In the article, the application potential of the dew-point cooling tower (DPCT) in selected energy-intensive applications in temperate climates was analyzed and discussed. The applications selected for analysis are power generation with natural gas turbines and chilled water air conditioning systems. The study is based on a mathematical model derived from a modified ε-NTU model. The model was validated against experimental results and showed satisfactory agreement with the experimental data. DPCT was compared with a typical cooling tower limited by the wet-bulb temperature (wet-bulb cooling tower, WBCT). The simulation results showed that DPCT is able to provide significant energy savings in energy-intensive applications; therefore, its application potential in temperate climates can be considered justified. In the case of gas turbines, DPCT was able to generate 2 to 10 percentage points more capacity than operating on outdoor air and 1.8 to 5 percentage points more than operating with WBCT. In the case of air conditioning systems, the system equipped with DPCT achieved EERs (energy efficiency ratios) higher by 1 to 7.2 compared to dry cooling and by 0.3 to 5.1 compared to systems equipped with WBCT. The annual energy savings obtained by the system with DPCT were 14.7 MWh compared to WBCT and 30 MWh compared to dry cooling.

Background. Eye-controlled interfaces can support communication and selection tasks when physical interaction is difficult, but conventional multi-point calibration may be burdensome for users who fatigue quickly or have severe motor limitations. This study operationally evaluates a low-burden single-point adaptive gaze-to-cursor correction pipeline for a dwell-based selection interface. Methods. The pipeline combines central single-point offset estimation, robust median/median absolute deviation (MAD) sample handling, recorded-stream jump rejection, exponential moving average filtering, dwell-based selection, and post-selection offset adaptation. It was evaluated using application-level gaze-to-cursor coordinates from 43 participants across 129 sessions. The primary endpoint was target-proximity Root Mean Square (RMS) during automatically logged dwell-active selection periods. Because these periods were defined by the pipeline’s online state, the endpoint was algorithm-conditioned. Whole-quiz RMS was analyzed as a secondary full-trajectory descriptor rather than as independent validation. Results. Averaged across three repeated measurements, target-proximity RMS during dwell-active periods decreased from 193.40 pixels (px) (approximately 3.15°) for the recorded uncorrected coordinates to 56.02 px (0.91°) for the complete pipeline output. The mean reduction was 137.38 px (95% confidence interval (CI), 116.59–158.17 px; Cohen’s d_z = 2.03), and all 43 participants showed a reduction. Conclusions. The results support the practical feasibility of the complete correction pipeline for target localization in the evaluated dwell-based interface. They do not independently validate native eye-tracker accuracy or establish superiority over conventional multi-point calibration.