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As the EU and the US successively proposed the carbon border adjustment mechanism (CBAM) and the Clean Competition Act (CCA), importers are now required to declare the carbon emissions of their products. Particularly, wheel companies face increasing time pressure with the gradual development of implementation paths for net-zero emissions and implementing carbon pricing policies in various countries. This research uses the production data of Taiwan wheel companies to establish a mathematical model for the activity-based cost optimisation decision-making process. It explores the impact of different carbon emission costs on a company's product mix with pricing decisions. Further, the research proposes five mathematical programmes for single-period cost optimisation decision-making under carbon pricing. These models provide some detailed view, such as changes in profits and product mix caused by high carbon taxes or other changes for each resource. Furthermore, the production cost optimal model proposed in this research can be used by enterprises to fulfill their social responsibilities while ensuring profitability and serve as a reference for making net-zero transformation decisions. [ABSTRACT FROM AUTHOR]

In secondary lymphoid tissues, human immunodeficiency virus (HIV) can replicate in both the follicular and extrafollicular compartments. Yet, virus is concentrated in the follicular compartment in the absence of antiretroviral therapy, in part due to the lack of cytotoxic T lymphocyte (CTL)-mediated activity there. CTLs home to the extrafollicular compartment, where they can suppress virus load to relatively low levels. We use mathematical models to show that this compartmentalization can explain seemingly counter-intuitive observations. First, it can explain the observed constancy of the viral decline slope during antiviral therapy in the peripheral blood, irrespective of the presence of CTL in Simian Immunodeficiency Virus (SIV)-infected macaques, under the assumption that CTL-mediated lysis significantly contributes to virus suppression. Second, it can account for the relatively long times it takes for CTL escape mutants to emerge during chronic infection even if CTL-mediated lysis is responsible for virus suppression. The reason is the heterogeneity in CTL activity and the consequent heterogeneity in selection pressure between the follicular and extrafollicular compartments. Hence, to understand HIV dynamics more thoroughly, this analysis highlights the importance of measuring virus populations separately in the extrafollicular and follicular compartments rather than using virus load in peripheral blood as an observable; this hides the heterogeneity between compartments that might be responsible for the particular patterns seen in the dynamics and evolution of the HIV in vivo.

IntroductionCancer combination treatments involving immunotherapies with targeted radiation therapy are at the forefront of treating cancers. However, dosing and scheduling of these therapies pose a challenge. Mathematical models provide a unique way of optimizing these therapies.MethodsUsing a preclinical model of multiple myeloma as an example, we demonstrate the capability of a mathematical model to combine these therapies to achieve maximum response, defined as delay in tumor growth. Data from mice studies with targeted radionuclide therapy (TRT) and chimeric antigen receptor (CAR)-T cell monotherapies and combinations with different intervals between them was used to calibrate mathematical model parameters. The dependence of progression-free survival (PFS), overall survival (OS), and the time to minimum tumor burden on dosing and scheduling was evaluated. Different dosing and scheduling schemes were evaluated to maximize the PFS and optimize timings of TRT and CAR-T cell therapies.ResultsTherapy intervals that were too close or too far apart are shown to be detrimental to the therapeutic efficacy, as TRT too close to CAR-T cell therapy results in radiation related CAR-T cell killing while the therapies being too far apart result in tumor regrowth, negatively impacting tumor control and survival. We show that splitting a dose of TRT or CAR-T cells when administered in combination is advantageous only if the first therapy delivered can produce a significant benefit as a monotherapy.DiscussionMathematical models are crucial tools for optimizing the delivery of cancer combination therapy regimens with application along the lines of achieving cure, maximizing survival or minimizing toxicity.

Background and Purpose: Bifunctional small molecule degraders, which link the target protein with E3 ubiquitin ligase, could lead to the efficient degradation of the target protein. BGB‐16673 is a Bruton's tyrosine kinase (BTK) degrader. A translational PK/PD modelling approach was used to predict the human BTK degradation of BGB‐16673 from preclinical in vitro and in vivo data. Experimental Approach: A simplified mechanistic PK/PD model was used to establish the correlation between the in vitro and in vivo BTK degradation by BGB‐16673 in a mouse model. Human and mouse species differences were compared using the parameters generated from in vitro human or mouse blood, and human or mouse serum spiked TMD‐8 cells. Human PD was then predicted using the simplified mechanistic PK/PD model. Key Results: BGB‐16673 showed potent BTK degradation in mouse whole blood, human whole blood, and TMD‐8 tumour cells in vitro. Furthermore, BGB‐16673 showed BTK degradation in a murine TMD‐8 xenograft model in vivo. The PK/PD model predicted human PD and the observed BTK degradation in clinical studies both showed robust BTK degradation in blood and tumour at clinical dose range. Conclusion and Implications: The presented simplified mechanistic model with reduced number of model parameters is practically easier to be applied to research projects compared with the full mechanistic model. It can be used as a tool to better understand the PK/PD behaviour for targeted protein degraders and increase the confidence when moving to the clinical stage. [ABSTRACT FROM AUTHOR]

This work is devoted to the kinetic analysis and modeling of patterns of the formation of nanoforms of metallic gold and mechanisms of the formation of metal macroparticles. The kinetics of the process in a steady state in solution and in a flow simulating the processes of deposit formation is considered. The autocatalytic nature of the synthesis of gold nanoparticles is shown, where the reverse oxidation of Au0 by strong oxidizer Au3+ is of fundamental importance. The kinetic behavior of the system is modeled, depending on the initial concentration of the reagent (Au3+), concentrations of autocatalytic seeds (Au1+, Au0), and the concentration of the reducing agent. The dependence of the multi-stage process on temperature is analyzed. The formation of a gold placer in a flow is modeled mathematically, based on the theory of ideal displacement reactors. The model includes a zone of dispersed mineral gold (or nanodistributed gold), a zone of the hydroflow, and a zone of coagulation in the formation of macroparticles on a metal nucleus (a zone of precipitation). Calculations are made of the dependence of reagent distribution on the concentration of the reducing component, the rate of hydraulic flow, the concentration of coagulants, and the precipitation of seeds. [ABSTRACT FROM AUTHOR]

In this study, we particularly address the generalized (3+1)-dimensional Kortewegde Vries (KdV) problem as one variation of the KdV equation. This equation can be utilized to simulate a wide range of physical events in a variety of domains, such as nonlinear optics, fluid dynamics, plasma physics, and other fields where coupled wave dynamics are significant. We first construct a Hirota bilinear form for the generalized KdV equation, and then we derive two different Bäcklund transformations (BT). The first Bäcklund transformation includes eleven arbitrary parameters, while the second form contains eight parameters. Rational and exponential traveling wave solutions with random wave numbers are found based on the suggested bilinear Bäcklund transformation. These solutions of the rational and exponential functions lead to the formation of dark and bright solitons. Moreover, we utilize the bilinear form of the equation to fully comprehend the behavior of lump-kink, breather, rogue, two-wave, three-wave, and multi-wave solutions. In-depth numerical simulations using 3-D profiles and contour plots are carried out while carefully taking into account relevant parameter values, offering more insights into the unique characteristics of the solutions that are obtained. Our results demonstrate the effectiveness and efficiency of the method used to obtain analytical solutions for nonlinear partial differential equations. [ABSTRACT FROM AUTHOR]

Novel multihost pathogens can threaten endangered wildlife species, as well as humans and domestic animals. The zoonotic protozoan parasite Toxoplasma gondii is transmitted by members of Felidae and can infect a large number of animal species, including humans. This parasite can have significant health consequences for infected intermediate hosts and could further endanger wild carnivore populations of Madagascar. Building on an empirical characterization of the prevalence of the pathogen in local mammals, we used mathematical models of pathogen transmission in a multihost community to compare preventative measures that aim to limit the spread of this parasite in wild carnivores. Specifically, we examined the effect of hypothetical cat vaccination and population control campaigns on reducing the risk of infection by T. gondii in wild Eupleridae. Our model predicted that the prevalence of exposure to T. gondii in cats would be around 72% and that seroprevalence would reach 2% and 43% in rodents and wild carnivores, respectively. Reducing the rodent population in the landscape by half may only decrease the prevalence of T. gondii in carnivores by 10%. Similarly, cat vaccination and reducing the population of definitive hosts had limited impact on the prevalence of T. gondii in wild carnivorans of Madagascar. A significant reduction in prevalence would require extremely high vaccination, low turnover, or both in the cat population. Other potential control methods of T. gondii in endangered Eupleridae include targeted vaccination of wild animals but would require further investigation. Eliminating the threat entirely will be difficult because of the ubiquity of cats and the persistence of the parasite in the environment. [ABSTRACT FROM AUTHOR]

Purpose: Based on the critical power (Pc or critical force; Fc) concept, a recent mathematical model formalised the proportional link between the decrease in maximal capacities during fatiguing exercises and the amount of impulse accumulated above Fc. This study aimed to provide experimental support to this mathematical model of muscle fatigability in the severe domain through testing (i) the model identifiability using non-exhausting tests and (ii) the model ability to predict time to exhaustion (tlim) and maximal force (Fmax) decrease. Methods: The model was tested on eight participants using electrically stimulated adductor pollicis muscle force. The Fmax was recorded every 15 s for all tests, including five constant tests to estimate the initial maximal force (Fi), Fc, and a time constant (τ). The model's parameters were used to compare the predicted and observed tlim values of the incremental ramp test and Fmax(t) of the sine test. Results: The results showed that the model accurately estimated Fi, Fc, and τ (CI95% = 2.7%Fi and 9.1 s for Fc and τ, respectively; median adjusted r2 = 0.96) and predicted tlim and Fmax with low systematic and random errors (11 ± 20% and − 1.8 ± 7.7%Fi, respectively). Conclusion: This study revealed the potential applications of a novel mathematical formalisation that encompasses previous research on the critical power concept. The results indicated that the model's parameters can be determined from non-exhaustive tests, as long as maximal capacities are regularly assessed. With these parameters, the evolution of maximal capacities (i.e. fatigability) at any point during a known exercise and the time to exhaustion can be accurately predicted. [ABSTRACT FROM AUTHOR]

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic. [ABSTRACT FROM AUTHOR]

Despite the global implementation of COVID-19 mitigation measures, the disease continues to maintain transmission. Although mask wearing became one of the key measures for preventing the transmission of COVID-19 early in the pandemic period, many countries have relaxed the mandatory or recommended wearing of masks. The objective of the present study was to estimate the epidemiological impact of removing the mask-wearing recommendation in Japan. We developed a model to assess the consequences of declining mask-wearing coverage after the government revoked its recommendation in February 2023. The declining mask-wearing coverage was estimated using serial cross-sectional data, and a mathematical model was devised to determine the age-specific incidence of COVID-19 using the observed case count in Tokyo from week of October 3, 2022 to October 30, 2023. We explored model-based counterfactual scenarios to measure hypothetical situations in which the mask-wearing coverage decreases or increases relative to the observed coverage. The results show that mask-wearing coverage declined from 97% to 69% by the week of October 30, 2023, and that if the mask-wearing recommendation had continued, 427 lives could have been saved in Tokyo. If the mask-wearing coverage had declined to 25% of the observed level, the model suggests there might have been 1587 additional deaths. Thus, revoking the mask-wearing recommendation had a substantial epidemiological impact. In future pandemics, our proposed approach could provide a realtime quantification of the effects of relaxing countermeasures. [ABSTRACT FROM AUTHOR]

Numerous novel concepts in fractional mathematics have been created to provide numerical models for a variety of real-world, engineering, and scientific challenges because of the kernel’s memory and non-local effects. In this post, we have looked at a deadly illness known as rabies. For our analysis, we employed the Atangana–Baleanu fractional derivative in Caputo sense. Additionally, the mathematical answer was obtained by applying the Laplace transform. Our approach is distinct, and we illustrated the vital role immunizations play in limiting the spread of the illness using graphical data. Furthermore, in this article, we have shown that fractional order systems are preferable to integer order systems. [ABSTRACT FROM AUTHOR]

The paper contains a proposal an original, extended mathematical model of an automatic system of human vision reaction to a forcing light pulse. A comprehensive mathematical model of the vision process was proposed in the form of an equation described in the frequency (dynamics) domain. Mathematical modelling of human senses is very important. It enables better integration of automation systems with a human cooperating with them, also as an automation system. This provides the basis for reasoning based on a mathematical model instead of intuitive reasoning about human reactions to visual stimuli. A block diagram of the proposed system with five human reaction paths is given. The following can be distinguished in the scheme: the main track consisting of: the transport delay of the eye reaction, the transport delay of the afferent nerves, the inertia of the brain with a preemptive action, the transport delay of the centrifugal nerves and the inertial and transport delay of the neuromotor system. In addition, the scheme of the system includes four tracks of negative feedback of motor and force reactions: upper eyelid, lower eyelid, pupil and lens. In the proposed model, the components of each path along with their partial mathematical models are given and discussed. For each reaction path, their overall mathematical models are also given. Taking into account the comprehensive models of all five reaction paths, a complete mathematical model of the automatic system of human reaction to a forcing light impulse is proposed. The proposed mathematical model opens up many possibilities for synchronizing it with mathematical models of many mechatronics and automation systems and their research. Optimizing the parameters of this model and its synchronization with specific models of automation systems is difficult and requires many numerical experiments. This approach enables the design of automation systems that are better synchronized with human reactions to existing stimuli and the selection of optimal parameters of their operation already in the design phase. The proposed model allows, for example, accurate determination of difficulty levels in computer games. Another example of the use of the proposed model is the study of human reactions to various situations generated virtually, for example in flight simulators and other similar ones. [ABSTRACT FROM AUTHOR]

The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is an extremely contagious variant that has garnered global attention due to its potential for rapid spread and its impact on the effectiveness of vaccines and non-pharmacological measures. In this paper, we investigate mathematical models involving vaccinated individuals and control functions to analyze how the spread of coronavirus disease 2019 (COVID-19) infection evolves over time. In the process of constructing a mathematical model for COVID-19, there are many parameters whose values are not yet known with certainty. Therefore, the extended Kalman filter method is used as a tool to estimate these parameters in an effort to better understand the dynamics of the spread and evolution of this disease. This method helps align the mathematical model with existing empirical data, allowing us to make more accurate predictions about the course of the COVID-19 pandemic and plan more precise actions to address the situation. Furthermore, an optimal control design is applied to reduce the number of infected individuals by implementing seven strategies involving a combination of health education, vaccination, and isolation controls. The simulation results we conducted indicate that the use of optimal control strategies can lead to a significant decrease in the number of individuals infected with COVID-19. [ABSTRACT FROM AUTHOR]

Colonies of ants can complete complex tasks without the need for centralised control as a result of interactions between individuals and their environment. Particularly remarkable is the process of path selection between the nest and food sources that is essential for successful foraging. We have designed a stochastic model of ant foraging in the absence of direct communication. The motion of ants is governed by two components - a random change in direction of motion that improves ability to explore the environment, and a non-random global indirect interaction component based on pheromone signalling. Our model couples individual-based off-lattice ant simulations with an on-lattice characterisation of the pheromone diffusion. Using numerical simulations we have tested three pheromone-based model alternatives: (1) a single pheromone laid on the way toward the food source and on the way back to the nest; (2) single pheromone laid on the way toward the food source and an internal imperfect compass to navigate toward the nest; (3) two different pheromones, each used for one direction. We have studied the model behaviour in different parameter regimes and tested the ability of our simulated ants to form trails and adapt to environmental changes. The simulated ants behaviour reproduced the behaviours observed experimentally. Furthermore we tested two biological hypotheses on the impact of the quality of the food source on the dynamics. We found that increasing pheromone deposition for the richer food sources has a larger impact on the dynamics than elevation of the ant recruitment level for the richer food sources. [ABSTRACT FROM AUTHOR]

The development of antiviral treatments for SARS-CoV-2 was an important turning point for the pandemic. Availability of safe and effective antivirals has allowed people to return back to normal life. While SARS-CoV-2 antivirals are highly effective at preventing severe disease, there have been concerning reports of viral rebound in some patients after cessation of antiviral treatment. In this study, we use a mathematical model of viral infection to study the potential of different antivirals to prevent viral rebound. We find that antivirals that block production are most likely to result in viral rebound if the treatment time course is not sufficiently long. Since these antivirals do not prevent infection of cells, cells continue to be infected during treatment. When treatment is stopped, the infected cells will begin producing virus at the usual rate. Antivirals that prevent infection of cells are less likely to result in viral rebound since cells are not being infected during treatment. This study highlights the role of antiviral mechanism of action in increasing or reducing the probability of viral rebound. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the stability of a class of nonlinear stochastic operators on a finite‐dimensional simplex. We identify a number of properties of the set of the class of stochastic operators, including the stability of each operator. We then use these operators to define non‐homogeneous Markov measures that rely on initial data and carry out an investigation into the absolute continuity and singularity of these measures. [ABSTRACT FROM AUTHOR]

AbstractDrying serves as an effective method to truncate the post-harvest wastage of agricultural products. This research paper presents a new thin-layer drying model to predict drying kinetics in the falling rate region. The semi-empirical model is derived as a modification of Fick’s second law and includes one drying constant. By determining this drying constant at a specific temperature, the model allows for the prediction of drying kinetics at any temperature. A Python program was used to evaluate the drying constant through nonlinear regression. Additionally, the model was simplified to obtain the required drying time at any given temperature. The study examined the drying kinetics of several agricultural products, including potato slices, green peas, and banana slices, at temperatures of 60 °C, 70 °C, and 80 °C, respectively. Experimental data of these agricultural products at various drying temperatures were validated against several known thin-layer drying models and the proposed model. The evaluation of model performance was conducted using statistical measures, specifically R2 and MAE. The higher R2 values (0.991-0.999) and lower MAE values (0.008-0.032) suggest strong agreement between the proposed model and experimental data, demonstrating its efficacy compared to previously reported models. Additionally, the proposed model exhibited the capability to predict drying time with high accuracy (error ± 2.9%) for any given temperature. [ABSTRACT FROM AUTHOR]

In the process of natural gas hydrate exploitation, the secondary hydrate formation may occur in both the reservoir and the completion layer. The secondary hydrate formed during the exploitation process and the sand particles falling from the reservoir will block the flow channel and reduce the gas production. In this paper, the permeability change of sand filling layer under the influence of sand and hydrate blockage is studied from a three-dimensional perspective, and the permeability change characteristics of sand mixing and secondary hydrate formation in filling layer caused by pore space blockage are summarized. A three-dimensional model for predicting the permeability of gravel layer is developed. In previous studies, the mathematical model parameters derived from the empirical formula are always not unique, which will cause inaccurate permeability prediction results. In this study, the simulated annealing algorithm is used to optimize the parameters such as boundary conditions and cooling tables. The undetermined parameters in the existing mathematical prediction model of gravel layer permeability are optimized into a determined maximum value to solve the optimization problem of the undetermined parameters of the permeability model. The algorithm in this paper takes the actual value of gravel layer permeability with different sand content and hydrate concentration in the experiment as the standard, and takes the mean square error of the predicted value and the experimental value of the prediction model as the objective function. The simulated annealing algorithm is used to estimate and optimize the undetermined parameters of the model. The algorithm results show that the mean square error between the predicted value and the experimental value is minimized with n = 22.7 and τ = 1.014(n and τ are the parameters to be optimized in the permeability model of this paper). The fitting degree between the optimized permeability prediction model and the experimental value is higher than that before optimization, which verifies the correctness and rationality of the algorithm in this paper. • Simulated annealing algorithm is used to optimize the permeability model. • The simulated annealing algorithm is optimized to have faster solution speed. • The NMSE error calculation method is used to make the predicted permeability model better fit the actual value. • The optimized model is combined with the capacity model. [ABSTRACT FROM AUTHOR]

The testing of relay protection devices is an extremely important task on which the stable and safe operation of the power systems depends. One of the most complex and responsible kinds of protection in networks with rotating generation, which includes hydrogen fuel generation, is protection against loss-of-synchronism. Integration of heterogeneous generating electrical devices based on renewable energy sources and based on a hydrogen fuel cell is associated with the need for reliable relay protection. It disconnects tie-lines between the systems in out-of-step conditions. And it is a challenging task to simulate input signals corresponding to the exact settings of the out-of-step protection to make sure it operates correctly. This is because one has to simulate exact parameters of impedance locus (its radius, coordinates of the center in the impedance plane, spin direction, speed, etc.) while available tools embedded in testing devices often don't support such parameters directly. To deal with the problem, the authors present the approach to simulate instantaneous values of line currents and phase-to-ground voltages during the out-of-step operation. These electrical signals form the locus of the impedance vector with the desired parameters to test the operation of the out-of-step protection devices. The software based on the proposed algorithm also provides a graphical representation of the simulation results. All the proposed solutions proved to reduce the time required for commissioning of out-of-step protection and thereby increase tests quality and speed. (provided to the authors on 29.08.2024 for printing in IJHE from the archive of Fermaltech Montenegro Limited, made by A.L. Gusev using Designer. On the DALL E 3 platform.). [Display omitted] [ABSTRACT FROM AUTHOR]

The article presents a method of developing a mathematical model of the arc surfacing process performed using the self-shielded flux-cored filler metal wire with the chromium cast iron (Fe15) weld deposit. A three-level design (static, determined, and complete) was used to determine the function of the test object, thus enabling the simulation of deposition rate in relation to wire feed speed and electrode extension. The deposition rate for the specified set of surfacing parameters amounted to between 4.31 kg/h and 11.25 kg/h. The study was also concerned with identifying the effect of the significance level of test factors and interactions between them on the resultant factor, as well as an assessment of the adequacy of the test object function. In relation to significance level α = 0.01, regression coefficients b0, b1, b2, and b11 significantly affected the deposition rate of the surfacing process. Coefficient b22 was significant at a level of 0.40, whereas coefficient b12 was significant at a level of 0.15. The mathematical model presenting the effect of wire feed speed and electrode extension, as well as interactions between them on the deposition rate of the surfacing process, was adequate for the adopted level of significance α = 0.05. [ABSTRACT FROM AUTHOR]

This research paper attempts to describe the transmission dynamic of zoonotic visceral leishmaniasis with the aid of a mathematical model by considering the asymptomatic stages in humans and animals. The disease is endemic in several countries. Data used in the research are obtained from the literature while some are assumed based on the disease dynamic. The consideration of both asymptomatic and the symptomatic infected individuals is incorporated in both humans and animals (reservoir), as well as lines of treatment for the human population. It is found that the model has two fixed points; the VL-free fixed point and the VL-endemic fixed point. Stability analysis of the fixed points shows that the VL-free fixed point is globally asymptotically stable whenever the basic reproduction number is less than one and the VL-endemic fixed point is globally asymptotically stable whenever the basic reproduction number is greater than one. Sensitivity analysis is conducted for the parameters in the basic reproduction number, and the profile of each state variable is also depicted using the data obtained from the literature and those assumed. The transmission probability from infected sandflies to animals, transmission probability from infected animals to sandflies, per capita biting rate of sandflies of animals, and rate of transfer from symptomatic infected animals to the recovered class are among the most sensitive parameters that have the greatest influence on the basic reproduction number. Moreover, the value of the basic reproduction number is obtained to be 0.98951, which may require further study, as the margin between potential disease control and outbreak is thin. [ABSTRACT FROM AUTHOR]

Hashimoto's thyroiditis (HT) is a prevalent autoimmune disorder marked by chronic inflammation of the thyroid gland, predominantly affecting children and adolescents. In a previous study, we developed a "maximal" mathematical model of thyroid physiology to simulate the complex interactions within the thyroid gland. The present research introduces an enhanced version of the "maximal" model, integrating the pathophysiological impacts of HT. It specifically models the adverse effects of thyroid peroxidase (TPO) and thyroglobulin (Tg) antibodies (TPOAb and TgAb) on TPO, Tg, sodium iodide symporter (NIS), albeit indirectly, and thyroid volume. Additionally, we present a new "minimal" model offering a streamlined interpretation of thyroid physiology and pathophysiology, designed for faster computational analysis while maintaining essential physiological interactions. Both models were fitted against longitudinal clinical data from patients with HT, assessing the concentrations of Thyroid Stimulating Hormone (TSH), Thyroxine (T4), and thyroid volume over 36 months, in both untreated patients and those receiving levothyroxine (LT4) treatment. The adaptation of the models to data shows that both of them accurately reproduce the available observed clinical outcomes, with the "maximal" model providing more detailed physiological insights but requiring extensive data and longer computation times. In contrast, the "minimal" model, despite exhibiting less realistic TSH oscillations, offers rapid parameter estimation and may be more feasible in clinical settings. These models hold significant potential as tools for detailed study and management of HT, enabling simulations of disease progression and therapeutic responses, thus paving the way for personalized treatment strategies. [ABSTRACT FROM AUTHOR]

Deforestation exerts profound ramifications on soil quality and biodiversity, thereby exerting substantial economic repercussions. The depletion of organic matter and structural integrity of soil following tree removal for agricultural purposes underscores the severity of this issue. In elucidating the soil pollution stemming from deforestation, this research employs a sophisticated five-compartment SDIFR model integrating fractal dimension and fractional order dynamics. The rigorous analysis, including the application of Picard Lindelof’s fixed point theorem, establishes the existence and uniqueness of explicit solutions. Furthermore, the examination of local and global stability sheds light on the system’s behavior, delineating between pollution-free equilibrium and pollution-extinct equilibrium states. To regulate system behavior, an adaptive control framework grounded in fractal fractional order is proposed, leveraging the Adams-Bashforth numerical approximation scheme for implementation. Through numerical simulations, the study underscores the pivotal role of parameters, thus substantiating the significance of the proposed model in comprehensively addressing the complexities of soil pollution induced by deforestation. [ABSTRACT FROM AUTHOR]

This paper introduces a novel stochastic inverse method that utilizes perturbation theory and advanced intelligence techniques to solve the multi‐parameter identification problem of concrete dams using displacement field monitoring data. The proposed method considers the uncertainties associated with the dam displacement monitoring data, which are comprised of two distinct sources: the first is related to stochastic mechanical properties of the dam, and the second is due to observation errors. The displacements at different measuring points generated by dam mechanical properties exhibit spatial correlation, while the observation errors at different points can be considered statistically random. In this context, the inversion formulas are derived for unknown stochastic parameters of the dam by combining perturbation equations and Taylor expansion methods. An improved meta‐heuristic optimization method is employed to identify the mean of stochastic parameters, while mathematical and statistical methods are used to determine the variance of stochastic parameters. The feasibility of the proposed method is verified through numerical examples of a typical dam section under different conditions. Additionally, the paper discusses and demonstrates the applicability of this method in a practical dam project. Results indicate that this method can effectively capture the uncertainty of dam's mechanical properties and separates them from observation errors. [ABSTRACT FROM AUTHOR]

Background and Purpose: Endothelin‐1 (ET‐1) receptor A (ETA) antagonists reduce proteinuria and prevent renal outcomes in chronic kidney disease (CKD) patients, but their utility has been limited because of associated fluid retention, resulting in increased heart failure risk. Understanding the mechanisms responsible for fluid retention could result in solutions that preserve renoprotective effects while mitigating fluid retention, but the complexity of the endothelin system has made identification of the underlying mechanisms challenging. Approach: We utilized a previously developed mathematical model of ET‐1 kinetics, ETA receptor antagonism, kidney function, haemodynamics, and sodium and water homeostasis to evaluate hypotheses for mechanisms of fluid retention with ETA antagonism. To do this, we simulated the RADAR clinical trial of atrasentan in patients with type 2 diabetes and CKD and evaluated the ability of the model to predict the observed decreases in haematocrit, urine albumin creatinine ratio (UACR), mean arterial pressure (MAP), and estimated glomerular filtration rate (eGFR). Background and Key Results: An effect of ETA antagonism on venodilation and increased venous capacitance was found to be the critical mechanism necessary to reproduce the simultaneous decrease in both MAP and haematocrit observed in RADAR. Conclusions and Impact: These findings indicate that fluid retention with ETA antagonism may not be caused by a direct antidiuretic effect within the kidney but is instead be an adaptive response to venodilation and increased venous capacity, which acutely tends to reduce cardiac filling pressure and cardiac output, and that fluid retention occurs in an attempt to maintain cardiac filling and cardiac output. [ABSTRACT FROM AUTHOR]

This paper evaluates the nonlinear dynamics of a melanoma cancer model through the iterative technique of finding compact invariant sets (LMCIS). The objective is to discover equilibrium points, ascertain their stability qualities, and determine the presence of compact invariant sets within the model. For instance, this approach is assessed in a model demonstrating the interplay between four cellular populations and administrating interleukin-12 (IL-12) immunotherapy. Nevertheless, the mechanism by which this anti-cancer therapy operates has yet to be completely defined. The approach is evaluated using severe treatment situations to provide evidence for the presence of equilibrium points inside the iteratively confined zone and to assess its effectiveness. The outcomes derived from using this approach were then compared with a conventional iteration of Newton's method. Newton's approach extends to the resolution of the system of equations inside the model, facilitating the identification of the equilibrium points with a small tolerance error. While the iterations of the localization algorithm were set close to the results obtained by this method. Finally, the simulation outcomes are shown via the utilization of MATLAB R2020b. The findings demonstrate the temporal progression of the model under the effect of immunotherapy and without it. [ABSTRACT FROM AUTHOR]

This article is devoted to planning water-energy regimes for hydropower plants, taking into account economic and ecologic criteria. A new methodology based on a probabilistic model of water inflow has been proposed. The probabilistic method requires the calculation of low-water and average-water year typical hydrographs based on the probability curve. This allows the determination of the guaranteed hydropower plant generation schedule with a month time-step. According to the method considered, the mathematical model of the reservoir filling and normal power station operation has been designed. The software for the automated water-energy mode calculation is presented in this paper. The economic feasibility of maximum replacement of thermal power plants in the energy system with more environmentally friendly hydropower plant is substantiated. The methodology of water resources cost calculation and economic efficiency assessment under various hydropower plant regime scenarios have been proposed in the paper. Using the data and characteristics of HPPs and TPPs, an assessment of energy efficiency will be obtained in accordance with the developed methodology to determine the price of water for HPPs and all participants in the water management complex. The results of the implementation of the developed approach indicate that the price of electricity sales in a competitive electricity market can be brought into line with the price of electricity sales generated by thermal power plants, which increases the economic feasibility of the maximum replacement of thermal power plant capacity in the system with more economical and environmentally friendly hydropower plant. The developed method allows for an increase in the efficiency of water resources use and the efficiency of hydropower plant participation in the energy balance, which makes it possible to displace part of the power generated by thermal power plants. [ABSTRACT FROM AUTHOR]

This paper presents a compelling argument for optimizing the logistics flow of wiring harnesses within the automotive industry to address the rising production demands of vehicle manufacturers. It introduces an innovative assembly line structure specifically designed to boost efficiency and enhance responsiveness to client needs. Drawing from data gathered from an actual assembly line dedicated to producing engine harnesses for K9K engines, this study offers a practical and impactful foundation for its proposed optimization strategies. The new assembly structure effectively merges the benefits of a dynamic line—which emphasizes efficient space utilization and flexibility—with the strengths of a rotary line, particularly in light of the increasing complexity associated with harness production. The paper features a mathematical model that calculates cycle times for workstations within this new system architecture, optimizing the entire production process. Moreover, it illustrates how advanced modeling, simulation, and optimization techniques using WITNESS Horizon Version Release 25.0 can identify necessary adjustments for achieving optimal assembly line balance. Additionally, this research addresses pressing environmental concerns by proposing a robust recycling strategy for the scrap produced during wiring harness manufacturing. By advocating for sustainable practices and responsible waste management, the study highlights the importance of minimizing the ecological footprint of the automotive manufacturing process. In summary, this research provides essential insights and practical solutions for optimizing wiring harness logistics flow in the automotive industry. By implementing these strategies, manufacturers can significantly enhance their production capacity, improve operational efficiency, and maintain competitiveness in an ever-evolving market landscape. [ABSTRACT FROM AUTHOR]

Tight sandstone water-bearing gas reservoirs typically exhibit low porosity and low permeability, with reservoir rocks characterized by complex pore structures, often featuring micron-scale or smaller pore throats. This intricate reservoir structure significantly restricts fluid flow within the reservoir, necessitating a certain threshold pressure gradient (TPG) to be overcome before flow can commence. This study focuses on the Ordos Basin and explores the influence of high water content tight sandstone gas reservoirs on TPG under different water saturation and formation pressure conditions through experiments. A mathematical model of TPG is established using multiple linear regression method. The results show that TPG is primarily affected by water saturation, followed by formation pressure. As the water saturation increases, the TPG of the core decreases, and the change becomes more pronounced when the water saturation exceeds 50%. As formation pressure increases, the weakening of the slippage effect in gas molecules leads to TPG stabilization, especially when local pressure exceeds 25.0 MPa. The research also shows that low-permeability cores exhibit greater TPG variation with pressure changes, while high-permeability cores remain more stable. A mathematical model was developed and validated to predict TPG based on permeability, water saturation, and formation pressure. These findings highlight the need to monitor formation water content during tight sandstone gas reservoir development to optimize production strategies, providing valuable insights for improving reservoir management and guiding future research. [ABSTRACT FROM AUTHOR]

To reveal the velocity distribution law of a gas lift reverse circulation well washing flow field in drilling shaft sinking, a velocity mathematical model of well washing flow field is established. The theoretical analytical solutions and velocity distribution laws in the drill pipe and bottom hole were provided and validated through numerical simulations and similar model tests. Furthermore, this study explores the impacts of mud circulation, wind pressure, rock debris, and mud properties on the flow field distribution, presenting the sensitivity order of the factors. This study indicated the following findings. (1) The velocity theoretical model of well washing flow field was validated through numerical simulation and similar model tests, and the relative error ranged from 4.2 to 18.5%, demonstrating the high consistency. (2) The rock debris and mud experienced slight and consistent deceleration within the drill pipe. Upon reaching the gas input end, the axial upward return of the multiphase flow resulted in a "jumping" sharp increase. (3) The mud circulation, wind pressure, and void fraction positively correlated with the multiphase fluid velocity in the drill pipe, but negatively correlated with the mud density and slag content. The axial upward velocity of the rock debris decreased with increasing particle size and density. (4) The sensitivity of each factor to the discharge speed of rock debris was summarized as follows: air content > mud circulation > wind pressure > mud density > rock debris density > rock debris particle size > slag content. These findings could offer a theoretical basis for selecting well washing parameters and enhancing drilling and washing efficiency. [ABSTRACT FROM AUTHOR]

Background: In Taiwan, the human immunodeficiency virus (HIV) is primarily transmitted among men who have sex with men (MSM). We believe that the primary source of heterogeneity influencing transmission is from whether the infected individuals engage in behaviors that promote transmission. Therefore, we differentiated and modeled the statuses of infected individuals, estimated parameters, and predicted future infection trends to better understand HIV transmission dynamics among MSM. Methods: We devised a computational approach to accurately estimate the number of infectious individuals during early surveillance years in Taiwan using monthly data of newly diagnosed patients with HIV from January 2005 to December 2006. Subsequently, using annual incident cases from 2005 to 2019, we estimated the susceptibility rate, transmission rates, and duration of risk and conducted short-term forecasts of HIV infection among MSM through mathematical modeling based on the susceptible-infected-removed model. Parameters and their values, such as HIV and acquired immunodeficiency syndrome (AIDS) transmission rates and removal rates, were also estimated. Results: A total of 850 infectious individuals with HIV were identified in 2005. From 2005 to 2019, approximately 0.68% (95% confidence interval: 0.64–0.71%) of Taiwan's male population were estimated to be susceptible to HIV infection via MSM. The transmission rate did not significantly differ between individuals with HIV and AIDS (0.0110 and 0.107 per 10,000 individuals, respectively). The average infection duration between infection and AIDS onset was 4.5 years and between AIDS onset and death was 2.1 years. In 2019, the proportion of infectious infected individuals continuously decreased. By 2026, only 11.85% and 10.21% of individuals with HIV and AIDS, respectively, will be infectious. Conclusions: Our study presents a new dynamic mathematical model for estimating infectious HIV cases among MSM in Taiwan. By accounting for the heterogeneity of infectious HIV/AIDS within the MSM population, we effectively estimated the number of infections during early surveillance years and implement concentrated measures, thereby reducing infectious HIV/AIDS cases, which contributes to the overall reduction in HIV/AIDS cases. With these estimations, identifying target populations and implementing effective intervention measures at appropriate times can be improved. [ABSTRACT FROM AUTHOR]

Cancer is a systemic heterogeneous disease involving complex molecular networks. Tumor formation involves an epithelial‐mesenchymal transition (EMT), which promotes both metastasis and plasticity of cancer cells. Recent experiments have proposed that cancer cells can be transformed into adipocytes via a combination of drugs. However, the underlying mechanisms for how these drugs work, from a molecular network perspective, remain elusive. To reveal the mechanism of cancer‐adipose conversion (CAC), this study adopts a systems biology approach by combing mathematical modeling and molecular experiments, based on underlying molecular regulatory networks. Four types of attractors are identified, corresponding to epithelial (E), mesenchymal (M), adipose (A) and partial/intermediate EMT (P) cell states on the CAC landscape. Landscape and transition path results illustrate that intermediate states play critical roles in the cancer to adipose transition. Through a landscape control approach, two new therapeutic strategies for drug combinations are identified, that promote CAC. These predictions are verified by molecular experiments in different cell lines. The combined computational and experimental approach provides a powerful tool to explore molecular mechanisms for cell fate transitions in cancer networks. The results reveal underlying mechanisms of intermediate cell states that govern the CAC, and identified new potential drug combinations to induce cancer adipogenesis. [ABSTRACT FROM AUTHOR]

In a fast-changing world, understanding how organisms adapt to their environment is a pressing necessity. Research has focused on genetic adaptation, while our understanding of non-genetic modes is still in its infancy. The host-associated microbiome can be considered a non-genetic mode of adaptation, which can strongly influence an organism's ability to cope with its environment. However, the role of the microbiome in host ecological dynamics is largely unexplored, particularly in animal communities. Here, we discuss the following hypothesis: invasive species may rapidly adapt to local conditions by adopting beneficial microbes from similar co-occurring native species. This occurs when the invader's fitness is influenced by adaptation to local conditions that is facilitated by microbes acquired from native microbiomes. We present a minimal mathematical model to explore this hypothesis and show that a delayed acquisition of native microbes may explain the occurrence of an invasion lag. Overall, our results contribute to broadening the conceptualization of rapid adaptation via microbiome transfer and offer insights towards designing early intervention strategies for invasive species management. [ABSTRACT FROM AUTHOR]

Introduction: Treating extensive burn injury requires an individually tailored resuscitation protocol that includes hourly-titrated intravenous fluid infusion to avert both hypovolemic shock and edema. Due to the complexity of burn pathophysiology and significant variability in treatment protocols, there is an ongoing effort to optimize burn resuscitation. The goal of this work is to contribute to this effort by developing a mathematical model of burn pathophysiology and resuscitation for in silico testing of burn resuscitation protocols and decision-support systems. Methods: In our previous work, we developed and validated a mathematical model consisting of volume kinetics, burn-induced perturbations, and kidney function. In this work, we expanded our previous mathematical model to incorporate novel mathematical models of cardiovascular system and hormonal system (renin-angiotensin-aldosterone (RAAS) system and antidiuretic hormone) which affect blood volume and pressure regulation. We also developed a detailed mathematical model of kidney function to regulate blood volume, pressure, and sodium levels, including components for glomerular filtration rate, reabsorption rates in nephron tubules, Tubuglomerular feedback, and myogenic mechanisms. We trained and validated the expanded mathematical model using experimental data from 15 pigs and 9 sheep with extensive burns to quantitatively evaluate its prediction accuracy for hematocrit, cardiac output, mean arterial pressure, central venous pressure, serum sodium levels, and urinary output. We then trained and tested the mathematical model using a clinical dataset of 233 human burn patients with demographic data and urinary output measurements. Results: The mathematical model could predict all tested variables very well, while internal variables and estimated parameters were consistent with the literature. Discussion: To the best of our knowledge, this is the first mathematical model of burn injury and resuscitation which is extensively validated to replicate actual burn patients. Hence, this in silico platform may complement large animal pre-clinical testing of burn resuscitation protocols. Beyond its primary purpose, the mathematical model can be used as a training tool for healthcare providers delivering insight into the pathophysiology of burn shock, and offering novel mathematical models of human physiology which can be independently used for other purposes and contexts. [ABSTRACT FROM AUTHOR]

This paper proposes an Improved Lemur Optimization algorithm (ILO), which combines the advantages of the Spider Monkey Optimization algorithm, Simulated Annealing algorithm, and Lemur Optimization algorithm. Through the use of an adaptive nonlinear decrement model, adaptive learning factors, and updated jump rates, the algorithm enhances its global exploration and local exploitation capabilities. A Gaussian function model is used to simulate the mountain environment, and a mathematical model for UAV flight is established based on constraints and objective functions. The fitness function is employed to determine the minimum cost for avoiding obstacles in a designated airspace, and cubic spline interpolation is used to smooth the flight path. The Improved Lemur Optimization algorithm was tested using the CEC2017 benchmark set, assessing its search capability, convergence speed, and accuracy. The simulation results show that ILO generates high-quality, smooth paths with fewer iterations, overcoming the issues of premature convergence and insufficient local search ability in traditional genetic algorithms. It adapts to complex terrain, providing an efficient and reliable solution. [ABSTRACT FROM AUTHOR]

The fourth-order nonlinear Boussinesq water wave equation, which describes the propagation of long waves in the intertidal zone, is investigated in this study. The exact wave patterns of the equation were computed using the t a n h method. As stability decreased, soliton wave structures were derived using similarity transformations. Numerical simulations supported these findings. The t a n h method introduced a Galilean modification, leading to the discovery of several new exact solutions. Subsequently, the fourth-order nonlinear Boussinesq wave equation was transformed into a planar dynamical system using the travelling wave transformation. The quasi-periodic, cyclical, and nonlinear behaviors of the analyzed equation were particularly examined. Numerical simulations revealed that varying the physical parameters impacts the system's nonlinear behavior. Graphs represent all possible examples of phase portraits in terms of these parameters. Furthermore, the study was proven to be highly beneficial for addressing issues such as shock waves and highly active travelling wave processes. Sensitivity analysis theory and the Lyapunov exponent were employed, offering a wide variety of linear periodic and first-frequency periodic characteristics. Sensitivity analysis and multistability analysis of the Boussinesq water wave equation were thoroughly investigated. [ABSTRACT FROM AUTHOR]

This article discusses the option of upgrading an ore-thermal furnace to produce technical silicon by installing strain gage sensors with signal converters and a rangefinder in the electrode lifting system. This upgrade will enable the reception of real-time data on changes in mass (carbon monoxide) or electrode breakage and the position of the working end of the electrode. The latter parameter is associated with the position of the reaction zone in the furnace, which directly affects the specific amount of electricity consumption, the temperature and volume of silicon leaving the furnace, and heat losses and dust removal with exhaust gases. The upgrade will increase the observability of the control object, which will improve the quality of control of the technological process of carbothermic silicon reduction. [ABSTRACT FROM AUTHOR]

This study investigated the effect of hot water blanching (HWB), high‐humidity air‐impingement blanching (HHAIB), different HHAIB blanching times (2, 4, 6, 8, and 10 min), and different HHAIB blanching temperatures (80, 85, 90, and 95°C) on texture quality, lignin content, weight loss, color, microstructure, and drying kinetics of bamboo shoots. After HWB treatment, the lignin content of bamboo shoots was apparently lower than that of HHAIB and the samples obtained the highest weight loss value of 6.13%. Both the texture values (brittleness and chewiness) and lignin content of bamboo shoots exhibited an overall downward trend as the HHAIB blanching time and blanching temperature increased. Specifically, the lignin content of bamboo shoots decreased from 5.59% to 4.58% with an increase in HHAIB blanching time from 2 to 10 min and dropped from 5.48% to 4.63% as HHAIB blanching temperature increased from 80 to 85°C, respectively. The lignin content was proved to be positively correlated (p < 0.05) with texture attributes (brittleness and chewiness). A second polynomial model was obtained for fitting the variation kinetics of lignin content during thermal processing. Reducing the HHAIB blanching time and blanching temperature would obtain a lower weight loss and a better color performance (ΔE and L*). Additionally, microstructure observation revealed that the distribution density of microchannels initially increased and then decreased with the extension of blanching time, while it continuously became firmer as HHAIB blanching temperature increased. Overall, the optimal processing parameters were achieved under the HHAIB blanching temperature of 85°C for 6 min, ensuring a high‐quality performance of bamboo shoot products. [ABSTRACT FROM AUTHOR]

Sowing depth is a critical factor in crop growth and is determined by both the soil conditions and the force of the opener. The trend for the future is to control sowing depth based on soil dynamic parameters. Therefore, this paper developed a downforce measurement and control system based on the 'T'-shaped furrow opener and investigated the influence of soil dynamic parameters and opener downforce on sowing depth. A test-rig was constructed and the accuracy of the system in measuring downforce and controlling downforce and sowing depth was verified. The study shows that at different sowing depths, soil moisture, bulk density and their interaction have a significant effect on downforce (P < 0.01). As the moisture content decreases and the bulk density increases, the required downforce increases for the same sowing depth. A mathematical model of downforce-sowing depth-soil bulk density-soil moisture content was established using experimental data, with an R2 of 0.916, VIF <5 and a Durbin-Watson value of 1.628. Field experiments show that, at an operating speed of 6 km h−1, the control strategy based on the soil dynamic parameters predicted by downforce theory significantly outperformed the strategy of adjusting the downforce in response to perceived changes in downforce. This indicates that after dynamic and rapid measurement of soil bulk density and moisture content during field operations, sowing depth can be accurately controlled based on the directed downforce of the opener. The mathematical model provides a theoretical basis for sowing depth control based on soil dynamic parameters. • Proposing a sowing depth control strategy based on soil dynamic parameters. • System developed under static conditions and validated through dynamic experiments. • A mathematical model for the precise control of sowing depth was established. [ABSTRACT FROM AUTHOR]

This paper aims to investigate appropriate mathematical models devoted to the optimization of some cleaning processes related to pharmaceutical contaminant removal. In our recent works, we found the rehabilitation of the existing cleaning plants as a viable solution for the removal of this type of micropollutants from waters by introducing efficient techniques such as adsorption on granulated active carbon filters and micro-, nano-, or ultrafiltration. To have these processes under better control and to assure the transfer from small- to large-scale treatment stations, specific mathematical models are necessary. Starting from Navier–Stokes equations and imposing proper boundary conditions, some mathematical physics problems are obtained for which adequate solving methods via variational methods and surjectivity results are proposed. The importance of these solution characterizations consists in their continuation in adequate numerical methods and the possibility to visualize the result by using a CFD program. [ABSTRACT FROM AUTHOR]

Optimal disturbances of the periodic solution of the hepatitis B dynamics model corresponding to the chronic recurrent form of the disease are found. The dependence of the optimal disturbance on the phase of periodic solution is analyzed. Four phases of the solution are considered, they correspond to clinically different periods of development of the immune response and severity of the disease, namely, activation of antiviral immune reactions, attenuation of reactions, peak and minimum viral load. The possibility of using optimal disturbances to exit the domain of attraction of the considered periodic solution using minimal impact is studied. The components of disturbances that may underlie the phenomenon of spontaneous recovery from chronic hepatitis B observed in clinical practice are identified. [ABSTRACT FROM AUTHOR]

In this work, we obtained a general formulation for the mating probability and fertile egg production in helminth parasites, focusing on the reproductive behavior of polygamous parasites and its implications for transmission dynamics. By exploring various reproductive variables in parasites with density-dependent fecundity, such as helminth parasites, we departed from the traditional assumptions of Poisson and negative binomial distributions to adopt an arbitrary distribution model. Our analysis considered critical factors such as mating probability, fertile egg production, and the distribution of female and male parasites among hosts, whether they are distributed together or separately. We show that the distribution of parasites within hosts significantly influences transmission dynamics, with implications for parasite persistence and, therefore, with implications in parasite control. Using statistical models and empirical data from Monte Carlo simulations, we provide insights into the complex interplay of reproductive variables in helminth parasites, enhancing our understanding of parasite dynamics and the transmission of parasitic diseases. [ABSTRACT FROM AUTHOR]

Thermal energy storage (TES) is an innovative technology that can help mitigate environmental problems and make energy consumption in air conditioning systems more efficient. TES also helps to decouple the production and use of cooling. In this work, a mathematical model was used to obtain the thermal loads of the environment based on Brazilian standards and to simulate the operation of an air conditioning system integrated with TES. A refrigeration system capable of providing cooling capacity for the selected environment was used. It simulated the operation of refrigeration systems to evaluate the coefficient of performance of the refrigeration cycle and the refrigeration system, including the TES, for the city of Teresina, in the northeast region of Brazil. On each day, the efficiency of the air-cooled chiller coupled with a TES that used paraffin with phase change material was verified. Based on the data obtained from the mathematical model, the reduction in energy consumption achieved by coupling TES with the air conditioning system varied between 10.6% and 1.3%. [ABSTRACT FROM AUTHOR]