Search Results (281)

Cyclic triaxial tests are widely used in laboratory studies to assess the liquefaction susceptibility of soils. Although standardized procedures exist for conducting these tests, there is no universally accepted criterion for defining liquefaction. The choice of a liquefaction criterion significantly influences the interpretation of test results and subsequent engineering analyses. This study evaluates the impact of different liquefaction criteria by analyzing 42 cyclic triaxial tests performed on soil mixtures containing plastic fines. Both stress-based and strain-based liquefaction criteria were applied to assess their influence on test outcomes. The analyses focused on two key parameters: the number of loading cycles required to initiate liquefaction and the normalized dissipated energy per unit volume needed for liquefaction to occur. Results indicate that for soils susceptible to liquefaction failures, these parameters remain relatively consistent across different failure criteria. However, for soils prone to cyclic mobility failures, the number of loading cycles and the dissipated energy required for liquefaction vary significantly depending on the selected failure criterion. These findings highlight the importance of carefully selecting a liquefaction criterion, as it directly affects the assessment of soil behavior under cyclic loading. A better understanding of these variations can improve the accuracy of liquefaction susceptibility evaluations and inform geotechnical design and hazard mitigation strategies. Full article

Cardiovascular disease and hypertension are major global health challenges, and increasing dietary salt intake is a known contributor. Emerging evidence suggests that excessive salt exposure during pregnancy may impact fetal development, yet its effects on early embryogenesis remain poorly understood. In this study, we used zebrafish (Danio rerio) embryos as a model to investigate the developmental and molecular consequences of high-salt exposure during early vertebrate development. Embryos subjected to elevated salt levels exhibited delayed hatching, reduced heart rates, and significant alterations in gene expression profiles. Transcriptomic analysis revealed over 4000 differentially expressed genes, with key disruptions identified in calcium signaling, MAPK signaling, cardiac muscle development, and vascular smooth muscle contraction pathways. These findings indicate that early salt exposure can perturb crucial developmental processes and signaling networks, offering insights into how prenatal environmental factors may contribute to long-term cardiovascular risk. Full article

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

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 $R^2$ 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 $R^2$ scores of $0.998\pm 0.001$ and $0.996\pm 0.001$, 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

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

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

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

This paper presents a comparative analysis of performance for several antenna prototypes using a screen-printing process. This analysis was performed using various bow-tie antenna configurations, including single-band and multi-band antennas with linear or circular polarization over multiple operating frequency ranges. For antenna implementations, three different conductive inks and two resolutions of screen masks were tested. The performance of the fabricated prototypes has then been compared to the copper laser-etched antennas. This study revealed that with the proper selection of ink thinness, screen-printed bow-tie antennas achieve similar performances to copper laser-etched bow-tie antennas up to 6 GHz, even for linearly polarized and circularly polarized antennas. However, the printing resolution should be improved by reducing the ink thickness for bow-tie antennas at higher operating frequencies. The measurement results show a successful agreement after improving the printing resolution of the fabricated 5.8 GHz and 15 GHz bi-band bow-tie antennas. Full article

The Multi-Objective Optimization Problem (MOOP) in Wireless Sensor Networks (WSNs) is a challenging issue that requires balancing multiple conflicting objectives, such as maintaining coverage, connectivity, and network lifetime all together. These objectives are important for a functioning WSN safety-critical applications, whether in environmental monitoring, military surveillance, or smart cities. To address these challenges, we propose a novel bio-inspired Bird Flocking Node Scheduling algorithm, which takes inspiration from the natural flocking behavior of birds migrating over long distance to optimize sensor node activity in a distributed and energy-efficient manner. The proposed algorithm integrates the Lyapunov function to maintain connected coverage while optimizing energy efficiency, ensuring service availability and reliability. The effectiveness of the algorithm is evaluated through extensive simulations, namely MATLAB R2018b simulator coupled with a Pareto front, comparing its performance with our previously developed BAT node scheduling algorithm. The results demonstrate significant improvements across key performance metrics, specifically, enhancing network coverage by 8%, improving connectivity by 10%, and extending network lifetime by an impressive 80%. These findings highlight the potential of bio-inspired Bird Flocking optimization techniques in advancing WSN dependability, making them more sustainable and suitable for real-world WSN safety-critical systems. Full article

Quantum dots with sizes between 1 and 100 nm possess unique optical and electronic properties, making them valuable in energy, bioimaging, and optoelectronics fields. While traditional synthesis methods offer control over QD properties, they face challenges in scalability and reproducibility. Integrating microfluidics addresses these issues, providing precise control and high-throughput capabilities. This review highlights the transition from PDMS-based devices to additive-manufactured microfluidics, emphasizing their ability to overcome limitations in traditional methods. These advancements smooth the way for scalable, cost-effective, and sustainable QD production with enhanced application potential. Full article

The main goal of this study is to investigate the possibility of using residual materials (biomass derived from used cooking oils and lignocellulosic biomass from plant waste) on a large scale for producing renewable fuels and, in particular, the best way to collect them. The methodology of Geographic Information Systems (GIS) as well as spatial analysis (SA) techniques were used to investigate the Greek case for this. The data recorded in the geographic database were quantities of waste cooking and household oils as well as quantities of lignocellulosic biomass. The most common global and local indices of spatial autocorrelation were used. Concerning the biomass derived from used cooking oils, it was found that their quantities were important (163.17 million L/year), and these can be used to produce green diesel in the context of the circular economy. Although the dispersion of the used cooking oils was wide, there is no doubt that their concentration in large cities and tourist areas is higher. This finding suggests a collection process that could be carried out mainly in these areas through the development of small autonomous collection units in each neighborhood and central processing plants in small regional units. The investigation of the geographical–spatial distribution of residual lignocellulosic biomass showed the geographical fragmentation and heterogeneity of the distributions. The quantities recorded were significant (4.5 million tons/year) but widely dispersed, such that the cost of collecting and transporting the biomass to central processing plants could be prohibitive. The “geography” of the problem itself suggests solutions of small mobile collection units in every part of the country. The lignocellulosic biomass would be collected and converted in situ into bio-oil by rapid pyrolysis carried out in a tanker vehicle. This would transport the produced bio-oil to the nearest oil refineries for the conversion of bio-oil into biofuels through deoxygenation processes. Full article

Crude oil spills create environmental hazards, leading to air pollution and respiratory health risks in under-five children due to their developing organs. This study compares ambient air quality (AAQ) and the respiratory health (RH) of under-five children in crude oil-impacted and less-impacted communities. The study involved 450 under-five children (mean age: 3 years) from three Niger Delta communities: Bodo, K-Dere, and Beeri. AAQ was measured using sensors, and RH was assessed through interviewer-administered questionnaires between July and October 2022. Mean concentrations of pollutants, including PM_2.5, PM₁₀, TVOCs, and HCHO, were consistently higher in Bodo and K-Dere (oil-impacted communities) compared to Beeri (less-impacted community), with levels frequently exceeding both WHO and national standards. These concentrations were highest near spill sites and during evening periods, highlighting localized and temporal factors influencing air pollution. Respiratory symptoms such as cough, difficulty breathing, and persistent nasal congestion were significantly more prevalent among children in oil-impacted communities. Logistic regression analysis indicated a higher likelihood of respiratory issues in these communities, with odds ratios ranging from 2.53 to 14.18 for various symptoms. Elevated air pollution from crude oil spills correlates with a higher prevalence of respiratory conditions in children from impacted communities, underscoring the need for public health interventions in these areas. Full article

The primary objective of this study is to explore how machine learning techniques can be incorporated into the analysis of material deformation. Neural network algorithms are applied to the study of mechanical properties of wire rods subjected to cold plastic deformations. Specifically, this study explores how pre-trained neural networks with appropriate architecture can be exploited to predict apparently distinct but internally related features. Tentative predictions are made by observing only an insignificant cropped fraction of the material’s profile. The neural network models are trained and calibrated using 6400 image fractions with a resolution of $120\times 90$ pixels. Different architectures are developed with a focus on two particular aspects. Firstly, different possible architectures are compared, particularly between multi-output and multi-label convolutional neural networks (CNNs). Moreover, a hybrid model is employed, essentially a conjunction of a CNN with a multi-layer perceptron (MLP). The neural network’s input constitutes combined numerical and visual data, and its architecture primarily consists of seven dense layers and eight convolutional layers. By proper calibration and fine-tuning, observed improvements over the standard CNN models are reflected by good training and test accuracies in order to predict the material’s mechanical properties, with efficiency demonstrated by the loss function’s rapid convergence. Secondly, the role of the pre-training process is investigated. The obtained CNN-MLP model can inherit the learning from a pre-trained multi-label CNN, initially developed for distinct features such as localization and number of passes. It is demonstrated that the pre-training effectively accelerates the learning process for the target feature. Therefore, it is concluded that appropriate architecture design and pre-training are essential for applying machine learning techniques to realistic problems. Full article

(1) Background: The literature indicates that pesticide use and exposure can lead to neurodegenerative and carcinogenic effects in living organisms. Additionally, pesticides have been reported to influence lipid metabolism. Based on this, the objective of this analysis was to identify the most relevant authors, countries, institutions, and journals addressing the relationship between pesticides and lipoproteins; (2) Methods: The analysis was conducted using the Web of Science database and bibliometric tools, including Bibliometrix/Biblioshiny and VOSViewer software; (3) Results: A total of 72 publications from 1977 to 2014 were identified, spanning 49 sources, 3453 references, and 390 authors. The journal Pesticide Biochemistry and Physiology stood out, with seven articles and an h-index of 5. The most relevant author was Samira Salihovic. China was the top country in terms of scientific output on this topic. The United Kingdom and Spain were notable for their international collaborations. Additionally, Duk Hee Lee and Monica P. Lind were found to have the highest co-citation relationship; (4) Conclusions: This analysis highlights the relatively small number of publications on pesticides and lipoproteins between 1977 and 2024, despite growing interest in the field due to its health implications. Expanding collaborations between developed and emerging countries is essential for advancing knowledge in this critical area. Full article

A typical Mashrabiya in Islamic architecture represents an integral climatic and sustainable solution, not only by offering recycling and the responsible use of small pieces of wood assembled in stunning geometrical and natural abstract lattice panels, but also because it offers air cooling, filtration, and flow from the exterior to the interior of a building. This leads to the air flow being cooled by the water spray offered by the interior patio fountains, in addition to protecting the sanctity and privacy of a building’s inhabitants, which complies with religious beliefs and social considerations. This integral sustainable solution acts on multiple scales: material recycling and responsible use, as well as climatic and socio-cultural considerations similar to Gaudi’s approach with Trencadís technology, not far from the Arabic and Islamic architectural influence revived in the Catalan Modernism contemporary to his time. In these footsteps, we explore the Mashrabiya of our time: an interactive and living architectural membrane, a soft interface that reacts by growing, giving shade, filtrating air, and transforming in time. Despite attempts to design a contemporary concept of the Mashrabiya, none of them have adopted the living organism to form an interactive living lattice architectural system. In this work, we propose the biodigital micro-cellular Mashrabiya as a novel idea and a proof of concept based on employing the authors’ previously published research findings to utilize eco-friendly biopolymers inoculated with useful native–domestic microbial strains to act as soft and living membranes, where these organisms grow and vary in their chemical and physical characteristics, sustainable function, and industrial value. This study implements an analytical–descriptive methodology to analyze the key characteristics of a traditional Mashrabiya as an integral sustainable solution and how the proposed micro-cellular biodigital Mashrabiya system can fulfill these criteria to be integrated into the built environment, forging future research trajectories on the bio-/micro-environmental compatibility of this biomaterial-based biodigital Mashrabiya system by understanding these materials’ physical, chemical, and physiological traits and their sustainable value. In this work, a biodigital Mashrabiya is proposed based on employing previous research findings on experimentally analyzed biomaterials from a biomineralized calcium-phosphate-based hydrogel and bio-welded seashell–mycelium biocomposite in forging the lattice system of a biodigital Mashrabiya, analyzing the feasibility and sustainability impact of these systems for integration into the architectural built environment. Full article