Influence Of Different Settings Research Articles

Effects of sensor configuration optimization on airflow estimation in urban environment: A case study with a building group model

The complicated flow field in an urban area can be properly estimated using sparse sensor measurements and several estimation methods. Because sensor measurements are the only real-time information provided for estimations, it is necessary to investigate the effects of the sensor placement on the estimation accuracy. This study compared the performance of uniform and optimized sensor configurations in estimating the streamwise velocity field for a block-arrayed building group model. The optimized configuration was produced by a QR decomposition method, which is a data-driven optimization method that uses the input POD11POD-LSE: proper orthogonal decomposition and linear stochastic estimation; POD: proper orthogonal decomposition; LSE: linear stochastic estimation; SCO: sensor configuration optimization; CFD: computational fluid dynamics; LES: large-eddy simulation; ANN: artificial neural network; RMSE: root mean squared error. (Proper Orthogonal Decomposition) modes and deploys sensors to the positions where dominant flow structures can be efficiently measured. The robustness of two estimation methods, inverse POD and POD-LSE (Linear Statistic Estimation), against different sensor configurations was analyzed. The influence of different settings, like the number of POD modes and the number of sensors, on the optimization and estimation were also explored. According to the results, the optimal configuration can reduce the root mean square error (RMSE) by about 86% in inverse POD and 10% in POD-LSE when compared to the uniform one. Applying LSE can relieve the deficiency caused by poorly placed sensors, but since it can only supplement linear information, the configuration optimization is still critical especially to nonlinear estimations.

Integrated method for intelligent structural design of steel frames based on optimization and machine learning algorithm

Optimization methods using metaheuristic algorithms have been widely used in steel frame design to improve the inefficient traditional design method due to repeated model tuning and massive mechanical analysis. However, the random search feature of them may easily result in poor performances. In this paper, combining metaheuristic algorithms and machine learning methods, a highly integrated method based on an on-line model training, updating and parameter tuning process is proposed to improve the performance of the optimization algorithm with general forms and parameters. It reduces the impact of the iterative mechanism and parameter setting of metaheuristic algorithms on their performance. Such method is introduced to intelligent structural design of steel frames including three steps. The standard optimization process is conducted to search optimal design and simultaneously collect the mechanical analysis data of the structure. Then the data is adopted to generate and update surrogate models of structural responses dynamically while an analysis-based feature engineering and an automatic model tuning technique are employed to improve the model accuracy. Finally, a much more efficient procedure is presented to obtain potential solutions which are used to improve the convergence rate and performance of standard optimization. Four cases are used to study the effectiveness of the integrated method and the influence of different settings is discussed, as well as its generality. As a conclusion, the proposed method can achieve structural safety and economic benefit of steel frames, which exhibits superiorly in terms of robustness, optimal results and computational cost even in large-scale optimization problems of complicated frames.

HOW AI-SUPPORTED SEARCHES THROUGH OTHER PERSPECTIVES AFFECT IDEATION OUTCOMES

Seeking inspiration from other perspectives is a prominent mechanism to support ideation. AI-based language models can help overcome information processing limits and efficiently structure large solution spaces spanned by prior ideas. However, it remains unclear how the search through a solution space affects the subsequent idea generation. This study explores the influence of different sets of prior idea stimuli pre-structured by an AI-supported clustering on ideation outcomes. The sets varied in quantity and semantic diversity. In a survey experiment, 181 participants generated 447 ideas evaluated according to major idea performance characteristics. Results indicate that seeing an extensive set of ideas from various clusters improves idea novelty and positively and semantic diversity. In a survey experiment, 181 participants generated 447 ideas evaluated according to major idea performance characteristics. Results indicate that seeing an extensive set of ideas from various clusters improves idea novelty and positively interacts with domain-specific knowledge. However, it negatively affects idea feasibility and specificity. These findings encourage innovators seeking particularly novel ideas to complement their current processes with AI-supported clustering tools while taking steps to avoid vagueness.

A hybrid intelligent genetic algorithm for truss optimization based on deep neutral network

The truss optimization problem has been extensively investigated, and the optimized trusses have been widely used in various fields. Truss optimization is a challenging optimization involving many difficulties, such as discrete variables and non-convex problems. In order to solve these problems, various metaheuristic optimization methods have been proposed. Due to the stochastic feature of these methods, it is always computationally intensive, and the optimization results may vary greatly in different optimization runs. In this paper, a hybrid intelligent genetic algorithm (HIGA) is proposed to improve the effectiveness and efficiency of truss optimization problems. This method systematically integrates deep neural network (DNN) and genetic algorithm (GA) in the optimization process. A two-step training procedure is proposed where the data generated during the optimization process is exploited to update DNN. Next, based on the generated DNN model, an optimization prediction procedure is proposed to seek more optimized trusses in an efficient manner. Through the investigation of three classical truss problems (size optimization, shape optimization and size-shape integrated optimization), the effectiveness of the proposed method is validated. In addition, the influence of different settings on the optimization performance and efficiency is investigated to demonstrate the applicability and robustness of the proposed method.

Epizootic dynamics of yellow fever in forest fragments: An agent-based model to explore the influence of vector and host parameters

Yellow fever (YF) is an acute infectious hemorrhagic disease caused by the yellow fever virus (YFV) and transmitted to humans by infected mosquitoes. In Brazil and other South American countries, the disease has been restricted to the sylvatic cycle, in which the virus circulates among mosquitoes and non-human primates in forested areas. Frequent outbreaks in the Amazon basin that spread to other Brazilian ecoregions have been observed in recent years. The most recent started in 2014 and spread to forests in densely populated areas on the Brazilian Atlantic Coast, resulting in the death of hundreds of humans and thousands of monkeys. However, the underlying ecological mechanisms that support YFV amplification and the severity of an epizootic and persistence of the virus on a microgeographic scale in these forest patches are still poorly understood. Here, we developed an agent-based model that simulates the dynamics of YFV transmission in a hypothetical forest fragment. The proposed model contains individual agents representing mosquitoes, breeding sites, howler monkeys (Alouatta) and other vertebrate species living and interacting in an environment where the YFV has emerged. The model simulations aimed to investigate the isolated and interaction effects of important input parameters linked to mosquitoes, monkeys, the environment and hypothetical alternative hosts on the following outcomes: (1) maximum proportion of infected mosquitoes, (2) proportion of dead monkeys and (3) YFV persistence in the environment. Local and global sensitivity analyses were used to assess the influence of different sets of input parameter values on the outputs. The model simulations indicated that mosquito abundance had the greatest influence on the outputs and made a major contribution to monkey mortality. Additionally, most of the variation in the outputs was due to complex and indirect effects of the different input parameters. These results suggest that mosquito density is one of the main factors responsible for YFV amplification during epizootics and reinforce the hypothesis that the severity and persistence of an outbreak depend on a complex web of interactions between different factors associated with vectors, hosts and the environment.

Influence Analysis in Music: Artists and Genres

Music is an important part of human civilization, which is accompanied by the emergence and development of human civilization. While music is developing, different genres of music or musicians’ styles are all influencing each other. Therefore, it is significant to understand and measure the influence of music influencers and their followers in different genres of music with social development. Address to identify the influence network of American music, firstly, a PageRank model was proposed to calculate the music influence of every artist among the network. According to the results, we built the subnetworks to illustrate the influence of different sets. Then, we did research on distinguishing between genres, which utilized the random forest classifier. Furthermore, Gini impurity is used to demonstrate the feature's importance. Our result shows that Pop is the best influential genre, and acousticness is the most distinguishing feature.

Can Dental Office Lighting Intensity Conditions Influence the Accuracy of Intraoral Scanning?

The aim of this study was to evaluate the influence of different settings of ambient light intensity inside the dental office on the accuracy (trueness and precision) of an intraoral scanner (IOS). A full crown preparation was conducted on a resin molar which was scanned using a high resolution extraoral scanner to obtain a reference model. Six light settings were chosen based on the most clinically relevant light conditions inside the workspace, and the preparation was scanned using an intraoral scanner (PlanScan, Planmeca). The obtained data was analyzed using a professional 3D quality control software (Geomagic Control X). There was no statistically relevant difference between the groups when regarding trueness, although a slight influence of the light intensity could be observed on the trueness values. Regarding precision, the best results were obtained in the 3800 lux group, with the other groups presenting close values, excepting the extreme values (400 lux and 11 000 lux) groups that proved to be the most deficient.

Corrective feedback: Beliefs and practices of Vietnamese primary EFL teachers

This study investigates Vietnamese EFL teachers’ beliefs and practices regarding oral corrective feedback, exploring and seeking to explain some of the relationships between beliefs and classroom practices. Data were collected in primary schools in Vietnam, and consist of 24 classroom observations and interviews with six teachers. Overall, the teachers showed high levels of awareness of the benefits of oral corrective feedback. They nominated pronunciation errors as the most important target for correction in the primary context. In practice, although pronunciation and grammar accounted for the majority of the total errors, leading to the majority of total feedback moves, the frequency of feedback per error was much higher for vocabulary errors. Prompts were reported by teachers to be more effective and more favourable than reformulations, but this preference was not reflected in the classroom observations, in which a large number of didactic recasts were used. The observed discrepancies are interpreted in relation to contextual factors and the influence of different sets of beliefs on practices. It was also noted that the linguistic realizations of these teachers’ feedback moves contained some inaccuracies. Implications for educational practice are discussed.

ViMo: Multiperson Vital Sign Monitoring Using Commodity Millimeter-Wave Radio

The continuous development of 802.11ad technology provides new opportunities in wireless sensing. In this work, we propose ViMo, a calibration-free remote vital sign monitoring system that can detect stationary/nonstationary users and estimate the respiration rates (RRs) as well as heart rates (HRs) built upon a commercial 60-GHz WiFi. The design of ViMo consists of two key components. First, we design an adaptive object detector that can identify static objects, stationary human subjects, and human in motion without any calibration. Second, we devise a robust HR estimator, which eliminates the respiration signal from the phase of the channel impulse response (CIR) to remove the interference of the harmonics from breathing and adopts dynamic programming (DP) to resist the random measurement noise. The influence of different settings, including the distance between a human and the device, user orientation and incidental angle, blockage material, body movement, and conditions of multiuser separation is investigated by extensive experiments. The experimental results show that ViMo monitors user’s vital signs accurately, with a median error of 0.19 and 0.92 breaths per minute (BPM), respectively, for RR and HR estimation.

A new computational model for flow in karst-carbonates containing solution-collapse breccias

We develop a new three-scale (micro/meso/macro) computational model based on a reiterated homogenization procedure to describe flow in carbonate rocks containing complex geological structures, such as fractures and solution-collapse breccias in the sense of Loucks (AAPG Bull. 83(11), 1795–1834, 1999). In this setting, we construct a hierarchical karst-fracture model wherein the larger geological objects are incorporated explicitly whereas the higher density microscopic structures are homogenized and replaced by equivalent continua with properties computed from self-consistent homogenization schemes. In the upscaling method, we subdivide the different clastic arrangements in the breccia into crackle, mosaic, and chaotic substructures where equivalent permeability and elastic constants are assigned to each layer within the breccia. After reconstructing the mesoscopic coefficients, we adopt a flow-based upscaling to the macroscale, where the characteristic length is associated with a typical coarse grid cell of a reservoir simulator. The mesoscopic flow equations are constructed based on the discrete fracture model (DFM) and discretized by a robust computationally scheme with the ability to handle strong heterogeneity induced by the collapse breccia and pressure jumps across flow barriers. In addition to the scenario wherein the collapse breccia network is composed of disconnected objects (isolated chambers), we also develop a reduced model for the case of connected karst facies playing the role of a network of enlarged fractures. Numerical results, with input data extracted from outcrops drone images, are presented illustrating the influence of different settings on flow patterns and their effect upon the magnitude of macroscopic properties.

Analysis of the Influence of Different Settings of Scan Sequence Parameters on Vibration and Noise Generated in the Open-Air MRI Scanning Area

A system of gradient coils of the magnetic resonance imaging (MRI) device produces significant vibration and noise. Energetic relations of these phenomena are analyzed depending on MRI scan parameters (sequence type, repetition time (TR), echo time (TE), slice orientation, body weight). This issue should be investigated because of negative physiological and psychological effects on a person exposed to vibration and acoustic noise. We also measured the sound pressure level in the MRI scanning area and its vicinity in order to minimize these negative impacts, depending on intensity and time duration of exposition. From the recorded vibration and noise signals, the energy parameters were determined and statistically analyzed, and the obtained results were visually and numerically compared. Finally, subjective evaluation by a listening test method was used to analyze the influence of the generated MRI noise on the human psyche.

Psychedelic-Assisted Therapies—Palliative Care Clinical & Research Priorities (TH317)

•Identify types of suffering of psychological, emotional, social, spiritual, or existential nature that are potential indications for supervised therapy involving psychedelic medications, as well as important contraindications to this class of drugs.•Critically evaluate published findings from the expanding evidence base of clinical research into psychedelic-assisted treatment of patients with depression, anxiety, demoralization, and existential and spiritual suffering. People with advanced medical illness often experience anxiety, feelings of hopelessness and loss of meaning and value of life. Some conclude that their life is not worth living and desire to hasten their deaths. Currently available treatments for depression, anxiety, and spiritual distress often fail to alleviate suffering among people who are seriously ill. Research involving psilocybin and related compounds have shown significant benefit suggesting that this class of drugs may offer therapeutic potential in treating persistent, non-physical suffering. During the 2018 AAHPM-HPNA Assembly Meeting a concurrent session on psychedelic therapy drew approximately 600 participants, indicating substantial interest among palliative care providers in the research, cautions, and potential clinical application of these medications. The 2019 session will build on this interest and the content of the previous session to begin defining priorities for research and clinical use of psychedelics within palliative care practice. Recent and ongoing clinical studies pertaining to psychedelic-assisted therapies in care of seriously ill patients will be reviewed. Session faculty will present results of key informant interviews conducted with palliative care clinicians and researchers regarding their priorities for future studies and therapeutic application of these medications. Survey items will include considerations of: a) patient selection and screening, b) frequency and duration of treatment sessions with specific drugs, c) selection of medications (entheogens and empathogens) in treating syndromes of depression, anxiety, demoralization and PTSD, d) necessary levels of supervision, e) safety and therapeutic influence of different settings. These results will inform discussion among session participants. A distillation of this interactive discussion will inform priorities for a developing Special Interest Group on Psychedelic Therapies.

Calculations of cross-sections, dissociation rate constants and transport coefficients of Xe2+ colliding with Xe.

A quantum formalism and classical treatment have been used for electrons and nuclei, respectively, in a hybrid method in order to study the dynamics of electronically ground-state ionic xenon dimer, Xe2+, in its parent gas. A semiempirical Diatomics In Molecules approach has been used to model the effective electronic Hamiltonian with different sets of input diatomic potentials (ionic and neutral). Non-reactive scattering and collision induced dissociation cross-sections have first been calculated and then injected in a Monte Carlo code for the simulations of the transport coefficients and dissociation rate constant calculated at ambient temperature and atmospheric pressure. Selected transport coefficients, such as Xe2+ mobility for which experimental measurements are available, have been compared to experimental results while transversal and longitudinal diffusion coefficients are compared to pseudo-experimental data obtained from inverse method calculations. Investigation of rotational-vibrational effects and the influence of different sets of ionic and neutral diatomic potentials have been studied.

P 130—The influence of different settings on accuracies of gait algorithms

P 130—The influence of different settings on accuracies of gait algorithms

Study on kinematic and compliance test of suspension

Chassis performance development is a major difficulty in vehicle research and development, which is the main factor restricting the independent development of vehicles in China. These years, through a large number of studies, chassis engineers have found that the suspension K&C characteristics as a quasi-static characteristic of the suspension provides a technical route for the suspension performance R&D, and the suspension K&C test has become an important means of vehicle benchmarking, optimization and verification. However, the research on suspension K&C test is less in china, and the test conditions and setting requirements vary greatly from OEM to OEM. In this paper, the influence of different settings on the characteristics of the suspension is obtained through experiments, and the causes of the differences are analyzed; in order to fully reflect the suspension characteristics, the author recommends the appropriate test case and settings.

Making a case for the on-demand multiple distributed message queue system in a Hadoop cluster

In this paper, we present a framework that can provide users with a simple, convenient and powerful way to deploy multiple message queue system on demand in a Hadoop cluster. Specifically, we are leveraging the Apache Kafka which is one of the state of art distributed message queue systems that can achieve high throughput, low latency, and good load balancing. Our framework provides automation of setting up and starting Kafka brokers on the fly and users can leverage the framework to quickly adopt Kafka without spending much efforts on installation and configuration challenges. In addition, the framework supports users to run their Kafka-based applications without detailed knowledge about the Hadoop YARN APIs and underlying mechanisms. We present a use case of the framework to evaluate Kafka’s performance with various test cases and working scenarios. The experimental results allow Kafka’s potential users to perceive the influences of different settings on the queuing performance.

Geographies of external voting: the Tunisian elections abroad since the 2011 Uprising

This contribution draws on the developing literature on the participation of citizens’ abroad in elections of their country of origin. It looks at Tunisia as a case study. Although it focuses on a single case study, this paper is comparative to the extent that it puts the case of Tunisia in perspective with other cases from the literature, and that it discusses the influence of different settings on the voter turnout and the voting behaviour of the Tunisians abroad. By doing so, it shows how Tunisian specificities contribute to academic discussions on the diffusion of new citizenship norms in the age of globalization and on the extent to which migration contributes to political change in labour sending countries. The paper first explores the adoption of external voting provisions by the former Tunisian president Zine El Abidine Ben Ali as soon as 1988, and the extension of external voting rights after the 2011 Uprising. The paper further adopts a geographic, diachronic, and multi-level approach to analyse the voter turnout and the voting behaviour among the Tunisians abroad since 2011. Results first show that the adoption and implementation of external voting in Tunisia is closely articulated to domestic processes of political change, regardless of the type of regime (authoritarian or democratic). Furthermore, results show that the voting behaviour of the Tunisians abroad follows the same trends than their Tunisian counterparts, but that it varies significantly depending on their place of residence (country, city, and neighbourhood). Such observation challenges the idea according to which migrants socialised with liberal values in Western societies tend to promote such values in their homeland. In contrast, this paper argues that the voter turnout and the voting behaviour in external elections depend on pre-migration variables and on the local context in which migrants are embedded.

An Ecological Perspective of Latina/o College Student Ethnicity

ABSTRACTAlthough Latinos are the largest minority population in the United States and the largest minority population on American campuses, there is little research concerning Latino college student ethnic identity. This study incorporates an ecological model to examine the levels of influence of different settings and backgrounds of Latino college students. The findings show that Latinos from different countries of origin and within the same countries of origin identify their ethnic identity differently depending on several factors including generation, immigration status, country of origin, language preferences, and discrimination.

Exploration of a physiologically-inspired hearing-aid algorithm using a computer model mimicking impaired hearing

Objective: To use a computer model of impaired hearing to explore the effects of a physiologically-inspired hearing-aid algorithm on a range of psychoacoustic measures. Design: A computer model of a hypothetical impaired listener’s hearing was constructed by adjusting parameters of a computer model of normal hearing. Absolute thresholds, estimates of compression, and frequency selectivity (summarized to a hearing profile) were assessed using this model with and without pre-processing the stimuli by a hearing-aid algorithm. The influence of different settings of the algorithm on the impaired profile was investigated. To validate the model predictions, the effect of the algorithm on hearing profiles of human impaired listeners was measured. Study sample: A computer model simulating impaired hearing (total absence of basilar membrane compression) was used, and three hearing-impaired listeners participated. Results: The hearing profiles of the model and the listeners showed substantial changes when the test stimuli were pre-processed by the hearing-aid algorithm. These changes consisted of lower absolute thresholds, steeper temporal masking curves, and sharper psychophysical tuning curves. Conclusion: The hearing-aid algorithm affected the impaired hearing profile of the model to approximate a normal hearing profile. Qualitatively similar results were found with the impaired listeners’ hearing profiles.

Modeling TCP Startup Performance*

The paper proposes a Markovian approach to the performance evaluation of the ESSE (early slow start exit) modification of the TCP congestion control mechanism. ESSE takes advantage of estimating the optimal pipesize at the sender side to properly select initial slow start threshold. Previous simulative experiments have shown that ESSE allows one to speed-up TCP connections and significantly reduces the packet drop rate at the bottleneck. This work takes a step further in understanding the ESSE behavior by developing a model of TCP source to evaluate the influence of different settings of slow start threshold on a TCP performance. As confirmed by comparison with simulations, the model provides, significantly faster than simulations, accurate estimates of typical performance indicators such as the average completion time and average drop rate of short-lived TCP connections.