In this paper, we introduce PortLaneNet, an optimized lane detection model specifically designed for the unique challenges of enclosed container terminal environments. Unlike conventional lane detection scenarios, this model addresses complexities such as intricate ground markings, tire crane lane lines, and various types of regional lines that significantly complicate detection tasks. Our approach includes the novel Scene Prior Perception Module, which leverages pre-training to provide essential prior information for more accurate lane detection. This module capitalizes on the enclosed nature of container terminals, where images from similar area scenes offer effective prior knowledge to enhance detection accuracy. Additionally, our model significantly improves understanding by integrating both high- and low-level image features through attention mechanisms, focusing on the critical components of lane detection. Through rigorous experimentation, PortLaneNet has demonstrated superior performance in port environments, outperforming traditional lane detection methods. The results confirm the effectiveness and superiority of our model in addressing the complex challenges of lane detection in such specific settings. Our work provides a valuable reference for solving lane detection issues in specialized environments and proposes new ideas and directions for future research.
<p>Oil and natural gas, as fluid minerals, flow within the Earth’s crust under the influence of various driving forces such as pressure, buoyancy, and gravity. This phenomenon is known as hydrocarbon migration. Hydrocarbon migration is a crucial component of the reservoir formation process, and accurately analyzing its direction affects the precision of trap prediction, well positioning, reservoir size, and morphology evaluation, thereby influencing the difficulty and cost of hydrocarbon development. However, most of the currently discovered hydrocarbon reservoirs have undergone multiple transformations or destructions, increasing the challenges of hydrocarbon development. Through an extensive literature review, this paper summarizes and categorizes the main current methods of studying hydrocarbon migration, including sedimentological methods, geochemical tracers, numerical simulation, and geophysical methods. Furthermore, this paper discusses and explores the frontier trends in hydrocarbon migration, mainly reflected in artificial intelligence (AI) methods, digital oil fields, geological big data analysis, and high-resolution seismic imaging technology. Looking to the future, there are significant opportunities in hydrocarbon migration research in data integration and intelligent analysis, high-resolution detection technology, digitization and automation, and the application of green technologies. However, there are also severe challenges regarding data quality and integration, the complexity and uncertainty of models, environmental and safety concerns, technology costs, and interdisciplinary collaboration. In conclusion, this paper clarifies the hydrocarbon migration process by reviewing, summarizing, and analyzing existing literature to understand hydrocarbon reservoirs’ formation and distribution patterns. It also delves into the mainstream methods, frontier trends, and prospects of hydrocarbon migration technology, providing valuable insights for future research.</p>
<p><span lang="AZ-LATIN">Tourist hotels (or tourist accommodations) are located near tourist attractions, primarily serving tourists. In recent years, with the gradual improvement of people’s living standards around the globe, tourists’ demands and standards for tourist hotel construction have been rising accordingly. In the context of technologization and informatization, various hotel booking platforms (Agoda, Booking, Trip, etc.) cover a large amount of review data in evaluating systems to reflect tourists’ demands. Meanwhile, identifying demand-oriented reviews and extracting core consumer demands from them is crucial for optimizing hotel services and enhancing tourist satisfaction. Therefore, this study explores the demands of tourists in tourist hotels from the perspective of text sentiment analysis and takes Macao, a famous tourist destination, as an example, based on reviews of tourist hotels on the Agoda site platform. Specifics are as follows: (1) Based on pointwise mutual information (PMI) and information entropy (IE), it realizes the identification of sentiment words in the field of tourist hotels and constructs a sentiment dictionary to address the problem of poor relevance between word segmentation results; (2) It summarizes the five types of reviews containing tourist demands (positive, negative, suggestion, demand, and comparison) and their characteristics to solve the ambiguity of texts and further accurately reveal the main demands of tourists; (3) It classifies tourist demands and group similar tourist demands into the same categories to address the problem of multiple expressions for the same demand. The present study provides empirical experiences from Macao’s hotels and contributes to the literature on text sentiment analysis in tourist hotels. Furthermore, the study results could enhance the mining accuracy and provide a detailed summarization of consumer demands and directions for the sustainable optimization improvement of tourist services.</span></p>
<p><span lang="AZ-LATIN">Tourist hotels (or tourist accommodations) are located near tourist attractions, primarily serving tourists. In recent years, with the gradual improvement of people’s living standards around the globe, tourists’ demands and standards for tourist hotel construction have been rising accordingly. In the context of technologization and informatization, various hotel booking platforms (Agoda, Booking, Trip, etc.) cover a large amount of review data in evaluating systems to reflect tourists’ demands. Meanwhile, identifying demand-oriented reviews and extracting core consumer demands from them is crucial for optimizing hotel services and enhancing tourist satisfaction. Therefore, this study explores the demands of tourists in tourist hotels from the perspective of text sentiment analysis and takes Macao, a famous tourist destination, as an example, based on reviews of tourist hotels on the Agoda site platform. Specifics are as follows: (1) Based on pointwise mutual information (PMI) and information entropy (IE), it realizes the identification of sentiment words in the field of tourist hotels and constructs a sentiment dictionary to address the problem of poor relevance between word segmentation results; (2) It summarizes the five types of reviews containing tourist demands (positive, negative, suggestion, demand, and comparison) and their characteristics to solve the ambiguity of texts and further accurately reveal the main demands of tourists; (3) It classifies tourist demands and group similar tourist demands into the same categories to address the problem of multiple expressions for the same demand. The present study provides empirical experiences from Macao’s hotels and contributes to the literature on text sentiment analysis in tourist hotels. Furthermore, the study results could enhance the mining accuracy and provide a detailed summarization of consumer demands and directions for the sustainable optimization improvement of tourist services.</span></p>
Accurate descriptions of the formation period and filling characteristics of underground rivers are crucial for prospecting karst reservoirs. Based on drilling logs, cores, seismic and thin sections, qualitative and quantitative analyses of the fillings of the S65 karst conduit in the denudation area of the Tahe Oilfield were performed. Conventional seismic data, wave impedance inversion seismic data, mud inversion seismic data, and well drilling data were used to characterize the transverse filling trends and combinations. By selecting key coring wells and calibrating the sensitive logs (acoustic time difference (AC), density (DEN), deep-shallow dual lateral resistivity (RD-RS), natural gamma ray (GR), and shale content (Vsh)) with cores, various quantitative identification standards were established. Breakdown breccia and calcite filling could be identified using the AC-DEN crossplot; the former had high DEN values (2.64–2.72) and medium AC values (>49.5), whereas the latter had higher DEN values (2.66–2.76) and lower AC values (<49.5). The mechanical and unfilled or low-filled component could be identified using GR-RS or △R (absolute values for deep and shallow resistivity difference) -Vsh crossplots; the GR value of 20 API and the Vsh of 5% in the respective plots represented the boundary between the two. The filling characteristics of the underground river and the underlying mechanism thereof were explained, and the conduits in different periods were compared: (i) Sandstone and sandstone-cemented breakdown breccia primarily filled with a combination of mechanical and collapse fillings developed in the doline. Mudstone and calcite developed in the corridor, while breakdown breccia developed at the intersection of faults. Uncemented breakdown breccia often formed in the branches of the underground river, and calcite developed on the blind side. (ii) The top conduit of the S65 underground river formed during the period of sea level rise in the Carboniferous; the reservoir connectivity in the lateral direction was poor due to severe mud percolation. In contrast, the bottom, which was mainly composed of Silurian sandstone and sandstone-cemented breakdown breccia that formed during the period of denudation in the early Hercynian, played a significant role in the high oil and gas production of the underground river.
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