Mathematical Calculations Research Articles

A Robust Decision-Making Model for Medical Supplies via Selecting Appropriate Unmanned Aerial Vehicle

Recently, Unmanned Aerial Vehicles (UAVs) have been used in many fields, including the field of health care, especially in delivering the necessary medical equipment and supplies, due to the many advantages they have compared to other traditional methods and the presence of different types of UAVs, to improve healthcare and provide it with the medical supplies and equipment necessary to save the lives of patients. Choosing the appropriate UAV for a specific situation represents a problem facing decision-makers, which is considered a multi-criteria decision-making problem. Since the decision-making process is cumbersome and complex, and deals with uncertainty and ambiguity. In this research, we proposed multi-criteria decision-making (MCDM) model using CRITERIA (Criteria Importance through Intercriteria Correlation) and MARICA (Multi-Attribute Rating Analysis with Ideal Concepts) methods integrated with neutrosophic logic, which is considered a powerful tool in dealing with uncertainty and ambiguity. The CRITIC method calculates the weight of criteria, whereas it takes into account the correlations and relationships between the criteria, whether they are positive or negative, unlike other methods that consider the criteria separately, which allows for a more accurate and comprehensive analysis of the decision problem. The MARICA method is used also to rank the alternatives. It allows decision-makers to evaluate alternatives according to how well they perform across multiple criteria by considering several factors at once. This helps increase the effectiveness of judgments by taking into account all relevant factors. Moreover, MARICA is a user-friendly method that doesn't require complex mathematical calculations, making it accessible to anyone who wants to make sound choices. The UAV with the highest ranking is the one that will be chosen and represents the best among the alternatives. The proposed model proved its effectiveness by applying it to an experimental case.

Menumbuhkan Kreativitas Matematika melalui Pendekatan Computational Thinking

This study aims to explain the implementation of computational thinking-based mathematics learning at SMA Negeri 2 Medan. This research is classified as qualitative descriptive research. Data sources used in this study include the learning process, informants from Mathematics teachers of SMA Negeri 2 Medan, teaching modules, and field notes about the learning process based on computational thinking. Data collection techniques include observation, interviews, and document analysis. The results of this study show that computational thinking-based mathematics learning is able to increase teacher creativity in teaching, increase students' thinking to be open to the surrounding environment which is actually closely related to mathematics, and be able to solve problems structurally and computational thinking-based learning gets a good response by students

SOFTWARE OPTIMIZATION OF RESOURCE ALLOCATION PROCESS IN METRO CONSTRUCTION ORGANIZATIONS: ANALYSIS OF EFFICIENCY OF EXISTING METHODS

This article explores the objectives of optimal resource allocation in metro construction organizations, including improving work efficiency, reducing project duration, and minimizing costs. The primary optimization methods, such as mathematical modeling, critical path analysis, and the use of network graphs, are discussed in the context of achieving these goals. The role of project management software in providing comprehensive control over planning, resources, and execution is emphasized. Additionally, the article underscores the significance of utilizing the Python programming language and its libraries, such as Pandas, Scikit-learn, and Matplotlib, in analyzing and optimizing resource allocation in metro construction. The paper contains a case study of a software implementation of risk analysis of delivery delays in metro construction using the Python programming language and its key libraries. Pandas for data processing and analysis, NumPy for performing complex mathematical calculations, and Matplotlib for visualizing the results were used.

Data envelopment analysis for identification of bank branches appropriate locations in terms of their efficient operation

One of the main banking activities is the branch network. It provides the effective banking, the profitability. Choosing the right location of branches is very important for the bank effective operation. Random or intuitively locations can have a negative impact on key performance indicators in the future. The correct and thoughtful choice of the bank branch location ultimately determines its maximum efficiency. The paper concerns with the determining of the bank branch location in terms of thorough mathematical calculation and many factors influencing bank activity. We developed a special calculation method using the DEA (Data Envelope Analysis) methodology, considering financial, branch location, location features, etc. The paper dwells on the issue of one large branch effectiveness comparing to several smaller branches covering a wider consumer area on the example of the branch on A. Sharifzade street, Baku, Azerbaijan. Indeed, it is important to consider the location in terms of its geolocation and landscape. However, one of the main issues is expediency of establishing one large or several small branches in densely populated areas. Moreover, it is necessary to take into account the infrastructure to improve the bank performance. To consider this issue, we use both hypothetical and statistical data of Capital Bank. It is one of the largest banks in Azerbaijan. Also it has a large network of branches. The article examines expediency of establishing one large or several small branches in the area close to the existing branch of Capital Bank on A. Sharifzade street. The efficiency of the current operating branch was compared with the total efficiency of virtual branches in several combinations. We analysed the indicators of the Inshaatchylar branch and virtual branches in different combinations in different locations in accordance with DEA CRS (Constant Return to Scale) method. Additionally, we determined the effectiveness of both the Inshaatchylar branch and virtual branches in various combinations. Thus, the highest score is the most effective one.

Design of Radial Distribution System to Study Load-Flow and Short Circuit Analysis Using ETAP Software

This work's analysis provides information on fault analysis and load flow studies, which help to understand how radial distribution networks operate both in normal and emergency circumstances. The research explores optimization methods, describing capacitor placement, transformer size, and feeder reconfiguration in order to lower losses and increase system efficacy. Moreover, the work looks at reliability-focused maintenance techniques, stressing the need of predictive maintenance and condition monitoring in raising the dependability of radial distribution networks. Modern electrical power networks depend heavily on their radial distribution system, which effectively and dependable distributes electricity to customers. Many radial distribution system-related topics are covered in detail on this website, including analytical methods, optimization processes, and dependability enhancement strategies. Power flow, voltage control, and losses in radial networks are investigated in this paper under a range of operational situations. The last and crucial stage of a power system, the electrical distribution system makes sure that customers have a steady supply of electricity. Regretfully, because it is often neglected and exposed to environmental risks, our distribution network has a number of problems. Our distribution feeder is rigorously analyzed for load flow and short-circuits in order to overcome these challenges and find and fix problems. The main topics of this study are the load flow and short-circuit analysis of a radial distribution feeder, which is mostly constructed with (ETAP). The final and crucial part of a power system, the electrical distribution system ensures that consumers receive an uninterrupted and steady supply of electricity. Our distribution feeder undergoes a rigorous and cautious process to address these issues front on because of the uncontrollable nature and susceptibility of our network to environmental hazards. This paper delves deeply into the issues of short-circuiting and load flow in radial distribution feeders. Both symmetrical and asymmetrical faults are thoroughly investigated by mathematical computations and ETAP simulations; the results of the software simulations are contrasted with those of the mathematical analysis. These discoveries will definitely influence the development and management of our system and point us in the direction of workable and practical solutions.

Penerapan pembelajaran STAD berbantuan media PANAMA untuk meningkatkan keterampilan berhitung

This research aims to determine the improvement of Mathematics calculation skills by implementing STAD learning assisted by PANAMA media (Mathematics Napier Board). This research is Classroom Action Research (PTK) with the research subjects being 27 class IV students at SD Negeri Depok 1, Sleman. The object of this research is mathematical calculation skills in the arithmetic operations of multiplication and division. The instrument used in this research was test questions supported by observation sheets. The results of the research at the pre-cycle stage showed a class average of 68.07 with the number of students completing only 10 students or 37.0%. These results increased significantly after taking action in cycle I with a class average of 81.18 and in cycle II the class average was 90.59. In cycles I and II, it was seen that there was an increase in Mathematics calculation skills by 22.2% with the number of students who achieved KKTP at the end of cycle II being 23 students or 85.1%. Based on these results, it can be concluded that the application of STAD learning assisted by PANAMA media) can improve calculation skills, especially in the calculation operations of multiplication and division.

A Numerical Study on Dust Control: Evaluating the Impact of Spray Angle and Airflow Speed in the Coalescence of Droplets and Dust

Spray dust reduction is one of the most economical and effective technologies for controlling coal dust in coal mining faces. We aimed to reproduce a spray dust reduction process in a simulation and investigate the mechanism by which the spray angle and airflow speed influence the dust reduction effect. Based on the DPM (discrete phase model) and the mixture model, we constructed a spray dust reduction evaluation model by considering two-way momentum coupling between the discrete phase and the continuous phase. The results showed that installing nozzles near the dust source (coal mining drum) significantly reduced the dust concentration at the coal mining face from 0.0005 kg/m3 to 0.0001 kg/m3. The increase in airflow speed and spray angle enhanced the horizontal transportation of droplets and dust, providing opportunities for the droplets to condense the dust; however, if the droplets have too large an angle, this will result in an insufficient concentration of droplets in the vicinity of the dust source. When the spray angle is 45°, increasing the airflow speed provides a better dust reduction effect. The nozzle position should also be set scientifically according to the airflow speed. Based on simulation results, a mathematical calculation model of spray dust reduction efficiency was constructed. These results can guide the key parameters of spray dust reduction systems, such as the installation position of the nozzle, the spray angle, and the airflow speed. This paper provides ideas for simulating spray dust reduction for other dust types.

The Michaelis–Menten Reaction at Low Substrate Concentrations: Pseudo-First-Order Kinetics and Conditions for Timescale Separation

We demonstrate that the Michaelis–Menten reaction mechanism can be accurately approximated by a linear system when the initial substrate concentration is low. This leads to pseudo-first-order kinetics, simplifying mathematical calculations and experimental analysis. Our proof utilizes a monotonicity property of the system and Kamke’s comparison theorem. This linear approximation yields a closed-form solution, enabling accurate modeling and estimation of reaction rate constants even without timescale separation. Building on prior work, we establish that the sufficient condition for the validity of this approximation is s0≪K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$s_0 \\ll K$$\\end{document}, where K=k2/k1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K=k_2/k_1$$\\end{document} is the Van Slyke–Cullen constant. This condition is independent of the initial enzyme concentration. Further, we investigate timescale separation within the linear system, identifying necessary and sufficient conditions and deriving the corresponding reduced one-dimensional equations.

REFORMS: Consensus-based Recommendations for Machine-learning-based Science.

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear recommendations for conducting and reporting ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist (recommendations for machine-learning-based science). It consists of 32 questions and a paired set of guidelines. REFORMS was developed on the basis of a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility.

A Comparative Study of Two Smartphone Applications and a Traditional Orthodontic Model Analysis Method

Introduction An orthodontic study used to determine whether teeth are positioned appropriately and whether there is an opportunity for orthodontic therapy or tooth extractions by model analysis. While certain manual model studies can be laborious and time-consuming, there are smartphone applications that can simplify the mathematical calculations necessary for orthodontic cast model analysis.  Objective The purpose of this research is to compare the outcomes and processing times of model analysis utilizing two smartphone apps, such as Model Analysis App and iModel Analysis, and the traditional technique.  Materials And Methods This is a comparative analytic study. The samples are made up of thirty dental casts that underwent several model studies, including as the Boltons, Ashley-Howe, Carey’s and Arch Perimeter assessments. A comparison in results and time was carried out for these model analyses using three methods - conventional method, iModel Analysis App and Model Analysis App.  Results The conventional technique gave results for a Bolton's overall ratio analysis of 3.3967±3.44579, iModel Analysis produced results of 3.4333±3.77709, while the Model Analysis app produced results of 4.6200±6.83744. The conventional technique yielded results for Bolton's anterior ratio analysis of 4.9133±5.81969; iModel Analysis produced results of 4.7700±5.57743; and the Model Analysis app produced results of 4.7500±5.59068. The results of arch perimeter discrepancy analysis for conventional method were 4.0000±3.15135, those for iModel Analysis was 4.0000±3.15135, and for Model Analysis app it was 4.0000±3.15135. The results of Carey’s discrepancy analysis for conventional method were 3.8667±2.59620, those for iModel Analysis was 3.8667±2.59620, and for Model Analysis app it was 3.8667±2.59620. The results of Ashley - Howe PMBAW% analysis for conventional method were 44.0200±3.71255, those for iModelAnalysis was 42.1367±5.74531, and for Model Analysis app it was 43.8133±3.71787. The results of Pont’s expansion analysis for conventional method, iModel Analysis and for Model Analysis app showed p value 0.114 (not significant = p>0.05). The results of period required for the analysis by conventional method were 903.3000±30.19951, while those for iModelAnalysis was 399.2000±42.94375, and for Model Analysis app it was 392.0333±35.58233.  Conclusion There was no critical distinction within the results of the examinations done by the different methods. However, there was a noteworthy contrast within the time duration required to carry out the examination by conventional methods and that of iModelAnalysis and Model Analysis App.

Modelling and assessing new SME digital business status for visualising virtual economics and sustainability economic indicators: Empirical evidence from poultry business

SMEs are generally recognised as financial contributors to regional economic development. Despite the enormous contributions of digital SMEs to sustainable economic growth and regional development have not yet been reported. This paper assesses and models SME businesses to gain a digital business status and model. These days, visualising economic growth is challenging and powerful to identify business performance and realise sustainable regional development. This research analyses empirical data obtained from a poultry business to form a standard model for implementing a new SME digital business model and status. This standard model is a guideline to measure the other SME businesses and productions. Localisation of the SDGs was analysed using thematic analysis. Mathematical computation is used to visualise virtual economic growth geographically. Hence, our digital business model and status demonstrated business performance, management, and economic growth.

A generalized risk assessment index for forecasting insect population under the effect of temperature

Life history traits have been studied under various environmental factors, but the ability to combine them into a simple function to assess pest response to climate is still lacking complete understanding. This study proposed a risk index derived by combining development, mortality, and fertility rates from a stage-structured dynamic mathematical model. The first part presents the theoretical framework behind the risk index. The second part of the study is concerned with the application of the index in two case studies of major economic pest: the brown planthopper (Nilaparvata lugens) and the spotted wing drosophila (Drosophila suzukii), pests of rice crops and soft fruits, respectively. The mathematical calculations provided a single function composed of the main thermal biodemographic rates. This function has a threshold value that determines the possibility of population increase as a function of temperature. The tests carried out on the two pest species showed the capability of the index to describe the range of favourable conditions. With this approach, we were able to identify areas where pests are tolerant to climatic conditions and to project them on a geospatial risk map. The theoretical background developed here provided a tool for understanding the biogeography of Nilaparvata lugens and Drosophila suzukii. It is flexible enough to deal with mathematically simple (N. lugens) and complex (D. Suzukii) case studies of crop insect pests. It produces biologically sound indices that behave like thermal performance curves. These theoretical results also provide a reasonable basis for addressing the challenge of pest management in the context of seasonal weather variations and climate change. This may help to improve monitoring and design management strategies to limit the spread of pests in invaded areas, as some non-invaded areas may be suitable for the species to develop.

The capacity of Plectranthus sp. to perform phytoremediation on heavy metal mercury (Hg) in the tailing of gold processing industries

Sludge (tailing), a waste product of the amalgamation process used in the mining of gold, contains a high concentration of the heavy metal mercury (Hg). It is well known that mercury builds up in large quantities in Plectranthus sp. herbaceous plants in the Pancurendang Village Small-Scale Gold Mining area. The study intends to: 1) quantify the amounts of mercury present in plant organs and media tailing during phytoremediation treatment; 2) examine the mechanisms and capacities of plant phytoremediation using values of the Biological Accumulation Coefficient (BAC), Biological Concentration Factor (BCF), and Translocation Factor (TF); and 3) determine the impact of percentage variations in tailing and time on Plectranthus sp.’s capacity to accumulate mercury. On a lab scale, phytoremediation of tailings is done with concentrations of 0%, 30%, 50%, and 100%. Planting medium is combined with dried tailings. Pots are used for phytoremediation, and the residence times are 10, 20, and 30 days. With the use of a mercury analyzer, the amount of mercury present in plant media, roots, and shoots is determined. Using the BAC, BCF, and TF values, the mathematical calculation of phytoremediation ability is performed. Hg content ranged from 1.82 to 16.23 ppm in shoots and 5.15 to 37.19 ppm in roots. Throughout the course of treatment, the tailings Hg concentration varied between 13.18 and 220.05 ppm. Plectranthus sp. is categorized as a medium accumulator with a phytoextraction mechanism, according to the analysis’s findings. Hg phytomining is possible with Plectranthus sp., the TF value is 2.38. Within 10 days, Plectranthus sp. most effectively accumulates 50% Hg in tailings.

Morphological analysis of ballast particles: Characterization and simplified analysis of particle morphology using imaging data

This paper introduces a novel algorithm for the rigorous characterization of three-dimensional (3D) particles, particularly for railway ballast. Degraded railway ballast must be replaced with fresh material for efficient functioning. This study examined the shape and form of degraded (used) ballast to guide future maintenance efforts. Laboratory-generated used ballast, obtained via the Los Angeles abrasion test, was compared to fresh ballast. Thirteen fundamental morphological parameters of fresh and used ballasts were investigated by utilizing the shape information obtained through 3D scanning. The algorithm efficiently processed datasets comprising multiple irregular particles and monitored the morphological characteristics of ballasts based on the shape of the particles. The trimesh library was imported for 3D processing, facilitating the mathematical calculation of diverse parameters using the developed algorithm. The algorithm also incorporated mechanisms for simultaneously storing parameters provided in various 3D configuration models. With the support of the trimesh library, a morphology analyzer was used to analyze various 3D model file formats, such as .stl, .obj, and csg. This method demonstrated its efficacy with reduced runtime and computation cost. Thus, the proposed algorithm has emerged as a valuable resource for researchers investigating the influence of ballast particle shape on the mechanical behavior of granular assemblies.

Comorbid Word Reading and Mathematics Computation Difficulty at Start of First Grade.

The purpose of this analysis was to describe cognitive processes associated with comorbid difficulty between word reading (WR) and mathematics computation (MC) at the start of first grade among children selected for WR and MC delays. A sample of 234 children (mean age 6.50 years, SD = 0.31) was assessed on WR, MC, core cognitive processes (phonological processing, rapid automatized naming, verbal counting [VC]), and domain-general cognitive processes (working memory, oral language, nonverbal reasoning, attentive behavior). Structural equation modeling was used to predict a latent Comorbidity factor, which modeled shared variance between WR and MC, and to identify processes associated with that Comorbidity factor. Results identified each of the core cognitive processes, especially VC, and each of the domain-general cognitive processes, especially working memory, as explaining shared variance between WR and MC. Implications for understanding comorbid difficulty at the start of first grade and designing coordinated first-grade interventions are discussed.

Applicability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to Oil and Gas Research

Anomaly detection in equipment processes is crucial for the oil and gas sector. Algorithms for detecting anomalies in measured data are best understood in Computer Science and Mathematics. Therefore, a possible transfer of knowledge from the latter area to the former can have a profound impact. This paper explores the potential for the knowledge transfer by analyzing bibliometric data of Computer Science and Mathematics papers published in MDPI journals, as well as publications available on SPE search platform. The research shows that the main algorithms extensively studied in MDPI publications and found in SPE publications, which address the anomaly detection problem, include Random Forest, Support Vector Machine, Long-term Memory Method and Recurrent Neural Network. The main advantages and disadvantages of these methods are briefly described. This paper provides examples of classical and highly cited publications that describe the work of these algorithms along with examples of articles that illustrate their application in the oil and gas industry. The sections of SPE disciplines with the largest number of publications using the algorithms that are frequently used for anomaly detection are presented.

Numerical treatment of linear Volterra integro differential equations using variational iteration algorithm with collocation

To numerically solve the linear Volterra integro-differential equation, this study employs fourth-kind Chebyshev polynomials and the variational iteration algorithm with collocation, which is a combination of the variational iteration strategy and collocation technique. By applying fourth-kind Chebyshev polynomials to the variational iteration method with collocation for solving Volterra integro-differential equations, mathematical problems with a broad range of multidisciplinary applications are addressed, and numerical techniques that produce more accurate and efficient results are developed. The recommended method is then used, and the fourth-kind Chebyshev polynomials generated for the given integro-differential equation serve as the trial functions for the approximation. As a result, the suggested method's significance probably goes beyond a particular equation or application, as it contributes to the larger field of mathematical modeling and numerical analysis. Research methods employing a variational iteration algorithm with collocation aim to provide general techniques that can be applied to a wide range of problems. Additionally, numerical examples were provided to highlight the applicability and dependability of the proposed methodology. The mathematical computations were carried out using the Maple 18 software.

Use of dynamic simulation and Forrester diagrams to describe the growth of lettuce (Lactuca sativa L.) under field conditions

The use of computational tools to describe some processes of crop growth has evolved in recent decades and remains an area of active research, where more and more applications are incorporated with the integration of a greater number of mathematical tools, statistics, and computational calculation efficiency, simplifying the tasks of modeling and visualizing the components of the system used. Current research proposes a dynamic growth model for lettuce cultivation using Forrester diagrams to evaluate different scenarios involving five growth functions and five lettuce cultivars in field conditions of the Bailadores region (Venezuelan Andes, 2550 m a.s.l.). The lettuce variety Coastal Star achieved the greatest accumulation of dry matter used as a response in each model. The logistics of growth function was properly adjusted to the experimental data compared to the other models. The proposed diagram model can be used as a basis for the construction of more complex models that incorporate other physiological variables of the crop and the growth environment.

Handwritten Calculator Using Contour Detection

This project introduces a novel Handwritten Calculator that merges traditional handwritten calculations with modern computing techniques. By leveraging computer vision, machine learning, and human-computer interaction, the calculator interprets handwritten digits and operators to perform mathematical calculations. Through contour detection and Convolutional Neural Network (CNN) model prediction, the calculator offers users a seamless and intuitive way to compute. The project explores the development, applications, and impact of this innovative technology, with potential implications for education, accessibility, and software engineering. The Handwritten Calculator represents a significant advancement in computational methods, bridging the gap between analog and digital approaches to calculation.

Using Mathematical Models and Extensions to Solve a Robot’s Equidistant Returning Path

The main purpose of this project is to study the Rusty the Robot problem where a robot travels equidistantly from the starting point back to the starting point. We mainly use mathematical models such as trigonometric functions, recursive sequences, and mathematical induction to explore, deduce and demonstrate findings, while using mathematical computer software for calculation and verification. In the beginning, Geogebra was used to explore the original question and allow us to obtain some properties and results. Then, the topic was extended and the relationship between the number of steps and angles was explored, as well as the recursive relationship between the landing points during the travel process. Next, we altered the starting positions of Rusty to see how this affects our findings. Finally, the two intersecting straight lines are extended into three straight lines intersecting at the same point. Based on the angles between them, we discuss the number of returning paths from the starting point to the starting point in an equidistant traveling method. Based on the mathematical model constructed, this study constructs a return path diagram for the robot to return to the starting point in an equidistant manner according to different starting point positions and obtains the recursive relationship of the relative positions on this return path. During the research process of this study, some interesting mathematical theories were obtained. It is expected that these results can be applied to related fields of AI robot movement patterns in the future.