We propose a new, data-driven model for the prediction of the outcomes of NBA and possibly other basketball league games by using machine learning methods. The paper starts with a strict mathematical formulation of the basketball statistical quantities and the performance indicators derived from them. The backbone of our model is the extended team efficiency index, which consists of two asymmetric parts: (i) the team efficiency index, generally based on some individual efficiency index—in our case, the NBA player efficiency index, and (ii) the comparing part, in which the observed team is rewarded for every selected feature in which it outperforms its rival. Based on the average of the past extended indices, the predicted extended indices are calculated symmetrically for both teams competing in the observed future game. The relative value of those indices defines the win function, which predicts the game outcome. The prediction model includes the concept of the optimal time window (OTW) for the training data. The training datasets were extracted from maximally four and the testing datasets from maximally two of the five consecutive observed NBA seasons (2013/2014–2017/2018). The model uses basic, derived, advanced, and league-wise basketball game elements as its features, whose preparation and extraction were briefly discussed. The proposed model was tested for several choices of the training and testing sets’ seasons, without and with OTWs. The average obtained prediction accuracy is around 66%, and the maximal obtained accuracy is around 78%. This is satisfactory and in the range of better results in the works of other authors.

In this paper, we present a novel compression method based on partial differential equations complemented by block sorting and symbol prediction. Block sorting is performed using the Burrows–Wheeler transform, while symbol prediction is performed using the context mixing method. With these transformations, the range coder is used as a lossless compression method. The objective and subjective quality evaluation of the reconstructed image illustrates the efficiency of this new compression method and is compared with the current standards, JPEG and JPEG 2000.

Thermography is probably the most used method of measuring surface temperature by analyzing radiation in the infrared part of the spectrum which accuracy depends on factors such as emissivity and reflected radiation. Contrary to popular belief that thermographic images represent temperature maps, they are actually thermal radiation converted into an image, and if not properly calibrated, they show incorrect temperatures. The objective of this study is to analyze commonly used image processing techniques and their impact on radiometric data in thermography. In particular, the extent to which a thermograph can be considered as an image and how image processing affects radiometric data. Three analyzes are presented in the paper. The first one examines how image processing techniques, such as contrast and brightness, affect physical reality and its representation in thermographic imaging. The second analysis examines the effects of JPEG compression on radiometric data and how degradation of the data varies with the compression parameters. The third analysis aims to determine the optimal resolution increase required to minimize the effects of compression on the radiometric data. The output from an IR camera in CSV format was used for these analyses, and compared to images from the manufacturer’s software. The IR camera providing data in JPEG format was used, and the data included thermographic images, visible images, and a matrix of thermal radiation data. The study was verified with a reference blackbody radiation set at 60 ${}^{°}$C. The results highlight the dangers of interpreting thermographic images as temperature maps without considering the underlying radiometric data which can be affected by image processing and compression. The paper concludes with the importance of accurate and precise thermographic analysis for reliable temperature measurement.

Deep Q-Networks (DQNs) have achieved strong performance across a range of benchmark tasks; however, their reliability under varying reward structures and planning horizons remains insufficiently characterized. This study presents a systematic cross-environment analysis of DQN agents evaluated across eight environments spanning simple control, arcade, and strategic domains. Rather than pursuing state-of-the-art performance, the objective is to investigate structural conditions under which standard value-based reinforcement learning succeeds, degrades, or fails. Across controlled experiments with consistent training budgets and statistical validation, three recurring failure patterns are identified: (i) sparse-reward exploration failure, (ii) reward exploitation without functional task competence, and (iii) strategic planning limitations in long-horizon or adversarial environments. Within-environment ablation studies further demonstrate that moderate network scaling (2–4× parameter increases) does not significantly alter learning outcomes when reward functions remain unchanged, suggesting that reward alignment and task horizon dominate architectural capacity as determinants of performance. The results provide a structured diagnostic perspective on DQN reliability, clarify the limits of reward shaping in complex environments, and offer practical guidance for identifying when standard value-based methods are likely to become unstable or insufficient.

In this paper an automatic image optimization workflow for web applications is proposed. The workflow consists of several parts: a JPEG encoder, a perceptual visual quality metric (PVQM) and a system for automatic iterative compression of the images. The system for automatic compression iteratively compresses the image and tries to determine the highest compression rate for each image, without compromising the perceptual quality of the image. To evaluate the proposed PVQM parameter used for stopping the iterative compression, a study is performed where 155 users rated random selection from 559 versions of differently compressed images from a public dataset. All images are high quality images in high resolution, which makes them suitable for use in web applications. For JPEG encoder we evaluate very popular open source software package: mozjpeg from Mozilla. The results show which values of PVQM metric should be used to obtain a desired trade-off between compression ratio and subjective loss of quality.