The three key design criteria for nearly zero-energy buildings (nZEBs) and climate-neutral buildings are minimizing energy use, ensuring high occupant comfort, and reducing environmental impact. Thermal comfort is one of the main components of indoor environmental quality (IEQ), strongly affecting occupants’ health, well-being, and productivity. As energy-efficiency requirements become more demanding, the appropriate selection of heating systems, their automated control, and the management of solar heat gains are becoming increasingly important. This study investigates the influence of two low-temperature radiant heating systems—underfloor and wall-mounted—and the use of Venetian blinds on perceived thermal comfort in a highly glazed public nZEB building located in a densely built urban area within a temperate climate zone. The assessment was based on the PMV (Predicted Mean Vote) index, commonly used in IEQ research. The results show that both heating systems maintained indoor conditions corresponding to comfort or slight thermal stress under steady state operation. However, during periods of strong solar exposure in the room without blinds, PMV values exceeded 2.0, indicating substantial heat stress. In contrast, external Venetian blinds significantly stabilized the indoor microclimate—reducing PMV peaks by an average of 50.2% and lowering the number of discomfort hours by 94.9%—demonstrating the crucial role of solar protection in highly glazed spaces. No significant whole-body PMV differences were found between underfloor and wall heating. Overall, the findings provide practical insights into the control of thermal conditions in radiant-heated spaces and highlight the importance of solar shading in mitigating heat stress. These results may support the optimization of HVAC design, control, and operation in both residential and non-residential nZEB buildings, contributing to improved occupant comfort and enhanced energy efficiency.

This study evaluates sensor-based pre-concentration using XRT technology as an alternative to selective mining for low-grade European copper ores (0.48% Cu), addressing the need for sustainable beneficiation amid declining ore grades and environmental pressures in green mining initiatives. Copper ore samples from Złote Hory mine (Czech Republic) were selectively extracted, mixed (1:1:1 ore 8–16 mm/ore 16–32 mm/waste rock 8–32 mm), and analyzed on Comex’s LSX-MAX laboratory sorter with dual-energy XRT sensors, calibrated for maximum product recovery via density-based classification into High-Density (product) and Low-Density (waste) fractions. Sorting achieved a 1:1 product-to-waste mass split from feed (Cu = 0.5%, 100% mass), yielding pre-concentrate at 0.91% Cu (52.08% mass yield, 95.67% recovery) and waste at 0.04% Cu (47.92% mass, 4.33% loss)—a 1.82x grade upgrade superior to mixed feed and 1.42x superior to selective mining (0.64% Cu at 66.21% yield). Combined approaches promise further optimization; future work will assess downstream grinding/flotation impacts, industrial scaling, and economic/environmental benefits.

The emergence of Artificial Neural Networks (ANNs) and their deep learning form called Artificial Intelligence (AI) opened a new path to improve energy efficiency and the indoor environment. A small collaborating network team is now extending the passive house approach, in a book entitled Retrofitting, the Energy and Environment of Buildings (Gruyter Publishers), and presenting generalized AI modeling in the following paper. This concept uses a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and test) for optimalization to hourly data collected for one full year. The non-residential buildings are less affected by the space occupants. This paper examines the feasibility of a uniform, climate modified technology, as our objective is to create a universal and affordable approach to buildings assisting in slowing the rate of climate change. Hence, the idea of creating a generalized neural network for predicting electricity consumption linked with weather conditions was born. This network is to forecast the electricity consumption for buildings linked to the local weather conditions, but different categories of buildings are put together in one set. While this will lower the large set precision, still our question is if such a network would work. If so, in the future we will create multi-variant, local residential systems with the capability of predicting energy use.

The emergence of Artificial Neural Networks (ANN) and its deep learning form, called Artificial Intelligence (AI), opened a new path to improve energy efficiency and the indoor environment. A small collaborating network team is now extending the passive house approach in a book entitled "Retrofitting: The Energy and Environment of Buildings" (Gruyter Publishers [5]) and presenting generalized AI modeling in the following paper. This concept utilizes a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and testing) for the optimization of hourly data collected over one full year. The non-residential buildings are less affected by space occupants. This paper examines the feasibility of a uniform, climate-modified technology, as our objective is to create a universal and affordable approach to buildings, assisting in slowing the rate of climate change. Hence, the idea of creating a generalized neural network for predicting electricity consumption in relation to weather conditions was born. This network is designed to forecast electricity consumption for buildings linked to local weather conditions; however, different categories of buildings are grouped in one set. While this will lower the large set precision, our question is whether such a network would work. If so, in the future we will create multi-variant, local residential systems with the capability of predicting energy use.