60419 research outputs found
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Neural-Network-Based Sensor Data Fusion for Multi-Hole Fluid Velocity Probes
No full textFor measuring three components of velocity in unknown flow fields, multi-hole pressure probes possess a significant advantage. Unlike methods such as hot-wire anemometry, laser-Doppler velocimetry and particle-image velocimetry, multi-hole pressure probes can provide not only the three components of local velocity, but also static and stagnation pressures. However, multi-hole probes do require exhaustive calibration. The traditional technique for calibrating these probes is based on either look-up tables or polynomial curve fitting, but with the low cost and easy availability of powerful computing resources, neural networks are increasingly being used. Here, we explore the possibility to further reduce measurement uncertainty by implementing neural-network-based methods that have not been previously used for probe calibration, including supervised and unsupervised learning neural networks, regression models and elastic-map methods. We demonstrate that calibrating probes in this way can reduce the uncertainty in flow angularity by as much as 50% compared to conventional techniques
Metallic iron in cornflakes
No full textIron is an essential element, and typically cornflake-style cereals are fortified with iron to a level up to 14 mg iron per 100 g. Even single cornflakes exhibit magnetic behaviour. We extracted iron microparticles from samples of two own-brand supermarket cornflakes using a strong permanent magnet. Synchrotron iron K-edge x-ray absorption near-edge spectroscopic data were consistent with identification as metallic iron, and x-ray diffraction studies provided unequivocal identification of the extracted iron as body-centred cubic (BCC) -iron. Magnetometry measurements were also consistent with ca. 14 mg/100 g BCC iron. These findings emphasise that attention must be paid to the speciation of trace elements, in relation to their bioavailability. To mimic conditions in the stomach, we suspended the iron extract in dilute HCl (pH 1.0-2.0) at 37oC (body temperature) and found by ICP-MS that over a period of 5 hours, up to 13% of the iron dissolved. This implies that despite its metallic form in the cornflakes, the iron is potentially bioavailable for oxidation and absorption into the body
Mesoporous strontium-doped phosphate-based sol-gel glasses for biomedical applications
No full textMesoporous phosphate-based glasses have great potential as biomedical materials being able to simultaneously induce tissue regeneration and controlled release of therapeutic molecules. In the present study, a series of mesoporous phosphate-based glasses in the P2O5-CaO-Na2O system doped with 1, 3, and 5 mol % of Sr2+ were prepared using the sol-gel method combined with supramolecular templating. A sample without strontium addition was prepared for comparison. The non-ionic triblock copolymer EO20PO70EO20 (P123) was used as a templating agent. SEM images revealed that all synthesized glasses have an extended porous structure. This was confirmed by N2 adsorption-desorption analysis at 77 K that shows a porosity typical of mesoporous materials. 31P magic angle spinning nuclear magnetic resonance (31P MAS-NMR) and Fourier transform infrared (FTIR) spectroscopies have shown that the glasses are mainly formed by Q1 and Q2 phosphate groups. Degradation of the glasses in deionized water assessed over a 7-day period shows that phosphate, Ca2+, Na+ and Sr2+ ions can be released in a controlled matter over time. In particular, a direct correlation between strontium content and degradation rate was observed. This study shows that Sr-doped mesoporous phosphate-based glasses have great potential in bone tissue regeneration as materials for controlled delivery of therapeutic ions
When do team members learn from each other? The importance of team members’ expertness, work team identification and need for closure and the moderating effect of team psychological safety.
No full textOrganisations increasingly rely on teams to get work done. Teams can provide more creative, responsive, productive, efficient, and effective outcomes than individuals working alone. However, the successful use of teams in organisations depends on team members’ ability to utilize their own specialized expertise while integrating the diverse expertise of their colleagues. This can be achieved through team learning.
Team learning enables that teams can combine their members’ existing knowledge structures and develop innovative solutions to changing problems. Team learning is a function of the members’ learning, which through their interactions produce mutual understanding that leads to an increase in the collective level of knowledge. By means of learning, team members can gain knowledge on how to structure themselves, communicate with other groups, conduct work processes, make decisions, and put these decisions into action. However, teams do not always learn, as learning can be conditioned by the members’ characteristics, the team emergent states and the members’ interactions while they are working together.
By studying the process of learning in teams, this thesis presents three studies that extend our understanding of the antecedents and contextual factors that determine when and from whom team members learn within their team. This thesis therefore contributes to the research on teams and learning in four ways: (1) by studying how members’ expertness, work team identification and need for closure influence team learning; (2) by reviewing learning from a multilevel contingency perspective; (3) by zooming in at the process of learning, that is studying dyadic learning in teams through the use of social network analysis; and, (4) by getting insights of learning as a process that can be studied from a dyadic (longitudinal) perspective. Our findings strengthen the knowledge that organisations have to promote learning in teams, such that they can create more effective policies and practices that enable both the social and the cognitive processes that stimulate the emergence of learning within teams
Preference-assisted multi- and many-objective evolutionary optimization.
No full textIn the real world, multi-objective optimization problems (MOPs) are very common and often involve multiple conflicting objectives. Consequently, no solutions can simultaneously satisfy all the objectives but a trade-off solutions will be obtained. The conventional multiobjective
evolutionary algorithms (MOEAs) are dedicated to finding a solution set with a good balance between the convergence and diversity to represent the Pareto optimal front (PoF). However, in practice, the decision-maker (DM) may be only interested in some parts of the PoF. Accordingly, the past decades of years have witnessed the development of the preference-driven MOEAs, seeking several solutions or regions of the PoF of the MOPs to satisfy the preference from the DM. Notably, the DMs may face a great challenge
in the articulation of explicit preference, when they have insufficient a priori knowledge of the problems. Therefore, the search of natural solutions of interest such as the knee points has become a new line of research in recent years. Nevertheless, little work has been reported focusing on designing multi-objective problems whose Pareto front contains complex knee regions. Likewise, few performance indicators dedicated to evaluating an algorithm's ability of accurately locating all knee points in high-dimensional objective space have been suggested. Additionally, the a posteriori knee identification methods implicitly
assume that the given solutions are well distributed over the whole Pareto optimal front (PoF) and able to provide sufficient information for identifying the knee solutions. However, this assumption may fail in practice, in particular when the number of objectives is very large or when the shape of the PoF is complex. Furthermore, most a priori methods mainly search knee regions in low-dimensional objective spaces and fail to achieve good
performance in locating the knee regions in high-dimensional objective space. Accordingly, this thesis aims to fill the above gaps.
To begin with, we proposed a set of multi-objective optimization test problems which Pareto front consists of complex knee regions, aiming to assess the capability of evolutionary algorithms to accurately identify all knee points. Various features related to knee points have been taken into account in designing the test problems, including symmetry, differentiability, degeneration. These features are also combined with other challenges in
solving optimization problems, such as multimodality, linkage between decision variables, non-uniformity and scalability of the Pareto front. The proposed test problems are scalable to both decision and objective spaces. Accordingly, new performance indicators are suggested for evaluating the capability of optimization algorithms in locating the knee points. The proposed test problems together with the performance indicators offer a new
means to develop and assess preference-based evolutionary algorithms for solving multiand many-objective optimization problems.
After that, an a posteriori MOEA has been proposed to alleviate the concern from the assumption. The basic idea is to augment the given solution set by generating solutions near the promising knee regions, thereby improving the performance of knee point identification.
In the method, we first transform the PoF into a multimodal auxiliary function, whose minimums correspond to the knee points of the PoF. Then, a surrogate model is built to approximate the auxiliary function and a variant of differential evolution is employed to search the basins of the approximated auxiliary functions, so that additional
solutions in the detected basins can be generated. After that, these new solutions in the objective space are mapped to the decision space with the help of an inverse model and
are evaluated by the original objective functions. Finally they are added to update the given solution set. Besides, a new method is introduced to search the knee candidates in
terms of the above augmentation strategy. Accordingly, the performance of the proposed and other knee identification methods will be greatly improved, and the concerns of the
assumption will be eased to much extent.
Additionally, an a priori MOEA using two localized dominance relationships has been proposed for the search of knee regions in high-dimensional objective space. In the environmental selection, the a-dominance is applied to each subpopulation partitioned by a set of predefined reference vectors, thereby guiding the search towards different potential knee regions while removing possible dominance resistant solutions. A knee-oriented dominance measure making use of the extreme points is then proposed to detect knee solutions in convex knee regions and discard solutions in concave knee regions. Without the misleading from the discarded solutions, the search process can be guided to the potential knee regions and the knee candidates can be found in high-dimensional objective space. Consequently, the knee candidates in high-dimensional objective space will be found by the proposed method.
Finally, we also conduct investigations of the proposed methods on a real application (hybrid electrical vehicle controller design problem with seven objectives). This study provides an insight into dealing with MOPs or MaOPs when the DM cannot specify explicit preference, and hopefully contributes the EMO, especially preference-driven EMO to much extent
Sestrins induce natural killer function in senescent-like CD8⁺ T cells
No full textAging is associated with remodeling of the immune system to enable the maintenance of life-long immunity. In the CD8⁺ T cell compartment, aging results in the expansion of highly differentiated cells that exhibit characteristics of cellular senescence. Here we found that CD27⁻CD28⁻CD8⁺ T cells lost the signaling activity of the T cell antigen receptor (TCR) and expressed a protein complex containing the agonistic natural killer (NK) receptor NKG2D and the NK adaptor molecule DAP12, which promoted cytotoxicity against cells that expressed NKG2D ligands. Immunoprecipitation and imaging cytometry indicated that the NKG2D-DAP12 complex was associated with sestrin 2. The genetic inhibition of sestrin 2 resulted in decreased expression of NKG2D and DAP12 and restored TCR signaling in senescent-like CD27⁻CD28⁻CD8⁺ T cells. Therefore, during aging, sestrins induce the reprogramming of non-proliferative senescent-like CD27⁻CD28⁻CD8⁺ T cells to acquire a broad-spectrum, innate-like killing activity
Mixed-numerology Signals Transmission and Interference Cancellation for Radio Access Network Slicing
No full textA clear understanding of mixed-numerology signals multiplexing and isolation in the physical layer is of importance to enable spectrum efficient radio access network (RAN) slicing, where the available access resource is divided into slices to cater to services/users with optimal individual design. In this paper, a RAN slicing framework is proposed and systematically analyzed from a physical layer perspective. According to the baseband and radio frequency (RF) configurations imparities among slices, we categorize four scenarios and elaborate on the numerology relationships of slices configurations. By considering the most generic scenario, system models are established for both uplink and downlink transmissions. Besides, a low out of band emission (OoBE) waveform is implemented in the system for the sake of signal isolation and inter-service/slice-band-interference (ISBI) mitigation. We propose two theorems as the basis of algorithms design in the established system, which generalize the original circular convolution property of discrete Fourier transform (DFT). Moreover, ISBI cancellation algorithms are proposed based on a collaboration detection scheme, where joint slices signal models are implemented. The framework proposed in the paper establishes a foundation to underpin extremely diverse user cases in 5G that implement on a common infrastructure
Road Traffic Noise Exposure and Filled Prescriptions for Antihypertensive Medication: A Danish Cohort Study
No full textBackground:
Epidemiological research on effects of transportation noise on incident hypertension is inconsistent.
Objectives:
We aimed to investigate whether residential road traffic noise increases the risk for hypertension.
Methods:
In a population-based cohort of 57,053 individuals 50–64 years of age at enrollment, we identified 21,241 individuals who fulfilled our case definition of filling ≥2
prescriptions and ≥180 defined daily doses of antihypertensive drugs (AHTs) within a year, during a mean follow-up time of 14.0 y. Residential addresses from 1987 to 2016 were obtained from national registers, and road traffic noise at the most exposed façade as well as the least exposed façade was modeled for all addresses. Analyses were conducted using Cox proportional hazards models.
Results:
We found no associations between the 10-y mean exposure to road traffic noise and filled prescriptions for AHTs, with incidence rate ratios (IRRs) of 0.999 [95% confidence intervals (CI): 0.980, 1.019)] per 10-dB increase in road traffic noise at the most exposed façade and of 1.001 (95% CI: 0.977, 1.026) at the least exposed façade. Interaction analyses suggested an association with road traffic noise at the least exposed façade among subpopulations of current smokers and obese individuals.
Conclusion:
The present study does not support an association between road traffic noise and filled prescriptions for AHTs. https://doi.org/10.1289/EHP627
Covid-19 and acute kidney injury in hospital: summary of NICE guidelines
No full textAcute kidney injury (AKI), a sudden reduction in kidney
function, is seen in some people with covid-19 infection. A
subset of patients develop severe AKI and require renal
replacement therapy (RRT). As in many settings, the
development of AKI is associated with an increased risk of
mortality.1 2 Although our understanding is incomplete, a picture is emerging from case reports and autopsy series of covid-19 specific causes of AKI. Intrinsic renal pathology including thrombotic vascular processes, viral mediated tubular cell injury, and glomerulonephritis have been reported, as well as AKI resulting from extrinsic factors such as fluid depletion, multi-organ failure, and rhabdomyolysis.3-7 Anecdotal reports
have emerged of proximal tubular injury with Fanconi syndrome that manifests as hypokalaemia, hypophosphataemia, normal anion gap metabolic acidosis, and hypovolaemia from salt wasting. Importantly, AKI can occur at all stages of covid-19 infection, so clinical vigilance and consideration of risk factors for AKI alongside early detection and diagnosis are essential components of general supportive care. Fluid management is
central to this. This article summarises key points from the National Institute for Health and Care Excellence (NICE) covid-19 rapid guideline on AKI in hospital
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