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Abstract The conditions for a feasible Cyber‐Physical System (CPS) in a welding environment are explored for the manufacturing technology components while also focusing on machine learning tools. Increasing manufacturing efficiency means making digitalisation feasible for all technologies, including welding, given today's challenges. Early versions of manufacturing management, such as Computer Integrated Manufacturing, are already leading the way, and one of the latest milestones in these developments is CPS. It can be shown that the digital migration of specific sub‐domains (e.g. visual inspection of the weld seam during quality assurance) is significantly more challenging and unimaginable without artificial intelligence applications. However, it is also true that the full integration needed to achieve autonomous manufacturing has yet to be fully achieved, although there is a strong demand in the industry for these CPS to work. In some areas, the digital switchover has already been prepared. However, the interconnection of these subsystems requires modern information systems or, in the case of existing ones, their upgrading to the appropriate level. This research area is set to be addressed comprehensively by initiating several projects. In the initial phase, the aim is to develop an architecture that integrates the various Information Technology applications. In this work, the digital manufacturing environment under CPS is studied, the relevant components are explored, the conditions for the transition from traditional to CPS‐based manufacturing are examined and examples of planned further specific studies on the components are listed.

Abstract This work focuses on detecting defects in welding seams using the most advanced You Only Look Once (YOLO) algorithms and transfer learning. To this end, the authors prepared a small dataset of images using manual welding and compared the performance of the YOLO v5, v6, v7, and v8 methods after two‐step training. Key findings reveal that YOLOv7 demonstrates superior performance, suggesting its potential as a valuable tool in automated welding quality control. The authors’ research underscores the importance of model selection. It lays the groundwork for future exploration in larger datasets and varied welding scenarios, potentially contributing to defect detection practices in manufacturing industries. The dataset and the code repository links are also provided to support our findings.

Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. In recent years, manual model tuning has been gradually replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. We quantified the performance of the algorithms in terms of the best solutions found and in terms of convergence speed. We identified several algorithms, including covariance matrix adaptation evolution strategy and particle swarm optimization, that consistently, without any fine-tuning, found good solutions in all of our use cases. By contrast, some other algorithms including all local search methods provided good solutions only for the simplest use cases, and failed completely on more complex problems. We also demonstrate the versatility of Neuroptimus by applying it to an additional use case that involves tuning the parameters of a subcellular model of biochemical pathways. Finally, we created an online database that allows uploading, querying and analyzing the results of optimization runs performed by Neuroptimus, which enables all researchers to update and extend the current benchmarking study. The tools and analysis we provide should aid members of the neuroscience community to apply parameter search methods more effectively in their research.

Abstract The application of industrial technologies is undergoing significant changes. Finding the level at which to use efficient cyberphysical systems is perhaps one of the most important technical preparatory tasks in implementing digital manufacturing. Welding technology systems are investigated, and a framework for capturing the data sets required for data-driven manufacturing is developed. To make full autonomy in a manufacturing environment meaningful, formerly isolated groups of equipment need to be organized into a production information system. In our research, a test system is created that can implement a digital virtual interface and achieve new levels of efficiency with a future digital twin system. In the discourse of the study, the technological parameters of welding test pieces were investigated, namely the available measurement data sets of current, and voltage data. In the summary section, most of the tasks and research directions are presented, which can be envisaged as a continuation of the present study. Our study will be followed by further research, already testing a complete digital twin system, thus reaching another milestone on the way to autonomous manufacturing.

Cydia interscindana (Möschler, 1866) has spread through several European countries in the past few years, becoming an invasive pest of ornamental trees. It was collected in Hungary for the first time in a pheromone trap set for Cydia pomonella (Linnaeus, 1758) in 2014. Here we discuss its recent distribution in Hungary based on intensive sampling between 2018 and 2020, which showed the dispersal of the pest by humans. Two formerly unknown host plants are also recorded. The damage caused by the larvae, the external morphology of the adult male, larva, pupa (described for the first time) and pupal exuviae are presented. We also analyse DNA barcodes, identifying this pest for the first time via DNA sequencing of immature stages. Introduction Cydia interscindana is native in the Mediterranean region, where it was described by Möschler in 1866 from Andalusia. It is distributed in Mediterranean countries including Portugal (Corley 2004), Spain (Férriz et al. 2006), France (Lévêque et al. 2017) and Italy (Minelli 1995). Later the species was recorded in the British Isles (Knill-Jones 2020), Belgium (De Prins 2016), Switzerland (Swisslepteam 2010), Slovakia (Pastorális et al. 2018) and Russia (Caucasus; Schurov et al. 2017). In Hungary, Cydia interscindana adults were caught by a sticky delta pheromone trap (CSALOMON RAG type) for Cydia pomonella (Linnaeus 1758) in 2014 during a study on swarming dynamics of the latter pest in Budapest. This provided the first record of the species in the Carpathian basin (Szabóky 2014; Takács and Szabóky 2015). In the Mediterranean region larvae feed on Juniperus oxycedrus (L.) (Miller 1990). In Belgium the larva was recorded on Juniperus spp. (Meert et al. 2019). J. oxycedrus is not native in Hungary, but Cupressus × leylandii A.B. Jacks. & Dallim 1926, Platycladus orientalis (L.) Franco 1949 and Chamaecyparis lawsoniana (A. Murray bis) Parl. 1864 are popular evergreens used as ornamental trees both in parks and gardens. In Hungary several pests of these plants have been recorded, all probably introduced with imported plants; in the literature, 11 Lepidoptera, nine Coleoptera and six Hemiptera species have been mentioned already (Csóka and Kovács 1999; Maráczi 2013; Bozsik et al. 2016; Schurov et al. 2017). However, until the end of the 2000s, only Scolytidae (Coleoptera) species caused serious damage (Bozsik and Szőcs 2017). In 2012, an outbreak of the formerly detected (Muskovits 2001) Lamprodila festiva (Linnaeus 1767) (Buprestidae) took place in Budapest (Németh 2012) causing serious damage on Platycladus orientalis and several ornamental gymnosperm species. This outbreak was certainly caused by introduced specimens, that had arrived with trees from the Mediterranean region where this beetle is a well-known pest (Merkl 2016), whose abundance in Hungary increases due to climatic change (Csóka et al. 2018). Based on the available data, in Hungary this beetle pest has also been blamed for all the damage caused on Cupressus, Platycladus and Chamaecyparis trees and management has been carried out only against them. In 2018, a larva of L. festiva, an unidentified caterpillar and a freshly emerged specimen of Cydia interscindana were collected simultaneously from a Leyland cypress in Székesfehérvár (Central Hungary). In that year, similar Lepidoptera larvae were found in three neighbouring villages: Velence, Sukoró and Pákozd. To identify the sampled caterpillar, DNA analysis was undertaken. Additionally, in 2019–2020 a country-wide investigation was carried out to map the distribution and abundance of C. interscindana and gather data on bionomics of this pest in the Carpathian basin.

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The thermal characterization of power devices is an inevitable task in the industry. Thermal transient testing is one of the major tools for this characterization, as it is not only capable of giving information about the actual thermal parameters but may also reveal the root cause of potential device failures. The testing may occur on single packages or modules on a dedicated standard test bench, or “in situ”, in an actual assembly. The testing process itself is very fast in both cases, on the order of seconds, but the transient measurement needs to be preceded by a calibration step to determine the temperature dependence of a temperature-sensitive parameter (TSP) of the semiconductor device. This may require a long time, as the device has to be measured at many stabilized temperatures, which in the case of power devices may take hours. It also has to be considered that, especially in “in situ” measurements, reaching the highest device temperatures of power devices may even damage other parts of the surrounding electronics. Moreover, the temperature distribution inside a module will be different at calibration time than during operation with the same junction temperature, as bond wires and copper traces do not reach the chip temperature in the latter case. This paper presents a methodology that can be used to replace the lengthy measurement of one parameter at many temperatures with a fast single I–V characteristics measurement at room temperature. Physics-based calculations assign a unique temperature-sensitive parameter to each item of interest in the system. After the presentation of the theoretical background, the usability of the method is demonstrated by verifying measurements on silicon power devices.