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Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed performance. Notes that 18 papers from the Symposium are grouped in the area of automated optimal design. Describes the main challenges that condition computational electromagnetism’s future development. Concludes by itemizing the range of applications from small activators to optimization of induction heating systems in this third chapter.

AI-based robo-advisory (RA) represents a FinTech application that is already replacing retail investment advisors. In private banking (PB), clients also increasingly expect service provision across different digital channels, but with a higher degree of personalization. Hence, the present study investigates the impact of intelligent RA on the PB investment advisory process to derive both process (re)design knowledge and strategic guidance for artificial intelligence (AI) usage for PB investment advisory.

The present study applies an AI process impact analysis approach by decomposing AI-based RA into three AI application types: conversational agent, customer segmentation and predictive analytics. The analysis results along a reference PB investment advisory process reveal sub-process transformations which are applied for process redesign integrating AI.

The study results imply that AI systems (1) enable seamless client journeys, (2) increase advisor flexibility, (3) support the client–advisor relationship by applying an omnichannel approach and (4) demand advisor skills to be augmented with technical and statistical knowledge.

The research study contributes (1) an AI process impact analysis approach, (2) derives process (re)design knowledge for AI deployment and (3) develops strategic guidance for AI usage in PB investment advisory.

The aim of the study is to assess the resilience of tourism enterprises in the face of the pandemic crisis and the war in Ukraine. It was achieved on the base of the results of surveys conducted among Małopolska (Poland) tourism enterprises in the years 2021–2022. The research was conducted using the CAWI technique, it has been collected 517 completed questionnaires. Regression analysis was used to check the relationships between the studied variables. The findings confirm that the surveyed companies react agilely to changing, volatile, uncertain, and complex business conditions. These companies undertook numerous innovative (most often in the scale of the company) solutions in the organisational area. Changes noticed in the behaviour of contractors were also confirmed by constant monitoring of the environment forced by dynamic changes in conditions. Among the factors determining the activities of tourist companies in the context of international factors, one can undoubtedly mention the uncertainty caused by the war in Ukraine (and thus the energy crisis). The research focuses on the important subject of resilience, organisational changes, and agility in adapting to conditions in the face of volatility and uncertainty in the environment.

An attempt is made of formulating the optimal design problem of air‐cored inductors in a systematic and straightforward way. The numerical solution is obtained by means of nonlinear constrained optimization techniques. Reference is made to air‐cored multilayer inductors of rectangular cross section.

This work aims to determine how innovation orientation (IO), built from six dimensions (strategic, structural-process, human resources, technological, organizational culture and market) affects organizational performance (OP) with the inclusion of knowledge management (KM) as a mediator and technological readiness (TR) as a moderator in the model.

Questionnaires completed by business service companies were analyzed using multiple regression analysis (path analysis), including the mediating variable (KM) and moderating variable (TR). The construct was validated with positive outcomes.

Of the eight hypotheses, six were supported. The study results show that strategic, technological, organizational culture and market dimensions of IO positively influence KM. On the other hand, KM plays an important role as a mediator in supporting the relationship between the four dimensions of IO and performance. Moreover, TR, as a moderator, positively affects the relationship between KM and OP.

The study is the first to explore the relationship between six dimensions of IO and KM in business service sector. Furthermore, this study provides evidence that TR can be beneficial for companies with respect to effective KM, which leads to the better performance.

The paper is concerned with a design and a validation of a neurocontroller for a pulse magnetiser for magnetising permanent magnets. The goal is to register the peak time and crest current in order to pick up an optimal intermittent duty conditions regime for the magnetiser. This is usually done by solving a set of coupled ordinary differential equations describing current waveforms and the temperature rise in the magnetising winding. The neurocontroller is based on a one‐layer feedforward neural network which is trained using the Levenberg‐Marquardt learning rule. We present the results produced by the neurocontroller and we compare them with the numerical and measurement results. The neurocontroller is intended to serve later as a part of a global optimising algorithm.

While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ significantly. This study aims to explore whether decomposing aggregated search queries based on the terminals from which these queries are generated can enhance tourism demand forecasting.

Mount Siguniang, a national geopark in China, is taken as a case study in this paper; another case, Kulangsu in China, is used as the robustness check. The authors decomposed the total Baidu search volume into searches from mobile devices and PCs. Weekly rolling forecasts were used to test the roles of decomposed and aggregated search queries in tourism demand forecasting.

Search queries generated from PCs can greatly improve forecasting performance compared to those from mobile devices and to aggregate search volumes from both terminals. Models incorporating search queries generated via multiple terminals did not necessarily outperform those incorporating search queries generated via a single type of terminal.

Major players in the tourism industry, including hotels, tourist attractions and airlines, can benefit from identifying effective search terminals to forecast tourism demand. Industry managers can also leverage search indices generated through effective terminals for more accurate demand forecasting, which can in turn inform strategic decision-making and operations management.

This study represents one of the earliest attempts to apply decomposed search query data generated via different terminals in tourism demand forecasting. It also enriches the literature on tourism demand forecasting using search engine data.

This study aims to apply an open-source approach to protect the 3D printing industry from innovation stagnation due to broad patenting of obvious materials.

To do this, first an open-source implementation of the first five conditions of an open-source algorithm developed to identify all obvious 3-D printing materials was implemented in Python, and the compound combinations of two and three constituents were tested on ten natural and synthetic compounds. The time complexity for combinations composed of two constituents and three constituents is determined to be O(n²) and O(n³), respectively.

Generating all combinations of materials available on the Chemical Abstracts Services (CAS) registry on the fastest processor on the market will require at least 73.9 h for the latter, but as the number of constituents increases the time needed becomes prohibitive (e.g. 3 constituents is 1.65 million years). To demonstrate how machine learning (ML) could help prioritize both theoretical as well as experimental efforts a three-part biomaterial consisting of water, agar and glycerin was used as a case study. A decision tree model is trained with the experimental data and is used to fill in missing physical properties, including Young's modulus and yield strength, with 84.9 and 85.1% accuracy, respectively.

The results are promising for an open-source system that can theoretically generate all possible combinations of materials for 3-D printing that can then be used to identify suitable printing material for specific business cases based on desired material properties.

The purpose of this paper is to solve the optimization problem for a permanent magnet linear actuator with moving magnet for driving a needle in a knitting machine. The optimization is carried out with respect to the maximal average force along the stroke.

The optimization factors are the dimensions of the permanent magnet and the pole length. For obtaining the objective function for each parameter combination, the static force‐stroke characteristic is computed. Three‐dimensional finite element analysis is employed for obtaining the electromagnetic force. The objective function is then approximated by secondary models on the basis of design of experiment. The optimization is carried out using sequential linear programming.

Optimal solution to the problem has been obtained and the force‐stroke characteristic of the optimal actuator is given.

The proposed approach employs sequential linear programming with move limits using trust region concept, 3D finite element analysis, design of experiments and secondary models for optimization of a permanent magnet linear actuator for individual needle driving in a knitting machine.

This study evaluates the potential of using the material extrusion (MEX) process for recycling waste tire rubber (WTR). By investigating the process parameters, mechanical behaviour and morphological characterisation of a thermoplastic polyurethane-waste tire rubber composite filament (TPU-WTR), this study aims to establish a framework for end-of-life tire (ELT) recycling using the MEX technology.

The research assesses the impact of various process parameters on the mechanical properties of the TPU-WTR filament. Hysteresis analysis and Poisson’s ratio estimation are conducted to investigate the material’s behaviour. In addition, the compressive performance of diverse TPU-WTR triply periodic minimal surface lattices is explored to test the filament suitability for printing intricate structures.

Results demonstrate the potential of the TPU-WTR filament in developing sustainable structures. The MEX process can, therefore, contribute to the recycling of WTR. Mechanical testing has provided insights into the influence of process parameters on the material behaviour, while investigating various lattice structures has challenged the material’s capabilities in printing complex topologies.

This research holds significant social implications addressing the growing environmental sustainability and waste management concerns. Developing 3D-printed sustainable structures using recycled materials reduces resource consumption and promotes responsible production practices for a more environmentally conscious society.

This study contributes to the field by showcasing the use of MEX technology for ELT recycling, particularly focusing on the TPU-WTR filament, presenting a novel approach to sustainable consumption and production aligned with the United Nations Sustainable Development Goal 12.