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The important task (NBI neutral beam injector for fusion) and the complexity of radiofrequency ion source need large tests (as ELISE and SPIDER) and intermediate scale tests (as NIO1 in this poster) for optimization. Conditioning (with gas) was found necessary in Cs-free operation. Cs-based operation has begun: stability improves at lower oven temperature; in both cases, lateral view cameras show beam quality. Energy recovery plugin almost ready for NIO1 (or at a test station at TRIPS).

The important task (NBI neutral beam injector for fusion) and the complexity of radiofrequency ion source need large tests (as ELISE and SPIDER) and intermediate scale tests (as NIO1 in this poster) for optimization. Conditioning (with gas) was found necessary in Cs-free operation. Cs-based operation has begun: stability improves at lower oven temperature; in both cases, lateral view cameras show beam quality. Energy recovery plugin almost ready for NIO1 (or at a test station at TRIPS).

This study is a follow-up on the the implementation of a business model, which was created in a co-creation process between two universities and an established SME specialized in monitoring municipal and industrial water distribution systems.. A shared collaboration sustainability innovation project between two universities and SME company on research, development and innovation context results was analyzed. A product was innovated for water systems monitoring service's needs and generated from two universities and SMEs shared RDI pilot. Authors elaborate findings for commercialization possibilities success factors and challenges for the co-creation concept. Evaluation criteria used are implementation feasibility and environmental & economical value. InTo tool is utilized for after commercialization phase to evaluate business model in this case study. Literature review focuses on collaboration enhanced sustainability innovations, which is used as comparison frame. Authors suggest efficient actions for collaborative innovation and hurdles to stay away for smooth environmental area innovation commercialization.

This study is a follow-up on the the implementation of a business model, which was created in a co-creation process between two universities and an established SME specialized in monitoring municipal and industrial water distribution systems.. A shared collaboration sustainability innovation project between two universities and SME company on research, development and innovation context results was analyzed. A product was innovated for water systems monitoring service's needs and generated from two universities and SMEs shared RDI pilot. Authors elaborate findings for commercialization possibilities success factors and challenges for the co-creation concept. Evaluation criteria used are implementation feasibility and environmental & economical value. InTo tool is utilized for after commercialization phase to evaluate business model in this case study. Literature review focuses on collaboration enhanced sustainability innovations, which is used as comparison frame. Authors suggest efficient actions for collaborative innovation and hurdles to stay away for smooth environmental area innovation commercialization.

The smart grid aims to enhance the electric grid’s dependability, security, and effciency by deploying digital information and control technology. However, the increasing reliance on communication technology exposes these systems to cyberattacks, posing signifcant cyber threats to the availability and functionality of the smart grid. To mitigate such threats, effective intrusion detection algorithms are crucial. In this context, we propose a hybrid deep learning algorithm that focuses on distributed denial of service (DDoS) attacks on the communication infrastructure of the smart grid. The proposed algorithm combines convolutional neural network (CNN) and gated recurrent unit (GRU) algorithms to provide real-time analysis and state estimation-based techniques for effcient control implementation. We conduct simulations using a benchmark cyber-security dataset from the Canadian institute of cybersecurity intrusion detection system. The results demonstrate that our hybrid deep learning algorithm outperforms existing intrusion detection algorithms, achieving an impressive overall accuracy rate of 99.7 %. In the context of supervisory control and data acquisition (SCADA) systems, which monitor and control industrial machinery, communication network vulnerabilities can lead to cyber-attacks introducing false data into the operational network. We propose a restricted Boltzmann machine-based nature-inspired artifcial root foraging optimization algorithm for identifying and classifying cyber-attacks to address this issue. We optimize data features using this algorithm and evaluate its performance against traditional supervised machine learning algorithms such as artifcial neural networks, convolutional neural networks, and support vector machines. The proposed algorithm outperforms its counterparts in accuracy, precision, recall, and f1 score. Furthermore, we address the security vulnerabilities in SCADA systems by introducing the genetically seeded fora transformer neural network (GSFTNN) intrusion detection algorithm. ...

The smart grid aims to enhance the electric grid’s dependability, security, and effciency by deploying digital information and control technology. However, the increasing reliance on communication technology exposes these systems to cyberattacks, posing signifcant cyber threats to the availability and functionality of the smart grid. To mitigate such threats, effective intrusion detection algorithms are crucial. In this context, we propose a hybrid deep learning algorithm that focuses on distributed denial of service (DDoS) attacks on the communication infrastructure of the smart grid. The proposed algorithm combines convolutional neural network (CNN) and gated recurrent unit (GRU) algorithms to provide real-time analysis and state estimation-based techniques for effcient control implementation. We conduct simulations using a benchmark cyber-security dataset from the Canadian institute of cybersecurity intrusion detection system. The results demonstrate that our hybrid deep learning algorithm outperforms existing intrusion detection algorithms, achieving an impressive overall accuracy rate of 99.7 %. In the context of supervisory control and data acquisition (SCADA) systems, which monitor and control industrial machinery, communication network vulnerabilities can lead to cyber-attacks introducing false data into the operational network. We propose a restricted Boltzmann machine-based nature-inspired artifcial root foraging optimization algorithm for identifying and classifying cyber-attacks to address this issue. We optimize data features using this algorithm and evaluate its performance against traditional supervised machine learning algorithms such as artifcial neural networks, convolutional neural networks, and support vector machines. The proposed algorithm outperforms its counterparts in accuracy, precision, recall, and f1 score. Furthermore, we address the security vulnerabilities in SCADA systems by introducing the genetically seeded fora transformer neural network (GSFTNN) intrusion detection algorithm. ...

Photon-enhanced thermionic emission (PETE) devices applied to solar energy may be considered as high-temperature solar cells. A cathode structure for PETE was designed for high temperature energy conversion in solar concentrating systems. Surface-hydrogenated diamond is one of the few semiconductors showing negative electron affinity and a work function as low as 1.7 eV if nitrogen doped, that is connected to a significant thermionic emission at moderate temperatures (up to 800 °C, where hydrogen desorbs inducing a positive electron affinity). But diamond is transparent to solar radiation, consequently advanced techniques for preparing an efficient sunlight absorbing diamond are necessary.