The Impact of Urbanization on Urban Heat Island: Predictive Approach Using Google Earth Engine and CA-Markov Modelling (2005-2050) of Tianjin City, China.

Urbanization has adverse environmental effects, such as rising surface temperatures. This study analyzes the relationship between the urban heat island (UHI) intensity and Tianjin city's land cover characteristics. The land use cover change (LUCC) effects on the green areas and the land surface temperature (LST) were also studied. The land cover characteristics were divided into five categories: a built-up area, an agricultural area, a bare area, a forest, and water. The LST was calculated using the thermal bands of spatial images taken from 2005 to 2020. The increase in the built-up area was mainly caused by the agricultural area decreasing by 11.90%. The average land surface temperature of the study area increased from 23.50 to 36.51 °C, and the region moved to a high temperature that the built-up area's temperature increased by 1.5%. Still, the increase in vegetation cover was negative. From 2020 to 2050, the land surface temperature is expected to increase by 9.5 °C. The high-temperature areas moved into an aerial distribution, and the direction of urbanization determined their path. Urban heat island mitigation is best achieved through forests and water, and managers of urban areas should avoid developing bare land since they may suffer from degradation. The increase in the land surface temperature caused by the land cover change proves that the site is becoming more urbanized. The findings of this study provide valuable information on the various aspects of urbanization in Tianjin and other regions. In addition, future research should look into the public health issues associated with rapid urbanization.

Gastronomic Tourism and Tourist Motivation: Exploring Northern Areas of Pakistan.

Gastronomic tourism is becoming an essential consideration among tourists when choosing a tourist destination. One of the main reasons for visiting a specific destination for almost 15% of tourists is "gastronomy". This has led to the development of a new kind of tourism called "Gastronomic Tourism". However, there has been minimal research on gastronomy tourism, specifically in Pakistan. The primary purpose of this study is to measure the level of satisfaction in a tourist destination and furthermore consider gastronomy as a component of visitor motivation. A survey of 307 tourists who had recently visited Pakistan's northern areas was undertaken to conduct the study. This has enabled us to better understand the variables that influence the behaviors and attitudes of tourists toward this popular tourist attraction. Gastronomy motivation impacts tourism location selection, and gastronomic experience influences satisfaction, according to the research. Specifically, tourists show a keen interest in gastronomic experiences after feeling satisfied with the destination and local foods, developing loyalty toward the destination.

In-TFT-array-process micro defect inspection using nonlinear principal component analysis.

Defect inspection plays a critical role in thin film transistor liquid crystal display (TFT-LCD) manufacture, and has received much attention in the field of automatic optical inspection (AOI). Previously, most focus was put on the problems of macro-scale Mura-defect detection in cell process, but it has recently been found that the defects which substantially influence the yield rate of LCD panels are actually those in the TFT array process, which is the first process in TFT-LCD manufacturing. Defect inspection in TFT array process is therefore considered a difficult task. This paper presents a novel inspection scheme based on kernel principal component analysis (KPCA) algorithm, which is a nonlinear version of the well-known PCA algorithm. The inspection scheme can not only detect the defects from the images captured from the surface of LCD panels, but also recognize the types of the detected defects automatically. Results, based on real images provided by a LCD manufacturer in Taiwan, indicate that the KPCA-based defect inspection scheme is able to achieve a defect detection rate of over 99% and a high defect classification rate of over 96% when the imbalanced support vector machine (ISVM) with 2-norm soft margin is employed as the classifier. More importantly, the inspection time is less than 1 s per input image.