Computational Experiments Research Articles

A Computational Investigation on Chitosan Derivatives using Pharmacophore- based Screening, Molecular Docking, and Molecular Dynamics Simulations against Kaposi Sarcoma.

Cancer is one of the most dangerous illnesses to the human body due to its severity and progressive nature. Kaposi's Sarcoma (KS) tumor can appear as painless purple spots on the legs, foot, or face. This cancer develops in the lining of lymph arteries and blood vessels. Along with the enlargement of lymph nodes, the vaginal region and the mouth portion are the additional target areas of KS. DNA-binding proteins known as Sox proteins are found in all mammals and belong to the HMG box superfamily. They controlled a wide range of developmental procedures, such as the formation of the germ layer, the growth of organs, and the selection of the cell type. Human developmental abnormalities and congenital illnesses are frequently caused by the deletion or mutation of the Sox protein. The purpose of this study is to determine the promising Kaposi's sarcoma inhibitors through computational studies. In this present study computational approaches were used to evaluate the anti- carcinogenic efficacy against Kaposi's sarcoma. Ligand-based pharmacophore screening was performed utilising four different chemical libraries (Asinex, Chembridge, Specs, and NCI Natural products (NSC)) depending on the top hypothesis. The top hits were examined using molecular docking, absorption, distribution, metabolism and excretion. Highest occupied molecular orbital and lowest unoccupied molecular orbital were analysed to determine the lead compounds' biological and pharmacological efficacy. The results of the study indicated that the leading candidates were possible SOX protein inhibitors. A pharmacophore model to inhibit the production of SOX protein in Kaposi Sarcoma was generated in this computational experiment using a set of 19 Chitosan compounds. The results revealed that the top hits responded to all of the pharmacological druglikening criteria and had the best interaction residues, fitness scores, and docking scores. The resulting leads might be potential Kaposi's Sarcoma alternative treatments.

Machine learning methods for financial forecasting and trading profitability: Evidence during the Russia–Ukraine war

PurposeThis study aims to evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine.Design/methodology/approachThis study made computational experiments using support vector machine (SVM) classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making.FindingsOn average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia–Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process.Practical implicationsInvestors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendations, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy.Originality/valueThis paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, which is of potential value for researchers in quantitative finance and market professionals who operate in the financial segment.

Towards Environmentally Sustainable Aviation: A Review on Operational Optimization

In recent years, the rapid growth of air traffic has intensified pressure on the air transport system, leading to congestion problems in airports and airspace. The projected increase in demand exacerbates these issues, necessitating immediate attention. Additionally, there is a growing concern regarding the environmental impact of the aviation sector. To tackle these challenges, the adoption of advanced methods and technologies shows promise in expanding current airspace capacity and improving its management. This paper presents an overview of sustainable aviation, drawing on publications from academia and industry. The emphasis is on optimizing both flight and ground operations. Specifically, the review delves into recent advancements in airline operations, airport operations, flight operations, and disruption management, analyzing their respective research objectives, problem formulations, methodologies, and computational experiments. Furthermore, the review identifies emerging trends, prevailing obstacles, and potential directions for future research.

A Revisit to Sunk Cost Fallacy for Two-Stage Stochastic Binary Decision Making

This paper undertakes a revisit of the sunk cost fallacy, which refers to the tendency of people to persist investing resources into something, even if it is destined to have no good outcome. We emphasize that the utilities associated with different alternatives are not static for decision makers, which is exactly opposite to the traditional perspective. This paper argues that the utility of an option may change due to the choice of another option, suggesting that decisions considered irrational by the traditional analytical method, i.e., sunk cost fallacy, may be rational. We propose a novel analytical method for decision making with sunk cost when considering the utility change and validate the effectiveness of this method through mathematical modeling and computational experiments. This paper mathematically describes such decision-making problems, analyzing the impact of changes in the utilities across different alternatives on decision making with a real-world example. Furthermore, we develop a two-stage stochastic optimization model for such decision-making problems and employ the sample average approximation (SAA) method to solve them. The results from computational experiments indicate that some decisions traditionally considered irrational are, in fact, rational when the utility of an option changes as a result of choosing another option. This paper, therefore, highlights the significance of incorporating utility changes into the decision-making process and stands as a valuable addition to the literature, offering a refreshed and effective decision-making method for improved decision making.

Simulation study to evaluate when Plasmode simulation is superior to parametric simulation in estimating the mean squared error of the least squares estimator in linear regression.

Simulation is a crucial tool for the evaluation and comparison of statistical methods. How to design fair and neutral simulation studies is therefore of great interest for both researchers developing new methods and practitioners confronted with the choice of the most suitable method. The term simulation usually refers to parametric simulation, that is, computer experiments using artificial data made up of pseudo-random numbers. Plasmode simulation, that is, computer experiments using the combination of resampling feature data from a real-life dataset and generating the target variable with a known user-selected outcome-generating model, is an alternative that is often claimed to produce more realistic data. We compare parametric and Plasmode simulation for the example of estimating the mean squared error (MSE) of the least squares estimator (LSE) in linear regression. If the true underlying data-generating process (DGP) and the outcome-generating model (OGM) were known, parametric simulation would obviously be the best choice in terms of estimating the MSE well. However, in reality, both are usually unknown, so researchers have to make assumptions: in Plasmode simulation studies for the OGM, in parametric simulation for both DGP and OGM. Most likely, these assumptions do not exactly reflect the truth. Here, we aim to find out how assumptions deviating from the true DGP and the true OGM affect the performance of parametric and Plasmode simulations in the context of MSE estimation for the LSE and in which situations which simulation type is preferable. Our results suggest that the preferable simulation method depends on many factors, including the number of features, and on how and to what extent the assumptions of a parametric simulation differ from the true DGP. Also, the resampling strategy used for Plasmode influences the results. In particular, subsampling with a small sampling proportion can be recommended.

Desensitizing the autonomic nervous system to mitigate anti-GD2 monoclonal antibody side effects

BackgroundAnti-GD2 monoclonal antibodies (mAbs) have shown to improve the overall survival of patients with high-risk neuroblastoma (HR-NB). Serious adverse events (AEs), including pain, within hours of antibody infusion, have limited the development of these therapies. In this study, we provide evidence of Autonomic Nervous System (ANS) activation as the mechanism to explain the main side effects of anti-GD2 mAbs.MethodsThrough confocal microscopy and computational super-resolution microscopy experiments we explored GD2 expression in postnatal nerves of infants. In patients we assessed the ANS using the Sympathetic Skin Response (SSR) test. To exploit tachyphylaxis, a novel infusion protocol (the Step-Up) was mathematically modelled and tested.ResultsThrough confocal microscopy, GD2 expression is clearly visible in the perineurium surrounding the nuclei of nerve cells. By computational super-resolution microscopy experiments we showed the selective expression of GD2 on the cell membranes of human Schwann cells in peripheral nerves (PNs) significantly lower than on NB. In patients, changes in the SSR were observed 4 minutes into the anti-GD2 mAb naxitamab infusion. SSR latency quickly shortened followed by gradual decrease in the amplitude before disappearance. SSR response did not recover for 24 hours consistent with tachyphylaxis and absence of side effects in the clinic. The Step-Up protocol dissociated on-target off-tumor side effects while maintaining serum drug exposure.ConclusionWe provide first evidence of the ANS as the principal non-tumor target of anti-GD2 mAbs in humans. We describe the development and modeling of the Step-Up protocol exploiting the tachyphylaxis phenomenon we demonstrate in patients using the SSR test.

Synthesis of Imidazo[1,2‐b]pyridazine Scaffold Based Amide Derivatives as Potential Inhibitors for Bacterial Activity

AbstractA series of fifteen novel amide derivatives based on imidazo[1,2‐b]pyridazine scaffold were synthesized by taking imidazo[1,2‐b]pyridazine‐3‐carboxylic acid intermediates with respective substituted amines to yield good to excellent. The molecular structures of the newly synthesized compounds were elucidated using spectroscopic techniques (1H NMR, 13C NMR, and LCMS) and supported by single‐crystal X‐ray diffraction (SXRD) for the two compounds, 9 and 18. These compounds were evaluated in vitro for antimicrobial activity against two Gram‐positive bacteria (S. aureus and B. subtilis) and two Gram‐negative bacteria (E. coli and P. aeruginosa). Interestingly, compounds 10 and 16, displayed comparable antibacterial activity to the positive control (Tetracycline: 6.25 μg/mL) against Gram‐negative bacteria, particularly Pseudomonas aeruginosa, with a minimum inhibitory concentration (MIC) of 6.25 μg/mL. Compound 17 exhibited moderate antibacterial activity with an MIC of 10 μg/mL against the Gram‐positive bacterium Bacillus subtilis, whereas the positive control demonstrated an MIC of 6.25 μg/mL. These antibacterial activities were found to have a good correlation with Insilco computational molecular docking experiments using the receptor UDP‐N‐acetylglucosamine‐enolpyruvate reductase, Mtb MurB.

Minimizing Flight Hours Losses by Utilizing Deep-Q Learning Techniques in Scheduling Aircraft Maintenance

Currently, there are insufficiently good or efficient tools or procedures for long-term scheduling of aviation maintenance activities in the world, and Vietnam specifically. Airlines are impacted by a variety of elements, including the quantity of aircraft they operate, their capacity, their personnel resources, their maintenance resources, and unforeseen and urgent occurrences that cause schedule disruptions. There are no fast, automatic solutions to the above-described problems existing in aviation today. The reinforcement learning method as described here could potentially be one answer to these problems. The idea is to plan in years running across a specified period such that the aircraft is brought as close as possible to the inspection deadline. From there, the airworthiness of the aircraft increases while the maintenance inspection decreases, reducing the cost of maintenance. Application optimization of the scheduling plan is done using the Deep Q-learning method. The results achieved by the Q-learning and Deep Q-learning algorithms are better in terms of computation times as compared to the other current techniques. The research results of the checks showed reinforcement learning potential in dealing with this problem, where the fly hours loss of planned inspections was reduced by using data from Vietnam Airlines. Computational experiments show that our methods adapt for different purposes and settings of reality. After teaching the model with these simulated conditions, they show how well a reinforcement learning application quickly arrives at lean repair plans.

Spectral-like conjugate gradient methods with sufficient descent property for vector optimization.

Several conjugate gradient (CG) parameters resulted in promising methods for optimization problems. However, it turns out that some of these parameters, for example, 'PRP,' 'HS,' and 'DL,' do not guarantee sufficient descent of the search direction. In this work, we introduce new spectral-like CG methods that achieve sufficient descent property independently of any line search (LSE) and for arbitrary nonnegative CG parameters. We establish the global convergence of these methods for four different parameters using Wolfe LSE. Our algorithm achieves this without regular restart and assumption of convexity regarding the objective functions. The sequences generated by our algorithm identify points that satisfy the first-order necessary condition for Pareto optimality. We conduct computational experiments to showcase the implementation and effectiveness of the proposed methods. The proposed spectral-like methods, namely nonnegative SPRP, SHZ, SDL, and SHS, exhibit superior performance based on their arrangement, outperforming HZ and SP methods in terms of the number of iterations, function evaluations, and gradient evaluations.

The home health care routing with heterogeneous electric vehicles and synchronization

AbstractThis paper studies the problem of heterogeneous electric vehicles, fast chargers, and synchronized jobs that have time windows in home healthcare routing and scheduling. We consider a problem that aims to establish daily routes and schedules for healthcare nurses to provide a variety of services to patients located in a scattered area. Each nurse should be assigned to an electric vehicle (EV) from a heterogeneous fleet of EVs to perform the assigned jobs within working hours. We consider three different types of EVs in terms of battery capacity and energy consumption. We aim to minimize the total cost of energy consumption, fixed nurse cost, and costs arising from the patients that cannot be served within the working day. We model the problem as a mixed integer programming formulation. We develop a hybrid metaheuristic based on a greedy random adaptive search procedure heuristic, to generate good quality initial solutions quickly, and an adaptive variable neighborhood search algorithm to generate high quality solutions in reasonable time. The hybrid metaheuristic employs a set of new advanced efficient procedures designed to handle the complex structure of the problem. Through extensive computational experiments, the performance of the mathematical model and the hybrid metaheuristic are evaluated. We conduct analyses on the robustness of the metaheuristic and the performance contribution of employing adaptive probabilities. We analyze the impact of problem parameters such as competency requirements, job duration, and synchronized jobs.

Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

Optimisation of medication reconciliation using queueing theory: a computer experiment.

Medication reconciliation (MedRec) in hospitals is an important tool to enhance the continuity of care, but completing MedRec is challenging. The aim of this study was to investigate whether queueing theory could be used to compare various interventions to optimise the MedRec process to ultimately reduce the number of patients discharged prior to MedRec being completed. Queueing theory, the mathematical study of waiting lines or queues, has not been previously applied in hospital pharmacies but enables comparisons without interfering with the baseline workflow. Possible interventions to enhance the MedRec process (replacing in-person conversations with telephone conversations, reallocating pharmacy technicians (PTs) or adjusting their working schedule) were compared in a computer experiment. The primary outcome was the percentage of patients with an incomplete discharge MedRec. Due to the COVID-19 pandemic, it was possible to add a real-life post hoc intervention (PTs starting their shift later) to the theoretical interventions. Descriptive analysis was performed. The queueing model showed that the number of patients with an incomplete discharge MedRec decreased from 37.2% in the original scenario to approximately 16% when the PTs started their shift 2h earlier and 1 PT was reassigned to prepare the discharge MedRec. The number increased with the real-life post hoc intervention (PTs starting later), which matches a decrease in the computer experiment when started earlier. Using queueing theory in a computer experiment could identify the most promising theoretical intervention to decrease the percentage of patients discharged prior to MedRec being completed.

A case study on entropy generation in MHD nanofluid flow in L- shaped triangular corrugated permeable enclosure

Enclosure with irregular boundaries by providing surface corrugations is considered an efficient strategy to elevate the heat transfer mechanism in laser technology, cooling of electronic instrument, photonic systems in material processing, magnetic resonance, and the automobile industry are few to mention. In view of such mesmerizing utilization present work is articulated by considering L-shaped corrugated enclosure saturated with water and copper (Cu) nanoparticles are included. The physical impacts of a horizontally oriented magnetic field and permeable medium are accounted along with measurement of entropy. Mathematical modelling of the problem is attained in dimensionless version through a set of similar variables. Computational experiments are conducted through the application of commercial software (COMSOL) based on the finite element approach. A grid independence and comparison tests are also accounted to ensure credibility of simulations. Impact of parameters is revealed through sketches. Quantities of interest, including local and average Nusselt numbers, Bejan number, and total entropy, are also evaluated for flat and correlated surfaces. Induction of nanoparticles in the base fluids tends to reduce the entropy generation by up to 12.45 %. Growth in NuAvg (3.08 % approx.) is attained by increasing the frequency of imposed triangular waved corrugation.

Decision support for allocating farmed fish to customer orders using a bi-objective optimization model

Aquaculture is an important industry in certain coastal areas. Focusing on farming of salmon and trout, an operational planning problem arises with the goal of allocating supply of fish to demand expressed through customer orders. This paper provides a conceptual model of such a planning problem, defines a corresponding bi-objective mathematical programming model. The problem is novel with respect to the structure of the transportation of fish, and the rules for satisfying customer orders with respect to fish size, quality, certification, and health status. Computational experiments are conducted to gain further insights into the use of the provided model to provide support for planners involved in the operational decision making. Results indicate that the bi-objective optimization model can be useful in situations where supply is insufficient to cover all the demand within a given planning horizon.

On Construction of Nearly Orthogonal Latin Hypercube Designs

Orthogonal Latin hypercube designs are becoming popular in designing computer experiments. Available literature on construction of orthogonal or nearly orthogonal LHDs has one or more restriction in terms of either runs or factors. In this article, we have proposed a method of construction for obtaining nearly orthogonal Latin hypercube designs capable of accommodating flexible number of runs or factors.

Energy‐consumption model for rotary‐wing drones

AbstractWith technological advancement, the use of drones in delivery systems has become increasingly feasible. Many companies have developed rotary‐wing drone (RWD) technologies for parcel delivery. At present, the limited endurance is the main disadvantage of RWD delivery. The energy consumption of RWDs must be carefully managed, and it is necessary to develop an effective energy‐consumption model to support RWD flight planning. Because the interaction between the forces on the RWD and its flying environment is very complex, it is challenging to estimate accurately the RWD energy consumption. This study summarizes several energy‐consumption models proposed in the literature, then we develop an RWD energy‐consumption model (called the integrated model) based on analyzing the dynamic equilibrium of forces and power consumption in flight phases (including climb, descent, hover, and horizontal flight). Computational experiments involving several commercial RWDs indicate that the integrated model is more effective than several models in the literature. In the case where an RWD completed one flight segment, on average, 87.63% of the battery capacity was consumed in the horizontal flight phase. We also analyzed the effects of the total mass and horizontal airspeed on the RWD endurance and found that a larger mass corresponded to shorter endurance, and in the experimental range of the horizontal airspeed, a higher horizontal airspeed corresponded to longer endurance. Moreover, the total mass affected the RWD endurance more significantly than the horizontal airspeed.

Synergizing Solar Power with Enhanced Oil Recovery: Integrating Solar-Assisted Methods into Existing EOR Techniques

This study investigates the utilization of solar energy to enhance the efficiency of oil recovery processes, known as Solar Assist-Enhanced Oil Recovery (SA-EOR). The project objectives involve assessing the feasibility and effectiveness of integrating solar technologies with traditional oil recovery methods, such as steam injection or water flooding, to improve oil production rates and reduce operational costs. Methods employed include computational modeling, laboratory experiments, and field trials to evaluate various solar-assisted techniques and their impact on oil recovery. Key findings suggest that solar integration can enhance oil recovery by optimizing thermal processes, improving fluid mobility, and reducing environmental footprint. Additionally, the study underscores the economic viability and sustainability of SA-EOR solutions in the context of evolving energy landscapes. Overall, this research contributes valuable insights into the potential of solar technologies to revolutionize conventional oil recovery practices and advance towards more efficient and environmentally friendly energy production methods. Methods, termed Solar Assist-Enhanced Oil Recovery (SA-EOR). The objective is to investigate the efficacy of utilizing solar power to optimize thermal processes in oil extraction, such as steam injection or water flooding. Through computational modeling, laboratory experiments, and field trials, various SA-EOR techniques are evaluated for their potential to enhance oil recovery rates and reduce environmental impact. Findings indicate promising opportunities for leveraging solar energy to improve fluid mobility, optimize reservoir conditions, and ultimately increase oil production efficiency. This research underscores the significance of SA-EOR methods in advancing sustainable energy practices within the oil industry while addressing pressing environmental concerns.

Vibrating roller with compacted soil interaction modelling

Introduction. Vibrating rollers are the most common means of compacting soils in construction. The nature of stress development on the contact surface of the roller with the ground depends on the technical characteristics of the vibrating roller (the mass of the roller, the mass of the roller frame, the frequency and driving force of vibrations, the number and characteristics of the roller shock absorbers) and the properties of the soil.Materials and methods. Simulation of the interaction of a vibrating roller with compacted soil was carried out using a three-mass rheological model of the frame-roller-soil system. Differential equations of mass motion in contact and separation modes were solved numerically. To determine the numerical values of the loading time (increase in contact stresses from zero to the maximum value) and the unloading time (decrease in contact stresses from the maximum value to zero), as well as the maximum reaction force of the soil, a computational experiment was conducted on a rheological model. The mass of the vibrating roller module (the mass of the front axle) and the relative driving force were used as independent parameters of the vibrating roller. The coefficients of elastic and viscous resistance of the soil were chosen as independent parameters of the soil. The total number of combinations of factors was 192. The values of the time of loading and unloading of the soil, as well as the maximum strength of the soil reaction, were determined by oscillograms of changes in the strength of the soil reaction over time.Results. Using the STATISTICA program, regression equations, to calculate the numerical values of the loading and unloading time of the soil, as well as the maximum reaction force of the soil and the corresponding values of the reliability coefficients of the multiple approximation, were obtained.Discussion and conclusion. The rheological model reproduces the asymmetric nature of changes in contact stresses during soil compaction by a vibrating roller, observed in experimental stress oscillograms obtained during field experimental studies. The results obtained are important for calculating the depth of stress propagation in the ground and the distribution of stresses in the ground after the passage of a vibrating roller using a wave approach to describing stress propagation in the ground. In the future, it is advisable to conduct a computational experiment with an expanded list of independent parameters of the roller, including the oscillation frequency.

REASSIGNMENT OF THE STRUCTURE OF A TRYPTOPHAN-CONTAINING CYCLIC TRIPEPTIDE PRODUCED BY THE BIARYLITIDE CROSSLINKING CYTOCHROME P450BLT.

The structure of the sidechain crosslinked Tyr-Leu-Trp peptide produced by the biarylitide crosslinking cytochrome P450Blt from Micromonospora sp. MW-13 has been reanalysed by a series of NMR, computational and isotope labelling experiments and shown to contain a C-N rather than a C-O bond. Additional in vivo experiments using such a modified peptide show there is a general tolerance of biarylitide crosslinking P450 enzymes for histidine to tryptophan mutations within their minimal peptide substrate sequences despite the lack of such residues noted in natural biarylitide gene clusters. This work further highlights the impressive ability of P450s from biarylitide biosynthesis pathways as biocatalysts for the formation of a range of sidechain crosslinked tripeptides.

The Effect of the Countercyclical Capital Buffer on the Stability of the Housing Market

AbstractAfter the great turmoil of the latest financial crisis, the criticism of the regulatory frameworks became increasingly stronger. The rules that banks needed to comply with are presumed to be procyclical and unable to prevent and mitigate the extent of strong financial and economic cycles. As a result, Basel III introduced a set of macroprudential tools to overcome these regulatory shortfalls. One tool that strives to counteract the issue of procyclicality is the countercyclical capital buffer ($$CCyB$$ CCyB ). This paper introduces a heterogeneous agent-based model that investigates the implication of the new regulatory measure. We develop a housing and a financial market where economic agents trade residential property that is financed by financial institutions. To examine the macroeconomic performance of the $$CCyB$$ CCyB , we evaluate the dynamics of key stability indicators of the housing and the financial market under four different market conditions: in an undisturbed market and in times of three different structural shocks. Computational experiments reveal that the $$CCyB$$ CCyB is effective in stabilizing the housing and the financial market in all market settings. The new macroprudential tool helps to mitigate economic fluctuations and to stabilize market conditions, especially in the aftermath of a crisis. It is not able to prevent any of the crises tested. However, the extent of the stabilizing effect varies according to market conditions. In the shock scenarios, the $$CCyB$$ CCyB performs better in dampening market fluctuations and increasing banking soundness than in the base scenario.