Quick Calculation Research Articles

Quick Electrical Drive Selection Method for Bus Retrofitting

The article concerns the issue of retrofitting (i.e., the conversion of worn-out diesel buses into electric buses). As this solution is often cheaper than purchasing new electric buses, it can be attractive for low-population areas with a weaker economic infrastructure. The article aims to present an original method for rapidly selecting components for the electric traction system, such as the electric motor, inverter, and transmission systems, combined with a battery installed in a drawer. The battery swapping solution is dedicated to regions with underdeveloped power infrastructure that does not allow for fast charging of bus batteries using pantographs. A mathematical model in the form of a polynomial was developed to estimate the energy losses for a given route. This model consists of a bus physics model, an energy loss model in the propulsion system, and a battery model. The weight coefficients of the polynomials were determined based on an analytical analysis of the model dependencies. The obtained models were reduced using the Lasso regularization method in linear regression. The input data for the model includes route characteristics (or driving cycle) and technical characteristics of the traction system components. The model output provides a detailed profile of electric energy consumption and peak values of the drive system characteristics (e.g., maximum torque of the motor) which must not be exceeded. Implemented as computer software, the model—combined with a database of motors, inverters, drive transmission systems, and batteries—allows for a quick calculation of the possibilities of applying a selected configuration to cover a given route. The approach proposed in the article enables the rapid composition of electric traction devices based on required driving conditions during the initial vehicle prototyping stage. At the same time, it allows the state of the bus battery to be monitored and estimates the remaining range during the operation of upgraded buses.

Improving Clinical Decision-Making: A Focus on Early Warning Systems and Scoring Methods

Dear Editor, Examinations, laboratory tests, radiology, and clinical experience are required to make the most appropriate clinical decisions. There is no single universal clinical decision-making method advocated in routine medical literature. Often, this process is driven by experience, exploration, and clinical gestalt. Clinician management serves as a subjective decision tool in disease management. It has been extensively studied in the literature, particularly in entities such as pulmonary embolism, difficult airway prediction, and severe COVID-19 (1). By combining parameters such as laboratory-vital parameters and combined hematological parameters, clinical decision-making tools have been developed (2-3). Early warning systems (EWS) help predict which patients will require critical care by evaluating physiological parameters in busy and crowded workspaces. These scores, which can be measured through vital parameters and a simple physical examination, ensure the effective use of resources (4). In intensive care units (ICUs), complex systems are used to predict mortality. APACHE II is the most well-known score used in intensive care, consisting of many parameters that are computationally difficult. Vital parameters, biochemical tests, and blood gas analysis results are used when calculating APACHE II (5). On the other hand, important features of scores used in the emergency department are their quick and easy calculation, requiring no professional knowledge. The VitalPAC Early Warning Score (ViEWS), developed by Prytherch et al. in 2010, is an EWS that includes six physiological parameters. This score records the pulse rate, systolic blood pressure, respiratory minute rate, body temperature, level of consciousness (assessed by AVPU), and peripheral oxygen saturation (SpO2) parameters. ViEWS is created based on the degree of deviation of these six parameters from their normal ranges. Additional points are given if the patient receives oxygen support. The total ViEWS value, calculated by considering each parameter, provides information about the patient's prognosis (6). By adding the rapid lactate level to ViEWS (ViEWS-L), a score that better predicts outcomes in the geriatric population was developed (7). The Modified Early Warning Score (MEWS) is one of the triage scoring methods used to identify patients in need of intensive and critical care, and to expedite their transfer to the intensive care unit. It evaluates systolic blood pressure, heart rate, respiratory rate, body temperature, and neurological status. MEWS is included in routine nursing care procedures in some countries and is routinely assessed by nurses. ICU admission rates and mortality rates increase for those who score 5 or more on this scale, although this threshold may vary in different populations or clinical situations. In a Korean study analyzing sepsis patients, combining lactate with the MEWS score improved the prediction of the need for intensive care (8). The National Early Warning Score (NEWS) is another scoring system used to determine clinical deterioration and follow-up level for all hospitalized patients, as well as to predict clinically high-risk patients. It is calculated by evaluating respiratory rate, oxygen saturation, oxygen support status, blood pressure, body temperature, and neurological status. The NEWS-L score, obtained by combining NEWS with lactate, has been reported to be superior to NEWS alone in geriatric critically ill patients (9). However, contrary to the geriatric patient study, Özkan's study reported that NEWS-L was not superior to NEWS and that they exhibited similar predictive abilities (10). As a result, researchers are actively working on new parameters and scoring systems to optimize resource utilization and predict critical illness. Researchers should be encouraged to investigate studies on EWS lactate combinations or other combinations.

Deriving analytical expressions of the spatial information entropy index on riverine water quality dynamics

Information entropy theory has been largely applied in hydrology and water resource engineering. Recently, entropy theory has received significant attention for describing reactive solute mixing and transport processes in ground water and surface water and for its applications in water quality management. However, analytical expressions of the entropy index of riverine water quality dynamics are often not included in the literature. In this study, the analytical expressions of the spatial information entropy index (Gx) in the 1D steady-state transport process under full-extent observation and tracking observations are derived after the definition of the system boundary and probability space. The expressions of Gx were successfully validated by field data from two tracer experiments, and the different characteristics of Gx under different riverine hydraulic situations were uncovered. The formula of Gx shows a reasonable linkage with the Peclet number (Pe) in hydrodynamics. This indicates that Gx is a physical quantity describing or linking to the real world more than a statistical index, and in practice, similar decision or management measurements based on entropy theory can be made for rivers with similar Pe conditions. Hydrologists will benefit in many ways from analytical expressions, such as theoretical analysis, quick calculation of the entropy index, preliminary engineering design, etc. A clearly defined example of maximum entropy time (tGmax) and its application in water quality evaluation, monitoring optimization and pollution source identification are demonstrated. These new findings will provide a convenient tool for catchment water quality management.

Modeling hydraulic heads with impulse response functions in different environmental settings of the Baltic countries

Study region:The Baltic countries (Estonia, Latvia, Lithuania), North Eastern Europe. Study focus:Time series models are a convenient tool for modeling hydraulic head time series due to their parsimonious parameterization and quick calculation time. However, it is unclear how the model structure and the outcome relate to the respective site characteristics. Time series modeling with impulse response functions was used to model the head time series. The correlations between the model performance and the environmental settings and between the model parameters and the environmental settings were analyzed using Spearman rank correlation and Random Forest regression analyses. New hydrological insights:From the 460 analyzed head time series, 145 were modeled with satisfactory goodness-of-fit in the calibration period, using only precipitation, potential evaporation, and temperature as model inputs. This number decreased to 68 time series with a satisfactory fit in the validation period. Including additional drivers in wells where a substantial influence is characteristic could improve modeling results. The model results suggest that snow processes are affecting head dynamics in many wells in the Baltic countries. Most correlations between the model performance and the environmental setting were observed with the geological and climatic setting of the site. Models performed best in monitoring wells with shallow groundwater and lower precipitation seasonality.

αCharges: partial atomic charges for AlphaFold structures in high quality.

The AlphaFold2 prediction algorithm opened up the possibility of exploring proteins' structural space at an unprecedented scale. Currently, >200 million protein structures predicted by this approach are deposited in AlphaFoldDB, covering entire proteomes of multiple organisms, including humans. Predicted structures are, however, stored without detailed functional annotations describing their chemical behaviour. Partial atomic charges, which map electron distribution over a molecule and provide a clue to its chemical reactivity, are an important example of such data. We introduce the web application αCharges: a tool for the quick calculation of partial atomic charges for protein structures from AlphaFoldDB. The charges are calculated by the recent empirical method SQE+qp, parameterised for this class of molecules using robust quantum mechanics charges (B3LYP/6-31G*/NPA) on PROPKA3 protonated structures. The computed partial atomic charges can be downloaded in common data formats or visualised via the powerful Mol* viewer.The αCharges application is freely available at https://alphacharges.ncbr.muni.cz with no login requirement.

ANALISIS LAPORAN KEUANGAN GUNA MENILAI KINERJA KEUANGAN PT CAMPINA ICE CREAM INDUSTRI TBK

This study aims to describe the company's financial performance PT Campina Ice Cream Industri Tbk with liquidity ratio analysis. The research method uses a descriptive quantitative approach. Secondary quantitative data from the financial reports of PT Campina Ice Cream Industri Tbk for the 2018-2021 period. Data collection by literature study and documentation. The results showed that the financial performance of PT. Campina Ice Cream Industri Tbk as a whole on average from 2019-2021 in terms of the liquidity ratio, according to the current ratio and quick ratio calculations it is good because the resulting ratio is above 1. However, the cash ratio shows that it is not favorable because the ratio's result is too low—below 1—when viewed from this perspective.

Global transportation infrastructure exposure to the change of precipitation in a warmer world

Transportation infrastructures are generally designed to have multi-decadal service lives. Transport infrastructure design, however, is largely based on historical conditions. Yet, in the face of global warming, we are likely going to experience more intense and frequent extreme events, which may put infrastructure at severe risk. In this study, we comprehensively analyze the exposure of road and railway infrastructure assets to changes in precipitation return periods globally. Under ~2 degrees of warming in mid-century (RCP 8.5 scenario), 43.6% of the global transportation assets are expected to experience at least a 25% decrease in design return period of extreme rainfall (a 33% increase in exceedance probability), which may increase to 69.9% under ~4 degrees of warming by late-21st century. To accommodate for such increases, we propose to incorporate a safety factor for climate change adaptation during the transportation infrastructure design process to ensure transportation assets will maintain their designed risk level in the future. Our results show that a safety factor of 1.2 would work sufficient for most regions of the world for quick design process calculations following the RCP4.5 path.

Two-frame Phase-shifting Interferometry by Minimizing the Residual-background

Demodulation of two random phase shift fringe patterns is an ill-posed problem. Existing methods often need to remove the fringe background by pre filtering in the frequency, which takes more time, and have a great influence on the phase reconstruction because of the frequency aliasing, these limits the application of two frame phase-shift interferometry in actual production. In this case, this paper based on the idea of Statistical Average, proposes a Statistical Average technology for phase extraction of two frame randomly phase-shifted interferograms from rough to precision without pre filtering. The proposed methods based on minimizing the residual background, firstly estimates the average background by calculating the average for all pixels, and then adding and subtracting the two frames with the background removed. The phase shift and phase are solved by using the properties of trigonometric functions. Then the amplitude is solved using the obtained phase and phase shift. Finally, the background is solved and the original light intensity is updated. Compared with existing methods, our method has the advantages such as rapid convergence and quick calculation by offering the right initial value. The accuracy of my proposed method is approximately double that of existing methods. Simulation and experimental results show that our method is a practical method of two frame phase shift interferences, both simple and reliable.

Measuring pituitary tumor volume: a comparison of the simplified and non-simplified ellipsoid equation with the 3D planimetric volume assessment.

The ellipsoid equation came in an attempt to facilitate the estimation of tumor volume, by measuring the height, width, and anteroposterior length of the lesion. The estimated tumor volume can sometimes be different between methods, thus, it is of extreme interest to evaluate if the methods are significantly different, as well as to discuss the main limitations of each one. This is an observational, analytical, cross-sectional study. A systematic review of the literature was also performed in order to discuss the results observed in the present study. A total of 82 patients (43 males and 39 females) ranging in age from 15 to 78 years (mean 47.95 ± 14.76) were included in the study. Seven patients were classified as Knosp grade 0 (8.5%), 36 Knosp grade 1 (44%), 14 Knosp grade 2 (17%), 20 Knosp grade 3 (24.4%), 5 Knosp grade 4 (6.1%). The tumor volume estimated by 3D planimetric assessment, non-simplified ellipsoid equation, and simplified ellipsoid formula averaged 10.68cm³, 10.36cm³, and 9.9cm³ respectively. A simplified form of the ellipsoid equation increases the divergence between the measurement obtained in planimetry, and should be discouraged, in view of the new automated methods of performing quick calculations using periodic digits. The non-simplified form underestimated the tumor volume by 2.9% on average but did so regularly. In clinical practice, measurement should be accompanied by an evaluation of tumor morphology.

Effects of Atmospheric Correction on Remote Sensing Statistical Inference in an Aquatic Environment

Atmospheric correction (AC) plays a critical role in the preprocessing of remote sensing images. Although AC is necessary for applications based on remote sensing inversion, it is not always required for those based on remote sensing classification. Recently, remote sensing statistical inference has been proposed for evaluating water quality. However, input data for these models have always been remote sensing reflectance (Rrs), which requires AC. This raises the question of whether AC is necessary for remote sensing statistical inference. We conducted a theoretical analysis and image validations by testing 24 water bodies observed by Landsat-8 and compared their spectral probability distributions (SPDs) calculated from Rrs before and after AC (using the ACOLITE model). Additionally, we tested and found that, if we use remote sensing inference as a tool to quantitatively infer statistical parameters of a specific waterbody, it is better to perform atmospheric correction. However, if the quantitative inference is applied to a large number of water bodies and high inference accuracy is not required, atmospheric correction may not be necessary, and a quick calculation based on the strong correlations between Rrs at the surface and sensor-observed reflectance can be used as a substitute.

Design of the 'SI ULET' Accounting Information System for Quick Calculation of Merchandise Inventory at AtariaShop Plastic Houseware Bandung Barat

Accounting information systems are very important to business activities. The amount of merchandise inventory owned by the company must be monitored for its availability because it is sold for profit. It is known that AtariaShop Plastic Houseware still uses a manual system to calculate its merchandise inventory. This causes the calculation of merchandise inventory to be slow, and the resulting information is often inaccurate. The qualitative approach method was used by the researchers in this study. Assisting in the calculation of computerized merchandise inventory requires the design of an accounting information system named SI ULET. This system as a whole displays the final report in the form of merchandise stock cards. SI ULET is proven to help business activities run effectively because this system can display the mobility of in-out merchandise due to purchases and sales so that the amount of merchandise inventory in stores can be quickly identified.

Elevation Angle Characterization for LEO Satellites: First and Second Order Statistics

The elevation angle θ is relevant for the Low Earth orbit (LEO) satellite communications since it is always changing its relative position with respect to fixed Earth stations (ES’s), and this affects the link length and received power, PR. This article provides a new methodology to compute the probability density function (PDF) and cumulative distribution function (CDF) of the elevation angle, θ, for diverse ES locations. This methodology requires as input parameters an ES latitude, ϕ, an orbit inclination value, i, and an orbit altitude, h. The elevation angle is characterized through a well known random variable, which facilitates the computation of the first and second-order statistics, and helps to determine the expected value and measures of dispersion of the angle θ for a particular ES location. The proposed methodology allows an easy and quick calculation of the elevation angle’s CDF, facilitating comparisons against CDF’s of more ES’s located at different latitudes, and longitudes, λ; as well as the comparisons of CDF’s of the elevation angle produced by different orbits. Extensive simulation results are summarized in a small table, which allows computation of the elevation angle’s CDF and PDF for multiple ES locations without requiring of simulations and statistical fitting. Finally, the proposed methodology is validated through an extensive error analysis that show the suitability of the obtained results to characterize the elevation angle.

SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment

Navigating robots through large-scale environments while avoiding dynamic obstacles is a crucial challenge in robotics. This study proposes an improved deep deterministic policy gradient (DDPG) path planning algorithm incorporating sequential linear path planning (SLP) to address this challenge. This research aims to enhance the stability and efficiency of traditional DDPG algorithms by utilizing the strengths of SLP and achieving a better balance between stability and real-time performance. Our algorithm generates a series of sub-goals using SLP, based on a quick calculation of the robot's driving path, and then uses DDPG to follow these sub-goals for path planning. The experimental results demonstrate that the proposed SLP-enhanced DDPG path planning algorithm outperforms traditional DDPG algorithms by effectively navigating the robot through large-scale dynamic environments while avoiding obstacles. Specifically, the proposed algorithm improves the success rate by 12.33% compared to the traditional DDPG algorithm and 29.67% compared to the A*+DDPG algorithm in navigating the robot to the goal while avoiding obstacles.

Revealing Lithium Configuration in Aged Layered Oxides for Effective Regeneration.

Layered oxides LiNixCoyMnzO2 are widely used as the main cathode material for high-energy lithium-ion batteries. Over long-term cycling, irreversible phase transformations in layered oxides usually occur along with the loss of active lithium, which directly reflects in the sharp decrease of capacity. However, it is difficult to accurately and rapidly determine lithium content in aged materials, raising extreme impediments in the direct recycling of layered oxides. Herein, we propose a facile method for quick and accurate calculation of the residual lithium content through the developed relationship of shear strain and the states of charge. Based on this recognization, a discharge capacity close to the original capacity of the pristine material is achieved in the regenerated material by combining a hydrothermal method with annealing treatment. The recycled material demonstrates a dramatic improvement in electrochemical properties, especially the high rate performance. This method not only effectively realizes the quantitative regeneration of cathode materials but also provides a possible strategy for the future development of direct regeneration.

Optimization of a thermoacoustic system

Clean combustion such as hydrogen combustion for the reduction of NOx emission is prone to thermoacoustic instabilities, which may cause structural vibrations and equipment failures. In the present work, an adjoint-based sensitivity analysis is applied to a low-order thermoacoustic model with a non-zero mean flow. The adjoint-based analysis can be a powerful tool to yield information for optimization techniques to eliminate or control such instabilities. With quick and low-cost calculations, structural sensitivity and base-state sensitivity are achieved and the conditions most sensitive to instabilities are identified.

Evaluation of Gait Parameters using Wearable Sensors in Simulated Freezing of Gait Episodes

Background and Hypothesis: Freezing of gait (FoG), the failure to initiate or maintain locomotion, is a common symptom in Parkinsonism that severely impacts patients’ quality of life. Wearable technology such as inertial measurement units (IMU) are being widely investigated as a method of detecting these episodes with increasing sensitivity and specificity. The aim of this initial study is to collect objective gait data using IMU sensors via Perception Neuron software and to develop an analysis pipeline for quick in-clinic calculation of gait parameters.
 Experimental Design: 16 IMU’s were attached to the feet, legs, arms, waist, back, and head in individuals without any gait abnormalities. Subjects were instructed to walk 10 meters normally, followed by 10 meters with interspersed, simulated FoG episodes. This data was processed and analyzed through Axis Studio and MATLAB to identify kinematic markers associated with gait. The correlation between the right and left foot gyroscopic data in the z-axis was determined and plotted against time, with any correlation less than 0.5 being considered a FoG episode. Further, parameters of gait including stride duration, stance duration, swing duration, and stride length were calculated from input of the raw data to establish a control comparison for future studies with Parkinson’s patients.
 Results: Data analysis demonstrated increased stride duration, stance duration and swing duration with shorter stride length in the stimulated FoG episodes as compared to normal gait. The developed MATLAB code was further able to accurately distinguish the simulated FoG episodes from normal gait given the correlation parameter difference of <0.5.
 Conclusion: Our findings showed that data obtained from Perception Neuron IMU can be processed to succinctly detect FoG episodes and calculate gait parameters. Ultimately, this data will be used as the baseline for a future study investigating the use of spinal cord stimulation as treatment for FoG in individuals with Parkinson’s Disease.

Atomic displacement damage energy model up to high recoil energy

Current analytical displacement damage models are derived for recoil energy below 24.9AZ4/3 keV. The present work studies the damage energy beyond this theoretical limit using SRIM Full Cascade (FC) and DART calculations. Notably, the three damage energies computed with different methods from the same SRIM simulation are not equal, whereas they are believed to be the same in principle. The method of directly subtracting the ionization energy from total recoil energy is recommended for both FC and Quick Calculation (QC) options. It is also observed that the damage energy seems to unphysically depend on the density of monatomic material in SRIM FC. While the classical analytical models imply the saturated damage energy for high recoil energy, this saturation is not observed in SRIM FC nor in the numerical solution of the Lindhard equation using reasonable electronic stopping powers.

AN APPRAISAL ALGORITHM FOR TESTS OF DIVISIBILITY USING MODULAR ARITHMETIC

Knowledge to execute wild conceptual mathematical computations helped immensely even out of the park. Knowing these quick calculations has been of great interest ever since. Divisibility tests were required to know whether a number (large enough) was divisible by a given integer or not? Let 0,m  and a be any integer. The symbol, mod , am was used to represent the residue when a was divided by m. In this piece of treatised work, modulo residue theory was employed to find tests of divisibilty for even numbers < 60 and elaborated the use of modular arithmetic from number theory in finding different tests of divisibility. Particularly, b adic expension of an integer N and its congruence modulo b was used to characterise a given integer regarding its divisibility rule. One of the characterisations was stated and proved that an integer N was divisible by 40 if and only if0 1 2 10( 2 ) a a a was divisible by 40, where 0 1 2 ,, a a awere the digits of N in its decimal representation. Finally, the framework proposed reduced the pitfalls by demonstating each established rule with the help of their recursive applications on large integers.

Prediksi Kuat Tekan Beton Agregat Recycled Campuran Air Laut dengan Schmidt Rebound Hammer Test

Nowadays, using recyled aggregate and seawater as replacing natural aggregate and freshwater in mixing have been main priority to be a new concrete construction material. However, in order to asses their strength performance in fast and accurate, an appropriate method needs to be determined. Therefore, the aim of this research is to evaluate and predict the compressive strength of recycled aggregate concrete (RAC) mixed seawater using Schmidt rebound hammer test (SRH) and the result value was corresponded to concrete compressive testing machine (CTM) as control test of concrete specimens. Total 72 samples divided into three concrete mixtures were designed and prepared in cylindrical samples with w/c ratio of 0.45 and target compressive strength of 25 MPa. The first mixture was named natural aggregate concrete (NAC) or BAN as control concrete with 100% natural aggregate and fresh water. The second and third mixtures were named BAR-50 and BAR-100 with the BAN replacement of 50% and 100% RAC respectively. Seawater as mixing was performed in mixture concrete of BAR-50 and BAR-100. All sample mixtures were then wet cured at different age of 7, 28, 56 and 90 days in fresh water. The results show a good correlation between SRH and CTM tests with having high correlation of R2=0.9908. Also, a proposed mathematical model, equation 1, is created for quick calculation in predicting compressive strength of RCA. Therefore, the usage of SRH test is a realistic alternative to predict the compressive strength of recycled concrete with different percentage of replacement of NAC and mixing seawater.

On volatile growth: Simple fitting of exponential functions taking into account values of every observation with any signs, applied to readily calculate a novel covariance-invariant CAGR

The commonly used compound annual growth rate does not consider volatility, and its calculation fails for time series beginning or terminating with a zero or negative value, which may be the case for a company’s earnings history. Thus, a modification of the standard definition is proposed, derived from a covariance-invariant mapping of observations to a two-parameter exponential model. The novel growth rate is called “covariance-invariant “, which becomes for the special case of steady growth at a constant rate. It can be obtained using different options such as a chart, look-up table or formula. Further, the extension of the model by an additive constant may be used if negative values dominate. The approach is viewed as easy to apply as the log-linear model but with a superior performance. Compared to nonlinear least-squares regression, unique solutions can be obtained that allow a rather quick calculation.