Quaternionic Quantum Mechanics: the Particles, Their q-Potentials and Mathematical Electron Model

In this work we show the quaternionic quantum descriptions of physical processes from the Planck to macro scale. The results presented here are based on the concepts of the Cauchy continuum and the elementary cell at the Planck scale. The structurally symmetric quaternion relations and the postulate of the quaternion velocity have been important in the present development. The momentum of the expansion and compression u̇0(t, x) is the consequence of the scalar term σ0(t, x) in the quaternionic deformation potential. The quaternionic G0(m)(σ0 + φ̂ ), vectorial G0(m)φ̂ and scalar G0(m)σ0 propagators are used to generate the second order PDE systems for the proton, electron and neutron. A mathematical model of an electron is formulated. It is described by the hyperbolic-elliptic partial differential system of quaternion equations with the initial-boundary conditions. The boundary conditions are generated by the quaternion energy flux that is found with the use of the Gauss theorem, the Cauchy–Riemann derivative and other mathematical formulas. The rigorous assessment of the second order PDE systems allows the proposal of two second order PDE systems for the u and d quarks from the up and down groups. It was verified that both the proton and the neutron obey experimental findings and are formed by three quarks. The proton and neutron are formed by the d-u-u and d-d-u complexes, respectively. The u and d quarks do not comply with the Cauchy equation of motion. The inconsistencies of the quarks’ PDE with the quaternion forms of the Cauchy equation of motion account for their short lifetime and the observed Quarks Chains. That is, they explain the Wilczek phenomenological paradox: Quarks are Born Free, but everywhere they are in Chains

A DEEP LEARNING DRIVEN TEXT CLASSIFICATION APPROACH WITH NAMED ENTITY RECOGNITION

In natural language processing with text data, which forms the basis of the studies in the field of Artificial Intelligence, various studies such as semantics and natural language generation are carried out, especially the solution of classification problems. This study aims to analyze the effect of detected named entities on text classification performance to make the text preprocessing stage more effective. In order to reduce the analysis time and increase the performance, after the classical preprocessing stage, word filtering was performed with Named Entity Recognition according to the thresholds determined in the 5% and 10% ranges. Analysis was performed with various machine learning, deep learning algorithms, Bidirectional Encoder Representations from Transformers (BERT) and the obtained results are discussed in the last part of the study. In the problem of classifying 50,000 news texts, 93% with Support Vector Machine (SVM) algorithm in statistical classification with machine learning, 87% with Long shortterm memory (LSTM), and 83% with BERT success was achieved. In the analyses performed with LSTM and BERT, although the model performances were numerically lower, it was observed that the semantic integrity was stronger in text classification and that the success increased after Named Entity Recognition (NER) filtering in general. Thus, it can be interpreted that the dataset that is passed through the NER filter according to the threshold values positivelyaffects the model\u27s success in terms of time and performance

The Influence of Modified Inorganic Binders Intended for 3D Printing on Selected Properties of Thermally Cured Moulding Sands – Conventionally and with Microwaves

This study determined the impact of thermal curing on the basic properties of moulding compounds made with commercial inorganic binders and binders based on them, modified for use in 3D printing technology (Binder Jetting). Two inorganic binders based on sodium silicate and a binder based on aluminosilicates were tested. As part of the work, the parameters for thermal curing of the mixtures were selected: for curing in a dryer, the best properties were obtained for mixtures containing 2.0 p.p.w. of binder cured for 10 min at 160°C. In the case of microwave curing, the best properties were obtained for moulding sands containing 2.0 p.p.w. of binder cured for 6 min at a device power of 800 W. The tests showed that the basic properties of moulding compounds with binders developed on the basis of commercial binders for use in 3D printing technology, thermally cured in a dryer, do not differ significantly from the properties of compounds with commercial binders. In the case of microwave curing, a reduction in the strength of compounds with new binders was observed in relation to compounds with classic binders. Thermal deformation tests of compounds with classic and modified binders confirmed the typical behavior observed for inorganic systems. It was proven that new, modified inorganic binders developed for 3D printing of moulds and cores using Binder Jetting technology can be used as binding materials in thermally cured moulding sands. Both thermal curing methods were assessed as suitable for curing moulding compounds with new binders

Enhancing Building Energy Efficiency through the Implementation of Renewable Energy Sources

Considering increasing sustainability requirements and the urgent need to reduce greenhouse gas emissions, improving the energy efficiency of buildings has become a key challenge in the construction sector. One of the most promising approaches involves the integration of renewable energy sources (RES) as alternatives to traditional, high-emission energy systems. This paper presents an analysis of the potential for using RES—such as solar, geothermal, and biomass energy—to enhance the energy efficiency of residential and public buildings. The economic and environmental benefits of implementing modern energy technologies are discussed, along with examples of technical solutions and hybrid system models. Legal, technical, and social aspects related to the implementation of such systems are also considered. The results of the analysis indicate that well-designed and properly managed RES systems can significantly reduce the demand for primary energy and CO₂ emissions while increasing the energy independence of buildings

The influence of the trajectory of a borehole heat exchanger on the power exchanged with the rock mass

The article presents the influence of the trajectory of a borehole heat exchanger on the power exchanged with the rock mass. The focus is on the thermal parameters of rocks, which include thermal conductivity. This parameter can be determined using literature, laboratory tests, or in-situ using a thermal response test. The design of the borehole heat exchanger as an inclined borehole maximizes the power exchanged with the rock mass by increasing the length of the borehole exchanger in the layer with the best thermal parameters. Mathematical simulations and thermal response tests show the advantage of inclined wells over vertical borehole heat exchangers in terms of the amount of power obtained from the rock mass

ENHANCED GNN WITH CAUSAL PROXIMITY VECTORS: BRIDGING CAUSALITY AND PROXIMITY IN GRAPH NEURAL NETWORKS

A knowledge graph is a structured representation of entities and their relationships, often used in biomedical domains to model complex interactions. Graph Neural Networks (GNNs), which utilize these graphs, are effective for predicting interactions missing in the knowledge graph. However, GNN lacks the ability to incorporate causal reasoning, which is crucial in biomedical applications. Additionally, they limit their ability to generalize to unseen data. In oncology, where treatment regimens are intricate and patient responses are highly variable, predicting Adverse Drug Reactions (ADRs) is particularly difficult. Existing models fail to capture the indirect, high-granularity information needed for accurate ADR prediction. To address these challenges, we propose the Causality and Proximity-based  Relational Multihead Attention Model (CPRMAM). This model leverages a knowledge graph of ADR-related cancer case studies and introduces a causal proximity vector to prioritize relevant relationships. By employing an inductive GNN approach, CPRMAM generalizes to unseen data, improving ADR prediction

ENHANCING PRIVACY AND ACCURACY IN FEDERATED LEARNING FOR REGRESSION WITH SERVER-SIDE FILTERING TO ADDRESS OUTLIERS

In the contemporary landscape characterized by extensive data proliferation, the amalgamation of information derived from a multitude of devices is imperative for the  advanced machine learning models. Nevertheless, the centralization of such data engenders significant apprehensions regarding privacy, particularly when the data is fetched from a heterogeneous array of devices including mobile phones, cameras, sensors, computers, and workstations. Federated Learning proffers a solution to these privacy-related dilemmas by maintaining a decentralized architecture, thereby enabling local devices to preserve their data while concurrently exchanging model parameters. Despite its promise, Federated Learning encounters substantial obstacles concerning data quality, which may arise from inherent biases, the presence of outliers, and the utilization of compromised devices. To mitigate these challenges, we advocate for the implementation of a server-side filtering methodology within Federated Learning, specifically tailored for regression-related problems. Based on this architecture, local devices train the model on their own data sets and then send the learned parameters to a central server. The server is then tasked with the filtration of erroneous contributions, thereby enhancing the overall accuracy of the model. This methodology is substantiated through the application of the Mean Squared Error  metric, a widely recognized standard within regression analysis, thereby augmenting both the efficiency and dependability of the learning process while safeguarding user privacy an essential component of Federated Learning

EXPLAINING ADDITIVE WEIBULL MODEL PARAMETER ESTIMATION WITH XAI: A SHAP AND LIME ANALYSIS

Conventional machine learning models face limitations in conducting time-to-event analyses because of censoring issues. This study introduces a Deep Additive Weibull (DAW) model that utilizes deep learning techniques for the survival analysis of right-censored COVID-19 patient data. Also, we explore several methods for "opening the black box" of the DAW model, including local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP), to enhance model trustworthiness. The DAW model leverages neural networks for survival analysis, specifically to estimate survival probabilities for each patient using an autoencoder-based network. The DAW model achieved a concordance index of 0.9699 for training and 0.92339 for testing. Our findings show that the DAW model effectively captures nonlinearities and complex interactions. We also assessed the impact of specific features on the model\u27s prediction, providing valuable insights. Both SHAP and LIME plots highlight similar features as important, such as pneumonia, diabetes, age and inmsupr, indicating consistent model behavior across different explanation methods. Moreover, we demonstrated that explainable machine learning (ML) can elucidate how models make prediction, which is crucial for increasing trust and adoption of innovative ML techniques in healthcare

MULTI-OBJECTIVE-OPTIMIZATION APPROACH FOR OPTIMAL TASK SCHEDUL-ING THROUGH IN DELAY SENSITIVE CLOUD ENVIRONMENT

Optimizing task scheduling in cloud computing is a major challenge that impacts system performance and resource use. Balancing different workloads, given the limits of the system and user needs, is difficult. Poor management of both underused and overburdened states can lead to problems like high energy use and hardware failures. Therefore, distributing tasks among virtual machines (VMs) is crucial in cloud task scheduling. This work introduces a dynamic load balancing algorithm called CHHO (Cuckoo Harris Hawk multi-objective Optimization). CHHO is a new hybrid method that combines Cuckoo Search Optimization (CSO) and Harris Hawk Optimization (HHO). This combination uses the strengths of both algorithms to address the complex issues of cloud task scheduling. Specifically, CHHO uses Cuckoo Search Optimization to widen the search area of Harris Hawk Optimization, aiming to improve factors such as cost, response time, and resource use. The CHHO algorithm is designed to improve system performance by increasing VM throughput, effectively distributing workloads across VMs, and maintaining a balance among task priorities through dynamic adjustments in task waiting times. To test the performance of CHHO, the algorithm is implemented in the CloudSim simulation environment. It is compared with existing load balancing algorithms on various performance measures. Our simulation results clearly show that CHHO performs better than existing algorithms, providing a strong and efficient solution for load-balancing in cloud computing. Introducing CHHO offers a significant advancement in the field, providing a dynamic and adaptable approach that improves cloud task scheduling and enhances the overall efficiency and effectiveness of cloud computing systems

Video games and contemporary art: Polygonal painting and environmental storytelling in art installation

Artykuł opisuje dwie formy relacji między współczesną sztuką i grami wideo. Pierwsza część artykułu dotyczy współczesnego malarstwa, w którym odnaleźć można inspiracje stylistyką cyfrowych poligonów. Część druga stanowi opis podobieństw sposobu przekazywania narracji we współczesnej instalacji artystycznej i w grach wykorzystujących environmental storytelling.This article describes two kinds of relations between contemporary art and video games. The first part of the article is about contemporary painting, which draws inspiration from the style of polygon graphics. In the second part the author describes similarities between the methods of transmitting narrations in contemporary art installation and videogames, which use environmental storytelling