A Bibliometric Analysis of Operations Research and Management Science

Bibliometric analysis is the quantitative study of bibliographic material. It provides a general picture of a research field that can be classified by papers, authors and journals. This paper presents a bibliometric overview of research published in operations research and management science in recent decades. The main objective of this study is to identify some of the most relevant research in this field and some of the newest trends according to the information found in the Web of Science database. Several classifications are made, including an analysis of the most influential journals, the two hundred most cited papers of all time and the most productive and influential authors. The results obtained are in accordance with the common wisdom, although some variations are found.European Commission PIEF-GA-2011-300062 Chilean Government 116028

Crowding, Attention and Consciousness: in support of the inference hypothesis

One of the most important topics in current work on consciousness is what relationship it has to attention. Recently, one of the focuses of this debate has been on the phenomenon of identity crowding. Ned Block has claimed that identity crowding involves consciously perceiving an object that we are unable to pay attention to. Others have offered different interpretations, emphasising the role of cognitive inference over conscious perception. In this paper, we draw upon a range of empirical findings to argue against Block’s interpretation of the data. We also argue that current empirical evidence strongly supports one particular version of the inference hypothesis . Finally, we consider the additional evidence Block gives in favour of his view, and argue that it fails to establish his position.Leverhulme Trust; Isaac Newton Trust; Royal Institute of Philosophy; Swiss National Science Foundation; FW

Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models

Laparoscopic Surgery and the debate on its safety during COVID-19 pandemic: A systematic review of recommendations

IntroductionThe transmission of COVID-19 virus since the outbreak of viral pneumonia due to SARS-CoV-2 gave rise to protective operative measures. Aerosol generating procedures such as laparoscopic surgery are known to be associated with increased risks of viral transmission to the healthcare workers. The safety of laparoscopy during the pandemic was then debated. We aimed to systematically review the literature regarding the safe use of laparoscopy during COVID-19.MethodsWe performed a systematic search using PubMed and ScienceDirect databases from inception to 1st May, 2020. The following search terms were used: ‘‘laparoscopic surgery and COVID-19’’; ‘‘minimally invasive surgery and COVID-19’’. Search items were considered from the nature of the articles, date of publication, aims and findings in relation to use of laparoscopic surgery during COVID-19. The study protocol was registered with PROSPERO register for systematic reviews (CRD42020183432).ResultsAltogether, 174 relevant citations were identified and reviewed for this study, of which 22 articles were included. The analysis of the findings in relation to laparoscopic surgery during the pandemic were presented in tabular form. We completed the common recommendations for performing laparoscopy during the COVID-19 pandemic in forms of pre-, intra- and postoperative phases.ConclusionThere is no scientific evidence to date for the transmission of COVID-19 by laparoscopic surgery. Laparoscopy can be used with precautions because of its benefits compared to open surgery. If safe, conservative management is the primary alternative during the pandemic. We concluded that recommended precautions should be respected while performing laparoscopy during the pandemic.</div

A Microscopic Simulation Laboratory for Evaluation of Off-street Parking Systems

The parking industry produces an enormous amount of data every day that, properly analyzed, will change the way the industry operates. The collected data form patterns that, in most cases, would allow parking operators and property owners to better understand how to maximize revenue and decrease operating expenses and support the decisions such as how to set specific parking policies (e.g. electrical charging only parking space) to achieve the sustainable and eco-friendly parking. However, there lacks an intelligent tool to assess the layout design and operational performance of parking lots to reduce the externalities and increase the revenue. To address this issue, this research presents a comprehensive agent-based framework for microscopic off-street parking system simulation. A rule-based parking simulation logic programming model is formulated. The proposed simulation model can effectively capture the behaviors of drivers and pedestrians as well as spatial and temporal interactions of traffic dynamics in the parking system. A methodology for data collection, processing, and extraction of user behaviors in the parking system is also developed. A Long-Short Term Memory (LSTM) neural network is used to predict the arrival and departure of the vehicles. The proposed simulator is implemented in Java and a Software as a Service (SaaS) graphic user interface is designed to analyze and visualize the simulation results. This study finds the active capacity of the parking system, which is defined as the largest number of actively moving vehicles in the parking system under the facility layout. In the system application of the real world testbed, the numerical tests show (a) the smart check-in device has marginal benefits in vehicle waiting time; (b) the flexible pricing policy may increase the average daily revenue if the elasticity of the price is not involved; (c) the number of electrical charging only spots has a negative impact on the performance of the parking facility; and (d) the rear-in only policy may increase the duration of parking maneuvers and reduce the efficiency during the arrival rush hour. Application of the developed simulation system using a real-world case demonstrates its capability of providing informative quantitative measures to support decisions in designing, maintaining, and operating smart parking facilities