Efficient Analysis Methodology Research Articles

Facilitating inorganic arsenic speciation and quantification in waters: Polymer inclusion membrane preconcentration and X-ray fluorescence detection

BackgroundArsenic, classified as a priority pollutant and human carcinogen by the IARC, is subject to stringent regulatory limits in food and water. Among various arsenic species found in water samples, arsenite (As(III)) is identified as the most toxic form. Given the limitations of conventional spectroscopic techniques in speciation analysis, there is a crucial need for innovative and sustainable methodologies that enable arsenic speciation. Simplifying these methodologies is essential for widespread applicability and effective environmental monitoring. ResultsThis study proposes a simple and cost-effective analytical methodology for speciating inorganic arsenic in water samples. The method involves extracting As(III) into a polymer inclusion membrane (PIM) containing the extractant Cyanex 301 (bis(2,4,4-trimethylpentyl) dithiophosphinic acid), followed by analysis using energy dispersive X-ray fluorescence (EDXRF) spectrometry. The concentration of arsenate was measured after a reduction step using a thiosulfate/iodide mixture. This simple methodology allows a limit of quantification for trivalent arsenic (2 μg L−1), which is well below the World Health Organization's recommended maximum permissible level of As in drinking water (10 μg L−1). The method that is developed allows the determination of As at trace levels in waters with naturally occurring arsenic. Significance and noveltyThis study represents a significant advance in the field, providing a novel and efficient methodology for arsenic speciation analysis in water samples. By combining the advantages of polymer inclusion membrane (PIM) extraction with energy dispersive X-ray fluorescence (EDXRF) spectrometry, this study offers a cost-effective and environmentally friendly approach to address the critical issue of arsenic contamination in water sources, thereby contributing to enhanced environmental monitoring and public health protection.

Methodology for Energy Efficiency Analysis and Optimization of Ship Systems using Exergy Modeling

Methodology for Energy Efficiency Analysis and Optimization of Ship Systems using Exergy Modeling

Photoluminescence Emission Efficiency Analysis Methodology by Integrating Raman Spectroscopy of the A1(LO) and E2(high) Phonons in a GaInN/GaN Heterostructure

Microscopic lattice vibration images of the E2(high) mode (E2H) and another mode of A1(LO) (A1L) or the higher energy branch of LO‐phonon−plasmon coupling mode (LOPC+) in a Ga0.95In0.05N film on a GaN template are obtained by Raman scattering spectroscopy using a 325 nm laser. The increase in temperature by increasing the laser power is obtained from the decrease in the energy of E2H and the theoretical formula comprising two terms based on the mode energy variation of the bulk material and the thermal strain effect. Using the obtained temperature and the energy shift of the LOPC+, the mapping images of the temperature and electron density in the x–y plane are simultaneously obtained. This image provides the spatial variation of photoluminescence (PL) emission efficiency, given as PL intensity per electron. This method enables the quantitative discussion on photo‐emission efficiency even in the regions of low or high carrier density affected by carrier transport. In the investigated area, a region with a lower PL efficiency is found despite a higher electron density and lower temperature increase than the surrounding region. This imaging analysis is feasible in integrating the carrier and thermal energy transports and recombination processes in carrier dynamics study.

Efficient V-Shaped Substrate for Surface and Volume Enhanced Raman Spectroscopic Analysis of Bioaerosol: Prevention from Potential Health Risk.

Bioaerosols pose a great threat to human life and health, and developing highly efficient and accurate identification and analysis methodologies for bioaerosols provides prerequisite knowledge to evaluate their toxicity on human health. In this paper, a rapid and economical method is proposed for the detection of bioaerosols based on surface-enhanced Raman scattering (SERS). The SERS substrate was prepared using an anodic aluminum oxide (AAO) template with an inverted conical (V-shaped) nanopore array structure that was subsequently deposited with Ag nanoparticles (AgNPs) through magnetron sputtering. This tailored nanostructure essentially enables the differentiated and efficient detection of Escherichia coli and Staphylococcus epidermidis health risks within 10 min, along with corresponding detection limits of 103 cells/mL and 104 cells/mL, respectively. Notably, by comparing the Raman spectrum of the two bacteria obtained from the plain plane deposited with the same AgNPs, the V-shaped nanopore array structure provides a more robust enhancement effect in the Raman signal relative to the ordinary plane. More importantly, the applicability of developed AgNPs@V-shaped AAO SERS substrate was validated using ambient bioaerosols, thus emphasizing the great potential of the application of SERS-based technique for a cost-effective, rapid label-free and culture-free bioaerosol analysis without experiencing conventional time-consuming and laborious incubation processes.

SOME THOUGHTS ON THE BUSINESS EFFICIENCY AND THE METHODS FOR ITS ANALYSIS

The issue of enterprises’ business is relevant at all phases and stages of their development. The survival of enterprises and the continuation of their business in the context of the complex and contradictory economic reality require them to be financially stable, properly oriented and positioned on the competitive market that is characterized with certain level of entropy. The achievement and maintaining of market and financial stability require the enterprises to implement measures for the continuous increase of their business efficiency. The importance and relevance of this issue are even higher today due to the effect of a system of internal and external factors, and as a result of the existing crises some of which are still experienced. For example, the enterprises’ business is subject to the influence of the Covid-19 pandemic consequences and the health crisis, supply chain disturbance, closing and restriction of many business and activities in the country, etc., it has caused. The business of the Bulgarian enterprises is also influenced by the wars led in the vicinity of the territory of Bulgaria, the occurred gas crisis, the unstable political situation, the high inflation rate, the increase of energy and other resource prices and a number of other factors.The author’s interest to the issues of enterprises’ business efficiency and the methods for its analysis is a long-term interest. Observations show that the specialized literature in the country does not offer theoretically substantiated general methodology for efficiency analysis, which is feasible and useful for the business practice. Only individual methodologies for analysis of different aspects of the efficiency are offered, such as methodology for analysis of the efficient use of workforce (analysis of the labour efficiency indicator). Thus, the objectively existing dialectic relations between the resultative indicators that characterise the various aspects of efficiency and the factor indicators that affect the improvement or worsening of the business in general and its specific aspects, cannot be covered. This publication reviews the methodology for analysis of enterprises’ business efficiency. The concept of efficiency is consistently highlighted, the sources of the information necessary for its analysis are described, and the system of indicators for business efficiency analysis is justified. The author’s objective is to suggest an improved general methodology for analysis of business efficiency, which integrates the individual private methods for analysis of its different aspects in the course of the enterprises’ business.

Comprehensive energy efficiency analysis of ultra-supercritical thermal power units

At present, ultra-supercritical power plant is the most advanced technology, which can achieve ultra-low pollutant emissions and greatly improve the energy efficiency of power plant. In this paper, the key factors affecting the energy efficiency of thermal power unit are analyzed. Moreover, the way in which these factors affect the energy efficiency of thermal power unit is obtained. Besides, this study lay a solid foundation for subsequent improvements in the energy efficiency of thermal power unit. First, a mathematical mechanism model of ultra-supercritical thermal power units is established. In addition, Aspen Plus software is used to build a full process simulation model of thermal power unit. In addition, a methodology for energy efficiency analysis based on the exergy balance theory is presented, and energy efficiency evaluation indicators are defined. Finally, the TOPSIS method was used to rank the key factors affecting the energy efficiency of thermal power unit. Based on experimental analysis, this study found that in comparison to general unit, the ultra-supercritical thermal power units has improved boiler efficiency by approximately 2%, power generation efficiency by approximately 6.77%, carbon emissions by approximately 0.027 t/h and the exergy efficiency by approximately 2.91%, providing better energy efficiency and lower carbon emissions.

Comparison and extension of advanced frequency domain non-active power decompositions

This research paper introduces new prospects in the field of power quality and energy transmission efficiency analysis and system design methodology through advanced frequency-domain power definitions. This proposal presents an improved extension of the power theory Currents’ Physical Components (CPC) by L.S. Czarnecki and compares its improvements with the CPC and an extension of the CPC by H. Lev-Ari. This proposal increases the resolution of physical phenomena reducing the power quality and hence contributes to its analysis and improvement. For this purpose, four additional non-active power components compared to the CPC are introduced. In contrast to the extension by H. Lev-Ari, these additional components are obtained from different definitions. In particular, the decomposition of the unbalance component into a spectral uniform and a spectral non-uniform component is novel to frequency-domain power definitions. As the CPC and its extension through H. Lev-Ari already decompose the collinear and shift components into spectral uniform and non-uniform components, the proposal for the unbalance components finally completes this structure. This yields a consistent and intuitive designation of physical phenomena related to spectral non-uniformity constituting as a concretization to the well-known distortion non-active power. Conclusive examples illustrate this improvement using the appertaining non-active power components.

Multivariate sensitivity analysis for a large-scale climate impact and adaptation model

Abstract We apply a new efficient methodology for Bayesian global sensitivity analysis for large-scale multivariate data. A multivariate Gaussian process is used as a surrogate model to replace the expensive computer model. To improve the computational efficiency and performance of the model, compactly supported correlation functions are used. The goal is to generate sparse matrices, which give crucial advantages when dealing with large data sets. The method was applied to multivariate data from the IMPRESSIONS Integrated Assessment Platform version 2. Our empirical results on Integrated Assessment Platform version 2 data show that the proposed methods are efficient and accurate for global sensitivity analysis of complex models.

Oxidation and reduction analysis of therapeutic recombinant human interleukin-15 by HPLC and LC-MS

Being an important immune stimulant of T lymphocytes and NK cells, the recombinant human interleukin-15 (rhIL-15) has been extensively researched in tumor immunotherapy or as a vaccine adjuvant. However, the rhIL-15 manufacturing level lags far behind its growing clinical demand due to the lack of efficient and exact analysis methodologies to characterize the trace by-products, typically redox and deamidation. In order to improve the production and quality control of rhIL-15, here we developed an expanded resolution reverse-phase high-performance liquid chromatography (ExRP-HPLC) approach to quickly and accurately analyze the oxidation and reduction by-products of rhIL-15, which may appear during the purification processes. Firstly, we developed RP-HPLC methods which can separate rhIL-15 fractions with different levels of oxidization or reduction, respectively, and the redox status of each peak was then determined by measuring the intact mass with a high-resolution mass spectrometer (UPLC-MS). To further clarify the complex pattern of oxidization of specific residues, the peaks with various oxidation levels were digested into pieces for peptide mapping to pinpoint the exact changes of oxygen and hydrogen atoms in the rhIL-15 by-products. In addition, we performed the ExRP-HPLC and UPLC-MS analysis of partially deamidated rhIL-15 to characterize their oxidation and reduction. Our work is the first in-depth characterization of the redox by-products of rhIL-15, even for deamidated impurities. The ExRP-HPLC method we reported can facilitate the rapid and accurate quality analysis of rhIL-15, which is substantially helpful for streamlining the industrial manufacturing of rhIL-15 to better meet the demands of clinical applications. KEYPOINTS: • The oxidization and reduction rhIL-15 by-products were characterized for the first time. • The changes of oxygen and hydrogen atoms in rhIL-15 redox by-products were accurately determined by UPLC-MS. • Oxidation and reduction by-products of deamidated rhIL-15 were further analyzed.

Exhaustive modal analysis of large-scale power systems using model order reduction

This paper presents an efficient modal analysis methodology that computes all modes of any given large-scale power system in exhaustive manner using the model order reduction techniques. For this, a reduced order model is generated using the Balanced Truncation (BT) method for which the controllability and observability gramians are approximated using the low-rank Cholesky factors. This leads to a rapid identification of classes of coupled dynamic devices of the original system. Next, approximated oscillatory modes are computed for each class. Finally, the exact values of the oscillatory modes of the overall power system are determined by iterative computations (the Modified Arnoldi method) initialized to the approximated modes found at the first step. The proposed methodology is able to put into evidence all coupling modes of any given large-scale power system (containing power electronics or any other specific dynamic devices). No a priori knowledge about the pattern of oscillations is needed. The accuracy and efficiency of the proposed methodology are thoroughly validated on several power systems with different orders, including a large scale model of the interconnected European power system.

Energy Efficiency of IoT Networks for Environmental Parameters of Bulgarian Cities

Building modern Internet of Things (IoT) systems is associated with a number of challenges. One of the most significant among them is the need for wireless technology, which will serve to build connectivity between the individual components of this technology. In the larger cities of Bulgaria, measures to ensure low levels of harmful emissions, reduce noise levels, and ensure comfort in urban environments have been taken. LoRa technology shows more advantages in transmission distance and low energy consumption compared to other technologies. That is why this technology was chosen for the design of wireless sensor networks (WSN) for six cities in Bulgaria. These networks have the potential to be used in IoT configurations. Appropriate modules and devices for building WSN for cities in Bulgaria have been selected. It has been found that the greater number of nodes in the WSN leads to an increase in the average power consumed in the network. On the other hand, depending on the location of these nodes, the energy consumed may decrease. The performance of wireless sensor networks can be optimized by applying appropriate routing protocols, which are proposed in the available literature. The methodology for energy efficiency analysis of WSN can be used in the design of wireless sensor networks to determine the parameters of the environment, with the possibility of application in IoT.

Efficient multiscale methodology for local stress analysis of metallic railway bridges based on modal superposition principles

This paper presents an advanced submodelling methodology for local stress analysis of complex details of existing metallic railway bridges. The fatigue assessment of connections of large structures based on local methods leads inherently to a multiscale problem that can only be solved by adopting efficient numerical procedures. Aiming to overcome such limitations that influence the analysis process, submodelling techniques and modal superposition principles are combined to fully represent numerically the local geometrical, material and contact properties of the fatigue-critical details. The results of experimental in situ tests are proposed to characterise the numerical models and respective multiscale relation, implementing optimisation and validation procedures. In this work, the suggested efficient multiscale methodology for stress analysis aims to allow the subsequent local fatigue assessment, according to the real mechanism of loading transference, reducing sources of conservatism. All numerical procedures and respective validation thru experimental techniques are illustrated using a real case study.

Screening of specific quantitative peptides of beef by LC–MS/MS coupled with OPLS-DA

A rapid, simple, and efficient analysis methodology for screening specific quantitative peptides of beef was established based on liquid chromatography-tandem mass spectrometry (LC–MS/MS) coupled with orthogonal partial least squares-discriminant analysis (OPLS-DA). The OPLS-DA model was built to select species-specific peptides that make a significant contribution to classification. Peptides with statistical significance were selected based on the variable importance in the projection (VIP) values and univariate P values. After the workflow of the statistical process, three specific quantitative peptides were identified by using homemade products with different beef contents. A quantification method for selected specific quantitative peptides was established by using LC–MS/MS. The quantitative results were applied to commercialized beef products. The developed method has high sensitivity, specificity, and repeatability. The results of this study proved that the integration of LC–MS/MS coupled with OPLS-DA is an efficient method for screening specific quantitative peptides and identification of the authenticity of meat products.

Predicting fatigue damage in composites subjected to general loading conditions

A computationally efficient fatigue analysis methodology is proposed that uses a cycle jump approach to drive the accumulation of fatigue damage and a unified local fatigue cohesive zone model that can predict the initiation and propagation of cracks using a reduced set of model parameters. A new definition of the stress ratio is proposed for general loading conditions, which include negative load ratios, unsynchronized loads, and problems where the mode mixity changes during a cycle. The cycle jump procedure was verified by performing analyses of standard double cantilever beam and mixed mode bending tests. Then, the model was validated by performing analyses of double notch shear tests and initiation of transverse matrix cracking in a [0/90] laminate using models that account for residual thermal stresses. The results indicate that the analysis methodology can reproduce experimentally measured fatigue damage initiation and evolution for a variety of different configurations and load combinations.(Keywords: C. Cohesive interface modelling, B Fatigue, B. Delamination, A. Polymer-matrix composites (PMCs))

An efficient sentiment analysis methodology based on long short-term memory networks

Sentiment analysis is the process of determining the sentiment polarity (positivity, neutrality or negativity) of the text. As online markets have become more popular over the past decades, online retailers and merchants are asking their buyers to share their opinions about the products they have purchased. As a result, millions of reviews are generated daily, making it difficult to make a good decision about whether a consumer should buy a product. Analyzing these enormous concepts is difficult and time-consuming for product manufacturers. Deep learning is the current research interest in Natural language processing. In the proposed model, Skip-gram architecture is used for better feature extraction of semantic and contextual information of words. LSTM (long short-term memory) is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are optimized by the adaptive particle Swarm Optimization algorithm. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models in different metrics.

Measuring the far-right in Europe: comparative analysis of the xenophobic and anti-immigrant sentiment in Germany, Poland and Russia

The rising significance of the far-right as political actors is one of the most noticeable political trends in Europe within the recent decades. It causes constant research activity aimed at the analysis of nature of public support of right-wing forces. However, the researchers, dealing with the issues of assessing the public support of the far-right in different European countries frequently have confront the problem of lack of relevant measurement tools, as well as efficient methodology for valid cross-country analysis. This stems not only from different “political background” and “political environment”, connected with political traditions and political practices, but also from the nature of public support of the far-right, having not only “overt”, but also “covert” component. Whereas “overt” component is on the surface and is good measurable (mainly by conventional tools such as polling), the “covert” one, encompassing the in-depth social sentiments, is hardly measurable. This pose real problem for assessing the genuine amount of public support far-right forces taking into account the fact, that “covert” component is to be crucial in achieving that goal. In October 2020 to tackle this problem by the international group of researchers there was initiated the research project, aiming at development of the specific research methodology on the combination of traditional and “digital” research methods such as comparative analysis, expert interviews and “social media listening” (SML) for analysing the xenophobic and anti-immigrant sentiments in three European societies – in Germany, Poland and Russia as a “covert” component. The accomplishment of the research project is expected to provide not only the insights into specifics of the public support of the far-right in different European countries by assessing the “covert” component, but also help to assess its genuine (gross) scale as well as to single out the factors influencing the public support of the far-right by putting emphasis on the ongoing political and social processes. The research project is expected to deliver the information for elaboration of the gauges (indicators) for valid cross-country analysis of the far-right politics in different countries allowing to overcome some research “weak points” – ranging from huge differences in political traditions to scarcity of relevant data. The aim of this article is to present and discuss the elaborated methodological approach.

GAKTpore: Stereological Characterisation Methods for Porous Foams in Biomedical Applications.

In tissue engineering, scaffolds are a key component that possess a highly elaborate pore structure. Careful characterisation of such porous structures enables the prediction of a variety of large-scale biological responses. In this work, a rapid, efficient, and accurate methodology for 2D bulk porous structure analysis is proposed. The algorithm, “GAKTpore”, creates a morphology map allowing quantification and visualisation of spatial feature variation. The software achieves 99.6% and 99.1% mean accuracy for pore diameter and shape factor identification, respectively. There are two main algorithm novelties within this work: (1) feature-dependant homogeneity map; (2) a new waviness function providing insights into the convexity/concavity of pores, important for understanding the influence on cell adhesion and proliferation. The algorithm is applied to foam structures, providing a full characterisation of a 10 mm diameter SEM micrograph (14,784 × 14,915 px) with 190,249 pores in ~9 min and has elucidated new insights into collagen scaffold formation by relating microstructural formation to the bulk formation environment. This novel porosity characterisation algorithm demonstrates its versatility, where accuracy, repeatability, and time are paramount. Thus, GAKTpore offers enormous potential to optimise and enhance scaffolds within tissue engineering.

DAPP: automatic detection and analysis of prototype pollution vulnerability in Node.js modules

The safe maintenance of Node.js modules is critical in the software security industry. Most server-side web applications are built on Node.js, an environment that is highly dependent on modules. However, there is clear lack of research on Node.js module security. This study focuses particularly on prototype pollution vulnerability, which is an emerging security vulnerability type that has also not been studied widely. To this point, the main goal of this paper is to propose patterns that can identify prototype pollution vulnerabilities. We developed an automatic static analysis tool called DAPP, which targets all the real-world modules registered in the Node Package Manager. DAPP can discover the proposed patterns in each Node.js module in a matter of a few seconds, and it mainly performs and integrates a static analysis based on abstract syntax tree and control flow graph. This study suggests an improved and efficient analysis methodology. We conducted multiple empirical tests to evaluate and compare our state-of-the-art methodology with previous analysis tools, and we found that our tool is exhaustive and works well with modern JavaScript syntax. To this end, our research demonstrates how DAPP found over 37 previously undiscovered prototype pollution vulnerabilities among 30,000 of the most downloaded Node.js modules. To evaluate DAPP, we expanded the experiment and ran our tool on 100,000 Node.js modules. The evaluation results show a high level of performance for DAPP along with the root causes for false positives and false negatives. Finally, we reported the 37 vulnerabilities, respectively, and obtained 24 CVE IDs mostly with 9.8 CVSS scores.

An efficient methodology for aspect-based sentiment analysis using BERT through refined aspect extraction

Aspect-Based Sentiment Analysis (ABSA) has become a trending research domain due to its ability to transform lives as well as the technical challenges involved in it. In this paper, a unique set of rules has been formulated to extract aspect-opinion phrases. It helps to reduce the average sentence length by 84% and the complexity of the text by 50%. A modified rank-based version of Term-Frequency - Inverse-Document-Frequency (TF-IDF) has been proposed to identify significant aspects. An innovative word representation technique has been applied for aspect categorization which identifies both local as well as global context of a word. For sentiment classification, pre-trained Bidirectional Encoder Representations from Transformers (BERT) has been applied as it helps to capture long-term dependencies and reduce the overhead of training the model from scratch. However, BERT has drawbacks like quadratic drop in efficiency with an increase in sequence length which is limited to 512 tokens. The proposed methodology mitigates these drawbacks of a typical BERT classifier accompanied by a rise in efficiency along with an improvement of 8% in its accuracy. Furthermore, it yields enhanced performance and efficiency compared to other state-of-the-art methods. The assertions have been established through extensive analysis upon movie reviews and Sentihood data-sets.

A study on the efficiency analysis between large and mid-sized Korean construction companies using DEA technique.

Background/Objectives: In this study, we analyze the efficiency of Korean large and mid-sized construction enterprises by utilizing DEA techniques to identify characteristics and review selectable strategies. Methods/Statistical analysis: This study presents a two-stage efficiency analysis methodology in total. The first stage overall DEA calculates the overall efficiency of inputs and outputs. The second stage fundamental DEA calculates the efficiency of individual combinations of inputs and outputs to estimate the variables that fundamentally affect the results of the first stage DEA. As a result, the improvement strategy for becoming an efficient entity is reviewed by deriving inefficient variables. Findings: The first stage DEA found that large/mid-sized construction companies showed different efficiency results. It was analyzed that about 83.5 percent of large construction companies need to scale down, while about 61.1 percent of mid-sized construction firms need to scale up. Statistically, there was also a difference in efficiency analysis of scale between groups of large/mid-sized construction companies. Therefore, it was understood that the efficiency analysis of the construction company should be carried out by considering the differences between the two groups rather than by aggregating them without considering the differences between the groups of scale. On the other hand, the second stage DEA found that even if the overall inputs were more efficient, the partial inputs could be inefficient. That is, the opposite result of the first stage DEA was shown in the second stage DEA. Thus, it was noted that the efficiency analysis of a construction company would need to analyze the second stage fundamental DEA model at the same time, not just the first stage overall DEA model. Improvements/Applications: Although a typical first stage DEA is efficient, a second stage DEA may be different, so it is suggested that a second stage DEA should be combined for efficiency analysis.