Mathematical Model For Prediction Research Articles

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<15% and RR<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

Novel Payloads to Mitigate Maladaptive Inward Arterial Remodeling in Drug-Coated Balloon Therapy.

Drug-coated balloon therapy is a minimally invasive endovascular approach to treat obstructive arterial disease, with increasing utilization in the peripheral circulation due to improved outcomes as compared to alternative interventional modalities. Broader clinical use of drug-coated balloons is limited by an incomplete understanding of device- and patient-specific determinants of treatment efficacy, including late outcomes that are mediated by postinterventional maladaptive inward arterial remodeling. To address this knowledge gap, we propose a predictive mathematical model of pressure-mediated femoral artery remodeling following drug-coated balloon deployment, with account of drug-based modulation of resident vascular cell phenotype and common patient comorbidities, namely, hypertension and endothelial cell dysfunction. Our results elucidate how postinterventional arterial remodeling outcomes are altered by the delivery of a traditional anti-proliferative drug, as well as by codelivery with an anti-contractile drug. Our findings suggest that codelivery of anti-proliferative and anti-contractile drugs could improve patient outcomes following drug-coated balloon therapy, motivating further consideration of novel payloads in next-generation devices.

A Model for Predicting the Duration of Viral Shedding in Patients Who Had Been Hospitalized with Mild COVID-19: A Single-Center Retrospective Study.

Clinical decision-making is enhanced by the development of a mathematical model for prognosis prediction. Screening criteria associated with viral shedding time and developing a prediction model facilitate clinical decision-making and are, thus, of great medical value. This study comprised 631 patients who were hospitalized with mild COVID-19 from a single center and 30 independent variables included. The data set was randomly divided into the training set (80%) and the validation set (20%). The outcome variable included viral shedding time and whether the viral shedding time >14 days, LASSO was used to screen the influencing factors. There were 321 males and 310 females among the 631 cases, with an average age of 62.1 years; the median viral shedding time was 12 days, and 68.8% of patients experienced viral shedding within 14 days, with fever (50.9%) and cough (44.2%) being the most common clinical manifestations. Using LASSO with viral shedding time as the outcome variable, the model with lambda as 0.1592 (λ = 0.1592) and 13 variables (eg the time from diagnosis to admission, constipation, cough, hs-CRP, IL-8, IL-1β, etc.) was more accurate. Factors were screened by LASSO and multivariable logistic regression with whether the viral shedding time >14 days as the outcome variable, five variables, including the time from diagnosis to admission, CD4 cell count, Ct value of ORF1ab, constipation, and IL-8, were included, and a nomogram was drawn; after model validation, the consistency index was 0.888, the AUC was 0.847, the sensitivity was 0.744, and the specificity was 0.830. A clinical model developed after LASSO regression was used to identify the factors that influence the viral shedding time. The predicted performance of the model was good, and it was useful for the allocation of medical resources.

Changes in Groundwater Levels and its Salinity (Badra Basin Iraq)

A fact that human-induced greenhouse gas emissions have led to an increased frequency and/or intensity of some weather and climate extremes since pre-industrial time, in particular for temperature extremes which affected lots of regions by reducing precipitations. Accordingly, this reduction causes a lowering of groundwater levels which was adopted as a goal of this study and its effects on quality of it. The study area is Badra - Jassan basin located to the north of Kut city, the center of Wasit governorate, and bordered by the international boundary with Iran from the east and northern east covering an area of about (1020) Km2. Badra - Jassan basin is an important hydrogeological basin in which groundwater can be used for agricultural purposes; especially with decreasing surface water supply. The research studied a predictive mathematical model of groundwater by ten wells distributed randomly within the boundaries of the study area; all of them fall within the unconfined aquifer the mathematical model for the study area has been developed using the Geographic Information System (GIS) software. The Groundwater level map for the ten existing wells was drawn from data in (2021) and a comparison with else published in (2002) for the study area. Likewise, the salinity concentration map variations for the same periods were estimated. The present study showed a decline in groundwater levels which conjunction with an Increasing in the range and value of groundwater salinity concentrations.

Experimental Study on Carbon Fiber-Reinforced Polymer Groove Machining by High-Power Water-Jet-Guided Laser.

Due to the excellent properties of carbon fiber-reinforced polymers (CFRPs), such as high strength and strong corrosion resistance, the traditional water-jet-guided laser (WJGL) technology has problems with fiber pull-out and has a small cutting depth when processing CFRPs. Therefore, in this study, we used high-power water-jet-guided laser (HPWJGL) technology to perform groove processing experiments on CFRPs. The effects of four key process parameters, high laser power, pulse frequency, feed rate, and water-jet pressure, on the cutting depth were investigated by a single-factor experiment. The formation mechanism of groove cross-section morphology and the processing advantages of high-power water-jet-guided lasers were analyzed. On this basis, the mathematical prediction model of cutting depth was established by using the response surface method (RSM), and the optimal combination of process parameters was obtained. The mathematical prediction model was verified by experiments, and the error was only 1.84%, indicating that the model had a high reference value. This study provides a reference for the precision machining of HPWJGL technology.

Efficient Machine Learning and Factional Calculus Based Mathematical Model for Early COVID Prediction

Diseases are increasing with exponential rate worldwide. Its detection is challenging task due to unavailability of the experts. Machine learning models provide automated mechanism to detect diseases once trained. It has been used to predict and detect many diseases such as cancer, heart attack, liver infections, kidney infections. The new coronavirus has become one of the deadliest diseases. Its case escalated in unexpected ways. In the literature, many machine learning models such as Extreme Gradient Boosting (XGBoosting), Support Vector Machine (SVM), regression, and Logistic regression have been used. It has been observed that these models can predict COVID cases early but are unable to find the peak point and deadline of the disease. Hence, mathematical models have been designed to early predict and find peak point and dead-line in disease prediction. These mathematical models use integral calculus-based Ordinary Differential Equations (ODEs) to predict COVID cases. Governments are dependent on these models’ pre- diction for early preparation of hospitalization, medicines, and many more. Hence, higher prediction accuracy is required. It has been found in the literature that fractional calculus-based models are more accurate in disease prediction and detection. Fractional models provides to choose order of derivative with fractional value due to which information processing capability increases. In the present work, mathematical model using fractional calculus has been devised for prediction of COVID cases. In the model, quarantine, symptomatic and asymptomatic cases have been incorporated for accurate prediction. It is found that the proposed fractional model not only predicts COVID cases more accurately but also gives peak point and dead-line of the disease.

Pressure Drop and Heat Transfer Characteristics of TiO2/R1234yf Nanorefrigerant: A Numerical Approach

Global warming is one of the most dangerous ecological issues facing the globe. Refrigerants are a major contributor to global warming. This investigation mainly focuses on the analysis of a greener nanorefrigerant. Nanorefrigerant can improve the efficiency of refrigeration and air conditioning systems that use vapor compression. In the present investigation, mathematical and computational methods are used to assess the heat transfer and pressure drop properties of TiO2/R1234yf. In order to analyze the heat transfer characteristics and the transport features of the innovative nanorefrigerant, appropriate mathematical predictive models were adapted from earlier investigations. The models are validated by the experiments using TiO2/POE nanolubricant as a test fluid. The investigation was conducted with a temperature range of 10 °C to 40 °C and a volume percentage of nano-sized TiO2 particles in R1234yf refrigerant ranging from 0.2 to 1%. According to the research, the introduction of nanoparticles increases viscosity, thermal conductivity, and density. However, as the amount of nanoparticles rises, the specific heat capacity of the nano-enhanced refrigerant decreases. The nanorefrigerant’s heat transfer coefficient and pressure drop are improved by 134.03% and 80.77%, respectively. The outcomes observed from the predictive technique and the simulation approach had an average absolute variation of 9.91%.

A regression-based approach for the explicit modeling of simultaneous heat and mass transfer of air-to-refrigerant microchannel heat exchangers

With the increasing utilization of mathematical models for heat exchanger optimization studies, there lies the demand for robust and accurate yet fast modeling methodologies on the simultaneous heat and mass transfer process. This research conducted an in-depth review of mathematical models for heat exchanger performance prediction. We categorized typical modeling methodologies into lumped models, zone models, and numerical methods. The review of mathematical models revealed the iterative approaches for performance prediction of fully wet conditions are computationally expensive. Also, the assumptions and derivations based on adiabatic fin tips do not allow flexibility with advanced fin designs. In order to address these two issues, this research developed a systematic regression approach under a common range of geometrical and thermal parameters under both wet and dry conditions, single-phase and two-phase refrigerant flow. Such regression analysis provides an explicit calculation procedure for micro-channel heat exchangers' performance prediction. The improved semi-regression models show advances in accuracy and calculation speed within the selected ranges of geometries and working conditions for heat pump applications.

Construction and Application of a Nomogram for Predicting Benign and Malignant Parotid Tumors.

A prediction model of benign and malignant differentiation was established by magnetic resonance signs of parotid gland tumors to provide an important basis for the preoperative diagnosis and treatment of parotid gland tumor patients. The data from 138 patients (modeling group) who were diagnosed based on a pathologic evaluation in the Department of Stomatology of Jilin University from June 2019 to August 2021 were retrospectively analyzed. The independent factors influencing benign and malignant differentiation of parotid tumors were selected by logistic regression analysis, and a mathematical prediction model for benign and malignant tumors was established. The data from 35 patients (validation group) who were diagnosed based on pathologic evaluation from September 2021 to February 2022 were collected for verification. Univariate and multivariate logistic regression analysis showed that tumor morphology, tumor boundary, tumor signal, and tumor apparent diffusion coefficient (ADC) were independent risk factors for predicting benign and malignant parotid gland tumors ( P < 0.05). Based on multivariate logistic regression analysis of the modeling group, a mathematical prediction model was established as follows: Y = the ex/(1 + ex) and X = 0.385 + (1.416 × tumor morphology) + (1.473 × tumor border) + (1.306 × tumor signal) + (2.312 × tumor ADC value). The results showed that the area under the receiver operating characteristic curve of the model was 0.832 (95% confidence interval, 0.75-0.91), the sensitivity was 82.6%, and the specificity was 70.65%. The validity of the model was verified using validation group data, for which the sensitivity was 85.71%, the specificity was 96.4%, and the correct rate was 94.3%. The results showed that the area under receiver operating characteristic curve was 0.936 (95% confidence interval, 0.83-0.98). Combined with tumor morphology, tumor ADC, tumor boundary, and tumor signal, the established prediction model provides an important reference for preoperative diagnosis of benign and malignant parotid gland tumors.

Predicting medicine demand using deep learning techniques: A review

Abstract The supply and storage of drugs are critical components of the medical industry and distribution. The shelf life of most medications is predetermined. When medicines are supplied in large quantities it is exceeding actual need, and long-term drug storage results. If demand is lower than necessary, this has an impact on consumer happiness and medicine 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. Artificial intelligence applications and predictive modeling have used machine learning (ML) and deep learning algorithms to build prediction models. This model allows for the optimization of inventory levels, thus reducing costs and potentially increasing sales. Various measures, such as mean squared error, mean absolute squared error, root mean squared error, and others, are used to evaluate the prediction model. This study aims to review ML and deep learning approaches of forecasting to obtain the highest accuracy in the process of forecasting future demand for pharmaceuticals. Because of the lack of data, they could not use complex models for prediction. Even when there is a long history of accessible demand data, these problems still exist because the old data may not be very useful when it changes the market climate.