Effects of Intellectual and Social Alignment on Organizational Agility: A Configurational Theory Approach

Literature has shown that business-information technology (IT) alignment can exert both positive and negative influences on organizational agility, giving rise to the IT alignment-agility paradox. To better understand this paradox at a more granular level, we conceptualize the sensing and responding dimensions of organizational agility as two independent constructs and suggest a nonlinear analytical approach. Based on configurational and contextual perspectives, this study investigates how intellectual and social alignment and organizational and environmental elements combine into multiple configurations to affect sensing and responding capabilities. Fuzzy-set qualitative comparative analysis (fsQCA) is used to analyze the survey data from 135 dyads of business and IT executives from the Chinese shipbuilding industry. Results show that different equifinal pathways can be used to achieve high sensing and responding capabilities, in which intellectual and social alignment play heterogeneous roles depending on the specific contexts. This study extends the IT-enabled agility literature by deepening our understanding of the effects of multidimensional IT alignment on multidimensional organizational agility and providing new insights into the IT alignment-agility paradox

Life Cycle Assessment of Bridges Using Bayesian Networks and Fuzzy Mathematics

[EN] At present, reducing the impact of the construction industry on the environment is the key to achieving sustainable development. Countries all over the world are using software systems for bridge environmental impact assessment. However, due to the complexity and discreteness of environmental factors in the construction industry, they are difficult to update and determine quickly, and there is a phenomenon of data missing in the database. Most of the lost data are optimized by Monte Carlo simulation, which greatly reduces the reliability and accuracy of the research results. This paper uses Bayesian advanced fuzzy mathematics theory to solve this problem. In the research, a Bayesian fuzzy mathematics evaluation and a multi-level sensitivity priority discrimination model are established, and the weights and membership degrees of influencing factors were defined to achieve comprehensive coverage of influencing factors. With the support of theoretical modelling, software analysis and fuzzy mathematics theory are used to comprehensively evaluate all the influencing factors of the five influencing stages in the entire life cycle of the bridge structure. The results show that the material manufacturing, maintenance, and operation of the bridge still produce environmental pollution; the main source of the emissions exceeds 53% of the total emissions. The effective impact factor reaches 3.01. At the end of the article, a big data sensitivity model was established. Through big data innovation and optimization analysis, traffic pollution emissions were reduced by 330 tonnes. Modeling of the comprehensive research model; application; clearly confirms the effectiveness and practicality of the Bayesian network fuzzy number comprehensive evaluation model in dealing with uncertain factors in the evaluation of the sustainable development of the construction industry. The research results have made important contributions to the realization of the sustainable development goals of the construction industry.This research was funded by the Spanish Ministry of Economy and Competitiveness, along with FEDER (Fondo Europeo de Desarrollo Regional), project grant number: BIA2017-85098-RZhou, Z.; Alcalá-González, J.; Kripka, M.; Yepes, V. (2021). Life Cycle Assessment of Bridges Using Bayesian Networks and Fuzzy Mathematics. Applied Sciences. 11(11):1-31. https://doi.org/10.3390/app11114916S131111

Astrotourism and night sky brightness forecast:First probabilistic model approach

Celestial tourism, also known as astrotourism, astronomical tourism or, less frequently, star tourism, refers to people’s interest in visiting places where celestial phenomena can be clearly observed. Stars, skygazing, meteor showers or comets, among other phenomena, arouse people’s interest, however, good night sky conditions are required to observe such phenomena. From an environmental point of view, several organisations have surfaced in defence of the protection of dark night skies against light pollution, while from an economic point of view; the idea also opens new possibilities for development in associated areas. The quality of dark skies for celestial tourism can be measured by night sky brightness (NSB), which is used to quantify the visual perception of the sky, including several light sources at a specific point on earth. The aim of this research is to model the nocturnal sky brightness by training and testing a probabilistic model using real NSB data. ARIMA and artificial neural network models have been applied to open NSB data provided by the Globe at Night international programme, with the results of this first model approach being promising and opening up new possibilities for astrotourism. To the best of the authors’ knowledge, probabilistic models have not been applied to NSB forecasting

Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

Reliability Modeling and Optimization Strategy for Manufacturing System Based on RQR Chain

Accurate and dynamic reliability modeling for the running manufacturing system is the prerequisite to implement preventive maintenance. However, existing studies could not output the reliability value in real time because their abandonment of the quality inspection data originated in the operation process of manufacturing system. Therefore, this paper presents an approach to model the manufacturing system reliability dynamically based on their operation data of process quality and output data of product reliability. Firstly, on the basis of importance explanation of the quality variations in manufacturing process as the linkage for the manufacturing system reliability and product inherent reliability, the RQR chain which could represent the relationships between them is put forward, and the product qualified probability is proposed to quantify the impacts of quality variation in manufacturing process on the reliability of manufacturing system further. Secondly, the impact of qualified probability on the product inherent reliability is expounded, and the modeling approach of manufacturing system reliability based on the qualified probability is presented. Thirdly, the preventive maintenance optimization strategy for manufacturing system driven by the loss of manufacturing quality variation is proposed. Finally, the validity of the proposed approach is verified by the reliability analysis and optimization example of engine cover manufacturing system

Applying decision tree-based model in tender evaluation: case of Technical University of Mombasa

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityUnfair tender evaluation and contract award in public procurement is prevalent in Kenya. This has contributed to low quality of goods, services and projects. Successful implementation of building projects is heavily impacted by taking the right decision during tendering processes. Manning tender procedures can be complex and uncertain, involving coordination of numerous tasks and persons with different priorities and objectives. Bias and inconsistent decision are inevitable if the decision-making process is wholly dependent on intuition, subjective judgement or emotions. In making transparent decision and beneficial competition tendering, there is need for a flexible tool that could facilitate fair decision making. The purpose of this research was to present a model of an IT solution integrating the concepts of supervised machine learning techniques in the context of tender evaluation in public procurement. A dataset of 100 instances comprising of 53 positive and 47 negative examples was used to train J48 decision tree classifier to build the model. After attribute selection in a WEKA environment, 4 of the 7 attributes of the dataset were used as independent variables (inputs) namely, Experience, Capacity, Number of personnel and Professionalism. A set criteria was used to determine the values of the independent variables. The dependent variable (output) was a category class with either “PASS” or “FAIL” values. To determine the class of an entity the J48 model considers all the values of the independent variables based on set rules. This algorithm was preferred due to its relatively simple model among other benefits stated herein. The dataset from TUM was divided into test data and training data for the model. The performance appraisal of the model was based on the accuracy of the classification, the precision, recall ratio, ROC curve and the F- Measure. The model was proven to be impressively accurate with an accuracy of 91.1765 % while the precision obtained was 0.857. The recall ratio was 1 and an F-measure of 0.923