Incremental Learning with Neural Network Algorithm for the Monitoring Pre-Convective Environments Using Geostationary Imager

Early warning of severe weather caused by intense convective weather systems is challenging. To help such activities, meteorological satellites with high temporal and spatial resolution have been utilized for the monitoring of instability trends along with water vapor variation. The current study proposes a retrieval algorithm based on an artificial neural network (ANN) model to quickly and efficiently derive total precipitable water (TPW) and convective available potential energy (CAPE) from Korea’s second geostationary satellite imagery measurements (GEO-KOMPSAT-2A/Advanced Meteorological Imager (AMI)). To overcome the limitations of the traditional static (ST) learning method such as exhaustive learning, impractical, and not matching in a sequence data, we applied an ANN model with incremental (INC) learning. The INC ANN uses a dynamic dataset that begins with the existing weight information transferred from a previously learned model when new samples emerge. To prevent sudden changes in the distribution of learning data, this method uses a sliding window that moves along the data with a window of a fixed size. Through an empirical test, the update cycle and the window size of the model are set to be one day and ten days, respectively. For the preparation of learning datasets, nine infrared brightness temperatures of AMI, six dual channel differences, temporal and geographic information, and a satellite zenith angle are used as input variables, and the TPW and CAPE from ECMWF model reanalysis (ERA5) data are used as the corresponding target values over the clear-sky conditions in the Northeast Asia region for about one year. Through the accuracy tests with radiosonde observation for one year, the INC NN results demonstrate improved performance (the accuracy of TPW and CAPE decreased by approximately 26% and 26% for bias and about 13% and 12% for RMSE, respectively) when compared to the ST learning. Evaluation results using ERA5 data also reveal more stable error statistics over time and overall reduced error distribution compared with ST ANN.

Structural process model of absorptive capacity for stakeholder’s integration in decision-making: dynamic capability perspective

Purpose This paper aims to present empirical test to analyse a structural process model based on the constructs of absorptive capacity considering sustainability aspects. It integrates dynamic capability perspective to strengthen the existing literature. Findings of the study may help organisations to renovate business processes related to absorptive capacity and improve decision-making considering sustainability attributes. Design/methodology/approach Sample for data analysis covers 389 managers of firms demonstrating some traits of sustainability orientation and operating in a developing economy like India. Structural equation modelling is applied to test a casual model. Findings Result of this study reveals that a formal system that drives knowledge sharing, storing and exploitation of knowledge, a structured way to appropriate and reward intellectual property rights can be fundamental to development of absorptive capacity. Such a system conducive to knowledge exploration and acquisition must lead to exploration of new knowledge sources and new knowledge. A firm’s knowledge acquisition system shall be able to deliver to acquire external as well as internal knowledge spread across departments. Research limitations/implications This study considers commercial output as an aspect of innovative capability, so this attribute is not included in absorptive capacity. Practical implications The study may help organisations to develop a systemic approach to evolve decision-making and develop suitable processes linked to absorptive capacity considering sustainability challenges and uncertain market conditions. Social implications The study presents a comprehensive view of absorptive capacity considering societal stakeholder’s knowledge in addition to technological and market knowledge. Originality/value There are hardly any research articles which link absorptive capacity, dynamic capability and sustainability strategies.

The malleability of international entrepreneurial cognitions: a natural quasi-experimental study on voluntary and involuntary shocks

PurposeThe authors investigate the durability of international entrepreneurial cognitions. Specifically, they examine how advanced business education and the Covid-19 pandemic influence international entrepreneurial orientation disposition (IEOD), and subsequently entrepreneurial intentions (EIs), to better understand the psychological dynamics underpinning the drivers of international entrepreneurship.Design/methodology/approachAgainst the backdrop of emerging entrepreneurial cognition and international entrepreneurial orientation research, the authors theorize that both a planned business education intervention (voluntary) and an unforeseeable radical environmental (involuntary) change constitute cognitive shocks impacting the disposition and intention to engage in entrepreneurial efforts. The authors use pre- and post-Covid-19 panel data (n = 233) and uniquely identify the idiosyncratic cognitive effects of Covid-19 through changes in the OCEAN personality assessment.FindingsFindings demonstrate that when individuals' perceived psychological impact of Covid-19 is low, business education increases IEOD. Conversely, the effects of a strongly perceived Covid-19 impact reduce the risk-taking and proactiveness components of the IEOD scale. The authors trace the same effects forward to EIs.Research limitations/implicationsThis paper contributes to a greater understanding of the resilience of entrepreneurial dispositions through an empirical test of the IEOD scale and shows its boundary conditions under planned intervention as well as unplanned externally induced shock.Practical implicationsThe study offers a first benchmark to practitioners of the malleability of international entrepreneurial dispositions and discusses the potential to encourage international entrepreneurial behaviour and the individual-level dispositional risk posed by exogenous shocks.Originality/valueThe study uniquely employs a baseline measure of all our constructs pre-Covid-19 to discern and isolate the pandemic impact on entrepreneurial dispositions and intentions, responding to recent calls for more experimental designs in entrepreneurship research.

Motorcycle Accident Reconstruction

Accident reconstruction utilizes principles of physics and empirical data to analyze the physical, electronic, video, audio, and testimonial evidence from a crash, to determine how and why the crash occurred, how the crash could have been avoided, or to determine whose description of the crash is most accurate. This process draws together aspects of mathematics, physics, engineering, materials science, human factors, and psychology, and combines analytical models with empirical test data. Different types of crashes produce different types of evidence and call for different analysis methods. Still, the basic philosophical approach of the reconstructionist is the same from crash type to crash type, as are the physical principles that are brought to bear on the analysis. This book covers a basic approach to accident reconstruction, including the underlying physical principles that are used, then details how this approach and the principles are applied when reconstructing motorcycle crashes. This second edition of Motorcycle Accident Reconstruction presents a thorough, systematic, and scientific overview of the available methods for reconstructing motorcycle crashes. This new edition contains: Additional theoretical models, examples, case studies, and test data. An updated bibliography incorporating the newest studies in the field. Expanded coverage of the braking capabilities of motorcyclists. Updated, refined, and expanded discussion of the decelerations of motorcycles sliding on the ground. A thoroughly rewritten and expanded discussion of motorcycle impacts with passenger vehicles. Updated coefficients of restitution for collisions between motorcycles and cars. A new and expanded discussion of using passenger car EDR data in motorcycle accident reconstruction. A new section covering recently published research on post-collision frozen speedometer readings on motorcycles. A new section on motorcycle interactions with potholes, roadway deterioration, and debris and expanded coverage of motorcycle falls. This second edition of Motorcycle Accident Reconstruction is a must-have title for accident reconstructionists, forensic engineers, and all interested in understanding why and how motorcycle crashes occur.

Positioning big data analytics capabilities towards financial service agility

PurposeEnterprises are increasingly taking actionable steps to transform existing business models through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility.Design/methodology/approachThe study relied on survey data from 485 banking professionals' perspectives with BDA usage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed.FindingsBased on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources.Practical implicationsThe study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products.Originality/valueThis study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations.

Intrahousehold Bargaining Power in Spain: An Empirical Test of the Collective Model

This paper analyzes the intrahousehold bargaining power of spouses in Spanish families, in a collective framework. We estimate household labor supply equations and, under certain testable restrictions, we obtain a theoretically derived sharing rule for household income, which characterizes intrahousehold bargaining power. Then, using unique data on decision-making in the household, we construct Pareto weights, and study the validity of the collective model by comparing the theoretical sharing rule and the constructed Pareto weight. The results reveal that both the observed Pareto weight and the theoretical sharing rule display qualitative similarities, thus providing direct empirical support to the collective model. Furthermore, the results suggest that Spanish wives behave more altruistically, while husbands behave more egoistically. This should be taken into account by policy makers and researchers when analyzing inequality in the household, and contemplating specific policies affecting the household.