Analysis Procedure Research Articles

Recent Advancement in the Fabrication of Colorimetric and Fluorometric Sensors for the Detection of Basic Amino Acids

Abstract:: Due to the biological importance of amino acids, the development of optical probes for these compounds has become a popular research topic in recent years. The amino acid fluorescence or colorimetric sensors are organized according to the reactions the amino acids go through and the related structural classification. Works on reaction-based chemosensors are categorized as either imine formation, Michael addition, thiazinane or thiazolidine formation, cleavage of a sulfonate ester, cleavage of a disulfide, metal complexes-displace coordination, or other mechanisms depending on the mechanisms between sensors and amino acids. Amino acids (AA), which comprise most proteins, are small molecules with a wide range of functional side-chain groups. Amino acids, therefore, have a variety of roles in physiological processes. Histidine (His), a member of this family, is required for tissue growth and repair as well as for weight gain. Lysine (Lys), another member of this family, is intimately linked to the Krebs-Henseleit cycle and polyamine synthesis, and an adequate amount of lysine in the diet is necessary for animals' metabolic processes and weight gain. Because it controls the transfer of metal elements in biological bases, tryptophan (Trp) is crucial to biological processes like protein synthesis, animal growth, and plant development. The majority of proteins are composed of amino acids (AA), which are tiny molecules with a variety of functional side chain groups. As a result, amino acids play a number of different roles in physiological processes. This family member's histidine (His) is necessary for weight gain, tissue growth, and repair. Another member of this family, lysine (Lys), is crucial for the Krebs-Henseleit cycle and polyamine production, and animals' metabolic activities and weight gain depend on appropriate lysine intake. Tryptophan (Trp) is an essential component of biological processes like protein synthesis, animal growth, and plant development because it regulates the transfer of metal elements in biological bases. Numerous attempts have been made to create new procedures for amino acid analysis as a result of the rising focus on human health, disease diagnosis, and therapy. Currently, spectroscopic, chromatographic, or electrochemical analytical methods are most frequently employed to identify and characterize amino acids. However, each method has certain disadvantages, such as the need for equipment and trained personnel, operational simplicity, analytical cost, test speed, and detection. Based on the important distinguishing characteristics of various amino acids to date, much research has been done on optical probes using indicator-displacement tests, metal complex coordination, particular interactions between probes and amino acids, and other techniques. We further subdivided the reaction-based probes into the following groups: production of imines, Michael addition, thiazinane or thiazolidine, cleavage of sulfonate ester, cleavage of disulfide, and others. Metal complexes-displace coordination. Due to some amino acids' similarity in structure and reactivity, it is still challenging to develop sensors that can selectively and sensitively identify amino acids from one another, such as the three biological thiols Cys, Hcy, and GSH. Specific reactions between probes and amino acids and other techniques have been extensively researched based on the significant characteristic features of diverse amino acids to date. We further divided the reaction-based probes into the following categories: metal complexes-displace coordination, imine creation, Michael addition, thiazinane or thiazolidine formation, cleavage of a sulfonate ester, cleavage of a disulfide; and others.

Framework to Quantify Uncertainty in Microplastic Concentrations in Wastewaters and Sludges Incorporating Analytical Recovery Information into Data Analysis.

Wastewater treatment plants (WWTPs) serve a pivotal role in transferring microplastics (MPs) from wastewater to sludge streams, thereby exerting a significant influence on their release into the environment and establishing wastewater and biosolids as vectors for MP transport and delivery. Hence, an accurate understanding of the fate and transport of MPs in WWTPs is vital. Enumeration is commonly used to estimate concentrations of MPs in performance evaluations of treatment processes, and risk assessment also typically involves MP enumeration. However, achieving high accuracy in concentration estimates is challenging due to inherent uncertainty in the analytical workflow to collect and process samples and count MPs. Here, sources of random error in MP enumeration in wastewater and other matrices were investigated using a modeling approach that addresses the sources of error associated with each step of the analysis. In particular, losses are reflected in data analysis rather than merely being measured as a validation step for MP extraction methods. A model for addressing uncertainty in the enumeration of microorganisms in water was adapted to include key assumptions relevant to the enumeration of MPs in wastewater. Critically, analytical recovery, the capacity to successfully enumerate particles considering losses and counting error, may be variable among MPs due to differences in size, shape, and type (differential analytical recovery) in addition to random variability between samples (nonconstant analytical recovery). Accordingly, differential analytical recovery among the categories of MPs was added to the existing model. This model was illustratively applied to estimate MP concentrations from simulated data and quantify uncertainty in the resulting estimates. Increasing the number of replicates, counting categories of MPs separately, and accounting for both differential and nonconstant analytical recovery improved the accuracy of MP enumeration. This work contributes to developing guidelines for analytical procedures quantifying MPs in diverse types of samples and provides a framework for enhanced interpretation of enumeration data, thereby facilitating the collection of more accurate and reliable MP data in environmental studies.

Enhancing crashworthiness characteristics of modified hexagonal honeycomb structural panels through stretching and bending of ribs

The effects of stretching and bending of concave, convex, and horizontal ribs on the energy absorption characteristics of modified hexagonal honeycomb structural panels are investigated under in-plane quasi-static compression loading conditions. In this regard, five hierarchical modified structural panel configurations were fabricated using polylactic acid material (PLA) via a fused filament 3D printer, ensuring uniform wall thickness in the 1.5–2 mm range. The panels included the regular hexagonal panel (RHP), regular hexagonal panel with concave rib (RHCP), regular hexagonal panel with convex rib (RHXP), regular hexagonal panel with concave and horizontal ribs (RHCRP), and regular hexagonal panel with convex and horizontal ribs (RHXRP). In-plane quasi-static compressive loading tests were conducted to analyze crush resistance characteristics, and buckling modes of the modified honeycomb panels were examined through experimental and finite element analysis procedures. The result indicates that the specific energy absorption capacity (SEA) of RHXRP is increasing significantly compared to the SEA capacity of other categories of the structures. The changes in failure modes and increased crush energy absorption characteristics of modified RHP with the introduction of concave, convex, and horizontal ribs are elaborated.

Phoenix–A model-based human reliability analysis methodology: Data sources and quantitative analysis procedure

Human Reliability Analysis (HRA) has continuously evolved to adopt more advanced human error probability (HEP) quantification approaches. Despite several efforts and enhancements of HRA methods, many still present limitations. Phoenix HRA Methodology aims to overcome known issues by incorporating strong elements of current HRA good practices, leveraging lessons learned from empirical studies and existing and emerging HRA methods' features. Phoenix models performance influencing factors (PIFs) and their impact on failure modes through Bayesian Networks (BBNs). Despite increased use in HRA applications, the large amount of data BBNs may require is still challenging. Thus, to populate Phoenix BBNs, several data sources were combined, including other HRA methods, expert judgment, and operating experience. The paper discusses Phoenix's quantitative analysis process, the data sources used to estimate the BBN parameters, and the applied data aggregation method. It further provides the quantitative analysis procedure guide with the significant steps and sub-steps an HRA analyst requires to implement this Phoenix methodology phase. Furthermore, the method for mapping the data to Phoenix BBN parameters and for aggregating the data considering non-homogeneous data sources can be further used for other HRA methods that apply BBNs or other applications.

A Numerical Investigation of Seismic Load Analysis for a Concrete Gravity Dam Utilizing Abaqus Software

Current research efforts focus on ensuring the safety of extant dams against seismic loads and designing earthquake-resistant new dams. Dependable analytical procedures are essential for a safe design that can withstand the forces induced by earthquakes. In this research, the Pine Flat Dam in California, United States, was subjected to modal, static, and dynamic analyses utilizing the Abaqus software to evaluate the impact of hydrostatic forces and dynamic stresses on the dam, taking into account flexible foundations. Using the vertical component and the horizontal component Taft Lincoln School Tunnel earthquake data, dynamic linear and nonlinear performance for concrete gravity dam were conducted. The results obtained from the nonlinear analysis exhibited a noteworthy decrease in the highest primary stress at the heel of the concrete dam, dropping from 3.17 MPa to 1.31 MPa. Conversely, no variation was observed in the minimum principal stress. In addition, the dam's maximum principal stress has shifted from its base to its neck. The findings from these analyses can contribute to enhancing new dam designs and evaluating seismic safety for existing dams. This study highlights the importance of accurate analytical procedures in ensuring dam safety and provides significant observations regarding the behavior of concrete dams during earthquake loading.

Fan-out panel-level package warpage and reliability analyses considering the fabrication process

With the continuous advancement of IC packaging technology, 3D and 2.5D packaging systems have emerged as the prevailing trends. The advantages of large area and high I/O density have made fan-out panel-level packaging (FOPLP) been considered as one of the best package types for 3D and 2.5D packaging applications. The discussions about FOPLP have been popular recently. Warpage and reliability of FOPLP are the main concerns of packaging engineers. However, it was found that warpage and reliability of FOPLP are strongly related to the fabrication process but not many research works have been published. Thus, reliability analysis procedure considering the residual stress of fabrication process was proposed in this paper to observe warpage and stress distribution during fabrication process. Then, this research procedure facilitates subsequent fatigue life calculations and predicts reliability of the package.First, the thermal stresses and warpage of the redistribution layer-first (RDL-first) process were conducted for an entire panel. Next, warpage and thermal stress analyses were conducted for a strip encapsulated with an epoxy molding compound (EMC) during compression molding. Then, a reliability analysis was conducted with an IC unit and incorporated the consideration of the residual stress induced during the fabrication processes.The compression molding analysis utilized the pressure–volume–temperature–cure (P–V–T–C) model to account for both the volume shrinkage caused by a thermal mismatch and the chemical shrinkage. Furthermore, dual shift factor model was used to model the viscoelastic behavior of EMC in this study during PMC analysis effectively.An observation of the stress distribution for an IC unit revealed that the maximum stress position was at the solder ball near the periphery, with a stress value of 147.309 MPa. The fatigue life calculated using the modified Coffin–Manson fatigue model was 417 cycles.

Spectrophotometric Determination of Hydroxycinnamic Acids in Herbal Drugs. Part 2. Determination of Hydroxycinnamic Acids in the Presence of Polyphenolic Compounds (Review)

INTRODUCTION. Polyphenolic compounds of various classes have pronounced absorption bands overlapping in almost the same ultraviolet region. This superposition of bands complicates the quantification of total hydroxycinnamic acids in herbal drugs by direct spectrophotometry without additional sample preparation.AIM. This study aimed to establish the possibility of quantifying hydroxycinnamic acids in herbal drugs by spectrophotometry in the presence of other polyphenolic compounds.DISCUSSION.  This  review  analyses  publications   on   analytical   procedures   for the spectrophotometric quantification of hydroxycinnamic acids that are based on the preliminary separation or chemical modification of polyphenolic compounds (combining the use of spectrophotometry, chromatography, extraction, Arnow’s reagent, etc.). The described sample preparation can help to resolve absorption bands in the spectra of polyphenolic compounds. The results of assessing the characteristic parameters of these analytical procedures (selectivity, sensitivity, labour intensity, etc.) can serve as a basis for searching novel approaches to the quantitative determination of hydroxycinnamic acids in plants.CONCLUSIONS. To determine the content of hydroxycinnamic acids in mixtures of polyphenolic compounds, analysts may use special sample preparation (combining spectrophotometry with chromatography and extraction) or chemical modification of the initial compounds. The acquired spectra could benefit from specific mathematical processing (derivative photometry and Vierordt’s method). However, even with the abovementioned approaches, it may not always be possible to increase the selectivity of an analytical procedure. Therefore, there is a need to further improve existing analytical procedures for the quantification of hydroxycinnamic acids and develop new ones.

Spectrophotometric Determination of Hydroxycinnamic Acids in Herbal Drugs. Part 1. Direct Spectrophotometry (Review)

INTRODUCTION. Current scientific research aims to discover new plant sources of hydroxycinnamic acids for the development of novel medicinal products. Most commonly, the quantitative determination of hydroxycinnamic acids in herbal drugs involves testing procedures based on spectrophotometry and its modifications.AIM. This study aimed to establish the possibility of the quantification of hydroxycinnamic acids in herbal drugs by direct spectrophotometry.DISCUSSION. This review presents organised information on the use of direct spectrophotometry to quantify hydroxycinnamic acids in 70 plant species growing or cultivated in the Russian Federation. The review covers publications in Russian for the period from 2007 to 2023. According to the reviewed publications, direct spectrophotometry is an easy-to-use and highly reproducible method that does not require additional reagents or labour-intensive steps. Direct spectrophotometry may result in an overestimation of the hydroxycinnamic acid content due to overlapping absorption bands of polyphenolic compounds with different structures.CONCLUSIONS. Direct spectrophotometry can be considered as a method for measuring the total content of phenolic compounds (as well as hydroxycinnamic acids) in herbal drugs. Different approaches to direct spectrophotometric quantification of hydroxycinnamic acids in herbal drugs by can serve as a methodological basis for developing analytical procedures specific to the chemical compositions of biologically active compounds in herbal drugs and their extracts.

Examining incubated and nonincubated startups from fear of failure and entrepreneurial well-being during crises

PurposeThis research aimed to examine entrepreneurial fear of failure and entrepreneurial well-being from the perspectives of incubated and nonincubated startups during crises.Design/methodology/approachData were collected by distributing online questionnaires to 152 respondents comprising 43 incubated and 109 nonincubated startups in Indonesia. A multivariate discriminant analysis procedure was used to examine the interrelationships between both groups at the discovery, validation, customer creation and construction stages.FindingsThe result showed a significant difference between these startups at various stages, which was analyzed to provide insights into the relevant dimensions of fear of failure for startups. The essence of entrepreneurial well-being during crises is in accordance with the role of business incubators in an emerging market economy.Practical implicationsStartups need to innovate in order to grow while considering other factors such as work-life balance and financial resource availability. This is important to ensure they have sufficient motivating dosage of fear of failure.Originality/valueThe present study evaluates incubated and nonincubated startups in an emerging market economy by using both the entrepreneurial fear of failure and well-being to capture possible differences between groups. The context of pandemic crises helps us formulate appropriate approaches taken by incubators and startups in the future crises.

Smartphone-Based Color Evaluation of Passive Samplers for Gases: A Review

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedures for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods—whenever available—is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review, the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

N.A. Blatov and his ideas of balance analysis: history and modernity

This article is devoted to the contribution to the analysis of the balance sheet of one of the famous scientists in the accounting – Nikolai Alexandrovich Blatov. Being a model of flows of three types values, «Blatov’s Square» illustrates the boundaries of balance sheet as an enterprise model. Half of the requirements, noted by Blatov for checking the correctness of the balance, are subjective and cast doubt on the possibility of drawing up the correct balance sheet. Blatov is one of the first authors who presented algorithms for balance analysis techniques and formalized analytical procedures, calling it «economic analysis». These methods remain relevant. The calculation of turnover and liquidity indicators proposed by the author may complement the methods of modern practice. An assumption was made about the relevance of using knowledge of the balance studies in modern conditions. Based on the study of Blatov’s works, it can be concluded that to a greater extent it is advisable to internal rather than external users to apply his ideas for the enterprise analysis.

Enhanced multi-springs model for estimating suction caisson responses under cyclic lateral loading

Offshore wind turbines (OWTs) are situated in intricate marine environments and consistently subjected to cyclic loadings, leading to an accumulation of foundation displacement along with stiffness variation. Employing the multi-spring system, improvements are made to the p-y and t-z model of multi-springs, according to the bounding surface plasticity-based theory, supporting the ongoing loading and unloading behaviors at the caisson-soil interface, which is also verified by the monopod caisson's tests under both uni-directional and multi-directional cyclic lateral loadings. The comparative analysis further indicates that ignoring the combined effect of p-y and t-z springs may result in overstating the deformation of the caisson. Then, incorporating the enhanced multi-spring model into the soil-structure module, allows for the implementation of the dynamic analysis procedure designed for caisson-based OWTs under multi-directional cyclic lateral loading. An increase in the cycle count leads to a larger load deflection angle for the case studied, initiating a continuous accumulation of plastic displacement and the disturbance at caisson-soil interface. This process gives rise to a certain degree of shrinkage within the soil stress boundary, ultimately affecting the overall stability of the structure.

Efficient estimation of gas exchange and respiration kinetics in apple using pathlength-resolved GASMAS

Optimization of controlled atmosphere (CA) storage of pome fruit crucially depends on understanding of respiratory gas exchange. Different fruit properties are involved in describing gas transfer and respiration kinetics, which can vary between cultivars, fruit batches and individual fruit. Determination of these properties currently requires a combination of different time-consuming techniques and data analysis procedures. This study introduces a more efficient single method for O2 gas exchange characterization of fruit based on the non-destructive pathlength-resolved GASMAS (gas in scattering media absorption spectroscopy) technique. GASMAS was used to measure the absolute intercellular O2 concentration in fruit of different apple cultivars (Malus x domestica Borkh.) with distinct porosity including 'Jonagold', 'Braeburn', and 'Nicoter'. To resolve the ambiguity between variation in pathlength and O2 concentration inherent to GASMAS, the pathlength travelled through pores cavities was quantified through a second GASMAS measurement at the absorption line of H2O vapour. Dynamic experiments were then performed in which the fruit was subjected to varying external O2 concentrations while measuring the internal O2 response. The observed dynamic profiles of external and internal O2 concentration were fitted with a lumped -respiration model to estimate the effective mass transfer coefficient, maximum respiration rate and Michaelis Menten constant of intact fruit. The model showed a good fit (R2>0.95) with the measured data and reliable estimates for the model parameters. The proposed method provides a novel and more efficient way to estimate the gas exchange properties of fruit that are essential for optimizing CA storage conditions. Future challenges for the proposed method are with applications to other gasses such as CO2 and ethylene.

Multi-criteria decision analysis: technique for order of preference by similarity to ideal solution for selecting greener analytical method in the determination of mifepristone in environmental water samples

This work proposes the use of multi-criteria decision analysis (MCDA) to select a more environmentally friendly analytical procedure. TOPSIS, which stands for Technique for Order of Preference by Similarity to Ideal Solution, is an example of a MCDA method that may be used to rank or select best alternative based on various criteria. Thirteen analytical procedures were used in this study as TOPSIS input choices for mifepristone determination in water samples. The input data, which consisted of these choices, was described using assessment criteria based on 12 principles of green analytical chemistry (GAC). Based on the objective mean weighting (MW), the weights for each criterion were assigned equally. The most preferred analytical method according to the ranking was solid phase extraction with micellar electrokinetic chromatography (SPE-MEKC), while solid phase extraction combined with ultra-high performance liquid chromatography tandem mass spectrometry (SPE-UHPLC-MS/MS) was ranked last. TOPSIS ranking results were also compared to the green metrics NEMI, Eco-Scale, GAPI, AGREE, and AGREEprep that were used to assess the greenness of thirteen analytical methods for mifepristone determination. The results demonstrated that only the AGREE metric tool correlated with TOPSIS; however, there was no correlation with other metric tools. The analysis results suggest that TOPSIS is a very useful tool for ranking or selecting the analytical procedure in terms of its greenness and that it can be easily integrated with other green metrics tools for method greenness assessment.

Virtual reality with e-learning model in business management and knowledge complex environment for gaming analysis

Nearly real-world experiences in a variety of industries may be had with virtual reality (VR) technology. Virtual reality (VR) settings are widely used, and as a result, a large quantity of data is being collected from them. The data-driven development paradigm must be advanced and analytical procedures must be created as the amount of data increases. There are several trends and possibilities that have emerged from integration of artificial intelligence (AI) as well as machine learning (ML) in business intelligence. Modern technology has completely changed how companies gather data, draw conclusions, decide. In order to analyse complex environments, this study uses gamification and virtual reality in business intelligence modelling for e-learning. Here, virtual reality-based e-learning is modelled for business intelligence, and gamification using reinforcement alexnet Q-kernel component neural networks is used to analyse the network’s complexity. Prediction accuracy, AUC, average precision, sensitivity, quadratic normalised square error are metrics used in experimental study. The evaluation’s findings demonstrate the usefulness of our solution for classifying and forecasting pay levels in the business sector, which in turn helps business analytics in intricate AI settings.

Диагностический потенциал регуляторных не кодирующих белок РНК при хроническом лимфоцитарном лейкозе

This paper reviews current knowledge about regulatory non-coding protein RNAs (ncRNAs) involved in the pathogenesis of chronic lymphocytic leukemia (CLL) and their potential capabilities as diagnostic markers. Diversity of clinical course as well as absence of detectable chromosomal aberrations and somatic mutations in 20 % of patients increase the interest to study the epigenetic aspects of pathogenesis. In this context, ncRNAs are believed to be promising diagnostic markers since their expression is commonly tissue-specific and they are quite stable in body fluids. Among the regulatory ncRNAs involved in the CLL pathogenesis, microRNAs and long (lncRNAs) have been most studied, whereas ring-like, or circulatory, ncRNAs (circRNAs) require further analysis. Aberrant expression of ncRNAs may account for the resistance to treatment in CLL patients without detected genomic abnormalities. Bioinformatics analysis of RNA sequencing databases allows to isolate novel candidate ncRNA molecules, including those associated with RNA-mediated suppression of the Piwi protein-interacting transposons. This paper proposes new independent predictive models based on the expression of 2 (LNC-KIA1755-4, LNC-IRF2-32-LNCRNA), 4 (miR-125b, miR-15b, miR-181c, miR-412), and 6 (PRKCQ, TRG.AS1, LNC00467, LNC01096, PCAT6, SBF2.AS1) simultaneously assessed different ncRNAs. Since risk- and stage classification of hematological malignancies is performed not only on the basis of clinical but also molecular genetic markers, the monitoring of regulatory ncRNA expression can provide an additional tool for more effective stratification of patients. The present review is concerned with the methodology issues in analytical procedures which impede widespread use of laboratory ncRNA tests.

Application of statistics in biomedical research

Abstract Statistical tools for data analysis vary depending on the kind of data and measurement scales used. The objective of this review is to offer a deep insight into these various data types and measuring systems. This overview offers comprehensive details on the various data kinds and measurement scales along with examples. Applying the proper statistical techniques and drawing reliable conclusions require knowledge of the relevant data type or analysis procedure.

Core Analysis Using Nuclear Magnetic Resonance

Core analysis is critical for oil and gas exploration and recovery. Nuclear magnetic resonance (NMR) has been widely applied to the petroleum industry for many years, and NMR core analysis plays an important role in supporting borehole NMR formation evaluation and understanding the mechanisms of petrophysics and petrochemistry. It’s unfortunate, however, that there has been a lack of commented descriptions of the basic procedures for NMR rock core analysis in the literature, which has led to issues of comparability and repeatability of NMR measurements between different laboratories. In this paper, we present a brief guideline for NMR rock core analysis of conventional T2, T1, T1-T2, and D-T2 measurements. As an established procedure in daily laboratory work, NMR core analysis includes three sophisticated steps: rock sample preparation, an NMR experiment, and data analysis. The acquisition and saturation of rock samples are described, and corresponding solutions are also considered. Detailed NMR experimental measurements, data setting, and calibration are addressed. Laplace inversion for different NMR experiments and models for estimation of porosity, irreducible water, permeability, pore structure, and oil saturation are included. The aim of this paper is to try to standardize general NMR experiments for rock core analysis.

Predicting maritime accident risk using Automated Machine Learning

Machine learning (ML), particularly, Automated machine learning (AutoML) offers a range of possibilities for analysing large volume of historical maritime accident records data with advanced algorithms for integrating predictive analytics in operational and policy decision making for improving maritime safety. This study explores historical data of maritime accidents in Norwegian waters over the 40 years. The data has been utilised for analysing five major maritime accident categories: grounding, contact damage, fire or explosion, collision, and heavy weather damage. A total of 29 classification ML algorithms were trained, and the Light Gradient Boosted Trees Classifier was found best performing model with highest predictive accuracy. The three most impactful factors for accident risk are: category of navigation waters, phase of operation, and gross tonnage of vessel. Based on feature effect results, vessels sailing in narrow coastal waters, in along the way operational phase, and fishing vessels are highly vulnerable to grounding relative to other types of accidents. The results can be used as input for the entire procedure of risk analysis, from hazard identification to quantification of accident consequences, and the best performing ML algorithm can be utilized in developing a decision support system for real-time maritime accident risk assessment.

Mapping knowledge landscapes and emerging trends of Marburg virus: A text-mining study

BackgroundMarburg virus (MARV), a close relative of Ebola virus, could induce hemorrhagic fevers in humans with high mortality rate. In recent years, increasing attention has been paid to this highly lethal virus due to sporadic outbreaks observed in various African nations. This bibliometric analysis endeavors to elucidate the trends, dynamics, and focal points of knowledge that have delineated the landscape of research concerning MARV. MethodsRelevant literature on MARV from 1968 to 2023 was extracted from the Web of Science Core Collection database. Following this, the data underwent bibliometric analysis and visualization procedures utilizing online analysis platform, CiteSpace 6.2R6, and VOSviewer 1.6.20. Three different types of bibliometric indicators including quantitative indicator, qualitative indicators, and structural indicators were used to gauge a researcher's productivity, assess the quality of their work, and analyze publication relationships, respectively. ResultsMARV is mainly prevalent in Africa. And approximately 643 confirmed cases have been described in the literature to date, and mortality observed was 81.2 % in overall patients. A total of 1014 papers comprising 869 articles and 145 reviews were included. The annual publications showed an increasing growth pattern from 1968 to 2023 (R2 = 0.8838). The United States stands at the forefront of this discipline, having dedicated substantial financial and human resources to scientific inquiry. However, co-authorship analysis showed the international research collaboration needs to be further strengthened. Based on reference and keywords analysis, contemporary MARV research encompasses pivotal areas: primarily, prioritizing the creation of prophylactic vaccines to impede viral spread, and secondarily, exploring targeted antiviral strategies, including small-molecule antivirals or MARV-specific monoclonal antibodies. Additionally, a comprehensive grasp of viral transmission, transcription, and replication mechanisms remains a central focus in ongoing investigations. And future MARV studies are expected to focus on evaluating clinical trial safety and efficacy, developing inhibitors to contain viral spread, exploring vaccine immunogenicity, virus-host association studies, and elucidating the role of neutralizing antibodies in MARV treatment. ConclusionThe present study offered comprehensive insights into the contemporary status and trajectories of MARV over the past decades. This enables researchers to discern novel collaborative prospects, institutional partnerships, emerging topics, and research forefronts within this domain.