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Motivated by a practical problem, this paper investigates the integrated planning of maintenance operations and workload allocation on a set of machines in a workshop. Given quantities of products to be produced per period on a planning horizon must be processed on unrelated flexible machines. Moreover, each machine has to undergo one or more maintenance operations that must be planned within a given time window and impact products differently. The main goal is to find a feasible plan that satisfies the machine capacity by allocating the production quantities to machines and assigning maintenance operations as late as possible in their time windows. Various original mathematical models are presented. In particular, we propose models that allow maintenance operations and some production quantities to overlap two consecutive periods. Computational experiments based on industrial data show that allowing this overlapping helps the earliness of maintenance operations to be significantly reduced in the most difficult instances, going for example from a total of 14 periods to only 1 period, and by more than 35% on average. [ABSTRACT FROM AUTHOR]

IntroductionIn high-burden settings, low-complexity screening tests for tuberculosis (TB) could expand the reach of community-based case-finding efforts. The potential costs and cost-effectiveness of approaches incorporating these tests are poorly understood.MethodsWe developed a microsimulation model assessing 3 approaches to community-based case-finding in hypothetical populations (India-, South Africa-, The Philippines-, Uganda-, and Vietnam-like settings) with TB prevalence 4 times that of national estimates: (1) screening with a point-of-care C-reactive protein (CRP) test, (2) screening with a more sensitive "Hypothetical Screening test" (95% sensitive for Xpert Ultra-positive TB, 70% specificity; equipment/labor costs similar to Xpert Ultra, but using a $2 cartridge) followed by sputum Xpert Ultra if positive, or (3) testing all individuals with sputum Xpert Ultra. Costs are expressed in 2023 US dollars and include treatment costs.ResultsUniversal Xpert Ultra was estimated to cost a mean $4.0 million (95% uncertainty range: $3.5 to $4.6 million) and avert 3200 (2600 to 3900) TB-related disability-adjusted life years (DALYs) per 100 000 people screened ($670 [The Philippines] to $2000 [Vietnam] per DALY averted). CRP was projected to cost $550 (The Philippines) to $1500 (Vietnam) per DALY averted but with 44% fewer DALYs averted. The Hypothetical Screening test showed minimal benefit compared to universal Xpert Ultra, but if specificity were improved to 95% and per-test cost to $4.5 (all-inclusive), this strategy could cost $390 (The Philippines) to $940 (Vietnam) per DALY averted.ConclusionsScreening tests can meaningfully improve the cost-effectiveness of community-based case-finding for TB but only if they are sensitive, specific, and inexpensive.

The application and development of methods using hydrogen as a gas for testing active surface sites is of great interest in the study of oxide catalysts. In this work a systematic study of Pt/CeO 2 catalysts, depending on the platinum loading and calcination temperature was carried out using the method of temperature-programmed reduction by hydrogen (TPR-H 2). Experimental TPR-H 2 curves demonstrated a complex structure over a wide temperature range, including the presence of abnormally narrow consumption peaks (ANCP-H 2) at low temperatures. To describe the kinetics, mathematical modeling of H 2 consumption by various platinum active centers has been used, including isolated platinum ions (single atoms) and PtO x clusters of 2D and 3D structures on the surface of ceria nanoparticles. We proposed a criterion that makes it possible to extend the analysis of the TPR-H 2 curves for reducible metal-supported oxide systems and unambiguously establish the action of the autocatalytic mechanism in hydrogen consumption. The kinetics of ANCP-H 2 by cluster PtO x centers with a half-width of several degrees on the base of the autocatalysis mechanism was described on the base of the proposed model. The simulations show that ANCP-H 2 are described by synchronous fast formation of metallic centers that provide a sharp acceleration of H 2 consumption. In general, the obtained results establish the fundamental aspects of the H 2 interaction with heterogeneous catalysts and are of particular interest in the light of various practical applications of hydrogen energy. [Display omitted] • Pt/CeO 2 catalysts were studied depending on Pt loading using TPR-H 2 method. • TPR-H 2 curves are capable to distinguish Pt2+-SA sites and PtOx cluster. • Abnormally narrow peaks (ANCP-H 2) were observed due to 2D- and 3D-PtO x clusters. • ANCP-H 2 are well simulated on the base of the autocatalytic stages of PtO x reduction. • ANCP-H 2 is due to strong difference in the oxygen reactivity of Pt–O –Pt and Pt–O–Ce. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel temporally and spatially segregated numerical scheme to discretize the coupled electromechanical model of myocardium. We perform several numerical experiments with activation of a myocardial slab with structural inhomogeneity and evaluate the dependence of numerical errors on the size of spatial and temporal discretization steps. In our study, we show that the spatial step for the mechanical equations hm⩽2.5 mm yields reasonable results with noticeable errors only in the region of myocardial inhomogeneity. We also show that time step τm⩽1 ms can be used for temporal discretization of mechanical equations, and the propagation velocity of the activation and contraction fronts differs from the reference one by no more than 1.3%for such time step. Finally, we show that the increase of time discretization steps of the mechanical equations τm and the monodomain equation τe leads to phase errors with opposite signs, and we can compensate these errors by tuning the relationship between the time steps. [ABSTRACT FROM AUTHOR]

Sulfur dioxide (SO2) belongs to the highly reactive group of gases familiar as "Oxides of Sulfur". SO2 has lots of adverse effects on plants, respiratory system and many other environmental issues. Sulfur dioxide is a primary pollutant which is regulated worldwide, due to the combustion of fuel. Different approaches are adopted to economically control the SO2 in the environment which causes the production of sulfuric acid that is reflected in acid rain. The aim of this study is to investigate the invariant regions and solution pathways for the formation of H2SO4 in a multi-step reaction mechanism. The employed Model Reduction Techniques (MRTs) such as Spectral Quasi Equilibrium Manifold (SQEM) and Intrinsic Low Dimensional Manifold (ILDM) give the solution curves, which functions as a primary approximation to invariant manifold. It is achieved that each chemical specie can be assessed rather than taking the overall mechanism. The new discovery suggests that we could achieve the invariant regions for SO2 and H2SO4. SO2 emissions, along with emission norms, will be disclosed. The comparison of MRTs is depicted through tabular and graphical representations, while theoretical results are demonstrated through computer simulations using MATLAB. [ABSTRACT FROM AUTHOR]

This paper proposes a new bi-objective aircraft and crew recovery model. Unlike previous researches, the proposed model considers all individual flight legs in the recovery process. The objective functions minimize the recovery cost regarding crew swapping, flight cancellation, flight delay, and crew deadhead costs. Besides, the crew sit time is reduced considering the maximum legal working hours and the minimum essential rest time. Moreover, maintenance operations can be done during the ground time, not necessarily at the end of duty day. The model is never infeasible because flight cancelation is an alternative solution for recovering a schedule. A modified Tabu Search (TS) approach has been developed to solve the model. The performance of the model and solution approach is examined by several test problems in small, medium, and large scales. The results indicate that TS reaches optimal and close-optimal in a short computational time, especially in large scale problems. [ABSTRACT FROM AUTHOR]

Ficus carica Linn leaves represent a rich source of various bioactive compounds providing various features. Drying was inevitably preferred as a hopeful technique aiming to provide the availability of leaves and effective utilization of them in various fields. In the present study, the effects of microwave drying of dried Ficus carica L. leaves on optical, rehydration and bioactivity parameters were examined. The leaves were dried using a variety of microwave output power levels (180–900 W) with sample amounts (20–100 g). The effect of microwave drying on the optical properties of leaves was discussed based on the colour parameters (L, a, b) and total colour change (∆E). The final value of L, a and b and colour change (∆E) were respectively found as 37.60 ± 0.49, − 10.07 ± 0.33, 16.08 ± 0.18 and 10.19 ± 0.062 for constant sample amount (20 g) at 900 W microwave output power level. On the other hand, the final value of L, a, b and colour change (∆E) were respectively found as 33.70 ± 2.25, − 8.79 ± 0.87, 13.99 ± 0.53 and 6.52 ± 0.99 for the 100g sample amount at 360 W microwave output power level. The bioactive compounds of the leaves were handled in terms of total phenolic content (TPC) and scavenging activity (IC50) corresponding to the capability to scavenge 50% of radicals. The results explored that the TPC of fresh leaves, 46.77 ± 0.39 (mg GAE/g DE), increased to 53.15 ± 1.25 (mg GAE/g DE) by increasing microwave power intensity. Thereby, the IC50 value of fresh leaves, 768.77 ± 1.58 (μg/mL), decreased to 577.14 ± 1.85 (μg/mL). The kinetics and empirical models were successfully fitted to the experimental data obtained for microwave power intensity applied. Finally, it was concluded that the microwave technique could be safely applied for drying Ficus carica Linn leaves without detecting any undesirable change in the quality parameters monitored such as colour, rehydration and bioactive compounds. [ABSTRACT FROM AUTHOR]

Efficient steam energy production was essential for reducing energy consumption and operational costs while enhancing productivity, particularly in industrial settings prone to explosions due to boiler parameter control issues. This challenge was especially acute in the food and beverage industry amid rising energy costs and stricter environmental regulations, highlighting the importance of optimizing steam energy production. This study focused on refining operational parameters in a steam production plant to maximize steam energy output. It utilized mathematical models and optimization tools to identify ideal operational conditions and investigate extreme scenarios. Design-Expert version 13.0 statistical software and Response Surface Methodology (RSM) via Centre Composite Design (CCD) were employed to create a comprehensive design matrix encompassing key variables like time, pressure levels, temperature, mass flow rate, and steam energy production across three experimental levels. The research revealed that increased pressure and time significantly boosted steam energy production by leveraging water's energy content rise under initial conditions, thus improving efficiency by reducing required water mass circulation. Moreover, elevated temperature and extended operation enhanced economizer efficiency, leading to increased heat recovery and reduced steam generation. Steam generation also increased with temperature and time due to the pressure rise during boiling, necessitating more energy for steam conversion. An optimum yield of steam energy of 620 Cal was attained at a time, pressure, temperature, and mass flow rate of 1 h, 16.97 MPa, 249.5°C, and 59.85 kg/s, respectively. The mathematical model developed is accurate, reliable, responsive, and can replicate the experimental data due to the high F-value (24.48), low CV (0.94) low p -value (< 0.005), and high R 2 (0.9821) value close to 1. This research promises to enhance the efficiency of steam energy production in the food and beverage industry by reducing the need for resource-intensive experimental procedures, thus lowering costs and resource consumption. [ABSTRACT FROM AUTHOR]

This paper focuses on in-service teacher education as a crucial agent in driving educational paradigm shifts for the teaching and learning of mathematical modelling. Our paper presents empirical research based on two case studies, examining training courses for in-service secondary school mathematics teachers. The first course integrates different modelling approaches, while the second one is based on the anthropological theory of the didactic and uses the proposal of the study and research paths for teacher education. Both experiences conclude with teachers working on the design of modelling tasks to be implemented in secondary schools. Both courses are analysed through the lenses of how teachers progress on the collective identification of conditions and constraints for modelling, when teachers deal with the design of modelling tasks and analyse their implementations. First, the results show the kind of modelling tasks teachers design and the level of disciplinary specificity at which they plan modelling to be implemented. Second, we analyse how teachers address the "ecological analysis", that is, the identification of conditions and constraints that facilitate or hinder the integration of modelling in secondary school classrooms after their experiences. [ABSTRACT FROM AUTHOR]

Mathematical modelling of (re)emerging infectious respiratory diseases among humans poses multiple challenges for modellers, which can arise as a result of limited data and surveillance, uncertainty in the natural history of the disease, as well as public health and individual responses to outbreaks. Here, we propose a COVID-19-inspired health state diagram (HSD) to serve as a foundational framework for conceptualising the modelling process for (re)emerging respiratory diseases, and public health responses, in the early stages of their emergence. The HSD aims to serve as a starting point for reflection on the structure and parameterisation of a transmission model to assess the impact of the (re)emerging disease and the capacity of public health interventions to control transmission. We also explore the adaptability of the HSD to different (re)emerging diseases using the characteristics of three respiratory diseases of historical public health importance. We outline key questions to contemplate when applying and adapting this HSD to (re)emerging infectious diseases and provide reflections on adapting the framework for public health-related interventions. [ABSTRACT FROM AUTHOR]

Harvesting energy from nonlinear systems has been at the centre of research in the energy harvesting community. Many such proposed systems are single nonlinear harvester. While these systems show an increase in bandwidth of harvesting frequency, overall, they are not effective enough in power generation. This article studies power harvesting and frequency bandwidth characteristics of an array of harvesters. Multiple harvesters are considered with linear and nonlinear coupling between the harvesters. The phenomena of internal resonance (IR) and stochastic resonance (SR) are reported. The IR in multiple coupled nonlinear harvesters is explored using multiple-scale analysis. A parametric study is conducted to demonstrate the effect of coupling strength, frequency mistuning, innate nonlinearity and other parameters. The parametric study helped establish effective ways to increase bandwidth. Moreover, a stochastically loaded linearly coupled bistable harvester array is numerically analysed to find the effect of coupling strength and array size on the phenomenon of SR and on the system's harvesting performance. Through these studies, the potential of multiple coupled nonlinear harvesters in enhanced energy harvesting is demonstrated under both harmonic and stochastic excitation. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'. [ABSTRACT FROM AUTHOR]

Background: Recently, several novel RSV immunisation products that protect infants and older adults against RSV disease have been licensed in Europe. We estimated the effectiveness and efficiency of introducing these RSV immunisation strategies in Germany. Methods: We used a Bayesian framework to fit a deterministic age-structured dynamic transmission model of RSV to sentinel surveillance and RSV-specific hospitalisation data in Germany from 2015 to 2019. The calibrated model was used to evaluate different RSV intervention strategies over 5 years: long-acting, single-dose monoclonal antibodies (mAbs) in high-risk infants aged 1–5 months; long-acting mAbs in all infants aged 1–5 months; seasonal vaccination of pregnant women and one-time seasonal vaccination of older adults (75 + /65 + /55 + years). We performed sensitivity analysis on vaccine uptake, seasonal vs. year-round maternal vaccination, and the effect of under-ascertainment for older adults. Results: The model was able to match the various RSV datasets. Replacing the current short-acting mAB for high-risk infants with long-acting mAbs prevented 1.1% of RSV-specific hospitalisations in infants per year at the same uptake. Expanding the long-acting mAB programme to all infants prevented 39.3% of infant hospitalisations per year. Maternal vaccination required a larger number to be immunised to prevent one additional hospitalisation than a long-acting mAB for the same uptake. Vaccination of adults older than 75 years at an uptake of 40% in addition to Nirsevimab in all infants prevented an additional 4.5% of all RSV hospitalisations over 5 years, with substantial uncertainty in the correction for under-ascertainment of the RSV burden. Conclusions: Immunisation has the potential to reduce the RSV disease burden in Germany. [ABSTRACT FROM AUTHOR]

Uncertainty is ubiquitous in biological systems. For example, since gene expression is intrinsically governed by noise, nature shows a fascinating degree of variability. If we want to use a model to predict the behaviour of such an intrinsically stochastic system, we have to cope with the fact that the model parameters are never exactly known, but vary according to some distribution. A key question is then to determine how the uncertainties in the parameters affect the model outcome. Knowing the latter uncertainties is crucial when a model is used for, e.g., experimental design, optimisation, or decisionmaking. To establish how parameter and model prediction uncertainties are related, Monte Carlo approaches could be used. Then, the model is evaluated for a huge number of parameters sets, drawn from the multivariate parameter distribution. However, when model solutions are computationally expensive this approach is intractable. To overcome this problem, so-called spectral expansion (SE) methods have been developed to quantify prediction uncertainty within a probabilistic framework. Such SE methods have a basis in, e.g., computational mathematics, engineering, physics, and fluid dynamics, and, to a lesser extent, systems biology. The computational costs of SE schemes mainly stem from the calculation of the expansion coefficients. Furthermore, SE effectively leads to a surrogate model which captures the dependence of the model on the uncertainty parameters, but is much simpler to execute compared to the original model. In this paper, we present an innovative scheme for the calculation of the expansion coefficients. It guarantees that the model has to be evaluated only a restricted number of times. Especially for models of high complexity this may be a huge computational advantage. By applying the scheme to a variety of examples we show its power, especially in challenging situations where solutions slowly converge due to high computational costs, bifurcations, and discontinuities. [ABSTRACT FROM AUTHOR]

Four fundamental models that describe the spread of Monkeypox disease are analyzed: the SIR-SIR, SEIR-SIR, SIR-SEIR, and SEIR-SEIR models. They form the basis of most Monkeypox diseases models that are currently discussed in the literature. It is shown that the way the model subpopulations are organized in disease outbreaks and evolve relative to each other is determined by the relevant unstable system eigenvectors, also called order parameters. For all models, analytical expressions of the order parameters are derived. Under appropriate conditions these order parameters describe the initial outbreak phases of exponential increase in good approximation. It is shown that all four models exhibit maximally two order parameters and maximally one human–animal order parameter. The human–animal order parameter firmly connects the outbreak dynamics in the animal system with the dynamics in the human system. For the special case of the SIR-SIR model, it is found that the two possible order parameters completely describe the dynamics of infected humans and animals during entire infection waves. Finally, a simulation of a Monkeypox infection wave illustrates that in line with the aforementioned analytical results the leading order parameter explains most of the variance in the infection dynamics. [ABSTRACT FROM AUTHOR]

Two-dimensional liquid foam systems comprised of an odd number N of bubbles arranged in a staircase configuration are studied as they are packed within a confined straight channel. Energy minimization is employed to characterize the permitted topology and geometry of the system at equilibrium. It is known that in an infinite staircase, bubble films are flat. The configuration for a finite number of bubbles, however, deviates from that of an infinite staircase at the edges. It is known that films near the edges are not flat and structures with a few bubbles (up to N=3) are well characterized. To study what happens as more bubbles are added, and how the behaviour approaches that of an infinite staircase, structures comprised of N=[5,7,...] bubbles are studied here. For these N values, minimum and maximum bubble areas are found that can pack in a staircase configuration within a straight channel. However, for an infinite staircase and also for N=3 , there are maximum areas, but no minima. Staircases with very short films, which make the structure susceptible to break in various ways, are also identified. [ABSTRACT FROM AUTHOR]

AbstractThe submerged arc welding (SAW) process generates a large quantity of slag, which, when disposed-of, harms the environment and wastes valuable ingredients. A technology was developed to recycle the SAW slag generated in stainless steel cladding, enabling its reuse as flux. Bead geometry and dilution play crucial roles in weld cladding. Dilution determines the minimum number of layers needed to achieve the desired chemistry, while bead width dictates the fewest passes required to cover the entire surface area. Additionally, bead geometry influences the mechanical strength of the clad layers. The microstructure and solidification mode also significantly affect clad quality. Therefore, understanding the impact of recycled slag on bead geometry, dilution, and microstructure is essential. This paper presents a systematic investigation into the effects of process variables and recycled slag on bead geometry and dilution in clad welding. It includes a comparison of microstructures obtained using fresh flux versus recycled slag. The results indicate that increasing the welding current (I) by 37.5% enhances weld penetration (p), bead width (w), reinforcement (r), and dilution (d) by 50.9%, 14.4%, 69.5%, and 5.5%, respectively. Conversely, increasing the travel speed (V) by 90%, decreases p, w, and r by 19.29%, 11.9%, and 25.3%, respectively; however, dilution (d) increases by 17.6%. On the other hand, increasing the open-circuit voltage (V) by 26.7% increases bead width (w) and dilution (d) by 10.4% and 5.4%, respectively, while reinforcement (r) decreases by 7.5%. Both single and multilayer claddings prepared with fresh flux and recycled slag exhibit similar microstructures. [ABSTRACT FROM AUTHOR]

The aim of this study was to optimise ultrasound-assisted extraction (UAE) of the herb Sanguisorba officinalis L. in terms of the antioxidant activity (DPPH and FRAP method) and total polyphenol content (TPC). Optimisation was performed using the response surface methodology (RSM) with a third-degree (33) Central Composite Design (CCD) approach. The RSM was applied to obtain the optimal combination of (1) raw material content (2.25–7.5 g raw material/100 mL of solvent), (2) ethanol concentration (20–60% v/v), and (3) extraction time (1–15 min). The optimal conditions for the extraction of polyphenols and antioxidant potential were a raw material content of 7.5 g/100 mL of solvent (solid/solvent ratio 13.3 mL/g), an ethanol concentration of 47% v/v, and an extraction time of 10 min. At these optimal extraction parameters, the maximum extraction of polyphenols and antioxidant activity obtained experimentally was found to be very close to its predicted value and was 12.9 mmol Trolox/L (DPPH method), 19.4 mmol FeSO4/L (FRAP method), and 2.1 g GA/L (TPC). The mathematical model developed was found to fit with the experimental data on the antioxidant potential and polyphenol extraction. The n-octanol/water partition coefficient of the optimised extract was used to determine their lipophilicity. Our studies have shown that the optimised extract is highly hydrophilic (log P < 0). Optimal parameters can be used for the industrial extraction of the S. officinalis herb for the needs of, among others, the pharmaceutical or cosmetic industry. [ABSTRACT FROM AUTHOR]

While existing studies acknowledge the importance of using technology in the mathematical modelling process, questions about how to integrate digital tools into mathematical modelling are not still answered. This study aims to examine pre-service mathematics teachers' designing and solving mathematical modelling problems by using different digital tools through the collaboration of two different courses in their education program. The GeoGebra-supported modelling cycle was adopted as the conceptual framework of the study. The case study was used to design the research. The participants of the study were three volunteer pre-service secondary mathematics teachers, who were able to use and integrate digital tools in mathematics education. The pre-service teachers were asked to design a mathematical modelling problem by integrating different digital tools as the final assignment in both courses. The designed mathematical modelling problem, the video records of pre-service teachers' explanations including the designing and solving process, and the video records of the focus group interview were the data of the study. The results showed that the pre-service teachers were able to design and solve an effective and comprehensive mathematical modelling problem using technology by combining their skills that they had learnt through different courses. In particular, they were able to use different digital tools in both designing and solving a mathematical modelling problem. Their solution also led to the extension of the adopted framework by adding new technological actions into the modelling stages. [ABSTRACT FROM AUTHOR]

Climatic conditions are a key determinant of malaria transmission intensity, through their impacts on both the parasite and its mosquito vectors. Mathematical models relating climatic conditions to malaria transmission can be used to develop spatial maps of climatic suitability for malaria. These maps underpin efforts to quantify the distribution and burden of malaria in humans, enabling improved monitoring and control. Previous work has developed mathematical models and global maps for the suitability of temperature for malaria transmission. In this paper, existing temperature-based models are extended to include two other important bioclimatic factors: humidity and rainfall. This model is combined with fine spatial resolution climatic data to produce a more biologically-realistic global map of climatic suitability for Plasmodium falciparum malaria. The climatic suitability index developed corresponds more closely than previous temperature suitability indices with the global distribution of P. falciparum malaria. There is weak agreement between the Malaria Atlas Project estimates of P. falciparum prevalence in Africa and the estimates of suitability solely based on temperature (Spearman Correlation coefficient of ρ = 0.24 ). The addition of humidity and then rainfall improves the comparison ( ρ = 0.62 when humidity added; ρ = 0.70 when both humidity and rainfall added). By incorporating the impacts of humidity and rainfall, this model identifies arid regions that are not climatically suitable for transmission of P. falciparum malaria. Incorporation of this improved index of climatic suitability into geospatial models can improve global estimates of malaria prevalence and transmission intensity. [ABSTRACT FROM AUTHOR]

Key points Previous studies demonstrated that the mitochondrial Ca2+ uniporter MCU and the Na+‐Ca2+ exchanger NCLX exist in proximity to the sarcoplasmic reticulum (SR) ryanodine receptor RyR and the Ca2+ pump SERCA, respectively, creating a mitochondria‐SR Ca2+ interaction. However, the physiological relevance of the mitochondria‐SR Ca2+ interaction has remained unsolved. Furthermore, although mitochondrial Ca2+ has been proposed to be an important factor regulating mitochondrial energy metabolism, by activating NADH‐producing dehydrogenases, the contribution of the Ca2+‐dependent regulatory mechanisms to cellular functions under physiological conditions has been controversial. In this study, we constructed a new integrated model of human ventricular myocyte with excitation‐contraction‐energetics coupling and investigated systematically the contribution of mitochondria‐SR Ca2+ interaction, especially focusing on cardiac energetics during dynamic workload transitions in exercise. Simulation analyses revealed that the spatial coupling of mitochondria and SR, particularly via mitochondrial Ca2+ uniport activity‐RyR, was the primary determinant of mitochondrial Ca2+ concentration, and that the Ca2+‐dependent regulatory mechanism facilitated mitochondrial NADH recovery during exercise and contributed to the stability of NADH in the workload transition by about 40%, while oxygen consumption rate and cytoplasmic ATP level were not influenced. We concluded that the mitochondria‐SR Ca2+ interaction, created via the uneven distribution of Ca2+ handling proteins, optimizes the contribution of the mitochondrial Ca2+‐dependent regulatory mechanism to stabilizing NADH during exercise. The mitochondrial Ca2+ uniporter protein MCU and the Na+‐Ca2+ exchanger protein NCLX are reported to exist in proximity to the sarcoplasmic reticulum (SR) ryanodine receptor RyR and the Ca2+ pump SERCA, respectively, creating a mitochondria‐SR Ca2+ interaction in cardiomyocytes. Mitochondrial Ca2+ (Ca2+mit) has been proposed to be an important factor regulating mitochondrial energy metabolism, by activating NADH‐producing dehydrogenases. Here we constructed an integrated model of a human ventricular myocyte with excitation‐contraction‐energetics coupling and investigated the role of the mitochondria‐SR Ca2+ interaction in cardiac energetics during exercise. Simulation analyses revealed that the spatial coupling particularly via mitochondrial Ca2+ uniport activity‐RyR is the primary determinant of Ca2+mit concentration, and that the activation of NADH‐producing dehydrogenases by Ca2+mit contributes to NADH stability during exercise. The mitochondria‐SR Ca2+ interaction optimizes the contribution of Ca2+mit to the activation of NADH‐producing dehydrogenases. [ABSTRACT FROM AUTHOR]

Background/Objectives: The present work proposes a mathematical model able to describe the dissolution of poly-disperse drug spherical particles in a solution (Dissolution Rate Test—DRT). DRT is a pivotal test performed in the pharmaceutical field to qualitatively assess drug bioavailability. Methods: The proposed mathematical model relies on the key hallmarks of DRT, such as particle size distribution, solubility, wettability, hydrodynamic conditions in the dissolving liquid of finite dimensions, and possible re-crystallization during the dissolution process. The spherical shape of the drug particles was the only cue simplification applied. Two model drugs were considered to check model robustness: theophylline (both soluble and wettable) and praziquantel (both poorly soluble and wettable). Results: The DRT data analysis within the proposed model allows us to understand that for theophylline, the main resistance to dissolution is due to the boundary layer surrounding drug particles, whereas wettability plays a negligible role. Conversely, the effect of low wettability cannot be neglected for praziquantel. These results are validated by the determination of drug wettability performed while measuring the solid–liquid contact angle on four liquids with decreasing polarities. Moreover, the percentage of drug polarity was determined. Conclusions: The proposed mathematical model confirms the importance of the different physical phenomena leading the dissolution of poly-disperse solid drug particles in a solution. Although a comprehensive mathematical model was proposed and applied, the DRT data of theophylline and praziquantel was successfully fitted by means of just two fitting parameters. [ABSTRACT FROM AUTHOR]

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. [ABSTRACT FROM AUTHOR]

Cryopreservation of bovine oocytes and embryos is essential for long‐term preservation and widespread distribution of genetic material, particularly in bovine in vitro embryo production, which has witnessed substantial growth in the past decade due to advancements in reproductive biotechnologies. Among current cryopreservation methods, vitrification has emerged as the preferred cryopreservation technique over slow freezing for preserving oocytes and in vitro‐produced (IVP) embryos, as it effectively addresses membrane chilling injury and ice crystal formation. Nonetheless, challenges remain and a simple and robust vitrification protocol that guarantees the efficiency and viability after warming has not yet been developed. Furthermore, although slow cooling can easily be adapted for direct transfer, an easier and more practical vitrification protocol for IVP embryos is required to allow the transfer of IVP embryos on farms using in‐straw dilution. In addition, the susceptibility of bovine oocytes and embryos to cryoinjuries highlights the need for novel strategies to improve their cryotolerance. This manuscript examines various methodological approaches for increasing the viability of bovine oocytes and IVP embryos during vitrification. Strategies such as modifying lipid content or mitigating oxidative damage have shown promise in improving cryotolerance. Additionally, mathematical modelling of oocyte and embryo membrane permeability has facilitated the rational design of cryopreservation protocols, optimizing the exposure time and concentration of cryoprotectants to reduce cytotoxicity. [ABSTRACT FROM AUTHOR]

The purpose of the study is to investigate the changing mathematical connection and metacognitive skills of students in the process of applying mathematical modelling. The data used for the study were collected from modelling activities, students’ written responses to modelling activities, audio recordings, semi-structured interviews, and observation notes. The data obtained were analysed using content analysis. As a result of the data analysis, it was found that metacognitive skills were used in each activity in the mathematical modelling process and were developed in interaction with each other. Although inter-concept and real-world connection skills are most commonly used in mathematical connection skills, modelling activities were found to enhance connection skills by allowing students to use connection skills with different disciplines and between different representations of the concept. It was found that metacognitive skills and mathematical connection skills develop interdependently, and it was found that mathematical modelling activities can be used as a tool for developing both of these skills. [ABSTRACT FROM AUTHOR]

During the last years, mathematical modelling has been entering the field of engineering education because it is a bridge between mathematics and industry that also promotes the development of skills that require the engineer of the twenty-first century. Nevertheless, the modelling process is involved and requires the deployment of cognitive skills of superior level, particularly metacognitive skills. This contribution presents the results of a multiple qualitative case study on metacognitive strategies used by groups of engineering students associated with each transition of the mathematical modelling process. During the development of the course, observations and retrospective interviews were carried out, which evidenced the use of metacognitive planning strategies from simplification to interpretation; metacognitive monitoring strategies throughout the modelling process, metacognitive regulation strategies from mathematization to mathematical work and metacognitive evaluation strategies during simplification, mathematization and validation. The results expand the classification framework of metacognitive strategies for engineering students. [ABSTRACT FROM AUTHOR]

Clinical pharmacology is often the nexus in any cross‐disciplinary team that is seeking solutions for human healthcare issues. The use and application of real‐world data and artificial intelligence to better understand our ecosystem has influenced our view at the world and how we do things. This has resulted in remarkable advancements in the healthcare space and development of personalized medicines with great attributes from the application of models and simulations, contributing to a more efficient healthcare development process. A cross‐disciplinary symposium was held in December 2023, whereby experts from different scientific disciplines engaged in a high‐level discussion on the opportunities and challenges of mathematical models in different fields, possible future developments and decision making. A strong interlink amongst the disciplines represented was apparent, with clinical pharmacology identified as the one which integrates various scientific disciplines. Deliberate and strategic cross‐disciplinary dialogues are required to break out of the silos and implement integration for efficiency and cost‐effectiveness of novel interventions. [ABSTRACT FROM AUTHOR]

• Critical releasable charges (CRCs) are modeled by mechanism analysis. • Predicted CRCs are generally within 16 % compared with measured and simulated values. • Effect of air speed on concentration field under CRCs is studied by CFD. • Transition value of air speed is 0.1 and 0.2 m/s for 8 g and 14.2 g CRC respectively. Propane (R290) is a promising refrigerant for split-type household air conditioners with the advantages of energy saving, environmentally friendliness and low charge. Nevertheless, the large scale use of R290 has been hindered by its high flammability. The potential fire hazards can be relieved by reducing R290 releasable charge, and numerous methods have been put forward. In the authors' previous papers, the method of refrigerant pump-down was proposed and a mathematical model of critical releasable charge (CRC, ignition threshold of leakage) was developed by dimensional analysis, but it is difficult to reflect the actual physical process of R290 dispersion. To reveal the leakage and dispersion mechanism of R290, a new numerical model of CRC is developed based on mechanism analysis in this work, in which the turbulent jet model, Coanda effect and dimensional analysis are involved. The predicted CRCs agree well with experimental and simulated data. Besides, the influence of indoor air speed with its direction parallel to the IDU panel on R290 concentration field is investigated by computational fluid dynamics (CFD) under different releasable charges. It is found that there is a transition value of air speed, below which the fire hazards decrease greatly with the increase of air speed, on the contrary, when the air speed is above the transition value, raising air speed has little impact on fire hazards. [ABSTRACT FROM AUTHOR]

When testing biological samples with atomic force microscopy (AFM) nanoindentation using pyramidal indenters, Sneddon's equation is commonly used for data processing, approximating the indenter as a perfect cone. While more accurate models treat the AFM tip as a blunted cone or pyramid, these are complex and lack a direct relationship between applied force and indentation depth, complicating data analysis. This paper proposes a new equation derived from simple mathematical processes and physics-based criteria. It is accurate for small indentation depths and serves as a viable alternative to complex classical approaches. The proposed equation has been validated for ℎ < 3R (where h is the indentation depth and R is the tip radius) and confirmed through simulations with blunted conical and pyramidal indenters, as well as experiments on prostate cancer cells. It is a reliable method for experiments where the tip radius cannot be ignored, such as in shallow indentations on thin samples to avoid substrate effects. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic underlined the need for pandemic planning but also brought into focus the use of mathematical modelling to support public health decisions. The types of models needed (compartment, agent-based, importation) are described. Best practices regarding biological realism (including the need for multidisciplinary expert advisors to modellers), model complexity, consideration of uncertainty and communications to decisionmakers and the public are outlined. Methods: A narrative review was developed from the experiences of COVID-19 by members of the Public Health Agency of Canada External Modelling Network for Infectious Diseases (PHAC EMN-ID), a national community of practice on mathematical modelling of infectious diseases for public health. Results: Modelling can best support pandemic preparedness in two ways: 1) by modelling to support decisions on resource needs for likely future pandemics by estimating numbers of infections, hospitalized cases and cases needing intensive care, associated with epidemics of "hypothetical-yet-plausible" pandemic pathogens in Canada; and 2) by having ready-to-go modelling methods that can be readily adapted to the features of an emerging pandemic pathogen and used for long-range forecasting of the epidemic in Canada, as well as to explore scenarios to support public health decisions on the use of interventions. Conclusion: There is a need for modelling expertise within public health organizations in Canada, linked to modellers in academia in a community of practice, within which relationships built outside of times of crisis can be applied to enhance modelling during public health emergencies. Key challenges to modelling for pandemic preparedness include the availability of linked public health, hospital and genomic data in Canada. [ABSTRACT FROM AUTHOR]

Water hyacinth is one of the most significant sources of biomass in tropical regions that can be used to create biogas. This strategy aims to improve the sustainability, precise energy content, and ease of transport of the original biofuel feedstock, as well as to extract gases. An experimental investigation on the biomethanation of water hyacinth took place in a semi-batch digester. Temperature, stirring speed, and catalyst concentration all have an impact on the rate of biogas production. The catalyst has been discovered to primarily boost the rate of biogas production from water hyacinth (Eichhornia crassipes). As the catalyst is used here to boost up the biomethanation reaction, the effect of the catalyst on different kinetic parameters is investigated. The key conclusions of the research indicate that the maximum value of acidogenic cell mass concentration is 0.13 kg/m3d while the minimum value of methanogenic cell mass concentration is 0.014 kg/m3d at 50 ppm catalyst concentration. Moreover, the maximum specific growth rate of the entire process increases as the catalyst concentration rises, reaching a maximum level of 0.312 d−1 at a 50 ppm catalyst concentration. This is proof that using a catalyst can expedite the biomethanation process. As the catalyst concentration increases, so does the overall biomass concentration. Since it increases the precision of the parameter estimates, the simultaneous estimation of the parameters is a crucial part of the estimation process. [ABSTRACT FROM AUTHOR]

Manufacturing plays a vital role in fostering economic growth, yet it is one of the major consumers of energy. Considering energy consumption when devising an Aggregate Production Plan (APP) is a determinant of a more energy-efficient production schedule at the operational level. Manufacturers in emerging economies envisage planning challenges when energy reforms are introduced. A guide for analysis and decision-making in the transitional phase of phasing out energy subsidies is essential. This work tackles this problem by developing two mixed-integer linear programming models for energy-aware production planning. The first model targets maximising profit while explicitly using the energy cost as one of the cost elements. The second is an energy-driven model to minimise energy consumption while including profit as a constraint. The proposed models were validated via a real-life dataset. The models provide the decision-makers with the flexibility to improve their production plans' energy efficiency by either developing plans that reduce energy consumption without sacrificing profit or developing production plans that increase profit without increasing energy consumption. The results imply that adopting Energy-Efficient Production Planning (EEPP) is substantial with high subsidy levels. For low subsidy levels, strategic decision-making entailing the deployment of technological solutions is more efficient. [ABSTRACT FROM AUTHOR]

Most organisms have evolved specific mechanisms to respond to changes in environmental conditions such as light and temperature over the course of day. These periodic changes in the physiology and behaviour of organisms, referred to as circadian rhythms, are a consequence of intricate molecular mechanisms in the form of transcription and translational feedback loops. The plant circadian regulatory network is a complex web of interconnected feedback loops involving various transcription factors such as CCA1, LHY, PRRs, TOC1, LUX, ELF3, ELF4, RVE8, and more. This network enables plants to adapt and thrive in diverse environmental conditions. It responds to entrainment signals, including light, temperature, and nutrient concentrations and interacts with most of the physiological functions such as flowering, growth and stress response. Mathematical modelling of these gene regulatory networks enables a deeper understanding of not only the function but also the perturbations that may affect the plant growth and function with changing climate. Over the years, numerous mathematical models have been developed to understand the diverse aspects of plant circadian regulation. In this review, we have delved into the systematic development of these models, outlining the model components and refinements over time. We have also highlighted strengths and limitations of each of the models developed so far. Finally, we conclude the review by describing the prospects for investigation and advancement of these models for better understanding of plant circadian regulation.Key message: Plant circadian rhythms are regulated by a complex network of interactions. This review summarizes the mathematical models that have been developed until now to explore plant circadian rhythms. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusion To develop a semimechanistic model that describes the kinetic profile and variability of antibody (Ab) concentrations following vaccination with Ad26.COV2.S at different doses and dosing intervals.Data were collected from participants randomized into 5 clinical trials receiving the Ad26.COV2.S vaccine. The model considered key elements of humoral immune response, dose proportionality and the evolutionary processes of the immune response. Interindividual variability and covariates were explored.Fast and slow kinetic phases of Ab and their evolution over time were differentiated. After first and second administrations, Ab concentrations of both phases increased less than dose proportionally, indicating a saturation of B‐cell production processes. Ab concentrations produced during the fast kinetic phase increased significantly after the second administration, indicating an underlying evolutive process after antigen exposures. For the slow kinetic phase, a less pronounced increase occurred after the second and third administrations but was relatively higher in subjects who had low concentrations after the first administration. Ab concentrations of the slow phase were higher in females and decreased with age. After multiple administrations, the fast phase had Ab maximum concentrations about 5 times higher than the slow phase. The limiting kinetic factors in the fast and slow phases were the elimination rates of Ab itself and Ab producing cells, respectively.The model appears suitable to quantitatively describe the inter‐ and intraindividual kinetics of the immune response and the impact of covariates after multiple administrations of a vaccine. [ABSTRACT FROM AUTHOR]

Background: Including structural determinants (e.g. criminalisation, stigma, inequitable gender norms) in dynamic HIV transmission models is important to help quantify their population-level impacts and guide implementation of effective interventions that reduce the burden of HIV and inequalities thereof. However, evidence-based modelling of structural determinants is challenging partly due to a limited understanding of their causal pathways and few empirical estimates of their effects on HIV acquisition and transmission. Methods: We conducted a scoping review of dynamic HIV transmission modelling studies that evaluated the impacts of structural determinants, published up to August 28, 2023, using Ovid Embase and Medline online databases. We appraised studies on how models represented exposure to structural determinants and causal pathways. Building on this, we developed a new methodological framework and recommendations to support the incorporation of structural determinants in transmission dynamics models and their analyses. We discuss the data and analyses that could strengthen the evidence used to inform these models. Results: We identified 17 HIV modelling studies that represented structural determinants and/or interventions, including incarceration of people who inject drugs (number of studies [n] = 5), violence against women (n = 3), HIV stigma (n = 1), and housing instability (n = 1), among others (n = 7). Most studies (n = 10) modelled exposures dynamically. Almost half (8/17 studies) represented multiple exposure histories (e.g. current, recent, non-recent exposure). Structural determinants were often assumed to influence HIV indirectly by influencing mediators such as contact patterns, condom use, and antiretroviral therapy use. However, causal pathways' assumptions were sometimes simple, with few mediators explicitly represented in the model, and largely based on cross-sectional associations. Although most studies calibrated models using HIV epidemiological data, less than half (7/17) also fitted or cross-validated to data on the prevalence, frequency, or effects of exposure to structural determinants. Conclusions: Mathematical models can play a crucial role in elucidating the population-level impacts of structural determinants and interventions on HIV. We recommend the next generation of models reflect exposure to structural determinants dynamically and mechanistically, and reproduce the key causal pathways, based on longitudinal evidence of links between structural determinants, mediators, and HIV. This would improve the validity and usefulness of predictions of the impacts of structural determinants and interventions. [ABSTRACT FROM AUTHOR]

The evaluation and prediction of reliability and testability of mining machinery and equipment are crucial, as advancements in mining technology have increased the importance of ensuring the safety of both the technological process and human life. This study focuses on developing a reliability model to analyze the controllability of mining equipment. The model, which examines the reliability of a mine cargo-passenger hoist, utilizes statistical methods to assess failures and diagnostic controlled parameters. It is represented as a transition graph and is supported by a system of equations. This model enables the estimation of the reliability of equipment components and the equipment as a whole through a diagnostic system designed for monitoring and controlling mining equipment. A mathematical and logical model is proposed to calculate availability and downtime coefficients for different structures within the mining equipment system. This analysis considers the probability of failure-free operation of the lifting unit based on the structural scheme, with additional redundancy for elements with lower reliability. The availability factor of the equipment for monitoring and controlling the mine hoisting plant is studied for various placements of diagnostic systems. Additionally, a logistic concept is introduced for organizing preventive maintenance systems and reducing equipment recovery time by optimizing spare parts, integrating them into strategies aimed at enhancing the reliability of mine hoisting plants. [ABSTRACT FROM AUTHOR]

Background: After abandoning the zero-case strategy for COVID-19 in December 2022 in mainland China, the Hong Kong Special Administrative Region of China (HKSAR) has undergone an anticipated surge of the prevalence of COVID-19, as well as other influenzas, such as influenza A/H1N1, influenza A/H3N2, and influenza B as well. Noteworthy, people are usually concerned about the mutual influences between two families of respiratory viruses, like co-circulation or co-infection. Methods: We fitted a unified model to weekly reported severe COVID-19 cases and the confirmed influenza A laboratory cases in HKSAR, respectively, using the R package POMP to obtain the best fitting and parameter estimates. The reconstructed transmission rates of the COVID-19 (/influenza A) versus the weekly reported influenza A (/COVID-19) confirmations between April 2022 and April 2024 were also compared. Results: Our numerical results suggest that influenza confirmations remained either at a very low level or were absent before 2023, while starting from 2023, the influenza epidemic re-emerged as expected because of the resumption of international travels and other social communications. Besides, the peak of influenza cases in April 2023 favored the form of the peak of COVID-19 between May–June, 2023. Additionally, during the sudden abolishment of the zero-case policy in mainland China (December 2022 to January 2023), we estimated that there were approximately 381 cases imported from mainland China into HKSAR. Conclusions: We estimated the potential number of imported COVID-19 severe cases from mainland China to Hong Kong and revealed some potential population-level interference between the two families of respiratory viruses. [ABSTRACT FROM AUTHOR]

Plant–soil feedback (PSF), the reciprocal interaction between plants and their soil environment, is a fundamental ecological process that can influence coexistence and functional structure in plant communities. Current theory establishes that PSF may enhance diversity or lead to exclusion depending on whether soil conditioning disproportionately benefits heterospecific or conspecific individuals. However, a more complete picture of the impact of PSF requires understanding how PSF interacts with competition. To that end, here we propose an integrated mathematical model combining trait‐based competition and soil‐explicit PSF. Contrary to the current paradigm, we find that soil conditioning that disproportionately favours conspecific individuals can promote coexistence. Additionally, we show that priority effects are common when soil‐conditioning species differ in their edaphic preferences. These effects can allow species with large differences in competitive ability to coexist under certain soil conditions. Our results provide testable predictions tying community‐level functional patterns in plant communities to PSF and competition. [ABSTRACT FROM AUTHOR]

Based on assumptions and limitations, a mathematical model of the non-stationary thermal state of a composite coating during laser remelting on a metal substrate to obtain an alloy of equimolar or close to equimolar composition has been developed. The model includes the boundary value problem of calculating the nonstationary thermal state of the temperature distribution in the solid, two-phase and liquid regions, considering the heat release of crystallization according to the theory of a quasi-equilibrium two-phase zone in the presence of porosity of a mixture of metal powders. Based on the mathematical model, the computer program "Composite coating thermal state" was created, which allows to determine the non-stationary temperature distribution in the formed coating and substrate, the depth of penetration of the high-entropy coating layer, to evaluate the shrinkage of the molten coating under different technological conditions: laser power, the diameter of its focal spot, as well as the speed of movement along the surface of the processed material. The created program also allows to determine the dynamics of the position of two-dimensional liquidus and solidus lines for each metal included in the mixture. Using the created program, a computer simulation of the process of unsteady heating of a mixture of three metal powders in the same mass fractions: nickel, chromium and iron was carried out. Non-stationary temperature fields in the composite coating are obtained, considering its penetration and heating of the substrate. The depth of penetration of all metals, including the most low-melting one, was determined during laser remelting on a metal substrate in the production of a high-entropy alloy. The practical significance of the developed computer model lies in its use to predict the penetration depth of a composite coating under specified technological modes of laser remelting. At the same time, its adaptation is necessary, based on experimental data on the porosity and thickness of the coating before and after melting of simple metal systems with similar thermophysical properties. [ABSTRACT FROM AUTHOR]

Based on expectancy-value theory, this study adopted a person-centred approach to explore the heterogeneous profiles of secondary German and Taiwanese students' mathematical modelling task values, and examined the differences in their mathematical modelling performance, controlling for the variable of intra-mathematical knowledge among the heterogeneous profiles. Authors conducted a survey study of 452 ninth graders (201 German students and 251 Taiwanese students). The results showed that German and Taiwanese students respectively displayed three profiles of mathematical modelling task values: a) moderate utility and moderate interest/attainment, b) high utility but low interest/attainment, and c) low utility but high interest/attainment. Furthermore, different profiles of mathematical modelling task values showed significant differences in mathematical modelling performance for Taiwanese students but not for German students, even after removing the variable of intra-mathematical knowledge. This study advances the understanding of students' mathematical modelling task values and its relation with their mathematical modelling performance by the expectancy-value model of achievement motivation and person-centred analyses, and sheds light on the learning and teaching of mathematical modelling. [ABSTRACT FROM AUTHOR]

Working with approximations is a common practice in physics. This paper presents an exploratory study of student understanding of some of the elementary aspects of approximations encountered in college physics. For this purpose, a questionnaire (a set of 14 multiple-choice questions) was developed to probe how students dealt with these aspects in a variety of contexts. The questionnaire was finalized after a trial of its pilot version on 7 students on the basis of their written scripts and interviews. After content validation by three external experts, the test was administered to a sample of 141 senior undergraduate physics students from a diverse set of colleges and national institutes. Data analysis reveals several significant student pitfalls in the topic related principally to (i) the identification of a dimensionless ratio to characterize an approximation, (ii) the notion of the order of approximation and consistent handling of a problem up to the required order, and (iii) the notion of the order of infinitesimal in calculus-based derivations. The findings are of direct pedagogic relevance. [ABSTRACT FROM AUTHOR]

Fibrous dysplasia (FD) is a mosaic non-inheritable genetic disorder of the skeleton in which normal bone is replaced by structurally unsound fibro-osseous tissue. There is no curative treatment for FD, partly because its pathophysiology is not yet fully known. We present a simple mathematical model of the disease incorporating its basic known biology, to gain insight on the dynamics of the involved bone-cell populations, and shed light on its pathophysiology. We develop an analytical study of the model and study its basic properties. The existence and stability of steady states are studied, an analysis of sensitivity on the model parameters is done, and different numerical simulations provide findings in agreement with the analytical results. We discuss the model dynamics match with known facts on the disease, and how some open questions could be addressed using the model. [ABSTRACT FROM AUTHOR]

Every year, the problem of environmental pollution from metallurgical waste is gaining momentum. In particular, these are metallurgical wastes containing residual metals. However, with the help of innovative methods of processing lead-containing slags, they can be turned into valuable resources with minimal impact on the environment. This article discusses the importance of recycling lead-containing slag and describes various methods of recycling. The scientific novelty of the article is to analyze the influence of the content of toxic metals, such as lead, chromium, zinc, and copper, in the products obtained after roasting around the waste storage. The results of the optimization of experimental data allowed to specify the composition of the mixture temperature of the process of ignition of residual and corrosion-protective metals chromium and nickel, with the indicators of dependencies in the three-dimensional model. The results of the mathematical planning of the processing of waste slag and galvanic sludge showed that the recovery of lead reached 93-95%, chromium-94-96% nickel-93%, and copper-90%. The obtained product is identified as a composite material by its elemental composition and microstructure. In the general structure of the surface, firing products are characterized by the presence of the aluminosilicate minerals melilite, merwinite, and ockermanite, as well as calcium monosilicates, in the form of irregular forms of light gray lamellar crystals. [ABSTRACT FROM AUTHOR]

Background: Throughout the SARS-CoV-2 pandemic, policymakers have had to navigate between recommending voluntary behaviour change and policy-driven behaviour change to mitigate the impact of the virus. While individuals will voluntarily engage in selfprotective behaviour when there is an increasing infectious disease risk, the extent to which this occurs and its impact on an epidemic is not known. Methods: This paper describes a deterministic disease transmission model exploring the impact of individual avoidance behaviour and policy-mediated avoidance behaviour on epidemic outcomes during the second wave of SARS-CoV-2 infections in Ontario, Canada (September 1, 2020 to February 28, 2021). The model incorporates an information feedback function based on empirically derived behaviour data describing the degree to which avoidance behaviour changed in response to the number of new daily cases COVID-19. Results: Voluntary avoidance behaviour alone was estimated to reduce the final attack rate by 23.1%, the total number of hospitalizations by 26.2%, and cumulative deaths by 27.5% over 6 months compared to a counterfactual scenario in which there were no interventions or avoidance behaviour. A provincial shutdown order issued on December 26, 2020 was estimated to reduce the final attack rate by 66.7%, the total number of hospitalizations by 66.8%, and the total number of deaths by 67.2% compared to the counterfactual scenario. Conclusion: Given the dynamics of SARS-CoV-2 in a pre-vaccine era, individual avoidance behaviour in the absence of government action would have resulted in a moderate reduction in disease however, it would not have been sufficient to entirely mitigate transmission and the associated risk to the population in Ontario. Government action during the second wave of the COVID-19 pandemic in Ontario reduced infections, protected hospital capacity, and saved lives. [ABSTRACT FROM AUTHOR]

Computational thinking is of great importance in developing students' ability to analyse complex problems in a systematic way, to develop appropriate strategies for solving these problems, and to use technology effectively in applying these solutions. In this context, mathematical modelling activities, which involve solving complex real-life situations through mathematics, stand out as an effective method for developing these skills in students. The purpose of this study is to investigate the effect of mathematical modelling activities on students' computational thinking (CT) self-efficacy. The participants of the study were English preparatory students at a high academic level public school in Istanbul. The modelling activities used in the study were restructured by considering the steps of computational thinking skills. Among these steps, the Python software language was used in the 'algorithmic thinking' step, and the students translated their problem-solving systems into software language through Python. As a data collection tool, in addition to the activities, the CT self-efficacy scale was used as a pre- and post-test. The mathematical modelling activities were applied to the high school preparatory class students, who were the research group, for a period, and the change in the students' CT self-efficacy was evaluated in the process. The results showed that modelling activities led to a significant increase in students' CT self-efficacy. It was found that students felt more competent in sub-dimensions such as problem solving, analytical thinking and algorithmic thinking. These findings suggest that mathematical modelling activities are a powerful tool for developing computational thinking skills. In conclusion, this study suggests that mathematical modelling activities should be more widely incorporated into the secondary school curriculum to develop computational thinking skills and draws attention to the importance of professional support for teachers in this process. [ABSTRACT FROM AUTHOR]

Tailings dams generally face problems such as a large number of hazardous reservoirs and difficulties in supervision, and the current numerical models related to tailings dam failure have shortcomings such as weak targeting and reliance on empirical formulas. In order to solve the above problems, based on the CTGAN data enhancement algorithm of 132 dam break cases database, a tailings dam break numerical model (hereinafter referred to as TDB_S) is established and applied to the analysis of 18 tailings dam break cases. By comparing the simulation results of the TDB-S model with three other common dam failure models (DAMBRK model, BREACH model, and Cellular Automaton model), it was found that the TDB-S model can effectively predict the expansion of the dam breach and the peak flow rate under most operating conditions. The comparison results with other models show that the average flow error of the TDB-S model in predicting the collapse process of 18 tailings dams worldwide is 12.36%; the average error of the breach is 13.28%. The above conclusion proves that the TDB-S model has better simulation effect and higher accuracy compared to other models, and can provide a certain reference basis for the study of tailings dam breach safety issues. [ABSTRACT FROM AUTHOR]

Radical or partial nephrectomy, commonly used for the treatment of kidney tumors, is a surgical procedure with a risk of high blood loss. The primary aim of this study is to quantify blood loss and elucidate the redistribution of blood flux and pressure between the two kidneys and the abdominal aorta during renal resection. We have developed a robust research methodology that introduces a new lumped-parameter mathematical model, specifically focusing on the vasculature of both kidneys using a non-Newtonian Carreau fluid. This model, a first-order approximation, accounts for the variation in the total impedance of the vasculature when various vessels are severed in the diseased kidney (assumed to be the left in this work). The model offers near real-time estimations of the flow–pressure redistribution within the vascular network of the two kidneys and the downstream aorta for several radical or partial nephrectomy scenarios. Notably, our findings indicate that the downstream aorta receives an approximately 1.27 times higher percentage of the redistributed flow from the diseased kidney compared to that received by the healthy kidney, in nearly all examined cases. The implications of this study are significant, as they can inform the development of surgical protocols to minimize blood loss and can assist surgeons in evaluating the adequacy of the remaining kidney vasculature. [ABSTRACT FROM AUTHOR]

The estimation of machine's centre of gravity (COG) dislocation is presented in this article. Various types of agricultural tractors' parameters were analysed. Their parameters such as track width in the range of 〈1222 mm, 1900 mm〉 and COG height in the range of 〈830 mm, 1340 mm〉 are known from the reports of OECD tests. The static stability of the machine is defined by the critical angle of labile equilibrium or critical overturning angle in static disposition, which is determined from measurement on the tilt table. The critical overturning angle can be calculated from the known parameters of COG dislocation. The critical overturning angle for each analysed machine can be evaluated from the obtained dataset. The mean and standard deviation of parameters were used as input parameters into Monte Carlo simulation (MCS). The MCS method was used for the estimation of the critical overturning angle and height of COG for 2500 data. Simulation was performed using a Mathcad® Prime 9.0 software. The basic statistical analysis was performed using a Spyder software in the Python language. The polynomial approximation equation was set up, and then COG heights were calculated. The critical overturning angle was calculated from the real and simulated parameters. The functions of the critical overturning angle were compared, and the fit of approximation was evaluated by the R2 parameter. The designed model was verified by the F-distribution test. [ABSTRACT FROM AUTHOR]

A fundamental analysis is presented to describe the dissolution behavior of lime (CaO) in FeO-containing slags. FeO present in steelmaking slag is seen to penetrate preferentially into the lime particle and form a (CaO–FeO) layer around it, thereby a pathway for outward diffusion of CaO into the slag and subsequent formation of dicalcium silicate. In the present work, the lime dissolution behavior is described in terms of the chemical potential gradient of CaO between the lime particle and slag. The chemical potential gradient is more fundamental for calculating fluxes in concentrated melts in which the activities are far from ideal, which has been ignored by previous researchers. The model developed in the present work is used to compare the mass transfer coefficients for a variety of experimental conditions. In addition, other factors affecting the dissolution are lime particle morphology, activity of silica in slag, slag viscosity, temperature and turbulence in slag. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, the Chinese government implemented nationwide public health interventions to control its spread. However, the impact of these measures on other infectious diseases remains unclear. Methods: The incidence of three types of notifiable infectious diseases in China were analyzed between 2013 and 2021. The seasonal Mann-Kendall test and Mann-Kendall mutation test were employed to examine trends and mutations in the time series. Based on the counterfactual inference, historical incidence rates were employed to construct SARIMA models and predict incidence between January 2020 and December 2021. Differences between reported and predicted incidences during the pandemic were compared using the Mann-Whitney U test. Results: Between 2013 and 2019, the incidence rate of three types of notifiable infectious diseases fluctuated between 494.05/100,000 and 550.62/100,000. No discernible trend was observed for types A and B infectious diseases (Z = −1.344, P = 0.18). A significant upward trend was observed for type C infectious diseases (Z = 2.56, P = 0.01). In 2020, the overall incidence rate of three types of notifiable infectious diseases decreased to 367.08/100,000. Compared to predicted values, the reported incidence of three types of infectious diseases was, on average, 30.05% lower in 2020 and 16.58% lower in 2021. Conclusion: The public health interventions implemented during the pandemic had a positive consequence on the prevention and control of other infectious diseases, with a particularly notable effect on type C infectious diseases. Among the diseases with different transmission routes, respiratory diseases and gastrointestinal or enteroviral diseases decreased significantly. [ABSTRACT FROM AUTHOR]