To investigate the fundamental causes of utility air voids content failures in asphalt layers to achieve Specification for the Reinstatement of Openings in Highways (SROH) compliant performance

The linkage between air voids content and durability in footways reinstatements with the limits currently in SROH is non-proven and unsupported by evidential research or trial data. Compounding of errors, particularly in density measurement of core samples and subsequent variability, generate biased air void content results that make the compliance largely a matter of chance. This led to a very wide range of predicted outcomes, putting both the contractor and the client at unacceptable risk. The use of a measured in situ air voids content criteria in a specification for footway reinstatements, where the entire operation is in restricted areas with hand laying process using recipe mixed materials, cannot be sustained on technical grounds with respect to relevant British Standard and Transport Research Laboratory (TRL) guide. Taking account of the service loads, nature and scale of works in footways, an in-service guarantee by the undertaker for an agreed extended period, linked to an allowable intervention level, could be a simple, realistic and acceptable solution, ensuring a durable reinstatement that removes the financial risk of failure from the highway authority

Vulnerable to Misinterpretation: Disabled People, 'Vulnerability' and the Fight for Legal Recognition

Hate crime is now an established term in the fields of racist and religious attacks and is acknowledged in the cultural proscription against attacks on Lesbian, gay, bisexual and transgender men and women. Disabled people, as so often is the case, are late in being afforded statutory recognition in hate crime. This can be explained in terms of wider constructions of disability and more pernicious and muddled constructions of disabled people as categorically, “Vulnerable”. This construction has arguably weakened the impetus to introducing hate crime provisions and legal justice for disabled people. There is now ample evidence of hate crime being evident and pervasive in the lives of many disabled people (Higgins, 2006; Quarmby, 2008). This paper will argue that the continued use of the term in criminal justice proceedings both perpetuates notions that disabled people are helpless and personally responsible for ‘their’ vulnerability, whilst ironically continuing to weaken legal protections for disabled people against such hate crimes and incidents. It will be argued that vulnerability is the product of poor community safeguards for disabled people and is better understood as a socio-political space rather than an inherent quality of an individual

Radiomic Texture Feature Descriptor to Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal MRI

Despite multimodal aggressive treatment with chemo-radiation-therapy, and surgical resection, Glioblastoma Multiforme (GBM) may recur which is known as recurrent brain tumor (rBT), There are several instances where benign and malignant pathologies might appear very similar on radiographic imaging. One such illustration is radiation necrosis (RN) (a moderately benign impact of radiation treatment) which are visually almost indistinguishable from rBT on structural magnetic resonance imaging (MRI). There is hence a need for identification of reliable non-invasive quantitative measurements on routinely acquired brain MRI scans: pre-contrast T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) that can accurately distinguish rBT from RN. In this work, sophisticated radiomic texture features are used to distinguish rBT from RN on multimodal MRI for disease characterization. First, stochastic multiresolution radiomic descriptor that captures voxel-level textural and structural heterogeneity as well as intensity and histogram features are extracted. Subsequently, these features are used in a machine learning setting to characterize the rBT from RN from four sequences of the MRI with 155 imaging slices for 30 GBM cases (12 RN, 18 rBT). To reduce the bias in accuracy estimation our model is implemented using Leave-one-out crossvalidation (LOOCV) and stratified 5-fold cross-validation with a Random Forest classifier. Our model offers mean accuracy of 0.967 ± 0.180 for LOOCV and 0.933 ± 0.082 for stratified 5-fold cross-validation using multiresolution texture features for discrimination of rBT from RN in this study. Our findings suggest that sophisticated texture feature may offer better discrimination between rBT and RN in MRI compared to other works in the literature

Domain Adaptive Federated Learning for Multi-Institution Molecular Mutation Prediction and Bias Identification

Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed domain adaptive FL MGMT classification inherently offers differential privacy (DP) for the patient data. For domain adaptation two techniques e.g., mixture of experts (ME) with a gating network and adversarial alignment are used for comparison. The proposed method is evaluated using publicly available multi-institution (UPENN-GBM, UCSF-PDGM, RSNA-ASNR-MICCAI BraTS-2021) data set with a total of 1007 patients. Our experiments with 5-fold cross validation suggest that domain adaptive FL offers improved performance with a mean accuracy of 69.93% ± 4.8 % and area under curve of 0.655 ± 0.055 across multiple institutions. In addition, further analysis of probability density of gating network for domain adaptive FL identifies the institution that may bias the global model prediction due to increased heterogeneity for a given input. Our comparison analysis shows that the proposed method with bias identification offers the best predictive performance when compared to different commonly employed FL and baseline methods in the literature

Seagrass restoration trials in Kavaratti Lagoon, Lakshadweep: Growth patterns of transplants and their impact on overgrazing

It is essential to restore degraded seagrass habitats as they are among the major blue carbon ecosystems undergoing degradation at alarming proportions throughout the globe. As our earlier attempts at seagrass transplanting trials ended up in grazing by herbivores, fresh trials in enclosed rafts were initiated which resulted in an 80% survival rate. The results indicated the magnitude of overgrazing on seagrass shoots and the height of transplants after 37 days in the enclosed rafts was 105 mm registering a net height of 71.05±9.1mm, while in the exposed rafts the leaves of the transplants were found grazed and the final mean height was only 13.3 mm registering a net height of shoots far below its initial height. Any initiative to restore seagrass meadows in the degraded areas must be taken up under protected mode or the existing seagrass meadows should be allowed to recover on their own by preventing overgrazing and checking man-made interferences

Opioid Use Disorder Prediction Using Machine Learning of fMRI Data

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical analysis of functional magnetic resonance imaging (fMRI) methods to analyze the neurobiology of Opioid addictions in humans. In this work, for the first time in the literature, we propose a machine learning (ML) framework to predict OUD users utilizing clinical fMRI-BOLD (Blood oxygen level dependent) signal from OUD users and healthy controls (HC). We first obtain the features and validate these with those extracted from selected brain subcortical areas identified in our previous statistical analysis of the fMRI-BOLD signal discriminating OUD subjects from that of the HC. The selected features from three representative brain areas such as default mode network (DMN), salience network (SN), and executive control network (ECN) for both OUD participants and HC subjects are then processed for OUD and HC subjects’ prediction. Our leave one out cross validated results with sixty-nine OUD and HC cases show 88.40% prediction accuracies. These results suggest that the proposed techniques may be utilized to gain a greater understanding of the neurobiology of OUD leading to novel therapeutic development

Cost-effectiveness of alternative changes to a national blood collection service.

OBJECTIVES: To evaluate the cost-effectiveness of changing opening times, introducing a donor health report and reducing the minimum inter-donation interval for donors attending static centres. BACKGROUND: Evidence is required about the effect of changes to the blood collection service on costs and the frequency of donation. METHODS/MATERIALS: This study estimated the effect of changes to the blood collection service in England on the annual number of whole-blood donations by current donors. We used donors' responses to a stated preference survey, donor registry data on donation frequency and deferral rates from the INTERVAL trial. Costs measured were those anticipated to differ between strategies. We reported the cost per additional unit of blood collected for each strategy versus current practice. Strategies with a cost per additional unit of whole blood less than £30 (an estimate of the current cost of collection) were judged likely to be cost-effective. RESULTS: In static donor centres, extending opening times to evenings and weekends provided an additional unit of whole blood at a cost of £23 and £29, respectively. Introducing a health report cost £130 per additional unit of blood collected. Although the strategy of reducing the minimum inter-donation interval had the lowest cost per additional unit of blood collected (£10), this increased the rate of deferrals due to low haemoglobin (Hb). CONCLUSION: The introduction of a donor health report is unlikely to provide a sufficient increase in donation frequency to justify the additional costs. A more cost-effective change is to extend opening hours for blood collection at static centres

Performance of DB2 Enterprise-Extended Edition on NT with Virtual Interface Architecture

Abstract. DB2 Universal Database Enterprise-Extended Edition (DB2 UDB EEE) is a parallel relational database management system using a sharednothing architecture. DB2 UDB EEE uses multiple nodes connected by an interconnect and partitions data across these nodes. The communication protocol used between nodes of DB2 UDB EEE has historically been Transmission Control Protocol (TCP) / Internet Protocol (IP) but has now been extended to include the Virtual Interface (VI) Architecture. This paper discusses a new protocol termed Virtual Interface Protocol (VIP), built on top of the primitives provided by the VI Architecture. DB2 UDB EEE with VIP on a fast interconnect has shown significant improvement in reducing the elapsed time of queries when compared with TCP/IP over fast ethernet. This paper discusses the implementation and performance results on a Transaction Processing Council's Decision (TPC-D) support database