Simulated Study Research Articles

A Systematic Survey of Moon-forming Giant Impacts. I. Nonrotating Bodies

In the leading theory of lunar formation, known as the giant impact hypothesis, a collision between two planet-size objects resulted in a young Earth surrounded by a circumplanetary debris disk from which the Moon later accreted. The range of giant impacts that could conceivably explain the Earth–Moon system is limited by the set of known physical and geochemical constraints. However, while several distinct Moon-forming impact scenarios have been proposed—from small, high-velocity impactors to low-velocity mergers between equal-mass objects—none of these scenarios have been successful at explaining the full set of known constraints, especially without invoking controversial post-impact processes. In order to bridge the gap between previous studies and provide a consistent survey of the Moon-forming impact parameter space, we present a systematic study of simulations of potential Moon-forming impacts. In the first paper of this series, we focus on pairwise impacts between nonrotating bodies. Notably, we show that such collisions require a minimum initial angular momentum budget of approximately 2 J EM in order to generate a sufficiently massive protolunar disk. We also show that low-velocity impacts (v ∞ ≲ 0.5 v esc) with high impactor-to-target mass ratios (γ → 1) are preferred to explain the Earth–Moon isotopic similarities. In a follow-up paper, we consider impacts between rotating bodies at various mutual orientations.

Interrelatedness of steering and lateral position parameters: Recommendations for the assessment of driving performance

Introduction: Loss of attention leads to less steady driving within the lane and is one of the main causes of road accidents. To improve road safety, vehicle-based parameters such as steering wheel angle and lateral position are used to objectively assess driving performance, especially in monotonous driving tasks. Method: The present driving simulator study investigated the extent to which eight commonly used parameters are independent indicators of driving performance. Fifteen participants undertook a monotonous highway driving task for 1 h. Four steering angle parameters were examined: average steering angle (ASA), standard deviation of steering angle (SDSA), steering angle range (SAR), and steering reversal rate (SRR); as well as four lateral position parameters: mean lateral position (MLP), standard deviation of lateral position (SDLP), lateral position range (LPR), and the out-of-lane duration. Measurements were averaged across 2-minute epochs. Repeated measures correlation analysis evaluated the similarity between each parameter, and the variance inflation factor test evaluated the multicollinearity of all the parameters. Results: The results demonstrated that some parameters are highly correlated and should not be used together to assess driving performance. It is recommended that the optimal combination is ASA and SAR to assess steering angle, and SDLP and out-of-lane to assess lateral position. Out-of-lane, as a factor directly contributing to road safety, is recommended because it has the least correlation with other parameters. Practical Applications: If implemented, these recommendations may improve the assessment of driving performance in future studies.

A Comparative Study of Network Simulators

A network simulator is a tool that can simulate and analyze the behavior of computer networks. They are widely used by the research community to test and evaluate the performance of networks in a virtual environment before deploying them in the real world. The simulators allow the users to test the network without the need to physical hardware or any real time connections. We have studied and compared the detail features of the Network Simulators including the Qualnet simulator which we have implemented in our college.

Integrated biomarker responses: a further improvement of IBR and IBRv2 indexes to preserve data variability in statistical analyses.

In biomonitoring and laboratory studies, it is typical to measure a battery of molecular, biochemical, and cytogenetic biomarkers to evaluate the effects of xenobiotics in biota. However, summarizing the results of several biomarkers to inform laypersons and environmental agencies is still a challenge for researchers and environmental specialists. To address this issue, researchers have developed indexes such as the Integrated Biomarker Responses (IBR) and Integrated Biomarker Response version 2 (IBRv2) to summarize all biomarkers responses into a single value. Unfortunately, these indexes do not preserve the original biological variability, which hampers subsequent statistical analyses. In this study, we created new versions of IBR and IBRv2, which preserve individual data variability and can be used in typical statistical analyses. The new Integrated Biomarker Responses individual (IBRi), Integrated Biomarker Responses version 2 individual (IBRv2i) and Weighted Integrated Biomarker Responses version 2 individual (Weighted IBRv2i) indexes correlated with the original IBR and IBRv2 indexes and were able to detect differences among experimental groups in a simulated and case studies. Using the IBRi, IBRv2i, or Weighted IBRv2i indexes is advantageous because they maintain the data variability of the experimental groups and can be analyzed using hypothesis testing statistics like any other parameter. Additionally, this approach can help translate technical scientific terminology into a more accessible language suitable for environmental governmental agencies and decision-makers.

Effects of vehicle vibration on driver alertness: a pilot study

Road accidents resulting from the loss of driving alertness cause significant social and economic damage. In recent years, studies have revealed that whole-body vibration affects driver drowsiness directly. However, the impact of different frequency ranges of vibration on driver alertness has not been extensively explored. This driving simulator study aimed to investigate the impact of low frequency (1-4 Hz) and high frequency (16-32 Hz) whole-body vibration on a driver's reaction time during a monotonous highway driving task. Nine participants completed the task for one hour, with reaction tasks presented every 8-12 seconds. After 25 minutes of exposure to whole-body vibration, participants exhibited significantly longer reaction times for both low and high frequency ranges when compared to the control condition. The low frequency range had a stronger effect on driver vigilance, with reaction times increasing by as much as 135 ms within 35 minutes. Subjective evaluations using the Karolinska Sleepiness Scale (KSS) were consistent with the reaction time results. Overall, this study confirms that both low and high frequency vibration can reduce driver alertness, with the effect of low frequency vibration being more pronounced. Further research is suggested with larger sample sizes and wider frequency ranges.

Compact size antenna for skin implantable medical devices

Abstract This research work presents an implantable antenna that operates at 5.8 GHz. By using a radiator with a loop-based design, the antenna can be made smaller. Radiator is made up of three connected rectangular loops. On the substrate’s back side, an I-shaped ground plane is used. As substrate and superstrate, polydimethylsiloxane (PDMS) with dimensions of 7 mm × 5 mm × 0.3 mm is used. The conducting sections are made using copper foil that is 30 µm thick. The suggested antenna is examined by the implantable medical device using realistic human scalp phantom models and a homogenous skin box. Simulated study revealed that it operates around 5.8 GHz with a bandwidth from 5.69 to 5.92 GHz. The specific absorption rate was 0.28 and 0.26 W/kg for skin box and human scalp phantoms, respectively, at 1 mW input power across 1 g volume tissue.

How to Choose the Most Proper Representative Climate Model Over a Study Region? a Case Study of Precipitation Simulations in Ireland with NEX-GDDP-CMIP6 Data

How to Choose the Most Proper Representative Climate Model Over a Study Region? a Case Study of Precipitation Simulations in Ireland with NEX-GDDP-CMIP6 Data

Comparison of new and old BacT/ALERT aerobic bottles for detection of Candida species.

A new version of aerobic blood culture media has been developed for the BacT/ALERT (bioMérieux) blood culture system. We evaluated the time to detection and yeast cell counts in positive blood cultures for each Candida spp. according to changes in media. Isolates from defibrinated horse blood were inoculated into three types of bottles: the old version of aerobic bottle, new version of aerobic bottle, and anaerobic bottle. All bottles were incubated in the BacT/ALERT Virtuo blood culture system. The time to detection was monitored for each bottle, and yeast cell counts were performed immediately after testing positive, determined via the plate count method. Clinical retrospective data of the candidemia samples before and after aerobic bottle change also were analyzed. The median time to detection was 52.47 hours in the old aerobic bottles versus 19.92 hours in the new aerobic bottles (P < 0.001) for Candida glabrata, and standard and clinical strains showed similar results. C. albicans (27.6 to 24.95 hours) and C. guilliermondii (28.92 to 26.9 hours) had shorter time to detection. However, C. auris (25.43 to 28.25 hours) had a longer time to detection in the new aerobic bottle. The retrospective clinical analysis showed a significant decrease in time to detection (45.0 to 19.4 hours) for C. glabrata, which is consistent with our simulated study result for C. glabrata. As a result of analysis including all blood specimens, C. tropicalis showed a significant delay in time to detection in new aerobic bottles. In an analysis limited to peripheral blood specimens, the time to detection of C. parapsilosis was longer in new aerobic bottles than in old aerobic bottles. Most Candida species did not show remarkable TTD differences, but TTD of C. glabrata was markedly reduced in the New FA Plus bottle. The reduction of time to detection enables faster detection and therapeutic approach for C. glabrata infections.

False Alarm Effects in Early Warnings for Emergency Vehicles: Exploring Drivers' Move-Over Behavior.

This study investigated drivers' move-over behavior when receiving an Emergency Vehicle Approaching (EVA) warning. Furthermore, the possible effects of false alarms, driver experience, and modality on move-over behavior were explored. EVA warnings are one solution to encourage drivers to move over for emergency vehicles in a safe and timely manner. EVA warnings are distributed based on the predicted path of the emergency vehicle causing a risk of false alarms. Previous EVA studies have suggested a difference between inexperienced and experienced drivers' move-over behavior. A driving simulator study was conducted with 110 participants, whereof 54 inexperienced and 56 experienced drivers. They were approached by an emergency vehicle three times. A control group received no EVA warnings, whereas the experimental groups received either true or false warnings, auditory or visual, 15seconds before the emergency vehicle overtook them. Drivers who received EVA warnings moved over more quickly for the emergency vehicle compared to the control group. Drivers moved over more quickly for each emergency vehicle interaction. False alarms impaired move-over behavior. No difference in driver behavior based on driver experience or modality was observed. EVA warnings positively affect drivers' move-over behavior. However, false alarms can decrease drivers' future willingness to comply with the warning. The findings regarding measurements of delay can be used to optimize the design of future EVA systems. Moreover, this research should be used to further understand the effect of false alarms in in-car warnings.

Non-ideal magnetohydrodynamics on a moving mesh II: Hall effect

ABSTRACT In this work, we extend the non-ideal magnetohydrodynamics (MHD) solver in the moving-mesh code arepo to include the Hall effect. The core of our algorithm is based on an estimation of the magnetic field gradients by a least-squares reconstruction on the unstructured mesh, which we also used in the companion paper for Ohmic and ambipolar diffusion. In an extensive study of simulations of a magnetic shock, we show that without additional magnetic diffusion our algorithm for the Hall effect becomes unstable at high resolution. We can however stabilize it by artificially increasing the Ohmic resistivity, ηOR, so that it satisfies the condition ηOR ≥ ηH/5, where ηH is the Hall diffusion coefficient. Adopting this solution, we find second-order convergence for the C-shock and are also able to accurately reproduce the dispersion relation of the whistler waves. As a first application of the new scheme, we simulate the collapse of a magnetized cloud with constant Hall parameter ηH and show that, depending on the sign of ηH, the magnetic braking can either be weakened or strengthened by the Hall effect. The quasi-Lagrangian nature of the moving-mesh method used here automatically increases the resolution in the forming core, making it well suited for more realistic studies with non-constant magnetic diffusivities in the future.

Leveraging Artificial Intelligence for Climate Change Mitigation: Predictive Models, Optimization Techniques, and Sustainable Solutions

Climate change presents a critical global challenge necessitating innovative solutions. This paper explores the transformative potential of artificial intelligence (AI) in climate change mitigation through predictive modeling, optimization techniques, and sustainable solutions. By integrating AI with climate science, the research aims to enhance the accuracy of climate predictions, optimize renewable energy resource utilization, and develop AI-driven systems for sustainable urban planning. The introduction outlines the pervasive impact of climate change and identifies current limitations in traditional mitigation approaches. The objective is to harness AI's capabilities to revolutionize climate science by developing advanced predictive models capable of capturing complex climate dynamics and regional variations. Optimization techniques, including genetic algorithms and reinforcement learning, are employed to maximize the efficiency of renewable energy systems, thereby reducing dependency on fossil fuels and mitigating greenhouse gas emissions. Methodologically, the study involves comprehensive data collection from satellite imagery, climate records, and renewable energy outputs. Machine learning and deep learning models, such as convolution neural networks (CNNs) and recurrent neural networks (RNNs), are selected for climate prediction, with rigorous training and validation processes to assess performance metrics. Results showcase the effectiveness of AI in improving climate predictions and optimizing energy resource management. Case studies in smart city simulations and renewable energy integration demonstrate the practical implications of AI-driven solutions for sustainable development.

Enlightening mode awareness

Driving an automated vehicle requires a clear understanding of its automation capabilities and resulting duties on the driver’s side. This is true across all levels of automation but especially so on SAE levels 3 and below, where the driver has an active driving task performance and/or monitoring role. If the automation capabilities and a driver’s understanding of them do not match, misuse can occur, resulting in decreased safety. In this paper, we present the results from a simulator study that investigated driving mode awareness support via ambient lights across automation levels 0, 2, and 3. We found lights in the steering wheel to be useful for momentary and lights below the windshield for permanent indication of automation-relevant information, whereas lights in the footwell showed to have little to no positive effects on driving mode awareness.

Extended centroid position method in liquid-crystal-based interrogation system for electric-field-modulated Fabry-Perot sensing

A polarized low-coherence interferometer (PLCI) based on a liquid crystal (LC) wedge is designed, and an associated demodulation method encompassing the tunability feature is proposed for tunable, standalone optical sensing. The application of an electric field to the LC material effectively decreases the birefringence value and the related dispersion relation, which in turn enhances the resolution of detection. The effect of the electric field on the demodulation of the cavity length is addressed by the successive determinations of the centroid positions of the PLCI interferograms. Through a comprehensive study of numerical simulations, the effectiveness of the proposed approach is explored relative to the conventional envelope detection methodology. In order to verify this method, an experiment with a Fabry-Perot-based fiber optic displacement sensor is carried out using a 5CB LC wedge-based PLCI setup in the presence of an electric field. The measurement accuracy of the cavity length is found to be 0.74% of full scale, rendering it more precise and robust than the conventional envelope detection method.

Driver Adaptability When Traffic Side Is Switched from Left to Right and Vice Versa: A Driving Simulator Study with Chinese and Pakistani Drivers

Driver Adaptability When Traffic Side Is Switched from Left to Right and Vice Versa: A Driving Simulator Study with Chinese and Pakistani Drivers

Selling antibiotics without prescriptions among community pharmacies and drug outlets: a simulated client study from Ghana

ABSTRACT Background Selling antibiotics without prescriptions is mostly illegal worldwide, including in Ghana, and promotes antimicrobial resistance. We evaluated the prevalence and practice of selling antibiotics without prescriptions among community pharmacies (CPs) and drug outlets, for the first time, in Ghana to quantify and characterize this issue to inform future interventions. Research design and methods Two scenarios utilizing the Simulated Client Methodology were enacted: an upper respiratory tract infection of viral origin (scenario one); and pediatric diarrhea (scenario two). CPs/Outlets were selected by stratified proportional random sampling from four metropolitan cities (~14% of the total Ghanaian population). Selling of antibiotics was assessed at three demand levels and its overall prevalence was estimated, then stratified by the study variables. Results Out of the 265 sampled CPs/outlets, the prevalence of selling antibiotic without prescription was 88.3% (n = 234/265), with variations not only across the four regions [92.5% (n = 123/133) in Kumasi, 87.5% (n = 14/16) in Cape Coast, 84.1% (n = 69/82) in Accra, and 82.4% (n = 28/34) in Tamale] but also across CPs [90% (n = 121/134)] and drug outlets [86% (n = 113/131)]. Conclusions A very high prevalence/sub-optimal practice of selling antibiotics without prescriptions was found. This highlights the need to increase compliance with antibiotic dispensing legislation through evidence-based interventions including education of key stakeholders.

How important is the linearity assumption in a sample size calculation for a randomised controlled trial where treatment is anticipated to affect a rate of change?

BackgroundFor certain conditions, treatments aim to lessen deterioration over time. A trial outcome could be change in a continuous measure, analysed using a random slopes model with a different slope in each treatment group. A sample size for a trial with a particular schedule of visits (e.g. annually for three years) can be obtained using a two-stage process. First, relevant (co-) variances are estimated from a pre-existing dataset e.g. an observational study conducted in a similar setting. Second, standard formulae are used to calculate sample size. However, the random slopes model assumes linear trajectories with any difference in group means increasing proportionally to follow-up time. The impact of these assumptions failing is unclear.MethodsWe used simulation to assess the impact of a non-linear trajectory and/or non-proportional treatment effect on the proposed trial’s power. We used four trajectories, both linear and non-linear, and simulated observational studies to calculate sample sizes. Trials of this size were then simulated, with treatment effects proportional or non-proportional to time.ResultsFor a proportional treatment effect and a trial visit schedule matching the observational study, powers are close to nominal even for non-linear trajectories. However, if the schedule does not match the observational study, powers can be above or below nominal levels, with the extent of this depending on parameters such as the residual error variance. For a non-proportional treatment effect, using a random slopes model can lead to powers far from nominal levels.ConclusionsIf trajectories are suspected to be non-linear, observational data used to inform power calculations should have the same visit schedule as the proposed trial where possible. Additionally, if the treatment effect is expected to be non-proportional, the random slopes model should not be used. A model allowing trajectories to vary freely over time could be used instead, either as a second line analysis method (bearing in mind that power will be lost) or when powering the trial.

Adsorptive removal of Pb2+ ions using stable imine linked covalent organic frameworks: A simulated and experimental studies

Porous organic frameworks that are bound covalently are eye-catching materials in the current research. The present work describes the solvothermal synthesis of a combination of 3,3′,5,5′-tetramethyl-[1,1′-biphenyl] 4,4′-diamine and benzene-1,3,5-tricarbaldehyde through covalent bonding to generate TBBT-covalent organic framework (TBBT-COF). The structural, morphological, and computational characterizations confirm the formation of COF. TBBT-COF has been used as an adsorbent for the removal of Pb2+ ions from aqueous media. The effects of pH, initial metal ion concentration, competing ions, and adsorbent dosage were optimized to attain maximum adsorption of Pb2+. The kinetics follow pseudo-second-order and govern the chemisorption of Pb2+ with the imine group of TBBT-COF. The X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) analyses of TBBT-COF after adsorption of Pb2+ support the chemisorption of Pb2+with TBBT-COF. The 2D contour and 3D surface response plots were used to assess the specific and comparative effects of the experimental variables. An analysis of variance (ANOVA) of the regression model was used to establish the relevance of the primary influencing variables on the adsorption of Pb2+over TBBT-COF. It was found to remove 99 % of Pb2+ in 90 min. The results of the real-sample analysis show the efficient removal of Pb2+ even in the presence of other cations. The statistical analysis of the adsorption has been conducted, which indicates the suggested models closely match the experimental data. The high surface area, covalency, and stability of TBBT-COF show its good adsorbent properties.

Significant thermal upgrade via cascade high temperature heat pump with low GWP working fluids

High temperature heat pump is a promising solution for efficient high temperature heat supply. However, the poor performance when it operates in large temperature lift hinders it from utilizing low temperature waste heat. To solve this problem, a cascade high temperature heat pump with economized vapor injection was established and a simulation model was developed accordingly. The tests were conducted using R134a/R245fa and R1234ze(E)/HP-1 at heat source temperature from 5 to 30 °C and heat sink temperature from 90 to 120 °C, with the coefficient of performance (COP) varying in the range of 1.72–2.73 and 1.77–2.75, respectively. The COP about 2.20 was achieved with R1234ze(E)/HP-1 at optimal intermediate temperature and optimal economized vapor injection pressure when the temperature lift is 100 °C. In the simulated case study, the heat pump water boiler based on the experimental prototype decreases CO2 emission by 62.8 % compared to electric boiler. Moreover, using low GWP working fluids reduces 1.32 × 104 kg direct CO2 emission in TEWI. Under the current technological, pricing, and operational condition, it takes 16 years for heat pump to have economic benefit over electric boiler, but this duration can be shortened with reduced initial cost, improved COP, and increased operating hours and electricity price. In summary, this work presented a high-efficient, environmentally friendly, and economically viable cascade high temperature heat pump system which has potential to utilize the abundant environmental heat source and deliver high temperature heat, being an alternative to the conventional heat generation equipment such as boilers.

Simulational and theoretical study of electron scattering cross section by Chlormethine-DNA complex

Studying phenomena happening in therapies to conquer cancer has been the aim of extensive research projects in recent years. One of the most practical treatments is utilizing electrons as interacting particles in body tissues. So, studying electron interactions with biological molecules is highly important. Chlormethine as an alkylating agent has always been used since the initial era of cancer chemotherapy. The drug makes interstrand and intrastrand covalent cross-links between two constituents in DNA. In this paper the physical interaction of electrons with Chlormethine drug was reported for the first time. Molecular dynamic (MD) simulations and free energy calculations were carried out to investigate near approach binding of the drug with DNA. Electron scattering cross sections on the system of DNA bases along with Chlormethine as an anticancer drug taken from MD simulations are studied in this paper. Calculations include relativistic Dirac partial-wave which is combined with a local interaction potential. Electron scattering is modelled by the independent atom model (IAM) considering a screening corrected coefficient over an energy range.

Driving Frequency Modulates Correlations Between Executive Functions and Driving Performance: A Driving Simulator Study.

Driving a car requires a complex combination of various cognitive functions (e.g., visual perception, motor control, decision making, and others), and deficits in any of these processes may compromise driving safety. Amongst these, executive functions such as inhibitory control, task switching, and decision-making are important, as they enable drivers to process information from their surroundings and respond appropriately to changing road conditions. Although previous research has focused on laboratory measures of individual executive functions, it remains unclear whether performance on such laboratory tests readily translates to actual on-the-road driving performance, especially since drivers' skill levels can vary widely, based on their driving frequency. To this end, we divided 30 participants into two categories based on their driving frequency (i.e., daily commuter vs. weekend only drivers), and we used three well-known executive functioning tasks (the stop signal task, Iowa gambling task or IGT, and a task-switching test) to see whether scores on these tasks predicted such driving performances and behaviors such as braking time, lane-keeping, speed limit violations, and inter-vehicle distance (e.g., in a driving simulator). Participants went through a follow-lead-car scenario in the driving simulator for 20minutes and then completed the three executive tasks. We found that stop signal reaction time (SSRT) best predicted driving performance, and remained predictive against driver distraction, as well as variabilities in driving frequency. The IGT predicted speed limit violations in high-frequency drivers, whereas task-switching cost predicted lane keeping performance in low-frequency drivers. Together, these results highlight the importance of driving frequency when considering correlates between executive functions and driving performance and behavior. They also imply that executive tasks better predict driving performance in low-frequency (or inexperienced) drivers, while driver temperament (i.e., impulsiveness as indicated by IGT) better predicted driving performance in high-frequency (or experienced) drivers.