Input Function Research Articles

Is the One-Compartment Model with First Order Absorption a Useful Approximation?

PurposeThe one-compartment model with first order absorption (ka1C) has been extensively used to fit oral data. But when the disposition parameters of the drug are not available, the bias in the parameter estimates remains unclear. In this paper, the effect of potential misspecification of the area under the curve (AUC) and the mean absorption time (MAT) was evaluated for three relatively slowly absorbed drugs/formulations.MethodsAssuming a three-compartment disposition model with an input (absorption) rate described as a sum of two inverse Gaussian functions (2IG3C) as the true model, the deviations of AUC and MAT estimated with simpler models were analyzed. Simpler models, as the ka1C model (Bateman function), the one-compartment model with IG input function (IG1C) and the gamma density function were fitted to the oral data alone, and compared to the fits obtained with the 2IG3C model which also uses the 3C disposition parameters of the drug. Data from pharmacokinetic studies of trospium, propiverine and ketamine in healthy volunteers were analyzed using a population approach.ResultsThe Bateman function (ka1C) allowed a robust estimation of the population mean AUC, but the individual estimates were highly biased. It failed in evaluating MAT. The simple alternative models did not improve the situation.ConclusionsThe Bateman function appears to be useful for estimating the population mean value of AUC after oral administration. The results reemphasize the fact that insight into the absorption process can be only gained when also intravenous reference data are available.

PET Evaluation of the Novel F-18 Labeled Reversible Radioligand [18F]GEH200449 for Detection of Monoamine Oxidase-B in the Non-Human Primate Brain.

Positron emission tomography (PET) using radioligands for the enzyme monoamine oxidase B (MAO-B) is increasingly applied as a marker for astrogliosis in neurodegenerative disorders. In the present study, a novel reversible fluorine-18 labeled MAO-B compound, [18F]GEH200449, was evaluated as a PET radioligand in non-human primates. PET studies of [18F]GEH200449 at baseline showed brain exposure (maximum concentration: 3.4-5.2 SUV; n = 5) within the range of that for suitable central nervous system radioligands and a regional distribution consistent with the known localization of MAO-B. Based on the quantitative assessment of [18F]GEH200449 data using the metabolite-corrected arterial plasma concentration as input function, the Logan graphical analysis was selected as the preferred method of quantification. The binding of [18F]GEH200449, as calculated based on regional estimates of the total distribution volume, was markedly inhibited (occupancy >80%) by the administration of the selective MAO-B ligands L-deprenyl (0.5 and 1.0 mg/kg) or rasagiline (0.75 mg/kg) prior to radioligand injection. Radioligand binding was displaceable by the administration of L-deprenyl (0.5 mg/kg) at 25 min after radioligand injection, thus supporting reversible binding to MAO-B. These observations support that [18F]GEH200449 is a reversible MAO-B radioligand suitable for applied studies in humans.

InVivo Cerebral Imaging of Mutant Huntingtin Aggregates Using 11C-CHDI-180R PET in a Nonhuman Primate Model of Huntington Disease.

Huntington disease (HD) is a neurodegenerative disorder caused by an expanded polyglutamine (CAG) trinucleotide expansion in the huntingtin (HTT) gene that encodes the mutant huntingtin protein (mHTT). Visualization and quantification of cerebral mHTT will provide a proxy for target engagement and a means to evaluate therapeutic interventions aimed at lowering mHTT in the brain. Here, we validated the novel radioligand 11C-labeled 6-(5-((5-methoxypyridin-2-yl)methoxy)benzo[d]oxazol-2-yl)-2-methylpyridazin-3(2H)-one (11C-CHDI-180R) using PET imaging to quantify cerebral mHTT aggregates in a macaque model of HD. Methods: Rhesus macaques received MRI-guided intrastriatal delivery of a mixture of AAV2 and AAV2.retro viral vectors expressing an HTT fragment bearing 85 CAG repeats (85Q, n = 5), a control HTT fragment bearing 10 CAG repeats (10Q, n = 4), or vector diluent only (phosphate-buffered saline, n = 5). Thirty months after surgery, 90-min dynamic PET/CT imaging was used to investigate 11C-CHDI-180R brain kinetics, along with serial blood sampling to measure input function and stability of the radioligand. The total volume of distribution was calculated using a 2-tissue-compartment model as well as Logan graphical analysis for regional quantification. Immunostaining for mHTT was performed to corroborate the invivo findings. Results: 11C-CHDI-180R displayed good metabolic stability (51.4% ± 4.0% parent in plasma at 60 min after injection). Regional time-activity curves displayed rapid uptake and reversible binding, which were described by a 2-tissue-compartment model. Logan graphical analysis was associated with the 2-tissue-compartment model (r 2 = 0.96, P < 0.0001) and used to generate parametric volume of distribution maps. Compared with controls, animals administered the 85Q fragment exhibited significantly increased 11C-CHDI-180R binding in several cortical and subcortical brain regions (group effect, P < 0.0001). No difference in 11C-CHDI-180R binding was observed between buffer and 10Q animals. The presence of mHTT aggregates in the 85Q animals was confirmed histologically. Conclusion: We validated 11C-CHDI-180R as a radioligand to visualize and quantify mHTT aggregated species in a HD macaque model. These findings corroborate our previous work in rodent HD models and show that 11C-CHDI-180R is a promising tool to assess the mHTT aggregate load and the efficacy of therapeutic strategies.

BPM: Blended Piecewise Möbius Maps

AbstractWe propose a novel Möbius interpolator that takes as an input a discrete map between the vertices of two planar triangle meshes, and outputs a continuous map on the input domain. The output map interpolates the discrete map, is continuous between triangles, and has low quasi‐conformal distortion when the input map is discrete conformal. Our map leads to considerably smoother texture transfer compared to the alternatives, even on very coarse triangulations. Furthermore, our approach has a closed‐form expression, is local, applicable to any discrete map, and leads to smooth results even for extreme deformations. Finally, by working with local intrinsic coordinates, our approach is easily generalizable to discrete maps between a surface triangle mesh and a planar mesh, i.e., a planar parameterization. We compare our method with existing approaches, and demonstrate better texture transfer results, and lower quasi‐conformal errors.

Artificial intelligence in healthcare: the value co-creation process and influence of other digital health transformations

PurposeDigital transformations are altering service models and care delivery methods in healthcare. Artificial Intelligence (AI) represents the next wave of transformation in healthcare. This study aims to understand patient perceptions of AI and its impact on value co-creation.Design/methodology/approachA conceptual model was developed to investigate how value co-creation operant resources (digital self-efficacy and relational service quality) impact value co-creation engagement (shared decision-making) and value co-creation outcomes (anticipatory AI value co-creation and intention to adopt AI). Data were collected from 332 respondents and analyzed using structural equation modeling.FindingsThe results indicate that the value co-creation process for AI technologies is a function of inputs, experiences and AI outputs. Operant resources were found to be positively associated with shared decision-making. However, not all operant resources directly and positively impacted AI outcomes. The indirect and positive mediated relationships through shared decision-making to AI outcomes suggest an interactive AI value co-creation process.Research limitations/implicationsAI technologies are still in early stages of consumer adoption in healthcare. Future research is warranted that investigates the validity of the model through maturing service life cycles.Practical implicationsCustomer perceptions of new digital innovations are formed in the context of previous digital experiences. Marketers need to understand how customers view their current non-AI technologies. Strong engagement and perceived value of current technologies will help ease customers into the usage of AI technologies.Originality/valueThis study investigates the unique stages of the value co-creation process for AI technologies in healthcare. The results demonstrate that the value co-creation process is a function of inputs, tech-enabled experiences and AI outputs.

BelNet: basis enhanced learning, a mesh-free neural operator

Operator learning trains a neural network to map functions to functions. An ideal operator learning framework should be mesh-free in the sense that the training does not require a particular choice of discretization for the input functions, allows for the input and output functions to be on different domains, and is able to have different grids between samples. We propose a mesh-free neural operator for solving parametric partial differential equations. The basis enhanced learning network (BelNet) projects the input function into a latent space and reconstructs the output functions. In particular, we construct part of the network to learn the ‘basis’ functions in the training process. This generalized the networks proposed in Chen &amp; Chen (Chen and Chen 1995 IEEE Trans. Neural Netw. 49 , 911–917. ( doi:10.1109/72.392253 ) and 6 , 904–910. ( doi:10.1109/IJCNN.1993.716815 )) to account for differences in input and output meshes. Through several challenging high-contrast and multiscale problems, we show that our approach outperforms other operator learning methods for these tasks and allows for more freedom in the sampling and/or discretization process.

Designing spiking neural networks for robust and reconfigurable computation

Networks of spiking neurons constitute analog systems capable of effective and resilient computing. Recent work has shown that networks of symmetrically connected inhibitory neurons may implement basic computations such that they are resilient to system disruption. For instance, if the functionality of one neuron is lost (e.g., the neuron, along with its connections, is removed), the system may be robustly reconfigured by adapting only one global system parameter. How to effectively adapt network parameters to robustly perform a given computation is still unclear. Here, we present an analytical approach to derive such parameters. Specifically, we analyze k-winners-takes-all (k-WTA) computations, basic computational tasks of identifying the k largest signals from a total of N input signals from which one can construct any computation. We identify and characterize different dynamical regimes and provide analytical expressions for the transitions between different numbers k of winners as a function of both input and network parameters. Our results thereby provide analytical insights about the dynamics underlying k-winner-takes-all functionality as well as an effective way of designing spiking neural network computing systems implementing disruption-resilient dynamics.

Rancang Bangun Sistem Pengajuan Surat Pengantar Nikah Berbasis Web Di Kelurahan Pengadegan Jakarta Selatan

&#x0D; The process of making a marriage cover letter which is done manually by having to come directly to the local RT or RW at the Pengadengan village office currently creates an obstacle for prospective brides who want to register their marriage, especially during a pandemic such as at this time who are required to carry out physical distancing in order to breaking the chain of spread of the covid-19 or corona virus. The purpose of this research is to create a web-based online marriage cover letter making system. It is hoped that this system will be able to simplify matters of making marriage cover letters without having to come to the village or sub-district office. The system development method that will be used is the Waterfall method. This method is still often used because it has a sequential flow and is suitable for developing software with unchanging specifications. This model provides a software life-flow approach starting from the analysis, design, coding, testing, and support stages. This application is tested using the black box test, which is testing the software in terms of functional specifications. Testing is intended whether the input and output functions of the device comply with the required specifications. The results of this application test concluded that the expected functions were all successful and accepted in accordance with the wishes of the research objectives.&#x0D;

Explainable Deep Ensemble Model for Bearing Fault Diagnosis Under Variable Conditions

Deep learning-based intelligent diagnostic methods have been widely used in aerospace, rail transportation, automotive, rail vehicles, and other fields. However, deep neural networks are composed of multi-layer non-linear transformation to learn the feature maps of input, leading to the black box characteristic. It is usually criticized to be non-transparent and less interpretable in predictions and maintenance decision-making. Meanwhile, due to the variability of machine working conditions in the manufacturing process, there is still a gap between traditional deep learning diagnosis methods and the practical application. To address such problems, an explainable deep ensemble model is proposed for fault diagnosis of bearings which both leverages feature learning ability and improves model interpretability. Firstly, a residual network integrated with bi-directional long short-term memory (BiLSTM) is designed as the backbone learning framework to perform associative feature extraction from the original 1D time-series signals. Then, a multi-branch model with a multi-scale ensemble learning strategy is constructed for feature fusion and final fault diagnosis. After the model is trained, an enhanced gradient-weighted class activation mapping technique, named 1D Grad-CAM ++ is embedded to visualize the region of interest in the convolutional layer, where the prior knowledge gained from the bearing fault mechanism model is effectively combined to provide a better understanding of the learned features and decision-making of model. Finally, three fault diagnosis cases of rolling bearings under different operation conditions are used for algorithm verification. The results demonstrate the proposed approach shows superior explainable capability and diagnosis performance in comparison with other methods.

Dose-consistent dynamic SPECT

BackgroundCoronary flow reserve (CFR) values measured by dynamic SPECT systems are typically consistent with other modalities (e.g., PET). However, large discrepancies are often observed for individual patients. Positioning of the region-of-interest (ROI), representing the arterial input function (AIF) could explain some of these discrepancies. We explored the possibility of positioning the ROI in a manner that evaluates its consistency with patient-based injected radiotracer doses. MethodsDose-consistent dynamic SPECT methodology was introduced, and its application was demonstrated in a twenty-patient clinical study. The effect of various ROI positions was investigated and comparison to myocardial perfusion imaging was performed. ResultsMean AIF ratios were consistent with the injected dose ratios for all examined ROI positions. Good agreement (> 80%) between total perfusion deficit and CFR was found in the detection of obstructive CAD patients for all ROIs considered. However, for individual patients, significant dependence on ROI position was observed (altering CFR by typically 30%). The proposed methodology’s uncertainty was evaluated (~ 7%) and found to be smaller than the variability due to choice of ROI position. ConclusionDose-consistent dynamic SPECT may contribute to evaluating uncertainty of CFR measurements and may potentially decrease uncertainty by allowing improved ROI positioning for individual patients.

Imaging the brain's immune response to alcohol with [11C]PBR28 TSPO Positron Emission Tomography.

In humans, the negative effects of alcohol are linked to immune dysfunction in both the periphery and the brain. Yet acute effects of alcohol on the neuroimmune system and its relationships with peripheral immune function are not fully understood. To address this gap, immune response to an alcohol challenge was measured with positron emission tomography (PET) using the radiotracer [11C]PBR28, which targets the 18-kDa translocator protein, a marker sensitive to immune challenges. Participants (n = 12; 5 F; 25-45 years) who reported consuming binge levels of alcohol (>3 drinks for females; >4 drinks for males) 1-3 months before scan day were enrolled. Imaging featured a baseline [11C]PBR28 scan followed by an oral laboratory alcohol challenge over 90 min. An hour later, a second [11C]PBR28 scan was acquired. Dynamic PET data were acquired for at least 90 min with arterial blood sampling to measure the metabolite-corrected input function. [11C]PBR28 volume of distributions (VT) was estimated in the brain using multilinear analysis 1. Subjective effects, blood alcohol levels (BAL), and plasma cytokines were measured during the paradigm. Full completion of the alcohol challenge and data acquisition occurred for n = 8 (2 F) participants. Mean peak BAL was 101 ± 15 mg/dL. Alcohol significantly increased brain [11C]PBR28 VT (n = 8; F(1,49) = 34.72, p > 0.0001; Cohen's d'=0.8-1.7) throughout brain by 9-16%. Alcohol significantly altered plasma cytokines TNF-α (F(2,22) = 17.49, p < 0.0001), IL-6 (F(2,22) = 18.00, p > 0.0001), and MCP-1 (F(2,22) = 7.02, p = 0.004). Exploratory analyses identified a negative association between the subjective degree of alcohol intoxication and changes in [11C]PBR28 VT. These findings provide, to our knowledge, the first in vivo human evidence for an acute brain immune response to alcohol.

Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling.

The tertiary structures of an increasing number of biological macromolecules have been determined using cryo-electron microscopy (cryo-EM). However, there are still many cases where the resolution is not high enough to model the molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3-4.5 Å), improvement in the map quality facilitates structure modeling. We report EM-GAN, a novel approach that modifies an input cryo-EM map to assist protein structure modeling. The method uses a 3D generative adversarial network (GAN) that has been trained on high- and low-resolution density maps to learn the density patterns, and modifies the input map to enhance its suitability for modeling. The method was tested extensively on a dataset of 65 EM maps in the resolution range of 3-6 Å and showed substantial improvements in structure modeling using popular protein structure modeling tools. https://github.com/kiharalab/EM-GAN, Google Colab: https://tinyurl.com/3ccxpttx.

Application of ANFIS to Minimize Total Harmonic Distortion in Brushless DC Motor

Abstract: Total harmonic distortion provides direct impact on power quality in distributed systems. The work presents reduction of Total harmonic distortion (THD) in Brushless DC motor (BLDC) using adaptiveneuro controller. The configured controller is provided with a rule base, input and output membership functions. The BLDC motor is modelled in MATLAB-SIMULINK with load variation that have direct impact on the functioning of a system. The parameters include load, resistance and inertia of BLDC motor. The work results are obtained after simulating BLDC system under different load configurations. The conclusion is provided with the calculation of THD so produced using Adaptive neuro configured controller

A systematic evaluation of five different image-derived input functions for the clinical implementation of 18F-NaF bone PET/CT in patients with chronic kidney disease-mineral and bone disorder.

The aim of this study was to investigate the impact of the use of varying input parameters on resulting bone plasma clearance (Ki ) and other kinetic modelling parameters in a group of patients with chronic kidney disease-mineral and bone disorder (CKD-MBD). Raw PET/CT data and blood data were systematically analyzed using five different VOIs for the input functions in the left ventricle and in the thoracic aorta. Standardized VOIs were placed in four thoracic vertebrae and the results pooled and averaged. The basic image-derived input functions (IDIFs) were corrected for partial volume effect and spill-over and modified by substitution of the terminal image exponential with the corresponding plasma-exponentials derived from blood samples. Ki was then calculated using both a non-linear regression (NLR) analysis and a graphical Patlak analysis and compared. Our original results were reproducible with an inter-observer difference of approximately 6%. The correction factors varied with the VOI volumes from 0.73 ± 0.17 for the largest LV-VOI (48.7 ± 25.3 cm3) to 0.99 ± 0.10 for the AO-VOI (3.4 ± 1.2 cm3). The mean NLR-Ki results varied between 0.0378 ± 0.0112 and 0.0432 ± 0.0095 ml/min ml-1 with a fixed vB and 0.0408 ± 0.0111 and 0.045 ± 0.0102 ml/min ml-1 with a free-fitted vB. The corresponding Patl-Ki -results varied between 0.0302 ± 0.0071 and 0.0325 ± 0.0070 ml/min ml-1, having lesser differences and variances. The input functions with least variance and mean differences compared with NLR results were derived from the left ventricle with a VOI volume of 19.2 ± 11.3 cm3 corrected for PVE and Bg with a mean Ki -difference: 0.0097 ± 0.0370 ml/min ml-1 and 95% confidence limits (-0.023 to 0.004). Our results indicated that a VOI with a volume of approximately 20 cm3 with a correction factor of 0.83 ± 0.13 results in Patlak results with the least variance and difference compared with the NLR results. The use of free-fitted vB in the NLR analysis showed the most robust results in all input series. The Patlak results were in comparison generally lower than the NLR results (-17.3% to -23.4%) but very robust across the various input series and with results comparable to previously published data and are therefore recommended for future analysis.

Impact of iron fertilisation on atmospheric CO2 during the last glaciation

Abstract. While several processes have been identified to explain the decrease in atmospheric CO2 during glaciations, a better quantification of the contribution of each of these processes is needed. For example, enhanced aeolian iron input into the ocean during glacial times has been suggested to drive a 5 to 28 ppm atmospheric CO2 decrease. Here, we constrain this contribution by performing a set of sensitivity experiments with different aeolian iron input patterns and iron solubility factors under boundary conditions corresponding to 70 000 years before present (70 ka), a time period characterised by the first observed peak in glacial dust flux. We show that the decrease in CO2 as a function of Southern Ocean iron input follows an exponential decay relationship. This exponential decay response arises due to the saturation of the biological pump efficiency and levels out at ∼21 ppm in our simulations. We show that the changes in atmospheric CO2 are more sensitive to the solubility of iron in the ocean than the regional distribution of the iron fluxes. If surface water iron solubility is considered constant through time, we find a CO2 drawdown of ∼4 to ∼8 ppm. However, there is evidence that iron solubility was higher during glacial times. A best estimate of solubility changing from 1 % during interglacials to 3 % to 5 % under glacial conditions yields a ∼9 to 11 ppm CO2 decrease at 70 ka, while a plausible range of CO2 drawdown between 4 to 16 ppm is obtained using the wider but possible range of 1 % to 10 %. This would account for ∼12 %–50 % of the reconstructed decrease in atmospheric CO2 (∼32 ppm) between 71 and 64 ka. We further find that in our simulations the decrease in atmospheric CO2 concentration is solely driven by iron fluxes south of the Antarctic polar front, while iron fertilisation elsewhere plays a negligible role.

Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function

PurposeIn Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. Materials and MethodA total of 13 healthy subjects (younger (<40 y/o): 8, older (> 67 y/o): 5) were recruited and underwent 25-min DCE-MRI scans. The 25 min data were retrospectively truncated to 10 min to simulate a reduced scan time of 10 min. A deep learning network was trained to predict the CIF using simulated tissue contrast dynamics with two vascular transport models. The BBB permeability (PS) was measured using 3 methods: (i) Ca-25min, using DCE-MRI data of 25 min with individually sampled AIF (Ca); (ii) Ca-10min, using truncated 10min data with AIF (Ca); and (iii) Cp-10min, using truncated 10 min data with CIF (Cp). The PS estimates from the Ca-25min method were used as reference standard values to assess the accuracy of the Ca-10min and Cp-10min methods in estimating the PS values. ResultsWhen compared to the reference method(Ca-25min), the Ca-10min and Cp-10min methods resulted in an overestimation of PS by 217 ± 241 % and 48.0 ± 30.2 %, respectively. The Bland Altman analysis showed that the mean difference from the reference was 8.85 ± 1.78 (x10−4 min−1) with the Ca-10min, while it was reduced to 1.63 ± 2.25 (x10−4 min−1) with the Cp-10min, resulting in an average reduction of 81%. The limits of agreement also reduced by up to 39.2% with the Cp-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group. ConclusionsWe demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.

Intercept Method for Accurately Estimating Residual Fluid Saturation and Approximate Transient Solutions with Production Time Scales in Centrifuge Core Plug Experiments

Summary The centrifuge experiment is used to measure capillary pressure in core plugs by forced displacement (imbibition or drainage): Strong gravitational forces (imposed by rotation) displace fluid held in place by capillary forces. This setup is also used to measure and establish residual saturation, the saturation where a fluid loses connectivity and can no longer flow. Obtaining this saturation is challenging as the capillary end effect causing outlet fluid accumulation theoretically only vanishes at infinite rotation speed. First, we derive a novel “intercept method” to estimate residual saturation with a centrifuge: Plotting steady-state average saturation data against inverse squared rotation speed gives a straight line at high speeds where the intercept equals the residual saturation. The linear behavior starts once the core saturation profile contains the residual saturation. The result is theoretically valid for all input parameters and functions, derived assuming uniform gravity along the core at a given speed. Then the saturation profile near the outlet is invariant and compresses at a higher speed. The method was, however, demonstrated numerically to be highly accurate even for extremely nonuniform gravity: The saturation data are linear and the correct residual saturation value is estimated. This is because when the residual saturation enters, most of the end effect profile is located in a narrow part of the core and thus uniformly compressed. Several experimental and numerical data sets validated the method. Second, an analytical solution (using all relevant input) is derived for transient production toward equilibrium after the rotation speed is increased starting from an arbitrary initial state. For this result, we assume the outlet (or initial) profile compresses also transiently. The displacing and displaced regions have fixed mobilities but occupy different lengths with time. Time as a function of production has a linear term and logarithmic term (dominating late time behavior). Production rate can thus be constant most of the time or gradually reducing, resulting in very distinct profiles. The correlation could fit experimental data well and confirmed the possible profile shapes. A time scale was derived analytically that scales all production curves to end (99.5% production) at same scaled time. The solution predicted similar time scales and trends in time scale with rotation speed and viscosity as numerical simulations. Numerical simulations indicated that the saturations near the residual saturation traveled slowly, which caused production to tail and span 5 log units of time (the analytical solution predicted 2–3). The correlation better matched low-speed data where the residual saturation had not entered.

Performance And Functional Testing With The Black Box Testing Method

Software testing is an essential stage in software development because it plays a role in maintaining the quality of the software produced. The objective of software testing is for verification and validation, namely ensuring that the software being developed works according to the expected functions when the software is designed. In addition to improving quality, the software being developed must also have minimal errors and test whether the software is feasible to use or not. This research employed the black box testing method. Black box technical testing was a test of the input or output functionality of the software. The research also described the testing model using automated testing, which was carried out by Apache JMeter tools on performance tests and Katalon Studio to functional tests on the Perbanas portal. The final results of this research depicted the test model from studying business processes, creating test scenarios for automated testing to analyzing the test results. The performance test results aimed to find out how much throughput (transactions per second) and Response Time are. Meanwhile, functional test results aimed to find out whether the expected functions and outputs were appropriate or not. The results of the performance test on the login feature that had been carried out, the best was taken, i.e., sample data that entered approximately 5000 samples for 250 users for a load test per 5 minutes; only 0.06% failed with an average of 15 transactions per second (TPS) by a maximum response time of 68 seconds and a minimum response time of 0.2 seconds. KEYWORDS: Apache JMeter, automation test, black box test, functional test, Katalon Studio, performance test

An experimental investigation and hybrid neural network modelling of thermal management of lithium-ion batteries using a non-paraffinic organic phase change material, Myristyl alcohol

The usage of electric vehicles is substantially increased worldwide to reduce greenhouse gas emissions and to decrease the dependence on crude oil. Li-ion battery (LiB) has a promising future due to their lightweight, high-energy density, and can be easily charged and discharged, with a relatively long lifespan. The lifespan/durability and performance of a battery depends dominantly on its operating temperature. Hence, cooling strategies for Li-ion battery is essential for better lifespan, safety, and performance. In this work, experiments are conducted using a stack of 20 Li-ion cells in a 5-series-4-parallel configuration at various discharge C-rates for natural convection air and PCM-assisted cooling methods. In comparison with the natural convection air-cooling scenario, the battery module maximum temperature is reduced by 21.66%, 25.96%, 31.71%, 31.72%, and 34.48% in the case of PCM-assisted cooling for discharge C-rates of 1C, 1.5C, 2C, 2.5C, and 3C, respectively.The use of PCM has increased the time for which the battery module is maintained below the desired operating temperature compared to the natural convection air cooling by 127.84%, 98.18%, and 115.38% for the 2C, 2.5C, and 3C discharge C-rates, respectively. The fraction of total discharge time under desired operating conditions maintained during the PCM cooling scenario is 100%, 75.69%, and 46.66% for discharge C-rates of 2C, 2.5C, and 3C, respectively. The battery module's maximum temperature is maintained under desired operating conditions by PCM cooling up to a discharge C-rate of 2C. This work shows that passive thermal management with Myristyl alcohol as a phase change material is safe and effective for high-current and high-energy-density Li-ion batteries. In addition, a prediction model using ANFIS has been created to estimate the battery module's maximum temperature values and found that a combination of gauss input and linear output membership function is the best model with a coefficient of determination of 0.99.

Stabilization of a class of underactuated parallel robots via energy shaping: Application to cable driven manipulators

In this brief paper, potential energy shaping of underactuated parallel robots (UPRs) based on the method called interconnection and damping assignment passivity-based control (IDA-PBC) is presented. To this aim, to assign a new Hamiltonian to the closed-loop system, due to fewer actuators than degrees of freedom, a set of partial differential equations (PDEs) related to assigning a desired potential energy should be solved analytically, which are basically the stumbling block of IDA-PBC approach. Although in UPRs, the input mapping matrix is complex and configuration-dependent, a general solution for the potential energy PDE is derived such that it is independent of degrees of underactuation.