Tutorial : The discrete-sectional method to simulate an evolving aerosol

The discrete-sectional method to solve the general dynamic equations is a useful tool for the simulation of an evolving aerosol population. This tutorial is intended to equip the reader with the necessary knowledge to implement this method for a single component system. To this end, we provide step-by-step instructions on the construction of a discrete-sectional model, including details on simulation bin configurations and all the necessary equations to describe relevant physical processes in an aerosol, i.e. condensation/evaporation, coagulation, and external particle losses. Supplementary to the text is a functional, open source MATLAB code that implements the framework introduced in this tutorial. The interested readers can use the code either for learning purposes or to meet research demands. Lastly, we designed six test cases not only to verify the validity of our discrete-sectional model, but also to help the reader gain insight into the evolution of aerosol systems.Peer reviewe

Long-Term Pavement Performance Indicators for Failed Materials

State Transportation Agencies (STAs) use quality control/quality assurance (QC/QA) specifications to guide the testing and inspection of road pavement construction. Although failed materials of pavement rarely occur in practice, it is critical to have a sound decision framework to assist in making data-driven, informed decisions regarding failed materials because such decisions have profound impacts on the long-term performance of the pavement and the operation and maintenance costs of the responsible highway agencies. A performance-related specification (PRS) is a quality acceptance (QA) specification that specifies the acceptable levels of key acceptance quality characteristics (AQCs) that are directly related to fundamental engineering properties, which in turn, determine the long-term performance of the constructed end products. Two PRS tools, PaveSpec for Portland Cement Concrete Pavement (PCCP) and Quality Related Specification Software (QRSS) for QC/QA Hot Mixed Asphalt (HMA) pavement, were investigated in this study to develop decision frameworks for PCCP and HMA pavement to assist the decision-making regarding failed materials at INDOT. A large number of simulations of various scenarios in the context of INDOT pavement construction were conducted to fully develop and implement the decision framework. For PCCP, the newly developed decision framework based on PaveSpec was validated using data from an INDOT construction project. The framework is readily implementable to assist INDOT in making informed decision regarding failed materials for PCCP. For QC/QA pavement, it was found that QRSS is not an appropriate PRS tool to estimate the long-term performance because of its limitations, the misalignment between QRSS process and INDOT practice, and erroneous simulation results

Zebrafish foxo3b Negatively Regulates Antiviral Response through Suppressing the Transactivity of irf3 and irf7

Forkhead box O (FOXO)3, a member of the FOXO family of transcription factors, plays key roles in various cellular processes, including development, longevity, reproduction, and metabolism. Recently, FOXO3 has also been shown to be involved in modulating the immune response. However, how FOXO3 regulates immunity and the underlying mechanisms are still largely unknown. In this study, we show that zebrafish (Danio rerio) foxo3b, an ortholog of mammalian FOXO3, is induced by polyinosinic-polycytidylic acid stimulation and spring viremia of carp virus (SVCV) infection. We found that foxo3b interacted with irf3 and irf7 to inhibit ifr3/irf7 transcriptional activity, thus resulting in suppression of SVCV or polyinosinic-polycytidylic acid-induced IFN activation. By suppressing expression of key antiviral genes, foxo3b negatively regulated the cellular antiviral response. Furthermore, upon SVCV infection, the expression of the key antiviral genes was significantly enhanced in foxo3b-null zebrafish larvae compared with wild-type larvae. Additionally, the replication of SVCV was inhibited in foxo3b-null zebrafish larvae, leading to a higher survival rate. Our findings suggest that by suppressing irf3/irf7 activity, zebrafish foxo3b negatively regulates the antiviral response, implicating the vital role of the FOXO gene family in innate immunity.</p

A Synthesis Study on Collecting, Managing, and Sharing Road Construction Asset Data

Accurate and complete construction records and as-built data are the key prerequisites to the effective management of transportation infrastructure assets throughout their life cycle. The construction phase is the best time to collect such data. Assets such as underground drainage and culverts are visible and physically accessible only during construction. For assets such as guardrails, signals, and pavement, it is safer and more efficient to collect data during construction than after construction when the road segment is open to traffic. The purpose of this project was to conduct a synthesis study to 1) assess the current status at INDOT regarding the collection of asset data during the construction phase and the use of such data in the operation and maintenance (O&M) phase, and 2) develop a framework for INDOT to leverage the construction inspection and documentation process to collect data for assets. Data needs during O&M were identified through rounds of meetings with relevant INDOT business units. The current practice in construction documentation was investigated in detail. A survey of state highway agencies (SHAs) was conducted to assess the state-of-the-practice. A practical framework was developed to leverage the construction inspection and documentation practice to collect asset data that are needed in O&M. The framework uses specific pay items—construction activities that result in physical structures—as the bridge to connect plan assets (i.e. physical structures specified in the design documents) to their corresponding counterparts in the asset management systems. The framework is composed of 1) a data needs component for determining the information requirements from the O&M perspective, 2) a construction documentation module, and 3) a mapping mechanism to link data items to be collected during the construction documentation to data items in the asset management systems. The mapping mechanism was tested and validated using four priority asset classes—underdrains, guardrails, attenuators, and small culverts—from an INDOT construction project. The testing results show that the newly developed framework is viable and solid to collect asset data during the construction phase for O&M use in the future, without adding extra workload to construction crews. The framework can reduce/eliminate the duplicate data collection efforts at INDOT, leading to savings and efficiency gains in the long term

Neural Collapse Inspired Federated Learning with Non-iid Data

One of the challenges in federated learning is the non-independent and identically distributed (non-iid) characteristics between heterogeneous devices, which cause significant differences in local updates and affect the performance of the central server. Although many studies have been proposed to address this challenge, they only focus on local training and aggregation processes to smooth the changes and fail to achieve high performance with deep learning models. Inspired by the phenomenon of neural collapse, we force each client to be optimized toward an optimal global structure for classification. Specifically, we initialize it as a random simplex Equiangular Tight Frame (ETF) and fix it as the unit optimization target of all clients during the local updating. After guaranteeing all clients are learning to converge to the global optimum, we propose to add a global memory vector for each category to remedy the parameter fluctuation caused by the bias of the intra-class condition distribution among clients. Our experimental results show that our method can improve the performance with faster convergence speed on different-size datasets.Comment: 11 pages, 5 figure

Risk-Based Construction Inspection

Construction inspection is a critical component in the quality assurance (QA) program to ensure the quality and long-term performance of pavements. Over the years, INDOT has been developing and modifying its standard specification to set requirements for construction inspection and material testing. With the retirement of experienced employees, INDOT is challenged with the lack of knowledge to effectively inspect the critical elements of construction results/deliverables such as pavement, soil embankment, and bridge (decks). There is a critical need for INDOT to allocate limited resources to the riskiest areas and equip construction inspectors with necessary knowledge to conduct inspection, ensure the quality of construction results, and minimize risks to INDOT. This study developed a risk-based inspection guide that has addressed the aforementioned problems of shortage in staffing and loss and lack of knowledge by providing answers in aspects of what, when, how, and how often to inspect. A comprehensive list of testing and inspection activities were extracted from INDOT’s material testing manual, INDOT’s standard specification, and the QA implementation at the Ohio River Bridge (ORB) project. This list was narrowed down to a core set of items based on survey responses and interviews with INDOT domain experts. Testing and inspection activities in the core set were aligned with the construction process. The risk associated with each inspection activity was assessed by considering both the probability of failure and consequence severity of failure in four dimensions: cost, time, quality, and safety. A composite risk index was developed as a single measure for the overall risk. All inspection activities were prioritized based on the composite index. For implementation, a linking mechanism was developed to link inspection activity, pay item, and check items (extracted from specification). This linking mechanism aligns with the business process of construction inspection at INDOT: starting with a pay item, field inspectors retrieve the associated check items and their inspection priority (based on risk), inspection frequency, and inspection criteria. A digital, ontology- and risk-based inspection system was proposed and its conceptual model was delivered to INDOT for its incorporation in the field application of construction documentation, a component of the e-Construction initiatives at INDOT. It will be tested on Project R-30397 through a pilot study

A hybrid active contour segmentation method for myocardial D-SPECT images

Ischaemic heart disease has become one of the leading causes of mortality worldwide. Dynamic single-photon emission computed tomography (D-SPECT) is an advanced routine diagnostic tool commonly used to validate myocardial function in patients suffering from various heart diseases. Accurate automatic localization and segmentation of myocardial regions is helpful in creating a three-dimensional myocardial model and assisting clinicians to perform assessments of myocardial function. Thus, image segmentation is a key technology in preclinical cardiac studies. Intensity inhomogeneity is one of the common challenges in image segmentation and is caused by image artefacts and instrument inaccuracy. In this paper, a novel region-based active contour model that can segment the myocardial D-SPECT image accurately is presented. First, a local region-based fitting image is defined based on information related to the intensity. Second, a likelihood fitting image energy function is built in a local region around each point in a given vector-valued image. Next, the level set method is used to present a global energy function with respect to the neighbourhood centre. The proposed approach guarantees precision and computational efficiency by combining the region-scalable fitting energy (RSF) model and local image fitting energy (LIF) model, and it can solve the issue of high sensitivity to initialization for myocardial D-SPECT segmentation

Survival probability of new atmospheric particles : closure between theory and measurements from 1.4 to 100 nm

The survival probability of freshly nucleated particles governs the influences of new particle formation (NPF) on atmospheric environments and the climate. It characterizes the probability of a particle avoiding being scavenged by the coagulation with pre-existing particles and other scavenging processes before the particle successfully grows up to a certain diameter. Despite its importance, measuring the survival probability has been challenging, which limits the knowledge of particle survival in the atmosphere and results in large uncertainties in predicting the influences of NPF. Here we report the proper methods to retrieve particle survival probability using the measured aerosol size distributions. Using diverse aerosol size distributions from urban Beijing, the Finnish boreal forest, a chamber experiment, and aerosol kinetic simulations, we demonstrate that each method is valid for a different type of aerosol size distribution, whereas misapplying the conventional methods to banana-type NPF events may underestimate the survival probability. Using these methods, we investigate the consistency between the measured survival probability of new particles and the theoretical survival probability against coagulation scavenging predicted using the measured growth rate and coagulation sink. With case-by-case and time- and size-resolved analysis of long-term measurement data from urban Beijing, we find that although both the measured and theoretical survival probabilities are sensitive to uncertainties and variations, they are, on average, consistent with each other for new particles growing from 1.4 (the cluster size) to 100 nm.Peer reviewe

Optimizing Methanol Blending Performance of Electronically Controlled Diesel Engines through Fuzzy Analysis

This paper presents a comprehensive optimization approach for enhancing the performance of a methanol/diesel Exhaust Gas Recirculation (EGR) engine. Initially, a hybrid fuel engine combustion chamber model was developed using AVL-FIRE software, and the simulated results were compared with the values obtained from bench tests. An orthogonal experimental design was employed to optimize five key factors, namely methanol blending ratio, EGR rate, injection advance angle, intake pressure, and intake temperature. Evaluation indexes were established, with indicated power and NO emissions assigned weights of 0.35 and 0.65, respectively. The optimal parameter combinations were determined as follows: methanol blending ratio (a1=20%), EGR rate (a2=12.5%), injection advance angle (a3=16.6°CA), intake temperature (a4 = 315.15 K), and intake pressure (a5=0.173 MPa). The indicated power of the optimized configuration reached 47.8 kW, slightly lower than the original 55 kW, while the NO emission mass fraction decreased to 1.9×10-4%, representing a significant reduction of 77.6% compared to the original value of 8.5×10-4%. This optimization methodology demonstrates the effective reduction of NO emissions without compromising power performance in methanol/diesel EGR engines

Effect of Injection Advance Angle on the Performance of Butanol-Diesel Dual-fuel Engines

In order to further investigate the performance of the butanol-diesel dual-fuel engine, this paper uses the 4190ZLC-2 marine medium-speed diesel engine as a prototype and establishes a dual-fuel engine high-pressure cycle model using AVL-FIRE simulation software. The injection advance angle was set to 16.6°, 18.6°, 20.6° and 22.6° respectively, and its effect on the performance of the dual-fuel engine was investigated by varying the injection advance angle. The results show that as the injection advance angle increases, the incylinder pressure and temperature also increase. When the injection advance angle is 22.6°CA, compared with the original engine, CO emission is reduced by 16.8%, NO emission is increased by 7.4%, carbon smoke emission is reduced by 16.9%, and the indicated power is 52.6kW, which is increased by 1.8%