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Earlier studies relied on simplified methods, assuming uniform or concentrated loads directly transferred from the superstructure to the raft through a fixed boundary condition neglecting the influence of the superstructure's stiffness on the soil-foundation interaction. Consequently, these simplifications result in inaccurate predictions of raft bending moments, pile loads, and overestimation of the reaction forces with insignificant settlement underestimation. In this paper, 3D finite element analyses were conducted for a 20-story building with three different structural systems to study the influence of the superstructure stiffness on the performance of piled raft under wind and earthquake loads. An iterative procedure was undertaken between PLAXIS 3D and ETABS to achieve displacements compatibility between the geotechnical and structural models. A layered soil profile was studied, consisted of soft to medium clay overlying a dense sand layer. The impact of using a 2m replacement sand layer below the raft also was evaluated on the soil-foundation response. Including the stiffness of the superstructure increased the load shared by the piles by (5.5% to 6.4%), (6.0% to 7.0%), and (1.7% to 7.6%), and reduced the raft differential settlement by (25.3% to 37.2%), (24.4% to 37.4%), and (29.7%) under gravity, wind, and earthquake loadings, respectively. Moreover, employing a 2-meter replacement layer beneath the raft had a negligible impact on the behavior of the piled raft foundation. Thus, the interaction of soil, foundation, and superstructure significantly influenced the structural response.

Currently, dual-layer tubes play a pivotal role in sectors like nuclear, oil, gas, and steam generation. Recent research focuses on enhancing energy absorption in these tubes using the spinning forming process. This thin-walled nature benefits their application as efficient energy absorbers in transportation like trains and aircraft. A novel approach integrates coarse and fine-threaded ridges between steel and aluminum layers to create a mechanical interlock using the spinning forming technique. This interlock enhances the bond through flow forming, resulting in robust dual-layer tubes. Interlayer connection strength is rigorously tested through shear tests, pre and post flow forming, indicating a significant post-flow forming enhancement. This process substantially bolsters the overall structural integrity of dual-layer tubes. In crush tests, both single-layer and dual-layer steel and aluminum tubes undergo pressing through flow forming. Comparative analysis of energy absorption parameters—specific energy and crushing percentage—reveals notable performance differences. An annealed version of the tube with an aluminum layer is also included due to prior aluminum layer failures. Among dual-layer samples, the flow formed variant with fine-threaded ribs exhibits superior energy absorption and bonding. Finite element simulation of the pressed dual-layer sample demonstrates an 8.42 % deviation between simulated and experimental energy absorption outcomes. The study highlights that tightly bonded layers result in enhanced bonding characteristics.

In response to the escalating issue of water scarcity, the United Nations has allocated Sustainable Development Goal 6 of ‘Clean Water and Sanitation’ to address the issue by providing clean water and improved sanitation. Solar stills are an attractive solution to water scarcity as they are simple, cost-effective, and convenient for communities with limited resources. However, they have shortcomings, such as limited production and nocturnal ineffectiveness. The present study proposes several alternatives to address these issues using the conceptual design technique. The customer requirements were met using the Quality Function Deployment (QFD) method targeted during the design stage. An integrated AHP-TOPSIS was used to evaluate the design of alternatives considering seven criteria. This proposed method includes many factors, including system efficiency, cost, and ease of operation and maintenance. The three alternatives combine solar stills with adsorption desalination units. Two weighting methods were used, consistency-based ranking index for decision making (CRITIC) and Entropy, to evaluate the results' reliability. The findings showed that the most favorable alternative with CRITIC value of 0.975 and entropy of 0.988, combines a pyramid solar still and an evacuated tube solar collector. The purpose of this investigation is to build on the body of knowledge of solar desalination and support decision-makers in the evaluation process of selecting an appropriate solar still system.

Motivation: The motivation of this recent article is to study dusty Walter’s B fluid flow due to its wide range of applications in biology and polymer industry. The fluid is traveling peristaltically through an asymmetric channel with wall slip. A discussion is also presented to examine heat transfer effects with thermal radiation and slip. Methodology: The regular perturbation technique is employed to evaluate the mathematical model of the problem, which is first simplified by using stream functions. Mathematical results are simulated to illustrate flow characteristics of fluid and solid particles in salient quantities. Also, graphs of temperature distribution of fluid and dust particles have been discussed to study the impacts of various parameters. Outcomes: Walter’s B fluid parameter reduces speed of both fluid and dust particles. By increasing thermal slip parameter, temperature transference becomes slower through the fluid, while Brinkman number significantly raises the temperature profile of both fluid and particles. This article presents a theoretical analysis of the problem. Moreover, the characteristics of liquids involved in the plastic industry and medical science can also be understood using the current analysis. Originality/Value: Walter’s B fluid with dust particle suspension has not been investigated for slip and thermal radiation effects.

This work investigates a new class of statistical models and presents a specific example from this class. We created a new family of distributions using trigonometric functions, known as the cosine pie-power odd-G family. The paper details the fundamental properties of this proposed family of distributions. By using the Weibull distribution as the underlying model, we present a specific distribution within this family that exhibits various hazard function shapes, including bathtub, reverse-j, increasing, and j-shaped curves. The statistical characteristics of this new distribution are thoroughly analyzed. Parameters of the suggested distribution are determined using the maximum likelihood estimation (MLE) method. To verify the precision of this estimation process, Monte Carlo simulations are conducted, which show a decrease in biases and mean square errors as sample sizes increase, even when samples are small. To demonstrate the practical utility of the proposed distribution, two real-world datasets are analyzed. The performance of the proposed distribution model is assessed through various criteria of model selection and fitness results. Results from these assessments indicate that the recommended model execute better than seven other existing models.

The Industrial Internet of Things (IIoT) infrastructure is inherently complex, often involving a multitude of sensors and devices. Ensuring the secure operation and maintenance of these systems is increasingly critical, making anomaly detection a vital tool for guaranteeing the success of IIoT deployments. In light of the distinctive features of the IIoT, graph-based anomaly detection emerges as a method with great potential. However, traditional graph neural networks, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have certain limitations and significant room for improvement. Moreover, previous anomaly detection methods based on graph neural networks have focused only on capturing dependencies in the spatial dimension, lacking the ability to capture dynamics in the temporal dimension. To address these shortcomings, we propose an anomaly detection method based on Spatio-Temporal Gated Attention Networks (STGaAN). STGaAN learns a graph structure representing the dependencies among sensors and then utilizes gated graph attention networks and temporal convolutional networks to grasp the spatio-temporal connections in time series data of sensors. Furthermore, STGaAN optimizes the results jointly based on both reconstruction and prediction loss functions. Experiments on public datasets indicate that STGaAN performs better than other advanced baselines. We also visualize the learned graph structures to provide insights into the effectiveness of graph-level anomaly detection.

Hybrid nanoliquids are not underestimated for their involvement in microelectronics, transportation, coolant processes, ships, biological processes and power generation. Hence motivation for current work is to examine homogeneous-heterogeneous reactions in entropy optimized flow by curved sheet stretched with nonlinear velocity. Trihybrid nanoliquid is synthesized through 2 % of ferrous oxides(Fe3O4), 2 % of silver(Ag) and 2 % of copper(Cu). Kerosene oil is employed as the base fluid. These specific kinds of nanoparticles are taken into consideration because of their numerous applications in heat dissipation, air filters, dynamic sealing, biosensors and catalysts process. Kerosene oil may have applications in many different domains such as energy storage, cleaning agent, portable heaters, fuel additive and industrial lubricants. Darcy-Forchheimer model is employed. Analysis in presence of radiation, dissipation, Ohmic heating, entropy generation and heat generation/absorption are organized. Unlike the previous considerations, the thermal expression here consists of impacts through Darcy-Forchheimer relation. Adequate transformations are implemented. Computations have been arranged by applying finite difference technique (FDM) using MATLAB. Quantities for physical interest are addressed. The presented analysis may have relevance for solar systems, chemical reacting processes, cooling towers and polymer data processes. The conclusions are also organized for important key findings. It is noticed that trihybrid nanomaterial has more entropy rate when compared with hybrid and classical nanoliquids when volume fraction is chosen as 2 % for trihybrid fluid. It is proposed that the ability to transfer heat is enhanced when nanoparticles are added to conventional fluids. Decay in concentration is noticed for both homogeneous and heterogeneous parameters. Reduction for Bejan number is noticed through homogeneous diffusion factor. Comparison with previous study is also presented and found great consensus between them.

We extend the virtual element method to the two-dimensional time-fractional parabolic PDE, characterized by a fractional derivative of order α∈(0,1) in time. To illustrate the working of this fractional virtual element scheme, a numerical investigation of the following time-fractional problem over distorted polygonal meshes is conducted. cDtαu(z,t)−Δu=f(z,t)inz∈Ω,t∈(0,T],where Ω is a spatial domain, α is fractional order, and t is time variable. Our methodology is based on the fundamental technical component, fractional version of the Grunwald–Letnikov approximation. We prove the method’s well-posedness, that is the approximate solution’s existence and uniqueness. The fully discrete scheme inherently maintains stability and consistency by leveraging the discrete maximal regularity and the energy projection operator. The convergence in the L2-norm and H1-norm over distorted mesh configuration is validated by numerical results, underlining the practical effectiveness of the proposed method.

Cyberattack classification involves applying deep learning (DL) and machine learning (ML) models to categorize digital threats based on their features and behaviors. These models examine system logs, network traffic, or other associated data patterns to discriminate between standard activities and malicious actions. Efficient cyberattack classification is vital for on-time threat detection and response, permitting cybersecurity specialists to categorize and reduce potential risks to a system. Handling class-imbalanced data in cyberattack classification using DL is critical for achieving exact and robust models. In cybersecurity databases, instances of normal behavior frequently significantly outnumber instances of cyberattacks, foremost due to biased methods that may complete poorly on minority classes. To address this issue approaches such as oversampling the lesser class, undersampling the popular class, or using more advanced systems can be used. These plans defend that the DL technique is more complex when determining cyberattacks, so it increases complete performance and adapts the effect of the imbalance class on the classification results. This study presents a novel Hybrid Salp Swarm Algorithm with a DL Approach for Cyberattack Classification (HSSADL-CAC) technique. The HSSADL-CAC method intends to resolve class imbalance data handling with an optimum DL model for the recognition of cyberattacks. At first, the HSSADL-CAC method experiences data normalization as a pre-processing stage. The HSSADL-CAC technique uses the ADASYN approach to handle class imbalance problems. In addition, the HSSADL-CAC technique applies an HSSA-based feature selection approach. The HSSADL-CAC technique detects cyberattacks using a deep extreme learning machine (DELM) model. Finally, the hyperparameter tuning of the ELM model takes place by utilizing the beluga whale optimization (BWO) model. The performance analysis of the HSSADL-CAC technique employs a benchmark database. The comprehensive comparison research indicates the superior performance of the HSSADL-CAC technique in the cyberattack detection procedure.

Asymmetrical probability models are helpful for analyzing skewed data sets since they allow you to describe the form of the distribution and anticipate the chance of extreme events. This article defines a novel approach to continuous moment exponential distribution called the exponentiated generalized moment exponential model. The extension has two additional parameters accounting for the distribution’s shape. We extend this distribution probability density, cumulative distribution, hazard rate, and survival functions and establish different key statistical properties. Parameter estimation is obtained using different procedures, notably maximum likelihood estimation, least square, and Bayesian methods. A Monte Carlo simulation experiment is conducted to assess parameter performance and indicator risk measures. This article examines two distinct actual data sets in order to highlight the significance of the proposed model as well as its application in a variety of settings. The new model is compared to a large number of well-known extensions that were developed by other businesses.

The primary focus of this study is to introduce some kinds of piecewise fractional derivatives (PFDs). These derivatives are defined using fractional derivatives in both the Atangana–Baleanu and Caputo senses. They are considered to generate a novel collection of fractional variational problems that rely on an indefinite integral. A numerical method established on the piecewise Chebyshev cardinal functions (as a suitable family of basis functions for such situations) is utilized to solve these problems. To this end, some operational matrices for PFDs of the expressed cardinal functions are derived and used to generate the presented method. Using the proposed technique, solving the desired problems is converted into solving associated algebraic systems. The effectiveness of the procedure is checked by solving some illustrative examples.

The accuracy and reliability of IoT-based sensor networks depend on validating sensed data, including detecting outliers at the node level. This study proposes an online outlier detection approach using Multiple Linear Regression-based adaptive thresholds for real-time IoT/WSN sensor nodes. IoT sensors experience two outlier types: Errors, from sensor malfunctions or low battery, and Events, from sudden environmental changes. The Adaptive Threshold Based Outlier Detection (ATBOD) approach differentiates errors from events using an adaptive threshold that adjusts to real-time data patterns. Unlike existing methods that are used in literature, which lack automated model evolution and suffer from delays and high computational time, ATBOD enhances outlier detection sensitivity without increasing false alarms, which is crucial for efficient IoT sensor board operation. It also improves sensor board lifespan by discarding errors at the node level, preventing energy wastage from transmitting error data to the cloud. ATBOD outperforms existing algorithms, which are referenced for comparison, such as Enhanced Efficient Outlier Detection and Classification Approach (EEODCA), K Nearest Neighbor approximate outlier detection (KNN), and Modified Local Outlier Factor (LOF), in Error Detection Rate, Error False Positive Rate, and Energy Saving Ratio. These advancements represent a significant leap in performance, making ATBOD a superior method for real-time outlier detection in IoT sensor networks.

Uncommon genetic illnesses pose significant challenges for detection due to their low occurrence and intricate genetic makeup. Traditional targeted genetic testing methods are often limited, missing rare or unidentified genetic variations. This project proposes a novel strategy that leverages Whole-Genome Sequencing (WGS) data and Random Forest (RF) analysis to overcome these limitations. WGS provides a comprehensive view of an individual's genetic profile, capturing a wide array of genetic variations that targeted approaches might overlook. By employing the RF method, which excels at handling complex datasets and detecting non-linear interactions, this project aims to uncover intricate links between rare genetic diseases and gene variations. The Swedish Genome Reference dataset will serve as the foundation for this research. RF analysis will be applied to this extensive dataset to identify patterns and connections that might reveal new genetic markers and previously unknown risk factors for these illnesses. This approach allows for exploring vast genetic datasets to detect structures and associations, providing deeper insights into the genetic underpinnings of rare diseases. Combining WGS with RF analysis offers a powerful tool for discovering genetic indicators and risk factors contributing to rare genetic disorders, achieving an accuracy rate of 97 %. This innovative approach can significantly enhance understanding, diagnosis, and treatment of these conditions. By highlighting the value of advanced computational techniques and comprehensive WGS databases, the project aims to pave the way for more personalized and specialized medical treatments, ultimately improving patient outcomes for those affected by rare genetic diseases.

Vibration is inevitable in operation of mechanical equipment, but severe vibrations will cause serious harm to wind turbines (WTs). This paper takes the drivetrain of 8 MW WT as the object, established a multi-body electromechanical coupling model of WT and verified the accuracy of the established model through the WT drivetrain in the laboratory. Subsequently, based on this model, time-frequency domain analysis was conducted on the vibration response of the WT drivetrain under different control strategies of the generator, and the influence of the generator control strategy on the vibration of the drivetrain was studied. The result shows that the generator control strategy has a significant impact on the vibration response amplitude of the drivetrain, but has a relatively small impact on the frequency component. Meanwhile, the sliding mode controller designed in this paper has good anti-interference ability, which can make the system quickly reach dynamic stability, and effectively suppress the vibration of the main components in the drivetrain. Under the new controller’s control, the transverse vibration displacement of some parts of the WT drivetrain can be reduced by up to nearly 60 %. This paper provides new ideas and new methods for the active vibration reduction control of WT drivetrain.

The active ingredient in traditional Chinese medicine, silybin (SBN), is known to inhibit the proliferation of cancer cells and synergistically enhance the anticancer effects of the chemotherapy drug doxorubicin (DOX). However, the combination of SBN and DOX faces challenges such as a lack of targeting, short half-lives, different administration routes, and pharmacokinetic processes, which prevent the drug combination from effectively targeting tumors and diminish their synergistic potential, thereby limiting their in vivo antitumor effects. In response, this study proposes the development of a novel nanocarrier system based on molybdenum disulfide (MoS₂) modified with polyethylene glycol (PEG) and sialic acid (SA) to co-deliver SBN and DOX. This system is designed to trigger the release of the drugs in response to dual stimuli of pH and near-infrared light, increasing the intracellular concentration of the drugs and enhancing their synergistic interaction. The MoS₂-LPN-SBN/DOX nanosystem circulates within the body and specifically accumulates at tumor sites due to the enhanced permeability and retention (EPR) effect and SA-mediated active targeting. Upon exposure to near-infrared light, the system provides combined chemo- and photothermal therapy, leading to significant tumor suppression. This research offers a new approach and strategy for the clinical treatment of lung cancer, suggesting a promising pathway for enhancing the efficacy of traditional therapies.

Building and construction projects are associated with the country's internal and external conditions, which exposes them to risks throughout their lifespan; hence, the construction industry is one of the industries influencing the country's economy. Therefore, the study aims to identify and evaluate the risks associated with Egyptian building and construction sector conditions. Consequently, this study explained and examined the risk management concept and its emergence in construction projects, along with the five stages of risk management. On the other hand, the study analyzed and demonstrated the concept, characteristics, and significance of the construction industry, its influential role in development plans, the position of that industry in Egypt, and many other factors that hinder this pivotal industry. Therefore, the study investigated many previous studies to identify the most significant risks facing the Egyptian construction sector due to the lack of a fixed database of risks for all projects and classify them according to quality, cost, performance, and schedule. Finally, to assess the significance of these risks and rank them according to the occurrence probability, the hazard size, and the threat degree relative to the Egyptian building and construction industry. A field study was conducted through interviews and a questionnaire based on the Chi-Square test in light of the architectural, urban, and infrastructure projects Egypt is currently undertaking within Vision 2030. Consequently, it will facilitate dealing with, managing, and treating risks and reducing and preventing their consequences as a standardized database.

The research applied ASPRS standards to determine the necessary number of check points to evaluate accuracy, with a proposed application of a simple method to identify outliers in elevation differences. Tests were conducted in the Latakia Governorate after dividing it into three areas: a moderate slope area, a moderate steep slope area, and a steep slope area. As for the check points, they were extracted from a topographic map with a scale of 1/25000.The results showed a strong positive correlation between reference elevations and elevations extracted from both the SRTM and ASTER models in all test areas, with a Pearson coefficient value of 0.99. It was also found that the vertical accuracy of the SRTM model is better than that of the ASTER model in the moderate slope area, where this accuracy reached 11.899m. In the case of the moderate steep slope area, the research found that the elevations extracted from the SRTM model are more accurate than those extracted from the ASTER model, with a vertical accuracy value of 21.609m for the SRTM model and 23.145m for the ASTER model. In the case of steep slope area, it was found that the elevations extracted from the ASTER model are more accurate than those extracted from the SRTM model, with a vertical accuracy of 36.770 meters for the ASTER model and 40.538 meters for the SRTM model.

The hot, arid climate of the Qassim region in Saudi Arabia poses significant challenges for energy-efficient building design, especially in terms of passive cooling strategies. Courtyards, a traditional architectural element, offer potential for improving thermal performance in such climates. However, the impact of courtyard plan aspect ratio and orientation on energy consumption in semi-detached houses remains understudied. This research addresses this gap by investigating the thermal behavior of U-shaped courtyards in arid climates through IES simulations. The study aims to assess the influence of different courtyard shapes (square and rectangular) and orientations (north, east, west, and south) on cooling loads in adjacent spaces. Two semi-detached house configurations, each featuring either a square or rectangular U-shaped courtyard, were modeled and analyzed. The findings indicate that square courtyards perform better than rectangular, reducing cooling loads during summer months. South-facing courtyards exhibited the highest energy consumption due to prolonged solar exposure, while west-facing courtyards had the lowest cooling loads. Furthermore, the results show that the internal zoning of rooms adjacent to the courtyard should be strategically planned based on thermal performance, as specific zones experienced varying cooling demands depending on courtyard shape and orientation. In conclusion, optimizing courtyard design, including plan aspect ratio and orientation, can significantly enhance energy efficiency and thermal comfort in arid climates. These insights provide architects with practical guidelines for designing more sustainable and energy-efficient courtyard houses. Future research could explore the integration of social and environmental aspects, as well as the impact of natural light on adjacent spaces

The importance of the rural community is shaped in Egypt, where more than 57.8% of the population lives. The Egyptian village is the smallest urban environmental unit that has social, cultural and political characteristics different from the city and has an economic base. Therefore, the importance of sustainable environmental planning for green infrastructure appears to improve the rural community and provide it with Health security, social security, food security, poverty eradication, and activating the role of rural women to keep pace with the wheel of development and achieve economic competitiveness.The research addresses the concepts of green infrastructure and procedures for dealing with its challenges to achieve sustainable rural development and enhance the performance of green infrastructure and presents green infrastructure standards and strategies.The research found that the integration between green and gray infrastructure elements in urban areas, as well as the presence of connectivity in the green infrastructure network along traffic paths, works to improve the performance of the green element in rural urban areas, which also enhances their efficiency and enhances the green economy. The research reached a set of indicators for green infrastructure to enhance its performance and overcome challenges from the perspective of the green economy and partnership between non-governmental organizations and the public sector, which leads to formulating a system to activate green infrastructure indicators in achieving sustainable rural development

This study explores an innovative architectural framework that integrates design and technology to regulate crowd management and enforce social distancing protocols in public spaces. Grounded in the principles of functionality and humanism, architecture serves as a medium for societal adaptation. The primary objective of this research is to transform the management of vertical movement in densely populated public areas by implementing interactive staircase designs. Public access staircases, prevalent in environments such as university campuses, present significant challenges for crowd management and the control of social distancing. This study proposes the adoption of interactive staircases that not only facilitate physical distancing but also encourage physical activity.Employing a multi-stage methodology, this research combines empirical observation with advanced technological applications. The process encompasses a comprehensive literature review, on-site data collection through time-lapse photography and video recordings, and collaborative brainstorming sessions to develop interactive scenarios. The project leverages Arduino microcontrollers, ultrasonic sensors, and LED lights to create a responsive staircase environment. A 1:5 scale physical model is utilized as a prototype to test and refine these interactive elements, thereby integrating architectural design principles with smart technology. This approach aims to manage crowd flow and promote social distancing through dynamic, user-responsive illumination.

This paper introduces a high-efficiency and a wide-frequency band RF-to-DC rectifier with an enhanced voltage conversion ratio (VCR) for implantable biomedical devices. The rectifier utilizes bootstrapped CMOS devices for threshold potential reduction. These devices (NMOS and PMOS) have bootstrapped gates. This is used to reduce the needed RF input voltage which turns rectifying devices ON, effectively adding DC voltages (positive or negative, respectively) to the input RF signal. This allows rectifier devices to conduct at lower input RF voltage levels. The proposed RF-to-DC rectifier circuit is designed and simulated in 180nm CMOS technology. Simulation results show a peak power conversion efficiency (PCE) of 81.3% at RF input power of -12.4dBm, with a load resistor and capacitor of 12 kΩ and 1pF, respectively. With the same loading, the proposed rectifier achieves a peak VCR of 87% at RF peak voltage of 1V. The obtained dynamic range of the proposed circuit is 15.7dB (PCE > 30%) at an input frequency of 402MHz. With an input frequency of 434MHz, the proposed circuit has a PCE of 80.85% at -12.8dBm RF input power, while having a load resistor and capacitor of 12 kΩ and 1pF, respectively. The rectifier achieves 87.14% VCR, and a dynamic range of 15.4dB (PCE>30%).

Indoor environmental conditions in classrooms affect students' health and academic performance. studies have proven that the quality of the indoor environment is important in classrooms. Indoor environmental quality (IEQ) is represented in achieving thermal comfort, indoor air quality, visual comfort, as well as acoustic comfort. The research aims to determine the extent to which the shape and orientation of the classroom in Egypt affect the quality of its internal environment, and the thermal performance, as well as achieving good ventilation for the classroom. The research conducted an environmental simulation for the classroom in Cairo, Alexandria, and Aswan using the environmental simulation program Design Builder. To study the extent to which the design, shape and orientation of classroom openings in accordance with the design standards of the Educational Building Authority affect thermal comfort and air velocity in the classroom in selected study areas in Egypt. The model has been calibrated via field measurements in a standard classroom.The research found that the expected percentage of dissatisfaction (PPD) inside the classroom in Aswan is higher than the PPD for the classroom in Cairo by a rate exceedingly 40%. The predicted mean vote (PMV) for the classroom in Cairo is the best during the school months, as its ranges from zero to less than (+1 and -1). The classroom with the southern orientation in Alexandria represents the lowest in (PPD) in the school months from (December to May), as decreases by 20% compared to the classrooms in the other directions in Alexandria

The article explores the aesthetic transfer approach as a pivotal and distinctive element of contemporary visual arts pedagogy, emphasizing the "learning by making" method, which prioritizes hands-on exploration and creation for acquiring artistic skills and knowledge. It advocates moving from traditional architectural education towards a more experimental approach. The study assesses a visual arts course at a private university in Cairo, Egypt, where students designed and implemented a full-scale wooden chair inspired by a celebrity. The results reveal significant success in fostering team collaboration, enhancing practical skills, and bridging the theoretical and physical design gap. Comparisons with related research highlight the impact of instructional shifts, such as adapting to virtual platforms during the COVID-19 pandemic and employing alternative design techniques like Shape Grammars, on student learning and final project outcomes. The findings emphasize the importance of effective composition and practical experience in art education while identifying areas for future improvement.

The relentless rise in heart disease incidence, a leading global cause of death, presents a significant public health challenge. Precise prediction of heart disease risk and early interventions are crucial. This study investigates the performance improvement of heart disease prediction models using machine learning and deep learning algorithms. Initially, we utilized the Heart Failure Prediction Dataset from Kaggle. After preprocessing to ensure data quality, three distinct feature engineering techniques were applied: PCA for dimensionality reduction, ET for feature selection, and Pearson's correlation coefficient for feature selection. We assessed their impact on model performance. The dataset was then partitioned into three different data split ratios—1:9, 2:8, and 3:7—to determine their specific effects on model performance. Twelve machine learning classifiers—LGBM, Adaboost, XGB, RF, DT, KNN, LR, GNB, ET, SVC, GB, and Bagging—were trained and evaluated based on five key metrics: accuracy, recall, precision, F1 score, and training time. The influence of different feature engineering methods and data partitioning ratios on model performance were systematically analyzed using paired-sample t-tests. Among the feature engineering methods compared, the Bagging classifier, when combined with feature selection via ET, exhibited superior performance. It achieved an accuracy of 97.48 % and an F1-Score of 97.48 % with a data split ratio of 1:9 between the test and training sets. With a 2:8 split, the accuracy was 94.96 % and the F1-Score was 94.95 %. For a 3:7 split, the accuracy was 94.12 % and the F1-Score was 94.11 %. Paired sample T-test results indicate that feature selection using Pearson correlation coefficient can shorten training duration, but this also leads to a decline in classifier performance. After applying PCA dimensionality reduction, compared with the control group, there was no significant difference in the training efficiency and efficacy of the classifier. However, feature selection through ET significantly reduced the training time for various classifiers while ensuring their performance.

Hydroxylamine nitrate (HAN) is a new type of high-energy oxidant used in controllable propulsion. The present study explores the reaction mechanism of HAN using density functional theory and constructs reaction mechanism diagrams to understand the mechanism of small-molecule gas formation during the thermal decomposition of HAN. Based on transition state theory, the half-life of each reaction is calculated under standard conditions and the kinetic parameters of each reaction are scanned across the temperature range 298.15–1200 K. The research revealed a vast half-life time scale forreaction of NO generation, NO2 generation, NO+NO2 generation, N2O generation and N2 generation process, meaning thatthese reaction can not occur. The half-life of RDS5 is very short. However, the reaction is also limited by the concentration of the reactant HNO. However, increasing the temperature rapidly decreases the reaction half-life and the reaction can easily proceed. Taking 300 s as the easily reactive boundary point, the cut-off points of the rate-determining steps of Processes 1 (NO generation), 2 (NO2 generation), 3 (NO + NO2 generation), 4 and 5 (N2O generation) and 6 (N2 generation) are 466 K, 468 K, 576 K, 587 K and 402 K, respectively. The calculated reaction mechanism revealed two isomeric transformations of HNO2 and H2N2O2 and three isomeric transformations of H2N2O. Both HNO2 and H2N2O2 are transformed via H-atom transfer, while H2N2O is transformed either by H-atom transfer or intramolecular rotation. As the formation reaction of NO2 does not have the lowest free energy, the NO2 product is easily converted to other products. When NO2 coexists with NO, it is also easily converted to the stable products N2O, N2 and NH3.

Researchers have devoted decades to striving to create a plethora of distinctive distributions in order to meet specific objectives. The argument is that traditional distributions have typically been found to lack fit in real-world situations, which include pharmaceutical studies, the field of engineering, hydrology, environmental science, and a number of others. The Weibull-inverse Nadarajah Haghighi (WINH) distribution is developed by combining the Weibull and inverse Nadarajah Haghighi distributions. The proposed distribution's fundamental characteristics have been established and analyzed. Several plots of the distributional properties, notably probability density function (PDF) with corresponding cumulative distribution function (CDF) are displayed. The estimation of model parameter is performed via the MLE procedure. Simulation-based research is conducted to demonstrate the performance of proposed estimator’s using some measure, like the average bias, variance, and associated mean square error (MSE). Two real datasets represent the morality due to COVID 19 in France and Canada are illustrated to see the practicality of the recommended model.

This paper introduces a distributed control approach for optimizing the operation of islanded DC microgrids (µGs), accounting for cyber delays and network failures. Initially, an integrated network emulation platform is utilized to calculate realistic cyber delays for islanded DC µGs employing IEC 61850 communication standard. To address the instability issues arising from cyber delays, the Artstein-Kwon-Pearson reduction technique is employed to convert the system with cyber delays into one without delays. The Subsequently, a consensus-based generation-cost optimization algorithm is designed for maintaining the economic operation of the µG. This algorithm efficiently equalizes the incremental costs (ICs) of all distributed generators (DGs) in a fully distributed manner, while adhering to the DGs capacity limits. Additionally, a fully distributed voltage regulation control is developed to stabilize µG average voltage, ensuring the balance between generation and demand. The efficacy of the formulated control technique is substantiated through diverse case studies, thereby underscoring its superior operational performance.

The essence of the research is the search for geometric, material and technological limits of the new concept of spinning spindles. Considering the trend of introducing new generation electric drives into the segment of high-speed spinning spindles (over 120,000 rpm), it is necessary to map and describe the basic physical connections, to find the necessary dependencies and correlations of individual design parameters and their limits for the design of a new generation of spindles. Due to the design limitations of the original and years-used spindle drive concept with a belt and the requirement for the production and development of a new generation of spun fibers, it is necessary to expand the basic knowledge for the design of spindles of the new concept, the design of which will be based on the knowledge and dependencies obtained by measuring and examining the basic principles and determining parameters for the design of spindle structures of a new progressive concept.

The article shows how the orthotropic material properties of the textile composite layer change if the ratio of longitudinal and transversal fiber bundles in the textile is modified in the case of a composite reinforced with plain weave textile. I determine the material properties using model-cell and finite element simulation. The density of the longitudinal fiber bundles is the same in each of the five examined layer models. I change the ratio of the fiber bundles of the textile by varying the distance of the transversal roving’s in each model.

Nowadays, a lot of data is generated in production and also in the domain of assembly, from which different patterns can be extracted using machine learning methods with the support of data mining. With the support of various modern technical and Information Technology (IT) tools, the recording, storage and processing of large amounts of data is now a routine activity. Based on machine learning, efficiency metrics including Overall Equipment Effectiveness (OEE), can be partially predicted, but industrial companies need more accurate and reliable methods. The analyzed algorithms can be used in general for all production units or machines where production data is recorded by Manufacturing Execution System (MES) or other Enterprise Resource Planning (ERP) systems are available. This paper presents and determinates which most used machine learning methods should be combined with each other in order to achieve a better prediction result.

An interesting field of studying nonlinear systems is their sensitivity study. With sensitivity study the most influential parameters on a system can be obtained and then the simplification and improvement of the model will be possible. In this paper the global sensitivity study of a Duffing-type vibration system is carried out with Sobol’s variance-based method taking the root mean square of acceleration and the maximum acceleration as output variables. With the sensitivity study it was observed that the parameters of the excitation signal like the amplitude and the angular velocity are the most influential. It was also found that a single parameter has less influence on the system than the parameter combinations. The aim of the research is to carry out the global sensitivity study of a relatively simple nonlinear system. The study is the basis for further research tasks in order to perform the sensitivity study of more complex systems.

Based on the flow modeling and operational experience of the previous cylindrical loop reactor, we designed a sheet reactor combined with a loop reactor. The design with a cylindrical cross-section was applied to an equivalent cross-section (sheet) of several squares arranged next to each other. In accordance with the sedimentation processes experienced in the outlet branch, we created an algae trap, thereby reducing the flow to the level necessary for sedimentation. With this, I would like to achieve the already experienced, nearly 10-fold increase in algae concentration compared to the one in the mainstream.

The paper presents a strength analysis of a pressed joint of two conical pipes with the aim of finding out whether this method of connection is suitable for telecommunication masts. In this paper, a solution using an engineering approach and a finite element method (FEM) numerical analysis for transversal direction will be compared.

In the course of the comparative investigation, energetic and working-quality tests were carried out with three frequently used soil-loosening implement types in field-land conditions – such as wide blade, duck-foot and chisel undercutting (‘Paraplow’) type loosening elements. When evaluating the results of the test, we qualified the implement-tools with the help an arbitrarily defined constructional factor; in this way, an order of the tool constructions was determined.

In the past decade, magnetorheological fluids have been applied in various devices. This article focuses on developing a new computational procedure to determine the shear strength-shear strain relationship of magnetorheological fluids from the measurement data obtained by using an Anton-Paar rotational viscometer.

This paper describes design and application of a torsional property measurement stand used for analyzing rubber-metal components. The paper covers mechanical, electrical and control systems. Design of the stand takes into consideration reusing existing components, that come from a measuring stand that was used for analyzing automotive dual-mass flywheels. Paper covers initial design proposal as well as final design in CAD model form and the finished assembled stand. A Section of this paper is dedicated to describing the components and software used to control the stand and collect data from sensor. Programming of the excitation movement cycle in TIA-Portal software is also covered in this section. Lastly it covers experimental analysis. Our findings and evaluation of the machine itself is found in the conclusion chapter.

In this work, cam profiles of a camshaft are measured by a roundness measurement machine. First, the measured cam profiles are transformed into their actual sizes, based on the measurement of the bearing locations. The 8 cams of the camshaft are of two types. From the 4–4 cam profiles of each type an approximate profile is created by fitting them to one another using their base circles as reference, and calculating the center of gravity of the cumulative points of these fitted profiles corresponding to an arc segment.

The paper is devoted to investigation of spectral parameters of constricted arc plasma, as a foundation for further development of basic approaches to intellectual controlling of plasma-arc technologies of material treatment and welding. The relevance of application of spectrometric analysis of the welding arc consists in obtaining the possibility of sufficiently accurate control of its temperature and energy input into the product being welded, recording initiation of weld defects (including internal pores), determination of atmospheric oxygen ingress into the weld pool, etc. The spectral composition, temperature and concentration of the constricted welding arc plasma were determined in the paper to more precisely define its physical characteristics at 80 and 100 A currents, in order to establish the further applicability of dynamic spectral analysis of a constricted plasma arc in control of welding processes. As modern spectrometers with CCD-detectors of the spectral range of 200÷1100 nm have the resolution of the order of 0.35 nm, which 2-3 times exceeds the quantization step by the measured wavelength, and may lead to considerable errors, an approach is proposed to increase the accuracy of spectrometric measurements to acceptable values. Radiation spectra of the constricted welding arc measured in the wavelength range of 650÷1000 nm allowed within the model of local thermodynamic equilibrium calculating the excitation temperature of the main spectral range components: Ar I and O I. It was established that the excitation temperature of electron levels of Ar atoms in the welding arc plasma only weakly depends on the discharge current, and it is equal to 13500 ± 500 K. Excitation temperature of O I electron levels was equal to 10000 ± 500 К at arc current of 100 A and to 7200 ± 500 К at 80 A current. Electron density of high-current welding arc plasma was measured by Stark broadening of Ar I lines (696.543; 772.4; 912.3 nm). It was found that with the rise of current of the constricted welding arc by 20%, an increase of charge concentration in the arc plasma by 1.5 times and of its temperature by 1.2 times are observed.

The work covers the issues related to the diagnosis of selected occupational safety hazards in Polish wood processing enterprises. The main objective is to build models in order to identify occupational accident profiles in these enterprises on the basis of individual records characterizing the casualties, provided by Statistics Poland. The modelling task employed the latent class analysis (LCA) data mining technique. In order to enhance the process of building the LCA model and to support the procedure of selecting input variables relevant to the model, an iterative algorithm was elaborated by the authors. The impact of an enterprise size on occupational accident consequences was statistically confirmed. Following this result, LCA models were developed independently for smaller (micro and small), and for larger (medium and large) enterprises. Latent classes, presenting occupational accident profiles, were visualized in the form of heat maps. Similarities and differences between the occupational accident profiles identified for the two types of enterprises were indicated. It has been shown that employees of smaller enterprises are at greater risk of suffering more serious injury from accidents at work than employees of larger enterprises. However, in both cases, the most critical latent classes concern occupational accidents related to operating machinery; they affect workers with a low level of job seniority, and result in injuries (often traumatic amputations) involving upper limbs in particular.

The objective of the research was to assess the feasibility of utilizing copper oxide particles as a reinforcing and conductive additive in a thermoplastic elastomer. This study focused on examining the influence of particle size on primary strength characteristics and aging resistance, as well as conducting an impedance measurement to gauge the impact of copper oxide particles on electrical conductivity. The findings of the study revealed that incorporating copper oxide particles at the nanoscale leads to enhanced mechanical and conductive properties in comparison to both the base material and the composite with microparticles. However, it was observed that the addition of microparticles in a 5% by weight ratio failed to provide significant improvements in strength and electrical conductivity. Additionally, the study found that the incorporation of copper oxide nanoparticles improves the aging resistance of the composite.

This paper presents a procedure for the application of a multifractal spectrum to analyse vibration signals obtained during static bending tests of selected fibre reinforced polymer matrix composite materials. The analyses were designed to determine the failure course of the composites tested under bending conditions. Non-stationary changes occurring in the signals include low- and high-energy symptoms of progressive damage in materials, which are difficult to identify and interpret, especially in the early stages. Determined using fractal leaders, the multifractal spectra of the vibration signals calculated for the different stages of the bending test allowed qualitative identification of the changes caused by progressive damage with different energy responses. In addition, the quantitative measures of change in the characteristics of the multifractal spectrum determined clearly indicated the changes occurring in the recorded signals. It can be concluded from the research that the proposed signal processing method based on a multifractal spectrum determined from non-stationary vibration signals is sensitive to changes occurring in these signals.

This paper delves into a significant area of research, exploring the application of the standard boundary element method (BEM) to analyze inverse acoustic frequency scattering problems in 2D space using a rigid corrugated circular object. The inverse problem is reformulated as an optimization problem, with the boundary of the scatterer parametrized to reduce the number of optimization variables. The influence of these parameters on imaging results based on near-field data is examined. The analysis uses a flat wave illuminating the object along the positive x-axis direction. The study evaluates the accuracy of the solution across various parameters defining the boundary of the analyzed object. The findings, which significantly contribute to advancements in computational methods, non-destructive testing, and the understanding of functional properties of materials and structures, offer valuable insights into numerical techniques and their practical engineering applications.

Recently an extensive growth in the market for Unmanned Aerial Vehicles (UAVs, drones) has been observed. Because of numerous advantages, their application in various areas is wide, starting with military, through civilian – specialized up to amateur. The world market for civilian commercial drones in the year 2022 has achieved a value of more than USD 30 billion, and it is estimated that by 2032 it will have achieved USD 125 billion. Great expectations of reduction of world CO2 emissions and development of clean economy are linked with drones application in transportation and agriculture. Unfortunately, despite of the UAV-related advantages mentioned above, the problem of their comprehensive recycling seems to remain unseen. Actions aimed at recycling lithium-ion batteries used in drones have been taken, whereas the challenge of recycling the construction materials applied in UAVs seems to be ignored, which, taking into consideration the estimated growth of the UAV market and a relatively short product lifecycle, may constitute a significant ecological problem in the near future. The aim of this article is to draw attention of scientific community to the crucial but unnoticed problem, which UAV recycling will become in the near future. The authors tried to assess the extent of drones recycling problem by review of UAVs market, emphasizing the most common construction materials applied in their production, and to outline the issues related to their recycling. Proposals of solutions facilitating introduction of circular economy into UAVs market have also been put forward.

Nowadays, the progress of technology covers, among other things, the development of modern techniques and high technologies used in land surveys. Unmanned aerial vehicles (UAVs), as a good alternative to conventional land survey techniques, have currently played an increasing role. The advantages of using unmanned aerial vehicles in photogrammetric measurements include a relatively short mission time for large-area surveys. In addition, photogrammetric products have a wider range of applications compared with conventional geodetic surveys. Many scientific publications delve into the quality of photogrammetric products, but the accuracy of UAVs in the context of geodetic standards has not been investigated in full. In this paper, we attempt to fill the observed research gap. Our research has analysed the position of objects recorded in geodetic databases referring to their counterparts based on an accurate orthophotomap from a photogrammetry campaign employing an unmanned aerial vehicle. The outcomes were referenced with land survey accuracy standards set out by relevant legislation. To ensure a smooth assessment of the result's accuracy we designed a computing algorithm with a module for selecting comparable points and verifying the results. The tool can be implemented in surveys carried out in any area thanks to open-source GIS software. Our analysis showed that a detailed orthophotomap delivered using UAVs can be a valuable data source on objects recorded in geodetic databases covering selected cadastral and topographic objects and land development components. A general verification of the accuracy and validity of a geodetic numerical map and preliminary detection of areas for potential updates can be a particularly useful application of photogrammetry.

Unmanned aerial vehicles are a synergistic technology that complements other new technologies and is in constant development. The paper focuses on using artificial intelligence (AI) in security surveillance. This article aims to develop an unmanned aerial system to monitor border areas and detect human silhouettes in challenging environmental conditions. For this purpose, thermal imaging technology was used for remote sensing in combination with artificial intelligence, particularly Yolo algorithms. After testing various Yolo versions, the target algorithm was implemented on an NVIDIA Jetson Xavier edge device. Prototyping of the AI-based thermal detection system was carried out on the DJI S900 multi-rotor aircraft. The final solution was implemented on a vertical take-off and landing aircraft. A summary containing observations and conclusions, as well as perspectives for the development of future work, are included at the end of the paper.

The work presents the process of designing a plasma reactor with a gliding arc discharge. The topic discussed is motivated by the large and still modern application potential of non-thermal plasma in the processing of gas mixtures and plasmachemical treatment with the afterglow product. The process of designing a completely new plasma reactor with 10 arc-shaped electrodes is described. In the design process, the author relies on experience with existing sliding discharge reactors at the Lublin University of Technology. The design process is carried out using the modern Autodesk Inventor solid modeler environment. Partial elements of the plasma reactor were designed and assembled into an assembly. The whole work is complemented by the presentation of selected, more important physical parameters of these parts. A special part is devoted to presenting a realistic render of reactor body.

The article contains the procedure of image acquisition, including sampling of analyzed material as well as technical solutions of hardware and preprocessing used in research. A dataset of digital images containing identified objects were obtained with help of automated mechanical system for controlling the microscope table and used to train the YOLO models. The performance of YOLOv4 as well as YOLOv8 deep learning networks was compared on the basis of automatic image analysis. YOLO constitutes a one-stage object detection model, aiming to examine the analyzed image only once. By utilizing a single neural network, the image is divided into a grid of cells, and predictions are made for bounding boxes, as well as object class probabilities for each box. This approach allows real-time detection with minimal accuracy loss. The study involved ciliated protozoa Vorticella as a test object. These organisms are found both in natural water bodies and in treatment plants that employ the activated sludge method. As a result of its distinct appearance, high abundance and sedentary lifestyle, Vorticella are good subjects for detection tasks. To ensure that the training dataset is accurate, the images were manually labeled. The performance of the models was evaluated using such metrics as accuracy, precision, and recall. The final results show the differences in metrics characterizing the obtained outputs and progress in the software over subsequent versions of the YOLO algorithm.

The aim of this paper is to present mathematical models of all the propulsion components of hybrid drivetrains. These models are based on very limited amounts of data, nevertheless they match real characteristics with high accuracy based on numerous measurement data points, thus they allow the modelling process of fuel and energy consumption for a particular vehicle in any homologation driving cycle to be performed in a very short time at the early stage of vehicle design. The characteristics provided in the article concern: the full load engine torque curve, full load electric motor power curve, combustion engine efficiency characteristic, including dynamic states and warm-up period, electric motor efficiency characteristic, inverter efficiency characteristic, battery characteristics in a wide temperature range and efficiency of the drivetrain. Moreover, the main principles concerning gear ratio selection are provided to conduct subsequent steps of transmission modelling from scratch. Simulations of drivetrain performance and vehicle fuel consumption in WLTP and US06 tests prove the suitability of the presented models.

In the paper there are presented models for moisture assessment applying the two reflectometric sensors in the cellular concrete samples. The readouts express the dependence between the cellular concrete moisture, measured in gravimetric way and the apparent permittivity values achieved by the Time Domain Reflectometry method and two surface sensors. According to observed relationships, the two types of calibration models were derived – the first model is a traditional one-variable model covering only time of signal propagation and the second one two-variable model which together with signal propagation time takes into account signal attenuation. The aim of this paper is to verify the efficiency of multiple regression to improve the accuracy of moisture estimation using the TDR technique. The applied models that consider amplitude attenuation are used for this type of analysis for the first time. With the conducted research and analyses, it was shown that the measurement quality of the method could be improved by obtaining more favorable values of the determination coefficient, Residual Standard Error, Root Mean Squared Error. Also the correlation analysis shows a better fit of two-variable models than one-variable to the obtained data.

This study presents the results of a dynamic two-point bending test and a quasi-static three-point bending test of the quail femurs. Bones from females and males of two genetic strains of Japanese quail belonging to various utility types were analyzed. Mechanical parameters obtained under impact and quasi-static loading conditions were investigated. The mechanical strength of eggshells under both loading conditions was also examined. The obtained results showed that the bones of males of both analyzed types of quails were characterized by statistically significant higher strength obtained under impact loading conditions compared to the bones of females. Moreover, the mechanical strength of the eggshell measured under impact loading conditions was characterized by higher values compared to the result obtained under quasi-static load conditions. The observed differences between quail genetic lines were not statistically significant. SEM–EDS qualitative elemental distribution analysis showed a higher content of calcium in the female femurs. The damage present on the fracture surface indicates that these bones were more brittle.