Marketing Analysis for the Construction of a Business Plan: Analysis of the Restaurant Sector in the Commercial Center of a City Located in the Brazilian’s Interior

Nowadays, when competitiveness has become a frequent factor among companies, they must be prepared and structured, in order to acquire competitive advantages in the market. The present work aims to analyze the restaurant market in the commercial center of the city of Lorena, state of São Paulo - SP, providing information on the profile of consumers, companies that compete in the market and influencing aspects in decision making in choosing the preferred establishment, thus providing the necessary subsidies for the elaboration of a possible business plan. The investigation was conducted through a bibliographic review and a market research applied to regulars in the commercial center of the municipality. The results obtained demonstrate the existence of a heterogeneous market, composed of consumers of the most varied ages, occupations and preferences for companies already established in the local commerce. The study also presented as the most relevant factors in the process of choosing the preferred restaurant the quality of the products, the service and the variety. Thus, the feasibility of proceeding to a detailed business plan, incorporating financial and marketing aspects, is concluded.

Automatic Extraction of Indoor Spatial Information from Floor Plan Image: A Patch-Based Deep Learning Methodology Application on Large-Scale Complex Buildings

Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded.

The Classroom Character Education in English Lessons Based on the 2013 Curriculum

The morality and character of the Indonesian nation are decreasing. This is evidenced by many cases of violence, sexuality, drugs, cybercrime, bullying. Some students also commit deviant actions such as cheating and smoking. This study aims to analyze the practice of character education in learning English. This type of research is qualitative. The methods used to collect data are observation, interviews, and questionnaires. The instruments used in this study were interview guides, note-takers, lesson plan analysis checklists, and observation checklists. The technique used to analyze the data is descriptive qualitative analysis. The results showed that the teacher had inserted the character values in the lesson plans contained in the indicators, learning materials, learning steps, assessment instruments, and the teaching and learning process. Furthermore, learning topics and indicators affect the differences in character values contained in the learning process. While the assessment of character education in the teaching and learning process used by the teacher is an investigation in the form of an advice and assessment rubric. This research implies that teachers can instill character values in students so that they can form superior student characters.

Evaluation of Deep Learning-Based Automatic Floor Plan Analysis Technology: An AHP-Based Assessment

This study proposes a technology that allows automatic extraction of vectorized indoor spatial information from raster images of floor plans. Automatic reconstruction of indoor spaces from floor plans is based on a deep learning algorithm, which trains on scanned floor plan images and extracts critical indoor elements such as room structures, junctions, walls, and openings. The newly developed technology proposed herein can handle complicated floor plans which could not be automatically extracted by previous studies because of its complexity and difficulty in being trained in deep learning. Such complicated reconstruction solely from a floor plan image can be digitized and vectorized either through manual drawing or with the help of newly developed deep learning-based automatic extraction. This study proposes an evaluation framework for assessing this newly developed technology against manual digitization. Using the analytical hierarchy process, the hierarchical aspects of technology value and their relative importance are systematically quantified. The analysis suggested that the automatic technology using a deep learning algorithm had predominant criteria followed by, substitutability, completeness, and supply and demand. In this study, the technology value of automatic floor plan analysis compared with that of traditional manual edits is compared systemically and assessed qualitatively, which had not been done in existing studies. Consequently, this study determines the effectiveness and usefulness of automatic floor plan analysis as a reasonable technology for acquiring indoor spatial information.

Metro Train Operation Plan Analysis Based on Station Travel Time Reliability

The train operation plan plays an essential role in metro systems and directly affects transportation organization efficiency and passenger service level. In metro systems, passengers have paid more attention to the travel time reliability (TTR), reflecting the reliability of metro operation management. This article proposes an analysis method of train operation plan based on TTR in the station dimension. First, an automated fare collection (AFC) data-driven framework is established to calculate the station travel time reliability (STTR) and analyze the train operation plan at different periods. The framework structure consists of four steps: AFC data preprocessing, STTR calculation and assignment, clustering algorithm design based on SOM neural network, and train operation plan analysis and optimization. Second, the proposed method is applied to the Beijing metro network as a case study. Several promising results are analyzed that allow the optimization of the existing train operation plan. Our research shows that STTR is a good supplement for the existing metro operation assignment studies, which can help analyze and optimize the train operation plan effectively. This study is also applicable to other metro networks with AFC systems.