A Python tool to determine the thickness of the hydrate layer around clinker grains using SEM-BSE images.

To accurately simulate the hydration process of cementitious materials, understanding the growth rate of C-S-H layers around clinker grains is crucial. Nonetheless, the thickness of the hydrate layer shows substantial variation around individual grains, depending on their surrounding. Consequently, it is not feasible to measure hydrate layers manually in a reliable and reproducible manner. To address this challenge, a software has been developed to statistically determine the C-S-H thickness, requiring minimal manual interventions for thresholding and for setting limits like particle size or circularity. This study presents a tool, which automatically identifies suitable clinker grains and and perform statistical measurements of their hydrate layer up to a specimen age of 28 days. The findings reveal a significant increase in the C-S-H layer, starting from 0.45  µ m $\umu {\rm {m}}$ after 1 day and reaching 3.04  µ m $\umu {\rm {m}}$ after 28 days. However, for older specimens, the measurement of the C-S-H layer was not feasible due to limited pore space and clinker grains.

Feasibility Study: Towards a Robot-Assisted Gait Training in Ophthalmological Rehabilitation.

The idea of using mobile assistance robots for gait training in rehabilitation has been increasingly explored in recent years due to the associated benefits. This paper describes how the previous results of research and praxis on gait training with a mobile assistance robot in orthopedic rehabilitation can be transferred to ophthalmic-related orientation and mobility training for blind and visually impaired people. To this end, the specific requirements for such orientation and mobility training are presented from a therapeutic perspective. Using sensory data, it is investigated how the analysis of training errors can be automated and transferred back to the training person. These pre-examinations are the prerequisite for any form of robot-assisted mobile gait training in ophthamological rehabilitation, which does not exist so far and which is expected to be of great benefit to these patients.

OHO: A Multi-Modal, Multi-Purpose Dataset for Human-Robot Object Hand-Over.

In the context of collaborative robotics, handing over hand-held objects to a robot is a safety-critical task. Therefore, a robust distinction between human hands and presented objects in image data is essential to avoid contact with robotic grippers. To be able to develop machine learning methods for solving this problem, we created the OHO (Object Hand-Over) dataset of tools and other everyday objects being held by human hands. Our dataset consists of color, depth, and thermal images with the addition of pose and shape information about the objects in a real-world scenario. Although the focus of this paper is on instance segmentation, our dataset also enables training for different tasks such as 3D pose estimation or shape estimation of objects. For the instance segmentation task, we present a pipeline for automated label generation in point clouds, as well as image data. Through baseline experiments, we show that these labels are suitable for training an instance segmentation to distinguish hands from objects on a per-pixel basis. Moreover, we present qualitative results for applying our trained model in a real-world application.

Cluster Randomized Pragmatic Clinical Trial Testing Behavioral Economic Implementation Strategies to Improve Tobacco Treatment for Patients With Cancer Who Smoke.

PURPOSE: Few cancer centers systematically engage patients with evidence-based tobacco treatment despite its positive effect on quality of life and survival. Implementation strategies directed at patients, clinicians, or both may increase tobacco use treatment (TUT) within oncology. METHODS: We conducted a four-arm cluster-randomized pragmatic trial across 11 clinical sites comparing the effect of strategies informed by behavioral economics on TUT engagement during oncology encounters with cancer patients. We delivered electronic health record (EHR)-based nudges promoting TUT across four nudge conditions: patient only, clinician only, patient and clinician, or usual care. Nudges were designed to counteract cognitive biases that reduce TUT engagement. The primary outcome was TUT penetration, defined as the proportion of patients with documented TUT referral or a medication prescription in the EHR. Generalized estimating equations were used to estimate the parameters of a linear model. RESULTS: From June 2021 to July 2022, we randomly assigned 246 clinicians in 95 clusters, and collected TUT penetration data from their encounters with 2,146 eligible patients who smoke receiving oncologic care. Intent-to-treat (ITT) analysis showed that the clinician nudge led to a significant increase in TUT penetration versus usual care (35.6% v 13.5%; OR = 3.64; 95% CI, 2.52 to 5.24; P < .0001). Completer-only analysis (N = 1,795) showed similar impact (37.7% clinician nudge v 13.5% usual care; OR = 3.77; 95% CI, 2.73 to 5.19; P < .0001). Clinician type affected TUT penetration, with physicians less likely to provide TUT than advanced practice providers (ITT OR = 0.67; 95% CI, 0.51 to 0.88; P = .004). CONCLUSION: EHR nudges, informed by behavioral economics and aimed at oncology clinicians, appear to substantially increase TUT penetration. Adding patient nudges to the implementation strategy did not affect TUT penetration rates.

Laser-Based Door Localization for Autonomous Mobile Service Robots.

For autonomous mobile service robots, closed doors that are in their way are restricting obstacles. In order to open doors with on-board manipulation skills, a robot needs to be able to localize the door's key features, such as the hinge and handle, as well as the current opening angle. While there are vision-based approaches for detecting doors and handles in images, we concentrate on analyzing 2D laser range scans. This requires less computational effort, and laser-scan sensors are available on most mobile robot platforms. Therefore, we developed three different machine learning approaches and a heuristic method based on line fitting able to extract the required position data. The algorithms are compared with respect to localization accuracy with help of a dataset containing laser range scans of doors. Our LaserDoors dataset is publicly available for academic use. Pros and cons of the individual methods are discussed; basically, the machine learning methods could outperform the heuristic method, but require special training data when applied in a real application.

Few-Shot Object Detection: A Comprehensive Survey.

Humans are able to learn to recognize new objects even from a few examples. In contrast, training deep-learning-based object detectors requires huge amounts of annotated data. To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection (FSOD) aims to learn from few object instances of new categories in the target domain. In this survey, we provide an overview of the state of the art in FSOD. We categorize approaches according to their training scheme and architectural layout. For each type of approach, we describe the general realization as well as concepts to improve the performance on novel categories. Whenever appropriate, we give short takeaways regarding these concepts in order to highlight the best ideas. Eventually, we introduce commonly used datasets and their evaluation protocols and analyze the reported benchmark results. As a result, we emphasize common challenges in evaluation and identify the most promising current trends in this emerging field of FSOD.

Clotting Propensity in Trauma Patients According to Marijuana Use: A Descriptive Analysis.

Marijuana use has been reported to promote hypercoagulable states among trauma patients, particularly respecting venous thromboembolism (VTE), a major contributor to morbidity and mortality in patients sustaining traumatic injury. We sought to investigate this further through a retrospective, single institutional study performed from January 2018 through June 2021, utilizing data from patients presenting to a Level 1 Trauma Center as a trauma activation. We observed less frequent VTE development in the marijuana-positive group compared to the marijuana-negative group, with patient thromboelastography revealing a longer mean R-time in the marijuana-positive group. Overall occurrence of VTE was too low for definitive conclusions, but a trend toward reduction in VTE frequency among marijuana users compared to nonusers was noted. More studies with larger populations and more VTE occurrences are needed to confirm a potential correlation between marijuana use and VTE development.

Digital media in intergenerational communication: Status quo and future scenarios for the grandparent-grandchild relationship.

Communication technologies play an important role in maintaining the grandparent-grandchild (GP-GC) relationship. Based on Media Richness Theory, this study investigates the frequency of use (RQ1) and perceived quality (RQ2) of established media as well as the potential use of selected innovative media (RQ3) in GP-GC relationships with a particular focus on digital media. A cross-sectional online survey and vignette experiment were conducted in February 2021 among N = 286 university students in Germany (mean age 23 years, 57% female) who reported on the direct and mediated communication with their grandparents. In addition to face-to-face interactions, non-digital and digital established media (such as telephone, texting, video conferencing) and innovative digital media, namely augmented reality (AR)-based and social robot-based communication technologies, were covered. Face-to-face and phone communication occurred most frequently in GP-GC relationships: 85% of participants reported them taking place at least a few times per year (RQ1). Non-digital established media were associated with higher perceived communication quality than digital established media (RQ2). Innovative digital media received less favorable quality evaluations than established media. Participants expressed doubts regarding the technology competence of their grandparents, but still met innovative media with high expectations regarding improved communication quality (RQ3). Richer media, such as video conferencing or AR, do not automatically lead to better perceived communication quality, while leaner media, such as letters or text messages, can provide rich communication experiences. More research is needed to fully understand and systematically improve the utility, usability, and joy of use of different digital communication technologies employed in GP-GC relationships.

Can Communication Technologies Reduce Loneliness and Social Isolation in Older People? A Scoping Review of Reviews.

BACKGROUND: Loneliness and social isolation in older age are considered major public health concerns and research on technology-based solutions is growing rapidly. This scoping review of reviews aims to summarize the communication technologies (CTs) (review question RQ1), theoretical frameworks (RQ2), study designs (RQ3), and positive effects of technology use (RQ4) present in the research field. METHODS: A comprehensive multi-disciplinary, multi-database literature search was conducted. Identified reviews were analyzed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. A total of N = 28 research reviews that cover 248 primary studies spanning 50 years were included. RESULTS: The majority of the included reviews addressed general internet and computer use (82% each) (RQ1). Of the 28 reviews, only one (4%) worked with a theoretical framework (RQ2) and 26 (93%) covered primary studies with quantitative-experimental designs (RQ3). The positive effects of technology use were shown in 55% of the outcome measures for loneliness and 44% of the outcome measures for social isolation (RQ4). CONCLUSION: While research reviews show that CTs can reduce loneliness and social isolation in older people, causal evidence is limited and insights on innovative technologies such as augmented reality systems are scarce.

Clinical characterization of familial hypercholesterolemia due to an amish founder mutation in Apolipoprotein B.

BACKGROUND: Familial hypercholesterolemia (FH) due to a founder variant in Apolipoprotein B (ApoBR3500Q) is reported in 12% of the Pennsylvania Amish community. By studying a cohort of ApoBR3500Q heterozygotes and homozygotes, we aimed to characterize the biochemical and cardiac imaging features in children and young adults with a common genetic background and similar lifestyle. METHODS: We employed advanced lipid profile testing, carotid intima media thickness (CIMT), pulse wave velocity (PWV), and peripheral artery tonometry (PAT) to assess atherosclerosis in a cohort of Amish ApoBR3500Q heterozygotes (n = 13), homozygotes (n = 3), and their unaffected, age-matched siblings (n = 9). ApoBR3500Q homozygotes were not included in statistical comparisons. RESULTS: LDL cholesterol (LDL-C) was significantly elevated among ApoBR3500Q heterozygotes compared to sibling controls, though several ApoBR3500Q heterozygotes had LDL-C levels in the normal range. LDL particles (LDL-P), small, dense LDL particles, and ApoB were also significantly elevated among subjects with ApoBR3500Q. Despite these differences in serum lipids and particles, CIMT and PWV were not significantly different between ApoBR3500Q heterozygotes and controls in age-adjusted analysis. CONCLUSIONS: We provide a detailed description of the serum lipids, atherosclerotic plaque burden, vascular stiffness, and endothelial function among children and young adults with FH due to heterozygous ApoBR3500Q. Fasting LDL-C was lower than what is seen with other forms of FH, and even normal in several ApoBR3500Q heterozygotes, emphasizing the importance of cascade genetic testing among related individuals for diagnosis. We found increased number of LDL particles among ApoBR3500Q heterozygotes but an absence of detectable atherosclerosis.

Quantification of cracks in concrete thin sections considering current methods of image analysis.

Image analysis is used in this work to quantify cracks in concrete thin sections via modern image processing. Thin sections were impregnated with a yellow epoxy resin, to increase the contrast between voids and other phases of the concrete. By the means of different steps of pre-processing, machine learning and python scripts, cracks can be quantified in an area of up to 40 cm2 . As a result, the crack area, lengths and widths were estimated automatically within a single workflow. Crack patterns caused by freeze-thaw damages were investigated. To compare the inner degradation of the investigated thin sections, the crack density was used. Cracks in the thin sections were measured manually in two different ways for validation of the automatic determined results. On the one hand, the presented work shows that the width of cracks can be determined pixelwise, thus providing the plot of a width distribution. On the other hand, the automatically measured crack length differs in comparison to the manually measured ones.

Neonatal Fever in the COVID-19 Pandemic: Odds of a Serious Bacterial Infection.

OBJECTIVES: A sepsis workup is recommended in young infants 56 days or younger with fever to rule out a serious bacterial infection (SBI). Given the reduction in non-severe acute respiratory syndrome - coronavirus 2 viral infections observed in multiple studies during the coronavirus diseases 2019 (COVID-19) pandemic, we sought to determine if the reduction in viral infections led to a change in the incidence of SBI in this vulnerable patient population. METHODS: We performed a multicenter, retrospective study of infants 56 days or younger presenting with fever to emergency departments of 6 community hospitals. We compared the incidence of SBIs, viral meningitis, and viral bronchiolitis during March 2020 to February 2021 (pandemic year) with the same calendar months in the 2 preceding years (prepandemic years). RESULTS: From March 2018 to February 2021, 543 febrile infants presented to the emergency departments, 95 during the pandemic year (March 2020 to February 2021) compared with 231 and 217 in the prepandemic years (March 2018 to February 2019 and March 2019 to February 2020, respectively).During the pandemic year, 28.4% of infants (27 of 95) were diagnosed with an SBI compared with 11.7% and 6.9% (P < 0.001) in the prepandemic years (27 of 231 and 15 of 217, respectively). Five patients were diagnosed with bacterial meningitis over the 3-year period, 4 of them during the pandemic year (4 of 95 [4.2%]). Positivity for viral cerebrospinal fluid polymerase chain reaction during the pandemic year was 6.4% (3 of 47) compared with 20.8% (25 of 120) and 20.4% (23 of 113) in prepandemic years (P = 0.070). During the pandemic year, 2.1% (2 of 95) febrile young infants were admitted with a comorbid diagnosis of bronchiolitis compared with 4.3% and 6.0% in the prepandemic years (P = 0.310). CONCLUSIONS: The COVID-19 pandemic led to an increase in the incidence of SBIs in febrile infants 56 days or younger, likely a result of reduction in non-severe acute respiratory syndrome - coronavirus 2 viral infections. Greater vigilance is thus warranted in the evaluation of febrile infants during the COVID-19 pandemic.

Agricultural Injury Surveillance Using a Regional Trauma Registry.

BACKGROUND: The burden of traumatic injury among workers in agriculture is substantial. Surveillance can inform injury prevention efforts to reduce farmworkers' risk. We posited that the regional trauma registry can provide surveillance for agricultural injury requiring trauma-center care. METHODS: The Northeast Texas regional trauma registry was queried for patients injured in agricultural settings during 2016-2019 occurring in the 23,580 square mile study area subdivided into 219 US Census Zip Code Tract Area (ZCTA). Population at risk was estimated from the 2017 Census of Agriculture. Kuldorff's SaTScan identified case hot spots. A multivariable, geographically weighted regression model was fit for cases/1000 workers. RESULTS: In total, 273 cases occurred, (mean 68 cases per year [95% confidence interval 55.1-80.9]) among 96 ZCTA. The mean injury rate was 3.9 (95% confidence interval 3.4-4.3) cases per 1000 farmworkers. Animals and farm machinery were the most common injury mechanisms, 52.0% and 20.9%, respectively. Trauma ZCTA demonstrated more farms (median 170 versus 95.5, P < 0.001), greater farm acreage (53,900 acres versus 32,800, P = 0.004), and higher median total product sales ($6.5 million versus $3.9 million, P < 0.001). Six hot spots were identified with relative risks from 2.85 to 5.31. The multivariable model of cases/1000 workers demonstrated significant associations with workers per ZCTA (a mean ß-coefficient of 0.004 with P values <0.05 in 145 of 219 [66.5%] ZCTA) and percent rural population (ß = -6.62, P values <0.05 in 76.1% of ZCTA). CONCLUSIONS: Regional trauma registry data, combined with census data and spatial analyses, can identify actionable geographic areas of high agriculture-related injury risk.

Comparing Smoking Cessation Interventions among Underserved Patients Referred for Lung Cancer Screening: A Pragmatic Trial Protocol.

Smoking burdens are greatest among underserved patients. Lung cancer screening (LCS) reduces mortality among individuals at risk for smoking-associated lung cancer. Although LCS programs must offer smoking cessation support, the interventions that best promote cessation among underserved patients in this setting are unknown. This stakeholder-engaged, pragmatic randomized clinical trial will compare the effectiveness of four interventions promoting smoking cessation among underserved patients referred for LCS. By using an additive study design, all four arms provide standard "ask-advise-refer" care. Arm 2 adds free or subsidized pharmacologic cessation aids, arm 3 adds financial incentives up to $600 for cessation, and arm 4 adds a mobile device-delivered episodic future thinking tool to promote attention to long-term health goals. We hypothesize that smoking abstinence rates will be higher with the addition of each intervention when compared with arm 1. We will enroll 3,200 adults with LCS orders at four U.S. health systems. Eligible patients include those who smoke at least one cigarette daily and self-identify as a member of an underserved group (i.e., is Black or Latinx, is a rural resident, completed a high school education or less, and/or has a household income <200% of the federal poverty line). The primary outcome is biochemically confirmed smoking abstinence sustained through 6 months. Secondary outcomes include abstinence sustained through 12 months, other smoking-related clinical outcomes, and patient-reported outcomes. This pragmatic randomized clinical trial will identify the most effective smoking cessation strategies that LCS programs can implement to reduce smoking burdens affecting underserved populations. Clinical trial registered with clinicaltrials.gov (NCT04798664). Date of registration: March 12, 2021. Date of trial launch: May 17, 2021.

Reconstruction of calcium silicate hydrates using multiple 2D and 3D imaging techniques: Light microscopy, µ-CT, SEM, FIB-nT combined with EDX.

This study demonstrates the application and combination of multiple imaging techniques [light microscopy, micro-X-ray computer tomography (µ-CT), scanning electron microscopy (SEM) and focussed ion beam - nano-tomography (FIB-nT)] to the analysis of the microstructure of hydrated alite across multiple scales. However, by comparing findings with mercury intrusion porosimetry (MIP), it becomes obvious that the imaged 3D volumes and 2D images do not sufficiently overlap at certain scales to allow a continuous quantification of the pore size distribution (PSD). This can be overcome by improving the resolution and increasing the measured volume. Furthermore, results show that the fibrous morphology of calcium-silicate-hydrates (C-S-H) phases is preserved during FIB-nT. This is a requirement for characterisation of nano-scale porosity. Finally, it was proven that the combination of FIB-nT with energy-dispersive X-ray spectroscopy (EDX) data facilitates the phase segmentation of a 11 × 11 × 7.7 µm3 volume of hydrated alite.

Robot-Assisted Gait Self-Training: Assessing the Level Achieved.

This paper presents the technological status of robot-assisted gait self-training under real clinical environment conditions. A successful rehabilitation after surgery in hip endoprosthetics comprises self-training of the lessons taught by physiotherapists. While doing this, immediate feedback to the patient about deviations from the expected physiological gait pattern during training is important. Hence, the Socially Assistive Robot (SAR) developed for this type of training employs task-specific, user-centered navigation and autonomous, real-time gait feature classification techniques to enrich the self-training through companionship and timely corrective feedback. The evaluation of the system took place during user tests in a hospital from the point of view of technical benchmarking, considering the therapists' and patients' point of view with regard to training motivation and from the point of view of initial findings on medical efficacy as a prerequisite from an economic perspective. In this paper, the following research questions were primarily considered: Does the level of technology achieved enable autonomous use in everyday clinical practice? Has the gait pattern of patients who used additional robot-assisted gait self-training for several days been changed or improved compared to patients without this training? How does the use of a SAR-based self-training robot affect the motivation of the patients?

Newborn Pulse Oximetry for Infants Born Out-of-Hospital.

BACKGROUND AND OBJECTIVES: Conventional timing of newborn pulse oximetry screening is not ideal for infants born out-of-hospital. We implemented a newborn pulse oximetry screen to align with typical midwifery care and measure its efficacy at detecting critical congenital heart disease. METHODS: Cohort study of expectant mothers and infants mainly from the Amish and Mennonite (Plain) communities with limited prenatal ultrasound use. Newborns were screened at 1 to 4 hours of life ("early screen") and 24 to 48 hours of life ("late screen"). Newborns were followed up to 6 weeks after delivery to report outcomes. Early screen, late screen, and combined results were analyzed on the basis of strict algorithm interpretation ("algorithm") and the midwife's interpretation in the field ("field") because these did not correspond in all cases. RESULTS: Pulse oximetry screening in 3019 newborns (85% Plain; 50% male; 43% with a prenatal ultrasound) detected critical congenital heart disease in 3 infants. Sensitivity of combined early and late screen was 66.7% (95% confidence interval [CI] 9.4% to 99.2%) for algorithm interpretation and 100% (95% CI 29.2% to 100%) for field interpretation. Positive predictive value was similar for the field interpretation (8.8%; 95% CI 1.9% to 23.7%) and algorithm interpretation (5.4%; 95% CI 0.7% to 18.2%). False-positive rates were ≤1.2% for both algorithm and field interpretations. Other pathologies (noncritical congenital heart disease, pulmonary issues, or infection) were reported in 12 of the false-positive cases. CONCLUSIONS: Newborn pulse oximetry can be adapted to the out-of-hospital setting without compromising sensitivity or prohibitively increasing false-positive rates.

Point Cloud Hand-Object Segmentation Using Multimodal Imaging with Thermal and Color Data for Safe Robotic Object Handover.

This paper presents an application of neural networks operating on multimodal 3D data (3D point cloud, RGB, thermal) to effectively and precisely segment human hands and objects held in hand to realize a safe human-robot object handover. We discuss the problems encountered in building a multimodal sensor system, while the focus is on the calibration and alignment of a set of cameras including RGB, thermal, and NIR cameras. We propose the use of a copper-plastic chessboard calibration target with an internal active light source (near-infrared and visible light). By brief heating, the calibration target could be simultaneously and legibly captured by all cameras. Based on the multimodal dataset captured by our sensor system, PointNet, PointNet++, and RandLA-Net are utilized to verify the effectiveness of applying multimodal point cloud data for hand-object segmentation. These networks were trained on various data modes (XYZ, XYZ-T, XYZ-RGB, and XYZ-RGB-T). The experimental results show a significant improvement in the segmentation performance of XYZ-RGB-T (mean Intersection over Union: 82.8% by RandLA-Net) compared with the other three modes (77.3% by XYZ-RGB, 35.7% by XYZ-T, 35.7% by XYZ), in which it is worth mentioning that the Intersection over Union for the single class of hand achieves 92.6%.

Mobile Robot-Based Gait Training after Total Hip Arthroplasty (THA) Improves Walking in Biomechanical Gait Analysis.

There are multiple attempts to decrease costs in the healthcare system while maintaining a high treatment quality. Digital therapies receive increasing attention in clinical practice, mainly relating to home-based exercises supported by mobile devices, eventually in combination with wearable sensors. The aim of this study was to determine if patients following total hip arthroplasty (THA) could benefit from gait training on crutches conducted by a mobile robot in a clinical setting. METHOD: This clinical trial was conducted with 30 patients following total hip arthroplasty. Fifteen patients received the conventional physiotherapy program in the clinic (including 5 min of gait training supported by a physiotherapist). The intervention group of 15 patients passed the same standard physiotherapy program, but the 5-min gait training supported by a physiotherapist was replaced by 2 × 5 min of gait training conducted by the robot. Length of stay of the patients was set to five days. Biomechanical gait parameters of the patients were assessed pre-surgery and upon patient discharge. RESULTS: While before surgery no significant difference in gait parameters was existent, patients from the intervention group showed a significant higher absolute walking speed (0.83 vs. 0.65 m/s, p = 0.029), higher relative walking speed (0.2 vs. 0.16 m/s, p = 0.043) or shorter relative cycle time (3.35 vs. 3.68 s, p = 0.041) than the patients from the control group. CONCLUSION: The significant higher walking speed of patients indicates that such robot-based gait training on crutches may shorten length of stay (LOS) in acute clinics. However, the number of patients involved was rather small, thus calling for further studies.

Development, design, and testing of a microwave-driven compact rotating-target D-D fast neutron generator for imaging applications.

A compact D-D fast neutron generator system was developed with an emphasis on imaging applications. It is equipped with an electron cyclotron resonance microwave ion source, an electric field method electron suppression electrode, and a rotating beam target. The design, mechanical assembling, and initial performance of the system is presented in this work. Electrostatics and charged particle tracing simulations were employed to dimension components in the vacuum system and estimate the size of the ion beam spot on the target surface. Stable operation of the neutron generator was possible with accelerating potentials of up to -120 kV and an estimated averaged ion beam current of around 0.5 mA. At these conditions, the neutron yield was estimated to 6 ⋅ 107 s-1 based on a combination of detailed Monte Carlo MCNP6 models and the ambient dose rate reading of an LB6411 neutron probe. The neutron emitting spot size was compared to the ion beam spot size and experimentally determined with an attenuating edge technique to be between 1.7 and 2.6 mm. With these neutron yield and emitting spot size values, the system compares favorably with commercially available neutron generators, and this context is also presented.