Many probability-based uncertainty quantification (UQ) schemes require a large amount of sampled data to build credible probability density function (PDF) models for uncertain parameters. Unfortunately, the data collected in compressor blades of aero-engine are mostly limited due to expensive and time-consuming tests. In this paper, we develop a preconditioner-based data-driven polynomial chaos (PDDPC) method that can efficiently deal with uncertainty propagation of limited sampled data. The calculation accuracy of PDDPC method is closely related to the sample size of collected data. Therefore, the influence of sample size on PDDPC method is investigated using a nonlinear test function. Subsequently, we consider the real manufacturing errors of stagger angle for compressor blades. Under three different operating conditions, PDDPC method is applied to investigate the effect of stagger angle error on UQ results of multiple aerodynamic parameters of a two-dimensional compressor blade. The results show that as the sample size of measured data increases, UQ results of aerodynamic performance obtained by PDDPC method gradually converge. There exists a critical sample size that ensures accurate UQ analysis of compressor blades. The probability information contained in the machining error data is analyzed through Kullback-Leibler divergence, and the critical sample size is determined. The research results can make a valuable reference for the fast and cheap UQ analysis of compressor blades in practical engineering.

X-ray tomography is an effective non-destructive testing method for industrial quality control. Limited angle tomography can be used to reduce the amount of data that needs to be acquired and thus speed up the process. In some industrial applications, however, objects are flat and layered, and laminography is preferred. It can deliver 2D images of the structure of a layered object at a particular depth from very limited data. An image at a particular depth is obtained by summing those parts of the data that contribute to that slice. This produces a sharp image of that slice while superimposing a blurred version of structures present at other depths. In this paper, we investigate an optimal experimental design (OED) problem for laminography that aims to determine the optimal source positions. Not only can this be used to mitigate imaging artifacts, it can also speed up the acquisition process in case where moving the source and detector is time consuming (e.g., in robotic arm imaging systems). We investigate the imaging artifacts in detail through a modified Fourier Slice Theorem. We address the experimental design problem within the Bayesian risk framework using empirical Bayes risk minimization. Finally, we present numerical experiments on simulated data.

In the present work, a methodology for wind field reconstruction based on the Meteo Particle Model (MPM) from Numerical Weather Prediction (NWP) data is presented. The development of specific wind forecast services is a challenging research topic, in particular for what concerns the availability of accurate local weather forecasts in highly populated areas. Currently, even if NWP Limited Area Models (LAMs) are run at spatial resolution of about 1 km, this level of information is not sufficient for many applications, for example to support drone operation in urban contexts. The coupling of MPM with the NWP Limited Area Model COSMO has been implemented in such a way that the MPM reads the NWP output over a selected area and provides wind values in the generical point considered for the investigation. The numerical results obtained reveal a good behavior of the method in reproducing the general trend of wind speed, as confirmed also by the power spectra analysis. The MPM is able to step over the intrinsic limitations of the NWP model in terms of spatial and temporal resolution even if the MPM inherits the bias that inevitably affects the COSMO output.

Granulomatosis with polyangiitis (GPA) is an autoimmune disease which is a type of anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis that frequently affects the lungs and kidneys. However, GPA limited to a single organ has also been reported. A 73-year-old man was admitted for back pain and fever. We detected elevated levels of inflammatory markers and myeloperoxidase-ANCA. Magnetic resonance imaging indicated diffuse inflammation of the back and psoas muscles. Histology showed degenerated muscle fibers and granulomatosis vasculitis with mixed lymphoplasma cell infiltration. High-dose methylprednisolone therapy improved his symptoms. A final diagnosis of GPA limited to the muscles was made.

Unlike the contiguous windthrows, the diffuse windthrows occurred as a result of wind gusts of lower speed (100-140 km/h) than in the first case (>140 km/h) are much more difficult to detect due to their much lower areas and due to their very large number, of several hundreds in the wooded mountain massifs. The objective of this research is to present a rapid procedure for the detection of the diffuse windthrows based on low cost, Landsat type images, knowing that certain sensors cannot be accessed without significant investments. Our application is based on the study of effects caused by the Xynthia storm in the Vosges Mountains in the North-East of France, on 28 February 2010. Thus, based on two sets of Landsat satellite images, we used the “dark object” approach and the Disturbance Index, as well as a classification of the images before and after the storm, resulting in a change map. Following the detection process, 257 scattered polygons were detected, totalling 229 ha. For validation purposes, high-resolution images and orthophotoplans taken before and after storm were used. The error matrix was calculated, achieving an overall accuracy of 86%, which confirms the quality of our analysis and supports this procedure for detecting diffuse windthrow based on low cost resources.

Microfluidics technology has not impacted the delivery and accessibility of point of care health services like diagnosis of infectious disease diagnosis, monitoring health or delivering interventions. Most microfluidics prototypes from academic research are not easy to manufacture with industrial scale fabrication techniques and cannot be operated without complex manipulations of supporting equipment and additives such as labels or reagents. We propose a label- and reagent-free inertial spiral microfluidic device to separate red blood, white blood and dendritic cells from blood fluid for applications in health monitoring and immunotherapy. We demonstrate that using larger channel widths in the range of 200 to 600 µm allows separation of cells into multiple streams according to different size ranges and we utilize a novel technique to collect the closely separated focused cell streams without constricting the channel. When tested on actual human blood cells, 77% of dendritic cells were separated and 80% of cells remained viable after our assay. Our contribution is a method to adapt spiral inertial microfluidic designs to separate more than two cell types in the same device which is robust against clogging, simple to operate and suitable for fabrication and deployment in resource-limited populations.

This study aimed to identify and differentiate the factors that may affect the incidence of temporomandibular joint (TMJ) dysfunction in children with type 1 diabetes. We analyzed the TMJ symptoms, clinical and laboratory parameters of type 1 diabetes in a cohort of 101 consecutive patients. The participants were evaluated regarding the presence of pain in the masticatory muscles, TMJ sounds, mouth opening, maximum mouth opening, lateral and protrusive excursions, and Fonseca scores. The overall cohort showed a mean Fonseca score of 20 ± 13.89, referred to mild TMJ dysfunction. Of patients, 34.65% presented at least one clinical symptom and were defined as having dysfunction. The most frequently recorded symptom was TMJ sounds, presented in 16.8% of children. In subjects with dysfunction compared to those without dysfunction, a significant difference was found at height standard deviation scores, Fonseca scores and maximum mouth opening (p<0.001). HbA1c levels, diabetes duration, age and gender did not reveal any significant differences between the patients with and without dysfunction. Type 1 diabetes is often associated with limited mouth opening and mild TMJ dysfunction. This dysfunction may reflect a more chronic condition and possibly exceeds the prevalence of cheiroartropathy. This study could be a starting point towards enhancing our understanding of clinically evident TMJ involvement in type 1 diabetes.

The theory of limited numbers says that the number is not infinite, that is, the number is limited in parts or completely. To reach a conclusion about the limited number, one must understand the meaning of mathematics and varied spaces, therefore, one must understand that mathematics and spaces interact. Mathematics has the function of describing the world precisely, where numbers represent elements or facts that rationally belong to space, and spaces have different intensities of specific physical concepts in each space; therefore, there is no infinite element or fact due to differences in the intensities of specific physical concepts in spaces that do not allow for the fact or fact in all spaces.

The use of spherical radial basis functions (SRBFs) in regional gravity field modeling has become popular in recent years. However, to our knowledge, their potential for combining gravity data from multiple sources, particularly data with different spectrum information in the frequency domain, has not been extensively explored. Therefore, band-limited SRBFs, which have good lo-calization characteristics in the frequency domain, are the main tool in this study. We propose a residual and a-prior accuracy comparative analysis method to determine the optimal degree of expansion of SRBFs for gravity data. Using this methodology, we constructed a high-resolution geoid model called ColSRBF2023 in Colorado. The degrees of expansion for terrestrial and airborne data were set to 5200 and 1840, respectively. Results indicate that ColSRBF2023 has a standard deviation (STD) value of 2.3 cm compared to the GSVS17 validate data. This value is 2-6 mm lower than models obtained using different degrees of expansion for gravity data and models from other institutions considered in this study. Additionally, at the 1'×1' grid in the entire target area, ColSRBF2023 has an STD value of 2.4 cm when compared to the validation model. This value is also the best among the options examined in this study. These findings highlight the importance of determining the optimal expansion degree of gravity data, particularly when constructing high-resolution gravity field models in rugged mountainous regions.

In past years, lithium-ion batteries (LIBs) can be found in every aspect of life, and batteries, as energy storage systems (ESSs), need to offer electric vehicles (EVs) more competition to be accepted in markets for automobiles. Thick electrode design could reduce the use of non-active materials in batteries that its energy density would be improved and its cost would be cut. However, thick electrodes are limited by their weak mechanical stability and poor electrochemical performance, these limitations could be classified as the critical cracking thickness (CCT) and the limited penetration depth (LPD). The understanding of the CCT and the LPD have been proposed and the recent works on breaking the CCT and improving the LPD are listed in this article. By comprising these attempts, some thick electrodes could not offer higher mass loading or higher accessible areal capacity that would defeat the purpose.

The 2019 Coronavirus pandemic which was initially referred to as 2019-nCoV, was first identified in Wuhan, China. Early response from the Chinese government included quarantine of infected persons, isolation and total lockdown of Wuhan province to prevent further spread. With the spread of the disease across national borders and declaration of the disease as a global pandemic, there has been a robust response by the international community to contain this deadly virus and prevent its further spread worldwide. Africa is not left out of this rampaging pandemic with documented cases in over 40 countries and still rising. Although extensive studies have been carried out on the novel SARS-CoV-2 on its pathogenesis, mode of infection and virulence but much is still unknown. However, potentially infectious samples are received routinely in the medical laboratory for analysis. This technical note reviews good laboratory practice (GLP) and processes across the different specialities of Medical Laboratory practice that should minimize the risk of infection to laboratory staff especially in resource-limited settings.

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

The Indorama Eleme Petrochemical Limited (IEPL) is found in the southern part of Nigeria more specifically on the Rivers State southern religion Nigeria. It consists of five distinct units, each of which generates its own unique trash at a rate of around 3115.98 tonnes per year. This study had the goal of concentrating on the management of the processing wastes in order to minimize the negative effects that they have on the environment. It is necessary to have an understanding of the number, nature, and make-up of IEPL's industrial wastes in order to effectively manage and regulate the development of waste there. As a result, we decided to collect data through the use of questionnaires. The classification of industrial wastes was accomplished by doing a comparative study and synthesis of research that were relevant to the management of petrochemical waste. Because an individual coding system is required for the integrated management of industrial waste, we gave each type of trash a code that consists of thirteen digits. The primary components of trash were catalysts (9.58 percent), metallic materials (7.62 percent), plastic barrels (35.77 percent), coke (12.66 percent), wood (4.47%), oil (4.15%), glass (0.028 percent), cooling tower packaging (6.7 percent), and other material (18.83 percent). According to the findings of the investigation into the physical qualities of the wastes, only 11.81% of these residues were liquid, while 88.19% were solid. The current handling of these wastes has been experiencing significant difficulties as of late. garbage management at IEPL was particularly challenging due to the wide variety of wastes produced there and the dangerous nature of the wastes themselves (88.19% of garbage was hazardous waste). Recycling and reuse of garbage may have been the greatest decision in some situations; yet, incineration and disposal of waste are also necessary options.

Gradient based methods are popularly used in training neural networks and can be broadly categorized into first and second order methods. Second order methods have shown to have better convergence compared to first order methods, especially in solving highly nonlinear problems. The BFGS quasi-Newton method is the most commonly studied second order method for neural network training. Recent methods have shown to speed up the convergence of the BFGS method using the Nesterov’s acclerated gradient and momentum terms. The SR1 quasi-Newton method though less commonly used in training neural networks, are known to have interesting properties and provide good Hessian approximations when used with a trust-region approach. Thus, this paper aims to investigate accelerating the Symmetric Rank-1 (SR1) quasi-Newton method with the Nesterov’s gradient for training neural networks and briefly discuss its convergence. The performance of the proposed method is evaluated on a function approximation and image classification problem.

Using co-precipitation to synthesize (CeO2)0.95(Y2O3)0.05 (YDC) and solid reaction method to synthesize (CeO2)0.75(ZrO2)0.25 (ZDC), and the characterization for both crystal structure and micro-structure of the two materials was conducted with X-ray diffraction (XRD) and scanning electron microscope (SEM) methods. Prepare the YDC and ZDC based limited current O2 sensor by employing platinum pasting bonding method. Sensing characteristics of the sensor were obtained at different conditions and study on the impact of temperature, O2 concentration as well as water vapor pressure on the sensing characteristics had been conducted. XRD results show that the phase structure of both YDC and ZDC is cubic phase. SEM results show that both YDC and ZDC layers are dense layers, which are then qualified to be the composition materials of the sensor. This limited current O2 sensor shows good sensing performance and conforms to the Knudsen model. Log(IL•T) depends linearly on 1000/T with R2 of 0.9904, IL depends linearly on x(O2) with R2 of 0.9726 and sensing characteristics are not affected by p(H2O).

Lithium-metal batteries offer high energy density, but are challenged by dendrite growth, which can lead to short circuits and battery failure. Various models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. In this study, a simple simulation model to predict macroscale dendrite growth on lithium metal electrodes is presented. The model uses a 3D single-particle diffusion-limited aggregation (DLA) algorithm, biased by the electric field, to simulate dendrite growth. The model was validated using 3D MRI of dendrite growth in a lithium symmetric cell. It was used to simulate deposition between consecutive measured dendrite structures. The model also introduces an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. This parameter was calibrated using the overlap and displacement of deposition sites metrics, achieving optimal results at c = 0.7. The model predictions were in good agreement with experimental data confirmed by fractal dimension analysis. This model provides a useful tool for predicting and studying dendrite formation in lithium and other metal batteries.

In Differentiated Services (DiffServ) architecture, Priority Service System (PSS) plays a vital responsibility to provide Quality-of-Service (QoS) for the applications based on networks. Priority queuing systems has always been a subject of interest for analytical modelling and evaluating the performance. However, previous works have mainly focused on performing priority queuing under range dependent traffics, namely Short Range Dependent (SRD) or Long Range Dependent (LRD). Recent studies revealed that realistic traffic demonstrates a heterogeneous nature for the modern networks that provide multiple services. In this paper, the results of analytical and simulation models of dynamic priority service systems is reviewed to study the heterogeneous traffic impact on designing and performance of the systems based on networks. In this paper, the results of analytical and simulation models of dynamic priority service systems is reviewed to investigate the impact of heterogeneous traffic on the design and performance of network-based systems. Here, the time of presence of requests in limited and unlimited buffer system is restricted. If the restriction is violated, the requests will be lost. The results of experiments conducted on both models are compared and it is identified that they differ in the allowable limit (2-9) %.

Radiotherapy commonly utilizes CBCT for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into HU values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow FOV systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent GAN processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm’s ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

The goods and services produced by forest and vegetation ecology system is the power by which human society can be promoted fast in high-quality and sustainable way. With the increase of population and economic development in water-limited regions, there is an increasing demand for the quantity and variety of forest vegetation ecosystem products and services. To meet the demands of this situation, most of the original forest has changed into farmland, non-native forest and grass land. As a result, water-plant relationship changed from equilibrium to non-equilibrium, which led to soil drought, soil degradation and vegetation decline in dry years or waste of soil water in rainy years in most of water-limited regions. In order to solve the questions and realize the sustainable utilization of soil water resources and the high quality and sustainable development of social economy, it is necessary to apply the utilization limit theory of soil water resources by plants and the theory of soil water carrying capacity for vegetation to adjust the relationship between plant growth and soil moisture to obtain the maximum yield and benefit of vegetation and serve high-quality and sustainable development.

Diagnostics is believed to drive about 70% of medical interventions, but this may not be true for all demographics. With more than 30% of the African population living in extreme poverty ($1.9 per day - 2022); and healthcare costs competing unfavorably against unbridled access to antibiotics and herbal concoctions, especially in Sub-Saharan Africa, how do you convince such a people to choose diagnostics first? More importantly, how do you design a medical device targeted at meeting the diagnostic needs of this demographic? In developing diagnostic devices for such a market, what is worth sacrificing? How dire is the need for advanced technologies in devices developed for such a demographic? These are questions that remain unaddressed by the technological breakthroughs and current research in medical device development, especially for infectious disease diagnostics. The objective of this paper is to underscore critical concerns which must be considered in the bid to successfully design medical diagnostic devices for Africa and perhaps, other limited-resource settings. It is also written as a suggestive guidance document for researchers whose interest is in the development of infectious disease diagnostic platforms for rural Africa and similar limited-resource environments.

The goods and services produced by forest vegetation ecosystem are the driving force for the rapid, high-quality and sustainable development of human society. In history, with the increase of population and economic development, there is an increasing demand for the quantity and variety of forest vegetation ecosystem products and services in a country or a region. To meet the demands, most of the original forest has become farmland, plantation and grass and crops land. As a result, the plant water relationship changed from equilibrium to non-equilibrium, which easily led to soil drought, soil degradation and vegetation decline in dry years or waste of soil water in rainy years in most of water-limited regions. In order to solve the question and realize the sustainable utilization of soil water resources and the high-quality sustainable development of forest, it is necessary to apply the limit theory of plant utilization of soil water resources and the vegetation carrying capacity theory in the critical period of plant-water relationship regulation to adjust the relationship between plant growth and soil moisture to obtain the maximum yield and benefit and realize high-quality and sustainable development.

Continual learning, a crucial facet of machine learning, involves the perpetual acquisition of valuable insights from incoming data, sans the necessity for full dataset access. Esteemed as a fundamental goal in artificial intelligence, continual learning grapples with an enduring challenge—catastrophic forgetting. A proficient model must exhibit adaptability for new data assimilation and robustness to retain existing knowledge. Class-incremental learning (CIL) aids the gradual integration of knowledge from newly introduced classes, forming a universal classifier. However, directly training the model with fresh class instances triggers a problem—forgetting distinguishing features of prior classes, causing a performance decline. Addressing such issues in machine learning, this survey aims to delineate significant challenges, outcomes, and recent advancements, including our contributions to CIL techniques, especially in image classification.