Cournot Competition Yields Spatial Avoiding Competition in Groups

This paper characterizes the properties of equilibrium location patterns in an Anderson-Neven-Pal model and uses these characteristics to comprehensively find the subgame perfect Nash equilibria, most of which are not yet found in the literature. Since the external competition effect may be exactly canceled out, or internal competition strictly dominates external competition, or the internal competition effect is consistent with the external competition effect, therefore without any externality and prior collusion, a competitive group structure may form endogenously in equilibrium and firms tend to avoid competition inside each group. The analyses of an Anderson-Neven-Pal model are instructive in studying the conditions for a capacity to implement a ``Nash combination."Cournot competition; Spatial competition; Nash equilibrium

Leadership, Regulatory Focus and Project Performance

Leadership is one critical factor of effective teamwork, such as information system (IS) projects. The mission of project leaders is to motivate followers and create an effective working environment that allows project teams to effectively meet the predefined goals. However, based on regulatory focus theory, a team may strive to the optional situation (promotion focus) or try to avoid not meeting the minimum requirements (prevention). The aim of this paper is to explore the effect of leadership styles (transformational and transactional) on the regulatory focus of one team (promotion and prevention), and investigate the relationship between regulatory focus and project team performance. Based on data collected from 154 IS professionals, we found that transformational leadership is associated with promotion focus and transactional leadership leads to prevention focus. Furthermore, while promotion focus orientated teams can perform effectively, prevention focus oriented teams are less efficient. Implications toward academia and practitioners are provided

On the Patent Claim Eligibility Prediction Using Text Mining Techniques

With the widespread of computer software in recent decades, software patent has become controversial for the patent system. Of the many patentability requirements, patentable subject matter serves as a gatekeeping function to prevent a patent from preempting future innovation. Software patents may easily fall into the gray area of abstract ideas, whose allowance may hinder future innovation. However, without a clear definition of abstract ideas, determining the patent claim subject matter eligibility is a challenging task for examiners and applicants. In this research, in order to solve the software patent eligibility issues, we propose an effective model to determine patent claim eligibility by text-mining and machine learning techniques. Drawing upon USPTO issued guidelines, we identify 66 patent cases to design domain knowledge features, including abstractness features and distinguishable word features, as well as other textual features, to develop the claim eligibility prediction model. The experiment results show our proposed model reaches the accuracy of more than 80%, and domain knowledge features play a crucial role in our prediction model

Hyper-Restormer: A General Hyperspectral Image Restoration Transformer for Remote Sensing Imaging

The deep learning model Transformer has achieved remarkable success in the hyperspectral image (HSI) restoration tasks by leveraging Spectral and Spatial Self-Attention (SA) mechanisms. However, applying these designs to remote sensing (RS) HSI restoration tasks, which involve far more spectrums than typical HSI (e.g., ICVL dataset with 31 bands), presents challenges due to the enormous computational complexity of using Spectral and Spatial SA mechanisms. To address this problem, we proposed Hyper-Restormer, a lightweight and effective Transformer-based architecture for RS HSI restoration. First, we introduce a novel Lightweight Spectral-Spatial (LSS) Transformer Block that utilizes both Spectral and Spatial SA to capture long-range dependencies of input features map. Additionally, we employ a novel Lightweight Locally-enhanced Feed-Forward Network (LLFF) to further enhance local context information. Then, LSS Transformer Blocks construct a Single-stage Lightweight Spectral-Spatial Transformer (SLSST) that cleverly utilizes the low-rank property of RS HSI to decompose the feature maps into basis and abundance components, enabling Spectral and Spatial SA with low computational cost. Finally, the proposed Hyper-Restormer cascades several SLSSTs in a stepwise manner to progressively enhance the quality of RS HSI restoration from coarse to fine. Extensive experiments were conducted on various RS HSI restoration tasks, including denoising, inpainting, and super-resolution, demonstrating that the proposed Hyper-Restormer outperforms other state-of-the-art methods

THE IDENTIFICATION OF NOTEWORTHY HOTEL REVIEWS FOR HOTEL MANAGEMENT

The rapid emergence of user-generated content (UGC) inspires knowledge sharing among Internet users. A good example is the well-known travel site TripAdvisor.com, which enables users to share their experiences and express their opinions on attractions, accommodations, restaurants, etc. The UGC about travel provide precious information to the users as well as staff in travel industry. In particular, how to identify reviews that are noteworthy for hotel management is critical to the success of hotels in the competitive travel industry. We have employed two hotel managers to conduct an examination on Taiwan’s hotel reviews in Tripadvisor.com and found that noteworthy reviews can be characterized by their content features, sentiments, and review qualities. Through the experiments using tripadvisor.com data, we find that all three types of features are important in identifying noteworthy hotel reviews. Specifically, content features are shown to have the most impact, followed by sentiments and review qualities. With respect to the various methods for representing content features, LDA method achieves comparable performance to TF-IDF method with higher recall and much fewer features

Changes in endotracheal tube cuff pressure during laparoscopic surgery in head-up or head-down position

BACKGROUND: The abdominal insufflation and surgical positioning in the laparoscopic surgery have been reported to result in an increase of airway pressure. However, associated effects on changes of endotracheal tube cuff pressure are not well established. METHODS: 70 patients undergoing elective laparoscopic colorectal tumor resection (head-down position, n = 38) and laparoscopic cholecystecomy (head-up position, n = 32) were enrolled and were compared to 15 patients undergoing elective open abdominal surgery. Changes of cuff and airway pressures before and after abdominal insufflation in supine position and after head-down or head-up positioning were analysed and compared. RESULTS: There was no significant cuff and airway pressure changes during the first fifteen minutes in open abdominal surgery. After insufflation, the cuff pressure increased from 26 ± 3 to 32 ± 6 and 27 ± 3 to 33 ± 5 cmH(2)O in patients receiving laparoscopic cholecystecomy and laparoscopic colorectal tumor resection respectively (both p < 0.001). The head-down tilt further increased cuff pressure from 33 ± 5 to 35 ± 5 cmH(2)O (p < 0.001). There six patients undergoing colorectal tumor resection (18.8%) and eight patients undergoing cholecystecomy (21.1%) had a total increase of cuff pressure more than 10 cm H(2)O (18.8%). There was no significant correlation between increase of cuff pressure and either the patient's body mass index or the common range of intra-abdominal pressure (10-15 mmHg) used in laparoscopic surgery. CONCLUSIONS: An increase of endotracheal tube cuff pressure may occur during laparoscopic surgery especially in the head-down position