Silicon has been a central material in electronics. Integrated circuits made from silicon as a substrate continue to improve in performance due to the miniaturization and stacking of metal-oxide semiconductor (MOS) transistors, and silicon is expected to remain a core material in electronics for the foreseeable future. Additionally, in recent years, spin qubits have become the focus of active research. As a result, not only is room-temperature operation gaining attention, but the low-temperature operation of MOS transistors is also receiving renewed interest. In fact, research into their interface properties at low temperatures is becoming increasingly active.

However, in the field of low-temperature condensed matter physics (such as superconductivity and superfluidity) for two-dimensional electron systems, there has been virtually no research involving silicon. This field has largely been dominated by GaAs and two-dimensional materials like graphene, which offer higher mobility than silicon. Nevertheless, the author believes that silicon is an important system from the perspective of low-temperature solid-state physics and its future applications in electronics. Silicon’s central role in electronics is primarily due to its excellent insulating film interface. For example, the quantum Hall effect, which won a Nobel Prize, was discovered in silicon, and phenomena such as the metal-insulator transition can be observed more clearly and conveniently in silicon than in GaAs and other materials. By utilizing these characteristics, novel physical properties are expected to emerge.

Against this backdrop, we have begun research focusing on electron correlations to explore the potential for new phenomena, such as superconductivity and superfluidity, at the silicon MOS transistor interface. Here, we introduce some recent studies related to these efforts.

Interface concepts fundamentally determine all qualities of interacting with our digital devices.  Since the plugs and switches of ENIAC in 1946, many revolutions have influenced the field (punched cards, CLI, GUI, mouse) taking us closer and closer to the most natural 3D environments. Various motion- and spatial sensing technologies of the past years enabled the real-time perception of the 3D relationship between the user and the environment.  As a result, most likely, Virtual, Augmented and Mixed reality (often referred to as XR technologies) shall define the next level of HCI evolution. In my talk, I am going to enlighten the history that finally leads to spatial HCI technologies.  

The second part of the presentation summarizes the results and ideas of a newly developed software engine, called MAXWHERE that provides effective working environments with spatial (Virtual Reality) multimedia arrangement.

The fundamental idea behind MAXWHERE is the generalization of the Document Object Model (DOM) introducing the Where Object Model (WOM) concept that covers the conventional WEB contents as well as the VR/AR building blocks in a coherent way.

Typical applications of MAXWHERE includes industrial monitoring and facility support, context-based collaborative working environment, industrial training, and Interactive live presentations.

Interesting examples will be delivered touching the fields of everyday computing, education, and industrial applications.

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In 2019, to face the challenges presented by climate change, the European Union (EU) presented the European Green Deal strategy, which according to the EU aims to transform EU the first climate neutral continent by 2050 and decouple economic growth from resource use. It is expected that the European Green Deal will transform the EU into a modern, resource-effective, and competitive economy. 

Within the scope of energy, simultaneously with promoting the use of renewable energy sources, the EU presented a set of initiatives focused on the energy end-users. These initiatives focused on the energy end-users tend to accelerate the change of power system organization paradigm, previously essentially centralized. Among these initiatives there is the promotion of prosumers (i.e., who both consume and produce energy) and stated that the decentralized production of energy by prosumers must constitute a relevant component of EU’s energy policy. The appearance of prosumers has enhanced the appearance of so-called energy communities. The EU introduced the concept of energy communities in its legislation through the Clean Energy for All European Package adopted in 2019, with the Citizen Energy Communities and Renewable Energy Communities designations.

According to EU estimates, by 2030 energy communities can account for 17 % of all installed wind capacity and 21 % of all photovoltaic capacity installed in the EU. Thus, it is possible to anticipate the great impact of energy communities in the future. However, it is not only necessary to create energy communities, but more importantly, it is necessary to develop intelligent and sustainable communities, and for this it is necessary to develop efficient energy management systems for energy communities.

This talk will focus on the above challenge, starting by the development of efficient energy management systems for single prosumers, and extending this approach to energy communities, using the concept of an intelligent energy aggregator. 

An important task in industrial applications is the proper heat treatment of workpieces to enhance their material properties. To achieve precise heat treatment, it is crucial to know the Heat Transfer Coefficient. This characteristic cannot be measured directly; instead, its value is typically inferred through experiments and inverse mathematical models.

Finding solutions to this Inverse Heat Conduction Problem is an active area of interest, with several methods based on metaheuristics. However, these methods tend to be resource-intensive, so any opportunities for acceleration can provide significant practical benefits.

This presentation will introduce the use of graphics accelerator cards (GPUs) in this field. It will discuss the data-parallel optimization of the widely used finite difference model-based algorithm for cooling simulation. Additionally, it will present the operation of metaheuristics, such as genetic algorithms and particle swarm optimization, that are based on this model. Finally, the presentation will explore how machine learning can provide approximate solutions to the problem and facilitate the random initialization of metaheuristics.