Manual Setup Research Articles

Measuring the Reproducibility of Scientific Research Based on Computing Environment Provisioning Methods (SDG)

Objective: This study investigates three different methods for provisioning computational environments in scientific research, focusing on the level of reproducibility offered by each one. The methods are manual setup, container-based, and one based on Infrastructure-as-Code principles, the Environment Code-First (ECF) framework. Method: The methodology adopted for this research comprises a comparative study design evaluating three distinct approaches to computational environment provisioning: manual setup, container-based setup, and the ECF framework. The study involved reproducing a research experiment using the Inception-V3 model to identify canine hip dysplasia from X-ray images across different computational setups. Data was collected through performance metrics such as reproducibility rate, consistency of results, and ease of setup. Results and Discussion: The results revealed that while offering complete control over the environment, the manual setup needed to be more consistent and more accessible to reproduce, leading to variability in the results. The container-based method improved reproducibility but required manual intervention to set up the container infrastructure. The ECF-based approach, however, demonstrated superior performance by fully automating the environment provisioning process, ensuring 100% reproducibility and consistency across different platforms. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied to advance practices in computational research and scientific reproducibility. These implications could encompass the broader adoption of IaC tools in scientific experiments, potentially leading to more reliable and reproducible research outcomes. Originality/Value: This study contributes to the literature by highlighting the innovative application of the Infrastructure-as-Code approach to achieving reproducibility in scientific research. The potential impact of adopting IaC tools on improving the reliability and consistency of research outcomes evidences the relevance and value of this research.

Advanced shooting target with bullet collector, semi-automatic bulls-eye paper positioning and automatic shooting score

Shooting exercises in Indonesia typically use simple bulls-eye targets on wooden boards with sand backstops, requiring manual setup and score calculation. This setup is inefficient, especially for long-range shooting, as operators must walk far to retrieve targets, and bullets embedded in sand are hard to recycle. This project developed an advanced shooting target featuring a bullet collector, semi-automatic target setup, automatic scoring, and target monitoring. A system with such complete features is not available in the market. This target system has a roll of bulls-eye paper and the roller is powered by a servo motor controlled by a switch to command a fresh new page of bulls-eye its positioning is helped by an infrared sensor to detect markers in the paper for correct positioning. This system is equipped with a bullet collector system by directing the bullet to a container using 450 angled armor and a layer of sand in the container to stop the bullet. This system is also equipped with a camera pointing to the bulls-eye paper and its output is transmitted to a monitor close to the shooter to identify bullet tracks for evaluating his shooting performance and to improve his shooting strategy. The image from the same camera is used for image processing with the OpenCV library and Python scripts to calculate the shooting score automatically. Several physical tests have been conducted and the system proves to perform reasonably well in the tests with some errors of around 3% for single bullet holes and simple multiple bullet holes. Based on test results, the pistol bullets have quite different properties from the rifle bullets. Pistol bullets follow the impact deflection with a coefficient of restitution e = 0 while rifle bullets follow the impact deflection with e ≈ 0.5. The pistol bullets are completely disintegrated after impact while the rifle bullets are just distorted.

Addressing OSH challenges in non-standardized work practices in small-scale FMCG units by introducing context-specific ergonomic pouch/sachet cutting apparatus.

The FMCG manufacturing industry in industrially developing countries operates in a manual or semi-automatic setup, employing a vast labor force. Several non-standardized work activities prevail on the FMCG shop floor and remain prone to safety-related risks involving repetitive motions, forceful exertions, and awkward postures. Among those, the rework of defective pouches/sachets is an unsafe activity of prime concern. It is prone to minor nicks, cuts, and injuries due to inadequate tools being used. It involves sharp cutters/blades and extensive forceful manual hand squeezing, which leads to drudgery and safety concerns. There lies the lack of standardized tools/apparatus for rework activity, and efforts towards its mitigation are required. Current research aims to address occupational safety-related issues in non-standardized rework activity in small-scale FMCG units through an innovative product design approach. An ergo-audit was conducted in eight small-scale FMCG units to identify the prevailing ergonomic stressors and safety concerns. The most critical area of concern, i.e., rework activity, was chosen through card-sorting sessions and discussions held with the stakeholders. An appropriate context-specific apparatus was designed/developed to ensure better safety and occupational health utilizing a systematic product design method involving three phases: initial field survey, design and development, and field trials. The apparatus, which was developed and factory-trialed, was evaluated for productivity improvement and ensuring user compatibility from various human factors' perspectives. In field trials, the developed apparatus was found effective in mitigating safety concerns and various ergonomic stressors associated with FMCG rework.

A libraries reproducibility hackathon: connecting students to University research and testing the longevity of published code

Reproducibility is a basis of scientific integrity, yet it remains a significant challenge across disciplines in computational science. This reproducibility crisis is now being met with an Open Science movement, which has risen to prominence within the scientific community and academic libraries especially. To address the need for reproducible computational research and promote Open Science within the community, members of the Open Science and Data Collaborations Program at Carnegie Mellon University Libraries organized a single-day hackathon centered around reproducibility. Partnering with a faculty researcher in English and Digital Humanities, this event allowed several students an opportunity to interact with real research outputs, test the reproducibility of data analyses with code, and offer feedback for improvements. With Python code and data shared by the researcher in an open repository, we revealed that students could successfully reproduce most of the data visualizations, but they required completing some manual setup and modifications to address depreciated libraries to successfully rerun the code. During the event, we also investigated the option of using ChatGPT to debug and troubleshoot rerunning this code. By interacting with a ChatGPT API in the code, we found and addressed the same roadblocks and successfully reproduced the same figures as the participating students. Assessing a second option, we also collaborated with the researcher to publish a compute capsule in Code Ocean. This option presented an alternative to manual setup and modifications, an accessible option for more limited devices like tablets, and a simple solution for outside researchers to modify or build on existing research code.

Early prehospital mechanical cardiopulmonary resuscitation use for out-of-hospital cardiac arrest: an observational study

BackgroundThe use of mechanical cardiopulmonary resuscitation device has been very prevalent in out-of-hospital cardiac arrest rescue. This study aimed to investigate whether the timing of mechanical cardiopulmonary resuscitation device set-up correlated with the the outcome of cardiac arrest patients.MethodsWe retrospectively reviewed adult nontrauma cardiac arrest cases in New Taipei City, Taiwan, from January to December 2022. Demographic data, intervention-related factors, and the time variables of mechanical cardiopulmonary resuscitation were collected. The outcomes included the return of spontaneous circulation and 24-hour survival. We compared patients who achieved spontaneous circulation and those who did not with univariate and multivariable regression analyses.ResultsIn total, 1680 patients who received mechanical cardiopulmonary resuscitation were included in the analysis. Reducing the time interval from manual chest compression initiation to device setup was independently associated with the return of spontaneous circulation and 24-hour survival, especially in the subgroup of patients of initial shockable rhythm. Receiver operating characteristic analysis revealed that the outcome of patients with an initial shockable rhythm could be predicted by the mechanical cardiopulmonary resuscitation setup time, with areas under the curve of 60.8% and 63.9% for ROSC and 24-hour survival, respectively. The cutoff point was 395.5 s for patients with an initial shockable rhythm.ConclusionA positive correlation was found between early mechanical cardiopulmonary resuscitation intervention and the outcomes of out-of-hospital cardiac arrest patients. The time between manual chest compression and device setup could predict the return of spontaneous circulation and 24-hour survival in the subgroup of patients with initially shockable rhythm with the optimal cutoff point at 395.5 s.

A dosimetrically motivated pathfinding approach for non-isocentric dynamic trajectory radiotherapy

Objective.Non-isocentric dynamic trajectory radiotherapy (DTRT) involves dynamic table translations in synchrony with intensity modulation and dynamic gantry, table, and/or collimator rotation. This work aims to develop and evaluate a novel dosimetrically motivated path determination technique for non-isocentric DTRT.Approach.The path determination considers all available beam directions, given on a user-specified grid of gantry angle, table angle, and longitudinal, vertical, and lateral table position. Additionally, the source-to-target distance of all beam directions can be extended by moving the table away from the gantry along the central beam axis to increase the collision-free space. The path determination uses a column generation algorithm to iteratively add beam directions to paths until a user-defined total path length is reached. A subsequent direct aperture optimization of the intensity modulation along the paths creates deliverable plans. Non-isocentric DTRT plans using the path determination and using a manual path setup were created for a craniospinal and a spinal irradiation case. Furthermore, VMAT, isocentric DTRT, and non-isocentric DTRT plans are created for a breast, head and neck (H&N), and esophagus case. Additionally, a HyperArc plan is created for the H&N case. The plans are compared in terms of the dosimetric treatment plan quality and estimated delivery time.Main results.For the craniospinal and spinal irradiation case, using path determination results in dose distributions with improved conformity but a slightly worse target homogeneity compared to manual path setup. The non-isocentric DTRT plans maintained target coverage while reducing the mean dose to organs-at-risk on average by 1.7 Gy (breast), 1.0 Gy (H&N), and 1.6 Gy (esophagus) compared to the VMAT plans and by 0.8 Gy (breast), 0.6 Gy (H&N), and 0.8 Gy (esophagus) compared to the isocentric DTRT plans.Significance.A general dosimetrically motivated path determination applicable to non-isocentric DTRT plans is successfully developed, further advancing the treatment planning for non-isocentric DTRT.

Instrument Interactions as Source of Information in Robot-Assisted Surgery

Abstract The interaction between the surgical instrument and tissue is a yet little-exploited source of information. Vibroacoustic signals resulting from an interaction can be analyzed to extract relevant information related to the tissue. This work presents a summary of feasibility studies with synthetic-, animal-, and ex-vivo tissue specimen. A standard palpation probe is equipped with a vibration measurement system. A manual and robot-assisted setup is used for data acquisition. Simple features derived from the signals are used in a subsequent classification step using kNN and SVM. The obtained results show the capability to differentiate various tissues, such as subchondral bone with an accuracy of 98.44%. This demonstrates the potential of the approach to provide tissue related information based on instrument interactions.

Single-cell ionic current phenotyping elucidates non-canonical features and predictive potential of cardiomyocytes during automated drug experiments.

All new drugs must go through preclinical screening tests to determine their proarrhythmic potential. While these assays effectively filter out dangerous drugs, they are too conservative, often misclassifying safe compounds as proarrhythmic. In this study, we attempt to address this shortcoming with a novel, medium-throughput drug-screening approach: we use an automated patch-clamp system to acquire optimized voltage clamp (VC) and action potential (AP) data from human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) at several drug concentrations (baseline, 3×, 10× and 20× the effective free plasma concentrations). With our novel method, we show correlations between INa block and upstroke slowing after treatment with flecainide or quinine. Additionally, after quinine treatment, we identify significant reductions in current during voltage steps designed to isolate If and IKs. However, we do not detect any IKr block by either drug, and upon further investigation, do not see any IKr present in the iPSC-CMs when prepared for automated patch experiments (i.e. in suspension) - this is in contrast to similar experiments we have conducted with these cells using the manual patch setup. In this study, we: (1) present a proof-of-concept demonstration of a single-cell medium-throughput drug study, and (2) characterize the non-canonical electrophysiology of iPSC-CMs when prepared for experiments in a medium-throughput setting. KEY POINTS: Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) offer potential as an in vitro model to study the proarrhythmic potential of drugs, but insights from these cells are often limited by the low throughput of manual patch-clamp. In this study, we use a medium-throughput automated patch-clamp system to acquire action potential (AP) and complex voltage clamp (VC) data from single iPSC-CMs at multiple drug concentrations. A correlation between AP upstroke and INa transients was identified and drug-induced changes in ionic currents found. We also characterize the substantially altered physiology of iPSC-CMs when patched in an automated system, suggesting the need to investigate differences between manual and automated patch experiments.

SayHello: An intelligent peer-to-peer polyglot voice-to-voice conversation application

As the entire world is becoming increasingly a global village, the need for reliable, smooth, and easy-to-use applications that facilitate the communication process between people speaking different languages worldwide becomes essential, especially in the tourism industry. While numerous online and mobile applications attempt to bridge the linguistic gap using text-to-text, text-to-voice, or voice-to-text-to-voice translators, they often fall short due to constraints such as the need for a single shared device, manual setup of speaker’s gender and preferred language, and an inability to communicate from a distance. These applications struggle to mimic the practical nature of real-time multilingual conversations where immediate and clear communication is paramount. This paper introduces an intelligent peer-to-peer polyglot voice-to-voice mobile application to facilitate the communication of people speaking different languages worldwide transparently mimicking a live conversation whether the involved parties are close to each other or at a nearby distance. People can interact with others transparently using their preferred language, irrespective of others’ languages, while the application automatically recognizes the language, gender of the speaker, and spoken words with very high accuracy. Five languages were implemented in the developed application as a proof-of-concept, and it is designed to smoothly and simply adapt more in future updates.

3MSF: A Multi-Modal Adaptation of the 6TiSCH Minimal Scheduling Function for the Industrial IoT.

Although wireless devices continuously gain communication capabilities, even state-of-the-art Industrial Internet of Things (IIoT) architectures, such as Internet Protocol version 6 over the Time-Slotted Channel Hopping (TSCH) mode of IEEE 802.15.4 (6TiSCH), continue to use network-wide, fixed link configurations. This presents a missed opportunity to (1) forego the need for rigorous manual setup of new deployments; and (2) provide full coverage of particularly heterogeneous and/or dynamic industrial sites. As such, we devised the Multi-Modal Minimal Scheduling Function (3MSF) for the TSCH link layer, which, combined with previous work on the routing layer, results in a 6TiSCH architecture able to dynamically exploit modern multi-modal hardware on a per-link basis through variable-duration timeslots, simultaneous transmission, and routing metric normalization. This paper describes, in great detail, its design and discusses the rationale behind every choice made. Finally, we evaluate three basic scenarios through simulations, showcasing both the functionality and flexibility of our 6TiSCH implementation.

Automatic Texture Alignment by Optimization Method.

Microstructure analysis via electron backscatter diffraction has become an indispensable tool in materials science and engineering. In order to interpret or predict the anisotropy in crystalline materials, the texture is assessed, e.g. via pole figure diagrams. To ensure a correct characterization, it is crucial to align the measured sample axes as closely as possible with the manufacturing process directions. However, deviations are inevitable due to sample preparation and manual measurement setup. Postprocessing is mostly done manually, which is tedious and operator-dependent. In this work, it is shown that the deviation can be calculated using the contour of the crystal orientations. This can also be utilized to define the axis symmetry of pole figure diagrams through an objective function, allowing for symmetric alignment by minimization. Experimental textures of extruded profiles and synthetically generated textures were used to demonstrate the general applicability of the method. It has proven to work excellently for deviations of up to 5∘, which are typical for careful manual sample preparation and mounting. While the performance of the algorithm is reduced with increasing misalignment, good results have also been obtained for deviations up to 15∘.

Evaluation of digital and manual orthodontic diagnostic setups in non-extraction cases using ABO model grading system: an in-vitro study

BackgroundTo evaluate the outcome quality of manual and digital orthodontic diagnostic setups in non-extraction cases according to the American Board of Orthodontics model grading system and to calculate the laboratory time needed for orthodontic diagnostic setup construction.MethodsThe sample consisted of 60 pretreatment models of non-extraction orthodontic cases with age ranges of 18–30. The study models were duplicated and scanned with 3Shape R-750 scanner. Digital and manual diagnostic setups were constructed according to their respective treatment plans. Digital diagnostic setups were 3D printed and then both manual and digital setups were assessed using the modified American Board of Orthodontics Cast Radiograph evaluation score (ABO CRE), which includes alignment, marginal ridge, buccolingual inclination, occlusal contacts, occlusal relationships, interproximal contacts, and overjet. The laboratory time needed for orthodontic setups was measured in minutes.ResultsThe total ABO CRE score of the digital diagnostic setup group (5.93 ± 2.74) was significantly lower than that of the manual diagnostic setup group (13.08 ± 3.25). The manual diagnostic setup had significantly larger scores in marginal ridge, overjet, overbite, buccolingual inclination, occlusal relationship, and total scores (P < 0.01). However, the digital diagnostic setup had a statistically larger occlusal contacts score than the manual diagnostic setup (P < 0.01). There was no significant difference between the alignment and the interproximal contacts scores in either group. The manual diagnostic setup needed significantly longer laboratory time (187.8 ± 14.22) than the digital setup (93.08 ± 12.65) (P < 0.01). Comparison between broken teeth was performed by using the chi-square test which found no significant difference between different tooth types.ConclusionsDigital diagnostic setup is a reliable tool for orthodontic diagnostic setup construction providing excellent quality setup models. Manual diagnostic setup is time consuming with a technique-sensitive laboratory procedure.

SCALING: plug-n-play device-free indoor tracking

24/7 continuous recording of in-home daily trajectories is informative for health status assessment (e.g., monitoring Alzheimer’s, dementia based on behavior patterns). Indoor device-free localization/tracking are ideal because no user efforts on wearing devices are needed. However, prior work mainly focused on improving the localization accuracy. They relied on well-calibrated sensor placements, which require hours of intensive manual setup and respective expertise, feasible only at small scale and by mostly researchers themselves. Scaling the deployments to tens or hundreds of real homes, however, would incur prohibitive manual efforts, and become infeasible for layman users. We present SCALING, a plug-and-play indoor trajectory monitoring system that layman users can easily set up by walking a one-minute loop trajectory after placing radar nodes on walls. It uses a self calibrating algorithm that estimates sensor locations through their distance measurements to the person walking the trajectory, a trivial effort without taxing layman users physically or cognitively. We evaluate SCALING via simulations and two testbeds (in lab and home configurations of sizes 3×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document}6 sq m and 4.5×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document}8.5 sq m). Experimental results demonstrate that SCALING outperformed the baseline using the approximate multidimensional scaling (MDS, the most relevant method in the context of self calibration) by 3.5 m/1.6 m in 80-percentile error of self calibration and tracking, respectively. Notably, only 1% degradation in performance has been observed with SCALING compared to the classical multilateration with known sensor locations (anchors), which costs hours of intensive calibrating effort. In addition, we conduct Monte Carlo experiments to numerically analyze the impact of sensor placements and develop practical guidelines for deployment in real life scenarios.

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms.

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

A Deep-Learning-Based Method for Extracting an Arbitrary Number of Individual Power Lines from UAV-Mounted Laser Scanning Point Clouds

In recent years, laser scanners integrated with Unmanned Aerial Vehicles (UAVs) have exhibited great potential in conducting power line inspections in harsh environments. The point clouds collected for power line inspections have numerous advantages over remote image data. However, point cloud-based individual power line extraction, which is a crucial technology required for power line inspections, still poses several challenges such as massive 3D points, imbalanced category points, etc. Moreover, in various power line scenarios, previous studies often require manual setup and careful adjustment of different thresholds to separate different power lines, which is inefficient for practical applications. To handle these challenges, in this paper, we propose a multi-branch network to automatically extract an arbitrary number of individual power lines from point clouds collected by UAV-based laser scanners. Specifically, to handle the massive 3D point clouds in complex outdoor scenarios, we propose to leverage deep neural network for efficient and rapid feature extraction in large-scale point clouds. To mitigate imbalanced data quantities across different categories, we propose to design a weighted cross-entropy loss function to measure the varying importance of each category. To achieve the effective extraction of an arbitrary number of power lines, we propose leveraging a loss function to learn the discriminative features that can differentiate the points belonging to different power lines. Once the discriminative features are learned, the Mean Shift method can distinguish the individual power lines by clustering without supervision. The evaluations are executed on two datasets, which are acquired at different locations with UAV-mounted laser scanners. The proposed method has been thoroughly tested and evaluated, and the results and discussions confirm its outstanding ability to extract an arbitrary number of individual power lines in point clouds.

Influence Of The Quantity Of Parts Per Batch On The Productivity Of Series Production Via Powder Bed Fusion With A Laser Beam of Metals Considering Personnel Shifts

Powder Bed Fusion with a Laser Beam of Metals (PBF-LB/M) is an Additive Manufacturing technology that allows the production of various complex geometries in one batch. For series production, usually a specific batch is defined and qualified that must not be changed later for qualification reasons. Usually, the geometric maximum of parts per build job is packed for high productivity, leading to high durations per batch of up to several days. A PBF-LB/M machine must be set up manually at the beginning and end of a build job in a comparably short time. Consequently, a 1- or 2-personnel shift system can potentially be used to reduce personnel cost but keep productivity high by carrying out the manual set-up activities for when personnel are present and keeping the machine productive with the long additive build-up during non-shift times. However, it is unclear whether the decision to pack more parts per batch always results in the higher productivity. Manual setup operations could be required during non-shift times, causing unproductive waiting time of the machine until personnel is available again. Technological progress could make it possible in future to change the number of parts without the need for new qualification. However, it remains to be determined if this could increase productivity. This work investigated these open questions based on a simulation of production schedules. In the simulation, scenarios with different assumptions like the number of machines, personnel quantity and shift-systems, and part models are considered. The results show that for series production with identical batches, more parts per batch do not always lead to higher productivity compared to less densely packed batches. Moreover, productivity could be increased by a value that depends on the simulation scenario if the number of parts per build job could be flexibly adapted to the availability of personnel.

Multi-taper-based automatic correction of non-anechoic antenna measurements

Prototype measurements belong to the key steps in the development of antenna structures. Although accurate validation of their far-field performance can be realized in dedicated facilities, such as anechoic chambers, the high cost of their construction and maintenance might not be justified if the main goal of measurements is to support teaching or low-budget research. Instead, they can be performed in non-anechoic conditions and then refined using appropriate correction algorithms. Unfortunately, the existing post-processing methods suffer from multiple challenges that include manual setup of parameters as well as validation of performance in idealized conditions. In this communication, a multi-taper-based framework for correction of antenna characteristics obtained in non-anechoic environments has been proposed. The algorithm augments one-shot measurements of the structure under test in order to extract the line-of-sight responses while attenuating the interferences pertinent to multi-path propagation and noise from external sources of radiation. The performance of the proposed correction routine has been demonstrated in two test sites using a geometrically small Vivaldi radiator and validated against state-of-the-art techniques from the literature. The uncertainty budget for the measurements performed using the approach amounts to 0.26 dB, which is low given challenging propagation conditions considered for experiments.

Shortest Path Routing Performance Evaluation over SDN Environment

Static routing has a manual configuration setup system, and the scope of static routing in an SDN network is just for small networks. The solution to this problem rises up with the new technology defined as software-defined networking (SDN) based on shortest path first dynamic routing. SDN has the facility of a centralized controller that smooth the controls and routes computation over a data packet. The performance analysis of SDN networks that have SDN switches connected to the network based on the shortest path first protocol are simulated on Mininet. The POX controller with Mininet programming feature for creating smart topologies was chosen. In this research, the SDN network using Dijkstra’s algorithm, Bellman-Ford algorithm, extended Dijkstra’s algorithm and Floyd Warshall Algorithm were implemented. The quality factors of SDN created by using four algorithms are measured in terms of delay, jitter, latency, packet loss, transmit, received, throughput, and bandwidth based on experimental results and European Telecommunications Standards Institute (ETSI) data. The performance parameters of SDN network topology created using Dijkstra’s, bellman ford, extended Dijkstra’s, and Floyd Warshall algorithms were compared and the experimental results showed that Bellman-Ford algorithm is better in terms of performance parameters than the other three algorithms.

Setup Uncertainty of Pediatric Brain Tumor Patients Receiving Proton Therapy: A Prospective Study.

This study quantifies setup uncertainty in brain tumor patients who received image-guided proton therapy. Patients analyzed include 165 children, adolescents, and young adults (median age at radiotherapy: 9 years (range: 10 months to 24 years); 80 anesthetized and 85 awake) enrolled in a single-institution prospective study from 2020 to 2023. Cone-beam computed tomography (CBCT) was performed daily to calculate and correct manual setup errors, once per course after setup correction to measure residual errors, and weekly after treatments to assess intrafractional motion. Orthogonal radiographs were acquired consecutively with CBCT for paired comparisons of 40 patients. Translational and rotational errors were converted from 6 degrees of freedom to a scalar by a statistical approach that considers the distance from the target to the isocenter. The 95th percentile of setup uncertainty was reduced by daily CBCT from 10 mm (manual positioning) to 1-1.5 mm (after correction) and increased to 2 mm by the end of fractional treatment. A larger variation existed between the roll corrections reported by radiographs vs. CBCT than for pitch and yaw, while there was no statistically significant difference in translational variation. A quantile mixed regression model showed that the 95th percentile of intrafractional motion was 0.40 mm lower for anesthetized patients (p=0.0016). Considering additional uncertainty in radiation-imaging isocentricity, the commonly used total plan robustness of 3 mm against positional uncertainty would be appropriate for our study cohort.

Delivery time reduction for mixed photon-electron radiotherapy by using photon MLC collimated electron arcs

Objective. Electron arcs in mixed-beam radiotherapy (Arc-MBRT) consisting of intensity-modulated electron arcs with dynamic gantry rotation potentially reduce the delivery time compared to mixed-beam radiotherapy containing electron beams with static gantry angle (Static-MBRT). This study aims to develop and investigate a treatment planning process (TPP) for photon multileaf collimator (pMLC) based Arc-MBRT. Approach. An existing TPP for Static-MBRT plans is extended to integrate electron arcs with a dynamic gantry rotation and intensity modulation using a sliding window technique. The TPP consists of a manual setup of electron arcs, and either static photon beams or photon arcs, shortening of the source-to-surface distance for the electron arcs, initial intensity modulation optimization, selection of a user-defined number of electron beam energies based on dose contribution to the target volume and finally, simultaneous photon and electron intensity modulation optimization followed by full Monte Carlo dose calculation. Arc-MBRT plans, Static-MBRT plans, and photon-only plans were created and compared for four breast cases. Dosimetric validation of two Arc-MBRT plans was performed using film measurements. Main results. The generated Arc-MBRT plans are dosimetrically similar to the Static-MBRT plans while outperforming the photon-only plans. The mean heart dose is reduced by 32% on average in the MBRT plans compared to the photon-only plans. The estimated delivery times of the Arc-MBRT plans are similar to the photon-only plans but less than half the time of the Static-MBRT plans. Measured and calculated dose distributions agree with a gamma passing rate of over 98% (3% global, 2 mm) for both delivered Arc-MBRT plans. Significance. A TPP for Arc-MBRT is successfully developed and Arc-MBRT plans showed the potential to improve the dosimetric plan quality similar as Static-MBRT while maintaining short delivery times of photon-only treatments. This further facilitates integration of pMLC-based MBRT into clinical practice.