Predicting product quality in continuous manufacturing processes using a scalable robust Gaussian Process approach

This work describes an Artificial Intelligence (AI)-based solution that predicts product quality when applied to a continuous manufacturing process. The proposed solution uses process parameters and product quality measurements that are obtained from a production line. The work detailed herein is problem-driven, showing an application within one of the UK's foundation industries and identifying five key criteria an AI solution should ideally satisfy in continuous manufacturing applications; scalability, modularity, stable out-of-data performance, uncertainty quantification and robustness to unrepresentative data. The shortcomings, relative to these five criteria, of available AI approaches are discussed before a potential solution is presented. The proposed approach involves the application of a generalised product-of-expert Gaussian process whose noise model is constructed from a Dirichlet process. The ability of the model to fulfil the five key criteria and its performance when applied to the foundation industry case study is demonstrated

Population Enumeration and Household Utilization Survey Methods in the Enterics for Global Health (EFGH): <i>Shigella</i> Surveillance Study.

BackgroundAccurate estimation of diarrhea incidence from facility-based surveillance requires estimating the population at risk and accounting for case patients who do not seek care. The Enterics for Global Health (EFGH) Shigella surveillance study will characterize population denominators and healthcare-seeking behavior proportions to calculate incidence rates of Shigella diarrhea in children aged 6-35 months across 7 sites in Africa, Asia, and Latin America.MethodsThe Enterics for Global Health (EFGH) Shigella surveillance study will use a hybrid surveillance design, supplementing facility-based surveillance with population-based surveys to estimate population size and the proportion of children with diarrhea brought for care at EFGH health facilities. Continuous data collection over a 24 month period captures seasonality and ensures representative sampling of the population at risk during the period of facility-based enrollments. Study catchment areas are broken into randomized clusters, each sized to be feasibly enumerated by individual field teams.ConclusionsThe methods presented herein aim to minimize the challenges associated with hybrid surveillance, such as poor parity between survey area coverage and facility coverage, population fluctuations, seasonal variability, and adjustments to care-seeking behavior

Plasma Protein Biomarkers Distinguish Multisystem Inflammatory Syndrome in Children From Other Pediatric Infectious and Inflammatory Diseases.

BackgroundMultisystem inflammatory syndrome in children (MIS-C) is a rare but serious hyperinflammatory complication following infection with severe acute respiratory syndrome coronavirus 2. The mechanisms underpinning the pathophysiology of MIS-C are poorly understood. Moreover, clinically distinguishing MIS-C from other childhood infectious and inflammatory conditions, such as Kawasaki disease or severe bacterial and viral infections, is challenging due to overlapping clinical and laboratory features. We aimed to determine a set of plasma protein biomarkers that could discriminate MIS-C from those other diseases.MethodsSeven candidate protein biomarkers for MIS-C were selected based on literature and from whole blood RNA sequencing data from patients with MIS-C and other diseases. Plasma concentrations of ARG1, CCL20, CD163, CORIN, CXCL9, PCSK9 and ADAMTS2 were quantified in MIS-C (n = 22), Kawasaki disease (n = 23), definite bacterial (n = 28) and viral (n = 27) disease and healthy controls (n = 8). Logistic regression models were used to determine the discriminatory ability of individual proteins and protein combinations to identify MIS-C and association with severity of illness.ResultsPlasma levels of CD163, CXCL9 and PCSK9 were significantly elevated in MIS-C with a combined area under the receiver operating characteristic curve of 85.7% (95% confidence interval: 76.6%-94.8%) for discriminating MIS-C from other childhood diseases. Lower ARG1 and CORIN plasma levels were significantly associated with severe MIS-C cases requiring inotropes, pediatric intensive care unit admission or with shock.ConclusionOur findings demonstrate the feasibility of a host protein biomarker signature for MIS-C and may provide new insight into its pathophysiology

Development of a smart hybrid drive system with advanced logistics for railway applications

Fuel Hydrogen cells become nowadays a major candidate to replace the diesel in powering the traction locomotives since they offer low emission of pollutant gases, high efficiency, and flexible modular structure without the need for installation of electrification infrastructure for railway networks. However, they cannot respond appropriately to the fast load transients due to their slow internal electrochemical and thermodynamic responses. Therefore, they shall be integrated with fast dynamics Li-ion battery cells to form a hybrid power source for traction systems. Accordingly, this paper presents a development of a new smart integrated power source of fuel hydrogen and Li-ion battery cells to supply dual three-phase machines for driving the trains in railway networks. The developed control management system is regulating the two power sources according to the train operational modes as well as the battery state of charge. In addition to that, dual three-phase machines offer several superiorities over the conventional three-phase machines such as lower harmonic distortion and power losses along with higher power density and fault tolerance. A designated railway line is nominated through selection criteria in this paper for the implementation of the new smart drive traction system in HIL platform using Typhoon HIL real time simulator. Moreover, an optimization analysis has been carried out to reduce the overall hydrogen consumption with the same journey time spent for a designated railway line using particle swarm optimization approach. Finally, a case study is presented to illustrate a proof of concept for designing the hydrogen refueling station for such railways which are supplied by the new integrated power source of fuel hydrogen and Li-ion battery cells. The case study investigates the site selection, operational assessment, and equipment requirements

A qualitative study of clinicians' experience of a clinical trial for displaced distal radius fractures.

AimsThe aim of this study was to explore clinicians' experience of a paediatric randomized controlled trial (RCT) comparing surgical reduction with non-surgical casting for displaced distal radius fractures.MethodsOverall, 22 staff from 15 hospitals who participated in the RCT took part in an interview. Interviews were informed by phenomenology and analyzed using thematic analysis.ResultsAnalysis of the findings identified the overarching theme of "overcoming obstacles", which described the challenge of alleviating staff concerns about the use of non-surgical casting and recruiting families where there was treatment uncertainty. In order to embed and recruit to the Children's Radius Acute Fracture Fixation Trial (CRAFFT), staff needed to fit the study within clinical practice, work together, negotiate treatment decisions, and support families.ConclusionRecruiting families to this RCT was challenging because staff were uncertain about longer-term patient outcomes, and the difficulties were exacerbated by interdisciplinary tensions. Strong family and clinician beliefs, coupled with the complex nature of emergency departments and patient pathways that differed site-by-site, served as barriers to recruitment. Cementing a strong research culture, and exploring families' treatment preferences, helped to overcome recruitment obstacles

Co-N-C axially coordination regulated H2O2 selectivity via water medicated recombination of solute •OH: A new route

The cobalt, earth abundant transition metal, embedded in nitrogen doped carbon material as single atom site (Co-N-C) has been manifested as promising electrochemical oxygen reduction reaction (ORR) catalyst, however the unsatisfying production selectivity has hampered its widespread applications. Herein, the H2O2 selectivity of Co-N-C catalyst has been tailored with Co axial functional groups. Thermodynamically, the selectivity is regulated due to the fine-tuning of the adsorption of the key reaction intermediates (ΔG*OOH), and five functional groups, including −O, –OH, –CN, −CH3 and −SO3, endow the Co-N-C catalyst with superior H2O2 selectivity. Importantly, we unravel a new water medicated recombination of solute •OH reaction pathway for H2O2 production, which was the result of dissociation of *HOOH in explicit water environment. That is, two •OH species reaction in the liquid environment which originated from the creaking of *OOH intermediates due to the weakened O-O bond by the interaction with surrounding water. This study provides foundational understanding for the ORR catalytic mechanism at the electrochemical interface and opens up new avenues for rational design of targeted high efficiency electrocatalysts

Exploring SVA Insertion Polymorphisms in Shaping Differential Gene Expressions in the Central Nervous System.

Transposable elements (TEs) are repetitive elements which make up around 45% of the human genome. A class of TEs, known as SINE-VNTR-Alu (SVA), demonstrate the capacity to mobilise throughout the genome, resulting in SVA polymorphisms for their presence or absence within the population. Although studies have previously highlighted the involvement of TEs within neurodegenerative diseases, such as Parkinson's disease and amyotrophic lateral sclerosis (ALS), the exact mechanism has yet to be identified. In this study, we used whole-genome sequencing and RNA sequencing data of ALS patients and healthy controls from the New York Genome Centre ALS Consortium to elucidate the influence of reference SVA elements on gene expressions genome-wide within central nervous system (CNS) tissues. To investigate this, we applied a matrix expression quantitative trait loci analysis and demonstrate that reference SVA insertion polymorphisms can significantly modulate the expression of numerous genes, preferentially in the trans position and in a tissue-specific manner. We also highlight that SVAs significantly regulate mitochondrial genes as well as genes within the HLA and MAPT loci, previously associated within neurodegenerative diseases. In conclusion, this study continues to bring to light the effects of polymorphic SVAs on gene regulation and further highlights the importance of TEs within disease pathology

Self-training guided disentangled adaptation for cross-domain remote sensing image semantic segmentation

Remote sensing (RS) image semantic segmentation using deep convolutional neural networks (DCNNs) has shown great success in various applications. However, the high dependence on annotated data makes it challenging for DCNNs to adapt to different RS scenes. To address this challenge, we propose a cross-domain RS image semantic segmentation task that considers ground sampling distance, remote sensing sensor variation, and different geographical landscapes as the main factors causing domain shifts between source and target images. To mitigate the negative impact of domain shift, we propose a self-training guided disentangled adaptation network (ST-DASegNet) that consists of source and target student backbones to extract source-style and target-style features. To align cross-domain single-style features, we adopt feature-level adversarial learning. We also propose a domain disentangled module (DDM) to extract universal and distinct features from single-domain cross-style features. Finally, we fuse these features and generate predictions using source and target student decoders. Moreover, we employ an exponential moving average (EMA) based cross-domain separated self-training mechanism to ease the instability and disadvantageous effect during adversarial optimization. Our experiments on several prominent RS datasets (Potsdam, Vaihingen, and LoveDA) demonstrate that ST-DASegNet outperforms previous methods and achieves new state-of-the-art results. Visualization and analysis also confirm the interpretability of ST-DASegNet. The code is publicly available at https://github.com/cv516Buaa/ST-DASegNet