Methodology For Identification Research Articles

Advanced Process Control Strategies for Efficient Methanol Production from Natural Gas

Natural gas-to-methanol plants are receiving renewed interest with the significant increase in shale gas availability. Methanol serves as a crucial raw material for producing various industrial and consumer goods as well as key platform chemicals, including acetic acid, methyl tertiary butyl ether, dimethyl ether, and methylamine. In this research, a dynamic model is developed for Natgasoline’s methanol manufacturing plant. A hierarchical control system comprising Dynamic Matrix Control (DMC) and a basic regulatory control loop is constructed using this dynamic model to minimize methanol losses and utility costs under various process upsets. A subspace identification methodology is used to develop rigorous DMCplus controller models. The simulation results in the ASPEN manufacturing software platform show that the DMCplus controller developed in this study can reduce methanol losses by 96% and utility requirements by 40%. The controller is robust to feed flow variations of ±10%. Furthermore, disturbances due to the variation in hydrogen content in the syngas are also successfully rejected by the controller. This hierarchical multivariable control system performs significantly better than the traditional regulatory PID control strategy in optimizing the methanol process under process constraints.

Machine Learning-Based In Situ Identification of the High-Dimensional Parameter Space for an Equivalent Circuit Model

Abstract Accurate online identification of equivalent circuit model (ECM) parameters of lithium-ion battery electrochemical impedance spectroscopy (EIS) remains a challenge, particularly with high-dimensional parameter spaces. Here, 11-dimensional adapted Randles ECM (AR-ECM) is reduced to two low-dimensional models, and the EIS frequency ranges for each AR-ECM parameter were determined by distinguishing the frequency bands representing different electrochemical processes. A multi-step parameter in-situ identification methodology was developed to minimize onboard training costs by selecting an optimal training set for machine learning based on the Euclidean distance between the collected and generated EIS data. A Gaussian process regression model was constructed by correlating the AR-ECM parameters and EIS to estimate the AR-ECM parameters. Model performance was validated using 12 cells at different temperatures. Experimental results show that a simulated database covering the main EIS and AR-ECM characteristics can be established, whose scale is on the order of 1e^6, much smaller than the order of 1e^11 resulting from each AR-ECM parameter being varied among 10 values. The estimation errors of the key AR-ECM parameters are approximately 5% at different temperatures. The maximum estimation error of all parameters is as low as 9.03%, 13.96% lower than that based on the complex nonlinear least squares method.

Dynamic regulation of ferroelectric polarization using external stimuli for efficient water splitting and beyond.

Establishing and regulating the ferroelectric polarization in ferroelectric nano-scale catalysts has been recognized as an emerging strategy to advance water splitting reactions, with the merits of improved surface charge density, high charge transfer rate, increased electronic conductivity, the creation of real active sites, and optimizing the chemisorption energy. As a result, engineering and tailoring the ferroelectric polarization induced internal electric field provides significant opportunities to improve the surface and electronic characteristics of catalysts, thereby enhancing the water splitting reaction kinetics. In this review, an interdisciplinary and comprehensive summary of recent advancements in the construction, characterization, engineering and regulation of the polarization in ferroelectric-based catalysts for water splitting is provided, by exploiting a variety of external stimuli. This review begins with a detailed overview of the classification, benefits, and identification methodologies of the ferroelectric polarization induced internal electric field; this offers significant insights for an in-depth analysis of ferroelectric-based catalysts. Subsequently, we explore the underlying structure-activity relationships for regulating the ferroelectric polarization using a range of external stimuli which include mechanical, magnetic, and thermal fields to achieve efficient water splitting, along with a combination of two or more fields. The review then highlights emerging strategies for multi-scale design and theoretical prediction of the relevant factors to develop highly promising ferroelectric catalysts for efficient water splitting. Finally, we present the challenges and perspectives on the potential research avenues in this fascinating and new field. This review therefore delivers an in-depth examination of the strategies to engineer the ferroelectric polarization for the next-generation of water electrolysis devices, systems and beyond.

A Force-Sensor-Less Approach for Rapid Young's Modulus Identification of Heterogeneous Soft Tissue.

Due to individual differences, accurate identification of tissue elastic parameters is essential for biomechanical modeling in surgical guidance for hepatic venous injections. This paper aims to acquire the absolute Young's modulus of heterogeneous soft tissues during endoscopic surgery with 2D ultrasound images. First, we introduced a force-sensor-less approach that utilizes a pre-calibrated soft patch with a known Young's modulus and its ultrasound images to calculate the external forces exerted by the probe on the tissue. Second, we introduced a Kriging-based inverse algorithm to identify the relative Young's modulus (RYM) between the inclusion and the background tissue. The RYM was estimated based on 2D plane strain approximation and mapped to the RYM of 3D soft tissue through a trained Kriging model. Finally, we developed a direct method to identify the background Young's modulus (BYM) based on calculated external forces and RYM. The simulation results demonstrate the high efficiency and robustness of the Kriging-based inverse algorithm in identifying RYM. Physical experiments on the three phantoms show that the errors of the identified BYM and RYM are all below 15%. The proposed methodology for Young's modulus identification is feasible and achieves satisfactory accuracy and computational efficiency in both simulations and physical experiments.

Species Identification and Orthologous Allergen Prediction and Expression in the Genus Aspergillus

The genus Aspergillus comprises a diverse group of fungi that can cause a range of health issues, including systemic infections and allergic reactions. In this regard, A. fumigatus has been recognized as the most prevalent allergen-producing species. This genus taxonomic classification has been subject to frequent updates, which has generated considerable difficulties for its classification when traditional identification methodologies are employed. To demonstrate the feasibility of this approach, we sequenced the whole genomes of 81 Aspergillus isolates and evaluated a WGS-based pipeline for precise species identification. This pipeline employed two methodologies: (i) BLASTn web using four barcode genes and (ii) species tree inference by OrthoFinder. Furthermore, we conducted a prediction of allergenic capacity based on a homology analysis across all the isolated species and confirmed by RT-qPCR the expression of three orthologous allergens (Asp f 1, Asp f 3 and Asp f 22) in fifteen different Aspergillus species. The species-level identification rate with the barcoding and the species tree were calculated at 64.2% and 100%, respectively. The results demonstrated that A. fumigatus, A. flavus and A. niger were the most prevalent species. The species A. hortae, A. uvarum, A. spinulosporus, A. sydowii, A. westerdijkiae, A. amoenus and A. rhizopodus identified in this study represent the inaugural report of their presence in our region. The results of the homology analysis indicated the presence of orthologous allergens in a wide range of non-fumigatus species. This study presents a novel approach based on WGS that enables the classification of new species within the genus Aspergillus and reports the genomic sequences of a great diversity of species isolated in our geographic area that had never been reported before. Additionally, this approach enables the prediction of allergens in species other than A. fumigatus and demonstrates their genetic expression, thereby contributing to the understanding of the allergenic potential of different species within this fungal genus.

Revisiting the trading activity of high-frequency trading firms around ultra-fast flash events

We investigate high-frequency traders’ behavior in the context of the fastest and most extreme price movements (EPMs) that can be observed in the market, specifically ultra-fast flash events, challenging the methodologies employed in the academic and practitioner literature for identifying sudden liquidity black holes. To refine the price-shock identification methodology, we introduce a new approach called sequence-based flash events (SFEs), which relies on tick sequences instead of predetermined fixed-time intervals within which all flash events in the sample are assumed to occur. This alternative methodology offers the advantage of pinpointing the exact time and duration of a crash, which, in turn, provides a way to more accurately define the observation windows around it. We compare our sample of SFEs with both the so-called “mini flash crashes”, as identified by the Nanex detection algorithm, and the so-called EPMs, as identified by Brogaard et al. (2018). We use close and open prices, as well as high and low prices. Based on our sample of SFEs, we find no evidence that HFTs trigger extreme price shocks. However, we find that HFTs exacerbate SFEs by increasing the net imbalance in the direction of these shocks as they occur. Finally, we show that the choice of the price-shock identification methodology is critical. Thus, we urge regulators to exercise caution and avoid hasty conclusions regarding HFTs’ contribution to price stability in stressful market conditions.

Publisher Correction: An automatic methodology for lithology identification in a tight sandstone reservoir using a bidirectional long short-term memory network combined with Borderline-SMOTE

Publisher Correction: An automatic methodology for lithology identification in a tight sandstone reservoir using a bidirectional long short-term memory network combined with Borderline-SMOTE

System Identification of a Servo-Valve Controlled Hydraulic Cylinder Operating Under Variable Load

This work presents an in-depth study on the system identification of a servo-valve controlled hydraulic cylinder operating under variable load. This research addresses the growing demand for improved control systems (enhancing time response, settling time, and precision) in variable load hydraulic actuators, such as those used in blade pitching systems of wind turbines. The paper begins by detailing the experimental setup, followed by the development of the system’s mathematical model, a fourth-order transfer function (TF). The experimental data collected by a proposed data acquisition system are used for the dynamic identification of the hydraulic setup using periodical signals as commands. All possible combinations of TFs up to order 8 are identified. After an initial visual preselection of the 15 most accurate ones, analyses comparing quality indicators between the measured (experimental) and the TF (simulated) step and sinusoidal responses are conducted to determine the most accurate TF. The paper concludes with the presentation and analysis of the dynamic model, identified as being a fourth-order TF, which replicates the system dynamics with the greatest fidelity. It provides an identification methodology with significant potential for industry practitioners aiming to improve, optimize, and enhance control strategies for variable load hydraulic actuators.

Optimized breath analysis: customized analytical methods and enhanced workflow for broader detection of VOCs

IntroductionBreath Volatile organic compounds (VOCs) are promising biomarkers for clinical purposes due to their unique properties. Translation of VOC biomarkers into the clinic depends on identification and validation: a challenge requiring collaboration, well-established protocols, and cross-comparison of data. Previously, we developed a breath collection and analysis method, resulting in 148 breath-borne VOCs identified.ObjectivesTo develop a complementary analytical method for the detection and identification of additional VOCs from breath. To develop and implement upgrades to the methodology for identifying features determined to be “on-breath” by comparing breath samples against paired background samples applying three metrics: standard deviation, paired t-test, and receiver-operating-characteristic (ROC) curve.MethodsA thermal desorption (TD)-gas chromatography (GC)-mass spectrometry (MS)-based analytical method utilizing a PEG phase GC column was developed for the detection of biologically relevant VOCs. The multi-step VOC identification methodology was upgraded through several developments: candidate VOC grouping schema, ion abundance correlation based spectral library creation approach, hybrid alkane-FAMES retention indexing, relative retention time matching, along with additional quality checks. In combination, these updates enable highly accurate identification of breath-borne VOCs, both on spectral and retention axes.ResultsA total of 621 features were statistically determined as on-breath by at least one metric (standard deviation, paired t-test, or ROC). A total of 38 on-breath VOCs were able to be confidently identified from comparison to chemical standards.ConclusionThe total confirmed on-breath VOCs is now 186. We present an updated methodology for high-confidence VOC identification, and a new set of VOCs commonly found on-breath.

A Review of Health Monitoring and Model Updating of Vibration Dissipation Systems in Structures

Given that numerous countries are located near active fault zones, this review paper assesses the seismic structural functionality of buildings subjected to dynamic loads. Earthquake-prone countries have implemented structural health monitoring (SHM) systems on base-isolated structures, focusing on modal parameters such as frequencies, mode shapes, and damping ratios related to isolation systems. However, many studies have investigated the dissipating energy capacity of isolation systems, particularly rubber bearings with different damping ratios, and demonstrated that changes in these parameters affect the seismic performance of structures. The main objective of this review is to evaluate the performance of damage detection computational tools and examine the impact of damage on structural functionality. This literature review’s strength lies in its comprehensive coverage of prominent studies on SHM and model updating for structures equipped with dampers. This is crucial for enhancing the safety and resilience of structures, particularly in mitigating dynamic loads like seismic forces. By consolidating key research findings, this review identifies technological advancements, best practices, and gaps in knowledge, enabling future innovation in structural health monitoring and design optimization. Various identification techniques, including modal analysis, model updating, non-destructive testing (NDT), and SHM, have been employed to extract modal parameters. The review highlights the most operational methods, such as Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification (SSI). The review also summarizes damage identification methodologies for base-isolated systems, providing useful insights into the development of robust, trustworthy, and effective techniques for both researchers and engineers. Additionally, the review highlights the evolution of SHM and model updating techniques, distinguishing groundbreaking advancements from established methods. This distinction clarifies the trajectory of innovation while addressing the limitations of traditional techniques. Ultimately, the review promotes innovative solutions that enhance accuracy, reliability, and adaptability in modern engineering practices.

Ecological quality assessment of estuarine macrobenthic communities using an integrative approach

Macrobenthic communities play a crucial role in the functioning of estuarine ecosystems and serve as bioindicators of environmental quality. This study assessed the ecological quality of an estuarine system using the AMBI and M-AMBI indices. The following parameters were considered: (i) environmental factors (total organic matter, organic carbon, grain size, calcium carbonate), (ii) sediment trace metals (Pb, Zn, Cu, Cr, Co, Ni, Hg, Li, As), (iii) species composition (morphological and molecular identification), and (iv) anthropogenic activities. The results demonstrated notable differences between study areas, reflecting hydrodynamic processes and human activities. The AMBI index indicated that all areas exhibited conditions classified as “slightly disturbed.” However, the composition of the ecological groups and M-AMBI results differed according to the identification method. This approach allowed for a more complete understanding of communities, by integrating the influence of anthropogenic activities on the sediment and macrobenthic communities, highlighting the importance of using both identification methodologies.

Forensic characterization of Brazilian gemstones: A pilot study employing raman spectroscopy and multivariate analysis.

Forensic gemstone analysis faces many challenges. One of the most critical aspects of gemological research is the classification of a gemstone. It is necessary to understand the mineral species, purity, origin, and identification of treatments to identify and classify a gem correctly, as well as assign a monetary value to it. However, the identification methodology must be neither destructive nor invasive to the material. Raman spectroscopy offers several advantages, such as fast, non-destructive analysis, requiring little material, being fully reproducible, and requiring no sample preparation. Furthermore, the development of portable Raman spectrometers allows the analysis to be carried out anywhere. Fourteen Brazilian gemstones (agate, amazonite, amethyst, yellow calcite, orange calcite, citrine, hematite, yellow jasper, tiger's eye, onyx, blue quartz, rose quartz, green quartz, and sodalite) were analyzed using the TacticID N 785 nm portable Raman system. For each of the fourteen types of gemstones, three fragments of rough and three polished gemstones were analyzed. Three spectra of each piece in different faces were taken, totaling nine spectra for each species and 252 spectra in all. We used one univariate technique (Pearson correlation coefficient) and one classificatory technique-Soft Independent Modelling of Class Analogy (SIMCA) to evaluate the spectra. Results showed that the univariate method was not able to discriminate the classes. However, SIMCA analysis showed promising results for classification. These results were achieved after removing outliers and making a variable selection. More specifically, some samples presented anomalous behavior due to the nature of the technique applied. Some regions of the spectrum did not offer information, or that information was redundant; because of these, it was removed. This suggests that Raman spectroscopy and multivariate methods can be essential tools for classifying gemstones, not only Brazilian ones but of any origin.

Using statistical analysis to explore the influencing factors of data imbalance for machine learning identification methods of human transcriptome m6A modification sites.

RNA methylation, particularly through m6A modification, represents a crucial epigenetic mechanism that governs gene expression and influences a range of biological functions. Accurate identification of methylation sites is crucial for understanding their biological functions. Traditional experimental methods, however, are often costly and can be influenced by experimental conditions, making machine learning, especially deep learning techniques, a vital tool for m6A site identification. Despite their utility, current machine learning models struggle with unbalanced datasets, a common issue in bioinformatics. This study addresses the RNA methylation site data imbalance problem from three key perspectives: feature encoding representation, deep learning models, and data resampling strategies. Using the K-mer one-hot encoding strategy, we effectively extracted RNA sequence features and developed classification prediction models utilizing long short-term memory networks (LSTM) and its variant, Multiplicative LSTM (mLSTM). We further enhanced model performance by ensemble and weighted strategy models. Additionally, we utilized the sequence generative adversarial network (SeqGAN) and the synthetic minority resampling technique (SMOTE) to construct balanced datasets for RNA methylation sites. The prediction results were rigorously analyzed using the Wilcoxon test and multivariate linear regression to explore the effects of different K-mer values, model architectures, and sampling methods on classification outcomes. The analysis underscored the significant impact of feature selection, model architecture, and sampling techniques in addressing data imbalance. Notably, the optimal prediction performance was achieved with a K value of 5 using the mLSTM-ensemble model. These findings not only offer new insights and methodologies for RNA methylation site identification but also provide valuable guidance for addressing similar challenges in bioinformatics.

Evaluating climatic vulnerability in India: Moving from risk to resilience

Climate adaptation has been a top priority in the UN climate negotiations due to the increasing frequency of extreme weather occurrences. Assessments of Climate Vulnerability and Risk are crucial for designing adaptation strategies and identifying vulnerable locations and individuals. However, there aren't many development projects or efforts in India that focus on Climate Vulnerability and Risk Assessment (VRA), and investment plans don't include climate resilience. The scopes and methodologies of risk identification and vulnerability assessments that are currently in use in India are evaluated in this article. It then offers recommendations for growing adaptation activities at the federal level, along with methodology based on global best practices.

Maximizing Identification Precision of Hymenoptera and Brachycera (Diptera) With a Non-Destructive DNA Metabarcoding Approach.

In recent years, DNA metabarcoding has been used for a more efficient assessment of bulk samples. However, there remains a paucity of studies examining potential disparities in species identification methodologies. Here, we explore the outcomes of diverse clustering and filtering techniques on data from a non-destructive metabarcoding approach, compared to species-level morphological identification of Brachycera (Diptera) and Hymenoptera of two bulk samples collected with Malaise traps. The study evaluated four distinct approaches, namely clustering to Amplicon Sequence Variants (ASVs) or ASVs clustered to Operational Taxonomic Units (OTUs) coupled with subsequent filtering using the LULU algorithm at 84% and 96% minimum match. In total, 114 species of Brachycera (35 families) and 85 species of Hymenoptera (27 families) were identified morphologically. Depending on the selected approach, DNA metabarcoding results strongly varied in terms of detected molecular units blasted to brachyceran and hymenopteran species. For Brachycera, ASVs clustered into OTUs followed by LULU using a 96% minimum match (OTU96) inferred the number of molecular units closest to the number of morphologically identified species. Using Syrphidae as an exemplary family, we found an overlap ranging from 9% to 81% between the morphological identification and the different clustering and filtering approaches, OTU96 being also here the closest one. For Hymenoptera, while OTU96 also yielded the highest number of molecular units, it was still considerably low compared to the number of morphologically identified species. Our results show that metabarcoding methodology needs to be significantly improved to be applied to Hymenoptera. Conversely, for Brachycera, we acknowledge the promise of employing a non-destructive metabarcoding approach, incorporating ASV clustering into OTUs and filtering with LULU, to derive dependable species lists. Such lists hold significant potential for applications in biomonitoring, conservation efforts, and other related fields.

Improved Interconnected MRAS Parameter Identification for Speed Sensorless Control of Linear Induction Motor

After eliminating the speed sensor in the linear induction motor (LIM) high-performance closed-loop control system, the speed feedback information is missing in the speed closed loop. The accuracy of speed observation results is affected by changes in magnetizing inductance and primary resistance. This effect can cause significant oscillations in the results of the speed sensorless control system, preventing them from converging. An enhanced model reference adaptive system (MRAS) multi-parameter parallel identification methodology based on the interconnected second-order super-twisting algorithm (SOSTA) is proposed. To enhance the system’s dynamic performance, we designed an improvement to the MRAS observer based on the SOSTA, with a focus on the LIM state-space equation that considers dynamic edge-end effects. The impact of parameter alterations on the LIM system is examined. To improve speed observation accuracy and system stability, a two-parameter MRAS identification model was created. The Popov hyperstability principle was used to formulate control laws for these two parameters, ultimately enabling the identification of these two parameters. The identified values were fed back to the speed observation and control system, which reduces the coupling of these two parameters and speed. Simulation and hardware-in-the-loop experiments demonstrate that the observation system estimates speed accurately when these two parameters undergo abrupt changes within the rated speed range, enhancing the precision and robustness of the speed sensorless control system.

Unsupervised Identification for 2-Additive Capacity by Principal Component Analysis and Kendall’s Correlation Coefficient in Multi-Criteria Decision-Making

With the Multi-Criteria Decision-Making (MCDM) problems becoming increasingly complex, traditional MCDM methods cannot effectively handle ambiguous, incomplete, or uncertain data. While several novel types of MCDM methods have been proposed to address this limitation, they fail to consider the potentially complex interactions among decision criteria. An effective capacity identification methodology is definitely needed to conquer this issue. In this paper, we develop a novel unsupervised method for identifying 2-additive capacities by means of Principal Component Analysis (PCA) and Kendall’s correlation coefficient. During the process, some significant results are achieved. Firstly, the Shapley values of decision criteria are derived by using the PCA, through a combination of the variance contribution rate of each Principal Component (PC) and its corresponding eigenvector. Secondly, Kendall’s correlation coefficient stemmed from the decision data created to help identify the Shapley interaction index for each pair of criteria by unsupervised learning. The optimization model equipped with a new form of monotonicity conditions is then established to further determine the optimal Shapley interaction index. With these two kinds of indices, a desired monotone 2-additive capacity is finally identified in an objective and efficient manner. Numerical experiments demonstrate that our proposal can adequately consider the importance of criteria and accurately identify the types of Shapley interaction indices between criteria, and is thus able to produce more convincing and logical results compared with other unsupervised identification methods.

How Do Researchers Identify and Recruit Dementia Caregivers? A Scoping Review.

Studies involving dementia caregivers are essential to transform care and inform new policies. However, identifying and recruiting this population for research is an ongoing challenge. This scoping review aimed to capture the current methodology for identifying and recruiting dementia caregivers in clinical studies. A focus was placed on methods for underrepresented populations and pragmatic trials to guide pragmatic and equitable clinical studies. Researchers conducted a literature search using PubMed, PsycINFO, EMBASE, and Web of Science databases. Studies conducted in the US that enrolled at least 10 caregivers and were published within the last 10 years (2013-2023) were included. Overall, 148 articles were included in the review. The most common method for identification was community outreach, and paper advertisements for recruitment. Caregivers were most often approached in community settings, formal organizations, and/or dementia research centers. Most enrolled caregivers were female, White, and spouses of persons living with dementia. Race and ethnicity were underreported, as were the target recruitment goals. Limited studies were self-reported as pragmatic. Additionally, limited studies reported adaptations for methods of identification and recruitment in underrepresented populations. We identified gaps in current practices for the identification and recruitment of dementia caregivers. Future identification and recruitment methodologies should be tailored to the intervention's intent, health care setting, and the research questions that need to be answered, while balancing available resources. Additionally, transparent reporting of identification and recruitment procedures, target recruitment goals, and comprehensive demographic data is warranted.

Methodology for Identification the Author of Destructive Textual Data

The article is devoted to determining the authorship of short comments of social network users, including communities associated with destructive content. The study includes classification with a fixed and open set of authors. In the latter case, several experiments were conducted, including detection of destructive content by introducing authors of the such content. The results show, that the proposed methodology achieves a high accuracy of 85 %.

Adipose Tissue as a Major Launch Spot for Circulating Extracellular Vesicle-Carried MicroRNAs Coordinating Tissue and Systemic Metabolism.

Circulating microRNAs (miRNAs), especially transported by extracellular vesicles (EVs), have recently emerged as major new participants in interorgan communication, playing an important role in the metabolic coordination of our tissues. Among these, adipose tissue displays an extraordinary ability to secrete a vast list of EV-carried miRNAs into the circulation, representing new hormone-like factors. Despite the limitations of current methodologies for the unequivocal identification of the origin and destination of EV-carried miRNAs in vivo, recent investigations clearly support the important regulatory role of adipose-derived circulating miRNAs in shaping the metabolism and function of other tissues including the liver, muscle, endocrine pancreas, cardiovascular system, gastrointestinal tract, and brain. Here, we review the most recent findings regarding miRNAs transported by adipose-derived EVs (AdEVs) targeting other major metabolic organs and the implications of this dialog for physiology and pathology. We also review here the current and potential future diagnostic and therapeutic applications of AdEV-carried miRNAs.