Machining Simulation Research Articles

Discovering supply chain operation towards sustainability using machine learning and DES techniques: a case study in Vietnam seafood

PurposeTo achieve the Sustainable Development Goals (SDGs) in the era of Logistics 4.0, machine learning (ML) techniques and simulations have emerged as highly optimized tools. This study examines the operational dynamics of a supply chain (SC) in Vietnam as a case study utilizing an ML simulation approach.Design/methodology/approachA robust fuel consumption estimation model is constructed by leveraging multiple linear regression (MLR) and artificial neural network (ANN). Subsequently, the proposed model is seamlessly integrated into a cutting-edge SC simulation framework.FindingsThis paper provides valuable insights and actionable recommendations, empowering SC practitioners to optimize operational efficiencies and fostering an avenue for further scholarly investigations and advancements in this field.Originality/valueThis study introduces a novel approach assessing sustainable SC performance by utilizing both traditional regression and ML models to estimate transportation costs, which are then inputted into the discrete event simulation (DES) model.

Accuracy of a new photometric jaw tracking system in the frontal plane at different recording distances: An in-vitro study

ObjectivesTo evaluate the accuracy of a new photometric jaw tracking system (JTS) in recording linear vertical movements in the frontal plane at different distances. MethodsA mandibular plaster cast of a patient was placed on a simulation machine capable of linear movements along two spatial axes. Cyclops JTS (Itaka) was adapted to the plaster cast, while the head frame was attached to the simulation machine. The latter performed five linear movements from 20 to 40 mm in the y-axis; each movement was repeated five times at five different recording distance (380 to 420 mm). The recorded movements were measured and compared with those obtained with a laser Doppler vibrometer (LDV) for accuracy analysis. Data were statistically processed (α = 0.05). ResultsNo statistically significant differences were found between Cyclops and LDV measurements on the y- and z-axes (p = 0.5). Changes in linear vertical motion and distance positions did not affect the accuracy, which remained relatively constant with similar trends and values less than 1 % for each parameter variation. The best condition observed was linear vertical movement of 30 mm at 420 mm (0.010 ± 0.023 mm). ConclusionsCyclops has proven to be an accurate JTS in recording linear vertical movements in the frontal plane at different recording distances. For optimal recordings, the scanner should be placed as close as possible to the markers; excessive vertical movements decreased the accuracy. However, this study has limitations and requires in-vivo confirmations. Clinical significanceThe tested JTS proved accurate in recording linear vertical movements in the frontal plane. However, given the limitations of the study, further investigation under real conditions is needed to support prosthetic and gnathological rehabilitations.

Establishing a unified viscoplastic constitutive equation for EA4T steel: Comparative analysis with Arrhenius model

Quasi-static and impact compression experiments were conducted on EA4T steel using a Gleeble-3800 thermal simulation machine. The study aimed to investigate the complex microstructural evolution and thermal deformation behavior of EA4T steel under various experimental conditions, including temperatures ranging from 970 °C to 1170 °C and strain rates ranging from 0.01s−1 to 1s−1. To precisely elucidate these phenomena, we meticulously constructed a unified visco-plastic constitutive model using the internal variable methodology. The model's parameterization was achieved through the effective application of genetic algorithm optimization techniques. Rigorous validation of the model was performed by meticulously comparing its outputs with experimental data, including key metrics such as average grain size, recrystallized fraction, and effective flow stress. In addition,a comparative analysis with the improved Arrhenius model highlights the superior performance of the unified visco-plastic constitutive equation in capturing the intricate microstructural evolution and thermal deformation behavior exhibited by EA4T steel.

A Mathematical Modeling of Computer Numerical Control Skiving Process for Manufacturing Helical Face Gears Using Sensitivity Matrix Combined With Levenberg–Marquardt Algorithm

Abstract Currently, numerous studies have applied gear skiving processes to produce face gear. However, there remains a significant challenge in achieving a flexible computing model for manufacturing a precise tooth surface for face gear. This study proposes a novel mathematical model that combines the cutter modification method and computer numerical control (CNC)-axis motion modification methods within a unified “closed-loop optimization.” This approach aims to enhance the tooth surface accuracy of skived helical face gears by determining optimal coefficients. Applying the Levenberg–Marquardt algorithm and sensitivity matrix enables the calculation of new polynomial coefficients, ensuring the attainment of gear surfaces with an accuracy grade of B6 (according to the ANSI/AGMA 2009-B01 standard) for each target surface. The proposed methodology involves the generation of a helical skiving cutter using a corrected rack. Subsequently, the cutting path on the CNC machine is optimized by incorporating additional motions expressed in polynomials. A comprehensive skiving simulation is conducted to achieve the desired face-gear surface, which is corrected by specified polynomial coefficients. The proposed model is validated through numerical and machining simulations using vericut software. The results affirm the practicality and efficacy of our approach in achieving the desired accuracy in producing helical face gears through power skiving processes.

Improved hot workability of TiAl composite with core-shell structure via in-situ synthesized multi-phases ceramic particles

TiAl composite with core-shell structure was in-situ synthesized incorporating BN nanosheets and graphene oxide into TiAl alloy using SPS, and the hot deformation behaviors were subsequently investigated on a Gleeble-1500D thermal simulation machine. The microstructural evolution and the influence of ceramic particles on hot deformation were revealed using OM, SEM, TEM, and EBSD. The results demonstrated a refined microstructure of TiAl composite induced by ceramic particles providing extra nucleation sites for DRX, contributing to lower flow stress, with peak stresses of 387 and 360 MPa for TiAl alloy and TiAl composite deformed at 1200 °C/1s−1, respectively. Moreover, dislocation slip was inhibited by ceramic particles leading to dislocation pile-up, facilitating the nucleation and growth of DRX at lamellar colony boundaries and α2/γ interfaces. Notably, the “shell” composed of TiB2 and Ti2AlN nanoparticles coordinated the deformation of lamellar colonies with different orientations, improving the hot workability of TiAl composite.

Artificial Intelligence Applications in Health

General practices (GPs), called family physicians in certain countries, are the cornerstone of primary health care. The increase in average lifespan and, thereby, the number of chronic diseases has recently increased the workload of GPs and decreased the time spent on the patient. Implementations of Artificial intelligence (AI)-powered systems are essential in GPs to facilitate the jobs of health professionals. Implementing AI-driven systems is expected to help health professionals diagnose and treat. AI involves the machine simulation of human cognitive capabilities, encompassing a range of technologies, including deep learning and machine learning. AI is currently being used across various applications in medicine and continues to evolve, and its role in medicine is expected to become increasingly prominent. AI-enhance sensor systems can continuously monitor physiological parameters and generate personalized medicinal therapy. However, the employment of AI in GPs is still in the very early phase. AI is a tool to aid healthcare professionals in improving the accuracy and speed of diagnosis rather than a replacement for their expertise. This review will focus on applying artificial intelligence in general practices (GPs).

Effect of Nb content and cooling rate on the microstructure transformation and mechanical properties of hot-rolled rebar

In this study, the synergistic effect of the Nb content and cooling rate on the microstructural transformation, precipitation behavior, and mechanical properties of hot-rolled rebar was studied using a Gleeble-3800 thermal simulation machine, scanning electron microscopy, transmission electron microscopy, and a universal tensile testing machine. The results showed that the synergistic effect of Nb content and cooling rate could optimize the microstructure and precipitation behavior of rebar, promote ferrite and pearlite transformation, and reduce the ferrite grain size, pearlite lamellar spacing, and precipitated phase size, thereby improving the strength and plasticity of Nb-containing rebar. When the cooling rate was 3 °C/s and the Nb content was 0.035 wt%, the ferrite grain size decreased to 21.45 μm, the pearlite lamellar spacing refined to 0.222 μm, and the particle size of the (Nb, Ti, V) C precipitates reduced to 5 nm. Accordingly, the yield and tensile strength increased to 779 ± 6 MPa and 973 ± 8 MPa, respectively, the elongation at fracture increased to 16.35%, and the microhardness increased to 311.39 ± 8.0 HV.

Dynamic modeling of a synchronous reluctance machine for transient simulation of vibrations under variable rotor magnetization

This work introduces a new approach for the dynamic simulation of a permanent magnet-assisted synchronous reluctance machine with the ability to consider dynamic changes in the rotor magnetization. The aim is to comprehensively analyze the dynamics of a machine through transient simulations of the occurring magnetic and mechanical forces that influence the noise and vibration characteristics. A simplified magnetic model considering the effects of magnetic reluctances, leakage flux, and magnetic saturation is utilized to efficiently calculate the dynamically changing magnetic forces in the air gap. Unlike conventional designs employing rare earth magnets in the rotor, the design at hand utilizes non-rare earth magnets that enable adjustments of the magnets’ flux output. The novelty of the presented approach lies in its ability to consider these dynamic changes when calculating the air gap flux. The magnetic forces are then applied to an elastic multibody model of the motor, which includes the rotor, stator, bearings, and the housing, for the computation of the bearing forces and housing deformations. The presented multi-physical model allows for transient simulations of the forces acting on the bearings and the housing, capturing the dynamic response of the motor under varying rotor magnetization, air gaps, and loads. With the proposed approach, this study offers predictions regarding critical vibration characteristics that occur during dynamic operation, providing valuable insights for noise reduction efforts.

IMPROVING THE ACCURACY OF FINITE ELEMENT MODELING OF A HIGH-FREQUENCY EDDY CURRENT PROBE

The paper considers the issues of finite element modeling of an eddy current probe designed to measure the thickness of a dielectric coating on an electrically conductive non-magnetic base metal. The quality criteria of the model are formulated and the requirements for them are presented: the precision and error of calculating the signals of the finite element model of an eddy current probe should not exceed ±(0.001h + 0.1) microns when the thickness h of the coating changes. To achieve this task, an analysis of the parameters affecting precision and error was carried out, the criteria for their assessment were justified and an approach to the problem of increasing the frequency of the finite element grid in critical places of the finite element model was formulated. To confirm the achieved characteristics, a real eddy current probe was manufactured, its characteristics were evaluated using a coating thickness simulator machine and measures of specific electrical conductivity. An algorithm for calibrating the signals of a real eddy current probe is presented, which is used to validate a finite element model.

The near-miss effect in online slot machine gambling: A series of conceptual replications.

Near-misses are a structural characteristic of gambling products that can be engineered within modern digital games. Over a series of preregistered experiments using an online slot machine simulation, we investigated the impact of near-miss outcomes on subjective ratings (motivation, valence) and two behavioral measures (speed of gambling, bet size). Participants were recruited using Prolific and gambled on an online three-reel slot machine simulator that delivered a one in three rate of X-X-O near-misses. Study 1 measured trial-by-trial subjective ratings of valence and motivation (Study 1a, n = 169; Study 1b, n = 148). Study 2 (n = 170) measured spin initiation latencies as a function of the previous trial outcome. Study 3 (n = 172) measured bet size as a function of the previous trial outcome. In Study 1a, near-misses increased the motivation to continue gambling relative to full-misses, supporting Hypothesis 1. On valence ratings, near-misses were rated significantly more positively than full-misses, in the opposite direction to Hypothesis 2; this effect was confirmed in a close replication (Study 1b). In Study 2, participants gambled faster following near-misses relative to full-misses, supporting Hypothesis 3. In Study 3, participants significantly increased their bet size following near-misses relative to full-misses, supporting Hypothesis 4. Across all dependent variables, near-miss outcomes yielded statistically significant differences from objectively equivalent full-miss outcomes, corroborating the "near-miss effect" across both subjective and behavioral measures, and in the environment of online gambling. The unexpected findings on valence ratings are considered in terms of boundary conditions for the near-miss effect, and competing theoretical accounts based on frustration/regret, goal generalization, and skill acquisition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Matrix metalloproteinase/Fas ligand (MMP/FasL) interaction dynamics in COVID-19: An in silico study and neuroimmune perspective

BackgroundThe initiator of cytokine storm in Coronavirus disease (COVID-19) is still unknown. We recently suggested a complex interaction of matrix metalloproteinases (MMPs), Fas ligand (FasL), and viral entry factors could be responsible for the cytokine outrage In COVID-19. We explored the molecular dynamics of FasL/MMP7-9 in COVID-19 conditions in silico and provide neuroimmune insights for future. MethodsWe enrolled and analyzed a clinical cohort of COVID-19 patients, and recorded their blood Na + levels and temperature at admission. A blood-like molecular dynamics simulation (MDS) box was then built. Four conditions were studied; MMP7/FasL (healthy), MMP7/FasL (COVID-19), MMP9-FasL (healthy), and MMP9/FasL (COVID-19). MDS was performed by GROningen MAchine for Chemical Simulation (GROMACS). We analyzed bonds, short-range energies, and free binding energies to draw conclusions on the interaction of MMP7/MMP9 and FasL to gain insights into COVID-19 immunopathology. Genevestigator was used study RNA-seq/microarray expression data of MMPs in the cells of immune and nervous systems. Finally, epitopes of MMP/FasL complexes were identified as drug targets by machine learning (ML) tools. ResultsMMP7-FasL (Healthy), MMP7-FasL (COVID-19), MMP9-FasL (Healthy), and MMP9-FasL (COVID-19) systems showed 0, 1, 4, and 2 salt bridges, indicating MMP9 had more salt bridges. Moreover, in both COVID-19 and normal conditions, the number of interacting residues and surface area was higher for MMP9 compared to MMP7 group. The COVID-19 MMP9-FasL group had more H-bonds compared to MMP7-FasL group (12 vs. 7). 15 epitopes for FasL-MMP9 and 10 epitopes for FasL-MMP7 were detected. Extended MD simulation for 100 ns confirmed stronger binding of MMP9 based on Molecular Mechanics Generalized Borne Surface analysis (MM-GBSA) and Coul and Leonard-Jones (LJ) short-range energies. ConclusionsMMP9 interacts stronger than MMP7 with FasL, however, both molecules maintained strong interaction through the MDS. We suggested epitopes for MMP-FasL complexes as valuable therapeutic targets in COVID-19. These data could be utilized in future immune drug and protein design and repurposing efforts.

A Neurosurgical Readmissions Reduction Program in an Academic Hospital Leveraging Machine Learning, Workflow Analysis, and Simulation.

Predicting 30-day hospital readmissions is crucial for improving patient outcomes, optimizing resource allocation, and achieving financial savings. Existing studies reporting the development of machine learning (ML) models predictive of neurosurgical readmissions do not report factors related to clinical implementation. Train individual predictive models with good performance (area under the receiver operating characteristic curve or AUROC > 0.8), identify potential interventions through semi-structured interviews, and demonstrate estimated clinical and financial impact of these models. Electronic health records were utilized with five ML methodologies: gradient boosting, decision tree, random forest, ridge logistic regression, and linear support vector machine. Variables of interest were determined by domain experts and literature. The dataset was split divided 80% for training and validation and 20% for testing randomly. Clinical workflow analysis was conducted using semi-structured interviews to identify possible intervention points. Calibrated agent-based models (ABMs), based on a previous study with interventions, were applied to simulate reductions of the 30-day readmission rate and financial costs. The dataset covered 12,334 neurosurgical intensive care unit (NSICU) admissions (11,029 patients); 1,903 spine surgery admissions (1,641 patients), and 2,208 traumatic brain injury (TBI) admissions (2,185 patients), with readmission rate of 13.13, 13.93, and 23.73%, respectively. The random forest model for NSICU achieved best performance with an AUROC score of 0.89, capturing potential patients effectively. Six interventions were identified through 12 semi-structured interviews targeting preoperative, inpatient stay, discharge phases, and follow-up phases. Calibrated ABMs simulated median readmission reduction rates and resulted in 13.13 to 10.12% (NSICU), 13.90 to 10.98% (spine surgery), and 23.64 to 21.20% (TBI). Approximately $1,300,614.28 in saving resulted from potential interventions. This study reports the successful development and simulation of an ML-based approach for predicting and reducing 30-day hospital readmissions in neurosurgery. The intervention shows feasibility in improving patient outcomes and reducing financial losses.

SIMULATOR BOILER HYBRID SEBAGAI FUNGSI PENGERING DAN PENGOLAH LIMBAH MEDIS JENIS PLASTIK

The hybrid boiler simulator is a simulation machine designed with two working functions, namely as a medical waste processing function and as a drying cabinet function. As a medical waste processor, this machine will focus on plastic medical waste, and as a dryer function, this machine will be a clothes-drying tool or cabinet. So based on the multifunctionality of the machine, this system has hybrid working characteristics. Hybrid boiler simulators can be a solution, providing the function of processing plastic medical waste and providing the advantageous function of economical and environmentally friendly drying. Therefore, this research aims to create a hybrid boiler simulator that can be used as a plastic medical waste processing machine and also as a clothes drying machine. The hybrid boiler system is designed by applying the concept of appropriate technology so that its implementation can be done easily and practically. The research implementation method is a classification of plastic medical waste, concept design, and analysis, construction design of a hybrid boiler system combined with an incinerator for burning medical waste, system function testing, and observation of test data. The focus of his research is the heat of burning plastic waste, heat radiation, circulators, and flow control systems as well as the characteristics of the results of burning plastic waste. The results of this research are that the hybrid boiler simulator machine can function as an incinerator machine to burn plastic medical waste and function as a clothes dryer; The function of the incinerator in this machine is only through two processes, namely the process of burning plastic medical waste raw materials and the process of burning carbon gas or smoke produced from the process of burning raw materials; the sumulator machine is not yet equipped with a fogging process for the function of smoke cleaning filter and fogging control; the hybrid boiler simulator machine works on the principle of automatic control based on temperature detection using a temperature sensor; The combustion temperature in the boiler machine can be controlled at a standard of 830 °C and the drying room temperature can be set at a room temperature of 50 – 150 °C.

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

An Integrated Framework for Dynamic Vehicle Routing Problems with Pick-up and Delivery Time Windows and Shared Fleet Capacity Planning

This paper proposes a novel route optimization framework to solve the problem of instant pick-up and delivery for e-grocery orders. The proposed framework extends the traditional time-windowed package delivery problem. We demonstrate the effectiveness of our approach for this integrated problem using actual delivery data from HepsiJet, a leading e-commerce logistics provider in Turkey. We first employ several machine learning algorithms and simulations to investigate the capacity of the courier. Subsequently, a dynamic route planning workflow is executed with a highly specialized and novel routing algorithm. Our proposed heuristic approach considers combined fleet operations for delivering regular packages originating from a central depot and dynamic e-grocery orders picked up at local supermarkets and delivered to the customers. The heuristic algorithm constitutes k-opt and node transfer operation variations customized for this integrated problem. We report the performance of our approach in problem instances from the literature and instances from HepsiJet’s daily operations, which we also publicly share as new route optimization problem instances. Our results suggest that, despite the more complex nature of the integrated problem, our proposed algorithm and solution framework produce more efficient and cost-effective solutions that offer additional business opportunities for companies such as HepsiJet. The computational analyses reveal that implementing our proposed approach yields significant efficiency gains and cost reductions for the company, with a distance reduction of over 30%, underscoring our approach’s effectiveness in achieving substantial cost savings and enhanced efficiency through integrating two distinct delivery operations.

Calculation and Experimental Verification of Zn–Al–Mg Phase Diagram

The liquid phase projection diagram, three-dimensional phase diagram, and vertical section diagram of the Zn–x%Al–x%Mg alloy system was calculated using the phase diagram calculation software Pandat. Simultaneously making full use of the self-developed hot-dip galvanizing process simulation machine by China Steel Research produced a 75%Zn–19%Al–6%Mg coating. A method combining phase diagram calculations and experimental verification was used to investigate the equilibrium phases and solidification process of the alloy. The microstructure of the 75%Zn–19%Al–6%Mg coating was studied using scanning electron microscopy and energy dispersive spectrometry. The results indicate that the coating structure consists of primary Al dendrite phase, MgZn2 inter-metallic compound and Zn-rich phase. There is no ternary eutectic structure in the coating structure. Al dendrites grow on the surface of the coating, while there are no Al dendrites on the cross-section. The experimental results strongly concur with the calculated results from the Pandat phase diagram. The solidification sequence of the 75%Zn–19%Al–6%Mg coating is L→L + Al→L + Al + MgZn2→Al + MgZn2 + Zn. The phase diagram guides industrial production significantly, avoiding the waste of transitional materials and zinc caused by small scale trial and error experiments, thus reducing unnecessary production costs. The factory can select a reasonable coating composition designing scheme in the phase diagram, based on the performance requirements of customers for the coating.

Determining pre-procedure fasting alert time using procedural and scheduling data.

Before a medical procedure requiring anesthesia, patients are required to not eat or drink non-clear fluids for 6h and not drink clear fluids for 2h. Fasting durations in standard practice far exceed these minimum thresholds due to uncertainties in procedure start time. The aim of this retrospective, observational study was to compare fasting durations arising from standard practice with different approaches for calculating the timepoint at which patients are instructed to stop eating and drinking. Scheduling data for procedures performed in the cardiac catheterization laboratory of an academic hospital in Canada (January 2020 to April 2022) were used. Four approaches utilizing machine learning (ML) and simulation were used to predict procedure start times and calculate when patients should be instructed to start fasting. Median fasting duration for standard practice was 10.08h (IQR 3.5) for both food and clear fluids intake. The best performing alternative approach, using tree-based ML models to predict procedure start time, reduced median fasting from food/non-clear fluids to 7.7h (IQR 2) and clear liquids fasting to 3.7h (IQR 2.4). 97.3% met the minimum fasting duration requirements (95% CI 96.9% to 97.6%). Further studies are required to determine the effectiveness of operationalizing this approach as an automated fasting alert system.

Investigation of friction modeling on numerical Ti6Al4V cutting simulations

Friction significantly influences chip formation, thereby highlighting its modeling critical in numerical cutting simulations. Notably, issues like the underestimation of feed force in simulations are often attributed to inadequate friction models. Nevertheless, diverse conclusions in the literature regarding friction’s behavior complicate the accurate implementation of this input. Additionally, the enormous number of the available friction models and their varied calibration methods introduce further debates over which aspects of friction modeling should receive more focus. These controversies and deficiencies hinder progress in further understanding friction’s role in chip formation through numerical studies. Instead of proposing or calibrating new friction models, the current study, based on Ti6Al4V machining simulations, attempts to readdress the aforementioned controversies in a neutral stance by conducting sensitivity studies using a hybrid Smoothed Particle Hydrodynamics (SPH) - Finite Element Method (FEM) solver with Graphics Processing Units (GPU) acceleration, which is capable of efficiently executing high-resolution computations. The significant impact of friction particularly at the end of the tool-chip contact on the chip formation, physical contact states and process forces are highlighted. The behaviors of physical parameters including normal contact pressure, sliding velocity, and temperature-dependent friction models in the literature are also evaluated. Several aspects such as the shear flow stress limit, the relationship between friction and process forces, and the selection of different models are discussed. In conclusion, pragmatic recommendations for friction modeling and cutting simulation work are provided. On the one hand, the available complex physical parameter dependent friction models could not prove their necessity and should be approached cautiously. Instead, the constant Coulomb friction model without the shear stress limit, despite its simplicity, demonstrates effective and sufficient for a single set of metal cutting simulations. On the other hand, reliable on-site measurement techniques of the coefficient of friction (COF) at the tool-chip sliding contact area should be developed, with consideration of the contact length and the state of material flow. Combined with careful cutting edge preparation and suitable constitutive models, the overall accuracy of numerical cutting simulations including the feed force prediction is expected to be improved.

Identification of Novel PI3Kα Inhibitor Against Gastric Cancer: QSAR-, Molecular Docking-, and Molecular Dynamics Simulation-Based Analysis.

Gastriccancer (GC) is a malignant tumor with global incidence and death ranking fifth and fourth, respectively. GC patients nevertheless have a poor prognosis despite the effectiveness of more advanced chemotherapy and surgical treatment options. The second most frequently mutated gene in GC is PI3Kalpha, a confirmed oncogene that results in abnormal PI3K/AKT/mTOR signaling, causing enhanced translation, proliferation, and survival, and is mutated in 7-25% of GC patients. The protein PI3Kalpha was targeted in the present study by utilizing machine learning (ML), molecular docking, and simulation. A total of 9214 molecules from the DrugBank database were chosen for the first screening. A training set for 6770 compounds tested against PI3Kalpha was assessed to create a quantitative structure-activity relationship-based machine learning model using five different classification algorithms: random forest, random tree, J48 pruned tree, decision stump, and REPTree. Furthermore, consideration was given to the random forest classifier for screening based on its performance index (Kappa statistics, ROC, and MCC). Overall, 1539 of the 9214 drug bank compounds were predicted to be active. Thereafter, three pharmacological filters, Lipinski's rule, Ghose filter, and Veber rule, were applied to test the drug-like properties of the screened compounds. Twenty-six of 1593 compounds showed excellent drug-like properties and were further considered for molecular docking. Thereafter, two compounds were screened as hits because they possessed the molecular docked position with the lowest binding energy and an excellent bonding profile. The binding stability of the selected compounds was further assessed through molecular dynamics simulations for up to 100 ns. Furthermore, compound 1-(3-(2,4-dimethylthiazol-5-YL)-4-oxo-2,4-dihydroindeno[1,2-C]pyrazol-5-YL)-3-(4-methylpiperazin-1-YL) urea was selected as a potential hit in the final screening by analyzing a number of parameters, including the Rg, RMSD, RMSF, H bonding, and SASA profile. Therefore, we conclude that compound 1-(3-(2, 4-dimethylthiazol-5-YL)-4-oxo-2,4-dihydroindeno[1,2-C]pyrazol-5-YL)-3-(4-methylpiperazin-1-YL) urea has efficient inhibitory potential against PI3Kalpha protein and could be utilized for the development of effective drugs against GC.

Method for controlling the area of the electrolyte outlets in the flow field simulation and experimental verification of electrochemical machining

Electrochemical machining (ECM) is regarded as a promising and cost-effective manufacturing method for difficult-to-cut materials with complex shapes and structures. Many methods have been successfully developed for the flow field state of machining gaps, which is a key factor affecting machining performance in ECM engineering practice. However, little attention has been given to the area of the electrolyte outlets. This study attempts to present a method for controlling the area of the electrolyte outlets in the flow field simulation and experimental verification of electrochemical machining. The simulation results indicate that the convergent outlet mode can effectively enhance the turbulent effect of the fluid in the inter-electrode gap, especially in the near-wall region, and improve the mass transfer effect of electrolyte. Meanwhile, the simulation results also showed that better material removal rate (MRR) and surface roughness can be obtained when the convergent degree is about 50–70% combined with the effects of bubble volume compression and flow velocity reduction. The experimental results confirmed that the convergent outlet mode can obtain better MRR and surface roughness. Energy-dispersive X-ray spectroscopy results of machined specimens showed that oxides with strong adhesion on the specimen’s surface were effectively eliminated due to stronger turbulence effect under convergent outlet conditions. This should be considered as the main reason for improving machining performance by changing the electrolyte outlet area. These conclusions can provide some useful assistance for ECM engineering practice.