Traditional Experimental Procedures Research Articles

Dynamic flexural strength of Aluminosilicate glass with a perspective of impulsive and quasi-impulsive responses: An experimental–numerical coupled evaluation

The flexural strength of glass is a critical design parameter for applications encountering impact loadings. However, the micro defects, specimen geometry, loading rate, and load transformation from a quasi-dynamic to quasi-impulsive state may influence the measurement accuracy. Due to the stochastic and amorphous nature of the material, an accurate determination of the flexural strength remains a challenge. In this two-fold study, a coupled experimental–numerical strategy was devised to evaluate the dynamic flexural strength. In the first phase, three-point bending experiments were conducted on a novel “Electromagnetic Split Hopkinson Pressure Bar (ESHPB)”. The incident stress signal and fracture time were recorded from experimental data, while the flexural strength was indirectly computed from a numerical algorithm. A quantitative comparison of the flexural strength with those in existing literature established the accuracy of the proposed methodology. Results of the study indicate that the specimen response became independent of the support conditions under impulsive loading. That being said, the specimen behaved like it had an infinite span length, and the measured flexural strength remained the same whether the specimen was supported or not. Besides, the specimen also maintained contact at the interfaces of the incident bar and fixture supports for the entire loading duration. In the second part of this study, the computed flexural strength was used to calibrate the existing JH-2 model. Numerical prediction of the damage propagation corroborated with that obtained from reprography images, though qualitatively. This work presents a precise and robust methodology to determine the dynamic flexural strength of brittle ceramics like Aluminosilicate glass over traditional experimental procedures to facilitate its adoption.

Predicting moisture penetration dynamics in paper with machine learning approach

In this work, we predicted the gradient of the deformational moisture dynamics in a sized commercial paper by observing the curl deformation in response to the one-sided water application. The deformational moisture is a part of the applied liquid which ends up in the fibers causing swelling and subsequent mechanical response of the entire fiber network structure. The adapted approach combines traditional experimental procedures, advanced machine learning techniques and continuum modeling to provide insights into the complex phenomenon relevant to ink-jet digital printing in which the sized and coated paper is often used, meaning that not all the applied moisture will reach the fibers. Key material properties including elasticity, plastic parameters, viscoelasticity, creep, moisture dependent behavior, along with hygroexpansion coefficients are identified through extensive testing, providing vital data for subsequent simulation using a continuum model. Two machine learning models, a Feedforward Neural Network (FNN) and a Recurrent Neural Network (RNN), are probed in this study. Both models are trained using exclusively numerically generated moisture profile histories, showcasing the value of such data in contexts where experimental data acquisition is challenging. These two models are subsequently utilized to predict moisture profile history based on curl experimental measurements, with the RNN demonstrating superior accuracy due to its ability to account for temporal dependencies. The predicted moisture profiles are used as inputs for the continuum model to simulate the associated curl response comparing it to the experiment representing “never seen” data. The result of comparison shows highly predictive capability of the RNN. This study melds traditional experimental methods and innovative machine learning techniques, providing a robust technique for predicting moisture gradient dynamics that can be used for both optimizing the ink solution and paper structure to achieve desirable printing quality with lowest curl propensities during printing.

Quantification of caffeine and catechins and evaluation of bitterness and astringency of Pu-erh ripen tea based on portable near-infrared spectroscopy

Rapid and accurate assaying catechins, caffeine, bitterness and astringency prediction are integral to quality assessment and control of Pu-erh ripen tea (PRT). The traditional experimental procedures for content quantification and sensory evaluation are time-consuming and lab-dependent, while near-infrared (NIR) technology has the potential to address this issue. In this work, an efficient and environment-friendly approach was established for caffeine and catechins prediction, and bitterness and astringency evaluation of PRT, using a portable NIR spectrometer coupled with chemometrics. A total of 100 PRT samples were collected for analysis and seven different spectral preprocessing methods and two variable selection algorithms were employed to improve the prediction performance of the partial least squares regression (PLSR) model. The optimized models achieved satisfactory results for the prediction of caffeine and catechins (catechin, catechin gallate, gallocatechin, epicatechin gallate, epigallocatechin, and epigallocatechin gallate), as well as bitterness and astringency, with the correlation coefficient of the prediction set (Rp) above 0.9, and residual prediction deviation (RPD) over 2.5. This research offers an alternative for portable and rapid quantification of caffeine and eight catechins and evaluation of bitterness and astringency of PRT.

Simulation of mechanical properties of stir cast aluminum matrix composites through Artificial Neural Networks (ANN)

Aluminum Metal Matrix Composites (AMMCs) are materials that were researched to tackle the requirements of a material with a high strength-to-weight ratio in civil and military industries. A wide range of reinforcements has coalesced with the Aluminum 6061 matrix. This study focuses on implementing an Artificial Neural Network (ANN) model to map the correlation between weight percentages of reinforcements used, ultimate tensile strength (UTS) & Brinell Hardness (BHN) of the corresponding composite. Through extensive data collection and analysis, a model suitable enough to further simulate mechanical properties on anonymous data is presented. Results have shown that the proposed model has a low Mean Squared Error (0.0058606) and a Regression (0.96118) value close to unity, demonstrating its ability to replace traditional experimental procedures, thereby reducing the cost & time of the analysis of aluminum composites. A trained model was further used to simulate mechanical properties on random data and gave satisfactory results.

Machine learning prediction of specific capacitance in biomass derived carbon materials: Effects of activation and biochar characteristics

The preparation process of biomass-based biochar materials is usually screened using traditional trial-and-error experiments. In this approach, the electrochemical properties of biochar are correlated with properties called descriptors. In this work, several simple and efficient machine learning (ML) models were used to predict the electrical capacity of biochar through activation conditions, biochar properties, and testing conditions. The established ML model predicted the capacitance of biochar with 9 descriptors that are readily available values during the preparation of biochar. The prediction performance of four regression methods (Decision Tree (DT), Artificial Neural Network (ANN), eXtreme Gradient Boosting (XGBoost) and Random Forest (RF)) were evaluated with a test set/training set ratio of 8 to 2. Among the four regression methods, XGBoost had the best prediction effect on the electrochemical performance of biochar with a low mean root mean square error (RMSE) and coefficient of determination (R2) close to 1. In addition, the analysis of the importance of the features under each model combined with the existing research verifies the rationality of the model. The accuracy and simplicity of this system demonstrate that the electrochemical performance of biochar can be easily predicted without time-consuming traditional experimental procedures and can be a method to guide the direction of experiments.

Security of Microfluidic Biochip

With the advancement of system miniaturization and automation, Lab-on-a-Chip (LoC) technology has revolutionized traditional experimental procedures. Microfluidic Biochip (MFB) is an emerging branch of LoC with wide medical applications such as DNA sequencing, drug delivery, and point of care diagnostics. Due to the critical usage of MFBs, their security is of great importance. In this article, we exploit the vulnerabilities of two types of MFBs: Flow-based Microfluidic Biochip (FMFB) and Digital Microfluidic Biochip (DMFB). We propose a systematic framework for applying Reverse Engineering (RE) attacks and Hardware Trojan (HT) attacks on MFBs as well as for practical countermeasures against the proposed attacks. We evaluate the attacks and defense on various benchmarks where experimental results prove the effectiveness of our methods. Security metrics are defined to quantify the vulnerability of MFBs. The overhead and performance of the proposed attacks as well as countermeasures are also discussed.

IMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition

Membrane proteins execute significant roles in cellular processes of living organisms, ranging from cell signaling to cell adhesion. As a major part of a cell, the identification of membrane proteins and their functional types become a challenging job in the field of bioinformatics and proteomics from last few decades. Traditional experimental procedures are slightly applicable due to lack of recognized structures, enormous time and space. In this regard, the demand for fast, accurate and intelligent computational method is increased day by day.In this paper, a two-tier intelligent automated predictor has been developed called iMem-2LSAAC, which classifies protein sequence as membrane or non-membrane in first-tier (phase1) and in case of membrane the second-tier (phase2) identifies functional types of membrane protein. Quantitative attributes were extracted from protein sequences by applying three discrete features extraction schemes namely amino acid composition, pseudo amino acid composition and split amino acid composition (SAAC). Various learning algorithms were investigated by using jackknife test to select the best one for predictor. Experimental results exhibited that the highest predictive outcomes were yielded by SVM in conjunction with SAAC feature space on all examined datasets. The true classification rate of iMem-2LSAAC predictor is significantly higher than that of other state-of- the- art methods so far in the literature. Finally, it is expected that the proposed predictor will provide a solid framework for the development of pharmaceutical drug discovery and might be useful for researchers and academia.

Computational Predictions of Glass-Forming Ability and Crystallization Tendency of Drug Molecules

Amorphization is an attractive formulation technique for drugs suffering from poor aqueous solubility as a result of their high lattice energy. Computational models that can predict the material properties associated with amorphization, such as glass-forming ability (GFA) and crystallization behavior in the dry state, would be a time-saving, cost-effective, and material-sparing approach compared to traditional experimental procedures. This article presents predictive models of these properties developed using support vector machine (SVM) algorithm. The GFA and crystallization tendency were investigated by melt-quenching 131 drug molecules in situ using differential scanning calorimetry. The SVM algorithm was used to develop computational models based on calculated molecular descriptors. The analyses confirmed the previously suggested cutoff molecular weight (MW) of 300 for glass-formers, and also clarified the extent to which MW can be used to predict the GFA of compounds with MW < 300. The topological equivalent of Grav3_3D, which is related to molecular size and shape, was a better descriptor than MW for GFA; it was able to accurately predict 86% of the data set regardless of MW. The potential for crystallization was predicted using molecular descriptors reflecting Hückel pi atomic charges and the number of hydrogen bond acceptors. The models developed could be used in the early drug development stage to indicate whether amorphization would be a suitable formulation strategy for improving the dissolution and/or apparent solubility of poorly soluble compounds.

Disentangling the Translational Sciences: A Social Science Perspective

In this article the author first attempts to disentangle a number of issues in translational science from a social science perspective. As expected in a fledgling field of study being approached from various disciplines, there are marked differences in the research literature on terminology, definition of terms, and conceptualization of staging of clinical research from the pilot phase to widespread dissemination in the community. The author asserts that translational efforts in the social sciences are at a crossroads, and its greatest challenge involves the movement of interventions gleaned from clinical trials to community settings. Four strategies for reaching this goal are discussed: the use of methods derived from health services research, a yet-to-be-developed strategy where decisions to modify aspects of an intervention derived from a clinical trial are triggered by data-based criteria, community based participatory action research (CBPR), and a hybrid system wherein methods from CBPR and traditional experimental procedures are combined to achieve translation. The author ends on an optimistic note, emphasizing the impressive advances in the area over the existing barriers and calling for a unified interdisciplinary science of translation.

Determination of ΔKth by Compression Pre-Cracking in a Structural Steel

AbstractThe traditional experimental procedures used to generate thresholds (known as “ΔK-decreasing” and “constant Kmax”) have been challenged because it seems they affect the experimental results, sometimes in a non conservative way. In order to fix this problem, different experimental procedures (“compression pre-cracking constant amplitude” and “compression pre-cracking load reduction” (CPLR)) based on a compression-compression pre-cracking of fracture mechanics specimens have been proposed. Up till now, such procedures are not yet wide-spread between fracture mechanics experimentalists. In particular, due to the recent introduction, CPLR has been applied only to few cases: Al alloys, Ti alloys, and high strength steels (ultimate tensile strength&amp;gt;1300 MPa), all in the shape of M(T) or C(T) specimens subjected to positive stress ratios. The present paper deals with the application of these novel “compression pre-cracking” procedures, with particular attention to CPLR, to the unexplored and technically very important case of the structurally mild A1N steel grade (very commonly used to produce European railway axles) in the shape of SE(B) specimens subjected to stress ratios varying between −2 and 0.85.

Rapid simultaneous analysis of prostaglandin E2, 12-hydroxyeicosatetraenoic acid and arachidonic acid using high performance liquid chromatography/electrospray ionization mass spectrometry.

Arachidonic acid (AA) can be metabolized to a variety of lipid mediators including prostaglandins (PGE), and hydroxyeicosatetraenoic acids (HETE) by cyclooxygenase, lipoxygenase and cytochrome P450-dependent monooxygenase enzymatic pathways. Traditional experimental procedures to quantify these lipid mediators require purification, often by high performance liquid chromatography (HPLC), prior to derivatization for gas chromatography/mass spectrometry (GC/MS) analysis. This paper describes a rapid and simple technique for the simultaneous quantitative analysis of PGE2, 12-HETE, and AA by HPLC/electrospray ionization mass spectrometry on cultured human dermal fibroblast supernatants. Extension of the method to analyse 5-HETE and 15-HETE was investigated. The advantages of this method include minimal sample preparation and elimination of the problem associated with thermal stability for GC/MS analysis. A detection limit of 20pg on column for PGE2 and 5pg on column for 12-HETE and AA was determined.

Selective reaching: evidence for action-centered attention.

Most studies of selective attention briefly present static 2-dimensional stimuli and require arbitrary responses such as verbal naming or a keypress. Many of our perceptual-motor interactions with the environment, however, require reaching directly toward an object while ignoring other objects in the scene. A series of experiments examines selective attention in the latter reaching situation. Effects previously observed in the traditional experimental procedures were obtained, suggesting that the models developed (which propose inhibitory mechanisms, e.g.) apply to ecologically valid situations. Attention accesses action-centered internal representations during such tasks.

Remembering autobiographically consequential experiences: Content analysis of psychologists' accounts of their lives.

The autobiographies of 49 eminent psychologists were content analyzed in terms of autobiographically consequential experiences (ACEs). Most memories for ACE were not single episodes. Episodic ACEs did, however, share many characteristics of "flashbulb" and vivid memories elicited in studies using more traditional experimental procedures. Memories were concentrated during the college and early adult years. Thus, as in other autobiographical memory studies that have used older subjects, there was a pronounced reminiscence effect. These results were considered in light of Erikson's theory of adult personality development.

Electronic structure of transition metals under pressure

In this short review we have tried to demonstrate the advances made in the intensive study of the behavior of the electronic structure of metals under pressure in recent years. The alloys of transition metals, including intermetallic ordered compounds, have for the time being proved to lie outside the scope of this review, mainly because of the lack, in practice, of theoretical calculations in this area. Clearly, progress in this field is also being held up by the lack of detailed experimental investigations devoted to the behavior of alloys under pressure. This is due to the complexities involved in carrying out many traditional experimental procedures under high pressures. We should like to emphasize, however, that at the present time theoretical calculations have taken on a predictive power and this gives reason to hope that progress in calculations will in turn stimulate progress in experimentation.

Additivity failures in heterochromatic brightness matching

AbstractThe often poor correlation between luminance and perceived brightness has been attributed to the facts that first, the CIE standard luminous efficiency function V(Λ) does not represent adequately heterochromatic brightness matching of monochromatic stimuli, and second, the additivity law does not always hold in heterochromatic brightness matching. By means of a variation of the traditional experimental procedures, Guth found that two types of additivity failure can occur: additivity failures of the enhancement type and of the cancellation type. It is shown here that the two types of additivity failure can also be deduced by calculation from brightness‐matching data obtained by the traditional experimental procedure. However, the magnitude of the additivity failures and sometimes the type of failure seem to depend strongly upon the individual observers involved. New experimental data confirm Guth's results except in the middle region of the spectrum. Whereas Guth's observers find additivity failures of the cancellation type between 500 and 600 nm, the new data suggest that in that region additivity holds fairly well.

CONTINUITIES AND INDIVIDUAL DIFFERENCES IN CONSERVATION

An analysis of present conceptions of conservation capacity suggested that they might be unduly influenced by developmental stage theory, to the extent that continuities and individual differences at one particular stage of development are insufficiently stressed. In order to achieve a finer discrimination between individual differences in conservation of continuous quantity (solid and liquid), a new test instrument was devised. This departs from traditional experimental procedures by being presented largely in pictorial form, and by progressively increasing the number of standards, variables and distributions and redistributions of material. The results indicate that ordinal scaling of conservation at one particular developmental level is possible. It is suggested that the demonstrated individual differences may reflect differences in syllogistic reasoning and information‐processing capacity.