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Volume 8
Issue 3
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J, Volume 8, Issue 3 (September 2025) – 5 articles

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31 pages, 5844 KiB  
Article
Cyclic Triaxial Testing: A Primer
by Carmine Polito
J 2025, 8(3), 25; https://doi.org/10.3390/j8030025 - 7 Jul 2025
Viewed by 163
Abstract
Cyclic triaxial tests are frequently used in the laboratory to assess the liquefaction susceptibility of soils. This paper will serve a two-fold purpose: First, it will serve to explain how the mechanics of the tests represent the stresses that occur in the field. [...] Read more.
Cyclic triaxial tests are frequently used in the laboratory to assess the liquefaction susceptibility of soils. This paper will serve a two-fold purpose: First, it will serve to explain how the mechanics of the tests represent the stresses that occur in the field. Topics covered include the differences in the stress paths for the soil in the field and in the lab, the differences in the actual stresses applied in the lab and the field, the differences between stress-controlled and strain-controlled tests, and the effects of other aspects of the testing methodology. The development of adjustment factors for converting the laboratory test results to the field is also briefly discussed. The second purpose of the paper is to serve as a guide to interpreting cyclic triaxial test results. The topics covered will include an examination of the two main liquefaction modes and the impact that the failure criteria selected have on the analysis, the differences between stress-controlled and strain-controlled test results, energy dissipation, and pore pressure generation. The author has run more than 1500 cyclic triaxial tests over the course of his career. He has found that, while the test is fairly straightforward to perform, it requires a much deeper understanding of the test mechanics and data interpretation in order to maximize the information gained from performing the test. This paper is intended as a guide, helping engineers to gain further insights into the test and its results. It has a target audience encompassing both those who are running their first tests and those who are looking to increase their understanding of the tests they have performed. Full article
(This article belongs to the Section Engineering)
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22 pages, 1291 KiB  
Article
Kolmogorov-Arnold Networks for Interpretable Analysis of Water Quality Time-Series Data
by Ignacio Sánchez-Gendriz, Ivanovitch Silva and Luiz Affonso Guedes
J 2025, 8(3), 24; https://doi.org/10.3390/j8030024 - 6 Jul 2025
Viewed by 133
Abstract
Kolmogorov–Arnold networks (KANs) represent a promising modeling framework for applications requiring interpretability. In this study, we investigate the use of KANs to analyze time series of water quality parameters obtained from a publicly available dataset related to an aquaponic environment. Two water quality [...] Read more.
Kolmogorov–Arnold networks (KANs) represent a promising modeling framework for applications requiring interpretability. In this study, we investigate the use of KANs to analyze time series of water quality parameters obtained from a publicly available dataset related to an aquaponic environment. Two water quality indices (WQIs) were computed—a linear case based on the weighted average WQI, and a non-linear case using the weighted quadratic mean (WQM) WQI, both derived from three water parameters: pH, total dissolved solids (TDS), and temperature. For each case, KAN models were trained to predict the respective WQI, yielding explicit algebraic expressions with low prediction errors and clear input–output mathematical relationships. Model performance was evaluated using standard regression metrics, with R2 values exceeding 0.96 on the hold-out test set across all cases. Specifically for the non-linear WQM case, we trained 15 classical regressors using the LazyPredict Python library. The top three models were selected based on validation performance. They were then compared against the KAN model and its symbolic expressions using a 5-fold cross-validation protocol on a temporally shifted test set (approximately one month after the training period), without retraining. Results show that KAN slightly outperforms the best tested baseline regressor (multilayer perceptron, MLP), with average R2 scores of 0.998±0.001 and 0.996±0.001, respectively. These findings highlight the potential of KAN in terms of predictive performance, comparable to well-established algorithms. Moreover, the ability of KAN to extract data-driven, interpretable, and lightweight symbolic models makes it a valuable tool for applications where accuracy, transparency, and model simplification are critical. Full article
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23 pages, 360 KiB  
Article
Depicting Falsifiability in Algebraic Modelling
by Achim Schlather and Martin Schlather
J 2025, 8(3), 23; https://doi.org/10.3390/j8030023 - 4 Jul 2025
Viewed by 106
Abstract
This paper investigates how algebraic structures can encode epistemic limitations, with a focus on object properties and measurement. Drawing from philosophical concepts such as underdetermination, we argue that the weakening of algebraic laws can reflect foundational ambiguities in empirical access. Our approach supplies [...] Read more.
This paper investigates how algebraic structures can encode epistemic limitations, with a focus on object properties and measurement. Drawing from philosophical concepts such as underdetermination, we argue that the weakening of algebraic laws can reflect foundational ambiguities in empirical access. Our approach supplies instruments that are necessary and sufficient towards practical falsifiability. Besides introducing this new concept, we consider, exemplarily and as a starting point, the following two fundamental algebraic laws in more detail: the associative law and the commutative law. We explore and analyze weakened forms of these laws. As a mathematical feature, we demonstrate that the existence of a weak neutral element leads to the emergence of several transversal algebraic laws. Most laws are individually weaker than the combination of associativity and commutativity, but many pairs of two laws are equivalent to this combination. We also show that associativity and commutativity can be combined to a simple, single law, which we call cyclicity. We illustrate our approach with many tables and practical examples. Full article
(This article belongs to the Section Computer Science & Mathematics)
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13 pages, 736 KiB  
Article
Exploring Polymeric Surfaces Manufactured Under Different Temperature Conditions—A Preliminary Experimental Study of Hardness
by Bozhana Chuchulska, Mariya Dimitrova, Boyan Dochev and Kliment Georgiev
J 2025, 8(3), 22; https://doi.org/10.3390/j8030022 - 28 Jun 2025
Viewed by 213
Abstract
Polymers are essential materials in the fabrication of partial and complete dentures, where their mechanical properties directly impact durability, comfort, and clinical performance. This study examines the influence of different manufacturing temperatures on the surface hardness of polymeric materials used in dental applications. [...] Read more.
Polymers are essential materials in the fabrication of partial and complete dentures, where their mechanical properties directly impact durability, comfort, and clinical performance. This study examines the influence of different manufacturing temperatures on the surface hardness of polymeric materials used in dental applications. A total of 60 experimental samples with a rectangular shape of Vertex ThermoSens polymer (Vertex Dental, 3D Systems, Soesterberg, The Netherlands) were fabricated through injection molding at 280 °C and 300 °C and analyzed over time to assess changes in their properties. Hardness measurements, conducted using the EQUOTIP Shore D hardness tester (Proceq SA, Schwerzenbach, Canton of Zürich, Switzerland), indicated increased hardness over time, with higher values observed in samples fabricated at 300 °C. A two-way ANOVA was performed to evaluate the statistical significance of temperature and time on hardness, revealing a significant effect (F = 14.73, p = 0.0185). These findings suggest that processing polymers at elevated temperatures improves surface hardness, significant for denture longevity and patient comfort. Increased hardness contributes to greater wear resistance. Optimizing polymer manufacturing conditions can thus lead to improved clinical outcomes, ensuring more durable and biocompatible dental prostheses. Full article
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11 pages, 1089 KiB  
Article
The Impact of Temperature, Humidity, and Precipitation on COVID-19 Cases: A Study Across National and Subnational Levels in Pakistan
by Ishtiaq Ahmad, Mustajab Ali, Hadiya Asghar, Miyoko Okamoto, Yoshihisa Shirayama, Zoofa Talha, Aida Uzakova, Hafiz Sultan Ahmad and Motoyuki Yuasa
J 2025, 8(3), 21; https://doi.org/10.3390/j8030021 - 23 Jun 2025
Viewed by 223
Abstract
Meteorological variables play a significant role in the transmission of viruses such as influenza and the coronavirus pandemic (COVID-19). Previous studies have identified the relationship between changes in meteorological variables, humidity, rainfall, and temperature, and the infection rate of COVID-19 at the national [...] Read more.
Meteorological variables play a significant role in the transmission of viruses such as influenza and the coronavirus pandemic (COVID-19). Previous studies have identified the relationship between changes in meteorological variables, humidity, rainfall, and temperature, and the infection rate of COVID-19 at the national level in Pakistan. However, the current study applied the logistic regression analysis technique to determine such a relationship on a more detailed scale, that is, subnational levels in addition to the national level in Pakistan, using a long-term analysis of two years of COVID-19 data. At the subnational level, the logistic regression analysis technique was applied, with infection rate as the predictive variable. The results showed an increase in the infection rate of COVID-19 with increasing humidity levels. In contrast, an increase in temperature has slowed the spread of COVID-19 cases at both the national and subnational levels. The minimum temperature was statistically significant (p < 0.001) for provinces, KPK and Sindh. Also, two federal territories, AJK and Islamabad, showed statistically significant p-values. At the national level, both maximum temperature and humidity showed such values that is, p < 0.001. We believe that this is the first study conducted in Pakistan to explore the direct and indirect relationship between variables such as temperature (min and max), humidity, and rainfall as predictive parameters for COVID-19 infection rates at a detailed level. The pattern observed in this study can help us predict the future spread of COVID-19, subject to climatic parameters in Pakistan at both the national and subnational levels. Full article
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