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1 – 10 of 151Kiri Mealings and Joerg M. Buchholz
The purpose of this paper is to systematically map research on the effect of classroom acoustics and noise on high school students’ listening, learning and well-being, as well as…
Abstract
Purpose
The purpose of this paper is to systematically map research on the effect of classroom acoustics and noise on high school students’ listening, learning and well-being, as well as identify knowledge gaps to inform future research.
Design/methodology/approach
This scoping review followed the PRISMA-ScR protocol. A comprehensive search of four online databases (ERIC, PubMed, Scopus and Web of Science) was conducted. Peer-reviewed papers were included if they conducted a study on the effect of classroom acoustics or noise on students’ listening, learning or well-being; had a clear definition of the noise level measurement; were conducted with high school students; and had the full text in English available.
Findings
In total, 14 papers met the criteria to be included in the review. The majority of studies assessed the impact of noise on students’ listening, learning or well-being. Overall, the results showed that higher noise levels have a negative effect on students’ listening, learning and well-being. Effects were even more pronounced for students who were non-native speakers or those with special educational needs such as hearing loss. Therefore, it would be beneficial to limit unnecessary noise in the classroom as much as possible through acoustic insulation, acoustic treatment and classroom management strategies.
Originality/value
This paper is the first review paper to synthesize previous research on the effect of classroom acoustics and noise on high school students’ listening, learning and well-being. It provides an analysis of the limitations of existing literature and proposes future research to help fill in these gaps.
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Pratheek Suresh and Balaji Chakravarthy
As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a…
Abstract
Purpose
As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.
Design/methodology/approach
This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R2) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.
Findings
The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.
Research limitations/implications
The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.
Originality/value
The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.
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Bingbing Qi, Lijun Xu and Xiaogang Liu
The purpose of this paper is to exploit the multiple-Toeplitz matrices reconstruction method combined with quadratic spatial smoothing processing to improve the…
Abstract
Purpose
The purpose of this paper is to exploit the multiple-Toeplitz matrices reconstruction method combined with quadratic spatial smoothing processing to improve the direction-of-arrival (DOA) estimation performance of coherent signals at low signal-to-noise ratio (SNRs).
Design/methodology/approach
An improved multiple-Toeplitz matrices reconstruction method is proposed via quadratic spatial smoothing processing. Our proposed method takes advantage of the available information contained in the auto-covariance matrices of individual Toeplitz matrices and the cross-covariance matrices of different Toeplitz matrices, which results in a higher noise suppression ability.
Findings
Theoretical analysis and simulation results show that, compared with the existing Toeplitz matrix processing methods, the proposed method improves the DOA estimation performance in cases with a low SNR. Especially for the cases with a low SNR and small snapshot number as well as with closely spaced sources, the proposed method can achieve much better performance on estimation accuracy and resolution probability.
Research limitations/implications
The study investigates the possibility of reusing pre-existing designs for the DOA estimation of the coherent signals. The proposed technique enables achieve good estimation performance at low SNRs.
Practical implications
The paper includes implications for the DOA problem at low SNRs in communication systems.
Originality/value
The proposed method proved to be useful for the DOA estimation at low SNR.
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Han Sun, Song Tang, Xiaozhi Qi, Zhiyuan Ma and Jianxin Gao
This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose…
Abstract
Purpose
This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.
Design/methodology/approach
Distinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.
Findings
Based on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.
Originality/value
In this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.
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Birgit Muskat, Girish Prayag, Sameer Hosany, Gang Li, Quan Vu and Sarah Wagner
Food is a key element in tourism experiences. This study aims to investigate the interplay of sensory and non-sensory factors in food tourism experiences and models their…
Abstract
Purpose
Food is a key element in tourism experiences. This study aims to investigate the interplay of sensory and non-sensory factors in food tourism experiences and models their influence on satisfaction and behavioural intentions.
Design/methodology/approach
The study focuses on the culinary experiences of 304 tourists dining at ethnic restaurants and uses causal relationship discovery modelling to analyse data.
Findings
Sensory factors are important in tourists’ culinary experiences with cleanliness, noise levels and room temperature at the top of the causal chain. Results also indicate the interplay between sensory and non-sensory factors to explain overall satisfaction, intention to return and intention to say positive things.
Originality/value
Using embodied cognition theory, the study offers novel insights into the role of senses in food tourism experiences at rural destinations.
研究目的
美食是乡村旅游的主要吸引物之一。本研究的目的是调查游客在用餐体验中感官和非感官因素的相互作用, 以及这些因素如何影响游客的满意度和行为意愿。
研究设计/研究方法
本研究使用因果关系建模的方法来分析 304 名在某地方特色餐厅用餐的游客的问卷数据。
研究结果
结果显示, 对于游客的用餐体验而言, 感官和非感官因素具备同等的重要性。此外, 结果发现, 游客感知到的噪音水平、适宜的室内温度及清洁度在与其他因素的相互作用中非常重要, 并能激发游客的满意度和重游意愿。
原创性/研究价值
基于认知理论, 本研究为更好地理解感官因素和非感观因素在乡村旅游情境下的游客用餐体验中的作用提供了新的知识。
Propósito
La comida es un elemento clave en las experiencias turísticas. Este estudio investiga la interacción de factores sensoriales y no sensoriales en las experiencias de turismo gastronómico y modela su influencia en la satisfacción y las intenciones de comportamiento.
Diseño/metodología/enfoque
El estudio se centra en las experiencias culinarias de 304 turistas que cenan en restaurantes étnicos y utiliza modelos de descubrimiento de relaciones causales para analizar los datos.
Resultados
Los factores sensoriales son importantes en las experiencias culinarias de los turistas con la limpieza, los niveles de ruido y la temperatura ambiente en la parte superior de la cadena causal. Los resultados también indican la interacción entre factores sensoriales y no sensoriales para explicar la satisfacción general, la intención de regresar y la intención de decir cosas positivas.
Originalidad/valor
Utilizando la teoría de la cognición incorporada, el estudio ofrece nuevos conocimientos sobre el papel de los sentidos en las experiencias de turismo gastronómico en destinos rurales.
Details
Keywords
- Food tourism
- Experiences
- Senses
- Embodied cognition theory
- Overall satisfaction
- Intention to return and intention to say positive things
- 美食旅游
- 体验
- 感官因素
- 认知理论
- 满意度
- 重游意愿
- 好评意愿
- Turismo gastronómico
- experiencias
- sentidos
- teoría de la cognición encarnada
- satisfacción general
- intención de regresar e intención de decir cosas positivas
José Luis Alfaro-Navarro and María Encarnación Andrés-Martínez
Being awarded world heritage status is a distinguishing factor when it comes to promoting tourism in a city. Tourism in these cities should be developed in a way that does not…
Abstract
Purpose
Being awarded world heritage status is a distinguishing factor when it comes to promoting tourism in a city. Tourism in these cities should be developed in a way that does not compromise either the city’s heritage or the inhabitants' quality of life. Thus, the main purpose of this paper is to analyze the effects of a European city achieving world heritage status on the subjective quality of life of its citizens.
Design/methodology/approach
First of all, we classify European cities according to whether or not they have been declared world heritage sites. Then, we analyze the effect of this classification on the main aspects used to measure the residents' perception of quality of life that are available in the Flash Eurobarometer 419.
Findings
The results show that achieving world heritage status has a negative effect on residents' perceptions of the noise level, air quality and feeling of safety. However, it does not affect their perceptions of public transport or cleanliness. In addition, world heritage status positively affects residents’ perceptions of the cultural activities in the city and their ease of finding a job. Residents report high levels of happiness in both world heritage and non-heritage cities, although levels are somewhat higher in non-heritage cities.
Originality/value
Residents' perceptions of the influence of tourism on their quality of life are undoubtedly of major importance; however, due to a lack of available data, few studies have examined this subjective quality of life at the city level.
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Zhixue Liao, Xinyu Gou, Qiang Wei and Zhibin Xing
Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that…
Abstract
Purpose
Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.
Design/methodology/approach
The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.
Findings
The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.
Originality/value
First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.
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Johnny Kwok Wai Wong, Mojtaba Maghrebi, Alireza Ahmadian Fard Fini, Mohammad Amin Alizadeh Golestani, Mahdi Ahmadnia and Michael Er
Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes…
Abstract
Purpose
Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.
Design/methodology/approach
To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.
Findings
The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.
Originality/value
The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.
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Cemalettin Akdoğan, Tolga Özer and Yüksel Oğuz
Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of…
Abstract
Purpose
Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).
Design/methodology/approach
Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.
Findings
In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.
Originality/value
An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.
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Taining Wang and Daniel J. Henderson
A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production…
Abstract
A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.
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