modeling, economic integration, and real-world validation, particularly in data-constrained rural networks. From a practical perspective, the findings indicate that no single optimization technique is universally superior; rather, algorithm selection should be guided by network characteristics, data availability, and implementation constraints. The paper concludes by outlining context-specific research directions to support the deployment of optimization-based capacitor planning in developing power systems such as those in Uganda.

The use of optimization methods in geotechnics dates back to the 1950s. They were used in slope stability analysis (Bishop) and evolved to a wide range of applications in ground engineering. We present here a non-exhaustive review of recent publications that relate to the use of different optimization techniques in geotechnical engineering. Metaheuristic methods are present in almost all the problems in geotechnics that deal with optimization. In a number of cases, they are used as single techniques, in others in combination with other approaches, and in a number of situations as hybrids. Different results are discussed showing the advantages and issues of the techniques used. Computational time is one of the issues, as well as the assumptions those methods are based on. The article can be read as an update regarding the recent tendencies in the use of optimization techniques in geotechnics.

Purpose: To investigate dose distributions in partial‐volume irradiation experiments in small experimental animals, in particular the parotid gland of rat.Materials and methods: High‐resolution magnetic resonance imaging images were made that provided the outlines of the parotid glands, which were used to design collimators with conformal radiation ports for 100 and 50% cranial/caudal partial‐volume irradiation. A protocol for absolute dosimetry was designed and relative dose measurements were performed. From the three‐dimensional topographical data and the three‐dimensional dose distribution, dose–volume histograms were determined.Results: The standard uncertainty of absorbed entrance dose was within 3%. Radiochromic film, thermoluminescence dosemeters and ionization chamber dose measurements revealed that the relative doses measured were in good agreement. The 20–80% penumbra of the beam across the 50% field edge was only 0.4 mm at a 6 mm depth. The gradient of the percentage depth dose from the skin of the rat to a depth of 12 mm was 1.5% mm−1. The absorbed doses in the cranial 50% and the caudal 50% partial volumes were comparable. This finding was reflected in the calculated dose–volume histograms of the different regions, which were similar. The dose in the shielded area between the left and right ports was about 14% of the dose near the centres of the beams.Conclusion: The designed set‐up showed that irradiation of small volumes could be performed with high accuracy allowing the study of differences in radiation damage. Similar doses were given to the 50% cranial and 50% caudal gland volumes and, therefore, a possible difference in radiosensitivity in these volumes was not a dose effect. The approach used was also applicable for the irradiation of small volumes of other tissues.

Optimization of stereotactic body radiotherapy treatment planning using a multicriteria optimization algorithm