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Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms, whereas consumers often rely on websites to search and compare prices when purchasing real property. In addition to being time consuming, this search process renders it difficult for agents and consumers to understand the status changes of objects. In this study, Python is used to write web crawler and image recognition programs to capture object information from the web pages of real estate agents; perform data screening, arranging, and cleaning; compare the text of real estate object information; as well as integrate and use the convolutional neural network of a deep learning algorithm to implement image recognition. In this study, data are acquired from two business-to-consumer real estate agency networks, i.e., the Sinyi real estate agent and the Yungching real estate agent, and one consumer-toconsumer real estate agency platform, i.e., the, FiveNineOne real estate agent. The results indicate that text mining can reveal the similarities and differences between the objects, list the number of days that the object has been available for sale on the website, and provide the price fluctuations and fluctuation times during the sales period. In addition, 213,325 object amplification images are used as a database for training using deep learning algorithms, and the maximum image recognition accuracy achieved is 95%. The dynamic recommendation system for real estate objects constructed by combining text mining and image recognition systems enables developers in the real estate industry to understand the differences between their commodities and other businesses in approximately 2 min, as well as rapidly determine developable objects via comparison results provided by the system. Meanwhile, consumers require less time in searching and comparing prices after they have understood the commodity dynamic information, thereby allowing them to use the most efficient approach to purchase real estate objects of their interest. [ABSTRACT FROM AUTHOR]

The advantages of a cloud computing service are cost advantages, availability, scalability, flexibility, reduced time to market, and dynamic access to computing resources. Enterprises can improve the successful adoption rate of cloud computing services if they understand the critical factors. To find critical factors, this study first reviewed the literature and established a three-layer hierarchical factor table for adopting a cloud computing service based on the Technology-Organization-Environment framework. Then, a hybrid method that combines two multi-criteria decision-making tools--called the Fuzzy Analytic Network Process method and the concept of VlseKriterijumska Optimizacija I Kompromisno Resenje acceptable advantage--was used to objectively identify critical factors for the adoption of a cloud computing service, replacing the subjective decision of the authors. The results of this study determined five critical factors, namely data access security, information transmission security, senior management support, fallback cloud management, and employee acceptance. Finally, the paper presents the findings and implications of the study. [ABSTRACT FROM AUTHOR]

This paper proposes a solution to the open vehicle routing problem with time windows (OVRPTW) considering third-party logistics (3PL). For the typical OVRPTW problem, most researchers consider time windows, capacity, routing limitations, vehicle destination, etc. Most researchers who previously investigated this problem assumed the vehicle would not return to the depot, but did not consider its final destination. However, by considering 3PL in the B2B e-commerce, the vehicle is required back to the nearest 3PL location with available space. This paper formulates the problem as a mixed integer linear programming (MILP) model with the objective of minimizing the total travel distance. A coordinate representation particle swarm optimization (CRPSO) algorithm is developed to obtain the best delivery sequencing and the capacity of each vehicle. Results of the computational study show that the proposed method provides solution within a reasonable amount of time. Finally, the result compared to PSO also indicates that the CRPSO is effective. [ABSTRACT FROM AUTHOR]

In the competitive global marketplace, production scheduling plays a vital role in planning in manufacturing. Scheduling deals directly with the time to output products quickly and with a low production cost. This research examines case study of a Radio-Frequency Identification labeling department at Avery Dennison. The main objective of the company is to have a method that allows for the sequencing and scheduling of a set of jobs so it can be completed on or before the customer's due date to minimize the number of late orders. This study analyzes the flexible flow shop scheduling problem with a sequence dependent setup by modifying the processing time and setup time to minimize the makespan on multiple machines. Based on the defined mathematical model, this study includes an alternative approach and application of heuristic algorithm with the input being big data. Both optimization programs are used in this study and compared to determine which method can better solve the company's problems. The proposed algorithm is able to improve machine utilization with large-scale problems. [ABSTRACT FROM AUTHOR]

A 58-year-old woman with underlying medically controlled hypertension presented after an episode of sudden-onset chest pain. Chest computed tomography imaging revealed a left renal artery aneurysm (RAA) measuring 1.6 cm in diameter with mural thrombi in the distal left renal artery at bifurcation level. An interval enlargement of approximately 0.4 cm in diameter was noted within a 6-month period; however, endovascular intervention was not feasible because of the complex RAA pattern. She was hospitalized and received a hand-assisted laparoscopic nephrectomy, ex vivo repair of the RAA, and autotransplantation into the left iliac fossa. The procedure was successful, and the postoperative course went smoothly. The kidney graft was evaluated using a magnetic resonance angiography 1-year postoperatively, which showed no signs of surgical complications or RAA recurrence. [ABSTRACT FROM AUTHOR]

Most previous research studies on the general factors related to the adoption of mobile services have used multiple regression methods (the technology acceptance mode or structural equation modeling) and some have used MCDM tools. However, these studies still have some shortcomings, so they cannot provide the enough precise information about these factors and their weightings. This information is needed by firms so that they can allocate their limited resources to the most important factors and draw appropriate strategies to improve the content and quality of their mobile services. This study reviewed the literature and constructed a three-layer hierarchical table of the factors that affect consumers' adoption of mobile services. A pair-wise questionnaire was then designed and distributed to managers who are familiar with the mobile services of Chunghwa Telecom, a leading telecommunication company. From the data collected, the weight of each factor was directly calculated, using the fuzzy analytic hierarchy process (FAHP). The paper analyzes the importance of factors and three implications are also discussed. Hopefully, this knowledge will enable firms to better utilize their limited resources, by devising management strategies based on the weights of these factors, and therefore, to meet consumer demand at lower cost and with greater efficiency. [ABSTRACT FROM AUTHOR]