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This study utilizes the transfer matrix method to analyze the modified Timoshenko beam with and without cracks. The massless torsional spring is assumed to represent the section where the crack is located. The matrix equation is simplified using boundary conditions and solved using MATLAB. Additionally, the influence of different crack depths and positions on the first three natural frequencies is compared to finite element analysis using three common types of beams as examples. The results indicate that increasing the crack depth leads to a decrease in the natural frequency of the beam. However, the impact on certain specific positions is insignificant, with a maximum error between the two methods not exceeding 2.73%. Furthermore, the study investigates the influence of crack depth on natural frequency under different span-to-height ratios. The findings reveal that increasing the span-to-height ratio reduces the influence of crack depth on natural frequency, thereby validating the proposed method and its applicability in modified Timoshenko cracked beams. [ABSTRACT FROM AUTHOR]

We present TDS Simulator, a new software tool aimed at modelling thermal desorption spectroscopy (TDS) experiments. TDS is a widely used technique for quantifying key characteristics of hydrogen-material interactions, such as diffusivity and trapping. However, interpreting the output of TDS experiments is non-trivial and requires appropriate post-processing tools. This work introduces the first software tool capable of simulating TDS curves for arbitrary choices of material parameters and hydrogen trap characteristics, using the primary hydrogen diffusion and trapping models (Oriani, McNabb–Foster). Moreover, TDS Simulator contains a specific functionality for loading experimental TDS data and conducting the inverse calibration of a selected transport model, providing automatic estimates of the density and binding energy of each hydrogen trap type in the material. In its first version, TDS Simulator is provided as a MATLAB App, which is made freely available to the community and provides a simple graphical user interface (GUI) to make use of TDS Simulator straightforward. As reported in the present manuscript, the outputs of TDS Simulator have been extensively validated against literature data. Demonstrations of automatic determination of trap characteristics from experimental data through the optimization tool are also provided. The present work enables an efficient and straightforward characterization of hydrogen-material characteristics relevant to multiple applications, from nuclear fusion to the development of hydrogen-compatible materials for the hydrogen economy. TDS Simulator can be downloaded from https://mechmat.web.ox.ac.uk/codes. • TDS Simulator is a free, user–friendly App for simulating H desorption experiments. • Multiple H transport theories (Oriani, McNabb-Foster) and trap sites are considered. • The provided inference algorithm enables identifying trap quantities from test data. • Each of the elements implemented in TDS Simulator is extensively validated. • Lab data is analysed, proving strong inference abilities and bringing new insight. [ABSTRACT FROM AUTHOR]

When dealing with large-scale applications, the availability of simple and efficient algorithms is essential. We focus on the algorithm for calculating the order statistics, i.e. for selecting the kth smallest element of an array X. Many statistical procedures rely on this basic operation, that is usually solved by sorting all the elements and selecting the one in position k. If the dimension of the array to sort is quite large, this simple operation can become excessively time consuming. For this purpose, we propose an original randomised algorithm that reduces the dimension of the selection problem by focusing only on a small subset of elements that contains the solution. Despite its random nature, it always returns the target value. Empirical results shows that, for arrays of dimensions running from 10 5 to 10 8 , our procedure resulted to be remarkably (up to almost 10 times) faster than the naïve procedure, independently of the programming environment and of the sorting algorithm, and with a relative advantage that tends to growth with the dimension of the array. [ABSTRACT FROM AUTHOR]

This study was carried out within the offshore Niger Delta Basin to generate novel predictive models for estimating rock parameters. MATLAB was employed in obtaining models for four different rock parameter relationships including unconfined compressive strength (UCS) against bulk density, UCS against sonic transit time (STT), shear wave velocity against STT, and permeability against bulk density using multiple ordinary least-squares regression (OLSR) methods. Also, the Adaptive-Neuro Fuzzy Inference System (ANFIS) artificial intelligence network was utilized for modeling and optimization of the data. Statistical tools including the Sum of Squares Total (SST), the Sum of Squares Error (SSE), the Sum of Squares Regression (SSR), and Correlation Coefficient (R-squared) were applied in investigating the prediction performances of the models. Results of OLSR analysis show that only the UCS against bulk density model gave high prediction performance in all the OLSR models with R-squared values of 0.8637, 0.8848, 0.8216, 0.9956, and 0.8108 for linear, quadratic, power, logarithmic, and exponential models respectively. ANN model results revealed that UCS against bulk density, UCS against STT, and shear wave velocity against STT models all gave high prediction performances with respective R-squared values of 0.89635, 0.99365, and 0.52703, while the permeability against bulk density model gave low performance (0.03378). These findings imply that all the OLSR models can be applied for the prediction of rock UCS from bulk density information only, while ANN-generated models can be used in predicting UCS from bulk density and STT, in addition to shear wave velocity from STT in the study area and similar geologic environments. [ABSTRACT FROM AUTHOR]

The study presents a new technique that combines hyperspectral imaging and machine learning to identify minerals. Overcoming the challenges of processing hyperspectral data, the solution offers a user-friendly hyperspectral analysis tool tailored for constructing datasets and enhancing mineral identification and concentration estimation. The tool integrates hyperspectral data processing with segmentation, simplifying complex operations and making mineral identification accessible to non-experts. The tool's capabilities extend to handling multi-spectral data efficiently, potentially leading to energy-efficient analysis when combined with dimensionality reduction techniques. Presented as a novel approach, it improves geological surveys in mining areas, enabling industrial applications and mineral research. [ABSTRACT FROM AUTHOR]

This work presents the development of a model for generating trajectories for an autonomous naval vehicle using genetic algorithms implemented in MATLAB. The primary objective is to optimize the routes the vehicle must follow, minimizing the traveled distance and ensuring efficient navigation. Various scenarios were tested by varying model parameters such as the number of environmental control points, the number of generations, and the number of individuals to evaluate the genetic algorithm's performance. In each scenario, results were analyzed in terms of minimum traveled distance and the optimal sequence of trajectory points (FITNESS). The results show that the genetic algorithm can find efficient solutions, adapting to different configurations of points and generations. Specific examples illustrate the optimal generated trajectories, accompanied by graphical representations visualizing the sequence of points. This study demonstrates the effectiveness of genetic algorithms in route planning for autonomous naval vehicles and provides a solid foundation for future research and applications in autonomous navigation. [ABSTRACT FROM AUTHOR]

Hybrid nanofluids, which incorporate two distinct nanoparticles, are an innovative class of nanofluids designed to improve thermal and mechanical properties. These fluids have garnered considerable interest in numerous engineering and scientific fields. The fundamental goal of this research is to investigate the heat-transfer increase of MnZnFe2O4-NiZnFe2O4/C10H22 hybrid nanofluids in the presence of magnetohydrodynamics, nonlinear thermal radiation, and the Biot number on a stretched sheet. In this case, nanomaterials (MnZnFe2O4 and NiZnFe2O4) are combined with a base fluid C10H22. To do this, the system's partial differential equations are transformed into a set of nonlinear ordinary differential equations using systematic similarity transformations. The shooting approach is then used in combination with MATLAB's BVP4C solver to solve the resultant ordinary differential equations. The study presents the impact of various physical parameters, including the porosity parameter, magnetic parameter, Prandtl number, thermal-radiation parameter, Biot number, and Schmidt number, on the velocity and temperature fields, illustrated through graphs and tables. The velocity field reduces for increasing values of both magnetic and porosity parameters. The thermal-distribution profile is increased for increasing variations of the temperature-ratio parameter, Biot number, volume fraction of nanoparticles, and the thermal-radiation parameter. The MnZnFe2O4-NiZnFe2O4/C10H22 hybrid nanofluids combine thermal, magnetic, and fluidic properties, making them versatile for applications in thermal management, medicine, industrial processes, environmental remediation, and advanced sensing technologies. Their multifunctional characteristics provide significant advantages in improving efficiency, performance, and control in various engineering and scientific fields. This research has potential applications in heat transfer, biomedical research, manufacturing, aerospace technology, and beyond. [ABSTRACT FROM AUTHOR]

The bioconvection effects with nanofluid are major application in biofuels. This analysis aimed to observe the bioconvection effect in unsteady two-dimensional Maxwell nanofluid flow containing gyrotactic motile microorganisms across a stretching/shrinking cylinder evaluating the consequences of thermal radiation and activation energy. The Cattaneo-Christov double diffusion theory is also observed. Nanofluids are quickly perceptive into many solicitations in the latest technology. The current research has noteworthy implementations in the modern nanotechnology, microelectronics, nano-biopolymer field, biomedicine, biotechnology, treatment of cancer therapy, cooling of atomic reactors, fuel cells, and power generation. By using the proper similarity transformation, the partial differential equations that serve as the basis for the current study are gradually reduced to a set of highly nonlinear forms of ordinary differential equations, which are then numerically, approached using a well-known shooting scheme and the bvp4c tool of the MATLAB software. Investigated is the profile behavior of the flow regulating parameters for the velocity field, thermal field, and volumetric concentration of nanoparticles and microorganisms. From the results, it is concluded that velocity is reduced with a larger bioconvection Rayleigh number. The thermal field is increased with a larger amount of thermal Biot number and thermal radiation. The concentration of nanoparticles increases with an increment in the thermophoresis parameter. Furthermore, the microorganism's field is decreased with a larger Lewis number. The findings demonstrate that by optimizing the concentration of nanoparticles and microorganisms, the thermal efficiency of biofuels can be significantly improved. This leads to more sustainable and efficient energy production. By optimizing the concentration of nanoparticles and microorganisms in biofuels, the thermal properties can be significantly improved, leading to more efficient combustion processes. This can reduce the overall cost and increase the yield of biofuels. Improved cooling systems for medical imaging devices such as MRI machines can be developed using nanofluids, ensuring better performance and patient safety. [ABSTRACT FROM AUTHOR]

In this work, the permeation flux equations, Richardson and Paripatyadar kinetic model, and lumen-shell mass balances were combined to develop a simulation model for the integrated CO 2 separation-dry reforming of methane (DRM) reaction in La 0.6 Sr 0.4 Co 0.8 Fe 0.2 O 3-δ (LSCF)-carbonate hollow fibre membrane reactor. The interactions of the reactants for the DRM and reverse water gas shift reactions were simulated at different operating parameters. Greater membrane area gave higher CO 2 and CH 4 conversions and H 2 /CO (syngas) molar ratio of ∼1. Lower temperature of 700 °C provided higher DRM performance in membrane reactor, which was due to the higher CO 2 permeation from the higher electronic conductivity of LSCF. Higher CH 4 amount in the sweep gas facilitated CO 2 permeation, conversion, syngas yield, and ratio. The optimum DRM performance of the membrane reactor was achieved at a lumen-to-shell flow rate ratio of 0.5, whereas the limiting factor was the CH 4 availability at the shell side. [Display omitted] • Richardson and Paripatyadar (R–P) kinetic model was used to simulate DRM and RWGS. • Permeation of CO 2 across LSCF-molten carbonate hollow fibre membrane was simulated. • MATLAB simulation of DRM in a ceramic-carbonate membrane reactor was developed. • Parametric study on temperature, membrane area, and flow conditions were presented. • RSM was used to optimize the DRM performance of membrane reactor. [ABSTRACT FROM AUTHOR]

The increasing adoption of 2D/3D laser line sensors in industrial and research applications necessitates accurate and efficient simulation tools for tasks such as surface inspection, dimensional verification, and quality control. This paper presents a novel algorithm developed in MATLAB for simulating the measurements of any 2D/3D laser line sensor on STL CAD models. The algorithm uses a modified fast-ray triangular intersection method, addressing challenges such as overlapping triangles in assembly models and incorporating sensor resolution to ensure realistic simulations. Quantitative analysis shows a significant reduction in computation time, enhancing the practical utility of the algorithm. The simulation results exhibit a mean deviation of 0.42 mm when compared to real-world measurements. Notably, the algorithm effectively handles complex geometric features, such as holes and grooves, and offers flexibility in generating point cloud data in both local and global coordinate systems. This work not only reduces the need for physical prototyping, thereby contributing to sustainability, but also supports AI training by generating accurate synthetic data. Future work should aim to further optimize the simulation speed and explore noise modeling to enhance the realism of simulated measurements. [ABSTRACT FROM AUTHOR]

With the chemical formula CaCl2, calcium chloride is a salt as well as an inorganic material. At room temperature, it has the consistency of a white, crystalline solid and is very water-soluble. It can be created by neutralizing calcium hydroxide with hydrochloric acid. Calcium chloride is a solution with a large enthalpy change. It is extensively utilized in research facilities, manufacturing facilities, and pharmaceuticals, including all types of food-graded applications, the treatment of acute illnesses, packaging for drying tubes, dust controllers, and de-icing, among other uses. In this paper, firstly we compute the topological indices, coindices, and reverse indices of CaCl2. Further, we employ machine learning strategies to capture the best suitable set of indices for the proximity of the prediction of distinct physio-chemical properties of CaCl2. To strengthen the results, different regression techniques are implemented to predict HOF of CaCl2 based on our features, and the most influential features were detected to verify our results. [ABSTRACT FROM AUTHOR]

Fuzzy implication models play a crucial role in the field of fuzzy logic. The reason behind this reality is the fact that fuzzy implications are influenced by the properties of the model used for their creation. The importance of the mentioned models increases due to the fact that there is a need for new fuzzy implications for use in artificial intelligence and other applications. So, this paper aims to resolve this problem by creating a new model. This model, named (S,T,N) by the authors, is an evolution from previous models as it utilizes all of the basic logical fuzzy connectives in a new composition that emphasizes the use of as many connectives as practically possible. Moreover, a computer program has been developed to display various interpretations of the proposed model and allow the readers to form a deeper understanding of the paper's research. The results provided by the research conducted are mainly due to the development of the new fuzzy implication model and, secondarily, the new tool for displaying the capabilities of the implication model. Finally, the conclusions drawn from the paper proved that the search for new fuzzy implications should not only be targeted at new research directions but also at more established ones. Furthermore, the program displayed the strong capabilities of computer-assisted computations since it allowed for rapid checking of multiple implications, thus easing the researcher's task of practically verifying the new model's validity. [ABSTRACT FROM AUTHOR]

Systems biology aims to understand living organisms through mathematically modeling their behaviors at different organizational levels, ranging from molecules to populations. Modeling involves several steps, from determining the model purpose to developing the mathematical model, implementing it computationally, simulating the model's behavior, evaluating, and refining the model. Importantly, model simulation results must be reproducible, ensuring that other researchers can obtain the same results after writing the code de novo and/or using different software tools. Guidelines to increase model reproducibility have been published. However, reproducibility remains a major challenge in this field. In this paper, we tackle this challenge for physiologically-based pharmacokinetic (PBPK) models, which represent the pharmacokinetics of chemicals following exposure in humans or animals. We summarize recommendations for PBPK model reporting that should apply during model development and implementation, in order to ensure model reproducibility and comprehensibility. We make a proposal aiming to harmonize abbreviations used in PBPK models. To illustrate these recommendations, we present an original and reproducible PBPK model code in MATLAB, alongside an example of MATLAB code converted to Systems Biology Markup Language format using MOCCASIN. As directions for future improvement, more tools to convert computational PBPK models from different software platforms into standard formats would increase the interoperability of these models. The application of other systems biology standards to PBPK models is encouraged. This work is the result of an interdisciplinary collaboration involving the ELIXIR systems biology community. More interdisciplinary collaborations like this would facilitate further harmonization and application of good modeling practices in different systems biology fields. [ABSTRACT FROM AUTHOR]

In the field of structural damage detection through vibration measurements, most existing methods demand extensive data collection, including vibration readings at multiple levels, strain data, temperature measurements, and numerous vibration modes. These requirements result in high costs and complex instrumentation processes. Additionally, many approaches fail to account for model uncertainties, leading to significant discrepancies between the actual structure and its numerical reference model, thus compromising the accuracy of damage identification. This study introduces an innovative computational method aimed at minimizing data requirements, reducing instrumentation costs, and functioning with fewer vibration modes. By utilizing information from a single vibration sensor and at least three vibration modes, the method avoids the need for higher-mode excitation, which typically demands specialized equipment. The approach also incorporates model uncertainties related to geometry and mass distribution, improving the accuracy of damage detection. The computational method was validated on a steel frame structure under various damage conditions, categorized as single or multiple damage. The results indicate up to 100% accuracy in locating damage and up to 80% accuracy in estimating its severity. These findings demonstrate the method's potential for detecting structural damage with limited data and at a significantly lower cost compared to conventional techniques. [ABSTRACT FROM AUTHOR]

Multi-user collaborative robotic simulation has great potential for applications in industry and education. Unity is a powerful software for simulation and online multi-user experience, which can be enhanced with third-party mathematical analysis and multiplayer servers. Unity can become a much more capable and user-friendly robotic simulation package through integration with other software. These include MATLAB for computations and the Photon Unity Engine (PUN) for online multi-user capabilities. This study developed a flexible robotic simulation framework that can be adapted to different scenarios for industrial and educational applications. Several simulation scenarios were developed to identify the most efficient data communication methods between MATLAB and Unity. TCP/IP, Shared Memory, Firebase, and MQTT, were selected to assess their performance and interaction with data in Unity and MATLAB. Next, an independent PUN application was created. Both components were integrated into the simulator for evaluation and performance optimization. The performance of this simulation framework was assessed through two case studies. The results demonstrated that the integrated framework is viable, efficient, and flexible for robotic simulation and digital twins. Future research will expand the framework by adding diverse functionalities to provide users with a better interface, enhancing its performance, and integrating additional software packages. [ABSTRACT FROM AUTHOR]

This paper introduces an instructional framework for process identification, combining theoretical concepts with practical laboratory exercises, focusing particularly on the identification of first‐order‐plus‐time‐delay models. Our methodology emphasizes guiding students through the various stages involved in the system identification process, namely mastering techniques, such as data acquisition and preprocessing, identification and validation stages, and method comparison. The laboratory assignment is structured into three distinct stages: an initial prelab task working with simulated data, the hands‐on work with laboratory equipment, and the assignment report writing and oral presentation. The assessment of students' learning outcomes is conducted using a detailed rubric. Feedback from a focus group interview indicates that the majority of students appreciated the well‐balanced content, highlighting a strong link between theoretical concepts and practical application. [ABSTRACT FROM AUTHOR]

The laminar flow reactor (LFR) is one of the most comprehensive problems in chemical reaction engineering, as its modeling involves mass, heat, and momentum conservation equations coupled with chemical reaction rate equations. It is relatively easy to grasp its basic operation, but the solution of the problem is far from trivial. Although there are analytical solutions that simplify the problem, students and tutors must use efficient numerical strategies to appropriately solve some LFR problems. In the present investigation, we solve several different cases of two‐dimensional cylindrical LFR, beginning with the isothermal case, as a benchmark. Calculations were performed using the Chebyshev orthogonal collocation technique by custom scripts in Scilab, Mathematica©, and Matlab®, and were compared with solutions available from the ECRE Version of COMSOL®, which was used as the reference, as well as available analytical solutions in the literature. After analyzing the case of the isothermal LFR with Newtonian fluid, we explored non‐Newtonian fluids, including Carreau and Bingham fluids, whose LFR results are not available in the preceding literature. For Newtonian fluids, besides (a) the isothermal case, we also explore other three nonisothermal design cases: (b) cooling jacket with a fixed heat exchange coefficient at the wall, (c) adiabatic operation, (d) nonisothermal LFR with isothermal wall. Through a performance criterion, the different operation models are compared and we show that nonisothermal design cases perform better than the isothermal case. Computation times, for different scenarios, are quite short and taking 25 nodal points or more suffice for an accurate and timely solution of the more complex problem Case (b). The numerical modeling approach is useful from a pedagogical standpoint, as one can compare numerical results with classical assumptions, and progress from a more restrictive conceptual model (segregated flow) to an array of different kinds of operation with the LFR in which one needs to consider diffusion and temperature distribution. [ABSTRACT FROM AUTHOR]

Most exoskeletons designed to assist users in load-bearing tasks face a mechanical dilemma in their conception. Designers may find a compromise between stiff active actuators-based architectures which are powerful but bulky and compliant actuator-based designs which are much less assistive but less constraining for users. This article presents a new open-source simulation-based design tool and a human-centered method that lets orthosis designers explore different device configurations and evaluate some performance criteria. This framework was applied in three different young-adult subjects. The effects of design personalization on user morphology and gait were studied. First, an ankle–foot orthosis designed to support a 20 kg backpack was defined according to the user's height, weight, and walking speed. Then, a simulation of the subjects fitted with their customized design walking at a self-selected speed on flat ground carrying this additional load was performed. First, the results showed that the designed method inspired by natural joint stiffness behavior provided viable personalized mechanisms. Second, significant reductions in peak joint torque and mean joint activity were observed when comparing muscle-generated torques while the subject was wearing the 20 kg backpack with ankle–foot orthoses on both legs or without. Finally, it shows the value of an open-access tool for exploring the coupling of passive and active actuators to generate lighter and more compliant designs. [ABSTRACT FROM AUTHOR]

Background: Diabetes is more apparent in adulthood but may be dormant in childhood and originates during early fetal development. In fetal biometry, femur length (FL) is crucial for assessing fetal growth and development. This study aimed to assess potential associations between fetal femur growth and prediabetic biomarkers in Bangladeshi children. Methods: A cohort study embedded in a population-based maternal food and micronutrient supplementation (MINIMat) trial was conducted in Matlab, Bangladesh. The children in the cohort were followed up until 15 years of age. In the original trial, pregnancy was confirmed by ultrasound before 13 gestational weeks (GWs). Afterward, ultrasound assessments were performed at 14, 19, and 30 GWs. FL was measured from one end to the other, capturing a complete femoral image. The FL was standardized by GW, and a z-score was calculated. FBG and HbA1c levels were determined in plasma and whole blood, and the triglyceride–glucose index, a biomarker of insulin resistance, was calculated as Ln [fasting triglycerides (mg/dl) × fasting glucose (mg/dl)/2]. Multivariable linear regression analysis using a generalized linear model was performed to estimate the effects of FL at 14, 19 and 30 GWs on prediabetic biomarkers at 9 and 15 years of age. Maternal micronutrient and food supplementation group, parity, child sex, and BMI at 9 years or 15 years were included as covariates. Results: A total of 1.2% (6/515) of the participants had impaired fasting glucose during preadolescence, which increased to 3.5% (15/433) during adolescence. At 9 years, 6.3% (32/508) of the participants had elevated HbA1c%, which increased to 28% (120/431) at 15 years. Additionally, the TyG index increased from 9.5% (49/515) (during preadolescence) to 13% (56/433) (during adolescence). A one standard deviation decrease in FL at 14 and 19 GWs was associated with increased FBG (β = − 0.44 [− 0.88, − 0.004], P = 0.048; β = − 0.59 [− 1.12, − 0.05], P = 0.031) and HbA1c (β = − 0.01; [− 0.03, -0.005], P = 0.007; β = − 0.01 [− 0.03, − 0.003], P = 0.018) levels at 15 years. FL was not associated with diabetic biomarkers at 9 years. Conclusion: Mid-trimester impaired femur growth may be associated with elevated prediabetic biomarkers in Bangladeshi adolescents. [ABSTRACT FROM AUTHOR]

Novel Stitched Foam‐filled Honeycomb Sandwich (SFHS) panels have been fabricated using vacuum‐assisted resin transfer molding to address the weak interfaces between the face sheets and the core in the Foam‐filled Honeycomb Sandwich (FHS) panel. The SFHS panels have shown better load‐bearing capacity and performance characteristics compared to FHS panel after Low‐Velocity Impact (LVI) tests. After the LVI test, MATLAB image processing was used to analyze the impact damage areas and failure mechanisms. In addition, Machine Learning regression algorithms were employed to predict the optimal amount of energy absorbed during low‐velocity impact testing of fabricated panels with a maximum impactor drop height of 700 mm. The results indicated that nylon yarn stitching significantly improved energy absorption and interfacial behavior compared to unstitched honeycomb panels. This research also revealed that SFHS1 panels with adjacent honeycomb cell stitching are more impact resistant, provide increased load carrying capacity, and are cost‐effective. These panels can be utilized by modern engineers to increase economy, durability, and functionality in industrial, automotive, and construction applications. Highlights: Stitched Foam Filled Honeycomb Sandwich (SFHS) panels, manufactured via resin transfer molding, and eliminates weak interfaces between the face sheets and core.SFHS panels outperformed unstitched panels in load‐bearing and energy absorption during low‐velocity impact, as confirmed by MATLAB image analysis showing reduced damage and failure.Machine learning algorithms particularly polynomial regression model predicted maximum absorption energy precisely with 99.9% accuracy, close to experimental results.SFHS panels can be used in automotive and industrial applications due to their through‐thickness stitching. [ABSTRACT FROM AUTHOR]

Background/Objectives: The precise quantification of myocardial infarction is crucial for evaluating therapeutic strategies. We developed a robust, color-based semi-automatic algorithm capable of infarct region detection, isolation and quantification with four different histological staining techniques, and of the isolation and quantification of diffuse fibrosis in the heart. Methods: Our method is developed based on the color difference in the infarct and non-infarct regions after histological staining. Mouse cardiac tissues stained with Masson's trichrome (MTS), hematoxylin and eosin (H&E), 2,3,5-Triphenyltetrazolium chloride and picrosirius red were included to demonstrate the performance of our method. Results: We demonstrate that our algorithm can effectively identify and produce a clear visualization of infarct tissue in the four staining techniques. Notably, the infarct region on an H&E-stained tissue section can be clearly visualized after processing. The MATLAB-based program we developed holds promise for infarct quantification. Additionally, our program can isolate and quantify diffuse fibrotic elements from an MTS-stained cardiac section, which suggests the algorithm's potential for evaluating pathological cardiac fibrosis in diseased cardiac tissues. Conclusions: We demonstrate that this color-based algorithm is capable of accurately identifying, isolating and quantifying cardiac infarct regions with different staining techniques, as well as diffuse and patchy fibrosis in MTS-stained cardiac tissues. [ABSTRACT FROM AUTHOR]

This study introduces fBrake, a novel simulation method now designed for use in periodic technical inspections of M1 and N1 vehicle categories, addressing challenges posed by Directive 2014/45/EU. The directive mandates that braking efficiency must be measured relative to the vehicle's maximum mass, which often results in underperformance during inspections due to vehicles typically being unladen. This discrepancy arises because the maximum braking forces are proportional to the vertical load on the wheels, causing empty vehicles to lock their wheels prematurely compared to laden ones. fBrake simulates the braking forces of unladen vehicles to reflect a laden state by employing an optimal brake-force distribution curve that aligns with the vehicle's inherent braking behavior, whether through proportioning valves or through electronic brake distribution systems in anti-lock-braking-system-equipped vehicles. Our methodology, previously applied to heavy vehicles, involved extensive experimentation with a roller brake tester, comparing the actual braking performances of dozens of vehicles to those of their simulated counterparts using fBrake. The results demonstrate that fBrake reliably replicates the braking efficiency of laden vehicles, validating its use as an accurate and effective tool for braking system assessments in periodic inspections, irrespective of the vehicle's load condition during the test. This approach ensures compliance with regulatory requirements while enhancing the reliability and safety of vehicle inspections. [ABSTRACT FROM AUTHOR]

This article discusses the software tool (Simscape—Multibody program of MATLAB) primarily intended for dynamic and kinematic processes with practical applications in static calculations. Currently, there are few published scientific works utilizing this tool for tasks like basic static calculations of truss systems. We were interested in comparing the calculation using the tools we use in our work and research activities for theoretical calculation; the potential reliance on simulations in the future could help to avoid the necessity of complex theoretical calculations, which can be time-consuming and prone to errors. Despite the fact that the structure may appear simple, in practice, there may not always be time for a verification calculation in the theoretical field (proper model creation, inclusion of all conditions, etc.). The beam system is intentionally both externally and internally statically indeterminate. For this reason, it is logically necessary to also consider deformation conditions. The achieved results were interesting in terms of accuracy compared to SOLIDWORKS, which was used for computation verification. Through very simple optimization, we were able to further increase the calculation accuracy without complicating other parameters. [ABSTRACT FROM AUTHOR]

Selecting the most preferred software for teaching power systems engineering at the undergraduate level is a complex problem in developing countries, and it requires making an informed decision by compromising on various criteria. This study proposes a multi-criteria framework to determine the most preferred software solution for instructing undergraduate power system modules using the Fuzzy-ARAS (additive ratio assessment) method and expert opinions. Twelve evaluation criteria were used to evaluate eight widely used software packages. A questionnaire was designed to capture views from professionals in academia and industry on the criteria weights and ranking of software options. Linguistic terms were used to represent the experts' judgment, and weights were assigned to each criterion. The Fuzzy-ARAS multi-criteria decision approach was applied to obtain ratings for each software alternative. Based on the result, MATLAB emerged as the most preferred software for instructing power systems analysis, whereas MATPOWER (V 8.0) was rated as the least preferred choice. In addition, the Fuzzy-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach was used, producing a separate ranking; the most preferred software was MATPOWER, while the least preferred software was NEPLAN (V 360 10.5.1). A new coefficient that combines the findings of the two approaches was suggested to reconcile the ranks. The combined ranking aligns with the result of the Fuzzy-TOPSIS method by returning MATLAB as the most preferred, while the least preferred software was NEPLAN. This study significantly contributes to the choice of software for undergraduate power systems analysis instruction by providing direction to educators and institutions looking for software solutions to improve undergraduate power systems analysis education. [ABSTRACT FROM AUTHOR]

The building sector constitutes 40% of global electric energy consumption, making it vital to address for achieving the global net-zero emissions goal by 2050. This study focuses on enhancing electric load forecasting systems' performance and interactivity by investigating the impact of weather and building usage parameters. Hourly electricity meter readings from a Texas university campus building (2012–2015) were employed, applying pre-processing techniques and machine learning algorithms such as linear regression, decision trees, and support vector machines using MATLAB R2023a. Exponential Gaussian Process Regression (GPR) showed the best performance at a one-year training data size, yielding an average normalized root mean square error (nRMSE) value of 0.52%, equivalent to a 0.3% reduction compared to leading methods. The developed system is presented through an interactive GUI and allows for prediction of external factors like PV and EV integration. Through a case study implementation, the combined system achieves 12.8% energy savings over a typical year simulated using ETAP 22 and Trimble ProDesign software version 2021.0.19. This holistic solution precisely models the electric demand management scenario of grid-interactive efficient buildings (GEBs), simultaneously enhancing reliability and flexibility to accommodate diverse applications. [ABSTRACT FROM AUTHOR]

To enhance the power generation efficiency of the photovoltaic system, it is necessary to ensure that it can operate stably at the global maximum power point (MPP). This paper presents a study on the maximum power point tracking(MPPT) technology of photovoltaic system and a new MPPT technique based on the improved multi‐subgroup parallel optimization algorithm with shuffled frog leaping algorithm (IMSPO‐SFLA) is proposed, which combines the improved multi‐subgroup parallel optimization algorithm with the shuffled frog leaping algorithm. Moreover, the developed MPPT technique improves the tracking accuracy of the MPP, increased the speed of MPP tracking, the problem of the traditional frog leaping algorithm being prone to falling into a local optimum is successfully overcame by it. In order to verify the effectiveness of the method, a simulation model is established in MATLAB in this paper. The Simulation results show that the method proposed in this paper can make the photovoltaic power generation system be operated more efficiently, which contributes to the development of the renewable energy field and promotes the progress of clean energy technology. Compared with the traditional algorithm, it has obvious advantages in realizing the MPPT of PV power generation system. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The gravimetric forward method is crucial in geophysical applications for a gravimetric interpretation of the Earth's inner structure. In this study, we present the gravimetric forward modelling open‐source software that incorporates a graphical user interface. This software allows data preparation, manipulation and result interpretation both spatially and spectrally. For spatial domain modelling, it uses prism and tesseroid elements, whereas in the spectral domain, it extends Parker's formulas within specified boundaries. The software's utility is demonstrated through synthetic models and real‐world applications, including calculating corrections for topography, sediments and consolidated crust using ETOPO1 and CRUST1.0 models. Performance comparisons show that Parker's method delivers computation speed superior to that of the prism, tesseroid and Terrain gravity forward (TGF) software, with variances ranging within ±12 mGal for Gz${{G}_z}$ and ±0.3 E for Gzz${{G}_{zz}}$ across different geological scenarios. [ABSTRACT FROM AUTHOR]

A novel method is presented for determining the derating factors of a three‐phase induction motor under the condition of the unbalanced supply voltages. In this method, a mechanical system is used which consist of the a centrifugal pump, two valves, a DC motor, which are connected to the shaft of the three‐phase induction motor. A sliding mode control system is used for position control of the DC motor for adjusting the valve angle for derating the induction motor. The authors present the results of an experiment in which a three‐phase induction motor was subjected to various unbalanced voltage conditions. The results of simulations were used to look into what happened when there were different levels of imbalanced voltage. This was done to determine how these situations changed an induction motor's speed, torque, and efficiency. For this system, the stator current would be greater than the rated current if there was an imbalance in the supply voltage. Therefore, to reduce the amount of power that the three‐phase induction motor can produce, the control system uses a DC motor to reduce the angle of one of the two valves. This decreasing angle continues until the root mean square value of the stator current returns to the rated current. At this point, the derating factor may be calculated by dividing the output power of the three‐phase induction motor in the unbalanced condition by the output power when there are ideal sinusoidal. The MATLAB SIMULINK environment is utilised to perform simulations of the proposed system. [ABSTRACT FROM AUTHOR]

The authors present a model for diagnosing motor faults based on machine learning, demonstrating advantages over other algorithms in terms of both improved fitness values and reduced running time. The structure of the model involves three primary phases: feature extraction, feature selection and classification. During the feature extraction phase, crucial features are identified using empirical mode decomposition, fast Fourier transform and multiresolution analysis, resulting in a total of 144 features. The feature selection stage employs a new strategy that combines symmetrical uncertainty in the filter approach with the binary grey wolf optimiser and emperor penguin optimiser in the wrapper approach. Finally, a support vector machine is used for classification to generate fitness values. To validate the model's effectiveness and accuracy, motor fault current signal datasets, case Western Reserve University (CWRU) benchmark datasets and mechanical failure prevention technology benchmark datasets are utilised. In the motor fault current signal dataset, the highest average accuracy achieved is 99.95%, with a minimum average running time of 88.02 s obtained under ∞dB conditions. Regarding benchmark datasets and mechanical failures at CWRU, using the prevention technology benchmark dataset resulted in classification accuracies of 99.54% and 99.52%, respectively. Comparative analysis with traditional algorithms reveals that symmetric uncertainty and emperor penguin–grey wolf optimisation model outperforms traditional models in terms of performance. [ABSTRACT FROM AUTHOR]

The purpose of this research is to explore and apply the use of wavelet transformation for the segmentation of digital images, utilizing MATLAB version R2010B. The study aims to analyze how wavelet transform can be used to enhance the accuracy and effectiveness of image segmentation, which is a critical process in image processing and computer vision. The research contributes to the field of digital image processing by demonstrating the application of wavelets transformation for segmenting digital images. In this study, the study provides insights into how wavelets can be utilized to improve the detection of image features, especially in identifying image features more accurately. The experimental results show that only the Canny operator's edge detection method has the best edge detector in detecting the edges of objects in wavelet images. The technique of determining the threshold value (thresholding) can be carried out in two ways, namely, automatic method and technique carried out by trial and error. Finally, improving automatic thresholding techniques using AI-driven algorithms to reduce the reliance on trial-and-error methods could provide more consistent results in varied application areas. [ABSTRACT FROM AUTHOR]

Aim: Herein, the oxidation of chemical compounds as sound signals, prepared either by chemical, physical, mechanical, biological methods were reported. Objectives: To fabricate the synthesized material for example, nanoparticle, ceramic, electro catalyst as electrode, by mixing the synthesized material with a suitable binder or they may be mixed with a solvent to function as an electrolyte. In this case, 40 % formalin as electrolyte, platinum and calomel electrode as positive and negative electrodes respectively have been used to formulate an electrochemical cell. Methodology: This cell is connected with the sound card to process the sound signals and analyzed using Sig view software. The sound signals after noise deduction were further processed using MATLAB to get information about the signals. Results: For example, Frequency, Amplitude, etc. of those cells can be obtained. The FFT spectrum obtained by this method correlates well with the FTIR spectrum of formalin. Any Conductive chemical oxidation could be processed in this way and their chemical information could be digitized and saved in cloud. [ABSTRACT FROM AUTHOR]

This paper presents a semester-long bottle design project for an undergraduate Numerical Analysis course. Students implement numerical methods in MATLAB to design and 3D print a bottle. Employing mathematical methods to compute features of their designs enhances students' understanding of numerical techniques in design applications. Student feedback highlights the project's efficacy in demonstrating the intersection of mathematics and design, fostering creativity, and instilling a deeper appreciation for mathematical principles in real-world contexts. [ABSTRACT FROM AUTHOR]

Heat transfer improvement in industrial and engineering applications has attained a lot of interest from investigators in recent years. This is due to fact that the improvement of the much equipment in this sector such as electronic devices and heat exchangers are highly dependent on rate of the thermal transport. Due to their low thermal conductivity, base fluids such as oil, water, and ethylene glycol limit the heat transport rate. Because of its many functions in various manufacturing problems, hybrid convective heat transfer has piqued the interest of many engineers. The heat transfer flow with magnetic dipole features in hybrid nanoparticles $ ({\textrm{CuO} - \textrm{CoF}{\textrm{e}_\textrm{2}}{\textrm{O}_\textrm{4}}}) $ (CuO − CoF e 2 O 4 ) , $ ({\textrm{CoF}{\textrm{e}_\textrm{2}}{\textrm{O}_\textrm{4}}}) $ (CoF e 2 O 4 ) and used blood as a base fluid over a spreading sheet are investigated. The flow problem of PDEs is transmuted into structure of nonlinear ODEs by employing suitable similarity conversions. For numerical solution, bvp4c solver in MATLAB computing software is considered. Graphs depict the effects of important controlling flow parameters on velocity distribution and temperature profile. Furthermore, the table discusses the thermo physical characteristics of nanoparticles and hybrid nanofluids. [ABSTRACT FROM AUTHOR]

The submerging of nanoparticles in the base fluid is the latest technique to enhance the heat efficiency of regular fluids. The suspension of the solid particles and base fluid is known as nanofluid. The study of bioconvection properties in the incompressible flow of non-Newtonian Williamson nanoliquid with gyrotactic motile microorganisms through melting wedge with thermal radiation, thermal and exponential space-based heat source is analyzed by utilizing governing equations, i.e. continuity's equation, momentum's equation, temperature's equation, concentration's equation and microorganism's equation. The system that comprises differential equations of partial derivatives is restricted into an ordinary one via suitable similarity variables and then integrated numerically through powerful bvp4c solver with shooting algorithm in MATLAB. The developed mathematical method is focused on Brownian and thermophoresis diffusions with bioconvection. The effects of prominent parameters on the flowing characteristics are scrutinized from a physical point of view. The numerical results of modeled system are explored and mentioned in detail with the aid of tabular data. The results show that the velocity of the fluid is improved with an augmentation in mixed convective and melting parameters. It is concluded that the occurrence of magnetic field resists the flow of fluid and relevant boundary layer reduces. [ABSTRACT FROM AUTHOR]

A computer program was developed using the MATLAB programming language to simulate the electronics readout for a radiation detector system. The function of each stage of this system is described by a mathematical model in the Laplace domain. The electrical signals have been shown and analyzed at two main outputs of the system. They are described according to their related circuit parameters. The obtained results of the simulation can be used to achieve a best design of the concerned circuits and to provide appropriate details about the system operation. Validation of the simulated signals by comparison with available experimental results has been achieved. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous development of urban transportation, the durability of concrete bridge structures has become increasingly prominent. Many in-service concrete bridges exhibit issues such as cracks, excessive deflection, and a significant reduction in load-bearing capacity during their service life. To meet the requirements of sustainable development, it is urgent to assess the durability of bridges accurately. Therefore, this paper establishes a scientific and reliable evaluation index system for the durability of in-service concrete bridges. Based on a review of the literature and relevant standards and specifications, this paper establishes an evaluation index system for the durability of in-service concrete bridges and calculates the weights using a combination weighting method. The durability of the bridges is assessed using the extension cloud model theory, and MATLAB is utilized for efficient and accurate computation and analysis, ultimately determining the durability grades of the bridges. The method was applied to the durability assessment of five in-service concrete bridges, and the results were in high agreement with the measured data, proving its effectiveness and accuracy. The research results provide a scientific basis for the management and maintenance of concrete bridges, which can help extend their service life. Future studies will expand the sample to cover more bridge types to comprehensively assess and enhance the durability of bridges.Article Highlights: A scientific and reliable durability evaluation index system for in-service concrete Bridges has been established. Weight optimization: Based on grey correlation weight and entropy weight to find the combined weight. The perfect integration of extension theory and cloud model. [ABSTRACT FROM AUTHOR]

The rheological property of biodiesel is determined by the methods of production, which constitute an important role in atomization, combustion, performance, and emission. Surface tension has a major contribution to droplet size in atomization characteristics. Therefore, it is necessary to examine the surface tension to enhance atomization. The surface tension of six biodiesels, Karanja, jatropha, soybean, palm, sunflower, and rapeseed oil, was measured experimentally and compared with three mathematical models, including the MacLeod–Sugden model, the Gibbs free energy model, and the Dalton type mass average model using MATLAB software. One more parameter was added to the Gibbs free model, which showed results that were more similar to experimental results. The proposed models use the temperature range of 300 to 360 K on changes in biodiesel properties. The study showed that the Gibbs free energy model exhibited a 1.6% to 6.8% error, the MacLeod–Sugden model 3.2% to 12.1%, and the Dalton type mass average model 9.52% to 14.16% with experimentation. [ABSTRACT FROM AUTHOR]

Advanced Driving Assistance Systems (ADAS), such as collision avoidance systems and adaptive cruise control, are important features of autonomous driving and are gaining importance day by day in terms of increasing road safety. To increase the reliability of the system, virtual simulation environments are used during the design and development stages. This study examines the effect of ADAS features on energy parameters during the driving cycle in a virtual simulation environment. The discussion focuses on the simulation of an electric vehicle and the relationship between energy use and ADAS functions. For ADAS modeling and simulation, a dynamic model was developed in MATLAB and examined throughout the NEDC drive cycle. [ABSTRACT FROM AUTHOR]

For more sustainable shipping operation in coastal areas and port cities, shore side power (SSP) systems are attracting widespread interest as a solution to reduce ship auxiliary engine emissions, noise and vibration. The potential of these systems can be further improved by integrating renewable energy into the electricity grid. However, the majority of prior research has focused on investigating SSP systems for large ports in large shipping hub countries. Therefore, in this study, SSP technology is investigated for an inland waterway in Egypt on the Suez Canal utilising real ferries operational data. Green electricity from solar sunshade structures is generated for the SSP system utilising the Egyptian excellent solar energy potential. For this study, the ferry diesel generator, battery and solar systems are modelled in MATLAB/Simulink environment to investigate the proposed SSP system. Results indicate that the proposed SSP system could eliminate annually 1420 tonnes of emissions as well reduce the grid ${CO2}$ CO 2 emissions by 1204 tonnes through the green electricity supplied to the grid. Moreover, the cash flow and net present value analyses have shown good profitability with a payback period between 7.4 and 12 years. [ABSTRACT FROM AUTHOR]

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles. [ABSTRACT FROM AUTHOR]

Network Intrusion Detection Systems (NIDS) are a difficult task for determining in any managerial information system or IT sectors, if a user is a normal user or an attacker. The main objectives of the proposed system are to enhance operational efficiency, decreasing the occurrence of false positives, to minimize the time complexity of the process. It is an excellent way for dealing with various types of network problems. Research focusses the various classifiers are applied to detect various types of network assaults. Performance of network intrusion detection by two classifiers are used to compare the results. Probabilistic Neural Network (PNN) and Feed Forward Neural Network (FFNN) classifiers are employed this suggested study. The performance results comparison between full featured and reduced features are presented. MATLAB software application is applied to test the performance of both test and train dataset. Detecting network intrusions is a critical challenge within managerial information systems and the IT sector, as it involves the complex task of distinguishing between legitimate users and potential attackers. Maintaining a secure network environment is paramount to safeguarding sensitive information and operations. In the arena of network intrusion detection, the research predominantly revolves around the deployment of diverse classifiers to identify various types of network attacks. This paper, proposes the evaluation of two specific classifiers, the PNN and the FFNN, with the objective of comparing their performance in the context of network intrusion detection. We systematically assess their effectiveness in both full-featured and reduced-feature scenarios, utilizing MATLAB software to rigorously analyze their capabilities across test and training datasets. In essence, this research delves into the intricate realm of Network Intrusion Detection Systems (NIDS), investigating how the PNN and FFNN classifiers function in the critical role of safeguarding networks against a multitude of potential threats. Through comprehensive analysis, we aim to illuminate the most efficient approach to enhancing network security in the constantly evolving landscape of cybersecurity. As a result, it is recommended that FFNN approaches be adopted as a means of improving detection efficiency and reducing the False Positive Rate (FPR) in network intrusion detection systems. [ABSTRACT FROM AUTHOR]

Математические модели некоторых динамических процессов можно существенно уточнить, используя в них производные и интегралы нецелого порядка, учитывая эффекты, которые не описать с помощью обыкновенных производных. Так, например, с помощью дробных производных Герасимова-Капуто постоянного и переменного порядка можно учитывать эффект памяти в модели процесса, а порядок производной будет связан с интенсивностью процесса. В частности, авторами ранее разработана эредитарная α-модель объемной активности радона, где параметр α связан с проницаемостью среды. Однако возникает вопрос об определении оптимальных значений как α, так и других параметров модели. Для решения проблемы можно решать обратную задачу — распространенный тип задач во многих научных областях, где необходимо определить значения параметров модели на основе наблюдаемых данных, но невозможно провести прямые измерения этих параметров. Необходимость такого подхода часто возникает при работе с геологическими данными. В статье описывается программная реализация программного комплекса PRPHMM 1.0, способного восстанавливать оптимальные значения эредитарных математических моделей на основе производной Герасимова-Капуто. Адаптирован и реализован на языке MATLAB алгоритм безусловной оптимизации ньютоновского типа Левенберга-Марквардта. Реализованы подпрограммы для чтения, обработки и визуализации экспериментальных и модельных данных. Приводится тестовый пример, решающий на основе экспериментальных данных радонового мониторинга обратную задачу для эредитарной α-модели на параметры α и λ0-коэффициент воздухообмена. Показано, что PRPHMM 1.0 позволяет для эредитарных математических моделей на восстанавливать значения параметров, близкие к оптимальным.

Формулювання проблеми. Формування навичок застосування класичних математичних інструментів при розв’язанні реальних проблем є однією із задач викладання математичних дисциплін у ЗВО та ВВНЗ. Це вимагає постійного поповнення бази сучасних прикладних задач. Значна частина їх не має "красивих" розв’язків та вимагає застосування програмного забезпечення. Виникає проблема об’єднання теоретичної математичної бази, прикладного застосування та використання інформаційних технологій. Для цього доцільно проводити інтегровані заняття з математики, спеціальності та комп’ютерних наук. Матеріали і методи. Для виконання дослідження використано стохастичний підхід до математичного моделювання бою, який полягає в побудові графу станів марковського процесу із вказанням інтенсивностей переходу від стану до стану та відповідної системи диференціальних рівнянь Колмогорова. Для побудови програми у системі MATLAB використано вбудовані функції для розв’язання диференціальних рівнянь з початковими умовами та для знаходження границь функцій. Результати. В роботі надано розробку інтегрованого заняття професійного спрямування з математичного моделювання “високоорганізованого” бою Ланчестера. Детально описано розв’язання задачі стохастичним підходом для початкових значень у найпростішому випадку. Представлено рекомендації для самостійної побудови курсантами (студентами) алгоритму розв’язання в MATLAB для більш складних випадків. Висновки. Проведення інтегрованого заняття підвищує зацікавленість курсантів у вивченні математики та застосуванні її інструментів у професійній діяльності. Детальний опис розв’язання розглянутої в роботі моделі Ланчестера можна використовувати для побудови та розв’язання подібних стохастичних моделей у військовій справі, економіці, інженерії та ін. Запропоноване інтегроване заняття може бути впроваджене при вивченні таких математичних дисциплін, як "Теорія ймовірностей", "Теорія випадкових процесів" та "Системи масового обслуговування".

Abstract With the chemical formula CaCl2, calcium chloride is a salt as well as an inorganic material. At room temperature, it has the consistency of a white, crystalline solid and is very water-soluble. It can be created by neutralizing calcium hydroxide with hydrochloric acid. Calcium chloride is a solution with a large enthalpy change. It is extensively utilized in research facilities, manufacturing facilities, and pharmaceuticals, including all types of food-graded applications, the treatment of acute illnesses, packaging for drying tubes, dust controllers, and de-icing, among other uses. In this paper, firstly we compute the topological indices, coindices, and reverse indices of CaCl2. Further, we employ machine learning strategies to capture the best suitable set of indices for the proximity of the prediction of distinct physio-chemical properties of CaCl2. To strengthen the results, different regression techniques are implemented to predict HOF of CaCl2 based on our features, and the most influential features were detected to verify our results.

Hydraulic pumps are the heart of hydraulic systems that convert electrical energy into mechanical energy. As the behavior of a specific type of hydraulic pump must be identified to cater to the pump's limits and operating range, precise pump selection for particular applications is a must. Hence, this paper presents the research findings on the characteristics of hydraulic pumps, focusing on pump parameters such as pump power, flow rate, and operating pressure. These parameters were evaluated under no-load operating conditions without actuators. The simulation setup is realized through Simscape on the MATLAB platform. The laboratory test rig exploits the advantages of Bosch Rexroth hydraulic pumps and other hydraulic components manufactured by the Rexroth group of companies. The findings from this research find a relationship between pump power, pressure, and flow rate. The simulation and experimental results showed good agreement with a very slight percentage difference, where the factors that contribute to the slight tolerance are thoroughly elaborated.

Abstract The authors present a model for diagnosing motor faults based on machine learning, demonstrating advantages over other algorithms in terms of both improved fitness values and reduced running time. The structure of the model involves three primary phases: feature extraction, feature selection and classification. During the feature extraction phase, crucial features are identified using empirical mode decomposition, fast Fourier transform and multiresolution analysis, resulting in a total of 144 features. The feature selection stage employs a new strategy that combines symmetrical uncertainty in the filter approach with the binary grey wolf optimiser and emperor penguin optimiser in the wrapper approach. Finally, a support vector machine is used for classification to generate fitness values. To validate the model's effectiveness and accuracy, motor fault current signal datasets, case Western Reserve University (CWRU) benchmark datasets and mechanical failure prevention technology benchmark datasets are utilised. In the motor fault current signal dataset, the highest average accuracy achieved is 99.95%, with a minimum average running time of 88.02 s obtained under ∞dB conditions. Regarding benchmark datasets and mechanical failures at CWRU, using the prevention technology benchmark dataset resulted in classification accuracies of 99.54% and 99.52%, respectively. Comparative analysis with traditional algorithms reveals that symmetric uncertainty and emperor penguin–grey wolf optimisation model outperforms traditional models in terms of performance.

Abstract A novel method is presented for determining the derating factors of a three‐phase induction motor under the condition of the unbalanced supply voltages. In this method, a mechanical system is used which consist of the a centrifugal pump, two valves, a DC motor, which are connected to the shaft of the three‐phase induction motor. A sliding mode control system is used for position control of the DC motor for adjusting the valve angle for derating the induction motor. The authors present the results of an experiment in which a three‐phase induction motor was subjected to various unbalanced voltage conditions. The results of simulations were used to look into what happened when there were different levels of imbalanced voltage. This was done to determine how these situations changed an induction motor's speed, torque, and efficiency. For this system, the stator current would be greater than the rated current if there was an imbalance in the supply voltage. Therefore, to reduce the amount of power that the three‐phase induction motor can produce, the control system uses a DC motor to reduce the angle of one of the two valves. This decreasing angle continues until the root mean square value of the stator current returns to the rated current. At this point, the derating factor may be calculated by dividing the output power of the three‐phase induction motor in the unbalanced condition by the output power when there are ideal sinusoidal. The MATLAB SIMULINK environment is utilised to perform simulations of the proposed system.

Abstract In recent years, with the continuous development of urban transportation, the durability of concrete bridge structures has become increasingly prominent. Many in-service concrete bridges exhibit issues such as cracks, excessive deflection, and a significant reduction in load-bearing capacity during their service life. To meet the requirements of sustainable development, it is urgent to assess the durability of bridges accurately. Therefore, this paper establishes a scientific and reliable evaluation index system for the durability of in-service concrete bridges. Based on a review of the literature and relevant standards and specifications, this paper establishes an evaluation index system for the durability of in-service concrete bridges and calculates the weights using a combination weighting method. The durability of the bridges is assessed using the extension cloud model theory, and MATLAB is utilized for efficient and accurate computation and analysis, ultimately determining the durability grades of the bridges. The method was applied to the durability assessment of five in-service concrete bridges, and the results were in high agreement with the measured data, proving its effectiveness and accuracy. The research results provide a scientific basis for the management and maintenance of concrete bridges, which can help extend their service life. Future studies will expand the sample to cover more bridge types to comprehensively assess and enhance the durability of bridges.