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The purpose of this paper is to explore students' mathematization through a flow of lessons using the Thailand Lesson Study Incorporated Open Approach (TLSOA) to improve the excellence of instruction.

A total of 16 Grade 4 students were selected because they have been taught using the TLSOA model for four years. Six Lesson Study (LS) team members participated, and two instruments were utilized, namely student worksheets, and field notes. An ethnographic research design was employed.

The results revealed that the students' mathematical ideas were developed from the real world to the mathematical world through a flow of lessons based on the four phases of the Open Approach (OA).

Firstly, the students demonstrated their ability to represent the real world independently when the teacher posed an open-ended problem. Secondly, the students demonstrated their ability to use semi-concrete aids to develop their ideas while self-learning. Thirdly, the students showed how they developed their ideas to solve the open-ended problem using relevant objects or related concepts as part of a whole-class discussion and comparison exercise. Finally, the students demonstrated their abilities to represent the mathematical world using numbers and symbols to communicate their ideas when they were required to make a summary by connecting their mathematical ideas.

This study adds new insight to the literature on students' mathematization using the TLSOA model.

Efficient management of earthmoving equipment is critical for decision-makers in construction engineering management. Thus, the purpose of this paper is to prudently identify, select, manage and optimize the associated decision variables (e.g. capacity, number and speed) for trucks and loaders equipment to minimize cost and time objectives.

This paper addresses an innovative multiobjective and multivariable mathematical optimization model to generate a Pareto-optimality set of solutions that offers insights of optimal tradeoffs between minimizing earthmoving activity’s cost and time. The proposed model has three major stages: first, define all related decision variables for trucks and loaders and detect all related constraints that affect the optimization model; second, derive the mathematical optimization model and apply the multiobjective genetic algorithms and classify all inputs and outputs related to the mathematical model; and third, model validation.

The efficiency of the proposed optimization model has been validated using a case study of earthmoving activities based on data collected from the real-world construction site. The outputs of the conducted optimization process promise the model’s originality and efficiency in generating optimal solutions for optimal time and cost objectives.

This model provides the decision-maker with an efficient tool to select the optimal design variables to minimize the activity's time and cost.

The purpose of this study is to present a general review that provides an overview of the concept of sustainability and the effectiveness of mathematics curricula in courses where deeper work on economic and environmental sustainability has become central.

A qualitative methodology consisting of a review based on a pre-defined systematic method was used to exhaustively search and identify the most relevant answers to the research question: What is the role of mathematics to sustainability? To facilitate answering such a broad question, several concrete questions were formulated. Answers from published and unpublished documents were analysed. The quality of the extracted data was assessed, and the results were synthesized.

It was concluded that, on the one hand, the discipline of mathematics has much to contribute to solving the problems of sustainability; on the other hand, new mathematics is appearing stimulated by new challenges.

This work presents social implications in an innovative way. It allows for an increase in educational sustainability by bringing the academic community closer to the business world and the challenges of society and, furthermore, by having a major impact on the motivation of teachers and students to develop cooperative work within university institutions.

The originality is based on an a priori analysis for the construction and implementation of didactic tools for university teacher training in the area of mathematics within the framework of sustainable development, both economically and environmentally.

The stress concentration factor (SCF) is commonly utilized to assess the fatigue life of a tubular T-joint in offshore structures. Parametric equations derived from experimental testing and finite element analysis (FEA) are utilized to estimate the SCF efficiently. The mathematical equations provide the SCF at the crown and saddle of tubular T-joints for various load scenarios. Offshore structures are subjected to a wide range of stresses from all directions, and the hotspot stress might occur anywhere along the brace. It is critical to incorporate stress distribution since using the single-point SCF equation can lead to inaccurate hotspot stress and fatigue life estimates. As far as we know, there are no equations available to determine the SCF around the axis of the brace.

A mathematical model based on the training weights and biases of artificial neural networks (ANNs) is presented to predict SCF. 625 FEA simulations were conducted to obtain SCF data to train the ANN.

Using real data, this ANN was used to create mathematical formulas for determining the SCF. The equations can calculate the SCF with a percentage error of less than 6%.

Engineers in practice can use the equations to compute the hotspot stress precisely and rapidly, thereby minimizing risks linked to fatigue failure of offshore structures and assuring their longevity and reliability. Our research contributes to enhancing the safety and reliability of offshore structures by facilitating more precise assessments of stress distribution.

Precisely determining the SCF for the fatigue life of offshore structures reduces the potential hazards associated with fatigue failure, thereby guaranteeing their longevity and reliability. The present study offers a systematic approach for using FEA and ANN to calculate the stress distribution along the weld toe and the SCF in T-joints since ANNs are better at approximating complex phenomena than standard data fitting techniques. Once a database of parametric equations is available, it can be used to rapidly approximate the SCF, unlike experimentation, which is costly and FEA, which is time consuming.

The purpose of this study is to determine the impact of two-phase power law nanofluid on a curved arterial blood flow under the presence of ovelapped stenosis. Over the past couple of decades, the percentage of deaths associated with blood vessel diseases has risen sharply to nearly one third of all fatalities. For vascular disease to be stopped in its tracks, it is essential to understand the vascular geometry and blood flow within the artery. In recent scenarios, because of higher thermal properties and the ability to move across stenosis and tumor cells, nanoparticles are becoming a more common and effective approach in treating cardiovascular diseases and cancer cells.

The present mathematical study investigates the blood flow behavior in the overlapped stenosed curved artery with cylinder shape catheter. The induced magnetic field and entropy generation for blood flow in the presence of a heat source, magnetic field and nanoparticle (Fe₃O₄) have been analyzed numerically. Blood is considered in artery as two-phases: core and plasma region. Power-law fluid has been considered for core region fluid, whereas Newtonian fluid is considered in the plasma region. Strongly implicit Stone’s method has been considered to solve the system of nonlinear partial differential equations (PDE’s) with 10–6 tolerance error.

The influence of various parameters has been discussed graphically. This study concludes that arterial curvature increases the probability of atherosclerosis deposition, while using an external heating source flow temperature and entropy production. In addition, if the thermal treatment procedure is carried out inside a magnetic field, it will aid in controlling blood flow velocity.

The findings of this computational analysis hold great significance for clinical researchers and biologists, as they offer the ability to anticipate the occurrence of endothelial cell injury and plaque accumulation in curved arteries with specific wall shear stress patterns. Consequently, these insights may contribute to the potential alleviation of the severity of these illnesses. Furthermore, the application of nanoparticles and external heat sources in the discipline of blood circulation has potential in the medically healing of illness conditions such as stenosis, cancer cells and muscular discomfort through the usage of beneficial effects.

This study aims to perform innovation analysis for a product based on market, design and process dimensions. This integrated approach provides sustainability for product design and development.

A significant aspect of innovation is investigated to provide energy from the wastes collected in the reverse chain. First, the indicators related to the product opportunity gap were collected and ranked by the structural equation modeling (SEM) method. Indicators with a factor loading above 0.6 are selected and inserted into the proposed mathematical model. The proposed mathematical model was implemented in GAMS 28.2.0 to maximize energy production from waste and minimize the cost of product innovation. A case study on pistachio new packaging process innovation is investigated.

The results showed that in today’s competitive world where sustainability and the environment are important, the index of converting waste into energy is one of the main indicators of innovation. Consequently, flammability is extracted from the mathematical model as one of the most significant indicators leading to higher energy production with the lowest innovation cost.

New product development (NPD) is significant to sustain market share and satisfy customer needs. Different approaches are proposed to handle NPD, mostly focusing on the customer and design requirements.

This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.

A new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.

According to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.

An integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

The next generation of mobility is arising, and various challenging mobilities have entered the limelight. One of the most exciting of these is urban air mobility (UAM), and one of its challenges is constructing effective and efficient UAM service network. This study took a quantitative approach to the problem in an effort to support and facilitate the UAM service industry.

This study derived a multi-objective and multi-period (MOMP) location optimization model to support strategic UAM service network design. The model, based on its long-term service plan, determines where and when to open UAM airports. In addition, this study applied a modified e-constraint algorithm to derive managerial decisions on the Pareto relationship in consideration of multiple objectives and multiple periods.

Each Pareto solution represents a different UAM service network configuration. Thus, the model can analyze the trade-offs between Pareto decisions for the UAM service network. A case study of UAM service network design in South Korea demonstrates the validity of the proposed mathematical model and algorithm.

The design of a UAM service network should consider various aspects. Its construction and operation would require significant investments of time, capital and people, which would redound to society over a significant span of time. The results of this study provide quantitative guidelines for derivation and analysis of various UAM service network configurations in consideration of multiple objectives and multiple periods.

This paper proposes MOMP optimization, which approach is suitable to the fundamental characteristics of expanding UAM service networks and their design. It is expected that the present study will make significant contributions to the efforts of those deriving and analyzing future UAM service networks.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

It is only logical that a firm aims to make a profit after entering the market. However, some firms enter the market with the goal of market expansion and even burn money to pursue market share, which is counterintuitive in practice. To explore the theoretical foundations behind this rare phenomenon, this paper focuses on discussing the impact of the market expansion entry strategy on the entrant firm and the incumbent firm.

Using a game theory model of a supply chain with an incumbent and an entrant, this paper explores the mathematical conditions for the entrant to adopt either the traditional or the market expansion entry strategy and investigates the incumbent’s benefits and losses under different entry strategies.

The results show that when the market-expansion effect and the selling price ceiling are moderate, the entrant firm always adopts the market expansion entry strategy, and the incumbent firm obtains a free ride from the entrant firm and benefits from it. The entire industry profits and the industry consumer surplus are increased. In particular, we further investigate the cases in which the incumbent firm has a first-mover advantage or there is a troublesome cost, and the results confirm the aforementioned conclusions.

By considering market share as the entrant’s goal, this paper contributes to the dual-purpose literature. Moreover, based on the model’s mathematical results, this paper offers relevant management insights for the entrant and its stakeholders in the e-commerce platform.