Online Adaptation Research Articles

Adaptive fuzzy neural network finite‐time command filtered control of n‐link robotic systems with actuator saturation

AbstractThe finite time tracking control of n‐link robotic system is studied for model uncertainties and actuator saturation. Firstly, a smooth function and adaptive fuzzy neural network online learning algorithm are designed to address the actuator saturation and dynamic model uncertainties. Secondly, a new finite‐time command filtered technique is proposed to filter the virtual control signal. The improved error compensation signal can reduce the impact of filtering errors, and the tracking errors of system quickly converge to a smaller compact set within finite time. Finally, adaptive fuzzy neural network finite‐time command filtered control achieves finite‐time stability through Lyapunov stability criterion. Simulation results verify the effectiveness of the proposed control.

2D antiscatter grid and scatter sampling based CBCT method for online dose calculations during CBCT guided radiation therapy of pelvis.

Online dose calculations before the delivery of radiation treatments have applications in dose delivery verification, online adaptation of treatment plans, and simulation-free treatment planning. While dose calculations by directly utilizing CBCT images are desired, dosimetric accuracy can be compromised due to relatively lower HU accuracy in CBCT images. In this work, we propose a novel CBCT imaging pipeline to enhance the accuracy of CBCT-based dose calculations in the pelvis region. Our approach aims to improve the HU accuracy in CBCT images, thereby improving the overall accuracy of CBCT-based dose calculations prior to radiation treatment delivery. An in-house developed quantitative CBCT pipeline was implemented to address the CBCT raw data contamination problem. The pipeline combines algorithmic data correction strategies and 2D antiscatter grid-based scatter rejection to achieve high CT number accuracy. To evaluate the effect of the quantitative CBCT pipeline on CBCT-based dose calculations, phantoms mimicking pelvis anatomy were scanned using a linac-mounted CBCT system, and a gold standard multidetector CT used for treatment planning (pCT). A total of 20 intensity-modulated treatment plans were generated for five targets, using 6 and 10 MV flattening filter-free beams, and utilizing small and large pelvis phantom images. For each treatment plan, four different dose calculations were performed using pCT images and three CBCT imaging configurations: quantitative CBCT, clinical CBCT protocol, and a high-performance 1D antiscatter grid (1D ASG). Subsequently, dosimetric accuracy was evaluated for both targets and organs at risk as a function of patient size, target location, beam energy, and CBCT imaging configuration. When compared to the gold-standard pCT, dosimetric errors in quantitative CBCT-based dose calculations were not significant across all phantom sizes, beam energies, and treatment sites. The largest error observed was 0.6% among all dose volume histogram metrics and evaluated dose calculations. In contrast, dosimetric errors reached up to 7% and 97% in clinical CBCT and high-performance ASG CBCT-based treatment plans, respectively. The largest dosimetric errors were observed in bony targets in the large phantom treated with 6 MV beams. The trends of dosimetric errors in organs at risk were similar to those observed in the targets. The proposed quantitative CBCT pipeline has the potential to provide comparable dose calculation accuracy to the gold-standard planning CT in photon radiation therapy for the abdomen and pelvis. These robust dose calculations could eliminate the need for density overrides in CBCT images and enable direct utilization of CBCT images for dose delivery monitoring or online treatment plan adaptations before the delivery of radiation treatments.

Air–ground coordinated unmanned swarm systems: A multitasking framework for control design

An air–ground heterogeneous unmanned swarm system coordination is considered. The system consists of N unmanned aerial vehicles (UAVs) and one unmanned ground vehicle (UGV). This forms a complicated mission, which consists of the following four different tasks. First, the aerial vehicles are in a compact formation, while avoiding collision with each other. Second, the aerial vehicles should stay close to the ground, while avoiding collision with the ground. Third, the aerial vehicles should stay close to the ground vehicle. Fourth, the ground vehicle should follow a desired trajectory. These tasks reflect two seemingly contradictory nature: close to (due to tracking) and away from (due to avoidance). The effective control design should address all four tasks even in the presence of uncertainty. By two creative transformations, this multitude of tasks are consolidated in a χ-measure. An adaptive robust control, which includes a robust control scheme and an online adaptation law, is then proposed to render guarantee boundedness performance of this χ-measure. As a result, the control design is able to accomplish the combined tracking-avoidance mission for the uncertain swarm system. Despite the presence of conflicting aspects between these tasks, the designed controller exhibits outstanding performance.

Adaptive Multioutput Gradient RBF Tracker for Nonlinear and Nonstationary Regression.

Multioutput regression of nonlinear and nonstationary data is largely understudied in both machine learning and control communities. This article develops an adaptive multioutput gradient radial basis function (MGRBF) tracker for online modeling of multioutput nonlinear and nonstationary processes. Specifically, a compact MGRBF network is first constructed with a new two-step training procedure to produce excellent predictive capacity. To improve its tracking ability in fast time-varying scenarios, an adaptive MGRBF (AMGRBF) tracker is proposed, which updates the MGRBF network structure online by replacing the worst performing node with a new node that automatically encodes the newly emerging system state and acts as a perfect local multioutput predictor for the current system state. Extensive experimental results confirm that the proposed AMGRBF tracker significantly outperforms existing state-of-the-art online multioutput regression methods as well as deep-learning-based models, in terms of adaptive modeling accuracy and online computational complexity.

Remote, unsupervised measurement of dyadic subjective cognitive change in the Brain Health Registry

AbstractBackgroundEarly, efficient identification of Alzheimer’s disease (AD) risk is a critical need. Dyadic subjective report from a participant and an informant/study partner (SP) who knows the participant well, may be a reliable and accurate method for assessing AD risk.MethodThe Caregiver and Study Partner Portal within the Brain Health Registry (BHR; an online registry and cohort) is a scalable tool for remotely obtaining dyadic data. All BHR participants are asked to identify a potential SP, who is invited to separately enroll and consent. SPs complete a demographic survey and questionnaires pertaining to cognition, behavioral symptoms, activities of daily living, and the health of the participant. Participants and SPs both complete an online adaptation of the Everyday Cognition Scale (ECog) to assess subjective cognitive change. We analyzed demographic characteristics of the dyads enrolled in BHR and measured correlation between SP and self‐report ECog scores. We used ADNI ECog data to estimate cut points likely to indicate cognitive impairment (1.43 for SP‐report scores and 1.65 for self‐report scores) and determined the number of BHR participants who are possibly impaired based on these cut points.ResultOut of 93,134 BHR participants, 10,494 (11.3%) have an enrolled study partner (Figure 1). The mean SP‐report ECog score is 1.35±0.50 and mean self‐report ECog score is 1.46±0.47. SP and self‐report ECog scores are correlated (r = 0.47, p < 0.001) (Figure 1). Out of 8,374 participants with available SP‐report ECog scores, 2,194 (26.2%) are possibly impaired. Out of 10,018 participants with available self‐report ECog scores, 2,384 (23.8%) of participants are possibly impaired (Figure 2).ConclusionThese findings demonstrate the feasibility of collecting online, dyad‐report demographic and subjective cognitive change data from a large cohort of participant‐SP dyads. The study failed to recruit an ethnoculturally and educationally diverse cohort of dyads. The positive correlation between self‐ and SP‐report ECog scores suggests dyad concordance in subjective report of change. Future efforts will focus on improving study partner enrollment and BHR task completion, increasing dyad diversity, and investigating the ability of remote dyadic measures to track cognitive decline and clinical progression along the AD continuum.

A Computation-efficient Online Secondary Path Modeling Technique for Modified FXLMS Algorithm

This paper proposes an online secondary path modelling (SPM) technique to improve the performance of the modified filtered reference Least Mean Square (FXLMS) algorithm. It can effectively respond to a time-varying secondary path, which refers to the path from a secondary source to an error sensor. Unlike traditional methods, the proposed approach switches modes between adaptive ANC and online SPM, eliminating the use of destabilizing components such as auxiliary white noise or additional filters, which can negatively impact the complexity, stability, and noise reduction performance of the ANC system. The system operates in adaptive ANC mode until divergence is detected due to secondary path changes. At this moment, it switches to SPM mode until the path is remodeled and then returns to ANC mode. Furthermore, numerical simulations in the paper demonstrate that the proposed online technique effectively copes with the secondary path variations.

Empowering Change: An Advanced NGO Management System for Efficient Operations, Enhanced Donor Engagement, and Transparent Community Development

This paper proposes solution to handle complex societal concerns, including social welfare, environmental preservation, and humanitarian aid, non-governmental organisations (NGOs) are essential players. Realising an NGO's objective requires effective administration of its many facets of operations, including donor connections, project tracking, volunteer coordination, and event planning. This article introduces the "An Advanced NGO Management System for Efficient Operations, Enhanced Donor Engagement, and Transparent Community Development" a powerful web-based tool designed to streamline NGOs' administrative procedures while promoting accountability and openness. In a world with constantly changing difficulties, NGOs need adaptive and user-friendly tools to successfully manage their range of activities. This system has been painstakingly designed to deliver a dynamic and adaptable online application that responds to the complex needs of NGOs and their stakeholders by utilising the power of Java Servlets technology. The NGO Management System functions as a central hub for the comprehensive management of NGO operations, enhancing their overall impact and contribution to the betterment of society.

Ameliorating epistemic injustice in practice: Communication strategies in a research project with refugee youth coresearchers.

Many researchers want to include seldom involved groups, such as refugees and youth, in patient and public involvement (PPI), but experience a number of barriers. The PPI research community calls for critical evaluations that are prospective, data-based and conducted by researchers and public contributors together. In this study, we conducted a longitudinal evaluation of a core activity in all collaborations: communication strategies. The aim was to evaluate the communication strategies adopted throughout a research project with refugee youth coresearchers. This article is based on the evaluation of a project where refugee youth were involved in the online adaptation of a group-based programme for youth with posttraumatic stress. Behavioural observations and field notes collected during the project were analysed with qualitative content analysis and a readability index, and discussed through the lens of epistemic injustice. The article was cowritten by researchers and refugee youth. Four manifest categories were identified: Facilitating engagement through coplanning and circumstantial flexibility; Different needs require different channels; It's not just about the channel-facilitation skills matter; Finding a common language opens a communicative flow. In addition, a latent underlying theme reflecting the role of trust was identified: Interactive moments facilitate trust-trust facilitates richer involvement. At the core of the identified communication strategies were strengthening relationship-building and actively facilitating involvement. Establishing trusting relationships enabled refugee youth to share input. The communication strategies increased hermeneutical justice by contributing to a common understanding; thus, taking a step towards ameliorating epistemic injustice. This article is a participatory analysis of a PPI process; it was written in collaboration between researchers and refugee youth coauthors, who were all previously involved in the evaluated project.

Transfer multi-source knowledge via scale-aware online domain adaptation in depth estimation for autonomous driving

This paper deals with the challenging online monocular depth adaptation task that aims to train an initial depth estimation model in a source domain and continuously adapt the model against a constantly changing target domain. Due to the high cost of real-world data collection and the camera-dependent nature of the depth estimation, previous works tend to simulate the environment using a virtual-world dataset for training (e.g. Virtual KITTI), and employ a real-world dataset for testing (e.g. KITTI, which shares the same camera settings) and are therefore vulnerable to novel domains with unknown statistics. We propose a meta-learning-based online domain adaptation framework that can leverage multi-source domain to transfer the learned knowledge from the virtual world to the real world better with the metrically accurate scale. Our learn-to-adapt algorithm mimics domain shifts during training by creating fictitious testing domains and incorporating a meta-optimization objective for optimizing the performance of the testing domains after updating the training domains in each mini-batch step. The algorithm is augmented with gradient surgery to alleviate unreliable optimization of inconsistent regions. To facilitate multi-source domain training and testing, we introduce a camera conversion technique for transforming images and depth cues from different camera settings to a unified one. During online adaptation, we also apply the exact statistics of the test-time input to the network's normalization layers to ensure a more robust adaptation. Extensive experiments demonstrate that our method can robustly adapt and outperform the virtual-to-real state-of-the-art methods on the standard KITTI Eigen benchmark of both full-length videos and isolated frames, a feat never attempted before, as well as generalizing to other real-world datasets without retraining.

Designing adaptive online support for problem-based learning

This paper describes the work-in-progress of a novel solution to the problem of designing an LMS course structure that supports students in a problem-based learning course. The students come with a wide variety of prior experience in the field, they need to learn a set of complex skills that build on each other, they need to work partially asynchronously, and the design solution needs to be scalable. A lean weekly structure is proposed that is embedded with the synchronous teaching, intricately connected to a parallel hub of support material whose design allows students to construct their own bespoke learning journey. The course design draws on cognitive load theory to direct student attention only to what will be useful to them at any point in the course, and the support materials themselves follow universal design for learning principles to cater for a wide range of student learning needs.

Event-triggered adaptive control for upper-extremity therapeutic robot in active-assist mode: A simulation study

Upper extremity rehabilitation exercises are essential for individuals recovering from injuries or conditions that affect their arm and hand functionality. Robot-assisted therapy has gained popularity as it offers precise control, objective assessment, and customizable treatment programs. However, several challenges persist, including uncertainties in the patient’s and robot dynamics, limited communication, and the need to maintain a compliant patient-robot interaction. Therefore, an event-triggered adaptive backstepping (ETAB) admittance control strategy is proposed in this work to address these challenges. Initially, the framework of the robot-assisted therapeutic process is briefly explained. The architecture of the proposed control strategy is formulated with two control modules. Thereafter, the adaptive backstepping technique is employed with an online adaptation law to deal with dynamic uncertainties. Moreover, the problem of limited communication is handled using the proposed design of a Lyapunov-based event-triggered mechanism. The admittance controller is integrated to maintain a compliant patient-robot interaction and consider the participation of the patient in the therapeutic sessions. The effectiveness of the proposed control strategy is verified using an end-effector type rehabilitation robot performing two different rehabilitation exercises. Furthermore, a comparative performance analysis is carried out with the proportional-integral-derivative controller (PID) and the adaptive sliding mode controller (ASMC). Based on the simulation runs, the proposed controller has shown promising tracking behavior, appropriate compliant interaction, and considerable reduction in the transmitted signals during the passive and active-assist training exercises.

Advice complexity of adaptive priority algorithms

The priority model was introduced to capture “greedy-like” algorithms. Motivated by the success of advice complexity in the area of online algorithms, the fixed priority model was extended to include advice, and a reduction-based framework was developed for proving lower bounds on the amount of advice required to achieve certain approximation ratios in this rather powerful model. To capture most of the algorithms that are considered greedy-like, the even stronger model of adaptive priority algorithms is needed. We extend the adaptive priority model to include advice. We modify the reduction-based framework from the fixed priority case to work with the more powerful adaptive priority algorithms, simplifying the proof of correctness and strengthening all previous lower bounds by a factor of two in the process.As evidence that adding advice to adaptive priority algorithms extends both adaptive priority algorithms and online algorithms with advice, we present a purely combinatorial adaptive priority algorithm with advice for Minimum Vertex Cover on triangle-free graphs of maximum degree three. Our algorithm achieves optimality and uses at most 7n/22 bits of advice. No adaptive priority algorithm without advice can achieve optimality without advice, and we prove that an online algorithm with advice needs more than 7n/22 bits of advice to reach optimality.We show connections between exact algorithms and priority algorithms with advice. The branching in branch-and-reduce algorithms can be seen as trying all possible advice strings, and all priority algorithms with advice that achieve optimality define corresponding exact algorithms, priority exact algorithms. Lower bounds on advice-based adaptive algorithms imply lower bounds on running times of exact algorithms designed in this way.

Advancing Cultural Heritage Structures Conservation: Integrating BIM and Cloud-Based Solutions for Enhanced Management and Visualization

Preserving and managing historical cultural heritage necessitates innovative approaches that harmonize technology with historical documentation. This paper presents a case study showcasing an adaptable online database tailored for the incorporation of Building Information Modeling (BIM) models and associated historical records. Notably, this approach offers distinctive integration with historical data, enabling users to contextualize heritage assets historically. The study delves into the database’s capabilities, focusing on its role in cultural heritage structure management. It explores interactive exploration, semantic querying, and attachment features, highlighting BIM’s potential in digital conservation, documentation, and archival efforts for historical structures. Additionally, the paper underscores the database’s significance as a valuable tool for education, research, and collaboration among heritage professionals and the public. While addressing specific modeling challenges, it emphasizes the importance of enhancing software solutions to elevate historical asset management. Historical BIM emerges as a powerful resource bridging the gap between historical structures’ conservation and modern technology.

Nine years of plan of the day for cervical cancer: Plan library remains effective compared to fully online-adaptive techniques

Background and purposeSince 2011, our center has been using a library-based Plan-of-the-Day (PotD) strategy for external beam radiotherapy of cervical cancer patients to reduce normal tissue dose while maintaining adequate target coverage. With the advent of fully online-adaptive techniques such as daily online-adaptive replanning, further dose reduction may be possible. However, it is unknown how this reduction relates to plan library approaches, and how the most recent PotD strategies relate to no adaptation. In this study we compare the performance of our current PotD strategy with non-adaptive and fully online-adaptive techniques in terms of target volume size and normal tissue sparing. Materials and methodsTreatment data of 376 patients treated with the PotD protocol between June 2011 and April 2020 were included. The size of the Planning Target Volumes (PTVs) was reconstructed for different strategies: full online adaptation, no adaptation, and the latest clinical version of the PotD protocol. Normal tissue sparing was estimated by the difference in margin volume to construct the PTV and the volume overlap of the PTV with bladder and rectum. ResultsThe current version of our PotD approach reduced the PTV margin volume by a median of 250 cm3 compared to no adaptation. Bladder-PTV overlap decreased from a median of 142 to 71 cm3, and from 39 to 16 cm3 for rectum-PTV. Fully online-adaptive approaches could further decrease the PTV volume by 144 cm3 using a 5 mm margin for residual errors. In this scenario, bladder-PTV overlap was reduced to 35 cm3 and rectum-PTV overlap to 11 cm3. ConclusionThe current version of the PotD protocol is an effective technique to improve normal tissue sparing compared to no adaptation. Further sparing can be achieved using fully online-adaptive techniques, but at the cost of a more complex workflow and with a potentially limited impact. PotD-type protocols can therefore be considered as a suitable alternative to fully online-adaptive approaches.

Intrafraction Motion and Margin Assessment for Ethos Online Adaptive Radiotherapy Treatments of the Prostate and Seminal Vesicles

IntroductionOnline adaptive radiotherapy (OART) utilizes daily imaging to identify changes in the patient's anatomy and generate a new treatment plan adapted to these changes for each fraction. The aim of this study was to determine the intra-fraction motion and planning target volume (PTV) margins required for an OART workflow on the Varian Ethos system. MethodSixty-five fractions from 13 previously treated OART patients were analyzed for this retrospective study. The prostate and seminal vesicles were contoured by a radiation oncologist on two cone beam computed tomography scans (CBCT) for each fraction; the initial CBCT at the start of the treatment session, and the verification CBCT immediately before beam-on. In part 1 of the study, PTVs of different sizes were defined on the initial CBCT, and the geometric overlap with the clinical target volume (CTV) on the verification CBCT was used to determine the optimal OART margin. This was performed with and without a patient re-alignment shift by registering the verification CBCT to the initial CBCT. In part 2 of the study, the margins determined in part 1 were used for simulated Ethos OART treatments on all 65 fractions. The resultant coverage to the CTV on the verification CBCT, was compared to an image guided radiotherapy (IGRT) workflow with 7mm margins. ResultsPart 1 of the study found, if a verification CBCT and shift is performed, a 4 mm margin on the prostate and 5 mm on the seminal vesicles resulted in 95% of the CTV covered by the PTV in more than 90% of fractions, and 98% of the CTV covered by the PTV in more than 80% of fractions. Part 2 of the study found when these margins were used in an Ethos OART workflow, they resulted in CTV coverage that was superior to an IGRT workflow with 7mm margins. ConclusionA 4 mm prostate margin and 5 mm seminal vesicles margin in an OART workflow with verification imaging are adequate to ensure coverage on the Varian Ethos system. Larger margins may be required if using an OART workflow without verification imaging.

Optimizing Online Adaptation Timing in the Treatment of Locally Advanced Cervical Cancer

Purpose/Objective: Online adaptive radiotherapy (ART) has emerged as a new treatment modality for cervical cancer. Daily online adapting improves target coverage and OAR sparing compared to traditional image-guided radiation therapy (IGRT); however, the required resources may not be feasible in a busy clinical setting. Less frequent adapting may still benefit cervical cancer patients due to large volume changes of the uterocervix of the treatment course. In this study, the dosimetry from different online adapt-on-demand schedules is compared.Materials/Methods: A retrospective cohort of 10 patients with cervical cancer treated with 260 fractions of definitive daily online adaptive radiotherapy was included. Plans with different adaptation schedules were simulated with adaptations weekly, every other week, once during treatment, and no adaptations (IGRT). These plans were applied to the synthetic CTs and contours generated during the patient's delivered daily adaptive workflow. The dosimetry of the weekly replan, every other week replan, once replan, and IGRT plans were compared using a paired t-test.Results: When compared to traditional IGRT plans, weekly and every other week ART plans had similar CTV coverage, but statistically significant improved sparing of OARs. Weekly and every other week ART had reduced bowel bag V40 by 1.57% and 1.41%, bladder V40 by 3.82% and 1.64%, rectum V40 by 8.49% and 7.50%, and bone marrow Dmean by 0.81% and 0.61%, respectively. Plans with a single adaptation had statistically significantly worse target coverage, and moderate improvements in OAR sparing. Of the 18 dose metrics evaluated, improvements were seen in 15 for weekly ART, 14 for every other week ART, and 10 for single ART plans when compared to IGRT. When every other week ART was compared to weekly ART, both plans had similar CTV coverage and OAR sparing with only small improvements in bone marrow dosimetry with weekly ART.Conclusions: This retrospective work compares different adapt on demand treatment schedules using data collected from patients treated with daily online adaptive radiotherapy. Results suggest weekly or every other week online ART is beneficial for reduced OAR dose compared to IGRT by exploiting the gradual changes in the uterocervix target volume.

MRI-guided Pelvic Radiation Therapy: A Primer for Radiologists.

Radiation therapy (RT) is a core pillar of oncologic treatment, and half of all patients with cancer receive this therapy as a curative or palliative treatment. The recent integration of MRI into the RT workflow has led to the advent of MRI-guided RT (MRIgRT). Using MRI rather than CT has clear advantages for guiding RT to pelvic tumors, including superior soft-tissue contrast, improved organ motion visualization, and the potential to image tumor phenotypic characteristics to identify the most aggressive or treatment-resistant areas, which can be targeted with a more focal higher radiation dose. Radiologists should be familiar with the potential uses of MRI in planning pelvic RT; the various RT techniques used, such as brachytherapy and external beam RT; and the impact of MRIgRT on treatment paradigms. Current clinical experience with and the evidence base for MRIgRT in the settings of prostate, cervical, and bladder cancer are discussed, and examples of treated cases are illustrated. In addition, the benefits of MRIgRT, such as real-time online adaptation of RT (during treatment) and interfraction and/or intrafraction adaptation to organ motion, as well as how MRIgRT can decrease toxic effects and improve oncologic outcomes, are highlighted. MRIgRT is particularly beneficial for treating mobile pelvic structures, and real-time adaptive RT for tumors can be achieved by using advanced MRI-guided linear accelerator systems to spare organs at risk. Future opportunities for development of biologically driven adapted RT with use of functional MRI sequences and radiogenomic approaches also are outlined. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Distributed Photovoltaic Installation Admittance Optimization: An End-to-End Approach

Distributed photovoltaic installation admittance capacity (PVAC) refers to the maximum capacity of photovoltaics that can be accommodated by the distribution system. The economic and reliable PVAC evaluation is critical for distributed photovoltaic capacity planning and configuration. However, lacking sufficient measurement devices and unachievable to full-scale perception of the practical distribution system impose challenges for PVAC assessment. This paper proposes an end-to-end approach to evaluate PVAC using the data from gird terminals and smart meters, where network topology and line parameters will be unnecessary. Specifically, an end-to-end power flow variables mapping model is proposed, which is essentially using the adaptive online deep learning algorithm to model and train the functional relationship of terminal active power, reactive power and voltage. Based on the above mapping relationship analysis, an end-user data driven photovoltaic installation admittance optimization model is established, which models the PVAC evaluation issue as a convex optimization model with differentiable objective function. Then a projected gradient descent algorithm based on adversarial attacks mechanism is proposed to solve the optimization model to obtain the maximum PVAC. Finally, the effectiveness of the proposed method is verified on IEEE 33-node, IEEE 123-node and actual distribution systems.

Treatment of Central Nervous System Tumors on Combination MR-Linear Accelerators: Review of Current Practice and Future Directions.

Magnetic resonance imaging (MRI) provides excellent visualization of central nervous system (CNS) tumors due to its superior soft tissue contrast. Magnetic resonance-guided radiotherapy (MRgRT) has historically been limited to use in the initial treatment planning stage due to cost and feasibility. MRI-guided linear accelerators (MRLs) allow clinicians to visualize tumors and organs at risk (OARs) directly before and during treatment, a process known as online MRgRT. This novel system permits adaptive treatment planning based on anatomical changes to ensure accurate dose delivery to the tumor while minimizing unnecessary toxicity to healthy tissue. These advancements are critical to treatment adaptation in the brain and spinal cord, where both preliminary MRI and daily CT guidance have typically had limited benefit. In this narrative review, we investigate the application of online MRgRT in the treatment of various CNS malignancies and any relevant ongoing clinical trials. Imaging of glioblastoma patients has shown significant changes in the gross tumor volume over a standard course of chemoradiotherapy. The use of adaptive online MRgRT in these patients demonstrated reduced target volumes with cavity shrinkage and a resulting reduction in radiation dose to uninvolved tissue. Dosimetric feasibility studies have shown MRL-guided stereotactic radiotherapy (SRT) for intracranial and spine tumors to have potential dosimetric advantages and reduced morbidity compared with conventional linear accelerators. Similarly, dosimetric feasibility studies have shown promise in hippocampal avoidance whole brain radiotherapy (HA-WBRT). Next, we explore the potential of MRL-based multiparametric MRI (mpMRI) and genomically informed radiotherapy to treat CNS disease with cutting-edge precision. Lastly, we explore the challenges of treating CNS malignancies and special limitations MRL systems face.

FACE-TO-FACE VS. ONLINE LEARNING in SCIENCE and ENGINEERING COURSES DURING COVID-19 EPOCH

ABSTRACT Background The new teaching scenario after COVID-19 results in the adoption of a complete online environment, providing a unique opportunity to compare in site (face-to-face) vs virtual (online) teaching. Due to the pandemic, practical activities have been replaced by written protocols supported with photos, video-recorded demos, linearsimulations, remote-controlled real laboratory instruments and so forth. Purpose This article describes the adaptation from in site to online teaching in science and engineering subjects in a Spanish University during COVID-19. Advantages and drawbacks, along with the process adopted (from education tools through virtualization tools) are comprehensively explained, including an inquiry-based learning (IBL) methodology for teaching laboratory courses. Sample Courses in Chemistry (cohort size of 100 students), Pharmacy (50 students), Industrial Electronics and Automation Engineering (110 and 30 students) at the University of Alcalá. Methods The outcomes of the teaching-learning process were evaluated via online tests and rubrics. The impact of online adaptation was assessed by comparing the marks of the different test and the dropout rate in the subjects with those obtained from groups of previous years in the face-to-face mode and feedback from the students. Anonymous survey was conducted to attain a comprehensive understanding of the students’ experiences with virtual learning. Results The developed approaches are beneficial for the teaching-learning process and lead to a higher number of students passing the subjects compared to the face-to-face modality. Some challenges students encountered were internet connection, managing workload andovercoming distractions. Nonetheless, most students benefited from the flexibility and better accessibility to the course material, timesaving and relax environment. Conclusion Our findings provided evidence in favor of remote learning. Both students and instructors regard the strategy as very positive. The new methodologies reported herein can be extended to online teaching courses beyond the COVID-19 scenario.