Hardness Parameters Research Articles

Microstructure and Mechanical Behavior of Modern Universal-Chromatic and Bulk-Fill Resin-Based Composites Developed to Simplify Dental Restorative Procedures.

One of the recent trends in the development of resin-based composites (RBCs) focuses on universal coloring to avoid time-consuming color matching and RBC layering for a clinically appropriate esthetic impact. We evaluated an experimental material for posterior restorations combining universal coloring with the possibility of bulk-fill placement. Clinically established materials were analyzed as a reference, including a bulk-fill and a universal chromatic RBC. Microstructural features were described using scanning electron microscopy and related to macroscopic and microscopic mechanical behavior. Standards to be met before market launch were supplemented by fractography, Weibull analysis, and aging behavior assessment. Quasi-static and viscoelastic behavior were evaluated on a microscopic scale, incorporating a large number of parameters and increasingly aggressive immersion media. All materials complied with the standard requirements even after aging. The latter had little impact on the measured parameters, except for strength. Strength, modulus of elasticity, and hardness parameters on the one hand and damping behavior on the other were mutually exclusive. Despite considerable differences in the microstructure and type of filler, an increased filler amount remained critical for better mechanical properties. The lower proportion of inorganic fillers was directly transferred to the elastic modulus values, which, in turn, restricts the experimental material in its clinical applications to smaller occlusal fillings.

Formulation And Evaluation Of The Preparation Of Hard Candy Source Of Sweet Orange (Citrus Sinensis (L.) Osbeck) As A Nutracetical

Candy is used as a source of high calories, so it is often eaten during activities such as work, studying, and exercise. In general, the candy that is widely circulated in the community is hard candy, which has a hard texture and a shiny appearance. The benefits of candy for the human body are that it adds nutrients, minerals, and other vitamin content because this candy is made from real fruit juice. The purpose of this study was to formulate hard candy with the main ingredient of sweet orange juice with a variety of sucrose in order to get a delicious formula in the form of taste that is expected to meet the nutritional requirements of both children and adults. formulated candy. Hard candy is made using the open pan method. The research is experimental and includes the processing of sweet orange ingredients, making sweet orange juice, making hard candy and hard candy test parameters including organoleptic tests, pH tests, weight uniformity tests, water content tests, ash content tests and tests of preference level (hedonic test) by the respondent. Based on the results of the study, sweet orange juice can be formulated into hard candy preparations with sucrose 1,5; 2 and 3 g. The obtained results with candy shape, taste, and hardness are quite good; they have a pH of 5, and they meet weight uniformity requirements with a coefficient of variation of 1.08, 1.30, and 1.11%, moisture content 2.4, 2.3 and 1.7%, ash content 1.0, 0.13 and 0%. The results of the organoleptic preference level test in terms of liking, taste, and texture showed that F3 was the most preferred formula. The sucrose levels have an effect on the level of liking.

Anharmonic Properties of Rocksalt Structure Solids of SOECs

An effort has been made for obtaining the anharmonic properties of rocksalt structure solid starting from primary physical parameters VIZ. nearest neighbour distance and hardness parameter assuming long- and short-range potentials at an elevated temperature. The elastic energy density for a deformed crystal is expanded as power series of strains for obtaining coefficients of quadratic cubic and quartic terms which are known as the second, third and fourth order elastic constant respectively. When the values of the higher order elastic constants are known for a crystal, many of the anharmonic properties of the crystal can be treated within the limit of the continuum approximation in a quantitative manner. The higher order elastic constant are computed at 300K for rocksalt structure solids which are Alkali cyanides, Sodium and Potassium Halides. The first order pressure derivatives of second and third order elastic constants, the second order pressure derivative of second order elastic constant and partial contractions are evaluated at room temperature for these substances. The results thus obtained are compared with experimental and theoretical data and found in well agreement with present values.

Taguchi analysis of shot peening parameters for surface hardness, wear resistance, roughness, and residual stress in Ti-6Al-4V alloy

Taguchi analysis of shot peening parameters for surface hardness, wear resistance, roughness, and residual stress in Ti-6Al-4V alloy

Intrinsic hard magnetism and thermal stability of a ThMn12-type permanent magnet

Herein, we theoretically demonstrate that simple metal (Ga and Al) substitutional atoms, rather than the conventional transition metal substitutional elements, not only stabilize the ThMn12-type SmFe12 and Sm(Fe,Co)12 phases thermodynamically but also further improve their intrinsic magnetic properties such that they are superior to those of the widely investigated SmFe11Ti and Sm(Fe,Co)11Ti magnets, and even to the state-of-the-art permanent magnet Nd2Fe14B. More specifically, the quaternary Sm(Fe,Co,Al)12 phase has the highest uniaxial magnetocrystalline anisotropy (MCA) of about 8 MJ m−3, anisotropy field of 18.2 T, and hardness parameter of 2.8 at room temperature and a Curie temperature of 764 K. Simultaneously, the Al and Ga substitutional atoms improve the single-domain size of the Sm(Fe,Co)12 grains by nearly a factor of two. Numerical results of MCA and MCA-driven hard magnetic properties can be described by the strong spin-orbit coupling and orbital angular momentum of the Sm 4f-electron orbitals.

Leveraging convergence behavior to balance conflicting tasks in multi-task learning

Multi-Task Learning is a learning paradigm that uses correlated tasks to improve performance generalization. A common way to learn multiple tasks is through the hard parameter sharing approach, in which a single architecture is used to share the same subset of parameters, creating an inductive bias between them during the training process. Due to its simplicity, potential to improve generalization, and reduce computational cost, it has gained the attention of the scientific and industrial communities. However, tasks often conflict with each other, which makes it challenging to define how the gradients of multiple tasks should be combined to allow simultaneous learning. To address this problem, we use the idea of multi-objective optimization to propose a method that takes into account temporal behaviour of the gradients to create a dynamic bias that adjusts the importance of each task during backpropagation. The result of this method is to give more attention to tasks that are diverging or not being benefited during the last iterations, ensuring that the simultaneous learning is heading to the performance maximization of all tasks. As a result, we empirically show that the proposed method outperforms the state-of-the-art approaches on learning conflicting tasks. Unlike the adopted baselines, our method ensures that all tasks reach good generalization performances.

Evaluation of patient satisfaction with pharyngeal airway changes after orthognathic surgery in patients with Class III skeletal anomalies.

The aims of our study were to determine the changes in the pharyngeal airway after treating patients with Class III malocclusion who received double jaw surgery, to determine the hard tissue and soft tissue parameters and the hyoid bone and tongue positions after surgery, to determine the changes to compare the pretreatment and post-treatment measurements of the patients in the double jaw group with the measurements of the control group patients, and to evaluate postoperative patient satisfaction. The study included thirty-two Class III adult patients that received double jaw surgery and twenty-five Class I patients. Cephalometric records were taken before treatment (T1) and after treatment (T2). After double jaw orthognathic surgery, there were statistically significant changes in all pharyngeal airway linear and areal parameters (p<0.001). There were significant superior and posterior movements of the tongue and hyoid bone post-surgery. The post-treatment analysis of the double jaw surgery group and control group were compared, with statistically significant differences in the mandibular dental parameters and pharyngeal airway measurements in patients in the double jaw surgery group. Despite the narrowings detected, high satisfaction scores were observed in the patient satisfaction questionnaire and the patients did not experience respiratory problems.

Feature Extraction Method of Art Visual Communication Image Based on 5G Intelligent Sensor Network

5G intelligent sensor network technology realizes the perception, processing, and transmission of information. It forms the three pillars of information technology together with computer technology and communication technology and is an important part of the Internet of Things technology. The 5G smart sensor network is a wireless communication module added to the sensor nodes, and a wireless communication network is formed by a large number of stationary or movable sensor nodes in the form of self-organization and multihop transmission. This paper proposes a keypoint feature extraction method based on deep learning, which can extract keypoint local features for matching. This method uses the convolutional network structure, which is pretrained based on the Siamese network structure and then adjusted to the ternary network structure to continue training to improve the accuracy. This paper proposes a high-art visual communication image classification based on multifeature extraction and classification decision fusion. In the data preprocessing stage, the correlation alignment algorithm is performed on the datasets of different domains (source domain and target domain) to reduce the difference in spatial distribution, and then, a multifeature extractor is designed to extract artistic visual communication images and spatial information. In the process, the multitask learning method is introduced to jointly train the networks of multiple data sets to reduce the degree of overfitting of the model, solve the problem of insufficient labeled samples in the target domain data set, and affect the classification accuracy of high-art visual communication images. Finally, the classification results are obtained through the fusion of voting decisions. The experimental results show that the advantage of this framework is that it utilizes the artistic visual communication image and spatial structure information from the source and target scenes, which can significantly reduce the dependence on the number of labeled samples in the target domain and improve the classification performance. In this paper, a dual-channel deep residual convolutional neural network is designed. The multiple convolution layers of the residual module in the network use hard parameters to share, so that the deep feature representation on the joint spatial spectrum dimension can be automatically extracted. The features extracted by the network are transferred to maximize the auxiliary role of the labeled samples in the source domain and avoid the negative transfer problem caused by the forced transfer between irrelevant samples.

Impact of AlN-SiC Nanoparticle Reinforcement on the Mechanical Behavior of Al 6061-Based Hybrid Composite Developed by the Stir Casting Route

The enhancement of composites’ mechanical characteristics (tensile, compressive, and hardness) is a constant demand for technological advancement. The stir casting process is used to make the hybrid aluminium alloy metal matrix composites Al 6061-SiC-AlN in our present study. To create mechanical qualities such as tensile, compressive, and hardness, silicon carbide and aluminium nitride (both 3% and 6%) were utilized as the reinforcement. The tensile strength, compressive strength, and hardness of the Al 6061-SiC-AlN hybrid composites samples were determined. The tensile, compressive, and hardness parameters of Al 6061-SiC-AlN hybrid composites are estimated and evaluated to those of the matrix Al 6061 alloy. With the inclusion of silicon carbide and AlN nanoparticles, the tensile strength, compressive strength, and hardness increased from 328 to 385 MPa, 145 to 178 Mpa, and 302 to 724 VHN, respectively.

Applying a Neural Network-Based Machine Learning to Laser-Welded Spark Plasma Sintered Steel: Predicting Vickers Micro-Hardness

This paper presents an artificial neural network (ANN) approach to the estimation of the Vickers hardness parameter at the weld zone of laser-welded sintered duplex stainless steel. The sintered welded stainless-steel hardness is primarily determined by the sintering conditions and laser welding processing parameters. In the current investigation, the process parameters for both the sintering and welding processes were trained by ANNs machine learning (ML) model using a TensorFlow framework for the microhardness predictions of laser-welded sintered duplex stainless steel (DSS 2507 grade). A neural network is trained using a thorough dataset. The mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R2 for the train and test data were calculated. The predicted values were in good agreement with the measured hardness values. Based on the results obtained, the ANN method can be effectively used to predict the mechanical properties of materials.

The new dimeric copper(II) complex from anticancer drug cytosine arabinoside

In this study, the new copper(II) based cytosine arabinoside (ara-C) analog was synthesized and characterized by analytical and spectroscopic methods. Especially the data obtained from FT-IR spectroscopy showed that ara-C coordinated with copper(II) ions through –C=O and -Nring groups and formed a complex compound in a tetrahedral structure. The proposed structure of this dimeric copper(II) complex was defined as [Cu2(ara-C)2Cl4]. Also, the ability of both compounds to bind to double helix fish sperm DNA (dsDNA) and the damage to the HeLa cell line was investigated. All theoretical chemical activity analyses (hardness and softness parameters, Fukui functions, net charges) of the ara-C and [Cu2(ara-C)2Cl4] molecules were performed with DFT with mixed basis set including LANL2DZ for Cu atom and 6-311G(d,p) for carbon, oxygen, nitrogen, chlorine and hydrogen atoms with B3LYP functional was applied. The stability of the molecule arising from hyperconjugative interactions, charge delocalization was analyzed by using natural bond orbital analysis (NBO) for optimized structure of [Cu2(ara-C)2Cl4]. Also, the interactions between the studied molecules (ara-C and [Cu2(ara-C)2Cl4] with DNA bases such as adenine, cytosine, guanine, and thymine were investigated by using the ECT (electrophilicity-based charge transfer) method. Furthermore, the best binding sites of the DNA (PDB:1BNA) protein to the ligands (ara-C and [Cu2(ara-C)2Cl4] were examined and the binding energies and interaction states were determined by molecular docking study.

Pumpkin and kale fibers - A potential hydrocolloids in processed cheese sauce formulations with different fat source

In recent years there has been a huge increase in consumer interest in products with the reduced fat content and the additional health-promoting properties. The interactions between the fiber and the ingredients of the cheese sauce have not yet been investigated and described. The aim of the research was examination of the effect of pumpkin and kale fibers on physicochemical properties and stability of the processed cheese sauces (PCS) obtained with the whey protein concentrate (WPC80), acid casein (AC) and different fat sources. In samples with the kale (KF) and rapeseed oil (RO) addition an increase in the value of characteristics such as hardness, adhesiveness, product stability (TSI) and surface roughness was observed. The results of measurements of storage (G′) and loss (G″) moduli showed the same upward trend with an increase in the amount of added kale fiber. They were related to the hardness and roughness parameters determined by measure of surface properties using the optical profilometer and the stability examined using Turbiscan. In the PCS with kale fiber addition based on RO an increase in stability (6.41–1.86 TSI) and roughness (2.2–16 qm) values along with the amount of fiber added was observed. Also optical microscopy images present the compacted structure of produced sauces. This article provides results and relevant literature, discussing the impact of different fibers and other ingredients on structure of PCS. In the future, these fibers may replace typical hydrocolloids used in processed cheese sauces production.

Anisotropic Gigahertz Antiferromagnetic Resonances of the Easy-Axis van der Waals Antiferromagnet CrSBr.

We report measurements of antiferromagnetic resonances in the van der Waals easy-axis antiferromagnet CrSBr. The interlayer exchange field and magnetocrystalline anisotropy fields are comparable to laboratory magnetic fields, allowing a rich variety of gigahertz-frequency dynamical modes to be accessed. By mapping the resonance frequencies as a function of the magnitude and angle of applied magnetic field, we identify the different regimes of antiferromagnetic dynamics. The spectra show good agreement with a Landau-Lifshitz model for two antiferromagnetically coupled sublattices, accounting for interlayer exchange and triaxial magnetic anisotropy. Fits allow us to quantify the parameters governing the magnetic dynamics: At 5 K, the interlayer exchange field is μ0HE = 0.395(2) T, and the hard and intermediate-axis anisotropy parameters are μ0Hc = 1.30(2) T and μ0Ha = 0.383(7) T. The existence of within-plane anisotropy makes it possible to control the degree of hybridization between the antiferromagnetic resonances using an in-plane magnetic field.

The physical properties of a semi-hard magnetic Fe3Pd alloy with negative enthalpy of formation

• A ferromagnetic Fe 3 Pd ordered structure with negative enthalpy of formation is report for the first time. • The structure is mechanically and dynamically stable in the ferromagnetic state. • It is a semi-hard magnetic material suitable for magnetically coupled devices. • Electronic structure, elastic properties and thermal expansion have been determined. This study presents a theoretical calculation on the physical properties of the tetragonal Z1-type ordered Fe 3 Pd structure, and a comparison with other reported structures is performed. The Z1-Fe 3 Pd is thermodynamically, mechanically, and dynamically stable in the ferromagnetic state. Note that this tetragonal structure is the first Fe 3 Pd structure found to have a negative enthalpy of formation. Compared with the P 4/ mbm -Fe 3 Pd, the Z1-Fe 3 Pd has larger elastic modulus and higher uniaxial magnetocrystalline anisotropy constant. From the Pugh’s ratio, however, the P 4/ mbm -Fe 3 Pd is more ductile than the Z1-Fe 3 Pd. The magnetic hardness parameter shows that both Z1 and P 4/ mbm structures are semihard magnetic materials suitable for magnetically coupled devices. Unlike other Fe 3 Pd structures reported previously, for the Z1 structure the magnetic moment of the two types of Fe atoms are quite different. The Fe magnetic moment discrepancy in the Z1 structure and the thermal expansion discrepancy between the Z1 structure and other structures are analyzed with the electronic structure and the linear coefficient of thermal expansion, respectively.

Hybrid Model Compression for Multi-Task Network

In recent years, deep neural networks have made significant achievements in various fields in science and engineering. To achieve outstanding performance, neural network models with very deep structures and an extensive number of parameters are usually used, leading to huge memory storage and computational resources requirements of the hardware systems, and thus preventing them from being deployed in devices with hardware limits. In particular, such problem will be further amplified when multiple neural network models for several tasks are to be integrated into one single platform at the same time. In this paper, a hybrid joint-network optimization model is proposed to solve this multi-task multi-model compression problem. The idea of hard parameter sharing in multi-task learning is adopted. Specifically, we turn the sub-network of the same structure of multiple models into a hybrid network model, in which the same parameters are shared for all the tasks. In addition, a unique sparse matrix for each task is added to each task independently. Experimental results show that the proposed compression methods can not only achieve a high compression ratio but also get performance improvement for some tasks.

Nanoindentation applied to ion-irradiated and neutron-irradiated Fe-9Cr and Fe-9Cr-NiSiP model alloys

Nanoindentation of ion-irradiated materials has attracted much interest as a tool envisaged to derive the dose dependence of bulk-equivalent hardness from small samples. A major challenge arises from the steep damage gradient in the thin ion-irradiated layer and its unavoidable interplay with the indentation size effect. The present study relies on a number of choices aimed at simplifying the interpretation of the results and strengthening the conclusions. The studied alloys are two ferritic Fe-9Cr model alloys differing in controlled amounts of Ni, Si, and P known to enhance irradiation hardening. Both ion-irradiated (5 MeV Fe2+ ions) and neutron-irradiated samples along with the unirradiated references were investigated using Berkovich tips. According to the collaborative nature of the study, tests were conducted in two different laboratories using different equipment. A generalized Nix–Gao approach was applied to derive the bulk-equivalent hardness and characteristic length scale parameters for the homogeneous unirradiated and neutron-irradiated samples. Comparison with Vickers hardness indicates a 6% overestimation of the bulk-equivalent hardness as compared to the ideal correlation. For the case of ion irradiation, a first model assumes a homogeneous irradiated layer on a homogeneous substrate, while a second model explicitly takes into account the damage gradient. The first model was combined with both the original and the generalized Nix–Gao relation. We have found that the results revealed for Fe-9Cr vs Fe-9Cr-NiSiP are compatible with expectations based upon known irradiation-induced microstructures. The bulk-equivalent hardness derived for ion-irradiated samples reasonably agrees with the observation for neutron-irradiated samples.

Facial Profile Convexity in Skeletal Class II Malocclusion: How Soft Tissue Angle of Facial Convexity (SA-FC) Correlate with Angle ANB in Skeletal Class II Subjects

OBJECTIVE: The research was conducted to determine the correlation between Soft tissue angle of facial convexity (SA-FC) and angle ANB in skeletal class II malocclusion. The outcome of the study will help in determining if the routine consideration of ANB angle while the lack of defined thresholds for convexity of soft tissue profile justified for choosing the treatment modality. METHODOLOGY: Lateral cephalograms of 141 skeletal class II subjects (ANB&gt;4°) were obtained. Angular parameters including soft tissue angle of facial convexity (SA-FC) and angle ANB were determined. Gender dimorphism of the variables was assessed by Mann Whitney U test. Correlation between SA-FC and angle ANB were determined utilizing Spearman's correlation coefficient. RESULTS: The angle SA-FC and ANB depicted moderately positive correlation (r = 0.662, p&lt;0.001). Gender dimorphism exists with increased mean value of ANB (7.88±1.90) and SA-FC (23.22±7.61) in females. CONCLUSIONS: Angle SA-FC depicts moderately positive correlation with angle ANB among skeletal class II subjects, hence suggesting the need of soft tissue guidelines along-with hard tissue parameters for selection of treatment modality. KEYWORDS: Cephalometry, Malocclusion, Diagnosis, soft tissue, correlation.

Magnetic Evaluation of Heat-Resistant Martensitic Steel Subjected to Microstructure Degradation.

The present paper investigates the use of the magnetic hysteresis loop technique to nondestructively evaluate microstructural degradation in heat-resistant martensitic (HRM) steels. The degradation impairs the safe operation of thermal power plants and it is thus essential to periodically assess it using nondestructive evaluation (NDE) techniques. In this contribution, HRM steels are thermally aged up to 16,000 h at 675 °C to simulate the microstructural degradation, then the changes in the magnetic coercivity, hardness, and microstructure are systematically characterized and the relations between them are determined. Both coercivity and hardness decrease with thermal aging duration, which can be interpreted in terms of the microstructure parameters’ evolution based on the pinning of crystal defects on domain walls and dislocations. Coercivity and hardness share the same softening trend with aging time, and good linear relations between coercivity, hardness, and microstructure parameters are found. These results provide a key to understanding the magnetic parameter evolution in HRM steels and suggest the possibility of using magnetic technologies for the NDE of microstructure degradation in thermal power plants.

Interactions of coumarin derivatives with monoamine oxidase biomarkers: In silico approach

A list of coumarin derivatives (A-P) were investigated in this work for recognizing their reactivity features and their functions towards the monoamine oxidase (MAO) enzyme biomarkers. In this regard, the models showed that he additional of molecular groups to the original scaffold of coumarin could significantly change the reactivity features leading to various tendency for contributing to reactions with other substances. In this case, were varied based on the obtained values of chemical hardness and softness parameters. Subsequently, formations of interacting ligand-target complexes indicated the coumarin derivatives could work as selective substances for interacting with each of MAOA (D) and MAOB (L) enzyme biomarkers, in which a common substance (E) was also observed for formation of interacting complexes with both of MAOA and MAOB targets. As a consequence, the models of coumarin were seen suitable for interacting with the MAO enzyme biomarkers with the purposes of detection and medication. All required information of this work were obtained in the in silico medium.

Research on correction method of inertia factor of starting section of gap flow opening in hydraulic rock drill

ABSTRACT Large pulsation and impact occur during the working process of hydraulic rock drill, which has a negative influence on the system. Aiming at this phenomenon, the related characteristics of hydraulic fluid inertia are studied in this paper. The dynamic model of impact and reversing mechanism of rock drill is established. Through the parameter analysis of the model, the concepts of hard parameter quantity and soft parameter quantity are proposed. The influence coefficient C of the initial section of crevice flow including the influence of fluid inertia is deduced, and the comprehensive factors such as hydraulic oil velocity, unstable flow and friction resistance are analyzed, The value of soft parameter quantity ζ is (ξ+1) ~ (ξ+ 1.5)。The pressure test of the working chamber is designed and 7 groups of data are obtained. Through data analysis, the influence of C on the simulation results is obtained, which has a great influence on the reversing valve. The deviation of simulation data of left and right valve chamber pressure is increased from ± 8% to ± 3%. It has a certain impact on the impact piston. The deviation of simulation data increases from ± 5% to ± 3%, and has little impact on the accumulator. The deviation of simulation data increases from ± 4% to ± 3%.