Optimal Data Collection Research Articles

Measuring the effects of ice thickness on resolution in single particle cryo-EM

Ice thickness is a critical parameter in single particle cryo-EM – too thin ice can break during imaging or exclude the sample of interest, while ice that is too thick contributes to more inelastic scattering that precludes obtaining high resolution reconstructions. Here we present the practical effects of ice thickness on resolution, and the influence of energy filters, accelerating voltage, or detector mode. We collected apoferritin data with a wide range of ice thicknesses on three microscopes with different instrumentation and settings. We show that on a 300 kV microscope, using a 20 eV energy filter slit has a greater effect on improving resolution in thicker ice; that operating at 300 kV instead of 200 kV accelerating voltage provides significant resolution improvements at an ice thickness above 150 nm; and that on a 200 kV microscope using a detector operating in super resolution mode enables good reconstructions for up to 200 nm ice thickness, while collecting in counting instead of linear mode leads to improvements in resolution for ice of 50–150 nm thickness. Our findings can serve as a guide for users seeking to optimize data collection or sample preparation routines for both single particle and in situ cryo-EM.We note that most in situ data collection is done on samples in a range of ice thickness above 150 nm so these results may be especially relevant to that community.

Feasibility of RESTORE: An online Acceptance and Commitment Therapy intervention to improve palliative care staff wellbeing

Background: Acceptance and Commitment Therapy is a form of Cognitive Behavioural Therapy which uses behavioural psychology, values, acceptance and mindfulness techniques to improve mental health and wellbeing. Acceptance and Commitment Therapy is efficacious in treating stress, anxiety and depression in a broad range of settings including occupational contexts where emotional labour is high. This approach could help palliative care staff to manage work-related stress and promote wellbeing. Aim: To develop, and feasibility test, an online Acceptance and Commitment Therapy intervention to improve wellbeing of palliative care staff. Design: A single-arm feasibility trial of an 8-week Acceptance and Commitment Therapy based intervention for staff, consisting of three online facilitated group workshops and five online individual self-directed learning modules. Data was collected via online questionnaire at four time-points and online focus groups at follow-up. Setting/participants: Participants were recruited from Marie Curie hospice and nursing services in Scotland. Results: Twenty five staff commenced and 23 completed the intervention (93%). Fifteen participated in focus groups. Twelve (48%) completed questionnaires at follow-up. Participants found the intervention enjoyable, informative and beneficial. There was preliminary evidence for improvements in psychological flexibility (Cohen’s d = 0.7) and mental wellbeing (Cohen’s d = 0.49) between baseline and follow-up, but minimal change in perceived stress, burnout or compassion satisfaction. Conclusion: Online Acceptance and Commitment Therapy for wellbeing is acceptable to palliative care staff and feasible to implement using Microsoft Teams in a palliative care setting. Incorporating ways to promote long-term maintenance of behaviour changes, and strategies to optimise data collection at follow-up are key considerations for future intervention refinement and evaluation.

Penerapan Metode Prototyping dalam Pengembangan Sistem Audit Internal Tingkat Program Studi (Studi Kasus: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Lampung)

PerBAN-PT No. 5 concerning APS Instruments, Accreditation is an assessment activity to determine the eligibility of a Study Program. The purpose of accreditation is to be able to determine the eligibility of a Study Program based on criteria that refer to the National Higher Education Standards and to guarantee the quality of the Study Program externally both in the academic and non-academic fields. Accreditation status is a benchmark for the performance of the study program concerned and describes the quality, efficiency, and relevance of an organized study program. The person in charge of assessing is called an auditor, the auditor needs information about the study program obtained from completing the accreditation assessment instrument by first making a study program self-evaluation document. In practice, staff must collect data along with evidence to the auditor to carry out an assessment in accordance with the standards set by BAN-PT, but study program staff in data collection still often experience data being scattered from one another, because the data collected is not small. This also sometimes results in less than optimal data collection to the auditor so that it can reduce the assessment. This problem can be overcome by making an Audit Information System so that data collection can be more organized. This information system was built using the prototype method with three stages, namely Listen to Customer, Build/Revise Mock Up, and Test Drive Mock-Up. This system uses the Laravel framework while for coding this system uses the PHP programming language. The testing method used is the Black Box Testing method with the Equivalence Partitioning type. The result of this study is a Web-Based Internal Audit Information System that can facilitate the internal auditing of a study program.

Energy Consumption Minimization for Data Collection From Wirelessly-Powered IoT Sensors: Session-Specific Optimal Design With DRL

Reliable and energy-efficient data collection from resource-limited sensors is essential to the success of future Internet of Things (IoT). In this article, we study the energy consumption minimization problem during the data collection from a generic wirelessly-powered sensor. Specifically, we determine the optimal data collection parameters, in terms of charging duration and charging power as well as sensor transmission rate, in real time according to the instantaneous channel condition while satisfying a certain latency constraint. For the scenario of ideal rate-adaptive transmission with linear energy harvesting, we derive closed-form expressions for optimal transmission parameters. We also establish the condition on channel quality for successful data collection under a latency constraint. For the more practical case of finite block-length transmission with nonlinear energy harvesting, we develop a deep reinforcement learning (DRL) solution for efficient online implementation. We also propose an online tuning scheme to cater for model inaccuracy and environment variation. The accuracy and effectiveness of our proposed approaches are verified by comparing with benchmark schemes. Our DRL-based approach has broad applicability and can solve other real-time optimal design problems in wireless communications.

Evaluating clinician emotion regulation during a serious illness conversation in oncology using multimodal assessment: A pilot study.

439 Background: Clinician emotion regulation (ER), self and the patient’s, likely moderates successful prognostic discussions with patients, yet challenges around its evaluation limit its investigation. We performed a pilot study to develop and assess an experimental framework that uses multimodal assessment (self-report, observer, and biometric data) to measure clinician ER during a simulated, serious illness conversation (SIC). Methods: We developed our experimental framework in four steps: 1) drafted a patient case and assessment framework; 2) refined the data collection strategy using a multidisciplinary research team; 3) trained our actor; and 4) iteratively piloted the case to optimize data collection. For the assessment, we conducted a cross-sectional, case series pilot study with physicians trained in SIC to assess its feasibility and acceptability, defined a priori as an enrollment rate >60% of approached clinicians, > 90% completion rate of survey items, < 20% missing data from wearable heart rate variability (HRV) sensors. To characterize clinician ER strategies, we analyzed the visit dialogue, physician interviews performed while watching the visit back, and physician SIC documentation generated post visit. We used a hybrid approach of inductive and deductive coding and theme development based on preexisting ER theory. Results: The development phase yielded two major modifications: 1) abandoned use of AppleWatch since it did not provide continuous HRV measurement; and 2) used telehealth with video given context of COVID-19 pandemic. We approached 12 physicians and 11 enrolled, of which 5 were female and 10 white, 5 were medical oncologists, and 6 specialty palliative care physicians. All participants completed all study survey items. The results of our three HRV sensors were as follows: Empatica E4 and Polar H10 met our pre-specified HRV collection in all 11 resting tasks and SIC encounters, and the Scoche R24 the benchmark in 7/11 resting tasks and 4/11 of simulated encounters. Preliminary qualitative analysis suggests investigators can characterize clinician use of intrapersonal and interpersonal ER strategies. Conclusions: The use of multimodal assessment of clinician ER in a simulated, telehealth SIC visit was acceptable and feasible.

Mapping and Recording of Ancient Shipwrecks by Using Marine Remote Sensing Techniques: Case Studies From Turkish Coasts

Within the context of the “Shipwreck Inventory Project of Turkey (SHIPT),” numerous acoustic remote sensing and photogrammetric surveys have been carried out along the coast of Turkey with the aim of detecting, mapping and documenting underwater cultural heritage. During the surveys, many ancient shipwreck sites have been discovered thanks to advanced technologies such as high-resolution side-scan sonar (SSS), sub-bottom profiler (SBP), and remotely operated vehicle (ROV). Once acoustic anomalies have been located, shipwreck sites have been verified by ROV and documented by 3D photogrammetric survey. Analysis of the collected data confirmed that the survey design and data acquisition parameters are the most important criteria for obtaining best quality image. Performing high-resolution remote sensing and photogrammetric survey for shipwrecks using optimal data collection techniques provides rapid results, decreasing time and budget and increasing the quantity of underwater cultural heritage that can be recorded. This paper discusses proper survey stages for the rapid, effective, high quality detection and recording of the shipwrecks using advanced technology. Acoustic images of shipwrecks comparing cargo, period, and form are also introduced in this paper, for the first time, which provide data on interpreting anomalies accurately for further exploration and monitoring surveys.

A joint optimized data collection algorithm based on dynamic cluster-head selection and value of information in UWSNs

Underwater wireless sensor networks (UWSNs) have become one of the enabling technologies for the development of future ocean monitoring systems, where autonomous underwater vehicles (AUVs) provide a very attractive way for the data collection of sensor nodes. The data obtained by nodes may have different importance and time sensitivity, which is recorded as the value of information (VoI). The VoI often decays with time. The obtained VoI based on AUV is closely related to the path length of AUV. A longer AUV path lead to more time to collect data, along which less VoI could be obtained finally. Moreover, as sensor nodes are battery-powered and therefore energy-constrained, selecting the nodes with larger residual energy as the AUV path nodes helps to prolong the network lifetime. Therefore, we focus on AUV path planing with the objective of maximizing the lifetime of network while satisfying the constraint on VoI. In this paper, considering the node energy and VoI, we provide an Integer Linear Programming (ILP) model to find an optimal path of AUV. Then, a joint optimized data collection algorithm (JODA) that taking in account the influence of residual energy of nodes and VoI is proposed, which reduces the computational complexity. In our experiments the effectiveness of JODA has been proved by comparing its performance with the theoretical value determined by the ILP model. We also compare the performance of JODA with that of other data collection algorithms, namely TSP, GAAP and EEDA. The simulation results demonstrate that the JODA always outperforms all other algorithms in terms of the total VoI and network lifetime.

Making the best use of clinical datasets: with examples from urogynaecology

Key content Healthcare professionals are often asked to collect data and contribute to national and international databases. It is important for us to understand the role data structure has in data performance, particularly focusing on data analysis. This article explores the principles of the role of data, the law and the ethics around its use and its interpretation. The importance of good data in health care underpins the principles of the International Consortium on Health Outcome Measures (ICHOM). Learning objectives To understand concepts of database design, what the perfect database looks like and the challenges to achieving this, as well as principles of extracting and interrogating data. To be able to define questions to obtain valid data and know how to optimise data collection. To understand the governing principles of the ICHOM. Ethical issues How do ethical guidelines, the Caldicott Principles and the General Data Protection Regulation (GDPR) apply to clinical datasets? Who owns data?

Introductory Curriculum to Health Disparities for Medical Physics Trainees: A Pilot Experience

PurposeThe COVID-19 pandemic brought to the forefront longstanding and pervasive health disparities (HD) in the United States that are driven by underlying structural inequities rooted in racism and intersecting systems of power. It is imperative that medical physics training programs increase their commitment and investment in teaching trainees about HD, especially as the profession becomes more patient-facing. To address this current training gap, we designed an introductory HD curriculum for medical physics graduate students and residents.MethodsA HD scholar and medical physicist collaborated to design this course. The curriculum consisted of four weekly 1.5-hour synchronous online sessions. Sessions were developed based on transformative learning theory and involved didactic lectures, case studies, large and small group discussions, and reflection exercises. Session topics included social determinants of equity, structural racism, implicit bias, public outreach, and critical reflection. Participants were asked to fill out pre-post surveys containing open- and close-ended questions for each session and the overall course to evaluate the curriculum's impact on participants. Due to the relevance and timeliness of this topic, trainees were encouraged to attend any part of the course as getting exposure to at least some HD training was prioritized over having a consistent course cohort for optimal data collection.ResultsFifteen trainees attended at least part of the course with 8-11 attendees/session. Most participants reported that weekly sessions increased their feelings of competence to explain the relevance of HD to their role in medical physics (4/7), address mistrust, bias, and stereotyping during patient-provider encounters (6/10), engage in critical reflection (7/8), and design public engagement strategies to reduce HD (5/5). Among participants that completed a pre-post survey for the overall course (N=4), 75% reported they will likely/very likely explore issues related to HD in their future education, research, and/or practice. All would recommend this course to colleagues noting satisfaction with topics, atmosphere to discuss sensitive issues, virtual format, activities, and facilitators.DiscussionThe course was well-attended despite being outside of program requirements, which showed there was interest in the subject. Topics presented resonated with participants and were viewed as timely and relevant to their practice. The implementation of this course shows that it is possible to successfully incorporate HD instruction into the medical physics curriculum in an introductory manner. More robust and consistent data collection is necessary to fully assess the impact of the course on participants.

Geometric Algorithm for Finding Time-Sensitive Data Gathering Path in Energy Harvesting Sensor Networks

To perform large-scale monitoring of sensitive events, energy harvesting wireless sensor network is considered where a mobile data sink <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MS$ </tex-math></inline-formula> collects data while travelling on a fixed path <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{ms}$ </tex-math></inline-formula> . The sensor nodes sense environmental data continuously at a pre-specified rate. The sensors close to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{ms}$ </tex-math></inline-formula> are referred as gateways. Sensors forward their data to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MS$ </tex-math></inline-formula> through the gateways. In practice, the usage of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$MS$ </tex-math></inline-formula> is not suitable for time-sensitive applications due to its long data gathering delay. Time-bound data gathering for path constrained environment is not accounted in literature. We aim at finding energy-efficient maximum data gathering sub-path for the MS for a given data gathering period <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> . To deal with the problem a novel optimal deterministic data collection sub-path finding algorithm is proposed which is based on the geometric properties of the sensors’ communication disks and the data gathering path. It maximizes the data collection and reduces the energy consumption by jointly optimizing the data gathering sub-path selection and the data forwarding path optimization. The performance of the proposed algorithm is compared with an existing baseline algorithm DDGA and a heuristic algorithm H-DGSPF. The simulation results show that our proposed algorithm outperforms DDGA and H-DGSPF in terms of data collection, data delivery success ratio, and energy consumption

Assessment of the configuration accuracy of two UWB local positioning systems, six antennae and 18 Hz vs. eight antennae and 33 Hz, to measure movement patterns in physical education

The use of valid, accurate and reliable systems is decisive for ensuring optimal data collection and correct interpretation of values. Among other factors, it seems that sampling rate and the number of nodes determine data accuracy using LPS. Thus, the aim of this study was to assess and compare the accuracy of two UWB configurations (i.e. UWB6_18HZ: 6 antennae and 18 Hz capacity vs. UWB8_33HZ: 8 antennae and 33 Hz capacity) to measure locomotion on court. A healthy and well-trained former soccer player (age: 38 years, mass: 76.34 kg, height 1.85 m) ran 9 m along the middle line of a volleyball court (n=10, samples= 424). UWB6_18HZ and UWB8_33HZ configurations have shown high accuracy in sport movement patterns monitoring (mean difference between actual measurements and UWB = 0.014±0.03m for UWB6_18HZ and 0.013±0.03m for UWB8_33HZ), although UWB8_33HZ presented higher accuracy (8.99±0.03 m) than UWB6_18HZ (8.94±0.03). Therefore, manufacturers should consider future developments based on sampling rates and number of antennae installed around the court.

Investigation of ground-penetrating radar defect detection capabilities, influence of moisture content, and optimal data collection orientation in condition assessments of concrete bridge decks

Eighteen specimens with simulated bridge deck defects were tested in different moisture contents and directions using ground-penetrating radar (GPR). When analyzing reflection amplitudes from the top reinforcement mat, it was found that GPR could not effectively locate delaminations, vertical cracks, concrete deterioration, poorly-constructed concrete, and clay-filled voids. Nonetheless, GPR could detect chloride-induced corrosion and water-filled voids in the cover layer. Hypothesis testing revealed that an increase in moisture content generally causes an increase in variance and a decrease in mean of reflection amplitudes, and reinforcement intersections decrease the mean of data collected while oriented perpendicular to the bottom bar in the top reinforcement mat.

Characterizing Dysarthria Diversity for Automatic Speech Recognition: A Tutorial from the Clinical Perspective.

Despite significant advancements in automatic speech recognition (ASR) technology, even the best performing ASR systems are inadequate for speakers with impaired speech. This inadequacy may be, in part, due to the challenges associated with acquiring a sufficiently diverse training sample of disordered speech. Speakers with dysarthria, which refers to a group of divergent speech disorders secondary to neurologic injury, exhibit highly variable speech patterns both within and across individuals. This diversity is currently poorly characterized and, consequently, difficult to adequately represent in disordered speech ASR corpora. In this paper, we consider the variable expressions of dysarthria within the context of established clinical taxonomies (e.g., Darley, Aronson, and Brown dysarthria subtypes). We also briefly consider past and recent efforts to capture this diversity quantitatively using speech analytics. Understanding dysarthria diversity from the clinical perspective and how this diversity may impact ASR performance could aid in (1) optimizing data collection strategies for minimizing bias; (2) ensuring representative ASR training sets; and (3) improving generalization of ASR across users and performance for difficult-to-recognize speakers. Our overarching goal is to facilitate the development of robust ASR systems for dysarthric speech using clinical knowledge.

A Combining Strategy of Energy Replenishment and Data Collection in Wireless Sensor Networks

In wireless rechargeable sensor networks, the mobile vehicle (MV) combining energy replenishment and data collection often collects all sensed data, which may lead to data overflow and larger delay. To avoid these defects, we propose a novel data collection policy, where the MV acts as an auxiliary mobile data collector to gather buffered data as much as possible and the base station receives and processes remaining sensed data. As the mobile vehicle acts the data collector and the mobile charger simultaneously, how to scheduling it to minimize energy consumption and improve data transmission performance without network lifetime degradation is a challenge. To address this, some algorithms are proposed under the new policy. First, according to the residual lifetime of nodes, a classic charging algorithm is utilized to dispatch the MV to charge nodes to maximize the network lifetime. After determining the trajectory of the MV, considering data type and buffer state, we divide data collection into two sub-problems: how to collect sensed data from neighbor clusters and roadside clusters respectively. To assign optimal data collection time and select best roadside clusters, some algorithms are proposed which can maximize the sum of normalized saved energy of neighbor clusters and roadside clusters. Finally, we evaluate the performance by simulations. The results show that our proposed algorithms can significantly improve the network performance, such as the delay, data overflow, energy consumption, network lifetime, and throughput.

Optimum Data Transmission Allocation in Multimodal Communication of Underwater Sensor Networks

Multimodal communication (MC) plays a crucial role in the Underwater Sensor Networks (UWSNs). The appropriate resource allocation of MC can significantly optimize the UWSNs performance. This letter investigates the optimum data transmission allocation in the underwater MC. Considering the fundamental MC tradeoff, the MC energy efficiency model is established and formulated as a mixed integer nonlinear programming problem with the provable convex optimization properties. Then this letter designs two schemes for the various data amounts to optimize the optical-acoustic transmission ratio and an optimized data collection algorithm to save the MC energy. Simulation results validate the effectiveness of this letter.

586 Graft Loss: 5-year Review of a Single Burn Center’s Experience with an Institutional Grading Scale

IntroductionDespite increasing attention on provisioning quality burn care, there continues to be no consensus definition of burn “graft loss”, nor a uniformly adopted scale to grade severity. The American Burn Association Burn Quality Improvement Project metric is a binary code identifying only "failure to achieve >95% autograft take." We introduced an institutional graft loss scale in 2014 for quality improvement and reported initial results and interrater reliability. With additional data now available for analysis, a secondary review was undertaken.MethodsAll patients with graft loss were identified on departmental Morbidity and Mortality (M&M) reports between 7/2016-7/2021. Graft loss grades were assigned during clinical care per institutional scale (Table 1). Non-burn acute surgical wounds that underwent skin grafting were included, but chronic non-healing wounds were excluded. Data abstracted included demographics, medical history, injury details, surgical procedures, graft loss, and lengths of stay. In situations where the graft loss grade recorded on M&M documentation was discordant with the grade documented in the medical record, the grade assigned at the later point in time was used for analysis.ResultsGraft loss was noted in 260 instances for 200 patients. Mean age was 51.4(±7.5y)years. The majority were male (60.7%) and African American (48.0%). Smoking (26.0%) and diabetes (37.5%) were prevalent. Overall mortality was 4% (8/200). Graft loss percentages by grade are 28% (grade I), 28% (grade II), 13% (grade III), and 22% (grade IV). An additional 9% were deemed either unknown or due to a technical error. Overall reporting of graft loss grades improved over time as surgeons became more familiar with the scale. Reported percentages of Grades I and II graft loss also increased over time, indicating better compliance with the overall goals of this quality improvement project.ConclusionsA graft loss grading scale can be applied to track split thickness autografting outcomes among a diverse group of surgeons. Regular reporting of low grade graft loss needs to be done to understand the complete distribution of graft loss after surgical intervention, including documentation of allograft and other skin substitute loss. Improved reporting in the medical record can optimize data collection for quality assessment purposes.

Combining farm and household surveys with modelling approaches to improve post-harvest loss estimates and reduce data collection costs

While there is growing awareness of the issue of food losses at the political level, official post-harvest loss data for informing policymaking and reporting on SDG Indicator 12.3.1. (a) Food Loss Index is scarce. Representative sample-based surveys are necessary to obtain information on on-farm losses at the country level, but due to the issue’s complexity, a loss module covering several key questions is needed. One main strategy proposed by the 50x2030 Initiative for optimizing data collection is sub-sampling for some of the survey modules. This paper examines whether modelling approaches can be combined with sub-sampling to improve harvest and post-harvest loss estimates and allow for further sample and cost reduction. The paper first presents the loss models generated on four selected surveys conducted in Malawi, Zimbabwe, and Nigeria, which were built using the Classification and Regression Tree (CART) method. The performance of each model is assessed for different sizes of sub-samples to improve the sample-based estimates, either by model-based estimates or by model-based imputation. The research concludes that the model-based estimates improve the loss estimates of the sub-samples due to post-stratification implied in the CART method, whereby they can constitute a cost-effective complement to sub-sampling strategies, while model-based imputations should only be used on a reduced number of missing observations. The models perform best when the survey invests in obtaining more detailed on-farm loss data and considers some key variables identified as relevant for on-farm loss models. Sub-sampling allows for investment in more detailed questionnaires and some considerations are derived for its design.

Semi-Automated, Object-Based Tomography of Dislocation Structures.

The characterization of the three-dimensional arrangement of dislocations is important for many analyses in materials science. Dislocation tomography in transmission electron microscopy is conventionally accomplished through intensity-based reconstruction algorithms. Although such methods work successfully, a disadvantage is that they require many images to be collected over a large tilt range. Here, we present an alternative, semi-automated object-based approach that reduces the data collection requirements by drawing on the prior knowledge that dislocations are line objects. Our approach consists of three steps: (1) initial extraction of dislocation line objects from the individual frames, (2) alignment and matching of these objects across the frames in the tilt series, and (3) tomographic reconstruction to determine the full three-dimensional configuration of the dislocations. Drawing on innovations in graph theory, we employ a node-line segment representation for the dislocation lines and a novel arc-length mapping scheme to relate the dislocations to each other across the images in the tilt series. We demonstrate the method for a dataset collected from a dislocation network imaged by diffraction-contrast scanning transmission electron microscopy. Based on these results and a detailed uncertainty analysis for the algorithm, we discuss opportunities for optimizing data collection and further automating the method.

Accuracy Improvement Method Based on Characteristic Database Classification for IMRT Dose Prediction in Cervical Cancer: Scientifically Training Data Selection.

PurposeConsistent training and testing datasets can lead to good performance for deep learning (DL) models. However, a large high-quality training dataset for unusual clinical scenarios is usually not easy to collect. The work aims to find optimal training data collection strategies for DL-based dose prediction models.Materials and MethodsA total of 325 clinically approved cervical IMRT plans were utilized. We designed comparison experiments to investigate the impact of (1) beam angles, (2) the number of beams, and (3) patient position for DL dose prediction models. In addition, a novel geometry-based beam mask generation method was proposed to provide beam setting information in the model training process. What is more, we proposed a new training strategy named “full-database pre-trained strategy”.ResultsThe model trained with a homogeneous dataset with the same beam settings achieved the best performance [mean prediction errors of planning target volume (PTV), bladder, and rectum: 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%] compared with that trained with large mixed beam setting plans (mean errors of PTV, bladder, and rectum: 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4%). A homogeneous dataset is more accessible to train an accurate dose prediction model (mean errors of PTV, bladder and rectum: 2.2 ± 0.15%, 5 ± 2.1%, and 3.23 ± 1.53%) than a non-homogeneous one (mean errors of PTV, bladder and rectum: 2.55 ± 0.12%, 6.33 ± 2.46%, and 4.76 ± 2.91%) without other processing approaches. The added beam mask can constantly improve the model performance, especially for datasets with different beam settings (mean errors of PTV, bladder, and rectum improved from 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4% to 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%).ConclusionsA consistent dataset is recommended to form a patient-specific IMRT dose prediction model. When a consistent dataset is not accessible to collect, a large dataset with different beam angles and a training model with beam information can also get a relatively good model. The full-database pre-trained strategies can rapidly form an accuracy model from a pre-trained model. The proposed beam mask can effectively improve the model performance. Our study may be helpful for further dose prediction studies in terms of training strategies or database establishment.

Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work

In the face of a continuous increase in the number of tourists in the Alps, the associated traffic volumes, and the resulting negative externalities, there is an urgent need to design policies capable of managing tourist traffic efficiently and to invest in transport systems and infrastructure wisely, given the limited financial, spatial and environmental resources. Unfortunately, while there is a considerable research interest in long-distance travel and arrival/departure patterns of tourists, research on tourist mobility during the stay at the destination is almost non-existent. This prevents policy-makers from making informed decisions backed by scientific evidence. The paper presents a data collection study that contributes to filing this gap. This ‘data paper’ reports on the motivation, design and administration of a bespoke travel-activity survey of tourists at vacation destinations. It informs other researchers about the encountered difficulties and helps them optimize data collection effort in their future studies. Besides the contribution to survey methodology, the paper reports on a rare and policy-relevant dataset, provides descriptive results, and thereby contributes to evidence-based design of transport policies in tourist regions. With this paper, we also want to inspire and invite researchers to conduct further quantitative and modeling work in this under-studied field.