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Modern innovative models have the possibility of transferring research and development (R&D) output through technology transfer from scientific and research institutions or other enterprises. The complex process of technology transfer is significantly dependent on cooperation among academia, industry, and governments (I4.0) in response to the technological developments driven together through Industry 4.0. As a result, numerous technology transfer factors must be addressed for I4.0 to become a reality. However, the abundance of literature on I4.0 and associated technologies, the key ingredients, and insights for effectively executing I4.0 technology transfer are fairly limited. This study focuses on the success factors of technology transfer for I4.0. The framework is based on systematic literature to outline significant results and factors. Furthermore, this study summarizes, analysis, and criticizes the actual models and their influential variables for I4.0 technology transfer. One of the findings of this study is the significance of cooperation between technology recipients, agents, and inventors for I4.0 technology transfer. Another impressive finding is the significance of the ecosystem component in technology transfer. Combining I4.0 technologies and open innovation is a game-changer, enabling businesses to significantly save time and cost. This article will assist decision-makers in developing policies and strategies to improve the I4.0 technology transfer process. Furthermore, this involves identifying the kind of government assistance that will help accelerate the transition to I4.0 via technology transfer.

Water-in-diesel (W/D) emulsion fuel is a potentially viable diesel fuel that can simultaneously enhance engine performance and reduce exhaust emissions in a current diesel engine without requiring engine modifications or incurring additional costs. In a consistent manner, the current study examines the impact of adding water, in the range of 5–30% wt. (5% increment) and 2% surfactant of polysorbate 20, on the performance in terms of brake torque (BT) and exhaust emissions of a four-cylinder four-stroke diesel engine. The relationship between independent factors, including water addition and engine speed, and dependent factors, including different exhaust released emissions and BT, was initially generated using machine learning support vector regression (SVR). Subsequently, a robust and modern optimization of the sea-horse optimizer (SHO) was run through the SVR model to find the optimal water addition and engine speed for improving the BT and lowering exhaust emissions. Furthermore, the SVR model was compared to the artificial neural network (ANN) model in terms of R-squared and mean square error (MSE). According to the experimental results, the BT was boosted by 3.34% compared to pure diesel at 5% water addition. The highest reduction in carbon monoxide (CO) and unburned hydrocarbon (UHC) was 9.57% and 15.63%, respectively, at 15% of water addition compared to diesel fuel. The nitrogen oxides (NOx) emissions from emulsified fuel were significantly lower than those from pure diesel, with a maximum decrease of 67.14% at 30% water addition. The suggested SVR-SHO model demonstrated superior prediction reliability, with a significant R-Squared of more than 0.98 and a low MSE of less than 0.003. The SHO revealed that adding 15% water to the W/D emulsion fuel at an engine speed of 1848 rpm yielded the optimum BT, CO, UHC, and NOx values of 49.5 N.m, 0.5%, 57 ppm, and 369 ppm, respectively. Finally, these outcomes have important implications for the potential of the SVR-SHO approach to minimize engine exhaust emissions while maximizing engine performance.

Water-in-diesel (W/D) emulsion fuel is a potential alternative fuel that can simultaneously lower NOx exhaust emissions and improves combustion efficiency. Additionally, there are no additional costs or engine modifications required when using W/D emulsion fuel. The proportion of water added and engine speed is crucial factors influencing engine behavior. This study aims to examine the impact of the W/D emulsion diesel fuel on engine performance and NOx pollutant emissions using a compression ignition (CI) engine. The emulsion fuel had water content ranging from 0 to 30% with a 5% increment, and 2% surfactant was employed. The tests were performed at speeds ranging from 1000 to 3000 rpm. All W/D emulsion fuel was compared to a standard of pure diesel in all tests. A four-cylinder, four-stroke, water-cooled, direct-injection diesel engine test bed was used for the experiments. The performance and exhaust emissions of the diesel engine were measured at full load and various engine speeds using a dynamometer and an exhaust gas analyzer, respectively. The second purpose of this study is to illustrate the application of two optimizers, grey wolf optimizer (GWO) and intelligent grey wolf optimizer (IGOW), along with using multivariate polynomial regression (MPR) to identify the optimum (W/D) emulsion blend percentage and engine speed to enhance the performance, reduce fuel consumption, and reduce NOX exhaust emissions of a diesel engine operating. The engine speed and proportion of water in the fuel mixture were the independent variables (inputs), while brake power (BP), brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), and NOx were the dependent variables (outcomes). It was experimentally observed that utilizing emulsified gasoline generally enhances engine performance and decreases emissions in general. Experimentally, at 5% water content and 2000 rpm, the BSFC has a minimal value of 0.258 kJ/kW·h. Under the same conditions, the maximum BP of 11.6 kW and BTE of 32.8% were achieved. According to the IGWO process findings, adding 9% water to diesel fuel and running the engine at a speed of 1998 rpm produced the highest BP (11.2 kW) and BTE (33.3%) and the lowest BSFC (0.259 kg/kW·h) and reduced NOx by 14.3% compared with the CI engine powered by pure diesel. The accuracy of the model is high, as indicated by a correlation coefficient R2 exceeding 0.97 and a mean absolute error (MAE) less than 0.04. In terms of the optimizer, the IGWO performs better than GWO in determining the optimal water addition and engine speed. This is attributed to the IGWO has excellent exploratory capability in the early stages of searching.

Technology transfer, particularly within the context of Industry 4.0, is a complicated process full of challenges. Integrating and commercializing advanced technologies of Industry 4.0, such as automation and robotics, into industry necessitates an in-depth understanding of the factors influencing effective technology transfer. This dissertation addresses these challenges and has three primary objectives. Firstly, it aims to identify and analyze existing gaps and challenges in the technology transfer process within Industry 4.0, focusing on understanding the factors contributing to effective technology transfer. Secondly, the dissertation investigates predictors to enhance the effectiveness of Technology Transfer Offices in managing Industry 4.0 technologies, particularly in automation and robotics. These predictors provide practical guidance and proven methods for technology transfer offices to enhance patent licensing success. Lastly, the dissertation intends to build a predictive model of patent licensing success specific to automation and robotics, targeting improving university technology transfer's patent portfolio management capabilities. This model aims to increase technology transfer office performance, increase technology commercialization, and facilitate self-sustainability through revenue generation.This dissertation employs a systematic literature review to incorporate existing knowledge, statistical analysis to explore patent variables and their relation with patent licensing, and supervised machine learning classification to develop a predictive model. These approaches enable an extensive investigation of the technology transfer process, facilitating the development of predictive models to promote innovation and enrich technology transfer effectiveness in the automation and robotics sectors within Industry 4.0. In conclusion, an Industry 4.0 Technology Transfer Model and Conceptual Framework are proposed, identifying novel predictors for automation and robotics patents, including independent claims, success rate of technology transfer office, and inventor experience. Additionally, a classification model is developed to predict patent licensing success, further contributing to the advancement of technology transfer in the Industry 4.0 era.