Mathematical Model For Prediction Research Articles

Properties of alkali-activated slag mortar and prediction of its compressive strength

An investigation was carried out on alkali-activated slag AAS mortar mixtures cast with three different water-to-binder ratios W/B; 0.35, 0.40, and 0.45, eight different silica modulus Ms, wide range of Na2O%, activated using a mixture of two different activators; waterglass and sodium hydroxide, and cured with two different curing methodologies; sealing and full immersion in water. The standard 70.6 mm mortar cubes were tested under compression at the age of 3 days and 7 days. Furthermore, their flow, setting time, and longitudinal shrinkage were measured. The results showed that not only the percentage of Na2O is important; but, its source. Additionally, curing methodology affected the compressive strength at both ages where the full immersion in water may cause early development of strength, with no later strength gain. Also, flow test results showed that although increasing the W/B ratio can cause an increase in the flow, however, the strength may be affected negatively at the highest W/B ratio. Drying Shrinkage of AAS mortar mixtures was also found to be very sensitive to the W/B ratio. Furthermore, four mathematical models were used to generate prediction equations of the compressive strength of AAS mortar based on the experimental data, and for both curing methods. A comparison between predictions of different mathematical models was made based on the highest coefficient of determination R2, score, and standard error. The models were verified using four AAS mortar mixtures that were casted and tested at the age of 7 days under compression. The results showed that two models closely predicted the actual strengths, except at compressive strength higher than 60 MPa, where the models were significantly conservative.

Mathematical model for prediction of compressive strength of ternary blended cement concrete utilizing gene expression programming

In order to encourage the utilization of rice husk ash (RHA), ceramic waste powder (CWP) and glass waste powder (GWP) in their ternary combinations in the production of concrete as well as determine the variability in the resulting compressive strength (CS), the application of soft-computing algorithms is of great interest. Therefore, this study developed a predictive model using the Gene Expression Programming (GEP) technique. The predictive model was trained and developed using extensive and trustworthy data of the compressive strength of ternary blended cement concrete comprising four (4) input parameters, including the RHA, CWP, GWP, and curing day (CD). The model was developed based on 70 % training datasets, and the model's accuracy was checked using 15 % testing datasets; the model's outcomes were validated using 15 % experimental datasets. The performance of the developed model was further assessed by applying statistical checks, comparing regression models and sensitivity analysis. The R-values in the training, testing and validation phases of GEP models are 0.95, 0.93 and 0.89 respectively with an objective function (OF) of 0.04. Based on the optimum GEP model, a closed-form mathematical equation is presented, proving to have excellent adaptability, predictive ability and capability of accurately estimating the compressive strength of concrete made from ternary blended cement mixes. Therefore, the outcomes of this study can help the construction sector predict the properties of pozzolanic concrete and manage scarce resources.

Comprehensive analysis of dust impact on photovoltaic module temperature: Experimental insights and mathematical modeling

Dust accumulation substantially impacts the efficiency and thermal behavior of photovoltaic (PV) modules. Addressing a current knowledge gap, this article presents a comprehensive assessment of the impact of dust on PV module temperature. This endeavor includes a combination of systematic experiments with a novel developed mathematical model that uniquely incorporates key dust parameters such as emissivity, absorbance, and transmittance. The model enhances accuracy in estimating PV module temperature compared to existing mathematical models, which often overlook heat absorption due to dust and specific dust characteristics. These parameters are measured using specialized devices to ensure realistic values. Outdoor experiments are conducted to validate the model's predictions under real weather conditions, further highlighting the importance of accurate PV temperature estimation in dusty environments. Additionally, a comparative analysis is performed with existing mathematical models for PV temperature prediction, demonstrating the superior performance of the proposed model, which achieves the lowest average prediction error (mean absolute error of 1.4). These findings provide valuable insights into the estimation of PV temperature in dusty conditions, bridging the gap between theoretical modeling and practical application and underscoring the novelty and innovation introduced in this research.

Transient polymorphisms in parental care strategies drive divergence of sex roles

The parental roles of males and females differ considerably between and within species. By means of individual-based evolutionary simulations, we strive to explain this diversity. We show that the conflict between the sexes creates a sex bias (towards maternal or paternal care), even if the two sexes are initially identical. When including sexual selection, there are two outcomes: either female mate choice and maternal care or no mate choice and paternal care. Interestingly, the care pattern drives sexual selection and not vice versa. Longer-term simulations exhibit rapid switches between alternative parental care patterns, even in constant environments. Hence, the evolutionary lability of sex roles observed in phylogenetic studies is not necessarily caused by external changes. Overall, our findings are in striking contrast to the predictions of mathematical models. We show that the discrepancies are caused by transient within-sex polymorphisms in parental strategies, a factor largely neglected in current sex-role theory.

Simplified electrocoagulation for efficient biodiesel washing water treatment

Biodiesel is commonly purified by washing with water, but its improper disposal poses serious environmental and economic issues. Electrocoagulation (EC) is an attractive technique for treating biodiesel fuel wastewater, owing to its effectiveness, ease of operation, and low cost operation. In this study, an experimental factorial design was employed to investigated the minimum parameters required for efficient and cost-effective treatment (pH: 3 – 9; voltage: 5 – 20 V). All experiments were conducted at room temperature (25º C) using a 1 L acrylic monopolar batch reactor. Aluminum electrode (12x5x 0.1cm) were used. The effluents were characterized before and after treatments, measuring Chemical Oxygen Demand (COD), pH, color, turbidity, conductivity, Dissolved Oxygen (DO), Oil & Grease (O&G), Total Dissolved Solids (TDS), fixed solids, and volatile solids. Kinetic experiments were performed to determine the minimum operating time (30 min). The obtained results demonstrated significant removal of turbidity (94.5%), color (96.4%), COD (83.9%), conductivity (37%), and O&G (93%). Additionally, the experimental and theoretical values showed good agreement, allowing for the development of predictive mathematical models for the analytical responses.

Temperature modulates PVN pre-sympathetic neurones via transient receptor potential ion channels.

The paraventricular nucleus (PVN) of the hypothalamus plays a vital role in maintaining homeostasis and modulates cardiovascular function via autonomic pre-sympathetic neurones. We have previously shown that coupling between transient receptor potential cation channel subfamily V Member 4 (Trpv4) and small-conductance calcium-activated potassium channels (SK) in the PVN facilitate osmosensing, but since TRP channels are also thermosensitive, in this report we investigated the temperature sensitivity of these neurones. Methods: TRP channel mRNA was quantified from mouse PVN with RT-PCR and thermosensitivity of Trpv4-like PVN neuronal ion channels characterised with cell-attached patch-clamp electrophysiology. Following recovery of temperature-sensitive single-channel kinetic schema, we constructed a predictive stochastic mathematical model of these neurones and validated this with electrophysiological recordings of action current frequency. Results: 7 thermosensitive TRP channel genes were found in PVN punches. Trpv4 was the most abundant of these and was identified at the single channel level on PVN neurones. We investigated the thermosensitivity of these Trpv4-like channels; open probability (Po) markedly decreased when temperature was decreased, mediated by a decrease in mean open dwell times. Our neuronal model predicted that PVN spontaneous action current frequency (ACf) would increase as temperature is decreased and in our electrophysiological experiments, we found that ACf from PVN neurones was significantly higher at lower temperatures. The broad-spectrum channel blocker gadolinium (100µM), was used to block the warm-activated, Ca2+-permeable Trpv4 channels. In the presence of gadolinium (100µM), the temperature effect was largely retained. Using econazole (10µM), a blocker of Trpm2, we found there were significant increases in overall ACf and the temperature effect was inhibited. Conclusion: Trpv4, the abundantly transcribed thermosensitive TRP channel gene in the PVN appears to contribute to intrinsic thermosensitive properties of PVN neurones. At physiological temperatures (37°C), we observed relatively low ACf primarily due to the activity of Trpm2 channels, whereas at room temperature, where most of the previous characterisation of PVN neuronal activity has been performed, ACf is much higher, and appears to be predominately due to reduced Trpv4 activity. This work gives insight into the fundamental mechanisms by which the body decodes temperature signals and maintains homeostasis.

A Field-Verified Model to Estimate Landfill Methane Flux Using Surface Methane Concentration Measurements under Calm Wind Conditions

A significant portion of anthropogenic greenhouse gas emissions––that are suspected of contributing to global warming––are emitted from landfills accepting biodegradable organic waste. Thus, the assessment of fugitive methane emissions from landfill surfaces has become an important and integral part of determining the overall carbon budgets of individual countries. Quantifying fugitive methane emissions from landfills using indirect methods, such as predictive mathematical models, which has been the norm for a long time, have proven to be ineffective for current needs. In recent years, attention has shifted to direct measurements, such as the flux chamber method, which is also costly and labor intensive, considering the vast areas covered by landfill surfaces. However, collecting surface methane concentration (SMC) data through an instantaneous emission measurement technique is relatively simple and inexpensive. Although the SMC is only a qualitative indicator of the methane flux of a landfill, recent literature on this topic has pointed to a potentially strong correlation between SMCs and methane flux. In this context, establishing a simple but robust model, capable of estimating methane surface flux using SMC values, was the primary aim of the research described here. In this study, we investigated the correlation between SMCs and methane flux across the soil–atmosphere boundary in a small-scale test cell under a partially controlled environment. The data collected from the test cell were used to propose a linear regression model that suggested that the methane flux under calm wind conditions could be simply predicted as 1.264 times the SMC. This model was then verified in a field lysimeter, which consisted of material similar to what is found in a landfill biocover. The model was able to predict the methane flux at the field lysimeter with an absolute average error of 40%. This error margin is quite reasonable because, as per the literature, error margins as high as 190% are not uncommon in estimating the methane flux at landfills using other commonly adopted methods.

Predictive analysis of the power spectral irradiance from blackbody radiation source using single pixel detector

Accurate spectral irradiance measurement in the near-infrared range is significant for the design and characterization of photodetector and photovoltaic cells. Approximation method is commonly used to solve for the input power using estimated spectral irradiance, where the dependency on wavelength and temperature remains uncertain. This study aims to determine the power spectrum at different radiation temperatures using a single pixel photodetector, taking into consideration factors such as transmission spectra of alumina radiator, CaF2 collimating lens, responsivity, and measured photocurrent information of photodetectors. Utilizing predictive mathematical model, five commercial photodetectors, including Silicon, Germanium, In0.53Ga0.47As, In0.73Ga0.27As, and In0.83Ga0.17As were used to solve for the power densities as a function of wavelengths at radiation temperatures of 1000 °C and 1500 °C. The spectral irradiance of photodetectors was determined with a percentage difference of <4.9 %, presenting an accurate power density estimation for the spectrum at a wide range of radiation temperatures. Power irradiance data obtained were validated in the narrow wavelength range with 1000 nm, 1400 nm, 1500 nm, and 2000 nm bandpass filters. The reported work demonstrates a simple and efficient way which could contribute to develop a cost-effective method of measuring and determining the spectrum irradiances of objects at different radiation temperatures. This predictive analysis method hopefully intensifies the progress of efforts to reduce the reliance on complex optoelectronic instruments in accurately solving power irradiance information.

Electrical power output prediction of combined cycle power plants using a recurrent neural network optimized by waterwheel plant algorithm

It is difficult to analyze and anticipate the power output of Combined Cycle Power Plants (CCPPs) when considering operational thermal variables such as ambient pressure, vacuum, relative humidity, and temperature. Our data visualization study shows strong non-linearity in the experimental data. We observe that CCPP energy production increases linearly with temperature but not pressure. We offer the Waterwheel Plant Algorithm (WWPA), a unique metaheuristic optimization method, to fine-tune Recurrent Neural Network hyperparameters to improve prediction accuracy. A robust mathematical model for energy production prediction is built and validated using anticipated and experimental data residuals. The residuals’ uniformity above and below the regression line suggests acceptable prediction errors. Our mathematical model has an R-squared value of 0.935 and 0.999 during training and testing, demonstrating its outstanding predictive accuracy. This research provides an accurate way to forecast CCPP energy output, which could improve operational efficiency and resource utilization in these power plants.

Novel experimental technique to evaluate soil thermal conductivity in a transient state

novel experimental technique to evaluate soil thermal conductivity in a transient state

Computer-aided drug repurposing to tackle antibiotic resistance based on topological data analysis

The progressive emergence of antimicrobial resistance has become a global health problem in need of rapid solution. Research into new antimicrobial drugs is imperative. Drug repositioning, together with computational mathematical prediction models, could be a fast and efficient method of searching for new antibiotics. The aim of this study was to identify compounds with potential antimicrobial capacity against Escherichia coli from US Food and Drug Administration-approved drugs, and the similarity between known drug targets and E. coli proteins using a topological structure-activity data analysis model. This model has been shown to identify molecules with known antibiotic capacity, such as carbapenems and cephalosporins, as well as new molecules that could act as antimicrobials. Topological similarities were also found between E. coli proteins and proteins from different bacterial species such as Mycobacterium tuberculosis, Pseudomonas aeruginosa and Salmonella Typhimurium, which could imply that the selected molecules have a broader spectrum than expected. These molecules include antitumor drugs, antihistamines, lipid-lowering agents, hypoglycemic agents, antidepressants, nucleotides, and nucleosides, among others. The results presented in this study prove the ability of computational mathematical prediction models to predict molecules with potential antimicrobial capacity and/or possible new pharmacological targets of interest in the design of new antibiotics and in the better understanding of antimicrobial resistance.

Mathematical prediction model construction of network packet loss rate and nonlinear mapping user experience under the Internet of Things

Abstract In order to further improve the prediction accuracy, the network packet loss rate (PLR) prediction mathematical model based on the Internet of Things (IoTs) was proposed. First, the network data transmission module was established, and the network PLR prediction process was developed based on IoTs; second, the prediction framework of PLR was designed to obtain more accurate prior information. The relationship between PLR and user experience quality QoE is univariate and nonlinear. The mapping between PLR and user experience quality QoE is established using univariate nonlinear regression analysis; finally, a mathematical model of network PLR prediction is constructed to further improve the prediction accuracy. Experimental results show that the delays of network nodes are all within 5 s, which can ensure the real-time nature of data transmission. When the total number of packets and the number of lost packets are the same, the PLR predicted by the mathematical model designed by the authors is consistent with the actual PLR. Conclusion: The prediction effect of the model is better and has higher promotion value.

Interfacial effects on the nucleation probability of gas hydrates in porous media

Understanding how interfacial properties affect the nucleation process of gas hydrates allows for the prediction of formation patterns of gas hydrates in natural sediments, facilitation of gas-hydrate production for energy storage, or inhibition of gas-hydrate formation for flow assurance. A predictive macroscopic mathematical model describing the kinetic nucleation of gas hydrates was developed based on Classical Nucleation Theory (CNT) in order to incorporate the interfacial properties of sediments and other interfaces. The model provides insights into the dependence of the formation mechanism of gas hydrates on a wide range of interfacial properties. The models for nucleation of gas hydrates presented in this work predict two different scenarios for gas hydrates formation in sediments. In hydrophilic sediments, gas hydrates start nucleating at the sediment wall towards a slowly nucleating hydrate phase at the center of the pore. In hydrophobic sediments, gas hydrates start nucleating at the center of the pores, growing towards the pore walls and a slowly nucleating hydrate phase anchored at the point where the gas, liquid and solid phases meet. The model predictions capture the qualitative information obtained from examination of gas-hydrate core samples and experimental observations of gas hydrate formation.

PREDICTING MEDICINE DEMAND USING DEEP LEARNING TECHNIQUES

Medication supply and storage are essential components of the medical industry and distribution. Most medications have a predetermined expiration date. When the demand is met in large quantities that exceed the actual need, this leads to the accumulation of medicines in the stores, and this leads to the expiration of the materials. If demand is too low, this will have an impact on consumer happiness and drug marketing.Therefore, it is necessary to find a way to predict the actual quantity required for the organization's needs to avoid material spoilage and storage problems. A mathematical prediction model is required to assist any management in achieving the required availability of medicines for customers and safe storage of medicines. The research question is to design a system based on deep learning that can predict the amount of drugs required with high efficiency and accuracy based on the chronology of previous years.Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) are used to build prediction models. Those models allow for the optimization of inventory levels, thus reducing costs and potentially increasing sales. Various measures such as mean squared error (MSE), mean absolute squared error (MASE), root mean squared error (RMSE), and others are used to evaluate the prediction models. RNN model achieved the best result with MSE: 0.019 MAE: 0.102, RMSE: 0.0.

Post-pandemic modeling of COVID-19: Waning immunity determines recurrence frequency

There are many factors in the current phase of the COVID-19 pandemic that signal the need for new modeling ideas. In fact, most traditional infectious disease models do not address adequately the waning immunity, in particular as new emerging variants have been able to break the immune shield acquired either by previous infection by a different strain of the virus, or by inoculation of vaccines not effective for the current variant. Furthermore, in a post-pandemic landscape in which reporting is no longer a default, it is impossible to have reliable quantitative data at the population level. Our contribution to COVID-19 post-pandemic modeling is a simple mathematical predictive model along the age-distributed population framework, that can take into account the waning immunity in a transparent and easily controllable manner. Numerical simulations show that under static conditions, the model produces periodic solutions that are qualitatively similar to the reported data, with the period determined by the immunity waning profile. Evidence from the mathematical model indicates that the immunity dynamics is the main factor in the recurrence of infection spikes, however, irregular perturbation of the transmission rate, due to either mutations of the pathogen or human behavior, may result in suppression of recurrent spikes, and irregular time intervals between consecutive peaks. The spike amplitudes are sensitive to the transmission rate and vaccination strategies, but also to the skewness of the profile describing the waning immunity, suggesting that these factors should be taken into consideration when making predictions about future outbreaks.

Mechanical predictive modeling of stereolithographic additive manufactured alumina microlattices

Alumina microlattices with stretch- and bending-dominated structures were manufactured to establish the intercorrelation between the quasi-static microlattice mechanical properties and the strut aspect ratio using the stereolithographic additive manufacturing method. In the present work, two kinds of novel mechanical prediction models were calibrated and validated through the experiment and simulation data, aiming to realize the topological optimization and effective design of the weight, strut size, and spatial geometrical structures of the microlattice according to the given load bearing and stiffness demands. First, the mathematical prediction models of the compression strength and Young’s modulus for the microlattices with different aspect ratios were developed based on the modified Gibson and Ashby’s law and Timoshenko beam theory. Then, the prediction models exhibited an excellent prediction achievement of the experiment and simulation results with the assistance of data fitting. Subsequently, the tensile and compressive damage models perfectly reappeared and tracked the actual fracture behaviors of the microlattice, demonstrating different collapse trends for the Simple cubic (SC) and Body-centered cubic (BCC) microlattices. Finally, the quasi-static mechanical properties of SC were far stronger than that of BCC under the same aspect ratio and from the experiment data, simulation results, and theory formulas. This work laid a solid foundation for the microlattice structure design in lightweight engineering.

OS09.7.A THE ROLE OF MICROSURGERY IN THE FIRST LINE TREATMENT OF VESTIBULAR SCHWANNOMA: IT IS ALL ABOUT THE ODDS

Abstract BACKGROUND Given the risk for a deterioration of facial function (FDR), first-line (1stline) microsurgery (MS) is discouraged in vestibular schwannoma (VS). In contrast, 1stline radiosurgery (SRS) preserves facial function but has a higher recurrence rate (RR). As salvage VS surgery is very challenging, it could be reasonable to accept a slightly increased FDR of 1stline MS in favor of the lower RR. The aim of the present study was to determine the FDR and RR values of salvage surgery and to calculate the break-even point of MS in the 1stline VS treatment. MATERIAL AND METHODS This retrospective study analyzed all patients with unilateral VS undergoing microsurgery between 03/2008 and 10/2021 (n=1470). We identified patient with 2ndline MS after previous MS (MS-MS) or radiosurgery (SRS-MS) as well as patients with 3rdline MS (MS-SRS-MS). After reviewing patients’ clinical and imaging data, we determined FDR and RR for each cohort. This data was compared to a 1stline MS cohort (CTRL) with a follow-up of at least 10 years (n=572). A mathematical prediction model (MPM) was applied to calculate the odds ratio (OR) of different treatment strategies (i.e., MS-MS, SRS-MS, MS-SRS-MS) depending on different FDR and RR values. RESULTS We identified 48 patients with 2ndline and 11 patients with 3rdline MS after failed MS/SRS. Time to recurrence in relation to the initial treatment was shortest in SRS-MS (35±34 mo) compared to MS-MS (128±179 mo) and MS-SRS-MS (38±17 mo). These patients had a significant worse preoperative facial function than CTRL patients. Extent-of-resection (EOR) was diminished in 2ndline (79% and 75%) compared to 1stline MS (97%). Both the FDR and RR were significantly increased in MS-MS (FDR:17%, RR:14%) and SRS-MS (FDR:45%, RR:5%) compared to CTRL (FDR:12%, RR:4%). Notably, MS-SRS-MS was associated with the highest FDR (60%) while having the lowest GTR rate (54%). MPM indicated that FDR&amp;lt;15% and RR&amp;lt;5% justify a 1stline MS. In the present cohort, these criteria were met for Koos 1-3 tumors (FDR:0-7%, RR:0-3%). CONCLUSION Recurrent VS surgeries fare much worse considering FDR, EOR and RR than first-line surgeries. Hence, reducing the RR of the first-line VS treatment is required. In smaller VS, the surgical oncofunctional outcome can reach the break-even point favoring microsurgery over radiosurgery as first-line treatment option. However, in situations with high FDR (e.g., large or cystic VS) a more conservative surgical approach might be beneficial.

Overwintering performance of bamboo leaves, and establishment of mathematical model for the distribution and introduction prediction of bamboos.

Bamboo has great economic values and is used extensively in many industries, and their natural distribution range was divided into 12 zones in China according to the temperature of their geographical distribution in previous works. Different bamboo species had significantly different abilities in low-temperature tolerance, which need to be considered carefully during ex-situ introduction. In this paper, we observed and evaluated the low-temperature damage of 19 bamboo species in winter, and measured the physiological changes of bamboo leaves. A total of 3060 leaf samples were obtained from 102 core collections in 34 bamboo species from the 5 regions of Chinese mainland for anatomical comparison, in order to screen out the key anatomical indicators related to their low-temperature tolerance and to establish a mathematical prediction model for bamboo introduction. The results showed that the low-temperature resistance of clustered bamboos was generally lower than that of the scattered bamboos. The decreased temperature led to the constant decrease of net photosynthetic rate and transpiration rate, but the increase of soluble sugar content in all bamboo species. There was no dormancy for all bamboo species in winter. The temperate bamboos showed lower photosynthesis as compared to tropical bamboos in winter. The leaf shape of bamboos was closely related to their distribution. A total of 13 leaf indicators were screened and more suitable to estimate the low-temperature tolerant abilities of bamboos and to predict their distribution. The MNLR (multiple nonlinear regression) mathematical model showed the highest fitting degree and the optimal prediction ability in the potential northernmost introduction range of bamboos. This study lay a foundation for bamboo introduction, and could also reduce the economic losses caused by the wrong introduction.

A Study on the Influence of Process Parameters on the Workpiece Surface Quality in the Cutting of Hard and Brittle Materials with Trepanning Drill.

Hard and brittle materials have excellent physical and mechanical properties and are widely used in the fields of microelectronics and optoelectronics. However, due to their high hardness and brittleness, the machining quality of a workpiece struggles to meet the requirements of practical applications. In order to improve the surface quality of deep-hole machining of hard and brittle materials, this article analyzes the formation mechanism of surface roughness and the exit-chipping width during the drilling machining of hard and brittle materials and establishes a mathematical prediction model for the surface roughness and the exit-chipping width of hard and brittle materials using a trepanning cutter. The experimental study on K9 optical glass machining shows that the surface roughness of the workpiece and the exit-chipping width increase with the increase in feed rate, and decrease with the increase in rotational speed. Through comparison and verification between theoretical and experimental values, the average errors of workpiece surface roughness and the exit-chipping width are 13.15% and 6.73%, respectively. This article analyzes the reasons for the errors. The results indicate that the theoretical model proposed in this article can be used to predict the surface roughness and the exit-chipping width of hard and brittle materials processed under the same conditions, providing a theoretical basis to optimize process parameters to improve the surface quality of the workpiece.

A mathematical prediction model for calculating the thermal conductivity of the asphalt mixture

A mathematical prediction model for calculating the thermal conductivity of the asphalt mixture