Uniform Assessment Research Articles

HARDeNN: Hardware-assisted attack-resilient deep neural network architectures

We propose HARDeNN, a low-overhead end-to-end inference accelerator methodology to armor the underlying pre-trained neural network architecture against black-box non-input adversarial attacks. In order to find the most vulnerable neural network architectures parameters, a hardware-assisted fault injection tool and a statistical stress model have been proposed to synergy uniform fault assessment across layers and targeted in-layer fault assessment to realize a holistic, rigorous fault evaluation in NN topologies susceptible to non-input adversarial black-box attacks. The key observation from the assessment shows that the weights and activation functions are the most vulnerable neural network parameters that are susceptible to both single-bit and multiple-bit flip attacks. Concerning the aforementioned parameters, a multi-objective design space exploration is conducted to find a superior design under different resource constraints. The error-resiliency magnitude offered by HARDeNN can be adjusted based on the given boundaries. The experimental results show that HARDeNN methodology enhances the error-resiliency magnitude of cnvW1A1 by 17.19% and 96.15% for 100 multi-bit upsets that target weight and activation layers, respectively, during CIFAR-10 classification.© 2017 Elsevier Inc. All rights reserved.

Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts

NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model’s performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented.Graphical Impact of various data learning approaches on NIR spectral data

Real-time determination of sandy soil stiffness during vibratory compaction incorporating machine learning method for intelligent compaction

An emerging real-time ground compaction and quality control, known as intelligent compaction (IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time assessment of uniformity of the compacted area, accurate determination of the soil stiffness required for quality control and design remains challenging. In this paper, a novel and advanced numerical model simulating the interaction of vibratory drum and soil beneath is developed. The model is capable of evaluating the nonlinear behaviour of underlying soil subjected to dynamic loading by capturing the variations of damping with the cyclic shear strains and degradation of soil modulus. The interaction of the drum and the soil is simulated via the finite element method to develop a comprehensive dataset capturing the dynamic responses of the drum and the soil. Indeed, more than a thousand three-dimensional (3D) numerical models covering various soil characteristics, roller weights, vibration amplitudes and frequencies were adopted. The developed dataset is then used to train the inverse solver using an innovative machine learning approach, i.e. the extended support vector regression, to simulate the stiffness of the compacted soil by adopting drum acceleration records. Furthermore, the impacts of the amplitude and frequency of the vibration on the level of underlying soil compaction are discussed. The proposed machine learning approach is promising for real-time extraction of actual soil stiffness during compaction. Results of the study can be employed by practising engineers to interpret roller drum acceleration data to estimate the level of compaction and ground stiffness during compaction.

Towards ‘one substance-one assessment’: an analysis of European chemical registration and environmental risk assessment frameworks

BACKGROUND AND AIM The registration and risk assessment of chemicals on the European market is currently fragmented across different legal frameworks, dependent on the chemical's use. A ‘one-substance one-assessment’ (OS-OA) approach has been proposed in order to better protect human and environmental health, and increase harmonisation and consistency across these different legal frameworks. We reviewed the five main European chemical registration frameworks and their risk assessment procedures for the freshwater environment, covering 1) medicines for human use, 2) veterinary medicines, 3) pesticides, 4) biocides and 5) industrial chemicals in order to provide an understanding of the differences between these chemical legislations and analyse how a shift towards an OS-OA approach might be realised. RESULTS Overall, the function of the current frameworks is similar, but important differences exist between the frameworks' environmental protection goals and risk assessment strategies. These differences result in inconsistent assessment outcomes for similar chemicals. Chemicals are also registered under multiple frameworks due to their multiple uses, and chemicals that are not approved under one framework are in some instances allowed on the market under other frameworks. In contrast, an OS-OA will require a uniform hazard assessment between all different frameworks. In addition, we show that industrial chemicals are the least hazardous for the freshwater environment, whilst biocides seem the most toxic following current regulatory assessment schemes. Finally, in order to facilitate a successful move towards an OS-OA approach we recommend a) harmonisation of protection goals and risk assessment strategies, b) that emission, use and production data should be made publicly available and that data sharing becomes a priority, and c) an alignment of the criteria used to classify problematic substances. KEYWORDS EU chemical legislation, Regulatory risk assessment, Water pollution, Environmental risk assessment

Uniform corrosion assessment in oil and gas pipelines using corrosion prediction models – Part 1: models performance and limitations for operational field cases

Internal and uniform corrosion caused by the presence of corrosive gases CO2 and/or H2S in the inner stream is the major inherent concern of the oil and gas industry regarding its carbon steel pipelines selected for the processes present in hydrocarbons production and transportation, which culminates in generalized loss of thickness along the pipelines surface. Distinct corrosion predictive models have been developed and updated for the prediction of this corrosive mechanism through main multiphase flow physical-chemical parameters, however, worries regarding models’ uncertainties and limitations remains. This study reviews and compares the most common predictive models in the industry, clarifying its classifications and input parameters. Based on that, NORSOK, OLI and Predict models are applied to oil and gas operational field cases from literature and the implementation of a preliminary sensitivity analysis of models’ parameters for these applications is done. The progress allowed the operational cases histories evaluation and the elucidation of the main constraints of corrosion data collection by industry and of predictive models application. Moreover, this study discusses preliminary perspectives about models’ uncertainty and presents the pH as the most important influential parameter in uniform corrosion prediction, forwarding the necessary future development for corrosion assessment in operational pipelines.

Language assessment in Mexico: Exploring university language teachers' backgrounds, practices, and opinions

Assessment is a vital aspect of any language teaching program and yet, no uniform assessment criteria exist in Mexico. To explore the role of language assessment in higher education in the country, a study employing a mixed-method approach was conducted. The data discussed in this article are based on a survey of university language teachers in Mexico investigating their educational backgrounds, their language assessment practices, and their opinions towards language assessment. Interviews with language teachers and program coordinators provided further insight into the university language teachers' experiences and concerns regarding language assessment in the country. The article also includes an overview of Mexico's particular educational context as it contributes to the overall dissatisfaction with the outcomes of language instruction. Since the results of the study indicate that the majority of language teachers in Mexico are part-time professors operating without clear guidelines and many of them feel somewhat insecure about language assessment, it is suggested that specific language assessment competencies be formulated for these teachers which should be met in future teacher training programs. Assessing the students' language learning outcomes at the individual program level is subsequently proposed as a concrete measure to improve the language education outcomes in Mexico.

A PROPOSED ASSESSMENT TOOL FOR SHARIA-COMPLIANT EQUITY CROWDFUNDING PROJECTS UTILIZING TOPSIS

On the market, the number of newly founded fintech companies that involve sharia-compliant businesses has increased recently, one of them being the sharia-crowdfunding company. The company targets countries with large Muslim populations, such as Indonesia and Malaysia. Since the sharia compliance is unique, and thus it influences decision-making in a business. Therefore, we are driven to propose and develop an assessment tool to assist the investment signaling process for the sharia-compliant equity crowdfunding projects on Platform X. Platform X is one of the pioneers of real estate Islamic crowdfunding platforms investing in sharia-compliant real estate projects in Southeast Asia's emerging countries. The firm must examine new projects locally for each newly created branch country as the firm aims to open new branches in some predominantly Muslim countries. However, each newly founded branch country lacks a mature and uniform assessment tool for sharia-based projects, resulting in glaring problems in the 'portability' and time-consuming problem of assessing the projects. Therefore, this research provides a technique for assessing sharia-equity crowd fundraising decisions using a multi-criteria decision analysis (MCDA), namely TOPSIS. We combine the concept of equity crowdfunding, Islamic principles, and Sharia compliance to develop a tailored assessment tool for sharia-based projects. As a result, we present both theoretical and managerial implications, and we find that the assessment tool is robust as the evaluation result for the portability aspect and the fast computational time were successfully examined through a simple experiment.

Cost effectiveness and health-related quality of life of chemoradiotherapy versus radiation therapy alone in elderly head and neck cancer patients

PurposeRadiotherapy (RT) constitutes a mainstay in the treatment of elderly patients with head and neck cancer (HNC), but use of simultaneous chemoradiotherapy (CRT) remains controversial. We have conducted a prospective analysis based on real-world patient data to examine the health-related quality of life (HRQoL) and cost effectiveness (CE) of CRT vs. RT in elderly HNC patients.MethodsEligible participants ≥ 65 years treated in a large tertiary cancer center between July 2019 and February 2020 who completed the validated EQ-5D-5L questionnaire (health state index [HI] and visual analog scale [VAS]) before and after RT were included. CE referred to direct medical costs, including diagnosis-related group (DRG)-based billings for inpatients and uniform assessment standard (EBM)-based costs for outpatients. The primary endpoint was cost (euros [€]) per quality-adjusted life year (QALY). The incremental cost-effectiveness ratios (ICERs) were calculated. Costs and QALYs were not discounted for short overall survival (OS).ResultsBaseline HRQoL was 0.878 (±0.11) in the CRT group and 0.857 (±0.17) in the RT group. Upon completion of therapy, HRQoL amounted to 0.849 (±0.14) in the CRT and 0.850 (±0.13) in the RT group. The mean treatment-related cost in the CRT cohort was €22,180.17 (±8325.26) vs. €18,027.87 (±26,022.48) in the RT group. The corresponding QALYs amounted to 2.62 in the CRT and 1.91 in the RT groups. The ICER was €5848.31.ConclusionThis is the first analysis from the German health care system demonstrating that the addition of chemotherapy to RT for selected elderly HNC patients is cost effective and not associated with a significant HRQoL decline.

Costs-revenue deficit of outpatient treatment of minor injuries in the emergency department

Many patients with minor injuries hastily present in the emergency department and tie up resources and personnel there. To establish the cost-revenue relationship of outpatient care of minor injuries in the traumatology emergency department. The calculation was based on the uniformly billed emergency flat rates of the uniform assessment standard (EBM). Using the current collective bargaining agreements for physicians and nurses, per minute costs were calculated. The time required for treatment was determined on the basis of 100 reference patients with minor injuries. The case cost calculation with the respective resources was carried out with the operational controlling of the University Hospital Frankfurt. A total of 4088 patients with minor injuries who presented in 2019 were included. Most common reasons for presentation were contusions of the lower (31.9%; n = 1303) and upper extremities (16.6%; n = 677). Atime expenditure of 166.7 min per day for the medical staff and 213.8 min per day for nursing staff was calculated. Atotal revenue of 29,384.31 € and total costs of 69,591.22 € were calculated. Thus, arevenue deficit of -40,206.91 € can be calculated for the year 2019. This corresponds to amonetary deficit of 9.84 € per patient. There is ashortage of the medical resource "personnel" to satisfactorily and economically manage the nowadays high volume of self-presenting pedestrian patients with minor injuries. The current remuneration of the treatment of minor injuries by the uniform assessment scale is insufficient for the hospital sector.

Parameters Affecting Worst-Case Gradient-Field Heating of Passive Conductive Implants.

Testing MRI gradient-induced heating of implanted medical devices is required by regulatory organizations and others. A gradient heating test of the ISO 10974 Technical Specification (TS) for active implants was adopted for this study of passive hip implants. All but one previous study of hip implants used nonuniform gradient exposure fields in clinical scanners and reported heating of less than 5 °C. This present study adapted methods of the TS, addressing the unmet need for identifying worst-case heating via exposures to uniform gradient fields. To identify gradient-field parameters affecting maximum heating in vitro for a hip implant and a cylindrical titanium disk. Computational simulations and experimental validation of induced heating. Tissue-simulating gel. 42 T/s RMS, sinusoidal, continuous B fields with high spatial uniformity ASSESSMENT: Hip implant heating at 1-10 kHz, via computational modeling, validated by limited point measurements. Experimental measurements of exposures of an implant at 42 T/s for 4, 6, and 9kHz, analyzed at 50, 100, and 150seconds. One sample student's t-test to assess difference between computational and experimental results. Experimental vs. computational results were not significantly different (p < 0.05). Maximum simulated temperature rise (10-minute exposure) was 10 °C at 1kHz and 0.66 °C at 10 kHz. The ratio of the rise for 21 T/s vs. 42 T/s RMS was 4, after stabilizing at 50seconds (dB/dt ratio squared). Heating of an implant is proportional to the frequency of the B field and the implant's cross-sectional area and is greater for a thickness on the order of its skin depth. Testing with lower values of dB/dt RMS with lower cost amplifiers enables prediction of heating at higher values for dB/dt squared (per ISO TS) with identical frequency components and waveforms, once thermal equilibrium occurs. 1 TECHNICAL EFFICACY: Stage 1.

Use of algorithms for identifying patients in a German claims database: learnings from a lung cancer case

BackgroundThe analysis of statutory health insurance (SHI) data is a little-used approach for understanding treatment and care as well as resource use of lung cancer (LC) patients in Germany. The aims of this observational, retrospective, longitudinal analysis of structured data were to analyze the healthcare situation of LC patients in Germany based on routine data from SHI funds, to develop an algorithm that sheds light on LC types (non-small cell / NSCLC vs. small cell / SCLC), and to gain new knowledge to improve needs-based care.MethodsAnonymized billing data of approximately four million people with SHI were analyzed regarding ICD-10 (German modification), documented medical interventions based on the outpatient SHI Uniform Assessment Standard Tariff (EBM) or the inpatient Operations and Procedure Code (OPS), and the dispensing of prescription drugs to outpatients (ATC classification). The study included patients who were members of 64 SHI funds between Jan-1st, 2015 and Dec-31st, 2016 and who received the initial diagnosis of LC in 2015 and 2016.ResultsThe analysis shows that neither the cancer type nor the cancer stage can be unambiguously described by the ICD-10 coding. Furthermore, an assignment based on the prescribed medication provides only limited information: many of the drugs are either approved for both LC types or are used off-label, making it difficult to assign them to a specific LC type. Overall, 25% of the LC patients were unambiguously identifiable as NSCLC vs SCLC based on the ICD-10 code, the drug therapy, and the billing data.ConclusionsThe current coding system appears to be of limited suitability for drawing conclusions about LC and therefore the SHI patient population. This makes it difficult to analyze the healthcare data with the aim of gathering new knowledge to improve needs-based care. The approach chosen for this study did not allow for development of a LC differentiation algorithm based on the available healthcare data. However, a better overview of patient specific needs could make it possible to modify the range of services provided by the SHI funds. From this perspective, it makes sense, in a first step, to refine the ICD-10 system to facilitate NSCLC vs. SCLC classification.

Christiansen uniformity revisited: Re-thinking uniformity assessment in rainfall simulator studies

Rainfall simulators have been used extensively for a variety of research applications. The uniformity and spatial distribution of simulated rainfall is an important metric to consider when conducting rainfall simulator experiments. Over the past 80 years, several methods of assessing rainfall distribution and uniformity over the plot surface below a rainfall simulator have been developed. However, the Christiansen Uniformity Coefficient (CU; Christiansen, 1942) is most frequently used. Here, we provide a critique of this established methodology and metric for quantifying rainfall distribution characteristics. Uniformity coefficients express the distribution of rainfall across the plot surface as a single percentage value. Therefore, if all collection beakers receive equal volumes of water during an experimental run, the rainfall simulator or irrigation system would be applying water with 100% uniformity. There are shortcomings with using a single percentage value to represent rainfall simulator rainfall distribution, especially over a larger plot surface, and CU is highly dependent on the sampling methodology employed. This paper assesses rainfall uniformity by conducting a series of controlled uniformity experiments and resampling different grid layouts of collection containers. Results demonstrate that the coarseness of the sampling methodology affects rainfall uniformity coefficient values. CU values of 45 – 51% were recorded under a dense (17 × 17) sampling grid layout, compared to CU values of 81% using a coarser (8 × 8) sampling methodology, despite being subjected to comparable rainfall events. This paper explores the sensitivity of CU to the resolution and spatial layout of the sampling methodology used and assesses whether the CU captures repeatability and localised variability of uniformity between experimental runs. A complementary statistical approach expressing variations in relation to their standard deviation from the mean is presented. Understanding how experimental setup (i.e. number, density, spatial configuration of collection containers) affects CU is critical when interpreting results from different rainfall simulator studies and an understanding of the factors which influence CU is critical to benchmark the results of uniformity testing across different rainfall simulator setups.

Assessment of Intelligent Compaction Quality Evaluation Index and Uniformity

Compaction Measurement Value (CMV) is the most widely used quality evaluation index for intelligent compaction. However, with the growth of compaction degree, CMV becomes less accurate due to increased reaction force from densified geomaterials. To solve this problem, this paper proposes a more accurate intelligent compaction quality evaluation index, acceleration intelligent compaction value (AICV). Based on field measurement data analysis, AICV turned out to be effective to elevate quality evaluation accuracy. Additionally, the singularities were detected by 3σ normal distribution in order to assess the uniformity of subgrade compaction quality. Based on the semivariogram analysis method of spatial statistics, CMV and AICV were analyzed for spatial correlation, and the influence range of intelligent compaction technology employed for subgrade was evaluated. The results from this study provide a basis for further research on precision control and uniformity analysis of intelligent compaction technology.

A survey of ultrasound Quality Assurance implementation in the United Kingdom.

In England, Quality Assurance is a legal requirement, regulated by the Care Quality Commission; equipment in use may exhibit only minor deterioration. There is evidence that a significant number of ultrasound transducers in use exhibit major faults and this may lead to suboptimal imaging in a large number of examinations. There is limited evidence suggesting that the uptake of Quality Assurance in the United Kingdom is low; the aim of this study was to gather more information on current Quality Assurance practice. A questionnaire to establish the level of Quality Assurance in place in National Health Service Hospital Trusts in the United Kingdom was sent by email to 170 National Health Service Chief Executives. There were 60 responses to the survey from individual National Health Service organisations, only four respondents (7%) indicating that no Quality Assurance was performed in their organisation. Twenty-three percent of respondents conducted only annual Quality Assurance. Eighteen percent of respondents conducted only user Quality Assurance. In the worst case, if no non-respondents had a Quality Assurance programme, only 33% of National Health Service Trusts in the United Kingdom have a Quality Assurance programme. The true figure will be between 33% and 93%. Annual Quality Assurance alone is insufficient to detect faults in a timely manner and user Quality Assurance alone may not detect more subtle faults. It is easy and straightforward for ultrasound users to perform Quality Assurance every day, in the form of a rapid visual inspection and uniformity assessment before every use.

Automated Grading of PowerPoint Presentations Using Latent Semantic Analysis

Manual grading of students’ work takes a long time and it is stressful. Evaluator may be holistic or analytic, lenient or non-lenient, experienced or inexperienced; which leads to non-uniformity in the assessment. Therefore, it is essential to do the automated grading of students' work to overcome human inadequacies through uniform assessment and also, it reduces workload of human evaluators. A novel automatic grading of students' PowerPoint presentation skills using Latent Semantic Analysis (LSA) is proposed. Program is implemented in python to extract features corresponding to the text appearance, graphics, footer, and hyperlink from the PowerPoint presentations. PowerPoint presentations are represented using feature vectors in the Latent Semantic Space using Singular Value Decomposition (SVD). SVD reveals relationships between features and PowerPoint presentations. The grades for the students' PowerPoint presentations are evaluated by finding Cosine similarity with reference presentations or finding k number of nearest reference presentations. The grades of such reference or nearest presentations are used to grade students' presentations. Kneighbors classifier used to find nearest neighbors. Kneighbors and Cosine Similarity approach give 90.90% and 81.81% accuracy, respectively, while predicting the grades for the students’ PowerPoint presentations.

Inter-observer agreement of preoperative cardiopulmonary exercise test interpretation in major abdominal surgery

BackgroundAccurate determination of cardiopulmonary exercise test (CPET) derived parameters is essential to allow for uniform preoperative risk assessment. The objective of this prospective observational study was to evaluate the inter-observer agreement of preoperative CPET-derived variables by comparing a self-preferred approach with a systematic guideline-based approach.MethodsTwenty-six professionals from multiple centers across the Netherlands interpreted 12 preoperative CPETs of patients scheduled for hepatopancreatobiliary surgery. Outcome parameters of interest were oxygen uptake at the ventilatory anaerobic threshold (V̇O2VAT) and at peak exercise (V̇O2peak), the slope of the relationship between the minute ventilation and carbon dioxide production (V̇E/V̇CO2-slope), and the oxygen uptake efficiency slope (OUES). Inter-observer agreement of the self-preferred approach and the guideline-based approach was quantified by means of the intra-class correlation coefficient.ResultsAcross the complete cohort, inter-observer agreement intraclass correlation coefficient (ICC) was 0.76 (95% confidence interval (CI) 0.57–0.93) for V̇O2VAT, 0.98 (95% CI 0.95–0.99) for V̇O2peak, and 0.86 (95% CI 0.75–0.95) for the V̇E/V̇CO2-slope when using the self-preferred approach. By using a systematic guideline-based approach, ICCs were 0.88 (95% CI 0.74–0.97) for V̇O2VAT, 0.99 (95% CI 0.99–1.00) for V̇O2peak, 0.97 (95% CI 0.94–0.99) for the V̇E/V̇CO2-slope, and 0.98 (95% CI 0.96–0.99) for the OUES.ConclusionsInter-observer agreement of numerical values of CPET-derived parameters can be improved by using a systematic guideline-based approach. Effort-independent variables such as the V̇E/V̇CO2-slope and the OUES might be useful to further improve uniformity in preoperative risk assessment in addition to, or in case V̇O2VAT and V̇O2peak are not determinable.

GNSS Timing Performance Assessment and Results Analysis.

Global Navigation Satellite System (GNSS) timing is a main service function. Each GNSS has its own time performance specification. However, a uniform timing performance assessment methodology and its outcomes do not exist. Firstly, the timing performance specifications of each GNSS are analyzed. Then, time transfer accuracy is considered as the key GNSS timing performance indicator. Secondly, an assessment method for the Coordinated Universal Time (UTC) published by the Bureau International des Poids et Mesures (BIPM) and UTC kept by the National Time Service Center of China (UTC(NTSC)) is proposed. Thirdly, the uncertainty budget of the assessment method is given. The timing performances of BDS, GPS, GLONASS, and Galileo are assessed and compared. The results show that the time transfer accuracy of BDS, GPS, GLONASS, and Galileo was 13.8 ns, 4.5 ns, 16.8 ns, and 4.2 ns, respectively, in 2021, meeting their performance requirements specified by GNSS (30 ns or 40 ns). Meanwhile, the assessment results of GPS and Galileo are much better than requirements, while the assessment results of BDS and GLONASS show fixed time offset and can still be improved further. If the local reference time of GNSS users can be connected with UTC, this assessment method can be used.

'CT and CT image-based texture image analysis in radiological diagnostics of allergic fungal rhinosinusitis'.

This prospective study is focused on evaluating radiological properties of AFRS. We analysed specific CT features related to the presence of AFRS, as well as explored the possible usefulness of the texture image analysis (TIA) as an additional diagnostical parameter. The CT images of maxillary sinuses of 37 adult patients diagnosed with chronic rhinosinusitis were analysed for homogeneity, high-attenuation areas, density of the soft tissue mass, bony wall thickness and density. TIA included assessment of uniformity, contrast, homogeneity and entropy of sinus content. In the F+ group, soft tissue mass was significantly more non-homogeneous, high-attenuation areas were more prevalent, while soft tissue densities were higher. The sinus wall showed a tendency towards decreased thickness and significantly higher density in the F+ group. Among TIA parameters only homogeneity was significantly lower in the F+ group. Presence of fungi should be suspected when the sinus is filled with a non-homogenous soft tissue content of a high CT density not necessarily presented as clearly visible hyperattenuation material. Additional criteria in radiological diagnostics of AFRS should encompass assessment of sinus bony wall density. TIA may serve as a tool for quantitative assessment of subjective CT features such as homogeneity of the soft tissue mass for investigative purposes. However, other TIA parameters showed limited potential.

The New Cranberry Wisconsin Research Station: Renovation Priorities of a ‘Stevens’ Cranberry Marsh Based on Visual Mapping, Genetic Testing, and Yield Data

ABSTRACT Cultivar contamination is a common issue in commercial cranberry production. Unknown or unwanted cranberry genotypes are found in commercial cranberry beds that are intended to be a single uniform genotype. Identification of contamination and the impact of contamination remain crucial issues for the cranberry industry to maintain long-term high productivity. To address this issue, tissue samples were taken from the former commercial beds of the new Wisconsin Cranberry Research Station (WCRS) for genetic fingerprinting analysis. The goal of this collection was to analyze the 10 beds for genetic uniformity to determine if any should be maintained or replaced, and to assess the accuracy of visual perception of genetic contamination in the field. A total of 288 DNA samples were collected in the 10 cranberry beds, and the ‘Stevens’ cultivar represented 180 samples or 69% of the 261 samples expected to be ‘Stevens.’ Therefore, the genotype contamination in the ‘Stevens’ beds was 31% overall. Overall, visual differentiation was accurate in distinguishing between genotypes and detecting large areas of contamination. A yield analysis was conducted along with the genotypic uniformity assessments, and a significant correlation was found between the 2017 yield of the beds and their level of genetic contamination. This study demonstrates the usefulness of genetic uniformity testing and mapping for cranberry bed management and renovation decision-making.

Medicaid long-term care workforce training intervention and value-based payment metrics.

To examine the impact of a scaled implementation of workforce training intervention on value-based payment measures in a large home-based Medicaid managed long-term care plan population in New York. Managed long-term care clients' health assessments from the Uniform Assessment System of New York merged with paid claims, home health aide operational visit data, and workforce training rosters between 2018 and early-2020. A quasi-experimental design was used. Exposure and control groups were constructed using the proportion of service hours delivered by trained aides between clients' baseline and follow-up/outcome assessments. Multivariate logistic generalized linear and additive models were estimated to examine associations between exposure to trained aides and value-based payment measures. The analytic sample consisted of 19,212 pairs of assessments from 13,320 long-term care clients continuously enrolled in the plan between baseline and follow-up/outcome assessments. Matched assessment pairs were 6-10months apart. Over 27% of the study population (n=3656 clients) received services from one or more of 8683 trained aides. Statistically significant associations were observed for four of seven value-based payment measures; however, the presence and magnitudes of positive training effects differed by client service needs. With covariate adjustment, workforce training had the largest estimated positive impacts on rates of flu vaccination among average-need clients (1.60%, standard error [SE]=0.01), not experiencing uncontrolled pain among above-average-need clients (0.69%, SE=0.001), stable/improved pain intensity among heavy-need clients (1.25%, SE=0.01), and stable/improved shortness of breath among light-need clients (0.88%, SE=0.003). Although we found mixed associations between scaled workforce training implementation and value-based payment metrics, we noted workforce training could benefit high-need long-term care recipients. Health indicators more sensitive to the daily support provided by direct care workers should be integrated into value-based health care models.